diff --git a/.DS_Store b/.DS_Store index 3a1ce30..36185f8 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/Algorithm, structure, complexity/.DS_Store b/Algorithm, structure, complexity/.DS_Store index dcb008f..3f1bdab 100644 Binary files a/Algorithm, structure, complexity/.DS_Store and b/Algorithm, structure, complexity/.DS_Store differ diff --git a/Algorithm, structure, complexity/Data structure /DiGraph.ipynb b/Algorithm, structure, complexity/Data structure /DiGraph.ipynb new file mode 100644 index 0000000..1034f9c --- /dev/null +++ b/Algorithm, structure, complexity/Data structure /DiGraph.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Directed graphs \n", + "\n", + "[Directed graphs](https://en.wikipedia.org/wiki/Directed_graph)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx\n", + "from typing import Hashable\n", + "\n", + "class DiGraph:\n", + " \"\"\"A directed graph with hashable node objects.\n", + "\n", + " Edges are between different nodes.\n", + " There's at most one edge from one node to another.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " self.out = dict() # a map of nodes to their out-neighbours\n", + "\n", + " def has_node(self, node: Hashable) -> bool:\n", + " \"\"\"Return True if and only if the graph has the node.\"\"\"\n", + " return node in self.out\n", + "\n", + " def has_edge(self, start: Hashable, end: Hashable) -> bool:\n", + " \"\"\"Return True if and only if edge start -> end exists.\n", + "\n", + " Preconditions: self.has_node(start) and self.has_node(end)\n", + " \"\"\"\n", + " return end in self.out[start]\n", + "\n", + " def add_node(self, node: Hashable) -> None:\n", + " \"\"\"Add the node to the graph.\n", + "\n", + " Preconditions: not self.has_node(node)\n", + " \"\"\"\n", + " self.out[node] = set()\n", + "\n", + " def add_edge(self, start: Hashable, end: Hashable) -> None:\n", + " \"\"\"Add edge start -> end to the graph.\n", + "\n", + " If the edge already exists, do nothing.\n", + "\n", + " Preconditions:\n", + " self.has_node(start) and self.has_node(end) and start != end\n", + " \"\"\"\n", + " self.out[start].add(end)\n", + "\n", + " def remove_node(self, node: Hashable) -> None:\n", + " \"\"\"Remove the node and all its attached edges.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " self.out.pop(node)\n", + " for start in self.out:\n", + " self.remove_edge(start, node)\n", + "\n", + " def remove_edge(self, start: Hashable, end: Hashable) -> None:\n", + " \"\"\"Remove edge start -> end from the graph.\n", + "\n", + " If the edge doesn't exist, do nothing.\n", + "\n", + " Preconditions: self.has_node(start) and self.has_node(end)\n", + " \"\"\"\n", + " self.out[start].discard(end)\n", + "\n", + " def nodes(self) -> set:\n", + " \"\"\"Return the graph's nodes.\"\"\"\n", + " all_nodes = set()\n", + " for node in self.out:\n", + " all_nodes.add(node)\n", + " return all_nodes\n", + "\n", + " def edges(self) -> set:\n", + " \"\"\"Return the graph's edges as a set of pairs (start, end).\"\"\"\n", + " all_edges = set()\n", + " for start in self.out:\n", + " for end in self.out[start]:\n", + " all_edges.add( (start, end) )\n", + " return all_edges\n", + "\n", + " def out_neighbours(self, node: Hashable) -> set:\n", + " \"\"\"Return the out-neighbours of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " return set(self.out[node]) # return a copy\n", + "\n", + " def out_degree(self, node: Hashable) -> int:\n", + " \"\"\"Return the number of out-neighbours of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " return len(self.out[node])\n", + "\n", + " def in_neighbours(self, node: Hashable) -> set:\n", + " \"\"\"Return the in-neighbours of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " start_nodes = set()\n", + " for start in self.out:\n", + " if self.has_edge(start, node):\n", + " start_nodes.add(start)\n", + " return start_nodes\n", + "\n", + " def in_degree(self, node: Hashable) -> int:\n", + " \"\"\"Return the number of in-neighbours of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " return len(self.in_neighbours(node))\n", + "\n", + " def neighbours(self, node: Hashable) -> set:\n", + " \"\"\"Return the in- and out-neighbours of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " return self.out_neighbours(node).union(self.in_neighbours(node))\n", + "\n", + " def degree(self, node: Hashable) -> int:\n", + " \"\"\"Return the number of in- and out-going edges of the node.\n", + "\n", + " Preconditions: self.has_node(node)\n", + " \"\"\"\n", + " return self.in_degree(node) + self.out_degree(node)\n", + "\n", + " def draw(self) -> None:\n", + " \"\"\"Draw the graph.\"\"\"\n", + " if type(self) == DiGraph:\n", + " graph = networkx.DiGraph()\n", + " else:\n", + " graph = networkx.Graph()\n", + " graph.add_nodes_from(self.nodes())\n", + " graph.add_edges_from(self.edges())\n", + " networkx.draw(graph, with_labels=True,\n", + " node_size=1000, node_color='lightblue',\n", + " font_size=12, font_weight='bold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding some nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph = DiGraph()\n", + "graph.add_node(\"Nuts\")\n", + "graph.add_node(\"Avocados\")\n", + "graph.add_node(\"Beans\")\n", + "graph.add_node(\"Apple\")\n", + "\n", + "graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph.add_edge(\"Beans\",\"Avocados\")\n", + "graph.draw()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Apple', 'Avocados', 'Beans', 'Nuts'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph.nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('Beans', 'Avocados'), ('Nuts', 'Apple'), ('Nuts', 'Avocados')}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph.edges()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Let's explore preferred combination of food" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph = DiGraph()\n", + "graph.add_node(\"Apple\")\n", + "graph.add_node(\"Cheese\")\n", + "graph.add_node(\"Biscuit\")\n", + "graph.add_node(\"Avocado\")\n", + "graph.add_node(\"Chilli\"),\n", + "graph.add_node(\"Chocolate\"),\n", + "graph.add_node(\"Green Tea\")\n", + "graph.add_node(\"Sushi\")\n", + "graph.add_node(\"Beans\")\n", + "graph.add_node(\"Rice\")\n", + "graph.add_node(\"carrot\")\n", + "graph.add_node(\"spinach\")\n", + "graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sushi: in 1 - out 0\n", + "Green Tea: in 0 - out 1\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph.add_edge(\"Green Tea\",\"Sushi\")\n", + "print(\"Sushi: in \", graph.in_degree(\"Sushi\"), \" - out \",\n", + " graph.out_degree(\"Sushi\"))\n", + "print(\"Green Tea: in \", graph.in_degree(\"Green Tea\"), \" - out \",\n", + " graph.out_degree(\"Green Tea\"))\n", + "graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "carrot : in 0 - out 0\n", + "Chocolate : in 1 - out 0\n", + "Apple : in 0 - out 0\n", + "Sushi : in 1 - out 0\n", + "Avocado : in 1 - out 0\n", + "Cheese : in 0 - out 0\n", + "Beans : in 1 - out 2\n", + "Rice : in 0 - out 1\n", + "Green Tea : in 0 - out 1\n", + "Chilli : in 1 - out 1\n", + "spinach : in 0 - out 0\n", + "Biscuit : in 0 - out 0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph.add_edge(\"Rice\",\"Beans\")\n", + "graph.add_edge(\"Beans\",\"Avocado\")\n", + "graph.add_edge(\"Beans\",\"Chilli\")\n", + "graph.add_edge(\"Chilli\",\"Chocolate\")\n", + "\n", + "for node in graph.nodes(): \n", + " print(node, \": in \", graph.in_degree(node), \" - out \",\n", + " graph.out_degree(node))\n", + "\n", + "graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "carrot : in 0 - out 1\n", + "Chocolate : in 1 - out 1\n", + "Apple : in 0 - out 1\n", + "Sushi : in 1 - out 0\n", + "Avocado : in 1 - out 0\n", + "Cheese : in 1 - out 1\n", + "Beans : in 1 - out 2\n", + "Rice : in 0 - out 3\n", + "Green Tea : in 0 - out 1\n", + "Chilli : in 1 - out 1\n", + "spinach : in 1 - out 0\n", + "Biscuit : in 2 - out 0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph.add_edge(\"Cheese\",\"Biscuit\")\n", + "graph.add_edge(\"Apple\",\"Cheese\")\n", + "graph.add_edge(\"Chocolate\",\"Biscuit\")\n", + "graph.add_edge(\"carrot\",\"Spinach\")\n", + "graph.add_edge(\"Rice\",\"spinach\")\n", + "\n", + "\n", + "for node in graph.nodes(): \n", + " print(node, \": in \", graph.in_degree(node), \" - out \",\n", + " graph.out_degree(node))\n", + "\n", + "graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'carrot': 1,\n", + " 'Chocolate': 2,\n", + " 'Apple': 1,\n", + " 'Sushi': 1,\n", + " 'Avocado': 1,\n", + " 'Cheese': 2,\n", + " 'Beans': 3,\n", + " 'Rice': 3,\n", + " 'Green Tea': 1,\n", + " 'Chilli': 2,\n", + " 'spinach': 1,\n", + " 'Biscuit': 2}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_item = {}\n", + "for node in graph.nodes(): \n", + " total_item[node] = graph.in_degree(node) + graph.out_degree(node)\n", + "\n", + "total_item" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_value = max(total_item.values())\n", + "max_value" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Beans', 'Rice']" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "keys = []\n", + "items = total_item.items()\n", + "for key, value in items:\n", + " if value == max_value:\n", + " keys.append(key)\n", + " \n", + "keys" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The food ['Beans', 'Rice'] have the most combinations. The have all been paired 3 times.\n" + ] + } + ], + "source": [ + "print (\"The food \" , keys, \" have the most combinations. The have all been paired \",\n", + " max_value, \" times.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Algorithm, structure, complexity/Data structure /sets and bags.ipynb b/Algorithm, structure, complexity/Data structure /sets and bags.ipynb new file mode 100644 index 0000000..377d5f3 --- /dev/null +++ b/Algorithm, structure, complexity/Data structure /sets and bags.ipynb @@ -0,0 +1,629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sets \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find more about sets mathematically use those sites:\n", + "\n", + "- [Introduction to sets](https://www.mathsisfun.com/sets/sets-introduction.html)\n", + "- [Symbols](https://www.mathsisfun.com/sets/symbols.html)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sets in Python\n", + "\n", + "We are going to use two sets...." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beans',\n", + " 'dairy products',\n", + " 'fish',\n", + " 'legumes',\n", + " 'meat',\n", + " 'nuts',\n", + " 'poultry',\n", + " 'seafood'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_food = {'meat','poultry','fish','seafood',\n", + " 'dairy products','nuts', 'legumes', 'beans', 'meat'}\n", + "protein_food" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apples',\n", + " 'beans',\n", + " 'berries',\n", + " 'broccoli',\n", + " 'dried fruits',\n", + " 'nuts',\n", + " 'potatoes',\n", + " 'whole grain'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fiber_food = {'beans','beans','broccoli','berries','apples',\n", + " 'nuts','potatoes','dried fruits','whole grain'}\n", + "fiber_food" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intersection\n", + "\n", + "Food that contain __both__ protein and fibers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beans', 'nuts'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fiber_food & protein_food" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Union\n", + "\n", + "Food that contain __either__ fiber __or__ protein. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apples',\n", + " 'beans',\n", + " 'berries',\n", + " 'broccoli',\n", + " 'dairy products',\n", + " 'dried fruits',\n", + " 'fish',\n", + " 'legumes',\n", + " 'meat',\n", + " 'nuts',\n", + " 'potatoes',\n", + " 'poultry',\n", + " 'seafood',\n", + " 'whole grain'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fiber_food | protein_food" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Difference\n", + "\n", + "Food that is a protein __but does not contain__ fibers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dairy products', 'fish', 'legumes', 'meat', 'poultry', 'seafood'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_food - fiber_food" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bag\n", + "\n", + "Bag is an unorder data structure. \n", + "\n", + "[Bags and sets](https://web.engr.oregonstate.edu/~sinisa/courses/OSU/CS261/CS261_Textbook/Chapter08.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class Bag(object):\n", + " def __init__(self):\n", + " self.collection = list()\n", + "\n", + " def add(self, item):\n", + " self.collection.append(item)\n", + "\n", + " def size(self):\n", + " return len(self.collection)\n", + "\n", + " def is_empty(self):\n", + " return len(self.collection) == 0\n", + "\n", + " def __iter__(self):\n", + " return iter(self.collection)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shopping bag\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "shopping_bag = Bag()\n", + "shopping_bag.add(\"apples\")\n", + "shopping_bag.add(\"nuts\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"beans\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"beans\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "apples\n", + "nuts\n", + "avocados\n", + "beans\n", + "broccoli\n", + "beans\n" + ] + } + ], + "source": [ + "for item in shopping_bag:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "other_shopping_bag = Bag()\n", + "other_shopping_bag.add(\"nuts\")\n", + "other_shopping_bag.add(\"beans\")\n", + "other_shopping_bag.add(\"chocolate\")\n", + "other_shopping_bag.add(\"beans\")\n", + "other_shopping_bag.add(\"rice\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nuts\n", + "beans\n", + "chocolate\n", + "beans\n", + "rice\n" + ] + } + ], + "source": [ + "for item in other_shopping_bag:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intersection" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['nuts', 'beans', 'beans']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "common_items = []\n", + "for item in shopping_bag:\n", + " if item in other_shopping_bag:\n", + " common_items.append(item)\n", + "common_items" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Union" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apples',\n", + " 'nuts',\n", + " 'avocados',\n", + " 'beans',\n", + " 'broccoli',\n", + " 'beans',\n", + " 'nuts',\n", + " 'beans',\n", + " 'chocolate',\n", + " 'beans',\n", + " 'rice']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_items = shopping_bag.collection + other_shopping_bag.collection\n", + "all_items" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Difference\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apples', 'avocados', 'broccoli']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "different_items = []\n", + "for item in shopping_bag:\n", + " if not item in other_shopping_bag:\n", + " different_items.append(item)\n", + "different_items" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Most commom item" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['apples',\n", + " 'nuts',\n", + " 'avocados',\n", + " 'beans',\n", + " 'broccoli',\n", + " 'beans',\n", + " 'apples',\n", + " 'nuts',\n", + " 'avocados',\n", + " 'broccoli',\n", + " 'broccoli',\n", + " 'broccoli',\n", + " 'broccoli',\n", + " 'beans',\n", + " 'broccoli',\n", + " 'beans',\n", + " 'apples',\n", + " 'nuts',\n", + " 'apples',\n", + " 'nuts',\n", + " 'avocados',\n", + " 'avocados',\n", + " 'avocados',\n", + " 'avocados',\n", + " 'avocados',\n", + " 'avocados',\n", + " 'avocados']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shopping_bag = Bag()\n", + "shopping_bag.add(\"apples\")\n", + "shopping_bag.add(\"nuts\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"beans\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"beans\")\n", + "shopping_bag.add(\"apples\")\n", + "shopping_bag.add(\"nuts\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"beans\")\n", + "shopping_bag.add(\"broccoli\")\n", + "shopping_bag.add(\"beans\")\n", + "shopping_bag.add(\"apples\")\n", + "shopping_bag.add(\"nuts\")\n", + "shopping_bag.add(\"apples\")\n", + "shopping_bag.add(\"nuts\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.add(\"avocados\")\n", + "shopping_bag.collection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Question:\n", + "In your opinion, why did we convert the list of items into a set?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apples', 'avocados', 'beans', 'broccoli', 'nuts'}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set_of_items = set(shopping_bag.collection)\n", + "set_of_items" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nuts': 4, 'avocados': 9, 'broccoli': 6, 'beans': 4, 'apples': 4}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_item = {}\n", + "for item in set_of_items:\n", + " total_item[item] = shopping_bag.collection.count(item)\n", + "total_item" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_value = max(total_item.values())\n", + "max_value\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['avocados']" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "keys = []\n", + "items = total_item.items()\n", + "for key, value in items:\n", + " if value == max_value:\n", + " keys.append(key)\n", + " \n", + "keys" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['avocados'] appeared the most in the bag. The item was purchased 9 times.\n" + ] + } + ], + "source": [ + "print(keys, \" appeared the most in the bag. \",\n", + " \"The item was purchased \", max_value , \" times.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Algorithm, structure, complexity/Turing machines and complexity classes/.DS_Store b/Algorithm, structure, complexity/Turing machines and complexity classes/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/Algorithm, structure, complexity/Turing machines and complexity classes/.DS_Store differ diff --git a/Algorithm, structure, complexity/Turing machines and complexity classes/Small18.pdf b/Algorithm, structure, complexity/Turing machines and complexity classes/Small18.pdf new file mode 100644 index 0000000..4ed0f7e Binary files /dev/null and b/Algorithm, structure, complexity/Turing machines and complexity classes/Small18.pdf differ diff --git a/Algorithm, structure, complexity/Turing machines and complexity classes/Turing machine.pdf b/Algorithm, structure, complexity/Turing machines and complexity classes/Turing machine.pdf new file mode 100644 index 0000000..4ed0f7e Binary files /dev/null and b/Algorithm, structure, complexity/Turing machines and complexity classes/Turing machine.pdf differ diff --git a/Algorithm, structure, complexity/Turing machines and complexity classes/review_complexity_classes.ipynb b/Algorithm, structure, complexity/Turing machines and complexity classes/review_complexity_classes.ipynb new file mode 100644 index 0000000..0f24b32 --- /dev/null +++ b/Algorithm, structure, complexity/Turing machines and complexity classes/review_complexity_classes.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Review of complexity classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some additional resources to read: \n", + "\n", + "- [wikipedia](https://en.wikipedia.org/wiki/Complexity_class) is informative and exhaustive - readers may required good set theories and other mathematical concepts\n", + "\n", + "- [Complexity classes P and NP](http://mercury.webster.edu/aleshunas/Support%20Materials/Presentations/Complexity%20Classes%20P%20and%20NP%20(13%20sep%2006).pdf) - an approachable presentation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's start with a selection sort ...." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[wikipedia](https://en.wikipedia.org/wiki/Selection_sort)\n", + "\n", + "[graphical representation](https://www.programiz.com/dsa/selection-sort)\n", + "\n", + "This procedure sort an array of values. The references to the array manipulates the array itself. We pass the array as an argument. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def selectionSort(array, size):\n", + " outer_steps = range(0,size)\n", + " for outer_step in outer_steps:\n", + " min_idx = outer_step\n", + " \n", + " internal_steps = range(outer_step + 1, size)\n", + " for index in internal_steps:\n", + " # to sort in descending order, change > to < in this line\n", + " # select the minimum element in each loop\n", + " if array[index] < array[min_idx]:\n", + " min_idx = index\n", + " \n", + " # put min at the correct position\n", + " (array[outer_step], array[min_idx]) = (array[min_idx], array[outer_step])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__The best case:__\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_list = [10,20,30,40,50]\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "size = len(a_list)\n", + "selectionSort(a_list, size)\n", + "a_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__The worst case:__\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[50, 40, 30, 20, 10]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_list = [50,40,30,20,10]\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "size = len(a_list)\n", + "selectionSort(a_list, size)\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7 µs, sys: 2 µs, total: 9 µs\n", + "Wall time: 10.3 µs\n" + ] + } + ], + "source": [ + "max_value = int(1e1)\n", + "a_list = [*range(max_value,0, -1)]\n", + "\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 250 µs, sys: 43 µs, total: 293 µs\n", + "Wall time: 294 µs\n" + ] + } + ], + "source": [ + "max_value = int(1e2)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 26 ms, sys: 0 ns, total: 26 ms\n", + "Wall time: 26 ms\n" + ] + } + ], + "source": [ + "max_value = int(1e3)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.46 s, sys: 0 ns, total: 2.46 s\n", + "Wall time: 2.47 s\n" + ] + } + ], + "source": [ + "max_value = int(1e4)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4min 13s, sys: 69.5 ms, total: 4min 13s\n", + "Wall time: 4min 14s\n" + ] + } + ], + "source": [ + "max_value = int(1e5)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time complexity\n", + "\n", + "Best and worst case complete $\\theta(n^2)$ comparisons, whether all the values or in ascending order or not. \n", + "\n", + "So, it is considered to be $\\theta(n^2)$, which is quadratic. Quadratic is a form a polynomial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is a polynomial?\n", + "\n", + "- [A simple explanation](https://www.mathsisfun.com/algebra/polynomials.html)\n", + "\n", + "- [A mathematical explanation](https://mathworld.wolfram.com/Polynomial.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "xs = range(-100, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + " \n", + "ys = [x for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('Linear - n^1')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,2) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('quadratic - n^2')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,3) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('cubic - n^3')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,4) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('biquadratic - n^4')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__What is a polynomial time?__\n", + "\n", + "It is a time complexity expressed as $O(n^k)$, where $k \\geq 1$ or $\\theta(n^k)$, where $k \\geq 1$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the complexity of a problem?\n", + "The complexity of the most efficient algorithm that\n", + "solves it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is a tractable algorithm?\n", + "\n", + "A problem is tractable if it has polynomial complexity, i.e. if the most efficient algorithm that\n", + "solves the problem has complexity $\\theta(n^c)$ or better, for input size 𝑛 and some constant 𝑐." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## P vs NP\n", + "\n", + "Class P is the set of tractable decision problems: those that can be solved in polynomial time.\n", + "\n", + "[P class](https://en.wikipedia.org/wiki/P_(complexity))\n", + "\n", + "[NP hard](https://en.wikipedia.org/wiki/NP_(complexity))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Turing machine\n", + "\n", + "[Turing machine](https://en.wikipedia.org/wiki/Turing_machine)\n", + "\n", + "[An implementation in Python](https://medium.com/practical-coding/turing-machines-in-python-8314fd6077d7)\n", + "\n", + "[Church-Turing thesis](https://en.wikipedia.org/wiki/History_of_the_Church–Turing_thesis)\n", + "\n", + "[Church-Turing thesis - simpler explanation](https://mathworld.wolfram.com/Church-TuringThesis.html)\n", + "\n", + "[History of computing](https://history-computer.com/the-church-turing-thesis-explained-what-it-is-when-it-was-formed/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Application of ML Algorithms/.DS_Store b/Application of ML Algorithms/.DS_Store index 426a831..829fab3 100644 Binary files a/Application of ML Algorithms/.DS_Store and b/Application of ML Algorithms/.DS_Store differ diff --git a/Application of ML Algorithms/Decision trees and random Forrest/titanic-random-forrest.ipynb b/Application of ML Algorithms/Decision trees and random Forrest/titanic-random-forrest.ipynb new file mode 100644 index 0000000..8570048 --- /dev/null +++ b/Application of ML Algorithms/Decision trees and random Forrest/titanic-random-forrest.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-27T13:29:00.362275Z","iopub.execute_input":"2023-02-27T13:29:00.362816Z","iopub.status.idle":"2023-02-27T13:29:00.369677Z","shell.execute_reply.started":"2023-02-27T13:29:00.362765Z","shell.execute_reply":"2023-02-27T13:29:00.368868Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"# Introduction\n\nData can be transformed into knowledge and then enhanced intelligence. We use the titanic datasets to explore first its features. The Titanic datasets contains the records of the Titanic passengers during its maiden voyage and tragic demise. \n\nWe apply some data engineering techniques to prepare the data for some various machine learning techniques - decision trees and random forrests for the purpose of predicting survivors. \n\nThe notebook is structured in this manner:\n\n\n- __[Upload libraires](#Libraries)__\n- __[Data engineering](#Data-engineering)__\n- __[Survival characteristics](#Survival-characteristics)__\n- __[Data preparation for classification](#Data-preparation-for-classification)__ \n- __[Method: Decision Trees](#Method-:-Decision-Trees)__ \n- __[Method: Random Forrest](#Method:-Random-Forrest)__\n\n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Libraries\n\nWe upload all the libraries required for all the operations of this notebook.","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport seaborn as sns\nimport os\nimport random as rand\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\nfrom sklearn.model_selection import train_test_split # Import train_test_split function\nfrom sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nimport scipy.stats as stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\n\nimport tensorflow as tf\nif (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed\n print('Please install tensorflow 2.0 to run this notebook')\nprint('Tensorflow version: ',tf.__version__)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:15.729194Z","iopub.execute_input":"2023-02-27T13:28:15.729546Z","iopub.status.idle":"2023-02-27T13:28:29.512628Z","shell.execute_reply.started":"2023-02-27T13:28:15.729512Z","shell.execute_reply":"2023-02-27T13:28:29.510853Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Tensorflow version: 2.11.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data engineering\n\nWe explore the files in the folder, sets the paths and file names. These variables will be used in each section.","metadata":{}},{"cell_type":"code","source":"!ls ../input/titanic/\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.514792Z","iopub.execute_input":"2023-02-27T13:28:29.515534Z","iopub.status.idle":"2023-02-27T13:28:29.801170Z","shell.execute_reply.started":"2023-02-27T13:28:29.515500Z","shell.execute_reply":"2023-02-27T13:28:29.800164Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"ls: cannot access '../input/titanic/': No such file or directory\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data_path = '../input/titanic/train.csv'\ntest_data_path = '../input/titanic/test.csv'","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.802873Z","iopub.execute_input":"2023-02-27T13:28:29.803472Z","iopub.status.idle":"2023-02-27T13:28:29.808818Z","shell.execute_reply.started":"2023-02-27T13:28:29.803437Z","shell.execute_reply":"2023-02-27T13:28:29.807498Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Import and explore the data \nExplore and import the training and test dataset provided by the competition.","metadata":{}},{"cell_type":"markdown","source":"### Training dataset","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.812696Z","iopub.execute_input":"2023-02-27T13:28:29.814300Z","iopub.status.idle":"2023-02-27T13:28:30.152138Z","shell.execute_reply.started":"2023-02-27T13:28:29.814236Z","shell.execute_reply":"2023-02-27T13:28:30.150673Z"},"trusted":true},"execution_count":5,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/207441726.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m 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\u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m 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\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m 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storage_options)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m )\n\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/titanic/train.csv'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '../input/titanic/train.csv'","output_type":"error"}]},{"cell_type":"code","source":"titanic_train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.153288Z","iopub.status.idle":"2023-02-27T13:28:30.154841Z","shell.execute_reply.started":"2023-02-27T13:28:30.154432Z","shell.execute_reply":"2023-02-27T13:28:30.154486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.156732Z","iopub.status.idle":"2023-02-27T13:28:30.157230Z","shell.execute_reply.started":"2023-02-27T13:28:30.156985Z","shell.execute_reply":"2023-02-27T13:28:30.157009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Test dataset","metadata":{}},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.159338Z","iopub.status.idle":"2023-02-27T13:28:30.159839Z","shell.execute_reply.started":"2023-02-27T13:28:30.159607Z","shell.execute_reply":"2023-02-27T13:28:30.159633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.161502Z","iopub.status.idle":"2023-02-27T13:28:30.161975Z","shell.execute_reply.started":"2023-02-27T13:28:30.161752Z","shell.execute_reply":"2023-02-27T13:28:30.161777Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.163512Z","iopub.status.idle":"2023-02-27T13:28:30.163966Z","shell.execute_reply.started":"2023-02-27T13:28:30.163747Z","shell.execute_reply":"2023-02-27T13:28:30.163770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" ## Meta data \n \n| Column name | Description|\n|---|---|\n|Passenger_id| unique row indentifier |\n|PClass | Categorical data (1 = 1st; 2 = 2nd; 3 = 3rd)|\n| Survival | Categoricial data (0 = No; 1 = Yes) |\n| Name | Characters - Name of passenger |\n| Sex | Categorical data male or female |\n| Age | integer values representing age |\n| SigSp | integer Number of Siblings/Spouses Aboard |\n| Parch | Number of Parents/Children Aboard |\n| Ticket | Ticket number |\n| Fare | Fare in GBP at time of travel|\n| Cabin | Cabin |\n| Embark | Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)|\n\n\nSource - http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf (7/12/2022)","metadata":{}},{"cell_type":"markdown","source":"# Survival characteristics\nWe explore the survival characteristics using several combinations of columns. We hope to understand better some features that may guide the predictions of survivors.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Passenger and survival\nThe training dataset suggests a minority of passengers survived (i.e., 38% approximately), 62% of passengers perished. Some further decomposition suggests first class passengers may have been more likely to survive than lower classes. The percentages of surviving decreases sharply.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Survived\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:48.498154Z","iopub.execute_input":"2023-02-27T13:28:48.498558Z","iopub.status.idle":"2023-02-27T13:28:48.526098Z","shell.execute_reply.started":"2023-02-27T13:28:48.498520Z","shell.execute_reply":"2023-02-27T13:28:48.525014Z"},"trusted":true},"execution_count":9,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/4256478334.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Survived\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"PassengerId\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtitanic_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'titanic_train' is not defined"],"ename":"NameError","evalue":"name 'titanic_train' is not defined","output_type":"error"}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp = temp.unstack()\ntemp","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:42.082092Z","iopub.execute_input":"2023-02-27T13:28:42.082458Z","iopub.status.idle":"2023-02-27T13:28:42.112345Z","shell.execute_reply.started":"2023-02-27T13:28:42.082431Z","shell.execute_reply":"2023-02-27T13:28:42.110307Z"},"trusted":true},"execution_count":8,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/2764600673.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m 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0.01$","metadata":{}},{"cell_type":"code","source":"\nsur_pclass = titanic_train.loc[titanic_train.Survived == 1, \"Pclass\"]\nperish_pclass = titanic_train.loc[titanic_train.Survived == 0, \"Pclass\"]\nfvalue, pvalue = stats.f_oneway(sur_pclass, perish_pclass)\nprint(fvalue, pvalue)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:29:37.721129Z","iopub.execute_input":"2023-02-01T14:29:37.721602Z","iopub.status.idle":"2023-02-01T14:29:37.732491Z","shell.execute_reply.started":"2023-02-01T14:29:37.721566Z","shell.execute_reply":"2023-02-01T14:29:37.731155Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"115.03127218827665 2.5370473879805644e-25\n","output_type":"stream"}]},{"cell_type":"code","source":"model = ols('Survived ~ Pclass', data=titanic_train).fit()\nanova_table = sm.stats.anova_lm(model, typ=2)\nanova_table","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:32:55.703937Z","iopub.execute_input":"2023-02-01T14:32:55.704329Z","iopub.status.idle":"2023-02-01T14:32:55.739204Z","shell.execute_reply.started":"2023-02-01T14:32:55.704285Z","shell.execute_reply":"2023-02-01T14:32:55.738138Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" sum_sq df F PR(>F)\nPclass 24.142900 1.0 115.031272 2.537047e-25\nResidual 186.584373 889.0 NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sum_sqdfFPR(>F)
Pclass24.1429001.0115.0312722.537047e-25
Residual186.584373889.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"__Interpretation__\n\nThe p value obtained from ANOVA analysis is significant (p < 0.01), and therefore, we conclude that there are significant differences among the classes who have perished or survived.","metadata":{}},{"cell_type":"markdown","source":"## Embarkment and survival\nThe port of embarkment appears to have less influence on the survival percentages. It appears most passengers embarked at Southampton (72% approximately), 18% of passengers at Cherbourg, and the remaining from Queenstown. Half of the Cherbourg passengers booked first class tickets. Other embarkment ports appears to be much lower. Half of the passengers from Southampton booked third class tickets. We could surmise the latter may have contributed to the lowest percentages of surviving the accident.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Embarked\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.612759Z","iopub.execute_input":"2023-02-01T14:50:09.613134Z","iopub.status.idle":"2023-02-01T14:50:09.626172Z","shell.execute_reply.started":"2023-02-01T14:50:09.613106Z","shell.execute_reply":"2023-02-01T14:50:09.625109Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Embarked\nC 0.188552\nQ 0.086420\nS 0.722783\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.631955Z","iopub.execute_input":"2023-02-01T14:50:09.632520Z","iopub.status.idle":"2023-02-01T14:50:09.652387Z","shell.execute_reply.started":"2023-02-01T14:50:09.632486Z","shell.execute_reply":"2023-02-01T14:50:09.651379Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Pclass 1 2 3\nEmbarked \nC 0.505952 0.101190 0.392857\nQ 0.025974 0.038961 0.935065\nS 0.197205 0.254658 0.548137","text/html":"
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Pclass123
Embarked
C0.5059520.1011900.392857
Q0.0259740.0389610.935065
S0.1972050.2546580.548137
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:10.164258Z","iopub.execute_input":"2023-02-01T14:50:10.164676Z","iopub.status.idle":"2023-02-01T14:50:10.185023Z","shell.execute_reply.started":"2023-02-01T14:50:10.164643Z","shell.execute_reply":"2023-02-01T14:50:10.183924Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked \nC 0.446429 0.553571\nQ 0.610390 0.389610\nS 0.663043 0.336957","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Embarked
C0.4464290.553571
Q0.6103900.389610
S0.6630430.336957
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:16.126671Z","iopub.execute_input":"2023-02-01T14:50:16.127079Z","iopub.status.idle":"2023-02-01T14:50:16.150013Z","shell.execute_reply.started":"2023-02-01T14:50:16.127043Z","shell.execute_reply":"2023-02-01T14:50:16.149263Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked Pclass \nC 1 0.154762 0.351190\n 2 0.047619 0.053571\n 3 0.244048 0.148810\nQ 1 0.012987 0.012987\n 2 0.012987 0.025974\n 3 0.584416 0.350649\nS 1 0.082298 0.114907\n 2 0.136646 0.118012\n 3 0.444099 0.104037","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
EmbarkedPclass
C10.1547620.351190
20.0476190.053571
30.2440480.148810
Q10.0129870.012987
20.0129870.025974
30.5844160.350649
S10.0822980.114907
20.1366460.118012
30.4440990.104037
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Gender and survival \nThe training dataset suggests that nearly two thirds of passengers were male, and a third were female. Women and girls appears to have a higher survival percentagers - three quarters of female passengers survived the accident, but only 19% of male survived.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Sex\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:21.461493Z","iopub.execute_input":"2023-02-01T14:50:21.461874Z","iopub.status.idle":"2023-02-01T14:50:21.474706Z","shell.execute_reply.started":"2023-02-01T14:50:21.461843Z","shell.execute_reply":"2023-02-01T14:50:21.473520Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Sex\nfemale 0.352413\nmale 0.647587\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:22.544412Z","iopub.execute_input":"2023-02-01T14:50:22.544835Z","iopub.status.idle":"2023-02-01T14:50:22.565483Z","shell.execute_reply.started":"2023-02-01T14:50:22.544801Z","shell.execute_reply":"2023-02-01T14:50:22.564390Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex \nfemale 0.257962 0.742038\nmale 0.811092 0.188908","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Sex
female0.2579620.742038
male0.8110920.188908
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:23.099261Z","iopub.execute_input":"2023-02-01T14:50:23.099666Z","iopub.status.idle":"2023-02-01T14:50:23.126110Z","shell.execute_reply.started":"2023-02-01T14:50:23.099635Z","shell.execute_reply":"2023-02-01T14:50:23.125241Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex Pclass \nfemale 1 0.013889 0.421296\n 2 0.032609 0.380435\n 3 0.146640 0.146640\nmale 1 0.356481 0.208333\n 2 0.494565 0.092391\n 3 0.610998 0.095723","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SexPclass
female10.0138890.421296
20.0326090.380435
30.1466400.146640
male10.3564810.208333
20.4945650.092391
30.6109980.095723
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age, siblings and parents\n\nThe age distribution appears to be multi-modal with some two peaks at around 0 and 25. Both training and testing datasets have a similar mean and standard deviation. However, some skewness may affect a normal distributions and any normalisation processes of the data.\n\nThe survivors and other passengers age appears to be of similar age at the point of centrality. We will need to complete some statistical tests to accept or reject the null hypothesis that the age distribution of survivors and non-survivors are the same. We surmise the values may have be unknown, without any data preparation the tests cannot be completed.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.246337Z","iopub.execute_input":"2023-02-01T14:50:24.247338Z","iopub.status.idle":"2023-02-01T14:50:24.633738Z","shell.execute_reply.started":"2023-02-01T14:50:24.247275Z","shell.execute_reply":"2023-02-01T14:50:24.632647Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"count 714.000000\nmean 29.699118\nstd 14.526497\nmin 0.420000\n25% 20.125000\n50% 28.000000\n75% 38.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.694278Z","iopub.execute_input":"2023-02-01T14:50:24.695165Z","iopub.status.idle":"2023-02-01T14:50:25.062419Z","shell.execute_reply.started":"2023-02-01T14:50:24.695120Z","shell.execute_reply":"2023-02-01T14:50:25.061338Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"count 332.000000\nmean 30.272590\nstd 14.181209\nmin 0.170000\n25% 21.000000\n50% 27.000000\n75% 39.000000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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y=\"Age\", data=titanic_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:25.141606Z","iopub.execute_input":"2023-02-01T14:50:25.142000Z","iopub.status.idle":"2023-02-01T14:50:25.355591Z","shell.execute_reply.started":"2023-02-01T14:50:25.141964Z","shell.execute_reply":"2023-02-01T14:50:25.354536Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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majority of passengers may be travelling on their own without a spouse, sibling, children of parents on board. However, passengers with 1 or 2 siblings/spouse appears to have survived; the percentages is in the range of 46% to 54%. Parents or individuals with one, two or three parents were less likely to perished - the percentages ranges between 50% and 60%.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"SibSp\"]).count()[\"PassengerId\"]/titanic_train.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.432856Z","iopub.execute_input":"2023-02-01T14:50:29.433512Z","iopub.status.idle":"2023-02-01T14:50:29.445537Z","shell.execute_reply.started":"2023-02-01T14:50:29.433478Z","shell.execute_reply":"2023-02-01T14:50:29.444361Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"SibSp\n0 0.682379\n1 0.234568\n2 0.031425\n3 0.017957\n4 0.020202\n5 0.005612\n8 0.007856\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"SibSp\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.890502Z","iopub.execute_input":"2023-02-01T14:50:29.890860Z","iopub.status.idle":"2023-02-01T14:50:29.915654Z","shell.execute_reply.started":"2023-02-01T14:50:29.890829Z","shell.execute_reply":"2023-02-01T14:50:29.914470Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSibSp \n0 0.654605 0.345395\n1 0.464115 0.535885\n2 0.535714 0.464286\n3 0.750000 0.250000\n4 0.833333 0.166667\n5 1.000000 NaN\n8 1.000000 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SibSp
00.6546050.345395
10.4641150.535885
20.5357140.464286
30.7500000.250000
40.8333330.166667
51.000000NaN
81.000000NaN
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.SibSp, bins = 8)\ntitanic_train.SibSp.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:30.548135Z","iopub.execute_input":"2023-02-01T14:50:30.548522Z","iopub.status.idle":"2023-02-01T14:50:30.775129Z","shell.execute_reply.started":"2023-02-01T14:50:30.548488Z","shell.execute_reply":"2023-02-01T14:50:30.774363Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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0.760943\n1 0.132435\n2 0.089787\n3 0.005612\n4 0.004489\n5 0.005612\n6 0.001122\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Parch\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.187336Z","iopub.execute_input":"2023-02-01T14:50:31.187728Z","iopub.status.idle":"2023-02-01T14:50:31.209460Z","shell.execute_reply.started":"2023-02-01T14:50:31.187695Z","shell.execute_reply":"2023-02-01T14:50:31.208365Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nParch \n0 0.656342 0.343658\n1 0.449153 0.550847\n2 0.500000 0.500000\n3 0.400000 0.600000\n4 1.000000 NaN\n5 0.800000 0.200000\n6 1.000000 NaN","text/html":"
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Survived01
Parch
00.6563420.343658
10.4491530.550847
20.5000000.500000
30.4000000.600000
41.000000NaN
50.8000000.200000
61.000000NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Parch, bins = 6)\ntitanic_train.Parch.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.433509Z","iopub.execute_input":"2023-02-01T14:50:31.434117Z","iopub.status.idle":"2023-02-01T14:50:31.664941Z","shell.execute_reply.started":"2023-02-01T14:50:31.434071Z","shell.execute_reply":"2023-02-01T14:50:31.664079Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.381594\nstd 0.806057\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 6.000000\nName: Parch, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We decided to add both fields _Parch_ and _SibSp_ together as a familly. The mean and median age appears to be quite close between the passengers who have survived and perished. For smaller families the spread appears to be smaller than for larger families. \n\nThe highest percentages of surviving the accident suggests that passengers in first and second class with no other familly members. These percentages are loweer than 30%.","metadata":{}},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train.SibSp + titanic_train.Parch\ntemp = titanic_train.groupby([\"fam_members\",\"Survived\"]).agg([np.median, np.mean, np.std])[\"Age\"]\ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.929453Z","iopub.execute_input":"2023-02-01T14:50:31.929858Z","iopub.status.idle":"2023-02-01T14:50:31.977029Z","shell.execute_reply.started":"2023-02-01T14:50:31.929823Z","shell.execute_reply":"2023-02-01T14:50:31.975764Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" median mean std \nSurvived 0 1 0 1 0 1\nfam_members \n0 29.0 30.0 32.414234 31.811538 13.334968 11.970452\n1 30.0 29.0 32.126984 30.781842 11.599836 14.916443\n2 30.5 22.0 31.500000 21.911887 13.776141 17.363697\n3 25.0 14.0 22.833333 16.972381 11.196726 15.054360\n4 12.5 21.0 17.000000 31.000000 15.528775 19.974984\n5 9.0 24.0 17.578947 23.666667 18.637822 0.577350\n6 9.0 11.0 14.875000 15.750000 15.169871 16.070159\n7 12.5 NaN 15.666667 NaN 14.361987 NaN\n10 NaN NaN NaN NaN NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medianmeanstd
Survived010101
fam_members
029.030.032.41423431.81153813.33496811.970452
130.029.032.12698430.78184211.59983614.916443
230.522.031.50000021.91188713.77614117.363697
325.014.022.83333316.97238111.19672615.054360
412.521.017.00000031.00000015.52877519.974984
59.024.017.57894723.66666718.6378220.577350
69.011.014.87500015.75000015.16987116.070159
712.5NaN15.666667NaN14.361987NaN
10NaNNaNNaNNaNNaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.fam_members, bins = 10)\ntitanic_train.fam_members.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.210511Z","iopub.execute_input":"2023-02-01T14:50:32.210873Z","iopub.status.idle":"2023-02-01T14:50:32.431170Z","shell.execute_reply.started":"2023-02-01T14:50:32.210842Z","shell.execute_reply":"2023-02-01T14:50:32.430235Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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twlqUP/Bzr6a6xtewKkAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"fam_members\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.505200Z","iopub.execute_input":"2023-02-01T14:50:32.505646Z","iopub.status.idle":"2023-02-01T14:50:32.533253Z","shell.execute_reply.started":"2023-02-01T14:50:32.505607Z","shell.execute_reply":"2023-02-01T14:50:32.532232Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nfam_members Pclass \n0 1 0.236111 0.268519\n 2 0.369565 0.195652\n 3 0.519348 0.140530\n1 1 0.087963 0.236111\n 2 0.086957 0.097826\n 3 0.075356 0.040733\n2 1 0.027778 0.083333\n 2 0.054348 0.114130\n 3 0.054990 0.040733\n3 1 0.009259 0.023148\n 2 0.016304 0.054348\n 3 0.006110 0.012220\n4 1 NaN 0.009259\n 2 NaN 0.005435\n 3 0.024440 NaN\n5 1 0.009259 0.009259\n 2 NaN 0.005435\n 3 0.034623 NaN\n6 3 0.016293 0.008147\n7 3 0.012220 NaN\n10 3 0.014257 NaN","text/html":"
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Survived01
fam_membersPclass
010.2361110.268519
20.3695650.195652
30.5193480.140530
110.0879630.236111
20.0869570.097826
30.0753560.040733
210.0277780.083333
20.0543480.114130
30.0549900.040733
310.0092590.023148
20.0163040.054348
30.0061100.012220
41NaN0.009259
2NaN0.005435
30.024440NaN
510.0092590.009259
2NaN0.005435
30.034623NaN
630.0162930.008147
730.012220NaN
1030.014257NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Futher analysis and discussions\nThe data in their current states suggests that the distribution for the field _Survived_ is likely to be binomial. It has a lowest occurrences of surviving, which is a shocking statistic.\n\nThe passenger class has more occurrences of third classes. However, First and second class female passengers were more likely to survive the accident. First class male passengers had the also the highest survival rate. The Age is skewed to the left; some age may be unknown. It appears (see below) the younger passengers may have been traveling with other members of a family and perhaps reduced their survival rates; the largest familly appears to be travelling in third class. Most occurrences were families made of 0, 1, or 3 family members. \n\nThis analysis suggests that perhaps the passenger class familly, and the gender may have contributed to a higher survival rate. However, the familly size may have contributed to survived too. The classifiers will need to identify other patterns that may have contributed to survive the accident. It is likely to be quite challenging as no linear relationships or grouping may be present in the data.\n\n","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=3).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.247279Z","iopub.execute_input":"2023-02-01T14:50:33.247672Z","iopub.status.idle":"2023-02-01T14:50:33.275585Z","shell.execute_reply.started":"2023-02-01T14:50:33.247640Z","shell.execute_reply":"2023-02-01T14:50:33.274507Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass fam_members Sex \n1 0 female 0.001821 0.096491\n male 0.091075 0.073099\n 1 female NaN 0.114035\n male 0.034608 0.035088\n 2 female NaN 0.038012\n male 0.010929 0.014620\n 3 female 0.003643 0.005848\n male NaN 0.008772\n 4 female NaN 0.005848\n 5 female NaN 0.005848\n male 0.003643 NaN\n2 0 female 0.005464 0.084795\n male 0.118397 0.020468\n 1 female 0.003643 0.049708\n male 0.025501 0.002924\n 2 female 0.001821 0.038012\n male 0.016393 0.023392\n 3 female NaN 0.026316\n male 0.005464 0.002924\n 4 female NaN 0.002924\n 5 female NaN 0.002924\n3 0 female 0.041894 0.108187\n male 0.422587 0.093567\n 1 female 0.025501 0.043860\n male 0.041894 0.014620\n 2 female 0.018215 0.035088\n male 0.030965 0.023392\n 3 female 0.001821 0.014620\n male 0.003643 0.002924\n 4 female 0.016393 NaN\n male 0.005464 NaN\n 5 female 0.009107 NaN\n male 0.021858 NaN\n 6 female 0.009107 0.008772\n male 0.005464 0.002924\n 7 female 0.003643 NaN\n male 0.007286 NaN\n 10 female 0.005464 NaN\n male 0.007286 NaN","text/html":"
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Survived01
Pclassfam_membersSex
10female0.0018210.096491
male0.0910750.073099
1femaleNaN0.114035
male0.0346080.035088
2femaleNaN0.038012
male0.0109290.014620
3female0.0036430.005848
maleNaN0.008772
4femaleNaN0.005848
5femaleNaN0.005848
male0.003643NaN
20female0.0054640.084795
male0.1183970.020468
1female0.0036430.049708
male0.0255010.002924
2female0.0018210.038012
male0.0163930.023392
3femaleNaN0.026316
male0.0054640.002924
4femaleNaN0.002924
5femaleNaN0.002924
30female0.0418940.108187
male0.4225870.093567
1female0.0255010.043860
male0.0418940.014620
2female0.0182150.035088
male0.0309650.023392
3female0.0018210.014620
male0.0036430.002924
4female0.016393NaN
male0.005464NaN
5female0.009107NaN
male0.021858NaN
6female0.0091070.008772
male0.0054640.002924
7female0.003643NaN
male0.007286NaN
10female0.005464NaN
male0.007286NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns = [\"Survived\",\"Pclass\",\"Age\", \"fam_members\"]\ntitanic_train = titanic_train[columns]\npd.plotting.scatter_matrix(titanic_train, diagonal='kde')","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.689687Z","iopub.execute_input":"2023-02-01T14:50:33.690876Z","iopub.status.idle":"2023-02-01T14:50:34.713667Z","shell.execute_reply.started":"2023-02-01T14:50:33.690832Z","shell.execute_reply":"2023-02-01T14:50:34.712910Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"array([[,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ]],\n dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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27mwWXVPL5qHw+/tweAijw35XkuSnNcFGQ6cDusuO3mxxZjkunqo4uAUhAIG/hDEfzhCIGQgScQpq4jQF2bn/0tXjzRFNOlOU4+vHgcN5wwgWmlaZMSSaPRHAMM2ESjlHovwbptfe0nIguBLKXUqSJyn4gsVkqtGKgcyUBEOGFSISdMKsQXjLByTzMba9rZWtvOwTY/G2vaaPWF8AUjBML9nyB22Cy4bBYyHDZKcpyML8zgxMmFzCrPYU5FLtNLs4c119CGmjYqb38WgAJg9Z0XDtu5+0OXbADfnQ433ZS+8lXfeSGhiOLKe99h1d5WADLsFkqynRw/qYC1e9uobfPSHjDvF4dFKMl18vkzp3DfG7vY2+glHapMWAAV/QDkuGyEIxECEYXDAhaLFbtVUIYCixAMG4BifH4m+ZkOPjjYhiCcOrWYr50/ncJMJ597ZDUrqpspyHTw/UtnU98epNkb5PzZY6gsykwoR+y9Ceb1jWXm7c/ii/neu/2cu15ke2PosO0vbjzAp/+2BqXg6oUV/PSa+T3af/3KFn7+khkAetqUAh765Ik92m99YDkvbGkEoLLAxetfO6tH+1/e2sUPn9uMUvD5pZP40nkze7Q/tmIv335qIwA/uOw4Prx4fKLLMGD+77lN/Omt3YgIv7thYb/2SYV9/gTgpejyy8CJQEqVfyxuh5VTpxZz6tTEqRQihsIXihAxoo9L959DtYKdNitOmyWtk8g1971JSvneVrgp1UL0gScQZkvtoYBBb8igxRvi7e1N+EIROgKHOgpBQ9HmDfHgsj3UtwfSQvED9O7KtPsPxb/4DLAQwR+9jRXmyFYE9rf6ONjuxx80j/DBgTY+ONBOntvOloPtBMMGzZ1Bnlh1gInFpsLfeKDtsMq/L3x9tMcq/kT85rWddD2y/91Uy097tf/prT3dy+/sjH86uhQ/QHWzP679/mXV3cf/+4r9ccr/oXerCUc3eOjd6qQr/ydW1xCJ/oN+89qOfu2TCneS6cAdIvIW8CkgL7ZRRG4RkZUisrKhoSEF4h0Zq0XIctrIddvNT4b5yctwdH/cDmtaK34Ad6oF6INLJqVagr7JdFqZVHxImblsQpbLTlVlPvluOxmOQ/eAzQJZLhvXHj+OgkwH6XJ39JYj02HFZResAg4LOO0WMh023NG/TpsFm1UozXExpSQTp82C02Zh+phsZozJZs7YXCaXZGGzWsh127lg7hhKcpzYrcLMsoEXIOrLDWN83pG3uOW0SVjE/L1nJOjYfeyEQ8p4wbi8uPaTJx9KXFCR64xr//DisQjm8S+bXx7X/pGqcVgtgtUifKRq3BFlHQgXzSnDImAV+OTJE/u1jxwy1w8PIvI5oE4p9Y9oBtDVSqmvJNrWmpGrbLkl3d/nVKRl5oi0ZUNNz7n37FALlZWVqRFmFLJ+83YkpyRhmwC9nywBLCJEhvmZOxyC2Yu3iBC16pDrtlOe56bNF8IfimARIS/DTosnREQpIoaB1WKJ+auwW4VMp40sp2lIaPeF8IUiiAj5GXZavCGUUj226c2aTds50rPe+14+2va9TV7a/ObowGaJfxFtr+vAHzXpWgRml/fcf9OB9h7/t97H31Lb0R0zZLUIs3odf2+zlzafef5ct53xBcl17Nhe14k/bI4n8zPs1O3arJRSR+zcp8Ls8w5wK/APoATT9NONiNxC1HXUmlNM2Y2/7G5bmWY26nQn1oYKUPTyd1m5cmWKpBn5bKxp4+0djVw8r5yKPDeZFdMo/ugvEm5rEbrNAF3YLWaKcW8oPQILbRawWixk2C34wwYZDiunTC3mV9cu4J+r9rOv2YtFhA/NGcOz6w/S7g/R1BmgMMtJU2eAoiwnDR0BJhVnMacil7NnlQLw5Jr9VDd6ETHraTy7/iAAs8tzOHf2mISyOMumHvFZ730vH237Zb95m7X7zRdElsPKyu+f36P9hB+9RG1HEDD/Tyt/3HP/qd94lth/W+/jz7njBTqiJjO3zcLKH36oR/vVv1vGqj0tACyakM/jnz6JZHLKna+wv9U0R502tYiHP3nC6r72GXblr5RaLSJlIuIBaoFXerX/gWiUsLNsanp0kUYB1XdeSFXVd1MtxojlgXd2871nNqEUzCzLoSLPTWVhBu4MK83eCDbAagNBqCzOoDTHjREOs2pfB+GIweSSLIqznSwal8urWxvYVtuOJ4nppRwWiBimaakgy05DR4BAWKEiCrfTSpbLhlXMXnmOy8aC8QXUdviZUJDJlJIsPqhpw2638LXzZgBwzsxSVu9rYVy+mykl2fhnRmjzhXDbrXiDETIc5l+33YovFGFxZUG3LGfOKGXN3hYq8txMLc0mOMug2RPssU1v5lTk0njYVvP+7XZWSOCBHds+rSherT1y8yJOv/sdfMEwf7lxcVz7y184kao73yJiGDzyiUVx7e9/9SQW/GQZAL+7bm5c+0tfOJnT7noTpRT/+MySuPbf31DFdX96t3s52dz/8cXceP8K3HYrf7xhPg9/su99ht3s0+PkIr8GXlVKPZmovaqqSumeavKoqqrSPf8BsGxnI9f/aTlnzyzlJ1fOJT/Djojo65lE9LVMLiKySil1xLfMsPf8RcSplApEv7bT90S+RpMywhGDO57+gPEFGfzq2vlkOEZEALtG0yep8PY5X0TeEJE3gFLgxRTIoNH0i/+sP8C2uk5uP3+GVvyaUUUqbP7/Bv493OfVaAbCA8v2MKk4k/OPSzxRqdGMVHTaSI3mMKzb18q6fa3ceKJOtKcZfWjlr9EchsdX7cNtt3LFwopUi6LRJB2t/DWaBIQjBv/dUMuZM0vIdtlTLY5Gk3S08tdoErB8dzNNniAXzy1LtSgazZCglb9Gk4Bn1h8g02HljOmJ0zdoNCMdrfw1ml4YhuKlTXUsnVEy7JXdNJrhQit/jaYXmw6209gZ5MwZutevGb0MSvmLyGQRcUaXzxCRz4tIXlIk02hSxBvbzFTih6vpoNGMBgbb8/8XEInW7v0DMA74+6Cl0mhSyBtbGziuIofi7Pi87RrNaGGwyt9QSoWBy4FfK6W+ilnXV6MZkbT7Q6za28Lp03SvXzO6GazyD4nIR4AbgWei67RTtGbEsmxHIxFDcfo0be/XjG4Gq/xvwqzB+yOl1G4RmQg8PHixNJrU8Ma2BrKdNhaMz0u1KBrNkDKoxG5KqU3A5wFEJB/IVkr9JBmCaTTDjVKKN7Y2cNKUQuxW7QinGd0M1tvndRHJEZECYDXwRxG5OzmiaTTDy476Tg60+bXJR3NMMNjuTa5Sqh24AnhIKbUEOHvwYmk0w0+Xi+dp04pSLIlGM/QMVvnbRKQMuIZDE74azYjkjW0NTCnJYmx+RqpF0WiGnMEq/+8DLwA7lFIrRGQSsH3wYmk0w4svGGH57mbt4qk5ZhjshO/jwOMx33cBVx5pHxFZAvwCMIAVSqkvDUYGjSYZvLe7iWDY0Mpfc8wwKOUvIi7gZmA24Opar5T6xBF22wOcqZTyi8jfRGSOUmrD4TauvP1ZAOZnw1PfvHAw4h5zLLn9Weqiy9V36mt3JN7Y2oDLbuH4iQX93scwFLf+dSUtnUGKsx1EIjC9LIsJhZlYxEJNq5fCTCdXLByLw2YOsrfWdrCltp2x+RnUtftZvquJFbub2N/qY2KhmwWV+Ww76OFgq5d9LX7CCkqybBRkONjd5CUSAQU4bEK2ywbKoCMQQSFkOwSxWLjhxEra/WG2HuygOMdBmy9EmzfMyZMKafGFyXPbmFCURWOnnyyXnQMtPrbWdTC7PJvibBd17X42H+zguiVjMQyhoTPI3IocdjZ4GZPrJNdtpzMQYe7YXOxWC7saOmnxhuK+z6nI7f7d/aHrWYfE92tX+6dOGMc3L5t72PZvnjeNTy2dGte+8Hsv4A1F+PdnT2V6eXZc+5badgIhgzkVuVgs8ZXbNh9sJxQxOK48cftIY7A1fB8GtgDnYZqArgc2H2kHpVRtzNcQEDncthtq2rrDhdd2DE7QY5G6mOXK259FT2Menje2NXDCpMKjyuK5t9lL+6Y6wob53SLwxvYG8jMd2ETwhiIUZzsJhA1uPKmS6kYPf1++hy21HThtFqqbPBxo9XXvv66mk3U1nXHnqe8MU98Z7rHOF1b4OkMxaxT+sAIM7n5pB3arEI6oaIvJmr2t2K0WbFbBbbdhKAO71UJTZwAFvLmtgYJMB02eIAJs2N9KXoaDDIeNf64Cm8VCXoYdl91KZWEmHf4Qs8pyeHrdAZQyo6Nnl8d894VY2s/keLHPeiJiXwx/fG9fnPKPbf/RC9vilP8ZP3uVZp95DS/8zZvs+HHPl8uuhk7+u8FUTcGIweLKnp2A7XUdPL/RbA8bioXj8/v1u9KZwdr8pyilvg14lFIPAhcCS/qzo4jMBYqjsQKx628RkZUisjLibRukeBpN3+xp8rC70cMZyTT5xHQMu8r/ihxaTjWmHPHC9CWecPjfELc6TX5rf4it0ZxI7NjfPIJ+1hEZbM+/q+vRKiLHAbVAn6/6aFzAbzC9hHqglPoDZpI4qqqqVGN0/SWTBinpMUgp9DD7VFV9N5XipC1dLp6nH2XhlvGFGZwwewytniDF2U4ihmL6mGzGFWRgEaG2zUdBlpPL5pk1gCcUZnL9kglsre1gbIGbujbT7LN8dxMHWnxMLM5gQWUB2w52crDVy55mHxEDSrLt5GfaqW7wmqMEBQ67hdwMG0QM2gNhDIQchwWxWvjYCZNo8QfZWttBaY6DNm+YVl+IkycX0ewNke+2Mb7QNPtku+zUtvrYUtfO3Ip8CjLtHGzzs7WuneuXVBI2DOo7Aswfl8eO+k5Kclzkue10BsIcV2GaeS6dX0GrN9j9/bL5FbREv/eXORW5dD3rjgTt1Xde2N27/+rSyUds/+Z50+LaX//qmVT94EU6A2H+/dlT49onFmVy4dwyAiGD2eU5ce1TSrL50BxFOKKYVRbfPhIRpVTfWx1uZ5FPYmb2nAvcD2QB31FK/e4I+9iAp4E7lFLvH+n4VVVVauXKlQOWT9OTqqoq9PWM5+YHVrC9vpM3vnpGjx5gX+jrmTz0tUwuIrJKKVV1pG0G6+3zp+jiG0B/++ZXA4uBn0YftK8rpd4djBya/rN2Xysl2U7Kcl1HpehGK4FwhGU7m7i6aqy+HppjigEpfxH58pHalVKHTfGglHoEeGQg59UMnuv++B7eYITZ5Tl868JZnDi5MNUipZQVu1vwhSLaxVNzzDHQCd/sPj6aNOUPH63i2xfNosMf5iN/fI/739mdapFSyqtb6nFYLcf8S1Bz7DGgnr9S6nvJFkQzPJwytYhTphZx3fHj+dJja/nefzbhsFm4fsmEVIs27CileOGDWk6dWkSGY7C+DxrNyGKwWT0fjK3ZKyL5IvKXQUulGXLcDiu/uW4BS6cX891/f8C7O5tSKs+ynY185fF1XHXfMj754EoeeX8v/tBhQ0CSwgcH2qlp9XHe7DFDeh6NJh0ZrJ//XKVUa9cXpVQLsGCQx9QMEzarhXs+soAJhRl88bE1tHqDwy5Dhz/EZ/+2iuv+uJyXNtVht1rYUd/B15/YwAW/eouNNUMX6/HCB7VYBM6aqVM4a449Bqv8LdEiLkC3/74eP48gsl12fnXtApo6g3zrqY0MxvX3aGnqDHDVfe/ywgd1fPW86Sz/xlk8cssJvPaVM3jgpsX4QhGu+f27vLW9YUjO/8IHtRw/sYDCLF2oXXPsMVjl/3PgPRH5gYj8AFgG/HTwYmmGk+Mqcvni2VN5Zv1Bnll/cFjO2e4P8bG/vE91k4cHblrMbUundKdWEBHOmF7Cv287mfEFGXzqoZWs3tuS1PNvr+tgW10n52uTj+YYZVDKXyn1EHA5ZiBpHXCFUkrX8B2BfPr0ycwbm8t3n/6Aps7AkJ7LMBRfenQtW2s7+N1HF3Hq1MRuliU5Lv76ySWU5rj45IMr2dfsTZoM/1pdg9UiXDSvPGnH1GhGEgNS/iLiEpEvishvgFOB3ymlftM7T49m5GCzWvjpVfPo9If5ztMfDOm5fv/mLl7ZUs+3L5rF0j5SKhRlOXngpuMJRww++7fVSZkEjhiKJ9fs54xpxRRpk4/mGGWgPf8HgSpgA/Ah4K6kSaRJGdPHZPOFs6fy7PqDPLdhaMw/y3c1cdeLW7lwThkfO7F/7qUTizL5+TXz2VDTxg+fHXz/4p0djdS1B7hy0dhBH0ujGakMVPnPUkrdoJT6PXAVcFoSZdKkkFtPm8Scily+/dRGmj3J9f5p7AzwuUfWMC7fzZ1XzjmqdArnzCrl1tMm8df39vLvtTWDkuOhd/dQkOngzH6mG9ZoRiMDVf7dicSVUuEjbagZWdisFn529Vza/SHuSKL5J2IovvjoWtp8Ie69fhHZLvtRH+Mr503n+MoCvv7EBrbXDazAQ3Wjh1e21HH9kvFHlbtfoxltDFT5zxOR9uinA5jbtSwi7ckUUDP8zBiTw+fOnMrT6w50F7AYLPe8sp23dzTyvUtmMytBytz+YLda+PV1C8hwWPnM31bjCRx9v+PPb+/GZhE+esKxF9Gs0cQyIOWvlLIqpXKin2yllC1meXQkuz7G+cwZk5lVlsO3ntpIyyDNP29tb+CeV7dzxcIKPrx43KCOVZrj4p6PLGBXQydff2LDUcUl7Gv28uiKvVy1aCwlOa6+d9BoRjGD9fPXjFLsVgt3XT2PVm+Q7z8z8EnWA60+vvjoWqaWZPHDy45LStrkkyYX8T/nTufpdQd4cFl1v/f72QtbsYjwhbPii31oNMcaWvlrDsus8hxuWzqFJ9fU8OIHR2/+6fCH+MQDKwiEDe69fmFSk6d95vTJnDOrlO89s4ln+xGY9vKmOp5ed4BbT5/MmFzd69dotPLXHJHblk5hdnkOX3ps7VHl2fGHInzmr6vZUd/JfTcsZEpJcjN9WyzCPdcuoGpCPl98bA3Pbzz8C2BPk4ev/Ws9M8Zk8/+WTkmqHBrNSEUrf80Rcdgs/PnGxeRlOPj4/e/zwYG+XwCdgTA33b+Cd3Y2cueVcw8bwTtY3A4rf7pxMcdV5PLpv67m7pe2EQj3DALbVtfBdX9cjqEU992wCIdN3/IaDWjlr+kHY3JdPHTz8ditFq753bv8Z92Bw060fnCgjUt+8zbvVzdz9zXzuGqIA6ly3XYe+dQJXLGggnte2c7Zd7/BXS9s5eH39vC1f67jwnveIhA2eOgTxzOxKHNIZdFoRhLDnoFTRMqBZ4BZQJaOExgZTC7O4qnbTuaWh1fxuUfW8OiKvVy/ZALzx+Vhswpbazt4cnUNT62toTDLycM3H89Jk4uGRTaX3crdH57PJfPLue/1nfz29R0oBZkOK1cuHMuXz51GSba282s0saQi/XIzcBbwZF8btra2UXn7swC4gc13Xji0ko0yuq4dQHUSrl1pjot/ffpEHnx3D394cyef/dvqHu2ZDis3nTyRz505hbwMx6DPd7ScMb2EM6aX4A9FaPeFyM90YLcO7eD23Z2NbK3tYGJhJnabBUPB8RMLepiXlFKs2mPWCj6+soD1+1tZu7eFlXua2VnXSWcwhDeoKM1xMm9cLm9sbyIQCBGMKBAYn+9GidDSGcRqteALRvCFI+S57GRn2HHbrexp6KAzBAJk2CCsBBFFYZaD0uwMwoZBUZaDDfvaCCqFwyp4AhGUUiilcNqtZDht5LrtZDls+CMRnBYLDrsVQyk6/GHmjc1lW30nRkSR4bQyLs9NoyfElNJMbjl1CgVZ5v98/f5W/rPuAC67lRtPmkBRVuIX74b9bTR5AiyuLAD6vl/7237jkgq+d/n8Hm2BQIALfv0unYEwf7lxMbPH5vZoNwyDvy7fizcQ4YYTJpDl6qka/f4wX/7nOnyhCD+7eu5hf9NIYtiVv1LKD/j74/K3zwNl0WXfkEo1+qm8/VmS0Q+3WS3cfMpEPnbiBNbua2VrbQeGUowvyGDJxELcjtRHzbrs1mGJ3vUGIzy74SDb6zrJz3CQ6bQyNj8Di4Ueo56dDZ28tb0RgNo2H+v3t/PW9gb2t3gJG4eO197gZUeDl94GtW0Nie/+Bk+IBk+oxzoFeMJdS1DTGqSmNYgFMHofIAZ/JEKbP8LBtp4ZXa0CEWX+3VLbgQBG9PsKi2ARYdPBdqwWK189bzoHWn38ffke3treSIbDRsRQfO38GXHnq23z8/LmOvPcIYMNNW3dz3oiYhV/IibHtD+4vCZO+d/26Hp2NnoAuPnBlbz3zbN6tL+8ub7ba8xqhU+dOrlH+09e3NJdV+I7T33AvTcsOqI8I4G0K7wiIrcAtwBYc4ZmolAzeOxWC4srC7p7bcciVhHsFrOH77Jbunv7mb1cWt0OGyKgFORlOHHYBIfNglWEcC9Vb7FA5EhaeoCIQNxb5XDbxuxjEVPZg/l7lVKIRNssggBWi5DnNn+z227FbbchIlgtQq47cRoPl92C1SJEDEWmc/Av6kynhfbA4S/cuDx39/8gxx2v9vIy7N3t+e74UWt5nrt7uSh7+Ee1Q4EMZ+WmHicWeR04+0g2/6KiIlVZWTlsMo02AmGDNl8Iq0UoyHCwu7qarKIybBYhP8NBEuKtjmmqq6vR92dyqK6uJru4nIihyHXbcWqvrEGxatUqpZQ64kVMu55/LJWVlaxcuTLVYoxYnttwkK21ZgK0yxdUcM7pJ/HZXz4OwJULxzK+MCOV4o14qqqqhvz+bPOFcNosoz4J3dz5C7npZ48CMK00mwvnHskIpOkLEVnd1zap8PaxA/8F5gEviMg3lFLLh1uOY4E5Fbnsb/GSl+GgPM+N224l02klP8Oho1zTnLX7WvnWUxvYWNOO1SKcPq2Yr39oBlNLkxssly7YrRbG5rtp8QaZU5Hb9w6aQZOKCd8QcHZ/tw9FDCKGGvU9n6FgXEEGt5x2aOLKYbP0+K45OpRSeIMRMhzWpOQoOhyr9rRw/Z/eozDTyVfPm067L8RjK/dx4T1vc9c187hkFJaeFDFHp6GISgungWOBtDb7RAzFn9/eTTBscPG8ch2ko0kpz2+sZUttB1NKsrh4iBSwJxDm84+soSTbxROfPam7zOQtp03iM39dzRceXYPNIlwwZ3SZRQyleGBZNZ2BMOfMKmV2ue79DzVpPasSihi0eIK0+0LsafKkWpxRQU2rlw5/qO8NNXHsiroK7m4cunvxz2/vpqbVx93XzOtRX7gwy8lDNx/PwvH5fPGxtWzY3/88SyOBcETR1Bmg3ReiutGbanGOCdJa+SsFj63Yx6Pv72XfED5wxwotniCfenAln3xwBfXt/lSLE0dnIMx7u5rY35KeD39pjpOdDZ2U5AxN0XdPIMwf39zFubNKqUrgQuuyW/njx6ooyHDwhcfW4AsOvph9uiACT66p4Z+r9rOldmAvtq21Haza00xoKHxlRyFprfybPUG8wTDBiMHjawZXt1UD7f4wbb4Q+1v8rN7bkmpx4nhhYy3v7mziydU1aanY6toDTC7Oor490PfGA+DpdQfoCIS59fTDz8sUZDq4+5p57G708PMXtw6JHKmgwx/GEwijlOLNbY1Hvf++Zi/PbTjIm9saWb6reQgkHH2ktfLPdNpw2CzYrMLC8fmpFmfEk+Uyw/cr8tzMH5uXanHiiMZLmcFDaRiDUFlozjlNGCIX2Ufe38v00mwWjs874nYnTSni2sXjeWBZ9YBrGacbmU4beZkOnHYrJ08pPOr9rZZDN4wlrbVa+pDWlynXbef0qUXMH5fLradPSrU4I56CTAefOLmSr50/nTExEYvpwsSiTNp8IUqynWkZ5HPOrBI+NGcM588ek/Rj723ysn5/G1ctGtsvT6KvnjedTKeNO/7zwVGVskxXHDYLl84rZ8nEAq6pSlzqs90forYtsbmyPM/NZQsqOHtmKUsmHv3L41gk/Z6wGJo9AV7e0sDK6lZ++t8tqRZnxNPYEeCXL2/nG09uoKY1/ezqG2vayXXb2d/io92ffslen1p7gP9uqOXJITBBvrjJrJR2Xj9fLAWZDr549lTe2dHEsp1NSZdnuPEGwjy+aj/v7Wrinld2xLW3eoM8/O4eHnl/72FNlhOLMpkzNrfHKCBZGIbi/d3NvLOjcdTMKaS18veGIgTDBqGIYvPB0TG8HU7e29nEZb95m08/vJIOf4hWb5CDbX5213fy2pb6VIsXx5SSLFq9QQqzHGQ7088LefWeFlZUN7NqT/LnS17cVMeMMdlHFXV93ZLxlOe6uOvFrSO+9x+KGDR3BGjxhNhRFz/h29ARYPXeFlbtaWHLwfZhl29LbQfv7Gjk/d3NrNnbOuznHwrSWvm7o4FdChibpyNSj5ZfvrKNnQ2dLNvZxIsf1BIxFBEFIQWShsqi1Rskx23HH4oQNtJPPkMpvMEwkSRfu2ZPkJXVzZx7lOYkp83K58+aypq9rbyahi/zo8FQipAyM4/Wd8a7IlsEcl12cty2lASBxSafyxglQWhprfx9wUh3IsLdTelnpkh3DMOMSPWHIjijuea72JeG7pRr97XyxrYGlu9qxh9KP2+fcERhKDP4MJks39WEoeD0aUefxfbKRWOZUJjB3S9tG9G9f2+Md1ebL175F2Y5CUUMOvxhirPiXW0bO/3c9rdVfPTPy1m+K/lmsAmFmVyzeBxXLKzguFGSfiKtlX/srRwcJXa24aQw047VIjisFnJcth4eNMXZ6TfhW9vmx+MP0eQJxNXiTQcMpXDZLBhJVv7v7WrCbbcyd+zRKxW71cLnz5zKBwfaeWlTXVLlGk7c9iP3rJs6A9itFrKdNho6411t1+xpYUd9JwfbfLyyeWhGQRV5biYUjp4sA2mt/GPnbVy2NPT9S3Pq2v0YShGMRKht8/foGTZ1BlMoWWLavCEaOoM0dgaxD8Gk3WCZWpLF1JIsppRmJfW4y3c3s2hC/oCrjl06v5zKwgx++fL2Edv7D0VUzHL8i99Q5v18sM2PJxDvDFCU7URECEfUqMm3P9SktfKPvY8jI/OeTilKBMMwH5wspw3FIYXqTMOXqYFZXtBmgY40DPIqyHLSEQhTmJk85dLiCbKltoMlEwdeFMdmtfC5M6ey6eDI7f2rmHF+okGfCITCBoYyEha7GV+QyYVzyrh0fsWAXT1DESMtR5xDRVor/1jbamcauv6lO76AOWdiKGjxhuLa0o0ddZ00eUIcaPVjScMebLMnyMSiLJo9ycuN9H61GY16wuTB+aaP9N5/7LOeaE5lf4uX9Qfa2HSwg/d3x9v0C7OcnDG9mAXj8wZkk2/xBPnz27v5wxu72HuMzC8OqfIXkUoRqROR10Xkxei6r4rI2yLyt2hu/34ea+Td0KmmzRfsVv6tvgCxsygqDa9nezThXMRQ1BwmmCeVLJ6QiycQZuGEvKQdc/muZpw2y4Ds/bGM9N6/y3bIzp/jilcL6/e1EYkYKKXY1dAZ176roYM7nt7Ez17Yyr/XHn0cRk2rD1/Q9DKrPkaSSA5Hz/8lpdQZSqlzRaQEWKqUOgVYD1zW34OEw+mnrNIdf0x1cF/I6GFG8x6h3mmqcNkt3a+nMTnp59r7y5d38tSamoRBSANl+e4mFo7Px2kbvPvgpfPNtOcjsfcf6z7rC8WP8mdVZBNREDbMALferKhuYV+Ll7p2P68PwO11SkkWEwozGJPrOmaKyQyH8l8qIm+JyJeAKuD16PqXgROPtGOsu1+rNvscNR0x12x3g6eH99T+1vQLmuv0m//vrsm9dGPdvhbafEE27k9OkFebN8Smg+0smTRwe38sZu9/CpsOtvPiCOv9x5p1O4PxHZOXN9V1uypvrIkP8hqfl0EwYuAPRSg6TNZVTyBMqzexo4PLbuXc2WO44Lgy8pM4p5PO9Ev5i0ipiPxZRP4b/T5LRG7ux64HgWnAUszqXVVA13+uDchLcK5bRGSliKw0fIf+yWk4P5n2xHT82dfcc6jcmIbePvboP1kEct3pF+FrAKHoBHoyWFHdjFIkNRfNJdGiR78aYb1/ex8PeGPHIffORCMDQ6Asx8mYHBc5znizUVNngAeWVfPAsuruutaxNHQEeOCd3dy/bDc76tOvYzQU9Lfn/wDwAtBVvmgb8MW+dlJKBZRSHqVUGHgG2AnkRJtzgNYE+/xBKVWllKoSd073+gSdAU0f2GLcJQtzevr1C+n3Ni3MdGARcNksWNMwNWOXO2IoSffi8t1NOKwWFvSRxfNoGKm9/1AfZt1JJYfca/Mz4nvm+Zn27numODtREFiQYNg0fR5o88W1N3QECEUUSsHBw8w3tXqDNCWIMRip9PcJK1JK/QOz80NUmffpLiIisdWmTwZ2AKdHv58NvHek/WNn/UdOHyZ9KMg41Hs+Z3ZJj7aizPTrWftCEVAQNhQ2a/q9nLpSTiQrwnf57mbmj89Len3qkdj7N/qQc8aYQ6okkR9/uy9MfUeAZm+Qvc3xE7aTizOZXZ7DpOJMFk2ITw8/tTSLmWVm+4IE6eMPtPp4cNkeHn5vz6gZGfRX+XtEpJCoDhaREzDNNn1xqoisEpFlQI1Sajnwpoi8DcwHnjp6kTX9pc13aHj81paeBTIMSUPlH4xgYCrZ9gQh/qOJDn+IjTVtnDAI//7DMRJ7/329ot7fdWie5UBrfM98Z30HvmCYQMhgZwJvIJvVwrmzx3Dp/IqE3kR2q4XzjzPbsxIkFWz2BDGUOTJo6Dh6k2mrN8iDy6p5cFk1bd70uLf7q/y/DDwNTBaRd4CHgM/1tZNS6jml1CKl1ElKqf+NrvuJUuoUpdR1SqkjXsU0DPIcUcT2+vLcth7Xc255doI9UosneGjCN5As20qasrK6BUPBkklDk3u+q/f/0+e3jIjAJUcfI71ITNSvL4ENeFyBGxFzBDF2CGpVTC3Jwm4VDGUwuzyn7x16sb2+k2ZPkGZPkG1pMnLol/JXSq3GNNecBNwKzFZKrR9KwczzDvUZRjexz0h1k7fH9UzHCNrYCeoW7+ixrSbivV2mvX+oKtTZrBa+c9EsdjZ4+N3ru4bkHMkk2EcIv8SkvkiUVHNrbScd/gi+kMG6/a1Jlg7W7m/lrW0NvLuziWU7E5eZXLWnJWEAGpi1BjIcVjIcViYWpUd+oH6N/UXkil6rpolIG7BBKTVkuWRHd99vePEEwj2G1nub4ie9Uo1VDqXxcDvSzyyVTN7b1cT8cXlDmp546YwSLp5Xzm9f28GFc8cwpST9Rntd9JUs78yphTy+sgYFzCyL73m/u7Ox+/7eUZf8IK0ddZ3sb/UDig9q2jn/uLIe7a9tqef//rsZFHz+rKlcNK+8R3tRlpNbTjOrEfanUttw0F+zz83An4Dro58/Av8LvCMiHx0i2dLQH2Xk0jsH/YE0rOQVO8mb4xq9yr/DH2JDTRsnJMm//0h856JZZDqt/L+/r8GXhqO9/rL2QMeh9O4JbPpzKg69EIoPk9jtvxsO8tCy3T1MSP1l2phsxhe4Kc9zc1yCaOx1+1vxBMJ4gmHWH2bkISJpo/ihnz3/6HYzlVJ1YPr9Y9r9lwBvAg8PhXDa6pM8pNcNb0TSL2guEOPuV98+es0+Xfb+E4bI3h9LcbaTX3x4Pjc9sIJvPbWRu66em1YKqIu+0rdsrjnkX9Lkjb93/T1cReN/3/Mba/nhs5sA2N/q5xsXzDwq+RaNz+fzZ00jbBgJ4zIunlvOqj0tKENxyfzyBEdIP/qr/Md1Kf4o9dF1zSKSHlPXmiOytaGnh0S9J/2Ufywf1DRz9uyyvjccgbzbZe9P4HI4FJwxvYTPnTmVe17ZTkW+my+fM21Yzns09DUoaYqJzE00PWDt44XW5g3iD0UwlLl8tFgswolHSL43uSSLX3x4PoZSlGSnX2qSRPRX+b8uIs8Aj0e/Xxldl0mCQC1N+tG7+NHJw2ByGAzN3pFrouiL17fWU1WZn3T//iPxpbOnUtvm455XtpPltHLLaZOH7dzJoCgmsCuRrTq2FEKiGJGJxZk4rRbChmLiEBVkKUpQYSyd6a/yvw24Ajgl+n0lUKqU8mCmbtCkOcFwz8Gw25ne+UvGF6ZfpbFksK/Zy7a6Tq6pGjes5xURfnz5HDzBCD9+bgvtvjD/c+60tDQBJSLLfcg332GPV/+xDgx1HfFxAMGwQWG2E0OBjI4SvIOmX8pfKaVEZBdwAnA1sBv411AKpkkuhtFzDmVnQ3xyrHSizZN+uYeSwWtbTee4s2aWDvu5bVYL91y7gByXjd+8toMWb5AfXHoclhEQUBOOsVJGElRzaQ8csj4bhyn2UpHvJhAymFka7y0UCEd4fmMtgZDBebPHkJvR72zzI5YjKn8RmQZ8JPppBB4DRCmle/sjDNXrgUhUJzWdaB1FOVRieX5jLZOKMlPm6221mCOAXLeD372xE08gzM+unjfgEpLDRU3LIQ+fRPF/s8fk8NpW0/8+UcpngOlRpe9IYG7bUd/JrgbTRXTd/lZOm1Y8WJHTnr56/luAt4CLlFI7AKKpmTUjjN7ZHJy29HalVJLeymggHGj18e6uJr5w1tSUyiEi3P6hGWS7bPzsha14ghF+/ZEFwzoHcbR4QkeeA/LFRggm8BMsyXHiD0Vo9gYTvnjLct047RbCEcX4gozBijsi6EsDXAFcC7wmIs8Dj6Ld70ckdkvPf1xZbnp7JMwee/Qh9OnOE6v3oxRcvqAi1aIAcNvSKWS7bHzn3x/wyQdX8sePVQ1p0Nlg6EusTOehDRINYt7YVs/fl+8hYigyHVbuuOS4Hu0FmQ5uPmUihkHCa2AYBn9fvpdA2OD6JeNxjYIgxCN2r5RSTymlrgVmAK9hpnEuEZH7ROTcYZBPkyR8oZ79oR118YEy6cTKnQ2pFiGp+EMRHli2h1OnFjFhiLxNBsLHTqzkrqvn8c7ORm5+cAXeYHq6AHtDR44DaI5JlpboJ7y4sZbOQARvyODNbYnTMzht1sO+/J5ce4A/vb2bh96t5v53qvstdzrT39w+HqXU35VSFwNjgTWYEb6aEUJvpw7DSG9XyuyM9B6ZHC0PLqumsTPAZ05PPxfLqxaN5e5r5vHeriY+8UB6vgAWxCQiTKSex+Q4u+/xrATR4WML3FhEsAD5CSZzw2GDv7y9m3te2U6HPz50yeMPEYoYhA1FRyD9rs9AOOqxi1KqBfhD9KMZIfS+3/Oz0tuV8vgp6R2H0BulFJsPdlDX4aciz82U4qxuL5rVe1v4xcvbOGdW6REDhVLJ5QvGYhHhS4+t5eP3r+D+jy8mM0Fq41QhtkOTuIksLrluBw4LRAwoz433t186vZQXN9XjD0X4cNXYuPZXttTzwge1ALjtFj7VKw7ikvkVrNnXSiAU4WMnVg7ux6QJ6fPf1QwpYwuy2BrT+19UOTzRpQOlODO9X06x7Gro5EuPrWXd/kMpCPIy7Cwan4/LYeWlTXWMyXHx48vnpLVf/aXzK5DuF8D73H/T8Qlz26cCFYmJ8E2U8dGIEIgOZpsTlCidOzaPb1wwE28gwqnTiuLaC7MctPlCGIYixx0/MujwhxlfkIFSZm7/MQnmzLpSqKfz/ziW9PjPaoac3Y2dPWrPvrOjkVtOn5I6gfrg3d2NVE2Mf0jTjR31nVz9u2WICD+47DhmleWws6GTldXNrN7bij8U4YoFFXzlvOkjIgL0knnlWAS+8OhaPv6X97n/psVkJyh+MtxsrD2UiDBRSdcXNx1KLrytPn4+y2IRTp16ePfNLJeNaSVZ+MMRxubHe/u0+0O0+cIYSiU0CzV7gvxz1T4iBly5sIKSnPiXw54m05U0XeZ8tPI/RnDbLD28fYqyUv9AH4lEaXvTjYih+OSDK7CI8K/PnERl1IVw0YT8YY/gTSYXzS3HIsLnH1nDxb9+m19du4B54/KG9Jx99ZUr8lzsbDRfAIkmKmN767bDxCxEDIWhVMKYBk/ALAOpgPqO+BgTt91Kqy+IYSgctvj9q5s8eKJDj12Nnjjlv62ug/+sPQDAxfPLmVaa+vTaae1MndbCjTDG5Lp75DyZVZ6XOmEOw9SSDATIsFuYXZaXanH6xBeK0OwJ8vuPLupW/KOFC+aU8bdPLiEYNrjivmXc8fQHQ1p+0JlAocYyIcb3Pscd32c9YXIRdqtgFRIWyGnzhfjz27v43es7qW6Mz/ef47IzZ2wux5XnMiZBr72+I0BDR4CGjmDCAu9TSrIoynZSkOlgegLFXtPiY9XeFlbtbaGmJT1qaaR1zz835p88pXB0eX8MBw7roWyJsyryWON2UJBhJ9Nh5cTJ6RfBOKU4B1/QINtlx5rmEacAWU4br95+ZsKasKOBJZMK+e8XTuMnL2zhwXereXrdAb50zjQ+snjcYXvXA6U010WXpX56SbzZJS8majfWp7+LC+eW886ORjr84YRJ6w62+WiPmm12N3riXtZTSrI4/7gyAqEIVQnmw9x2C+W5bgylyEpw/hyXnasXmRPJiYLlMhxWSnOc3cvpQHor/wwHYTFrup4+oyTV4ow4ppRks+mgWS908YR8/u20MaMsh4JMB2Pz0+9lOrsiB08wTEGWg2xn+ilUIT52dLQq/i5yM+z8+PI5XL9kPN/7zya+/dRGHn63mm9eOIvTk5gCwWmzMK4oE28owvlz4oPgqiYWkvfuHgIRgxMnxc8FFWQ6+MwZU2jxBDl+YrynWJ7bzu5GD75QhDNnxusSEWHREVJsTy3NZmyBm0DY4LiK+GIu9e1+Hl+1H8NQXLFoLBW96ggXZjm6zUKFWemRVDGtlX+LJ0hG9Gl7cVM93744tfKMNBaOz2NXgweX3cLUMTkYSrG40nwwWr1hctzpcRN28bGTKhmT42bO2Jy0jDQ9losLzS7P5bFbTuCFD2r58XNbuPEv77N0ejHfvHBmUspDCsIJkwvxhyIJ0y8sGJ/P5YvG0eELc/0J4+Pat9e187lHVuMPRrht6VRuPLmyR3urL8SUkiwAOnzxfvqBcIT/rDuALxjhkvkV5Pby+Nnd4GHD/jYihmLTwXZOmdLzxbfpQDvPbzyIUuYoorfyr23zd89r1Lb506KkZlorf3/YoGsAWN8xOrM8DiXb6zqJGAp/yGBvo4cMh43SHBcFmXYq8tPPlfKPb+7i5U11jMl1cd/1C0dFCP1oQkQ4/7gyls4o4cFl1fz6lR2c98u3+PDicXzxrKkJPVz6i8Nm4dSpRTR7QgljIXJcdr523gwC4Qh5GfGdlkdX7GV/iw+l4MH3dscp/0lFWUwuycIXDDM/weT1yuoWnlhdg2GYE8LXHt/zBbN6b4tZqUvBuzua4pT/nmZvd88+0ZyC4lDnIV06EWn9dMXOymc60t8GnG40dgYIGYqQoWjyhrBbheuWxPea0oWXNtVR0+qjvsNPTaufydGemia9cNrMYjBXLhzLPa9s5+/v7+XJ1TXccMJ4rl8yYcCT36dOLcYbjFCcHe8S6w2GufO/W2j2BPn06ZPjTC9dZkIRcNviR40Om4XFE/LxBMLkJ8j6GettZE2Q4rqxI0AgbOZFr2uPn/CdFfVOM5RiVnm8p9rk4izmRmv/TkmT+7pfyl9Efgr8EPABzwNzgS8ppf46hLKR7bJhwXxTnjQ1/X2+043GmLTI7YEASsHb2xvJz7QzuzzebplqDEPhC4YxbFYy9Ms+7SnMcvK9S4/jE6dM5O6XtvGXd6r541u7WTg+jzNnlHDG9BJmluUkVKa9iSjFg+9WEwgZnDatOM7+vmJ3M6v3tBA2FE+tqYlT/pctGMtf3t5FIGRwWYIautvqOvjq42sJhBWfPX0yl/RKrldVWcCHF4/DFwpz/nFj4vafVZ5NhsOKYSiOS/DsNHoC2K2CUkJTgiCz8jw3nzx1EpB4QjgV9Lfnf65S6msicjlQjZnt801gSJW/ihkfBRNFdmiOSJv/UP6e/66vpSMQ4tUtdThsFgoznQmjFFNJkydA2ADCBp2+EOSlWiJNf5hQmMmvrl3ANy+YyeOr9vP8xlruenEbd724jSynjQXj81g0IZ8F4/OZWpLFmBxXXAGZSEQRiCbqr0/Qs87LsNPuC+EPG7gTKM8H3tlNe8Dc/6H39vCpXgGMb21rYHejF6UUL26qi1P+YcOgsTOANxAhEDbobZGPGILbbpaBTFRsfl+Tl9p2P0qZAZWJSBel30V/lX/X7MeFwONKqbbBhDCLyC+AKmC1UuoLh9uuwx+iawD13u6mAZ9PA8qIEAgZLNvZhMsWb9NMB1o8ph952FDUtgeYOjrrt49aSnJc3LZ0CrctnUJ9h59lO5pYuaeZVXta+dUr27s7cy67hbH5GRRmOijKcvKb6xbgsFlYNCGfFm+QEybF2/zDEUXYiIBSeBPk9t8VU5kukVkmz2XFEwibZRx7VzYCVuxu4aUP6lBAfqaDG06Y0KN9c207tW0BFIpVe1u48eSe+1ssgkUERCVM7xCOGLy/uxmA4ycWJN1VdiD0V/k/LSJbMM0+nxGRYiD+CvcDEVkIZCmlTo2mhl6slFqRaNtwTD6CYFj3/AeDN6iIGAq7VRARWjxBKtMkzDwR/vDoyJx4rFKS7eKyBRVcFu1hd/hDbKhpY3ejh90NHmpafTR5guxv9XUryyNVz2ryBFEICLR6480qsdW9EnVL39zRRCSqTtbWtMW1Zzit2K0WwkqRnSAraJs3iC1q1gkE43v+00qzmVSUhaFUQlfQDTVtLI8q/wynLeGk83DTp/IXEQvwH+BnQJtSKiIiXuDSfuxbDjwDzMJU+OHoccpF5FfAy8CJQELlX57rImIRIkpx40mV/fxJmi4s0RgJgCmlWdQ6rIwvyCDDYUub/CKx5GfaafaEsFmFysL0mBTTJIdsl52TJhdx0uSBzd1NLclkSnEW3mCE4yvj/fgXT8jnvV0tKGBsgkpcU4qzsVlqUUpRnhvfvnB8PjefOhF/KMLp0+LjAK5bMoHl1c1EIoobTpwQ137i5EJCEYOIoTh9evxLLPaFkihILBX0qfyVUoaI/FYptSBmnQeI92eKpxk4C3gSunv9OcCXMF8epfSy7IrILcAtAOPHj+fZ206m3RfipCl6wvdoeerTx3P9X1ZRmuPkLzctYfG9dj531lTy3I7D1jlNJX//1BJ+9sI2zpxewtQx6Zfb5/uXzOIv7+zmowkefs3QUlmUxS2nT6bNF0qYoO3L582kPRBhT5OXX39kYVz7586eijcUprbNz48vPy6u3WoRzph++EDSGWU5PHDT8YQjinEJXi52q4WzZpYedv8pJdlcXWUq/USJ41KBKNW316mI3AW8Czyh+rND/P6vA2djKvVFwAtABPgQsF4pdU+i/VzZeUplmf9ou8XCjLLUB0aMZKqrq6msrEy1GCOWDTHmgjkVufp6JhF9LZPLqlWrlFLqiBML/bX53wp8GYiIiI9opLtS6mi7Z3nAaszRwOOYLqM9isLE9vxzi8vI//gvUcp82P7zuVOO8nSaWKqqqli5cmWqxRixTPvmcwQjCrtFWPnjC/T1TCJVVVV85d4neGdnE1//0AzK89IvCHEkISKr+9qmX8pfKZWsLncb0IA5WXwP0KCUer/XubqrhFVVVakblk6htt3PnVfNS5IIGs3A+MetJ3Dvazv51GmVqRZlVOIJmikWrAK/vHZB3ztoBkV/g7wEuB6YqJT6gYiMA8p6K+5+8C5wq1LqVhG5F3igrx2+eO70ozyFRjM0zB9fwB9uHFnlJUcSN58ykV0NnTy5pgZ/KJJ2fvGjjf46m96L6ZVzXfR7J/DbvnYSEbuIvAzMw7Tz2wG/iLwFRAbw8tBoNKOYpdNL8AYjrNvXmmpRRj39tfkvUUotFJE1YBZxF5E+3UWUUiHMid5Ylh+ljBqN5hhhwfg8ANbtb2VJgmAvTfLob88/JCJWognpokFeOupKo9EklcIsJ+MK3KzbFx+IpUku/VX+92D66peKyI+At4EfD5lUGo3mmGVWWQ5b6zpSLcaop7/ePn8TkVWYLpoAlymlNg+dWBqN5lhlakk2r2yuJxg2EhZL1ySHo7myGYA1uo92wtVoNEPC1NIswoaiuqk/SQQ0A6Vfyl9EvgM8CBQARcD9IvKtoRRMo9Ecm3QVO9lelzg1siY59Nfb53pgnlLKDyAidwJrMQu8aDQaTdKYXJyFiFmA5UJ0Xu+hor9mnwNAbOUPJ1CTfHE0Gs2xjstuZVx+BjsadM9/KDliz19Efo3p3tkGfCAiL0W/nwPoAC2NRjMkTCnJYme9Vv5DSV9mn66sVauIpmWO8vqQSJOA6oZOmjxBFiXI4a3RDCfBYJC3djRz6pQCHA4zxjEQjqDU4Uv0BcNmjnerRYgYCrfDmnDdUBOKGIQjw3OuZDClJIu3dzR2XydN8jmi8ldKPThcgiSixRPk7F+8iQIum1fOzz88P5XiaI5xTvzJG7R4g+S67Kz57rmEDcWf395NOKK4ZF45lUU9C+S0eII8umIf7b4QYcOsEHX6tGKW7Wyi3RckbECW08bF88qYVDx0xWva/SEeWb4Xf8jgQ3PGMK00/VOjTynOIhg22NfsjbuumuTQX2+fi0RkjYg0i0i7iHSISHvfew6ONl8IQymUUrxf3TzUp9Nojkibz6wx3B4wS0yGIgaBkNmL39vsjdv+YJsffyhCmy9EQ4efiKFYv78VfyhCuz9MfYcfQyn2JNg3mdS3B/AGIxhKUd04MtwnJ0c9fnZo08+Q0V9vn18CVwAbBlLMZaCU5bqxuuyEIgb/78wpw3VajSYhC8bnsWF/G7PKzTIWLpuVScWZBEIG88bmxW0/uSSTSfWZFGc7sYhZLWrp9BLe3dXUY938BPsmkwmFGUwtzaLDH2bhhPwhPVey6HL33NHQydkcvkKWZuD0V/nvAzYOp+IHcNotrPzuucN5So3msDz+6ZN6fBeBS+dXHHZ7p82asP1I+wwFdquFi+aWD+s5B0uu205JtlP3/IeQ/ir/rwHPicgbQKBrpVLq7iGRSqPRHPNMKcnSyn8I6a/y/xFmDn8XMGyVv9t9ISZ//VkMBTeeOJ47Lp0zXKceFdz/9i5+89pOsl02/nHLiakWZ8Qz9RvPEjLAZoEdP74QAE8gzKMr9rJ8VzOBcAQBrBYLc8flct7sUu57fRcd/jCzyrLJz3Ry8dwySnLMkBlfMMKTa2rwBsNcPK+c0hzXEc7eP/yhCH9fvocn19SQ67bzP+dOZ8H4kWHq6c2UkiyeXF2DUgqznpQmmfRX+ZcrpeJL3g8xdR0B8qOGpifWHNDK/yj51+oaguEITZ0Rnli7P9XijHhC0STm4Zhk5rsbPeyo7+RAq4+OQBgUuOwWsl02/r32ALVtfjyBMN5gmCUTC9l0sL1b+Vc3eahr9wOw6WB7UpT/vmYva/e10uwJEggZvLy5fkQr/45AmPqOQFKujaYn/Y3wfU5Eht34XpTpQDCrxZ85vWS4Tz/iOXd2KVaLkOWyc+GcMakWZ8TT5W4e2wcdX5hBRZ6bgkw7Y3KclOa6KMxyMCbXxXmzxpDrtpPjtjFzTA4Om4WpMW6W4woyyHXbzfUlyXH1rMh3M600mwyHjRy3nVOnFCXluKlgSrH2+BlKpD9zuCLSAWQCwehHAKWUyhlK4aqqqtTKlSvx+/24XPrNPxDC4TA2mznAq6qqYuXKlX3soTkSnZ2dZGWZSin2eiZ6jrpMFYZhYLFYDmu+GAqzhoq6SFssIyMlcqJ7s77dz/E/foU7Lp7Fx0+emCLJRiYiskopVXWkbfqbzz+lUSFa8Q+cLsWvSQ5dir83R1LeXQr4cNsMhT1bREa8nbw420lhpoONB4Y8pOiYpF+aQcy76HpgolLqByIyDigb6gLsHf4Qlbc/C8B5M4r4/ceXDOXpRh2r97Twsxe2UJLj4mdXzKXDH+Lsn79OcbaThz+xOO1eDPuavby3q4kJhZkcPzH90nlM/eazhCKHJnwV8OTq/fzlnWoyHFYmFmXgsFn5xMmV/H35PrbWtlOa68JlszC7IpcL5pThtlt5ZUs9nkAYiwgicNrUIlZUt+ALRVg6o4QMu5XnN9by77U1FGQ5+fxZU6jIy+i3nBFD8crmOpbvaiIYMZhZlsuFc8rIdtl4bWs97f4QS6eXkJcxcN8Nw1C8sa2BFm+QM6aXUJCZfD8QEWHB+DxW721J+rE1/Z/wvRezZu+ZwA8wPX9+CyweIrkAqG7ydid0fWFL41CealRy3xs72NvsZW+zl/9+UEtdewBHu5+6dj8Pv7eXm06ZlGoRe/DW9kbq2v3sb/ExsyybbJc91SL1IBQx/3ZN+PpDEf65ch/VjZ0oYFdDB5OKs7n7pe3sbuykrt3P5toO8jPstPvDlOW6KclxsulAO42dATyBMBMKM+nwh2noMD2oc90tlOe5eX7jQTYdbCfLaeOfq/bzhbOm9VvO3Y0e3treyLp9rfjDEVq9IUqynUwqzmT9frM27gpnC+fMGnjw1P4WH2v3tQLw/u4mzj9uaFIvLxifz8ub62n1Bgf1stLE01+D4BKl1G2AH0Ap1cIwuHw6Y0q42Ub2CDYlzBxjTsk4bFZml+fgspvX0261sDgNe9ZleaZ5Lz/DjvswidLSCbvVQnm+G4tFcNqsZLvsiMCiCXm47VZsVgs5Lhtuh5Ucl52yXBdFWU4cNgtZThv5mQ5EYFppFg6bBREzqr0oy0lJjgurRXDarUedi6coy0Fehh2n3YLbbiXbZaMsz0VhphNn9B4oyx2cKTU/096dzK4sd+gK+y2KRiSvqNa9/2TT3wnf5cBJwAql1EIRKQZeVEotGErhqqqq1Olf+T01rT4e/fQpQ3mqUcvavS0UZ7uoyHdTVVXFXX99jsqiDCqLhi6R2EBRStHkCZLjsqdl7dZVu1r45r/X8qNL57NoUj5VVVW88c577GroxCpCUbaTQMhgXGEG9e1+WjxBst12BMhw2MjNMEcynkCYcERhswrhiCI3w969rmsbbzBMbasfq1WYUHj0ic28wTCt3iCC4HZYu3vNvmAEfyhCfhLMNMk81uGcEYJhg4U/eImL55Xxf1fMHfR5jhWSNuEL3IOZ0rlERH4EXAUMSxnHn1+7aDhOM2qZ38vH+4wZ6esyKyIUZTlTLcZhWTQpn+e/tLTHukynjTkJcvOU5Li6/fl7k+mMf+x6r8tw2Jg0CPfPDIeNDEf8edwOa9LSOifzWIfDYbNw+vRiXtpUzw8v0+mdk0l/vX3+JiKrgLMw3TwvU0ptHlLJMF3kZn7rv4QMg/tvXMSp07Wv+tFQ0+LjFy9vY3x+Bp8/eyphQ/Hb13ZQkefmsgXDm1+mP7R6g6zZ18q4/IzuxF7pxFX3vsPqfa0cV57D0587FTBHK6v2NLOxpp1JxVmcOLkQu9XSvX5DTTsTizI5aXJRj9GMUorVe1vxBcPYrRZCEUXVhDyeXHuANm+I608Yn3DOY83eFjyBCIsn5uO0DU7xrtvXSrs/xOLKgh71CNbta6XNF+L4iQWHrVMwnFw8t5xn1x/k5c11nDdb64BkcTTuHnXAW9F93CKyUCm1emjEMtlW10lRdHbtEw+uYns0pF7TP3703GbW7WvhXWBmWTaNnQHe3NYAwMSiDOaNS6/Izxc/qKOm1cf6fW186rSJCXuuqWTl3lYA1tcccj3cXt/JU2sOsL2+k7H5buxWCydOLmRHzPqKfDcOm4WTJh8KuNrZ4OHNbQ00e4J4AmHGFWSwbn8Lb2475Njw6TMm9zj/7kYPr281/3+GUpw2rXjAv2Vfs5dXt9QDEI4olkZHhD3WGwZnzkh9Rs2zZ5ZQkefmj2/u4txZpSPehTVd6G8+/x8A6zHNPz+Pfu4aQrkAsFkPiWe3pp8NON3JdZnKU0TIz7BjjT40VouQk2aeNEC3CcFhs4yY4b3bbu2+N+1WCxnR3+B29FzfewLb7bAiAnarYI+OCAoynXTptRx3/P/Hbbd2t2cM0tzitFuwRA8Wa7px2a2H1tvT4+Vrs1r47NLJrNzTwnMbalMtzqihvxO+W4E5Sqng0It0iKqqKlV+4y9o8gR55/azhvPUo4JgMMKjK/cyoTCT06eXsGhRFT95+BnK89zMKs9NtXhxBMMGuxo7Kc12JWUSMdnc//ZOfv3qDm49bRK3njG1e5Jyf4vpTjsmx9WjIldNq489TR5Kc1xMTlCpq6bVhz8UwWWz4A8bTCrKZO2+Vlq8Ic6YVpQwOvdgmw9PIMLk4sxB94Br2/x0BkJMLs7qcay6dj8d/vj1Q0lf0ecRQ3HRr9+mxRPkpS+flnZuwOlGfyZ8+6v8/wV8RilVnyzh+kNXegdNctDpHZKLvp7Joz/Xcs3eFq68bxnXHj+eH1+ukzweiWR6+/wfsEZENtIzn/8lg5CvX9x8//s0eoI89skqnebhKHl7UwM3PPQ+WQ4LG7//IcIRxRX3vs1xZbl8Pw0fnidX7eWb/97E8ZX5PPCJ9IvmfnjZbn7/5m5uPnkCN506GaXgm09swB+OcMFxYxAR6jv85LkdnDu7tEfPfU+TB3/IYFppFnubvd3LiXrWe5u87GzoZNPBNg62+phaksXiiQXMLMtFRNjX7KUzEGZ6aTYrqptp6gxy7qxSnt90kAMtfj5+YiWOfpiFYo9jGQFmtgXj8/nEyRP509u7uXhuOSdOLky1SCOa/ir/B4GfABswI32Hhb3NHhqjE1xn//Id3tamn6PihofM7BudQYNT7nyZ7fUdtO5tY82+NiryXNy6dGqKJezJlx7fAMDr2xp5ctVeLl80PsUS9eQ7T29CAd97dgs3nTqZfS1e/rFyHxGleG1LHTluB55AmOJsFy2+EB853pR/X7OXJ1bXALCrIZsttR0AdAaKWDShZ7BdTauPB5bt5q3tDext8hI2FC67lcUTC7ht6RQq8tz8c5WZnnvN3haeXncApeCt7fW8taMJpRT7W318/9IjZ2Cva/fzr9X7UQpavMEek9HpzP+cO52XNtdx+xPref4Lpw25q+lopr+zqF6l1D1KqdeUUm90fYZUMiAcObTsD0UOv6GmTzr9YbosfEpBc7QYebpS3xHoe6NhpreBNGIoFOb1jBiKsKFQynTj9AUP3a/ByKH+kjfmPg6E4/tRwbBZED4cUd3nU0oRihgEwwahmGN1xPxPzf+v+aU/z0ooYnTvG0wgR7ridlj5yZVz2dPk5ecvbk21OCOa/vb83xKR/wOepqfZZ0hdPScVZ5JTlEFnIMzfbx7SNEKjkttOHc9v39qLAGvvOJ9ZT2SQl+tifEEGX79gVqrFi+P6JRU8sryGcfkubj0jvUYlANcuruCZdbWcO9t0fxxfkMGsiQX4QxEumluO3WahpsVHcbaT644f173f5OIszppZgj9ksGB8HpsPtuMLRhIWU59YlMlVi8YxY0wOq/Y0U9vuY0pxNktnFnP8xALsVgvnzCqlMxBm4fh8xuS6aOoMck3VOB58dzd17QG+1I88QGPzMzh3dintvjALJ+Ql7RoNBydMKuT6JeP5yzu7uWBuGQtHaLGaVNPfCd/XEqxWSqkzky/SIfSEb3LRE5TJRV/P5HG017LDH+K8X7yJy27lzx9fzMSio0+BMZpJZj7/pUdqF5EblVIPHo1w/WX67c8SAKrv1AFeA6ErJXbX9Zv73f8yuzybR25Nv1xJe/bsYel9GzltSj4PfPKkVIsTxzcfX8HfVtVz4Yw8fvvxkwF4dHk1nYEI4woyqG72UJrl5LKF42jzhdjT5KGmxYvTZmXuuLweqSu62tt9IfY1efEEwyyqLCAvw44nEMFhs9DpD1Hd6GHqmGxaPEHT7mSBTLuNg20+alp8zKzIAWWmh7CJheqmTiYWZ5Fht7J2XwuBUIQ54/KZWJRJY2eAzkAYq0UYl5+BLxTBbbfS2BEg02UjElHUtPkYX5BBSfYh54rOaBlKpaDJE2BW2ZHdhDsDYbyB8GHTWySDbJeduz88n089tJKld71ORZ6baaVZXDCnjCsXjh0RE9ippl89/z4PIrJaKbUwCfL0wFk2VZXd+Mvu7/oFcHR0Kf4uDj74Rbqu59zyLJ7+/OkpkOrwxMr7+dMq+fIFs1MoTTyx8lXfeSGlk2aR+eGfYcQ8QhaBC+aMIc/t4PmNtbT5QlgtQtXEfL514SxmjMmhoSPAr1/dxgsb62jxBglGzAO4bBZKcszkcA6bhYbOAJGIwmYRFAplFtDDKuALm/MLVjGDoJw2IWxAKGzgtltAhHZ/GAGynFYWVxbQ4g1yoM1PXoaDSUWZjC/IoNkTpKbVR36GnT1NXlp9IWaMyeb/nTmV+ePyaPOG+OvyPTS2+1m9rxWX3cplCyq6J7N70+4P8df39hAIGZw2LX5C+3AMdBRV2+bniTX72Vrbwfr9bexu9HDOrFJ+e93CtEwOOFz0p+efrKujX7MjjM0H07su6tPrD6ZahD7xhyL07jspBdtqO2j2BglEDAylMJSipTPYnbO/2ROkuTNIKGIQjnlzhCKGmd3TiP6N7h80zEngSMTAMBTBqOIHiEQnm4NhRTAcQaEIhA0C0UlfhTnhXNcewBuMEAobBIIRGjsD+EMRmjoDRAxFY2eQzoA5adzqDVEfLSzf6gsSDBu0BUJ4o5PYe5u8h70mbd4QgWil+4ZhmLQfk+vis2dM4VfXLuDV/zmd71w0i5c21fH1JzYkLK2pOUSy4reH5CqPyT4U5Vk4dCnDjwn+dnEe5//10Lv+rS+kX7ZUC4f8iF+//exUitIvKvLdZGQ58AbCZDsttPsN8jIc/OTKuTR0BIlEFFvr2slw2Li6amz3xOSUkiwumV9BWCn2R4vtRCIGs8pzmD4ml/p2P/lZDrbXdtDoCTI2z027L4QyO/7kua1sb/DiDUYozXbistsoyLCbx2vxMbnIjdNuY0V1MwYwryKXS+ZVsL2+g+omL4WZDhZV5mO3WjhhUgEbatoZX+Bif4ufrXWdnDy5iCWTTB/68QUZVFXmM7k4g7H5GbT7wlx3mF4/wNh8N4srC2j2Bjlh0vD64YsInzhlIm2+EL96ZTszy7L55KnpVbAonUiW2WfNUOT21xO+yUVPUCYXfT2TRzKvpWEoPvu31by4qZaHb17CyVNGRgxDMhlOs887STpOHP/3n+e56p5n+95Qk5DFtz/LI48cun7n/+RZXnzxxRRKdGR+9NR6du3alWoxDsuH7n6ux/dg2CAcNvAHwxiGIhw59L03hqEIhiL4A2GaO/10eoO0ewIcaOlgR30rje1e2j0Bmjt8tHb6ae30U93Yxva6VurbPOxtaqe6sZ39LR20dvppbPfSHN2utrWTA60dHGjpoLnDR7s3QDAUIRiK0O4JUN/uodMbpLHdi9cfIhiK4PWFaO7w0dju7V4fjhj4/WFaPf7u39P1VynzLxyKPej6m05YLMJd18xjSkkWt/19NfuaD2+mOpbpr6tnHvAxoJIYU5FS6vNDJRjoCd/BcqQJXzuwPc2uZ6y8f7ppJmdPT68he+8J32mz53H9//2dlXuayXRYmVqag9MmfHCgA6sFPn/WVI6faJo+2nwhHlpWzds7GtlW106nP4yIEDZUjwljqwXTiKrgSKFaXZNsIuY8Q++n2G23MDbfjVWE7fWdGIqoB4wi02GjNNtJXYcfTyBCRJnHy3XbWTghj7V7WwlFDBaOz2fuuLzuqmOGoXDarZw5o6Q7rYShFE6blQvmjGHqUZabjGUoRlHVjR4u+c3buB1W7r5m/jE1Akhmz/85TMW/AVgV89GMUNI7vhe++uiQ1woaNP6wwcE2Hy2eEJ2BCDsbOqlp8dPsCRCKKN7f3dy9bU2Lj4Ntfpo6A6bCNSAY6an4ASJGdBK3j3NH3w8YCRQ/mNHDde0BDrb5iES3iRiKiGGWeKzrMCeAI+rQ8TyBMJsPduANRjAMxZbaDtp9IfY2e2nzhahu8hAxFKv2tNDqDdHhN9cZSrG9Pv0cCCqLMnn0lhNx2qxc/6flXPKbt/nDmzvZ1ZB+sqaC/k74upRSXx5SSTRDSjEQ6z9z/NhUSdI//vXRmakWoU8yHVZmleXiDxm4HVZmleVgt4rZS1aKc2OqTk0qzmR2RQ6tviAhw6CpI4DFIoTDBt6wqYEtmHn2I0phQREMHz6Rlt1i9vhtNrMOcFeGBokeJ8dlY3pZNjYRVu1tJRwxsFvNYxdmOqgsyqS60UuzJ0ggbGCzQGmOk9OmFbNsRyPeUISlM4oZV5DBhKIMDAMqCzMRgaXTi1m9t5XGThuVhZkgMG9c3hBe6YEzqzyHF790Go++v5cn1tTw4+e28OPntlBZmMEZ00s4c0YJ88blkeOyJUyy5w2GqW70UtPq42CbDxGhNNvJrPIcKvLcI7qwTH/NPl8COoFn6JneofmwOyUBPeGbXPQEZXLR1zN5DNe13Nfs5fWt9by6pZ5lO5u68ytZLYLdKlhEsIp0F81p98fP3XSR7bIxqSiTsfkZ5GXYzY/bQW6GnVy3nTy3nbwMB3nR78NZEjOZKZ2DwM+Ab3JolKmAARllReQXQBWwWin1hYEcQ6PRaI6WcQUZfPTESj56YiX+UIR3dzWxs76TVm+IUHdcRjRpn1IUZzupLMpkXH4GZXkuULC/1cemA+1sre2gusnD5oPttPlCtPpCRHrb8WJw2izkZzjIz3RQkGm+GAqi3/Mz7GQ6bVhFsFoEi0WwiGnWC0fjQcwRnkEoomj2BGjoCFDfETDjRjxBWr0hzplVyi8+PL9f16K/yv9/gClKqcY+t+wDEVkIZCmlThWR+0RksVJqRaJtN9S0xU2yafpP3IRvzPXMBdal2fVM9/91X/L5QxGeWX8QfyjCBXPKKIhWI+ta3+EPYRiKtfta2bC/lepm37DJDj3jKLqwEp07wDQZlWQ7yc+0s7/FRyAUwWq1YLdYQBkUZDkJhg0aPQHsFgvTx2SbQWRhxSlTC7np5IkUZzn501u7eOT9veS47Hzn4lnUtpsK6pxZpZTnmQE7EUPx340HaeoMcvas1NQJdtmtLJ1ewtLpJUe1X0mOK2EyOaUUnYEwrd4QbT7z0+oN0eoLdq9r8QRp8YZo9gQ42NpOszdImy8UFyzYF1aLUJTloDjbSVGWk8nFWeRl2Jk7tv8V+vqr/HcAyfKXOgF4Kbr8MnAikFD5a4aOtlQL0AeVtz+bli+AI7GjvrPbrXBDTRunRwus72rwsK/ZS0NHgAOtPva3eNkzzIofEs8fxE4sK+juSYaj6apDhoEfAwvga/URMcztQhGDTQc7cEXrLa/Y3cL8cfkcV57LS5vqaPYE6fCHefjdvUwsNpOurd7b0q38D7T62F5nTryu3tMyhL96+BARsl12sl12xvW9eTcRQ9HmC+EJhDGUIhL1ADOUwiJgs1iwWSXmr3mewda57q/y9wBro9k9Y23+A3H1zAO6HLnbgB4JXETkFuAWAGtO8QAOrxkNzEy/Er59MjbfTYbDSjBaj7eLijxzfX6GHZfdYnrbtPvoCKSXfzyAy24hw26lzR8ibJi5isyC7qaLaChi4AsZWC1CYaYdi5iTyBX5biYXZ1Gc7WTamGx2NXpwO2wsnVFMqy9Euy/co45xcbaTvAw7bb5QwvrGxxJWi1CQ6egeKQ4X/Z3wvTHR+oFk8hSR24AGpdQ/ROQKYKxS6p7DbNsA7AGKgEGbnI5huq7fQmA16X89R4p8C4G9pLes/SXV13yk3JvpTtf1m6CUOmLvOSnpHY6GqM3/VqXUrSJyL/CAUur9PvZZ2dfMtebw9L5+6X49R5J86S5rf0mX35EucoxUjub69SvIS0Smisg/RWSTiOzq+gxEuGj1L7+IvAVE+lL8Go1Go0k+/bX53w98F/gFsBS4iUHkBdLunRqNRpNa+qvA3UqpVzDNRHuUUncAw+mK8YdhPNdopPf1S/frOZLkS3dZ+0u6/I50kWOk0u/r198J32XAKcA/gVeBGuBOpdT0gUqo0Wg0mtRxxJ6/iDwcXXwKyAA+DywCPgok9ADSaDQaTfpzxJ6/iGwCzgb+C5xBr3KNQ53bR6PRaDRDQ18Tvr8DXsHM4bMKohWkD/1Nr4TrGo1Go+kX/bX536eU+swwyNN1vkWYaR/ygFbgPaWUTp84CHrnUDpSTqXhRkRmY7r9bolZt0QptTyFYnUTvR/3AU3AFzA7PbXoe3PQ6Gc9dQx7kFdfRDN+OjHz/rQBOZimp7B2Ee0bEek9jyPRz3+B82LWPa+UOmc4ZUuEiPwcKMWsL1MEfEIp1SAiryqlzkytdCAif8a8XgHM+9AHbAXcwJ8ZgfemiFiBy+ildIGnlFKHz2GcfDn0sz5IBvPy7K+f/3CySCl1Wq91T4rImymRZuTRifkgd5nmTgfagSzMh4xo29yUSBfP4q7/t4jMBR4Xka+kWKZYpiilTgcQEY9SKjO6/JpS6klG5r35ALAe+Ds9le4DwA3DKId+1gdBr5fnZsz/400i8tH+vDzTUfmvFJHfY2b+bMf8QWdh5v3Q9M1m4HKlVBuYRR2AM4F/xvb0ReSlw+w/3FhFxKGUCiql1ovI5cBf6ZXwL4XEPiMvxdybBSJyFSPz3qxUSn2017o10aj74UQ/64NjUC/PtDP7AIjIAszUz3mYPZN3lVJrUirUCEFEyoAmpVQw9jtgxA7pRcQ2nEP8wyEixwPVSqn6mHVW4Gql1KOpk6xbltnAFqVUJPp9AXAycDzmCGvE3Zsi8lXMEeHrHFK6pwNvKaV+Osyy6Gd9gIjI3UAm8S/PgFLqi33un47KX6PRDC0iUoxZTW8RsBPYkS4OAJr+k+jlCdj687/Uyl+jOcYQkeeVUueLyBcxbf3PYI5m9iulvp5S4TT9JoFzBxyFM0c62vw1Gs3Q0lU15HJgqVLKAH4nIm+nUCbN0dPl3BFLv505jknlLyLfBK7DrGJnYNYXGJRPuYhcAsxSSt2ZBPk6lVIjuryRiESADZj32GbgRqVUwlKgInIH0KmUumv4JDymmSUiDwGTMb1FumpKulInkmYA9HDu6KK/zhzHnNlHRE4E7gbOUEoFRKQIcCilDvRj32GZJB0lyr/7N4jI34BVSqm7D7PtHWjlP2yIyISYrweUUiERyQJOVUr9N1VyaY6O3s4dMev7pacGnJN/BFMGNCqlAgBKqUal1AERqY6+CBCRKhF5Pbp8h4g8LCLvAA+LyHtRDxCi7a9Ht/+4iPxGRHJFZE+XPU5EMkVkn4jYRWSyiDwvIqtE5C0RmRHdZqKIvCsiG0Tkh8N8PYaDt4ApACLyMRFZLyLrYhIHdiMinxKRFdH2f4lIRnT91SKyMbr+zei62SLyvoisjR5z6rD+qhFKNC171ycUXdepFf/IQil1sLfij67vVwf1WFT+LwLjRGSbiNwrIqf3Y59ZwNlKqY8AjwHXQPebtyw2oi46BFuL6ToHcBHwQvQh+wPwOaXUIuArwL3RbX4F3KeUmgMcHOwPTCdExAZ8CNgQfWl+CzhTKTUPM1VCb55QSi2Otm8Gbo6u/w5wXnT9JdF1nwZ+pZSaj+m5sn/ofolGM7o45pS/UqoT073tFqABeExEPt7Hbk8rpbrsov8AroouX4NZ46A3jwEfji5fGz1HFnASZgTrWuD3mKMQMD0tHokux/WGRyju6O9ciVnk/M+YwWaPK6Ua4bBZYY+Ljoo2ANdzKNjrHeABEfkUYI2uexf4hoj8L2bBal/84TQaTSKOyQnfaMDO68DrUSVzIxDm0Muw98SXJ2bfGhFpiqYi+DBm77M3TwM/FpECzBfNq5jBGK3RXmpCsQb2a9IWX+/fKiKH2bQHDwCXKaXWRV/KZwAopT4tIkswK8itEpFFSqm/i8jy6LrnRORWpdSryfsJIwMRuQx4EpgZmxxPozkSx1zPX0Sm97INzwf2ANWYihrgyj4O8xjwNSBXKbW+d2N0dLEC05zzjFIqopRqB3aLyNVROURE5kV3eQdzhABmb3e08ipwtYgUAkRfjr3JBg6KiJ2YayEik5VSy5VS38EcsY0TkUnALqXUPcC/SZ98RcPNR4C3o39HJSLyeRHZHHUeGHF0zQmmWo5Yjjnlj5ng7EER2SQi6zHt+XcA3wN+JSIrMV1Aj8Q/MZX1P46wzWOYSbIei1l3PXCziKwDPgAuja7/AnBbdBRScXQ/Z+SglPoA+BHwRvQaJPL++TawHPOFGNuL/Vl0QnwjsAxYh2l22xg1Lx0HPDSE4qclUXPiKZhzI9dG11mi81lbROQlEXlOzDxEiMgiEXkj6nTwQnTeaiTwWeAcpdRo7hwdFjFTniQXpZT+6I/+jNAPZofiz9HlZZij16uA5zA7d2OAlug6e3Sb4uj2Hwb+kurf0I/f+DsgiBk38r+Ycz1ror9lenSbj2OWm30JcxT//4AvR7d7Dyg4wvFfB36BOT+1GVgMPAFsB34Ys90NwPuYDh2/B6zR9Z3AzzA7dC9j5n16HdgFXBIj37+j67cD3+3ncX+O2dE5BbgT2ISZkfWuQV/XVP9j9Ud/9GfgH8zUDOdElz8P3AX8ErgpZpsnosr/OMwEYGujnw3Ai6n+Df38ndWY9R5yMHPXgJma4l/R5Y8DOzDNhsWYeW4+HW37BfDFIxz7deAn0eUvAAcwnTGcmB5khcBM4D+APbrdvcDHossK+FB0+UlMj0I7MA9YGyPfweix3MBGTA+1vo57TXS5ELOORFdsVt5gr+kxOeGr0YwGonMmZwJzRERhekEpTAWUcBfgA6XUicMk4lCQi2m2nYr5W+0xba8ppTqADhFpw1SqYL7k+poPejpm2w+UUgcBRGQXMA6z570IWBF1XHADXZlog8DzMfsHlBk4twGojDnHS0qppuhxn4geM3yE40aAf0WX2wA/8GcReQbzpT8ojkWbv0YzWrgKeFgpNUEpVamUGgfsBpqBK6O2/1KiHlOYPcdiMaPciQYepkvdhP7yA0wlfxxwMT098wIxy0bMd4O+PRtjt+19HBvmi/NBpdT86Ge6UuqO6DYhFe2Ox+6vzJxJseft7dHXVQ/9cMf1q2gqcWUGbh2POd94EYdeNgNGK3+NZuTyEeJ7+f/CtPPvx7QP/xWzOEqbMqNBrwJ+Ep1wX4sZezKSyAVqossfH8bzvgJcJSIlYI66pGeajP5wTnQ/N2YZzXf6e9zoxH6uUuo54EuYJqVBoc0+Gs0IRSm1NMG6e8BUFkqpzqhb7fuY5giUUmuB3tWfRhI/xTT7fAt4drhOqpTaFD3ni2KmbgkBt2G6ifeX9zFfzmOBv6poZoB+Hjcb+LeIuDBHC18ezO+BYzCxm0ZzLCBmbqo8zPTNP1VKPZBKeTTph1b+Go1GcwyizT4ajeaYQER+i5lHK5ZfKaXuT4U8qUb3/DUajeYYRHv7aDQazTGIVv4ajUZzDKKVv0aj0RyDaOWv0Wg0xyD/H3TvaayBxTyOAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The percentages suggests passenger class and gender may be the factor that may lead to survival. ","metadata":{}},{"cell_type":"markdown","source":"# Data preparation for classification\nThis section prepares the data for classifiers. We transform the data in suitable data types supported by the classifiers, remove null values and imputes some values when required. Some columns are deleted; they may be either character or we surmise not suitable for classification.","metadata":{}},{"cell_type":"markdown","source":"## Integer to float\nWe upload the data for a cleaning and display the columns with their data types to float on both datasets.","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.368853Z","iopub.execute_input":"2023-02-01T14:50:35.370121Z","iopub.status.idle":"2023-02-01T14:50:35.386127Z","shell.execute_reply.started":"2023-02-01T14:50:35.370069Z","shell.execute_reply":"2023-02-01T14:50:35.385078Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.770203Z","iopub.execute_input":"2023-02-01T14:50:35.770631Z","iopub.status.idle":"2023-02-01T14:50:35.784625Z","shell.execute_reply.started":"2023-02-01T14:50:35.770596Z","shell.execute_reply":"2023-02-01T14:50:35.783551Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_train[\"SibSp\"] = titanic_train[\"SibSp\"].astype(float)\ntitanic_train[\"Parch\"] = titanic_train[\"Parch\"].astype(float)\ntitanic_train[\"Survived\"] = titanic_train[\"Survived\"].astype(float)\ntitanic_train[\"Pclass\"] = titanic_train[\"Pclass\"].astype(float)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.166628Z","iopub.execute_input":"2023-02-01T14:50:36.167303Z","iopub.status.idle":"2023-02-01T14:50:36.181459Z","shell.execute_reply.started":"2023-02-01T14:50:36.167252Z","shell.execute_reply":"2023-02-01T14:50:36.178943Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)\ntitanic_test[\"SibSp\"] = titanic_test[\"SibSp\"].astype(float)\ntitanic_test[\"Parch\"] = titanic_test[\"Parch\"].astype(float)\ntitanic_test[\"Pclass\"] = titanic_test[\"Pclass\"].astype(float)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.666190Z","iopub.execute_input":"2023-02-01T14:50:36.667397Z","iopub.status.idle":"2023-02-01T14:50:36.678991Z","shell.execute_reply.started":"2023-02-01T14:50:36.667345Z","shell.execute_reply":"2023-02-01T14:50:36.677862Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Null values \n\nWe remove all the nulls values from some of the columns; i.e., PassengerId, Fare, SibSp, Parch, and Embarked. Some fares were unknown, but all passengers ID was set to a unique number. ","metadata":{}},{"cell_type":"code","source":"titanic_train.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.489505Z","iopub.execute_input":"2023-02-01T14:50:37.489938Z","iopub.status.idle":"2023-02-01T14:50:37.497591Z","shell.execute_reply.started":"2023-02-01T14:50:37.489901Z","shell.execute_reply":"2023-02-01T14:50:37.496243Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.991239Z","iopub.execute_input":"2023-02-01T14:50:37.992524Z","iopub.status.idle":"2023-02-01T14:50:38.000114Z","shell.execute_reply.started":"2023-02-01T14:50:37.992478Z","shell.execute_reply":"2023-02-01T14:50:37.998884Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Fare.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.437569Z","iopub.execute_input":"2023-02-01T14:50:38.437966Z","iopub.status.idle":"2023-02-01T14:50:38.445766Z","shell.execute_reply.started":"2023-02-01T14:50:38.437933Z","shell.execute_reply":"2023-02-01T14:50:38.444961Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.994637Z","iopub.execute_input":"2023-02-01T14:50:38.995337Z","iopub.status.idle":"2023-02-01T14:50:39.002110Z","shell.execute_reply.started":"2023-02-01T14:50:38.995287Z","shell.execute_reply":"2023-02-01T14:50:39.000886Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Parch.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.436990Z","iopub.execute_input":"2023-02-01T14:50:39.437517Z","iopub.status.idle":"2023-02-01T14:50:39.445363Z","shell.execute_reply.started":"2023-02-01T14:50:39.437381Z","shell.execute_reply":"2023-02-01T14:50:39.444366Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.905392Z","iopub.execute_input":"2023-02-01T14:50:39.905832Z","iopub.status.idle":"2023-02-01T14:50:39.913740Z","shell.execute_reply.started":"2023-02-01T14:50:39.905797Z","shell.execute_reply":"2023-02-01T14:50:39.912816Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.307865Z","iopub.execute_input":"2023-02-01T14:50:40.308905Z","iopub.status.idle":"2023-02-01T14:50:40.316347Z","shell.execute_reply.started":"2023-02-01T14:50:40.308849Z","shell.execute_reply":"2023-02-01T14:50:40.315199Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_test[\"Fare\"].isnull(),\"Fare\"] = -1.0\ntitanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.604978Z","iopub.execute_input":"2023-02-01T14:50:40.605706Z","iopub.status.idle":"2023-02-01T14:50:40.614214Z","shell.execute_reply.started":"2023-02-01T14:50:40.605660Z","shell.execute_reply":"2023-02-01T14:50:40.613381Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.071733Z","iopub.execute_input":"2023-02-01T14:50:41.072626Z","iopub.status.idle":"2023-02-01T14:50:41.081041Z","shell.execute_reply.started":"2023-02-01T14:50:41.072577Z","shell.execute_reply":"2023-02-01T14:50:41.079958Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.270757Z","iopub.execute_input":"2023-02-01T14:50:41.271157Z","iopub.status.idle":"2023-02-01T14:50:41.282542Z","shell.execute_reply.started":"2023-02-01T14:50:41.271122Z","shell.execute_reply":"2023-02-01T14:50:41.281267Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.500496Z","iopub.execute_input":"2023-02-01T14:50:41.500902Z","iopub.status.idle":"2023-02-01T14:50:41.515629Z","shell.execute_reply.started":"2023-02-01T14:50:41.500870Z","shell.execute_reply":"2023-02-01T14:50:41.514309Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.775897Z","iopub.execute_input":"2023-02-01T14:50:41.776267Z","iopub.status.idle":"2023-02-01T14:50:41.789089Z","shell.execute_reply.started":"2023-02-01T14:50:41.776237Z","shell.execute_reply":"2023-02-01T14:50:41.787661Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.000121Z","iopub.execute_input":"2023-02-01T14:50:42.001023Z","iopub.status.idle":"2023-02-01T14:50:42.016796Z","shell.execute_reply.started":"2023-02-01T14:50:42.000983Z","shell.execute_reply":"2023-02-01T14:50:42.015509Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.207362Z","iopub.execute_input":"2023-02-01T14:50:42.207763Z","iopub.status.idle":"2023-02-01T14:50:42.218799Z","shell.execute_reply.started":"2023-02-01T14:50:42.207729Z","shell.execute_reply":"2023-02-01T14:50:42.217490Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.428396Z","iopub.execute_input":"2023-02-01T14:50:42.429337Z","iopub.status.idle":"2023-02-01T14:50:42.442620Z","shell.execute_reply.started":"2023-02-01T14:50:42.429286Z","shell.execute_reply":"2023-02-01T14:50:42.441246Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.657623Z","iopub.execute_input":"2023-02-01T14:50:42.658000Z","iopub.status.idle":"2023-02-01T14:50:42.668231Z","shell.execute_reply.started":"2023-02-01T14:50:42.657969Z","shell.execute_reply":"2023-02-01T14:50:42.666865Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.893929Z","iopub.execute_input":"2023-02-01T14:50:42.894325Z","iopub.status.idle":"2023-02-01T14:50:42.904773Z","shell.execute_reply.started":"2023-02-01T14:50:42.894278Z","shell.execute_reply":"2023-02-01T14:50:42.903541Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.119666Z","iopub.execute_input":"2023-02-01T14:50:43.120081Z","iopub.status.idle":"2023-02-01T14:50:43.128473Z","shell.execute_reply.started":"2023-02-01T14:50:43.120043Z","shell.execute_reply":"2023-02-01T14:50:43.127000Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.558402Z","iopub.execute_input":"2023-02-01T14:50:43.558778Z","iopub.status.idle":"2023-02-01T14:50:43.566705Z","shell.execute_reply.started":"2023-02-01T14:50:43.558746Z","shell.execute_reply":"2023-02-01T14:50:43.565387Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.776835Z","iopub.execute_input":"2023-02-01T14:50:43.777203Z","iopub.status.idle":"2023-02-01T14:50:43.786826Z","shell.execute_reply.started":"2023-02-01T14:50:43.777173Z","shell.execute_reply":"2023-02-01T14:50:43.785678Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.999708Z","iopub.execute_input":"2023-02-01T14:50:44.000611Z","iopub.status.idle":"2023-02-01T14:50:44.012435Z","shell.execute_reply.started":"2023-02-01T14:50:44.000573Z","shell.execute_reply":"2023-02-01T14:50:44.011295Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.273326Z","iopub.execute_input":"2023-02-01T14:50:44.273733Z","iopub.status.idle":"2023-02-01T14:50:44.285873Z","shell.execute_reply.started":"2023-02-01T14:50:44.273696Z","shell.execute_reply":"2023-02-01T14:50:44.284650Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.530974Z","iopub.execute_input":"2023-02-01T14:50:44.531405Z","iopub.status.idle":"2023-02-01T14:50:44.543425Z","shell.execute_reply.started":"2023-02-01T14:50:44.531367Z","shell.execute_reply":"2023-02-01T14:50:44.542121Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.768732Z","iopub.execute_input":"2023-02-01T14:50:44.769126Z","iopub.status.idle":"2023-02-01T14:50:44.779844Z","shell.execute_reply.started":"2023-02-01T14:50:44.769091Z","shell.execute_reply":"2023-02-01T14:50:44.779079Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.009603Z","iopub.execute_input":"2023-02-01T14:50:45.009952Z","iopub.status.idle":"2023-02-01T14:50:45.019372Z","shell.execute_reply.started":"2023-02-01T14:50:45.009923Z","shell.execute_reply":"2023-02-01T14:50:45.018131Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.259413Z","iopub.execute_input":"2023-02-01T14:50:45.259813Z","iopub.status.idle":"2023-02-01T14:50:45.270859Z","shell.execute_reply.started":"2023-02-01T14:50:45.259779Z","shell.execute_reply":"2023-02-01T14:50:45.269750Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.492004Z","iopub.execute_input":"2023-02-01T14:50:45.492453Z","iopub.status.idle":"2023-02-01T14:50:45.505989Z","shell.execute_reply.started":"2023-02-01T14:50:45.492416Z","shell.execute_reply":"2023-02-01T14:50:45.504737Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.736753Z","iopub.execute_input":"2023-02-01T14:50:45.737917Z","iopub.status.idle":"2023-02-01T14:50:45.751164Z","shell.execute_reply.started":"2023-02-01T14:50:45.737860Z","shell.execute_reply":"2023-02-01T14:50:45.749612Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.979633Z","iopub.execute_input":"2023-02-01T14:50:45.980021Z","iopub.status.idle":"2023-02-01T14:50:45.987927Z","shell.execute_reply.started":"2023-02-01T14:50:45.979987Z","shell.execute_reply":"2023-02-01T14:50:45.986675Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarkment \nWe remove any NAs from the embarked column. We replace NaNs values with unknown. However, only the training datasets has some unknown values. It could lower accuracy on the prediction on the testing dataset.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.403953Z","iopub.execute_input":"2023-02-01T14:50:46.404807Z","iopub.status.idle":"2023-02-01T14:50:46.413830Z","shell.execute_reply.started":"2023-02-01T14:50:46.404750Z","shell.execute_reply":"2023-02-01T14:50:46.412619Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' nan]\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Embarked'].isna(),'Embarked'] = 'U'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.636113Z","iopub.execute_input":"2023-02-01T14:50:46.637042Z","iopub.status.idle":"2023-02-01T14:50:46.643930Z","shell.execute_reply.started":"2023-02-01T14:50:46.637002Z","shell.execute_reply":"2023-02-01T14:50:46.642148Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"titanic_test.loc[titanic_test['Embarked'].isna(),'Embarked'] = 'U'\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.902953Z","iopub.execute_input":"2023-02-01T14:50:46.904202Z","iopub.status.idle":"2023-02-01T14:50:46.911244Z","shell.execute_reply.started":"2023-02-01T14:50:46.904144Z","shell.execute_reply":"2023-02-01T14:50:46.910042Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.161516Z","iopub.execute_input":"2023-02-01T14:50:47.162591Z","iopub.status.idle":"2023-02-01T14:50:47.169485Z","shell.execute_reply.started":"2023-02-01T14:50:47.162548Z","shell.execute_reply":"2023-02-01T14:50:47.168028Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' 'U']\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Sex.unique())\nprint(\"Testing : \" , titanic_test.Sex.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.378976Z","iopub.execute_input":"2023-02-01T14:50:47.379690Z","iopub.status.idle":"2023-02-01T14:50:47.386404Z","shell.execute_reply.started":"2023-02-01T14:50:47.379649Z","shell.execute_reply":"2023-02-01T14:50:47.385047Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTesting : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Passenger class\nNo unknown values is present in both datasets.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Pclass.unique())\nprint(\"Testing : \" , titanic_test.Pclass.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.799740Z","iopub.execute_input":"2023-02-01T14:50:47.800431Z","iopub.status.idle":"2023-02-01T14:50:47.807300Z","shell.execute_reply.started":"2023-02-01T14:50:47.800393Z","shell.execute_reply":"2023-02-01T14:50:47.806156Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Training : [3. 1. 2.]\nTesting : [3. 2. 1.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PClass and Fare\n\nThe Fare decreases as the passenger class decrease. However the range is can be quite large and the data data imbalanced; there are a lot more third class tickets than other classes. So we scale robustly the data based on non-parametric statistics.","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Fare\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x:100 * x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.235186Z","iopub.execute_input":"2023-02-01T14:50:48.235601Z","iopub.status.idle":"2023-02-01T14:50:48.274182Z","shell.execute_reply.started":"2023-02-01T14:50:48.235564Z","shell.execute_reply":"2023-02-01T14:50:48.272964Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"Fare 0.0000 4.0125 5.0000 6.2375 6.4375 6.4500 6.4958 \\\nPclass \n1.0 2.314815 NaN 0.462963 NaN NaN NaN NaN \n2.0 3.260870 NaN NaN NaN NaN NaN NaN \n3.0 0.814664 0.203666 NaN 0.203666 0.203666 0.203666 0.407332 \n\nFare 6.7500 6.8583 6.9500 ... 153.4625 164.8667 211.3375 \\\nPclass ... \n1.0 NaN NaN NaN ... 1.388889 0.925926 1.388889 \n2.0 NaN NaN NaN ... NaN NaN NaN \n3.0 0.407332 0.203666 0.203666 ... NaN NaN NaN \n\nFare 211.5000 221.7792 227.5250 247.5208 262.3750 263.0000 512.3292 \nPclass \n1.0 0.462963 0.462963 1.851852 0.925926 0.925926 1.851852 1.388889 \n2.0 NaN NaN NaN NaN NaN NaN NaN \n3.0 NaN NaN NaN NaN NaN NaN NaN \n\n[3 rows x 248 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Fare0.00004.01255.00006.23756.43756.45006.49586.75006.85836.9500...153.4625164.8667211.3375211.5000221.7792227.5250247.5208262.3750263.0000512.3292
Pclass
1.02.314815NaN0.462963NaNNaNNaNNaNNaNNaNNaN...1.3888890.9259261.3888890.4629630.4629631.8518520.9259260.9259261.8518521.388889
2.03.260870NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3.00.8146640.203666NaN0.2036660.2036660.2036660.4073320.4073320.2036660.203666...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n

3 rows × 248 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins=512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.451437Z","iopub.execute_input":"2023-02-01T14:50:48.451845Z","iopub.status.idle":"2023-02-01T14:50:49.547249Z","shell.execute_reply.started":"2023-02-01T14:50:48.451810Z","shell.execute_reply":"2023-02-01T14:50:49.546123Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 32.204208\nstd 49.693429\nmin 0.000000\n25% 7.910400\n50% 14.454200\n75% 31.000000\nmax 512.329200\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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NTauxtvkicn+XqSb7ex/lVrPzHJ19qYrm0XIJDkSW19d+ufnugAViDJEUm+leQzbb3nsd6V5DtJdiaZbW0TfR8ftuE+qa84WANXAWcsaLsY2F5Vm4HtbR0GY9/cHluBy9eoxnF5CHhnVZ0MnApc0P4b9jjeB4HTq+p5wBbgjCSnAn8DXFZVvwf8GDi/bX8+8OPWflnb7nBzEXDb0HrPYwV4eVVtGbqefbLv46o6LB/Ai4DPDa1fAlwy6brGNLZpYNfQ+u3Apra8Cbi9Lf8rcO5i2x2OD+AG4JW9jxf4LeCbwAsZfGpxQ2t/9D3N4EqzF7XlDW27TLr2ZYzxOAaBdjrwGSC9jrXVfRdwzIK2ib6PD9szd+BY4J6h9T2trUcbq2pfW74X2NiWu/kZtH+KPx/4Gp2Ot01T7AQOADcDdwI/qaqH2ibD43l0rK3/fuDpa1rwaP4BeBfwSFt/Ov2OFaCAzyfZ0b5aBSb8Pp7Y1w9oZaqqknR1/WqSpwLXAe+oqgeSPNrX03ir6mFgS5IjgeuB359sRasjyWuAA1W1I8lpEy5nrbykqvYmeQZwc5LvDXdO4n18OJ+5r6evONifZBNAez7Q2g/7n0GSJzAI9o9V1adbc7fjBaiqnwBfZDA1cWSS+ZOs4fE8OtbW/zvAfWtb6Yq9GHhtkrsYfCPs6cA/0udYAaiqve35AIP/cZ/ChN/Hh3O4r6evOLgROK8tn8dgbnq+/c3tr++nAvcP/TPwcS+DU/QrgNuq6oNDXd2NN8lUO2MnyW8y+NvCbQxC/g1ts4Vjnf8ZvAH4QrUJ2se7qrqkqo6rqmkGv5dfqKo30uFYAZI8JcnT5peBVwG7mPT7eNJ/iBjxjxhnAt9nMHf5nknXM6YxXQPsA37BYC7ufAbzj9uBO4D/Ao5u24bBFUN3At8BZiZd/zLH+hIGc5W3ADvb48wexws8F/hWG+su4L2t/STg68Bu4FPAk1r7k9v67tZ/0qTHsMJxnwZ8puextnF9uz1unc+iSb+P/foBSerQ4TwtI0k6CMNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdej/AYXmR/MxxoJxAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,8))\nplt.suptitle('')\ntitanic_train.boxplot(column=['Fare'], by='Pclass', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.549636Z","iopub.execute_input":"2023-02-01T14:50:49.550050Z","iopub.status.idle":"2023-02-01T14:50:49.804155Z","shell.execute_reply.started":"2023-02-01T14:50:49.550008Z","shell.execute_reply":"2023-02-01T14:50:49.803012Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.805690Z","iopub.execute_input":"2023-02-01T14:50:49.806699Z","iopub.status.idle":"2023-02-01T14:50:49.864940Z","shell.execute_reply.started":"2023-02-01T14:50:49.806662Z","shell.execute_reply":"2023-02-01T14:50:49.863879Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% max\nPclass \n1.0 216.0 84.154687 78.380373 0.0 30.92395 60.2875 93.5 512.3292\n2.0 184.0 20.662183 13.417399 0.0 13.00000 14.2500 26.0 73.5000\n3.0 491.0 13.675550 11.778142 0.0 7.75000 8.0500 15.5 69.5500","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0216.084.15468778.3803730.030.9239560.287593.5512.3292
2.0184.020.66218313.4173990.013.0000014.250026.073.5000
3.0491.013.67555011.7781420.07.750008.050015.569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_train.Fare.median()\nIQR_fare = titanic_train.Fare.quantile(0.75) - titanic_train.Fare.quantile(0.25)\ntitanic_train.loc[:,\"Fare\"] = (titanic_train.Fare - median_fare)/IQR_fare\nplt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.867034Z","iopub.execute_input":"2023-02-01T14:50:49.867360Z","iopub.status.idle":"2023-02-01T14:50:51.334840Z","shell.execute_reply.started":"2023-02-01T14:50:49.867301Z","shell.execute_reply":"2023-02-01T14:50:51.334033Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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S5fzh6XUpKNSb6d5Ikkjyf5dFdfMedJ0+G+XLdBWAE+UlVbVtI1u0N2J7B1Wm0XsL+qNgH7u/nV5E7efEwAbunOlS1V9cAS97ScXgU+W1WbgYuBnV12rJjzpOlwp6HbIGh4quoh4KVp5W3Anm56D3DFUva03GY5JqtWVR2vqke66VeAJ4ENrKDzpPVw3wA83zN/tKutZgV8K8mB7hYQmrSuqo530y8A65azmdPIDUke7YZtTtshiMWUZBS4EPgeK+g8aT3c9WYfrKr3MzlUtTPJh5e7odNNTV4f7DXCcCtwAbAFOA7cvKzdLIMk7wL2AZ+pqpd7l53u50nr4e5tEKapqmPd+0ngXiaHrgQnkqwH6N5PLnM/y66qTlTVa1X1OnAbq+xcSfI2JoP9rqr6eldeMedJ6+HubRB6JHlnkndPTQMfBQ699Varxv3A9m56O3DfMvZyWpgKsc6VrKJzJUmA24Enq+qLPYtWzHnS/C9Uu8u3/pY3boPwl8vb0fJJcj6T39Zh8tYTX12NxyPJXuASJm/hegK4CfgGcA9wLvAccFVVrZr/YJzlmFzC5JBMAUeA63vGm5uW5IPAvwGPAa935RuZHHdfEedJ8+EuSatR68MykrQqGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQf8Po+eCZUrdk2EAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:51.336034Z","iopub.execute_input":"2023-02-01T14:50:51.336529Z","iopub.status.idle":"2023-02-01T14:50:52.406610Z","shell.execute_reply.started":"2023-02-01T14:50:51.336498Z","shell.execute_reply":"2023-02-01T14:50:52.405714Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.407853Z","iopub.execute_input":"2023-02-01T14:50:52.408376Z","iopub.status.idle":"2023-02-01T14:50:52.415841Z","shell.execute_reply.started":"2023-02-01T14:50:52.408342Z","shell.execute_reply":"2023-02-01T14:50:52.414785Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We repeat the same process with the test dataset. The distribution is much different and therefore could lower the accuracy of the prediction.","metadata":{}},{"cell_type":"code","source":"titanic_test.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.418261Z","iopub.execute_input":"2023-02-01T14:50:52.418629Z","iopub.status.idle":"2023-02-01T14:50:52.472603Z","shell.execute_reply.started":"2023-02-01T14:50:52.418596Z","shell.execute_reply":"2023-02-01T14:50:52.471219Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nPclass \n1.0 107.0 94.280297 84.435858 0.0000 30.10 60.0000 134.500000 \n2.0 93.0 22.202104 13.991877 9.6875 13.00 15.7500 26.000000 \n3.0 218.0 12.397936 10.817256 -1.0000 7.75 7.8958 14.327075 \n\n max \nPclass \n1.0 512.3292 \n2.0 73.5000 \n3.0 69.5500 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0107.094.28029784.4358580.000030.1060.0000134.500000512.3292
2.093.022.20210413.9918779.687513.0015.750026.00000073.5000
3.0218.012.39793610.817256-1.00007.757.895814.32707569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_test.Fare.median()\nIQR_fare = titanic_test.Fare.quantile(0.75) - titanic_test.Fare.quantile(0.25)\ntitanic_test.loc[:,\"Fare\"] = (titanic_test.Fare - median_fare)/IQR_fare\nplt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.473824Z","iopub.execute_input":"2023-02-01T14:50:52.474155Z","iopub.status.idle":"2023-02-01T14:50:53.560939Z","shell.execute_reply.started":"2023-02-01T14:50:52.474123Z","shell.execute_reply":"2023-02-01T14:50:53.559872Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:53.562396Z","iopub.execute_input":"2023-02-01T14:50:53.562797Z","iopub.status.idle":"2023-02-01T14:50:54.622056Z","shell.execute_reply.started":"2023-02-01T14:50:53.562764Z","shell.execute_reply":"2023-02-01T14:50:54.620862Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.623442Z","iopub.execute_input":"2023-02-01T14:50:54.623854Z","iopub.status.idle":"2023-02-01T14:50:54.631562Z","shell.execute_reply.started":"2023-02-01T14:50:54.623823Z","shell.execute_reply":"2023-02-01T14:50:54.630628Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age\nWe normalise the age to bring more the data towards the median. The previous transformation have brought more data centrally.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.632748Z","iopub.execute_input":"2023-02-01T14:50:54.633113Z","iopub.status.idle":"2023-02-01T14:50:54.995183Z","shell.execute_reply.started":"2023-02-01T14:50:54.633084Z","shell.execute_reply":"2023-02-01T14:50:54.993205Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_train.Age.median()\nIQR_age = titanic_train.Age.quantile(0.75) - titanic_train.Age.quantile(0.25)\ntitanic_train.loc[:,\"Age\"] = (titanic_train.Age - median_age)/IQR_age\ntitanic_train.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.996341Z","iopub.execute_input":"2023-02-01T14:50:54.996637Z","iopub.status.idle":"2023-02-01T14:50:55.012393Z","shell.execute_reply.started":"2023-02-01T14:50:54.996609Z","shell.execute_reply":"2023-02-01T14:50:55.011269Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.014533Z","iopub.execute_input":"2023-02-01T14:50:55.015228Z","iopub.status.idle":"2023-02-01T14:50:55.377136Z","shell.execute_reply.started":"2023-02-01T14:50:55.015184Z","shell.execute_reply":"2023-02-01T14:50:55.376023Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.378688Z","iopub.execute_input":"2023-02-01T14:50:55.379745Z","iopub.status.idle":"2023-02-01T14:50:55.727506Z","shell.execute_reply.started":"2023-02-01T14:50:55.379709Z","shell.execute_reply":"2023-02-01T14:50:55.726302Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6N52X3A08A/AWdOKds7Gb2c/B7wWHd571DydRl/B3i0y/g48Jfd+t8GvgMcYvRS+denfJzfDTwwtGxdlu92lyeO/V0M7BhfBMx3x/gfgTMGlu804D+AN4+tG0y+SS6eoSpJDdqs0zKSpBOw3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatD/AmLJbG6fuoYqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_test.Age.median()\nIQR_age = titanic_test.Age.quantile(0.75) - titanic_test.Age.quantile(0.25)\ntitanic_test.loc[:,\"Age\"] = (titanic_test.Age - median_age)/IQR_age\ntitanic_test.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.731833Z","iopub.execute_input":"2023-02-01T14:50:55.732609Z","iopub.status.idle":"2023-02-01T14:50:55.747180Z","shell.execute_reply.started":"2023-02-01T14:50:55.732557Z","shell.execute_reply":"2023-02-01T14:50:55.746071Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.748583Z","iopub.execute_input":"2023-02-01T14:50:55.748898Z","iopub.status.idle":"2023-02-01T14:50:56.093344Z","shell.execute_reply.started":"2023-02-01T14:50:55.748868Z","shell.execute_reply":"2023-02-01T14:50:56.092150Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Gender \nWe replace the male with 1 and female with the value 2.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \", titanic_train['Sex'].unique())\nprint(\"Test : \", titanic_train['Sex'].unique())\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.094765Z","iopub.execute_input":"2023-02-01T14:50:56.095091Z","iopub.status.idle":"2023-02-01T14:50:56.103516Z","shell.execute_reply.started":"2023-02-01T14:50:56.095062Z","shell.execute_reply":"2023-02-01T14:50:56.102411Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTest : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_train[\"Sex\"] = titanic_train[\"Sex\"].astype(float)\ntitanic_train.groupby(\"Sex\").count()[\"PassengerId\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.104821Z","iopub.execute_input":"2023-02-01T14:50:56.105350Z","iopub.status.idle":"2023-02-01T14:50:56.122953Z","shell.execute_reply.started":"2023-02-01T14:50:56.105306Z","shell.execute_reply":"2023-02-01T14:50:56.122030Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 577\n2.0 314\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_test[\"Sex\"] = titanic_test[\"Sex\"].astype(float)\ntitanic_test.groupby(\"Sex\").count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.124259Z","iopub.execute_input":"2023-02-01T14:50:56.124612Z","iopub.status.idle":"2023-02-01T14:50:56.139408Z","shell.execute_reply.started":"2023-02-01T14:50:56.124581Z","shell.execute_reply":"2023-02-01T14:50:56.138058Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 266\n2.0 152\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sibling and parentage\n\nWe add both sibling, parents, and children into a family variables. ","metadata":{}},{"cell_type":"code","source":"titanic_train[\"SibSp\"].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.141402Z","iopub.execute_input":"2023-02-01T14:50:56.141813Z","iopub.status.idle":"2023-02-01T14:50:56.148230Z","shell.execute_reply.started":"2023-02-01T14:50:56.141777Z","shell.execute_reply":"2023-02-01T14:50:56.147382Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"array([1., 0., 3., 4., 2., 5., 8.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Parch\"].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.149745Z","iopub.execute_input":"2023-02-01T14:50:56.150349Z","iopub.status.idle":"2023-02-01T14:50:56.159952Z","shell.execute_reply.started":"2023-02-01T14:50:56.150294Z","shell.execute_reply":"2023-02-01T14:50:56.158924Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"array([0., 1., 2., 5., 3., 4., 6.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\ntitanic_train[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.161871Z","iopub.execute_input":"2023-02-01T14:50:56.162175Z","iopub.status.idle":"2023-02-01T14:50:56.176837Z","shell.execute_reply.started":"2023-02-01T14:50:56.162147Z","shell.execute_reply":"2023-02-01T14:50:56.175684Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.178360Z","iopub.execute_input":"2023-02-01T14:50:56.178747Z","iopub.status.idle":"2023-02-01T14:50:56.191340Z","shell.execute_reply.started":"2023-02-01T14:50:56.178698Z","shell.execute_reply":"2023-02-01T14:50:56.190355Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.195050Z","iopub.execute_input":"2023-02-01T14:50:56.195448Z","iopub.status.idle":"2023-02-01T14:50:56.209129Z","shell.execute_reply.started":"2023-02-01T14:50:56.195400Z","shell.execute_reply":"2023-02-01T14:50:56.207967Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.210664Z","iopub.execute_input":"2023-02-01T14:50:56.211090Z","iopub.status.idle":"2023-02-01T14:50:56.219640Z","shell.execute_reply.started":"2023-02-01T14:50:56.211049Z","shell.execute_reply":"2023-02-01T14:50:56.218550Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.221452Z","iopub.execute_input":"2023-02-01T14:50:56.222189Z","iopub.status.idle":"2023-02-01T14:50:56.231508Z","shell.execute_reply.started":"2023-02-01T14:50:56.222146Z","shell.execute_reply":"2023-02-01T14:50:56.230398Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.232676Z","iopub.execute_input":"2023-02-01T14:50:56.233089Z","iopub.status.idle":"2023-02-01T14:50:56.242657Z","shell.execute_reply.started":"2023-02-01T14:50:56.233048Z","shell.execute_reply":"2023-02-01T14:50:56.241737Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.244097Z","iopub.execute_input":"2023-02-01T14:50:56.244427Z","iopub.status.idle":"2023-02-01T14:50:56.251459Z","shell.execute_reply.started":"2023-02-01T14:50:56.244398Z","shell.execute_reply":"2023-02-01T14:50:56.250542Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.256041Z","iopub.execute_input":"2023-02-01T14:50:56.256485Z","iopub.status.idle":"2023-02-01T14:50:56.265940Z","shell.execute_reply.started":"2023-02-01T14:50:56.256450Z","shell.execute_reply":"2023-02-01T14:50:56.264711Z"},"trusted":true},"execution_count":113,"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.267253Z","iopub.execute_input":"2023-02-01T14:50:56.267740Z","iopub.status.idle":"2023-02-01T14:50:56.278020Z","shell.execute_reply.started":"2023-02-01T14:50:56.267696Z","shell.execute_reply":"2023-02-01T14:50:56.276748Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"titanic_test[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.315420Z","iopub.execute_input":"2023-02-01T14:50:56.315791Z","iopub.status.idle":"2023-02-01T14:50:56.322971Z","shell.execute_reply.started":"2023-02-01T14:50:56.315760Z","shell.execute_reply":"2023-02-01T14:50:56.322090Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## Columns to drop \nWe drop some columns; they may have too many unknown values. Some of them may be dependent statistical variables. We assume the price of a ticket may be dependent of the fare. ","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Name\", axis = 1, inplace = True)\ntitanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_train.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.722307Z","iopub.execute_input":"2023-02-01T14:50:56.722753Z","iopub.status.idle":"2023-02-01T14:50:56.744122Z","shell.execute_reply.started":"2023-02-01T14:50:56.722718Z","shell.execute_reply":"2023-02-01T14:50:56.743299Z"},"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.drop(\"Name\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_test.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.963356Z","iopub.execute_input":"2023-02-01T14:50:56.963753Z","iopub.status.idle":"2023-02-01T14:50:56.979754Z","shell.execute_reply.started":"2023-02-01T14:50:56.963719Z","shell.execute_reply":"2023-02-01T14:50:56.978543Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We make of both datasets. These copies will be used to analysed the predictions values from all the classifiers.","metadata":{}},{"cell_type":"code","source":"results_test = titanic_test.copy(deep = True)\nresults_train = titanic_train.copy(deep = True) ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:57.429442Z","iopub.execute_input":"2023-02-01T14:50:57.429827Z","iopub.status.idle":"2023-02-01T14:50:57.435755Z","shell.execute_reply.started":"2023-02-01T14:50:57.429796Z","shell.execute_reply":"2023-02-01T14:50:57.434439Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"markdown","source":"# Method : Logistic regression\n\nOur first classifier is a logistic regression. We surmise it may be the most suitable methods as two classes of labels exist; survived or not. The data is imbalanced towards perishing sadly. So we add some class weight to represent this situation in the data. \n\nWe choose the passenger class, sex, familly members. We surmise the passenger class, gender and being part of a familly or not may have influenced surviving the accident. The training dataset is split into training and validation for validating the model fitting. ","metadata":{}},{"cell_type":"markdown","source":"## Preparation Cross validation \nWe show how the transformation have affected both datasets","metadata":{}},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.108812Z","iopub.execute_input":"2023-02-01T14:50:58.109845Z","iopub.status.idle":"2023-02-01T14:50:58.118552Z","shell.execute_reply.started":"2023-02-01T14:50:58.109806Z","shell.execute_reply":"2023-02-01T14:50:58.117356Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.354904Z","iopub.execute_input":"2023-02-01T14:50:58.355573Z","iopub.status.idle":"2023-02-01T14:50:58.362764Z","shell.execute_reply.started":"2023-02-01T14:50:58.355531Z","shell.execute_reply":"2023-02-01T14:50:58.361542Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"(891, 8)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.590264Z","iopub.execute_input":"2023-02-01T14:50:58.591668Z","iopub.status.idle":"2023-02-01T14:50:58.600773Z","shell.execute_reply.started":"2023-02-01T14:50:58.591627Z","shell.execute_reply":"2023-02-01T14:50:58.599216Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.804713Z","iopub.execute_input":"2023-02-01T14:50:58.805085Z","iopub.status.idle":"2023-02-01T14:50:58.812599Z","shell.execute_reply.started":"2023-02-01T14:50:58.805054Z","shell.execute_reply":"2023-02-01T14:50:58.811376Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"(418, 7)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Split data sets for cross validation\n\nWe use a stratified shuffle split to aim at reducing the variation between the training and validation datasets.","metadata":{}},{"cell_type":"code","source":"\n\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n#X = X[x_cols]\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.267374Z","iopub.execute_input":"2023-02-01T14:50:59.267771Z","iopub.status.idle":"2023-02-01T14:50:59.288554Z","shell.execute_reply.started":"2023-02-01T14:50:59.267735Z","shell.execute_reply":"2023-02-01T14:50:59.287476Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We keep the passengers ids for building up the training dataset results. It will be used to compare all the classifier.","metadata":{}},{"cell_type":"code","source":"x_train_pass_id = X_train[\"PassengerId\"]\nx_train_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.704437Z","iopub.execute_input":"2023-02-01T14:50:59.704815Z","iopub.status.idle":"2023-02-01T14:50:59.714204Z","shell.execute_reply.started":"2023-02-01T14:50:59.704783Z","shell.execute_reply":"2023-02-01T14:50:59.713337Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"844 845.0\n316 317.0\n768 769.0\n255 256.0\n130 131.0\n ... \n476 477.0\n58 59.0\n736 737.0\n462 463.0\n747 748.0\nName: PassengerId, Length: 534, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"x_cols =[\"Pclass\",\"Sex\",\"fam_members\"]\nX_train = X_train[x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.933799Z","iopub.execute_input":"2023-02-01T14:50:59.934191Z","iopub.status.idle":"2023-02-01T14:50:59.947540Z","shell.execute_reply.started":"2023-02-01T14:50:59.934158Z","shell.execute_reply":"2023-02-01T14:50:59.946577Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n844 3.0 1.0 0.0\n316 2.0 2.0 1.0\n768 3.0 1.0 1.0\n255 3.0 2.0 2.0\n130 3.0 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
8443.01.00.0
3162.02.01.0
7683.01.01.0
2553.02.02.0
1303.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid[\"PassengerId\"]\nx_valid_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.149697Z","iopub.execute_input":"2023-02-01T14:51:00.150120Z","iopub.status.idle":"2023-02-01T14:51:00.160439Z","shell.execute_reply.started":"2023-02-01T14:51:00.150083Z","shell.execute_reply":"2023-02-01T14:51:00.159106Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"369 370.0\n541 542.0\n196 197.0\n810 811.0\n427 428.0\n ... \n174 175.0\n297 298.0\n244 245.0\n38 39.0\n371 372.0\nName: PassengerId, Length: 357, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.356159Z","iopub.execute_input":"2023-02-01T14:51:00.357062Z","iopub.status.idle":"2023-02-01T14:51:00.370786Z","shell.execute_reply.started":"2023-02-01T14:51:00.357017Z","shell.execute_reply":"2023-02-01T14:51:00.369619Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n369 1.0 2.0 0.0\n541 3.0 2.0 6.0\n196 3.0 1.0 0.0\n810 3.0 1.0 0.0\n427 2.0 2.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
3691.02.00.0
5413.02.06.0
1963.01.00.0
8103.01.00.0
4272.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test.copy(deep = True)\nX_test = X_test[x_cols]\nX_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.599111Z","iopub.execute_input":"2023-02-01T14:51:00.599521Z","iopub.status.idle":"2023-02-01T14:51:00.616521Z","shell.execute_reply.started":"2023-02-01T14:51:00.599483Z","shell.execute_reply":"2023-02-01T14:51:00.615356Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n0 3.0 1.0 0.0\n1 3.0 2.0 1.0\n2 2.0 1.0 0.0\n3 3.0 1.0 0.0\n4 3.0 2.0 2.0\n.. ... ... ...\n413 3.0 1.0 0.0\n414 1.0 2.0 0.0\n415 3.0 1.0 0.0\n416 3.0 1.0 0.0\n417 3.0 1.0 2.0\n\n[418 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
03.01.00.0
13.02.01.0
22.01.00.0
33.01.00.0
43.02.02.0
............
4133.01.00.0
4141.02.00.0
4153.01.00.0
4163.01.00.0
4173.01.02.0
\n

418 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model fitting","metadata":{}},{"cell_type":"markdown","source":"We fit the model using a stochastic average gradient. We achieve approximately 82% accuracy on the validation dataset. There is not sign of over fitting. ","metadata":{}},{"cell_type":"code","source":"classifier = LogisticRegression(random_state = 0, C = 1000, max_iter= 10000, \n solver=\"sag\", penalty=\"l2\",class_weight={0:6.,1:4})\nclassifier.fit(X_train, y_train)\nclassifier.coef_","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.275079Z","iopub.execute_input":"2023-02-01T14:51:01.275483Z","iopub.status.idle":"2023-02-01T14:51:01.291372Z","shell.execute_reply.started":"2023-02-01T14:51:01.275450Z","shell.execute_reply":"2023-02-01T14:51:01.290133Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([[-0.96687438, 2.71046703, -0.09242397]])"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_train = classifier.score(X_train, y_train)\nlog_reg_score_train","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.505908Z","iopub.execute_input":"2023-02-01T14:51:01.507102Z","iopub.status.idle":"2023-02-01T14:51:01.519460Z","shell.execute_reply.started":"2023-02-01T14:51:01.507059Z","shell.execute_reply":"2023-02-01T14:51:01.518123Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"0.7921348314606742"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_valid = classifier.score(X_valid, y_valid)\nlog_reg_score_valid","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.727365Z","iopub.execute_input":"2023-02-01T14:51:01.727743Z","iopub.status.idle":"2023-02-01T14:51:01.737787Z","shell.execute_reply.started":"2023-02-01T14:51:01.727712Z","shell.execute_reply":"2023-02-01T14:51:01.736406Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"0.8207282913165266"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nTwo confusion matrices show an improvement on predicting the validation dataset. We also store the predicted results in the results_train dataframe. We will use this dataframe later on to analyse difference between classifiers. \n\n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = classifier.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.212411Z","iopub.execute_input":"2023-02-01T14:51:02.212812Z","iopub.status.idle":"2023-02-01T14:51:02.223463Z","shell.execute_reply.started":"2023-02-01T14:51:02.212779Z","shell.execute_reply":"2023-02-01T14:51:02.222427Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"array([[297, 32],\n [ 79, 126]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.417280Z","iopub.execute_input":"2023-02-01T14:51:02.417687Z","iopub.status.idle":"2023-02-01T14:51:02.426591Z","shell.execute_reply.started":"2023-02-01T14:51:02.417653Z","shell.execute_reply":"2023-02-01T14:51:02.425177Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"Accuracy : 0.7921348314606742\nMisclassfication : 0.20786516853932585\nSensitivivity : 0.9027355623100304\nSpecificity : 0.6146341463414634\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = classifier.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.661227Z","iopub.execute_input":"2023-02-01T14:51:02.661653Z","iopub.status.idle":"2023-02-01T14:51:02.672901Z","shell.execute_reply.started":"2023-02-01T14:51:02.661618Z","shell.execute_reply":"2023-02-01T14:51:02.671790Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.907929Z","iopub.execute_input":"2023-02-01T14:51:02.908917Z","iopub.status.idle":"2023-02-01T14:51:02.916300Z","shell.execute_reply.started":"2023-02-01T14:51:02.908877Z","shell.execute_reply":"2023-02-01T14:51:02.915176Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"Accuracy : 0.8207282913165266\nMisclassfication : 0.1792717086834734\nSensitivivity : 0.9363636363636364\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.367005Z","iopub.execute_input":"2023-02-01T14:51:03.367441Z","iopub.status.idle":"2023-02-01T14:51:03.372440Z","shell.execute_reply.started":"2023-02-01T14:51:03.367404Z","shell.execute_reply":"2023-02-01T14:51:03.371375Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"y_pred = classifier.predict(X_train)\nlog_reg_pred = X_train.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_train\nlog_reg_pred[\"PassengerId\"] = x_train_pass_id\nlog_reg_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.610590Z","iopub.execute_input":"2023-02-01T14:51:03.610967Z","iopub.status.idle":"2023-02-01T14:51:03.632961Z","shell.execute_reply.started":"2023-02-01T14:51:03.610936Z","shell.execute_reply":"2023-02-01T14:51:03.631856Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n844 3.0 1.0 0.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 0.0 769.0\n255 3.0 2.0 2.0 0.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_memberslr_y_predyPassengerId
8443.01.00.00.00.0845.0
3162.02.01.01.01.0317.0
7683.01.01.00.00.0769.0
2553.02.02.00.01.0256.0
1303.01.00.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(log_reg_pred[[\"PassengerId\",\"y\", \"lr_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.870519Z","iopub.execute_input":"2023-02-01T14:51:03.870935Z","iopub.status.idle":"2023-02-01T14:51:03.899083Z","shell.execute_reply.started":"2023-02-01T14:51:03.870900Z","shell.execute_reply":"2023-02-01T14:51:03.898021Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 NaN NaN \n2 0.0 1.0 1.0 \n3 1.0 NaN NaN \n4 0.0 NaN NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.0NaNNaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.0NaNNaN
45.00.03.01.00.384615-0.2773632.00.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = classifier.predict(X_valid)\nlog_reg_pred = X_valid.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_valid\nlog_reg_pred[\"PassengerId\"] = x_valid_pass_id\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.094193Z","iopub.execute_input":"2023-02-01T14:51:04.094610Z","iopub.status.idle":"2023-02-01T14:51:04.120418Z","shell.execute_reply.started":"2023-02-01T14:51:04.094576Z","shell.execute_reply":"2023-02-01T14:51:04.119350Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n369 1.0 2.0 0.0 1.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 1.0 428.0\n.. ... ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 1.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 0.0 0.0 39.0\n371 3.0 1.0 1.0 0.0 0.0 372.0\n\n[357 rows x 6 columns]","text/html":"
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PclassSexfam_memberslr_y_predyPassengerId
3691.02.00.01.01.0370.0
5413.02.06.00.00.0542.0
1963.01.00.00.00.0197.0
8103.01.00.00.00.0811.0
4272.02.00.01.01.0428.0
.....................
1741.01.00.00.00.0175.0
2971.02.03.01.00.0298.0
2443.01.00.00.00.0245.0
383.02.02.00.00.039.0
3713.01.01.00.00.0372.0
\n

357 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"y\"] = log_reg_pred[\"y\"]\nresults_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"lr_y_pred\"] = log_reg_pred[\"lr_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.330333Z","iopub.execute_input":"2023-02-01T14:51:04.330729Z","iopub.status.idle":"2023-02-01T14:51:04.353404Z","shell.execute_reply.started":"2023-02-01T14:51:04.330694Z","shell.execute_reply":"2023-02-01T14:51:04.352359Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 1.0 1.0 \n2 0.0 1.0 1.0 \n3 1.0 1.0 1.0 \n4 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"We start with the training dataset. It may be quite unconventional, but it can help us understanding better the features of the data.","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.059608Z","iopub.execute_input":"2023-02-01T14:51:05.059995Z","iopub.status.idle":"2023-02-01T14:51:05.077377Z","shell.execute_reply.started":"2023-02-01T14:51:05.059959Z","shell.execute_reply":"2023-02-01T14:51:05.076249Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n255 3.0 2.0 2.0 1.0 0.0\n707 1.0 1.0 0.0 1.0 0.0\n172 3.0 2.0 2.0 1.0 0.0\n78 2.0 1.0 2.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
2553.02.02.01.00.0
7071.01.00.01.00.0
1723.02.02.01.00.0
782.01.02.01.00.0
2333.02.06.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We complete the same activities to the validation dataset. It appears many male first class passengers traveling alone may have survived more than we anticipated. ","metadata":{}},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.569495Z","iopub.execute_input":"2023-02-01T14:51:05.569879Z","iopub.status.idle":"2023-02-01T14:51:05.589621Z","shell.execute_reply.started":"2023-02-01T14:51:05.569846Z","shell.execute_reply":"2023-02-01T14:51:05.588487Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n340 2.0 1.0 2.0 1.0 0.0\n534 3.0 2.0 0.0 0.0 1.0\n279 3.0 2.0 2.0 1.0 0.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3402.01.02.01.00.0
5343.02.00.00.01.0
2793.02.02.01.00.0
6071.01.00.01.00.0
8043.01.00.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.798455Z","iopub.execute_input":"2023-02-01T14:51:05.799489Z","iopub.status.idle":"2023-02-01T14:51:05.813581Z","shell.execute_reply.started":"2023-02-01T14:51:05.799450Z","shell.execute_reply":"2023-02-01T14:51:05.812556Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 0.0 3\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 1.0 4\n 2.0 1.0 0.0 4\n 2.0 0.0 4\n 3.0 2.0 0.0 2\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.295513Z","iopub.execute_input":"2023-02-01T14:51:06.296134Z","iopub.status.idle":"2023-02-01T14:51:06.315914Z","shell.execute_reply.started":"2023-02-01T14:51:06.296088Z","shell.execute_reply":"2023-02-01T14:51:06.314875Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n130 3.0 1.0 0.0 0.0 0.0\n110 1.0 1.0 0.0 0.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
1303.01.00.00.00.0
1101.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.545374Z","iopub.execute_input":"2023-02-01T14:51:06.546123Z","iopub.status.idle":"2023-02-01T14:51:06.565170Z","shell.execute_reply.started":"2023-02-01T14:51:06.546085Z","shell.execute_reply":"2023-02-01T14:51:06.564022Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 2.0 1.0 9\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 0.0 3\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 1.0 4\n 2.0 1.0 0.0 10\n 2.0 0.0 5\n 3.0 1.0 0.0 2\n 2.0 0.0 1\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. It appears \n\nOther elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees classifiers. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.019601Z","iopub.execute_input":"2023-02-01T14:51:07.020764Z","iopub.status.idle":"2023-02-01T14:51:07.038884Z","shell.execute_reply.started":"2023-02-01T14:51:07.020723Z","shell.execute_reply":"2023-02-01T14:51:07.037796Z"},"trusted":true},"execution_count":146,"outputs":[{"execution_count":146,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.380694Z","iopub.execute_input":"2023-02-01T14:51:07.381775Z","iopub.status.idle":"2023-02-01T14:51:07.399161Z","shell.execute_reply.started":"2023-02-01T14:51:07.381734Z","shell.execute_reply":"2023-02-01T14:51:07.397965Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 6\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 1.0 11\n 2.0 1.0 0.0 7\n 2.0 0.0 5\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Predict with testing dataset","metadata":{}},{"cell_type":"code","source":"y_pred = classifier.predict(X_test)\nlog_reg_pred = X_test.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.781717Z","iopub.execute_input":"2023-02-01T14:51:07.782101Z","iopub.status.idle":"2023-02-01T14:51:07.809230Z","shell.execute_reply.started":"2023-02-01T14:51:07.782070Z","shell.execute_reply":"2023-02-01T14:51:07.808079Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 1.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 0.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
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PclassSexfam_memberslr_y_predPassengerId
03.01.00.00.0892.0
13.02.01.01.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.00.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
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418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.028966Z","iopub.execute_input":"2023-02-01T14:51:08.030264Z","iopub.status.idle":"2023-02-01T14:51:08.036547Z","shell.execute_reply.started":"2023-02-01T14:51:08.030211Z","shell.execute_reply":"2023-02-01T14:51:08.035240Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.293378Z","iopub.execute_input":"2023-02-01T14:51:08.294544Z","iopub.status.idle":"2023-02-01T14:51:08.309861Z","shell.execute_reply.started":"2023-02-01T14:51:08.294483Z","shell.execute_reply":"2023-02-01T14:51:08.308466Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":" PassengerId lr_y_pred\n0 892.0 0.0\n1 893.0 1.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 0.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdlr_y_pred
0892.00.0
1893.01.0
2894.00.0
3895.00.0
4896.00.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
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418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.513449Z","iopub.execute_input":"2023-02-01T14:51:08.513843Z","iopub.status.idle":"2023-02-01T14:51:08.535503Z","shell.execute_reply.started":"2023-02-01T14:51:08.513810Z","shell.execute_reply":"2023-02-01T14:51:08.534386Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 0.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_pred
0892.03.01.00.431373-0.2810053.00.00.0
1893.03.02.01.411765-0.3161762.01.01.0
2894.02.01.02.588235-0.2021843.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: K-Nearest-neighbourn","metadata":{}},{"cell_type":"markdown","source":"We explore whether a reduction of statistical variables may be beneficial to the classification. We focus our model fitting on the same statistical variables as the logistic regression. \n\n\nThe K-NN classifier overfits to the training dataset. We have yet to find a better result. So Decision tree may have found its limit. ","metadata":{}},{"cell_type":"markdown","source":"## Model fitting\nWe discover the hyper-parametrisation of approximately 7 neighbors and the algorithm set the brute.","metadata":{}},{"cell_type":"code","source":"neighbors = range(2, 100)\nfor neighbor in neighbors:\n knn = KNeighborsClassifier(n_neighbors = neighbor, algorithm=\"brute\", weights = \"distance\", p=2)\n knn.fit(X_train,y_train)\n train_score = knn.score(X_train, y_train)\n valid_score = knn.score(X_valid, y_valid)\n print(\" - n neighbor : \", neighbor , \" - train score : \", train_score, \" - valid score : \", valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:09.565134Z","iopub.execute_input":"2023-02-01T14:51:09.565542Z","iopub.status.idle":"2023-02-01T14:51:12.977246Z","shell.execute_reply.started":"2023-02-01T14:51:09.565506Z","shell.execute_reply":"2023-02-01T14:51:12.975689Z"},"trusted":true},"execution_count":152,"outputs":[{"name":"stdout","text":" - n neighbor : 2 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 3 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 4 - train score : 0.8089887640449438 - valid score : 0.7591036414565826\n - n neighbor : 5 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 6 - train score : 0.8164794007490637 - valid score : 0.7927170868347339\n - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 8 - train score : 0.8202247191011236 - valid score : 0.7899159663865546\n - n neighbor : 9 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 10 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 11 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 12 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 13 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 14 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 15 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 16 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 17 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 18 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 19 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 20 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 21 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 22 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 23 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 24 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 25 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 26 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 27 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 28 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 29 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 30 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 31 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 32 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 33 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 34 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 35 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 36 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 37 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 38 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 39 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 40 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 41 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 42 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 43 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 44 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 45 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 46 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 47 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 48 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 49 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 50 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 51 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 52 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 53 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 54 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 55 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 56 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 57 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 58 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 59 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 60 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 61 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 62 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 63 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 64 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 65 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 66 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 67 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 68 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 69 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 70 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 71 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 72 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 73 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 74 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 75 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 76 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 77 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 78 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 79 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 80 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 81 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 82 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 83 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 84 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 85 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 86 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 87 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 88 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 89 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 90 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 91 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 92 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 93 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 94 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 95 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 96 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 97 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 98 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 99 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors = 7, algorithm=\"brute\", weights = \"distance\", p=2)\nknn.fit(X_train,y_train)\nknn_train_score = knn.score(X_train, y_train)\nknn_valid_score = knn.score(X_valid, y_valid)\nprint(\" - n neighbor : \", 7 , \" - train score : \", knn_train_score, \" - valid score : \", knn_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:12.986323Z","iopub.execute_input":"2023-02-01T14:51:12.992081Z","iopub.status.idle":"2023-02-01T14:51:13.043083Z","shell.execute_reply.started":"2023-02-01T14:51:12.992006Z","shell.execute_reply":"2023-02-01T14:51:13.041491Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":" - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = knn.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.051296Z","iopub.execute_input":"2023-02-01T14:51:13.052276Z","iopub.status.idle":"2023-02-01T14:51:13.094020Z","shell.execute_reply.started":"2023-02-01T14:51:13.052210Z","shell.execute_reply":"2023-02-01T14:51:13.092537Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"array([[299, 30],\n [ 63, 142]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.104344Z","iopub.execute_input":"2023-02-01T14:51:13.109854Z","iopub.status.idle":"2023-02-01T14:51:13.138605Z","shell.execute_reply.started":"2023-02-01T14:51:13.109782Z","shell.execute_reply":"2023-02-01T14:51:13.137094Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"Accuracy : 0.8258426966292135\nMisclassfication : 0.17415730337078653\nSensitivivity : 0.9088145896656535\nSpecificity : 0.6926829268292682\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.141155Z","iopub.execute_input":"2023-02-01T14:51:13.151686Z","iopub.status.idle":"2023-02-01T14:51:13.183541Z","shell.execute_reply.started":"2023-02-01T14:51:13.151614Z","shell.execute_reply":"2023-02-01T14:51:13.181982Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.190465Z","iopub.execute_input":"2023-02-01T14:51:13.191601Z","iopub.status.idle":"2023-02-01T14:51:13.214243Z","shell.execute_reply.started":"2023-02-01T14:51:13.191536Z","shell.execute_reply":"2023-02-01T14:51:13.212831Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"Accuracy : 0.7871148459383753\nMisclassfication : 0.21288515406162464\nSensitivivity : 0.8818181818181818\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.216513Z","iopub.execute_input":"2023-02-01T14:51:13.217361Z","iopub.status.idle":"2023-02-01T14:51:13.226018Z","shell.execute_reply.started":"2023-02-01T14:51:13.217286Z","shell.execute_reply":"2023-02-01T14:51:13.224351Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(X_train)\nknn_pred = X_train.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_train_pass_id\nknn_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.228136Z","iopub.execute_input":"2023-02-01T14:51:13.229804Z","iopub.status.idle":"2023-02-01T14:51:13.289272Z","shell.execute_reply.started":"2023-02-01T14:51:13.229740Z","shell.execute_reply":"2023-02-01T14:51:13.287745Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n844 3.0 1.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 769.0\n255 3.0 2.0 2.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
8443.01.00.00.0845.0
3162.02.01.01.0317.0
7683.01.01.00.0769.0
2553.02.02.01.0256.0
1303.01.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(knn_pred[[\"PassengerId\", \"knn_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.297719Z","iopub.execute_input":"2023-02-01T14:51:13.302941Z","iopub.status.idle":"2023-02-01T14:51:13.361563Z","shell.execute_reply.started":"2023-02-01T14:51:13.302872Z","shell.execute_reply":"2023-02-01T14:51:13.359941Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = knn.predict(X_valid)\nknn_pred = X_valid.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_valid_pass_id\nknn_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.366098Z","iopub.execute_input":"2023-02-01T14:51:13.367128Z","iopub.status.idle":"2023-02-01T14:51:13.414267Z","shell.execute_reply.started":"2023-02-01T14:51:13.367081Z","shell.execute_reply":"2023-02-01T14:51:13.412764Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n369 1.0 2.0 0.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 428.0\n.. ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 1.0 39.0\n371 3.0 1.0 1.0 0.0 372.0\n\n[357 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
3691.02.00.01.0370.0
5413.02.06.00.0542.0
1963.01.00.00.0197.0
8103.01.00.00.0811.0
4272.02.00.01.0428.0
..................
1741.01.00.00.0175.0
2971.02.03.00.0298.0
2443.01.00.00.0245.0
383.02.02.01.039.0
3713.01.01.00.0372.0
\n

357 rows × 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(knn_pred.PassengerId), \"knn_y_pred\"] = knn_pred[\"knn_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.416656Z","iopub.execute_input":"2023-02-01T14:51:13.417577Z","iopub.status.idle":"2023-02-01T14:51:13.474919Z","shell.execute_reply.started":"2023-02-01T14:51:13.417518Z","shell.execute_reply":"2023-02-01T14:51:13.473392Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.552373Z","iopub.execute_input":"2023-02-01T14:51:13.552777Z","iopub.status.idle":"2023-02-01T14:51:13.575185Z","shell.execute_reply.started":"2023-02-01T14:51:13.552741Z","shell.execute_reply":"2023-02-01T14:51:13.573826Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n707 1.0 1.0 0.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0\n788 3.0 1.0 3.0 1.0 0.0\n183 2.0 1.0 3.0 1.0 0.0\n654 3.0 2.0 0.0 0.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
7071.01.00.01.00.0
2333.02.06.01.00.0
7883.01.03.01.00.0
1832.01.03.01.00.0
6543.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.851998Z","iopub.execute_input":"2023-02-01T14:51:13.852446Z","iopub.status.idle":"2023-02-01T14:51:13.868236Z","shell.execute_reply.started":"2023-02-01T14:51:13.852408Z","shell.execute_reply":"2023-02-01T14:51:13.867490Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 2.0 1.0 1\n 1.0 1.0 0.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 2\n 1.0 1.0 0.0 1\n 2.0 1.0 2\n 2.0 1.0 1.0 3\n 2.0 1.0 1\n 3.0 1.0 0.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 0.0 4\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 3.0 1.0 0.0 1\n 2.0 1.0 1\n 6.0 2.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.057420Z","iopub.execute_input":"2023-02-01T14:51:14.057804Z","iopub.status.idle":"2023-02-01T14:51:14.084011Z","shell.execute_reply.started":"2023-02-01T14:51:14.057773Z","shell.execute_reply":"2023-02-01T14:51:14.082464Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.355738Z","iopub.execute_input":"2023-02-01T14:51:14.356164Z","iopub.status.idle":"2023-02-01T14:51:14.375540Z","shell.execute_reply.started":"2023-02-01T14:51:14.356115Z","shell.execute_reply":"2023-02-01T14:51:14.374287Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n534 3.0 2.0 0.0 0.0 1.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0\n429 3.0 1.0 0.0 1.0 0.0\n501 3.0 2.0 0.0 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
5343.02.00.00.01.0
6071.01.00.01.00.0
8043.01.00.01.00.0
4293.01.00.01.00.0
5013.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.597501Z","iopub.execute_input":"2023-02-01T14:51:14.597895Z","iopub.status.idle":"2023-02-01T14:51:14.613504Z","shell.execute_reply.started":"2023-02-01T14:51:14.597865Z","shell.execute_reply":"2023-02-01T14:51:14.612422Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 1.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 1.0 6\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.104177Z","iopub.execute_input":"2023-02-01T14:51:15.104569Z","iopub.status.idle":"2023-02-01T14:51:15.123111Z","shell.execute_reply.started":"2023-02-01T14:51:15.104537Z","shell.execute_reply":"2023-02-01T14:51:15.121935Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n255 3.0 2.0 2.0 1.0 1.0\n130 3.0 1.0 0.0 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
2553.02.02.01.01.0
1303.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.344115Z","iopub.execute_input":"2023-02-01T14:51:15.344558Z","iopub.status.idle":"2023-02-01T14:51:15.362850Z","shell.execute_reply.started":"2023-02-01T14:51:15.344502Z","shell.execute_reply":"2023-02-01T14:51:15.361620Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 4\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 0.0 10\n 2.0 1.0 0.0 10\n 2.0 1.0 8\n 3.0 1.0 0.0 2\n 2.0 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.857448Z","iopub.execute_input":"2023-02-01T14:51:15.857837Z","iopub.status.idle":"2023-02-01T14:51:15.877163Z","shell.execute_reply.started":"2023-02-01T14:51:15.857806Z","shell.execute_reply":"2023-02-01T14:51:15.875923Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.132579Z","iopub.execute_input":"2023-02-01T14:51:16.132970Z","iopub.status.idle":"2023-02-01T14:51:16.150755Z","shell.execute_reply.started":"2023-02-01T14:51:16.132936Z","shell.execute_reply":"2023-02-01T14:51:16.149943Z"},"trusted":true},"execution_count":171,"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 1.0 1\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 0.0 4\n 2.0 1.0 0.0 7\n 2.0 1.0 4\n 3.0 2.0 1.0 2\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower.","metadata":{}},{"cell_type":"markdown","source":"## Prediction on the test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = knn.predict(X_test)\nknn_pred = X_test.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nknn_pred\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.910077Z","iopub.execute_input":"2023-02-01T14:51:16.910492Z","iopub.status.idle":"2023-02-01T14:51:16.964596Z","shell.execute_reply.started":"2023-02-01T14:51:16.910456Z","shell.execute_reply":"2023-02-01T14:51:16.963157Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 0.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 1.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
03.01.00.00.0892.0
13.02.01.00.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.01.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
\n

418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.178878Z","iopub.execute_input":"2023-02-01T14:51:17.179931Z","iopub.status.idle":"2023-02-01T14:51:17.185405Z","shell.execute_reply.started":"2023-02-01T14:51:17.179876Z","shell.execute_reply":"2023-02-01T14:51:17.184219Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"code","source":"knn_pred[[\"PassengerId\",\"knn_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.372559Z","iopub.execute_input":"2023-02-01T14:51:17.372948Z","iopub.status.idle":"2023-02-01T14:51:17.390909Z","shell.execute_reply.started":"2023-02-01T14:51:17.372914Z","shell.execute_reply":"2023-02-01T14:51:17.389533Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" PassengerId knn_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdknn_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(knn_pred[[\"PassengerId\",\"knn_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.671274Z","iopub.execute_input":"2023-02-01T14:51:17.672432Z","iopub.status.idle":"2023-02-01T14:51:17.693960Z","shell.execute_reply.started":"2023-02-01T14:51:17.672382Z","shell.execute_reply":"2023-02-01T14:51:17.692706Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred \n0 0.0 0.0 \n1 1.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 1.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.0
2894.02.01.02.588235-0.2021843.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method : Decision Trees\n\nWe use a decision tree classifier and some automated search of the hyper-parametrisation to discover suitable hyper-parameters and validate the quality of a model. \n","metadata":{}},{"cell_type":"code","source":"\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.109673Z","iopub.execute_input":"2023-02-01T14:51:18.110073Z","iopub.status.idle":"2023-02-01T14:51:18.128404Z","shell.execute_reply.started":"2023-02-01T14:51:18.110036Z","shell.execute_reply":"2023-02-01T14:51:18.127375Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = [\"Fare\",\"Pclass\",\"Sex\",\"Embarked\",\"fam_members\", \"Age\"]\nx_train_pass_id = X_train.PassengerId\nX_train = X_train [x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.370422Z","iopub.execute_input":"2023-02-01T14:51:18.370800Z","iopub.status.idle":"2023-02-01T14:51:18.388440Z","shell.execute_reply.started":"2023-02-01T14:51:18.370767Z","shell.execute_reply":"2023-02-01T14:51:18.387202Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538\n768 0.419921 3.0 1.0 3.0 1.0 0.000000\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769","text/html":"
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FarePclassSexEmbarkedfam_membersAge
844-0.2508363.01.02.00.0-1.000000
3160.5000432.02.02.01.0-0.461538
7680.4199213.01.03.01.00.000000
2550.0342843.02.04.02.0-0.076923
130-0.2840413.01.04.00.00.230769
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nx_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.609801Z","iopub.execute_input":"2023-02-01T14:51:18.610554Z","iopub.status.idle":"2023-02-01T14:51:18.628148Z","shell.execute_reply.started":"2023-02-01T14:51:18.610505Z","shell.execute_reply":"2023-02-01T14:51:18.626956Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154","text/html":"
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FarePclassSexEmbarkedfam_membersAge
3692.3753461.02.04.00.0-0.461538
5410.7285013.02.02.06.0-1.615385
196-0.2903563.01.03.00.00.000000
810-0.2844013.01.02.00.0-0.307692
4270.5000432.02.02.00.0-0.846154
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nX = titanic_test.copy(deep = True)\nX_test = X[x_cols]\nX_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.826406Z","iopub.execute_input":"2023-02-01T14:51:18.826797Z","iopub.status.idle":"2023-02-01T14:51:18.835436Z","shell.execute_reply.started":"2023-02-01T14:51:18.826766Z","shell.execute_reply":"2023-02-01T14:51:18.834526Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"Index(['Fare', 'Pclass', 'Sex', 'Embarked', 'fam_members', 'Age'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Decision Tree classifier\n\nWe explore the maximum depths hyper parameter using a deterministic and incremental search. Then we applied the most efficient parametrisation. We chose a low maximum depth, as the model may be overfitting.","metadata":{}},{"cell_type":"code","source":"\ndepths = range(3, 200)\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth = depth, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n # Train Decision Tree Classifer\n clf = clf.fit(X_train,y_train)\n train_score = clf.score(X_train,y_train)\n valid_score = clf.score(X_valid,y_valid)\n print(\"- depth : \", depth, \" - train score : \", train_score, \" - valid score : \", valid_score)\n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:19.301726Z","iopub.execute_input":"2023-02-01T14:51:19.302125Z","iopub.status.idle":"2023-02-01T14:51:20.492365Z","shell.execute_reply.started":"2023-02-01T14:51:19.302089Z","shell.execute_reply":"2023-02-01T14:51:20.491051Z"},"trusted":true},"execution_count":180,"outputs":[{"name":"stdout","text":"- depth : 3 - train score : 0.8295880149812734 - valid score : 0.8011204481792717\n- depth : 4 - train score : 0.8295880149812734 - valid score : 0.8151260504201681\n- depth : 5 - train score : 0.8595505617977528 - valid score : 0.8067226890756303\n- depth : 6 - train score : 0.8820224719101124 - valid score : 0.8235294117647058\n- depth : 7 - train score : 0.8895131086142322 - valid score : 0.8179271708683473\n- depth : 8 - train score : 0.9063670411985019 - valid score : 0.7927170868347339\n- depth : 9 - train score : 0.9119850187265918 - valid score : 0.7843137254901961\n- depth : 10 - train score : 0.9250936329588015 - valid score : 0.803921568627451\n- depth : 11 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n- depth : 12 - train score : 0.9550561797752809 - valid score : 0.773109243697479\n- depth : 13 - train score : 0.9625468164794008 - valid score : 0.7955182072829131\n- depth : 14 - train score : 0.9662921348314607 - valid score : 0.7787114845938375\n- depth : 15 - train score : 0.9700374531835206 - valid score : 0.7927170868347339\n- depth : 16 - train score : 0.9737827715355806 - valid score : 0.7787114845938375\n- depth : 17 - train score : 0.9756554307116105 - valid score : 0.7871148459383753\n- depth : 18 - train score : 0.9794007490636704 - valid score : 0.7871148459383753\n- depth : 19 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 20 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 21 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 22 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 23 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 24 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 25 - train score : 0.9812734082397003 - valid score : 0.7899159663865546\n- depth : 26 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 27 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 28 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 29 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 30 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 31 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 32 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 33 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 34 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 35 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 36 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 37 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 38 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 39 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 40 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 41 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 42 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 43 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 44 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 45 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 46 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 47 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 48 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 49 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 50 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 51 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 52 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 53 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 54 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 55 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 56 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 57 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 58 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 59 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 60 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 61 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 62 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 63 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 64 - train score : 0.9812734082397003 - 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valid score : 0.7703081232492998\n- depth : 197 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 198 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 199 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(max_depth = 8, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n\n# Train Decision Tree Classifer\nclf = clf.fit(X_train,y_train)\nclf_train_score = clf.score(X_train,y_train)\nclf_valid_score = clf.score(X_valid,y_valid)\nprint(\"- depth : \", 8, \" - train score : \", clf_train_score, \" - valid score : \", clf_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.494270Z","iopub.execute_input":"2023-02-01T14:51:20.494649Z","iopub.status.idle":"2023-02-01T14:51:20.508968Z","shell.execute_reply.started":"2023-02-01T14:51:20.494617Z","shell.execute_reply":"2023-02-01T14:51:20.507560Z"},"trusted":true},"execution_count":181,"outputs":[{"name":"stdout","text":"- depth : 8 - train score : 0.9082397003745318 - valid score : 0.8151260504201681\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover that the gender, Fare and age could be contribute to the classification. It constrast to our previous assumptions for KNN and logistic regression.","metadata":{}},{"cell_type":"code","source":"importances = clf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.510227Z","iopub.execute_input":"2023-02-01T14:51:20.510578Z","iopub.status.idle":"2023-02-01T14:51:20.523335Z","shell.execute_reply.started":"2023-02-01T14:51:20.510548Z","shell.execute_reply":"2023-02-01T14:51:20.521845Z"},"trusted":true},"execution_count":182,"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":" 0\n0.200193 Fare\n0.125949 Pclass\n0.315820 Sex\n0.025783 Embarked\n0.094918 fam_members\n0.237337 Age","text/html":"
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0
0.200193Fare
0.125949Pclass
0.315820Sex
0.025783Embarked
0.094918fam_members
0.237337Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.525411Z","iopub.execute_input":"2023-02-01T14:51:20.525712Z","iopub.status.idle":"2023-02-01T14:51:20.536265Z","shell.execute_reply.started":"2023-02-01T14:51:20.525685Z","shell.execute_reply":"2023-02-01T14:51:20.535549Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"array([[326, 3],\n [ 46, 159]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.736687Z","iopub.execute_input":"2023-02-01T14:51:20.737047Z","iopub.status.idle":"2023-02-01T14:51:20.744835Z","shell.execute_reply.started":"2023-02-01T14:51:20.737016Z","shell.execute_reply":"2023-02-01T14:51:20.743620Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"Accuracy : 0.9082397003745318\nMisclassfication : 0.09176029962546817\nSensitivivity : 0.9908814589665653\nSpecificity : 0.775609756097561\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.940682Z","iopub.execute_input":"2023-02-01T14:51:20.941080Z","iopub.status.idle":"2023-02-01T14:51:20.950745Z","shell.execute_reply.started":"2023-02-01T14:51:20.941045Z","shell.execute_reply":"2023-02-01T14:51:20.949939Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.156573Z","iopub.execute_input":"2023-02-01T14:51:21.157555Z","iopub.status.idle":"2023-02-01T14:51:21.164777Z","shell.execute_reply.started":"2023-02-01T14:51:21.157504Z","shell.execute_reply":"2023-02-01T14:51:21.163996Z"},"trusted":true},"execution_count":186,"outputs":[{"name":"stdout","text":"Accuracy : 0.8151260504201681\nMisclassfication : 0.18487394957983194\nSensitivivity : 0.9318181818181818\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.602984Z","iopub.execute_input":"2023-02-01T14:51:21.603408Z","iopub.status.idle":"2023-02-01T14:51:21.609433Z","shell.execute_reply.started":"2023-02-01T14:51:21.603369Z","shell.execute_reply":"2023-02-01T14:51:21.608257Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_train)\nclf_pred = X_train.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_train_pass_id\nclf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.801023Z","iopub.execute_input":"2023-02-01T14:51:21.801826Z","iopub.status.idle":"2023-02-01T14:51:21.826292Z","shell.execute_reply.started":"2023-02-01T14:51:21.801783Z","shell.execute_reply":"2023-02-01T14:51:21.825118Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769231.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(clf_pred[[\"PassengerId\", \"clf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.073441Z","iopub.execute_input":"2023-02-01T14:51:22.073853Z","iopub.status.idle":"2023-02-01T14:51:22.100768Z","shell.execute_reply.started":"2023-02-01T14:51:22.073817Z","shell.execute_reply":"2023-02-01T14:51:22.099989Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = clf.predict(X_valid)\nclf_pred = X_valid.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_valid_pass_id\nclf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.313331Z","iopub.execute_input":"2023-02-01T14:51:22.314186Z","iopub.status.idle":"2023-02-01T14:51:22.339255Z","shell.execute_reply.started":"2023-02-01T14:51:22.314149Z","shell.execute_reply":"2023-02-01T14:51:22.338531Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 0.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538460.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
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357 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(clf_pred.PassengerId), \"clf_y_pred\"] = clf_pred[\"clf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.503867Z","iopub.execute_input":"2023-02-01T14:51:22.504541Z","iopub.status.idle":"2023-02-01T14:51:22.530946Z","shell.execute_reply.started":"2023-02-01T14:51:22.504500Z","shell.execute_reply":"2023-02-01T14:51:22.529880Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.197164Z","iopub.execute_input":"2023-02-01T14:51:23.197598Z","iopub.status.idle":"2023-02-01T14:51:23.221279Z","shell.execute_reply.started":"2023-02-01T14:51:23.197559Z","shell.execute_reply":"2023-02-01T14:51:23.220173Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n220 -0.277363 3.0 1.0 2.0 0.0 -1.076923 1.0 0.0\n510 -0.290356 3.0 1.0 3.0 0.0 -0.076923 1.0 0.0\n724 1.673732 1.0 1.0 2.0 1.0 -0.230769 1.0 0.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
220-0.2773633.01.02.00.0-1.0769231.00.0
510-0.2903563.01.03.00.0-0.0769231.00.0
7241.6737321.01.02.01.0-0.2307691.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.536909Z","iopub.execute_input":"2023-02-01T14:51:23.537537Z","iopub.status.idle":"2023-02-01T14:51:23.553252Z","shell.execute_reply.started":"2023-02-01T14:51:23.537491Z","shell.execute_reply":"2023-02-01T14:51:23.552369Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 10\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n3.0 0.0 1.0 0.0 14\n 2.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.819458Z","iopub.execute_input":"2023-02-01T14:51:23.819831Z","iopub.status.idle":"2023-02-01T14:51:23.828371Z","shell.execute_reply.started":"2023-02-01T14:51:23.819802Z","shell.execute_reply":"2023-02-01T14:51:23.827545Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.944899Z","iopub.execute_input":"2023-02-01T14:51:23.945939Z","iopub.status.idle":"2023-02-01T14:51:24.401522Z","shell.execute_reply.started":"2023-02-01T14:51:23.945899Z","shell.execute_reply":"2023-02-01T14:51:24.400330Z"},"trusted":true},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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+rur1l3e4D1EgiBa5xjGQjnUzLfn+nI2z0zXff0FT3f92yT3+tROU4CYVWdkenI4Rd39xvX2xoA4GhxyijAsptnugXCR5P8WpIfOBph8HhSVb+Q6T6GvyQMHhtV9ec13ZR8/79D3rie419V3fYgn+9Hq+pwOoACOGocIQQAABiUI4QAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAkqaoXV9UHquo6624LABwrAiEAw6uqM5LcLUkn+ab1tgYAjh2BEACSByd5aZKnJnnIxsCq+uyq+tOq+nBVvayqHlNVe1bG37GqXlRV76+qf6uq7zj2TQeAq++kdTcAAI4DD07yy0n+MclLq+pm3f2uJL+R5GNJbp7kjCR/meRNSVJV10vyoiQ/m+QbkpyZ5EVV9erufu0x/x8AwNXgCCEAQ6uq7Ulul+TZ3f3yJP+Z5AFVtSXJtyb5ue7+rznkPW3lpfdOckl3/253f6q7/yXJHyX59mP8XwCAq00gBGB0D0nywu5+7/z8GfOw0zOdSfOWlWlXH98uyZdX1Qc3/pI8MNPRRAC4RnDKKADDqqpTknxHki1V9c558HWS3DDJzZJ8Ksmtk7x+HneblZe/JcnfdvfXHpvWAsDmq+5edxsAYC2q6txM1wneJcknV0Y9O8nLMoXBy5J8b5LbJnlhkjd39/aqOjXJq5M8Mskz59fdJclHu3vvsWg/ABwpp4wCMLKHJPnd7n5zd79z4y/Jr2c6/fOHk5yW5J1Jfi/JriSfSJLu/kiSeya5f5K3z9M8LtMRRgC4RnCEEAAOU1U9LsnNu/shh5wYAK4BHCEEgIOY7zN4p5p8WZIdSZ637nYBwGbRqQwAHNypmU4TvWWSdyV5fJLnr7VFALCJnDIKAAAwKKeMAgAADEogBAAAGNRariG8yU1u0mecccY63hoAAGAoL3/5y9/b3acfaNxaAuEZZ5yRiy++eB1vDQAAMJSqetPBxjllFAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGtWmBsKq2VNW/VNULNmueAAAAHD2beYTwEUn2buL8AAAAOIo2JRBW1a2T3CvJ72zG/AAAADj6NusI4ROS/GSST2/S/AAAADjKjjgQVtW9k7y7u19+iOkeVlUXV9XF73nPe470bQEAADhCm3GE8CuTfFNVXZLkmUnuXlW/v/9E3f3k7j6ru886/fTTN+FtAQAAOBJHHAi7+6e7+9bdfUaS+yf5m+7+riNuGQAAAEeV+xACAAAM6qTNnFl3vzjJizdzngAAABwdjhACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBDR0Id+3alW3btmXLli3Ztm1bdu3ate4mAQAAHDMnrbsB67Jr167s3LkzF154YbZv3549e/Zkx44dSZJzzz13za0DAAA4+qq7j/mbnnXWWX3xxRcf8/ddtW3btjzxiU/MOeecc/mw3bt357zzzsurX/3qNbYMAABg81TVy7v7rAOOGzUQbtmyJR//+Mdz8sknXz5s3759ue51r5vLLrtsjS0DAADYPEuBcNhrCLdu3Zo9e/ZcYdiePXuydevWNbUIAADg2Bo2EO7cuTM7duzI7t27s2/fvuzevTs7duzIzp071900AACAY2LYTmU2Oo4577zzsnfv3mzdujUXXHCBDmUAAIBhDHsNIQAAwAhcQwgAAMCVCIQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKCOOBBW1XWr6p+q6pVV9ZqqevRmNAwAAICj66RNmMcnkty9uz9aVScn2VNVf97dL92EeQMAAHCUHHEg7O5O8tH56cnzXx/pfAEAADi6NuUawqraUlWvSPLuJC/q7n/cjPkCAABw9GxKIOzuy7r7LkluneTLqmrb/tNU1cOq6uKquvg973nPZrwtAAAAR2BTexnt7g8m2Z3k6w8w7sndfVZ3n3X66adv5tsCAABwNWxGL6OnV9UN58enJPnaJK870vkCAABwdG1GL6O3SPK0qtqSKWA+u7tfsAnzBQAA4CjajF5G/zXJF29CWwAAADiGNvUaQgAAAK45BEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADCoIw6EVXWbqtpdVa+tqtdU1SM2o2EAAAAcXSdtwjw+leR/dvc/V9WpSV5eVS/q7tduwrwBAAA4So74CGF3v6O7/3l+/JEke5Pc6kjnCwAAwNG1qdcQVtUZSb44yT8eYNzDquriqrr4Pe95z2a+LQAAAFfDpgXCqrp+kj9K8qPd/eH9x3f3k7v7rO4+6/TTT9+stwUAAOBq2pRAWFUnZwqDf9Ddz92MeQIAAHB0bUYvo5XkwiR7u/uXj7xJAAAAHAubcYTwK5M8KMndq+oV8983bsJ8AQAAOIqO+LYT3b0nSW1CWwAAADiGNrWXUQAAAK45BEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMauhAuGvXrmzbti1btmzJtm3bsmvXrnU3CQAA4Jg5ad0NWJddu3Zl586dufDCC7N9+/bs2bMnO3bsSJKce+65a24dAADA0Vfdfczf9KyzzuqLL774mL/vqm3btuWJT3xizjnnnMuH7d69O+edd15e/epXr7FlAAAAm6eqXt7dZx1w3KiBcMuWLfn4xz+ek08++fJh+/bty3Wve91cdtlla2wZAADA5lkKhMNeQ7h169bs2bPnCsP27NmTrVu3rqlFAAAAx9awgXDnzp3ZsWNHdu/enX379mX37t3ZsWNHdu7cue6mAQAAHBPDdiqz0XHMeeedl71792br1q254IILdCgDAAAMY9hrCAEAAEbgGkIAAACuRCAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAY1EnrbgAcz6pq0+bV3Zs2LwAA2AyOEMKC7j7k3+1+6gWHNR0AABxvBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGNRJ624A61VVmzav7t60eQEAAEefI4SD6+5D/t3up15wWNMBAADXLAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFAnrbsBsC53fvQL86FL923KvM44/6Ijnsdpp5ycV/7cPTehNQAAcHgEQob1oUv35ZLH3mvdzbjcZoRKAAC4KpwyCgAAMCiBEAAAYFBOGT2BuUYOAABYIhCewFwjBwAALHHKKAAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAM6qR1NwAARnPm085cdxOu5FUPedW6mwDAGgiEAHCMCV8AHC+cMgoAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACD0ssowzp16/k582nnr7sZlzt1a5Lca93NAABgIAIhw/rI3sfmkscePwHsjPMvWncTAAAYjFNGAQAABiUQAgAADGpTAmFVPaWq3l1Vr96M+QEAAHD0bdYRwqcm+fpNmhcAAADHwKYEwu5+SZL3b8a8AAAAODZcQwgAADCoYxYIq+phVXVxVV38nve851i9LQAAAAdxzAJhdz+5u8/q7rNOP/30Y/W2AAAAHIRTRgEAAAa1Wbed2JXkH5J8QVW9tap2bMZ8AQAAOHpO2oyZdPe5mzEfAAAAjh2njAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAY1EnrbgBwzVVVmzav7t60eQEAcHgcIQSutu4+5N/tfuoFhzUdAADHnkAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwqJPW3QBYpzPOv2jdTbjcaaecvO4mAAAwGIGQYV3y2HttynzOOP+iTZsXAAAcS04ZBQAAGJRACAAAMKgT/pTRqtq0eXX3ps3rWDh16/k582nnr7sZlzt1a5I4tRIAAI4XJ3wgPJwQd6JeA/aRvY89rv5fx1MHLgAAgFNGAQAAhiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQATii7du3Ktm3bsmXLlmzbti27du1ad5MAjlsnrbsBAACbZdeuXdm5c2cuvPDCbN++PXv27MmOHTuSJOeee+6aWwdw/HGEEAA4YVxwwQW58MILc8455+Tkk0/OOeeckwsvvDAXXHDBupsGcFwSCAGAE8bevXuzffv2Kwzbvn179u7du6YWARzfBEIA4ISxdevW7Nmz5wrD9uzZk61bt66pRQDHN4EQADhh7Ny5Mzt27Mju3buzb9++7N69Ozt27MjOnTvX3TSA45JOZQCAE8ZGxzHnnXde9u7dm61bt+aCCy7QoQzAQVyjA+GdH/3CfOjSfZsyrzPOv+iI53HaKSfnlT93z01oDQBwdZ177rkCIMBhukYHwg9dui+XPPZe627G5TYjVAIAABwrriEEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMKiT1t0AgGuiM5925rqbcCWvesir1t0EAOAaRiAEuBqELwDgROCUUQAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAY1EnrbgAcz6rq8KZ73KGn6e4jbA0AAGwugRAWCHEAAJzInDIKAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIM6ad0NAI5Pd370C/OhS/dtyrzOOP+iI57HaaecnFf+3D03oTUAAGwQCIED+tCl+3LJY++17mZcbjNCJQAAV+SUUQAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAzqpHU3ADg+nbr1/Jz5tPPX3YzLnbo1Se617mYAAJxQBELggD6y97G55LHHTwA74/yL1t0EAIATjlNGAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKDcdgKATXfm085cdxOu5FUPedW6m8AmqqpNm1d3b9q8AK5pBEIANp3wxdF2OCHujPMvOq7upwpwPHLKKAAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAADDbtWtXtm3bli1btmTbtm3ZtWvXupsER9VJ624AAAAcD3bt2pWdO3fmwgsvzPbt27Nnz57s2LEjSXLuueeuuXVwdDhCCAAASS644IJceOGFOeecc3LyySfnnHPOyYUXXpgLLrhg3U2Do+YafYTw1K3n58ynnb/uZlzu1K1Jcq91NwMAgKth79692b59+xWGbd++PXv37l1Ti+Dou0YHwo/sfWwueezxE8DOOP+idTcBAICraevWrdmzZ0/OOeecy4ft2bMnW7duXWOr4OhyyigAACTZuXNnduzYkd27d2ffvn3ZvXt3duzYkZ07d667aXDUXKOPEAIAwGbZ6DjmvPPOy969e7N169ZccMEFOpThhCYQAgd1PJ0GfdopJ6+7CQAM4NxzzxUAGYpACBzQZl2fe8b5Fx1X1/oCAPAZriEEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAg9qUQFhVX19V/1ZV/1FV52/GPAEAADi6jjgQVtWWJL+R5BuSfGGSc6vqC490vgAAABxdm3GE8MuS/Ed3v6G7P5nkmUnuuwnzBQAA4Cg6aRPmcaskb1l5/tYkX77/RFX1sCQPS5Lb3va2m/C2kzPOv2jT5nWkTjvl5HU3AY6pqjq86R536Gm6+whbA5wo7vzoF+ZDl+7blHltxn7CaaecnFf+3D03oTWb48ynnbnuJlzJqx7yqnU3ATbFiOvXZgTCw9LdT07y5CQ566yzNmXP75LH3mszZpMzzr9o0+YFIxHigKPhQ5fuO66+l4+nH58T4QuOphHXr804ZfRtSW6z8vzW8zAAAACOY5sRCF+W5POq6nOq6tpJ7p/kTzZhvgAAABxFR3zKaHd/qqp+OMlfJtmS5Cnd/Zojbhmb4ng6zcU1lgAAcHzZlGsIu/vPkvzZZsyLzeMaSwAAYMmm3JgeAACAax6BEAAAYFDH7LYTAACH49St5+fMp52/7mZc7tStSeLSCeDEJBACAMeVj+x97HF17frx1EEbwGZzyigAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQJ627AQAA+zvj/IvW3YTLnXbKyetuAsBRIxACAMeVSx57r02ZzxnnX7Rp8wI4UTllFAAAYFACIQAAwKBO+FNGq+rwpnvcoafp7iNsDQAA63a4+4eHw/4h13Qn/BHC7t60PwAArvkOZ7/vdj/1AvuHDOGED4QAAAAcmEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGNRJ624AAABsljs/+oX50KX7NmVeZ5x/0RHP47RTTs4rf+6em9AaODoEQgAAThgfunRfLnnsvdbdjMttRqiEo8kpowAAAIMSCAEAAAbllFEAABjEmU87c91NuJJXPeRV627C0ARCAAAYhPDF/pwyCgAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIM6ad0NYL2q6vCme9yhp+nuI2wNAABwLAmEgxPiAABgXE4ZBQAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEHpZRQAgBPGqVvPz5lPO3/dzbjcqVuT5F7rbgYclEAIAMAJ4yN7H5tLHnv8BLAzzr9o3U2ARU4ZBQAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAINy2wkAAE4ox9OtHk475eR1NwEWCYQAAJwwNusehGecf9FxdT9DOFqcMgoAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgzpp3Q0AALiqqurwpnvcoafp7iNsDcA1l0AIAFzjCHEAm8MpowAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAJ5Rdu3Zl27Zt2bJlS7Zt25Zdu3atu0kAx62T1t0AAIDNsmvXruzcuTMXXnhhtm/fnj179mTHjh1JknPPPXfNrQM4/jhCCACcMC644IJceOGFOeecc3LyySfnnHPOyYUXXpgLLrhg3U0DOC4JhADACWPv3r3Zvn37FYZt3749e/fuXVOLAI5vAiEAcMLYunVr9uzZc4Vhe/bsydatW9fUIoDjm0AIAJwwdu7cmR07dmT37t3Zt29fdu/enR07dmTnzp3rbhrAcUmnMgDACWOj45jzzjsve/fuzdatW3PBBRfoUAbgIARCAOCEcu655wqAAIfJKaMAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABnXSuhsAAADHUlUd3nSPO/Q03X2ErYH1EggBABiKEAef4ZRRAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIM6okBYVd9eVa+pqk9X1Vmb1SgAAACOviM9QvjqJPdL8pJNaAsAAADH0ElH8uLu3pskVbU5rQEAAOCYcQ0hAADAoA55hLCq/irJzQ8wamd3P/9w36iqHpbkYUly29ve9rAbCAAAwNFxyEDY3ffYjDfq7icneXKSnHXWWb0Z8wQAAODqc8ooAADAoI70thPfUlVvTfIVSS6qqr/cnGYBAABwtB1pL6PPS/K8TWoLAAAAx5BTRgEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAwAll165d2bZtW7Zs2ZJt27Zl165d624SwHHriG5MDwBwPNm1a1d27tyZCy+8MNu3b8+ePXuyY8eOJMm555675tYBHH+qu4/5m5511ll98cUXH/P3BQBObNu2bcsTn/jEnHPOOZcP2717d84777y8+tWvXmPLANanql7e3WcdcJxACACcKLZs2ZKPf/zjOfnkky8ftm/fvlz3utfNZZddtsaWAazPUiB0DSEAcMLYunVr9uzZc4Vhe/bsydatW9fUIoDjm0AIAJwwdu7cmR07dmT37t3Zt29fdu/enR07dmTnzp3rbhrAcUmnMgDACWOj45jzzjsve/fuzdatW3PBBRfoUAbgIFxDCAAAcAJzDSEAAABXIhACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADAogRAAAGBQAiEAAMCgBEIAAIBBCYQAAACDEggBAAAGJRACAAAMSiAEAAAYlEAIAAAwKIEQAABgUAIhAADAoARCAACAQQmEAAAAgxIIAQAABiUQAgAADEogBAAAGJRACAAAMCiBEAAAYFACIQAAwKAEQgAAgEEJhAAAAIMSCAEAAAYlEAIAAAxKIAQAABiUQAgAADCo6u5j/6ZV70nypmP+xgd3kyTvXXcjjmPqs0x9lqnPMvVZpj7L1GeZ+ixTn2Xqs0x9lh1v9bldd59+oBFrCYTHm6q6uLvPWnc7jlfqs0x9lqnPMvVZpj7L1GeZ+ixTn2Xqs0x9ll2T6uOUUQAAgEEJhAAAAIMSCCdPXncDjnPqs0x9lqnPMvVZpj7L1GeZ+ixTn2Xqs0x9ll1j6uMaQgAAgEE5QggAADAogRAAgKOqqmrdbQAOTCA8xqpKzRf4wlimPsusX8ssP8vUZ5n1a5nl5+Cq6trtGqVFlp9ltj/LjrQ+insMVdV1uvvT627H8aqqTvWFcXDqs8z6tcwO2TLr1zLr1zLr18FV1TcmeUZV3aWqbrPu9hyPLD/LbH+WbUZ9BMJjZN4g/llVPayqvmbd7TneVNXXJdlVVT9fVd+x7vYcb9RnmfVrWVXdK8kLquobq+ou627P8cb6tcz6tcz6dUi7k/xVkgcleWRVffN6m3N8sfwss/1Ztln10cvoMVRVX53klkl+KsnvdvevrrlJx5Wq+oIkt0jypCS/k+TXu/vS9bbq+KE+y6xfy6rq25PcNsndk/xRdz9lzU06rli/llm/llm/rqyq7pTkQ939pvn5GUnOSvITSX6tu/9gjc07rlh+ltn+LNuM+py06a3iCqrqa5Ncu7sv6u6/nYe9Osnzq+qk7n78elu4XlX1rUmul+SZSd7Q3f9WVfdN8n+TXDvJBets37qpzzLr17Kq+m9JPt3d/9Tdf1hV10nyj0meVVXX6+4nrrmJa2X9Wmb9Wmb9Oriq+r0kn5PkbfPpkN/S3ZckuaSqPprkf1bVWzeWqxFZfpbZ/izb7Po4QngUVdUfJrlxksuSfCzJryX55+7+UFV9YZIXJvm57r5wjc1cm7k+N0jy8ST/leSvk7ygu99ZVXdI8qwkTx11o6g+y6xfy6rqmUlunqk2neThSd7e3Z+uqi9O8owkP9Pdz1tjM9fG+rXM+rXM+nVw8w8t53X32VV1UpLnZqrRA7r7Y1V1vSTfleSUJL+aJKNdP2f5WWb7s+xo1Mc1hEdJVZ2Z5Abd/TXdfc9Mv/rcP8lXzb/8vDbJtyX5+vk0iqFU1a2SXKe7v66775vkT5N8YZLvqKrTu/s/Ml1vcP+qOmudbV0H9Vlm/VpWVWcnuWl3n93d90ry1iSPS/JFSdLd/5Lk/CR3q6rTqsbq3c76tcz6tcz6dUivz3Qk8Ebd/anu/qZMO61/lCTd/bEkr0pyTpIbDxgGz47l56Bsf5YdrfoIhEfP25PcaOMCz+5+bJLXJPnmJKfP07wmyX8mOW0dDVyzDya5XVU9IEm6+xlJ9iS5fZKt87DXZvqV/sZrauM6fTDqs8T6tewNST4x/1KY7n54krcleVRVXXue5jVJbpTktNF2yGL9OhTr1zLr17KPJbl+krtuDOjuByS5VlU9cn7+/5K8JMlXjxZ4Yvk5FNufZUelPgLhUVBV1d3vy3Rdyl3m04/S3b+W5FNJfnF+/pEkr82U6ofZIM71+Vim63PuWlVfkSTd/dwkH0jy4yuTvyHTL/fDUJ9l1q/D8uEkr0vyxVV1WpJ0909m2ub/2vz8P5JcnOQeI9XH+rXM+nVYrF8LuvsNSZ6X5PFV9dVVtWUe9cuZTnHb8LQkfzlg4LH8HITtz7KjWR+B8ChY2bj9fZLPT3KvqvqiedhPJvlYVV13nvbpSZ4y0gZx5f/6z0k+lOQ+8ykU6e5HJ/lUVX32/PwFmXr8G4b6LLN+HdjqRr+7P5jpGoIHZDpt5ObzqPOTfHTlZU9K8qwR6rPB+nVgG8uP9evQrF8HtxH+eupB9AlJHpvk+6pqe5Lzktx0Y9rufu/848xQLD8HZ/uz7GjWR6cym2RO7Vcq5rwR/PZMPdm9Jsk9kry3ux9yjJt4XJpPmbhXkrtluqbgS5K8r7u/a60NO06oz8T6dfiqakt3XzY//qYkD06yN8mrM10X9/buftgam3jcsH5d2X7Lj/VrgfUrqar7Zzq48KIkH+7uT9TUw+Gn5vFfl+nU0TsleU93nzcPP+A2fSSWn4nv92XHqj4C4Saoqtt295ur6lrd/ekDjP+cJGckuU+mD+t/zcOH2CBW1bbufvXC+NMy3T/l25J8sOde/dTn8vGj18f6taCqfjjTaVgXJ9nb3R/db4fsrEynPX51ph2y8+fho9TH+rWgqn42ySeSvDHJi7r7A1V1cnfvm8ePvn7dorvfsbr9Wf2/j7x+VdUfJblupu3P25O8KclvdPeHq+o63f2JlWlXt0kH3JafiCw/y3y/LzuW9REIj1BVPTfThZzndPff7rfSH3SjN8oGsaqel+S+Se7T3RftN+4KNdhvI6k+6mP9OoSq2pXk1CSXJKkkJyf56e5+X033/vrkQV43Sn2sXwuq6sJMp/DtznRT7C9M8uCebr1h+ZnWr69M8q3d/bL9tj+XH0k9wOtO+PpU1U2S/HZ3f8v8/OuTfE2m27j8Yk/XMKWqvirJv6w8H2JHPrH8HIrv92XHuj6uITwCVfXNmX5ZfXiSP66qs3u6h8y1kmTlg3t4Vd169bWDLMxnJ3lPku9L8itVde/V8Sv1+f+q6nNXvyTUR32sX8vqM9cJ3Lu7fzjJryd5f5Jfraobb+zMV9W3VtXpK6+rQepzdqxfh+O87v7lJDuT/FOSP6qqm64sP6OuX9+d5GZJfinJk6rqS/fb/mycVjvk+pWpA4utNd1zMEn+MslFSa6T5J5JUlV3T3KHjTCYjHO/QcvPMt/vy9ZRn5OOrMnD+7skb5t/+bk0yfOr6r7d/eKNCarqpkne2d1vXVcj1+ifk7y/u/+1qj6Y5NeqaqOjhiTTLxlJ/rG7/3NdjVwj9Vlm/Vq2Jcltq+p7uvsp3b23qn4rycOS7KiqX05yZpKTu/s9Gy8aZYcs1q9FNXVvf51Mv0A/oacbhv9/SX4+yc9X1Y9m6sL87YOuX3+W5HXd/Q9V9ekkv1NV39vdL9uYoKq2Zrqf5VDr1xxaPlhVP5Pp3p3v7O6/r6q/z3S94H2S/FF3/816W7pWlp9lvt+XHfv6dLe/q/GXz5xuu2Vl2AMzdSd81vz8fvuNr3W3ew11utbK4/tluk7lnPn592faGKqP+uxfF+vX4dXpqzMd1bnX/PzkJN+S5KkHq+lIf9avQ9bnLpmu/Xrg/PykTJ1/PCXJqftNO0x9VrY/tTLsB5K8Mskd5+dfOWp9Vv7PN0vyiEw98Z49D7tOpjB0y3W3b411sfwcXn18vx9H9XGE8Grq+RPo7suqLu+u+w+q6mNJ/qqq3pbkr3q6t9UVXjOSng5xb9TnuVX10Uy/lH0iyf/r7ietTKs+6pPE+nU45l/p/7aqfiXJj9TUEcgfJ3leVT2iqj6/u1+/Mf1o9UmsX0tquobpFVX1oCRPrKnTj6cl+dequmOmLs1fvjH9SPVZ2f6snkb8m1X1X0l2VdXJSf4kU9fv2X/aUXT3u6rq+ZmvG6ypk5l7JHlrd799va1bH8vPMt/vy9ZVH53KbJJ552yjQ4I3JNnT3Q/ef9yo1GeZ+ixTn4OrqlMyXbPz+CR/kOSrkry5B+uae4nlZ1lVfWWmZec5Sf5bkv+0/BxYVV2S5O+6+0HrbsvxpKbeMs9O8qnufsI8bPh1a3+WnyuzfV52rOojEG6yqvq+TId0v39+PkRvSMnhLZhVdd9Mp2z96Pxcfa44zbD1ORwjr1+HUtM99e6c5Prd/dvzsGG+TK1fyw5Wn40a1NR9+W0zneq3a+k1o6qqRyb5/JWdsWGWn+TgPWMeqA6j1eZwjL78HIrv92VHuz4C4VV0qC/IqrpBd394fjzEwnxV/p9VdUp3X3pVX3dNpj6Hz/q17FA79YcadiKyfi07nP/nyMvPqoMFnpXxn93d75sfn/D1qarHJHlbpjPSfmsetnEK21C3aDkclp9lvt+Xrbs+AuEh1NRT34czdU7wsyvDF+8nM8ovq1X1f+eHl2Xqne79PZ33vLhhHIX6LLN+Lbu69RmF9WuZ+iy7KoFnv9O2TvjtT1U9MckdM3UY8z+TvC7JQ1ZqcPskb5qXp+F23hPLz6H4fl92vNXHfQgXVNXjknxRkr9I8tVVdWFVfW5yeWcFd9z4sGq+N8iGQRbmn03yuZnus3PzJI9OcnZNnVtcVlV3qKrbzNMOt6ypzzLr17Ijqc8IrF/L1GfZHHi+PNO9O7+nqp6+saPV3V1Vt5/Xr0/PO2irHYSc0Nufmq5Lvk2SH+/uZ3X3l2XqUfTClcl+MMlz5pqNGAYtPwt8vy87Husz3JfA4aqqk5LcOsnju/ul3f3VmW7E+hNVdZN5socnedmIv47NC+jNk/x+d78xyXdm+qXsWzLdhyjzsOdX1bXVR31WWb+Wqc8y69cy9Vkm8Bzc/P+9NNMtEu40b4uS5F5J7ljTvU6T5DGZbuOybQ3NXCvLzzLfX8uO1/oIhAcwr8CfytQl8JetfEA/mORGmX5xTU8dE/x9kq9fRzvXaV5AX5TkblV1h/n5/850+Pv752kuSPL/kuxYW0PXRH0Ozvq1TH0Ozfq1TH0OTuBZtnL04bWZlo07zsM/leTeSW5WVbdK8l+Zbkvy5nW0c10sP8t8fy07nusjEB7AfhvEM5N8aVVdbz6P97uS3L6qvnie5lmZvlRHtDfJJ5KcU1W37u5Pd/fPJPnCqrrHPM3jkjxzbS1cL/U5AOvXMvU5bNavZepzAALPsqrLr4F7VpI/T3JhVZ1VVZ/V3e/PtNN6/e7+ZJJndPeH1tjcY87ys8z317LjuT5uTL+gu19cVWck+YEk166qV3T3m6rqUyvT7FlbA9esu19XVX+e5Jsz1ecfu/viJB/KtCOS7n7LGpu4VuqzzPp1YCvXoajPAuvXga0sP+qzoLufNa9fF1bVDyV5bXe/v6o2As/bquoZg1zPdHJ370umHdaN09S6+3FV9fEk5yf5YE23JnlHd//bxrRrbPZarKxflp8Fvr+WHY/10cvoilrpzWdjpZ8fn5vkK5PcIVOI/lB3f+v6WroeVXXS/CvY/vW5R6aLq78tybuTfLy777u+lq6H+iyzfh1cVZ3a3R9Zeb7ay9jw9UmsX0uq6qbd/e6V56vLz/D1Sa5Uk9XHj0hytyQfTPI5Sd7d3eeuraHHWFX9UpLTkrwvyc919yfno4S1UqMz52nu0N1PnYcN0RNkcsXAPD+3/OzH9/uya0J9BMIkVfXjSf5wTuerK/rqh3azJDdNckZ3/+k8bIiLYavqfyf53e5+/cJCXUnOSHKz7n7pPEx91Mf6dQhV9YQkt0jyriT/dz6ys3/X5SPXx/q1oKp+Pcntk7wlyfO7+8/m4eqTpKq+O8lfdPc7FkLhkIGnqn47yU0y9UD7m0le3N0/fYDprtPdn1h5PsSykwjMh+L7fdk1qT7DB8Kq+p0k35rk9UnO7e43HOiDqKrTeuVc+YEW5l/NdEj7A0nO7u69deD7ouz/C/UoG0P1WWD9WlZVT05y4yQ/keQXMv26/GMHmG7U+li/FlTVkzItPz+cqU436e4fPsB0o9bnGUm+KsmeJP+zp9P4DrT9GS7wVNX2TPcX/M455NwmyR8m+cYkH1jZWf3BJP/U0+nGQxGYl/l+X3ZNq8/QncrMqfyTSb44ybOT7Kqq2/d0D5CTVqb73iRfsfraQRbmG2W6x871MvWY9ZKq2trTfVFOXpnuQUnuufraQXY21GeB9WtZVd05yWcn+e6ebg3wiCRfW1V32G+6HRmzPtavBTXdGPxGSR7e3e/K1Dvd9qr6b/tN910Zsz63TnKdJPdP8qokj6+qW83bny0r0/1Qps4dLjfC+pXkNUl+Zg6D18l0bel1k9xov+XjdYOGwe2ZwuB3dvcrknxHknOq6sbzEcKN6X4wAy4/vt+XXRPr4wjhtNPxsXmj+Mgk35Tku+bTk6q7u6ru1N3/uuamrsV+9fmJJD+Z5O7d/aqV+tyyu9++5qauhfoss34d3LxTekZ3/2dVXbe7P15Vf5fkEd39zyvT3bm7X7m+lq6P9WtZTb0ZvjvJtbr7E1X13EynHf/VyjS37u63rq2Ra1RVN8zU2+Mtknxvks9L8lPz6Vsby8853b17ne1cl1q5Lnd+/oIkD+ruD1TVw5M8vbv/ax43xFHlDfO25+bzWQnXyfTjwkuSfGt3/+fKdHfv7r9ZVzvXyff7smtafYY+Qpgk3f2BnrpPTnc/JskLkjxlPn3iZ6tq28aHtfqr0Cj2q88vJXl8khfORzF+qarO3tgZUx/12Z/16+B6Ou3xjfPjj8+D3565B8iq+smqus1GGBytPon161C6+23dva8/c7ra+5JcO0mq6jHzjwlvnZ+PWJ8Pdvcnu/tNSX4nyX8m+fmqummSH6uqm2+EwUHrc3knTfOgTyS5Q1X9QZL/vhEG52mHCYPJtO1J8u/z409094eTvDXTWQupqofXdCuOv5mfj7j8+H5fcE2rz/CBcFVN5+3+fJKLkrwpyZ26+9Ub40fbIK6qqmvNv2g8NtOpSa9PcrvufvHGNOqjPkusX1fWKx1czIM+lmmH7GlJvqhXbgswYn02WL+WrSw/H0hyu6q6MMmtVo8sj1yf2VszLTtvnB9v7+53bowcvD4by88pSZ6T5F3d/eDk+NhRXReB+fD5fl92TaiPQLiiP3Pe7lck2dVz168jbxA39HRPoo0F9swkz+7ub0/UJ1Gfw2H9WrRRg8uS/G6mzmUekqhPYv26Cj6S5NeSvK+7vztRnw3dfVl3fzDT9ZTP7O5vScaoz8b/8WD/1/5MJ02vztRxyo/N01/reNhRPQ4IzIfg+33ZNaE+wwXCQxW/qm6X5B+7+4Hz86E2iCu/NB9s/M2T/L/uvv/G9OpzhfGj18f6teAwdshek+SF3f0T8/Sj1cf6teBg9VnZ2Xhdkj/u7p/cmH6E+hwq8KxM93mZtj8bO/MnfH2q6rTMpxF3TzedX5j8iSs/RA3RE2QiMB8u3+/Lrun1GaJTmaq6RZJ93f3e+flhXRw9ygaxqj43SfqKF0ofskbqoz6J9etQrkp9qur63f3R+fEo9bF+LVCfZXPg+XjP11Ee7v97hPrU1O39rTKdovba7v61efj+ncns/3yYDmSuyvJT0zXdbznUdCcS3+/LTqT6nPCBsKp+P8nNknw0ySu7+1Hz8Ct8GHWAe1uNoKqeneSGSa6f6V5Nj+7ujx1gOvVRnyuxfi27CvUZcofM+rXsKtTnCsvPKK5C4Dnudr6Otqr6yST3SPLgJJ+f5ElJ/nTl6PHtM/WY+Uvra+V6CczLfL8vO9Hqc0KfMlpVD8v0YX1dpu7Kv6OqfjGZTrGpqs+rqsfPz4/7D2uzVdX9ktywu++Z5H5J7pTkUTV1ZZ65Pn+QqE/U50qsX8uuYn2usDM/yM6G9WvBVazPiGHwJ5PcNsl3J3lGkh9YWb8+VVW3r+lWJUPc9+wA3pzkr7v7nd39kiRfneR+VfWYefx1kty5qr5hbS1co6u4/Iy4ffb9vuBErM8JHQgz/erzH5mOhP57kq9Kcreq+j/z+HcmuX1V/cC6Grhmb0/yqaq6SU+9rd0/069l/yNJ5prdtKoeu8Y2rpP6LLN+LVOfZdavZeqzTOBZdmmSe1TVKUnS3e/OtA06u6rOztRT7z8m+ax1NXDNLD/LfH8tO+Hqc6IHwvcnuXGS2yfJfI7vNya5d1U9oLs/kuS3k3yspptEj+YtSS5Jcpeaboz9wSQ/kOTuVfXj8zQ/lOSSqrruepq4VuqzzPq1TH2WWb+Wqc8ygWdBdz8/007rnpo7kunpnp0vSnLqfNTiuZlORR6R5WeZ769lJ1x9TuhA2N0vy3Rj0d+qqtvM54F/IMkvJtn4Av3HJC+4phzS3Uzd/bZMvRo+PMlZVXXD7v5QkvMz90qW6X5Nf9CfuXH2MNRnmfVrmfoss34tU59lAs/BVdVJSdLd35vktUn+vqrOrKrrJ7lbko2Oit7W3e9aX0vXx/KzzPfXshOxPidsIFzZID4yySsyXVB975q6fT03ya3n8e/r7vevq53HUtVnusTdeNzdv5HkHzL98vw9VfUlSX4syWnz+P+af+kYysoXhPocgPVrmfpcme3PMvU5fALPFVXVWVV1l43n8zVwJ8+PH5Tkr5P8aJIXJHlrdz9hDc08blh+rmy/7c/G/o/vrwM4UetzwvQyWlVfl+S/krxs49fSqjq5u/fNj38g06HdOye5pLsftrbGrkFVXbu7P1krvR3VSk9IVfVNSb4w000z39zd562xucdcVZ2TZF+Sf+ruT87D1Gdm/VqmPstsf5apz7KqOivJp7r7FSvDVtevxyS5RaYd+Uu6+6HraOc6VNXzMx0x/twkz0ryku5+0Tzuuivbo+sluVF3v3V+PkzPq5afZRu12G+bs/p49O+vO2XKS69cGXbC1eeECIRV9bxMFwB/dqbD+6/t7gvncZdvEOfnn93d75sfD7FBrKlr3NsmuU93f2i/nY79u1Me8T5oT8+07Nw2yZ8keezGr+7qY/06FPVZZvuzTH2WCTwHV1V3TfKY7v6GmnqffUim65pe0t1/sjLdrZK8Y2UHdojbJiSWn0Opqt/MdJ3kD3X3R/cLOpeH5vn5iN9fT8u0Tn1xkt/s7gtWxp1Q9bnGnzJa0017r9vd35ip+9eXJblrVf1wkqys7F88f3gbH1Zd0z6sq6OqfizJGUn+Jclzq+q07r6s5otcN3Y2qurr543jxs7GKPX5xUxfAvdKcs9Mp4vcb2O8+li/lqjPMtufZeqzbA481+7ub0hyTqaOQL5uPmK6un7dKsmlKzvzQ9QnyaeSfG5NN0x/W6ZOLN6e5L/XdJ/BjaMXX7taj4HCoOVnQVVdkKku70vyhPkHp0/XZ06J3DiCOur31xMy3frnPknOTvKAqrrvxvgTrT7X+EA4+7yq2tpTL2wXZboo+PNq6ikqVfVtSbatJvlRNohJ/ibJT3T3I5K8OsnzNnY6Niaoqv+e5CarRzIGqs8/JfnpJOnudyS5MMnNVyeoqu0Ztz6J9etQ1OfgbH+Wqc8ygWdBT6ewPTPJg6vqpt39niS7knxOkvvMk/1Wdz91TU1cN8vPsj/N1GnVr2a65OFXV0JhJUlVfUuSO432/VVTz82vSXJeknT3fyT5nSQ32G+6+yU580Soz4lyyuhPJtma5Oe6+81VdeMkP5Xko939CyOdHnEgNZ+CVFXXztQD0p2T3L27u6ru2N2vW3MT16ami8g/sfJLz3cnObu7HzI/v1kPclH5wVi/lqnPMtufZeqzrKp+Psknkvx2d7+7qm6R5AlJ/l93/6r1q+6R5OuTvC3Js7r77fOPCDsynQa4cRRsyDpZfg5PVX1+kh9Mclp3f/c87PJTIEdUVTdP8r6V/cOfSHKL7v6x+fkVTum/prtGHyHc+AUjU9fAb07y41X1OT316vO0TPdvOm1jZV+Zfigbvzb31FnKz2TqCvdFVfW3+cyviEPq7o/2dDH1xrLx4SQfSpKq+sNM95UZkvVrmfocHtufZepzSC9JcqMkD6yqW85ncvxqkjvVdBrt6OvXXyX52yQ3y3Ta3/Yk/1+SDw56VHl/lp/D0N2vT/LkJO+qqifP259v3Rg/Yn26+5377R9emulIaqrqj5JcfvroiVCfE+IIYZLU1OXyN2W6juexSX44yet7sN7YDse84L4vyV909wPW3Z7jSVVty2fu83XpxpHC0Vm/lqnP4bP9WaY+V1ZV90nylZl68vu1JDszdd70P9fasDVbPbpVVZ+T5N5J7pLkA9394/tPMyrLz+Gbl6N/SvLX3X3/dbfneFJVd0vyzUlOz/Qbywm1f3jCBMIkqarrJHlAki9Icll375yHD79BXFVVv5zkZt39wPn5Na43pKOlpu6p/ylTb1I/NA9Tn1i/DkV9Do/tzzL1+QyB58oO9f+t+RYm8+Nhl53E8nNVbNShqv4gyUnd/Z3z8KGXoVVVdc8kf5ETdP/wGhkIr8oKfCJ9WIfrML4wvqC7/21+rD5XHHdKkm/v7qcfatoTlfVrmfoss/1Zpj7LBJ4rqqodSf490xkrL5uHXeH/faA6jPjdlVh+DuUw6nOXnu/XqD5XGnfbJPft7iceatpromtEIKyq/5HpGp2PdvdfzsMq+cx58SMuuBuubn1OtIX5YI6gPkMsU9avZeqzzPZnmfosE3gOrqqekun+ef+Qqdv73+/uX18Zf9Pufvf8+ISvx4FYfpYdZn2uVItRvtOOYPk54epz3HcqU1VPynTO7m2SPKmqfjyZvkjnw9u3mp9f3k3uSI6kPoNsDK9Kfa6wPpxoK/uBWL+Wqc8y259l6rNsDjwPztR51xPrM/fv3Lgx9k03no9Wn5rucfp5mW6JcH6S70/yyKr60Xn8ZyV59PyDwwlfjwOx/Cy7CvXpA9RnhP2fq7L8nPD7hyetuwFLauoe+POSfGt3v7eqLkrynDmZ/2JVnZTkF6vq7d39EyOs4KvUZ9nVqM8Jt4IvsfwsU59l6rNMfZatBJ6v6e5PVtWdk/xlTV25P2El8Ly+u39ltPpk6ljo9UluneQN3f3Kqrp7kj+rqnd39zOq6nlJ7lZTb5kfX5zbCcbys0x9ll2N+pzw+4fH+xHCdyV5VZIvmT+kf0/yHUl+qKp+oKf7fzw6yfVqumB4NOqzTH2Wqc8y9VmmPsvUZ9lq4Nm4yfrdk/xoVT2gu/8ryfOS3Limm0QPpbs/mOTjSZ64Muy1SX4kyfZ50L/N/w53dkIsP4eiPsvUZz/HdSCcE/nbk3xvklPnYf+W5P5JvqGqbpjkPUleMf87FPVZpj7L1GeZ+ixTn2Xqs0zgObiN09N66snwpKp64croVyW5RVV9Vne/KcljuvvSdbRznSw/y9Rnmfpc2XEbCDfOZ+7ux2W6EeRTquqW8+h/ztT2Ld39gSRP6e6Prqel66E+y9RnmfosU59l6rNMfZYJPFc2n0Kc5PJrlrbMj78uyaVV9cdV9UNJfj3J++cjGOnuT6ylwWtk+VmmPsvU58COu15Gq2pLd192gMe/melX1ncl2Zrkgz3gTXvVZ5n6HFzVFXsSU58rUp9l6rNMfZbNp81+auX5an2en6STvChTBw/v7O4d62npsVdVv5Dkhkle3t1PXRl+ec2q6sFJrpfkFt39s/OwIXrKTCw/h6I+y9Tn0I6bQFhV9+nuP50fX96d634f2jlJbp7k5t39K/OwITaI6rNMfZZV1c9k2uH4l+7etTJcfaI+h6I+y9RnmcBzcFX1W0lumuR3kjwrU6+iLz2M151w3d4fjOVnmfosU5/Dc1wEwqp6RpK7Jfmj7v7Redi15tMmlm4SOcQGUX2Wqc+yqnpykptl2tn42SQXdPfvzePUR30Wqc8y9Vkm8BxcVd0vyfd0973n509K8tokr0vyz939nnn49yR5Qc/3HByJ5WeZ+ixTn8O39msIq+qsJLdI8qBM5/I+IfnMOfQbX6ZV9b1VtXX1tSN8WOqzTH2WzTsct+7u+3b3MzJdMP2DVXXK6s5qVe1QH/XZn/osU59lK/W5X3f/WZJnJPnyqvq6qjp9ZbrvqfmeXxtGqE93PzfJtybTd1Sme6J9MskPJnl4VZ1cVddLcvKgYdDys0B9lqnPVbP2QNjdFyd5SJJ/yJTgr1dVT6iqk7v7sqq6VlVdO9NF1HvX2tg1UJ9l6nNIf5PkJ5NkrsNr5uEnr+ysXifJB9RHfQ5AfZapzwKB58Cq6kE1XVe62inMxUnu0N2/meSnkjwwybbu/lh3P2l+3RC9HW6w/CxTn2XqcxV191r+Mh3RedJ+w7YkuVOSC5P873nYg5N81so0ta42q8/x86c+h1Wf35gfn7TfuL/cqEOSb95vnPqoj/qoz2bU5zf3G3aXJLeaH98x02mRXzxofW6S5FFJbjA/v9YBpvmTJHddd1vXVB/Lj/qozzH+W+cRwj9P8vaqukFy+fm6l80f0mMy3QzyY0nu1XP3yknS86c2APVZpj7L/jzJe6rq1O7+VE22zEcxTkpyRlU9O1OPWpdTH/WZqc8y9Vn250netd/2+RXd/bYk6e7XZbop9BX2QQaqz2VJvijJuckVT0+bl6VnZTqq/PI1tW/dLD/L1GeZ+lwN6wyElyXZlpUN4vyhfbK735jki5M8p7u/MxnvVImoz6Goz7KNHY4HJJdv6LYk2Zekkjwnydu7+2Fra+F6qc8y9VmmPssEngU93X/yMUkeWVWX336kqm6W6XTRS7v7IfOw0b67EsvPoajPMvW5Gk469CRHR3d/oKoek+QFVfWR7n7Gxk59kq9O8qqe7wNSA/b2oz7L1GfZQerzySSpqo8keUfv1yPrGpt7zKnPMvVZpj7LDlSf5PLA892ZAs9D52FDde2+obtfWVXfneTXqura3f3U7n5XVf12d78vGXPZSSw/h6I+y9Tnaur1n+t7j0zdLD/0IOOvdG79SH/qoz6bXZ9MHReoj/qoj/qsoz6frT5XqNH2JG9M8ogkX78yfOjrmeYaWH7UR32O0d/xch/C7Ul+L8mvJHlDd79gHi65R30ORX2WrdTnCUn2dvcL5+FD/vq8P/VZpj7L1GfZfvX5t+7+i3m47fOsqj4vydcmuX2SV/fKzbNHZ/lZpj7L1OfwHReBMLnSBvFV3f20NTfpuKI+y9RnmR2OZeqzTH2Wqc8y9Tl8VXWD7v7wuttxPLH8LFOfZepzeI6bQLjKBnGZ+ixTn2Xqs0x9lqnPMvVZpj4cCcvPMvVZpj4Hd1wGQgAAAI6+dd52AgAAgDUSCAEAAAYlEAIAAAxKIAQAABiUQAhwgqiqM6rq0qp6xcqwy6rqFVX16qr6w6r6rIXXP6qqfvwYtPNuVfWauV2nHO33OxrmWr/6GL/nzrlu/zrX7ss3cd7fV1XPWnl+g6r6z6q6/UGmf2hV3XIT3/9z5//TRzdrngAcHoEQ4MTyn919l5Xnl3b3Xbp7W5JPJnn4epp1BQ9M8r/ndl267sasQ1WddBWn/4ok907yJd19pyT3SPKWTWzS7yS5TVXdY37+80me0t1vOMj0D01ywEBYVVuu6pt39/7LLQDHiEAIMI6/S3KHJKmqB89Hml5ZVb+3/4TzEaOXzeP/aOPIYlV9+3y08ZVV9ZJ52BdV1T/NR3j+db4R8AFV1fcm+Y4kv1BVf1BV16+qv66qf66qV1XVfefpzqiq11XVU6vq9fO096iqv6+qf6+qL1t4j0dV1dOq6u+q6k1Vdb+q+sV5/n9RVSfP0921qv62ql5eVX9ZVbeYh7+4qn6lqi6uqr1V9aVV9dz5fR+z8lYnze3aW1XPWanR0nyfUFUXJ3nEgWq54BZJ3tvdn0iS7n5vd7/9YO9XVadV1b9V1RfM0+yqqu872Mx7ugfVw5M8oarOSvI1SX7pIPX9tiRnJfmDjaO8VXVJVT2uqv45ybfP/9ez5ulvUlWXzI+3VNUvzcvWv1bV9x/i/w3AUSYQAgxgPiL1DUleVVVflOSRSe7e3XdO8ogDvOS53f2l8/i9SXbMw382ydfNw79pHvbwJL86H+E5K8lbD9aO7v6dJH+S5Ce6+4FJPp7kW7r7S5Kck+TxVVXz5HdI8vgkd5z/HpBke5IfT/Izh/gvf26Su89t/P0ku7v7zCSXJrnXHAqfmOTbuvuuSZ6S5IKV13+yu89K8ltJnp/kh5JsS/LQqvrseZovSPJ/u3trkg8n+cHDmO+1u/us7n78QWp5MC/MdATv9VX1f6vqq5PkYO/X3R9K8sNJnlpV909yo+7+7aU36O5/TfKXSf46yXnd/cmDTPecJBcneeB+R3nf191f0t3PXHibHUk+1N1fmuRLk3xfVX3OIf7vABxFV+mUFQCucU6pz1xT+HdJLkzy/Un+sLvfmyTd/f4DvG7bfDTshkmunykoJMnfZwoZz07y3HnYPyTZWVW3zhQk//0qtK+S/K+q+qokn05yqyQ3m8e9sbtflSRV9Zokf93dXVWvSnLGIeb75929b552S5K/mIdvvPYLMgW8F835c0uSd6y8/k9Wpn9Nd79jbscbktwmyQeTvKW7/36e7veT/Mj8PkvzfdbK4wPV8oC6+6NVddckd8sUnJ9VVednCmYHfL/uflFVfXuS30hy56X5r/iNJN/Q3S8+zOlXPevQk+SeSe40H2VMktOSfF6SN16N9wNgEwiEACe2S/e/NuszB+AWPTXJN3f3K6vqoUnOTpLufnhNnZncK8nLq+qu3f2MqvrHedifVdX3d/ffHGb7Hpjk9CR3nQPcJUmuO4/7xMp0n155/ukc+vtr49TKT1fVvvmUyNXXVqag9xVLr9/vffd/784V9WHM92OXT3zgWr7vYP+h7r4s/397d/NqVRXGcfz7EwIhTU0aSS94QREiBMFJI/8DBzYpIgoRwiY1cVI6apSDNKIGmiI0CZxYDRwYBiVcKKOBkRBSDSpyqBb49jRYW+5Oz/Xe4zFO1/39wIHNOuus/ezNgXMe1rPWhtPA6S7RfQn4dr7zJVkGbAL+AtZwl5nbnpvd615c6R1fZ64KaXmvPbTZx5NIkv4XLBmVpOH5grbOay1AkkdH9FkJ/N6VJL5wqzHJTFXNVtVe4CKtjHE9cKGqDtLKK5/p+p5Ksm6BWFYBf3bJ4DbgyUkvbpHOA4+lbdZCkoe6UtpxPHHr87Ry1q/GGXeee7kuyakRfTfm32szNwO/LHC+12nlvs8DR3prJ4/lLmswF+kS7Tsyn5+BLd3xjl77SeDVXiwbkjw8YSySpAk4QyhJA1NV55K8DXyZ5AbwHW3XyL63gFlaojLL3J//d7rEJLS1Zt8De4AXk1wD/qCVgC6jrQEcVY7a9zHwaTfj9Q3w44SXtyhVdbUrWzyYZBXt9/Bd4NwYw5wHdif5CPgB+GDMcUfdyy202bXbrQDeS7K6e/8nYNd850tyHdgJbK2qS92mNW8C+2gJ+29jXOcoR4EPk/wNjJoN3Q98kmQX8Hmv/RCtZPdst1b0IrB9wlgkSRPIXBWNJGkpS/IU8Fn3iIlpx/I08EpVvTHtWJaSJK8Bv1bViQU739v4jwCHq+q5/2L8SSW5XFUrph2HJA2JCaEkPSCSPA6coe32uHnK4UiLlmQGOA6srKqZaccjSUNiQihJWpKSvMydj8z4uqp2TyOeB1GS94Fnb2s+UFVHphGPJOn+MyGUJEmSpIFyl1FJkiRJGigTQkmSJEkaKBNCSZIkSRooE0JJkiRJGigTQkmSJEkaqH8ABilMl5Mom0UAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.403612Z","iopub.execute_input":"2023-02-01T14:51:24.404043Z","iopub.status.idle":"2023-02-01T14:51:24.424956Z","shell.execute_reply.started":"2023-02-01T14:51:24.404007Z","shell.execute_reply":"2023-02-01T14:51:24.423814Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0\n501 -0.290356 3.0 2.0 3.0 0.0 -0.692308 0.0 1.0\n17 -0.062981 2.0 1.0 2.0 0.0 0.000000 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
429-0.2773633.01.02.00.00.1538461.00.0
501-0.2903563.02.03.00.0-0.6923080.01.0
17-0.0629812.01.02.00.00.0000001.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.426286Z","iopub.execute_input":"2023-02-01T14:51:24.426719Z","iopub.status.idle":"2023-02-01T14:51:24.444950Z","shell.execute_reply.started":"2023-02-01T14:51:24.426673Z","shell.execute_reply":"2023-02-01T14:51:24.443790Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 3\n 1.0 1\n 1.0 2.0 0.0 1\n 2.0 1.0 1.0 1\n3.0 0.0 1.0 0.0 12\n 1.0 3\n 2.0 0.0 4\n 1.0 2\n 1.0 1.0 0.0 1\n 2.0 0.0 9\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 2\n 2.0 1.0 3\n 4.0 1.0 1.0 1\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.655585Z","iopub.execute_input":"2023-02-01T14:51:24.655981Z","iopub.status.idle":"2023-02-01T14:51:25.270872Z","shell.execute_reply.started":"2023-02-01T14:51:24.655946Z","shell.execute_reply":"2023-02-01T14:51:25.270073Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.272439Z","iopub.execute_input":"2023-02-01T14:51:25.273391Z","iopub.status.idle":"2023-02-01T14:51:25.295346Z","shell.execute_reply.started":"2023-02-01T14:51:25.273342Z","shell.execute_reply":"2023-02-01T14:51:25.294366Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 1.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
2550.0342843.02.04.02.0-0.0769231.01.0
130-0.2840413.01.04.00.00.2307690.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.310893Z","iopub.execute_input":"2023-02-01T14:51:25.311294Z","iopub.status.idle":"2023-02-01T14:51:25.332606Z","shell.execute_reply.started":"2023-02-01T14:51:25.311259Z","shell.execute_reply":"2023-02-01T14:51:25.331521Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 6\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 2\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 0.0 1\n 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 1.0 5\n 2.0 0.0 14\n 1.0 23\n 1.0 1.0 0.0 15\n 1.0 3\n 2.0 0.0 10\n 1.0 4\n 2.0 1.0 0.0 10\n 1.0 2\n 2.0 0.0 5\n 1.0 8\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.617532Z","iopub.execute_input":"2023-02-01T14:51:25.617910Z","iopub.status.idle":"2023-02-01T14:51:27.648580Z","shell.execute_reply.started":"2023-02-01T14:51:25.617879Z","shell.execute_reply":"2023-02-01T14:51:27.647383Z"},"trusted":true},"execution_count":201,"outputs":[{"execution_count":201,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.650898Z","iopub.execute_input":"2023-02-01T14:51:27.651397Z","iopub.status.idle":"2023-02-01T14:51:27.674977Z","shell.execute_reply.started":"2023-02-01T14:51:27.651353Z","shell.execute_reply":"2023-02-01T14:51:27.673660Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.676558Z","iopub.execute_input":"2023-02-01T14:51:27.676918Z","iopub.status.idle":"2023-02-01T14:51:27.695988Z","shell.execute_reply.started":"2023-02-01T14:51:27.676883Z","shell.execute_reply":"2023-02-01T14:51:27.694729Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 1.0 3\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 12\n 1.0 1.0 0.0 4\n 2.0 1.0 8\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 91\n 1.0 1\n 2.0 0.0 6\n 1.0 4\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 1.0 2\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 0.0 2\n 1.0 4\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.698581Z","iopub.execute_input":"2023-02-01T14:51:27.699104Z","iopub.status.idle":"2023-02-01T14:51:28.312451Z","shell.execute_reply.started":"2023-02-01T14:51:27.699061Z","shell.execute_reply":"2023-02-01T14:51:28.311698Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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If9pa21/7LqLElSVbuTPGk+fPoGoapek+QLk/xVa+3LV50H2N4USmDb2cpCOR9S+sFMe/4enOlzX1/U5uveLXleT80ghbKqzsy05/L+rbW3rDYNADAqh7wCXLc7ZLqExceS/HyS79yMMjmSqvrJTNex/GllcmtU1e/VdFH79V8/tupsnLiqutsxlu/HqmojJ9ACGJY9lAAAAHSxhxIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBIAkVfUnVfWhqjp11VkAYLtQKAE46VXVmUm+JElL8tWrTQMA24dCCQDJE5K8Nsnzk5y3dmdVfVpV/U5VfaSq/rqqnl5Vhxcev3dVvaaqPlhV/1BVX7/10QFgdXasOgAADOAJSX42yV8meW1V3b619t4kv5Tk40nukOTMJH+Q5K1JUlU3S/KaJD+e5KFJzk7ymqr6u9baG7f8fwAAK2APJQAntaraneTuSV7aWnt9kn9J8riqOiXJY5I8pbX2b3NJvGjhRx+e5LLW2q+11q5qrf1Nkpcl+bot/i8AwMoolACc7M5L8urW2vvn2y+a77ttpiN53r7w3MXv757kC6rqw2tfSb4x095MADgpOOQVgJNWVZ2W5OuTnFJV75nvPjXJLZPcPslVSe6S5B/nx+668ONvT/KnrbWv2Jq0ADCeaq2tOgMArERV7c30Ocn7JfnPhYdemuSvM5XJTyT5tiR3S/LqJG9rre2uqtOT/F2SJyd58fxz90vysdbaka3IDwCr5pBXAE5m5yX5tdba21pr71n7SvKLmQ5f/e4kZyR5T5JfT3IoyX8kSWvto0kenOSxSd41P+cZmfZwAsBJwR5KANigqnpGkju01s477pMB4CRgDyUAHMN8ncn71OTzk+xL8opV5wKAUTgpDwAc2+mZDnO9U5L3JvmZJK9caSIAGIhDXgEAAOjikFcAAAC6KJQAAAB0WclnKG9zm9u0M888cxWzBgAAOKm8/vWvf39r7babMe2VFMozzzwzr3vd61YxawAAgJNKVb11s6btkFcAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuO1YdADi6qtrQ81prm5wEAACOzh5KGFRr7Vpfd/+RV13rPgAAWBWFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKDLjlUH4Ibr7IvOXur0Lj3v0qVODwAAODEKJZtGAQQAgBs2h7wCAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4nXCir6q5VdUlVvbGq/r6qvmcZwbajQ4cO5ayzzsopp5ySs846K4cOHVp1JAAAgE2zYwnTuCrJD7TW/l9VnZ7k9VX1mtbaG5cw7W3j0KFD2b9/fw4ePJjdu3fn8OHD2bdvX5Jk7969K04HAACwfCe8h7K19u7W2v+bv/9okiNJ7nyi091uDhw4kIMHD2bPnj3ZuXNn9uzZk4MHD+bAgQOrjgYAALAplvoZyqo6M8n9k/zlUR57YlW9rqpe9773vW+Zsx3CkSNHsnv37mvct3v37hw5cmRFiQAAADbX0gplVd08ycuSfG9r7SPrH2+tPbe1dk5r7Zzb3va2y5rtMHbt2pXDhw9f477Dhw9n165dK0oEAACwuZZSKKtqZ6Yy+cLW2suXMc3tZv/+/dm3b18uueSSXHnllbnkkkuyb9++7N+/f9XRAAAANsUJn5SnqirJwSRHWms/e+KRtqe1E++cf/75OXLkSHbt2pUDBw44IQ8AAHCDtYyzvH5xkscnubSq/na+78daa7+7hGlvK3v37lUgAQCAk8YJF8rW2uEktYQsAAAAbCNLPcsrAAAAJw+FEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdNmx6gAA18fZF5291Oldet6lS50eAMDJRKEEtpWPHrkwl134sKVM68wLLl7KdAAATlYOeQUAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdNmx6gBsjqra8HNba5uYBAAAuKGyh/IGqrV2ra+7/8irjno/AABAD4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgy45VBwCS+z7t1bn8iis39NwzL7j4Oh8/47SdecNTHryMWAAAcJ0UShjA5VdcmcsufNhSpnW8wgkAAMvikFcAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAsBxHDp0KGeddVZOOeWUnHXWWTl06NCqIwHAEHasOgAAjOzQoUPZv39/Dh48mN27d+fw4cPZt29fkmTv3r0rTgcAq2UPJQBchwMHDuTgwYPZs2dPdu7cmT179uTgwYM5cODAqqMBwMoNu4fy7IvOXur0Lj3v0qVObyT3fdqrc/kVV27ouWdecPF1Pn7GaTvzhqc8eBmxAG4Qjhw5kt27d1/jvt27d+fIkSMrSgQA4xi2UN6QC+CyXX7FlbnswoctZVrHK5wAJ5tdu3bl8OHD2bNnz9X3HT58OLt27VphKgAYg0NeAeA67N+/P/v27csll1ySK6+8Mpdcckn27duX/fv3rzoaAKzcsHsoAWAEayfeOf/883PkyJHs2rUrBw4ccEIeAIhCCQDHtXfvXgUSAI7CIa8AAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAeA4Dh06lLPOOiunnHJKzjrrrBw6dGjVkQBgCDtWHQAARnbo0KHs378/Bw8ezO7du3P48OHs27cvSbJ3794VpwOA1bKHEgCuw4EDB3Lw4MHs2bMnO3fuzJ49e3Lw4MEcOHBg1dEAYOUUSgC4DkeOHMnu3buvcd/u3btz5MiRFSUCgHEolABwHXbt2pXDhw9f477Dhw9n165dK0oEAONQKAHgOuzfvz/79u3LJZdckiuvvDKXXHJJ9u3bl/379686GgCsnJPyAMB1WDvxzvnnn58jR45k165dOXDggBPyAEAUSgA4rr179yqQAHAUDnkFAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6LKUQllVz6uqf62qv1vG9AAAABjfsvZQPj/JQ5Y0LQAAALaBpRTK1tqfJfngMqYFAADA9uAzlAAAAHTZsVUzqqonJnliktztbnfbqtkCnLTOvujspU7v0vMuXer0AIDtb8sKZWvtuUmemyTnnHNO26r5ApysFEAAYLM55BUAAIAuy7psyKEkf5Hks6rqHVW1bxnTBQAAYFxLOeS1tbZ3GdMBAABg+3DIKwAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0GXHqgMAXF9nXnDxUqZzxmk7lzIdAICTlUIJbCuXXfiwDT3vzAsu3vBzAQDo45BXAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeXDQEAANhCZ1909lKnd+l5ly51eteHQgkAALCFVlkAl80hrwAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAwNUOHTqUs846K6ecckrOOuusHDp0aNWRgIHtWHUAAADGcOjQoezfvz8HDx7M7t27c/jw4ezbty9Jsnfv3hWnA0ZkDyUAAEmSAwcO5ODBg9mzZ0927tyZPXv25ODBgzlw4MCqowGDsoeSk15Vbfi5rbVNTAIAq3XkyJHs3r37Gvft3r07R44cWVEiYHT2UHLSa61d6+vuP/Kqo94PADdku3btyuHDh69x3+HDh7Nr164VJQJGp1ACAJAk2b9/f/bt25dLLrkkV155ZS655JLs27cv+/fvX3U0YFAOeQUAIMmnTrxz/vnn58iRI9m1a1cOHDjghDzAMSmUAABcbe/evQoksGEOeQUAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXXasOgAn7vRdF+Tsiy5Y0rSS5GFLmRYAAHDDplDeAHz0yIW57MLllMAzL7h4KdMBAABu+BzyCgAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6LJj1QEAYERVteHnttY2MQkAjMseSgA4itbatb7u/iOvOur9AHCyUigBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0GXHqgMAwAju+7RX5/Irrjzu88684OLrfPyM03bmDU958LJiAcDQFEoASHL5FVfmsgsfdsLTOV7hBIAbEoe8AgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0WUqhrKqHVNU/VNU/V9UFy5gmAAAAYzvhQllVpyT5pSQPTfLZSfZW1Wef6HQBAAAY2zL2UH5+kn9urb25tfafSV6c5JFLmC4AAAADW0ahvHOSty/cfsd8HwAAADdgO7ZqRlX1xCRPTJK73e1uWzXbLVFVG3pea22Tk7Bdnb7rgpx90XI+fnz6riR52FKmBVvFdnTjjBUn6uyLzl7q9C4979KlTm80yxyvzRwr2wZWZRmF8p1J7rpw+y7zfdfQWntukucmyTnnnHODWpPXvzDPvODiXHahN/Rs3EePXLi0debMCy5eynRgK9mObtzR3gwaL66PG3oBXLbtMl62o6zKMg55/eskn1FVn15VN07y2CS/vYTpAgAAMLAT3kPZWruqqr47yR8kOSXJ81prf3/CyQAAABjaUj5D2Vr73SS/u4xpwWa779NencuvuPK4zzveoaNnnLYzb3jKg5cVCwAAtp0tOykPjOLyK65cymcKfFYRAICT3TI+QwkAAMBJSKEEAACgi0Ner6dlff4u8Rk8AABge1Mor6dlff4u8Rk8AABge3PIKwAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLy4YAAMA24rrojEShBACAbcR10RmJQ14BAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALrsWHWA7eb0XRfk7IsuWNK0kuRhS5kWACdmWdt323Zgs3k/ykgUyuvpo0cuzGUXLudFd+YFFy9lOgCcuGVt323bgc3m/SgjccgrAAAAXRRKAAAAujjkFQAGdd+nvTqXX3Hlhp57vMPWzjhtZ97wlAcvIxYAXE2hBIBBXX7FlT4nBcDQHPIKAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6OKyIR2Wder1M07buZTpAAAArIJCeT1t5HpgZ15w8dKuGwYAADAqh7wCAADQRaEEAACgi0Nel6Cqrn3fM679vNbaFqSBk8/RXoPJtV+Hm/kaPPuis5c6vUvPu3Sp04PNdKzX4NH4XQhww6JQLoFfjrBaI7wGFUBOZkd7DTqfAMDJwSGvAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4uG3IDceYFFy9lOmectnMp0wEAAG74FMobgI1e58s1wQAAgGVyyCsAAABdFEoAAAC6OOQVALhe7vu0V+fyK6487vOO9/n+M07bmTc85cHLigXACiiUAMD1cvkVVy7lM/nLOqEcAKvjkFcAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6LJj1QEAOLnc92mvzuVXXHnc5515wcXHfc4Zp+3MG57y4GXEGtLpuy7I2RddsKRpJcnDljItAFijUAKwpS6/4spcduFyis1GSud29tEjFxorAIbmkFcAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF5cNuYGqqqPf/4xr39da2+Q0AADADZFCeQOlJAIAAJvNIa8AAAB0USgBAADo4pBXAJidecHFJzyNM07buYQkALA9KJQAkOSyCx923OececHFG3oeAJwsHPIKAABAF4USAACALg55hUEs47Nbic9vAQCwdRRKGMBGP5Pl81sAAIzEIa8AAAB0USgBAADoolACAADQRaEEAOBqhw4dyllnnZVTTjklZ511Vg4dOrTqSMDAnJQHAIAkU5ncv39/Dh48mN27d+fw4cPZt29fkmTv3r0rTgeMyB5KAACSJAcOHMjBgwezZ8+e7Ny5M3v27MnBgwdz4MCBVUcDBmUPJXBMZ1909lKnd+l5ly51esBqnL7rgpx90QVLmE6SuBTSSI4cOZLdu3df477du3fnyJEjK0oEjE6hBI5JAQSO5qNHLlzKNXHPvODiJaRhmXbt2pXDhw9nz549V993+PDh7Nq1a4WpgJE55BUAgCTJ/v37s2/fvlxyySW58sorc8kll2Tfvn3Zv3//qqMBg7KHEgCAJJ868c7555+fI0eOZNeuXTlw4IAT8gDHpFACAHC1vXv3KpDAhjnkFQAAgC4KJQAAAF0USgAAALr4DCUnHddPAwCA5VAoOem4fhoAACyHQ14BAADoolACAADQxSGvADCwZR1ef8ZpO5cyHQBYpFACwKA2+nnvMy+4eCmfDQeA68shrwAAAHRRKAEAAOjikFcAANhmfL6aUSiUAACwjWzkM9M+W81WccgrAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB02bHqAADA9nPmBRef8DTOOG3nEpIAsEoKJQBwvVx24cOO+5wzL7h4Q88DYHtzyCsAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuuxYdQAATi6n77ogZ190wZKmlSQPW8q0touqOvr9z7jm7dbaFqSZ573BTMnW5gJg8ymUAGypjx65MJdduJwSeOYFFy9lOtvJiIVsxEwAbA2HvAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdNmx6gAAACfq7IvOXur0Lj3v0qVOD+CGSqEEALY9BRBgNRzyCgAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADosmPVAYCjq6qj3/+Ma95urW1BGgAAuDaFEgalKAIAMDqHvAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuuxYdQAAGFFVHf3+Z1z7vtbaJqcBuG5H22bZXrEVFEoAOApvuoDtxDaLVXHIKwAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB02XEiP1xVX5fkqUl2Jfn81trrlhEKNtuZF1x8wtM447SdS0gCAADb1wkVyiR/l+TRSZ6zhCywJS678GHHfc6ZF1y8oecBAMDJ7IQKZWvtSJJU1XLSAAAAsG34DCUAAABdjruHsqr+MMkdjvLQ/tbaKzc6o6p6YpInJsnd7na3DQcEAODktNGj4Fprm5wEOJbjFsrW2oOWMaPW2nOTPDdJzjnnHK96AACu0/qi6BwHMB6HvAIAANDlhAplVX1NVb0jyRclubiq/mA5sQAAABjdiZ7l9RVJXrGkLAAAAGwjDnkFAACgi0IJAABAF4USAACALif0GUoAAFiG+z7t1bn8iiuP+7wzL7j4uM8547SdecNTHryMWMBxKJQAAKzc5VdcubRrTG6kdALL4ZBXAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeXDQFgyy3rlP5nnLZzKdMBVu/0XRfk7IsuWNK0kmQ5lyABrptCCcCW2sh15s684OKlXY8O2B4+euRC16GEbcghrwAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuLhsCAMAQXKMWth+FEgCAlXONWtieHPIKAABAF4USAACALgolAAAAXRRKAACudujQoZx11lk55ZRTctZZZ+XQoUOrjgQMzEl5AABIMpXJ/fv35+DBg9m9e3cOHz6cffv2JUn27t274nTAiOyhBAAgSXLgwIEcPHgwe/bsyc6dO7Nnz54cPHgwBw4cWHU0YFD2UAIAkCQ5cuRIdu/efY37du/enSNHjqwkT1Vd+75nXPt5rbUtSAMcjT2UAAAkSXbt2pXDhw9f477Dhw9n165dK8nTWtvQF7A6CiUAAEmS/fv3Z9++fbnkkkty5ZVX5pJLLsm+ffuyf//+VUcDBuWQVwAAknzqxDvnn39+jhw5kl27duXAgQNOyAMck0IJAMDV9u7dq0ACG+aQVwAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgy45VB4BVq6qj3/+Ma9/XWtvkNHByOtrr0GsQAManUHLS8wYVVs/rEAC2J4e8AgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdTqhQVtVPV9Wbqur/V1WvqKpbLikXAAAAgzvRPZSvSXJWa+0+Sf4xyY+eeCQAAAC2gxMqlK21V7fWrppvvjbJXU48EgAAANvBMj9D+a1Jfu9YD1bVE6vqdVX1uve9731LnC0AAACrsON4T6iqP0xyh6M8tL+19sr5OfuTXJXkhceaTmvtuUmemyTnnHNO60oLAADAMI5bKFtrD7qux6vqm5M8PMmXt9YURQAAgJPEcQvldamqhyT54SQPbK3923IiAQAAsB2c6GcofzHJ6UleU1V/W1XPXkImAAAAtoET2kPZWrvXsoIAAACwvSzzLK8AAACcRBRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHTZseoAAAAAm+Hsi85e6vQuPe/SpU7vhkChBAAAbpAUwM3nkFcAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQJcdqw4AAHBDdfZFZy91epeed+lSpwfLtMz13bq+fSiUAACbxJtiTibW95OTQ14BAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6KJQAAAB0USgBAADoolACAADQRaEEAACgi0IJAABAF4USAACALgolAAAAXRRKAAAAuiiUAAAAdFEoAQAA6KJQAgAA0EWhBAAAoItCCQAAQBeFEgAAgC4KJQAAAF0USgAAALoolAAAAHRRKAEAAOiiUAIAANBFoQQAAKCLQgkAAEAXhRIAAIAuCiUAAABdFEoAAAC6VGtt62da9b4kb13S5G6T5P1LmtayjJgpGTPXiJkSua6PETMlY+YaMVMyZq4RMyVyXR8jZkrGzDVipmTMXCNmSsbMNWKmRK7rY5mZ7t5au+2SpnUNKymUy1RVr2utnbPqHItGzJSMmWvETIlc18eImZIxc42YKRkz14iZErmujxEzJWPmGjFTMmauETMlY+YaMVMi1/UxYqajccgrAAAAXRRKAAAAutwQCuVzVx3gKEbMlIyZa8RMiVzXx4iZkjFzjZgpGTPXiJkSua6PETMlY+YaMVMyZq4RMyVj5hoxUyLX9TFipmvZ9p+hBAAAYDVuCHsoAQAAWAGFEoCTXlXVqjNsF8YK2E5sszafQnk9jLpCVtVwy3HgsRou14jLLxk6l2W4QcZqY6rqxm3Az38MuvyGHKtk2PEabn1PjNX1Yaw2btCxGnKbNfAy7Mo15H9mRAOvkKe21j656hyLqur0QcdquFwjLr9k6FzDvQ4HHivr+wZU1VcleVFV3a+q7rrqPGsGXdeHHKtk2PEabn1PjNX1Yaw2btCxGnKbNfAy7M6lUG5AVT0syauq6quq6n6rzrNmfqH8blU9saq+fNV5kqSqvjLJoar6iar6+lXnWTNirhGXXzJ0ruFehwOPlfV94y5J8odJHp/kyVX1qNXGGXNdnw03VsmY4zXq+m6sNs5YbdyIYzUbbps18DI8oVzO8rpBVfV1Se6W5MuSvKy19rwVR0qSVNUDk9wpyY8k+bXW2rNWHClV9VlJ7pjkOUl+NckvttauWG2qMXONuPySoXMN9zoceKys79ed5T5JLm+tvXW+fWaSc5L8UJKfb629cFXZ5jzDrOujj1Uy1nitGWl9X2SsNs5YbdxIYzX6NmvgZdifq7Xm6xhfSb4wyecv3D41ye4k70xy/gpzfUWSh6277+wkb07yAyvK9JgkT0hy4yQ75/vuneSPk+xf4VgNl2vE5Td4ruFehwOPlfV9Y5l+PcnhJC9J8op1jz0kyWuSPHAFuUZc14ccq4HHa7j13VgZq5NsrIbcZg28DJeSyx7KY6iqFye5Q5KPJ2lJviPJu1prn6yq+yd5UZIfa629Yotz/e8kt07yiTnbzyf5f621y6vqs5O8OslTWmsHtzjTLZL8e5J/S/JHSV7VWntPVd0r04v6+a21X9iqTKPmGnH5DZ5ruNfhwGNlfd9YpsdkeqNzblXtSPLyTOvW41prH6+qmyX5piSnJXlWkrQt+EU56Lo+5FjN2UYcr+HW9zmXsdp4LmO18VwjjtWQ26yBl+HScvkM5VFU1blJbtdaO7e19rAk70jyjCSfkySttb9JckGSL6mqM6q25qxWVXV2klu01r68tfbgJH+Z5LFJvrSqbtZae2OSr03ykHn3/lZkunOSU1trX9lae2SS30ny2Um+vqpu21r750zHrj+2qs7Zikyj5hpx+Q2e69wM9joceKys7xv3j0kuq6pbtdauaq19daZfpC9Lktbax5NcmmRPkltv0ZuNczPYuj4bbqySMcdr1PXdWF2vXOfGWG0017kZbKxmw22zBl6GS82lUB7dm5P8x9zO01r7jky7759aVTeen/P3SW6V5Iyt+iWa5F1JblXzh2VbaxfOOR6V5LYLuf4lyRlblOnDSe5eVY+bM70o06EG90iya77vjZn2jtx6izKNmmvE5TdyrhFfh6OO1Ydjfd+ojye5eZIHrN3RWntckhtV1ZPn2/83yZ8leeAWvREacV1PxhyrZMzxGnV9N1YbZ6w2bsSxSsbcZo26DJeaS6E8uo8keVOS+1fVGUnSWvvhTOP18/Ptf07yuiQP2qK/UlVr7QNJXpzkfjUdwpbW2s8nuSrJ/5hvfzTJGzP9hWFTc82ZPp7kQJIHVNUXzRlenuRDSX5w4elvzrTHZNONmGvE5TdyrtlQr8NRx8r6fv201t6c5BVJfqaqHlhVp8wP/Wymw37WXJTkD7bojdBQ6/qaQccqGWy8Rl7fY6yuD2O1cUON1ZrRtlmjLsNNydVW9CHQ0b4yn/F24fZDk1yc5BuS3GG+77OS/M+F5+xIcrMtzvkFmc7a+D1JPme+74wkv5bkJgvP27JcSe6V5MeT/Pck5y7c//Ikn7Zw++ZbPFbD5Rpx+Y2Uazu8DkcZq6PkWvn6fpTlN9RYJTll4ftvSfIXmT73s3tez35uK5fZumxDresjj9WI4zXPb4j13XbUWJ2MYzXyNmuUZbiZuZyUZ52qOqW19on5+6/OdMbEI0n+LtNnkN7VWnviFmWpdpQFVFW7k3xdkptl2h39oCTvb62dtxW5jmY+7OFhSb4k0/Hpn5vkA621b1pVplXnGnX5jZprXZYhXofbYazW5Rridbhu+a1srKrqsZn+Yv6aJB9prf1HVe1orV01P/6VmQ6Nuk+S97XWzp/vP+py34K8q1zXt9VYzfO2bbgOtqMbZ6w2bqCxGnKbNeoy3OxcJ32hrKrvzrQb/HVJjrTWPrZuhTwn0yFiD8y0Ql4w37/ZK+TdWmtvq6obtdY+eZTHPz3JmUkekWnB//fNzlVVZ7XW/u46Hj8j0/VrvjbJh9t8NsktGKvhco24/AbPNdzrcOCxGnF9//Ek/5HkLUle01r7UFXtbK1dOT++iu3Vy5LcJNN69a4kb03yS621j1TVqa21/1h47uK6dtTlvcRcd2ytvXtxPovjsKJ1fcixmucx4niNum2wHd14LmO18VwjjtWQ26yBl+Gm5zqpC2VVHUpyepLLklSSnUl+tLX2gaq6cWvtP4/xc5u9Qr4804di97TW/nTdL9Jjznszc1XVK5I8MskjWmsXX9d81/2y3+yxGi7XiMtv8FzDvQ4HHqsR1/eDSW6X5JJMF7X+7CRPaNPlSla1/G6T5Fdaa18z335Iki/PdEmV/9Gmz4Wkqr40yd8s3N7sX+qHknxxkse01v563Xp19V/9j/JzJ91YzfMYcbxG3TbYjm48l7HaeK4Rx2rIbdbAy3BLcp20J+WpqpskSWvt4a21707yi0k+mORZVXXrtRdJVT2mqm678HO1yQv+UZn+0v8dSX6rqs5t0zV9bjTnXVsJvqOq7rL4s5v44j03yfuS/NckP1dVDz/afKvq/6uqey6+YDd5rIbLNeLyGzzXcK/Dgcfq3Ay2vi84v7X2s0n2J/mrJC+rqtstLL8tHatMJxXYVdM1yZLkDzJ9hubUJA+eM31ZknutvdmYM23mm41vSXL7JD+d5DlV9Xnr1qu1Q8i29HdOBhyreZ7DjdfA2wbb0Y3nMlYbzzXcWM2G22YNvAy3Lldb0QdUV/2V6Vjh/5PkWxfuOzPTCS1+KMkpSe6X5LFbnOvTknze/P3jk1yehRNszPffLsmjtjDTLZLcZ/7+MZnOFPnwdc+5UZIHb/FYDZdrxOU3eK7hXocDj9WI6/uNk/xGku9duK+S/GSSZ2c6JOmuSb56CzOtHXnz6ExnsPvi+fbOJD+Q5PlbudwWct0+yRfN3z8pyRvW1rOF5+zKdAHuk3qsBh6vUbcNtqPG6mQZqyG3WQMvwy3LteWDPtJXpuO9/yrJw+bbO5N8zdFWyLWVeJPzrL1QFs9U9Y2ZTs98znz70ese3/Rc83xutPD9ozN9VmrPfPvbM11UfUszjZZr1OU3aq6FeQ3zOtwGYzXM+r4wn/tl+gzLN863d2Q6CcLzkpy+lctv3bxun+nMdb+69gs001+wfzfJnbYqx7r1qhbu+85MJene8+0vNlbjjtc22DbYjhqrG/RYrZvXMNusUZfhVufasgEf7WthoPdm2l3+qIXH/iTJZ64630LGR2W6cPnfJ3nWIJkenOlip29M8qsDjdUQuUZcfiPmGvl1ONpYHSPXStf3tV9EmT6/8sYk5y089n+TPGDFY3VmpqL9V0l+JNPZAH9tlZnW5Tsvyd9kOjvifzdW22O8Rts22I4aq5NtrOYMw22zRluGW5nrpD4pT5JU1WmZ3pT9TJIXJvnSJG9rKz79f3LNDxBX1ZuTHG6tPWH9Yyd7plFzjZhp1Fyjvg5HHKtRc1XVF2dadr+Z5AuT/Muql9+ams5CeG6Sq1prz5zvW9nyW1RVlyX589ba41edJRl7rJJxxmvQ16Dt6MYzGauNZxpyrNaMts0acRluRa6TvlCuqen6bffNdNHvX5nvG+KXaFX910y7p799vr3ZZ4Q67v+7qh6Z6VC7792KTCPnOp6tXn4bNWKuUV+HqxirEdf3Y2Vam29Npx6/W6ZDjg5d189sQrajnvXzaGMywro+53hypr/yr/1S35Jc23GsktWN13Ey2Y5uPJex2nguY3XNeW+7bdaIy3DOsSm5TqpCebw3Q8e7b6tzLTx+i9baRzY71/WZdlWd1lq7YrMzjZxrYZ5DLL/tnmuVr8ORxmrE9X0j097q5VdVT0/yzkwn73v2fF8lnzqb36p/gR/rTdDC45/WWvvA/P1JPVZzhiHGa908h9k2bCSX7ejGcxmrjeeyfT9qxm21DDc71w26UFbVz2b68OmNWms/vnD/hq5lNVquzfxLUFX9r/nbTyT5iSQfbK19YhXjM3quEZffDTHXZhp4rEZc30fM9AtJ7p3phAw/kORNmT6/ufZG4x5J3jrn3Mpf5Bt+E7S4Lm3ytn3IsZrnPeJ4jbptsB3d5FybyVhdr0xDbrNuaMtw2blusNehrKpnJPmcJL+f5IFVdbCq7plM11apqnuvDXDN12MZPdcm/gL98ST3zHS9rzskeVqSc6tq55zjXlV11/m5WzlWw+UacfndUHONmGmTx2rE9X3ETKdluhTJD7bWXtJa+/xMZ/w7uPC070rym/MvzK16s/ELSb4g03XavrWqXrD2C7u11qrqHvN69cn5F/3V69ImbtuHHKs524jjNeq2wXZ0C3KNmOkkHKsht1k3xGW47Fw3yEJZVTuS3CXJz7TWXttae2CmC6H+UFXdZn7adyT569rav24Ml2tewe6Q5Ddaa29J8g2Z/mL8NUkeMD/tG5K8sqpuvIVjNVyuEZefXNs/05xrxPV9xEzVpkN835DkPvPyTJKHJbl3VT17vv30TJdUOWuzM825hnsTNOpYzdlGHK9Rtw3D5Rox06i5Rsw0aq5Rt1kjjtWIuW5whXJeIa/KdDHWz18Y1O9KcqtMf2lPm05i8X+SPORkzjWvYK9J8iVVda/59k9l2n3+7fNzDmS6BMC+rcg0Yq5Rl59c2zvTmtHW94Ezrf1F9Y3zPO89339VkocnuX1V3TnJvyV5fZK3bXamUd8EjThWyZjjNeq2YcRcI2YaNdeImUbONeI2a9SxGjHXDa5Qrlshz07yeVV1szYdO/xNSe5RVfefn/OSTG+GTtpcsyNJ/iPJnqq6S2vtk621H0vy2VX1oPk5z0jy4i3MNFSuUZefXNs70zrDrO+DZ0pr7SVJfi/Jwao6p6pu2lr7YKZfpDdvrf1nkhe11i7fgizDvQlKrvFZxGHGas4z3HiNum0YMdeImUbNNWKmkXMt5BtmmzXqWI2Ya8fxn7I9tdb+pKrOTPKdSW5cVX/bWntrVV218JzDciWttTdV1e9lutjpjavqL1trr0tyeaY3k2mtvX0rM42aa8TlN2Ku+a9nbaRcI2Zal2vE9X2ITHXNkwvcaC62z6iqf09yQZIP13TJkne31v5hzrWlZ5xrrb1kXq8OVtWTkryxtfbBqlp7E/TOqnrRZuaq6bOtV8552ohjtbC+r3y81ht42zBMrhEzLRox12iZRluG22T7PsRYrRltGSa54ZzltRbOYLQ20PP3e5N8cZJ7ZSrQl7fWHnMy56qqHW36a/D6TA/KdJKEr03yr0n+vbX2yK3INGquEZffqLmq6vTW2kcXbi/+klhJrhEzzfO+XWvtX4+Ra5Xr+1Cvwar6liS/31p799HedMzfn53kjCT3aq09f332Tcx2rDzfk+RLknw4yacn+dfW2t7NzDLP96czjcMHkjyltfafVVWZfs+veqyuLrrz7ZWP1zxv29Ftmmkhy4jLcMRMwy3DwbfvluH1zbfdC2VV/WCS/z038sXBXVwBbp/kdknObK39znzfZl9LbrhcVfVTSX6ttfaP1/FiqSRnJrl9a+21m51p1FwjLr/Bcz0zyR2TvDfJ/2rTnq31p//f6vV9uEzz9H8xyT2SvD3JK1trvzvfv8r1fcTX4IuSfGmSw0l+oE17rI52LbRTW2v/sXB7s5ffcG+CqupXktwm01l4fznJn7TWfvQoz9vSsZrnMVzRtR3d3pnm6Q+3DEfMNE//mRlsGQ68fbcMe7XWtu1XpmvUfCjJXya5x3zfjY7yvDPW3b7Wc27ouZI8K8l/ZloZd833nXKU591u3e3a5LEaLteIy2/wXM9N8puZ9i78RpKfPcbztizXiJnm6T8nyf/OdEbLpyb5xWM8byvX9xFfg3dJ8rIku5Psz/QZzTuvz5bkSUnO2cxlti7Xi5K8Y12eo70GT92q9Woeo1ckufF8+65JXpvk1ovLKNPJGrZsrOZ5/sqc7X5J/iLJTx3jeVs5Xraj2zjTqMtwxEyjLsOBt++W4Ql8bduT8sxN/D+T3D/JS5Mcqqp7tOm6KzsWnvdtSb5o8Wfb5v4VYbhcNX0m5YNJbpbpzHl/VlW72nRdmp0Lz3t8kgevy7Rpu7BHzDXi8hs8132TfFqSb2nTpSW+J8lXVNW91j1v31blGjHTPL97ZDqpwHe01t6b6Sxsu6vqC9c975uydev7cK/BedrvyHTClr/K9Av0n5L8TFXdfc5W81Pf2KbPdG66qrpLklOTPDbJpXOeO8+vwVMWnvekTCdJuNpmrldJ/j7Jj7Vpz9+pmT7fepMkt1q3jN60VWOVJFW1O9Ne029orf1tkq9Psqeqbr2w/FJV35UtGi/b0e2daZ7fcMtwxEzz/IZchoNu3y3DE7WV7XXZX5nenK39VfbJmVbOz5xvrx3Oex+5rpXph5K8L8nZ6zLdacVjNUSuEZffqLmSnJLknvP3N5n//fMkn7vuefc9mTMtzPPOSXZm3iOT5OVJHrTuOXdZ4Xo1xGvwKBnvnuRAkosyHdLzA0nusPD4ph5JsTCfWya58ZznJzP9Zf3u68ZqzwrGZ8e626/KVCiT6TpkN13BWN0qn9rjfWqSWyT527XX5sLzvmyLx8p2dJtmGnwZjphp2GW4bv6jbN8twxPJuuoASx74H890PPZdkzwlyVkLj23JCrldcmU6c9a7M32A938mOXfVmUbNNeLyGylX1h1WkekU1Z8zf//DSe661blGzHSMnL+S5Kvm75+++EvBtuEamU7JVOaemumvyK9Y1TJbyDTEm6B1mdbe9LwsyecleWGSF6xwjIYrukfJaDu6jTKNvAxHzLQdluGI23fL8Pp/bdtDXtebP3j6E0kuTvLWTH9F+Lu1x9s80id7rqq60fzh4gszHXL3j5n+wv4nq8o0eq6Rlt+IudrCSUnmuz6e5F5VdVGmjd7bF567JblGzLRoIdeHkty9qg5m+gzJG1aVa9TX4DzfT7TWPpzpsNsXt9a+JkkWDo1ahXdkGqe3zN/vbq29Z+3BFY3V2np1WqbP3Ly3tfaEZDVj1RbOGDzf9R+ZXocvTPJfWmv/tvDck3r7PuI2a8RM6420DEfMtB2W4Yjbd8vw+rvBFMr2qWOFvyjJoTafLnfFbziGy9Wm6/usrXBnJ3lpa+3rVplp9Fzzt0MsvzWD5lqb9yeS/Fqm0/+fl6w014iZFn00yc8n+UBr7VuSk2/bsDbt482jqj4jyV8uFKQbrfKX5yreBB1vrNp8dt4kf5fpTK/fPz9/pWOVwYruGtvRbZ0pyZjLcMRMWdEy3Mbbd8vwetpWhXIDK+TdM62Q3zjf3pIVcsRcC3/JONbjd0jyf1trj92qTKPmGnH5bcdcC29m/z7Jq1trP7RVuUbMtDafY+Ra+2X1piS/1Vr74a3KNdJrsKrOyPSZxLTW2nVla639U2vt+xYybeaJEIZ7E3R9xirJLyy80diKS4MMW3RtR7d3puvKtfD4Kt5jDZfpunKtYhmOun1fyGcZLtHw16GsqjsmubK19v759oauTbUFbziGy1VV90yS1tq/LNx33FxbMFbD5Rpx+d1QclXVzVtrH9vsXCNmmqc/4vo+YqZfzXSSordmOpvfz8/372jzoZKbneEYuc5I8u9tvvbZRuc/yFitv70VFwDf8HhV1V3bfHjWKNuGdT9nOzpApuuba93PDTFWW5Xp+ubawvVq1O27ZbhJhi6UVfUbma7X9rEkb2itPXW+/xoDWAsX4j5Zc1XVSzN9qPnmmT5E/LTW2seP8rytHqvhco24/G4gubbszeyImebpb3R9v0auzTToa/CHkzwoyROSfGam63P+TvvUntp7JHlMa+2ntyLPQq7h3gSNOlbzvIcruraj2zvT9czlfcOAy3DUbZZluMnais4GdLyvJE9M8ppMh+V+RpI3JvkfC49/RpKfkaslyaMz7f5Okjsk+f1MJ4u480KmF65grIbLNeLyk2v7Z5rnO+L6Plymeb6PTfIjC7dvl+Sfkzx9vr0r0/XJHrqFmX44yavncfrSJEfWrVf3SPJDxmrc8Rp42zBcrhEzjZprxEyD5xpumzXwWA2Zq+dr5M9QvjXTClittX/K9AvrS6rqf86PvyfJParqO+XKu5JcVVW3adNZBh+b6a/G35dMx6YnuV1VXbiFmUbNNeLyk2v7Z0rGXN9HzJQkVyR5UFWdNuf410zL8dyqOjfTGWb/MslNtzDT25L8UWvtPa21P0vywCSPrqqnz4+fmuS+VfXQLcyUjDlWyZjjNeq2YcRcI2YaNdeImUbONeI2a9SxGjXX9TZyofxgkltn+itn2nRc8VcleXhVPa619tFM13D7eFWdcpLnenuSy5Lcr6pu0qYzD35nki+rqh+cn/OkJJdV1U22KNOouUZcfnJt/0zJmOv7iJnSWntlpl+kh2s+UUNr7V2Z/lJ7epsON3p5pkN0t8qIb4JGHatkzPEaddswYq4RM42aa8RMw+YadJs15FgNnOt6G7ZQttb+Osk/JXl2Vd11Pnb4Q0n+R5K1Nz5/meRVbQuPdR4xV2vtnZnO+vQdSc6pqlu21i7PdIHyG89Pe0emQ9v+fSsyjZprxOUn1/bPNOcacX0fLlNV7ZizfVumw3v+T1WdXVU3T/IlSdZOIPTO1tp7tyLTPL/h3gSNOlbzPIcbr4G3DcPlGjHTqLlGzDRqroVtwVDbrBHHauRcPYYslAu/RJ+c5G8zfaD34TWdwndvkrvMj3+gtfbBky1X1adOKbz2fWvtl5L8Raa9D99aVZ+b5PuTnDE//m/zXzq2xMJGZZhca3/dWfXyO0quIdar7ZBrpEyjvw5HeQ1W1TlVdb+12621q6pq5/z945P8UZLvTfKqJO9orT1zs7JcR8Yhitt2GKtkqPFafA2ure+jbRuGyDVipqNkHC7XiJlGylVV/7Wq7r92u7X2yYX3WkNss0YZq+2Sq9cQZ3mtqq9M8m9J/nrtr+RVtbO1duX8/Xdm2h183ySXtdaeeLLmqqobt9b+sxbOQlULZ4Kqqq9O8tmZLsb6ttba+ZudaZ7vniRXJvmr1tp/jpCrqr4myUeS/G1r7QNHyWS9GjzXiJnm+Q73Ohz0NfjKTHtC75nkJUn+rLX2mvmxmyws05sluVVr7R3rc29SrnOSXNVa+9uF+xbXq6cnueOc+7LW2jdvVpaF+Q85VvM8Rhyvna21K9et4yNs34fLNWKmeb73yfQ+9A0L9616rIbLNGquqnpIklck+fUkz2utvXa+v5LcuH3qUkJbvX3/tUwnoHv5wjZq8Xf1qpbhkLmWZeWFsqpekenD+5+W6dCYN7bWDs6PXf1LdL79aUcrBidLrppOLXy3JI9orV2+bkVcfzrhrbxe1AsyjdPdkvx2kgvbvMdjVbmq6mCmsw+enuRvknz/wlidurahm2+f1OvVqLlGzDRPf7jX4aCvwQdkOqvfQ6vqzknOy/RZkT9rrf32wvPunOTdC2+MNvuSBMMVt1HHap7HiOP1y5k+m/mk1trH1r2xvrrozre3ctswXK4RM83TvyjTOn7/JL/cWjuw8Niqxmq4TIPnum2Sn0/yfzP9sfJXW2uvX/ecrd6+/68kZyZ55OK4zI+t8n3DkLmWaaWHvNZ0se2btNa+KslXJvnrJA+oqu9OkoVfVPefXzRrA1ybvOCHy1VV359pZfybJC+vqjNaa5+oTx1acNX8vIfMK+faG8bNHqv/kelNxMOSPDjT4U6PXnt8FbnmX6C3nTM9PNNhA59RdfVhiWt/NTvp16tRc42YaZ7+cK/DEV+Ds6uS3LOmi9m/M9OJBd6V5L/UdB2ytb/IfsVijk1+s/GATH85f2iSPZlOLPOVNe29XVyv7pzkioVydNKN1TzP4carqg7MWT6Q5Jk1/YHkk/Wpw8fW/vK/1duG4XKNmGme/jOT3LK19ogk5yZ5XFU9cu3xFY3VcJlGzjX7SKajYj6Q5F+SPL6qnlnTkUWpqu/K1m7fT820E+Hr27RH/oFVtbuqzprnvar3DUPmWrYRPkP5GVW1q01nH7w40wf6P6Oms8Slqr42yVmLjX6zf4kOmuuPM13T63uS/F2SV6y9mV17QlX9lyS3WfxLxxaM1V8l+dF5Xu9OsrZn8GpVtXuLc704ydfP339PkgckeVaSn6qqR8yZHhPr1ei5Rsw04utwxNdg2nRo1ouTPKGqbtdae1+SQ0k+Pckj5qc9u7X2/M3Msc6QxW3QsUrGHK/fyXSSqWdlOhz+WQtFqeZMX5PkPlu8bRgx13CZajqb9N8nOX+e1z8n+dUkt1j3vEcnOXsrco2YaeRc8zxv1KY/zr8lyeuT/EKSL8z03mvtM/lbts2a/0hy00x/8L19VX1dkp/KdIms75vL7dpYbdn7hlFzbYYRDnn94UwXOX1Ka+1tVXXrJD+S5GOttZ+cG/qWhxwxV82H1lXVjTOdAeq+Sb6stdaq6t6ttTdtZZ45082T/MfCX8m+Jcm5rbXz5tu3b1t89sGFbKcl+V9J9if5WKYPht+ytfb91qvxc42Yac411Otw8Nfgg5I8JMk7k7yktfauuXDvy3QI3tpfZrdsWVbVTyT5jyS/0lr716q6Y5JnJvm/rbVnrXC9Gm6s5vkNOV5zts9M8l1Jzmitfct839WHi63KiLlGylRVd0jygYVt1g8luWNr7fvn29c4TP9kzTRyroV8e5PcJsnnZDok9xVJ7p3pYxdvmp+zldv3/5bky5N8Isk3zv8+KtPvxO9a4fZ9yFzLtLI9lGt/Hct0WvG3JfnBqvr0Np3J6KJM1007Y22AF55/UuZKkjbvBWnTCTd+LNOphF9TVX+aT/0Ve0u11j7Wpl34a+PwkSSXJ0lV/e9M19NZidbaFUm+tbX2rtbaR5K8NMldq+oW1qtxc42YadFor8PBX4N/mORPk9w+0yF3u5P8f0k+3Lb2SIpFf5bkVkm+saru1Ka9us9Kcp+aDgle1Xo14lglg45XkrTW/jHJc5O8t6qeO78GH7P2+CoyjZprpEyttfes22ZdkWkPaqrqZUmuPqRzq3KNmGnkXAv+Jck3JLlHa+0LMp2g52WLf1jdim3Wwv/9ZZnOmPpfknzG/Hv6z5Lcq6russL3WEPl2gwr30OZJDWdJv2rM31W6sIk353kH9sWnaF0u+VaM694H0jy+621x606T5LUdEz42jXurljbSzKCqnpRkn9trX3vinPcLwOuVyPmGjHTeqO9Dkd5DS7+xbWqPj3T55nvl+RDrbUfXP+cLc72iCRfnOmMej+f6SiGN7bWfmCrs8x5hh2red5Djdd685j9VZI/aq09dtV51oyYa9BMX5Jpb81tM/WPlb9vGDFTMmaumj5+8srWrnWymVVt3z8ryZOSnJ3kqZkOGX5va+1JW51lO+RaliEKZXL1h1Yfl+SzknyitbZ/vn+lu4FHzTVn+Nkkt2+tfeN8e+Vng6rp9PJ/lelMZE9ada6ajl+/VZLnJXl/a23ffL/1apvkGjHTotFeh6t8DR5vmdR8uZWtzLRu/sMUt9HHap7vMON1Xfmq6oVJdrTWvmG+f9WvweFyjZhpIduDM11OYYj3DaNmWnWu9a/19fOtFR9+u2h+3/C1Se6V5JTW2o/P94/wHmu4XMuw5YXy+gzaVr54R8y1gTccn9Va+4etzHS8XDV9bvHrWmsvON5ztzDTLZPsbq29ar49xFgd5bknda4RM83zGu51OMprsKr2JfmnTHtC/3q+b/2bjGuNyVb+8hyluG2HsdrI/FZRdDeQ6X5tvkbmYNuGLc81Yqbj5aqqu2W6nMIvHO+5N/RMI+Xq3WZtpe36vmHdc1f+h4pl2PRCWVXfl+lzUB9rrf3BfN/a5RvW/vK5ir9UD5erN9Nmb+hOINdmvjkbLtOJ5NpsI+YaMdOJ5NrkX+zDre9V9bxM1yb8i0yns/+N1tovLjx+u9bav65l3cI3Y8MVt1HHap7fiOO1kUzXmv8WbN+HyzVipuuRa6u3WcNlGjXXqNusUUvuqLm20qaelKeqnpPpWO+7JnlOVf1gMr0Baq21mq5dlbZwCuutMGKuE8m0yWXy+uS6xvq0iRu64TJ15LK+D5bpRHNt4pvr4db3mq4T+hmZLhdxQZJvT/Lkqvre+fGbJnnaXIS37EQy85ugJ2Q6EdEv1KeuW/rJ+fHbrd3ewuU35FjN8x5xvDaaqR0l02Zu34fLNWKm65lrK7dZw2UaNdeo26wT2V6djLm2XGttU76S3DHTNdtuM9/+jCRvSPLD8+0dSV6Y5Kc3K8N2yTViplFzjZhJru2fadRcI2aa53vLTNe7vMfCfZ+d5LIkj5tvPzjJTya5yRZlumeSP09y4/n2fZO8J8n3zrdvmuSXk3zfyT5Wo47XiJlGzTViplFzjZhp8FzDbbMGHqshc63iazP3UL43yaVJPremD+r+U6YLnj6pqr6zTR/cfVqSm9X0Yf+tMmKuETONmmvETHJt/0yj5hoxU1prH07y75kuZr123xuT/Lcku+e7/mH+d6v+IvuBJP+Y5C5znjck+bIk31tVj2ut/Vuma6TduqYLhm+JQccqGXO8Rsw0aq4RM42aa8RMw+YadJs15FgNnGvLbVqhbNOu3ncl+bYkp8/3/UOSxyZ5aE0nSnlfpuuyvG+zcmyHXCNmGjXXiJnk2v6ZRs01Yqa1w67adIbBHVX16oWHL01yx6q6aWvtrUme3qbrwW66Ed8EjTpWc6YPZ7DxGjHTqLlGzDRqrhEzjZpr1G3WiGM1cq6V2Izdnsl0sp/5++dnaud3mm+fmuRVST5tvr1jMzJsl1wjZho114iZ5Nr+mUbNNVKm9dPPdLrzte9fmeS3Ml1f6+IkB7dquS1kuNHC93+Q5NULtz99Hrubro3dyTxWo43XyJlGzTViplFzjZhptFyjb7NGGqvtkGtVX0s9y2tVndJa+8RRvv/lTH9df2+SXUk+3LbwAuAj5hox06i5Bs20/npMcm2jTKPmGjTTT2b6TM3rW2vPX7j/6muOVdUTktwsyR3bFl1Xq9Zd82zdWL0ySUvymkwnSnhPm69Bu5lGHav1GebbI4zXcJlGzTViplFzjZhp1FyjbrNGHKuRc41gKYWyqh7RWvud+furT4u7bqD3JLlDkju01n5uvm+zV8jhco2YadRcI2aap/9jmTbAf9NaO7Rwv1zbINOouQbN9Owkt0vyq0lekumsf6/dwM9t9mn2h3sTNOpYzfMYcbyGyzRqrhEzjZprxEyj5hp1mzXiWI2caxjtxHf5vijJ25M8c+G+G83/1nX83I1OdN7bLdeImUbNNWKmefrPzXQIyOOSvCnJ4xcek2vwTKPmGjTTo5O8auH2c5J8T5KvTHLbhfu/NcntNnOZrcv17CQvz/QX4I8m+cIN/txJN1YDj9dwmUbNNWKmUXONmGnUXKNus0Ycq5FzjfR1QiflqapzMp3W/vGZPrz7zOTqa62c0ubRrKpvq6pdiz/bNvevG8PlGjHTqLlGzDTP79FJ7tJae2Rr7UWZPnT9XVV12uJfoKpq38mea8RMo+YaMdM87Zcnecw872/LdJ2t/0zyXUm+o6p2VtXNkuxs8wWuN9vCWD26tfa7mf7w9AVV9ZVVdduF531rzdf+Wvj/nFRjNWcZbrxGzDRqrhEzjZprxEwj5xpxmzXqWI2aazgn2kiT3C3TCSLul+RXkjwz0wqYTGeRvXGSR29FOx4914iZRs01aKZbJjlr/v7GSe6c5C+S3GLhOafKNWamUXONlinTH3J+ed1990ty5/n7e2fai3r/dc855p7UJec7df7325JckeQ7M+3d/f+S7Mx0uNG3G6vxxmvkTKPmGjHTqLlGzDRartG3WSON1XbINdJX78A+Pslz1t13SpL7ZLoY6k/N9z0h8xmO5tubukKOmGvETKPmGjHTQq5fmr9ffza0P1ibf5JHrXvspMs1YqZRc42YaZ7+bZI8NXOhzVEO2Uny20kesJk5jjJWw70JGnGsRh2vETONmmvETKPmGjHT4LmG22YNPFZD5hr1q/eQ199L8q6qukVy9Qd0PzEP7NMzXcDz40ke1qaLeiZJ2jzSm2jEXCNmGjXXiJnWcr2vqk5vrV1Vk1Oq6sZJdiQ5s6pemunY+qudpLlGzDRqrhEzJcknknxOkr3z/K4+ZGfO+JIkH2qtvX6Tcyz6vSTvXbdt+NvW2jvnjG/KdHHpa/xOO0nHKhlzvEbMNGquETONmmvETCPnGnGbNepYjZprSL2F8hNJzsrCCjkP9H+21t6S5P5JfrO19g3JtJIuJe32zDViplFzjZhpLdfnZDphytrG4pQkVyapJL+Z5F2ttSduUZ6Rc42YadRcI2ZKa+1Dmf6A8+SquvqyJFV1+yQ/kuSK1tp5831b/Roc6U3QqGOVjDleI2YaNdeImUbNNWKmYXMNus0acqwGzjWm1r8r+L6ZzsL5uIX7bpRkTxYufJotPsPRiLlGzDRqrhEzHSvXfP8rk7xQrrEzjZprxEwL83xQkjcm+eaF+z5tpOWX5PZJLkjy/IX7tvxwo9HGatTxGjHTqLlGzDRqrhEzjZxrnudQ26xRx2rUXCN+nehAX2uFXPf4Sk6XO2KuETONmmvETMfKleRecm2PTKPmGjHTwrx3J3lLptPJP2Th/pX88hztTdDIYzXqeI2YadRcI2YaNdeImUbONc97qG3WqGM1aq7RvtZO+tCtqnYn+fUkP5fkza21V833r/RCniPmGjHTqLlGzLQu1zOTHGmtvXq+f9MvTr7dco2YadRcI2ZayPYZSb4iyT2S/F1buKDzivIsjtU/tNZ+f75/5RePHm2skjHHa8RMo+YaMdOouUbMNHKuOcNQ26xRx2rUXCM54UKZXGuFvLS1dtEJT3QJRsw1YqZkzFwjZkrG2wCvGTHXiJmSMXONmGm9qrpFa+0jA+QwVtfDiOM1YqZkzFwjZvr/t3f/MVeWdRzH3x/RpoWimWtGFIlFTkLGAzZXVhqrOVtZg9ZkFaUIheVqtlqaucpaEQt1mluIVFLhD7bol+QwjKhRQAISoomYTVpiy8RIED79cV9PHJ7OeX4cfpwb+Ly2ZzvnOt/7ur73tbPn7Lvruu8b6plXHXOC+ubVqC7/s+o6V3XNqy72S0G5V4c1+UL2VMe86pgT1DOvOuYEyWsg6pgT1DOvOuZUV5mrganjfNUxJ6hnXnXMCeqZVx1zgvrmVUd1nau65tVJ+72gjIiIiIiIiCNDu48NiYiIiIiIiCNcCsqIiIiIiIhoSwrKiIiIiIiIaEsKyoiIiIiIiGhLCsqIiMOEpOGStkt6oKFtl6QHJD0o6U5JL+7l+GslXXkQ8jxX0vqS13EHerwDocz1gwd5zKvKvK0tc/fG/dj3VEkLGt6fIOlRSae1iJ8i6RX7cfwR5Zy27a8+IyLi4EhBGRFxeHnU9piG99ttj7E9CtgBTO9MWnuZDHyt5LW908l0gqSjBxh/DvAuYKzt0cAE4In9mNIcYJikCeX9l4C5tje1iJ8CNC0oJQ0a6OC2e35vIyLiEJGCMiLiyLEMOB1A0ofKStcaSd/vGVhWrP5QPr+7e2VT0qSy2rlG0q9L25mSfl9WmNaWB0A3JelS4P3AlyXNlzRY0hJJqyWtk/SeEjdc0kOS5kl6uMROkLRc0iOSzu5ljGslfVfSMkmPS3qfpG+U/u+RdEyJ65J0v6RVkhZLOrW0L5X0LUkrJW2QNF7SwjLuVxqGOrrktUHSXQ1z1Fu/syWtBK5oNpe9OBXYavt5ANtbbT/ZajxJQyRtlDSyxPxQ0tRWnbt6hth0YLakccDbgZkt5nciMA6Y373KLGmzpK9LWg1MKuc6rsS/TNLm8nqQpJnlu7VW0rQ+zjsiImouBWVExBGgrIhdAKyTdCZwNXC+7bOAK5ocstD2+PL5BuCS0n4N8M7S/u7SNh24vqwwjQP+2ioP23OARcBnbE8G/gO81/ZY4DxgliSV8NOBWcDry9/FwJuBK4HP93HKI4DzS463A7+y/QZgO3BhKSpvBCba7gLmAtc1HL/D9jjgFuDHwAxgFDBF0sklZiRws+0zgH8BH+9Hvy+yPc72rBZz2covqVYQH5Z0s6S3ArQaz/YzwOXAPEkfAE6y/Z3eBrC9FlgMLAE+YXtHi7i7gJXA5B6rzE/bHmv7R70McwnwjO3xwHhgqqTX9HHuERFRYwPachMREYec47TnmsplwK3ANOBO21sBbP+jyXGjymrcicBgqkIDYDlVkXIHsLC0/Q64StIrqQrRRwaQn4CvSnoLsBsYCry8fPaY7XUAktYDS2xb0jpgeB/9/sL2zhI7CLintHcfO5KqQLy31K+DgC0Nxy9qiF9ve0vJYxMwDPgn8ITt5SXuduCTZZze+l3Q8LrZXDZle5ukLuBcqsJ7gaTPURV2Tcezfa+kScBNwFm99d/gJuAC20v7Gd9oQd8hvAMYXVY5AYYArwUea2O8iIiogRSUERGHt+09r03bswDYq3nARbbXSJoCvA3A9nRVN4O5EFglqcv2DyStKG0/lzTN9n39zG8ycArQVQrAzcCx5bPnG+J2N7zfTd+/X91bQ3dL2lm2dDYeK6pC8Zzeju8xbs+xzd7cj36f+19w87l8utUJ2d4FLAWWlkL5w8CqVuNJOgo4A/g3cBK9rBw32F3+2vFcw+sX2LML6tiGdlGtfi4mIiIOC9nyGhFx5LmP6jq3kwEkvbRJzPHAlrKlcnJ3o6QRtlfYvgZ4imob5mnAJts3UG0PHV1il0ga2kcuQ4C/l2LyPODV+3py/bQROEXVzW6QdEzZCjwQr+o+nmo77m8G0m+LuRwqaUmT2JHa+9rUMcDjfYz3KartyhcDtzVcO/o99XINaj89S/UdaWUz0FVeT2xoXwx8rCGX10l6yT7mEhERHZQVyoiII4zt9ZKuA+6XtAv4I9VdOxt9AVhBVeisYE/xMLMUNqK61m4N8Fngg5J2An+j2sJ6FNU1kM220zaaD/ykrLitBB7ax9PrF9s7yrbLGyQNofo9nA2sH0A3G4EZkuYCfwK+PcB+m81lF9XqXk+DgRslnVg+/zNwWavxJL0AXAqcbfvZctOfq4EvUhX8Tw7gPJuZB9wiaTvQbDX2m8Adki4DftbQPodqy/Hqcq3sU8BF+5hLRER0kPbsAoqIiEOZpOHAT8sjQjqdyyjgo7Y/3elcDiWSLgf+YntRn8Ht9X8CcKvtSQei/30laZvtwZ3OIyIi+i8FZUTEYULSMOC3VHfbHNPhdCL6TdII4G7geNsjOp1PRET0XwrKiIg4JEn6CP//yJPltmd0Ip/DkaSbgDf1aL7e9m2dyCciIuonBWVERERERES0JXd5jYiIiIiIiLakoIyIiIiIiIi2pKCMiIiIiIiItqSgjIiIiIiIiLakoIyIiIiIiIi2/BcFZTSiAninwAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.313663Z","iopub.execute_input":"2023-02-01T14:51:28.314115Z","iopub.status.idle":"2023-02-01T14:51:28.742585Z","shell.execute_reply.started":"2023-02-01T14:51:28.314085Z","shell.execute_reply":"2023-02-01T14:51:28.741404Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.744143Z","iopub.execute_input":"2023-02-01T14:51:28.744835Z","iopub.status.idle":"2023-02-01T14:51:29.161694Z","shell.execute_reply.started":"2023-02-01T14:51:28.744790Z","shell.execute_reply":"2023-02-01T14:51:29.160329Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.164872Z","iopub.execute_input":"2023-02-01T14:51:29.165348Z","iopub.status.idle":"2023-02-01T14:51:29.588614Z","shell.execute_reply.started":"2023-02-01T14:51:29.165277Z","shell.execute_reply":"2023-02-01T14:51:29.587528Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.590230Z","iopub.execute_input":"2023-02-01T14:51:29.591244Z","iopub.status.idle":"2023-02-01T14:51:29.999585Z","shell.execute_reply.started":"2023-02-01T14:51:29.591206Z","shell.execute_reply":"2023-02-01T14:51:29.998460Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = clf.predict(X_test)\ndecision_tree_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"clf_y_pred\": y_pred})\ndecision_tree_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.001184Z","iopub.execute_input":"2023-02-01T14:51:30.001710Z","iopub.status.idle":"2023-02-01T14:51:30.018740Z","shell.execute_reply.started":"2023-02-01T14:51:30.001660Z","shell.execute_reply":"2023-02-01T14:51:30.017976Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.019742Z","iopub.execute_input":"2023-02-01T14:51:30.020678Z","iopub.status.idle":"2023-02-01T14:51:30.025527Z","shell.execute_reply.started":"2023-02-01T14:51:30.020645Z","shell.execute_reply":"2023-02-01T14:51:30.024304Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.027690Z","iopub.execute_input":"2023-02-01T14:51:30.028212Z","iopub.status.idle":"2023-02-01T14:51:30.045818Z","shell.execute_reply.started":"2023-02-01T14:51:30.028170Z","shell.execute_reply":"2023-02-01T14:51:30.044552Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.048587Z","iopub.execute_input":"2023-02-01T14:51:30.048979Z","iopub.status.idle":"2023-02-01T14:51:30.075974Z","shell.execute_reply.started":"2023-02-01T14:51:30.048946Z","shell.execute_reply":"2023-02-01T14:51:30.074745Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred \n0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Random Forrest\n\nWe use Random Forrest to classify the titanic passengers as either surviving or not the accident. We use again the same statistical variable as Decisiont Trees.","metadata":{}},{"cell_type":"markdown","source":"## Model fitting and classification\n\nRandom Forrest overfits to the training dataset. ","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nn_estimators = range(1,20)\nmax_depths = range(1,40)\n\nfor est in n_estimators:\n for depth in max_depths:\n rf = RandomForestClassifier(n_estimators = est, max_depth = depth, \n random_state = 42, class_weight={0:6.,1:4},max_features = 6)\n rf.fit(X_train, y_train)\n train_score = rf.score(X_train, y_train)\n test_score = rf.score(X_valid, y_valid)\n print(\" - estimators : \", est, \n \" - max depths : \", depth, \n \" - train score : \", train_score,\n \" - valid score : \", valid_score)\n \n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.172233Z","iopub.execute_input":"2023-02-01T14:51:30.172931Z","iopub.status.idle":"2023-02-01T14:51:52.273980Z","shell.execute_reply.started":"2023-02-01T14:51:30.172890Z","shell.execute_reply":"2023-02-01T14:51:52.272764Z"},"trusted":true},"execution_count":213,"outputs":[{"name":"stdout","text":" - estimators : 1 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 2 - train score : 0.7771535580524345 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 3 - train score : 0.8071161048689138 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 4 - train score : 0.8277153558052435 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 5 - train score : 0.8314606741573034 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 6 - train score : 0.8651685393258427 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 7 - train score : 0.8820224719101124 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 8 - train score : 0.8857677902621723 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 9 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 10 - train score : 0.900749063670412 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 11 - train score : 0.9082397003745318 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 12 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 13 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 14 - train score : 0.9119850187265918 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 15 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 16 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 17 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 18 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 19 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 20 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 21 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 22 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 23 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 24 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 25 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 26 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 27 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 28 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 29 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 30 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 31 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 32 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 33 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 34 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 35 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 36 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 37 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 38 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 39 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 4 - train score : 0.848314606741573 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 5 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 6 - train score : 0.8689138576779026 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 7 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 8 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 10 - train score : 0.9213483146067416 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 11 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 12 - train score : 0.9288389513108615 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 13 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 14 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 15 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 16 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 17 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 18 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 19 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 20 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 21 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 22 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 23 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 24 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 25 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 26 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 27 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 28 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 29 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 30 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 31 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 32 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 33 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 34 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 35 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 36 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 37 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 38 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 39 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 4 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 5 - train score : 0.8707865168539326 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 6 - train score : 0.8838951310861424 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 7 - train score : 0.897003745318352 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 8 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 10 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 11 - train score : 0.9400749063670412 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 12 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 13 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 14 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 15 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 16 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 17 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 18 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 19 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 20 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 21 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 22 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 23 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 24 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 25 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 26 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 27 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 28 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 29 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 30 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 31 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 32 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 33 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 34 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 35 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 36 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 37 - train score : 0.9456928838951311 - 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estimators : 19 - max depths : 26 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 27 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 28 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 29 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 30 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 31 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 32 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 33 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 34 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 35 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 36 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 37 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 38 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 39 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover again the learning overfit on the training dataset. So we choose a maximum depth at around 6 and n estimator of 11. ","metadata":{}},{"cell_type":"code","source":"rf = RandomForestClassifier(n_estimators = 11, max_depth=6, random_state = 42, class_weight={0:6.,1:4}, max_features = 6)\nrf.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.275894Z","iopub.execute_input":"2023-02-01T14:51:52.276195Z","iopub.status.idle":"2023-02-01T14:51:52.312746Z","shell.execute_reply.started":"2023-02-01T14:51:52.276167Z","shell.execute_reply":"2023-02-01T14:51:52.311257Z"},"trusted":true},"execution_count":214,"outputs":[{"execution_count":214,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight={0: 6.0, 1: 4}, max_depth=6, max_features=6,\n n_estimators=11, random_state=42)"},"metadata":{}}]},{"cell_type":"code","source":"rf_train_score = rf.score(X_train, y_train)\nrf_train_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.314414Z","iopub.execute_input":"2023-02-01T14:51:52.314882Z","iopub.status.idle":"2023-02-01T14:51:52.329948Z","shell.execute_reply.started":"2023-02-01T14:51:52.314839Z","shell.execute_reply":"2023-02-01T14:51:52.328684Z"},"trusted":true},"execution_count":215,"outputs":[{"execution_count":215,"output_type":"execute_result","data":{"text/plain":"0.8801498127340824"},"metadata":{}}]},{"cell_type":"code","source":"rf_valid_score = rf.score(X_valid, y_valid)\nrf_valid_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.332102Z","iopub.execute_input":"2023-02-01T14:51:52.333087Z","iopub.status.idle":"2023-02-01T14:51:52.346061Z","shell.execute_reply.started":"2023-02-01T14:51:52.333051Z","shell.execute_reply":"2023-02-01T14:51:52.344862Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"0.8067226890756303"},"metadata":{}}]},{"cell_type":"markdown","source":"The age, the fare and the gender appears to contribute the most to predicting accurately the surviving or not the accident. It is surprising the passenger class influence less random forrest. ","metadata":{}},{"cell_type":"code","source":"importances = rf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.347466Z","iopub.execute_input":"2023-02-01T14:51:52.347785Z","iopub.status.idle":"2023-02-01T14:51:52.360347Z","shell.execute_reply.started":"2023-02-01T14:51:52.347756Z","shell.execute_reply":"2023-02-01T14:51:52.359060Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":" 0\n0.199528 Fare\n0.140924 Pclass\n0.390318 Sex\n0.023663 Embarked\n0.053330 fam_members\n0.192238 Age","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
0.199528Fare
0.140924Pclass
0.390318Sex
0.023663Embarked
0.053330fam_members
0.192238Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We found the classes of importances are Fares, Sex, and Age. ","metadata":{}},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = rf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.362231Z","iopub.execute_input":"2023-02-01T14:51:52.362868Z","iopub.status.idle":"2023-02-01T14:51:52.379545Z","shell.execute_reply.started":"2023-02-01T14:51:52.362825Z","shell.execute_reply":"2023-02-01T14:51:52.378290Z"},"trusted":true},"execution_count":218,"outputs":[{"execution_count":218,"output_type":"execute_result","data":{"text/plain":"array([[319, 10],\n [ 54, 151]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.381097Z","iopub.execute_input":"2023-02-01T14:51:52.381577Z","iopub.status.idle":"2023-02-01T14:51:52.391168Z","shell.execute_reply.started":"2023-02-01T14:51:52.381537Z","shell.execute_reply":"2023-02-01T14:51:52.390198Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"Accuracy : 0.8801498127340824\nMisclassfication : 0.1198501872659176\nSensitivivity : 0.9696048632218845\nSpecificity : 0.7365853658536585\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.392573Z","iopub.execute_input":"2023-02-01T14:51:52.393224Z","iopub.status.idle":"2023-02-01T14:51:52.412047Z","shell.execute_reply.started":"2023-02-01T14:51:52.393191Z","shell.execute_reply":"2023-02-01T14:51:52.410398Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.413222Z","iopub.execute_input":"2023-02-01T14:51:52.413582Z","iopub.status.idle":"2023-02-01T14:51:52.421900Z","shell.execute_reply.started":"2023-02-01T14:51:52.413554Z","shell.execute_reply":"2023-02-01T14:51:52.420658Z"},"trusted":true},"execution_count":221,"outputs":[{"name":"stdout","text":"Accuracy : 0.8067226890756303\nMisclassfication : 0.19327731092436976\nSensitivivity : 0.9227272727272727\nSpecificity : 0.6204379562043796\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.427307Z","iopub.execute_input":"2023-02-01T14:51:52.427779Z","iopub.status.idle":"2023-02-01T14:51:52.433953Z","shell.execute_reply.started":"2023-02-01T14:51:52.427746Z","shell.execute_reply":"2023-02-01T14:51:52.432477Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.435235Z","iopub.execute_input":"2023-02-01T14:51:52.435660Z","iopub.status.idle":"2023-02-01T14:51:52.465440Z","shell.execute_reply.started":"2023-02-01T14:51:52.435608Z","shell.execute_reply":"2023-02-01T14:51:52.464167Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.466837Z","iopub.execute_input":"2023-02-01T14:51:52.467622Z","iopub.status.idle":"2023-02-01T14:51:52.495143Z","shell.execute_reply.started":"2023-02-01T14:51:52.467589Z","shell.execute_reply":"2023-02-01T14:51:52.494000Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.496752Z","iopub.execute_input":"2023-02-01T14:51:52.497420Z","iopub.status.idle":"2023-02-01T14:51:52.520420Z","shell.execute_reply.started":"2023-02-01T14:51:52.497382Z","shell.execute_reply":"2023-02-01T14:51:52.519633Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.521415Z","iopub.execute_input":"2023-02-01T14:51:52.522394Z","iopub.status.idle":"2023-02-01T14:51:52.546457Z","shell.execute_reply.started":"2023-02-01T14:51:52.522351Z","shell.execute_reply":"2023-02-01T14:51:52.545447Z"},"trusted":true},"execution_count":226,"outputs":[{"execution_count":226,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.547614Z","iopub.execute_input":"2023-02-01T14:51:52.547908Z","iopub.status.idle":"2023-02-01T14:51:52.553613Z","shell.execute_reply.started":"2023-02-01T14:51:52.547880Z","shell.execute_reply":"2023-02-01T14:51:52.552611Z"},"trusted":true},"execution_count":227,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.554829Z","iopub.execute_input":"2023-02-01T14:51:52.555101Z","iopub.status.idle":"2023-02-01T14:51:52.580427Z","shell.execute_reply.started":"2023-02-01T14:51:52.555075Z","shell.execute_reply":"2023-02-01T14:51:52.579665Z"},"trusted":true},"execution_count":228,"outputs":[{"execution_count":228,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.581453Z","iopub.execute_input":"2023-02-01T14:51:52.582459Z","iopub.status.idle":"2023-02-01T14:51:52.610464Z","shell.execute_reply.started":"2023-02-01T14:51:52.582401Z","shell.execute_reply":"2023-02-01T14:51:52.609279Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y \n0 0.0 \n1 NaN \n2 0.0 \n3 NaN \n4 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_y
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.611523Z","iopub.execute_input":"2023-02-01T14:51:52.611803Z","iopub.status.idle":"2023-02-01T14:51:52.639513Z","shell.execute_reply.started":"2023-02-01T14:51:52.611776Z","shell.execute_reply":"2023-02-01T14:51:52.638365Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 1.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538461.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
\n

357 rows × 8 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.641337Z","iopub.execute_input":"2023-02-01T14:51:52.641775Z","iopub.status.idle":"2023-02-01T14:51:52.669655Z","shell.execute_reply.started":"2023-02-01T14:51:52.641731Z","shell.execute_reply":"2023-02-01T14:51:52.668451Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred \n0 0.0 NaN \n1 NaN 1.0 \n2 0.0 NaN \n3 NaN 1.0 \n4 NaN 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.670923Z","iopub.execute_input":"2023-02-01T14:51:52.671224Z","iopub.status.idle":"2023-02-01T14:51:52.693465Z","shell.execute_reply.started":"2023-02-01T14:51:52.671196Z","shell.execute_reply":"2023-02-01T14:51:52.692202Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 0.0\n233 0.733373 3.0 2.0 2.0 6.0 -1.923077 1.0 0.0\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n235 -0.299018 3.0 2.0 2.0 0.0 0.000000 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
2550.0342843.02.04.02.0-0.0769231.00.0
2330.7333733.02.02.06.0-1.9230771.00.0
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
235-0.2990183.02.02.00.00.0000000.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.694762Z","iopub.execute_input":"2023-02-01T14:51:52.695075Z","iopub.status.idle":"2023-02-01T14:51:52.711272Z","shell.execute_reply.started":"2023-02-01T14:51:52.695047Z","shell.execute_reply":"2023-02-01T14:51:52.710037Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 12\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n 2.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 0.0 5\n 1.0 4\n 1.0 1.0 0.0 2\n 2.0 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n 2.0 0.0 2\n 5.0 1.0 1.0 1\n 6.0 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.712948Z","iopub.execute_input":"2023-02-01T14:51:52.713356Z","iopub.status.idle":"2023-02-01T14:51:52.728466Z","shell.execute_reply.started":"2023-02-01T14:51:52.713299Z","shell.execute_reply":"2023-02-01T14:51:52.727135Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.729743Z","iopub.execute_input":"2023-02-01T14:51:52.730867Z","iopub.status.idle":"2023-02-01T14:51:53.319377Z","shell.execute_reply.started":"2023-02-01T14:51:52.730830Z","shell.execute_reply":"2023-02-01T14:51:53.318257Z"},"trusted":true},"execution_count":235,"outputs":[{"execution_count":235,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.320867Z","iopub.execute_input":"2023-02-01T14:51:53.321309Z","iopub.status.idle":"2023-02-01T14:51:53.344082Z","shell.execute_reply.started":"2023-02-01T14:51:53.321267Z","shell.execute_reply":"2023-02-01T14:51:53.342810Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n386 1.405213 3.0 1.0 2.0 7.0 -2.230769 0.0 1.0\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n824 1.092843 3.0 1.0 2.0 5.0 -2.153846 0.0 1.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3861.4052133.01.02.07.0-2.2307690.01.0
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
8241.0928433.01.02.05.0-2.1538460.01.0
429-0.2773633.01.02.00.00.1538461.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.345774Z","iopub.execute_input":"2023-02-01T14:51:53.346816Z","iopub.status.idle":"2023-02-01T14:51:53.369951Z","shell.execute_reply.started":"2023-02-01T14:51:53.346772Z","shell.execute_reply":"2023-02-01T14:51:53.368730Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 7\n 2.0 0.0 1\n 1.0 1.0 0.0 5\n 2.0 1.0 0.0 1\n 1.0 1\n 3.0 2.0 1.0 1\n 5.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n3.0 0.0 1.0 0.0 13\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 1.0 1\n 2.0 1.0 0.0 3\n 1.0 2\n 2.0 0.0 1\n 1.0 2\n 4.0 1.0 1.0 1\n 5.0 1.0 1.0 2\n 6.0 2.0 0.0 1\n 7.0 1.0 1.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.371853Z","iopub.execute_input":"2023-02-01T14:51:53.372272Z","iopub.status.idle":"2023-02-01T14:51:54.052559Z","shell.execute_reply.started":"2023-02-01T14:51:53.372234Z","shell.execute_reply":"2023-02-01T14:51:54.051607Z"},"trusted":true},"execution_count":238,"outputs":[{"execution_count":238,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.053862Z","iopub.execute_input":"2023-02-01T14:51:54.054160Z","iopub.status.idle":"2023-02-01T14:51:54.076180Z","shell.execute_reply.started":"2023-02-01T14:51:54.054133Z","shell.execute_reply":"2023-02-01T14:51:54.075083Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0\n110 1.626091 1.0 1.0 2.0 0.0 1.307692 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
130-0.2840413.01.04.00.00.2307690.00.0
1101.6260911.01.02.00.01.3076920.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.081370Z","iopub.execute_input":"2023-02-01T14:51:54.081697Z","iopub.status.idle":"2023-02-01T14:51:54.104120Z","shell.execute_reply.started":"2023-02-01T14:51:54.081668Z","shell.execute_reply":"2023-02-01T14:51:54.103001Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 4\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 3\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 0.0 11\n 1.0 24\n 1.0 1.0 0.0 15\n 1.0 2\n 2.0 0.0 9\n 1.0 4\n 2.0 1.0 0.0 9\n 1.0 3\n 2.0 0.0 5\n 1.0 6\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 6\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.106496Z","iopub.execute_input":"2023-02-01T14:51:54.106922Z","iopub.status.idle":"2023-02-01T14:51:55.631830Z","shell.execute_reply.started":"2023-02-01T14:51:54.106868Z","shell.execute_reply":"2023-02-01T14:51:55.630790Z"},"trusted":true},"execution_count":241,"outputs":[{"execution_count":241,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.633364Z","iopub.execute_input":"2023-02-01T14:51:55.633706Z","iopub.status.idle":"2023-02-01T14:51:55.655017Z","shell.execute_reply.started":"2023-02-01T14:51:55.633675Z","shell.execute_reply":"2023-02-01T14:51:55.653820Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.656793Z","iopub.execute_input":"2023-02-01T14:51:55.657669Z","iopub.status.idle":"2023-02-01T14:51:55.680263Z","shell.execute_reply.started":"2023-02-01T14:51:55.657616Z","shell.execute_reply":"2023-02-01T14:51:55.679008Z"},"trusted":true},"execution_count":243,"outputs":[{"execution_count":243,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 1.0 2\n 2.0 1.0 10\n 1.0 1.0 0.0 6\n 1.0 1\n 2.0 1.0 19\n 2.0 1.0 0.0 5\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 13\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 5\n 1.0 2\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 93\n 2.0 0.0 5\n 1.0 7\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 2.0 1.0 0.0 5\n 1.0 1\n 2.0 0.0 3\n 1.0 3\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 3\n 2.0 0.0 3\n 6.0 1.0 1.0 1\n 2.0 0.0 3\n 7.0 1.0 0.0 3\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.681765Z","iopub.execute_input":"2023-02-01T14:51:55.682091Z","iopub.status.idle":"2023-02-01T14:51:56.352496Z","shell.execute_reply.started":"2023-02-01T14:51:55.682062Z","shell.execute_reply":"2023-02-01T14:51:56.351351Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.353913Z","iopub.execute_input":"2023-02-01T14:51:56.355590Z","iopub.status.idle":"2023-02-01T14:51:56.788043Z","shell.execute_reply.started":"2023-02-01T14:51:56.355547Z","shell.execute_reply":"2023-02-01T14:51:56.786828Z"},"trusted":true},"execution_count":245,"outputs":[{"execution_count":245,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.789229Z","iopub.execute_input":"2023-02-01T14:51:56.789583Z","iopub.status.idle":"2023-02-01T14:51:57.215295Z","shell.execute_reply.started":"2023-02-01T14:51:56.789553Z","shell.execute_reply":"2023-02-01T14:51:57.214488Z"},"trusted":true},"execution_count":246,"outputs":[{"execution_count":246,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.216805Z","iopub.execute_input":"2023-02-01T14:51:57.217226Z","iopub.status.idle":"2023-02-01T14:51:57.574988Z","shell.execute_reply.started":"2023-02-01T14:51:57.217185Z","shell.execute_reply":"2023-02-01T14:51:57.574210Z"},"trusted":true},"execution_count":247,"outputs":[{"execution_count":247,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.576156Z","iopub.execute_input":"2023-02-01T14:51:57.576637Z","iopub.status.idle":"2023-02-01T14:51:57.924867Z","shell.execute_reply.started":"2023-02-01T14:51:57.576603Z","shell.execute_reply":"2023-02-01T14:51:57.923105Z"},"trusted":true},"execution_count":248,"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. Nonetheless, the various distribution of age and fare may lower the accuracy of the validation and testing datasets.","metadata":{}},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = rf.predict(X_test)\nrandom_forrest_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"rf_y_pred\": y_pred})\nrandom_forrest_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.926719Z","iopub.execute_input":"2023-02-01T14:51:57.927152Z","iopub.status.idle":"2023-02-01T14:51:57.950525Z","shell.execute_reply.started":"2023-02-01T14:51:57.927100Z","shell.execute_reply":"2023-02-01T14:51:57.949359Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.951752Z","iopub.execute_input":"2023-02-01T14:51:57.952061Z","iopub.status.idle":"2023-02-01T14:51:57.958199Z","shell.execute_reply.started":"2023-02-01T14:51:57.952032Z","shell.execute_reply":"2023-02-01T14:51:57.956976Z"},"trusted":true},"execution_count":250,"outputs":[]},{"cell_type":"code","source":"random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.959366Z","iopub.execute_input":"2023-02-01T14:51:57.960119Z","iopub.status.idle":"2023-02-01T14:51:57.977269Z","shell.execute_reply.started":"2023-02-01T14:51:57.960080Z","shell.execute_reply":"2023-02-01T14:51:57.976084Z"},"trusted":true},"execution_count":251,"outputs":[{"execution_count":251,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.978846Z","iopub.execute_input":"2023-02-01T14:51:57.979227Z","iopub.status.idle":"2023-02-01T14:51:58.007917Z","shell.execute_reply.started":"2023-02-01T14:51:57.979179Z","shell.execute_reply":"2023-02-01T14:51:58.006694Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 0.0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n4 0.0 1.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Neural AI \nIn this section we use some neural network to classify the data. We prepare the data so that it is more suitable for neural networks. We apply cross validation. ","metadata":{"execution":{"iopub.status.busy":"2023-01-09T16:59:50.819233Z","iopub.execute_input":"2023-01-09T16:59:50.819762Z","iopub.status.idle":"2023-01-09T16:59:50.825788Z","shell.execute_reply.started":"2023-01-09T16:59:50.819721Z","shell.execute_reply":"2023-01-09T16:59:50.823990Z"}}},{"cell_type":"markdown","source":"## Prepare data for Neural-AI","metadata":{"execution":{"iopub.status.busy":"2022-12-07T15:38:00.160610Z","iopub.execute_input":"2022-12-07T15:38:00.161030Z","iopub.status.idle":"2022-12-07T15:38:00.169322Z","shell.execute_reply.started":"2022-12-07T15:38:00.160998Z","shell.execute_reply":"2022-12-07T15:38:00.167957Z"}}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:58.009483Z","iopub.execute_input":"2023-02-01T14:51:58.009908Z","iopub.status.idle":"2023-02-01T14:51:58.023101Z","shell.execute_reply.started":"2023-02-01T14:51:58.009868Z","shell.execute_reply":"2023-02-01T14:51:58.021915Z"},"trusted":true},"execution_count":253,"outputs":[{"execution_count":253,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.431458Z","iopub.execute_input":"2023-02-01T14:55:47.431870Z","iopub.status.idle":"2023-02-01T14:55:47.444617Z","shell.execute_reply.started":"2023-02-01T14:55:47.431840Z","shell.execute_reply":"2023-02-01T14:55:47.443399Z"},"trusted":true},"execution_count":254,"outputs":[{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.696681Z","iopub.execute_input":"2023-02-01T14:55:47.697091Z","iopub.status.idle":"2023-02-01T14:55:47.706759Z","shell.execute_reply.started":"2023-02-01T14:55:47.697056Z","shell.execute_reply":"2023-02-01T14:55:47.705377Z"},"trusted":true},"execution_count":255,"outputs":[{"execution_count":255,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.965238Z","iopub.execute_input":"2023-02-01T14:55:47.965693Z","iopub.status.idle":"2023-02-01T14:55:47.976964Z","shell.execute_reply.started":"2023-02-01T14:55:47.965657Z","shell.execute_reply":"2023-02-01T14:55:47.975774Z"},"trusted":true},"execution_count":256,"outputs":[{"execution_count":256,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"I propose to keep Pclass,Sex, Age, SibSP,Parch,Ticket, Fare,Cabin, Embarked, Survived","metadata":{}},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked', 'Survived']\ntitanic_train = titanic_train.loc[:,columns_to_keep]\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.596834Z","iopub.execute_input":"2023-02-01T14:59:41.597224Z","iopub.status.idle":"2023-02-01T14:59:41.617029Z","shell.execute_reply.started":"2023-02-01T14:59:41.597192Z","shell.execute_reply":"2023-02-01T14:59:41.615728Z"},"trusted":true},"execution_count":259,"outputs":[{"execution_count":259,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name \\\n0 1 3 Braund, Mr. Owen Harris \n1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n2 3 3 Heikkinen, Miss. Laina \n3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n4 5 3 Allen, Mr. William Henry \n\n Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n1 female 38.0 1 0 PC 17599 71.2833 C85 C \n2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n3 female 35.0 1 0 113803 53.1000 C123 S \n4 male 35.0 0 0 373450 8.0500 NaN S \n\n Survived \n0 0 \n1 1 \n2 1 \n3 1 \n4 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
013Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
233Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
453Allen, Mr. William Henrymale35.0003734508.0500NaNS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked']\ntitanic_test = titanic_test.loc[:,columns_to_keep]\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.783983Z","iopub.execute_input":"2023-02-01T14:59:41.784720Z","iopub.status.idle":"2023-02-01T14:59:41.804682Z","shell.execute_reply.started":"2023-02-01T14:59:41.784681Z","shell.execute_reply":"2023-02-01T14:59:41.803270Z"},"trusted":true},"execution_count":260,"outputs":[{"execution_count":260,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Passengers ID\nTransforms to float","metadata":{}},{"cell_type":"code","source":"\ntitanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.301290Z","iopub.execute_input":"2023-02-01T14:59:42.302052Z","iopub.status.idle":"2023-02-01T14:59:42.309717Z","shell.execute_reply.started":"2023-02-01T14:59:42.302008Z","shell.execute_reply":"2023-02-01T14:59:42.308660Z"},"trusted":true},"execution_count":261,"outputs":[]},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.953004Z","iopub.execute_input":"2023-02-01T14:59:42.953443Z","iopub.status.idle":"2023-02-01T14:59:42.961302Z","shell.execute_reply.started":"2023-02-01T14:59:42.953406Z","shell.execute_reply":"2023-02-01T14:59:42.960093Z"},"trusted":true},"execution_count":262,"outputs":[{"execution_count":262,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.133436Z","iopub.execute_input":"2023-02-01T14:59:43.133821Z","iopub.status.idle":"2023-02-01T14:59:43.144899Z","shell.execute_reply.started":"2023-02-01T14:59:43.133790Z","shell.execute_reply":"2023-02-01T14:59:43.143759Z"},"trusted":true},"execution_count":263,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.317702Z","iopub.execute_input":"2023-02-01T14:59:43.318112Z","iopub.status.idle":"2023-02-01T14:59:43.329982Z","shell.execute_reply.started":"2023-02-01T14:59:43.318070Z","shell.execute_reply":"2023-02-01T14:59:43.328608Z"},"trusted":true},"execution_count":264,"outputs":[{"execution_count":264,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.526826Z","iopub.execute_input":"2023-02-01T14:59:43.527875Z","iopub.status.idle":"2023-02-01T14:59:43.538926Z","shell.execute_reply.started":"2023-02-01T14:59:43.527837Z","shell.execute_reply":"2023-02-01T14:59:43.538137Z"},"trusted":true},"execution_count":265,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.734794Z","iopub.execute_input":"2023-02-01T14:59:43.735200Z","iopub.status.idle":"2023-02-01T14:59:43.749137Z","shell.execute_reply.started":"2023-02-01T14:59:43.735165Z","shell.execute_reply":"2023-02-01T14:59:43.747731Z"},"trusted":true},"execution_count":266,"outputs":[{"execution_count":266,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.969440Z","iopub.execute_input":"2023-02-01T14:59:43.970219Z","iopub.status.idle":"2023-02-01T14:59:43.982089Z","shell.execute_reply.started":"2023-02-01T14:59:43.970157Z","shell.execute_reply":"2023-02-01T14:59:43.980764Z"},"trusted":true},"execution_count":267,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.130067Z","iopub.execute_input":"2023-02-01T14:59:44.130855Z","iopub.status.idle":"2023-02-01T14:59:44.141446Z","shell.execute_reply.started":"2023-02-01T14:59:44.130814Z","shell.execute_reply":"2023-02-01T14:59:44.140366Z"},"trusted":true},"execution_count":268,"outputs":[{"execution_count":268,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.375519Z","iopub.execute_input":"2023-02-01T14:59:44.376720Z","iopub.status.idle":"2023-02-01T14:59:44.387800Z","shell.execute_reply.started":"2023-02-01T14:59:44.376665Z","shell.execute_reply":"2023-02-01T14:59:44.386558Z"},"trusted":true},"execution_count":269,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.523725Z","iopub.execute_input":"2023-02-01T14:59:44.524363Z","iopub.status.idle":"2023-02-01T14:59:44.536192Z","shell.execute_reply.started":"2023-02-01T14:59:44.524322Z","shell.execute_reply":"2023-02-01T14:59:44.535041Z"},"trusted":true},"execution_count":270,"outputs":[{"execution_count":270,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.806439Z","iopub.execute_input":"2023-02-01T14:59:44.806827Z","iopub.status.idle":"2023-02-01T14:59:44.814441Z","shell.execute_reply.started":"2023-02-01T14:59:44.806794Z","shell.execute_reply":"2023-02-01T14:59:44.813111Z"},"trusted":true},"execution_count":271,"outputs":[{"execution_count":271,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.150811Z","iopub.execute_input":"2023-02-01T14:59:45.151188Z","iopub.status.idle":"2023-02-01T14:59:45.159387Z","shell.execute_reply.started":"2023-02-01T14:59:45.151156Z","shell.execute_reply":"2023-02-01T14:59:45.158248Z"},"trusted":true},"execution_count":272,"outputs":[{"execution_count":272,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.400597Z","iopub.execute_input":"2023-02-01T14:59:45.401226Z","iopub.status.idle":"2023-02-01T14:59:45.410601Z","shell.execute_reply.started":"2023-02-01T14:59:45.401186Z","shell.execute_reply":"2023-02-01T14:59:45.409380Z"},"trusted":true},"execution_count":273,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.540502Z","iopub.execute_input":"2023-02-01T14:59:45.541816Z","iopub.status.idle":"2023-02-01T14:59:45.555066Z","shell.execute_reply.started":"2023-02-01T14:59:45.541649Z","shell.execute_reply":"2023-02-01T14:59:45.553893Z"},"trusted":true},"execution_count":274,"outputs":[{"execution_count":274,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.765213Z","iopub.execute_input":"2023-02-01T14:59:45.766189Z","iopub.status.idle":"2023-02-01T14:59:45.777960Z","shell.execute_reply.started":"2023-02-01T14:59:45.766144Z","shell.execute_reply":"2023-02-01T14:59:45.776759Z"},"trusted":true},"execution_count":275,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.007744Z","iopub.execute_input":"2023-02-01T14:59:46.008172Z","iopub.status.idle":"2023-02-01T14:59:46.020782Z","shell.execute_reply.started":"2023-02-01T14:59:46.008134Z","shell.execute_reply":"2023-02-01T14:59:46.019374Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.158566Z","iopub.execute_input":"2023-02-01T14:59:46.158955Z","iopub.status.idle":"2023-02-01T14:59:46.171385Z","shell.execute_reply.started":"2023-02-01T14:59:46.158921Z","shell.execute_reply":"2023-02-01T14:59:46.170377Z"},"trusted":true},"execution_count":277,"outputs":[{"execution_count":277,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.365352Z","iopub.execute_input":"2023-02-01T14:59:46.365774Z","iopub.status.idle":"2023-02-01T14:59:46.377504Z","shell.execute_reply.started":"2023-02-01T14:59:46.365737Z","shell.execute_reply":"2023-02-01T14:59:46.376059Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.591674Z","iopub.execute_input":"2023-02-01T14:59:46.592065Z","iopub.status.idle":"2023-02-01T14:59:46.602473Z","shell.execute_reply.started":"2023-02-01T14:59:46.592030Z","shell.execute_reply":"2023-02-01T14:59:46.601375Z"},"trusted":true},"execution_count":279,"outputs":[{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.828954Z","iopub.execute_input":"2023-02-01T14:59:46.829390Z","iopub.status.idle":"2023-02-01T14:59:46.840546Z","shell.execute_reply.started":"2023-02-01T14:59:46.829349Z","shell.execute_reply":"2023-02-01T14:59:46.839434Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.034899Z","iopub.execute_input":"2023-02-01T14:59:47.036005Z","iopub.status.idle":"2023-02-01T14:59:47.045477Z","shell.execute_reply.started":"2023-02-01T14:59:47.035966Z","shell.execute_reply":"2023-02-01T14:59:47.044685Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.171565Z","iopub.execute_input":"2023-02-01T14:59:47.172636Z","iopub.status.idle":"2023-02-01T14:59:47.179309Z","shell.execute_reply.started":"2023-02-01T14:59:47.172596Z","shell.execute_reply":"2023-02-01T14:59:47.178195Z"},"trusted":true},"execution_count":282,"outputs":[{"execution_count":282,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.581953Z","iopub.execute_input":"2023-02-01T14:59:47.582616Z","iopub.status.idle":"2023-02-01T14:59:47.591105Z","shell.execute_reply.started":"2023-02-01T14:59:47.582574Z","shell.execute_reply":"2023-02-01T14:59:47.589952Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.831877Z","iopub.execute_input":"2023-02-01T14:59:47.832258Z","iopub.status.idle":"2023-02-01T14:59:47.839367Z","shell.execute_reply.started":"2023-02-01T14:59:47.832227Z","shell.execute_reply":"2023-02-01T14:59:47.838210Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.993877Z","iopub.execute_input":"2023-02-01T14:59:47.994253Z","iopub.status.idle":"2023-02-01T14:59:48.002543Z","shell.execute_reply.started":"2023-02-01T14:59:47.994221Z","shell.execute_reply":"2023-02-01T14:59:48.001550Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.201983Z","iopub.execute_input":"2023-02-01T14:59:48.202420Z","iopub.status.idle":"2023-02-01T14:59:48.212760Z","shell.execute_reply.started":"2023-02-01T14:59:48.202382Z","shell.execute_reply":"2023-02-01T14:59:48.211396Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_embarked_cat(data):\n factors = data['Embarked'].unique()\n gender_columns = pd.get_dummies(data['Embarked'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.431294Z","iopub.execute_input":"2023-02-01T14:59:48.432534Z","iopub.status.idle":"2023-02-01T14:59:48.437882Z","shell.execute_reply.started":"2023-02-01T14:59:48.432467Z","shell.execute_reply":"2023-02-01T14:59:48.437019Z"},"trusted":true},"execution_count":287,"outputs":[]},{"cell_type":"code","source":"\ntitanic_train = transform_embarked_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Embarked\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.629204Z","iopub.execute_input":"2023-02-01T14:59:48.629922Z","iopub.status.idle":"2023-02-01T14:59:48.642617Z","shell.execute_reply.started":"2023-02-01T14:59:48.629880Z","shell.execute_reply":"2023-02-01T14:59:48.641807Z"},"trusted":true},"execution_count":288,"outputs":[{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\ntitanic_test = transform_embarked_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Embarked\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.849824Z","iopub.execute_input":"2023-02-01T14:59:48.850216Z","iopub.status.idle":"2023-02-01T14:59:48.866727Z","shell.execute_reply.started":"2023-02-01T14:59:48.850182Z","shell.execute_reply":"2023-02-01T14:59:48.865657Z"},"trusted":true},"execution_count":289,"outputs":[{"execution_count":289,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"indices = range(0, titanic_test.shape[0])\ntitanic_test['U'] = [0 for i in indices]\ntitanic_test['U'] = titanic_test['U'].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.014240Z","iopub.execute_input":"2023-02-01T14:59:49.014659Z","iopub.status.idle":"2023-02-01T14:59:49.022051Z","shell.execute_reply.started":"2023-02-01T14:59:49.014622Z","shell.execute_reply":"2023-02-01T14:59:49.020812Z"},"trusted":true},"execution_count":290,"outputs":[]},{"cell_type":"markdown","source":"### Number of sibling","metadata":{}},{"cell_type":"code","source":"print(titanic_train[\"SibSp\"].describe())\nplt.hist(titanic_train[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.435498Z","iopub.execute_input":"2023-02-01T14:59:49.435873Z","iopub.status.idle":"2023-02-01T14:59:49.609979Z","shell.execute_reply.started":"2023-02-01T14:59:49.435843Z","shell.execute_reply":"2023-02-01T14:59:49.608818Z"},"trusted":true},"execution_count":291,"outputs":[{"name":"stdout","text":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":291,"output_type":"execute_result","data":{"text/plain":"(array([608., 209., 28., 16., 0., 18., 5., 0., 0., 7.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(titanic_test[\"SibSp\"].describe())\nplt.hist(titanic_test[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.640013Z","iopub.execute_input":"2023-02-01T14:59:49.640429Z","iopub.status.idle":"2023-02-01T14:59:50.199638Z","shell.execute_reply.started":"2023-02-01T14:59:49.640389Z","shell.execute_reply":"2023-02-01T14:59:50.198241Z"},"trusted":true},"execution_count":292,"outputs":[{"name":"stdout","text":"count 418.000000\nmean 0.447368\nstd 0.896760\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":292,"output_type":"execute_result","data":{"text/plain":"(array([283., 110., 14., 4., 0., 4., 1., 0., 0., 2.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def categorise_siblings(data):\n cut_labels_9 = ['sib_0','sib_1','sib_2','sib_3', \n 'sib_4','sib_5','sib_6','sib_7', 'sib_8']\n cut_bins = [0,1,2,3,4,5,6,7,8,9]\n data['Sib_cat'] = pd.cut(data['SibSp'], \n bins=cut_bins, \n labels=cut_labels_9)\n \n data['Sib_cat'] = data.Sib_cat.astype(str)\n data.loc[data[\"Sib_cat\"] == 'nan', \"Sib_cat\"] = \"Sib_Unknown\"\n \n return data\n\ndef transform_sibling_cat(data):\n factors = data['Sib_cat'].unique()\n gender_columns = pd.get_dummies(data['Sib_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.201993Z","iopub.execute_input":"2023-02-01T14:59:50.202490Z","iopub.status.idle":"2023-02-01T14:59:50.212938Z","shell.execute_reply.started":"2023-02-01T14:59:50.202445Z","shell.execute_reply":"2023-02-01T14:59:50.211676Z"},"trusted":true},"execution_count":293,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_siblings(titanic_train)\ntitanic_train = transform_sibling_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"SibSp\", axis = 1)\ntitanic_train = titanic_train.drop(\"Sib_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.214386Z","iopub.execute_input":"2023-02-01T14:59:50.214705Z","iopub.status.idle":"2023-02-01T14:59:50.237526Z","shell.execute_reply.started":"2023-02-01T14:59:50.214675Z","shell.execute_reply":"2023-02-01T14:59:50.236793Z"},"trusted":true},"execution_count":294,"outputs":[{"execution_count":294,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.431533Z","iopub.execute_input":"2023-02-01T14:59:50.432231Z","iopub.status.idle":"2023-02-01T14:59:50.438691Z","shell.execute_reply.started":"2023-02-01T14:59:50.432194Z","shell.execute_reply":"2023-02-01T14:59:50.437673Z"},"trusted":true},"execution_count":295,"outputs":[{"execution_count":295,"output_type":"execute_result","data":{"text/plain":"(891, 21)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_siblings(titanic_test)\ntitanic_test = transform_sibling_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"SibSp\", axis = 1)\ntitanic_test = titanic_test.drop(\"Sib_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.596205Z","iopub.execute_input":"2023-02-01T14:59:50.596606Z","iopub.status.idle":"2023-02-01T14:59:50.618154Z","shell.execute_reply.started":"2023-02-01T14:59:50.596574Z","shell.execute_reply":"2023-02-01T14:59:50.617093Z"},"trusted":true},"execution_count":296,"outputs":[{"execution_count":296,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.849255Z","iopub.execute_input":"2023-02-01T14:59:50.850520Z","iopub.status.idle":"2023-02-01T14:59:50.858028Z","shell.execute_reply.started":"2023-02-01T14:59:50.850477Z","shell.execute_reply":"2023-02-01T14:59:50.856953Z"},"trusted":true},"execution_count":297,"outputs":[{"execution_count":297,"output_type":"execute_result","data":{"text/plain":"(418, 20)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Transforming age into categories\nThe categorise the age into 9 categories; unknown and one for each decade. The categories are then transformed in hot_coding format. ","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.269486Z","iopub.execute_input":"2023-02-01T14:59:51.269885Z","iopub.status.idle":"2023-02-01T14:59:51.572232Z","shell.execute_reply.started":"2023-02-01T14:59:51.269851Z","shell.execute_reply":"2023-02-01T14:59:51.571214Z"},"trusted":true},"execution_count":298,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.573955Z","iopub.execute_input":"2023-02-01T14:59:51.574279Z","iopub.status.idle":"2023-02-01T14:59:51.588745Z","shell.execute_reply.started":"2023-02-01T14:59:51.574249Z","shell.execute_reply":"2023-02-01T14:59:51.587351Z"},"trusted":true},"execution_count":299,"outputs":[{"execution_count":299,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.763907Z","iopub.execute_input":"2023-02-01T14:59:51.764334Z","iopub.status.idle":"2023-02-01T14:59:52.129917Z","shell.execute_reply.started":"2023-02-01T14:59:51.764278Z","shell.execute_reply":"2023-02-01T14:59:52.128918Z"},"trusted":true},"execution_count":300,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.131621Z","iopub.execute_input":"2023-02-01T14:59:52.132130Z","iopub.status.idle":"2023-02-01T14:59:52.142285Z","shell.execute_reply.started":"2023-02-01T14:59:52.132091Z","shell.execute_reply":"2023-02-01T14:59:52.141264Z"},"trusted":true},"execution_count":301,"outputs":[{"execution_count":301,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"def transform_age_cat(data):\n factors = data['Age_cat'].unique()\n gender_columns = pd.get_dummies(data['Age_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.143629Z","iopub.execute_input":"2023-02-01T14:59:52.143919Z","iopub.status.idle":"2023-02-01T14:59:52.154584Z","shell.execute_reply.started":"2023-02-01T14:59:52.143891Z","shell.execute_reply":"2023-02-01T14:59:52.153409Z"},"trusted":true},"execution_count":302,"outputs":[]},{"cell_type":"code","source":"def categorise_age(data):\n cut_labels_8 = ['age_0-9','age_10-19','age_20-29','age_30-39', \n 'age_40-49','age_50-59','age_60-69','age_70-79']\n cut_bins = [0,10,20,30,40,50,60,70,80]\n data['Age_cat'] = pd.cut(data['Age'], \n bins=cut_bins, \n labels=cut_labels_8)\n data['Age_cat'] = data.Age_cat.astype(str)\n data.loc[data[\"Age\"].isna(), \"Age_cat\"] = \"Age_Unknown\"\n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.340509Z","iopub.execute_input":"2023-02-01T14:59:52.340896Z","iopub.status.idle":"2023-02-01T14:59:52.347606Z","shell.execute_reply.started":"2023-02-01T14:59:52.340863Z","shell.execute_reply":"2023-02-01T14:59:52.346572Z"},"trusted":true},"execution_count":303,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_age(titanic_train)\ntitanic_train = transform_age_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Age\", axis = 1)\ntitanic_train = titanic_train.drop(\"Age_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.546266Z","iopub.execute_input":"2023-02-01T14:59:52.546677Z","iopub.status.idle":"2023-02-01T14:59:52.572844Z","shell.execute_reply.started":"2023-02-01T14:59:52.546642Z","shell.execute_reply":"2023-02-01T14:59:52.571757Z"},"trusted":true},"execution_count":304,"outputs":[{"execution_count":304,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_age(titanic_test)\ntitanic_test = transform_age_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Age\", axis = 1)\ntitanic_test = titanic_test.drop(\"Age_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.811521Z","iopub.execute_input":"2023-02-01T14:59:52.812681Z","iopub.status.idle":"2023-02-01T14:59:52.836736Z","shell.execute_reply.started":"2023-02-01T14:59:52.812627Z","shell.execute_reply":"2023-02-01T14:59:52.835513Z"},"trusted":true},"execution_count":305,"outputs":[{"execution_count":305,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"### Gender transformation to hot-coding \nWe check the factor values are the same between both datasets. Then, we generate a hot coding of two columns; i.e., male and female. Both columns replace the Sex column.","metadata":{}},{"cell_type":"code","source":"titanic_train['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.188122Z","iopub.execute_input":"2023-02-01T14:59:53.189282Z","iopub.status.idle":"2023-02-01T14:59:53.197504Z","shell.execute_reply.started":"2023-02-01T14:59:53.189231Z","shell.execute_reply":"2023-02-01T14:59:53.196373Z"},"trusted":true},"execution_count":306,"outputs":[{"execution_count":306,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.420038Z","iopub.execute_input":"2023-02-01T14:59:53.420458Z","iopub.status.idle":"2023-02-01T14:59:53.428009Z","shell.execute_reply.started":"2023-02-01T14:59:53.420423Z","shell.execute_reply":"2023-02-01T14:59:53.426859Z"},"trusted":true},"execution_count":307,"outputs":[{"execution_count":307,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_gender(data):\n factors = data['Sex'].unique()\n gender_columns = pd.get_dummies(data['Sex'])\n columns = range(0,len(factors))\n \n for column in columns:\n data[factors[column]] = gender_columns.loc[:,factors[column]].astype(float)\n \n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.614253Z","iopub.execute_input":"2023-02-01T14:59:53.614984Z","iopub.status.idle":"2023-02-01T14:59:53.620854Z","shell.execute_reply.started":"2023-02-01T14:59:53.614945Z","shell.execute_reply":"2023-02-01T14:59:53.619727Z"},"trusted":true},"execution_count":308,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_gender(titanic_train)\ntitanic_train.drop(\"Sex\", axis = 1, inplace = True)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.853720Z","iopub.execute_input":"2023-02-01T14:59:53.854121Z","iopub.status.idle":"2023-02-01T14:59:53.868139Z","shell.execute_reply.started":"2023-02-01T14:59:53.854084Z","shell.execute_reply":"2023-02-01T14:59:53.867117Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_gender(titanic_test)\ntitanic_test.drop(\"Sex\", axis = 1,inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.077511Z","iopub.execute_input":"2023-02-01T14:59:54.078227Z","iopub.status.idle":"2023-02-01T14:59:54.117482Z","shell.execute_reply.started":"2023-02-01T14:59:54.078188Z","shell.execute_reply":"2023-02-01T14:59:54.116493Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Parch \\\n0 892.0 3 Kelly, Mr. James 0 \n1 893.0 3 Wilkes, Mrs. James (Ellen Needs) 0 \n2 894.0 2 Myles, Mr. Thomas Francis 0 \n3 895.0 3 Wirz, Mr. Albert 0 \n4 896.0 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 \n\n Ticket Fare Cabin Q S C ... age_30-39 age_40-49 \\\n0 330911 7.8292 NaN 1.0 0.0 0.0 ... 1.0 0.0 \n1 363272 7.0000 NaN 0.0 1.0 0.0 ... 0.0 1.0 \n2 240276 9.6875 NaN 1.0 0.0 0.0 ... 0.0 0.0 \n3 315154 8.6625 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n4 3101298 12.2875 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n\n age_60-69 age_20-29 age_10-19 age_50-59 age_0-9 age_70-79 male \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n\n female \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 1.0 \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameParchTicketFareCabinQSC...age_30-39age_40-49age_60-69age_20-29age_10-19age_50-59age_0-9age_70-79malefemale
0892.03Kelly, Mr. James03309117.8292NaN1.00.00.0...1.00.00.00.00.00.00.00.01.00.0
1893.03Wilkes, Mrs. James (Ellen Needs)03632727.0000NaN0.01.00.0...0.01.00.00.00.00.00.00.00.01.0
2894.02Myles, Mr. Thomas Francis02402769.6875NaN1.00.00.0...0.00.01.00.00.00.00.00.01.00.0
3895.03Wirz, Mr. Albert03151548.6625NaN0.01.00.0...0.00.00.01.00.00.00.00.01.00.0
4896.03Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.2875NaN0.01.00.0...0.00.00.01.00.00.00.00.00.01.0
\n

5 rows × 28 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Cabin and Pclass\n\nThe passenger class appears to drive whether a cabin is known. So, we propose to drop the cabin as the percentage of not known values is quite high. We apply an hot encoding the Pclass. ","metadata":{}},{"cell_type":"code","source":"titanic_train['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.494349Z","iopub.execute_input":"2023-02-01T14:59:54.494758Z","iopub.status.idle":"2023-02-01T14:59:54.503695Z","shell.execute_reply.started":"2023-02-01T14:59:54.494724Z","shell.execute_reply":"2023-02-01T14:59:54.502385Z"},"trusted":true},"execution_count":311,"outputs":[{"execution_count":311,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n 'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n 'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n 'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n 'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"print(\"percentage of cabin nan values - training \", titanic_train['Cabin'].isna().sum()/titanic_train.shape[0])\nprint(\"percentage of cabin nan values - test \", titanic_test['Cabin'].isna().sum()/titanic_test.shape[0])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.731246Z","iopub.execute_input":"2023-02-01T14:59:54.732185Z","iopub.status.idle":"2023-02-01T14:59:54.740154Z","shell.execute_reply.started":"2023-02-01T14:59:54.732142Z","shell.execute_reply":"2023-02-01T14:59:54.738880Z"},"trusted":true},"execution_count":312,"outputs":[{"name":"stdout","text":"percentage of cabin nan values - training 0.7710437710437711\npercentage of cabin nan values - test 0.7822966507177034\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.963015Z","iopub.execute_input":"2023-02-01T14:59:54.963847Z","iopub.status.idle":"2023-02-01T14:59:54.971020Z","shell.execute_reply.started":"2023-02-01T14:59:54.963804Z","shell.execute_reply":"2023-02-01T14:59:54.969855Z"},"trusted":true},"execution_count":313,"outputs":[{"execution_count":313,"output_type":"execute_result","data":{"text/plain":"array([3, 1, 2])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.182701Z","iopub.execute_input":"2023-02-01T14:59:55.183488Z","iopub.status.idle":"2023-02-01T14:59:55.190703Z","shell.execute_reply.started":"2023-02-01T14:59:55.183443Z","shell.execute_reply":"2023-02-01T14:59:55.189659Z"},"trusted":true},"execution_count":314,"outputs":[{"execution_count":314,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 1])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 1 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.447423Z","iopub.execute_input":"2023-02-01T14:59:55.447835Z","iopub.status.idle":"2023-02-01T14:59:55.464293Z","shell.execute_reply.started":"2023-02-01T14:59:55.447799Z","shell.execute_reply":"2023-02-01T14:59:55.463098Z"},"trusted":true},"execution_count":315,"outputs":[{"execution_count":315,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n1 1 C85\n3 1 C123\n6 1 E46\n11 1 C103\n23 1 A6\n.. ... ...\n871 1 D35\n872 1 B51 B53 B55\n879 1 C50\n887 1 B42\n889 1 C148\n\n[216 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
11C85
31C123
61E46
111C103
231A6
.........
8711D35
8721B51 B53 B55
8791C50
8871B42
8891C148
\n

216 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 2 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.639329Z","iopub.execute_input":"2023-02-01T14:59:55.640055Z","iopub.status.idle":"2023-02-01T14:59:55.656031Z","shell.execute_reply.started":"2023-02-01T14:59:55.640016Z","shell.execute_reply":"2023-02-01T14:59:55.655083Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n9 2 NaN\n15 2 NaN\n17 2 NaN\n20 2 NaN\n21 2 D56\n.. ... ...\n866 2 NaN\n874 2 NaN\n880 2 NaN\n883 2 NaN\n886 2 NaN\n\n[184 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
92NaN
152NaN
172NaN
202NaN
212D56
.........
8662NaN
8742NaN
8802NaN
8832NaN
8862NaN
\n

184 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 3 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.890762Z","iopub.execute_input":"2023-02-01T14:59:55.891773Z","iopub.status.idle":"2023-02-01T14:59:55.905616Z","shell.execute_reply.started":"2023-02-01T14:59:55.891731Z","shell.execute_reply":"2023-02-01T14:59:55.904841Z"},"trusted":true},"execution_count":317,"outputs":[{"execution_count":317,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n0 3 NaN\n2 3 NaN\n4 3 NaN\n5 3 NaN\n7 3 NaN\n.. ... ...\n882 3 NaN\n884 3 NaN\n885 3 NaN\n888 3 NaN\n890 3 NaN\n\n[491 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
03NaN
23NaN
43NaN
53NaN
73NaN
.........
8823NaN
8843NaN
8853NaN
8883NaN
8903NaN
\n

491 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"xs = titanic_train.loc[titanic_train['Fare'] > 0,'Pclass']\nys = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.128782Z","iopub.execute_input":"2023-02-01T14:59:56.129782Z","iopub.status.idle":"2023-02-01T14:59:56.360461Z","shell.execute_reply.started":"2023-02-01T14:59:56.129741Z","shell.execute_reply":"2023-02-01T14:59:56.359413Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"xs = titanic_test.loc[titanic_test['Fare'] > 0,'Pclass']\nys = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.362001Z","iopub.execute_input":"2023-02-01T14:59:56.362324Z","iopub.status.idle":"2023-02-01T14:59:56.593756Z","shell.execute_reply.started":"2023-02-01T14:59:56.362281Z","shell.execute_reply":"2023-02-01T14:59:56.592791Z"},"trusted":true},"execution_count":319,"outputs":[{"execution_count":319,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.scatter(titanic_train[\"Pclass\"],titanic_train[\"Fare\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.595546Z","iopub.execute_input":"2023-02-01T14:59:56.595846Z","iopub.status.idle":"2023-02-01T14:59:56.826882Z","shell.execute_reply.started":"2023-02-01T14:59:56.595817Z","shell.execute_reply":"2023-02-01T14:59:56.825559Z"},"trusted":true},"execution_count":320,"outputs":[{"execution_count":320,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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transform_Pclass(data):\n factors = data['Pclass'].unique()\n Pclass_columns = pd.get_dummies(data['Pclass'])\n columns = range(0,len(factors))\n \n for column in columns:\n col_name = 'Class_' + str(factors[column])\n data[col_name] = Pclass_columns.loc[:,factors[column]].astype(float)\n \n data.drop(\"Pclass\", axis = 1)\n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.829111Z","iopub.execute_input":"2023-02-01T14:59:56.829859Z","iopub.status.idle":"2023-02-01T14:59:56.838658Z","shell.execute_reply.started":"2023-02-01T14:59:56.829811Z","shell.execute_reply":"2023-02-01T14:59:56.837496Z"},"trusted":true},"execution_count":321,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_Pclass(titanic_train)\ntitanic_train.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.037884Z","iopub.execute_input":"2023-02-01T14:59:57.039017Z","iopub.status.idle":"2023-02-01T14:59:57.077228Z","shell.execute_reply.started":"2023-02-01T14:59:57.038961Z","shell.execute_reply":"2023-02-01T14:59:57.076108Z"},"trusted":true},"execution_count":322,"outputs":[{"execution_count":322,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch \\\n0 1.0 Braund, Mr. Owen Harris 0 \n1 2.0 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n2 3.0 Heikkinen, Miss. Laina 0 \n3 4.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n4 5.0 Allen, Mr. William Henry 0 \n\n Ticket Fare Survived S C Q U ... age_0-9 \\\n0 A/5 21171 7.2500 0 1.0 0.0 0.0 0.0 ... 0.0 \n1 PC 17599 71.2833 1 0.0 1.0 0.0 0.0 ... 0.0 \n2 STON/O2. 3101282 7.9250 1 1.0 0.0 0.0 0.0 ... 0.0 \n3 113803 53.1000 1 1.0 0.0 0.0 0.0 ... 0.0 \n4 373450 8.0500 0 1.0 0.0 0.0 0.0 ... 0.0 \n\n age_10-19 age_60-69 age_40-49 age_70-79 male female Class_3 Class_1 \\\n0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n2 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n4 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n\n Class_2 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 30 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareSurvivedSCQU...age_0-9age_10-19age_60-69age_40-49age_70-79malefemaleClass_3Class_1Class_2
01.0Braund, Mr. Owen Harris0A/5 211717.250001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
12.0Cumings, Mrs. John Bradley (Florence Briggs Th...0PC 1759971.283310.01.00.00.0...0.00.00.00.00.00.01.00.01.00.0
23.0Heikkinen, Miss. Laina0STON/O2. 31012827.925011.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
34.0Futrelle, Mrs. Jacques Heath (Lily May Peel)011380353.100011.00.00.00.0...0.00.00.00.00.00.01.00.01.00.0
45.0Allen, Mr. William Henry03734508.050001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
\n

5 rows × 30 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_Pclass(titanic_test)\ntitanic_test.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.320738Z","iopub.execute_input":"2023-02-01T14:59:57.321706Z","iopub.status.idle":"2023-02-01T14:59:57.358787Z","shell.execute_reply.started":"2023-02-01T14:59:57.321665Z","shell.execute_reply":"2023-02-01T14:59:57.357627Z"},"trusted":true},"execution_count":323,"outputs":[{"execution_count":323,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch Ticket \\\n0 892.0 Kelly, Mr. James 0 330911 \n1 893.0 Wilkes, Mrs. James (Ellen Needs) 0 363272 \n2 894.0 Myles, Mr. Thomas Francis 0 240276 \n3 895.0 Wirz, Mr. Albert 0 315154 \n4 896.0 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 3101298 \n\n Fare Q S C U Sib_Unknown ... age_20-29 age_10-19 \\\n0 7.8292 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n1 7.0000 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 \n2 9.6875 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n3 8.6625 0.0 1.0 0.0 0.0 1.0 ... 1.0 0.0 \n4 12.2875 0.0 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n\n age_50-59 age_0-9 age_70-79 male female Class_3 Class_2 Class_1 \n0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareQSCUSib_Unknown...age_20-29age_10-19age_50-59age_0-9age_70-79malefemaleClass_3Class_2Class_1
0892.0Kelly, Mr. James03309117.82921.00.00.00.01.0...0.00.00.00.00.01.00.01.00.00.0
1893.0Wilkes, Mrs. James (Ellen Needs)03632727.00000.01.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
2894.0Myles, Mr. Thomas Francis02402769.68751.00.00.00.01.0...0.00.00.00.00.01.00.00.01.00.0
3895.0Wirz, Mr. Albert03151548.66250.01.00.00.01.0...1.00.00.00.00.01.00.01.00.00.0
4896.0Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.28750.01.00.00.00.0...1.00.00.00.00.00.01.01.00.00.0
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Tickets and Fare\nWe remove the tickets, as it brings no additional characteristic for the prediction.\n\nOld version: We reduce the complexity of the Fare by using the log.\nNew version: The price appears to be dependent on the class, so we drop the price.","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.725423Z","iopub.execute_input":"2023-02-01T14:59:57.726055Z","iopub.status.idle":"2023-02-01T14:59:57.734724Z","shell.execute_reply.started":"2023-02-01T14:59:57.725995Z","shell.execute_reply":"2023-02-01T14:59:57.733640Z"},"trusted":true},"execution_count":324,"outputs":[]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\ntitanic_train.loc[titanic_train['Fare'] > 0,'Fare'] = log_10_values\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.977699Z","iopub.execute_input":"2023-02-01T14:59:57.978673Z","iopub.status.idle":"2023-02-01T14:59:57.991610Z","shell.execute_reply.started":"2023-02-01T14:59:57.978633Z","shell.execute_reply":"2023-02-01T14:59:57.990366Z"},"trusted":true},"execution_count":325,"outputs":[{"execution_count":325,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.256781\nstd 0.435553\nmin 0.000000\n25% 0.898198\n50% 1.159994\n75% 1.491362\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\ntitanic_test.loc[titanic_test['Fare'] > 0,'Fare'] = log_10_values\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.219678Z","iopub.execute_input":"2023-02-01T14:59:58.220097Z","iopub.status.idle":"2023-02-01T14:59:58.235301Z","shell.execute_reply.started":"2023-02-01T14:59:58.220059Z","shell.execute_reply":"2023-02-01T14:59:58.234195Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"count 417.000000\nmean 1.279591\nstd 0.437507\nmin 0.000000\n25% 0.897396\n50% 1.159994\n75% 1.498311\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.drop(\"Fare\", axis = 1, inplace = True)\ntitanic_test.drop(\"Fare\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.471730Z","iopub.execute_input":"2023-02-01T14:59:58.472149Z","iopub.status.idle":"2023-02-01T14:59:58.480205Z","shell.execute_reply.started":"2023-02-01T14:59:58.472111Z","shell.execute_reply":"2023-02-01T14:59:58.479227Z"},"trusted":true},"execution_count":327,"outputs":[]},{"cell_type":"markdown","source":"### Outcome of data preparations","metadata":{}},{"cell_type":"code","source":"\nprint(\"training datasets : \" , titanic_train.shape)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.947799Z","iopub.execute_input":"2023-02-01T14:59:58.948756Z","iopub.status.idle":"2023-02-01T14:59:58.957820Z","shell.execute_reply.started":"2023-02-01T14:59:58.948713Z","shell.execute_reply":"2023-02-01T14:59:58.956624Z"},"trusted":true},"execution_count":328,"outputs":[{"name":"stdout","text":"training datasets : (891, 28)\n","output_type":"stream"},{"execution_count":328,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_1 float64\nClass_2 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(\"testing datasets : \" , titanic_test.shape)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.211439Z","iopub.execute_input":"2023-02-01T14:59:59.211825Z","iopub.status.idle":"2023-02-01T14:59:59.222689Z","shell.execute_reply.started":"2023-02-01T14:59:59.211793Z","shell.execute_reply":"2023-02-01T14:59:59.221460Z"},"trusted":true},"execution_count":329,"outputs":[{"name":"stdout","text":"testing datasets : (418, 27)\n","output_type":"stream"},{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train_cols = titanic_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.478786Z","iopub.execute_input":"2023-02-01T14:59:59.479161Z","iopub.status.idle":"2023-02-01T14:59:59.488399Z","shell.execute_reply.started":"2023-02-01T14:59:59.479130Z","shell.execute_reply":"2023-02-01T14:59:59.487137Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"Index(['Survived'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.773416Z","iopub.execute_input":"2023-02-01T14:59:59.773881Z","iopub.status.idle":"2023-02-01T14:59:59.780592Z","shell.execute_reply.started":"2023-02-01T14:59:59.773845Z","shell.execute_reply":"2023-02-01T14:59:59.779730Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Name', 'Parch', 'Q', 'S', 'C', 'U', 'Sib_Unknown',\n 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', 'age_30-39',\n 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross validation preparation\nWe use a stratified sampling for the training into a train and test dataset. ","metadata":{}},{"cell_type":"code","source":"x_cols = [\"PassengerId\",'Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\nX = X[x_cols]\nX = X.apply(pd.to_numeric)\n\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.360627Z","iopub.execute_input":"2023-02-01T15:00:00.361572Z","iopub.status.idle":"2023-02-01T15:00:00.386989Z","shell.execute_reply.started":"2023-02-01T15:00:00.361528Z","shell.execute_reply":"2023-02-01T15:00:00.385873Z"},"trusted":true},"execution_count":332,"outputs":[{"name":"stdout","text":"0 0.616105\n1 0.383895\nName: Survived, dtype: float64 \n\n\n0 0.616246\n1 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = ['Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\nx_train_pass_id = X_train.PassengerId\nX_train = X_train[x_cols]\nX_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.656949Z","iopub.execute_input":"2023-02-01T15:00:00.657623Z","iopub.status.idle":"2023-02-01T15:00:00.667953Z","shell.execute_reply.started":"2023-02-01T15:00:00.657586Z","shell.execute_reply":"2023-02-01T15:00:00.666758Z"},"trusted":true},"execution_count":333,"outputs":[{"execution_count":333,"output_type":"execute_result","data":{"text/plain":"(534, 25)"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\n\nX_valid.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.982077Z","iopub.execute_input":"2023-02-01T15:00:00.982495Z","iopub.status.idle":"2023-02-01T15:00:00.991483Z","shell.execute_reply.started":"2023-02-01T15:00:00.982459Z","shell.execute_reply":"2023-02-01T15:00:00.990369Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"(357, 25)"},"metadata":{}}]},{"cell_type":"code","source":"y_train_encode=pd.get_dummies(y_train)\ny_valid_encode=pd.get_dummies(y_valid)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.303350Z","iopub.execute_input":"2023-02-01T15:00:01.303749Z","iopub.status.idle":"2023-02-01T15:00:01.310531Z","shell.execute_reply.started":"2023-02-01T15:00:01.303715Z","shell.execute_reply":"2023-02-01T15:00:01.309278Z"},"trusted":true},"execution_count":335,"outputs":[]},{"cell_type":"code","source":"train_cols = X_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.517778Z","iopub.execute_input":"2023-02-01T15:00:01.518178Z","iopub.status.idle":"2023-02-01T15:00:01.527798Z","shell.execute_reply.started":"2023-02-01T15:00:01.518142Z","shell.execute_reply":"2023-02-01T15:00:01.526659Z"},"trusted":true},"execution_count":336,"outputs":[{"execution_count":336,"output_type":"execute_result","data":{"text/plain":"Index([], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test[x_cols]\nX_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.807922Z","iopub.execute_input":"2023-02-01T15:00:01.808982Z","iopub.status.idle":"2023-02-01T15:00:01.817925Z","shell.execute_reply.started":"2023-02-01T15:00:01.808940Z","shell.execute_reply":"2023-02-01T15:00:01.816659Z"},"trusted":true},"execution_count":337,"outputs":[{"execution_count":337,"output_type":"execute_result","data":{"text/plain":"Parch int64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\nQ float64\nS float64\nC float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## ANN\n\nWe apply an ANN to predict the survival of passengers. We create a basic architecture made of 5 layers.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential, load_model\n\ntf.compat.v1.get_default_graph()\n\nno_columns = X_train.shape[1]\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Flatten(input_shape=(no_columns,)))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(2, activation=\"softmax\"))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.368188Z","iopub.execute_input":"2023-02-01T15:00:02.368622Z","iopub.status.idle":"2023-02-01T15:00:02.493557Z","shell.execute_reply.started":"2023-02-01T15:00:02.368582Z","shell.execute_reply":"2023-02-01T15:00:02.492272Z"},"trusted":true},"execution_count":338,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 25) 0 \n_________________________________________________________________\ndense (Dense) (None, 32) 832 \n_________________________________________________________________\ndense_1 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_2 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_3 (Dense) (None, 2) 66 \n=================================================================\nTotal params: 3,010\nTrainable params: 3,010\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"},{"name":"stderr","text":"2023-02-01 15:00:02.406449: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"\nrate = 0.00021\nopt = tf.keras.optimizers.Adam(learning_rate = rate)\nmodel.compile(optimizer= opt, \n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"])\ntf.compat.v1.get_default_graph()\nhistory = model.fit(X_train,\n y_train_encode,\n validation_data=(X_valid, y_valid_encode),\n epochs = 300,\n verbose = True)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.642006Z","iopub.execute_input":"2023-02-01T15:00:02.642833Z","iopub.status.idle":"2023-02-01T15:00:28.751910Z","shell.execute_reply.started":"2023-02-01T15:00:02.642783Z","shell.execute_reply":"2023-02-01T15:00:28.750794Z"},"trusted":true},"execution_count":339,"outputs":[{"name":"stderr","text":"2023-02-01 15:00:02.755801: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/300\n17/17 [==============================] - 1s 19ms/step - loss: 0.7885 - accuracy: 0.6161 - val_loss: 0.7708 - val_accuracy: 0.6162\nEpoch 2/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7574 - accuracy: 0.6161 - val_loss: 0.7429 - val_accuracy: 0.6162\nEpoch 3/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7326 - accuracy: 0.6161 - val_loss: 0.7213 - val_accuracy: 0.6162\nEpoch 4/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.7133 - accuracy: 0.6161 - val_loss: 0.7045 - val_accuracy: 0.6162\nEpoch 5/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.6161 - val_loss: 0.6921 - val_accuracy: 0.6162\nEpoch 6/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6878 - accuracy: 0.6161 - val_loss: 0.6832 - val_accuracy: 0.6162\nEpoch 7/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6802 - accuracy: 0.6161 - val_loss: 0.6771 - val_accuracy: 0.6162\nEpoch 8/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6750 - accuracy: 0.6161 - val_loss: 0.6727 - val_accuracy: 0.6162\nEpoch 9/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6717 - accuracy: 0.6161 - val_loss: 0.6698 - val_accuracy: 0.6162\nEpoch 10/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6692 - accuracy: 0.6161 - val_loss: 0.6682 - val_accuracy: 0.6162\nEpoch 11/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.6161 - val_loss: 0.6670 - val_accuracy: 0.6162\nEpoch 12/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6668 - accuracy: 0.6161 - val_loss: 0.6663 - val_accuracy: 0.6162\nEpoch 13/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6662 - accuracy: 0.6161 - val_loss: 0.6658 - val_accuracy: 0.6162\nEpoch 14/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.6161 - val_loss: 0.6655 - val_accuracy: 0.6162\nEpoch 15/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.6161 - val_loss: 0.6653 - val_accuracy: 0.6162\nEpoch 16/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6655 - accuracy: 0.6161 - val_loss: 0.6651 - val_accuracy: 0.6162\nEpoch 17/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6161 - val_loss: 0.6650 - val_accuracy: 0.6162\nEpoch 18/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6651 - accuracy: 0.6161 - val_loss: 0.6649 - val_accuracy: 0.6162\nEpoch 19/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6647 - val_accuracy: 0.6162\nEpoch 20/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6646 - val_accuracy: 0.6162\nEpoch 21/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6648 - accuracy: 0.6161 - val_loss: 0.6645 - val_accuracy: 0.6162\nEpoch 22/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6647 - accuracy: 0.6161 - val_loss: 0.6644 - val_accuracy: 0.6162\nEpoch 23/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6645 - accuracy: 0.6161 - val_loss: 0.6643 - val_accuracy: 0.6162\nEpoch 24/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6644 - accuracy: 0.6161 - val_loss: 0.6641 - val_accuracy: 0.6162\nEpoch 25/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.6161 - val_loss: 0.6640 - val_accuracy: 0.6162\nEpoch 26/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6641 - accuracy: 0.6161 - val_loss: 0.6639 - val_accuracy: 0.6162\nEpoch 27/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6637 - val_accuracy: 0.6162\nEpoch 28/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6636 - val_accuracy: 0.6162\nEpoch 29/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6637 - accuracy: 0.6161 - val_loss: 0.6634 - val_accuracy: 0.6162\nEpoch 30/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.6161 - val_loss: 0.6633 - val_accuracy: 0.6162\nEpoch 31/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6635 - accuracy: 0.6161 - val_loss: 0.6631 - val_accuracy: 0.6162\nEpoch 32/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6633 - accuracy: 0.6161 - val_loss: 0.6629 - val_accuracy: 0.6162\nEpoch 33/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6630 - accuracy: 0.6161 - val_loss: 0.6627 - val_accuracy: 0.6162\nEpoch 34/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6628 - accuracy: 0.6161 - val_loss: 0.6625 - val_accuracy: 0.6162\nEpoch 35/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6623 - val_accuracy: 0.6162\nEpoch 36/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6621 - val_accuracy: 0.6162\nEpoch 37/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6622 - accuracy: 0.6161 - val_loss: 0.6619 - val_accuracy: 0.6162\nEpoch 38/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6619 - accuracy: 0.6161 - val_loss: 0.6616 - val_accuracy: 0.6162\nEpoch 39/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6618 - accuracy: 0.6161 - val_loss: 0.6614 - val_accuracy: 0.6162\nEpoch 40/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6614 - accuracy: 0.6161 - val_loss: 0.6611 - val_accuracy: 0.6162\nEpoch 41/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6612 - accuracy: 0.6161 - val_loss: 0.6608 - val_accuracy: 0.6162\nEpoch 42/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6610 - accuracy: 0.6161 - val_loss: 0.6605 - val_accuracy: 0.6162\nEpoch 43/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.6161 - val_loss: 0.6601 - val_accuracy: 0.6162\nEpoch 44/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6598 - val_accuracy: 0.6162\nEpoch 45/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6594 - val_accuracy: 0.6162\nEpoch 46/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6595 - accuracy: 0.6161 - val_loss: 0.6590 - val_accuracy: 0.6162\nEpoch 47/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6590 - accuracy: 0.6161 - val_loss: 0.6586 - val_accuracy: 0.6162\nEpoch 48/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6161 - val_loss: 0.6581 - val_accuracy: 0.6162\nEpoch 49/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6580 - accuracy: 0.6161 - val_loss: 0.6576 - val_accuracy: 0.6162\nEpoch 50/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.6161 - val_loss: 0.6571 - val_accuracy: 0.6162\nEpoch 51/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6571 - accuracy: 0.6161 - val_loss: 0.6566 - val_accuracy: 0.6162\nEpoch 52/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6565 - accuracy: 0.6161 - val_loss: 0.6560 - val_accuracy: 0.6162\nEpoch 53/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6161 - val_loss: 0.6553 - val_accuracy: 0.6162\nEpoch 54/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6161 - val_loss: 0.6546 - val_accuracy: 0.6162\nEpoch 55/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6545 - accuracy: 0.6161 - val_loss: 0.6539 - val_accuracy: 0.6162\nEpoch 56/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6542 - accuracy: 0.6161 - val_loss: 0.6531 - val_accuracy: 0.6162\nEpoch 57/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6531 - accuracy: 0.6161 - val_loss: 0.6522 - val_accuracy: 0.6162\nEpoch 58/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6161 - val_loss: 0.6513 - val_accuracy: 0.6162\nEpoch 59/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6510 - accuracy: 0.6161 - val_loss: 0.6503 - val_accuracy: 0.6162\nEpoch 60/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6501 - accuracy: 0.6161 - val_loss: 0.6493 - val_accuracy: 0.6162\nEpoch 61/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6490 - accuracy: 0.6161 - val_loss: 0.6482 - val_accuracy: 0.6162\nEpoch 62/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6480 - accuracy: 0.6161 - val_loss: 0.6469 - val_accuracy: 0.6162\nEpoch 63/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6466 - accuracy: 0.6161 - val_loss: 0.6456 - val_accuracy: 0.6162\nEpoch 64/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6453 - accuracy: 0.6161 - val_loss: 0.6443 - val_accuracy: 0.6162\nEpoch 65/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6439 - accuracy: 0.6161 - val_loss: 0.6428 - val_accuracy: 0.6162\nEpoch 66/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6423 - accuracy: 0.6161 - val_loss: 0.6412 - val_accuracy: 0.6162\nEpoch 67/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6411 - accuracy: 0.6161 - val_loss: 0.6395 - val_accuracy: 0.6162\nEpoch 68/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6393 - accuracy: 0.6161 - val_loss: 0.6376 - val_accuracy: 0.6162\nEpoch 69/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6373 - accuracy: 0.6161 - val_loss: 0.6357 - val_accuracy: 0.6162\nEpoch 70/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6351 - accuracy: 0.6161 - val_loss: 0.6336 - val_accuracy: 0.6162\nEpoch 71/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6330 - accuracy: 0.6161 - val_loss: 0.6313 - val_accuracy: 0.6162\nEpoch 72/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6307 - accuracy: 0.6161 - val_loss: 0.6290 - val_accuracy: 0.6162\nEpoch 73/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6285 - accuracy: 0.6161 - val_loss: 0.6264 - val_accuracy: 0.6162\nEpoch 74/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6258 - accuracy: 0.6161 - val_loss: 0.6239 - val_accuracy: 0.6162\nEpoch 75/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6231 - accuracy: 0.6161 - val_loss: 0.6211 - val_accuracy: 0.6162\nEpoch 76/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6203 - accuracy: 0.6161 - val_loss: 0.6180 - val_accuracy: 0.6162\nEpoch 77/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6161 - val_loss: 0.6150 - val_accuracy: 0.6190\nEpoch 78/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6143 - accuracy: 0.6161 - val_loss: 0.6118 - val_accuracy: 0.6162\nEpoch 79/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6109 - accuracy: 0.6199 - val_loss: 0.6083 - val_accuracy: 0.6218\nEpoch 80/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6074 - accuracy: 0.6273 - val_loss: 0.6048 - val_accuracy: 0.6331\nEpoch 81/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6039 - accuracy: 0.6404 - val_loss: 0.6011 - val_accuracy: 0.6443\nEpoch 82/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6003 - accuracy: 0.6610 - val_loss: 0.5970 - val_accuracy: 0.6667\nEpoch 83/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5962 - accuracy: 0.6798 - val_loss: 0.5932 - val_accuracy: 0.6667\nEpoch 84/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5923 - accuracy: 0.6966 - val_loss: 0.5891 - val_accuracy: 0.7003\nEpoch 85/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7116 - val_loss: 0.5849 - val_accuracy: 0.7087\nEpoch 86/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5843 - accuracy: 0.7172 - val_loss: 0.5807 - val_accuracy: 0.7115\nEpoch 87/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5805 - accuracy: 0.7191 - val_loss: 0.5762 - val_accuracy: 0.7395\nEpoch 88/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7303 - val_loss: 0.5719 - val_accuracy: 0.7395\nEpoch 89/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5713 - accuracy: 0.7378 - val_loss: 0.5674 - val_accuracy: 0.7563\nEpoch 90/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5671 - accuracy: 0.7491 - val_loss: 0.5627 - val_accuracy: 0.7563\nEpoch 91/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7509 - val_loss: 0.5582 - val_accuracy: 0.7563\nEpoch 92/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5582 - accuracy: 0.7659 - val_loss: 0.5537 - val_accuracy: 0.7759\nEpoch 93/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.7640 - val_loss: 0.5492 - val_accuracy: 0.7871\nEpoch 94/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.5499 - accuracy: 0.7640 - val_loss: 0.5447 - val_accuracy: 0.7731\nEpoch 95/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5456 - accuracy: 0.7640 - val_loss: 0.5402 - val_accuracy: 0.7871\nEpoch 96/300\n17/17 [==============================] - 0s 13ms/step - loss: 0.5412 - accuracy: 0.7640 - val_loss: 0.5359 - val_accuracy: 0.7843\nEpoch 97/300\n17/17 [==============================] - 0s 12ms/step - loss: 0.5369 - accuracy: 0.7659 - val_loss: 0.5316 - val_accuracy: 0.7843\nEpoch 98/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.7603 - val_loss: 0.5275 - val_accuracy: 0.7955\nEpoch 99/300\n17/17 [==============================] - 0s 9ms/step - loss: 0.5294 - accuracy: 0.7715 - val_loss: 0.5233 - val_accuracy: 0.8039\nEpoch 100/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7753 - val_loss: 0.5196 - val_accuracy: 0.8039\nEpoch 101/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5217 - accuracy: 0.7734 - val_loss: 0.5158 - val_accuracy: 0.8039\nEpoch 102/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5181 - accuracy: 0.7753 - val_loss: 0.5120 - val_accuracy: 0.8039\nEpoch 103/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5145 - accuracy: 0.7753 - val_loss: 0.5085 - val_accuracy: 0.8011\nEpoch 104/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5113 - accuracy: 0.7715 - val_loss: 0.5049 - val_accuracy: 0.8039\nEpoch 105/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7715 - val_loss: 0.5016 - val_accuracy: 0.8039\nEpoch 106/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7715 - val_loss: 0.4983 - val_accuracy: 0.8039\nEpoch 107/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.7828 - val_loss: 0.4951 - val_accuracy: 0.8095\nEpoch 108/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7921 - val_loss: 0.4921 - val_accuracy: 0.8095\nEpoch 109/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7959 - val_loss: 0.4891 - val_accuracy: 0.8067\nEpoch 110/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7921 - val_loss: 0.4864 - val_accuracy: 0.8067\nEpoch 111/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4919 - accuracy: 0.7940 - val_loss: 0.4840 - val_accuracy: 0.8123\nEpoch 112/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7996 - val_loss: 0.4813 - val_accuracy: 0.8067\nEpoch 113/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7996 - val_loss: 0.4789 - val_accuracy: 0.8067\nEpoch 114/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7996 - val_loss: 0.4767 - val_accuracy: 0.8067\nEpoch 115/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4828 - accuracy: 0.7996 - val_loss: 0.4745 - val_accuracy: 0.8095\nEpoch 116/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4808 - accuracy: 0.7996 - val_loss: 0.4724 - val_accuracy: 0.8095\nEpoch 117/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4789 - accuracy: 0.7996 - val_loss: 0.4706 - val_accuracy: 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0.8179\nEpoch 173/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4420 - accuracy: 0.8240 - val_loss: 0.4308 - val_accuracy: 0.8123\nEpoch 174/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8240 - val_loss: 0.4305 - val_accuracy: 0.8123\nEpoch 175/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8179\nEpoch 176/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8123\nEpoch 177/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4297 - val_accuracy: 0.8151\nEpoch 178/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4410 - accuracy: 0.8258 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 179/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4294 - val_accuracy: 0.8151\nEpoch 180/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4402 - accuracy: 0.8240 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 181/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4400 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 182/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4397 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 183/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4395 - accuracy: 0.8240 - val_loss: 0.4286 - val_accuracy: 0.8151\nEpoch 184/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4393 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8123\nEpoch 185/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4392 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 186/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4389 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 187/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4390 - accuracy: 0.8240 - val_loss: 0.4278 - val_accuracy: 0.8123\nEpoch 188/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4279 - val_accuracy: 0.8151\nEpoch 189/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8151\nEpoch 190/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 191/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8240 - val_loss: 0.4274 - val_accuracy: 0.8151\nEpoch 192/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 193/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8221 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 194/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4375 - accuracy: 0.8240 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 195/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8240 - val_loss: 0.4272 - val_accuracy: 0.8151\nEpoch 196/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4370 - accuracy: 0.8240 - val_loss: 0.4271 - val_accuracy: 0.8151\nEpoch 197/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4368 - accuracy: 0.8240 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 198/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4367 - accuracy: 0.8221 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 199/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 0.8240 - val_loss: 0.4269 - val_accuracy: 0.8151\nEpoch 200/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4364 - accuracy: 0.8240 - val_loss: 0.4265 - val_accuracy: 0.8151\nEpoch 201/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4363 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 202/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4362 - accuracy: 0.8202 - val_loss: 0.4262 - val_accuracy: 0.8123\nEpoch 203/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4359 - accuracy: 0.8258 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 204/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4357 - accuracy: 0.8240 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 205/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8221 - val_loss: 0.4261 - val_accuracy: 0.8151\nEpoch 206/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 207/300\n17/17 [==============================] - 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[==============================] - 0s 5ms/step - loss: 0.4347 - accuracy: 0.8240 - val_loss: 0.4258 - val_accuracy: 0.8151\nEpoch 215/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 216/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4344 - accuracy: 0.8221 - val_loss: 0.4251 - val_accuracy: 0.8123\nEpoch 217/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4255 - val_accuracy: 0.8151\nEpoch 218/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 219/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4339 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 220/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 221/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 222/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4252 - val_accuracy: 0.8151\nEpoch 223/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4335 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 224/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4332 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 225/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 226/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4330 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 227/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 228/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 229/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 230/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4250 - val_accuracy: 0.8151\nEpoch 231/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4325 - accuracy: 0.8240 - val_loss: 0.4249 - val_accuracy: 0.8151\nEpoch 232/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 233/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4322 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 234/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 235/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 236/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 237/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 238/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 239/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 240/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 241/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 242/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4313 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 243/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 244/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 245/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8151\nEpoch 246/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8221 - val_loss: 0.4246 - val_accuracy: 0.8179\nEpoch 247/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 248/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 249/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 250/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4307 - accuracy: 0.8221 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 251/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8151\nEpoch 252/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8221 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 253/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 254/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 255/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4302 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 256/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 257/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 258/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 259/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 260/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 261/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 262/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 263/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 264/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 265/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 266/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 267/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 268/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 269/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 270/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 271/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 272/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 273/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 274/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 275/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4289 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 276/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 277/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4288 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 278/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 279/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 280/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 281/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 282/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 283/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 284/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 285/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 286/300\n17/17 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 287/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 288/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4282 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 289/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 290/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 291/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 292/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 293/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 294/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 295/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 296/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 297/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 298/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 299/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 300/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_train_accuracy = model.evaluate(X_train, y_train_encode)\nprint('Accuracy: %.4f' % (ann_train_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.756511Z","iopub.execute_input":"2023-02-01T15:00:28.757274Z","iopub.status.idle":"2023-02-01T15:00:28.874523Z","shell.execute_reply.started":"2023-02-01T15:00:28.757226Z","shell.execute_reply":"2023-02-01T15:00:28.873360Z"},"trusted":true},"execution_count":340,"outputs":[{"name":"stdout","text":"17/17 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221\nAccuracy: 0.8221\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_valid_accuracy = model.evaluate(X_valid, y_valid_encode)\nprint('Accuracy: %.4f' % (ann_valid_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.876387Z","iopub.execute_input":"2023-02-01T15:00:28.877663Z","iopub.status.idle":"2023-02-01T15:00:28.990657Z","shell.execute_reply.started":"2023-02-01T15:00:28.877614Z","shell.execute_reply":"2023-02-01T15:00:28.989441Z"},"trusted":true},"execution_count":341,"outputs":[{"name":"stdout","text":"12/12 [==============================] - 0s 2ms/step - loss: 0.4238 - accuracy: 0.8179\nAccuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ","metadata":{}},{"cell_type":"code","source":"\ny_pred = model.predict(X_train)\nY_pred = np.argmax(model.predict(X_train),axis=1)\ncm = confusion_matrix(y_train, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.993841Z","iopub.execute_input":"2023-02-01T15:00:28.994285Z","iopub.status.idle":"2023-02-01T15:00:29.270957Z","shell.execute_reply.started":"2023-02-01T15:00:28.994240Z","shell.execute_reply":"2023-02-01T15:00:29.269885Z"},"trusted":true},"execution_count":342,"outputs":[{"execution_count":342,"output_type":"execute_result","data":{"text/plain":"array([[304, 25],\n [ 70, 135]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.272190Z","iopub.execute_input":"2023-02-01T15:00:29.273301Z","iopub.status.idle":"2023-02-01T15:00:29.281676Z","shell.execute_reply.started":"2023-02-01T15:00:29.273267Z","shell.execute_reply":"2023-02-01T15:00:29.280517Z"},"trusted":true},"execution_count":343,"outputs":[{"name":"stdout","text":"Accuracy : 0.8220973782771536\nMisclassfication : 0.17790262172284643\nSensitivivity : 0.9240121580547113\nSpecificity : 0.6585365853658537\n","output_type":"stream"}]},{"cell_type":"code","source":"\ny_pred = model.predict(X_valid)\nY_pred = np.argmax(model.predict(X_valid),axis=1)\ncm = confusion_matrix(y_valid, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.283243Z","iopub.execute_input":"2023-02-01T15:00:29.283612Z","iopub.status.idle":"2023-02-01T15:00:29.451759Z","shell.execute_reply.started":"2023-02-01T15:00:29.283566Z","shell.execute_reply":"2023-02-01T15:00:29.450417Z"},"trusted":true},"execution_count":344,"outputs":[{"execution_count":344,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.453236Z","iopub.execute_input":"2023-02-01T15:00:29.453610Z","iopub.status.idle":"2023-02-01T15:00:29.461774Z","shell.execute_reply.started":"2023-02-01T15:00:29.453579Z","shell.execute_reply":"2023-02-01T15:00:29.460520Z"},"trusted":true},"execution_count":345,"outputs":[{"name":"stdout","text":"Accuracy : 0.8179271708683473\nMisclassfication : 0.18207282913165265\nSensitivivity : 0.9363636363636364\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.463445Z","iopub.execute_input":"2023-02-01T15:00:29.463787Z","iopub.status.idle":"2023-02-01T15:00:29.472285Z","shell.execute_reply.started":"2023-02-01T15:00:29.463752Z","shell.execute_reply":"2023-02-01T15:00:29.471294Z"},"trusted":true},"execution_count":346,"outputs":[]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_train),axis=1)\n\nann_pred = X_train.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_train_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.473634Z","iopub.execute_input":"2023-02-01T15:00:29.474711Z","iopub.status.idle":"2023-02-01T15:00:29.593403Z","shell.execute_reply.started":"2023-02-01T15:00:29.474675Z","shell.execute_reply":"2023-02-01T15:00:29.592290Z"},"trusted":true},"execution_count":347,"outputs":[{"execution_count":347,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n844 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n316 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n768 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n255 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n130 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n844 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n316 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n768 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n255 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n130 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n\n ann_y_pred PassengerId \n844 0 845.0 \n316 1 317.0 \n768 0 769.0 \n255 1 256.0 \n130 0 131.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
84401.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00845.0
31600.01.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01317.0
76800.01.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00769.0
25521.00.00.00.00.00.00.00.00.0...1.01.00.00.00.00.01.00.01256.0
13001.00.00.00.00.00.00.01.00.0...0.01.00.00.00.00.01.00.00131.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(ann_pred[[\"PassengerId\", \"ann_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.598604Z","iopub.execute_input":"2023-02-01T15:00:29.599029Z","iopub.status.idle":"2023-02-01T15:00:29.628142Z","shell.execute_reply.started":"2023-02-01T15:00:29.598995Z","shell.execute_reply":"2023-02-01T15:00:29.627332Z"},"trusted":true},"execution_count":348,"outputs":[{"execution_count":348,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 NaN \n2 0.0 NaN 0.0 \n3 NaN 1.0 NaN \n4 NaN 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_valid),axis=1)\nann_pred = X_valid.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_valid_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.629335Z","iopub.execute_input":"2023-02-01T15:00:29.629823Z","iopub.status.idle":"2023-02-01T15:00:29.739371Z","shell.execute_reply.started":"2023-02-01T15:00:29.629791Z","shell.execute_reply":"2023-02-01T15:00:29.738281Z"},"trusted":true},"execution_count":349,"outputs":[{"execution_count":349,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n369 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n541 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n196 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n810 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n427 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n369 0.0 ... 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n541 0.0 ... 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n196 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n810 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n427 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n\n ann_y_pred PassengerId \n369 1 370.0 \n541 1 542.0 \n196 0 197.0 \n810 0 811.0 \n427 1 428.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
36901.00.00.00.00.00.00.00.00.0...1.00.00.01.00.00.01.00.01370.0
54120.00.00.00.01.00.00.00.00.0...1.01.00.00.00.01.00.00.01542.0
19601.00.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00197.0
81001.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00811.0
42701.00.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01428.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(ann_pred.PassengerId), \"ann_y_pred\"] = ann_pred[\"ann_y_pred\"]\nresults_train.drop(\"rf_y_pred_y\", axis = 1)\nresults_train.drop(\"rf_y_pred_x\", axis = 1)\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.740869Z","iopub.execute_input":"2023-02-01T15:00:29.741291Z","iopub.status.idle":"2023-02-01T15:00:29.771294Z","shell.execute_reply.started":"2023-02-01T15:00:29.741249Z","shell.execute_reply":"2023-02-01T15:00:29.770286Z"},"trusted":true},"execution_count":350,"outputs":[{"execution_count":350,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 1.0 \n2 0.0 NaN 0.0 \n3 NaN 1.0 1.0 \n4 NaN 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Overall, the number of survivors misclassified were greater than misclassified passengers who perished. The next step is to identify those passengers to attempt to find the source of the misclassifcation. So far the lowest number of misclassified passengers who perished. ","metadata":{}},{"cell_type":"markdown","source":"## Predict test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\ny_pred = y_pred.argmax(1)\nann_pred = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"],\n \"ann_y_pred\" : y_pred})\nann_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.772619Z","iopub.execute_input":"2023-02-01T15:00:29.772938Z","iopub.status.idle":"2023-02-01T15:00:29.875387Z","shell.execute_reply.started":"2023-02-01T15:00:29.772908Z","shell.execute_reply":"2023-02-01T15:00:29.874334Z"},"trusted":true},"execution_count":351,"outputs":[{"execution_count":351,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.876431Z","iopub.execute_input":"2023-02-01T15:00:29.876729Z","iopub.status.idle":"2023-02-01T15:00:29.882726Z","shell.execute_reply.started":"2023-02-01T15:00:29.876701Z","shell.execute_reply":"2023-02-01T15:00:29.881480Z"},"trusted":true},"execution_count":352,"outputs":[]},{"cell_type":"code","source":"ann_pred[[\"PassengerId\",\"ann_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.884219Z","iopub.execute_input":"2023-02-01T15:00:29.884571Z","iopub.status.idle":"2023-02-01T15:00:29.900340Z","shell.execute_reply.started":"2023-02-01T15:00:29.884540Z","shell.execute_reply":"2023-02-01T15:00:29.899599Z"},"trusted":true},"execution_count":353,"outputs":[{"execution_count":353,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(ann_pred[[\"PassengerId\",\"ann_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.901356Z","iopub.execute_input":"2023-02-01T15:00:29.901844Z","iopub.status.idle":"2023-02-01T15:00:29.931394Z","shell.execute_reply.started":"2023-02-01T15:00:29.901814Z","shell.execute_reply":"2023-02-01T15:00:29.929969Z"},"trusted":true},"execution_count":354,"outputs":[{"execution_count":354,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred \n0 0.0 0.0 0.0 0.0 0 \n1 1.0 0.0 0.0 0.0 0 \n2 0.0 0.0 0.0 0.0 0 \n3 0.0 0.0 0.0 0.0 0 \n4 0.0 1.0 1.0 1.0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_predann_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.00
1893.03.02.01.411765-0.3161762.01.01.00.00.00.00
2894.02.01.02.588235-0.2021843.00.00.00.00.00.00
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.00
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.00
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Findings","metadata":{}},{"cell_type":"markdown","source":"We compile all the results in a basic structure. We discover that the logistic regression has achieved the highest accuracy on the validation datasets. ANN came close. Both methods appear not to overfit to the training dataset.","metadata":{}},{"cell_type":"code","source":"log_reg_results = {\n \"method\": \"Logistic regression\",\n \"training_accurary\": log_reg_score_train,\n \"valid_accuracy\": log_reg_score_valid\n}\n\nknn_results = {\n \"method\": \"KNN\",\n \"training_accurary\": knn_train_score,\n \"valid_accuracy\": knn_valid_score\n}\n\nclf_results = {\n \"method\": \"decision trees\",\n \"training_accurary\": clf_train_score,\n \"valid_accuracy\": clf_valid_score\n}\n\nrf_results = {\n \"method\": \"Random Forrest\",\n \"training_accurary\": rf_train_score,\n \"valid_accuracy\": rf_valid_score\n}\n\nann_results = {\n \"method\": \"ANN\",\n \"training_accurary\": ann_train_accuracy,\n \"valid_accuracy\": ann_valid_accuracy\n}\n\nresults = [log_reg_results, knn_results, clf_results, rf_results, ann_results]\nresults","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.932994Z","iopub.execute_input":"2023-02-01T15:00:29.933497Z","iopub.status.idle":"2023-02-01T15:00:29.947698Z","shell.execute_reply.started":"2023-02-01T15:00:29.933454Z","shell.execute_reply":"2023-02-01T15:00:29.946484Z"},"trusted":true},"execution_count":355,"outputs":[{"execution_count":355,"output_type":"execute_result","data":{"text/plain":"[{'method': 'Logistic regression',\n 'training_accurary': 0.7921348314606742,\n 'valid_accuracy': 0.8207282913165266},\n {'method': 'KNN',\n 'training_accurary': 0.8258426966292135,\n 'valid_accuracy': 0.7871148459383753},\n {'method': 'decision trees',\n 'training_accurary': 0.9082397003745318,\n 'valid_accuracy': 0.8151260504201681},\n {'method': 'Random Forrest',\n 'training_accurary': 0.8801498127340824,\n 'valid_accuracy': 0.8067226890756303},\n {'method': 'ANN',\n 'training_accurary': 0.8220973610877991,\n 'valid_accuracy': 0.8179271817207336}]"},"metadata":{}}]},{"cell_type":"markdown","source":"Less than 10% errors of passengers have been misclassified, when we compare all predictions together. So, it may be possible to identify some rules to increase accuracy. Nonetheless, these rules may also decrease the accuracy. So, a fine balance needs to be found. ","metadata":{}},{"cell_type":"code","source":"results_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.949130Z","iopub.execute_input":"2023-02-01T15:00:29.949749Z","iopub.status.idle":"2023-02-01T15:00:29.958469Z","shell.execute_reply.started":"2023-02-01T15:00:29.949702Z","shell.execute_reply":"2023-02-01T15:00:29.957602Z"},"trusted":true},"execution_count":356,"outputs":[{"execution_count":356,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked',\n 'fam_members', 'y', 'lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred']\nresults_train['merged_pred'] = results_train.loc[:,cols].apply(\n lambda x: ','.join(x.dropna().astype(str)),\n axis=1\n)\n\nresults_train['y_found'] = results_train.apply(lambda x: str(x.y) in x.merged_pred, axis=1)\nresults_train.drop(\"merged_pred\", axis = 1, inplace = True)\nresults_train.groupby(\"y_found\").count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.959997Z","iopub.execute_input":"2023-02-01T15:00:29.960444Z","iopub.status.idle":"2023-02-01T15:00:30.142912Z","shell.execute_reply.started":"2023-02-01T15:00:29.960402Z","shell.execute_reply":"2023-02-01T15:00:30.141887Z"},"trusted":true},"execution_count":357,"outputs":[{"execution_count":357,"output_type":"execute_result","data":{"text/plain":"y_found\nFalse 0.075196\nTrue 0.924804\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The set of passengers misclassified by all methods appear to have a lower expected fares and much more compact spread of fares. The median passenger class of misclassified passenger appear to be higher than those correctly classified. Both observations contractict each other and suggests some of fares being close to each other between passenger classes may be contributing in misclassifying passengers. \n\nThe misclassified passengers appears to be most women and their age appear to be older than the ones correctly classified by one method. The distribution to gender appears to match the overall observations for correctly classified passengers. Nonetheless, it is worth pointing out some of ages were inputed based on the number of siblings, spouse and parents aboard. This simple method of inputation may have impacted the classifiers; more research should be made to validate or improve inputting the missing information. \n\nOther aspects in the data may lead to misclassification.","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == False,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.144511Z","iopub.execute_input":"2023-02-01T15:00:30.145249Z","iopub.status.idle":"2023-02-01T15:00:30.180088Z","shell.execute_reply.started":"2023-02-01T15:00:30.145205Z","shell.execute_reply":"2023-02-01T15:00:30.178959Z"},"trusted":true},"execution_count":358,"outputs":[{"execution_count":358,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 67.000000 67.000000 67.000000 67.000000 67.000000 67.000000\nmean 2.149254 1.104478 0.129736 0.423026 0.343284 2.537313\nstd 0.908774 0.308188 0.721256 1.008879 0.844810 0.840785\nmin 1.000000 1.000000 -1.076923 -0.626005 0.000000 2.000000\n25% 1.000000 1.000000 -0.269231 -0.282777 0.000000 2.000000\n50% 2.000000 1.000000 0.000000 -0.062981 0.000000 2.000000\n75% 3.000000 1.000000 0.230769 0.694936 0.500000 3.000000\nmax 3.000000 2.000000 2.461538 3.318594 6.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count67.00000067.00000067.00000067.00000067.00000067.000000
mean2.1492541.1044780.1297360.4230260.3432842.537313
std0.9087740.3081880.7212561.0088790.8448100.840785
min1.0000001.000000-1.076923-0.6260050.0000002.000000
25%1.0000001.000000-0.269231-0.2827770.0000002.000000
50%2.0000001.0000000.000000-0.0629810.0000002.000000
75%3.0000001.0000000.2307690.6949360.5000003.000000
max3.0000002.0000002.4615383.3185946.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.182172Z","iopub.execute_input":"2023-02-01T15:00:30.183279Z","iopub.status.idle":"2023-02-01T15:00:30.213352Z","shell.execute_reply.started":"2023-02-01T15:00:30.183235Z","shell.execute_reply":"2023-02-01T15:00:30.212605Z"},"trusted":true},"execution_count":359,"outputs":[{"execution_count":359,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 824.000000 824.000000 824.000000 824.000000 824.000000 824.000000\nmean 2.321602 1.372573 -0.030604 0.796855 0.950243 2.455097\nstd 0.829129 0.483783 1.018913 2.217409 1.652334 0.790541\nmin 1.000000 1.000000 -2.275385 -0.626005 0.000000 1.000000\n25% 2.000000 1.000000 -0.615385 -0.284041 0.000000 2.000000\n50% 3.000000 1.000000 0.000000 0.001984 0.000000 2.000000\n75% 3.000000 2.000000 0.384615 0.719569 1.000000 3.000000\nmax 3.000000 2.000000 3.846154 21.562738 10.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count824.000000824.000000824.000000824.000000824.000000824.000000
mean2.3216021.372573-0.0306040.7968550.9502432.455097
std0.8291290.4837831.0189132.2174091.6523340.790541
min1.0000001.000000-2.275385-0.6260050.0000001.000000
25%2.0000001.000000-0.615385-0.2840410.0000002.000000
50%3.0000001.0000000.0000000.0019840.0000002.000000
75%3.0000002.0000000.3846150.7195691.0000003.000000
max3.0000002.0000003.84615421.56273810.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"incorrect = results_train.loc[results_train[\"y_found\"] == False,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == False,:].groupby(\"Sex\").count()[\"PassengerId\"]/incorrect","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.214494Z","iopub.execute_input":"2023-02-01T15:00:30.215455Z","iopub.status.idle":"2023-02-01T15:00:30.229684Z","shell.execute_reply.started":"2023-02-01T15:00:30.215404Z","shell.execute_reply":"2023-02-01T15:00:30.228567Z"},"trusted":true},"execution_count":360,"outputs":[{"execution_count":360,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.895522\n2.0 0.104478\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"correct = results_train.loc[results_train[\"y_found\"] == True,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == True,:].groupby(\"Sex\").count()[\"PassengerId\"]/correct","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.230783Z","iopub.execute_input":"2023-02-01T15:00:30.231538Z","iopub.status.idle":"2023-02-01T15:00:30.246006Z","shell.execute_reply.started":"2023-02-01T15:00:30.231506Z","shell.execute_reply":"2023-02-01T15:00:30.244736Z"},"trusted":true},"execution_count":361,"outputs":[{"execution_count":361,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.627427\n2.0 0.372573\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We analyse differences between each method on the testing and training data set. We add all the predictions to identify the passenger the classifier could not agree with. So, a total of 0 or 5 suggests all the classifiers have either identify passengers as survivor or not. Values in the range [1,4] indicates some disagreements in classification. Some methodologies appears to correclty classify passengers with at least one method.","metadata":{}},{"cell_type":"code","source":"results_train[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.247816Z","iopub.execute_input":"2023-02-01T15:00:30.248276Z","iopub.status.idle":"2023-02-01T15:00:30.473510Z","shell.execute_reply.started":"2023-02-01T15:00:30.248230Z","shell.execute_reply":"2023-02-01T15:00:30.472297Z"},"trusted":true},"execution_count":362,"outputs":[{"execution_count":362,"output_type":"execute_result","data":{"text/plain":"count 67.000000\nmean 0.447761\nstd 1.438471\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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3bXhOeAm5L843hHGr2qOtV9XATuY/my8FC0EHBv2d/kun/Quws4WVWfGvc8GyHJa5Ns7x6/guV/pP/hWIcasar6WFXtrKpdLP85/lpV/cGYxxqpJNu6f5gnyTbgncDQ3l12yQe8qp4Hzt+yfxK4Z8S37I9dki8A3wTekOSZJPvGPdOI3Qh8kOUzske7X+8a91AjtgN4KMn3WT5JebCqJuJtdRNmGvhGku8B3wK+XFVfHdaLX/JvI5Qkre6SPwOXJK3OgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXqfwEOtkCGTWOUBQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.475100Z","iopub.execute_input":"2023-02-01T15:00:30.475447Z","iopub.status.idle":"2023-02-01T15:00:30.691199Z","shell.execute_reply.started":"2023-02-01T15:00:30.475417Z","shell.execute_reply":"2023-02-01T15:00:30.690153Z"},"trusted":true},"execution_count":363,"outputs":[{"execution_count":363,"output_type":"execute_result","data":{"text/plain":"count 824.000000\nmean 1.577670\nstd 2.058981\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We explore how the techniques may predict differently and but accurately surviving the accident. \n\nKNN misclassified the most passengers who perished. Logistic regression and Random Tree classifier has the higest accuracy; both of them could be influencing the most the prediction, when only one classifier suggests a passenger has survived. ","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 1)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.692627Z","iopub.execute_input":"2023-02-01T15:00:30.692946Z","iopub.status.idle":"2023-02-01T15:00:30.712415Z","shell.execute_reply.started":"2023-02-01T15:00:30.692916Z","shell.execute_reply":"2023-02-01T15:00:30.711228Z"},"trusted":true},"execution_count":364,"outputs":[{"execution_count":364,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 3\n 1.0 0.0 0.0 3\n 1.0 0.0 0.0 0.0 10\n 1.0 0.0 0.0 0.0 0.0 3\n1.0 0.0 0.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 5\n 1.0 0.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 0.0 4\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 4)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.714064Z","iopub.execute_input":"2023-02-01T15:00:30.714806Z","iopub.status.idle":"2023-02-01T15:00:30.734553Z","shell.execute_reply.started":"2023-02-01T15:00:30.714762Z","shell.execute_reply":"2023-02-01T15:00:30.733458Z"},"trusted":true},"execution_count":365,"outputs":[{"execution_count":365,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 1.0 1.0 1.0 2\n 1.0 0.0 1.0 1.0 1.0 1\n1.0 0.0 1.0 1.0 1.0 1.0 6\n 1.0 0.0 1.0 1.0 1.0 1\n 1.0 0.0 1.0 1.0 2\n 1.0 0.0 1.0 2\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"A combination of Logistic regression and ANN may identify some survivors, when other methods do not. KNN in combination with another classifier may misclassify passengers who perished.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 2)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.743560Z","iopub.execute_input":"2023-02-01T15:00:30.743975Z","iopub.status.idle":"2023-02-01T15:00:30.762208Z","shell.execute_reply.started":"2023-02-01T15:00:30.743943Z","shell.execute_reply":"2023-02-01T15:00:30.761101Z"},"trusted":true},"execution_count":366,"outputs":[{"execution_count":366,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 1.0 1.0 0.0 4\n 1.0 0.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 0.0 2\n 1.0 1.0 0.0 0.0 0.0 5\n1.0 0.0 0.0 0.0 1.0 1.0 1\n 1.0 1.0 0.0 2\n 1.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 3)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.763835Z","iopub.execute_input":"2023-02-01T15:00:30.764145Z","iopub.status.idle":"2023-02-01T15:00:30.780211Z","shell.execute_reply.started":"2023-02-01T15:00:30.764116Z","shell.execute_reply":"2023-02-01T15:00:30.779475Z"},"trusted":true},"execution_count":367,"outputs":[{"execution_count":367,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 0.0 1.0 1.0 1\n 1.0 0.0 0.0 1.0 1.0 1\n 1.0 0.0 1.0 0.0 1\n1.0 0.0 1.0 1.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 1.0 0.0 1.0 1\n 1.0 0.0 0.0 1.0 2\n 1.0 0.0 3\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 5)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.781542Z","iopub.execute_input":"2023-02-01T15:00:30.781830Z","iopub.status.idle":"2023-02-01T15:00:30.798259Z","shell.execute_reply.started":"2023-02-01T15:00:30.781802Z","shell.execute_reply":"2023-02-01T15:00:30.796868Z"},"trusted":true},"execution_count":368,"outputs":[{"execution_count":368,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n1.0 1.0 1.0 1.0 1.0 1.0 65\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.799833Z","iopub.execute_input":"2023-02-01T15:00:30.800231Z","iopub.status.idle":"2023-02-01T15:00:30.808910Z","shell.execute_reply.started":"2023-02-01T15:00:30.800191Z","shell.execute_reply":"2023-02-01T15:00:30.808105Z"},"trusted":true},"execution_count":369,"outputs":[{"execution_count":369,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.809985Z","iopub.execute_input":"2023-02-01T15:00:30.810331Z","iopub.status.idle":"2023-02-01T15:00:30.821387Z","shell.execute_reply.started":"2023-02-01T15:00:30.810281Z","shell.execute_reply":"2023-02-01T15:00:30.820530Z"},"trusted":true},"execution_count":370,"outputs":[{"execution_count":370,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.822809Z","iopub.execute_input":"2023-02-01T15:00:30.823089Z","iopub.status.idle":"2023-02-01T15:00:30.834693Z","shell.execute_reply.started":"2023-02-01T15:00:30.823062Z","shell.execute_reply":"2023-02-01T15:00:30.833613Z"},"trusted":true},"execution_count":371,"outputs":[{"execution_count":371,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.sum_pred.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:01:14.796405Z","iopub.execute_input":"2023-02-01T15:01:14.796794Z","iopub.status.idle":"2023-02-01T15:01:14.805737Z","shell.execute_reply.started":"2023-02-01T15:01:14.796762Z","shell.execute_reply":"2023-02-01T15:01:14.804627Z"},"trusted":true},"execution_count":377,"outputs":[{"execution_count":377,"output_type":"execute_result","data":{"text/plain":"0.0 0.576880\n5.0 0.205387\n1.0 0.079686\n2.0 0.062851\n3.0 0.040404\n4.0 0.034792\nName: sum_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"}},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:36:25.295643Z","iopub.execute_input":"2023-02-01T15:36:25.296169Z","iopub.status.idle":"2023-02-01T15:36:25.314079Z","shell.execute_reply.started":"2023-02-01T15:36:25.296132Z","shell.execute_reply":"2023-02-01T15:36:25.312932Z"}},"execution_count":412,"outputs":[{"execution_count":412,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Are they any particular features that may have been picked up by each classifier?","metadata":{}},{"cell_type":"markdown","source":"### All classifiers agrees with the survival predictions\n\nWe found out that approximately 70% of the passengers who perished have been correclty classified by all the classifiers in agreement. But only, 20% of survivors have been correctly classified. Approximately 70% of the observations made in the training datasets have been correct and all the classifiers agree.","metadata":{}},{"cell_type":"code","source":"filter_rows = ((results_train[\"sum_pred\"] == 0.0) & (results_train[\"y\"] == 0))\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:06.719133Z","iopub.execute_input":"2023-02-01T15:45:06.719636Z","iopub.status.idle":"2023-02-01T15:45:06.733253Z","shell.execute_reply.started":"2023-02-01T15:45:06.719598Z","shell.execute_reply":"2023-02-01T15:45:06.732170Z"},"trusted":true},"execution_count":413,"outputs":[{"execution_count":413,"output_type":"execute_result","data":{"text/plain":"50.841750841750844"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"sum_pred\"] == 5.0) & (results_train[\"y\"] == 1)\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:07.933943Z","iopub.execute_input":"2023-02-01T15:45:07.935099Z","iopub.status.idle":"2023-02-01T15:45:07.947554Z","shell.execute_reply.started":"2023-02-01T15:45:07.935043Z","shell.execute_reply":"2023-02-01T15:45:07.946375Z"},"trusted":true},"execution_count":414,"outputs":[{"execution_count":414,"output_type":"execute_result","data":{"text/plain":"19.865319865319865"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"},"trusted":true},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:45.372534Z","iopub.execute_input":"2023-02-01T15:45:45.372921Z","iopub.status.idle":"2023-02-01T15:45:45.388445Z","shell.execute_reply.started":"2023-02-01T15:45:45.372891Z","shell.execute_reply":"2023-02-01T15:45:45.387062Z"},"trusted":true},"execution_count":415,"outputs":[{"execution_count":415,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## The classifiers disagree with each others on the survival predictions ?\n\nDecision Tree classifiers appears to have classified correctly the most passengers, when disagreements between classifiers exists. \n\nWe calculate the proportion of correct predictions, when some classifiers disagree. We found out that Decision tree appears to predict the most correct passengers who survive or perish the accident.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nno_correct = results_train.loc[filter_rows, :].shape[0]\nno_incorrect = results_train.loc[filter_rows, :].shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:30.920933Z","iopub.execute_input":"2023-02-01T16:00:30.921353Z","iopub.status.idle":"2023-02-01T16:00:30.932343Z","shell.execute_reply.started":"2023-02-01T16:00:30.921303Z","shell.execute_reply":"2023-02-01T16:00:30.930975Z"},"trusted":true},"execution_count":433,"outputs":[]},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.lr_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.369868Z","iopub.execute_input":"2023-02-01T16:00:32.370576Z","iopub.status.idle":"2023-02-01T16:00:32.381927Z","shell.execute_reply.started":"2023-02-01T16:00:32.370537Z","shell.execute_reply":"2023-02-01T16:00:32.381022Z"},"trusted":true},"execution_count":434,"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"44.329896907216494"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.knn_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.853276Z","iopub.execute_input":"2023-02-01T16:00:32.854476Z","iopub.status.idle":"2023-02-01T16:00:32.868855Z","shell.execute_reply.started":"2023-02-01T16:00:32.854418Z","shell.execute_reply":"2023-02-01T16:00:32.867407Z"},"trusted":true},"execution_count":435,"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"47.42268041237113"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.ann_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:33.395939Z","iopub.execute_input":"2023-02-01T16:00:33.396354Z","iopub.status.idle":"2023-02-01T16:00:33.410583Z","shell.execute_reply.started":"2023-02-01T16:00:33.396294Z","shell.execute_reply":"2023-02-01T16:00:33.409408Z"},"trusted":true},"execution_count":436,"outputs":[{"execution_count":436,"output_type":"execute_result","data":{"text/plain":"52.0618556701031"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.clf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:34.195555Z","iopub.execute_input":"2023-02-01T16:00:34.196776Z","iopub.status.idle":"2023-02-01T16:00:34.208545Z","shell.execute_reply.started":"2023-02-01T16:00:34.196733Z","shell.execute_reply":"2023-02-01T16:00:34.207295Z"},"trusted":true},"execution_count":437,"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"75.25773195876289"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.rf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:35.044699Z","iopub.execute_input":"2023-02-01T16:00:35.045127Z","iopub.status.idle":"2023-02-01T16:00:35.057811Z","shell.execute_reply.started":"2023-02-01T16:00:35.045090Z","shell.execute_reply":"2023-02-01T16:00:35.056488Z"},"trusted":true},"execution_count":438,"outputs":[{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"25.257731958762886"},"metadata":{}}]},{"cell_type":"markdown","source":"We change the predictions, that has been mispredicted by at least one classifier.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_train.loc[filter_rows, \"y\"] = results_train.clf_y_pred\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:38:03.184402Z","iopub.execute_input":"2023-02-01T16:38:03.184812Z","iopub.status.idle":"2023-02-01T16:38:03.191812Z","shell.execute_reply.started":"2023-02-01T16:38:03.184781Z","shell.execute_reply":"2023-02-01T16:38:03.191010Z"},"trusted":true},"execution_count":462,"outputs":[]},{"cell_type":"markdown","source":"The accuracy has been increased by a considerable level of accuracy. ","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train.Survived == results_train.y,:].count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:40:10.552687Z","iopub.execute_input":"2023-02-01T16:40:10.553066Z","iopub.status.idle":"2023-02-01T16:40:10.564469Z","shell.execute_reply.started":"2023-02-01T16:40:10.553036Z","shell.execute_reply":"2023-02-01T16:40:10.563190Z"},"trusted":true},"execution_count":467,"outputs":[{"execution_count":467,"output_type":"execute_result","data":{"text/plain":"0.9461279461279462"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying to results test\n\nThe distribution appears the be very similar as the training dataset.","metadata":{}},{"cell_type":"markdown","source":"__Testing dataset:__","metadata":{}},{"cell_type":"code","source":"results_test[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_test.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_test.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:04.962156Z","iopub.execute_input":"2023-02-01T16:28:04.962921Z","iopub.status.idle":"2023-02-01T16:28:05.177598Z","shell.execute_reply.started":"2023-02-01T16:28:04.962882Z","shell.execute_reply":"2023-02-01T16:28:05.176388Z"},"trusted":true},"execution_count":459,"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 1.590909\nstd 2.078233\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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dataset:__","metadata":{}},{"cell_type":"code","source":"results_train.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:10.931421Z","iopub.execute_input":"2023-02-01T16:28:10.931875Z","iopub.status.idle":"2023-02-01T16:28:11.153259Z","shell.execute_reply.started":"2023-02-01T16:28:10.931840Z","shell.execute_reply":"2023-02-01T16:28:11.152336Z"},"trusted":true},"execution_count":460,"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.492705\nstd 2.040242\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 3.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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z5aVb8YdT3DVlW/q6oLmb3D++Ik3U7DJXkncLyqDo26lhX21qq6iNkn6F7Tpl0HZjWHu485GBNt3vlrwK1V9fVR17OSquoZ4F5g24hLGaZLgXe1Oeh9wGVJ/nG0JQ1fVR1r78eB25mdah6Y1RzuPuZgDLR/XLwZeLSqPjPqelZCknOSbGjLZzJ70cAPR1rUEFXVJ6tqU1VtYfZ3fE9V/cWIyxqqJOvaBQIkWQe8HRjoVXCrNtyr6lngucccPArcNuTHHIxcki8D/wa8LsnRJDtGXdMKuBT4ALNncw+015WjLmrINgL3JnmQ2ZOYu6tqLC4PHCMTwH1JfgB8B7irqr45yA9YtZdCSpJOb9WeuUuSTs9wl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36PzFqarrIVm2TAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_test.loc[filter_rows, \"y\"] = results_test.clf_y_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:41:15.101164Z","iopub.execute_input":"2023-02-01T16:41:15.101563Z","iopub.status.idle":"2023-02-01T16:41:15.110450Z","shell.execute_reply.started":"2023-02-01T16:41:15.101523Z","shell.execute_reply":"2023-02-01T16:41:15.109235Z"},"trusted":true},"execution_count":468,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:46:41.923470Z","iopub.execute_input":"2023-02-01T16:46:41.923885Z","iopub.status.idle":"2023-02-01T16:46:43.051535Z","shell.execute_reply.started":"2023-02-01T16:46:41.923846Z","shell.execute_reply":"2023-02-01T16:46:43.050096Z"},"trusted":true},"execution_count":471,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:48:10.301809Z","iopub.execute_input":"2023-02-01T16:48:10.302423Z","iopub.status.idle":"2023-02-01T16:48:11.417688Z","shell.execute_reply.started":"2023-02-01T16:48:10.302370Z","shell.execute_reply":"2023-02-01T16:48:11.415704Z"},"trusted":true},"execution_count":472,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({\n \"PassengerId\": results_test[\"PassengerId\"].astype(int),\n \"Survived\": results_test[\"y\"]\n })\n\nsubmission = submission.astype({col: 'int32' for col in submission.select_dtypes('int64').columns})\nsubmission.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:09:39.297418Z","iopub.execute_input":"2023-02-01T17:09:39.297834Z","iopub.status.idle":"2023-02-01T17:09:39.311761Z","shell.execute_reply.started":"2023-02-01T17:09:39.297801Z","shell.execute_reply":"2023-02-01T17:09:39.310602Z"},"trusted":true},"execution_count":490,"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"PassengerId int32\nSurvived float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)\n!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:06:56.872660Z","iopub.execute_input":"2023-02-01T17:06:56.873348Z","iopub.status.idle":"2023-02-01T17:06:57.989149Z","shell.execute_reply.started":"2023-02-01T17:06:56.873282Z","shell.execute_reply":"2023-02-01T17:06:57.987753Z"},"trusted":true},"execution_count":488,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Application of ML Algorithms/Logistic regression/Logistic Regression: Understanding Its Significance.pdf b/Application of ML Algorithms/Logistic regression/Logistic Regression: Understanding Its Significance.pdf new file mode 100644 index 0000000..146a5b5 Binary files /dev/null and b/Application of ML Algorithms/Logistic regression/Logistic Regression: Understanding Its Significance.pdf differ diff --git a/Application of ML Algorithms/data-preparation-for-ml-techniques.ipynb b/Application of ML Algorithms/data-preparation-for-ml-techniques.ipynb new file mode 100644 index 0000000..e4d57be --- /dev/null +++ b/Application of ML Algorithms/data-preparation-for-ml-techniques.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-01T14:12:24.884916Z","iopub.execute_input":"2023-02-01T14:12:24.885428Z","iopub.status.idle":"2023-02-01T14:12:24.912232Z","shell.execute_reply.started":"2023-02-01T14:12:24.885325Z","shell.execute_reply":"2023-02-01T14:12:24.911114Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/titanic/train.csv\n/kaggle/input/titanic/test.csv\n/kaggle/input/titanic/gender_submission.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Introduction\n\nData can be transformed into knowledge and then enhanced intelligence. We use the titanic datasets to explore first its features. The Titanic datasets contains the records of the Titanic passengers during its maiden voyage and tragic demise. \n\nWe apply some data engineering techniques to prepare the data for some various machine learning techniques - including _registic regression, decision trees, random forrests, KNN and artificial neural network_ - for the purpose of predicting survivors. \n\nWe also analyse and compare the predictions from all the classifier methods to explore further how the data and our data preparation may have affected the model fitting as well as the prediction on unseen data.\n\n\nThe notebook is structured in this manner:\n\n\n- __[Upload libraires](#Libraries)__\n- __[Data engineering](#Data-engineering)__\n- __[Survival characteristics](#Survival-characteristics)__\n- __[Data preparation for classification](#Data-preparation-for-classification)__ \n- __[Method: Logistic regression](#Method-:-Logistic-regression)__\n- __[Method: K-Nearest neighorn](#Method:-K-Nearest-neighbourn)__\n- __[Method: Decision Trees](#Method-:-Decision-Trees)__ \n- __[Method: Random Forrest](#Method:-Random-Forrest)__\n- __[Method: Neural AI](#Method:-Neural-AI)__ \n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Libraries\n\nWe upload all the libraries required for all the operations of this notebook.","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport seaborn as sns\nimport os\nimport random as rand\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\nfrom sklearn.model_selection import train_test_split # Import train_test_split function\nfrom sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nimport scipy.stats as stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\n\nimport tensorflow as tf\nif (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed\n print('Please install tensorflow 2.0 to run this notebook')\nprint('Tensorflow version: ',tf.__version__)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:30:21.088212Z","iopub.execute_input":"2023-02-01T14:30:21.088884Z","iopub.status.idle":"2023-02-01T14:30:21.739453Z","shell.execute_reply.started":"2023-02-01T14:30:21.088844Z","shell.execute_reply":"2023-02-01T14:30:21.738425Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Tensorflow version: 2.6.4\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data engineering\n\nWe explore the files in the folder, sets the paths and file names. These variables will be used in each section.","metadata":{}},{"cell_type":"code","source":"!ls ../input/titanic/\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:36.483134Z","iopub.execute_input":"2023-02-01T14:12:36.484288Z","iopub.status.idle":"2023-02-01T14:12:37.583058Z","shell.execute_reply.started":"2023-02-01T14:12:36.484239Z","shell.execute_reply":"2023-02-01T14:12:37.581624Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"gender_submission.csv test.csv train.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data_path = '../input/titanic/train.csv'\ntest_data_path = '../input/titanic/test.csv'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.584978Z","iopub.execute_input":"2023-02-01T14:12:37.586181Z","iopub.status.idle":"2023-02-01T14:12:37.591422Z","shell.execute_reply.started":"2023-02-01T14:12:37.586143Z","shell.execute_reply":"2023-02-01T14:12:37.590256Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Import and explore the data \nExplore and import the training and test dataset provided by the competition.","metadata":{}},{"cell_type":"markdown","source":"### Training dataset","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.825672Z","iopub.execute_input":"2023-02-01T14:12:37.827141Z","iopub.status.idle":"2023-02-01T14:12:37.862872Z","shell.execute_reply.started":"2023-02-01T14:12:37.827090Z","shell.execute_reply":"2023-02-01T14:12:37.861760Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.735534Z","iopub.execute_input":"2023-02-01T14:12:40.736625Z","iopub.status.idle":"2023-02-01T14:12:40.784900Z","shell.execute_reply.started":"2023-02-01T14:12:40.736575Z","shell.execute_reply":"2023-02-01T14:12:40.783854Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 38.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.786409Z","iopub.execute_input":"2023-02-01T14:12:40.786701Z","iopub.status.idle":"2023-02-01T14:12:40.803610Z","shell.execute_reply.started":"2023-02-01T14:12:40.786675Z","shell.execute_reply":"2023-02-01T14:12:40.802382Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Test dataset","metadata":{}},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:45.642106Z","iopub.execute_input":"2023-02-01T14:12:45.642583Z","iopub.status.idle":"2023-02-01T14:12:45.662691Z","shell.execute_reply.started":"2023-02-01T14:12:45.642542Z","shell.execute_reply":"2023-02-01T14:12:45.661472Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.221543Z","iopub.execute_input":"2023-02-01T14:12:46.222107Z","iopub.status.idle":"2023-02-01T14:12:46.261833Z","shell.execute_reply.started":"2023-02-01T14:12:46.222059Z","shell.execute_reply":"2023-02-01T14:12:46.260714Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Age SibSp Parch Fare\ncount 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\nmean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\nstd 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\nmin 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\nmax 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.919995Z","iopub.execute_input":"2023-02-01T14:12:46.920463Z","iopub.status.idle":"2023-02-01T14:12:46.940798Z","shell.execute_reply.started":"2023-02-01T14:12:46.920405Z","shell.execute_reply":"2023-02-01T14:12:46.939404Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":" ## Meta data \n \n| Column name | Description|\n|---|---|\n|Passenger_id| unique row indentifier |\n|PClass | Categorical data (1 = 1st; 2 = 2nd; 3 = 3rd)|\n| Survival | Categoricial data (0 = No; 1 = Yes) |\n| Name | Characters - Name of passenger |\n| Sex | Categorical data male or female |\n| Age | integer values representing age |\n| SigSp | integer Number of Siblings/Spouses Aboard |\n| Parch | Number of Parents/Children Aboard |\n| Ticket | Ticket number |\n| Fare | Fare in GBP at time of travel|\n| Cabin | Cabin |\n| Embark | Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)|\n\n\nSource - http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf (7/12/2022)","metadata":{}},{"cell_type":"markdown","source":"# Survival characteristics\nWe explore the survival characteristics using several combinations of columns. We hope to understand better some features that may guide the predictions of survivors.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Passenger and survival\nThe training dataset suggests a minority of passengers survived (i.e., 38% approximately), 62% of passengers perished. Some further decomposition suggests first class passengers may have been more likely to survive than lower classes. The percentages of surviving decreases sharply.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Survived\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:50.918803Z","iopub.execute_input":"2023-02-01T14:12:50.919222Z","iopub.status.idle":"2023-02-01T14:12:50.940405Z","shell.execute_reply.started":"2023-02-01T14:12:50.919186Z","shell.execute_reply":"2023-02-01T14:12:50.939284Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Survived\n0 0.616162\n1 0.383838\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp = temp.unstack()\ntemp","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:18:15.229152Z","iopub.execute_input":"2023-02-01T14:18:15.229539Z","iopub.status.idle":"2023-02-01T14:18:15.255184Z","shell.execute_reply.started":"2023-02-01T14:18:15.229507Z","shell.execute_reply":"2023-02-01T14:18:15.254098Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass \n1 0.370370 0.629630\n2 0.527174 0.472826\n3 0.757637 0.242363","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Pclass
10.3703700.629630
20.5271740.472826
30.7576370.242363
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Null hypothesis: Pclass means are equal (no variation in means of groups)\nH0:$μ_0=μ-1$\n\nAlternative hypothesis: At least, one group mean is different from other groups\nH1: All μ are not equal\n\n$p_{value} = 0.01$","metadata":{}},{"cell_type":"code","source":"\nsur_pclass = titanic_train.loc[titanic_train.Survived == 1, \"Pclass\"]\nperish_pclass = titanic_train.loc[titanic_train.Survived == 0, \"Pclass\"]\nfvalue, pvalue = stats.f_oneway(sur_pclass, perish_pclass)\nprint(fvalue, pvalue)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:29:37.721129Z","iopub.execute_input":"2023-02-01T14:29:37.721602Z","iopub.status.idle":"2023-02-01T14:29:37.732491Z","shell.execute_reply.started":"2023-02-01T14:29:37.721566Z","shell.execute_reply":"2023-02-01T14:29:37.731155Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"115.03127218827665 2.5370473879805644e-25\n","output_type":"stream"}]},{"cell_type":"code","source":"model = ols('Survived ~ Pclass', data=titanic_train).fit()\nanova_table = sm.stats.anova_lm(model, typ=2)\nanova_table","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:32:55.703937Z","iopub.execute_input":"2023-02-01T14:32:55.704329Z","iopub.status.idle":"2023-02-01T14:32:55.739204Z","shell.execute_reply.started":"2023-02-01T14:32:55.704285Z","shell.execute_reply":"2023-02-01T14:32:55.738138Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" sum_sq df F PR(>F)\nPclass 24.142900 1.0 115.031272 2.537047e-25\nResidual 186.584373 889.0 NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sum_sqdfFPR(>F)
Pclass24.1429001.0115.0312722.537047e-25
Residual186.584373889.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"__Interpretation__\n\nThe p value obtained from ANOVA analysis is significant (p < 0.01), and therefore, we conclude that there are significant differences among the classes who have perished or survived.","metadata":{}},{"cell_type":"markdown","source":"## Embarkment and survival\nThe port of embarkment appears to have less influence on the survival percentages. It appears most passengers embarked at Southampton (72% approximately), 18% of passengers at Cherbourg, and the remaining from Queenstown. Half of the Cherbourg passengers booked first class tickets. Other embarkment ports appears to be much lower. Half of the passengers from Southampton booked third class tickets. We could surmise the latter may have contributed to the lowest percentages of surviving the accident.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Embarked\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.612759Z","iopub.execute_input":"2023-02-01T14:50:09.613134Z","iopub.status.idle":"2023-02-01T14:50:09.626172Z","shell.execute_reply.started":"2023-02-01T14:50:09.613106Z","shell.execute_reply":"2023-02-01T14:50:09.625109Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Embarked\nC 0.188552\nQ 0.086420\nS 0.722783\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.631955Z","iopub.execute_input":"2023-02-01T14:50:09.632520Z","iopub.status.idle":"2023-02-01T14:50:09.652387Z","shell.execute_reply.started":"2023-02-01T14:50:09.632486Z","shell.execute_reply":"2023-02-01T14:50:09.651379Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Pclass 1 2 3\nEmbarked \nC 0.505952 0.101190 0.392857\nQ 0.025974 0.038961 0.935065\nS 0.197205 0.254658 0.548137","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Pclass123
Embarked
C0.5059520.1011900.392857
Q0.0259740.0389610.935065
S0.1972050.2546580.548137
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:10.164258Z","iopub.execute_input":"2023-02-01T14:50:10.164676Z","iopub.status.idle":"2023-02-01T14:50:10.185023Z","shell.execute_reply.started":"2023-02-01T14:50:10.164643Z","shell.execute_reply":"2023-02-01T14:50:10.183924Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked \nC 0.446429 0.553571\nQ 0.610390 0.389610\nS 0.663043 0.336957","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Embarked
C0.4464290.553571
Q0.6103900.389610
S0.6630430.336957
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:16.126671Z","iopub.execute_input":"2023-02-01T14:50:16.127079Z","iopub.status.idle":"2023-02-01T14:50:16.150013Z","shell.execute_reply.started":"2023-02-01T14:50:16.127043Z","shell.execute_reply":"2023-02-01T14:50:16.149263Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked Pclass \nC 1 0.154762 0.351190\n 2 0.047619 0.053571\n 3 0.244048 0.148810\nQ 1 0.012987 0.012987\n 2 0.012987 0.025974\n 3 0.584416 0.350649\nS 1 0.082298 0.114907\n 2 0.136646 0.118012\n 3 0.444099 0.104037","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
EmbarkedPclass
C10.1547620.351190
20.0476190.053571
30.2440480.148810
Q10.0129870.012987
20.0129870.025974
30.5844160.350649
S10.0822980.114907
20.1366460.118012
30.4440990.104037
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Gender and survival \nThe training dataset suggests that nearly two thirds of passengers were male, and a third were female. Women and girls appears to have a higher survival percentagers - three quarters of female passengers survived the accident, but only 19% of male survived.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Sex\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:21.461493Z","iopub.execute_input":"2023-02-01T14:50:21.461874Z","iopub.status.idle":"2023-02-01T14:50:21.474706Z","shell.execute_reply.started":"2023-02-01T14:50:21.461843Z","shell.execute_reply":"2023-02-01T14:50:21.473520Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Sex\nfemale 0.352413\nmale 0.647587\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:22.544412Z","iopub.execute_input":"2023-02-01T14:50:22.544835Z","iopub.status.idle":"2023-02-01T14:50:22.565483Z","shell.execute_reply.started":"2023-02-01T14:50:22.544801Z","shell.execute_reply":"2023-02-01T14:50:22.564390Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex \nfemale 0.257962 0.742038\nmale 0.811092 0.188908","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Sex
female0.2579620.742038
male0.8110920.188908
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:23.099261Z","iopub.execute_input":"2023-02-01T14:50:23.099666Z","iopub.status.idle":"2023-02-01T14:50:23.126110Z","shell.execute_reply.started":"2023-02-01T14:50:23.099635Z","shell.execute_reply":"2023-02-01T14:50:23.125241Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex Pclass \nfemale 1 0.013889 0.421296\n 2 0.032609 0.380435\n 3 0.146640 0.146640\nmale 1 0.356481 0.208333\n 2 0.494565 0.092391\n 3 0.610998 0.095723","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SexPclass
female10.0138890.421296
20.0326090.380435
30.1466400.146640
male10.3564810.208333
20.4945650.092391
30.6109980.095723
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age, siblings and parents\n\nThe age distribution appears to be multi-modal with some two peaks at around 0 and 25. Both training and testing datasets have a similar mean and standard deviation. However, some skewness may affect a normal distributions and any normalisation processes of the data.\n\nThe survivors and other passengers age appears to be of similar age at the point of centrality. We will need to complete some statistical tests to accept or reject the null hypothesis that the age distribution of survivors and non-survivors are the same. We surmise the values may have be unknown, without any data preparation the tests cannot be completed.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.246337Z","iopub.execute_input":"2023-02-01T14:50:24.247338Z","iopub.status.idle":"2023-02-01T14:50:24.633738Z","shell.execute_reply.started":"2023-02-01T14:50:24.247275Z","shell.execute_reply":"2023-02-01T14:50:24.632647Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"count 714.000000\nmean 29.699118\nstd 14.526497\nmin 0.420000\n25% 20.125000\n50% 28.000000\n75% 38.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.694278Z","iopub.execute_input":"2023-02-01T14:50:24.695165Z","iopub.status.idle":"2023-02-01T14:50:25.062419Z","shell.execute_reply.started":"2023-02-01T14:50:24.695120Z","shell.execute_reply":"2023-02-01T14:50:25.061338Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"count 332.000000\nmean 30.272590\nstd 14.181209\nmin 0.170000\n25% 21.000000\n50% 27.000000\n75% 39.000000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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y=\"Age\", data=titanic_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:25.141606Z","iopub.execute_input":"2023-02-01T14:50:25.142000Z","iopub.status.idle":"2023-02-01T14:50:25.355591Z","shell.execute_reply.started":"2023-02-01T14:50:25.141964Z","shell.execute_reply":"2023-02-01T14:50:25.354536Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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majority of passengers may be travelling on their own without a spouse, sibling, children of parents on board. However, passengers with 1 or 2 siblings/spouse appears to have survived; the percentages is in the range of 46% to 54%. Parents or individuals with one, two or three parents were less likely to perished - the percentages ranges between 50% and 60%.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"SibSp\"]).count()[\"PassengerId\"]/titanic_train.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.432856Z","iopub.execute_input":"2023-02-01T14:50:29.433512Z","iopub.status.idle":"2023-02-01T14:50:29.445537Z","shell.execute_reply.started":"2023-02-01T14:50:29.433478Z","shell.execute_reply":"2023-02-01T14:50:29.444361Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"SibSp\n0 0.682379\n1 0.234568\n2 0.031425\n3 0.017957\n4 0.020202\n5 0.005612\n8 0.007856\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"SibSp\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.890502Z","iopub.execute_input":"2023-02-01T14:50:29.890860Z","iopub.status.idle":"2023-02-01T14:50:29.915654Z","shell.execute_reply.started":"2023-02-01T14:50:29.890829Z","shell.execute_reply":"2023-02-01T14:50:29.914470Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSibSp \n0 0.654605 0.345395\n1 0.464115 0.535885\n2 0.535714 0.464286\n3 0.750000 0.250000\n4 0.833333 0.166667\n5 1.000000 NaN\n8 1.000000 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SibSp
00.6546050.345395
10.4641150.535885
20.5357140.464286
30.7500000.250000
40.8333330.166667
51.000000NaN
81.000000NaN
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.SibSp, bins = 8)\ntitanic_train.SibSp.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:30.548135Z","iopub.execute_input":"2023-02-01T14:50:30.548522Z","iopub.status.idle":"2023-02-01T14:50:30.775129Z","shell.execute_reply.started":"2023-02-01T14:50:30.548488Z","shell.execute_reply":"2023-02-01T14:50:30.774363Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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0.760943\n1 0.132435\n2 0.089787\n3 0.005612\n4 0.004489\n5 0.005612\n6 0.001122\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Parch\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.187336Z","iopub.execute_input":"2023-02-01T14:50:31.187728Z","iopub.status.idle":"2023-02-01T14:50:31.209460Z","shell.execute_reply.started":"2023-02-01T14:50:31.187695Z","shell.execute_reply":"2023-02-01T14:50:31.208365Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nParch \n0 0.656342 0.343658\n1 0.449153 0.550847\n2 0.500000 0.500000\n3 0.400000 0.600000\n4 1.000000 NaN\n5 0.800000 0.200000\n6 1.000000 NaN","text/html":"
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Survived01
Parch
00.6563420.343658
10.4491530.550847
20.5000000.500000
30.4000000.600000
41.000000NaN
50.8000000.200000
61.000000NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Parch, bins = 6)\ntitanic_train.Parch.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.433509Z","iopub.execute_input":"2023-02-01T14:50:31.434117Z","iopub.status.idle":"2023-02-01T14:50:31.664941Z","shell.execute_reply.started":"2023-02-01T14:50:31.434071Z","shell.execute_reply":"2023-02-01T14:50:31.664079Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.381594\nstd 0.806057\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 6.000000\nName: Parch, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We decided to add both fields _Parch_ and _SibSp_ together as a familly. The mean and median age appears to be quite close between the passengers who have survived and perished. For smaller families the spread appears to be smaller than for larger families. \n\nThe highest percentages of surviving the accident suggests that passengers in first and second class with no other familly members. These percentages are loweer than 30%.","metadata":{}},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train.SibSp + titanic_train.Parch\ntemp = titanic_train.groupby([\"fam_members\",\"Survived\"]).agg([np.median, np.mean, np.std])[\"Age\"]\ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.929453Z","iopub.execute_input":"2023-02-01T14:50:31.929858Z","iopub.status.idle":"2023-02-01T14:50:31.977029Z","shell.execute_reply.started":"2023-02-01T14:50:31.929823Z","shell.execute_reply":"2023-02-01T14:50:31.975764Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" median mean std \nSurvived 0 1 0 1 0 1\nfam_members \n0 29.0 30.0 32.414234 31.811538 13.334968 11.970452\n1 30.0 29.0 32.126984 30.781842 11.599836 14.916443\n2 30.5 22.0 31.500000 21.911887 13.776141 17.363697\n3 25.0 14.0 22.833333 16.972381 11.196726 15.054360\n4 12.5 21.0 17.000000 31.000000 15.528775 19.974984\n5 9.0 24.0 17.578947 23.666667 18.637822 0.577350\n6 9.0 11.0 14.875000 15.750000 15.169871 16.070159\n7 12.5 NaN 15.666667 NaN 14.361987 NaN\n10 NaN NaN NaN NaN NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medianmeanstd
Survived010101
fam_members
029.030.032.41423431.81153813.33496811.970452
130.029.032.12698430.78184211.59983614.916443
230.522.031.50000021.91188713.77614117.363697
325.014.022.83333316.97238111.19672615.054360
412.521.017.00000031.00000015.52877519.974984
59.024.017.57894723.66666718.6378220.577350
69.011.014.87500015.75000015.16987116.070159
712.5NaN15.666667NaN14.361987NaN
10NaNNaNNaNNaNNaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.fam_members, bins = 10)\ntitanic_train.fam_members.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.210511Z","iopub.execute_input":"2023-02-01T14:50:32.210873Z","iopub.status.idle":"2023-02-01T14:50:32.431170Z","shell.execute_reply.started":"2023-02-01T14:50:32.210842Z","shell.execute_reply":"2023-02-01T14:50:32.430235Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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twlqUP/Bzr6a6xtewKkAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"fam_members\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.505200Z","iopub.execute_input":"2023-02-01T14:50:32.505646Z","iopub.status.idle":"2023-02-01T14:50:32.533253Z","shell.execute_reply.started":"2023-02-01T14:50:32.505607Z","shell.execute_reply":"2023-02-01T14:50:32.532232Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nfam_members Pclass \n0 1 0.236111 0.268519\n 2 0.369565 0.195652\n 3 0.519348 0.140530\n1 1 0.087963 0.236111\n 2 0.086957 0.097826\n 3 0.075356 0.040733\n2 1 0.027778 0.083333\n 2 0.054348 0.114130\n 3 0.054990 0.040733\n3 1 0.009259 0.023148\n 2 0.016304 0.054348\n 3 0.006110 0.012220\n4 1 NaN 0.009259\n 2 NaN 0.005435\n 3 0.024440 NaN\n5 1 0.009259 0.009259\n 2 NaN 0.005435\n 3 0.034623 NaN\n6 3 0.016293 0.008147\n7 3 0.012220 NaN\n10 3 0.014257 NaN","text/html":"
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Survived01
fam_membersPclass
010.2361110.268519
20.3695650.195652
30.5193480.140530
110.0879630.236111
20.0869570.097826
30.0753560.040733
210.0277780.083333
20.0543480.114130
30.0549900.040733
310.0092590.023148
20.0163040.054348
30.0061100.012220
41NaN0.009259
2NaN0.005435
30.024440NaN
510.0092590.009259
2NaN0.005435
30.034623NaN
630.0162930.008147
730.012220NaN
1030.014257NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Futher analysis and discussions\nThe data in their current states suggests that the distribution for the field _Survived_ is likely to be binomial. It has a lowest occurrences of surviving, which is a shocking statistic.\n\nThe passenger class has more occurrences of third classes. However, First and second class female passengers were more likely to survive the accident. First class male passengers had the also the highest survival rate. The Age is skewed to the left; some age may be unknown. It appears (see below) the younger passengers may have been traveling with other members of a family and perhaps reduced their survival rates; the largest familly appears to be travelling in third class. Most occurrences were families made of 0, 1, or 3 family members. \n\nThis analysis suggests that perhaps the passenger class familly, and the gender may have contributed to a higher survival rate. However, the familly size may have contributed to survived too. The classifiers will need to identify other patterns that may have contributed to survive the accident. It is likely to be quite challenging as no linear relationships or grouping may be present in the data.\n\n","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=3).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.247279Z","iopub.execute_input":"2023-02-01T14:50:33.247672Z","iopub.status.idle":"2023-02-01T14:50:33.275585Z","shell.execute_reply.started":"2023-02-01T14:50:33.247640Z","shell.execute_reply":"2023-02-01T14:50:33.274507Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass fam_members Sex \n1 0 female 0.001821 0.096491\n male 0.091075 0.073099\n 1 female NaN 0.114035\n male 0.034608 0.035088\n 2 female NaN 0.038012\n male 0.010929 0.014620\n 3 female 0.003643 0.005848\n male NaN 0.008772\n 4 female NaN 0.005848\n 5 female NaN 0.005848\n male 0.003643 NaN\n2 0 female 0.005464 0.084795\n male 0.118397 0.020468\n 1 female 0.003643 0.049708\n male 0.025501 0.002924\n 2 female 0.001821 0.038012\n male 0.016393 0.023392\n 3 female NaN 0.026316\n male 0.005464 0.002924\n 4 female NaN 0.002924\n 5 female NaN 0.002924\n3 0 female 0.041894 0.108187\n male 0.422587 0.093567\n 1 female 0.025501 0.043860\n male 0.041894 0.014620\n 2 female 0.018215 0.035088\n male 0.030965 0.023392\n 3 female 0.001821 0.014620\n male 0.003643 0.002924\n 4 female 0.016393 NaN\n male 0.005464 NaN\n 5 female 0.009107 NaN\n male 0.021858 NaN\n 6 female 0.009107 0.008772\n male 0.005464 0.002924\n 7 female 0.003643 NaN\n male 0.007286 NaN\n 10 female 0.005464 NaN\n male 0.007286 NaN","text/html":"
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Survived01
Pclassfam_membersSex
10female0.0018210.096491
male0.0910750.073099
1femaleNaN0.114035
male0.0346080.035088
2femaleNaN0.038012
male0.0109290.014620
3female0.0036430.005848
maleNaN0.008772
4femaleNaN0.005848
5femaleNaN0.005848
male0.003643NaN
20female0.0054640.084795
male0.1183970.020468
1female0.0036430.049708
male0.0255010.002924
2female0.0018210.038012
male0.0163930.023392
3femaleNaN0.026316
male0.0054640.002924
4femaleNaN0.002924
5femaleNaN0.002924
30female0.0418940.108187
male0.4225870.093567
1female0.0255010.043860
male0.0418940.014620
2female0.0182150.035088
male0.0309650.023392
3female0.0018210.014620
male0.0036430.002924
4female0.016393NaN
male0.005464NaN
5female0.009107NaN
male0.021858NaN
6female0.0091070.008772
male0.0054640.002924
7female0.003643NaN
male0.007286NaN
10female0.005464NaN
male0.007286NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns = [\"Survived\",\"Pclass\",\"Age\", \"fam_members\"]\ntitanic_train = titanic_train[columns]\npd.plotting.scatter_matrix(titanic_train, diagonal='kde')","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.689687Z","iopub.execute_input":"2023-02-01T14:50:33.690876Z","iopub.status.idle":"2023-02-01T14:50:34.713667Z","shell.execute_reply.started":"2023-02-01T14:50:33.690832Z","shell.execute_reply":"2023-02-01T14:50:34.712910Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"array([[,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ]],\n dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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27mwWXVPL5qHw+/tweAijw35XkuSnNcFGQ6cDusuO3mxxZjkunqo4uAUhAIG/hDEfzhCIGQgScQpq4jQF2bn/0tXjzRFNOlOU4+vHgcN5wwgWmlaZMSSaPRHAMM2ESjlHovwbptfe0nIguBLKXUqSJyn4gsVkqtGKgcyUBEOGFSISdMKsQXjLByTzMba9rZWtvOwTY/G2vaaPWF8AUjBML9nyB22Cy4bBYyHDZKcpyML8zgxMmFzCrPYU5FLtNLs4c119CGmjYqb38WgAJg9Z0XDtu5+0OXbADfnQ433ZS+8lXfeSGhiOLKe99h1d5WADLsFkqynRw/qYC1e9uobfPSHjDvF4dFKMl18vkzp3DfG7vY2+glHapMWAAV/QDkuGyEIxECEYXDAhaLFbtVUIYCixAMG4BifH4m+ZkOPjjYhiCcOrWYr50/ncJMJ597ZDUrqpspyHTw/UtnU98epNkb5PzZY6gsykwoR+y9Ceb1jWXm7c/ii/neu/2cu15ke2PosO0vbjzAp/+2BqXg6oUV/PSa+T3af/3KFn7+khkAetqUAh765Ik92m99YDkvbGkEoLLAxetfO6tH+1/e2sUPn9uMUvD5pZP40nkze7Q/tmIv335qIwA/uOw4Prx4fKLLMGD+77lN/Omt3YgIv7thYb/2SYV9/gTgpejyy8CJQEqVfyxuh5VTpxZz6tTEqRQihsIXihAxoo9L959DtYKdNitOmyWtk8g1971JSvneVrgp1UL0gScQZkvtoYBBb8igxRvi7e1N+EIROgKHOgpBQ9HmDfHgsj3UtwfSQvED9O7KtPsPxb/4DLAQwR+9jRXmyFYE9rf6ONjuxx80j/DBgTY+ONBOntvOloPtBMMGzZ1Bnlh1gInFpsLfeKDtsMq/L3x9tMcq/kT85rWddD2y/91Uy097tf/prT3dy+/sjH86uhQ/QHWzP679/mXV3cf/+4r9ccr/oXerCUc3eOjd6qQr/ydW1xCJ/oN+89qOfu2TCneS6cAdIvIW8CkgL7ZRRG4RkZUisrKhoSEF4h0Zq0XIctrIddvNT4b5yctwdH/cDmtaK34Ad6oF6INLJqVagr7JdFqZVHxImblsQpbLTlVlPvluOxmOQ/eAzQJZLhvXHj+OgkwH6XJ39JYj02HFZResAg4LOO0WMh023NG/TpsFm1UozXExpSQTp82C02Zh+phsZozJZs7YXCaXZGGzWsh127lg7hhKcpzYrcLMsoEXIOrLDWN83pG3uOW0SVjE/L1nJOjYfeyEQ8p4wbi8uPaTJx9KXFCR64xr//DisQjm8S+bXx7X/pGqcVgtgtUifKRq3BFlHQgXzSnDImAV+OTJE/u1jxwy1w8PIvI5oE4p9Y9oBtDVSqmvJNrWmpGrbLkl3d/nVKRl5oi0ZUNNz7n37FALlZWVqRFmFLJ+83YkpyRhmwC9nywBLCJEhvmZOxyC2Yu3iBC16pDrtlOe56bNF8IfimARIS/DTosnREQpIoaB1WKJ+auwW4VMp40sp2lIaPeF8IUiiAj5GXZavCGUUj226c2aTds50rPe+14+2va9TV7a/ObowGaJfxFtr+vAHzXpWgRml/fcf9OB9h7/t97H31Lb0R0zZLUIs3odf2+zlzafef5ct53xBcl17Nhe14k/bI4n8zPs1O3arJRSR+zcp8Ls8w5wK/APoATT9NONiNxC1HXUmlNM2Y2/7G5bmWY26nQn1oYKUPTyd1m5cmWKpBn5bKxp4+0djVw8r5yKPDeZFdMo/ugvEm5rEbrNAF3YLWaKcW8oPQILbRawWixk2C34wwYZDiunTC3mV9cu4J+r9rOv2YtFhA/NGcOz6w/S7g/R1BmgMMtJU2eAoiwnDR0BJhVnMacil7NnlQLw5Jr9VDd6ETHraTy7/iAAs8tzOHf2mISyOMumHvFZ730vH237Zb95m7X7zRdElsPKyu+f36P9hB+9RG1HEDD/Tyt/3HP/qd94lth/W+/jz7njBTqiJjO3zcLKH36oR/vVv1vGqj0tACyakM/jnz6JZHLKna+wv9U0R502tYiHP3nC6r72GXblr5RaLSJlIuIBaoFXerX/gWiUsLNsanp0kUYB1XdeSFXVd1MtxojlgXd2871nNqEUzCzLoSLPTWVhBu4MK83eCDbAagNBqCzOoDTHjREOs2pfB+GIweSSLIqznSwal8urWxvYVtuOJ4nppRwWiBimaakgy05DR4BAWKEiCrfTSpbLhlXMXnmOy8aC8QXUdviZUJDJlJIsPqhpw2638LXzZgBwzsxSVu9rYVy+mykl2fhnRmjzhXDbrXiDETIc5l+33YovFGFxZUG3LGfOKGXN3hYq8txMLc0mOMug2RPssU1v5lTk0njYVvP+7XZWSOCBHds+rSherT1y8yJOv/sdfMEwf7lxcVz7y184kao73yJiGDzyiUVx7e9/9SQW/GQZAL+7bm5c+0tfOJnT7noTpRT/+MySuPbf31DFdX96t3s52dz/8cXceP8K3HYrf7xhPg9/su99ht3s0+PkIr8GXlVKPZmovaqqSumeavKoqqrSPf8BsGxnI9f/aTlnzyzlJ1fOJT/Djojo65lE9LVMLiKySil1xLfMsPf8RcSplApEv7bT90S+RpMywhGDO57+gPEFGfzq2vlkOEZEALtG0yep8PY5X0TeEJE3gFLgxRTIoNH0i/+sP8C2uk5uP3+GVvyaUUUqbP7/Bv493OfVaAbCA8v2MKk4k/OPSzxRqdGMVHTaSI3mMKzb18q6fa3ceKJOtKcZfWjlr9EchsdX7cNtt3LFwopUi6LRJB2t/DWaBIQjBv/dUMuZM0vIdtlTLY5Gk3S08tdoErB8dzNNniAXzy1LtSgazZCglb9Gk4Bn1h8g02HljOmJ0zdoNCMdrfw1ml4YhuKlTXUsnVEy7JXdNJrhQit/jaYXmw6209gZ5MwZutevGb0MSvmLyGQRcUaXzxCRz4tIXlIk02hSxBvbzFTih6vpoNGMBgbb8/8XEInW7v0DMA74+6Cl0mhSyBtbGziuIofi7Pi87RrNaGGwyt9QSoWBy4FfK6W+ilnXV6MZkbT7Q6za28Lp03SvXzO6GazyD4nIR4AbgWei67RTtGbEsmxHIxFDcfo0be/XjG4Gq/xvwqzB+yOl1G4RmQg8PHixNJrU8Ma2BrKdNhaMz0u1KBrNkDKoxG5KqU3A5wFEJB/IVkr9JBmCaTTDjVKKN7Y2cNKUQuxW7QinGd0M1tvndRHJEZECYDXwRxG5OzmiaTTDy476Tg60+bXJR3NMMNjuTa5Sqh24AnhIKbUEOHvwYmk0w0+Xi+dp04pSLIlGM/QMVvnbRKQMuIZDE74azYjkjW0NTCnJYmx+RqpF0WiGnMEq/+8DLwA7lFIrRGQSsH3wYmk0w4svGGH57mbt4qk5ZhjshO/jwOMx33cBVx5pHxFZAvwCMIAVSqkvDUYGjSYZvLe7iWDY0Mpfc8wwKOUvIi7gZmA24Opar5T6xBF22wOcqZTyi8jfRGSOUmrD4TauvP1ZAOZnw1PfvHAw4h5zLLn9Weqiy9V36mt3JN7Y2oDLbuH4iQX93scwFLf+dSUtnUGKsx1EIjC9LIsJhZlYxEJNq5fCTCdXLByLw2YOsrfWdrCltp2x+RnUtftZvquJFbub2N/qY2KhmwWV+Ww76OFgq5d9LX7CCkqybBRkONjd5CUSAQU4bEK2ywbKoCMQQSFkOwSxWLjhxEra/WG2HuygOMdBmy9EmzfMyZMKafGFyXPbmFCURWOnnyyXnQMtPrbWdTC7PJvibBd17X42H+zguiVjMQyhoTPI3IocdjZ4GZPrJNdtpzMQYe7YXOxWC7saOmnxhuK+z6nI7f7d/aHrWYfE92tX+6dOGMc3L5t72PZvnjeNTy2dGte+8Hsv4A1F+PdnT2V6eXZc+5badgIhgzkVuVgs8ZXbNh9sJxQxOK48cftIY7A1fB8GtgDnYZqArgc2H2kHpVRtzNcQEDncthtq2rrDhdd2DE7QY5G6mOXK259FT2Menje2NXDCpMKjyuK5t9lL+6Y6wob53SLwxvYG8jMd2ETwhiIUZzsJhA1uPKmS6kYPf1++hy21HThtFqqbPBxo9XXvv66mk3U1nXHnqe8MU98Z7rHOF1b4OkMxaxT+sAIM7n5pB3arEI6oaIvJmr2t2K0WbFbBbbdhKAO71UJTZwAFvLmtgYJMB02eIAJs2N9KXoaDDIeNf64Cm8VCXoYdl91KZWEmHf4Qs8pyeHrdAZQyo6Nnl8d894VY2s/keLHPeiJiXwx/fG9fnPKPbf/RC9vilP8ZP3uVZp95DS/8zZvs+HHPl8uuhk7+u8FUTcGIweLKnp2A7XUdPL/RbA8bioXj8/v1u9KZwdr8pyilvg14lFIPAhcCS/qzo4jMBYqjsQKx628RkZUisjLibRukeBpN3+xp8rC70cMZyTT5xHQMu8r/ihxaTjWmHPHC9CWecPjfELc6TX5rf4it0ZxI7NjfPIJ+1hEZbM+/q+vRKiLHAbVAn6/6aFzAbzC9hHqglPoDZpI4qqqqVGN0/SWTBinpMUgp9DD7VFV9N5XipC1dLp6nH2XhlvGFGZwwewytniDF2U4ihmL6mGzGFWRgEaG2zUdBlpPL5pk1gCcUZnL9kglsre1gbIGbujbT7LN8dxMHWnxMLM5gQWUB2w52crDVy55mHxEDSrLt5GfaqW7wmqMEBQ67hdwMG0QM2gNhDIQchwWxWvjYCZNo8QfZWttBaY6DNm+YVl+IkycX0ewNke+2Mb7QNPtku+zUtvrYUtfO3Ip8CjLtHGzzs7WuneuXVBI2DOo7Aswfl8eO+k5Kclzkue10BsIcV2GaeS6dX0GrN9j9/bL5FbREv/eXORW5dD3rjgTt1Xde2N27/+rSyUds/+Z50+LaX//qmVT94EU6A2H+/dlT49onFmVy4dwyAiGD2eU5ce1TSrL50BxFOKKYVRbfPhIRpVTfWx1uZ5FPYmb2nAvcD2QB31FK/e4I+9iAp4E7lFLvH+n4VVVVauXKlQOWT9OTqqoq9PWM5+YHVrC9vpM3vnpGjx5gX+jrmTz0tUwuIrJKKVV1pG0G6+3zp+jiG0B/++ZXA4uBn0YftK8rpd4djBya/rN2Xysl2U7Kcl1HpehGK4FwhGU7m7i6aqy+HppjigEpfxH58pHalVKHTfGglHoEeGQg59UMnuv++B7eYITZ5Tl868JZnDi5MNUipZQVu1vwhSLaxVNzzDHQCd/sPj6aNOUPH63i2xfNosMf5iN/fI/739mdapFSyqtb6nFYLcf8S1Bz7DGgnr9S6nvJFkQzPJwytYhTphZx3fHj+dJja/nefzbhsFm4fsmEVIs27CileOGDWk6dWkSGY7C+DxrNyGKwWT0fjK3ZKyL5IvKXQUulGXLcDiu/uW4BS6cX891/f8C7O5tSKs+ynY185fF1XHXfMj754EoeeX8v/tBhQ0CSwgcH2qlp9XHe7DFDeh6NJh0ZrJ//XKVUa9cXpVQLsGCQx9QMEzarhXs+soAJhRl88bE1tHqDwy5Dhz/EZ/+2iuv+uJyXNtVht1rYUd/B15/YwAW/eouNNUMX6/HCB7VYBM6aqVM4a449Bqv8LdEiLkC3/74eP48gsl12fnXtApo6g3zrqY0MxvX3aGnqDHDVfe/ywgd1fPW86Sz/xlk8cssJvPaVM3jgpsX4QhGu+f27vLW9YUjO/8IHtRw/sYDCLF2oXXPsMVjl/3PgPRH5gYj8AFgG/HTwYmmGk+Mqcvni2VN5Zv1Bnll/cFjO2e4P8bG/vE91k4cHblrMbUundKdWEBHOmF7Cv287mfEFGXzqoZWs3tuS1PNvr+tgW10n52uTj+YYZVDKXyn1EHA5ZiBpHXCFUkrX8B2BfPr0ycwbm8t3n/6Aps7AkJ7LMBRfenQtW2s7+N1HF3Hq1MRuliU5Lv76ySWU5rj45IMr2dfsTZoM/1pdg9UiXDSvPGnH1GhGEgNS/iLiEpEvishvgFOB3ymlftM7T49m5GCzWvjpVfPo9If5ztMfDOm5fv/mLl7ZUs+3L5rF0j5SKhRlOXngpuMJRww++7fVSZkEjhiKJ9fs54xpxRRpk4/mGGWgPf8HgSpgA/Ah4K6kSaRJGdPHZPOFs6fy7PqDPLdhaMw/y3c1cdeLW7lwThkfO7F/7qUTizL5+TXz2VDTxg+fHXz/4p0djdS1B7hy0dhBH0ujGakMVPnPUkrdoJT6PXAVcFoSZdKkkFtPm8Scily+/dRGmj3J9f5p7AzwuUfWMC7fzZ1XzjmqdArnzCrl1tMm8df39vLvtTWDkuOhd/dQkOngzH6mG9ZoRiMDVf7dicSVUuEjbagZWdisFn529Vza/SHuSKL5J2IovvjoWtp8Ie69fhHZLvtRH+Mr503n+MoCvv7EBrbXDazAQ3Wjh1e21HH9kvFHlbtfoxltDFT5zxOR9uinA5jbtSwi7ckUUDP8zBiTw+fOnMrT6w50F7AYLPe8sp23dzTyvUtmMytBytz+YLda+PV1C8hwWPnM31bjCRx9v+PPb+/GZhE+esKxF9Gs0cQyIOWvlLIqpXKin2yllC1meXQkuz7G+cwZk5lVlsO3ntpIyyDNP29tb+CeV7dzxcIKPrx43KCOVZrj4p6PLGBXQydff2LDUcUl7Gv28uiKvVy1aCwlOa6+d9BoRjGD9fPXjFLsVgt3XT2PVm+Q7z8z8EnWA60+vvjoWqaWZPHDy45LStrkkyYX8T/nTufpdQd4cFl1v/f72QtbsYjwhbPii31oNMcaWvlrDsus8hxuWzqFJ9fU8OIHR2/+6fCH+MQDKwiEDe69fmFSk6d95vTJnDOrlO89s4ln+xGY9vKmOp5ed4BbT5/MmFzd69dotPLXHJHblk5hdnkOX3ps7VHl2fGHInzmr6vZUd/JfTcsZEpJcjN9WyzCPdcuoGpCPl98bA3Pbzz8C2BPk4ev/Ws9M8Zk8/+WTkmqHBrNSEUrf80Rcdgs/PnGxeRlOPj4/e/zwYG+XwCdgTA33b+Cd3Y2cueVcw8bwTtY3A4rf7pxMcdV5PLpv67m7pe2EQj3DALbVtfBdX9cjqEU992wCIdN3/IaDWjlr+kHY3JdPHTz8ditFq753bv8Z92Bw060fnCgjUt+8zbvVzdz9zXzuGqIA6ly3XYe+dQJXLGggnte2c7Zd7/BXS9s5eH39vC1f67jwnveIhA2eOgTxzOxKHNIZdFoRhLDnoFTRMqBZ4BZQJaOExgZTC7O4qnbTuaWh1fxuUfW8OiKvVy/ZALzx+Vhswpbazt4cnUNT62toTDLycM3H89Jk4uGRTaX3crdH57PJfPLue/1nfz29R0oBZkOK1cuHMuXz51GSba282s0saQi/XIzcBbwZF8btra2UXn7swC4gc13Xji0ko0yuq4dQHUSrl1pjot/ffpEHnx3D394cyef/dvqHu2ZDis3nTyRz505hbwMx6DPd7ScMb2EM6aX4A9FaPeFyM90YLcO7eD23Z2NbK3tYGJhJnabBUPB8RMLepiXlFKs2mPWCj6+soD1+1tZu7eFlXua2VnXSWcwhDeoKM1xMm9cLm9sbyIQCBGMKBAYn+9GidDSGcRqteALRvCFI+S57GRn2HHbrexp6KAzBAJk2CCsBBFFYZaD0uwMwoZBUZaDDfvaCCqFwyp4AhGUUiilcNqtZDht5LrtZDls+CMRnBYLDrsVQyk6/GHmjc1lW30nRkSR4bQyLs9NoyfElNJMbjl1CgVZ5v98/f5W/rPuAC67lRtPmkBRVuIX74b9bTR5AiyuLAD6vl/7237jkgq+d/n8Hm2BQIALfv0unYEwf7lxMbPH5vZoNwyDvy7fizcQ4YYTJpDl6qka/f4wX/7nOnyhCD+7eu5hf9NIYtiVv1LKD/j74/K3zwNl0WXfkEo1+qm8/VmS0Q+3WS3cfMpEPnbiBNbua2VrbQeGUowvyGDJxELcjtRHzbrs1mGJ3vUGIzy74SDb6zrJz3CQ6bQyNj8Di4Ueo56dDZ28tb0RgNo2H+v3t/PW9gb2t3gJG4eO197gZUeDl94GtW0Nie/+Bk+IBk+oxzoFeMJdS1DTGqSmNYgFMHofIAZ/JEKbP8LBtp4ZXa0CEWX+3VLbgQBG9PsKi2ARYdPBdqwWK189bzoHWn38ffke3treSIbDRsRQfO38GXHnq23z8/LmOvPcIYMNNW3dz3oiYhV/IibHtD+4vCZO+d/26Hp2NnoAuPnBlbz3zbN6tL+8ub7ba8xqhU+dOrlH+09e3NJdV+I7T33AvTcsOqI8I4G0K7wiIrcAtwBYc4ZmolAzeOxWC4srC7p7bcciVhHsFrOH77Jbunv7mb1cWt0OGyKgFORlOHHYBIfNglWEcC9Vb7FA5EhaeoCIQNxb5XDbxuxjEVPZg/l7lVKIRNssggBWi5DnNn+z227FbbchIlgtQq47cRoPl92C1SJEDEWmc/Av6kynhfbA4S/cuDx39/8gxx2v9vIy7N3t+e74UWt5nrt7uSh7+Ee1Q4EMZ+WmHicWeR04+0g2/6KiIlVZWTlsMo02AmGDNl8Iq0UoyHCwu7qarKIybBYhP8NBEuKtjmmqq6vR92dyqK6uJru4nIihyHXbcWqvrEGxatUqpZQ64kVMu55/LJWVlaxcuTLVYoxYnttwkK21ZgK0yxdUcM7pJ/HZXz4OwJULxzK+MCOV4o14qqqqhvz+bPOFcNosoz4J3dz5C7npZ48CMK00mwvnHskIpOkLEVnd1zap8PaxA/8F5gEviMg3lFLLh1uOY4E5Fbnsb/GSl+GgPM+N224l02klP8Oho1zTnLX7WvnWUxvYWNOO1SKcPq2Yr39oBlNLkxssly7YrRbG5rtp8QaZU5Hb9w6aQZOKCd8QcHZ/tw9FDCKGGvU9n6FgXEEGt5x2aOLKYbP0+K45OpRSeIMRMhzWpOQoOhyr9rRw/Z/eozDTyVfPm067L8RjK/dx4T1vc9c187hkFJaeFDFHp6GISgungWOBtDb7RAzFn9/eTTBscPG8ch2ko0kpz2+sZUttB1NKsrh4iBSwJxDm84+soSTbxROfPam7zOQtp03iM39dzRceXYPNIlwwZ3SZRQyleGBZNZ2BMOfMKmV2ue79DzVpPasSihi0eIK0+0LsafKkWpxRQU2rlw5/qO8NNXHsiroK7m4cunvxz2/vpqbVx93XzOtRX7gwy8lDNx/PwvH5fPGxtWzY3/88SyOBcETR1Bmg3ReiutGbanGOCdJa+SsFj63Yx6Pv72XfED5wxwotniCfenAln3xwBfXt/lSLE0dnIMx7u5rY35KeD39pjpOdDZ2U5AxN0XdPIMwf39zFubNKqUrgQuuyW/njx6ooyHDwhcfW4AsOvph9uiACT66p4Z+r9rOldmAvtq21Haza00xoKHxlRyFprfybPUG8wTDBiMHjawZXt1UD7f4wbb4Q+1v8rN7bkmpx4nhhYy3v7mziydU1aanY6toDTC7Oor490PfGA+DpdQfoCIS59fTDz8sUZDq4+5p57G708PMXtw6JHKmgwx/GEwijlOLNbY1Hvf++Zi/PbTjIm9saWb6reQgkHH2ktfLPdNpw2CzYrMLC8fmpFmfEk+Uyw/cr8tzMH5uXanHiiMZLmcFDaRiDUFlozjlNGCIX2Ufe38v00mwWjs874nYnTSni2sXjeWBZ9YBrGacbmU4beZkOnHYrJ08pPOr9rZZDN4wlrbVa+pDWlynXbef0qUXMH5fLradPSrU4I56CTAefOLmSr50/nTExEYvpwsSiTNp8IUqynWkZ5HPOrBI+NGcM588ek/Rj723ysn5/G1ctGtsvT6KvnjedTKeNO/7zwVGVskxXHDYLl84rZ8nEAq6pSlzqs90forYtsbmyPM/NZQsqOHtmKUsmHv3L41gk/Z6wGJo9AV7e0sDK6lZ++t8tqRZnxNPYEeCXL2/nG09uoKY1/ezqG2vayXXb2d/io92ffslen1p7gP9uqOXJITBBvrjJrJR2Xj9fLAWZDr549lTe2dHEsp1NSZdnuPEGwjy+aj/v7Wrinld2xLW3eoM8/O4eHnl/72FNlhOLMpkzNrfHKCBZGIbi/d3NvLOjcdTMKaS18veGIgTDBqGIYvPB0TG8HU7e29nEZb95m08/vJIOf4hWb5CDbX5213fy2pb6VIsXx5SSLFq9QQqzHGQ7088LefWeFlZUN7NqT/LnS17cVMeMMdlHFXV93ZLxlOe6uOvFrSO+9x+KGDR3BGjxhNhRFz/h29ARYPXeFlbtaWHLwfZhl29LbQfv7Gjk/d3NrNnbOuznHwrSWvm7o4FdChibpyNSj5ZfvrKNnQ2dLNvZxIsf1BIxFBEFIQWShsqi1Rskx23HH4oQNtJPPkMpvMEwkSRfu2ZPkJXVzZx7lOYkp83K58+aypq9rbyahi/zo8FQipAyM4/Wd8a7IlsEcl12cty2lASBxSafyxglQWhprfx9wUh3IsLdTelnpkh3DMOMSPWHIjijuea72JeG7pRr97XyxrYGlu9qxh9KP2+fcERhKDP4MJks39WEoeD0aUefxfbKRWOZUJjB3S9tG9G9f2+Md1ebL175F2Y5CUUMOvxhirPiXW0bO/3c9rdVfPTPy1m+K/lmsAmFmVyzeBxXLKzguFGSfiKtlX/srRwcJXa24aQw047VIjisFnJcth4eNMXZ6TfhW9vmx+MP0eQJxNXiTQcMpXDZLBhJVv7v7WrCbbcyd+zRKxW71cLnz5zKBwfaeWlTXVLlGk7c9iP3rJs6A9itFrKdNho6411t1+xpYUd9JwfbfLyyeWhGQRV5biYUjp4sA2mt/GPnbVy2NPT9S3Pq2v0YShGMRKht8/foGTZ1BlMoWWLavCEaOoM0dgaxD8Gk3WCZWpLF1JIsppRmJfW4y3c3s2hC/oCrjl06v5zKwgx++fL2Edv7D0VUzHL8i99Q5v18sM2PJxDvDFCU7URECEfUqMm3P9SktfKPvY8jI/OeTilKBMMwH5wspw3FIYXqTMOXqYFZXtBmgY40DPIqyHLSEQhTmJk85dLiCbKltoMlEwdeFMdmtfC5M6ey6eDI7f2rmHF+okGfCITCBoYyEha7GV+QyYVzyrh0fsWAXT1DESMtR5xDRVor/1jbamcauv6lO76AOWdiKGjxhuLa0o0ddZ00eUIcaPVjScMebLMnyMSiLJo9ycuN9H61GY16wuTB+aaP9N5/7LOeaE5lf4uX9Qfa2HSwg/d3x9v0C7OcnDG9mAXj8wZkk2/xBPnz27v5wxu72HuMzC8OqfIXkUoRqROR10Xkxei6r4rI2yLyt2hu/34ea+Td0KmmzRfsVv6tvgCxsygqDa9nezThXMRQ1BwmmCeVLJ6QiycQZuGEvKQdc/muZpw2y4Ds/bGM9N6/y3bIzp/jilcL6/e1EYkYKKXY1dAZ176roYM7nt7Ez17Yyr/XHn0cRk2rD1/Q9DKrPkaSSA5Hz/8lpdQZSqlzRaQEWKqUOgVYD1zW34OEw+mnrNIdf0x1cF/I6GFG8x6h3mmqcNkt3a+nMTnp59r7y5d38tSamoRBSANl+e4mFo7Px2kbvPvgpfPNtOcjsfcf6z7rC8WP8mdVZBNREDbMALferKhuYV+Ll7p2P68PwO11SkkWEwozGJPrOmaKyQyH8l8qIm+JyJeAKuD16PqXgROPtGOsu1+rNvscNR0x12x3g6eH99T+1vQLmuv0m//vrsm9dGPdvhbafEE27k9OkFebN8Smg+0smTRwe38sZu9/CpsOtvPiCOv9x5p1O4PxHZOXN9V1uypvrIkP8hqfl0EwYuAPRSg6TNZVTyBMqzexo4PLbuXc2WO44Lgy8pM4p5PO9Ev5i0ipiPxZRP4b/T5LRG7ux64HgWnAUszqXVVA13+uDchLcK5bRGSliKw0fIf+yWk4P5n2xHT82dfcc6jcmIbePvboP1kEct3pF+FrAKHoBHoyWFHdjFIkNRfNJdGiR78aYb1/ex8PeGPHIffORCMDQ6Asx8mYHBc5znizUVNngAeWVfPAsuruutaxNHQEeOCd3dy/bDc76tOvYzQU9Lfn/wDwAtBVvmgb8MW+dlJKBZRSHqVUGHgG2AnkRJtzgNYE+/xBKVWllKoSd073+gSdAU0f2GLcJQtzevr1C+n3Ni3MdGARcNksWNMwNWOXO2IoSffi8t1NOKwWFvSRxfNoGKm9/1AfZt1JJYfca/Mz4nvm+Zn27numODtREFiQYNg0fR5o88W1N3QECEUUSsHBw8w3tXqDNCWIMRip9PcJK1JK/QOz80NUmffpLiIisdWmTwZ2AKdHv58NvHek/WNn/UdOHyZ9KMg41Hs+Z3ZJj7aizPTrWftCEVAQNhQ2a/q9nLpSTiQrwnf57mbmj89Len3qkdj7N/qQc8aYQ6okkR9/uy9MfUeAZm+Qvc3xE7aTizOZXZ7DpOJMFk2ITw8/tTSLmWVm+4IE6eMPtPp4cNkeHn5vz6gZGfRX+XtEpJCoDhaREzDNNn1xqoisEpFlQI1Sajnwpoi8DcwHnjp6kTX9pc13aHj81paeBTIMSUPlH4xgYCrZ9gQh/qOJDn+IjTVtnDAI//7DMRJ7/329ot7fdWie5UBrfM98Z30HvmCYQMhgZwJvIJvVwrmzx3Dp/IqE3kR2q4XzjzPbsxIkFWz2BDGUOTJo6Dh6k2mrN8iDy6p5cFk1bd70uLf7q/y/DDwNTBaRd4CHgM/1tZNS6jml1CKl1ElKqf+NrvuJUuoUpdR1SqkjXsU0DPIcUcT2+vLcth7Xc255doI9UosneGjCN5As20qasrK6BUPBkklDk3u+q/f/0+e3jIjAJUcfI71ITNSvL4ENeFyBGxFzBDF2CGpVTC3Jwm4VDGUwuzyn7x16sb2+k2ZPkGZPkG1pMnLol/JXSq3GNNecBNwKzFZKrR9KwczzDvUZRjexz0h1k7fH9UzHCNrYCeoW7+ixrSbivV2mvX+oKtTZrBa+c9EsdjZ4+N3ru4bkHMkk2EcIv8SkvkiUVHNrbScd/gi+kMG6/a1Jlg7W7m/lrW0NvLuziWU7E5eZXLWnJWEAGpi1BjIcVjIcViYWpUd+oH6N/UXkil6rpolIG7BBKTVkuWRHd99vePEEwj2G1nub4ie9Uo1VDqXxcDvSzyyVTN7b1cT8cXlDmp546YwSLp5Xzm9f28GFc8cwpST9Rntd9JUs78yphTy+sgYFzCyL73m/u7Ox+/7eUZf8IK0ddZ3sb/UDig9q2jn/uLIe7a9tqef//rsZFHz+rKlcNK+8R3tRlpNbTjOrEfanUttw0F+zz83An4Dro58/Av8LvCMiHx0i2dLQH2Xk0jsH/YE0rOQVO8mb4xq9yr/DH2JDTRsnJMm//0h856JZZDqt/L+/r8GXhqO9/rL2QMeh9O4JbPpzKg69EIoPk9jtvxsO8tCy3T1MSP1l2phsxhe4Kc9zc1yCaOx1+1vxBMJ4gmHWH2bkISJpo/ihnz3/6HYzlVJ1YPr9Y9r9lwBvAg8PhXDa6pM8pNcNb0TSL2guEOPuV98+es0+Xfb+E4bI3h9LcbaTX3x4Pjc9sIJvPbWRu66em1YKqIu+0rdsrjnkX9Lkjb93/T1cReN/3/Mba/nhs5sA2N/q5xsXzDwq+RaNz+fzZ00jbBgJ4zIunlvOqj0tKENxyfzyBEdIP/qr/Md1Kf4o9dF1zSKSHlPXmiOytaGnh0S9J/2Ufywf1DRz9uyyvjccgbzbZe9P4HI4FJwxvYTPnTmVe17ZTkW+my+fM21Yzns09DUoaYqJzE00PWDt44XW5g3iD0UwlLl8tFgswolHSL43uSSLX3x4PoZSlGSnX2qSRPRX+b8uIs8Aj0e/Xxldl0mCQC1N+tG7+NHJw2ByGAzN3pFrouiL17fWU1WZn3T//iPxpbOnUtvm455XtpPltHLLaZOH7dzJoCgmsCuRrTq2FEKiGJGJxZk4rRbChmLiEBVkKUpQYSyd6a/yvw24Ajgl+n0lUKqU8mCmbtCkOcFwz8Gw25ne+UvGF6ZfpbFksK/Zy7a6Tq6pGjes5xURfnz5HDzBCD9+bgvtvjD/c+60tDQBJSLLfcg332GPV/+xDgx1HfFxAMGwQWG2E0OBjI4SvIOmX8pfKaVEZBdwAnA1sBv411AKpkkuhtFzDmVnQ3xyrHSizZN+uYeSwWtbTee4s2aWDvu5bVYL91y7gByXjd+8toMWb5AfXHoclhEQUBOOsVJGElRzaQ8csj4bhyn2UpHvJhAymFka7y0UCEd4fmMtgZDBebPHkJvR72zzI5YjKn8RmQZ8JPppBB4DRCmle/sjDNXrgUhUJzWdaB1FOVRieX5jLZOKMlPm6221mCOAXLeD372xE08gzM+unjfgEpLDRU3LIQ+fRPF/s8fk8NpW0/8+UcpngOlRpe9IYG7bUd/JrgbTRXTd/lZOm1Y8WJHTnr56/luAt4CLlFI7AKKpmTUjjN7ZHJy29HalVJLeymggHGj18e6uJr5w1tSUyiEi3P6hGWS7bPzsha14ghF+/ZEFwzoHcbR4QkeeA/LFRggm8BMsyXHiD0Vo9gYTvnjLct047RbCEcX4gozBijsi6EsDXAFcC7wmIs8Dj6Ld70ckdkvPf1xZbnp7JMwee/Qh9OnOE6v3oxRcvqAi1aIAcNvSKWS7bHzn3x/wyQdX8sePVQ1p0Nlg6EusTOehDRINYt7YVs/fl+8hYigyHVbuuOS4Hu0FmQ5uPmUihkHCa2AYBn9fvpdA2OD6JeNxjYIgxCN2r5RSTymlrgVmAK9hpnEuEZH7ROTcYZBPkyR8oZ79oR118YEy6cTKnQ2pFiGp+EMRHli2h1OnFjFhiLxNBsLHTqzkrqvn8c7ORm5+cAXeYHq6AHtDR44DaI5JlpboJ7y4sZbOQARvyODNbYnTMzht1sO+/J5ce4A/vb2bh96t5v53qvstdzrT39w+HqXU35VSFwNjgTWYEb6aEUJvpw7DSG9XyuyM9B6ZHC0PLqumsTPAZ05PPxfLqxaN5e5r5vHeriY+8UB6vgAWxCQiTKSex+Q4u+/xrATR4WML3FhEsAD5CSZzw2GDv7y9m3te2U6HPz50yeMPEYoYhA1FRyD9rs9AOOqxi1KqBfhD9KMZIfS+3/Oz0tuV8vgp6R2H0BulFJsPdlDX4aciz82U4qxuL5rVe1v4xcvbOGdW6REDhVLJ5QvGYhHhS4+t5eP3r+D+jy8mM0Fq41QhtkOTuIksLrluBw4LRAwoz433t186vZQXN9XjD0X4cNXYuPZXttTzwge1ALjtFj7VKw7ikvkVrNnXSiAU4WMnVg7ux6QJ6fPf1QwpYwuy2BrT+19UOTzRpQOlODO9X06x7Gro5EuPrWXd/kMpCPIy7Cwan4/LYeWlTXWMyXHx48vnpLVf/aXzK5DuF8D73H/T8Qlz26cCFYmJ8E2U8dGIEIgOZpsTlCidOzaPb1wwE28gwqnTiuLaC7MctPlCGIYixx0/MujwhxlfkIFSZm7/MQnmzLpSqKfz/ziW9PjPaoac3Y2dPWrPvrOjkVtOn5I6gfrg3d2NVE2Mf0jTjR31nVz9u2WICD+47DhmleWws6GTldXNrN7bij8U4YoFFXzlvOkjIgL0knnlWAS+8OhaPv6X97n/psVkJyh+MtxsrD2UiDBRSdcXNx1KLrytPn4+y2IRTp16ePfNLJeNaSVZ+MMRxubHe/u0+0O0+cIYSiU0CzV7gvxz1T4iBly5sIKSnPiXw54m05U0XeZ8tPI/RnDbLD28fYqyUv9AH4lEaXvTjYih+OSDK7CI8K/PnERl1IVw0YT8YY/gTSYXzS3HIsLnH1nDxb9+m19du4B54/KG9Jx99ZUr8lzsbDRfAIkmKmN767bDxCxEDIWhVMKYBk/ALAOpgPqO+BgTt91Kqy+IYSgctvj9q5s8eKJDj12Nnjjlv62ug/+sPQDAxfPLmVaa+vTaae1MndbCjTDG5Lp75DyZVZ6XOmEOw9SSDATIsFuYXZaXanH6xBeK0OwJ8vuPLupW/KOFC+aU8bdPLiEYNrjivmXc8fQHQ1p+0JlAocYyIcb3Pscd32c9YXIRdqtgFRIWyGnzhfjz27v43es7qW6Mz/ef47IzZ2wux5XnMiZBr72+I0BDR4CGjmDCAu9TSrIoynZSkOlgegLFXtPiY9XeFlbtbaGmJT1qaaR1zz835p88pXB0eX8MBw7roWyJsyryWON2UJBhJ9Nh5cTJ6RfBOKU4B1/QINtlx5rmEacAWU4br95+ZsKasKOBJZMK+e8XTuMnL2zhwXereXrdAb50zjQ+snjcYXvXA6U010WXpX56SbzZJS8majfWp7+LC+eW886ORjr84YRJ6w62+WiPmm12N3riXtZTSrI4/7gyAqEIVQnmw9x2C+W5bgylyEpw/hyXnasXmRPJiYLlMhxWSnOc3cvpQHor/wwHYTFrup4+oyTV4ow4ppRks+mgWS908YR8/u20MaMsh4JMB2Pz0+9lOrsiB08wTEGWg2xn+ilUIT52dLQq/i5yM+z8+PI5XL9kPN/7zya+/dRGHn63mm9eOIvTk5gCwWmzMK4oE28owvlz4oPgqiYWkvfuHgIRgxMnxc8FFWQ6+MwZU2jxBDl+YrynWJ7bzu5GD75QhDNnxusSEWHREVJsTy3NZmyBm0DY4LiK+GIu9e1+Hl+1H8NQXLFoLBW96ggXZjm6zUKFWemRVDGtlX+LJ0hG9Gl7cVM93744tfKMNBaOz2NXgweX3cLUMTkYSrG40nwwWr1hctzpcRN28bGTKhmT42bO2Jy0jDQ9losLzS7P5bFbTuCFD2r58XNbuPEv77N0ejHfvHBmUspDCsIJkwvxhyIJ0y8sGJ/P5YvG0eELc/0J4+Pat9e187lHVuMPRrht6VRuPLmyR3urL8SUkiwAOnzxfvqBcIT/rDuALxjhkvkV5Pby+Nnd4GHD/jYihmLTwXZOmdLzxbfpQDvPbzyIUuYoorfyr23zd89r1Lb506KkZlorf3/YoGsAWN8xOrM8DiXb6zqJGAp/yGBvo4cMh43SHBcFmXYq8tPPlfKPb+7i5U11jMl1cd/1C0dFCP1oQkQ4/7gyls4o4cFl1fz6lR2c98u3+PDicXzxrKkJPVz6i8Nm4dSpRTR7QgljIXJcdr523gwC4Qh5GfGdlkdX7GV/iw+l4MH3dscp/0lFWUwuycIXDDM/weT1yuoWnlhdg2GYE8LXHt/zBbN6b4tZqUvBuzua4pT/nmZvd88+0ZyC4lDnIV06EWn9dMXOymc60t8GnG40dgYIGYqQoWjyhrBbheuWxPea0oWXNtVR0+qjvsNPTaufydGemia9cNrMYjBXLhzLPa9s5+/v7+XJ1TXccMJ4rl8yYcCT36dOLcYbjFCcHe8S6w2GufO/W2j2BPn06ZPjTC9dZkIRcNviR40Om4XFE/LxBMLkJ8j6GettZE2Q4rqxI0AgbOZFr2uPn/CdFfVOM5RiVnm8p9rk4izmRmv/TkmT+7pfyl9Efgr8EPABzwNzgS8ppf46hLKR7bJhwXxTnjQ1/X2+043GmLTI7YEASsHb2xvJz7QzuzzebplqDEPhC4YxbFYy9Ms+7SnMcvK9S4/jE6dM5O6XtvGXd6r541u7WTg+jzNnlHDG9BJmluUkVKa9iSjFg+9WEwgZnDatOM7+vmJ3M6v3tBA2FE+tqYlT/pctGMtf3t5FIGRwWYIautvqOvjq42sJhBWfPX0yl/RKrldVWcCHF4/DFwpz/nFj4vafVZ5NhsOKYSiOS/DsNHoC2K2CUkJTgiCz8jw3nzx1EpB4QjgV9Lfnf65S6msicjlQjZnt801gSJW/ihkfBRNFdmiOSJv/UP6e/66vpSMQ4tUtdThsFgoznQmjFFNJkydA2ADCBp2+EOSlWiJNf5hQmMmvrl3ANy+YyeOr9vP8xlruenEbd724jSynjQXj81g0IZ8F4/OZWpLFmBxXXAGZSEQRiCbqr0/Qs87LsNPuC+EPG7gTKM8H3tlNe8Dc/6H39vCpXgGMb21rYHejF6UUL26qi1P+YcOgsTOANxAhEDbobZGPGILbbpaBTFRsfl+Tl9p2P0qZAZWJSBel30V/lX/X7MeFwONKqbbBhDCLyC+AKmC1UuoLh9uuwx+iawD13u6mAZ9PA8qIEAgZLNvZhMsWb9NMB1o8ph952FDUtgeYOjrrt49aSnJc3LZ0CrctnUJ9h59lO5pYuaeZVXta+dUr27s7cy67hbH5GRRmOijKcvKb6xbgsFlYNCGfFm+QEybF2/zDEUXYiIBSeBPk9t8VU5kukVkmz2XFEwibZRx7VzYCVuxu4aUP6lBAfqaDG06Y0KN9c207tW0BFIpVe1u48eSe+1ssgkUERCVM7xCOGLy/uxmA4ycWJN1VdiD0V/k/LSJbMM0+nxGRYiD+CvcDEVkIZCmlTo2mhl6slFqRaNtwTD6CYFj3/AeDN6iIGAq7VRARWjxBKtMkzDwR/vDoyJx4rFKS7eKyBRVcFu1hd/hDbKhpY3ejh90NHmpafTR5guxv9XUryyNVz2ryBFEICLR6480qsdW9EnVL39zRRCSqTtbWtMW1Zzit2K0WwkqRnSAraJs3iC1q1gkE43v+00qzmVSUhaFUQlfQDTVtLI8q/wynLeGk83DTp/IXEQvwH+BnQJtSKiIiXuDSfuxbDjwDzMJU+OHoccpF5FfAy8CJQELlX57rImIRIkpx40mV/fxJmi4s0RgJgCmlWdQ6rIwvyCDDYUub/CKx5GfaafaEsFmFysL0mBTTJIdsl52TJhdx0uSBzd1NLclkSnEW3mCE4yvj/fgXT8jnvV0tKGBsgkpcU4qzsVlqUUpRnhvfvnB8PjefOhF/KMLp0+LjAK5bMoHl1c1EIoobTpwQ137i5EJCEYOIoTh9evxLLPaFkihILBX0qfyVUoaI/FYptSBmnQeI92eKpxk4C3gSunv9OcCXMF8epfSy7IrILcAtAOPHj+fZ206m3RfipCl6wvdoeerTx3P9X1ZRmuPkLzctYfG9dj531lTy3I7D1jlNJX//1BJ+9sI2zpxewtQx6Zfb5/uXzOIv7+zmowkefs3QUlmUxS2nT6bNF0qYoO3L582kPRBhT5OXX39kYVz7586eijcUprbNz48vPy6u3WoRzph++EDSGWU5PHDT8YQjinEJXi52q4WzZpYedv8pJdlcXWUq/USJ41KBKNW316mI3AW8Czyh+rND/P6vA2djKvVFwAtABPgQsF4pdU+i/VzZeUplmf9ou8XCjLLUB0aMZKqrq6msrEy1GCOWDTHmgjkVufp6JhF9LZPLqlWrlFLqiBML/bX53wp8GYiIiI9opLtS6mi7Z3nAaszRwOOYLqM9isLE9vxzi8vI//gvUcp82P7zuVOO8nSaWKqqqli5cmWqxRixTPvmcwQjCrtFWPnjC/T1TCJVVVV85d4neGdnE1//0AzK89IvCHEkISKr+9qmX8pfKZWsLncb0IA5WXwP0KCUer/XubqrhFVVVakblk6htt3PnVfNS5IIGs3A+MetJ3Dvazv51GmVqRZlVOIJmikWrAK/vHZB3ztoBkV/g7wEuB6YqJT6gYiMA8p6K+5+8C5wq1LqVhG5F3igrx2+eO70ozyFRjM0zB9fwB9uHFnlJUcSN58ykV0NnTy5pgZ/KJJ2fvGjjf46m96L6ZVzXfR7J/DbvnYSEbuIvAzMw7Tz2wG/iLwFRAbw8tBoNKOYpdNL8AYjrNvXmmpRRj39tfkvUUotFJE1YBZxF5E+3UWUUiHMid5Ylh+ljBqN5hhhwfg8ANbtb2VJgmAvTfLob88/JCJWognpokFeOupKo9EklcIsJ+MK3KzbFx+IpUku/VX+92D66peKyI+At4EfD5lUGo3mmGVWWQ5b6zpSLcaop7/ePn8TkVWYLpoAlymlNg+dWBqN5lhlakk2r2yuJxg2EhZL1ySHo7myGYA1uo92wtVoNEPC1NIswoaiuqk/SQQ0A6Vfyl9EvgM8CBQARcD9IvKtoRRMo9Ecm3QVO9lelzg1siY59Nfb53pgnlLKDyAidwJrMQu8aDQaTdKYXJyFiFmA5UJ0Xu+hor9mnwNAbOUPJ1CTfHE0Gs2xjstuZVx+BjsadM9/KDliz19Efo3p3tkGfCAiL0W/nwPoAC2NRjMkTCnJYme9Vv5DSV9mn66sVauIpmWO8vqQSJOA6oZOmjxBFiXI4a3RDCfBYJC3djRz6pQCHA4zxjEQjqDU4Uv0BcNmjnerRYgYCrfDmnDdUBOKGIQjw3OuZDClJIu3dzR2XydN8jmi8ldKPThcgiSixRPk7F+8iQIum1fOzz88P5XiaI5xTvzJG7R4g+S67Kz57rmEDcWf395NOKK4ZF45lUU9C+S0eII8umIf7b4QYcOsEHX6tGKW7Wyi3RckbECW08bF88qYVDx0xWva/SEeWb4Xf8jgQ3PGMK00/VOjTynOIhg22NfsjbuumuTQX2+fi0RkjYg0i0i7iHSISHvfew6ONl8IQymUUrxf3TzUp9Nojkibz6wx3B4wS0yGIgaBkNmL39vsjdv+YJsffyhCmy9EQ4efiKFYv78VfyhCuz9MfYcfQyn2JNg3mdS3B/AGIxhKUd04MtwnJ0c9fnZo08+Q0V9vn18CVwAbBlLMZaCU5bqxuuyEIgb/78wpw3VajSYhC8bnsWF/G7PKzTIWLpuVScWZBEIG88bmxW0/uSSTSfWZFGc7sYhZLWrp9BLe3dXUY938BPsmkwmFGUwtzaLDH2bhhPwhPVey6HL33NHQydkcvkKWZuD0V/nvAzYOp+IHcNotrPzuucN5So3msDz+6ZN6fBeBS+dXHHZ7p82asP1I+wwFdquFi+aWD+s5B0uu205JtlP3/IeQ/ir/rwHPicgbQKBrpVLq7iGRSqPRHPNMKcnSyn8I6a/y/xFmDn8XMGyVv9t9ISZ//VkMBTeeOJ47Lp0zXKceFdz/9i5+89pOsl02/nHLiakWZ8Qz9RvPEjLAZoEdP74QAE8gzKMr9rJ8VzOBcAQBrBYLc8flct7sUu57fRcd/jCzyrLJz3Ry8dwySnLMkBlfMMKTa2rwBsNcPK+c0hzXEc7eP/yhCH9fvocn19SQ67bzP+dOZ8H4kWHq6c2UkiyeXF2DUgqznpQmmfRX+ZcrpeJL3g8xdR0B8qOGpifWHNDK/yj51+oaguEITZ0Rnli7P9XijHhC0STm4Zhk5rsbPeyo7+RAq4+OQBgUuOwWsl02/r32ALVtfjyBMN5gmCUTC9l0sL1b+Vc3eahr9wOw6WB7UpT/vmYva/e10uwJEggZvLy5fkQr/45AmPqOQFKujaYn/Y3wfU5Eht34XpTpQDCrxZ85vWS4Tz/iOXd2KVaLkOWyc+GcMakWZ8TT5W4e2wcdX5hBRZ6bgkw7Y3KclOa6KMxyMCbXxXmzxpDrtpPjtjFzTA4Om4WpMW6W4woyyHXbzfUlyXH1rMh3M600mwyHjRy3nVOnFCXluKlgSrH2+BlKpD9zuCLSAWQCwehHAKWUyhlK4aqqqtTKlSvx+/24XPrNPxDC4TA2mznAq6qqYuXKlX3soTkSnZ2dZGWZSin2eiZ6jrpMFYZhYLFYDmu+GAqzhoq6SFssIyMlcqJ7s77dz/E/foU7Lp7Fx0+emCLJRiYiskopVXWkbfqbzz+lUSFa8Q+cLsWvSQ5dir83R1LeXQr4cNsMhT1bREa8nbw420lhpoONB4Y8pOiYpF+aQcy76HpgolLqByIyDigb6gLsHf4Qlbc/C8B5M4r4/ceXDOXpRh2r97Twsxe2UJLj4mdXzKXDH+Lsn79OcbaThz+xOO1eDPuavby3q4kJhZkcPzH90nlM/eazhCKHJnwV8OTq/fzlnWoyHFYmFmXgsFn5xMmV/H35PrbWtlOa68JlszC7IpcL5pThtlt5ZUs9nkAYiwgicNrUIlZUt+ALRVg6o4QMu5XnN9by77U1FGQ5+fxZU6jIy+i3nBFD8crmOpbvaiIYMZhZlsuFc8rIdtl4bWs97f4QS6eXkJcxcN8Nw1C8sa2BFm+QM6aXUJCZfD8QEWHB+DxW721J+rE1/Z/wvRezZu+ZwA8wPX9+CyweIrkAqG7ydid0fWFL41CealRy3xs72NvsZW+zl/9+UEtdewBHu5+6dj8Pv7eXm06ZlGoRe/DW9kbq2v3sb/ExsyybbJc91SL1IBQx/3ZN+PpDEf65ch/VjZ0oYFdDB5OKs7n7pe3sbuykrt3P5toO8jPstPvDlOW6KclxsulAO42dATyBMBMKM+nwh2noMD2oc90tlOe5eX7jQTYdbCfLaeOfq/bzhbOm9VvO3Y0e3treyLp9rfjDEVq9IUqynUwqzmT9frM27gpnC+fMGnjw1P4WH2v3tQLw/u4mzj9uaFIvLxifz8ub62n1Bgf1stLE01+D4BKl1G2AH0Ap1cIwuHw6Y0q42Ub2CDYlzBxjTsk4bFZml+fgspvX0261sDgNe9ZleaZ5Lz/DjvswidLSCbvVQnm+G4tFcNqsZLvsiMCiCXm47VZsVgs5Lhtuh5Ucl52yXBdFWU4cNgtZThv5mQ5EYFppFg6bBREzqr0oy0lJjgurRXDarUedi6coy0Fehh2n3YLbbiXbZaMsz0VhphNn9B4oyx2cKTU/096dzK4sd+gK+y2KRiSvqNa9/2TT3wnf5cBJwAql1EIRKQZeVEotGErhqqqq1Olf+T01rT4e/fQpQ3mqUcvavS0UZ7uoyHdTVVXFXX99jsqiDCqLhi6R2EBRStHkCZLjsqdl7dZVu1r45r/X8qNL57NoUj5VVVW88c577GroxCpCUbaTQMhgXGEG9e1+WjxBst12BMhw2MjNMEcynkCYcERhswrhiCI3w969rmsbbzBMbasfq1WYUHj0ic28wTCt3iCC4HZYu3vNvmAEfyhCfhLMNMk81uGcEYJhg4U/eImL55Xxf1fMHfR5jhWSNuEL3IOZ0rlERH4EXAUMSxnHn1+7aDhOM2qZ38vH+4wZ6esyKyIUZTlTLcZhWTQpn+e/tLTHukynjTkJcvOU5Li6/fl7k+mMf+x6r8tw2Jg0CPfPDIeNDEf8edwOa9LSOifzWIfDYbNw+vRiXtpUzw8v0+mdk0l/vX3+JiKrgLMw3TwvU0ptHlLJMF3kZn7rv4QMg/tvXMSp07Wv+tFQ0+LjFy9vY3x+Bp8/eyphQ/Hb13ZQkefmsgXDm1+mP7R6g6zZ18q4/IzuxF7pxFX3vsPqfa0cV57D0587FTBHK6v2NLOxpp1JxVmcOLkQu9XSvX5DTTsTizI5aXJRj9GMUorVe1vxBcPYrRZCEUXVhDyeXHuANm+I608Yn3DOY83eFjyBCIsn5uO0DU7xrtvXSrs/xOLKgh71CNbta6XNF+L4iQWHrVMwnFw8t5xn1x/k5c11nDdb64BkcTTuHnXAW9F93CKyUCm1emjEMtlW10lRdHbtEw+uYns0pF7TP3703GbW7WvhXWBmWTaNnQHe3NYAwMSiDOaNS6/Izxc/qKOm1cf6fW186rSJCXuuqWTl3lYA1tcccj3cXt/JU2sOsL2+k7H5buxWCydOLmRHzPqKfDcOm4WTJh8KuNrZ4OHNbQ00e4J4AmHGFWSwbn8Lb2475Njw6TMm9zj/7kYPr281/3+GUpw2rXjAv2Vfs5dXt9QDEI4olkZHhD3WGwZnzkh9Rs2zZ5ZQkefmj2/u4txZpSPehTVd6G8+/x8A6zHNPz+Pfu4aQrkAsFkPiWe3pp8NON3JdZnKU0TIz7BjjT40VouQk2aeNEC3CcFhs4yY4b3bbu2+N+1WCxnR3+B29FzfewLb7bAiAnarYI+OCAoynXTptRx3/P/Hbbd2t2cM0tzitFuwRA8Wa7px2a2H1tvT4+Vrs1r47NLJrNzTwnMbalMtzqihvxO+W4E5Sqng0It0iKqqKlV+4y9o8gR55/azhvPUo4JgMMKjK/cyoTCT06eXsGhRFT95+BnK89zMKs9NtXhxBMMGuxo7Kc12JWUSMdnc//ZOfv3qDm49bRK3njG1e5Jyf4vpTjsmx9WjIldNq489TR5Kc1xMTlCpq6bVhz8UwWWz4A8bTCrKZO2+Vlq8Ic6YVpQwOvdgmw9PIMLk4sxB94Br2/x0BkJMLs7qcay6dj8d/vj1Q0lf0ecRQ3HRr9+mxRPkpS+flnZuwOlGfyZ8+6v8/wV8RilVnyzh+kNXegdNctDpHZKLvp7Joz/Xcs3eFq68bxnXHj+eH1+ukzweiWR6+/wfsEZENtIzn/8lg5CvX9x8//s0eoI89skqnebhKHl7UwM3PPQ+WQ4LG7//IcIRxRX3vs1xZbl8Pw0fnidX7eWb/97E8ZX5PPCJ9IvmfnjZbn7/5m5uPnkCN506GaXgm09swB+OcMFxYxAR6jv85LkdnDu7tEfPfU+TB3/IYFppFnubvd3LiXrWe5u87GzoZNPBNg62+phaksXiiQXMLMtFRNjX7KUzEGZ6aTYrqptp6gxy7qxSnt90kAMtfj5+YiWOfpiFYo9jGQFmtgXj8/nEyRP509u7uXhuOSdOLky1SCOa/ir/B4GfABswI32Hhb3NHhqjE1xn//Id3tamn6PihofM7BudQYNT7nyZ7fUdtO5tY82+NiryXNy6dGqKJezJlx7fAMDr2xp5ctVeLl80PsUS9eQ7T29CAd97dgs3nTqZfS1e/rFyHxGleG1LHTluB55AmOJsFy2+EB853pR/X7OXJ1bXALCrIZsttR0AdAaKWDShZ7BdTauPB5bt5q3tDext8hI2FC67lcUTC7ht6RQq8tz8c5WZnnvN3haeXncApeCt7fW8taMJpRT7W318/9IjZ2Cva/fzr9X7UQpavMEek9HpzP+cO52XNtdx+xPref4Lpw25q+lopr+zqF6l1D1KqdeUUm90fYZUMiAcObTsD0UOv6GmTzr9YbosfEpBc7QYebpS3xHoe6NhpreBNGIoFOb1jBiKsKFQynTj9AUP3a/ByKH+kjfmPg6E4/tRwbBZED4cUd3nU0oRihgEwwahmGN1xPxPzf+v+aU/z0ooYnTvG0wgR7ridlj5yZVz2dPk5ecvbk21OCOa/vb83xKR/wOepqfZZ0hdPScVZ5JTlEFnIMzfbx7SNEKjkttOHc9v39qLAGvvOJ9ZT2SQl+tifEEGX79gVqrFi+P6JRU8sryGcfkubj0jvUYlANcuruCZdbWcO9t0fxxfkMGsiQX4QxEumluO3WahpsVHcbaT644f173f5OIszppZgj9ksGB8HpsPtuMLRhIWU59YlMlVi8YxY0wOq/Y0U9vuY0pxNktnFnP8xALsVgvnzCqlMxBm4fh8xuS6aOoMck3VOB58dzd17QG+1I88QGPzMzh3dintvjALJ+Ql7RoNBydMKuT6JeP5yzu7uWBuGQtHaLGaVNPfCd/XEqxWSqkzky/SIfSEb3LRE5TJRV/P5HG017LDH+K8X7yJy27lzx9fzMSio0+BMZpJZj7/pUdqF5EblVIPHo1w/WX67c8SAKrv1AFeA6ErJXbX9Zv73f8yuzybR25Nv1xJe/bsYel9GzltSj4PfPKkVIsTxzcfX8HfVtVz4Yw8fvvxkwF4dHk1nYEI4woyqG72UJrl5LKF42jzhdjT5KGmxYvTZmXuuLweqSu62tt9IfY1efEEwyyqLCAvw44nEMFhs9DpD1Hd6GHqmGxaPEHT7mSBTLuNg20+alp8zKzIAWWmh7CJheqmTiYWZ5Fht7J2XwuBUIQ54/KZWJRJY2eAzkAYq0UYl5+BLxTBbbfS2BEg02UjElHUtPkYX5BBSfYh54rOaBlKpaDJE2BW2ZHdhDsDYbyB8GHTWySDbJeduz88n089tJKld71ORZ6baaVZXDCnjCsXjh0RE9ippl89/z4PIrJaKbUwCfL0wFk2VZXd+Mvu7/oFcHR0Kf4uDj74Rbqu59zyLJ7+/OkpkOrwxMr7+dMq+fIFs1MoTTyx8lXfeSGlk2aR+eGfYcQ8QhaBC+aMIc/t4PmNtbT5QlgtQtXEfL514SxmjMmhoSPAr1/dxgsb62jxBglGzAO4bBZKcszkcA6bhYbOAJGIwmYRFAplFtDDKuALm/MLVjGDoJw2IWxAKGzgtltAhHZ/GAGynFYWVxbQ4g1yoM1PXoaDSUWZjC/IoNkTpKbVR36GnT1NXlp9IWaMyeb/nTmV+ePyaPOG+OvyPTS2+1m9rxWX3cplCyq6J7N70+4P8df39hAIGZw2LX5C+3AMdBRV2+bniTX72Vrbwfr9bexu9HDOrFJ+e93CtEwOOFz0p+efrKujX7MjjM0H07su6tPrD6ZahD7xhyL07jspBdtqO2j2BglEDAylMJSipTPYnbO/2ROkuTNIKGIQjnlzhCKGmd3TiP6N7h80zEngSMTAMBTBqOIHiEQnm4NhRTAcQaEIhA0C0UlfhTnhXNcewBuMEAobBIIRGjsD+EMRmjoDRAxFY2eQzoA5adzqDVEfLSzf6gsSDBu0BUJ4o5PYe5u8h70mbd4QgWil+4ZhmLQfk+vis2dM4VfXLuDV/zmd71w0i5c21fH1JzYkLK2pOUSy4reH5CqPyT4U5Vk4dCnDjwn+dnEe5//10Lv+rS+kX7ZUC4f8iF+//exUitIvKvLdZGQ58AbCZDsttPsN8jIc/OTKuTR0BIlEFFvr2slw2Li6amz3xOSUkiwumV9BWCn2R4vtRCIGs8pzmD4ml/p2P/lZDrbXdtDoCTI2z027L4QyO/7kua1sb/DiDUYozXbistsoyLCbx2vxMbnIjdNuY0V1MwYwryKXS+ZVsL2+g+omL4WZDhZV5mO3WjhhUgEbatoZX+Bif4ufrXWdnDy5iCWTTB/68QUZVFXmM7k4g7H5GbT7wlx3mF4/wNh8N4srC2j2Bjlh0vD64YsInzhlIm2+EL96ZTszy7L55KnpVbAonUiW2WfNUOT21xO+yUVPUCYXfT2TRzKvpWEoPvu31by4qZaHb17CyVNGRgxDMhlOs887STpOHP/3n+e56p5n+95Qk5DFtz/LI48cun7n/+RZXnzxxRRKdGR+9NR6du3alWoxDsuH7n6ux/dg2CAcNvAHwxiGIhw59L03hqEIhiL4A2GaO/10eoO0ewIcaOlgR30rje1e2j0Bmjt8tHb6ae30U93Yxva6VurbPOxtaqe6sZ39LR20dvppbPfSHN2utrWTA60dHGjpoLnDR7s3QDAUIRiK0O4JUN/uodMbpLHdi9cfIhiK4PWFaO7w0dju7V4fjhj4/WFaPf7u39P1VynzLxyKPej6m05YLMJd18xjSkkWt/19NfuaD2+mOpbpr6tnHvAxoJIYU5FS6vNDJRjoCd/BcqQJXzuwPc2uZ6y8f7ppJmdPT68he+8J32mz53H9//2dlXuayXRYmVqag9MmfHCgA6sFPn/WVI6faJo+2nwhHlpWzds7GtlW106nP4yIEDZUjwljqwXTiKrgSKFaXZNsIuY8Q++n2G23MDbfjVWE7fWdGIqoB4wi02GjNNtJXYcfTyBCRJnHy3XbWTghj7V7WwlFDBaOz2fuuLzuqmOGoXDarZw5o6Q7rYShFE6blQvmjGHqUZabjGUoRlHVjR4u+c3buB1W7r5m/jE1Akhmz/85TMW/AVgV89GMUNI7vhe++uiQ1woaNP6wwcE2Hy2eEJ2BCDsbOqlp8dPsCRCKKN7f3dy9bU2Lj4Ntfpo6A6bCNSAY6an4ASJGdBK3j3NH3w8YCRQ/mNHDde0BDrb5iES3iRiKiGGWeKzrMCeAI+rQ8TyBMJsPduANRjAMxZbaDtp9IfY2e2nzhahu8hAxFKv2tNDqDdHhN9cZSrG9Pv0cCCqLMnn0lhNx2qxc/6flXPKbt/nDmzvZ1ZB+sqaC/k74upRSXx5SSTRDSjEQ6z9z/NhUSdI//vXRmakWoU8yHVZmleXiDxm4HVZmleVgt4rZS1aKc2OqTk0qzmR2RQ6tviAhw6CpI4DFIoTDBt6wqYEtmHn2I0phQREMHz6Rlt1i9vhtNrMOcFeGBokeJ8dlY3pZNjYRVu1tJRwxsFvNYxdmOqgsyqS60UuzJ0ggbGCzQGmOk9OmFbNsRyPeUISlM4oZV5DBhKIMDAMqCzMRgaXTi1m9t5XGThuVhZkgMG9c3hBe6YEzqzyHF790Go++v5cn1tTw4+e28OPntlBZmMEZ00s4c0YJ88blkeOyJUyy5w2GqW70UtPq42CbDxGhNNvJrPIcKvLcI7qwTH/NPl8COoFn6JneofmwOyUBPeGbXPQEZXLR1zN5DNe13Nfs5fWt9by6pZ5lO5u68ytZLYLdKlhEsIp0F81p98fP3XSR7bIxqSiTsfkZ5GXYzY/bQW6GnVy3nTy3nbwMB3nR78NZEjOZKZ2DwM+Ab3JolKmAARllReQXQBWwWin1hYEcQ6PRaI6WcQUZfPTESj56YiX+UIR3dzWxs76TVm+IUHdcRjRpn1IUZzupLMpkXH4GZXkuULC/1cemA+1sre2gusnD5oPttPlCtPpCRHrb8WJw2izkZzjIz3RQkGm+GAqi3/Mz7GQ6bVhFsFoEi0WwiGnWC0fjQcwRnkEoomj2BGjoCFDfETDjRjxBWr0hzplVyi8+PL9f16K/yv9/gClKqcY+t+wDEVkIZCmlThWR+0RksVJqRaJtN9S0xU2yafpP3IRvzPXMBdal2fVM9/91X/L5QxGeWX8QfyjCBXPKKIhWI+ta3+EPYRiKtfta2bC/lepm37DJDj3jKLqwEp07wDQZlWQ7yc+0s7/FRyAUwWq1YLdYQBkUZDkJhg0aPQHsFgvTx2SbQWRhxSlTC7np5IkUZzn501u7eOT9veS47Hzn4lnUtpsK6pxZpZTnmQE7EUPx340HaeoMcvas1NQJdtmtLJ1ewtLpJUe1X0mOK2EyOaUUnYEwrd4QbT7z0+oN0eoLdq9r8QRp8YZo9gQ42NpOszdImy8UFyzYF1aLUJTloDjbSVGWk8nFWeRl2Jk7tv8V+vqr/HcAyfKXOgF4Kbr8MnAikFD5a4aOtlQL0AeVtz+bli+AI7GjvrPbrXBDTRunRwus72rwsK/ZS0NHgAOtPva3eNkzzIofEs8fxE4sK+juSYaj6apDhoEfAwvga/URMcztQhGDTQc7cEXrLa/Y3cL8cfkcV57LS5vqaPYE6fCHefjdvUwsNpOurd7b0q38D7T62F5nTryu3tMyhL96+BARsl12sl12xvW9eTcRQ9HmC+EJhDGUIhL1ADOUwiJgs1iwWSXmr3mewda57q/y9wBro9k9Y23+A3H1zAO6HLnbgB4JXETkFuAWAGtO8QAOrxkNzEy/Er59MjbfTYbDSjBaj7eLijxzfX6GHZfdYnrbtPvoCKSXfzyAy24hw26lzR8ibJi5isyC7qaLaChi4AsZWC1CYaYdi5iTyBX5biYXZ1Gc7WTamGx2NXpwO2wsnVFMqy9Euy/co45xcbaTvAw7bb5QwvrGxxJWi1CQ6egeKQ4X/Z3wvTHR+oFk8hSR24AGpdQ/ROQKYKxS6p7DbNsA7AGKgEGbnI5huq7fQmA16X89R4p8C4G9pLes/SXV13yk3JvpTtf1m6CUOmLvOSnpHY6GqM3/VqXUrSJyL/CAUur9PvZZ2dfMtebw9L5+6X49R5J86S5rf0mX35EucoxUjub69SvIS0Smisg/RWSTiOzq+gxEuGj1L7+IvAVE+lL8Go1Go0k+/bX53w98F/gFsBS4iUHkBdLunRqNRpNa+qvA3UqpVzDNRHuUUncAw+mK8YdhPNdopPf1S/frOZLkS3dZ+0u6/I50kWOk0u/r198J32XAKcA/gVeBGuBOpdT0gUqo0Wg0mtRxxJ6/iDwcXXwKyAA+DywCPgok9ADSaDQaTfpzxJ6/iGwCzgb+C5xBr3KNQ53bR6PRaDRDQ18Tvr8DXsHM4bMKohWkD/1Nr4TrGo1Go+kX/bX536eU+swwyNN1vkWYaR/ygFbgPaWUTp84CHrnUDpSTqXhRkRmY7r9bolZt0QptTyFYnUTvR/3AU3AFzA7PbXoe3PQ6Gc9dQx7kFdfRDN+OjHz/rQBOZimp7B2Ee0bEek9jyPRz3+B82LWPa+UOmc4ZUuEiPwcKMWsL1MEfEIp1SAiryqlzkytdCAif8a8XgHM+9AHbAXcwJ8ZgfemiFiBy+ildIGnlFKHz2GcfDn0sz5IBvPy7K+f/3CySCl1Wq91T4rImymRZuTRifkgd5nmTgfagSzMh4xo29yUSBfP4q7/t4jMBR4Xka+kWKZYpiilTgcQEY9SKjO6/JpS6klG5r35ALAe+Ds9le4DwA3DKId+1gdBr5fnZsz/400i8tH+vDzTUfmvFJHfY2b+bMf8QWdh5v3Q9M1m4HKlVBuYRR2AM4F/xvb0ReSlw+w/3FhFxKGUCiql1ovI5cBf6ZXwL4XEPiMvxdybBSJyFSPz3qxUSn2017o10aj74UQ/64NjUC/PtDP7AIjIAszUz3mYPZN3lVJrUirUCEFEyoAmpVQw9jtgxA7pRcQ2nEP8wyEixwPVSqn6mHVW4Gql1KOpk6xbltnAFqVUJPp9AXAycDzmCGvE3Zsi8lXMEeHrHFK6pwNvKaV+Osyy6Gd9gIjI3UAm8S/PgFLqi33un47KX6PRDC0iUoxZTW8RsBPYkS4OAJr+k+jlCdj687/Uyl+jOcYQkeeVUueLyBcxbf3PYI5m9iulvp5S4TT9JoFzBxyFM0c62vw1Gs3Q0lU15HJgqVLKAH4nIm+nUCbN0dPl3BFLv505jknlLyLfBK7DrGJnYNYXGJRPuYhcAsxSSt2ZBPk6lVIjuryRiESADZj32GbgRqVUwlKgInIH0KmUumv4JDymmSUiDwGTMb1FumpKulInkmYA9HDu6KK/zhzHnNlHRE4E7gbOUEoFRKQIcCilDvRj32GZJB0lyr/7N4jI34BVSqm7D7PtHWjlP2yIyISYrweUUiERyQJOVUr9N1VyaY6O3s4dMev7pacGnJN/BFMGNCqlAgBKqUal1AERqY6+CBCRKhF5Pbp8h4g8LCLvAA+LyHtRDxCi7a9Ht/+4iPxGRHJFZE+XPU5EMkVkn4jYRWSyiDwvIqtE5C0RmRHdZqKIvCsiG0Tkh8N8PYaDt4ApACLyMRFZLyLrYhIHdiMinxKRFdH2f4lIRnT91SKyMbr+zei62SLyvoisjR5z6rD+qhFKNC171ycUXdepFf/IQil1sLfij67vVwf1WFT+LwLjRGSbiNwrIqf3Y59ZwNlKqY8AjwHXQPebtyw2oi46BFuL6ToHcBHwQvQh+wPwOaXUIuArwL3RbX4F3KeUmgMcHOwPTCdExAZ8CNgQfWl+CzhTKTUPM1VCb55QSi2Otm8Gbo6u/w5wXnT9JdF1nwZ+pZSaj+m5sn/ofolGM7o45pS/UqoT073tFqABeExEPt7Hbk8rpbrsov8AroouX4NZ46A3jwEfji5fGz1HFnASZgTrWuD3mKMQMD0tHokux/WGRyju6O9ciVnk/M+YwWaPK6Ua4bBZYY+Ljoo2ANdzKNjrHeABEfkUYI2uexf4hoj8L2bBal/84TQaTSKOyQnfaMDO68DrUSVzIxDm0Muw98SXJ2bfGhFpiqYi+DBm77M3TwM/FpECzBfNq5jBGK3RXmpCsQb2a9IWX+/fKiKH2bQHDwCXKaXWRV/KZwAopT4tIkswK8itEpFFSqm/i8jy6LrnRORWpdSryfsJIwMRuQx4EpgZmxxPozkSx1zPX0Sm97INzwf2ANWYihrgyj4O8xjwNSBXKbW+d2N0dLEC05zzjFIqopRqB3aLyNVROURE5kV3eQdzhABmb3e08ipwtYgUAkRfjr3JBg6KiJ2YayEik5VSy5VS38EcsY0TkUnALqXUPcC/SZ98RcPNR4C3o39HJSLyeRHZHHUeGHF0zQmmWo5Yjjnlj5ng7EER2SQi6zHt+XcA3wN+JSIrMV1Aj8Q/MZX1P46wzWOYSbIei1l3PXCziKwDPgAuja7/AnBbdBRScXQ/Z+SglPoA+BHwRvQaJPL++TawHPOFGNuL/Vl0QnwjsAxYh2l22xg1Lx0HPDSE4qclUXPiKZhzI9dG11mi81lbROQlEXlOzDxEiMgiEXkj6nTwQnTeaiTwWeAcpdRo7hwdFjFTniQXpZT+6I/+jNAPZofiz9HlZZij16uA5zA7d2OAlug6e3Sb4uj2Hwb+kurf0I/f+DsgiBk38r+Ycz1ror9lenSbj2OWm30JcxT//4AvR7d7Dyg4wvFfB36BOT+1GVgMPAFsB34Ys90NwPuYDh2/B6zR9Z3AzzA7dC9j5n16HdgFXBIj37+j67cD3+3ncX+O2dE5BbgT2ISZkfWuQV/XVP9j9Ud/9GfgH8zUDOdElz8P3AX8ErgpZpsnosr/OMwEYGujnw3Ai6n+Df38ndWY9R5yMHPXgJma4l/R5Y8DOzDNhsWYeW4+HW37BfDFIxz7deAn0eUvAAcwnTGcmB5khcBM4D+APbrdvcDHossK+FB0+UlMj0I7MA9YGyPfweix3MBGTA+1vo57TXS5ELOORFdsVt5gr+kxOeGr0YwGonMmZwJzRERhekEpTAWUcBfgA6XUicMk4lCQi2m2nYr5W+0xba8ppTqADhFpw1SqYL7k+poPejpm2w+UUgcBRGQXMA6z570IWBF1XHADXZlog8DzMfsHlBk4twGojDnHS0qppuhxn4geM3yE40aAf0WX2wA/8GcReQbzpT8ojkWbv0YzWrgKeFgpNUEpVamUGgfsBpqBK6O2/1KiHlOYPcdiMaPciQYepkvdhP7yA0wlfxxwMT098wIxy0bMd4O+PRtjt+19HBvmi/NBpdT86Ge6UuqO6DYhFe2Ox+6vzJxJseft7dHXVQ/9cMf1q2gqcWUGbh2POd94EYdeNgNGK3+NZuTyEeJ7+f/CtPPvx7QP/xWzOEqbMqNBrwJ+Ep1wX4sZezKSyAVqossfH8bzvgJcJSIlYI66pGeajP5wTnQ/N2YZzXf6e9zoxH6uUuo54EuYJqVBoc0+Gs0IRSm1NMG6e8BUFkqpzqhb7fuY5giUUmuB3tWfRhI/xTT7fAt4drhOqpTaFD3ni2KmbgkBt2G6ifeX9zFfzmOBv6poZoB+Hjcb+LeIuDBHC18ezO+BYzCxm0ZzLCBmbqo8zPTNP1VKPZBKeTTph1b+Go1GcwyizT4ajeaYQER+i5lHK5ZfKaXuT4U8qUb3/DUajeYYRHv7aDQazTGIVv4ajUZzDKKVv0aj0RyDaOWv0Wg0xyD/H3TvaayBxTyOAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The percentages suggests passenger class and gender may be the factor that may lead to survival. ","metadata":{}},{"cell_type":"markdown","source":"# Data preparation for classification\nThis section prepares the data for classifiers. We transform the data in suitable data types supported by the classifiers, remove null values and imputes some values when required. Some columns are deleted; they may be either character or we surmise not suitable for classification.","metadata":{}},{"cell_type":"markdown","source":"## Integer to float\nWe upload the data for a cleaning and display the columns with their data types to float on both datasets.","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.368853Z","iopub.execute_input":"2023-02-01T14:50:35.370121Z","iopub.status.idle":"2023-02-01T14:50:35.386127Z","shell.execute_reply.started":"2023-02-01T14:50:35.370069Z","shell.execute_reply":"2023-02-01T14:50:35.385078Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.770203Z","iopub.execute_input":"2023-02-01T14:50:35.770631Z","iopub.status.idle":"2023-02-01T14:50:35.784625Z","shell.execute_reply.started":"2023-02-01T14:50:35.770596Z","shell.execute_reply":"2023-02-01T14:50:35.783551Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_train[\"SibSp\"] = titanic_train[\"SibSp\"].astype(float)\ntitanic_train[\"Parch\"] = titanic_train[\"Parch\"].astype(float)\ntitanic_train[\"Survived\"] = titanic_train[\"Survived\"].astype(float)\ntitanic_train[\"Pclass\"] = titanic_train[\"Pclass\"].astype(float)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.166628Z","iopub.execute_input":"2023-02-01T14:50:36.167303Z","iopub.status.idle":"2023-02-01T14:50:36.181459Z","shell.execute_reply.started":"2023-02-01T14:50:36.167252Z","shell.execute_reply":"2023-02-01T14:50:36.178943Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)\ntitanic_test[\"SibSp\"] = titanic_test[\"SibSp\"].astype(float)\ntitanic_test[\"Parch\"] = titanic_test[\"Parch\"].astype(float)\ntitanic_test[\"Pclass\"] = titanic_test[\"Pclass\"].astype(float)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.666190Z","iopub.execute_input":"2023-02-01T14:50:36.667397Z","iopub.status.idle":"2023-02-01T14:50:36.678991Z","shell.execute_reply.started":"2023-02-01T14:50:36.667345Z","shell.execute_reply":"2023-02-01T14:50:36.677862Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Null values \n\nWe remove all the nulls values from some of the columns; i.e., PassengerId, Fare, SibSp, Parch, and Embarked. Some fares were unknown, but all passengers ID was set to a unique number. ","metadata":{}},{"cell_type":"code","source":"titanic_train.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.489505Z","iopub.execute_input":"2023-02-01T14:50:37.489938Z","iopub.status.idle":"2023-02-01T14:50:37.497591Z","shell.execute_reply.started":"2023-02-01T14:50:37.489901Z","shell.execute_reply":"2023-02-01T14:50:37.496243Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.991239Z","iopub.execute_input":"2023-02-01T14:50:37.992524Z","iopub.status.idle":"2023-02-01T14:50:38.000114Z","shell.execute_reply.started":"2023-02-01T14:50:37.992478Z","shell.execute_reply":"2023-02-01T14:50:37.998884Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Fare.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.437569Z","iopub.execute_input":"2023-02-01T14:50:38.437966Z","iopub.status.idle":"2023-02-01T14:50:38.445766Z","shell.execute_reply.started":"2023-02-01T14:50:38.437933Z","shell.execute_reply":"2023-02-01T14:50:38.444961Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.994637Z","iopub.execute_input":"2023-02-01T14:50:38.995337Z","iopub.status.idle":"2023-02-01T14:50:39.002110Z","shell.execute_reply.started":"2023-02-01T14:50:38.995287Z","shell.execute_reply":"2023-02-01T14:50:39.000886Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Parch.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.436990Z","iopub.execute_input":"2023-02-01T14:50:39.437517Z","iopub.status.idle":"2023-02-01T14:50:39.445363Z","shell.execute_reply.started":"2023-02-01T14:50:39.437381Z","shell.execute_reply":"2023-02-01T14:50:39.444366Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.905392Z","iopub.execute_input":"2023-02-01T14:50:39.905832Z","iopub.status.idle":"2023-02-01T14:50:39.913740Z","shell.execute_reply.started":"2023-02-01T14:50:39.905797Z","shell.execute_reply":"2023-02-01T14:50:39.912816Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.307865Z","iopub.execute_input":"2023-02-01T14:50:40.308905Z","iopub.status.idle":"2023-02-01T14:50:40.316347Z","shell.execute_reply.started":"2023-02-01T14:50:40.308849Z","shell.execute_reply":"2023-02-01T14:50:40.315199Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_test[\"Fare\"].isnull(),\"Fare\"] = -1.0\ntitanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.604978Z","iopub.execute_input":"2023-02-01T14:50:40.605706Z","iopub.status.idle":"2023-02-01T14:50:40.614214Z","shell.execute_reply.started":"2023-02-01T14:50:40.605660Z","shell.execute_reply":"2023-02-01T14:50:40.613381Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.071733Z","iopub.execute_input":"2023-02-01T14:50:41.072626Z","iopub.status.idle":"2023-02-01T14:50:41.081041Z","shell.execute_reply.started":"2023-02-01T14:50:41.072577Z","shell.execute_reply":"2023-02-01T14:50:41.079958Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.270757Z","iopub.execute_input":"2023-02-01T14:50:41.271157Z","iopub.status.idle":"2023-02-01T14:50:41.282542Z","shell.execute_reply.started":"2023-02-01T14:50:41.271122Z","shell.execute_reply":"2023-02-01T14:50:41.281267Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.500496Z","iopub.execute_input":"2023-02-01T14:50:41.500902Z","iopub.status.idle":"2023-02-01T14:50:41.515629Z","shell.execute_reply.started":"2023-02-01T14:50:41.500870Z","shell.execute_reply":"2023-02-01T14:50:41.514309Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.775897Z","iopub.execute_input":"2023-02-01T14:50:41.776267Z","iopub.status.idle":"2023-02-01T14:50:41.789089Z","shell.execute_reply.started":"2023-02-01T14:50:41.776237Z","shell.execute_reply":"2023-02-01T14:50:41.787661Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.000121Z","iopub.execute_input":"2023-02-01T14:50:42.001023Z","iopub.status.idle":"2023-02-01T14:50:42.016796Z","shell.execute_reply.started":"2023-02-01T14:50:42.000983Z","shell.execute_reply":"2023-02-01T14:50:42.015509Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.207362Z","iopub.execute_input":"2023-02-01T14:50:42.207763Z","iopub.status.idle":"2023-02-01T14:50:42.218799Z","shell.execute_reply.started":"2023-02-01T14:50:42.207729Z","shell.execute_reply":"2023-02-01T14:50:42.217490Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.428396Z","iopub.execute_input":"2023-02-01T14:50:42.429337Z","iopub.status.idle":"2023-02-01T14:50:42.442620Z","shell.execute_reply.started":"2023-02-01T14:50:42.429286Z","shell.execute_reply":"2023-02-01T14:50:42.441246Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.657623Z","iopub.execute_input":"2023-02-01T14:50:42.658000Z","iopub.status.idle":"2023-02-01T14:50:42.668231Z","shell.execute_reply.started":"2023-02-01T14:50:42.657969Z","shell.execute_reply":"2023-02-01T14:50:42.666865Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.893929Z","iopub.execute_input":"2023-02-01T14:50:42.894325Z","iopub.status.idle":"2023-02-01T14:50:42.904773Z","shell.execute_reply.started":"2023-02-01T14:50:42.894278Z","shell.execute_reply":"2023-02-01T14:50:42.903541Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.119666Z","iopub.execute_input":"2023-02-01T14:50:43.120081Z","iopub.status.idle":"2023-02-01T14:50:43.128473Z","shell.execute_reply.started":"2023-02-01T14:50:43.120043Z","shell.execute_reply":"2023-02-01T14:50:43.127000Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.558402Z","iopub.execute_input":"2023-02-01T14:50:43.558778Z","iopub.status.idle":"2023-02-01T14:50:43.566705Z","shell.execute_reply.started":"2023-02-01T14:50:43.558746Z","shell.execute_reply":"2023-02-01T14:50:43.565387Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.776835Z","iopub.execute_input":"2023-02-01T14:50:43.777203Z","iopub.status.idle":"2023-02-01T14:50:43.786826Z","shell.execute_reply.started":"2023-02-01T14:50:43.777173Z","shell.execute_reply":"2023-02-01T14:50:43.785678Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.999708Z","iopub.execute_input":"2023-02-01T14:50:44.000611Z","iopub.status.idle":"2023-02-01T14:50:44.012435Z","shell.execute_reply.started":"2023-02-01T14:50:44.000573Z","shell.execute_reply":"2023-02-01T14:50:44.011295Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.273326Z","iopub.execute_input":"2023-02-01T14:50:44.273733Z","iopub.status.idle":"2023-02-01T14:50:44.285873Z","shell.execute_reply.started":"2023-02-01T14:50:44.273696Z","shell.execute_reply":"2023-02-01T14:50:44.284650Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.530974Z","iopub.execute_input":"2023-02-01T14:50:44.531405Z","iopub.status.idle":"2023-02-01T14:50:44.543425Z","shell.execute_reply.started":"2023-02-01T14:50:44.531367Z","shell.execute_reply":"2023-02-01T14:50:44.542121Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.768732Z","iopub.execute_input":"2023-02-01T14:50:44.769126Z","iopub.status.idle":"2023-02-01T14:50:44.779844Z","shell.execute_reply.started":"2023-02-01T14:50:44.769091Z","shell.execute_reply":"2023-02-01T14:50:44.779079Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.009603Z","iopub.execute_input":"2023-02-01T14:50:45.009952Z","iopub.status.idle":"2023-02-01T14:50:45.019372Z","shell.execute_reply.started":"2023-02-01T14:50:45.009923Z","shell.execute_reply":"2023-02-01T14:50:45.018131Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.259413Z","iopub.execute_input":"2023-02-01T14:50:45.259813Z","iopub.status.idle":"2023-02-01T14:50:45.270859Z","shell.execute_reply.started":"2023-02-01T14:50:45.259779Z","shell.execute_reply":"2023-02-01T14:50:45.269750Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.492004Z","iopub.execute_input":"2023-02-01T14:50:45.492453Z","iopub.status.idle":"2023-02-01T14:50:45.505989Z","shell.execute_reply.started":"2023-02-01T14:50:45.492416Z","shell.execute_reply":"2023-02-01T14:50:45.504737Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.736753Z","iopub.execute_input":"2023-02-01T14:50:45.737917Z","iopub.status.idle":"2023-02-01T14:50:45.751164Z","shell.execute_reply.started":"2023-02-01T14:50:45.737860Z","shell.execute_reply":"2023-02-01T14:50:45.749612Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.979633Z","iopub.execute_input":"2023-02-01T14:50:45.980021Z","iopub.status.idle":"2023-02-01T14:50:45.987927Z","shell.execute_reply.started":"2023-02-01T14:50:45.979987Z","shell.execute_reply":"2023-02-01T14:50:45.986675Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarkment \nWe remove any NAs from the embarked column. We replace NaNs values with unknown. However, only the training datasets has some unknown values. It could lower accuracy on the prediction on the testing dataset.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.403953Z","iopub.execute_input":"2023-02-01T14:50:46.404807Z","iopub.status.idle":"2023-02-01T14:50:46.413830Z","shell.execute_reply.started":"2023-02-01T14:50:46.404750Z","shell.execute_reply":"2023-02-01T14:50:46.412619Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' nan]\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Embarked'].isna(),'Embarked'] = 'U'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.636113Z","iopub.execute_input":"2023-02-01T14:50:46.637042Z","iopub.status.idle":"2023-02-01T14:50:46.643930Z","shell.execute_reply.started":"2023-02-01T14:50:46.637002Z","shell.execute_reply":"2023-02-01T14:50:46.642148Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"titanic_test.loc[titanic_test['Embarked'].isna(),'Embarked'] = 'U'\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.902953Z","iopub.execute_input":"2023-02-01T14:50:46.904202Z","iopub.status.idle":"2023-02-01T14:50:46.911244Z","shell.execute_reply.started":"2023-02-01T14:50:46.904144Z","shell.execute_reply":"2023-02-01T14:50:46.910042Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.161516Z","iopub.execute_input":"2023-02-01T14:50:47.162591Z","iopub.status.idle":"2023-02-01T14:50:47.169485Z","shell.execute_reply.started":"2023-02-01T14:50:47.162548Z","shell.execute_reply":"2023-02-01T14:50:47.168028Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' 'U']\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Sex.unique())\nprint(\"Testing : \" , titanic_test.Sex.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.378976Z","iopub.execute_input":"2023-02-01T14:50:47.379690Z","iopub.status.idle":"2023-02-01T14:50:47.386404Z","shell.execute_reply.started":"2023-02-01T14:50:47.379649Z","shell.execute_reply":"2023-02-01T14:50:47.385047Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTesting : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Passenger class\nNo unknown values is present in both datasets.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Pclass.unique())\nprint(\"Testing : \" , titanic_test.Pclass.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.799740Z","iopub.execute_input":"2023-02-01T14:50:47.800431Z","iopub.status.idle":"2023-02-01T14:50:47.807300Z","shell.execute_reply.started":"2023-02-01T14:50:47.800393Z","shell.execute_reply":"2023-02-01T14:50:47.806156Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Training : [3. 1. 2.]\nTesting : [3. 2. 1.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PClass and Fare\n\nThe Fare decreases as the passenger class decrease. However the range is can be quite large and the data data imbalanced; there are a lot more third class tickets than other classes. So we scale robustly the data based on non-parametric statistics.","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Fare\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x:100 * x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.235186Z","iopub.execute_input":"2023-02-01T14:50:48.235601Z","iopub.status.idle":"2023-02-01T14:50:48.274182Z","shell.execute_reply.started":"2023-02-01T14:50:48.235564Z","shell.execute_reply":"2023-02-01T14:50:48.272964Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"Fare 0.0000 4.0125 5.0000 6.2375 6.4375 6.4500 6.4958 \\\nPclass \n1.0 2.314815 NaN 0.462963 NaN NaN NaN NaN \n2.0 3.260870 NaN NaN NaN NaN NaN NaN \n3.0 0.814664 0.203666 NaN 0.203666 0.203666 0.203666 0.407332 \n\nFare 6.7500 6.8583 6.9500 ... 153.4625 164.8667 211.3375 \\\nPclass ... \n1.0 NaN NaN NaN ... 1.388889 0.925926 1.388889 \n2.0 NaN NaN NaN ... NaN NaN NaN \n3.0 0.407332 0.203666 0.203666 ... NaN NaN NaN \n\nFare 211.5000 221.7792 227.5250 247.5208 262.3750 263.0000 512.3292 \nPclass \n1.0 0.462963 0.462963 1.851852 0.925926 0.925926 1.851852 1.388889 \n2.0 NaN NaN NaN NaN NaN NaN NaN \n3.0 NaN NaN NaN NaN NaN NaN NaN \n\n[3 rows x 248 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Fare0.00004.01255.00006.23756.43756.45006.49586.75006.85836.9500...153.4625164.8667211.3375211.5000221.7792227.5250247.5208262.3750263.0000512.3292
Pclass
1.02.314815NaN0.462963NaNNaNNaNNaNNaNNaNNaN...1.3888890.9259261.3888890.4629630.4629631.8518520.9259260.9259261.8518521.388889
2.03.260870NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3.00.8146640.203666NaN0.2036660.2036660.2036660.4073320.4073320.2036660.203666...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n

3 rows × 248 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins=512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.451437Z","iopub.execute_input":"2023-02-01T14:50:48.451845Z","iopub.status.idle":"2023-02-01T14:50:49.547249Z","shell.execute_reply.started":"2023-02-01T14:50:48.451810Z","shell.execute_reply":"2023-02-01T14:50:49.546123Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 32.204208\nstd 49.693429\nmin 0.000000\n25% 7.910400\n50% 14.454200\n75% 31.000000\nmax 512.329200\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,8))\nplt.suptitle('')\ntitanic_train.boxplot(column=['Fare'], by='Pclass', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.549636Z","iopub.execute_input":"2023-02-01T14:50:49.550050Z","iopub.status.idle":"2023-02-01T14:50:49.804155Z","shell.execute_reply.started":"2023-02-01T14:50:49.550008Z","shell.execute_reply":"2023-02-01T14:50:49.803012Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.805690Z","iopub.execute_input":"2023-02-01T14:50:49.806699Z","iopub.status.idle":"2023-02-01T14:50:49.864940Z","shell.execute_reply.started":"2023-02-01T14:50:49.806662Z","shell.execute_reply":"2023-02-01T14:50:49.863879Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% max\nPclass \n1.0 216.0 84.154687 78.380373 0.0 30.92395 60.2875 93.5 512.3292\n2.0 184.0 20.662183 13.417399 0.0 13.00000 14.2500 26.0 73.5000\n3.0 491.0 13.675550 11.778142 0.0 7.75000 8.0500 15.5 69.5500","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0216.084.15468778.3803730.030.9239560.287593.5512.3292
2.0184.020.66218313.4173990.013.0000014.250026.073.5000
3.0491.013.67555011.7781420.07.750008.050015.569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_train.Fare.median()\nIQR_fare = titanic_train.Fare.quantile(0.75) - titanic_train.Fare.quantile(0.25)\ntitanic_train.loc[:,\"Fare\"] = (titanic_train.Fare - median_fare)/IQR_fare\nplt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.867034Z","iopub.execute_input":"2023-02-01T14:50:49.867360Z","iopub.status.idle":"2023-02-01T14:50:51.334840Z","shell.execute_reply.started":"2023-02-01T14:50:49.867301Z","shell.execute_reply":"2023-02-01T14:50:51.334033Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:51.336034Z","iopub.execute_input":"2023-02-01T14:50:51.336529Z","iopub.status.idle":"2023-02-01T14:50:52.406610Z","shell.execute_reply.started":"2023-02-01T14:50:51.336498Z","shell.execute_reply":"2023-02-01T14:50:52.405714Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.407853Z","iopub.execute_input":"2023-02-01T14:50:52.408376Z","iopub.status.idle":"2023-02-01T14:50:52.415841Z","shell.execute_reply.started":"2023-02-01T14:50:52.408342Z","shell.execute_reply":"2023-02-01T14:50:52.414785Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We repeat the same process with the test dataset. The distribution is much different and therefore could lower the accuracy of the prediction.","metadata":{}},{"cell_type":"code","source":"titanic_test.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.418261Z","iopub.execute_input":"2023-02-01T14:50:52.418629Z","iopub.status.idle":"2023-02-01T14:50:52.472603Z","shell.execute_reply.started":"2023-02-01T14:50:52.418596Z","shell.execute_reply":"2023-02-01T14:50:52.471219Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nPclass \n1.0 107.0 94.280297 84.435858 0.0000 30.10 60.0000 134.500000 \n2.0 93.0 22.202104 13.991877 9.6875 13.00 15.7500 26.000000 \n3.0 218.0 12.397936 10.817256 -1.0000 7.75 7.8958 14.327075 \n\n max \nPclass \n1.0 512.3292 \n2.0 73.5000 \n3.0 69.5500 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0107.094.28029784.4358580.000030.1060.0000134.500000512.3292
2.093.022.20210413.9918779.687513.0015.750026.00000073.5000
3.0218.012.39793610.817256-1.00007.757.895814.32707569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_test.Fare.median()\nIQR_fare = titanic_test.Fare.quantile(0.75) - titanic_test.Fare.quantile(0.25)\ntitanic_test.loc[:,\"Fare\"] = (titanic_test.Fare - median_fare)/IQR_fare\nplt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.473824Z","iopub.execute_input":"2023-02-01T14:50:52.474155Z","iopub.status.idle":"2023-02-01T14:50:53.560939Z","shell.execute_reply.started":"2023-02-01T14:50:52.474123Z","shell.execute_reply":"2023-02-01T14:50:53.559872Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:53.562396Z","iopub.execute_input":"2023-02-01T14:50:53.562797Z","iopub.status.idle":"2023-02-01T14:50:54.622056Z","shell.execute_reply.started":"2023-02-01T14:50:53.562764Z","shell.execute_reply":"2023-02-01T14:50:54.620862Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.623442Z","iopub.execute_input":"2023-02-01T14:50:54.623854Z","iopub.status.idle":"2023-02-01T14:50:54.631562Z","shell.execute_reply.started":"2023-02-01T14:50:54.623823Z","shell.execute_reply":"2023-02-01T14:50:54.630628Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age\nWe normalise the age to bring more the data towards the median. The previous transformation have brought more data centrally.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.632748Z","iopub.execute_input":"2023-02-01T14:50:54.633113Z","iopub.status.idle":"2023-02-01T14:50:54.995183Z","shell.execute_reply.started":"2023-02-01T14:50:54.633084Z","shell.execute_reply":"2023-02-01T14:50:54.993205Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAASdUlEQVR4nO3dfYwk913n8ffn7BDAyWEbz632bO+NA4tRQGQdRo5RHmRiHtYGxQlCxiuUOOC7TSRbl9xF4uwgEeCE5LvLw4HuzmiDfXZOYeMQx8QKBrL4IiJOxMmsszjrJ7xO1nhX693BCXFEUJS1v/zRNZf2ZNY7M9U93fPb90tqddWvqrq+213z2apfV1elqpAkteVfTLoASdLoGe6S1CDDXZIaZLhLUoMMd0lq0OmTLgDgnHPOqdnZ2UmXIUkbyt69e/++qmaWmzYV4T47O8v8/Pyky5CkDSXJEyeaZreMJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUEnDfck5yf5dJKHkjyY5B1d+9lJ9iR5rHs+q2tPkt9LciDJA0leOe5/hCTp+VbyC9XjwLuq6v4kLwX2JtkDvBW4t6puSnIDcAPwn4DLga3d41XAzd2z9DyzN/zJ88YP3vRzE6pEas9J99yr6khV3d8Nfx14GDgXuBK4vZvtduCN3fCVwIdq4LPAmUk2j7pwSdKJrarPPckscBFwH7Cpqo50k54CNnXD5wJPDi12qGtb+lo7k8wnmV9YWFht3ZKkF7DicE/yEuBO4J1V9czwtBrciHVVN2Otql1VNVdVczMzy17UTJK0RisK9yQvYhDsH66qj3fNRxe7W7rnY137YeD8ocXP69okSetkJWfLBLgFeLiq3j806W7gmm74GuATQ+1v6c6auQT42lD3jSRpHazkbJlXA28GvphkX9f2buAm4KNJrgWeAK7qpt0DXAEcAL4B/MooC5YkndxJw72q/grICSZftsz8BVzXsy5JUg/+QlWSGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KCV3Gbv1iTHkuwfarsjyb7ucXDxDk1JZpP809C03x9j7ZKkE1jJbfZuA/4H8KHFhqr6pcXhJO8DvjY0/+NVtW1E9UmS1mAlt9n7TJLZ5aZ1N8++Cnj9iOuSJPXQt8/9tcDRqnpsqO2CJF9I8pdJXnuiBZPsTDKfZH5hYaFnGZKkYX3DfQewe2j8CLClqi4C/iPwh0n+5XILVtWuqpqrqrmZmZmeZUiShq053JOcDvwCcMdiW1V9s6qe7ob3Ao8DP9S3SEnS6vTZc/8p4JGqOrTYkGQmyWnd8MuArcCX+pUoSVqtlZwKuRv4a+DCJIeSXNtNuprnd8kAvA54oDs18mPA26vqKyOsV5K0Ais5W2bHCdrfukzbncCd/cuSJPXhL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQSu5zd6tSY4l2T/U9ptJDifZ1z2uGJp2Y5IDSR5N8rPjKlySdGIr2XO/Ddi+TPsHqmpb97gHIMnLGdxb9Ue6Zf7X4g2zJUnr56ThXlWfAVZ6k+srgY9U1Ter6svAAeDiHvVJktagT5/79Uke6LptzurazgWeHJrnUNf2HZLsTDKfZH5hYaFHGZKkpdYa7jcDPwBsA44A71vtC1TVrqqaq6q5mZmZNZYhSVrOmsK9qo5W1bNV9RzwQb7d9XIYOH9o1vO6NknSOlpTuCfZPDT6JmDxTJq7gauTvDjJBcBW4HP9SpQkrdbpJ5shyW7gUuCcJIeA9wCXJtkGFHAQeBtAVT2Y5KPAQ8Bx4LqqenYslUuSTuik4V5VO5ZpvuUF5v8d4Hf6FCVJ6sdfqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGnTScE9ya5JjSfYPtf23JI8keSDJXUnO7Npnk/xTkn3d4/fHWLsk6QRWsud+G7B9Sdse4Eer6seAvwVuHJr2eFVt6x5vH02ZkqTVOGm4V9VngK8saftUVR3vRj8LnDeG2iRJazSKPvdfBf50aPyCJF9I8pdJXjuC15ckrdJJb5D9QpL8OnAc+HDXdATYUlVPJ/lx4I+T/EhVPbPMsjuBnQBbtmzpU4YkaYk177kneSvw88AvV1UBVNU3q+rpbngv8DjwQ8stX1W7qmququZmZmbWWoYkaRlrCvck24FfA95QVd8Yap9Jclo3/DJgK/ClURQqSVq5k3bLJNkNXAqck+QQ8B4GZ8e8GNiTBOCz3ZkxrwN+O8m3gOeAt1fVV5Z9YUnS2Jw03KtqxzLNt5xg3juBO/sWJUnqx1+oSlKDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoNWFO5Jbk1yLMn+obazk+xJ8lj3fFbXniS/l+RAkgeSvHJcxUuSlrfSPffbgO1L2m4A7q2qrcC93TjA5QxujL0V2Anc3L9MSdJqrCjcq+ozwNIbXV8J3N4N3w68caj9QzXwWeDMJJtHUKskaYX69Llvqqoj3fBTwKZu+FzgyaH5DnVtz5NkZ5L5JPMLCws9ypAkLTWSL1SrqoBa5TK7qmququZmZmZGUYYkqdMn3I8udrd0z8e69sPA+UPznde1SZLWSZ9wvxu4phu+BvjEUPtburNmLgG+NtR9I0laB6evZKYku4FLgXOSHALeA9wEfDTJtcATwFXd7PcAVwAHgG8AvzLimiVJJ7GicK+qHSeYdNky8xZwXZ+iJEn9+AtVSWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCK7sS0nCQXAncMNb0M+A3gTODfAQtd+7ur6p61rkeStHprDveqehTYBpDkNOAwcBeDe6Z+oKreO4oCJUmrN6pumcuAx6vqiRG9niSph1GF+9XA7qHx65M8kOTWJGctt0CSnUnmk8wvLCwsN4skaY16h3uS7wLeAPxR13Qz8AMMumyOAO9bbrmq2lVVc1U1NzMz07cMSdKQUey5Xw7cX1VHAarqaFU9W1XPAR8ELh7BOiRJqzCKcN/BUJdMks1D094E7B/BOiRJq7Dms2UAkpwB/DTwtqHm/5pkG1DAwSXTJEnroFe4V9U/At+/pO3NvSqSJPXmL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ3qdbMOgCQHga8DzwLHq2ouydnAHcAsg7sxXVVVX+27LknSyoxqz/0nq2pbVc114zcA91bVVuDeblyStE7G1S1zJXB7N3w78MYxrUeStIxRhHsBn0qyN8nOrm1TVR3php8CNo1gPZKkFerd5w68pqoOJ/lXwJ4kjwxPrKpKUksX6v4j2AmwZcuWEZQhSVrUe8+9qg53z8eAu4CLgaNJNgN0z8eWWW5XVc1V1dzMzEzfMiRJQ3qFe5Izkrx0cRj4GWA/cDdwTTfbNcAn+qxHkrQ6fbtlNgF3JVl8rT+sqj9L8nngo0muBZ4Aruq5HknSKvQK96r6EvCKZdqfBi7r89qSpLXzF6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGjuM2eGjZ7w588b/zgTT83oUokrYZ77pLUIMNdkhq05nBPcn6STyd5KMmDSd7Rtf9mksNJ9nWPK0ZXriRpJfr0uR8H3lVV93c3yd6bZE837QNV9d7+5Wnc7FOX2rTmcK+qI8CRbvjrSR4Gzh1VYVo5A1rSUiM5WybJLHARcB/wauD6JG8B5hns3X91mWV2AjsBtmzZMooy1DlZ2C+dPqk6JI1P73BP8hLgTuCdVfVMkpuB/wxU9/w+4FeXLldVu4BdAHNzc9W3Dq0PA1vaGHqdLZPkRQyC/cNV9XGAqjpaVc9W1XPAB4GL+5cpSVqNNe+5JwlwC/BwVb1/qH1z1x8P8CZgf78S1Yr16g5abl0eYehU06db5tXAm4EvJtnXtb0b2JFkG4NumYPA23qsQ5K0Bn3OlvkrIMtMumft5UiSRsFry2hqDHelrLYbZT27fKSNwHBXL+MKVfvMpX4M9w2iz15tCwx7aXW8cJgkNcg991PAarpONkrf9UapU5qUJsL9VO+yUD92+ahFTYT7qca91vFazbV5/I9A08pwl8bIowJNil+oSlKD3HPXKcduLZ0Kmgv31V7L3MNkbRRuu1qN5sJdmqTVHhW80PyGt/ow3HtwT+rU0EI3jtvqqcdw1ymhhYBeysDWC2k+3DfKH/VGqVPrp08Xj0Gv5sNdGqdR9rFP0gsdBXiEsDGd8uG+mg3XPSnpO/mL3uk0tnBPsh34XeA04A+q6qZxrWuUpnXPStoo+vwNjfMo4VQ7AhlLuCc5DfifwE8Dh4DPJ7m7qh4ax/qmRYtXX1SbTrb9vdD0UYb3OJ3qf2Pj2nO/GDhQVV8CSPIR4Eqg6XB/Iaf6hqb11cr2Nqm97RaOIFJVo3/R5BeB7VX1b7vxNwOvqqrrh+bZCezsRi8EHl3Dqs4B/r5nueNgXas3rbVZ1+pMa10wvbX1qevfVNXMchMm9oVqVe0CdvV5jSTzVTU3opJGxrpWb1prs67Vmda6YHprG1dd47oq5GHg/KHx87o2SdI6GFe4fx7YmuSCJN8FXA3cPaZ1SZKWGEu3TFUdT3I98OcMToW8taoeHMOqenXrjJF1rd601mZdqzOtdcH01jaWusbyhaokabK8E5MkNchwl6QGbchwT7I9yaNJDiS5YcK13JrkWJL9Q21nJ9mT5LHu+awJ1HV+kk8neSjJg0neMQ21JfnuJJ9L8jddXb/VtV+Q5L7uM72j+yJ+3SU5LckXknxyyuo6mOSLSfYlme/apmE7OzPJx5I8kuThJD8x6bqSXNi9T4uPZ5K8c9J1dbX9h267359kd/f3MJZtbMOF+9ClDS4HXg7sSPLyCZZ0G7B9SdsNwL1VtRW4txtfb8eBd1XVy4FLgOu692nStX0TeH1VvQLYBmxPcgnwX4APVNUPAl8Frl3nuha9A3h4aHxa6gL4yaraNnRO9KQ/SxhcP+rPquqHgVcweO8mWldVPdq9T9uAHwe+Adw16bqSnAv8e2Cuqn6UwckmVzOubayqNtQD+Angz4fGbwRunHBNs8D+ofFHgc3d8Gbg0Sl43z7B4Fo/U1Mb8L3A/cCrGPxC7/TlPuN1rOc8Bn/0rwc+CWQa6urWfRA4Z0nbRD9L4PuAL9OdmDEtdS2p5WeA/zcNdQHnAk8CZzM4U/GTwM+OaxvbcHvufPsNWnSoa5smm6rqSDf8FLBpksUkmQUuAu5jCmrruj72AceAPcDjwD9U1fFulkl9pv8d+DXguW78+6ekLoACPpVkb3fpDpj8Z3kBsAD8764r6w+SnDEFdQ27GtjdDU+0rqo6DLwX+DvgCPA1YC9j2sY2YrhvKDX473hi55smeQlwJ/DOqnpmeNqkaquqZ2twyHweg4vM/fB617BUkp8HjlXV3knXcgKvqapXMuiOvC7J64YnTuizPB14JXBzVV0E/CNLujomuf13fddvAP5o6bRJ1NX18V/J4D/Ffw2cwXd26Y7MRgz3jXBpg6NJNgN0z8cmUUSSFzEI9g9X1cenqTaAqvoH4NMMDkXPTLL4o7pJfKavBt6Q5CDwEQZdM787BXUB/3+vj6o6xqD/+GIm/1keAg5V1X3d+McYhP2k61p0OXB/VR3txidd108BX66qhar6FvBxBtvdWLaxjRjuG+HSBncD13TD1zDo715XSQLcAjxcVe+fltqSzCQ5sxv+HgbfAzzMIOR/cVJ1VdWNVXVeVc0y2Kb+b1X98qTrAkhyRpKXLg4z6Efez4Q/y6p6CngyyYVd02UMLus98e2/s4Nvd8nA5Ov6O+CSJN/b/X0uvl/j2cYm9UVHzy8mrgD+lkFf7a9PuJbdDPrPvsVgT+ZaBn219wKPAX8BnD2Bul7D4LDzAWBf97hi0rUBPwZ8oatrP/AbXfvLgM8BBxgcRr94gp/ppcAnp6Wuroa/6R4PLm7zk/4suxq2AfPd5/nHwFlTUtcZwNPA9w21TUNdvwU80m37/wd48bi2MS8/IEkN2ojdMpKkkzDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+GQQx1jIEIXnhAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_train.Age.median()\nIQR_age = titanic_train.Age.quantile(0.75) - titanic_train.Age.quantile(0.25)\ntitanic_train.loc[:,\"Age\"] = (titanic_train.Age - median_age)/IQR_age\ntitanic_train.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.996341Z","iopub.execute_input":"2023-02-01T14:50:54.996637Z","iopub.status.idle":"2023-02-01T14:50:55.012393Z","shell.execute_reply.started":"2023-02-01T14:50:54.996609Z","shell.execute_reply":"2023-02-01T14:50:55.011269Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.014533Z","iopub.execute_input":"2023-02-01T14:50:55.015228Z","iopub.status.idle":"2023-02-01T14:50:55.377136Z","shell.execute_reply.started":"2023-02-01T14:50:55.015184Z","shell.execute_reply":"2023-02-01T14:50:55.376023Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.378688Z","iopub.execute_input":"2023-02-01T14:50:55.379745Z","iopub.status.idle":"2023-02-01T14:50:55.727506Z","shell.execute_reply.started":"2023-02-01T14:50:55.379709Z","shell.execute_reply":"2023-02-01T14:50:55.726302Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6N52X3A08A/AWdOKds7Gb2c/B7wWHd571DydRl/B3i0y/g48Jfd+t8GvgMcYvRS+denfJzfDTwwtGxdlu92lyeO/V0M7BhfBMx3x/gfgTMGlu804D+AN4+tG0y+SS6eoSpJDdqs0zKSpBOw3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatD/AmLJbG6fuoYqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_test.Age.median()\nIQR_age = titanic_test.Age.quantile(0.75) - titanic_test.Age.quantile(0.25)\ntitanic_test.loc[:,\"Age\"] = (titanic_test.Age - median_age)/IQR_age\ntitanic_test.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.731833Z","iopub.execute_input":"2023-02-01T14:50:55.732609Z","iopub.status.idle":"2023-02-01T14:50:55.747180Z","shell.execute_reply.started":"2023-02-01T14:50:55.732557Z","shell.execute_reply":"2023-02-01T14:50:55.746071Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.748583Z","iopub.execute_input":"2023-02-01T14:50:55.748898Z","iopub.status.idle":"2023-02-01T14:50:56.093344Z","shell.execute_reply.started":"2023-02-01T14:50:55.748868Z","shell.execute_reply":"2023-02-01T14:50:56.092150Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Gender \nWe replace the male with 1 and female with the value 2.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \", titanic_train['Sex'].unique())\nprint(\"Test : \", titanic_train['Sex'].unique())\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.094765Z","iopub.execute_input":"2023-02-01T14:50:56.095091Z","iopub.status.idle":"2023-02-01T14:50:56.103516Z","shell.execute_reply.started":"2023-02-01T14:50:56.095062Z","shell.execute_reply":"2023-02-01T14:50:56.102411Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTest : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_train[\"Sex\"] = titanic_train[\"Sex\"].astype(float)\ntitanic_train.groupby(\"Sex\").count()[\"PassengerId\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.104821Z","iopub.execute_input":"2023-02-01T14:50:56.105350Z","iopub.status.idle":"2023-02-01T14:50:56.122953Z","shell.execute_reply.started":"2023-02-01T14:50:56.105306Z","shell.execute_reply":"2023-02-01T14:50:56.122030Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 577\n2.0 314\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_test[\"Sex\"] = titanic_test[\"Sex\"].astype(float)\ntitanic_test.groupby(\"Sex\").count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.124259Z","iopub.execute_input":"2023-02-01T14:50:56.124612Z","iopub.status.idle":"2023-02-01T14:50:56.139408Z","shell.execute_reply.started":"2023-02-01T14:50:56.124581Z","shell.execute_reply":"2023-02-01T14:50:56.138058Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 266\n2.0 152\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sibling and parentage\n\nWe add both sibling, parents, and children into a family variables. ","metadata":{}},{"cell_type":"code","source":"titanic_train[\"SibSp\"].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.141402Z","iopub.execute_input":"2023-02-01T14:50:56.141813Z","iopub.status.idle":"2023-02-01T14:50:56.148230Z","shell.execute_reply.started":"2023-02-01T14:50:56.141777Z","shell.execute_reply":"2023-02-01T14:50:56.147382Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"array([1., 0., 3., 4., 2., 5., 8.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Parch\"].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.149745Z","iopub.execute_input":"2023-02-01T14:50:56.150349Z","iopub.status.idle":"2023-02-01T14:50:56.159952Z","shell.execute_reply.started":"2023-02-01T14:50:56.150294Z","shell.execute_reply":"2023-02-01T14:50:56.158924Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"array([0., 1., 2., 5., 3., 4., 6.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\ntitanic_train[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.161871Z","iopub.execute_input":"2023-02-01T14:50:56.162175Z","iopub.status.idle":"2023-02-01T14:50:56.176837Z","shell.execute_reply.started":"2023-02-01T14:50:56.162147Z","shell.execute_reply":"2023-02-01T14:50:56.175684Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.178360Z","iopub.execute_input":"2023-02-01T14:50:56.178747Z","iopub.status.idle":"2023-02-01T14:50:56.191340Z","shell.execute_reply.started":"2023-02-01T14:50:56.178698Z","shell.execute_reply":"2023-02-01T14:50:56.190355Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.195050Z","iopub.execute_input":"2023-02-01T14:50:56.195448Z","iopub.status.idle":"2023-02-01T14:50:56.209129Z","shell.execute_reply.started":"2023-02-01T14:50:56.195400Z","shell.execute_reply":"2023-02-01T14:50:56.207967Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.210664Z","iopub.execute_input":"2023-02-01T14:50:56.211090Z","iopub.status.idle":"2023-02-01T14:50:56.219640Z","shell.execute_reply.started":"2023-02-01T14:50:56.211049Z","shell.execute_reply":"2023-02-01T14:50:56.218550Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.221452Z","iopub.execute_input":"2023-02-01T14:50:56.222189Z","iopub.status.idle":"2023-02-01T14:50:56.231508Z","shell.execute_reply.started":"2023-02-01T14:50:56.222146Z","shell.execute_reply":"2023-02-01T14:50:56.230398Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.232676Z","iopub.execute_input":"2023-02-01T14:50:56.233089Z","iopub.status.idle":"2023-02-01T14:50:56.242657Z","shell.execute_reply.started":"2023-02-01T14:50:56.233048Z","shell.execute_reply":"2023-02-01T14:50:56.241737Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.244097Z","iopub.execute_input":"2023-02-01T14:50:56.244427Z","iopub.status.idle":"2023-02-01T14:50:56.251459Z","shell.execute_reply.started":"2023-02-01T14:50:56.244398Z","shell.execute_reply":"2023-02-01T14:50:56.250542Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.256041Z","iopub.execute_input":"2023-02-01T14:50:56.256485Z","iopub.status.idle":"2023-02-01T14:50:56.265940Z","shell.execute_reply.started":"2023-02-01T14:50:56.256450Z","shell.execute_reply":"2023-02-01T14:50:56.264711Z"},"trusted":true},"execution_count":113,"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.267253Z","iopub.execute_input":"2023-02-01T14:50:56.267740Z","iopub.status.idle":"2023-02-01T14:50:56.278020Z","shell.execute_reply.started":"2023-02-01T14:50:56.267696Z","shell.execute_reply":"2023-02-01T14:50:56.276748Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"titanic_test[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.315420Z","iopub.execute_input":"2023-02-01T14:50:56.315791Z","iopub.status.idle":"2023-02-01T14:50:56.322971Z","shell.execute_reply.started":"2023-02-01T14:50:56.315760Z","shell.execute_reply":"2023-02-01T14:50:56.322090Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## Columns to drop \nWe drop some columns; they may have too many unknown values. Some of them may be dependent statistical variables. We assume the price of a ticket may be dependent of the fare. ","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Name\", axis = 1, inplace = True)\ntitanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_train.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.722307Z","iopub.execute_input":"2023-02-01T14:50:56.722753Z","iopub.status.idle":"2023-02-01T14:50:56.744122Z","shell.execute_reply.started":"2023-02-01T14:50:56.722718Z","shell.execute_reply":"2023-02-01T14:50:56.743299Z"},"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.drop(\"Name\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_test.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.963356Z","iopub.execute_input":"2023-02-01T14:50:56.963753Z","iopub.status.idle":"2023-02-01T14:50:56.979754Z","shell.execute_reply.started":"2023-02-01T14:50:56.963719Z","shell.execute_reply":"2023-02-01T14:50:56.978543Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We make of both datasets. These copies will be used to analysed the predictions values from all the classifiers.","metadata":{}},{"cell_type":"code","source":"results_test = titanic_test.copy(deep = True)\nresults_train = titanic_train.copy(deep = True) ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:57.429442Z","iopub.execute_input":"2023-02-01T14:50:57.429827Z","iopub.status.idle":"2023-02-01T14:50:57.435755Z","shell.execute_reply.started":"2023-02-01T14:50:57.429796Z","shell.execute_reply":"2023-02-01T14:50:57.434439Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"markdown","source":"# Method : Logistic regression\n\nOur first classifier is a logistic regression. We surmise it may be the most suitable methods as two classes of labels exist; survived or not. The data is imbalanced towards perishing sadly. So we add some class weight to represent this situation in the data. \n\nWe choose the passenger class, sex, familly members. We surmise the passenger class, gender and being part of a familly or not may have influenced surviving the accident. The training dataset is split into training and validation for validating the model fitting. ","metadata":{}},{"cell_type":"markdown","source":"## Preparation Cross validation \nWe show how the transformation have affected both datasets","metadata":{}},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.108812Z","iopub.execute_input":"2023-02-01T14:50:58.109845Z","iopub.status.idle":"2023-02-01T14:50:58.118552Z","shell.execute_reply.started":"2023-02-01T14:50:58.109806Z","shell.execute_reply":"2023-02-01T14:50:58.117356Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.354904Z","iopub.execute_input":"2023-02-01T14:50:58.355573Z","iopub.status.idle":"2023-02-01T14:50:58.362764Z","shell.execute_reply.started":"2023-02-01T14:50:58.355531Z","shell.execute_reply":"2023-02-01T14:50:58.361542Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"(891, 8)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.590264Z","iopub.execute_input":"2023-02-01T14:50:58.591668Z","iopub.status.idle":"2023-02-01T14:50:58.600773Z","shell.execute_reply.started":"2023-02-01T14:50:58.591627Z","shell.execute_reply":"2023-02-01T14:50:58.599216Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.804713Z","iopub.execute_input":"2023-02-01T14:50:58.805085Z","iopub.status.idle":"2023-02-01T14:50:58.812599Z","shell.execute_reply.started":"2023-02-01T14:50:58.805054Z","shell.execute_reply":"2023-02-01T14:50:58.811376Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"(418, 7)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Split data sets for cross validation\n\nWe use a stratified shuffle split to aim at reducing the variation between the training and validation datasets.","metadata":{}},{"cell_type":"code","source":"\n\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n#X = X[x_cols]\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.267374Z","iopub.execute_input":"2023-02-01T14:50:59.267771Z","iopub.status.idle":"2023-02-01T14:50:59.288554Z","shell.execute_reply.started":"2023-02-01T14:50:59.267735Z","shell.execute_reply":"2023-02-01T14:50:59.287476Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We keep the passengers ids for building up the training dataset results. It will be used to compare all the classifier.","metadata":{}},{"cell_type":"code","source":"x_train_pass_id = X_train[\"PassengerId\"]\nx_train_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.704437Z","iopub.execute_input":"2023-02-01T14:50:59.704815Z","iopub.status.idle":"2023-02-01T14:50:59.714204Z","shell.execute_reply.started":"2023-02-01T14:50:59.704783Z","shell.execute_reply":"2023-02-01T14:50:59.713337Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"844 845.0\n316 317.0\n768 769.0\n255 256.0\n130 131.0\n ... \n476 477.0\n58 59.0\n736 737.0\n462 463.0\n747 748.0\nName: PassengerId, Length: 534, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"x_cols =[\"Pclass\",\"Sex\",\"fam_members\"]\nX_train = X_train[x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.933799Z","iopub.execute_input":"2023-02-01T14:50:59.934191Z","iopub.status.idle":"2023-02-01T14:50:59.947540Z","shell.execute_reply.started":"2023-02-01T14:50:59.934158Z","shell.execute_reply":"2023-02-01T14:50:59.946577Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n844 3.0 1.0 0.0\n316 2.0 2.0 1.0\n768 3.0 1.0 1.0\n255 3.0 2.0 2.0\n130 3.0 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
8443.01.00.0
3162.02.01.0
7683.01.01.0
2553.02.02.0
1303.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid[\"PassengerId\"]\nx_valid_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.149697Z","iopub.execute_input":"2023-02-01T14:51:00.150120Z","iopub.status.idle":"2023-02-01T14:51:00.160439Z","shell.execute_reply.started":"2023-02-01T14:51:00.150083Z","shell.execute_reply":"2023-02-01T14:51:00.159106Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"369 370.0\n541 542.0\n196 197.0\n810 811.0\n427 428.0\n ... \n174 175.0\n297 298.0\n244 245.0\n38 39.0\n371 372.0\nName: PassengerId, Length: 357, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.356159Z","iopub.execute_input":"2023-02-01T14:51:00.357062Z","iopub.status.idle":"2023-02-01T14:51:00.370786Z","shell.execute_reply.started":"2023-02-01T14:51:00.357017Z","shell.execute_reply":"2023-02-01T14:51:00.369619Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n369 1.0 2.0 0.0\n541 3.0 2.0 6.0\n196 3.0 1.0 0.0\n810 3.0 1.0 0.0\n427 2.0 2.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
3691.02.00.0
5413.02.06.0
1963.01.00.0
8103.01.00.0
4272.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test.copy(deep = True)\nX_test = X_test[x_cols]\nX_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.599111Z","iopub.execute_input":"2023-02-01T14:51:00.599521Z","iopub.status.idle":"2023-02-01T14:51:00.616521Z","shell.execute_reply.started":"2023-02-01T14:51:00.599483Z","shell.execute_reply":"2023-02-01T14:51:00.615356Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n0 3.0 1.0 0.0\n1 3.0 2.0 1.0\n2 2.0 1.0 0.0\n3 3.0 1.0 0.0\n4 3.0 2.0 2.0\n.. ... ... ...\n413 3.0 1.0 0.0\n414 1.0 2.0 0.0\n415 3.0 1.0 0.0\n416 3.0 1.0 0.0\n417 3.0 1.0 2.0\n\n[418 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
03.01.00.0
13.02.01.0
22.01.00.0
33.01.00.0
43.02.02.0
............
4133.01.00.0
4141.02.00.0
4153.01.00.0
4163.01.00.0
4173.01.02.0
\n

418 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model fitting","metadata":{}},{"cell_type":"markdown","source":"We fit the model using a stochastic average gradient. We achieve approximately 82% accuracy on the validation dataset. There is not sign of over fitting. ","metadata":{}},{"cell_type":"code","source":"classifier = LogisticRegression(random_state = 0, C = 1000, max_iter= 10000, \n solver=\"sag\", penalty=\"l2\",class_weight={0:6.,1:4})\nclassifier.fit(X_train, y_train)\nclassifier.coef_","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.275079Z","iopub.execute_input":"2023-02-01T14:51:01.275483Z","iopub.status.idle":"2023-02-01T14:51:01.291372Z","shell.execute_reply.started":"2023-02-01T14:51:01.275450Z","shell.execute_reply":"2023-02-01T14:51:01.290133Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([[-0.96687438, 2.71046703, -0.09242397]])"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_train = classifier.score(X_train, y_train)\nlog_reg_score_train","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.505908Z","iopub.execute_input":"2023-02-01T14:51:01.507102Z","iopub.status.idle":"2023-02-01T14:51:01.519460Z","shell.execute_reply.started":"2023-02-01T14:51:01.507059Z","shell.execute_reply":"2023-02-01T14:51:01.518123Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"0.7921348314606742"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_valid = classifier.score(X_valid, y_valid)\nlog_reg_score_valid","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.727365Z","iopub.execute_input":"2023-02-01T14:51:01.727743Z","iopub.status.idle":"2023-02-01T14:51:01.737787Z","shell.execute_reply.started":"2023-02-01T14:51:01.727712Z","shell.execute_reply":"2023-02-01T14:51:01.736406Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"0.8207282913165266"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nTwo confusion matrices show an improvement on predicting the validation dataset. We also store the predicted results in the results_train dataframe. We will use this dataframe later on to analyse difference between classifiers. \n\n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = classifier.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.212411Z","iopub.execute_input":"2023-02-01T14:51:02.212812Z","iopub.status.idle":"2023-02-01T14:51:02.223463Z","shell.execute_reply.started":"2023-02-01T14:51:02.212779Z","shell.execute_reply":"2023-02-01T14:51:02.222427Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"array([[297, 32],\n [ 79, 126]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.417280Z","iopub.execute_input":"2023-02-01T14:51:02.417687Z","iopub.status.idle":"2023-02-01T14:51:02.426591Z","shell.execute_reply.started":"2023-02-01T14:51:02.417653Z","shell.execute_reply":"2023-02-01T14:51:02.425177Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"Accuracy : 0.7921348314606742\nMisclassfication : 0.20786516853932585\nSensitivivity : 0.9027355623100304\nSpecificity : 0.6146341463414634\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = classifier.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.661227Z","iopub.execute_input":"2023-02-01T14:51:02.661653Z","iopub.status.idle":"2023-02-01T14:51:02.672901Z","shell.execute_reply.started":"2023-02-01T14:51:02.661618Z","shell.execute_reply":"2023-02-01T14:51:02.671790Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.907929Z","iopub.execute_input":"2023-02-01T14:51:02.908917Z","iopub.status.idle":"2023-02-01T14:51:02.916300Z","shell.execute_reply.started":"2023-02-01T14:51:02.908877Z","shell.execute_reply":"2023-02-01T14:51:02.915176Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"Accuracy : 0.8207282913165266\nMisclassfication : 0.1792717086834734\nSensitivivity : 0.9363636363636364\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.367005Z","iopub.execute_input":"2023-02-01T14:51:03.367441Z","iopub.status.idle":"2023-02-01T14:51:03.372440Z","shell.execute_reply.started":"2023-02-01T14:51:03.367404Z","shell.execute_reply":"2023-02-01T14:51:03.371375Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"y_pred = classifier.predict(X_train)\nlog_reg_pred = X_train.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_train\nlog_reg_pred[\"PassengerId\"] = x_train_pass_id\nlog_reg_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.610590Z","iopub.execute_input":"2023-02-01T14:51:03.610967Z","iopub.status.idle":"2023-02-01T14:51:03.632961Z","shell.execute_reply.started":"2023-02-01T14:51:03.610936Z","shell.execute_reply":"2023-02-01T14:51:03.631856Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n844 3.0 1.0 0.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 0.0 769.0\n255 3.0 2.0 2.0 0.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_memberslr_y_predyPassengerId
8443.01.00.00.00.0845.0
3162.02.01.01.01.0317.0
7683.01.01.00.00.0769.0
2553.02.02.00.01.0256.0
1303.01.00.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(log_reg_pred[[\"PassengerId\",\"y\", \"lr_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.870519Z","iopub.execute_input":"2023-02-01T14:51:03.870935Z","iopub.status.idle":"2023-02-01T14:51:03.899083Z","shell.execute_reply.started":"2023-02-01T14:51:03.870900Z","shell.execute_reply":"2023-02-01T14:51:03.898021Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 NaN NaN \n2 0.0 1.0 1.0 \n3 1.0 NaN NaN \n4 0.0 NaN NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.0NaNNaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.0NaNNaN
45.00.03.01.00.384615-0.2773632.00.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = classifier.predict(X_valid)\nlog_reg_pred = X_valid.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_valid\nlog_reg_pred[\"PassengerId\"] = x_valid_pass_id\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.094193Z","iopub.execute_input":"2023-02-01T14:51:04.094610Z","iopub.status.idle":"2023-02-01T14:51:04.120418Z","shell.execute_reply.started":"2023-02-01T14:51:04.094576Z","shell.execute_reply":"2023-02-01T14:51:04.119350Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n369 1.0 2.0 0.0 1.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 1.0 428.0\n.. ... ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 1.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 0.0 0.0 39.0\n371 3.0 1.0 1.0 0.0 0.0 372.0\n\n[357 rows x 6 columns]","text/html":"
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PclassSexfam_memberslr_y_predyPassengerId
3691.02.00.01.01.0370.0
5413.02.06.00.00.0542.0
1963.01.00.00.00.0197.0
8103.01.00.00.00.0811.0
4272.02.00.01.01.0428.0
.....................
1741.01.00.00.00.0175.0
2971.02.03.01.00.0298.0
2443.01.00.00.00.0245.0
383.02.02.00.00.039.0
3713.01.01.00.00.0372.0
\n

357 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"y\"] = log_reg_pred[\"y\"]\nresults_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"lr_y_pred\"] = log_reg_pred[\"lr_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.330333Z","iopub.execute_input":"2023-02-01T14:51:04.330729Z","iopub.status.idle":"2023-02-01T14:51:04.353404Z","shell.execute_reply.started":"2023-02-01T14:51:04.330694Z","shell.execute_reply":"2023-02-01T14:51:04.352359Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 1.0 1.0 \n2 0.0 1.0 1.0 \n3 1.0 1.0 1.0 \n4 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"We start with the training dataset. It may be quite unconventional, but it can help us understanding better the features of the data.","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.059608Z","iopub.execute_input":"2023-02-01T14:51:05.059995Z","iopub.status.idle":"2023-02-01T14:51:05.077377Z","shell.execute_reply.started":"2023-02-01T14:51:05.059959Z","shell.execute_reply":"2023-02-01T14:51:05.076249Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n255 3.0 2.0 2.0 1.0 0.0\n707 1.0 1.0 0.0 1.0 0.0\n172 3.0 2.0 2.0 1.0 0.0\n78 2.0 1.0 2.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
2553.02.02.01.00.0
7071.01.00.01.00.0
1723.02.02.01.00.0
782.01.02.01.00.0
2333.02.06.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We complete the same activities to the validation dataset. It appears many male first class passengers traveling alone may have survived more than we anticipated. ","metadata":{}},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.569495Z","iopub.execute_input":"2023-02-01T14:51:05.569879Z","iopub.status.idle":"2023-02-01T14:51:05.589621Z","shell.execute_reply.started":"2023-02-01T14:51:05.569846Z","shell.execute_reply":"2023-02-01T14:51:05.588487Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n340 2.0 1.0 2.0 1.0 0.0\n534 3.0 2.0 0.0 0.0 1.0\n279 3.0 2.0 2.0 1.0 0.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3402.01.02.01.00.0
5343.02.00.00.01.0
2793.02.02.01.00.0
6071.01.00.01.00.0
8043.01.00.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.798455Z","iopub.execute_input":"2023-02-01T14:51:05.799489Z","iopub.status.idle":"2023-02-01T14:51:05.813581Z","shell.execute_reply.started":"2023-02-01T14:51:05.799450Z","shell.execute_reply":"2023-02-01T14:51:05.812556Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 0.0 3\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 1.0 4\n 2.0 1.0 0.0 4\n 2.0 0.0 4\n 3.0 2.0 0.0 2\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.295513Z","iopub.execute_input":"2023-02-01T14:51:06.296134Z","iopub.status.idle":"2023-02-01T14:51:06.315914Z","shell.execute_reply.started":"2023-02-01T14:51:06.296088Z","shell.execute_reply":"2023-02-01T14:51:06.314875Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n130 3.0 1.0 0.0 0.0 0.0\n110 1.0 1.0 0.0 0.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
1303.01.00.00.00.0
1101.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.545374Z","iopub.execute_input":"2023-02-01T14:51:06.546123Z","iopub.status.idle":"2023-02-01T14:51:06.565170Z","shell.execute_reply.started":"2023-02-01T14:51:06.546085Z","shell.execute_reply":"2023-02-01T14:51:06.564022Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 2.0 1.0 9\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 0.0 3\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 1.0 4\n 2.0 1.0 0.0 10\n 2.0 0.0 5\n 3.0 1.0 0.0 2\n 2.0 0.0 1\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. It appears \n\nOther elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees classifiers. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.019601Z","iopub.execute_input":"2023-02-01T14:51:07.020764Z","iopub.status.idle":"2023-02-01T14:51:07.038884Z","shell.execute_reply.started":"2023-02-01T14:51:07.020723Z","shell.execute_reply":"2023-02-01T14:51:07.037796Z"},"trusted":true},"execution_count":146,"outputs":[{"execution_count":146,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.380694Z","iopub.execute_input":"2023-02-01T14:51:07.381775Z","iopub.status.idle":"2023-02-01T14:51:07.399161Z","shell.execute_reply.started":"2023-02-01T14:51:07.381734Z","shell.execute_reply":"2023-02-01T14:51:07.397965Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 6\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 1.0 11\n 2.0 1.0 0.0 7\n 2.0 0.0 5\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Predict with testing dataset","metadata":{}},{"cell_type":"code","source":"y_pred = classifier.predict(X_test)\nlog_reg_pred = X_test.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.781717Z","iopub.execute_input":"2023-02-01T14:51:07.782101Z","iopub.status.idle":"2023-02-01T14:51:07.809230Z","shell.execute_reply.started":"2023-02-01T14:51:07.782070Z","shell.execute_reply":"2023-02-01T14:51:07.808079Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 1.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 0.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
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PclassSexfam_memberslr_y_predPassengerId
03.01.00.00.0892.0
13.02.01.01.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.00.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
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418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.028966Z","iopub.execute_input":"2023-02-01T14:51:08.030264Z","iopub.status.idle":"2023-02-01T14:51:08.036547Z","shell.execute_reply.started":"2023-02-01T14:51:08.030211Z","shell.execute_reply":"2023-02-01T14:51:08.035240Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.293378Z","iopub.execute_input":"2023-02-01T14:51:08.294544Z","iopub.status.idle":"2023-02-01T14:51:08.309861Z","shell.execute_reply.started":"2023-02-01T14:51:08.294483Z","shell.execute_reply":"2023-02-01T14:51:08.308466Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":" PassengerId lr_y_pred\n0 892.0 0.0\n1 893.0 1.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 0.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdlr_y_pred
0892.00.0
1893.01.0
2894.00.0
3895.00.0
4896.00.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
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418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.513449Z","iopub.execute_input":"2023-02-01T14:51:08.513843Z","iopub.status.idle":"2023-02-01T14:51:08.535503Z","shell.execute_reply.started":"2023-02-01T14:51:08.513810Z","shell.execute_reply":"2023-02-01T14:51:08.534386Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 0.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_pred
0892.03.01.00.431373-0.2810053.00.00.0
1893.03.02.01.411765-0.3161762.01.01.0
2894.02.01.02.588235-0.2021843.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: K-Nearest-neighbourn","metadata":{}},{"cell_type":"markdown","source":"We explore whether a reduction of statistical variables may be beneficial to the classification. We focus our model fitting on the same statistical variables as the logistic regression. \n\n\nThe K-NN classifier overfits to the training dataset. We have yet to find a better result. So Decision tree may have found its limit. ","metadata":{}},{"cell_type":"markdown","source":"## Model fitting\nWe discover the hyper-parametrisation of approximately 7 neighbors and the algorithm set the brute.","metadata":{}},{"cell_type":"code","source":"neighbors = range(2, 100)\nfor neighbor in neighbors:\n knn = KNeighborsClassifier(n_neighbors = neighbor, algorithm=\"brute\", weights = \"distance\", p=2)\n knn.fit(X_train,y_train)\n train_score = knn.score(X_train, y_train)\n valid_score = knn.score(X_valid, y_valid)\n print(\" - n neighbor : \", neighbor , \" - train score : \", train_score, \" - valid score : \", valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:09.565134Z","iopub.execute_input":"2023-02-01T14:51:09.565542Z","iopub.status.idle":"2023-02-01T14:51:12.977246Z","shell.execute_reply.started":"2023-02-01T14:51:09.565506Z","shell.execute_reply":"2023-02-01T14:51:12.975689Z"},"trusted":true},"execution_count":152,"outputs":[{"name":"stdout","text":" - n neighbor : 2 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 3 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 4 - train score : 0.8089887640449438 - valid score : 0.7591036414565826\n - n neighbor : 5 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 6 - train score : 0.8164794007490637 - valid score : 0.7927170868347339\n - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 8 - train score : 0.8202247191011236 - valid score : 0.7899159663865546\n - n neighbor : 9 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 10 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 11 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 12 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 13 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 14 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 15 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 16 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 17 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 18 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 19 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 20 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 21 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 22 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 23 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 24 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 25 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 26 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 27 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 28 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 29 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 30 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 31 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 32 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 33 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 34 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 35 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 36 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 37 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 38 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 39 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 40 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 41 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 42 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 43 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 44 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 45 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 46 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 47 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 48 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 49 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 50 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 51 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 52 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 53 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 54 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 55 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 56 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 57 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 58 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 59 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 60 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 61 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 62 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 63 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 64 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 65 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 66 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 67 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 68 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 69 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 70 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 71 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 72 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 73 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 74 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 75 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 76 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 77 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 78 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 79 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 80 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 81 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 82 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 83 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 84 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 85 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 86 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 87 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 88 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 89 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 90 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 91 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 92 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 93 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 94 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 95 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 96 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 97 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 98 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 99 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors = 7, algorithm=\"brute\", weights = \"distance\", p=2)\nknn.fit(X_train,y_train)\nknn_train_score = knn.score(X_train, y_train)\nknn_valid_score = knn.score(X_valid, y_valid)\nprint(\" - n neighbor : \", 7 , \" - train score : \", knn_train_score, \" - valid score : \", knn_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:12.986323Z","iopub.execute_input":"2023-02-01T14:51:12.992081Z","iopub.status.idle":"2023-02-01T14:51:13.043083Z","shell.execute_reply.started":"2023-02-01T14:51:12.992006Z","shell.execute_reply":"2023-02-01T14:51:13.041491Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":" - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = knn.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.051296Z","iopub.execute_input":"2023-02-01T14:51:13.052276Z","iopub.status.idle":"2023-02-01T14:51:13.094020Z","shell.execute_reply.started":"2023-02-01T14:51:13.052210Z","shell.execute_reply":"2023-02-01T14:51:13.092537Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"array([[299, 30],\n [ 63, 142]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.104344Z","iopub.execute_input":"2023-02-01T14:51:13.109854Z","iopub.status.idle":"2023-02-01T14:51:13.138605Z","shell.execute_reply.started":"2023-02-01T14:51:13.109782Z","shell.execute_reply":"2023-02-01T14:51:13.137094Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"Accuracy : 0.8258426966292135\nMisclassfication : 0.17415730337078653\nSensitivivity : 0.9088145896656535\nSpecificity : 0.6926829268292682\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.141155Z","iopub.execute_input":"2023-02-01T14:51:13.151686Z","iopub.status.idle":"2023-02-01T14:51:13.183541Z","shell.execute_reply.started":"2023-02-01T14:51:13.151614Z","shell.execute_reply":"2023-02-01T14:51:13.181982Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.190465Z","iopub.execute_input":"2023-02-01T14:51:13.191601Z","iopub.status.idle":"2023-02-01T14:51:13.214243Z","shell.execute_reply.started":"2023-02-01T14:51:13.191536Z","shell.execute_reply":"2023-02-01T14:51:13.212831Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"Accuracy : 0.7871148459383753\nMisclassfication : 0.21288515406162464\nSensitivivity : 0.8818181818181818\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.216513Z","iopub.execute_input":"2023-02-01T14:51:13.217361Z","iopub.status.idle":"2023-02-01T14:51:13.226018Z","shell.execute_reply.started":"2023-02-01T14:51:13.217286Z","shell.execute_reply":"2023-02-01T14:51:13.224351Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(X_train)\nknn_pred = X_train.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_train_pass_id\nknn_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.228136Z","iopub.execute_input":"2023-02-01T14:51:13.229804Z","iopub.status.idle":"2023-02-01T14:51:13.289272Z","shell.execute_reply.started":"2023-02-01T14:51:13.229740Z","shell.execute_reply":"2023-02-01T14:51:13.287745Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n844 3.0 1.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 769.0\n255 3.0 2.0 2.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
8443.01.00.00.0845.0
3162.02.01.01.0317.0
7683.01.01.00.0769.0
2553.02.02.01.0256.0
1303.01.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(knn_pred[[\"PassengerId\", \"knn_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.297719Z","iopub.execute_input":"2023-02-01T14:51:13.302941Z","iopub.status.idle":"2023-02-01T14:51:13.361563Z","shell.execute_reply.started":"2023-02-01T14:51:13.302872Z","shell.execute_reply":"2023-02-01T14:51:13.359941Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = knn.predict(X_valid)\nknn_pred = X_valid.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_valid_pass_id\nknn_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.366098Z","iopub.execute_input":"2023-02-01T14:51:13.367128Z","iopub.status.idle":"2023-02-01T14:51:13.414267Z","shell.execute_reply.started":"2023-02-01T14:51:13.367081Z","shell.execute_reply":"2023-02-01T14:51:13.412764Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n369 1.0 2.0 0.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 428.0\n.. ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 1.0 39.0\n371 3.0 1.0 1.0 0.0 372.0\n\n[357 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
3691.02.00.01.0370.0
5413.02.06.00.0542.0
1963.01.00.00.0197.0
8103.01.00.00.0811.0
4272.02.00.01.0428.0
..................
1741.01.00.00.0175.0
2971.02.03.00.0298.0
2443.01.00.00.0245.0
383.02.02.01.039.0
3713.01.01.00.0372.0
\n

357 rows × 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(knn_pred.PassengerId), \"knn_y_pred\"] = knn_pred[\"knn_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.416656Z","iopub.execute_input":"2023-02-01T14:51:13.417577Z","iopub.status.idle":"2023-02-01T14:51:13.474919Z","shell.execute_reply.started":"2023-02-01T14:51:13.417518Z","shell.execute_reply":"2023-02-01T14:51:13.473392Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.552373Z","iopub.execute_input":"2023-02-01T14:51:13.552777Z","iopub.status.idle":"2023-02-01T14:51:13.575185Z","shell.execute_reply.started":"2023-02-01T14:51:13.552741Z","shell.execute_reply":"2023-02-01T14:51:13.573826Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n707 1.0 1.0 0.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0\n788 3.0 1.0 3.0 1.0 0.0\n183 2.0 1.0 3.0 1.0 0.0\n654 3.0 2.0 0.0 0.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
7071.01.00.01.00.0
2333.02.06.01.00.0
7883.01.03.01.00.0
1832.01.03.01.00.0
6543.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.851998Z","iopub.execute_input":"2023-02-01T14:51:13.852446Z","iopub.status.idle":"2023-02-01T14:51:13.868236Z","shell.execute_reply.started":"2023-02-01T14:51:13.852408Z","shell.execute_reply":"2023-02-01T14:51:13.867490Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 2.0 1.0 1\n 1.0 1.0 0.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 2\n 1.0 1.0 0.0 1\n 2.0 1.0 2\n 2.0 1.0 1.0 3\n 2.0 1.0 1\n 3.0 1.0 0.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 0.0 4\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 3.0 1.0 0.0 1\n 2.0 1.0 1\n 6.0 2.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.057420Z","iopub.execute_input":"2023-02-01T14:51:14.057804Z","iopub.status.idle":"2023-02-01T14:51:14.084011Z","shell.execute_reply.started":"2023-02-01T14:51:14.057773Z","shell.execute_reply":"2023-02-01T14:51:14.082464Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.355738Z","iopub.execute_input":"2023-02-01T14:51:14.356164Z","iopub.status.idle":"2023-02-01T14:51:14.375540Z","shell.execute_reply.started":"2023-02-01T14:51:14.356115Z","shell.execute_reply":"2023-02-01T14:51:14.374287Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n534 3.0 2.0 0.0 0.0 1.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0\n429 3.0 1.0 0.0 1.0 0.0\n501 3.0 2.0 0.0 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
5343.02.00.00.01.0
6071.01.00.01.00.0
8043.01.00.01.00.0
4293.01.00.01.00.0
5013.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.597501Z","iopub.execute_input":"2023-02-01T14:51:14.597895Z","iopub.status.idle":"2023-02-01T14:51:14.613504Z","shell.execute_reply.started":"2023-02-01T14:51:14.597865Z","shell.execute_reply":"2023-02-01T14:51:14.612422Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 1.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 1.0 6\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.104177Z","iopub.execute_input":"2023-02-01T14:51:15.104569Z","iopub.status.idle":"2023-02-01T14:51:15.123111Z","shell.execute_reply.started":"2023-02-01T14:51:15.104537Z","shell.execute_reply":"2023-02-01T14:51:15.121935Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n255 3.0 2.0 2.0 1.0 1.0\n130 3.0 1.0 0.0 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
2553.02.02.01.01.0
1303.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.344115Z","iopub.execute_input":"2023-02-01T14:51:15.344558Z","iopub.status.idle":"2023-02-01T14:51:15.362850Z","shell.execute_reply.started":"2023-02-01T14:51:15.344502Z","shell.execute_reply":"2023-02-01T14:51:15.361620Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 4\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 0.0 10\n 2.0 1.0 0.0 10\n 2.0 1.0 8\n 3.0 1.0 0.0 2\n 2.0 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.857448Z","iopub.execute_input":"2023-02-01T14:51:15.857837Z","iopub.status.idle":"2023-02-01T14:51:15.877163Z","shell.execute_reply.started":"2023-02-01T14:51:15.857806Z","shell.execute_reply":"2023-02-01T14:51:15.875923Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.132579Z","iopub.execute_input":"2023-02-01T14:51:16.132970Z","iopub.status.idle":"2023-02-01T14:51:16.150755Z","shell.execute_reply.started":"2023-02-01T14:51:16.132936Z","shell.execute_reply":"2023-02-01T14:51:16.149943Z"},"trusted":true},"execution_count":171,"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 1.0 1\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 0.0 4\n 2.0 1.0 0.0 7\n 2.0 1.0 4\n 3.0 2.0 1.0 2\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower.","metadata":{}},{"cell_type":"markdown","source":"## Prediction on the test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = knn.predict(X_test)\nknn_pred = X_test.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nknn_pred\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.910077Z","iopub.execute_input":"2023-02-01T14:51:16.910492Z","iopub.status.idle":"2023-02-01T14:51:16.964596Z","shell.execute_reply.started":"2023-02-01T14:51:16.910456Z","shell.execute_reply":"2023-02-01T14:51:16.963157Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 0.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 1.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
03.01.00.00.0892.0
13.02.01.00.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.01.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
\n

418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.178878Z","iopub.execute_input":"2023-02-01T14:51:17.179931Z","iopub.status.idle":"2023-02-01T14:51:17.185405Z","shell.execute_reply.started":"2023-02-01T14:51:17.179876Z","shell.execute_reply":"2023-02-01T14:51:17.184219Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"code","source":"knn_pred[[\"PassengerId\",\"knn_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.372559Z","iopub.execute_input":"2023-02-01T14:51:17.372948Z","iopub.status.idle":"2023-02-01T14:51:17.390909Z","shell.execute_reply.started":"2023-02-01T14:51:17.372914Z","shell.execute_reply":"2023-02-01T14:51:17.389533Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" PassengerId knn_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdknn_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(knn_pred[[\"PassengerId\",\"knn_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.671274Z","iopub.execute_input":"2023-02-01T14:51:17.672432Z","iopub.status.idle":"2023-02-01T14:51:17.693960Z","shell.execute_reply.started":"2023-02-01T14:51:17.672382Z","shell.execute_reply":"2023-02-01T14:51:17.692706Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred \n0 0.0 0.0 \n1 1.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 1.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.0
2894.02.01.02.588235-0.2021843.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method : Decision Trees\n\nWe use a decision tree classifier and some automated search of the hyper-parametrisation to discover suitable hyper-parameters and validate the quality of a model. \n","metadata":{}},{"cell_type":"code","source":"\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.109673Z","iopub.execute_input":"2023-02-01T14:51:18.110073Z","iopub.status.idle":"2023-02-01T14:51:18.128404Z","shell.execute_reply.started":"2023-02-01T14:51:18.110036Z","shell.execute_reply":"2023-02-01T14:51:18.127375Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = [\"Fare\",\"Pclass\",\"Sex\",\"Embarked\",\"fam_members\", \"Age\"]\nx_train_pass_id = X_train.PassengerId\nX_train = X_train [x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.370422Z","iopub.execute_input":"2023-02-01T14:51:18.370800Z","iopub.status.idle":"2023-02-01T14:51:18.388440Z","shell.execute_reply.started":"2023-02-01T14:51:18.370767Z","shell.execute_reply":"2023-02-01T14:51:18.387202Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538\n768 0.419921 3.0 1.0 3.0 1.0 0.000000\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769","text/html":"
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FarePclassSexEmbarkedfam_membersAge
844-0.2508363.01.02.00.0-1.000000
3160.5000432.02.02.01.0-0.461538
7680.4199213.01.03.01.00.000000
2550.0342843.02.04.02.0-0.076923
130-0.2840413.01.04.00.00.230769
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nx_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.609801Z","iopub.execute_input":"2023-02-01T14:51:18.610554Z","iopub.status.idle":"2023-02-01T14:51:18.628148Z","shell.execute_reply.started":"2023-02-01T14:51:18.610505Z","shell.execute_reply":"2023-02-01T14:51:18.626956Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154","text/html":"
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FarePclassSexEmbarkedfam_membersAge
3692.3753461.02.04.00.0-0.461538
5410.7285013.02.02.06.0-1.615385
196-0.2903563.01.03.00.00.000000
810-0.2844013.01.02.00.0-0.307692
4270.5000432.02.02.00.0-0.846154
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nX = titanic_test.copy(deep = True)\nX_test = X[x_cols]\nX_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.826406Z","iopub.execute_input":"2023-02-01T14:51:18.826797Z","iopub.status.idle":"2023-02-01T14:51:18.835436Z","shell.execute_reply.started":"2023-02-01T14:51:18.826766Z","shell.execute_reply":"2023-02-01T14:51:18.834526Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"Index(['Fare', 'Pclass', 'Sex', 'Embarked', 'fam_members', 'Age'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Decision Tree classifier\n\nWe explore the maximum depths hyper parameter using a deterministic and incremental search. Then we applied the most efficient parametrisation. We chose a low maximum depth, as the model may be overfitting.","metadata":{}},{"cell_type":"code","source":"\ndepths = range(3, 200)\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth = depth, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n # Train Decision Tree Classifer\n clf = clf.fit(X_train,y_train)\n train_score = clf.score(X_train,y_train)\n valid_score = clf.score(X_valid,y_valid)\n print(\"- depth : \", depth, \" - train score : \", train_score, \" - valid score : \", valid_score)\n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:19.301726Z","iopub.execute_input":"2023-02-01T14:51:19.302125Z","iopub.status.idle":"2023-02-01T14:51:20.492365Z","shell.execute_reply.started":"2023-02-01T14:51:19.302089Z","shell.execute_reply":"2023-02-01T14:51:20.491051Z"},"trusted":true},"execution_count":180,"outputs":[{"name":"stdout","text":"- depth : 3 - train score : 0.8295880149812734 - valid score : 0.8011204481792717\n- depth : 4 - train score : 0.8295880149812734 - valid score : 0.8151260504201681\n- depth : 5 - train score : 0.8595505617977528 - valid score : 0.8067226890756303\n- depth : 6 - train score : 0.8820224719101124 - valid score : 0.8235294117647058\n- depth : 7 - train score : 0.8895131086142322 - valid score : 0.8179271708683473\n- depth : 8 - train score : 0.9063670411985019 - valid score : 0.7927170868347339\n- depth : 9 - train score : 0.9119850187265918 - valid score : 0.7843137254901961\n- depth : 10 - train score : 0.9250936329588015 - valid score : 0.803921568627451\n- depth : 11 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n- depth : 12 - train score : 0.9550561797752809 - valid score : 0.773109243697479\n- depth : 13 - train score : 0.9625468164794008 - valid score : 0.7955182072829131\n- depth : 14 - train score : 0.9662921348314607 - valid score : 0.7787114845938375\n- depth : 15 - train score : 0.9700374531835206 - valid score : 0.7927170868347339\n- depth : 16 - train score : 0.9737827715355806 - valid score : 0.7787114845938375\n- depth : 17 - train score : 0.9756554307116105 - valid score : 0.7871148459383753\n- depth : 18 - train score : 0.9794007490636704 - valid score : 0.7871148459383753\n- depth : 19 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 20 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 21 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 22 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 23 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 24 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 25 - train score : 0.9812734082397003 - valid score : 0.7899159663865546\n- depth : 26 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 27 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 28 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 29 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 30 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 31 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 32 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 33 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 34 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 35 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 36 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 37 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 38 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 39 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 40 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 41 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 42 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 43 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 44 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 45 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 46 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 47 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 48 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 49 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 50 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 51 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 52 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 53 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 54 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 55 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 56 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 57 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 58 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 59 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 60 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 61 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 62 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 63 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 64 - train score : 0.9812734082397003 - 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valid score : 0.7703081232492998\n- depth : 197 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 198 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 199 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(max_depth = 8, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n\n# Train Decision Tree Classifer\nclf = clf.fit(X_train,y_train)\nclf_train_score = clf.score(X_train,y_train)\nclf_valid_score = clf.score(X_valid,y_valid)\nprint(\"- depth : \", 8, \" - train score : \", clf_train_score, \" - valid score : \", clf_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.494270Z","iopub.execute_input":"2023-02-01T14:51:20.494649Z","iopub.status.idle":"2023-02-01T14:51:20.508968Z","shell.execute_reply.started":"2023-02-01T14:51:20.494617Z","shell.execute_reply":"2023-02-01T14:51:20.507560Z"},"trusted":true},"execution_count":181,"outputs":[{"name":"stdout","text":"- depth : 8 - train score : 0.9082397003745318 - valid score : 0.8151260504201681\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover that the gender, Fare and age could be contribute to the classification. It constrast to our previous assumptions for KNN and logistic regression.","metadata":{}},{"cell_type":"code","source":"importances = clf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.510227Z","iopub.execute_input":"2023-02-01T14:51:20.510578Z","iopub.status.idle":"2023-02-01T14:51:20.523335Z","shell.execute_reply.started":"2023-02-01T14:51:20.510548Z","shell.execute_reply":"2023-02-01T14:51:20.521845Z"},"trusted":true},"execution_count":182,"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":" 0\n0.200193 Fare\n0.125949 Pclass\n0.315820 Sex\n0.025783 Embarked\n0.094918 fam_members\n0.237337 Age","text/html":"
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0
0.200193Fare
0.125949Pclass
0.315820Sex
0.025783Embarked
0.094918fam_members
0.237337Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.525411Z","iopub.execute_input":"2023-02-01T14:51:20.525712Z","iopub.status.idle":"2023-02-01T14:51:20.536265Z","shell.execute_reply.started":"2023-02-01T14:51:20.525685Z","shell.execute_reply":"2023-02-01T14:51:20.535549Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"array([[326, 3],\n [ 46, 159]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.736687Z","iopub.execute_input":"2023-02-01T14:51:20.737047Z","iopub.status.idle":"2023-02-01T14:51:20.744835Z","shell.execute_reply.started":"2023-02-01T14:51:20.737016Z","shell.execute_reply":"2023-02-01T14:51:20.743620Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"Accuracy : 0.9082397003745318\nMisclassfication : 0.09176029962546817\nSensitivivity : 0.9908814589665653\nSpecificity : 0.775609756097561\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.940682Z","iopub.execute_input":"2023-02-01T14:51:20.941080Z","iopub.status.idle":"2023-02-01T14:51:20.950745Z","shell.execute_reply.started":"2023-02-01T14:51:20.941045Z","shell.execute_reply":"2023-02-01T14:51:20.949939Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.156573Z","iopub.execute_input":"2023-02-01T14:51:21.157555Z","iopub.status.idle":"2023-02-01T14:51:21.164777Z","shell.execute_reply.started":"2023-02-01T14:51:21.157504Z","shell.execute_reply":"2023-02-01T14:51:21.163996Z"},"trusted":true},"execution_count":186,"outputs":[{"name":"stdout","text":"Accuracy : 0.8151260504201681\nMisclassfication : 0.18487394957983194\nSensitivivity : 0.9318181818181818\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.602984Z","iopub.execute_input":"2023-02-01T14:51:21.603408Z","iopub.status.idle":"2023-02-01T14:51:21.609433Z","shell.execute_reply.started":"2023-02-01T14:51:21.603369Z","shell.execute_reply":"2023-02-01T14:51:21.608257Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_train)\nclf_pred = X_train.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_train_pass_id\nclf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.801023Z","iopub.execute_input":"2023-02-01T14:51:21.801826Z","iopub.status.idle":"2023-02-01T14:51:21.826292Z","shell.execute_reply.started":"2023-02-01T14:51:21.801783Z","shell.execute_reply":"2023-02-01T14:51:21.825118Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769231.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(clf_pred[[\"PassengerId\", \"clf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.073441Z","iopub.execute_input":"2023-02-01T14:51:22.073853Z","iopub.status.idle":"2023-02-01T14:51:22.100768Z","shell.execute_reply.started":"2023-02-01T14:51:22.073817Z","shell.execute_reply":"2023-02-01T14:51:22.099989Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = clf.predict(X_valid)\nclf_pred = X_valid.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_valid_pass_id\nclf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.313331Z","iopub.execute_input":"2023-02-01T14:51:22.314186Z","iopub.status.idle":"2023-02-01T14:51:22.339255Z","shell.execute_reply.started":"2023-02-01T14:51:22.314149Z","shell.execute_reply":"2023-02-01T14:51:22.338531Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 0.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538460.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
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357 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(clf_pred.PassengerId), \"clf_y_pred\"] = clf_pred[\"clf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.503867Z","iopub.execute_input":"2023-02-01T14:51:22.504541Z","iopub.status.idle":"2023-02-01T14:51:22.530946Z","shell.execute_reply.started":"2023-02-01T14:51:22.504500Z","shell.execute_reply":"2023-02-01T14:51:22.529880Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.197164Z","iopub.execute_input":"2023-02-01T14:51:23.197598Z","iopub.status.idle":"2023-02-01T14:51:23.221279Z","shell.execute_reply.started":"2023-02-01T14:51:23.197559Z","shell.execute_reply":"2023-02-01T14:51:23.220173Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n220 -0.277363 3.0 1.0 2.0 0.0 -1.076923 1.0 0.0\n510 -0.290356 3.0 1.0 3.0 0.0 -0.076923 1.0 0.0\n724 1.673732 1.0 1.0 2.0 1.0 -0.230769 1.0 0.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
220-0.2773633.01.02.00.0-1.0769231.00.0
510-0.2903563.01.03.00.0-0.0769231.00.0
7241.6737321.01.02.01.0-0.2307691.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.536909Z","iopub.execute_input":"2023-02-01T14:51:23.537537Z","iopub.status.idle":"2023-02-01T14:51:23.553252Z","shell.execute_reply.started":"2023-02-01T14:51:23.537491Z","shell.execute_reply":"2023-02-01T14:51:23.552369Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 10\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n3.0 0.0 1.0 0.0 14\n 2.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.819458Z","iopub.execute_input":"2023-02-01T14:51:23.819831Z","iopub.status.idle":"2023-02-01T14:51:23.828371Z","shell.execute_reply.started":"2023-02-01T14:51:23.819802Z","shell.execute_reply":"2023-02-01T14:51:23.827545Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.944899Z","iopub.execute_input":"2023-02-01T14:51:23.945939Z","iopub.status.idle":"2023-02-01T14:51:24.401522Z","shell.execute_reply.started":"2023-02-01T14:51:23.945899Z","shell.execute_reply":"2023-02-01T14:51:24.400330Z"},"trusted":true},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.403612Z","iopub.execute_input":"2023-02-01T14:51:24.404043Z","iopub.status.idle":"2023-02-01T14:51:24.424956Z","shell.execute_reply.started":"2023-02-01T14:51:24.404007Z","shell.execute_reply":"2023-02-01T14:51:24.423814Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0\n501 -0.290356 3.0 2.0 3.0 0.0 -0.692308 0.0 1.0\n17 -0.062981 2.0 1.0 2.0 0.0 0.000000 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
429-0.2773633.01.02.00.00.1538461.00.0
501-0.2903563.02.03.00.0-0.6923080.01.0
17-0.0629812.01.02.00.00.0000001.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.426286Z","iopub.execute_input":"2023-02-01T14:51:24.426719Z","iopub.status.idle":"2023-02-01T14:51:24.444950Z","shell.execute_reply.started":"2023-02-01T14:51:24.426673Z","shell.execute_reply":"2023-02-01T14:51:24.443790Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 3\n 1.0 1\n 1.0 2.0 0.0 1\n 2.0 1.0 1.0 1\n3.0 0.0 1.0 0.0 12\n 1.0 3\n 2.0 0.0 4\n 1.0 2\n 1.0 1.0 0.0 1\n 2.0 0.0 9\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 2\n 2.0 1.0 3\n 4.0 1.0 1.0 1\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.655585Z","iopub.execute_input":"2023-02-01T14:51:24.655981Z","iopub.status.idle":"2023-02-01T14:51:25.270872Z","shell.execute_reply.started":"2023-02-01T14:51:24.655946Z","shell.execute_reply":"2023-02-01T14:51:25.270073Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.272439Z","iopub.execute_input":"2023-02-01T14:51:25.273391Z","iopub.status.idle":"2023-02-01T14:51:25.295346Z","shell.execute_reply.started":"2023-02-01T14:51:25.273342Z","shell.execute_reply":"2023-02-01T14:51:25.294366Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 1.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
2550.0342843.02.04.02.0-0.0769231.01.0
130-0.2840413.01.04.00.00.2307690.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.310893Z","iopub.execute_input":"2023-02-01T14:51:25.311294Z","iopub.status.idle":"2023-02-01T14:51:25.332606Z","shell.execute_reply.started":"2023-02-01T14:51:25.311259Z","shell.execute_reply":"2023-02-01T14:51:25.331521Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 6\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 2\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 0.0 1\n 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 1.0 5\n 2.0 0.0 14\n 1.0 23\n 1.0 1.0 0.0 15\n 1.0 3\n 2.0 0.0 10\n 1.0 4\n 2.0 1.0 0.0 10\n 1.0 2\n 2.0 0.0 5\n 1.0 8\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.617532Z","iopub.execute_input":"2023-02-01T14:51:25.617910Z","iopub.status.idle":"2023-02-01T14:51:27.648580Z","shell.execute_reply.started":"2023-02-01T14:51:25.617879Z","shell.execute_reply":"2023-02-01T14:51:27.647383Z"},"trusted":true},"execution_count":201,"outputs":[{"execution_count":201,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.650898Z","iopub.execute_input":"2023-02-01T14:51:27.651397Z","iopub.status.idle":"2023-02-01T14:51:27.674977Z","shell.execute_reply.started":"2023-02-01T14:51:27.651353Z","shell.execute_reply":"2023-02-01T14:51:27.673660Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.676558Z","iopub.execute_input":"2023-02-01T14:51:27.676918Z","iopub.status.idle":"2023-02-01T14:51:27.695988Z","shell.execute_reply.started":"2023-02-01T14:51:27.676883Z","shell.execute_reply":"2023-02-01T14:51:27.694729Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 1.0 3\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 12\n 1.0 1.0 0.0 4\n 2.0 1.0 8\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 91\n 1.0 1\n 2.0 0.0 6\n 1.0 4\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 1.0 2\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 0.0 2\n 1.0 4\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.698581Z","iopub.execute_input":"2023-02-01T14:51:27.699104Z","iopub.status.idle":"2023-02-01T14:51:28.312451Z","shell.execute_reply.started":"2023-02-01T14:51:27.699061Z","shell.execute_reply":"2023-02-01T14:51:28.311698Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.313663Z","iopub.execute_input":"2023-02-01T14:51:28.314115Z","iopub.status.idle":"2023-02-01T14:51:28.742585Z","shell.execute_reply.started":"2023-02-01T14:51:28.314085Z","shell.execute_reply":"2023-02-01T14:51:28.741404Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.744143Z","iopub.execute_input":"2023-02-01T14:51:28.744835Z","iopub.status.idle":"2023-02-01T14:51:29.161694Z","shell.execute_reply.started":"2023-02-01T14:51:28.744790Z","shell.execute_reply":"2023-02-01T14:51:29.160329Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.164872Z","iopub.execute_input":"2023-02-01T14:51:29.165348Z","iopub.status.idle":"2023-02-01T14:51:29.588614Z","shell.execute_reply.started":"2023-02-01T14:51:29.165277Z","shell.execute_reply":"2023-02-01T14:51:29.587528Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.590230Z","iopub.execute_input":"2023-02-01T14:51:29.591244Z","iopub.status.idle":"2023-02-01T14:51:29.999585Z","shell.execute_reply.started":"2023-02-01T14:51:29.591206Z","shell.execute_reply":"2023-02-01T14:51:29.998460Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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EAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBBcBfbs2ZPNmzdn3bp12bx5c/bs2TPrkQAAgHux9bMeoLs9e/Zkx44dueaaa3LRRRdl37592bp1a5Lk0ksvnfF0AADAvVGNMc7Yg23ZsmVcf/31Z+zx1oLNmzdn9+7dufjii49s27t3bxYWFnLDDTfMcDIAAGAtq6r3jTG2HPU2IThb69aty4EDB7Jhw4Yj2w4dOpRNmzblzjvvnOFkAADAWna8EPQcwRmbm5vLvn377rJt3759mZubm9FEAADAvZ0QnLEdO3Zk69at2bt3bw4dOpS9e/dm69at2bFjx6xHAwAA7qVcLGbGDl8QZmFhIfv378/c3Fx27tzpQjEAAMBp4zmCAAAA90KeIwgAAMARQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANHPCEKyqh1XV3qr6k6r6UFX9y8n286vqPVX155P/PuD0jwsAAMC0TuaI4B1JrhhjfFOS/z3JP6+qb0pyZZLfHGN8Q5LfnHwMAADAKnfCEBxjfGKM8YeT929Jsj/JQ5M8I8mbJ3d7c5LvOU0zAgAAsILu0XMEq+rCJH83yX9P8qAxxicmN30yyYNWdjQAAABOh5MOwaq6X5JfSvLiMcYXlt82xhhJxjE+7wVVdX1VXX/TTTdNNSwAAADTO6kQrKoNWYrA/zjG+OXJ5k9V1YMntz84yaeP9rljjDeMMbaMMbZccMEFKzEzAAAAUziZq4ZWkmuS7B9jvGrZTe9I8rzJ+89L8vaVHw8AAICVtv4k7vN/JHlukg9W1Qcm27YneUWSX6yqrUk+nOTZp2VCAAAAVtQJQ3CMsS9JHePmf7Sy4wAAAHC63aOrhgIAALD2CUEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgmROGYFW9sao+XVU3LNv2E1X1sar6wOTtqad3TAAAAFbKyRwRfFOS7zjK9p8bYzxu8vZrKzsWAAAAp8sJQ3CM8d4knz0DswAAAHAGTPMcwX9RVX88OXX0ASs2EQAAAKfVqYbg65J8fZLHJflEkp891h2r6gVVdX1VXX/TTTed4sMBAACwUk4pBMcYnxpj3DnG+HKSq5N8y3Hu+4YxxpYxxpYLLrjgVOcEAABghZxSCFbVg5d9+I+T3HCs+wIAALC6rD/RHapqT5InJnlgVX00ycuSPLGqHpdkJLkxyQtP34gAAACspBOG4Bjj0qNsvuY0zAIAAMAZMM1VQwEAAFiDhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhuAosLCxk06ZNqaps2rQpCwsLsx4JAE7I/gtg7RKCM7awsJDFxcXs2rUrt956a3bt2pXFxUU7UwBWtYWFhbz2ta/NAx7wgJx11ll5wAMekNe+9rX2XwBrRI0xztiDbdmyZVx//fVn7PHWgk2bNmXXrl25/PLLj2x71atele3bt+fAgQMznAwAjm3Dhg0599xzc+211+aiiy7Kvn378sxnPjNf+MIXcujQoVmPB0CSqnrfGGPLUW8TgrNVVbn11ltz9tlnH9l222235b73vW/O5N8NANwTVZVf+7Vfy3d+53ce2fbrv/7reepTn2r/BbBKHC8EnRo6Yxs3bszi4uJdti0uLmbjxo0zmggATs4NN9xw3I8BWL3Wz3qA7i677LJs27YtSTI/P5/FxcVs27Yt8/PzM54MAI7t/PPPz5VXXpl169Yd2X9deeWVOf/882c9GgAnwamhq8DCwkKuvvrqHDx4MBs3bsxll12W3bt3z3osADimPXv2ZH5+PrfffnsOHTqUDRs25D73uU8WFxdz6aWXzno8AOI5ggDAabBnz57s3Lkz+/fvz9zcXHbs2CECAVYRIQgAANCMi8UAAABwhBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgmROGYFW9sao+XVU3LNt2flW9p6r+fPLfB5zeMQEAAFgpJ3NE8E1JvuNu265M8ptjjG9I8puTjwEAAFgDThiCY4z3Jvns3TY/I8mbJ++/Ocn3rOxYAAAAnC6n+hzBB40xPjF5/5NJHrRC8wAAAHCaTX2xmDHGSDKOdXtVvaCqrq+q62+66aZpHw4AAIApnWoIfqqqHpwkk/9++lh3HGO8YYyxZYyx5YILLjjFhwMAAGClnGoIviPJ8ybvPy/J21dmHAAAAE63k3n5iD1Jfi/JN1bVR6tqa5JXJHlyVf15kidNPgYAAGANWH+iO4wxLj3GTf9ohWcBAADgDJj6YjEAAACsLUIQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAJySPXv2ZPPmzVm3bl02b96cPXv2zHokAE7S+lkPAACsPXv27MmOHTtyzTXX5KKLLsq+ffuydevWJMmll1464+kAOJEaY5yxB9uyZcu4/vrrz9jjAQCnx+bNm7N79+5cfPHFR7bt3bs3CwsLueGGG2Y4GQCHVdX7xhhbjnqbEAQA7ql169blwIED2bBhw5Fthw4dyqZNm3LnnXfOcDIADjteCHqOIABwj83NzWXfvn132bZv377Mzc3NaCIA7gkhCADcYzt27MjWrVuzd+/eHDp0KHv37s3WrVuzY8eOWY8GwElwsRgA4B47fEGYhYWF7N+/P3Nzc9m5c6cLxQCsEZ4jCAAAcC/kOYIAAAAcIQQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBBcBfbs2ZPNmzdn3bp12bx5c/bs2TPrkQDghBYWFrJp06ZUVTZt2pSFhYVZjwTASRKCM7Znz57s2LEju3fvzoEDB7J79+7s2LFDDAKwqi0sLGRxcTG7du3Krbfeml27dmVxcVEMAqwRNcY4Yw+2ZcuWcf3115+xx1sLNm/enN27d+fiiy8+sm3v3r1ZWFjIDTfcMMPJAODYNm3alF27duXyyy8/su1Vr3pVtm/fngMHDsxwMgAOq6r3jTG2HPU2IThb69aty4EDB7Jhw4Yj2w4dOpRNmzblzjvvnOFkAHBsVZVbb701Z5999pFtt912W+573/vmTP7bAoBjO14IOjV0xubm5rJv3767bNu3b1/m5uZmNBEAnNjGjRuzuLh4l22Li4vZuHHjjCYC4J4QgjO2Y8eObN26NXv37s2hQ4eyd+/ebN26NTt27Jj1aABwTJdddlm2bduWV73qVbntttvyqle9Ktu2bctll10269EAOAnrZz1Ad5deemmSpSfd79+/P3Nzc9m5c+eR7QCwGu3evTtJsn379lxxxRXZuHFj5ufnj2wHYHXzHEEAAIB7Ic8RBAAA4AghCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAKdkz5492bx5c9atW5fNmzdnz549sx4JgJO0ftYDAABrz549e7Jjx45cc801ueiii7Jv375s3bo1SXLppZfOeDoATqTGGGfswbZs2TKuv/76M/Z4AMDpsXnz5uzevTsXX3zxkW179+7NwsJCbrjhhhlOBsBhVfW+McaWo94mBAGAe2rdunU5cOBANmzYcGTboUOHsmnTptx5550znAyAw44Xgp4jCADcY3Nzc9m3b99dtu3bty9zc3MzmgiAe0IIAgD32I4dO7J169bs3bs3hw4dyt69e7N169bs2LFj1qMBcBJcLAYAuMcOXxBmYWEh+/fvz9zcXHbu3OlCMQBrhOcIAgAA3At5jiAAAABHCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJpZP80nV9WNSW5JcmeSO8YYW1ZiKAAAAE6fqUJw4uIxxmdW4OsAAABwBjg1FAAAoJlpQ3Akua6q3ldVL1iJgQAAADi9pj019KIxxseq6quTvKeq/nSM8d7ld5gE4guS5OEPf/iUDwcAAMC0pjoiOMb42OS/n07yK0m+5Sj3ecMYY8sYY8sFF1wwzcMBAACwAk45BKvqvlV1zuH3k1yS5IaVGgwAAIDTY5pTQx+U5Feq6vDXeesY47+uyFQAAACcNqccgmOMv0zy2BWcBQAAgDPAy0cAAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoJn1sx7g3qCqZj3CVxhjzHoEAFY5+y+AvhwRXAFjjBV5e8S2d67Y1wKAE7H/AuhLCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGbWz3oAAOCeeexPXpfP335o1mPcxYVXvmvWIxxx3n025I9edsmsxwBY1YQgAKwxn7/9UG58xdNmPcaqtZqiFGC1cmooAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzayf9QCz9NifvC6fv/3QrMe4iwuvfNesRzjivPtsyB+97JJZjwHA3Zwzd2Ue/eYrZz3GqnXOXJI8bdZjAKxqrUPw87cfyo2vsKM4ltUUpQD8L7fsf4X913HYfwGcmFNDAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIa9zCwkI2bdqUqsqmTZuysLAw65EAAFjlhCCsYQsLC1lcXMyuXbty6623ZteuXVlcXBSDAAAclxCENezqq6/OVVddlcsvvzxnn312Lr/88lx11VW5+uqrZz0aAACrmBCENezgwYOZn5+/y7b5+fkcPHhwRhMBALAWCEFYwzZu3JjFxcW7bFtcXMzGjRtnNBEAAGvB+lkPAJy6yy67LNu2bUuydCRwcXEx27Zt+4qjhAAAsJwQhDVs9+7dSZLt27fniiuuyMaNGzM/P39kOwAAHI0QhDVu9+7dwg8AgHvEcwQBGvM6lACsRU95ylNy1llnpapy1lln5SlPecqsR1pzhCBAU16HEoC16ClPeUquu+66zM/P5+abb878/Hyuu+46MXgPOTUUoKnlr0OZ5Mh/t2/f7nRjAFat97znPXnRi16U1772tUly5L93v5I6x+eIIEBTXocSgLVojJGXv/zld9n28pe/PGOMGU20NglBgKa8DiUAa1FV5aUvfeldtr30pS9NVc1oorXJqaEATXkdSgDWoic/+cl53etel2TpSOBLX/rSvO51r8sll1wy48nWFiEI0JTXoQRgLXr3u9+dpzzlKVlcXMzrXve6VFUuueSSvPvd7571aGuKEARozOtQArAWib7peY4gAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEKCxhYWFbNq0KVWVTZs2ZWFhYdYjAQBnwPpZDwDAbCwsLGRxcTFXXXVV5ufns7i4mG3btiVJdu/ePePpOJELr3zXrEdYtc67z4ZZjwCw6glBgKauvvrqXHXVVbn88suT5Mh/t2/fLgRXuRtf8bRZj3AXF175rlU3EwDH59RQgKYOHjyY+fn5u2ybn5/PwYMHZzQRAHCmCEGApjZu3JjFxcW7bFtcXMzGjRtnNBEAcKY4NRSgqcsuu+zIcwKXP0fw7kcJAYB7HyEI0NTh5wFu3749V1xxRTZu3Jj5+XnPDwSABoQgQGO7d+8WfgDQkOcIAgAANCMEAQAAmhGCAAAAzQhBAACAZlpfLOacuSvz6DdfOesxVq1z5pLkabMeAwAAWGGtQ/CW/a/Ija8QOsdy4ZXvmvUIAADAaeDUUAAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBGjsMY95TKrqyNtjHvOYWY8EAJwBQhCgqcc85jH54Ac/mKc//em56aab8vSnPz0f/OAHxSAANCAEAZo6HIFvf/vb88AHPjBvf/vbj8QgAHDv1vp1BAG6u+aaa77i4wsuuGBG0wDQRVXNeoSvMMaY9QhnlCOCAI1t3br1uB8DwOkwxliRt0dse+eKfa1uhCBAU49+9KPzjne8I894xjPymc98Js94xjPyjne8I49+9KNnPRoAcJo5NRSgqT/+4z/OYx7zmLzjHe84cjroox/96PzxH//xjCcDAE43IQjQmOgDgJ6cGgoAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIMxYVa26N/pYWFjIpk2bUlXZtGlTFhYWZj0SsEbMel9l/wXTEYIwY2OMFXl7xLZ3rtjXooeFhYUsLi5m165dufXWW7Nr164sLi6KQeCk2H/B2iYEAZq6+uqrc9VVV+Xyyy/P2WefncsvvzxXXXVVrr766lmPBgCcZkIQoKmDBw9mfn7+Ltvm5+dz8ODBGU0EAJwpQhCgqY0bN2ZxcfEu2xYXF7Nx48YZTQQAnCnrZz0AALNx2WWXZdu2bUmWjgQuLi5m27ZtX3GUEAC49xGCAE3t3r07SbJ9+/ZcccUV2bhxY+bn549sBwDuvYQgQGO7d+8WfgDQkOcIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCNLZnz55s3rw569aty+bNm7Nnz55ZjwQAnAHrZz0AALOxZ8+e7NixI9dcc00uuuii7Nu3L1u3bk2SXHrppTOeDgA4nRwRBGhq586dueaaa3LxxRdnw4YNufjii3PNNddk586dsx4NADjNhCBAU/v3789FF110l20XXXRR9u/fP6OJAIAzRQgCNDU3N5dnP/vZ2bRpU6oqmzZtyrOf/ezMzc3NejQA4DRr/xzBC69816xHWLXOu8+GWY8AnEYPfehD86u/+qt50YtelJe//OV56Utfmte97nW55JJLZj0aAHCatQ7BG1/xtFmPcBcXXvmuVTcTcO/1O7/zO3nOc56T9773vTn//PMzNzeX5zznObn22mtnPRoAcJo5NRSgqYMHD+ZJT3rSXbY96UlPysGDB2c0EQBwprQ+IgjQ2fr163PFFVfk2muvPfLyEc985jOzfr1dAwDc2zkiCNDUueeem5tvvjnvf//7c+jQobz//e/PzTffnHPPPXfWowEAp5kQBGjq5ptvzgtf+MJs3749973vfbN9+/a88IUvzM033zzr0QCA00wIAjQ1NzeXZz3rWTlw4EDGGDlw4ECe9axnefkIAGhACAI0tWPHjmzdujV79+7NoUOHsnfv3mzdujU7duyY9WicIVW1Im8fvuq7VuxrAXBmuCIAnKLH/uR1+fzth2Y9xl2sptfFPO8+G/JHL/N6dKvZpZdemiRZWFjI/v37Mzc3l507dx7Zzr3fGGPWIzAD9l/HZ/9FF0IQTtHnbz/kdR+PYzXt1AH4X+y/js/+iy6EIEBTe/bsyY4dO3LNNdccefmIrVu3JomjggBwL+c5ggBN7dy5M9dcc00uvvjibNiwIRdffHGuueaa7Ny5c9ajAQCnmSOCAE3t378/F1100V22XXTRRdm/f/+MJgJgtfMc0+NbS88xFYIATc3NzWXfvn25+OKLj2zbt2+fl48A4Jg8x/T4VlOUnohTQwGa8vIRANCXI4IATV166aX53d/93Xznd35nDh48mI0bN+ayyy5zoRgAaMARQYCm9uzZk7e97W158IMfnKrKgx/84LztbW/Lnj17Zj0aAHCaCUGApn7sx34s69atyxvf+MYcPHgwb3zjG7Nu3br82I/92KxHAwBOM6eGAjT10Y9+NNddd92Ri8VcfPHFectb3pJLLlkbVzsDTs05c1fm0W++ctZjrFrnzCWJi6Fw7ycEARrbu3dvXvKSl2T//v2Zm5vL05/+9FmPBJxmt+x/has+HsdauuojTMOpoQBNnX/++XnlK1+Z5z//+bnlllvy/Oc/P6985Stz/vnnz3o0AOA0E4IATZ199tk566yzcsUVV+S+971vrrjiipx11lk5++yzZz0aAHCaCUGApj72sY/ljjvuyIMe9KBUVR70oAfljjvuyMc+9rFZjwYAnGZCEKCx+fn5fPKTn8yXv/zlfPKTn8z8/PysRwIAzgAhCNDUGCNveMMbUlVH3t7whjdkjDHr0QCA00wIAjR25513HvdjAODeSQgCAAA0M1UIVtV3VNWfVdVfVJVXJgVYg84666y7/BcAuPc75ReUr6p1SV6T5MlJPprkD6rqHWOMP1mp4QA4vTZt2pQxRg4ePJgNGzakqnLgwIFZjwXAKnXO3JV59Jsd/zmWc+aS5GmzHuOknHIIJvmWJH8xxvjLJKmq/5TkGUmEIMAaceDAgVx44YX5jd/4jTzpSU/KjTfeOOuRAFjFbtn/itz4irUROrNw4ZXvmvUIJ22aEHxoko8s+/ijSb51unEAONNuvPHGPPKRj5z1GADAGTRNCJ6UqnpBkhckycMf/vDT/XAArVTVqvu6Xn4CAFa/aa4M8LEkD1v28ddOtt3FGOMNY4wtY4wtF1xwwRQPB8DdjTFO+W3jxo151KMedST6qiqPetSjsnHjxqm+LgCw+k1zRPAPknxDVf3tLAXg9yf5gRWZCtYAT5Y+vrX0ZOmuLrvssiwuLubf/Jt/k1d/9BF58dd+ONu2bcv8/PysRwNOs7X0PKYz7bz7bJj1CHBGnHIIjjHuqKp/keTdSdYleeMY40MrNhmscp4sfXz+kbH67d69O0myffv2HDx4MNs3bsz8/PyR7cC902rbd1145btW3UzQwVQvGjXG+LUxxqPGGF8/xti5UkMBcGbs3r07Bw4cyCO2vTMHDhwQgQDQxGm/WAzcmznqdWxOrQEAWL2EIJyi1XYai1NrAAA4WUIQYAYe+5PX5fO3H5r1GHexmo5wn3efDfmjl10y6zGA41jJl6+pq1bm67hyMZw8IQgwA5+//ZAjuMexmqIUODrRBWvbVBeLAQAAYO0RggAAAM0IQQAAgGY8RxBgBs6ZuzKPfvOVsx5j1TpnLkk8hxJgNfI87mNbSy+fJQQBZuCDz/vgrEe4Cy8/AsDJWKl9xUpedXaldLsAkhAEAADOqG7RtRoJQYA1zOt4AQCnQggCrGGiCwA4Fa4aCgAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQoLGFhYVs2rQpVZVNmzZlYWFh1iMBAGeAEARoamFhIYuLi9m1a1duvfXW7Nq1K4uLi2IQABoQggBNXX311bnqqqty+eWX5+yzz87ll1+eq666KldfffWsRwMATrMaY5yxB9uyZcu4/vrrz9jjnSlVNesRvsKZ/HtlOtYPs1JVufXWW3P22Wcf2Xbbbbflvve9rzUAAPcCVfW+McaWo93miOAKGGOsujfWjlmvFeunr40bN2ZxcfEu2xYXF7Nx48YZTQQAnCnrZz0AALNx2WWXZdu2bUmS+fn5LC4uZtu2bZmfn5/xZADA6SYEAZravXt3kmT79u254oorsnHjxszPzx/ZDgDce3mOIAAAwL2Q5wgCAABwhBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzQhBAACAZoQgAABAM0IQAACgGSEIAADQjBAEAABoRggCAAA0IwQBAACaEYIAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEAQAAGhGCAIAADQjBAEAAJoRggAAAM0IQQAAgGaEIAAAQDNCEAAAoBkhCAAA0IwQBAAAaEYIAgAANCMEAQAAmqkxxpl7sKqbknz4jD3g2vPAJJ+Z9RCsWdYP07B+mIb1wzSsH6Zh/RzfI8YYFxzthjMaghxfVV0/xtgy6zlYm6wfpmH9MA3rh2lYP0zD+jl1Tg0FAABoRggCAAA0IwRXlzfMegDWNOuHaVg/TMP6YRrWD9Owfk6R5wgCAAA044ggAABAM0JwDauqmvUMrF3WD9OwfpiG9cM0rB+mYf38L04NBQAAaMYRwTWoqn6wqn6hqr65qh4863lYW6wfplFVz6qqV1fVg6rq3FnPw9ri5w/TsH6Yhv3XV3JEcA2qqg1JFpKck+TxSX56jPEHs52KtcL64VRV1bokD0pyeZJKsj7JG8YYH5rpYKwZfv4wDeuHU2X/dXRCcA2pqq9PctYY488nH5+X5J8k+akk3zfG+G+znI/VzfphGpPfvn9pjPE3k48fk+RJSZ6V5IfHGO+f5Xysbn7+MA3rh2nYfx2bEFwjquoXk9w/yf2S/G6SfzXGuG1y2w8luTLJM8cYN8xqRlYv64dpVNV/yNJvUr+Y5IYxxv892b4+yXySp2VpZ/pXs5uS1crPH6Zh/TAN+6/j8xzBNaCqvjfJ/ccYlyT53iSbk/xUVT0sScYYb0ry2iTfX1UbXQ2J5awfplFVL8jSTvQpSX4syfdV1c8kyRjjjiRvTbIvyRMn97d+OMLPH6Zh/TAN+68TE4Jrw8eT3FFVDxxjfDLJ9yd5SJIXL7vPf0vyt5LcORzm5a6sH6bx4SR/kaUzSP48yT9IclFV/WySjDE+m+RPknz75GPrh+X8/GEa1g/TsP86ASG4NnwkyY1JHldVm8YYNyd5UZKLq+rFSTLGeF+Sg0mePqMZWb2sH6bx2STnJ/m6JBljfCbJU5M8taqeM9n2K0lur6p/NLMpWa38/GEa1g/TsP86gfWzHoATG2N8rKo+lKVzmQ9U1Q1jjJur6sokW5bd9f9O0u63GRyf9cM0xhh/UFV/nmRx8nycT4wxPjc5vearlt11V5K/mcWMrF5+/jAN64dp2H+dmCOCq9Dyc5QPvz/GeE2S38vSb8KeX1WPz9IlcM87fN8xxi1jjC+e4XFZxarqrMT64dRMLredMcaPJ/lAktcn+a6qekSSS5M8bNndP3r4Ag70Zf/FSrH/Yhr2XyfHVUNXmar6qjHGl6pq3Rjjzsm2s8YYX568//Qk35Tk25L89RhjYYbjsspU1cVJDiX5H2OML022WT+clKr6x0m+kOQDyy6zvXz9vChLp9g8NsmNY4wXzGxYVh37L6Zh/8U07L9OjRBcRSaXuH14ku8eY3z+bjvT9ZMrHB2+7/0O//Zr+UKnr6p6S5aeMP/wJO9I8ooxxi2T26wfjquqrknyNVl6oeb3J7l82c+fjWOMg8vu+7eOtqOlL/svpmH/xTTsv06dU0NXiaq6PMmFWVrAv1xV540x7lx2aPuOyf2+Y/KE6cM/BKv7IiapqlcmecAY42lJLkny97N0qe0k1g/HV1WvS3LBZP18V5KvTfINy07tOzi539+tqg3LdqLWD/ZfTMX+i2nYf03HxWJWj99K8ntjjN+rqv8nya9U1T8eY3z+8B2q6u8leeAY48DhbR0vdctR/Y8kb0mSMcYnlv127IiquijWD0f3n5L898n7/zLJE5L8P0neX1X/bYzxX6rq+5KcPcZ4/+FPsn6YsP9iGvZfTMP+awpODV1FDp9KU1VfleSVWTqP+R+OMUZV/Z0xxp/OeERWqaq6X5KDY4xDk4//zyRPHGM8b/Lxg8YYn5rljKx+VXWfLL04844kX8zSa3Xdf4xx+eS3p3YYHJX9F6fK/ouVYP91apwauoocPp958iTp7Vn6Dcd7qup3knz3LGdjdRtjfHGMcejwqRBZesL055Okqv5zll43B45rjHF7kuePMT4+xvhCkl9M8rCqOvfwTnTZGoMj7L84VfZfrAT7r1PjiOAqNlmwf5Pkv44xfmDW87B2VNXmJFdm6XVybj/8m1W4J6rqrUk+PcZ48axnYW2x/+JU2X+xEuy/To4jgqvbzyb59cM70Zq8pg6chE1JfiDJTctOr7F+OKGqOquq/lZVvT1L/wh78WS736RyT9h/carsvzgl9l/3nCOCM3Sic5ar6hvHGH82eb/9JW65q+Otn8m58s8aY7zlRPelpxOsn/snuWiM8c7Jx37+cBf2X0zD/otp2H+tHCF4BlXVS5L8dZIvjjHePdl2+PK2h89f/ooF64cgyVTrxw9BrB+mYv/FNPz8YRrWz+njUPsZUlWvT/I9SR6W5PVV9SPJ0gKeXFXtoZOPv3z3Q9h2otzD9XOX/6/9EMT6YRr2X0zDzx+mYf2cXl5H8Ayoqgcn+YYk3zfG+ExVvSvJtZPfVLyyqtYneWVVfXyM8aN2nCx3CuvHDz6OsH6Yhv0X0/Dzh2lYP6efI4JnxqeSfDDJ46tq/Rjjz5M8O8k/r6oXjTHuSPKTSe5bVX97loOyKlk/TMP6YRrWD9OwfpiG9XOaCcEzYPIbio8n+WdJzpls+7Mk35/kOydPbL0pyQcm/4UjrB+mYf0wDeuHaVg/TMP6Of2E4Gm27MmsVyW5Lckbq+ohk5v/MEt/B+vGGJ9L8sYxxhdnMymrkfXDNKwfpmH9MA3rh2lYP2eGq4aeJlW1boxx51Hef12WfqvxqSRzSW4eXmyXu7F+mIb1w6m6+1U+rR/uCeuHaVg/Z54QXGFV9d1jjP8yef/IZWvvtpgvTvI1Sb5mjPFzk20usY31w1SsH6ZRVduT3D/J+8cYe5Ztt344IeuHaVg/syEEV1BVvTXJ30/yS2OMF0+2nTUml9Q+1kItr3NCrB+mY/0wjap6Q5IHJXlbkn+VZOcY499PbrN+OC7rh2lYP7PjOYIrpKq2JHlwkucmWV9Vr06OvK7JusOLuKr+WVXNLf9cixjrh2lYP0yjqr43ydeOMZ4xxnhrkv8ryQ9X1X2W/yOsqrZaP9yd9cM0rJ/ZEoIrZIxxfZLnJfm9JP8uS5eyfXVVbRhj3FlVZ1XVVyX57Bhj/0yHZdWxfpiG9cOUfivJjyXJZJ18aLJ9w7J/hG1M8jnrh6OwfpiG9TNDQnBKVfXcqnp9kowx/nqMcTBLr3myO0tPav2pyV3/aZL1Y4xfnnxezWJeVhfrh2lYP0xjsn5eM8a4OcmfJskY40tjjI8l+UKSWyb3+54xxkHrh+WsH6Zh/awOQnB6v57k41V1bnLkfOU7s7SofzrJ+VV1a5KnjTFuO/xJntjKhPXDNKwfpvHrSW6qqnPGGHfUknWT38qvT3JhVf1ikqcu/yTrhwnrh2lYP6uAEJzenUk2J7k0OfKcnLMmv9X4qyR/N8m1Y4x/kvhNBl/B+mEa1g/TuDPJ/5bkB5Ij/8Bal+RQkkpybZKPjzFeMLMJWc2sH6Zh/awC62c9wFo3xvhcVf10kndW1S1jjLce/sdYkm9P8sExxtbE1Y34StYP07B+mMYx1s+XkqSqbknyiXG3K9DOcFxWGeuHaVg/q4OXj1ghVfWkJD+f5JVjjDcd5XaLmGOyfpiG9cM0jrZ+quqRY4y/mLxv/XBM1g/TsH5mSwiuoKq6KMm/T/JzSf5yjPHOyXYvdskJWT9Mw/phGsvWz6uT7B9jXDfZ7h9hnJD1wzSsn9kRgiusqr4hyZOTfF2WTst684xHYg2xfpiG9cM07rZ+bjja0WU4FuuHaVg/syEET6OqOneM8YVZz8HaZP0wDeuHaVg/TMP6YRrWz5kjBAEAAJrx8hEAAADNCEEAAIBmhCAAAEAzQhAAAKAZIQgAANCMEARg1auqC6vq9qr6wLJtO6rqQ1X1x1X1gar61hV+zDdV1V9NvvYfVtW3nWC+G1bwsX+mqj5ZVT+yUl8TAJZbP+sBAOAk/c8xxuOSZBJl35Xk8WOMg1X1wCRfdRoe80fHGNdW1SVJXp/kMafhMb7CGONHq+rWM/FYAPTkiCAAa9GDk3xmjHEwScYYnxljfDxJquoJVfU7VfW+qnp3VT24qs6rqj+rqm+c3GdPVV12Dx7vvUkeOfncR1bVb1TVH02OFH798jtOjg7+v5Pb/rCq/t5k+4Or6r2TI4w3VNXfr6p1kyOPN1TVB6vqJSvwZwMAJyQEAViLrkvysKr6/6rqtVX17UlSVRuS7E7yzDHGE5K8McnOMcbnk/yLJG+qqu9P8oAxxtX34PG+O8kHJ+//xySvGWM8NsnfS/KJu93300mePMZ4fJJ/kuTnJ9t/IMm7J0c1H5vkA0kel+ShY4zNY4xHJ/mFezATAJwyp4YCsOaMMb5YVU9I8veTXJzkbVV1ZZLrk2xO8p6qSpJ1mYTaGOM9VfWsJK/JUoidjJ+pqh9PclOSrVV1TpbC7VcmX/NAkkwe67ANSf5tVT0uyZ1JHjXZ/gdJ3jiJ1V8dY3ygqv4yyddV1e4k78pS4ALAaScEAViTxhh3JvntJL9dVR9M8rwk70vyoTHGV1zYparOSjKX5LYkD0jy0ZN4mB8dY1y77GuccxKf85Ikn8pSbJ6V5MBk3vdW1T9I8rQsHZl81RjjLVX12CRPSTKf5NlJnn8SjwEAU3FqKABrTlV9Y1V9w7JNj0vy4SR/luSCw1f4rKoNVfW/Te7zkiT7s3SK5i9Mjsylqt5SVd9yMo87xrglyUer6nsmn7uxqs6+293OS/KJMcaXkzw3S0clU1WPSPKpySmp/y7J4ycXuTlrjPFLSX48yePvwR8DAJwyRwQBWIvul2R3Vd0/yR1J/iLJC8YYX6qqZyb5+ao6L0v7uVdX1R1J/lmSbxlj3FJV781SeL0sS1cC/fg9eOznJnl9Vf1UkkNJnpXky8tuf22SX6qqH0zyX5McvvrnE5P8aFUdSvLFJD+Y5KFZitLDv5h96T2YAwBOWY0xZj0DABxXVV2Y5J1jjM0r/HXPTXLNGONZK/l1V0JV/USSL44x/s2sZwHg3sepoQCsBXcmOW/5C8qvhDHGF1ZpBP5Mkn+a/3U0EQBWlCOCAAAAzTgiCAAA0IwQBAAAaEYIAgAANCMEAQAAmhGCAAAAzfz/BujYj6sTGUYAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = clf.predict(X_test)\ndecision_tree_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"clf_y_pred\": y_pred})\ndecision_tree_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.001184Z","iopub.execute_input":"2023-02-01T14:51:30.001710Z","iopub.status.idle":"2023-02-01T14:51:30.018740Z","shell.execute_reply.started":"2023-02-01T14:51:30.001660Z","shell.execute_reply":"2023-02-01T14:51:30.017976Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.019742Z","iopub.execute_input":"2023-02-01T14:51:30.020678Z","iopub.status.idle":"2023-02-01T14:51:30.025527Z","shell.execute_reply.started":"2023-02-01T14:51:30.020645Z","shell.execute_reply":"2023-02-01T14:51:30.024304Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.027690Z","iopub.execute_input":"2023-02-01T14:51:30.028212Z","iopub.status.idle":"2023-02-01T14:51:30.045818Z","shell.execute_reply.started":"2023-02-01T14:51:30.028170Z","shell.execute_reply":"2023-02-01T14:51:30.044552Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.048587Z","iopub.execute_input":"2023-02-01T14:51:30.048979Z","iopub.status.idle":"2023-02-01T14:51:30.075974Z","shell.execute_reply.started":"2023-02-01T14:51:30.048946Z","shell.execute_reply":"2023-02-01T14:51:30.074745Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred \n0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Random Forrest\n\nWe use Random Forrest to classify the titanic passengers as either surviving or not the accident. We use again the same statistical variable as Decisiont Trees.","metadata":{}},{"cell_type":"markdown","source":"## Model fitting and classification\n\nRandom Forrest overfits to the training dataset. ","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nn_estimators = range(1,20)\nmax_depths = range(1,40)\n\nfor est in n_estimators:\n for depth in max_depths:\n rf = RandomForestClassifier(n_estimators = est, max_depth = depth, \n random_state = 42, class_weight={0:6.,1:4},max_features = 6)\n rf.fit(X_train, y_train)\n train_score = rf.score(X_train, y_train)\n test_score = rf.score(X_valid, y_valid)\n print(\" - estimators : \", est, \n \" - max depths : \", depth, \n \" - train score : \", train_score,\n \" - valid score : \", valid_score)\n \n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.172233Z","iopub.execute_input":"2023-02-01T14:51:30.172931Z","iopub.status.idle":"2023-02-01T14:51:52.273980Z","shell.execute_reply.started":"2023-02-01T14:51:30.172890Z","shell.execute_reply":"2023-02-01T14:51:52.272764Z"},"trusted":true},"execution_count":213,"outputs":[{"name":"stdout","text":" - estimators : 1 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 2 - train score : 0.7771535580524345 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 3 - train score : 0.8071161048689138 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 4 - train score : 0.8277153558052435 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 5 - train score : 0.8314606741573034 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 6 - train score : 0.8651685393258427 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 7 - train score : 0.8820224719101124 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 8 - train score : 0.8857677902621723 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 9 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 10 - train score : 0.900749063670412 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 11 - train score : 0.9082397003745318 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 12 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 13 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 14 - train score : 0.9119850187265918 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 15 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 16 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 17 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 18 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 19 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 20 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 21 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 22 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 23 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 24 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 25 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 26 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 27 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 28 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 29 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 30 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 31 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 32 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 33 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 34 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 35 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 36 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 37 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 38 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 39 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 4 - train score : 0.848314606741573 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 5 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 6 - train score : 0.8689138576779026 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 7 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 8 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 10 - train score : 0.9213483146067416 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 11 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 12 - train score : 0.9288389513108615 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 13 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 14 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 15 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 16 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 17 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 18 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 19 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 20 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 21 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 22 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 23 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 24 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 25 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 26 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 27 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 28 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 29 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 30 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 31 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 32 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 33 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 34 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 35 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 36 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 37 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 38 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 39 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 4 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 5 - train score : 0.8707865168539326 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 6 - train score : 0.8838951310861424 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 7 - train score : 0.897003745318352 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 8 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 10 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 11 - train score : 0.9400749063670412 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 12 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 13 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 14 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 15 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 16 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 17 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 18 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 19 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 20 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 21 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 22 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 23 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 24 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 25 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 26 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 27 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 28 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 29 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 30 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 31 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 32 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 33 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 34 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 35 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 36 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 37 - train score : 0.9456928838951311 - 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estimators : 19 - max depths : 26 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 27 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 28 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 29 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 30 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 31 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 32 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 33 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 34 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 35 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 36 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 37 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 38 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 39 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover again the learning overfit on the training dataset. So we choose a maximum depth at around 6 and n estimator of 11. ","metadata":{}},{"cell_type":"code","source":"rf = RandomForestClassifier(n_estimators = 11, max_depth=6, random_state = 42, class_weight={0:6.,1:4}, max_features = 6)\nrf.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.275894Z","iopub.execute_input":"2023-02-01T14:51:52.276195Z","iopub.status.idle":"2023-02-01T14:51:52.312746Z","shell.execute_reply.started":"2023-02-01T14:51:52.276167Z","shell.execute_reply":"2023-02-01T14:51:52.311257Z"},"trusted":true},"execution_count":214,"outputs":[{"execution_count":214,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight={0: 6.0, 1: 4}, max_depth=6, max_features=6,\n n_estimators=11, random_state=42)"},"metadata":{}}]},{"cell_type":"code","source":"rf_train_score = rf.score(X_train, y_train)\nrf_train_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.314414Z","iopub.execute_input":"2023-02-01T14:51:52.314882Z","iopub.status.idle":"2023-02-01T14:51:52.329948Z","shell.execute_reply.started":"2023-02-01T14:51:52.314839Z","shell.execute_reply":"2023-02-01T14:51:52.328684Z"},"trusted":true},"execution_count":215,"outputs":[{"execution_count":215,"output_type":"execute_result","data":{"text/plain":"0.8801498127340824"},"metadata":{}}]},{"cell_type":"code","source":"rf_valid_score = rf.score(X_valid, y_valid)\nrf_valid_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.332102Z","iopub.execute_input":"2023-02-01T14:51:52.333087Z","iopub.status.idle":"2023-02-01T14:51:52.346061Z","shell.execute_reply.started":"2023-02-01T14:51:52.333051Z","shell.execute_reply":"2023-02-01T14:51:52.344862Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"0.8067226890756303"},"metadata":{}}]},{"cell_type":"markdown","source":"The age, the fare and the gender appears to contribute the most to predicting accurately the surviving or not the accident. It is surprising the passenger class influence less random forrest. ","metadata":{}},{"cell_type":"code","source":"importances = rf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.347466Z","iopub.execute_input":"2023-02-01T14:51:52.347785Z","iopub.status.idle":"2023-02-01T14:51:52.360347Z","shell.execute_reply.started":"2023-02-01T14:51:52.347756Z","shell.execute_reply":"2023-02-01T14:51:52.359060Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":" 0\n0.199528 Fare\n0.140924 Pclass\n0.390318 Sex\n0.023663 Embarked\n0.053330 fam_members\n0.192238 Age","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
0.199528Fare
0.140924Pclass
0.390318Sex
0.023663Embarked
0.053330fam_members
0.192238Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We found the classes of importances are Fares, Sex, and Age. ","metadata":{}},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = rf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.362231Z","iopub.execute_input":"2023-02-01T14:51:52.362868Z","iopub.status.idle":"2023-02-01T14:51:52.379545Z","shell.execute_reply.started":"2023-02-01T14:51:52.362825Z","shell.execute_reply":"2023-02-01T14:51:52.378290Z"},"trusted":true},"execution_count":218,"outputs":[{"execution_count":218,"output_type":"execute_result","data":{"text/plain":"array([[319, 10],\n [ 54, 151]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.381097Z","iopub.execute_input":"2023-02-01T14:51:52.381577Z","iopub.status.idle":"2023-02-01T14:51:52.391168Z","shell.execute_reply.started":"2023-02-01T14:51:52.381537Z","shell.execute_reply":"2023-02-01T14:51:52.390198Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"Accuracy : 0.8801498127340824\nMisclassfication : 0.1198501872659176\nSensitivivity : 0.9696048632218845\nSpecificity : 0.7365853658536585\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.392573Z","iopub.execute_input":"2023-02-01T14:51:52.393224Z","iopub.status.idle":"2023-02-01T14:51:52.412047Z","shell.execute_reply.started":"2023-02-01T14:51:52.393191Z","shell.execute_reply":"2023-02-01T14:51:52.410398Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.413222Z","iopub.execute_input":"2023-02-01T14:51:52.413582Z","iopub.status.idle":"2023-02-01T14:51:52.421900Z","shell.execute_reply.started":"2023-02-01T14:51:52.413554Z","shell.execute_reply":"2023-02-01T14:51:52.420658Z"},"trusted":true},"execution_count":221,"outputs":[{"name":"stdout","text":"Accuracy : 0.8067226890756303\nMisclassfication : 0.19327731092436976\nSensitivivity : 0.9227272727272727\nSpecificity : 0.6204379562043796\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.427307Z","iopub.execute_input":"2023-02-01T14:51:52.427779Z","iopub.status.idle":"2023-02-01T14:51:52.433953Z","shell.execute_reply.started":"2023-02-01T14:51:52.427746Z","shell.execute_reply":"2023-02-01T14:51:52.432477Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.435235Z","iopub.execute_input":"2023-02-01T14:51:52.435660Z","iopub.status.idle":"2023-02-01T14:51:52.465440Z","shell.execute_reply.started":"2023-02-01T14:51:52.435608Z","shell.execute_reply":"2023-02-01T14:51:52.464167Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.466837Z","iopub.execute_input":"2023-02-01T14:51:52.467622Z","iopub.status.idle":"2023-02-01T14:51:52.495143Z","shell.execute_reply.started":"2023-02-01T14:51:52.467589Z","shell.execute_reply":"2023-02-01T14:51:52.494000Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.496752Z","iopub.execute_input":"2023-02-01T14:51:52.497420Z","iopub.status.idle":"2023-02-01T14:51:52.520420Z","shell.execute_reply.started":"2023-02-01T14:51:52.497382Z","shell.execute_reply":"2023-02-01T14:51:52.519633Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.521415Z","iopub.execute_input":"2023-02-01T14:51:52.522394Z","iopub.status.idle":"2023-02-01T14:51:52.546457Z","shell.execute_reply.started":"2023-02-01T14:51:52.522351Z","shell.execute_reply":"2023-02-01T14:51:52.545447Z"},"trusted":true},"execution_count":226,"outputs":[{"execution_count":226,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.547614Z","iopub.execute_input":"2023-02-01T14:51:52.547908Z","iopub.status.idle":"2023-02-01T14:51:52.553613Z","shell.execute_reply.started":"2023-02-01T14:51:52.547880Z","shell.execute_reply":"2023-02-01T14:51:52.552611Z"},"trusted":true},"execution_count":227,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.554829Z","iopub.execute_input":"2023-02-01T14:51:52.555101Z","iopub.status.idle":"2023-02-01T14:51:52.580427Z","shell.execute_reply.started":"2023-02-01T14:51:52.555075Z","shell.execute_reply":"2023-02-01T14:51:52.579665Z"},"trusted":true},"execution_count":228,"outputs":[{"execution_count":228,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.581453Z","iopub.execute_input":"2023-02-01T14:51:52.582459Z","iopub.status.idle":"2023-02-01T14:51:52.610464Z","shell.execute_reply.started":"2023-02-01T14:51:52.582401Z","shell.execute_reply":"2023-02-01T14:51:52.609279Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y \n0 0.0 \n1 NaN \n2 0.0 \n3 NaN \n4 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_y
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.611523Z","iopub.execute_input":"2023-02-01T14:51:52.611803Z","iopub.status.idle":"2023-02-01T14:51:52.639513Z","shell.execute_reply.started":"2023-02-01T14:51:52.611776Z","shell.execute_reply":"2023-02-01T14:51:52.638365Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 1.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538461.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
\n

357 rows × 8 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.641337Z","iopub.execute_input":"2023-02-01T14:51:52.641775Z","iopub.status.idle":"2023-02-01T14:51:52.669655Z","shell.execute_reply.started":"2023-02-01T14:51:52.641731Z","shell.execute_reply":"2023-02-01T14:51:52.668451Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred \n0 0.0 NaN \n1 NaN 1.0 \n2 0.0 NaN \n3 NaN 1.0 \n4 NaN 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.670923Z","iopub.execute_input":"2023-02-01T14:51:52.671224Z","iopub.status.idle":"2023-02-01T14:51:52.693465Z","shell.execute_reply.started":"2023-02-01T14:51:52.671196Z","shell.execute_reply":"2023-02-01T14:51:52.692202Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 0.0\n233 0.733373 3.0 2.0 2.0 6.0 -1.923077 1.0 0.0\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n235 -0.299018 3.0 2.0 2.0 0.0 0.000000 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
2550.0342843.02.04.02.0-0.0769231.00.0
2330.7333733.02.02.06.0-1.9230771.00.0
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
235-0.2990183.02.02.00.00.0000000.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.694762Z","iopub.execute_input":"2023-02-01T14:51:52.695075Z","iopub.status.idle":"2023-02-01T14:51:52.711272Z","shell.execute_reply.started":"2023-02-01T14:51:52.695047Z","shell.execute_reply":"2023-02-01T14:51:52.710037Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 12\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n 2.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 0.0 5\n 1.0 4\n 1.0 1.0 0.0 2\n 2.0 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n 2.0 0.0 2\n 5.0 1.0 1.0 1\n 6.0 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.712948Z","iopub.execute_input":"2023-02-01T14:51:52.713356Z","iopub.status.idle":"2023-02-01T14:51:52.728466Z","shell.execute_reply.started":"2023-02-01T14:51:52.713299Z","shell.execute_reply":"2023-02-01T14:51:52.727135Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.729743Z","iopub.execute_input":"2023-02-01T14:51:52.730867Z","iopub.status.idle":"2023-02-01T14:51:53.319377Z","shell.execute_reply.started":"2023-02-01T14:51:52.730830Z","shell.execute_reply":"2023-02-01T14:51:53.318257Z"},"trusted":true},"execution_count":235,"outputs":[{"execution_count":235,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.320867Z","iopub.execute_input":"2023-02-01T14:51:53.321309Z","iopub.status.idle":"2023-02-01T14:51:53.344082Z","shell.execute_reply.started":"2023-02-01T14:51:53.321267Z","shell.execute_reply":"2023-02-01T14:51:53.342810Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n386 1.405213 3.0 1.0 2.0 7.0 -2.230769 0.0 1.0\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n824 1.092843 3.0 1.0 2.0 5.0 -2.153846 0.0 1.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3861.4052133.01.02.07.0-2.2307690.01.0
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
8241.0928433.01.02.05.0-2.1538460.01.0
429-0.2773633.01.02.00.00.1538461.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.345774Z","iopub.execute_input":"2023-02-01T14:51:53.346816Z","iopub.status.idle":"2023-02-01T14:51:53.369951Z","shell.execute_reply.started":"2023-02-01T14:51:53.346772Z","shell.execute_reply":"2023-02-01T14:51:53.368730Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 7\n 2.0 0.0 1\n 1.0 1.0 0.0 5\n 2.0 1.0 0.0 1\n 1.0 1\n 3.0 2.0 1.0 1\n 5.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n3.0 0.0 1.0 0.0 13\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 1.0 1\n 2.0 1.0 0.0 3\n 1.0 2\n 2.0 0.0 1\n 1.0 2\n 4.0 1.0 1.0 1\n 5.0 1.0 1.0 2\n 6.0 2.0 0.0 1\n 7.0 1.0 1.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.371853Z","iopub.execute_input":"2023-02-01T14:51:53.372272Z","iopub.status.idle":"2023-02-01T14:51:54.052559Z","shell.execute_reply.started":"2023-02-01T14:51:53.372234Z","shell.execute_reply":"2023-02-01T14:51:54.051607Z"},"trusted":true},"execution_count":238,"outputs":[{"execution_count":238,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.053862Z","iopub.execute_input":"2023-02-01T14:51:54.054160Z","iopub.status.idle":"2023-02-01T14:51:54.076180Z","shell.execute_reply.started":"2023-02-01T14:51:54.054133Z","shell.execute_reply":"2023-02-01T14:51:54.075083Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0\n110 1.626091 1.0 1.0 2.0 0.0 1.307692 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
130-0.2840413.01.04.00.00.2307690.00.0
1101.6260911.01.02.00.01.3076920.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.081370Z","iopub.execute_input":"2023-02-01T14:51:54.081697Z","iopub.status.idle":"2023-02-01T14:51:54.104120Z","shell.execute_reply.started":"2023-02-01T14:51:54.081668Z","shell.execute_reply":"2023-02-01T14:51:54.103001Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 4\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 3\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 0.0 11\n 1.0 24\n 1.0 1.0 0.0 15\n 1.0 2\n 2.0 0.0 9\n 1.0 4\n 2.0 1.0 0.0 9\n 1.0 3\n 2.0 0.0 5\n 1.0 6\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 6\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.106496Z","iopub.execute_input":"2023-02-01T14:51:54.106922Z","iopub.status.idle":"2023-02-01T14:51:55.631830Z","shell.execute_reply.started":"2023-02-01T14:51:54.106868Z","shell.execute_reply":"2023-02-01T14:51:55.630790Z"},"trusted":true},"execution_count":241,"outputs":[{"execution_count":241,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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vSPQ8vytUm+ajb0dscZiffx/vENmfa/h0spj83q/bgl1gxwSy5PWAFwpM3Oav16rfWrGjz39yb5xVrr/Uto+6WZzhp8o4B9qMr05zF+c7f9XEr5qkxD4HaSD6y1/tFh1bcIs8DxhiQfOTt7R2OzM/3fV2v9M61rSZJSSj/J35wNBT8WSimvTvLxSX6m1vopresBThahFDgWDjOUzobH/l6mZyDvzfR7cJ9QZ7+LuODnemk6EkpLKWcyPYP60bXWX21bDQBwXBi+C3Dz3j/Tnyf5wyT/PMmXLSOQdkkp5R9m+junXyeQHo5Syr8tpfzhDpe/37o2Dq6U8uwbvL5/WErZz6RkAMeGM6UAAAA040wpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAsANlFJeU0r5/VLKk1vXAgDHlVAKADsopZxJ8meT1CR/vm01AHB8CaUAsLMvSvLaJK9M8oLLC0sp711K+ZFSyh+UUn62lPI1pZTx3P0fXkp5dSnl90opv1RK+cuHXzoAHB23ty4AADrqi5J8fZKfTvLaUsr71Vp/O8k3JvmjJO+f5EySH0vypiQppbx7klcn+eokn57kbJJXl1LeWGv9hUPfAgA4ApwpBYBrlFL6Se5K8n211tcn+e9JPq+UcirJ5yS5v9b6P2dB86G5hz4/yaO11m+vtb6j1vpfkvzrJP/7IW8CABwZQikAXO8FSR6utb51dvu7Z8uenukoozfPrTt//a4kH1dKedvlS5LPz/SsKgCwA8N3AWBOKeWOJH85yalSyv+YLX5ykqcleb8k70jyzCT/dXbfs+Ye/uYkP1lrfe7hVAsAR1+ptbauAQA6o5Synun3Rj8qyf+au+v7kvxspoH0nUn+epJnJ3k4ya/VWvullDuTvDHJVyX5ntnjPirJH9ZaJ4dRPwAcNYbvAsDVXpDk22utv1Zr/R+XL0n+RaZDcf9WktNJ/keS70yymeSPk6TW+liSe5N8bpLfnK3zYKZnWgGAHThTCgAHUEp5MMn711pfsOfKAMB1nCkFgJsw+x3SjyxTH5tkkOQHW9cFAEeViY4A4ObcmemQ3Wck+e0k/yTJDzWtCACOMMN3AQAAaMbwXQAAAJoRSgEAAGimM98pfZ/3eZ965syZ1mUAAAAce69//evfWmt9eus6kg6F0jNnzuR1r3td6zIAAACOvVLKm1rXcJnhuwAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0sNpaWUU6WU/1JKedUynwcAAICjadlnSr88yWTJzwEAAMARtbRQWkp5ZpL7kvzLZT0HAAAAR9syz5T+syR/L8m7brRCKeWFpZTXlVJe95a3vGWJpQAAANBFSwmlpZTnJ/mdWuvrd1uv1vqKWus9tdZ7nv70py+jFAAAADpsWWdKPzHJny+lPJrke5J8cinlu5b0XAAAABxRSwmltdaX1FqfWWs9k+Rzk/xErfULlvFcAAAAHF1+pxQAAIBmbl/2E9RaX5PkNct+HgAAAI4eZ0oBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmjkxoXRzczOrq6s5depUVldXs7m52bokAACAE+/21gUchs3NzQyHw2xsbKTf72c8HmcwGCRJ1tfXG1cHAABwcpVaa+sakiT33HNPfd3rXreUtldXV/Oyl70sa2trV5ZtbW3l3LlzeeMb37iU5wQAAOiqUsrra633tK4jOSGh9NSpU3n729+elZWVK8u2t7fzlKc8Je985zuX8pwAAABd1aVQeiK+U9rr9TIej69aNh6P0+v1GlUEAABAckJC6XA4zGAwyNbWVra3t7O1tZXBYJDhcNi6NAAAgBPtREx0dHkyo3PnzmUymaTX62U0GpnkCAAAoLET8Z1SAAAAnuA7pQAAABChFAAAgIaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIA6LDNzc2srq7m1KlTWV1dzebmZuuSAGChbm9dAACws83NzQyHw2xsbKTf72c8HmcwGCRJ1tfXG1cHAItRaq2ta0iS3HPPPfV1r3td6zIAoDNWV1fzspe9LGtra1eWbW1t5dy5c3njG9/YsDIAjrpSyutrrfe0riMRSgGgs06dOpW3v/3tWVlZubJse3s7T3nKU/LOd76zYWUAHHVdCqW+UwoAHdXr9TIej69aNh6P0+v1GlUEAIsnlAJARw2HwwwGg2xtbWV7eztbW1sZDAYZDoetSwOAhTHREQB01OXJjM6dO5fJZJJer5fRaGSSIwCOFd8pBQAAOGF8pxQAAAAilAIAANCQUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzdzeuoC9lFJ2XF5rPeRKAAAAWLTOnymttabWmrte/Kor1wVSAACA46HzoRQAAIDjSygFgA7b3NzM6upqTp06ldXV1WxubrYuCQAWqvPfKQWAk2pzczPD4TAbGxvp9/sZj8cZDAZJkvX19cbVAcBiOFMKAB01Go2ysbGRtbW1rKysZG1tLRsbGxmNRq1LA4CFEUoBoKMmk0n6/f5Vy/r9fiaTSaOKAGDxhFIA6Kher5fxeHzVsvF4nF6v16giAFg8oRQAOmo4HGYwGGRrayvb29vZ2trKYDDIcDhsXRoALIxQCseYWTvhaFtfX89oNMq5c+fylKc8JefOnctoNDLJEQDHitl34ZgyayccD+vr645ZAI41Z0rhmDJrJwAAR4FQCseUWTsBADgKhFI4pszaCQDAUSCUwjFl1k4AAI4CEx3BMXV5YpRz585lMpmk1+uZtRMAgM4RSuEYM2snAABdZ/guAAAAzQilAAAANCOUAgAA0IxQCgAAQDNLCaWllKeUUn6mlPKGUsrPl1IeWMbzAAAAcLQta/bdP07yybXWPyylrCQZl1L+ba31tUt6PgAAAI6gpYTSWmtN8oezmyuzS13GcwEAAHB0Le07paWUU6WUn0vyO0leXWv96WU9FwAAAEfT0kJprfWdtdaPSvLMJB9bSlm9dp1SygtLKa8rpbzuLW95y7JKAQAAoKOWPvturfVtSbaSfNoO972i1npPrfWepz/96csuBQAAgI5Z1uy7Ty+lPG12/Y4kz03yi8t4LgAAAI6uZc2++wFJHiqlnMo0+H5frfVVS3ouAAAAjqhlzb77/yX56GW0DQAAwPGx9O+UAgAAwI0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKVwjG1ubmZ1dTWnTp3K6upqNjc3W5cEAABXub11AcBybG5uZjgcZmNjI/1+P+PxOIPBIEmyvr7euDoAAJhyphSOqdFolI2NjaytrWVlZSVra2vZ2NjIaDRqXRoAAFwhlMIxNZlM0u/3r1rW7/czmUwaVQQAANcTSuGY6vV6GY/HVy0bj8fp9XqNKgIAgOsJpXBMDYfDDAaDbG1tZXt7O1tbWxkMBhkOh61LAwCAK0x0BMfU5cmMzp07l8lkkl6vl9FoZJIjAAA6RSiFY2x9fV0IBQCg0wzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAWADtvc3Mzq6mpOnTqV1dXVbG5uti4JABbK7LsA0FGbm5sZDofZ2NhIv9/PeDzOYDBIEjNrA3BsOFMKAB01Go2ysbGRtbW1rKysZG1tLRsbGxmNRq1LA4CFcab0CCql7Li81nrIlQCwTJPJJP1+/6pl/X4/k8mkUUUAsHjOlB5BtdYrl7te/Kor1wE4Xnq9Xsbj8VXLxuNxer1eo4oAYPGEUgDoqOFwmMFgkK2trWxvb2drayuDwSDD4bB1aQCwMIbvAkBHXZ7M6Ny5c5lMJun1ehmNRiY5AuBYEUoBoMPW19eFUACONcN3AQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKD1km5ubWV1dzalTp7K6uprNzc3WJQEAADRze+sCTpLNzc0Mh8NsbGyk3+9nPB5nMBgkSdbX1xtXBwAAcPicKT1Eo9EoGxsbWVtby8rKStbW1rKxsZHRaNS6NAAAgCaE0kM0mUzS7/evWtbv9zOZTBpVBAAA0JZQeoh6vV7G4/FVy8bjcXq9XqOKAAAA2hJKD9FwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAAIAmTHR0iC5PZnTu3LlMJpP0er2MRiOTHAEAACeWUHrI1tfXhVAAAIAZw3cBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAaDDNjc3s7q6mlOnTmV1dTWbm5utSwKAhTL7LgB01ObmZobDYTY2NtLv9zMejzMYDJLETO4AHBvOlAJAR41Go2xsbGRtbS0rKytZW1vLxsZGRqNR69IAYGGEUgDoqMlkkn6/f9Wyfr+fyWTSqCIAWDyhFAA6qtfrZTweX7VsPB6n1+s1qggAFk8oBYCOGg6HGQwG2drayvb2dra2tjIYDDIcDluXBgALY6IjAOioy5MZnTt3LpPJJL1eL6PRyCRHABwrQikAdNj6+roQCsCxZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQDQYZubm1ldXc2pU6eyurqazc3N1iUBwELd3roAAGBnm5ubGQ6H2djYSL/fz3g8zmAwSJKsr683rg4AFsOZUgDoqNFolI2NjaytrWVlZSVra2vZ2NjIaDRqXRoALIxQCgAdNZlM0u/3r1rW7/czmUwaVQQAiyeUAkBH9Xq9jMfjq5aNx+P0er1GFQHA4gmlANBRw+Ewg8EgW1tb2d7eztbWVgaDQYbDYevSAGBhTHQEAB11eTKjc+fOZTKZpNfrZTQameQIgGNFKAWADltfXxdCATjWDN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoPWSbm5tZXV3NqVOnsrq6ms3NzdYlAQAANGP23UO0ubmZ4XCYjY2N9Pv9jMfjDAaDJDGzIgAAcCI5U3qIRqNRNjY2sra2lpWVlaytrWVjYyOj0ah1aQAAAE04U3qIJpNJ+v3+Vcv6/X4mk0mTekop1y2rtTaoBAAAOKmcKT1EvV4v4/H4qmXj8Ti9Xq9JPbXW1Fpz14tfdeU6AADAYRJKD9FwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAAIAmDN89RJcnMzp37lwmk0l6vV5Go5FJjgAAgBNLKD1k6+vrQigAAMCM4bsAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUwjG2ubmZ1dXVnDp1Kqurq9nc3GxdEgAAXMVPwsAxtbm5meFwmI2NjfT7/YzH4wwGgyTxs0QAAHSGM6VwTI1Go2xsbGRtbS0rKytZW1vLxsZGRqNR69IAAOAKZ0rhmJpMJun3+1ct6/f7mUwm+3r82YfO7rnOIy945JZqAwCAy4RSOKZ6vV7G43HW1tauLBuPx+n1evt6vMAJAMBhMHwXjqnhcJjBYJCtra1sb29na2srg8Egw+GwdWkAAHCFM6VwTF2ezOjcuXOZTCbp9XoZjUYmOQIAoFOEUjjG1tfXhVAAADrN8F0AAACaEUoBAABoRigFAACgGaEUAACAZpYSSkspzyqlbJVSfqGU8vOllC9fxvMAAABwtC1r9t13JPk7tdb/XEq5M8nrSymvrrX+wpKeDwAAgCNoKWdKa62/VWv9z7PrjyWZJPnAZTwXAAAAR9fSv1NaSjmT5KOT/PQO972wlPK6Usrr3vKWtyy7FAAAADpmqaG0lPLUJP86yVfUWv/g2vtrra+otd5Ta73n6U9/+jJLAQAAoIOWFkpLKSuZBtJ/VWv9gWU9DwAAAEfXsmbfLUk2kkxqrV+/jOcAAADg6FvWmdJPTPKFST65lPJzs8tnLOm5AAAAOKKW8pMwtdZxkrKMtgEAADg+lj77LgAAANyIUAoAAEAzQikAAADNCKXA0m1ubmZ1dTWnTp3K6upqNjc3W5cEAEBHLGWiI4DLNjc3MxwOs7GxkX6/n/F4nMFgkCRZX19vXB0AAK05Uwos1Wg0ysbGRtbW1rKyspK1tbVsbGxkNBq1Lg0AgA4QSoGlmkwm6ff7Vy3r9/uZTCaNKgIAoEuEUmCper1exuPxVcvG43F6vV6jigAA6BKhFFiq4XCYwWCQra2tbG9vZ2trK4PBIMPhsHVpAAB0gImOgKW6PJnRuXPnMplM0uv1MhqNTHIEAEASoRQ4BOvr60IoAAA7MnwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKCZ21sXACxPKeW6ZbXWBpUsxnHbHrrn7ENn97XeIy94ZMmVAMDJIZTCMXY5sJ05fzGPXrivcTUHd9y2h+4RNgHg8Bm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzdzeugAopey4vNZ6yJUAAACHzZlSmqu1ptaau178qivXBVIAADgZhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmjnWPwlz9wMP59Lj20mSNz34/Ovuv+vFr8rpO1byhvvvPezSAAAAyDEPpZce386jF+6b3riw80+MnDl/8RArAgAAYJ7huwAAADQjlAJAhz3vec/LbbfdllJKbrvttjzvec9rXRIALJRQCgAd9bznPS8PP/xwvvRLvzRve9vb8qVf+qV5+OGHBVMAjpVj/Z1SADjKXv3qV+fLvuzL8k3f9E1JcuXfl7/85S3LAoCFcqYUADqq1pqv/dqvvWrZ137t16bWnSfvA4CjSCgFgI4qpeQlL3nJVcte8pKXpJTSqCIAWDyhFAA66rnPfW6++Zu/OS960Yty6dKlvOhFL8o3f/M357nPfW7r0gBgYXynFAA66sd+7MfyvOc9Ly9/+cvzzd/8zSml5N57782P/diPtS4NABZGKAWADhNAATjuDN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJrxO6X7cPahs3uu88gLHll6HXc/8HAuPb593fIz5y9euX76jpW84f57l14LAADAIgil+3AYgXM/Lj2+nUcv3LfrOvMBFQAAoOsM3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmvE7pSfQ3Q88nEuPb1+1bP73TU/fsZI33H/vYZcFwA42NzczGo0ymUzS6/UyHA6zvr7euiwAWBih9AS69Ph2Hr1w3w3vnw+oALSzubmZ4XCYjY2N9Pv9jMfjDAaDJBFMATg2DN8FgI4ajUbZ2NjI2tpaVlZWsra2lo2NjYxGo9alAcDCOFN6yEop1y2rtTaoBICum0wm6ff7Vy3r9/uZTCaNKgKAxXOm9JDVWlNrzV0vftWV6wCwk16vl/F4fNWy8XicXq/XqCIAWDyhFAA6ajgcZjAYZGtrK9vb29na2spgMMhwOGxdGgAsjOG7ANBRlyczOnfu3JXZd0ejkUmOADhWhFIA6LD19XUhFIBjzfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgE4VjY3N7O6uppTp05ldXU1m5ubrUs6kOO2PQBwLT8JA8Cxsbm5meFwmI2NjfT7/YzH4wwGgyQ5kj+rcty2BwB24kwpAMfGaDTKxsZG1tbWsrKykrW1tWxsbGQ0GrUu7ZYct+0BgJ04UwrAsTGZTNLv969a1u/3M5lMGlV0MMdte4C2SinXLau1NqgEruZMKQDHRq/Xy3g8vmrZeDxOr9drVNHBHLftAdqqtabWmrte/Kor16ELhFIAjo3hcJjBYJCtra1sb29na2srg8Egw+GwdWm35LhtDwDsxPBdAI6Ny5P/nDt3LpPJJL1eL6PR6MhOCnTctgcAdiKUAnCsrK+vH6vQdty2BwCuZfguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQB02ObmZlZXV3Pq1Kmsrq5mc3OzdUkAsFBm3wWAjtrc3MxwOMzGxkb6/X7G43EGg0GSmJEXgGPDmVIA6KjRaJSNjY2sra1lZWUla2tr2djYyGg0al0aACxMZ8+U3v3Aw7n0+PZVy86cv3jV7dN3rOQN9997mGUBwKGZTCbp9/tXLev3+5lMJo0qOj5KKdctq7U2qASAzobSS49v59EL9+26zrUhFQCOk16vl/F4nLW1tSvLxuNxer1ew6qOh8sB9Mz5i3t+3gBguQzfBYCOGg6HGQwG2drayvb2dra2tjIYDDIcDluXBgAL09kzpQBw0l2ezOjcuXOZTCbp9XoZjUYmOQLgWBFKAaDD1tfXhVAAjjXDdwEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFgA7b3NzM6upqTp06ldXV1WxubjZpAwCW5fbWBQAAO9vc3MxwOMzGxkb6/X7G43EGg0GSZH19/dDaAIBlcqYUADpqNBplY2Mja2trWVlZydraWjY2NjIajQ61DQBYJmdKaebuBx7Opce3r1p25vzFq26fvmMlb7j/3sMsiyUopey4vNZ6yJWwk51eH69NN0wmk/T7/auW9fv9TCaTQ20DYNH838M8oZRmLj2+nUcv3LfrOteGVI6my//JnDl/cc/XnMPn9emuXq+X8XictbW1K8vG43F6vd6htgGwaP7vYZ7huwDQUcPhMIPBIFtbW9ne3s7W1lYGg0GGw+GhtgEAy+RMKQB01OWJiM6dO5fJZJJer5fRaHRTExQtog0AWCahFAA6bH19/cABchFtAMCyGL4LAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM7e3LgAAADj+7n7g4Vx6fPu65WfOX7xy/fQdK3nD/fceZll0gFAKAAAs3aXHt/Pohft2XWc+oHJyGL4LAABAM0sLpaWUbyul/E4p5Y3Leg4AAACOtmWeKX1lkk9bYvsAAAAccUsLpbXW/5Dk95bVPgAAAEef75QCAADQTNPZd0spL0zywiR59rOf3bIUAOikUsp1y2qtDSoBYBnOPnR2z3UeecEjh1BJO01Daa31FUlekST33HOP/2EB4BqXA+iZ8xf3/CkFAI6e4x4498PwXQAAAJpZ5k/CbCb5qSQfVkr59VLKYFnPBQAAwNG0tOG7tdb1ZbUNAADA8WD4LgAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAcGg2NzezurqaU6dOZXV1NZubm61LAqCxpc2+CwAwb3NzM8PhMBsbG+n3+xmPxxkMpr8Yt75u0n6Ak8qZUgDgUIxGo2xsbGRtbS0rKytZW1vLxsZGRqNR69IAaMiZUoCbVEq5blmttUElcLRMJpP0+/2rlvX7/Uwmk0YVAYfpzt75nH3o/B7rJMl9h1IP3SGUAtykywH0zPmLefSC/zhhv3q9XsbjcdbW1q4sG4/H6fV6DasCDstjkwt7/r955vzFQ6qGLjF8FwA4FMPhMIPBIFtbW9ne3s7W1lYGg0GGw2Hr0gBoyJlSAOBQXJ7M6Ny5c5lMJun1ehmNRiY5AjjhhFIA4NCsr68LoQBcxfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAsrm5mdXV1Zw6dSqrq6vZ3NxsXRJwQph9FwDghNvc3MxwOMzGxkb6/X7G43EGg0GSmC0ZWDpnSgEATrjRaJSNjY2sra1lZWUla2tr2djYyGg0al0acAIIpQAAJ9xkMkm/379qWb/fz2QyaVQRcJIIpQAAJ1yv18t4PL5q2Xg8Tq/Xa1QRcJIIpQAAJ9xwOMxgMMjW1la2t7eztbWVwWCQ4XDYujTgBDDR0SG5+4GHc+nx7auWnTl/8arbp+9YyRvuv/cwy4LO2+nYSa4+fhw7AAdzeTKjc+fOZTKZpNfrZTQameQIOBRC6SG59Ph2Hr1w367rXBtSAccOwGFZX18XQoEmDN8FAACgGWdKAQCAQ7HX6KbTd6wcUiV0iVAKAAAs3U5fxzlz/uKeX9Ph+DN8FwAAgGaEUgAAAJoRSgEAgH3b3NzM6upqTp06ldXV1WxubrYuiSPOd0oBAIB92dzczHA4zMbGRvr9fsbjcQaDQZL4SSFumTOlAADAvoxGo2xsbGRtbS0rKytZW1vLxsZGRqNR69I4wpwpBQBoqJRy3bJaa4NKYG+TyST9fv+qZf1+P5PJpFFFHAfOlAIANFRrTa01d734VVeuQ1f1er2Mx+Orlo3H4/R6vUYVcRwIpQAAwL4Mh8MMBoNsbW1le3s7W1tbGQwGGQ6HrUvjCDN8FwAA2JfLkxmdO3cuk8kkvV4vo9HIJEcciFAKAADs2/r6uhDKQhm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0c6x/p/TO3vmcfej8HuskyX2HUg9X8/osz90PPJxLj29ftezM+YtXrp++YyVvuP/e5nUcZi0AQHeUUp64/uD031rrLbdx2SLauJV2OJhjHUofm1zIoxd2DzTXfkDm8Hh9lufS49u77tvD2q971XGYtQAA3bGI0He5jTPnL+75eWOZbXBwhu8CAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzt7cugP27s3c+Zx86v8c6SXLfodQDAABwUELpEfLY5EIevbB74Dxz/uIhVQMAAHBwhu8CAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADN3N66AI62Usp1y2qtDSoB4Ea8VwPQZc6UciC11tRac9eLX3XlOgDd4r0agC4TSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAODQbG5uZnV1NadOncrq6mo2NzdblwRAY7e3LgAAOBk2NzczHA6zsbGRfr+f8XicwWCQJFlfX29cHQCtOFMKAByK0WiUjY2NrK2tZWVlJWtra9nY2MhoNGpdGgANOVMKC3L2obP7Wu+RFzyy5Erg8JVSdlxeaz3kSuiyyWSSfr9/1bJ+v5/JZNKoonbufuDhXHp8+7rlZ85fvHL99B0recP99x5mWQBNCKWwIMImJ9nl8Hnm/MU8euG+xtXQVb1eL+PxOGtra1eWjcfj9Hq9hlW1cenx7T2PlfmACnCcGb4LAByK4XCYwWCQra2tbG9vZ2trK4PBIMPhsHVpADR07M+U7vVXxtN3rBxSJcBRt9Nwu2vfYwy3gxu7PJnRuXPnMplM0uv1MhqNTHIEcMId61C607AYQ8uAW2W4HRzc+vq6EArAVQzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaOda/UzqvlPLE9Qen/9ZaD+357+ydz9mHzu+xTpL4DVUAAODkODGh9DAD6E4em1zIoxd2D5xnzl88pGoAAAC6wfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgmdtbFwCwmzt753P2ofN7rJMk9x1KPQAALJZQCnTaY5MLefTC7oHzzPmLh1QNAACLZvguAAAAzThTCnBC3f3Aw7n0+PZVy+bPOp++YyVvuP/ewy4LADhhhFKAE+rS49u7Do02LBoAOAyG7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzXT2d0rv7J3P2YfO77FOktz4N/YAAKC1Usp1y2qtDSqBbupsKH1scmHXH3VP/LA7AADddzmAnjl/cc/Pt3ASGb4LAABAM509UwpA9939wMO59Pj2VcuuHcVy+o6VvOH+ew+zLADgCBFKAbhllx7f9lULAOBADN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaMZER8DS3Nk7n7MPnd9jnSTxm20AACeVUAoszWOTC2ZmBQBgV4bvAgAA0IwzpSfQXkMqDaeE5Sul7Li81nrIlQAn3d0PPJxLj2/nTQ8+/7r77nrxq5Ikp+9YyRvuv/ewSwNOCKH0BNprSKXhlLB88+HzzPmLew5zBliWS49vT9+DLtz4j2I+GwDLZPguAAAAzThTCgAdc3k45bXmz1YZTgnAcSGUAkDHXBlOuQvDKQE4LgzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBXCy7fXj76fvWDmkSgA4Ce5+4OFcenz7qmXz/xedvmMlb7j/3sMuC+BEE0pp5tEL9111+8z5i9ctA4BFuvT49q7/1+z1x1IAFs/wXQAAAJoRSgEAAGjG8F2g83z3GADg+BJKgU7b6btfvn8MAHB8GL4LAABAM0IpAAAAzSwtlJZSPq2U8kullF8upZxf1vMAAABwdC3lO6WllFNJvjHJc5P8epKfLaX8cK31F5bxfADA9e5+4OFcenz7qmXzE4edvmMlb7j/3sMuCwCusqyJjj42yS/XWn8lSUop35Pks5IIpQBwSC49vr3rpGB7zWwNAIdhWcN3PzDJm+du//psGQAAAFzR9CdhSikvTPLCJHn2s5993f3H7bcJj9v20F139s7n7EM3/ir3nb0k8ZMq0FV7HcPTdZLDOI4vDwF+04PPv+6+u178qiR7DwM++9DZfT3XIy945NaKJKWU65bVWvf12EX2t53quJladmvnZts4iC722YPsk72G8ieG8x91XeyzN2NZofQ3kjxr7vYzZ8uuUmt9RZJXJMk999xz1VF17XCjo/67hMdte+i2xyYXDNmDI2yvYzg5vOP4yhDgCzf+8LtXLV3anuPqcji5lc8Xi3x95kPSQT7rHGR7FqGLH9wPsk/2Gsp/uV2Ori722ZuxrOG7P5vkfyulfFAp5UlJPjfJDy/puQAAADiilnKmtNb6jlLK30ryY0lOJfm2WuvPL+O5AAAAboYhzd2ytO+U1lp/NMmPLqt9AACAW2FIc7csa/guAAAA7Knp7LsAHG1dmiUWADiahFIAbplZVQGAgzJ8FwAAgGacKQWWaq+zZKfvWDmkSgBYlp1mMk2u/j/ATKbAjQilwNJcO6yz1Y+gA7BcZjIFDsLwXQAAAJpxphQA4IQ7c/5i3vTg869bfteLX5Xk8L5qsdMwYEOA4fgTSgEATrArw24v1LaFZO9hwIYAw/Fk+C4AAADNCKUAAAA0I5QCAADQjFAKAABAMyY6AtinO3vnc/ah83uskyR+ixXgpNtpJuHEbMKwE6EUYJ8em1zw4/AA7MteMwkn/s+AywzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZs+8eMXvN0nb6jpVDqgQOXynliesPTv+ttTaqBuDW+Ympne21X07iPoGTQCg9QnaaVvzM+Yt7TjcOx4UAChwXfmJqZ3vtl5O4T+AkMHwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBkTHQGcUGa5BAC6QCgFOKHMcgkAdIHhuwAAADQjlAIAANCMUAoAAEAzQikAAADNmOiIW3L3Aw/n0uPbVy2bnxTl9B0recP99x52WczZbZKa03esHGIl3XD2obN7rvPICx45hEoA2I3/v+DkEUq5JZce3zZrZ4dd+9qcOX9x19frJBA4AbrP/19wMhm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMiY4ADtFOM1cnZq+mu+7snc/Zh87vsU6S7D4ZzV4T4JlVFXbmFw84CYRSgEO018zVidmr6ZbHJhcO3Gd3erxZVWF//OIBJ4HhuwAAADTjTCkAB2JYJgBwEEIpALfMD90DAAdl+C4AAADNOFMKAJwYe80mvJ+ZhFm+UsoT1x+c/ltrbVQNsGxCKQBwYuw1m7CZTLtBAIWTxfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGZMdAQAwIHsNavxdJ3EzMbAToRSAAAOZK9ZjRMzGwM3ZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM2YfReSnH3o7J7rPPKCRw6hEgDYv7sfeDiXHt++atn8LLen71jJG+6/97DLomP8ZA9dJ5RCBE4AjqZLj2/v+lMsfoaFxE/20H2G7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDO3ty4AABbh7gcezqXHt/OmB59/3X13vfhVOX3HSt5w/71Lr+PsQ2f3td4jL3hkyZUALd3ZO5+zD53fY50kue9Q6jlOLr/fzztz/uKV64f1fs/iCKUAHAuXHt/OoxfuSy7UHe+f/8CyTMImkCSPTS5M35N2cVjvS8fNlff7G7Bfjx7DdwEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoJnbWxcAwNFXSnni+oNPLK+1HloNd/bO5+xD53e5P0nuO7R6DurM+Yu73n/6jpU92zhu+wROIscxJ4FQCsCBHWb4vJHHJhfy6IUbfzDbK+R1yU7bceb8xV23byfHaZ/ASeU45iQwfBcAAIBmnCk9oXb7q9p+hoQBAAAsglB6Al07BORWhoQBAAAsguG7AAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADN3N66gL2UUp64/uATy2utDaoBAG7V/P/pyRP/r/s/HeBk63wo9R8VABwP/k8HYCeG7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzXT+d0oBuuTM+Yu73n/6jpVDqgQA4HgQSgH26dEL9111+8z5i9ctAwDg5hi+CwAAQDPOlEKHnH3o7L7We+QFjyy5ksUqpTxx/cEnltdaG1TT1p298zn70Pk91kkSZ2BZjN2GnBtuDkAXCKXQIUctbO7XSQyfN/LY5MKeQ373+t4q7Jch5wAcBYbvAgAA0IxQCgAAQDNCKQAAAM0IpQAAADRjoiMAAOBEMRt+twilAADAiWI2/G4xfBcAAIBmhFIAAACaEUoBAABoRigFAACgGRMdARyyvSZOOH3HyiFVAifTbsfgUTv+9ppB1OyhwFEglAIcop1m+jtz/uKeMwACi3HtsXbUj7+9ZhA1eyhwFBi+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMiY4AAKDDjtOM0bAToRQAADrquM0YDTsxfBcAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZsy+e8hKKU9cf3D6b621UTXdsNM+SewXjrf5fp94P4Cj6Nrj+DLHMcDNcab0kNVar7ucdDvtE/uF406/h6Pv8nF714tf5TgGOAChFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBQAAHCV3P/BwLj2+fdWyM+cvXnX79B0recP99x5mWQBHllAKAHATLj2+nUcv3LfrOteGVABuzPBdAAAAmnGm9AgqpVx9+8Hpv7XWBtXQZfN9RT8B6Ja9zqaevmPlkCoBaEsoPYKECvZLXwHopp2G/545f3HPYcEAx5HhuwAAADQjlAIAANCMUAoAAEAzQikAAADNmOgIANiVWWIBWCahFAC4oWtngzVDLACLZvguAAAAzSw8lJZS/vdSys+XUt5VSrln0e0DAEfX5uZmVldXc+rUqayurmZzc7N1SQA0tozhu29M8heTfMsS2gYAjqjNzc0Mh8NsbGyk3+9nPB5nMBgkSdbX1xtXB0ArCz9TWmud1Fp/adHtAgBH22g0ysbGRtbW1rKyspK1tbVsbGxkNBq1Lg2AhppOdFRKeWGSFybJs5/97JalcJPu7J3P2YfO73J/kpgIA6ALSilPXH9w+m+t9dDrmEwm6ff7Vy3r9/uZTCaHXgvAQZ196Oy+1nvkBY8suZKj75ZCaSnlx5O8/w53DWutP7Tfdmqtr0jyiiS55557Dv9/R27ZY5MLu86+uNfPBwBweFoE0J30er2Mx+Osra1dWTYej9Pr9RpWBXBrhM3FuaVQWmv91EUXAgAcb8PhMIPB4LrvlBq+C3Cy+Z1SAOBQXJ7M6Ny5c5lMJun1ehmNRiY5AjjhFh5KSyl/IcnLkjw9ycVSys/VWp+36OcBAI6e9fV1IRSAqyw8lNZafzDJDy66XQAAAI6fhf8kDAAAAOyXUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzSz8d0oBgMUppTxx/cHpv7XWRtWwDF5j9qMr/WS+jnn6LAfhTCkAdFit9boLx4vXmP3oSj+Zf/67XvwqfZaFEEoBAABoRigFAACgGaEUAACAZoRSAAAAmjH7LgAAsKu7H3g4lx7fvm75mfMXr1w/fcdK3nD/vYdZFseEUAoAAOzq0uPbefTCfbuuMx9Q4WYYvgsAAEAzQikAAADNCKUAAAA0I5QCAADQjImOTrBSyhPXH5z+W2ttVA0AAHASCaUnmAAKAAC0ZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDMmOgIAThwz0HMYzpy/uOv9p+9YOaRKoNuEUgDgxBFAWbZHL9x33bIz5y/uuBxOOsN3AQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGjm9tYFAADcjFLKdctqrQ0qgZPjzt75nH3o/B7rJMl9h1IPx4tQCgAcKZcD6JnzF/PoBR+A4TA8Nrmw5/F25vzFQ6qG48bwXQAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmTHQEcILtNinF6TtWDrES4FY5joGjTigFOKGunUXRTKZw9DiOgePA8F0AAACacaYUAAA4cfb6XVXD3w+PUAoAAJwohr53i+G7AAAANONMKQDATbizdz5nHzq/xzpJ4qwLwH4IpQAAN+GxyYU9h/nt9V01AJ5g+C4AAADNCKUAAAA0I5QCAADQjFAKAABAMyY64pbtNomDHxsGAAD2QyjllvjBYQAAYBEM3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmbm9dAAAsypnzF2943+k7Vg6xEjh6Njc3MxqNMplM0uv1MhwOs76+3ros4AQQSgE4Fh69cN9Vt8+cv3jdMmBnm5ubGQ6H2djYSL/fz3g8zmAwSBLBFFg6oRQAOBLufuDhXHp8+6pl82fHT9+xkjfcf+9hl3UsjEajbGxsZG1tLUmytraWjY2NnDt3TigFlk4oBQCOhEuPb+969nu34dvsbjKZpN/vX7Ws3+9nMpnsu4299r8h9CzKnb3zOfvQ+V3uTxIjZY4SoRQA4ITr9XoZj8dXzpQmyXg8Tq/X29fjd/pjgSH0LMtjkwv+QHXMmH0XAOCEGw6HGQwG2drayvb2dra2tjIYDDIcDluXBpwAzpQCAJxwl783eu7cuSuz745GI98nPUYMr6bLhFIAALK+vi6EHlNmJ6frDN8FAACgGWdKAQCAPRkCzLIIpQAAwK7MsMwyGb4LAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0c3vrAgAAAG7GmfMXb3jf6TtWDrESFkEoBQAAjoxHL9x31e0z5y9et4yjxfBdAAAAmnGmFAA4Eu7snc/Zh87vcn+SOFsCcNQIpQDAkfDY5MKuQ/R2+44ZAN1l+C4AAADNOFMKcJNKKU9cf3D6b621UTUAAEebUApwkwRQAIDFMXwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaub11AQBA95VSnrj+4PTfWmujauBocfzA7oRSAGBPPkDDrXP8wO4M3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmll4KC2lfF0p5RdLKf9fKeUHSylPW/RzAAAAcDws40zpq5Os1lo/Msl/TfKSJTwHAAAAx8DCQ2mt9eFa6ztmN1+b5JmLfg4AAACOh2V/p/SvJfm3S34OAAAAjqjbb+VBpZQfT/L+O9w1rLX+0GydYZJ3JPlXu7TzwiQvTJJnP/vZt1IKjZVSnrj+4PTfWmujaoBbcdyO4+O2PQBdMv8em7R9n/V+f3zcUiittX7qbveXUr44yfOTfErdpWfUWl+R5BVJcs899+hBR5ADH46+43YcH7ftAeiSLr3HdqkWDuaWQuluSimfluTvJfmkWuv/XHT7AAAAHB/L+E7pv0hyZ5JXl1J+rpTy8iU8BwAAAMfAws+U1lo/dNFtAgAAcDwte/ZdAAAAuKGFnykFADjuzpy/uOv9p+9YOaRKAI4+oRQA4CY8euG+q26fOX/xumUA7J9QCgAcGbudoXR2EuBoEkoBjrDNzc2MRqNMJpP0er0Mh8Osr6+3LguWwhlKgONJKAU4ojY3NzMcDrOxsZF+v5/xeJzBYJAkgikAcGSYfRfgiBqNRtnY2Mja2lpWVlaytraWjY2NjEaj1qUBAOybM6UAR9RkMkm/379qWb/fz2QyaVQR0Eop5YnrD07/rbU2qoau0k/oKmdKAY6oXq+X8Xh81bLxeJxer9eoIqCVWut1F7iWfkJXCaUAR9RwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAgH0zfBfgiLo8mdG5c+euzL47Go1McgQAHClCKcARtr6+LoQCAEea4bsAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDN+pxQA4BaUUp64/uATy2utDaoBboXjuBuEUgCAW+BDKxx9juNuMHwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBQBwc+5+4OFcenw7SfKmB59/3f13vfhVOX3HSt5w/72HXdotOfvQ2X2t98gLHllyJcBBlVKuvv3g9N9a66HVsJ/3FO8n0C1CKUeaD7OcRJce386jF+6b3riw8we9M+cvHmJFB+P4hOPjMMPnjXhPgaNHKOVI8x8PJ9GdvfM5+9D5PdZJkvsOpR4AgIMQSgGOmJ3+GHPm/MUnzp4CABwhQinAETb//a0W390CADgooRTgCBNAAYCjzk/CAAAA0IwzpQDAkWLYOsDxIpQCAEeKAApwvBi+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANFNqra1rSJKUUt6S5E27rPI+Sd66gKdaRDtdaaNLtXSljS7VYnuW00aXarE9y2mjS7V0pY0u1WJ7ltNGl2qxPctpo0u12J7ltNGlWvbTxl211qcf8HkWo9Z6JC5JXteVdrrSRpdq6UobXarF9tgnR62WrrTRpVq60kaXarE99slRq6UrbXSpFttjn3TtYvguAAAAzQilAAAANHOUQukrOtROV9pYVDvHqY1FtdOVNhbVznFqY1HtdKWNRbVznNpYVDvHqY1FtdOVNhbVznFqY1HtdKWNRbVznNpYVDtdaWNR7RynNhbVTlfaODSdmegIAACAk+conSkFAADgmBFKAYCrlFJKl9qBw9Cl/tqVWrwXcFiE0htYxMFTSjnw/l3gm8EiaunS9izi9fEaL6eNLr3GXdknC3mv7dA+6cTxt8BauvQaN399SilPqgv4bs8C2+lEf3McL6eOrtSyqP46a6srx2BX6ujE9iyqjS7V0pX3gkW0KZTuYBEHTynlybXWd7WuY4G1LKKNOxe0PQduZ0FteI2vb6Mz/4F1aJ8cuI1ZO13ps504/mbtHJt+ssBaDrRvSymfkeS7SykfVUp5VgfaWcRr3Il+0qVaulJHV2pZVH+dtXWg7enKseO9oPu1dOW9YK6tTy6lvEcp5bZaa73ZwCyUXqOUcl+SV5VSPqOU8lG32MZnJPnRUsoLSymf0qqOBdayiDael2SzlPIPSil/+VbaWFQ7C2rDa3x9G4vanq4cg51oY9ZOV/psJ46/WTvHpp8ssJZF7NutJD+e5AuTfFUp5bNbtbOg17gT/aRLtXSljo7VspB+v6D/Bztx7CyijkW105X3gi7V0pX3grm2npbkRUm+Msl3llLe/aYDc63V5ZpLkv89yd9JcjHJX7vFNj4pyXqSn0vy5a3qWGAti2jjw5I8J8kvzTrtHa3aWVAbXuPlbU9XjsFOtDFrpyt9thPH33HrJ61fnyQfmeSuudtnkvylJD+d5PNv4vkX0s6CX+NO9JMu1dKVOlrXsuj+epDt6cqx473g6NTSofeCldm/T5ldvjHJf0jyUTfVzq2+IMftkuTjk3zs3O0nJ+kn+Y0k5/bZxnOT3HfNsrNJfiXJ3zmsOhZYyyLa+JwkX5TkSXOd9sOT/ESS4U1sz4HbWVAbXuPlbU9XjsFOtNGxPtuJ4++49ZOuvD5JvjPJOMn3JvnBa+77tCSvTvJJh9jOIl7jTvSTLtXSlTq6Usui+usitqcrx473gu7X0pX3grnHfnWSr8/0Dw4fcM3yH03yQbPbt+3Z1s088XG9JPmeJK/J9K8Er0ryzMs7L8lHJ5kk+Qt7tPH/JPn3SR5O8oNJ1pKcnt33J5P8epLBsutYYC2LauPHkvxQks0kfz3J+8/u+9Akr99P519EOwtsw2u8nO3pyjHYiTY61mc7cfwdt37Sldcn00D7mtn125P88Kytd58te/ckX5LkK5KUZPr75ktsZxGvcSf6SZdq6UodXallUf11EduzqFo6VEcntmdRbXSplnTkvWCurX8+q+WvzF7nf5DkU+fuv5DpmfHb99XeflY6zpdMhzn9xNztlyf5V0nOzi37rEz/CnB6p4Mn079Q/Njc7fNJviXJZ84dhB8/60xnllXHAmtZRBsfmOSH525/3qz2v53k6bNlfzLJf0pyzy6vz4HbWVAbXuPlbc+B2+nQPjlwGx3rs504/o5bP+nY63M2ySuTvOfcsu9O8u/mbv+ZTD9Uvvce23Ogdhb0Gnein3Splq7U0aVaFtFfF7w9B6qlK3V0bHsO3EaXaklH3gvm1ntKkoeS/G+z2x+WZJhpEO3PrfcNSb7sRu3MX0x0ND3d/cellD+ZJLXWL8309PVLSylPmq3z80neM9O/RtQd2vjNJO95+YvGtdYLs8d8dpKnz7Xx3zN9kZdVx6JqWUQbb0tyVynl82ZtfHemQzk+OElvtuwXMh3a8V43aGNR7SyiDa/x8ranK8dgV9pIutNnF1HHItpIjlc/WVQ7b8vB9+0fJXlqko+5vKDW+nlJbiulfNXs9v+b6feDPqmUG07zv4h2FvEad6WfdKmWrtTRpVoW1e8XsT1dOXa8F+ysK7V05b0gs8e+fVbTS8p01vdfynS0znaSj51b9eFMz47vSShN/iDJLyb56FLK6SSptf69TPfNP5/d/uUkr0vyqdcePKWUUmv93UxPh39UKeVDZ4/550nekeT/nt1+LMkvJPlzNzgAD1THompZYBt/lGSU5GNKKZ8we8wPJPn9JH93bvVfyfQv+ddZRDsLbMNrvITtWUQ7Xdkni9qvXemzXTn+5hyLfrLgWg68b2utv5LpMLB/Ukr5pFLKqdldX5/knXOrPpTpX+l3/JCyoHYW8Z7SvJ90sJau1NGZWhbV7w+6PQuspSt1dGZ7FtRGJ2rpynvBfD2zq9+Z5PeSrJdS3m3utf9bpZQPma3zn5K8o5Tybju1dZW6j9Opx+2Sa05HJ/n0TMdW/5U88V2cD0vyj+fWuT2z0+M3aPPjMj2N/uVJPmK27HSSb0/ylLn13n2ZdSywlptuY4c6PjTTLzr/oyTPmVv+A5kbspHkqXtsz4HbWVAbR/o17tL2LGO/tOr3i2hjUX12GbXcSh2LaqMr/WQZbXTh9Ulyau76X03yU0m+NNMJLy4m+ae71b/odm71Ne5SP+lKLV2po2u1LLq/HnR7FllLF+o4SDuL6CeL6mtdquWg/X6RdVxua+7fU3niayNfcbmGTCc4Wp1bb89JjmqtV1Y+kUopp2qt75xd//OZzl44SfLGTH9P6TdrrS/c4XGl7rDjSin9TKdWfvdMT39/apK31lpfsIw6DqGWW2rjmvb+ZJL7kvzZJI8k+VNJfrfW+gX7bWNR7dxMG8ftNe7S9iyinS72+0UfO7N2bqnfd/E4PkgbLfvJovraovvsNW3sa9+WUj4307+IvzrJH9Ra/7iUcnut9R2z+5+X6ZC7j0zyllrruZ1qX1Q7u2zPLb2ndOX9pEu1dKWOlrUsu7/ezPZ05djp8nvBIj5ftP6Msog2uvJeMPf490jyx7XWP57dvr3W+o4yPRP+mUnWMv2Jmt9M8q5a6/N3q2fH5zhJobSU8rcyHTrwuiSTWusfXnPw3JPpEKdPyvTgOT9bfqVjlFKeXWv9tVLKbXWHH4UtpXxQpr/F9JmZdpJ/tEMbB65jgbV8dZI/TvKrSV5da/39UspKrXX7JtpYrbW+cZf9fjrJMzKdLvpttdaX3WB7DtzOgto4bq9xl7anK8fgIvr9gduY3V5En+3EcbzA94Ku9JMDt7HAWg60b0sp/zrTiSnememHhjcl+cZa6x+UUp58+YPG7DHz+/qqmhfYzgfUWn9rfvk127uf17gT/aRLtXSljq7Usqj+Olt2oD7blWOng+8Fi+gnXfqM0rzfL3KfzJZ9a6b/v/xakjfWWr9xtvzK54zZ7ecmeWet9Sdu1Nau6j5Opx6HS6Zfvn1Vkn+R6Y+6viKzoU1JnrTL426bu/4DSd6V2W8rXXPfDU9NX7PegetYYC0bSX4kyf+Z5J9l+mXk97+ZWjIdO/6uXPObSTeouexy34HbWVAbx+017tL2dOUYXES/P3AbC+yznTiOF9FGx/rJgdtYYC0H2rdJ3idzvxWY6e8Efl2Sf5jkzrnlf+6a29cO+1pUO5uZfrj50zts66la97VPOtFPulRLV+roSi2L6q+L6LOLqqVDdSxyew7aT7r0GaV5v1/kPpkte3GmnyuekWQtyS8l+Uc3amM/td7wMTf7gKN4yfQvOZtzt3uZTln8XUnea27552Q2jf7s9vx/7p89e5FfmOkkEs/ZaadnOnb+mcuqY1G1zO7fyGza6EyHAXxNpl9Ift99bs9zZh19kOS/Jnn+Ddb7v5J8yC51HLidBbVxrF7jjm1PJ47BRfT7BbZx4D67iFoWUccCt6UT/WSBfW0RtSzi9XlappNbfM7l/TVr9/+eW/bJSf7ajbZlUe1k+j2zn0hyLsl/zg4frvfxGnein3Splq7U0aVaFtFfF9hnD1xLV+pY4PYsop906TNKV/r9QvbJ3PLPS/KVc7c/INPJ8146t+z/TPKXdusz+7kc6MFH5ZLpB7X/NH9wZHra+x8l+cpMv6j7UUk+d5c23jtPvAF8YZJLlzvL3Drvm+Szl1nHAmt50qyDfsV8h8z0r1wvn3XqZyX587u08R5JPnJ2/XNmnfT516xzW5J799ieA7ezoDaO22vcpe3pyjG4iH5/4DYW2Gc7cRwvoo2O9ZMDt7HAWg60b5MrX9P5i5nO3PiJs9srSf5Oklfutg1LaOf9knzC7PrfTPKGy/tobp1eks/rej/pUi1dqaMrtSyqvy6izy6qlg7Vsah2FtFPuvQZpXm/X+Q+mXvsX8j0TOmT5pY9a/Yca7PbH7Tf42nX51pEI0fhkumY6Z/JbPjT7OD5CzsdPLn+LyhXZpmaW/b5mU6tfM/s9l+85v4b/cXhlutYQi0flen3AD5/dvv2TL+M/m2ZG26xRxvzwwX+YqbfabvcSb8kyZP3amNR7RykjeP2Gndpezp4DC6i3x+4jQX2+0VsT9Pjr0v9ZFF9bcF9dhGvz/tlOmPjv8wTf31/cqazJD7jRq/HItuZ2yfzZwa+LNMP1x8+u/2J++yznXg/6VItXamjS7UctN8vuM925dhp/l6wiH6yyDa6UMuC+/1C9sncOt+e5GeuWfbSXPOVkv20tevzHOTBR+Uy90KvJ/mxzP2FIclrkvyJm2lrrr3PzvTHy38+yTccZh0LqOXU7N9PyfQ3jV4wd9//m+RjbrGOezP94d5fSPIvD7A9t9TOEto4sq9xl7anQ8fggfv9Io+dg/bZRdbSheOvK/1kkW0sqZZbfX3OZBpifybT7wm9Osm338y2LLKdufZekOS/ZDoL5H6+q9SZftKVWrpSR9dqWUZ/vZU+u6xaWtdxkHYW0U8W1de6VMvltm613y+4jvk/iH53pmdHPyLTs7E/nuTLb7Xv7vh8i2ys65ckdyT5rCS/nOSBJFtJHrqFdub/UvUrSb5jp/uWXcciapmt94lJHk3yj5OMW+yTBW9P8zY6+Bp3Ynu6cgzO1ltEvz9wGx3bnuZ9rYP95Fi9t83WvSfJ3801w75vYf8upJ3Z4x5N8p03+Zgu9ZNO1NKVOrpWy6L766322WXU0oU6DtLOIvrJAvtal2rpynvB/JnZr810FNZrbqWtvS6X0/SJUqa/6XZ3pj8m/q2zZaXe5M4opfyNTE+pf8ns9m11h6mbl13Hfmq5UbuX15tNL/3sTIdcbO70mP3UVkr5rEyHlX3FTnUsqp1F1bKXo/QaH0YbCzx2DuUYXGa/v5k29rt9t9rvD/s4PsTjrxPv1YtqYz/tLPB99spv0l2zfKd1b7gti2pnj+35qkz/ev9Ft9JOx/pJJ2rpSh2HXcth9NfZY/fss105do7Ke8Ei+kmXPqN05Ri8mTp2WT7/G6fvkeR0rfXNt1LPrrXewmfjI2GvD297Ldutjbn736PW+ge30sYtvBncUi376Sx71XIzHa6Ucket9fGdHreIdhZVy9w6R/41PmrbcxjH4GH1+/1sy2H1+8M4jhd9/M3u6/R79c20cZB2DrpvSylfk+Q3ktRa68sv15LZgv0+x6LauabNHT/Mzt3/3rXW392t7a70ky7V0pU6WtayjP46e8xN99muHDsdfy9YWj/p0meUo/ZeUEr54CSP1VrfcqM2b/C4WzrJciPHJpSWUr4+0y8D31Zr/eq55fP/6e91cN9SG/MvyiLqWGAt3zRb/M4k/yDJ79Va37mf559r78BtdKmWY/gaH/ntWcI+6URf61ItXWlj1k5X+smB21hgLQfat6WUlyX58EwnHPk7mf5Uwwvm2v/gJG+atbnbh7RFtbPvD7PX7IeFvi+1fo0XXUtX6uhKLYvqr7N1D9Rnu3LsdPC9oFk/6WotXXkvmHvMD2Q6k/+fyPQnfX6m1vr/zd2/Umvd3qudhagLHg/c4pLkwUy/zPvxSX4y09/t+5C5+z88T0wIcluX21hgLV89a+ODkvw/Sb4p08lQVmb3f2iSZy27jS7Vcgxf40600aVautLXulRLV9roWD/pUr8/0L7N9HtD/ybJ3XPLfizJt83d/sdJfjDZdebJRbXzskwnO/krmU5+8h3z6yf54BP4Gh+bNrpSy6L66yL67KJq6VAdi2qneT/pWi1daWNu3U9L8u9m1587a/tr8sTP0tyV5EWZ+zmYZV5uyxFXSrk9yTOT/JNa62trrZ+U5B1JvrKU8j6z1b40yc/e6K85XWljgbXcluT9k3xXrfVXM32D+41Mp4P+mNlqfyXJD5VSnrSsNrpUyzF8jTvRRpdq6Upf61ItXWlj1k5X+kmX+v2B9u3sL+ePZ/qzEB85qylJ7kvy4aWUl89uf02mPyOzeoNtWVQ7d2T6+3V/t9b6vbXWj830ZyM25lZ7UZLvnz3nSXiNj00bXallUf111taB+mxXjp0Ovhc07yddq6UrbVxjJdOZdFNrfXWmv3v+riTPK6U8LdPfwP6iJF+wRzsLcaRD6ezgeUemUxR/7NwL8qIk75nk65KkTieD+E+Z/kWgk20ssp1ZJ3x1kj9bSvnQ2e2vzfRU/5fM1hll+pMRg2W10ZVajttr3JU2ulZLF/pa12rpShtd6Sdd6vez+w+0b2ud/ik705+GGWT6F/LMant+kvcrpXxgkv+Z5PVJfu0GdRy4nUV8mD1ur/FxaqNLtSyq3y+iz3bl2Onge0HzftKlWrrSxrVqrT+S5JdKKS+ctf9Ikh9K8nFJPrpO/1j6N5N8QinljlKmQ8mXph7C6dhlX5I8J8n3Jvn0JO8+W7aS6Wntj57d7id5WtfbWGAtH57pUJC/keSZc8t/Osmnzq4/K8l7LrONLtVyDF/jTrTRpVq60te6VEtX2uhYPzlwG13qs7N1Xjx7zD1J3m227DVJPmx2fbdhdmUR7czu/yuz9Vfnlr1XpkP9PjDT7y59fqazN56U1/jYtNHBWg7UXw/aZ9OxY+egdSx4e7rUTzpRS1famGvrtkx/z/TrMz0bevts+bkkF+ZuP3mvthZxufzXmCOt1vqaUsqZJF+W5EmllJ+rtb6plPKOuXXGy2pj9teFetA6FtXO7P5fLKX820x/dPdJpZSfrrW+LsmlJH88W+fNe9Rxy210sZbj8hovuJ8ct+1p3tcW3GebtrGE9wLv1de3cdP7tuwwa2+t9cFSytuTnE/ytjL9eaDfqrX+0qyNek0bVyavqLXWW21nh+353tk+2Sil/M0kv1Br/b1Syntm+pMEv1FK+e4btTN7Du9t9sl1bSyi399gv9xUn+3SsdPF94K5/dqJY6cLtXSljR32y7tKKT+c6RDeP5XknlLKS5N8RpLX1OmZ2dRa/3g/7R1U2cfx2jnl6t/LmZ+Naj3TH5D/0CS3J7lUa/2cZbRRSrmz1vrY3O35N4abqWNR7dx+ufNcsz2fmulp+L+U5HeSvL3W+lk7PP59a62/c4M69tVG12o5Tq/xAmtZxLHTpe3pSl9bVDsH2p4u7ZPZ+t6rr27jQPu2lPJXM52U4rfKDh9GZ9fPJjmd5ENrra+8dr/Nbn/dbJ3fTXJ/rfV/lVJKpp8Jbqadq2ZlvKaOL0/yZ5O8LdNJnH6n1rp+g33bvJ/M1u3Se5t98sT6C+n3N3j+m+qzXTl2uvZeMFvWiffqrtTSlTZ2c3nflOl3mj8i06HAT03yh7XWv3YzbS1EPYTTsYu6JPm7Se6aXb9tbvn8cIP3S3I2yWfOLbttwW38s0xPnf/zJB9++fE308ai2sn0+0d/Ynb91A22p2T6xvbxN2jjXyT50STfkuQzbqWNLtVyDF/jRbRx4H3Sse3pRF9bYJ9dxPZ0aZ94r17wvk3y3Ul+Pcn3JPnAneqcLXvyNbev3ZZvzXQ44Ecl+akkX3ttG/ts5+uSvGLW7540V//8Np/NdBjZF99gezvRTxb4Gi+iDftkCf1+tuyvJvmAHWrcV59NR46dRe2TBW5Pl96rO1FLV9qYW/ZvMjd78jX33X7N7aft1tYyL4f2RAcudPpbSb+f6fj2D95lx5++5vZtC27jFUm+P9MPDt+V5OtvUO8N21hUO0m+Icn/SvLbSXqzZad2aON9r7k936G/JdOfIXi/JC9N8i9uUMcN2+hSLcfwNV5EGwfeJx3bnk70tQX22UVsT5f2iffq62s50L7NdLbFf53ph9Rhrv4wOv8HiL+Z5J6d2p7d38/0Q+jlD8LPSvLaTL+3Nt8XXrRHOwf+MNuVfrLA17gT79XHaZ8sqt/P1jlQkEtHjp1F7ZMFbk+X3qs7UUtX2phb9mWz1/ZnknzDLq/lX7jmdrnRusu6HOqT3XKR0//IvynJmUx/xHe+w90+t95fT/JpS2zj7tmbwZ2z2++d5JFMhzTMrze4URuLaifTmbbuz/TLzeeSvCVPfBhdmVvvC5N8wQ3a+OAk35fkvWe33z3Jz2Xur/Sz5V9woza6VMsxfI0X0caB90nHtqcTfW2BfXYR29OlfeK9enn79mmZTnRyV5J/mOmH0btm910Or2s3evxcX7ncN56c5D1mtXzINet98i5tHPjDbFf6yQJf4068Vx+3fbLAfn/gIJeOHDsL3CeL2J4uvVd3opautHHNep+UJybK+qkk37jDOh+R5Mv3amvZl6ZPflOFTg+gywfyV2Wa+C8PVbt8EH7kMttIcuryAZvkKbN//2OSP3Vth9qjjkW1M789X5nph9Gz12zPM/Zo4wMz/TD75NntH8hsxse5dZ65WxtdquU4vcZL6ie3eux0dXta9/tF99lb3Z4u7RPv1Uvat3Pr3pVklOShJO+b6Yew95+7/4Z/4c71Q7VeldnMvpn+vt277dZOFvBhtiv9ZFGv8QL7iX2yvH7/tBw8yHXi2FngPjnQ9iyivy2yn3Shlq60sUObd8z+vTPTYPrNc/d92H7bWfaleQG3XHjy1UnGmf6l6f5cPYX2vk4530obuX64wPcm+YjZ9b+X5Fn7qWNR7VzTxvkkv5Xpl57/cZLn3EIb35rZ950y/U2su2+2jS7VctRf4yX1k1s6djq8PZ3oawvss4vYni7tE+/VC963mX5oeVqmQ4H/V5If3O/rce3zZPoX+T+d5F8l+Y59PvbAH2a70k8W9RovqZ/YJwvu97N27sotBrn5+7tw7CxinxxkexbR35Zx7LSspStt3KjvZRrefzLT76r+UJL/61Ze62VcbssRNJt96h8kuZjkTZn+5eONl++vs72+jDbq3Axns0V/lOT/b+/Mw3Yby/7/+e69jW1zyDxsUsZdG5U0i1RK4n2LI2QWJb1Kr0q94UeGsslQbWxJmQslQ4Reb1GKDKEBDSIpRW1snL8/ruvee+3bPT7rep77fO7n/B7HfTxrXeta3/U9z3Ve61nXuqZ1JJ1NCpg/9KKjFE+DI8+gdQxpAP19pC+A1/fDkTf/Dqwh6QxS95bbe+XwqGW83+OScdLgGWnZ8WaPt1iry1PKHi8+aXDFs3pBW/JmLd+a2XNm9jiwNXCemb0n86vbuRU0tCxGGrv0iJnt2guPVWZ3zklPk3xyLrCFmf27H3sGGSf5mKtnW117SnB49EmhuIc0tvQ44P68vaWZPdyHFjdlZ9DPgioG/az2osULRwvOZ5Vm5/87KV4OJM2ye0Qv548FxmWltHGzgNcA37I8BXI/hacARyPfc8BZpCm7d+tXRwkeS+tJNYJyI+ACM9tpBFoAniB9PXnMzD7YL4c3LXlz3N/jUlpKlJ1CWoYq7kvwlLDHm0/iWd0WbX3b/LetGGld4ObKy+Ok6stJNx7LyyUAd5LWo/tYK54uKPIy6yROwMmzDSaeT0rFfS/oVpHzUnbG2bPA07PaixYvHPOQK6aTgI8BF5nZLpnLR33QHDTXtvpB1+4TawCfquy3mnFqLDg+SnqoteUoxdOOu3L8JcDeNTl2Ai7swR4XWobwHo8FR1efOLPHRayV4hkje8bSJ/GsLuRb0lqAi/TK0y5fPzws2A2smUfVvx04jgXOdnCPx9uzLXxi5eK+qqEHLesCX6qjZTTLjrdnQcl4KxFrnrR44agce8HM+y3ybNRvbI3Fr1FoXEDSSsBcM/tr3pf1IFALLiY7phySpprZk80cpXgkTQMws99W8na1qTSHJy1DeI/HlKOdT5zZ4yLWPGnxwpH341n9Qi21fCtpFmlCpAeBu83spHx8iuUuf620t+Drlad5fwGtkpYCnjKzp7tdV9JqlruTDfIet+Pol8f7s3qYfFIq7nOenmO2lV+8lB2Hz4Jx+aweTS1eOCrHdwOmAFea2Z8q6c33suP+oOGmUirpG6QpnZ8Ebjezz+X05oCcbPO7GgySo1shrs0j6QLSAPappMHa/2Nm/2qhuZM9vXIsoKMGz6hqGcJ7PJYcbX3izB4XsVaKZ4ztGQufxLOaEcdsS99K+gSwFbAr8FLSuqaXm9kn8vG1gfea2XGtfDEKPLVfZr3ESZ887p/Vw+STUvGa89aqyHkpOw6fBePxWT2qWrxwVNLOJk3m9Etgc+Bk4IeWx0lLWtbM/tbKH+5gDpprgX2Aa0h97NcF7gaOrRxfFzhhPHAU1LIDcHXefglwJWlg/ioVjnNHm8OTliG8xy44PGnxEmuetHjhcBYnnuK+xP15H3BoZX8F4DfAkXn/5aQF1LcdbR7SzI5XZ1teD/yqySdrAx+fYPd4aDg8aSkY9yVi1kvZ8fQscBEnnrR44ajkXR34fmV/R9LY0z1J65suDnwfeH8vfIP++RjYmr4o/QaQmf2aVJhfJ+n4fPxhYG1J+48DjlI8DwHPSnqxpa8d7yN9fTsYIPOuIOmYUebwpGXY7rEXDk9avMSaJy1eOMBPnHiK+xK+nQNsJWmxfM5fspY3SnojaRblm0kvGJ1Qguf3wLVm9rCZ3UhaeH0HSUfm44sAm0jatgPHsN3jYeLwpKVU3JeIWS9lx9OzwEuceNLihQMAM/s98IykvfP+ReR1sElL4PwbmAkcojQhlmt4qZT+DViW9BUJS/2r3w68U9LOZvYEaV23f0ma7JyjFM8fgAeA6ZIWtTRT3P7AmyUdkvMcADwgadFR5PCkZdjusRcOT1q8xJonLV44wE+ceIr72r41s0tJLyr/qzwLopk9RPqavoSlrmiXkLoGt0UhnhIvs8N2j4eJw42WUnFPgZj1UnacPQtcxIkzLV44kObNvnshsLqkzTPf5cBNwExJi5nZlaR1Wv134TUHzbWWmpyPBK4l9YtuLPC6B7BH3l4OWHY8cBTUcgBpqvAtgaVz2tbAYXl7cdLDZVQ5PGkZwnvsgsOTFi+x5kmLFw5nceIp7kfs28Y18/Y5wI9Jy/lMBX4AfLSbDSV58vmzgFupzMpIeqnZLm+vAqw4we7x0HB40EJlhtC68Vo3ZktqqaljUgkdpXg8xIlHLYPmgAVn6gVWBr4IfB54fSX9QmCp5pjw/Bu8gAX/kZ4AXAFsT5rK+Rrg8PHCUYenGmRN2/8FnEtaU+iVpPFKXxgtjhack+ryFOKo/tMY6T2uzVFQy6QCHAON2WGL+9Llx1HZqc1RMO5dcLTwy0i19B0nwKbA9Ka0hSrbRwJnANcDsztcuwhP0/kjqtg2+WFEfi3B4U2LN45BaiF9sNm8Ka1alvuO1xYcPcXsaGgZoY69gVfU1VGKx0OceNYyUg7yUjuASvmkmQ9YNd/nWcCZwHdJ65D2xOXlN+az70raBvg38FMzeyqnLWRmc/P2/qQm7U2AB8xsH68cBbUsbGbPqDJjmBacNvpdwPqkRYB/b2YfHg2OnO9NwFzgFjN7ZoRaSnC8B/gncJuZPdaCoxe/1uYoqOWTpMWtf1hJ65fDU8wOW9yX0OKl7NTmyPlclMGC5bhEGfwa6aXupjYcHX0r6VJgYWAacD5wo5ldk48tWilLLwKWMbM/Nl+jMM+mwLNmdlslrVqOjwRWytd5wMx2b+GThcxsbpMf+vVrbQ5PWiRtTKrY3l5JG3MOL1oknUmaaGc90lqLn6kcW8TmL53SMV5z2pbAM2Z2SyWt+tzuGLOltBTQ8Tbg26QK7Jlm9pOcLmDhPnSU4jkGuMDMfl5J6zdOanPkfGeRPupdUnkWVX3bixYvHGeQys4edXyS8x5H+shxF/BjM7s13+dJZvac0lJEywHvAuaY2Vfyea6WfekIG8MaMKngXEHqU38CsGfl2KJNeZerbE/yxlFQyzeAG5nfxF79ujWliWPqaHHk/a8D3wPuAI6i0t2sDy0lOM7IHDeSBmhX7VmkiaOdX2tzFNSyBeml+mrgbZV09cHhKWaHLe5LaPFSdmpzeCqDJTgKlsGTSEsq0Pwjvfx19C0wgzxLIqkL32HA8cC7ms5dpem6zV21SvFcSpqV8T7gCOCtlWOLVrZfBKzaxienAWc37G06tlDT9dr5tTaHJy35/MuBPwKfajpnzDi8aCEtRfKdvL0GcB55Zup+4jWnNVqAfg0c0XRskcp2y5gtpaWujry9PPAt4MM57maM0Ce1eUjPtm83n5ePNT/bOj0fa3Hk/VNJ7xcLteDp9f3CC8dJwI9IExdt23ROz+U4p51M+l+4PXAo6f1rm3xscnP+Tlyef2N3ofS1qPGPdGnSDIWnAgc25XsFC3ZBkjeOglo+RhpkPpPUt3ypVgEGvI0FXxKKcuT9Y8kvXKSvezcCu7W4j520lOA4Dbgsby8JXAy8rIXeTn6tzVGYZ0q+P7sBlwFbtfDJeInZYYv7Elq8lJ3aHJ7KYAmOgmVwJqlXQGN/fdKSCkv2cX82IVUAG125ls/xdwywdk7bH9i9WVvTNWrzUKBiS/rocQ9pLNMsWlTkevBrbQ5PWoATgUvz9jqkFo139xlrtTk8aSG9RDcqhZ8lzTp6HXA4sFFO34/ucV+7QllCSwkdeX8R0kfEnYFDsq9PZH5F40PdfFKCh/RR9pbK/mrA6n3e49ocFVvOYX7ZewOpq/WGfWjxwvFVciWd9H/n6Mzb17Otkn4k8Nq8vRDwH8CdwJsreQ6kqRv3ePtNYmyxrqSXW5qd8Huk/tPrKs1KhqQdSTd9buMEy552yFGC5zrSmlUHkYLr25KWssqiv5K2AF5suTvWKHEA3AL8dz72Z1ILxUuqGXJ3lU48JTjOIxU2gINIL08zgaMlbZc53ktnv5bgKMkjUnehp0ld7Q6UdK6k92eOnUj/EMdDzJbg8BT3JXi8lJ0SHOCnDJYqf1C/DN4CLCNpmqR9gFNILUhHSnp75ng96Wt3S99a6vp4HrCrpBXM7FFSC8dawHY52+lmNruF/nkoxPMsME3Samb2J9JMjw8BW0haO9uzP6n1dF4XvyafXE56gZ9J6so/U9JUM3s+dylrdL3euINfS3C40KI0s/JdpBYrzOw3pMrtklWhknagTayV4PCmhfRh6XlJKwIvBd4C7EL6wLljzvPVbnFPatXfIW/vThpTfY6kwyVtlLXsR+eYLaGlto7cffNp4H7SpEgnA68mPe+eyNm6PgsK8dwErCNpOUm7kJ4FV0g6WtIG+TrdnrO1OZRmCF4cWBNYMT+TjyYtrXWwpA/lfDu043HEsTxpqMh78qFHSLMwr5Djb1KFo+O7XyMvabjGfjnPXDO7gPQhbV9JK0laBrjTzH7BeIaNYQ2YtLDwWeQvKKQpkb8AfKZxL8cLR0Etk/PfhUlft37YOA94WY86SnBMZcGvNR8Ezq7sd5xpsRRHJe9i2bcrk/4JHg58sc/7U5ujLk/lPuzL/K9c1wGPAu8YpzE7NHFfgsdL2SlZ/urGvSeOgmVwV9KL3k9zzK+QtRzfh0+3IrVIHgysnNO2IH1AaNt6PRo8pJkaP0V6UYLUun4+cFA/9yfnfWkuO2dV0pbr9fxSHIPWQvoIVC2DH2/Eat6fMhYc3rRU8le5Ns/lutd4bbRwrkiaUGyNHLNnA/9TzTOaWkrqAN5PqvSfTuqO+UlgNpX/O72Uw7o8pGfj88Bt2a5p+fzP9qGhNkfO9xHS0IJLSM/+hUmV7FP70OKCo5oP+HLmWqT5WI8xOpn04aFa/lbOcbdWq2uOx9+YtJQ2vjLmG/J70iKua5nZ37JDp+fWCWvK746jJA+A5RYZSxOSHEZ6oFwj6Qbmf/XuiEIcT1qa3KGh9Z/AP7L+C0lrKI06R4VrDmlq7IfM7J/ABcBqkpbsxa+lOOryNI6TWiG2V5pkYWHSmIBDJb1yvMTsMMZ9CR4vZadk+ct8LspgXY5SZdDMvk5qTdnfzP5maQ3Ci0nrwy3RyYaKlh8AN5Be2k5Uarn+DPC4dW69Hg2eG4FlgF0krWypdX0msLHSZEn9PCPvI3VXe0TSV3PZeW/j+FhxDFqLmT3cVAbnkFpdkXQx8O6x4PCmpcI3t7L7MeAvvcar5VZHM3uE1B31wRyzp5DK4KI2f/KYUdNSWMdvgf8kdbt/FanL6MVmdk83HSV4Ks+2r5CebfuZ2SNm9ltSN+XVlSZ/6uX5OGKOpvSLSRXbLYB18//lG0ktsav2+I4yUI5myvz3ZNI6oWvl8yZ30XEaMEvSVJj3nrIdMCMfw9K6s2uThpPMQ48x4xKDmH13OmlmqG1IY2AOBO6zNjNAeuYoyVPhE/AYcKWZ7TxAjg1JX9sWJs3itdsgOCpc3yT90/joIDlGyiNpVdI/rUVJE62YpDeY2Q19cEzHScwOa9yX4PFSdkqWv8znogyOlKNOGZRaz17Yj5Yqh6S1gHcC04G/m9khna4zGjw533bAa0kvNieRWk7vNrP/6nZuG761SF2drzWz9w2Kw4sWSa8jjWNcnvSuOJJyXJvDixalrojLkSr8j5nZXjl9xLODSjoPeNDMDh2klho6diSN253blN6XjpHydHi2nU+aDbarPSU4ms5bj7T280bA50itwI+Y2QHjjaPCNYU0zOJxM9u7S96jSB/BriD1DvqomT2Zjy2Z0+8ntUbfb2a79KvHLWwAzbOkwb4fJL3MHlVJ76e7kAuOkjz5nC8C51b2+545qxDHpqSuGKeMlKcuB9D4p3EpcMYI73FtjoJa3sz8RZI1Qg43MTuMcV+Cx0PZKcjhogwWLMe1y2DOvzhpwqSOWrrxUpmVstO9KcXTio/05f7DpK6/x/d6zVZ8pO6M548k3kpwONSydYEyWJtjrLV0ip0cb++vacskUsX428CsTtcdTS11dDRfi967ZRfh6cA/FfhOt2fbGHAsQhrv+1ng8yPhccTR6PK9PnBsD/lfDbyR1DX8JNKzeYEZ3UkT7b2hXRyM19/oko/gpnnlGCstwHpjwdGNh9SHftdueceAY2ngnXV80ivHaGlpsd92+u6SsVaKZyLFfQktjspOTxw9HBuzMjha5bh0GSTNfrhNsxZgT9KEFpu109lKdwt9RXh6PZaPd63Y9sAxfSw4PGnp4vPVgQ93y1uCY5BaasRrxxfpLlpaVihHQ8tY6mjBX4SnD3tWBHbsxl2CoxtPr/donHL0Oia11fj2l9S5/55/RbvvSjqYNNbsSTO7Kqc1+ptb3p9kTYshe+QYtJamLlq1OWryzEsbJo5Ba2niG/cxW4LDWdyX0BJx79ieJs5aWpTGqk4Dfkz6yv0NM/tyJd8KlsahduxSV5BnT9I6inPM7Kdt/NYt7nvheIGGpntcm8OTlhp+LcrhRUuf8dqtDLrQMsY6PD8L3MRsCXsGxDHirukVjvVJE+0tC6wHnGNms+pwuoUVqt2SBjTfAHwUeAA4pOn4KpXtdl/oXHB40jIge9p9BRoajgFp8XSPXcfsOPZJxL1ve0blHpNeHH9Ebm0krSX6MGksEKTuvqcBB7ezozDPmdmeY4Cf8MJ1hFfowScuODxp6ZOjXdzX5vCipVS8etLiSMdQPQs8afHC0c+P1CL/KHBeXS7PvzIkaTrs60jr3wGsC9wOfCLvTyGNzTjOO4cnLWFP+GQi2hM+CZ+MQ3uWJo37WbuStj6pgrtz3t8aOILKshOjwUOBl1kvHJ60eOHwpKVEvHrS4kWHM3uGLWZdcPT6gzLj28fLrwxJGnQ7MxeQxiQS6wEPkqbPh9Qv+lSa1tPxxuFJS9gTPpmI9oRPwifjzZ6c5xTge01p72L+unZrkF4gF2vHUYKHMi+zLjg8afHC4VBL7bj3osWLDk/2DFvMeuFowVlkrP14/xVZp9RS/+mHgL2AJXLavcD7gG0lLU1qdr4t/3XL4UlL2BM+mYj2hE/CJ+PJHqWlJbC0TMAUSVdXDt8BrCRpcTN7EDjS0rqrL0ApHjN7HHiKtC5eI+1u0mLwW+akexuX9czhSYsXDi9aSsWrJy1edHixpxSHJy1eOCTtKen1kjbL51vjvlfyzDvXzG7LaV3nFhnXqFurpVK7B2aTpsZeOe8vAnwXWC7vt5yq2guHJy1hT/hkItoTPgmfjAd7mvmozOZLWrbmO6T17b5HZVmEFtcvwlM5Z1Jl+yrg6sr+Wtm+xRv2eebwpMULx6C1lI5XT1oGrcOLPcMWs045xnQ86nj6jXj2XUmTzey5Ftunkb42P0JaR+dxa7MAvRcOT1rCnuH2SfNMbDV01OYZJg5PWrxweNIyTPZIOoLUfetWM5tdSZ9iZs/m7V2BFwErmdnhba5bimde/hb2XAoYcA3wduBhM9uzhU9ccHjS4oXDi5ZS8epJiyMdLuwpxeFJixeOyrnTSB9A32Jmz0jahFS5PcbMTpS0OHACcJ+Zfakdz7Ci70qppO3M7PK8XZ0uunqT3gS8hLSWzpdy2rzC44XDk5awZ0L45DDSP55fmNm3Kvr6tac2zzBxeNLihcOTlmGyR9LpwArALOB84K1m9hO6QC9cJqAUT+2XWS8cnrR44fCipVS8etLiSIcLe0pxeNLihaMKpeEgJwBHmdnvctr6wBXAYWb2TUlbA6/LeZ5q5hhqWB/NqsA3gT8AJ1bSJjXuXYfzJnnj8KQl7JkQPvkqqQvOzsA9wAcqx/qxpzbPMHF40uKFw5OWYbIH2AH4biX9K8BBwDbA8pX0Pah0v2rBV4rndOAS0tf5J4BXt8vbwScuODxp8cLhRUupePWkxZEOF/YMW8x64mhzvMhEWMP46z0jbAr8kLSA75dZ8AW92vd9L+Dlnjk8aQl7JoRPdgCuqOxvTVoMezEqL8PAnl3sqc0zTByetHjh8KRl2OzJxxfJf/cC5gD7kyq6nwEWIn0t37fd+aV4KPAy64XDkxYvHA611I57L1q86PBkz7DFrBeOFpxFxugO86+/zLA6afKH6cDXgBOBhRrOBhYGdhgPHJ60hD3D7RNS148N8/bCwCqkF+IlK3kW6UFHbZ5h4vCkxQuHJy3DYg/wAeC0prTpwCp5+2WkltdXNOXRaPBU9ea/dSoKLjg8afHCMWgtpePVk5ZB6/Biz7DFrFOO4pODDfOve4ZUeL7S7FRgY9I6PUfntF3JNfy8L28cnrSEPRPGJ6fk7eYH01WNvMD2Tcda2VOLZ5g4PGnxwuFJyxDa82Lgc+QKLC26ZgGXATOa05vy1OahwMusFw5PWrxweNJSIl49afGiw5k9wxazLjgqaUeQlo3ZvSm9OpP7rqTK7uc7cU2UXy/rlH4feEjSkkBjcPVz+aYcCSwr6V/AO8zs342TLHvWGYcnLWHPxPDJo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prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.633364Z","iopub.execute_input":"2023-02-01T14:51:55.633706Z","iopub.status.idle":"2023-02-01T14:51:55.655017Z","shell.execute_reply.started":"2023-02-01T14:51:55.633675Z","shell.execute_reply":"2023-02-01T14:51:55.653820Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.656793Z","iopub.execute_input":"2023-02-01T14:51:55.657669Z","iopub.status.idle":"2023-02-01T14:51:55.680263Z","shell.execute_reply.started":"2023-02-01T14:51:55.657616Z","shell.execute_reply":"2023-02-01T14:51:55.679008Z"},"trusted":true},"execution_count":243,"outputs":[{"execution_count":243,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 1.0 2\n 2.0 1.0 10\n 1.0 1.0 0.0 6\n 1.0 1\n 2.0 1.0 19\n 2.0 1.0 0.0 5\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 13\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 5\n 1.0 2\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 93\n 2.0 0.0 5\n 1.0 7\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 2.0 1.0 0.0 5\n 1.0 1\n 2.0 0.0 3\n 1.0 3\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 3\n 2.0 0.0 3\n 6.0 1.0 1.0 1\n 2.0 0.0 3\n 7.0 1.0 0.0 3\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.681765Z","iopub.execute_input":"2023-02-01T14:51:55.682091Z","iopub.status.idle":"2023-02-01T14:51:56.352496Z","shell.execute_reply.started":"2023-02-01T14:51:55.682062Z","shell.execute_reply":"2023-02-01T14:51:56.351351Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.353913Z","iopub.execute_input":"2023-02-01T14:51:56.355590Z","iopub.status.idle":"2023-02-01T14:51:56.788043Z","shell.execute_reply.started":"2023-02-01T14:51:56.355547Z","shell.execute_reply":"2023-02-01T14:51:56.786828Z"},"trusted":true},"execution_count":245,"outputs":[{"execution_count":245,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.789229Z","iopub.execute_input":"2023-02-01T14:51:56.789583Z","iopub.status.idle":"2023-02-01T14:51:57.215295Z","shell.execute_reply.started":"2023-02-01T14:51:56.789553Z","shell.execute_reply":"2023-02-01T14:51:57.214488Z"},"trusted":true},"execution_count":246,"outputs":[{"execution_count":246,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.216805Z","iopub.execute_input":"2023-02-01T14:51:57.217226Z","iopub.status.idle":"2023-02-01T14:51:57.574988Z","shell.execute_reply.started":"2023-02-01T14:51:57.217185Z","shell.execute_reply":"2023-02-01T14:51:57.574210Z"},"trusted":true},"execution_count":247,"outputs":[{"execution_count":247,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.576156Z","iopub.execute_input":"2023-02-01T14:51:57.576637Z","iopub.status.idle":"2023-02-01T14:51:57.924867Z","shell.execute_reply.started":"2023-02-01T14:51:57.576603Z","shell.execute_reply":"2023-02-01T14:51:57.923105Z"},"trusted":true},"execution_count":248,"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. Nonetheless, the various distribution of age and fare may lower the accuracy of the validation and testing datasets.","metadata":{}},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = rf.predict(X_test)\nrandom_forrest_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"rf_y_pred\": y_pred})\nrandom_forrest_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.926719Z","iopub.execute_input":"2023-02-01T14:51:57.927152Z","iopub.status.idle":"2023-02-01T14:51:57.950525Z","shell.execute_reply.started":"2023-02-01T14:51:57.927100Z","shell.execute_reply":"2023-02-01T14:51:57.949359Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.951752Z","iopub.execute_input":"2023-02-01T14:51:57.952061Z","iopub.status.idle":"2023-02-01T14:51:57.958199Z","shell.execute_reply.started":"2023-02-01T14:51:57.952032Z","shell.execute_reply":"2023-02-01T14:51:57.956976Z"},"trusted":true},"execution_count":250,"outputs":[]},{"cell_type":"code","source":"random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.959366Z","iopub.execute_input":"2023-02-01T14:51:57.960119Z","iopub.status.idle":"2023-02-01T14:51:57.977269Z","shell.execute_reply.started":"2023-02-01T14:51:57.960080Z","shell.execute_reply":"2023-02-01T14:51:57.976084Z"},"trusted":true},"execution_count":251,"outputs":[{"execution_count":251,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.978846Z","iopub.execute_input":"2023-02-01T14:51:57.979227Z","iopub.status.idle":"2023-02-01T14:51:58.007917Z","shell.execute_reply.started":"2023-02-01T14:51:57.979179Z","shell.execute_reply":"2023-02-01T14:51:58.006694Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 0.0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n4 0.0 1.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Neural AI \nIn this section we use some neural network to classify the data. We prepare the data so that it is more suitable for neural networks. We apply cross validation. ","metadata":{"execution":{"iopub.status.busy":"2023-01-09T16:59:50.819233Z","iopub.execute_input":"2023-01-09T16:59:50.819762Z","iopub.status.idle":"2023-01-09T16:59:50.825788Z","shell.execute_reply.started":"2023-01-09T16:59:50.819721Z","shell.execute_reply":"2023-01-09T16:59:50.823990Z"}}},{"cell_type":"markdown","source":"## Prepare data for Neural-AI","metadata":{"execution":{"iopub.status.busy":"2022-12-07T15:38:00.160610Z","iopub.execute_input":"2022-12-07T15:38:00.161030Z","iopub.status.idle":"2022-12-07T15:38:00.169322Z","shell.execute_reply.started":"2022-12-07T15:38:00.160998Z","shell.execute_reply":"2022-12-07T15:38:00.167957Z"}}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:58.009483Z","iopub.execute_input":"2023-02-01T14:51:58.009908Z","iopub.status.idle":"2023-02-01T14:51:58.023101Z","shell.execute_reply.started":"2023-02-01T14:51:58.009868Z","shell.execute_reply":"2023-02-01T14:51:58.021915Z"},"trusted":true},"execution_count":253,"outputs":[{"execution_count":253,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.431458Z","iopub.execute_input":"2023-02-01T14:55:47.431870Z","iopub.status.idle":"2023-02-01T14:55:47.444617Z","shell.execute_reply.started":"2023-02-01T14:55:47.431840Z","shell.execute_reply":"2023-02-01T14:55:47.443399Z"},"trusted":true},"execution_count":254,"outputs":[{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.696681Z","iopub.execute_input":"2023-02-01T14:55:47.697091Z","iopub.status.idle":"2023-02-01T14:55:47.706759Z","shell.execute_reply.started":"2023-02-01T14:55:47.697056Z","shell.execute_reply":"2023-02-01T14:55:47.705377Z"},"trusted":true},"execution_count":255,"outputs":[{"execution_count":255,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.965238Z","iopub.execute_input":"2023-02-01T14:55:47.965693Z","iopub.status.idle":"2023-02-01T14:55:47.976964Z","shell.execute_reply.started":"2023-02-01T14:55:47.965657Z","shell.execute_reply":"2023-02-01T14:55:47.975774Z"},"trusted":true},"execution_count":256,"outputs":[{"execution_count":256,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"I propose to keep Pclass,Sex, Age, SibSP,Parch,Ticket, Fare,Cabin, Embarked, Survived","metadata":{}},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked', 'Survived']\ntitanic_train = titanic_train.loc[:,columns_to_keep]\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.596834Z","iopub.execute_input":"2023-02-01T14:59:41.597224Z","iopub.status.idle":"2023-02-01T14:59:41.617029Z","shell.execute_reply.started":"2023-02-01T14:59:41.597192Z","shell.execute_reply":"2023-02-01T14:59:41.615728Z"},"trusted":true},"execution_count":259,"outputs":[{"execution_count":259,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name \\\n0 1 3 Braund, Mr. Owen Harris \n1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n2 3 3 Heikkinen, Miss. Laina \n3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n4 5 3 Allen, Mr. William Henry \n\n Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n1 female 38.0 1 0 PC 17599 71.2833 C85 C \n2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n3 female 35.0 1 0 113803 53.1000 C123 S \n4 male 35.0 0 0 373450 8.0500 NaN S \n\n Survived \n0 0 \n1 1 \n2 1 \n3 1 \n4 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
013Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
233Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
453Allen, Mr. William Henrymale35.0003734508.0500NaNS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked']\ntitanic_test = titanic_test.loc[:,columns_to_keep]\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.783983Z","iopub.execute_input":"2023-02-01T14:59:41.784720Z","iopub.status.idle":"2023-02-01T14:59:41.804682Z","shell.execute_reply.started":"2023-02-01T14:59:41.784681Z","shell.execute_reply":"2023-02-01T14:59:41.803270Z"},"trusted":true},"execution_count":260,"outputs":[{"execution_count":260,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Passengers ID\nTransforms to float","metadata":{}},{"cell_type":"code","source":"\ntitanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.301290Z","iopub.execute_input":"2023-02-01T14:59:42.302052Z","iopub.status.idle":"2023-02-01T14:59:42.309717Z","shell.execute_reply.started":"2023-02-01T14:59:42.302008Z","shell.execute_reply":"2023-02-01T14:59:42.308660Z"},"trusted":true},"execution_count":261,"outputs":[]},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.953004Z","iopub.execute_input":"2023-02-01T14:59:42.953443Z","iopub.status.idle":"2023-02-01T14:59:42.961302Z","shell.execute_reply.started":"2023-02-01T14:59:42.953406Z","shell.execute_reply":"2023-02-01T14:59:42.960093Z"},"trusted":true},"execution_count":262,"outputs":[{"execution_count":262,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.133436Z","iopub.execute_input":"2023-02-01T14:59:43.133821Z","iopub.status.idle":"2023-02-01T14:59:43.144899Z","shell.execute_reply.started":"2023-02-01T14:59:43.133790Z","shell.execute_reply":"2023-02-01T14:59:43.143759Z"},"trusted":true},"execution_count":263,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.317702Z","iopub.execute_input":"2023-02-01T14:59:43.318112Z","iopub.status.idle":"2023-02-01T14:59:43.329982Z","shell.execute_reply.started":"2023-02-01T14:59:43.318070Z","shell.execute_reply":"2023-02-01T14:59:43.328608Z"},"trusted":true},"execution_count":264,"outputs":[{"execution_count":264,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.526826Z","iopub.execute_input":"2023-02-01T14:59:43.527875Z","iopub.status.idle":"2023-02-01T14:59:43.538926Z","shell.execute_reply.started":"2023-02-01T14:59:43.527837Z","shell.execute_reply":"2023-02-01T14:59:43.538137Z"},"trusted":true},"execution_count":265,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.734794Z","iopub.execute_input":"2023-02-01T14:59:43.735200Z","iopub.status.idle":"2023-02-01T14:59:43.749137Z","shell.execute_reply.started":"2023-02-01T14:59:43.735165Z","shell.execute_reply":"2023-02-01T14:59:43.747731Z"},"trusted":true},"execution_count":266,"outputs":[{"execution_count":266,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.969440Z","iopub.execute_input":"2023-02-01T14:59:43.970219Z","iopub.status.idle":"2023-02-01T14:59:43.982089Z","shell.execute_reply.started":"2023-02-01T14:59:43.970157Z","shell.execute_reply":"2023-02-01T14:59:43.980764Z"},"trusted":true},"execution_count":267,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.130067Z","iopub.execute_input":"2023-02-01T14:59:44.130855Z","iopub.status.idle":"2023-02-01T14:59:44.141446Z","shell.execute_reply.started":"2023-02-01T14:59:44.130814Z","shell.execute_reply":"2023-02-01T14:59:44.140366Z"},"trusted":true},"execution_count":268,"outputs":[{"execution_count":268,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.375519Z","iopub.execute_input":"2023-02-01T14:59:44.376720Z","iopub.status.idle":"2023-02-01T14:59:44.387800Z","shell.execute_reply.started":"2023-02-01T14:59:44.376665Z","shell.execute_reply":"2023-02-01T14:59:44.386558Z"},"trusted":true},"execution_count":269,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.523725Z","iopub.execute_input":"2023-02-01T14:59:44.524363Z","iopub.status.idle":"2023-02-01T14:59:44.536192Z","shell.execute_reply.started":"2023-02-01T14:59:44.524322Z","shell.execute_reply":"2023-02-01T14:59:44.535041Z"},"trusted":true},"execution_count":270,"outputs":[{"execution_count":270,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.806439Z","iopub.execute_input":"2023-02-01T14:59:44.806827Z","iopub.status.idle":"2023-02-01T14:59:44.814441Z","shell.execute_reply.started":"2023-02-01T14:59:44.806794Z","shell.execute_reply":"2023-02-01T14:59:44.813111Z"},"trusted":true},"execution_count":271,"outputs":[{"execution_count":271,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.150811Z","iopub.execute_input":"2023-02-01T14:59:45.151188Z","iopub.status.idle":"2023-02-01T14:59:45.159387Z","shell.execute_reply.started":"2023-02-01T14:59:45.151156Z","shell.execute_reply":"2023-02-01T14:59:45.158248Z"},"trusted":true},"execution_count":272,"outputs":[{"execution_count":272,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.400597Z","iopub.execute_input":"2023-02-01T14:59:45.401226Z","iopub.status.idle":"2023-02-01T14:59:45.410601Z","shell.execute_reply.started":"2023-02-01T14:59:45.401186Z","shell.execute_reply":"2023-02-01T14:59:45.409380Z"},"trusted":true},"execution_count":273,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.540502Z","iopub.execute_input":"2023-02-01T14:59:45.541816Z","iopub.status.idle":"2023-02-01T14:59:45.555066Z","shell.execute_reply.started":"2023-02-01T14:59:45.541649Z","shell.execute_reply":"2023-02-01T14:59:45.553893Z"},"trusted":true},"execution_count":274,"outputs":[{"execution_count":274,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.765213Z","iopub.execute_input":"2023-02-01T14:59:45.766189Z","iopub.status.idle":"2023-02-01T14:59:45.777960Z","shell.execute_reply.started":"2023-02-01T14:59:45.766144Z","shell.execute_reply":"2023-02-01T14:59:45.776759Z"},"trusted":true},"execution_count":275,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.007744Z","iopub.execute_input":"2023-02-01T14:59:46.008172Z","iopub.status.idle":"2023-02-01T14:59:46.020782Z","shell.execute_reply.started":"2023-02-01T14:59:46.008134Z","shell.execute_reply":"2023-02-01T14:59:46.019374Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.158566Z","iopub.execute_input":"2023-02-01T14:59:46.158955Z","iopub.status.idle":"2023-02-01T14:59:46.171385Z","shell.execute_reply.started":"2023-02-01T14:59:46.158921Z","shell.execute_reply":"2023-02-01T14:59:46.170377Z"},"trusted":true},"execution_count":277,"outputs":[{"execution_count":277,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.365352Z","iopub.execute_input":"2023-02-01T14:59:46.365774Z","iopub.status.idle":"2023-02-01T14:59:46.377504Z","shell.execute_reply.started":"2023-02-01T14:59:46.365737Z","shell.execute_reply":"2023-02-01T14:59:46.376059Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.591674Z","iopub.execute_input":"2023-02-01T14:59:46.592065Z","iopub.status.idle":"2023-02-01T14:59:46.602473Z","shell.execute_reply.started":"2023-02-01T14:59:46.592030Z","shell.execute_reply":"2023-02-01T14:59:46.601375Z"},"trusted":true},"execution_count":279,"outputs":[{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.828954Z","iopub.execute_input":"2023-02-01T14:59:46.829390Z","iopub.status.idle":"2023-02-01T14:59:46.840546Z","shell.execute_reply.started":"2023-02-01T14:59:46.829349Z","shell.execute_reply":"2023-02-01T14:59:46.839434Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.034899Z","iopub.execute_input":"2023-02-01T14:59:47.036005Z","iopub.status.idle":"2023-02-01T14:59:47.045477Z","shell.execute_reply.started":"2023-02-01T14:59:47.035966Z","shell.execute_reply":"2023-02-01T14:59:47.044685Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.171565Z","iopub.execute_input":"2023-02-01T14:59:47.172636Z","iopub.status.idle":"2023-02-01T14:59:47.179309Z","shell.execute_reply.started":"2023-02-01T14:59:47.172596Z","shell.execute_reply":"2023-02-01T14:59:47.178195Z"},"trusted":true},"execution_count":282,"outputs":[{"execution_count":282,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.581953Z","iopub.execute_input":"2023-02-01T14:59:47.582616Z","iopub.status.idle":"2023-02-01T14:59:47.591105Z","shell.execute_reply.started":"2023-02-01T14:59:47.582574Z","shell.execute_reply":"2023-02-01T14:59:47.589952Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.831877Z","iopub.execute_input":"2023-02-01T14:59:47.832258Z","iopub.status.idle":"2023-02-01T14:59:47.839367Z","shell.execute_reply.started":"2023-02-01T14:59:47.832227Z","shell.execute_reply":"2023-02-01T14:59:47.838210Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.993877Z","iopub.execute_input":"2023-02-01T14:59:47.994253Z","iopub.status.idle":"2023-02-01T14:59:48.002543Z","shell.execute_reply.started":"2023-02-01T14:59:47.994221Z","shell.execute_reply":"2023-02-01T14:59:48.001550Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.201983Z","iopub.execute_input":"2023-02-01T14:59:48.202420Z","iopub.status.idle":"2023-02-01T14:59:48.212760Z","shell.execute_reply.started":"2023-02-01T14:59:48.202382Z","shell.execute_reply":"2023-02-01T14:59:48.211396Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_embarked_cat(data):\n factors = data['Embarked'].unique()\n gender_columns = pd.get_dummies(data['Embarked'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.431294Z","iopub.execute_input":"2023-02-01T14:59:48.432534Z","iopub.status.idle":"2023-02-01T14:59:48.437882Z","shell.execute_reply.started":"2023-02-01T14:59:48.432467Z","shell.execute_reply":"2023-02-01T14:59:48.437019Z"},"trusted":true},"execution_count":287,"outputs":[]},{"cell_type":"code","source":"\ntitanic_train = transform_embarked_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Embarked\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.629204Z","iopub.execute_input":"2023-02-01T14:59:48.629922Z","iopub.status.idle":"2023-02-01T14:59:48.642617Z","shell.execute_reply.started":"2023-02-01T14:59:48.629880Z","shell.execute_reply":"2023-02-01T14:59:48.641807Z"},"trusted":true},"execution_count":288,"outputs":[{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\ntitanic_test = transform_embarked_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Embarked\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.849824Z","iopub.execute_input":"2023-02-01T14:59:48.850216Z","iopub.status.idle":"2023-02-01T14:59:48.866727Z","shell.execute_reply.started":"2023-02-01T14:59:48.850182Z","shell.execute_reply":"2023-02-01T14:59:48.865657Z"},"trusted":true},"execution_count":289,"outputs":[{"execution_count":289,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"indices = range(0, titanic_test.shape[0])\ntitanic_test['U'] = [0 for i in indices]\ntitanic_test['U'] = titanic_test['U'].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.014240Z","iopub.execute_input":"2023-02-01T14:59:49.014659Z","iopub.status.idle":"2023-02-01T14:59:49.022051Z","shell.execute_reply.started":"2023-02-01T14:59:49.014622Z","shell.execute_reply":"2023-02-01T14:59:49.020812Z"},"trusted":true},"execution_count":290,"outputs":[]},{"cell_type":"markdown","source":"### Number of sibling","metadata":{}},{"cell_type":"code","source":"print(titanic_train[\"SibSp\"].describe())\nplt.hist(titanic_train[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.435498Z","iopub.execute_input":"2023-02-01T14:59:49.435873Z","iopub.status.idle":"2023-02-01T14:59:49.609979Z","shell.execute_reply.started":"2023-02-01T14:59:49.435843Z","shell.execute_reply":"2023-02-01T14:59:49.608818Z"},"trusted":true},"execution_count":291,"outputs":[{"name":"stdout","text":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":291,"output_type":"execute_result","data":{"text/plain":"(array([608., 209., 28., 16., 0., 18., 5., 0., 0., 7.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(titanic_test[\"SibSp\"].describe())\nplt.hist(titanic_test[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.640013Z","iopub.execute_input":"2023-02-01T14:59:49.640429Z","iopub.status.idle":"2023-02-01T14:59:50.199638Z","shell.execute_reply.started":"2023-02-01T14:59:49.640389Z","shell.execute_reply":"2023-02-01T14:59:50.198241Z"},"trusted":true},"execution_count":292,"outputs":[{"name":"stdout","text":"count 418.000000\nmean 0.447368\nstd 0.896760\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":292,"output_type":"execute_result","data":{"text/plain":"(array([283., 110., 14., 4., 0., 4., 1., 0., 0., 2.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def categorise_siblings(data):\n cut_labels_9 = ['sib_0','sib_1','sib_2','sib_3', \n 'sib_4','sib_5','sib_6','sib_7', 'sib_8']\n cut_bins = [0,1,2,3,4,5,6,7,8,9]\n data['Sib_cat'] = pd.cut(data['SibSp'], \n bins=cut_bins, \n labels=cut_labels_9)\n \n data['Sib_cat'] = data.Sib_cat.astype(str)\n data.loc[data[\"Sib_cat\"] == 'nan', \"Sib_cat\"] = \"Sib_Unknown\"\n \n return data\n\ndef transform_sibling_cat(data):\n factors = data['Sib_cat'].unique()\n gender_columns = pd.get_dummies(data['Sib_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.201993Z","iopub.execute_input":"2023-02-01T14:59:50.202490Z","iopub.status.idle":"2023-02-01T14:59:50.212938Z","shell.execute_reply.started":"2023-02-01T14:59:50.202445Z","shell.execute_reply":"2023-02-01T14:59:50.211676Z"},"trusted":true},"execution_count":293,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_siblings(titanic_train)\ntitanic_train = transform_sibling_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"SibSp\", axis = 1)\ntitanic_train = titanic_train.drop(\"Sib_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.214386Z","iopub.execute_input":"2023-02-01T14:59:50.214705Z","iopub.status.idle":"2023-02-01T14:59:50.237526Z","shell.execute_reply.started":"2023-02-01T14:59:50.214675Z","shell.execute_reply":"2023-02-01T14:59:50.236793Z"},"trusted":true},"execution_count":294,"outputs":[{"execution_count":294,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.431533Z","iopub.execute_input":"2023-02-01T14:59:50.432231Z","iopub.status.idle":"2023-02-01T14:59:50.438691Z","shell.execute_reply.started":"2023-02-01T14:59:50.432194Z","shell.execute_reply":"2023-02-01T14:59:50.437673Z"},"trusted":true},"execution_count":295,"outputs":[{"execution_count":295,"output_type":"execute_result","data":{"text/plain":"(891, 21)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_siblings(titanic_test)\ntitanic_test = transform_sibling_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"SibSp\", axis = 1)\ntitanic_test = titanic_test.drop(\"Sib_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.596205Z","iopub.execute_input":"2023-02-01T14:59:50.596606Z","iopub.status.idle":"2023-02-01T14:59:50.618154Z","shell.execute_reply.started":"2023-02-01T14:59:50.596574Z","shell.execute_reply":"2023-02-01T14:59:50.617093Z"},"trusted":true},"execution_count":296,"outputs":[{"execution_count":296,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.849255Z","iopub.execute_input":"2023-02-01T14:59:50.850520Z","iopub.status.idle":"2023-02-01T14:59:50.858028Z","shell.execute_reply.started":"2023-02-01T14:59:50.850477Z","shell.execute_reply":"2023-02-01T14:59:50.856953Z"},"trusted":true},"execution_count":297,"outputs":[{"execution_count":297,"output_type":"execute_result","data":{"text/plain":"(418, 20)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Transforming age into categories\nThe categorise the age into 9 categories; unknown and one for each decade. The categories are then transformed in hot_coding format. ","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.269486Z","iopub.execute_input":"2023-02-01T14:59:51.269885Z","iopub.status.idle":"2023-02-01T14:59:51.572232Z","shell.execute_reply.started":"2023-02-01T14:59:51.269851Z","shell.execute_reply":"2023-02-01T14:59:51.571214Z"},"trusted":true},"execution_count":298,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.573955Z","iopub.execute_input":"2023-02-01T14:59:51.574279Z","iopub.status.idle":"2023-02-01T14:59:51.588745Z","shell.execute_reply.started":"2023-02-01T14:59:51.574249Z","shell.execute_reply":"2023-02-01T14:59:51.587351Z"},"trusted":true},"execution_count":299,"outputs":[{"execution_count":299,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.763907Z","iopub.execute_input":"2023-02-01T14:59:51.764334Z","iopub.status.idle":"2023-02-01T14:59:52.129917Z","shell.execute_reply.started":"2023-02-01T14:59:51.764278Z","shell.execute_reply":"2023-02-01T14:59:52.128918Z"},"trusted":true},"execution_count":300,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.131621Z","iopub.execute_input":"2023-02-01T14:59:52.132130Z","iopub.status.idle":"2023-02-01T14:59:52.142285Z","shell.execute_reply.started":"2023-02-01T14:59:52.132091Z","shell.execute_reply":"2023-02-01T14:59:52.141264Z"},"trusted":true},"execution_count":301,"outputs":[{"execution_count":301,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"def transform_age_cat(data):\n factors = data['Age_cat'].unique()\n gender_columns = pd.get_dummies(data['Age_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.143629Z","iopub.execute_input":"2023-02-01T14:59:52.143919Z","iopub.status.idle":"2023-02-01T14:59:52.154584Z","shell.execute_reply.started":"2023-02-01T14:59:52.143891Z","shell.execute_reply":"2023-02-01T14:59:52.153409Z"},"trusted":true},"execution_count":302,"outputs":[]},{"cell_type":"code","source":"def categorise_age(data):\n cut_labels_8 = ['age_0-9','age_10-19','age_20-29','age_30-39', \n 'age_40-49','age_50-59','age_60-69','age_70-79']\n cut_bins = [0,10,20,30,40,50,60,70,80]\n data['Age_cat'] = pd.cut(data['Age'], \n bins=cut_bins, \n labels=cut_labels_8)\n data['Age_cat'] = data.Age_cat.astype(str)\n data.loc[data[\"Age\"].isna(), \"Age_cat\"] = \"Age_Unknown\"\n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.340509Z","iopub.execute_input":"2023-02-01T14:59:52.340896Z","iopub.status.idle":"2023-02-01T14:59:52.347606Z","shell.execute_reply.started":"2023-02-01T14:59:52.340863Z","shell.execute_reply":"2023-02-01T14:59:52.346572Z"},"trusted":true},"execution_count":303,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_age(titanic_train)\ntitanic_train = transform_age_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Age\", axis = 1)\ntitanic_train = titanic_train.drop(\"Age_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.546266Z","iopub.execute_input":"2023-02-01T14:59:52.546677Z","iopub.status.idle":"2023-02-01T14:59:52.572844Z","shell.execute_reply.started":"2023-02-01T14:59:52.546642Z","shell.execute_reply":"2023-02-01T14:59:52.571757Z"},"trusted":true},"execution_count":304,"outputs":[{"execution_count":304,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_age(titanic_test)\ntitanic_test = transform_age_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Age\", axis = 1)\ntitanic_test = titanic_test.drop(\"Age_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.811521Z","iopub.execute_input":"2023-02-01T14:59:52.812681Z","iopub.status.idle":"2023-02-01T14:59:52.836736Z","shell.execute_reply.started":"2023-02-01T14:59:52.812627Z","shell.execute_reply":"2023-02-01T14:59:52.835513Z"},"trusted":true},"execution_count":305,"outputs":[{"execution_count":305,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"### Gender transformation to hot-coding \nWe check the factor values are the same between both datasets. Then, we generate a hot coding of two columns; i.e., male and female. Both columns replace the Sex column.","metadata":{}},{"cell_type":"code","source":"titanic_train['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.188122Z","iopub.execute_input":"2023-02-01T14:59:53.189282Z","iopub.status.idle":"2023-02-01T14:59:53.197504Z","shell.execute_reply.started":"2023-02-01T14:59:53.189231Z","shell.execute_reply":"2023-02-01T14:59:53.196373Z"},"trusted":true},"execution_count":306,"outputs":[{"execution_count":306,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.420038Z","iopub.execute_input":"2023-02-01T14:59:53.420458Z","iopub.status.idle":"2023-02-01T14:59:53.428009Z","shell.execute_reply.started":"2023-02-01T14:59:53.420423Z","shell.execute_reply":"2023-02-01T14:59:53.426859Z"},"trusted":true},"execution_count":307,"outputs":[{"execution_count":307,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_gender(data):\n factors = data['Sex'].unique()\n gender_columns = pd.get_dummies(data['Sex'])\n columns = range(0,len(factors))\n \n for column in columns:\n data[factors[column]] = gender_columns.loc[:,factors[column]].astype(float)\n \n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.614253Z","iopub.execute_input":"2023-02-01T14:59:53.614984Z","iopub.status.idle":"2023-02-01T14:59:53.620854Z","shell.execute_reply.started":"2023-02-01T14:59:53.614945Z","shell.execute_reply":"2023-02-01T14:59:53.619727Z"},"trusted":true},"execution_count":308,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_gender(titanic_train)\ntitanic_train.drop(\"Sex\", axis = 1, inplace = True)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.853720Z","iopub.execute_input":"2023-02-01T14:59:53.854121Z","iopub.status.idle":"2023-02-01T14:59:53.868139Z","shell.execute_reply.started":"2023-02-01T14:59:53.854084Z","shell.execute_reply":"2023-02-01T14:59:53.867117Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_gender(titanic_test)\ntitanic_test.drop(\"Sex\", axis = 1,inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.077511Z","iopub.execute_input":"2023-02-01T14:59:54.078227Z","iopub.status.idle":"2023-02-01T14:59:54.117482Z","shell.execute_reply.started":"2023-02-01T14:59:54.078188Z","shell.execute_reply":"2023-02-01T14:59:54.116493Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Parch \\\n0 892.0 3 Kelly, Mr. James 0 \n1 893.0 3 Wilkes, Mrs. James (Ellen Needs) 0 \n2 894.0 2 Myles, Mr. Thomas Francis 0 \n3 895.0 3 Wirz, Mr. Albert 0 \n4 896.0 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 \n\n Ticket Fare Cabin Q S C ... age_30-39 age_40-49 \\\n0 330911 7.8292 NaN 1.0 0.0 0.0 ... 1.0 0.0 \n1 363272 7.0000 NaN 0.0 1.0 0.0 ... 0.0 1.0 \n2 240276 9.6875 NaN 1.0 0.0 0.0 ... 0.0 0.0 \n3 315154 8.6625 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n4 3101298 12.2875 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n\n age_60-69 age_20-29 age_10-19 age_50-59 age_0-9 age_70-79 male \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n\n female \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 1.0 \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameParchTicketFareCabinQSC...age_30-39age_40-49age_60-69age_20-29age_10-19age_50-59age_0-9age_70-79malefemale
0892.03Kelly, Mr. James03309117.8292NaN1.00.00.0...1.00.00.00.00.00.00.00.01.00.0
1893.03Wilkes, Mrs. James (Ellen Needs)03632727.0000NaN0.01.00.0...0.01.00.00.00.00.00.00.00.01.0
2894.02Myles, Mr. Thomas Francis02402769.6875NaN1.00.00.0...0.00.01.00.00.00.00.00.01.00.0
3895.03Wirz, Mr. Albert03151548.6625NaN0.01.00.0...0.00.00.01.00.00.00.00.01.00.0
4896.03Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.2875NaN0.01.00.0...0.00.00.01.00.00.00.00.00.01.0
\n

5 rows × 28 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Cabin and Pclass\n\nThe passenger class appears to drive whether a cabin is known. So, we propose to drop the cabin as the percentage of not known values is quite high. We apply an hot encoding the Pclass. ","metadata":{}},{"cell_type":"code","source":"titanic_train['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.494349Z","iopub.execute_input":"2023-02-01T14:59:54.494758Z","iopub.status.idle":"2023-02-01T14:59:54.503695Z","shell.execute_reply.started":"2023-02-01T14:59:54.494724Z","shell.execute_reply":"2023-02-01T14:59:54.502385Z"},"trusted":true},"execution_count":311,"outputs":[{"execution_count":311,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n 'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n 'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n 'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n 'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"print(\"percentage of cabin nan values - training \", titanic_train['Cabin'].isna().sum()/titanic_train.shape[0])\nprint(\"percentage of cabin nan values - test \", titanic_test['Cabin'].isna().sum()/titanic_test.shape[0])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.731246Z","iopub.execute_input":"2023-02-01T14:59:54.732185Z","iopub.status.idle":"2023-02-01T14:59:54.740154Z","shell.execute_reply.started":"2023-02-01T14:59:54.732142Z","shell.execute_reply":"2023-02-01T14:59:54.738880Z"},"trusted":true},"execution_count":312,"outputs":[{"name":"stdout","text":"percentage of cabin nan values - training 0.7710437710437711\npercentage of cabin nan values - test 0.7822966507177034\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.963015Z","iopub.execute_input":"2023-02-01T14:59:54.963847Z","iopub.status.idle":"2023-02-01T14:59:54.971020Z","shell.execute_reply.started":"2023-02-01T14:59:54.963804Z","shell.execute_reply":"2023-02-01T14:59:54.969855Z"},"trusted":true},"execution_count":313,"outputs":[{"execution_count":313,"output_type":"execute_result","data":{"text/plain":"array([3, 1, 2])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.182701Z","iopub.execute_input":"2023-02-01T14:59:55.183488Z","iopub.status.idle":"2023-02-01T14:59:55.190703Z","shell.execute_reply.started":"2023-02-01T14:59:55.183443Z","shell.execute_reply":"2023-02-01T14:59:55.189659Z"},"trusted":true},"execution_count":314,"outputs":[{"execution_count":314,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 1])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 1 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.447423Z","iopub.execute_input":"2023-02-01T14:59:55.447835Z","iopub.status.idle":"2023-02-01T14:59:55.464293Z","shell.execute_reply.started":"2023-02-01T14:59:55.447799Z","shell.execute_reply":"2023-02-01T14:59:55.463098Z"},"trusted":true},"execution_count":315,"outputs":[{"execution_count":315,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n1 1 C85\n3 1 C123\n6 1 E46\n11 1 C103\n23 1 A6\n.. ... ...\n871 1 D35\n872 1 B51 B53 B55\n879 1 C50\n887 1 B42\n889 1 C148\n\n[216 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
11C85
31C123
61E46
111C103
231A6
.........
8711D35
8721B51 B53 B55
8791C50
8871B42
8891C148
\n

216 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 2 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.639329Z","iopub.execute_input":"2023-02-01T14:59:55.640055Z","iopub.status.idle":"2023-02-01T14:59:55.656031Z","shell.execute_reply.started":"2023-02-01T14:59:55.640016Z","shell.execute_reply":"2023-02-01T14:59:55.655083Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n9 2 NaN\n15 2 NaN\n17 2 NaN\n20 2 NaN\n21 2 D56\n.. ... ...\n866 2 NaN\n874 2 NaN\n880 2 NaN\n883 2 NaN\n886 2 NaN\n\n[184 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
92NaN
152NaN
172NaN
202NaN
212D56
.........
8662NaN
8742NaN
8802NaN
8832NaN
8862NaN
\n

184 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 3 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.890762Z","iopub.execute_input":"2023-02-01T14:59:55.891773Z","iopub.status.idle":"2023-02-01T14:59:55.905616Z","shell.execute_reply.started":"2023-02-01T14:59:55.891731Z","shell.execute_reply":"2023-02-01T14:59:55.904841Z"},"trusted":true},"execution_count":317,"outputs":[{"execution_count":317,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n0 3 NaN\n2 3 NaN\n4 3 NaN\n5 3 NaN\n7 3 NaN\n.. ... ...\n882 3 NaN\n884 3 NaN\n885 3 NaN\n888 3 NaN\n890 3 NaN\n\n[491 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
03NaN
23NaN
43NaN
53NaN
73NaN
.........
8823NaN
8843NaN
8853NaN
8883NaN
8903NaN
\n

491 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"xs = titanic_train.loc[titanic_train['Fare'] > 0,'Pclass']\nys = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.128782Z","iopub.execute_input":"2023-02-01T14:59:56.129782Z","iopub.status.idle":"2023-02-01T14:59:56.360461Z","shell.execute_reply.started":"2023-02-01T14:59:56.129741Z","shell.execute_reply":"2023-02-01T14:59:56.359413Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"xs = titanic_test.loc[titanic_test['Fare'] > 0,'Pclass']\nys = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.362001Z","iopub.execute_input":"2023-02-01T14:59:56.362324Z","iopub.status.idle":"2023-02-01T14:59:56.593756Z","shell.execute_reply.started":"2023-02-01T14:59:56.362281Z","shell.execute_reply":"2023-02-01T14:59:56.592791Z"},"trusted":true},"execution_count":319,"outputs":[{"execution_count":319,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.scatter(titanic_train[\"Pclass\"],titanic_train[\"Fare\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.595546Z","iopub.execute_input":"2023-02-01T14:59:56.595846Z","iopub.status.idle":"2023-02-01T14:59:56.826882Z","shell.execute_reply.started":"2023-02-01T14:59:56.595817Z","shell.execute_reply":"2023-02-01T14:59:56.825559Z"},"trusted":true},"execution_count":320,"outputs":[{"execution_count":320,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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transform_Pclass(data):\n factors = data['Pclass'].unique()\n Pclass_columns = pd.get_dummies(data['Pclass'])\n columns = range(0,len(factors))\n \n for column in columns:\n col_name = 'Class_' + str(factors[column])\n data[col_name] = Pclass_columns.loc[:,factors[column]].astype(float)\n \n data.drop(\"Pclass\", axis = 1)\n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.829111Z","iopub.execute_input":"2023-02-01T14:59:56.829859Z","iopub.status.idle":"2023-02-01T14:59:56.838658Z","shell.execute_reply.started":"2023-02-01T14:59:56.829811Z","shell.execute_reply":"2023-02-01T14:59:56.837496Z"},"trusted":true},"execution_count":321,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_Pclass(titanic_train)\ntitanic_train.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.037884Z","iopub.execute_input":"2023-02-01T14:59:57.039017Z","iopub.status.idle":"2023-02-01T14:59:57.077228Z","shell.execute_reply.started":"2023-02-01T14:59:57.038961Z","shell.execute_reply":"2023-02-01T14:59:57.076108Z"},"trusted":true},"execution_count":322,"outputs":[{"execution_count":322,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch \\\n0 1.0 Braund, Mr. Owen Harris 0 \n1 2.0 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n2 3.0 Heikkinen, Miss. Laina 0 \n3 4.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n4 5.0 Allen, Mr. William Henry 0 \n\n Ticket Fare Survived S C Q U ... age_0-9 \\\n0 A/5 21171 7.2500 0 1.0 0.0 0.0 0.0 ... 0.0 \n1 PC 17599 71.2833 1 0.0 1.0 0.0 0.0 ... 0.0 \n2 STON/O2. 3101282 7.9250 1 1.0 0.0 0.0 0.0 ... 0.0 \n3 113803 53.1000 1 1.0 0.0 0.0 0.0 ... 0.0 \n4 373450 8.0500 0 1.0 0.0 0.0 0.0 ... 0.0 \n\n age_10-19 age_60-69 age_40-49 age_70-79 male female Class_3 Class_1 \\\n0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n2 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n4 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n\n Class_2 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 30 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareSurvivedSCQU...age_0-9age_10-19age_60-69age_40-49age_70-79malefemaleClass_3Class_1Class_2
01.0Braund, Mr. Owen Harris0A/5 211717.250001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
12.0Cumings, Mrs. John Bradley (Florence Briggs Th...0PC 1759971.283310.01.00.00.0...0.00.00.00.00.00.01.00.01.00.0
23.0Heikkinen, Miss. Laina0STON/O2. 31012827.925011.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
34.0Futrelle, Mrs. Jacques Heath (Lily May Peel)011380353.100011.00.00.00.0...0.00.00.00.00.00.01.00.01.00.0
45.0Allen, Mr. William Henry03734508.050001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
\n

5 rows × 30 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_Pclass(titanic_test)\ntitanic_test.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.320738Z","iopub.execute_input":"2023-02-01T14:59:57.321706Z","iopub.status.idle":"2023-02-01T14:59:57.358787Z","shell.execute_reply.started":"2023-02-01T14:59:57.321665Z","shell.execute_reply":"2023-02-01T14:59:57.357627Z"},"trusted":true},"execution_count":323,"outputs":[{"execution_count":323,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch Ticket \\\n0 892.0 Kelly, Mr. James 0 330911 \n1 893.0 Wilkes, Mrs. James (Ellen Needs) 0 363272 \n2 894.0 Myles, Mr. Thomas Francis 0 240276 \n3 895.0 Wirz, Mr. Albert 0 315154 \n4 896.0 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 3101298 \n\n Fare Q S C U Sib_Unknown ... age_20-29 age_10-19 \\\n0 7.8292 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n1 7.0000 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 \n2 9.6875 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n3 8.6625 0.0 1.0 0.0 0.0 1.0 ... 1.0 0.0 \n4 12.2875 0.0 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n\n age_50-59 age_0-9 age_70-79 male female Class_3 Class_2 Class_1 \n0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareQSCUSib_Unknown...age_20-29age_10-19age_50-59age_0-9age_70-79malefemaleClass_3Class_2Class_1
0892.0Kelly, Mr. James03309117.82921.00.00.00.01.0...0.00.00.00.00.01.00.01.00.00.0
1893.0Wilkes, Mrs. James (Ellen Needs)03632727.00000.01.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
2894.0Myles, Mr. Thomas Francis02402769.68751.00.00.00.01.0...0.00.00.00.00.01.00.00.01.00.0
3895.0Wirz, Mr. Albert03151548.66250.01.00.00.01.0...1.00.00.00.00.01.00.01.00.00.0
4896.0Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.28750.01.00.00.00.0...1.00.00.00.00.00.01.01.00.00.0
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Tickets and Fare\nWe remove the tickets, as it brings no additional characteristic for the prediction.\n\nOld version: We reduce the complexity of the Fare by using the log.\nNew version: The price appears to be dependent on the class, so we drop the price.","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.725423Z","iopub.execute_input":"2023-02-01T14:59:57.726055Z","iopub.status.idle":"2023-02-01T14:59:57.734724Z","shell.execute_reply.started":"2023-02-01T14:59:57.725995Z","shell.execute_reply":"2023-02-01T14:59:57.733640Z"},"trusted":true},"execution_count":324,"outputs":[]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\ntitanic_train.loc[titanic_train['Fare'] > 0,'Fare'] = log_10_values\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.977699Z","iopub.execute_input":"2023-02-01T14:59:57.978673Z","iopub.status.idle":"2023-02-01T14:59:57.991610Z","shell.execute_reply.started":"2023-02-01T14:59:57.978633Z","shell.execute_reply":"2023-02-01T14:59:57.990366Z"},"trusted":true},"execution_count":325,"outputs":[{"execution_count":325,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.256781\nstd 0.435553\nmin 0.000000\n25% 0.898198\n50% 1.159994\n75% 1.491362\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\ntitanic_test.loc[titanic_test['Fare'] > 0,'Fare'] = log_10_values\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.219678Z","iopub.execute_input":"2023-02-01T14:59:58.220097Z","iopub.status.idle":"2023-02-01T14:59:58.235301Z","shell.execute_reply.started":"2023-02-01T14:59:58.220059Z","shell.execute_reply":"2023-02-01T14:59:58.234195Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"count 417.000000\nmean 1.279591\nstd 0.437507\nmin 0.000000\n25% 0.897396\n50% 1.159994\n75% 1.498311\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.drop(\"Fare\", axis = 1, inplace = True)\ntitanic_test.drop(\"Fare\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.471730Z","iopub.execute_input":"2023-02-01T14:59:58.472149Z","iopub.status.idle":"2023-02-01T14:59:58.480205Z","shell.execute_reply.started":"2023-02-01T14:59:58.472111Z","shell.execute_reply":"2023-02-01T14:59:58.479227Z"},"trusted":true},"execution_count":327,"outputs":[]},{"cell_type":"markdown","source":"### Outcome of data preparations","metadata":{}},{"cell_type":"code","source":"\nprint(\"training datasets : \" , titanic_train.shape)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.947799Z","iopub.execute_input":"2023-02-01T14:59:58.948756Z","iopub.status.idle":"2023-02-01T14:59:58.957820Z","shell.execute_reply.started":"2023-02-01T14:59:58.948713Z","shell.execute_reply":"2023-02-01T14:59:58.956624Z"},"trusted":true},"execution_count":328,"outputs":[{"name":"stdout","text":"training datasets : (891, 28)\n","output_type":"stream"},{"execution_count":328,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_1 float64\nClass_2 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(\"testing datasets : \" , titanic_test.shape)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.211439Z","iopub.execute_input":"2023-02-01T14:59:59.211825Z","iopub.status.idle":"2023-02-01T14:59:59.222689Z","shell.execute_reply.started":"2023-02-01T14:59:59.211793Z","shell.execute_reply":"2023-02-01T14:59:59.221460Z"},"trusted":true},"execution_count":329,"outputs":[{"name":"stdout","text":"testing datasets : (418, 27)\n","output_type":"stream"},{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train_cols = titanic_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.478786Z","iopub.execute_input":"2023-02-01T14:59:59.479161Z","iopub.status.idle":"2023-02-01T14:59:59.488399Z","shell.execute_reply.started":"2023-02-01T14:59:59.479130Z","shell.execute_reply":"2023-02-01T14:59:59.487137Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"Index(['Survived'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.773416Z","iopub.execute_input":"2023-02-01T14:59:59.773881Z","iopub.status.idle":"2023-02-01T14:59:59.780592Z","shell.execute_reply.started":"2023-02-01T14:59:59.773845Z","shell.execute_reply":"2023-02-01T14:59:59.779730Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Name', 'Parch', 'Q', 'S', 'C', 'U', 'Sib_Unknown',\n 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', 'age_30-39',\n 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross validation preparation\nWe use a stratified sampling for the training into a train and test dataset. ","metadata":{}},{"cell_type":"code","source":"x_cols = [\"PassengerId\",'Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\nX = X[x_cols]\nX = X.apply(pd.to_numeric)\n\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.360627Z","iopub.execute_input":"2023-02-01T15:00:00.361572Z","iopub.status.idle":"2023-02-01T15:00:00.386989Z","shell.execute_reply.started":"2023-02-01T15:00:00.361528Z","shell.execute_reply":"2023-02-01T15:00:00.385873Z"},"trusted":true},"execution_count":332,"outputs":[{"name":"stdout","text":"0 0.616105\n1 0.383895\nName: Survived, dtype: float64 \n\n\n0 0.616246\n1 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = ['Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\nx_train_pass_id = X_train.PassengerId\nX_train = X_train[x_cols]\nX_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.656949Z","iopub.execute_input":"2023-02-01T15:00:00.657623Z","iopub.status.idle":"2023-02-01T15:00:00.667953Z","shell.execute_reply.started":"2023-02-01T15:00:00.657586Z","shell.execute_reply":"2023-02-01T15:00:00.666758Z"},"trusted":true},"execution_count":333,"outputs":[{"execution_count":333,"output_type":"execute_result","data":{"text/plain":"(534, 25)"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\n\nX_valid.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.982077Z","iopub.execute_input":"2023-02-01T15:00:00.982495Z","iopub.status.idle":"2023-02-01T15:00:00.991483Z","shell.execute_reply.started":"2023-02-01T15:00:00.982459Z","shell.execute_reply":"2023-02-01T15:00:00.990369Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"(357, 25)"},"metadata":{}}]},{"cell_type":"code","source":"y_train_encode=pd.get_dummies(y_train)\ny_valid_encode=pd.get_dummies(y_valid)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.303350Z","iopub.execute_input":"2023-02-01T15:00:01.303749Z","iopub.status.idle":"2023-02-01T15:00:01.310531Z","shell.execute_reply.started":"2023-02-01T15:00:01.303715Z","shell.execute_reply":"2023-02-01T15:00:01.309278Z"},"trusted":true},"execution_count":335,"outputs":[]},{"cell_type":"code","source":"train_cols = X_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.517778Z","iopub.execute_input":"2023-02-01T15:00:01.518178Z","iopub.status.idle":"2023-02-01T15:00:01.527798Z","shell.execute_reply.started":"2023-02-01T15:00:01.518142Z","shell.execute_reply":"2023-02-01T15:00:01.526659Z"},"trusted":true},"execution_count":336,"outputs":[{"execution_count":336,"output_type":"execute_result","data":{"text/plain":"Index([], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test[x_cols]\nX_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.807922Z","iopub.execute_input":"2023-02-01T15:00:01.808982Z","iopub.status.idle":"2023-02-01T15:00:01.817925Z","shell.execute_reply.started":"2023-02-01T15:00:01.808940Z","shell.execute_reply":"2023-02-01T15:00:01.816659Z"},"trusted":true},"execution_count":337,"outputs":[{"execution_count":337,"output_type":"execute_result","data":{"text/plain":"Parch int64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\nQ float64\nS float64\nC float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## ANN\n\nWe apply an ANN to predict the survival of passengers. We create a basic architecture made of 5 layers.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential, load_model\n\ntf.compat.v1.get_default_graph()\n\nno_columns = X_train.shape[1]\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Flatten(input_shape=(no_columns,)))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(2, activation=\"softmax\"))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.368188Z","iopub.execute_input":"2023-02-01T15:00:02.368622Z","iopub.status.idle":"2023-02-01T15:00:02.493557Z","shell.execute_reply.started":"2023-02-01T15:00:02.368582Z","shell.execute_reply":"2023-02-01T15:00:02.492272Z"},"trusted":true},"execution_count":338,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 25) 0 \n_________________________________________________________________\ndense (Dense) (None, 32) 832 \n_________________________________________________________________\ndense_1 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_2 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_3 (Dense) (None, 2) 66 \n=================================================================\nTotal params: 3,010\nTrainable params: 3,010\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"},{"name":"stderr","text":"2023-02-01 15:00:02.406449: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"\nrate = 0.00021\nopt = tf.keras.optimizers.Adam(learning_rate = rate)\nmodel.compile(optimizer= opt, \n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"])\ntf.compat.v1.get_default_graph()\nhistory = model.fit(X_train,\n y_train_encode,\n validation_data=(X_valid, y_valid_encode),\n epochs = 300,\n verbose = True)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.642006Z","iopub.execute_input":"2023-02-01T15:00:02.642833Z","iopub.status.idle":"2023-02-01T15:00:28.751910Z","shell.execute_reply.started":"2023-02-01T15:00:02.642783Z","shell.execute_reply":"2023-02-01T15:00:28.750794Z"},"trusted":true},"execution_count":339,"outputs":[{"name":"stderr","text":"2023-02-01 15:00:02.755801: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/300\n17/17 [==============================] - 1s 19ms/step - loss: 0.7885 - accuracy: 0.6161 - val_loss: 0.7708 - val_accuracy: 0.6162\nEpoch 2/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7574 - accuracy: 0.6161 - val_loss: 0.7429 - val_accuracy: 0.6162\nEpoch 3/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7326 - accuracy: 0.6161 - val_loss: 0.7213 - val_accuracy: 0.6162\nEpoch 4/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.7133 - accuracy: 0.6161 - val_loss: 0.7045 - val_accuracy: 0.6162\nEpoch 5/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.6161 - val_loss: 0.6921 - val_accuracy: 0.6162\nEpoch 6/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6878 - accuracy: 0.6161 - val_loss: 0.6832 - val_accuracy: 0.6162\nEpoch 7/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6802 - accuracy: 0.6161 - val_loss: 0.6771 - val_accuracy: 0.6162\nEpoch 8/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6750 - accuracy: 0.6161 - val_loss: 0.6727 - val_accuracy: 0.6162\nEpoch 9/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6717 - accuracy: 0.6161 - val_loss: 0.6698 - val_accuracy: 0.6162\nEpoch 10/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6692 - accuracy: 0.6161 - val_loss: 0.6682 - val_accuracy: 0.6162\nEpoch 11/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.6161 - val_loss: 0.6670 - val_accuracy: 0.6162\nEpoch 12/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6668 - accuracy: 0.6161 - val_loss: 0.6663 - val_accuracy: 0.6162\nEpoch 13/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6662 - accuracy: 0.6161 - val_loss: 0.6658 - val_accuracy: 0.6162\nEpoch 14/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.6161 - val_loss: 0.6655 - val_accuracy: 0.6162\nEpoch 15/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.6161 - val_loss: 0.6653 - val_accuracy: 0.6162\nEpoch 16/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6655 - accuracy: 0.6161 - val_loss: 0.6651 - val_accuracy: 0.6162\nEpoch 17/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6161 - val_loss: 0.6650 - val_accuracy: 0.6162\nEpoch 18/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6651 - accuracy: 0.6161 - val_loss: 0.6649 - val_accuracy: 0.6162\nEpoch 19/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6647 - val_accuracy: 0.6162\nEpoch 20/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6646 - val_accuracy: 0.6162\nEpoch 21/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6648 - accuracy: 0.6161 - val_loss: 0.6645 - val_accuracy: 0.6162\nEpoch 22/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6647 - accuracy: 0.6161 - val_loss: 0.6644 - val_accuracy: 0.6162\nEpoch 23/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6645 - accuracy: 0.6161 - val_loss: 0.6643 - val_accuracy: 0.6162\nEpoch 24/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6644 - accuracy: 0.6161 - val_loss: 0.6641 - val_accuracy: 0.6162\nEpoch 25/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.6161 - val_loss: 0.6640 - val_accuracy: 0.6162\nEpoch 26/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6641 - accuracy: 0.6161 - val_loss: 0.6639 - val_accuracy: 0.6162\nEpoch 27/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6637 - val_accuracy: 0.6162\nEpoch 28/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6636 - val_accuracy: 0.6162\nEpoch 29/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6637 - accuracy: 0.6161 - val_loss: 0.6634 - val_accuracy: 0.6162\nEpoch 30/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.6161 - val_loss: 0.6633 - val_accuracy: 0.6162\nEpoch 31/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6635 - accuracy: 0.6161 - val_loss: 0.6631 - val_accuracy: 0.6162\nEpoch 32/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6633 - accuracy: 0.6161 - val_loss: 0.6629 - val_accuracy: 0.6162\nEpoch 33/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6630 - accuracy: 0.6161 - val_loss: 0.6627 - val_accuracy: 0.6162\nEpoch 34/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6628 - accuracy: 0.6161 - val_loss: 0.6625 - val_accuracy: 0.6162\nEpoch 35/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6623 - val_accuracy: 0.6162\nEpoch 36/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6621 - val_accuracy: 0.6162\nEpoch 37/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6622 - accuracy: 0.6161 - val_loss: 0.6619 - val_accuracy: 0.6162\nEpoch 38/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6619 - accuracy: 0.6161 - val_loss: 0.6616 - val_accuracy: 0.6162\nEpoch 39/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6618 - accuracy: 0.6161 - val_loss: 0.6614 - val_accuracy: 0.6162\nEpoch 40/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6614 - accuracy: 0.6161 - val_loss: 0.6611 - val_accuracy: 0.6162\nEpoch 41/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6612 - accuracy: 0.6161 - val_loss: 0.6608 - val_accuracy: 0.6162\nEpoch 42/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6610 - accuracy: 0.6161 - val_loss: 0.6605 - val_accuracy: 0.6162\nEpoch 43/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.6161 - val_loss: 0.6601 - val_accuracy: 0.6162\nEpoch 44/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6598 - val_accuracy: 0.6162\nEpoch 45/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6594 - val_accuracy: 0.6162\nEpoch 46/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6595 - accuracy: 0.6161 - val_loss: 0.6590 - val_accuracy: 0.6162\nEpoch 47/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6590 - accuracy: 0.6161 - val_loss: 0.6586 - val_accuracy: 0.6162\nEpoch 48/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6161 - val_loss: 0.6581 - val_accuracy: 0.6162\nEpoch 49/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6580 - accuracy: 0.6161 - val_loss: 0.6576 - val_accuracy: 0.6162\nEpoch 50/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.6161 - val_loss: 0.6571 - val_accuracy: 0.6162\nEpoch 51/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6571 - accuracy: 0.6161 - val_loss: 0.6566 - val_accuracy: 0.6162\nEpoch 52/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6565 - accuracy: 0.6161 - val_loss: 0.6560 - val_accuracy: 0.6162\nEpoch 53/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6161 - val_loss: 0.6553 - val_accuracy: 0.6162\nEpoch 54/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6161 - val_loss: 0.6546 - val_accuracy: 0.6162\nEpoch 55/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6545 - accuracy: 0.6161 - val_loss: 0.6539 - val_accuracy: 0.6162\nEpoch 56/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6542 - accuracy: 0.6161 - val_loss: 0.6531 - val_accuracy: 0.6162\nEpoch 57/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6531 - accuracy: 0.6161 - val_loss: 0.6522 - val_accuracy: 0.6162\nEpoch 58/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6161 - val_loss: 0.6513 - val_accuracy: 0.6162\nEpoch 59/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6510 - accuracy: 0.6161 - val_loss: 0.6503 - val_accuracy: 0.6162\nEpoch 60/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6501 - accuracy: 0.6161 - val_loss: 0.6493 - val_accuracy: 0.6162\nEpoch 61/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6490 - accuracy: 0.6161 - val_loss: 0.6482 - val_accuracy: 0.6162\nEpoch 62/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6480 - accuracy: 0.6161 - val_loss: 0.6469 - val_accuracy: 0.6162\nEpoch 63/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6466 - accuracy: 0.6161 - val_loss: 0.6456 - val_accuracy: 0.6162\nEpoch 64/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6453 - accuracy: 0.6161 - val_loss: 0.6443 - val_accuracy: 0.6162\nEpoch 65/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6439 - accuracy: 0.6161 - val_loss: 0.6428 - val_accuracy: 0.6162\nEpoch 66/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6423 - accuracy: 0.6161 - val_loss: 0.6412 - val_accuracy: 0.6162\nEpoch 67/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6411 - accuracy: 0.6161 - val_loss: 0.6395 - val_accuracy: 0.6162\nEpoch 68/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6393 - accuracy: 0.6161 - val_loss: 0.6376 - val_accuracy: 0.6162\nEpoch 69/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6373 - accuracy: 0.6161 - val_loss: 0.6357 - val_accuracy: 0.6162\nEpoch 70/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6351 - accuracy: 0.6161 - val_loss: 0.6336 - val_accuracy: 0.6162\nEpoch 71/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6330 - accuracy: 0.6161 - val_loss: 0.6313 - val_accuracy: 0.6162\nEpoch 72/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6307 - accuracy: 0.6161 - val_loss: 0.6290 - val_accuracy: 0.6162\nEpoch 73/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6285 - accuracy: 0.6161 - val_loss: 0.6264 - val_accuracy: 0.6162\nEpoch 74/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6258 - accuracy: 0.6161 - val_loss: 0.6239 - val_accuracy: 0.6162\nEpoch 75/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6231 - accuracy: 0.6161 - val_loss: 0.6211 - val_accuracy: 0.6162\nEpoch 76/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6203 - accuracy: 0.6161 - val_loss: 0.6180 - val_accuracy: 0.6162\nEpoch 77/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6161 - val_loss: 0.6150 - val_accuracy: 0.6190\nEpoch 78/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6143 - accuracy: 0.6161 - val_loss: 0.6118 - val_accuracy: 0.6162\nEpoch 79/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6109 - accuracy: 0.6199 - val_loss: 0.6083 - val_accuracy: 0.6218\nEpoch 80/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6074 - accuracy: 0.6273 - val_loss: 0.6048 - val_accuracy: 0.6331\nEpoch 81/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6039 - accuracy: 0.6404 - val_loss: 0.6011 - val_accuracy: 0.6443\nEpoch 82/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6003 - accuracy: 0.6610 - val_loss: 0.5970 - val_accuracy: 0.6667\nEpoch 83/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5962 - accuracy: 0.6798 - val_loss: 0.5932 - val_accuracy: 0.6667\nEpoch 84/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5923 - accuracy: 0.6966 - val_loss: 0.5891 - val_accuracy: 0.7003\nEpoch 85/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7116 - val_loss: 0.5849 - val_accuracy: 0.7087\nEpoch 86/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5843 - accuracy: 0.7172 - val_loss: 0.5807 - val_accuracy: 0.7115\nEpoch 87/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5805 - accuracy: 0.7191 - val_loss: 0.5762 - val_accuracy: 0.7395\nEpoch 88/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7303 - val_loss: 0.5719 - val_accuracy: 0.7395\nEpoch 89/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5713 - accuracy: 0.7378 - val_loss: 0.5674 - val_accuracy: 0.7563\nEpoch 90/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5671 - accuracy: 0.7491 - val_loss: 0.5627 - val_accuracy: 0.7563\nEpoch 91/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7509 - val_loss: 0.5582 - val_accuracy: 0.7563\nEpoch 92/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5582 - accuracy: 0.7659 - val_loss: 0.5537 - val_accuracy: 0.7759\nEpoch 93/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.7640 - val_loss: 0.5492 - val_accuracy: 0.7871\nEpoch 94/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.5499 - accuracy: 0.7640 - val_loss: 0.5447 - val_accuracy: 0.7731\nEpoch 95/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5456 - accuracy: 0.7640 - val_loss: 0.5402 - val_accuracy: 0.7871\nEpoch 96/300\n17/17 [==============================] - 0s 13ms/step - loss: 0.5412 - accuracy: 0.7640 - val_loss: 0.5359 - val_accuracy: 0.7843\nEpoch 97/300\n17/17 [==============================] - 0s 12ms/step - loss: 0.5369 - accuracy: 0.7659 - val_loss: 0.5316 - val_accuracy: 0.7843\nEpoch 98/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.7603 - val_loss: 0.5275 - val_accuracy: 0.7955\nEpoch 99/300\n17/17 [==============================] - 0s 9ms/step - loss: 0.5294 - accuracy: 0.7715 - val_loss: 0.5233 - val_accuracy: 0.8039\nEpoch 100/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7753 - val_loss: 0.5196 - val_accuracy: 0.8039\nEpoch 101/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5217 - accuracy: 0.7734 - val_loss: 0.5158 - val_accuracy: 0.8039\nEpoch 102/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5181 - accuracy: 0.7753 - val_loss: 0.5120 - val_accuracy: 0.8039\nEpoch 103/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5145 - accuracy: 0.7753 - val_loss: 0.5085 - val_accuracy: 0.8011\nEpoch 104/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5113 - accuracy: 0.7715 - val_loss: 0.5049 - val_accuracy: 0.8039\nEpoch 105/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7715 - val_loss: 0.5016 - val_accuracy: 0.8039\nEpoch 106/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7715 - val_loss: 0.4983 - val_accuracy: 0.8039\nEpoch 107/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.7828 - val_loss: 0.4951 - val_accuracy: 0.8095\nEpoch 108/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7921 - val_loss: 0.4921 - val_accuracy: 0.8095\nEpoch 109/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7959 - val_loss: 0.4891 - val_accuracy: 0.8067\nEpoch 110/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7921 - val_loss: 0.4864 - val_accuracy: 0.8067\nEpoch 111/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4919 - accuracy: 0.7940 - val_loss: 0.4840 - val_accuracy: 0.8123\nEpoch 112/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7996 - val_loss: 0.4813 - val_accuracy: 0.8067\nEpoch 113/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7996 - val_loss: 0.4789 - val_accuracy: 0.8067\nEpoch 114/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7996 - val_loss: 0.4767 - val_accuracy: 0.8067\nEpoch 115/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4828 - accuracy: 0.7996 - val_loss: 0.4745 - val_accuracy: 0.8095\nEpoch 116/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4808 - accuracy: 0.7996 - val_loss: 0.4724 - val_accuracy: 0.8095\nEpoch 117/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4789 - accuracy: 0.7996 - val_loss: 0.4706 - val_accuracy: 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0.8179\nEpoch 173/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4420 - accuracy: 0.8240 - val_loss: 0.4308 - val_accuracy: 0.8123\nEpoch 174/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8240 - val_loss: 0.4305 - val_accuracy: 0.8123\nEpoch 175/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8179\nEpoch 176/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8123\nEpoch 177/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4297 - val_accuracy: 0.8151\nEpoch 178/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4410 - accuracy: 0.8258 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 179/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4294 - val_accuracy: 0.8151\nEpoch 180/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4402 - accuracy: 0.8240 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 181/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4400 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 182/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4397 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 183/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4395 - accuracy: 0.8240 - val_loss: 0.4286 - val_accuracy: 0.8151\nEpoch 184/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4393 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8123\nEpoch 185/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4392 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 186/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4389 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 187/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4390 - accuracy: 0.8240 - val_loss: 0.4278 - val_accuracy: 0.8123\nEpoch 188/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4279 - val_accuracy: 0.8151\nEpoch 189/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8151\nEpoch 190/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 191/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8240 - val_loss: 0.4274 - val_accuracy: 0.8151\nEpoch 192/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 193/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8221 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 194/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4375 - accuracy: 0.8240 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 195/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8240 - val_loss: 0.4272 - val_accuracy: 0.8151\nEpoch 196/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4370 - accuracy: 0.8240 - val_loss: 0.4271 - val_accuracy: 0.8151\nEpoch 197/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4368 - accuracy: 0.8240 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 198/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4367 - accuracy: 0.8221 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 199/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 0.8240 - val_loss: 0.4269 - val_accuracy: 0.8151\nEpoch 200/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4364 - accuracy: 0.8240 - val_loss: 0.4265 - val_accuracy: 0.8151\nEpoch 201/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4363 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 202/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4362 - accuracy: 0.8202 - val_loss: 0.4262 - val_accuracy: 0.8123\nEpoch 203/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4359 - accuracy: 0.8258 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 204/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4357 - accuracy: 0.8240 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 205/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8221 - val_loss: 0.4261 - val_accuracy: 0.8151\nEpoch 206/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 207/300\n17/17 [==============================] - 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[==============================] - 0s 5ms/step - loss: 0.4347 - accuracy: 0.8240 - val_loss: 0.4258 - val_accuracy: 0.8151\nEpoch 215/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 216/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4344 - accuracy: 0.8221 - val_loss: 0.4251 - val_accuracy: 0.8123\nEpoch 217/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4255 - val_accuracy: 0.8151\nEpoch 218/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 219/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4339 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 220/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 221/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 222/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4252 - val_accuracy: 0.8151\nEpoch 223/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4335 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 224/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4332 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 225/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 226/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4330 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 227/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 228/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 229/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 230/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4250 - val_accuracy: 0.8151\nEpoch 231/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4325 - accuracy: 0.8240 - val_loss: 0.4249 - val_accuracy: 0.8151\nEpoch 232/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 233/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4322 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 234/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 235/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 236/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 237/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 238/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 239/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 240/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 241/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 242/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4313 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 243/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 244/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 245/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8151\nEpoch 246/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8221 - val_loss: 0.4246 - val_accuracy: 0.8179\nEpoch 247/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 248/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 249/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 250/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4307 - accuracy: 0.8221 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 251/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8151\nEpoch 252/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8221 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 253/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 254/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 255/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4302 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 256/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 257/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 258/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 259/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 260/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 261/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 262/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 263/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 264/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 265/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 266/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 267/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 268/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 269/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 270/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 271/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 272/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 273/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 274/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 275/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4289 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 276/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 277/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4288 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 278/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 279/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 280/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 281/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 282/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 283/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 284/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 285/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 286/300\n17/17 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 287/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 288/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4282 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 289/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 290/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 291/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 292/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 293/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 294/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 295/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 296/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 297/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 298/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 299/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 300/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_train_accuracy = model.evaluate(X_train, y_train_encode)\nprint('Accuracy: %.4f' % (ann_train_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.756511Z","iopub.execute_input":"2023-02-01T15:00:28.757274Z","iopub.status.idle":"2023-02-01T15:00:28.874523Z","shell.execute_reply.started":"2023-02-01T15:00:28.757226Z","shell.execute_reply":"2023-02-01T15:00:28.873360Z"},"trusted":true},"execution_count":340,"outputs":[{"name":"stdout","text":"17/17 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221\nAccuracy: 0.8221\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_valid_accuracy = model.evaluate(X_valid, y_valid_encode)\nprint('Accuracy: %.4f' % (ann_valid_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.876387Z","iopub.execute_input":"2023-02-01T15:00:28.877663Z","iopub.status.idle":"2023-02-01T15:00:28.990657Z","shell.execute_reply.started":"2023-02-01T15:00:28.877614Z","shell.execute_reply":"2023-02-01T15:00:28.989441Z"},"trusted":true},"execution_count":341,"outputs":[{"name":"stdout","text":"12/12 [==============================] - 0s 2ms/step - loss: 0.4238 - accuracy: 0.8179\nAccuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ","metadata":{}},{"cell_type":"code","source":"\ny_pred = model.predict(X_train)\nY_pred = np.argmax(model.predict(X_train),axis=1)\ncm = confusion_matrix(y_train, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.993841Z","iopub.execute_input":"2023-02-01T15:00:28.994285Z","iopub.status.idle":"2023-02-01T15:00:29.270957Z","shell.execute_reply.started":"2023-02-01T15:00:28.994240Z","shell.execute_reply":"2023-02-01T15:00:29.269885Z"},"trusted":true},"execution_count":342,"outputs":[{"execution_count":342,"output_type":"execute_result","data":{"text/plain":"array([[304, 25],\n [ 70, 135]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.272190Z","iopub.execute_input":"2023-02-01T15:00:29.273301Z","iopub.status.idle":"2023-02-01T15:00:29.281676Z","shell.execute_reply.started":"2023-02-01T15:00:29.273267Z","shell.execute_reply":"2023-02-01T15:00:29.280517Z"},"trusted":true},"execution_count":343,"outputs":[{"name":"stdout","text":"Accuracy : 0.8220973782771536\nMisclassfication : 0.17790262172284643\nSensitivivity : 0.9240121580547113\nSpecificity : 0.6585365853658537\n","output_type":"stream"}]},{"cell_type":"code","source":"\ny_pred = model.predict(X_valid)\nY_pred = np.argmax(model.predict(X_valid),axis=1)\ncm = confusion_matrix(y_valid, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.283243Z","iopub.execute_input":"2023-02-01T15:00:29.283612Z","iopub.status.idle":"2023-02-01T15:00:29.451759Z","shell.execute_reply.started":"2023-02-01T15:00:29.283566Z","shell.execute_reply":"2023-02-01T15:00:29.450417Z"},"trusted":true},"execution_count":344,"outputs":[{"execution_count":344,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.453236Z","iopub.execute_input":"2023-02-01T15:00:29.453610Z","iopub.status.idle":"2023-02-01T15:00:29.461774Z","shell.execute_reply.started":"2023-02-01T15:00:29.453579Z","shell.execute_reply":"2023-02-01T15:00:29.460520Z"},"trusted":true},"execution_count":345,"outputs":[{"name":"stdout","text":"Accuracy : 0.8179271708683473\nMisclassfication : 0.18207282913165265\nSensitivivity : 0.9363636363636364\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.463445Z","iopub.execute_input":"2023-02-01T15:00:29.463787Z","iopub.status.idle":"2023-02-01T15:00:29.472285Z","shell.execute_reply.started":"2023-02-01T15:00:29.463752Z","shell.execute_reply":"2023-02-01T15:00:29.471294Z"},"trusted":true},"execution_count":346,"outputs":[]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_train),axis=1)\n\nann_pred = X_train.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_train_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.473634Z","iopub.execute_input":"2023-02-01T15:00:29.474711Z","iopub.status.idle":"2023-02-01T15:00:29.593403Z","shell.execute_reply.started":"2023-02-01T15:00:29.474675Z","shell.execute_reply":"2023-02-01T15:00:29.592290Z"},"trusted":true},"execution_count":347,"outputs":[{"execution_count":347,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n844 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n316 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n768 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n255 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n130 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n844 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n316 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n768 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n255 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n130 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n\n ann_y_pred PassengerId \n844 0 845.0 \n316 1 317.0 \n768 0 769.0 \n255 1 256.0 \n130 0 131.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
84401.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00845.0
31600.01.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01317.0
76800.01.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00769.0
25521.00.00.00.00.00.00.00.00.0...1.01.00.00.00.00.01.00.01256.0
13001.00.00.00.00.00.00.01.00.0...0.01.00.00.00.00.01.00.00131.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(ann_pred[[\"PassengerId\", \"ann_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.598604Z","iopub.execute_input":"2023-02-01T15:00:29.599029Z","iopub.status.idle":"2023-02-01T15:00:29.628142Z","shell.execute_reply.started":"2023-02-01T15:00:29.598995Z","shell.execute_reply":"2023-02-01T15:00:29.627332Z"},"trusted":true},"execution_count":348,"outputs":[{"execution_count":348,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 NaN \n2 0.0 NaN 0.0 \n3 NaN 1.0 NaN \n4 NaN 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_valid),axis=1)\nann_pred = X_valid.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_valid_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.629335Z","iopub.execute_input":"2023-02-01T15:00:29.629823Z","iopub.status.idle":"2023-02-01T15:00:29.739371Z","shell.execute_reply.started":"2023-02-01T15:00:29.629791Z","shell.execute_reply":"2023-02-01T15:00:29.738281Z"},"trusted":true},"execution_count":349,"outputs":[{"execution_count":349,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n369 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n541 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n196 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n810 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n427 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n369 0.0 ... 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n541 0.0 ... 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n196 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n810 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n427 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n\n ann_y_pred PassengerId \n369 1 370.0 \n541 1 542.0 \n196 0 197.0 \n810 0 811.0 \n427 1 428.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
36901.00.00.00.00.00.00.00.00.0...1.00.00.01.00.00.01.00.01370.0
54120.00.00.00.01.00.00.00.00.0...1.01.00.00.00.01.00.00.01542.0
19601.00.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00197.0
81001.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00811.0
42701.00.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01428.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(ann_pred.PassengerId), \"ann_y_pred\"] = ann_pred[\"ann_y_pred\"]\nresults_train.drop(\"rf_y_pred_y\", axis = 1)\nresults_train.drop(\"rf_y_pred_x\", axis = 1)\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.740869Z","iopub.execute_input":"2023-02-01T15:00:29.741291Z","iopub.status.idle":"2023-02-01T15:00:29.771294Z","shell.execute_reply.started":"2023-02-01T15:00:29.741249Z","shell.execute_reply":"2023-02-01T15:00:29.770286Z"},"trusted":true},"execution_count":350,"outputs":[{"execution_count":350,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 1.0 \n2 0.0 NaN 0.0 \n3 NaN 1.0 1.0 \n4 NaN 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Overall, the number of survivors misclassified were greater than misclassified passengers who perished. The next step is to identify those passengers to attempt to find the source of the misclassifcation. So far the lowest number of misclassified passengers who perished. ","metadata":{}},{"cell_type":"markdown","source":"## Predict test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\ny_pred = y_pred.argmax(1)\nann_pred = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"],\n \"ann_y_pred\" : y_pred})\nann_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.772619Z","iopub.execute_input":"2023-02-01T15:00:29.772938Z","iopub.status.idle":"2023-02-01T15:00:29.875387Z","shell.execute_reply.started":"2023-02-01T15:00:29.772908Z","shell.execute_reply":"2023-02-01T15:00:29.874334Z"},"trusted":true},"execution_count":351,"outputs":[{"execution_count":351,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.876431Z","iopub.execute_input":"2023-02-01T15:00:29.876729Z","iopub.status.idle":"2023-02-01T15:00:29.882726Z","shell.execute_reply.started":"2023-02-01T15:00:29.876701Z","shell.execute_reply":"2023-02-01T15:00:29.881480Z"},"trusted":true},"execution_count":352,"outputs":[]},{"cell_type":"code","source":"ann_pred[[\"PassengerId\",\"ann_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.884219Z","iopub.execute_input":"2023-02-01T15:00:29.884571Z","iopub.status.idle":"2023-02-01T15:00:29.900340Z","shell.execute_reply.started":"2023-02-01T15:00:29.884540Z","shell.execute_reply":"2023-02-01T15:00:29.899599Z"},"trusted":true},"execution_count":353,"outputs":[{"execution_count":353,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(ann_pred[[\"PassengerId\",\"ann_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.901356Z","iopub.execute_input":"2023-02-01T15:00:29.901844Z","iopub.status.idle":"2023-02-01T15:00:29.931394Z","shell.execute_reply.started":"2023-02-01T15:00:29.901814Z","shell.execute_reply":"2023-02-01T15:00:29.929969Z"},"trusted":true},"execution_count":354,"outputs":[{"execution_count":354,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred \n0 0.0 0.0 0.0 0.0 0 \n1 1.0 0.0 0.0 0.0 0 \n2 0.0 0.0 0.0 0.0 0 \n3 0.0 0.0 0.0 0.0 0 \n4 0.0 1.0 1.0 1.0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_predann_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.00
1893.03.02.01.411765-0.3161762.01.01.00.00.00.00
2894.02.01.02.588235-0.2021843.00.00.00.00.00.00
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.00
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.00
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Findings","metadata":{}},{"cell_type":"markdown","source":"We compile all the results in a basic structure. We discover that the logistic regression has achieved the highest accuracy on the validation datasets. ANN came close. Both methods appear not to overfit to the training dataset.","metadata":{}},{"cell_type":"code","source":"log_reg_results = {\n \"method\": \"Logistic regression\",\n \"training_accurary\": log_reg_score_train,\n \"valid_accuracy\": log_reg_score_valid\n}\n\nknn_results = {\n \"method\": \"KNN\",\n \"training_accurary\": knn_train_score,\n \"valid_accuracy\": knn_valid_score\n}\n\nclf_results = {\n \"method\": \"decision trees\",\n \"training_accurary\": clf_train_score,\n \"valid_accuracy\": clf_valid_score\n}\n\nrf_results = {\n \"method\": \"Random Forrest\",\n \"training_accurary\": rf_train_score,\n \"valid_accuracy\": rf_valid_score\n}\n\nann_results = {\n \"method\": \"ANN\",\n \"training_accurary\": ann_train_accuracy,\n \"valid_accuracy\": ann_valid_accuracy\n}\n\nresults = [log_reg_results, knn_results, clf_results, rf_results, ann_results]\nresults","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.932994Z","iopub.execute_input":"2023-02-01T15:00:29.933497Z","iopub.status.idle":"2023-02-01T15:00:29.947698Z","shell.execute_reply.started":"2023-02-01T15:00:29.933454Z","shell.execute_reply":"2023-02-01T15:00:29.946484Z"},"trusted":true},"execution_count":355,"outputs":[{"execution_count":355,"output_type":"execute_result","data":{"text/plain":"[{'method': 'Logistic regression',\n 'training_accurary': 0.7921348314606742,\n 'valid_accuracy': 0.8207282913165266},\n {'method': 'KNN',\n 'training_accurary': 0.8258426966292135,\n 'valid_accuracy': 0.7871148459383753},\n {'method': 'decision trees',\n 'training_accurary': 0.9082397003745318,\n 'valid_accuracy': 0.8151260504201681},\n {'method': 'Random Forrest',\n 'training_accurary': 0.8801498127340824,\n 'valid_accuracy': 0.8067226890756303},\n {'method': 'ANN',\n 'training_accurary': 0.8220973610877991,\n 'valid_accuracy': 0.8179271817207336}]"},"metadata":{}}]},{"cell_type":"markdown","source":"Less than 10% errors of passengers have been misclassified, when we compare all predictions together. So, it may be possible to identify some rules to increase accuracy. Nonetheless, these rules may also decrease the accuracy. So, a fine balance needs to be found. ","metadata":{}},{"cell_type":"code","source":"results_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.949130Z","iopub.execute_input":"2023-02-01T15:00:29.949749Z","iopub.status.idle":"2023-02-01T15:00:29.958469Z","shell.execute_reply.started":"2023-02-01T15:00:29.949702Z","shell.execute_reply":"2023-02-01T15:00:29.957602Z"},"trusted":true},"execution_count":356,"outputs":[{"execution_count":356,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked',\n 'fam_members', 'y', 'lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred']\nresults_train['merged_pred'] = results_train.loc[:,cols].apply(\n lambda x: ','.join(x.dropna().astype(str)),\n axis=1\n)\n\nresults_train['y_found'] = results_train.apply(lambda x: str(x.y) in x.merged_pred, axis=1)\nresults_train.drop(\"merged_pred\", axis = 1, inplace = True)\nresults_train.groupby(\"y_found\").count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.959997Z","iopub.execute_input":"2023-02-01T15:00:29.960444Z","iopub.status.idle":"2023-02-01T15:00:30.142912Z","shell.execute_reply.started":"2023-02-01T15:00:29.960402Z","shell.execute_reply":"2023-02-01T15:00:30.141887Z"},"trusted":true},"execution_count":357,"outputs":[{"execution_count":357,"output_type":"execute_result","data":{"text/plain":"y_found\nFalse 0.075196\nTrue 0.924804\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The set of passengers misclassified by all methods appear to have a lower expected fares and much more compact spread of fares. The median passenger class of misclassified passenger appear to be higher than those correctly classified. Both observations contractict each other and suggests some of fares being close to each other between passenger classes may be contributing in misclassifying passengers. \n\nThe misclassified passengers appears to be most women and their age appear to be older than the ones correctly classified by one method. The distribution to gender appears to match the overall observations for correctly classified passengers. Nonetheless, it is worth pointing out some of ages were inputed based on the number of siblings, spouse and parents aboard. This simple method of inputation may have impacted the classifiers; more research should be made to validate or improve inputting the missing information. \n\nOther aspects in the data may lead to misclassification.","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == False,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.144511Z","iopub.execute_input":"2023-02-01T15:00:30.145249Z","iopub.status.idle":"2023-02-01T15:00:30.180088Z","shell.execute_reply.started":"2023-02-01T15:00:30.145205Z","shell.execute_reply":"2023-02-01T15:00:30.178959Z"},"trusted":true},"execution_count":358,"outputs":[{"execution_count":358,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 67.000000 67.000000 67.000000 67.000000 67.000000 67.000000\nmean 2.149254 1.104478 0.129736 0.423026 0.343284 2.537313\nstd 0.908774 0.308188 0.721256 1.008879 0.844810 0.840785\nmin 1.000000 1.000000 -1.076923 -0.626005 0.000000 2.000000\n25% 1.000000 1.000000 -0.269231 -0.282777 0.000000 2.000000\n50% 2.000000 1.000000 0.000000 -0.062981 0.000000 2.000000\n75% 3.000000 1.000000 0.230769 0.694936 0.500000 3.000000\nmax 3.000000 2.000000 2.461538 3.318594 6.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count67.00000067.00000067.00000067.00000067.00000067.000000
mean2.1492541.1044780.1297360.4230260.3432842.537313
std0.9087740.3081880.7212561.0088790.8448100.840785
min1.0000001.000000-1.076923-0.6260050.0000002.000000
25%1.0000001.000000-0.269231-0.2827770.0000002.000000
50%2.0000001.0000000.000000-0.0629810.0000002.000000
75%3.0000001.0000000.2307690.6949360.5000003.000000
max3.0000002.0000002.4615383.3185946.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.182172Z","iopub.execute_input":"2023-02-01T15:00:30.183279Z","iopub.status.idle":"2023-02-01T15:00:30.213352Z","shell.execute_reply.started":"2023-02-01T15:00:30.183235Z","shell.execute_reply":"2023-02-01T15:00:30.212605Z"},"trusted":true},"execution_count":359,"outputs":[{"execution_count":359,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 824.000000 824.000000 824.000000 824.000000 824.000000 824.000000\nmean 2.321602 1.372573 -0.030604 0.796855 0.950243 2.455097\nstd 0.829129 0.483783 1.018913 2.217409 1.652334 0.790541\nmin 1.000000 1.000000 -2.275385 -0.626005 0.000000 1.000000\n25% 2.000000 1.000000 -0.615385 -0.284041 0.000000 2.000000\n50% 3.000000 1.000000 0.000000 0.001984 0.000000 2.000000\n75% 3.000000 2.000000 0.384615 0.719569 1.000000 3.000000\nmax 3.000000 2.000000 3.846154 21.562738 10.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count824.000000824.000000824.000000824.000000824.000000824.000000
mean2.3216021.372573-0.0306040.7968550.9502432.455097
std0.8291290.4837831.0189132.2174091.6523340.790541
min1.0000001.000000-2.275385-0.6260050.0000001.000000
25%2.0000001.000000-0.615385-0.2840410.0000002.000000
50%3.0000001.0000000.0000000.0019840.0000002.000000
75%3.0000002.0000000.3846150.7195691.0000003.000000
max3.0000002.0000003.84615421.56273810.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"incorrect = results_train.loc[results_train[\"y_found\"] == False,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == False,:].groupby(\"Sex\").count()[\"PassengerId\"]/incorrect","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.214494Z","iopub.execute_input":"2023-02-01T15:00:30.215455Z","iopub.status.idle":"2023-02-01T15:00:30.229684Z","shell.execute_reply.started":"2023-02-01T15:00:30.215404Z","shell.execute_reply":"2023-02-01T15:00:30.228567Z"},"trusted":true},"execution_count":360,"outputs":[{"execution_count":360,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.895522\n2.0 0.104478\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"correct = results_train.loc[results_train[\"y_found\"] == True,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == True,:].groupby(\"Sex\").count()[\"PassengerId\"]/correct","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.230783Z","iopub.execute_input":"2023-02-01T15:00:30.231538Z","iopub.status.idle":"2023-02-01T15:00:30.246006Z","shell.execute_reply.started":"2023-02-01T15:00:30.231506Z","shell.execute_reply":"2023-02-01T15:00:30.244736Z"},"trusted":true},"execution_count":361,"outputs":[{"execution_count":361,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.627427\n2.0 0.372573\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We analyse differences between each method on the testing and training data set. We add all the predictions to identify the passenger the classifier could not agree with. So, a total of 0 or 5 suggests all the classifiers have either identify passengers as survivor or not. Values in the range [1,4] indicates some disagreements in classification. Some methodologies appears to correclty classify passengers with at least one method.","metadata":{}},{"cell_type":"code","source":"results_train[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.247816Z","iopub.execute_input":"2023-02-01T15:00:30.248276Z","iopub.status.idle":"2023-02-01T15:00:30.473510Z","shell.execute_reply.started":"2023-02-01T15:00:30.248230Z","shell.execute_reply":"2023-02-01T15:00:30.472297Z"},"trusted":true},"execution_count":362,"outputs":[{"execution_count":362,"output_type":"execute_result","data":{"text/plain":"count 67.000000\nmean 0.447761\nstd 1.438471\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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3bXhOeAm5L843hHGr2qOtV9XATuY/my8FC0EHBv2d/kun/Quws4WVWfGvc8GyHJa5Ns7x6/guV/pP/hWIcasar6WFXtrKpdLP85/lpV/cGYxxqpJNu6f5gnyTbgncDQ3l12yQe8qp4Hzt+yfxK4Z8S37I9dki8A3wTekOSZJPvGPdOI3Qh8kOUzske7X+8a91AjtgN4KMn3WT5JebCqJuJtdRNmGvhGku8B3wK+XFVfHdaLX/JvI5Qkre6SPwOXJK3OgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXqfwEOtkCGTWOUBQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.475100Z","iopub.execute_input":"2023-02-01T15:00:30.475447Z","iopub.status.idle":"2023-02-01T15:00:30.691199Z","shell.execute_reply.started":"2023-02-01T15:00:30.475417Z","shell.execute_reply":"2023-02-01T15:00:30.690153Z"},"trusted":true},"execution_count":363,"outputs":[{"execution_count":363,"output_type":"execute_result","data":{"text/plain":"count 824.000000\nmean 1.577670\nstd 2.058981\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We explore how the techniques may predict differently and but accurately surviving the accident. \n\nKNN misclassified the most passengers who perished. Logistic regression and Random Tree classifier has the higest accuracy; both of them could be influencing the most the prediction, when only one classifier suggests a passenger has survived. ","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 1)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.692627Z","iopub.execute_input":"2023-02-01T15:00:30.692946Z","iopub.status.idle":"2023-02-01T15:00:30.712415Z","shell.execute_reply.started":"2023-02-01T15:00:30.692916Z","shell.execute_reply":"2023-02-01T15:00:30.711228Z"},"trusted":true},"execution_count":364,"outputs":[{"execution_count":364,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 3\n 1.0 0.0 0.0 3\n 1.0 0.0 0.0 0.0 10\n 1.0 0.0 0.0 0.0 0.0 3\n1.0 0.0 0.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 5\n 1.0 0.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 0.0 4\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 4)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.714064Z","iopub.execute_input":"2023-02-01T15:00:30.714806Z","iopub.status.idle":"2023-02-01T15:00:30.734553Z","shell.execute_reply.started":"2023-02-01T15:00:30.714762Z","shell.execute_reply":"2023-02-01T15:00:30.733458Z"},"trusted":true},"execution_count":365,"outputs":[{"execution_count":365,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 1.0 1.0 1.0 2\n 1.0 0.0 1.0 1.0 1.0 1\n1.0 0.0 1.0 1.0 1.0 1.0 6\n 1.0 0.0 1.0 1.0 1.0 1\n 1.0 0.0 1.0 1.0 2\n 1.0 0.0 1.0 2\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"A combination of Logistic regression and ANN may identify some survivors, when other methods do not. KNN in combination with another classifier may misclassify passengers who perished.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 2)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.743560Z","iopub.execute_input":"2023-02-01T15:00:30.743975Z","iopub.status.idle":"2023-02-01T15:00:30.762208Z","shell.execute_reply.started":"2023-02-01T15:00:30.743943Z","shell.execute_reply":"2023-02-01T15:00:30.761101Z"},"trusted":true},"execution_count":366,"outputs":[{"execution_count":366,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 1.0 1.0 0.0 4\n 1.0 0.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 0.0 2\n 1.0 1.0 0.0 0.0 0.0 5\n1.0 0.0 0.0 0.0 1.0 1.0 1\n 1.0 1.0 0.0 2\n 1.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 3)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.763835Z","iopub.execute_input":"2023-02-01T15:00:30.764145Z","iopub.status.idle":"2023-02-01T15:00:30.780211Z","shell.execute_reply.started":"2023-02-01T15:00:30.764116Z","shell.execute_reply":"2023-02-01T15:00:30.779475Z"},"trusted":true},"execution_count":367,"outputs":[{"execution_count":367,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 0.0 1.0 1.0 1\n 1.0 0.0 0.0 1.0 1.0 1\n 1.0 0.0 1.0 0.0 1\n1.0 0.0 1.0 1.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 1.0 0.0 1.0 1\n 1.0 0.0 0.0 1.0 2\n 1.0 0.0 3\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 5)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.781542Z","iopub.execute_input":"2023-02-01T15:00:30.781830Z","iopub.status.idle":"2023-02-01T15:00:30.798259Z","shell.execute_reply.started":"2023-02-01T15:00:30.781802Z","shell.execute_reply":"2023-02-01T15:00:30.796868Z"},"trusted":true},"execution_count":368,"outputs":[{"execution_count":368,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n1.0 1.0 1.0 1.0 1.0 1.0 65\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.799833Z","iopub.execute_input":"2023-02-01T15:00:30.800231Z","iopub.status.idle":"2023-02-01T15:00:30.808910Z","shell.execute_reply.started":"2023-02-01T15:00:30.800191Z","shell.execute_reply":"2023-02-01T15:00:30.808105Z"},"trusted":true},"execution_count":369,"outputs":[{"execution_count":369,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.809985Z","iopub.execute_input":"2023-02-01T15:00:30.810331Z","iopub.status.idle":"2023-02-01T15:00:30.821387Z","shell.execute_reply.started":"2023-02-01T15:00:30.810281Z","shell.execute_reply":"2023-02-01T15:00:30.820530Z"},"trusted":true},"execution_count":370,"outputs":[{"execution_count":370,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.822809Z","iopub.execute_input":"2023-02-01T15:00:30.823089Z","iopub.status.idle":"2023-02-01T15:00:30.834693Z","shell.execute_reply.started":"2023-02-01T15:00:30.823062Z","shell.execute_reply":"2023-02-01T15:00:30.833613Z"},"trusted":true},"execution_count":371,"outputs":[{"execution_count":371,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.sum_pred.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:01:14.796405Z","iopub.execute_input":"2023-02-01T15:01:14.796794Z","iopub.status.idle":"2023-02-01T15:01:14.805737Z","shell.execute_reply.started":"2023-02-01T15:01:14.796762Z","shell.execute_reply":"2023-02-01T15:01:14.804627Z"},"trusted":true},"execution_count":377,"outputs":[{"execution_count":377,"output_type":"execute_result","data":{"text/plain":"0.0 0.576880\n5.0 0.205387\n1.0 0.079686\n2.0 0.062851\n3.0 0.040404\n4.0 0.034792\nName: sum_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"}},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:36:25.295643Z","iopub.execute_input":"2023-02-01T15:36:25.296169Z","iopub.status.idle":"2023-02-01T15:36:25.314079Z","shell.execute_reply.started":"2023-02-01T15:36:25.296132Z","shell.execute_reply":"2023-02-01T15:36:25.312932Z"}},"execution_count":412,"outputs":[{"execution_count":412,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Are they any particular features that may have been picked up by each classifier?","metadata":{}},{"cell_type":"markdown","source":"### All classifiers agrees with the survival predictions\n\nWe found out that approximately 70% of the passengers who perished have been correclty classified by all the classifiers in agreement. But only, 20% of survivors have been correctly classified. Approximately 70% of the observations made in the training datasets have been correct and all the classifiers agree.","metadata":{}},{"cell_type":"code","source":"filter_rows = ((results_train[\"sum_pred\"] == 0.0) & (results_train[\"y\"] == 0))\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:06.719133Z","iopub.execute_input":"2023-02-01T15:45:06.719636Z","iopub.status.idle":"2023-02-01T15:45:06.733253Z","shell.execute_reply.started":"2023-02-01T15:45:06.719598Z","shell.execute_reply":"2023-02-01T15:45:06.732170Z"},"trusted":true},"execution_count":413,"outputs":[{"execution_count":413,"output_type":"execute_result","data":{"text/plain":"50.841750841750844"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"sum_pred\"] == 5.0) & (results_train[\"y\"] == 1)\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:07.933943Z","iopub.execute_input":"2023-02-01T15:45:07.935099Z","iopub.status.idle":"2023-02-01T15:45:07.947554Z","shell.execute_reply.started":"2023-02-01T15:45:07.935043Z","shell.execute_reply":"2023-02-01T15:45:07.946375Z"},"trusted":true},"execution_count":414,"outputs":[{"execution_count":414,"output_type":"execute_result","data":{"text/plain":"19.865319865319865"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"},"trusted":true},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:45.372534Z","iopub.execute_input":"2023-02-01T15:45:45.372921Z","iopub.status.idle":"2023-02-01T15:45:45.388445Z","shell.execute_reply.started":"2023-02-01T15:45:45.372891Z","shell.execute_reply":"2023-02-01T15:45:45.387062Z"},"trusted":true},"execution_count":415,"outputs":[{"execution_count":415,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## The classifiers disagree with each others on the survival predictions ?\n\nDecision Tree classifiers appears to have classified correctly the most passengers, when disagreements between classifiers exists. \n\nWe calculate the proportion of correct predictions, when some classifiers disagree. We found out that Decision tree appears to predict the most correct passengers who survive or perish the accident.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nno_correct = results_train.loc[filter_rows, :].shape[0]\nno_incorrect = results_train.loc[filter_rows, :].shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:30.920933Z","iopub.execute_input":"2023-02-01T16:00:30.921353Z","iopub.status.idle":"2023-02-01T16:00:30.932343Z","shell.execute_reply.started":"2023-02-01T16:00:30.921303Z","shell.execute_reply":"2023-02-01T16:00:30.930975Z"},"trusted":true},"execution_count":433,"outputs":[]},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.lr_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.369868Z","iopub.execute_input":"2023-02-01T16:00:32.370576Z","iopub.status.idle":"2023-02-01T16:00:32.381927Z","shell.execute_reply.started":"2023-02-01T16:00:32.370537Z","shell.execute_reply":"2023-02-01T16:00:32.381022Z"},"trusted":true},"execution_count":434,"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"44.329896907216494"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.knn_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.853276Z","iopub.execute_input":"2023-02-01T16:00:32.854476Z","iopub.status.idle":"2023-02-01T16:00:32.868855Z","shell.execute_reply.started":"2023-02-01T16:00:32.854418Z","shell.execute_reply":"2023-02-01T16:00:32.867407Z"},"trusted":true},"execution_count":435,"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"47.42268041237113"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.ann_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:33.395939Z","iopub.execute_input":"2023-02-01T16:00:33.396354Z","iopub.status.idle":"2023-02-01T16:00:33.410583Z","shell.execute_reply.started":"2023-02-01T16:00:33.396294Z","shell.execute_reply":"2023-02-01T16:00:33.409408Z"},"trusted":true},"execution_count":436,"outputs":[{"execution_count":436,"output_type":"execute_result","data":{"text/plain":"52.0618556701031"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.clf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:34.195555Z","iopub.execute_input":"2023-02-01T16:00:34.196776Z","iopub.status.idle":"2023-02-01T16:00:34.208545Z","shell.execute_reply.started":"2023-02-01T16:00:34.196733Z","shell.execute_reply":"2023-02-01T16:00:34.207295Z"},"trusted":true},"execution_count":437,"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"75.25773195876289"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.rf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:35.044699Z","iopub.execute_input":"2023-02-01T16:00:35.045127Z","iopub.status.idle":"2023-02-01T16:00:35.057811Z","shell.execute_reply.started":"2023-02-01T16:00:35.045090Z","shell.execute_reply":"2023-02-01T16:00:35.056488Z"},"trusted":true},"execution_count":438,"outputs":[{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"25.257731958762886"},"metadata":{}}]},{"cell_type":"markdown","source":"We change the predictions, that has been mispredicted by at least one classifier.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_train.loc[filter_rows, \"y\"] = results_train.clf_y_pred\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:38:03.184402Z","iopub.execute_input":"2023-02-01T16:38:03.184812Z","iopub.status.idle":"2023-02-01T16:38:03.191812Z","shell.execute_reply.started":"2023-02-01T16:38:03.184781Z","shell.execute_reply":"2023-02-01T16:38:03.191010Z"},"trusted":true},"execution_count":462,"outputs":[]},{"cell_type":"markdown","source":"The accuracy has been increased by a considerable level of accuracy. ","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train.Survived == results_train.y,:].count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:40:10.552687Z","iopub.execute_input":"2023-02-01T16:40:10.553066Z","iopub.status.idle":"2023-02-01T16:40:10.564469Z","shell.execute_reply.started":"2023-02-01T16:40:10.553036Z","shell.execute_reply":"2023-02-01T16:40:10.563190Z"},"trusted":true},"execution_count":467,"outputs":[{"execution_count":467,"output_type":"execute_result","data":{"text/plain":"0.9461279461279462"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying to results test\n\nThe distribution appears the be very similar as the training dataset.","metadata":{}},{"cell_type":"markdown","source":"__Testing dataset:__","metadata":{}},{"cell_type":"code","source":"results_test[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_test.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_test.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:04.962156Z","iopub.execute_input":"2023-02-01T16:28:04.962921Z","iopub.status.idle":"2023-02-01T16:28:05.177598Z","shell.execute_reply.started":"2023-02-01T16:28:04.962882Z","shell.execute_reply":"2023-02-01T16:28:05.176388Z"},"trusted":true},"execution_count":459,"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 1.590909\nstd 2.078233\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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dataset:__","metadata":{}},{"cell_type":"code","source":"results_train.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:10.931421Z","iopub.execute_input":"2023-02-01T16:28:10.931875Z","iopub.status.idle":"2023-02-01T16:28:11.153259Z","shell.execute_reply.started":"2023-02-01T16:28:10.931840Z","shell.execute_reply":"2023-02-01T16:28:11.152336Z"},"trusted":true},"execution_count":460,"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.492705\nstd 2.040242\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 3.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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z5aVb8YdT3DVlW/q6oLmb3D++Ik3U7DJXkncLyqDo26lhX21qq6iNkn6F7Tpl0HZjWHu485GBNt3vlrwK1V9fVR17OSquoZ4F5g24hLGaZLgXe1Oeh9wGVJ/nG0JQ1fVR1r78eB25mdah6Y1RzuPuZgDLR/XLwZeLSqPjPqelZCknOSbGjLZzJ70cAPR1rUEFXVJ6tqU1VtYfZ3fE9V/cWIyxqqJOvaBQIkWQe8HRjoVXCrNtyr6lngucccPArcNuTHHIxcki8D/wa8LsnRJDtGXdMKuBT4ALNncw+015WjLmrINgL3JnmQ2ZOYu6tqLC4PHCMTwH1JfgB8B7irqr45yA9YtZdCSpJOb9WeuUuSTs9wl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36PzFqarrIVm2TAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_test.loc[filter_rows, \"y\"] = results_test.clf_y_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:41:15.101164Z","iopub.execute_input":"2023-02-01T16:41:15.101563Z","iopub.status.idle":"2023-02-01T16:41:15.110450Z","shell.execute_reply.started":"2023-02-01T16:41:15.101523Z","shell.execute_reply":"2023-02-01T16:41:15.109235Z"},"trusted":true},"execution_count":468,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:46:41.923470Z","iopub.execute_input":"2023-02-01T16:46:41.923885Z","iopub.status.idle":"2023-02-01T16:46:43.051535Z","shell.execute_reply.started":"2023-02-01T16:46:41.923846Z","shell.execute_reply":"2023-02-01T16:46:43.050096Z"},"trusted":true},"execution_count":471,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:48:10.301809Z","iopub.execute_input":"2023-02-01T16:48:10.302423Z","iopub.status.idle":"2023-02-01T16:48:11.417688Z","shell.execute_reply.started":"2023-02-01T16:48:10.302370Z","shell.execute_reply":"2023-02-01T16:48:11.415704Z"},"trusted":true},"execution_count":472,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({\n \"PassengerId\": results_test[\"PassengerId\"].astype(int),\n \"Survived\": results_test[\"y\"]\n })\n\nsubmission = submission.astype({col: 'int32' for col in submission.select_dtypes('int64').columns})\nsubmission.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:09:39.297418Z","iopub.execute_input":"2023-02-01T17:09:39.297834Z","iopub.status.idle":"2023-02-01T17:09:39.311761Z","shell.execute_reply.started":"2023-02-01T17:09:39.297801Z","shell.execute_reply":"2023-02-01T17:09:39.310602Z"},"trusted":true},"execution_count":490,"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"PassengerId int32\nSurvived float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)\n!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:06:56.872660Z","iopub.execute_input":"2023-02-01T17:06:56.873348Z","iopub.status.idle":"2023-02-01T17:06:57.989149Z","shell.execute_reply.started":"2023-02-01T17:06:56.873282Z","shell.execute_reply":"2023-02-01T17:06:57.987753Z"},"trusted":true},"execution_count":488,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Cloud technology/web_mob_cloud.pdf b/Cloud technology/web_mob_cloud.pdf new file mode 100644 index 0000000..127577d Binary files /dev/null and b/Cloud technology/web_mob_cloud.pdf differ diff --git a/Data engineering and science/.DS_Store b/Data engineering and science/.DS_Store index 961cd9e..aa3e15d 100644 Binary files a/Data engineering and science/.DS_Store and b/Data engineering and science/.DS_Store differ diff --git a/Data engineering and science/More data exploration/titanic_exploration_knn.ipynb b/Data engineering and science/More data exploration/titanic_exploration_knn.ipynb new file mode 100644 index 0000000..c0b4993 --- /dev/null +++ b/Data engineering and science/More data exploration/titanic_exploration_knn.ipynb @@ -0,0 +1,5798 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9d0a2f3c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "7dca2520", + "metadata": {}, + "source": [ + "# Can we discover some interesting from the Titanic dataset? Can we demonstrate good and bad examples...." + ] + }, + { + "cell_type": "markdown", + "id": "1f0ff944", + "metadata": {}, + "source": [ + "This notebook uses the [Titanic dataset](https://www.kaggle.com/competitions/titanic/data?select=test.csv). It contains the list of passengers and information collated. We are assuming, at the time of writing, no individual listed on this dataset are still alive, therefore, the data protection act and other legistation should not apply to the data. However, the author will do their best to preserve privacy.\n", + "\n", + "Further information about the Titanic ship and disaster can be found on [Wikipedia](https://en.wikipedia.org/wiki/TitanicWikipedia) and [Google Scholar](https://scholar.google.co.uk/scholar?hl=en&as_sdt=0%2C5&q=titanic+sinking&oq=Titanic+) provides peer-reviewed publications. \n", + "\n", + "This notebook also simulate some suitable and unsuitable use of analytical techniques. The titanic data did not demonstrate such features. " + ] + }, + { + "cell_type": "markdown", + "id": "a2aa59e1", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c7b329a7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.model_selection import StratifiedShuffleSplit\n", + "\n", + "from scipy.stats import f_oneway\n", + "from scipy import stats\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "543165b1", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Understanding the data" + ] + }, + { + "cell_type": "markdown", + "id": "c06b184e", + "metadata": { + "hidden": true + }, + "source": [ + "Understanding the data and its structure should be the main concern at the early stages of data exploration. Understanding about the data structure as well as its properties assists on selecting the type of analytical processes for the purpose of pre-processing and analysing and predicting. \n", + "\n", + "Meta-data describes the data itself, i.e., it is some data about data. For example, databases use some schemas or column’s description for datasets. Meta-data should also inform about the source of the data to support its management and our judgement of reliability. \n", + "\n", + "Meta-data should support justification for selecting subset within a dataset to produce an analysis, creating a report, producing a graph. An example is provided in Figure X. Meta-data is often communicated in tables, diagrams or using a markup language. Data manager or analyst are often considered as the targeted audience, as they expect some descriptive, data type, and sometimes some dimensions. " + ] + }, + { + "cell_type": "markdown", + "id": "752adaac", + "metadata": { + "hidden": true + }, + "source": [ + "## data source\n" + ] + }, + { + "cell_type": "markdown", + "id": "46f7f7f5", + "metadata": { + "hidden": true + }, + "source": [ + "The dataset has been obtained from [Kaggle](https://www.kaggle.com/competitions/titanic/data?select=test.csv). It is free to use. We upload the data, explore the meta-data of both datasets. \n", + "\n", + "https://www.openml.org/search?type=data&sort=runs&id=40945&status=active\n", + "\n", + "We discover the training dataset has a survivor column, which the test dataset has not. The training dataset is also much larger than the test dataset, which is expected for machine learning exploration. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "00724e08", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data.csv test.csv train.csv\r\n" + ] + } + ], + "source": [ + "!ls data" + ] + }, + { + "cell_type": "markdown", + "id": "467d6d73", + "metadata": { + "hidden": true + }, + "source": [ + "## upload data and explore meta-data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2a07b360", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Survived int64\n", + "Pclass int64\n", + "Name object\n", + "Sex object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = pd.read_csv(\"data/train.csv\")\n", + "train.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3f0af28e", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(891, 12)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9d768670", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
50450511Maioni, Miss. Robertafemale16.00011015286.50B79S
25725811Cherry, Miss. Gladysfemale30.00011015286.50B77S
75976011Rothes, the Countess. of (Lucy Noel Martha Dye...female33.00011015286.50B77S
26226301Taussig, Mr. Emilmale52.01111041379.65E67S
55855911Taussig, Mrs. Emil (Tillie Mandelbaum)female39.01111041379.65E67S
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "504 505 1 1 \n", + "257 258 1 1 \n", + "759 760 1 1 \n", + "262 263 0 1 \n", + "558 559 1 1 \n", + "\n", + " Name Sex Age SibSp \\\n", + "504 Maioni, Miss. Roberta female 16.0 0 \n", + "257 Cherry, Miss. Gladys female 30.0 0 \n", + "759 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 \n", + "262 Taussig, Mr. Emil male 52.0 1 \n", + "558 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39.0 1 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "504 0 110152 86.50 B79 S \n", + "257 0 110152 86.50 B77 S \n", + "759 0 110152 86.50 B77 S \n", + "262 1 110413 79.65 E67 S \n", + "558 1 110413 79.65 E67 S " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.sort_values(by=\"Ticket\").head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cb7d8568", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Pclass int64\n", + "Name object\n", + "Sex object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = pd.read_csv(\"data/test.csv\")\n", + "test.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fd7bab97", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(418, 11)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "950a5e1b", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
33512271Maguire, Mr. John Edwardmale30.00011046926.00C106S
15810501Borebank, Mr. John Jamesmale42.00011048926.55D22S
23611281Warren, Mr. Frank Manleymale64.01011081375.25D37C
19110831Salomon, Mr. Abraham LmaleNaN0011116326.00NaNS
26611581Chisholm, Mr. Roderick Robert CrispinmaleNaN001120510.00NaNS
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" + ], + "text/plain": [ + " PassengerId Pclass Name Sex Age \\\n", + "335 1227 1 Maguire, Mr. John Edward male 30.0 \n", + "158 1050 1 Borebank, Mr. John James male 42.0 \n", + "236 1128 1 Warren, Mr. Frank Manley male 64.0 \n", + "191 1083 1 Salomon, Mr. Abraham L male NaN \n", + "266 1158 1 Chisholm, Mr. Roderick Robert Crispin male NaN \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "335 0 0 110469 26.00 C106 S \n", + "158 0 0 110489 26.55 D22 S \n", + "236 1 0 110813 75.25 D37 C \n", + "191 0 0 111163 26.00 NaN S \n", + "266 0 0 112051 0.00 NaN S " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.sort_values(by=\"Ticket\").head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "12128297", + "metadata": { + "hidden": true + }, + "source": [ + "We use instead a [complete dataset](https://www.openml.org/search?type=data&sort=runs&id=40945&status=active). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9b0c6342", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'pclass' int64\n", + "'survived' int64\n", + "'name' object\n", + "'sex' object\n", + "'age' object\n", + "'sibsp' int64\n", + "'parch' int64\n", + "'ticket' object\n", + "'fare' object\n", + "'cabin' object\n", + "'embarked' object\n", + "'boat' object\n", + "'body' object\n", + "'home.dest' object\n", + "dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"data/data.csv\")\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e36caf61", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'pclass' int64\n", + "'survived' int64\n", + "'name' object\n", + "'sex' object\n", + "'age' object\n", + "'sibsp' int64\n", + "'parch' int64\n", + "'ticket' object\n", + "'fare' object\n", + "'cabin' object\n", + "'embarked' object\n", + "'boat' object\n", + "'body' object\n", + "'home.dest' object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ab5e5a1a", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1309, 14)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "62281eae", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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'pclass''survived''name''sex''age''sibsp''parch''ticket''fare''cabin''embarked''boat''body''home.dest'
6711Cherry, Miss. Gladysfemale300011015286.5B77S8?London, England
24511Rothes, the Countess. of (Lucy Noel Martha Dye...female330011015286.5B77S8?London Vancouver, BC
19511Maioni, Miss. Robertafemale160011015286.5B79S8??
28911Taussig, Miss. Ruthfemale180211041379.65E68S8?New York, NY
29111Taussig, Mrs. Emil (Tillie Mandelbaum)female391111041379.65E67S8?New York, NY
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" + ], + "text/plain": [ + " 'pclass' 'survived' 'name' \\\n", + "67 1 1 Cherry, Miss. Gladys \n", + "245 1 1 Rothes, the Countess. of (Lucy Noel Martha Dye... \n", + "195 1 1 Maioni, Miss. Roberta \n", + "289 1 1 Taussig, Miss. Ruth \n", + "291 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) \n", + "\n", + " 'sex' 'age' 'sibsp' 'parch' 'ticket' 'fare' 'cabin' 'embarked' 'boat' \\\n", + "67 female 30 0 0 110152 86.5 B77 S 8 \n", + "245 female 33 0 0 110152 86.5 B77 S 8 \n", + "195 female 16 0 0 110152 86.5 B79 S 8 \n", + "289 female 18 0 2 110413 79.65 E68 S 8 \n", + "291 female 39 1 1 110413 79.65 E67 S 8 \n", + "\n", + " 'body' 'home.dest' \n", + "67 ? London, England \n", + "245 ? London Vancouver, BC \n", + "195 ? ? \n", + "289 ? New York, NY \n", + "291 ? New York, NY " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sort_values(by=\"'ticket'\").head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "a2b62b5c", + "metadata": { + "hidden": true + }, + "source": [ + "We chose data and surmise this data set has contains all the train and test dataset. " + ] + }, + { + "cell_type": "markdown", + "id": "74818cfb", + "metadata": { + "hidden": true + }, + "source": [ + "## Summarising the data" + ] + }, + { + "cell_type": "markdown", + "id": "ef4a88d2", + "metadata": { + "hidden": true + }, + "source": [ + "Data summary often complements meta-data; it informs about the features and properties of columns of a dataset. Analysts are keen to use descriptive statistical summaries and present them into tables; an example of a data summary is shown in figure X. \n", + "\n", + "Analysts can compute and communicate measures of extremes, centrality, and variability. The centre represents the halfway point of the observations – we often use the mean, the median, or both. The spread informs about the variability of the data. A small standard deviation, or interquartile range (IQR) may suggest the observations are clustered around a single value. Otherwise, some outliers may exist and affect the mean and the standard deviation.\n", + "\n", + "We first explore numerical values and their statistical descriptive summary. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4042adda", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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'pclass''survived''sibsp''parch'
count1309.0000001309.0000001309.0000001309.000000
mean2.2948820.3819710.4988540.385027
std0.8378360.4860551.0416580.865560
min1.0000000.0000000.0000000.000000
25%2.0000000.0000000.0000000.000000
50%3.0000000.0000000.0000000.000000
75%3.0000001.0000001.0000000.000000
max3.0000001.0000008.0000009.000000
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" + ], + "text/plain": [ + " 'pclass' 'survived' 'sibsp' 'parch'\n", + "count 1309.000000 1309.000000 1309.000000 1309.000000\n", + "mean 2.294882 0.381971 0.498854 0.385027\n", + "std 0.837836 0.486055 1.041658 0.865560\n", + "min 1.000000 0.000000 0.000000 0.000000\n", + "25% 2.000000 0.000000 0.000000 0.000000\n", + "50% 3.000000 0.000000 0.000000 0.000000\n", + "75% 3.000000 1.000000 1.000000 0.000000\n", + "max 3.000000 1.000000 8.000000 9.000000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "fbc22379", + "metadata": { + "hidden": true + }, + "source": [ + "We list the unique values of the other columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e6fcea27", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'pclass'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "658775f1", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2,)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'survived'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "96ab33c2", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2,)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'sex'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "454f211c", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'embarked'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "48d92f29", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7,)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'sibsp'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "45720a91", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(8,)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'parch'\"].unique().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "32344ee8", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(282,)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'fare'\"].unique().shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c8e946e1", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(370,)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'home.dest'\"].unique().shape" + ] + }, + { + "cell_type": "markdown", + "id": "06f23dc5", + "metadata": { + "hidden": true + }, + "source": [ + "## Statistical distribution" + ] + }, + { + "cell_type": "markdown", + "id": "e0493fa7", + "metadata": { + "hidden": true + }, + "source": [ + "Statistical distributions arrange some observations or values from the smallest to the largest value. Distributions are visually represented with some histograms; each bin (a bar on the histogram) counts the number of values within a subset of values. Our choice of number of bins can bring a level of granularity and clarity.\n", + "\n", + "Figure X1 has the lowest number of bins, which shows the data in with large bins. It is not easy to appreciate how the data is distributed; it is quite incomprehensible. However, when we increase the number of bins, the quality of the visual representation improves. A clearer shape arises, a perfect bell shape, in this case, is shown.\n", + "\n", + "In this example, we replace all the unknown values with a negative fare. It is better than 0. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4c238b32", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['211.3375', '151.55', '26.55', '77.9583', '0', '51.4792',\n", + " '49.5042', '227.525', '69.3', '78.85', '30', '25.925', '247.5208',\n", + " '76.2917', '75.2417', '52.5542', '221.7792', '26', '91.0792',\n", + " '135.6333', '35.5', '31', '164.8667', '262.375', '55', '30.5',\n", + " '50.4958', '39.6', '27.7208', '134.5', '26.2875', '27.4458',\n", + " '512.3292', '5', '47.1', '120', '61.175', '53.1', '86.5', '29.7',\n", + " '136.7792', '52', '25.5875', '83.1583', '25.7', '71', '71.2833',\n", + " '57', '81.8583', '106.425', '56.9292', '78.2667', '31.6792',\n", + " '31.6833', '110.8833', '26.3875', '27.75', '263', '133.65', '49.5',\n", + " '79.2', '38.5', '211.5', '59.4', '89.1042', '34.6542', '28.5',\n", + " '153.4625', '63.3583', '55.4417', '76.7292', '42.4', '83.475',\n", + " '93.5', '42.5', '51.8625', '50', '57.9792', '90', '30.6958', '80',\n", + " '28.7125', '25.9292', '39.4', '45.5', '146.5208', '82.1708',\n", + " '57.75', '113.275', '26.2833', '108.9', '25.7417', '61.9792',\n", + " '66.6', '40.125', '55.9', '60', '82.2667', '32.3208', '79.65',\n", + " '28.5375', '33.5', '34.0208', '75.25', '77.2875', '61.3792', '35',\n", + " '24', '13', '11.5', '10.5', '12.525', '39', '29', '21', '13.5',\n", + " '26.25', '36.75', '73.5', '31.5', '23', '32.5', '13.8583', '14.5',\n", + " '33', '65', '16', '12.275', '27', '15', '13.7917', '12.35',\n", + " '10.7083', '41.5792', '12', '12.875', '15.0458', '37.0042',\n", + " '15.5792', '19.5', '14', '9.6875', '30.0708', '13.8625', '15.05',\n", + " '12.7375', '15.0333', '18.75', '12.65', '15.75', '7.55', '20.25',\n", + " '7.65', '7.925', '7.2292', '7.25', '8.05', '9.475', '9.35',\n", + " '18.7875', '7.8875', '7.05', '8.3', '22.525', '7.8542', '31.275',\n", + " '7.775', '7.7958', '7.8958', '17.8', '31.3875', '7.225', '14.4583',\n", + " '15.85', '19.2583', '14.4542', '7.8792', '4.0125', '56.4958',\n", + " '7.75', '15.2458', '15.5', '16.1', '7.725', '7.0458', '7.2833',\n", + " '7.8208', '6.75', '8.6625', '7.7333', '7.4958', '7.6292', '15.9',\n", + " '8.1583', '10.5167', '10.1708', '6.95', '14.4', '24.15', '17.4',\n", + " '9.5', '20.575', '12.475', '13.9', '6.975', '15.1', '34.375',\n", + " '7.7417', '20.525', '7.85', '46.9', '8.3625', '9.8458', '8.85',\n", + " '19.9667', '14.1083', '6.8583', '8.9625', '12.2875', '6.45',\n", + " '7.0542', '8.1125', '6.4958', '8.6542', '11.1333', '23.45',\n", + " '9.825', '7.125', '8.4333', '7.5208', '13.4167', '7.8292',\n", + " '7.7375', '22.025', '12.1833', '9.5875', '9.4833', '25.4667',\n", + " '6.4375', '15.55', '7.5792', '7.1417', '23.25', '7.7875', '8.0292',\n", + " '8.4583', '15.7417', '11.2417', '7.8', '6.2375', '9.225', '3.1708',\n", + " '8.4042', '7.3125', '9.2167', '8.6833', '21.075', '39.6875',\n", + " '8.7125', '13.775', '7', '22.3583', '8.1375', '29.125', '7.7208',\n", + " '20.2125', '7.7292', '7.575', '69.55', '9.325', '21.6792', '16.7',\n", + " '7.7792', '27.9', '?', '9.8375', '10.4625', '8.5167', '9.8417',\n", + " '9', '18', '7.875'], dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'fare'\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "787f29ed", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 33.269280\n", + "std 51.747562\n", + "min -1.000000\n", + "25% 7.895800\n", + "50% 14.454200\n", + "75% 31.275000\n", + "max 512.329200\n", + "Name: 'fare', dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "fares = data[\"'fare'\"]\n", + "fares = fares.replace('?', \"-1\")\n", + "fares = fares.astype(float)\n", + "fares.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "b4517147", + "metadata": { + "hidden": true + }, + "source": [ + "We replace the negative values with the values 9999. It will support log 10." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8a3c6f7c", + "metadata": { + "hidden": true, + "tags": [ + "style-activity" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fares = fares.replace(-1, 999)\n", + "fares[fares == 999].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e8cbc38b", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fares = fares.replace(0, 1e-10)\n", + "fares[fares == 1e-10 ].count()" + ] + }, + { + "cell_type": "markdown", + "id": "2fb3f5a6", + "metadata": { + "hidden": true + }, + "source": [ + "__Incomprensible distribution__" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1110cd72", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1.309000e+03\n", + "mean 3.403322e+01\n", + "std 5.821815e+01\n", + "min 1.000000e-10\n", + "25% 7.895800e+00\n", + "50% 1.445420e+01\n", + "75% 3.127500e+01\n", + "max 9.990000e+02\n", + "Name: 'fare', dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4HqNHgRNa2QnAo237C8AFQ+1fancoPRjcz7EdeCdwKxAGdyou2XfOGVwZdlrbXtLaZbHHMMvxHg08sW+/x3me+e3d+se0ebsVePc4zjOwCnhgrvMKXAB8Yaj8d9od6DF27/SZ+aseli9SXw6a9ufsScA9wPFV9VSreho4vm2Py+/ic8AngN+0/WOB56tqT9sfHtdLY271L7T2h5LVwDTwpbak9cUkRzHG81xVu4DPAD8BnmIwb/cx3vO812zndaT5HsfQH3tJ3gB8HfhoVf18uK4GL/1jcx1ukvcCu6vqvsXuywJaApwMXFtVJwG/4Ld/8gNjOc/LGHz54mrgjcBRvHwZZOwtxLyOY+iP9Vc9JHkdg8C/vqpubsXPJDmh1Z8A7G7l4/C7OB14X5IfA19lsMRzNbA0yd6bC4fH9dKYW/3RwM8WssPzYCews6ruafs3MXgRGOd5fhfwRFVNV9WvgZsZzP04z/Nes53XkeZ7HEN/bL/qIUmALcDDVfXZoaptwN5P8NczWOvfW35huwrgVOCFoT8jDwlVdVlVraiqVQzm8s6q+iBwF3Bea7bvmPf+Ls5r7Q+pd8RV9TSwI8mbW9EZDL5+fGznmcGyzqlJXt/+ne8d89jO85DZzuvtwJlJlrW/kM5sZa/OYn+ocZA+KDkH+CHwI+BTi92feRzXnzH40+9+4PvtcQ6DtcztwGPAfwLHtPZhcCXTj4AfMLgyYtHHMcL43wHc2rb/CPgOMAX8C3BEKz+y7U+1+j9a7H7Pcax/Cky2uf43YNm4zzPwd8AjwAPAPwNHjNs8Azcw+Mzi1wz+otswl3kFPtTGPgVcNJs++DUMktSRcVzekSTth6EvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOvL/gpraHeTib6IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(fares, bins = 8)\n", + "fares.describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "78c2521b", + "metadata": { + "hidden": true + }, + "source": [ + "__A more comprensible distribution__" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "dadb18ad", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1.308000e+03\n", + "mean 3.329548e+01\n", + "std 5.175867e+01\n", + "min 1.000000e-10\n", + "25% 7.895800e+00\n", + "50% 1.445420e+01\n", + "75% 3.127500e+01\n", + "max 5.123292e+02\n", + "Name: 'fare', dtype: float64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fares = fares[fares < 999]\n", + "plt.hist(fares, bins = 100)\n", + "fares.describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b588ecf4", + "metadata": { + "hidden": true + }, + "source": [ + "__An even more comprehensible histogram:__\n", + " \n", + "[Logarithms explained](https://www.mathsisfun.com/algebra/exponents-logarithms.html)\n", + "\n", + "We appear to have zoomed in the distribution and see more peaks. So the distribution is multi-modal, with a tendency to have a lot of low fares." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "1319019a", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1291.000000\n", + "mean 33.733917\n", + "std 51.956349\n", + "min 3.170800\n", + "25% 7.925000\n", + "50% 14.500000\n", + "75% 31.331250\n", + "max 512.329200\n", + "Name: 'fare', dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fares = fares[fares > 1e-10]\n", + "plt.hist(np.log10(fares), bins = 100)\n", + "fares.describe()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "977d68ae", + "metadata": { + "hidden": true + }, + "source": [ + "__Age:__\n", + "\n", + "Some values are missing so the use again some values to remove those.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cf9d47f7", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 224.593328\n", + "std 388.674258\n", + "min 0.166700\n", + "25% 22.000000\n", + "50% 32.000000\n", + "75% 55.000000\n", + "max 999.000000\n", + "Name: 'age', dtype: float64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages = data[\"'age'\"]\n", + "ages = ages.replace (\"?\", 999)\n", + "ages = ages.astype(float)\n", + "ages.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "59ab316d", + "metadata": { + "hidden": true + }, + "source": [ + "The number of missing ages is quite large, and affecting negatively the distribution. \n", + "\n", + "\n", + "So any analysis will be affected by this situation. Some inputation may help alleviate the negative impact to a certain extend. \n", + "\n", + "The distribution is therefore spurious, but indicative nonetheless.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2a7a4454", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "263" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages[ages == 999].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "009df561", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1046.000000\n", + "mean 29.881135\n", + "std 14.413500\n", + "min 0.166700\n", + "25% 21.000000\n", + "50% 28.000000\n", + "75% 39.000000\n", + "max 80.000000\n", + "Name: 'age', dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ages = ages[ages < 999]\n", + "plt.hist(ages, bins = 64)\n", + "ages.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "fb03b26c", + "metadata": { + "hidden": true + }, + "source": [ + "__Siblings or spouse__\n", + "\n", + "Many passenger may have travelled on their own without any siblings or spouse. Many passengers also travelling without any children or parents. We surmise not many families may have been travelling on the Titanic." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a16321c5", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 0.498854\n", + "std 1.041658\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 1.000000\n", + "max 8.000000\n", + "Name: 'sibsp', dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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C\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'sibsp'\"], bins = 8)\n", + "data[\"'sibsp'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "bcde3d8d", + "metadata": { + "hidden": true + }, + "source": [ + "__Parent or children:__\n", + "\n", + "Some passengers travelled with their parents or their children." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "59619182", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 0.385027\n", + "std 0.865560\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 9.000000\n", + "Name: 'parch', dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'parch'\"], bins = 9)\n", + "data[\"'parch'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "612aecbb", + "metadata": { + "hidden": true + }, + "source": [ + "More examples of histogram using the other columns. Some of these columns appeared to be catetorical. So, we chose the number of bins as the number of values. (see above)." + ] + }, + { + "cell_type": "markdown", + "id": "bd666f8a", + "metadata": { + "hidden": true + }, + "source": [ + "__Passenger class:__" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "59d802bf", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 2.294882\n", + "std 0.837836\n", + "min 1.000000\n", + "25% 2.000000\n", + "50% 3.000000\n", + "75% 3.000000\n", + "max 3.000000\n", + "Name: 'pclass', dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'pclass'\"], bins = 3)\n", + "data[\"'pclass'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "4f3f3195", + "metadata": { + "hidden": true + }, + "source": [ + "__Survived or perished:__\n", + "\n", + "\n", + "More passengers would have perished the accident than survived. It is a shocking statistic." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "a1228cf1", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 0.381971\n", + "std 0.486055\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 1.000000\n", + "max 1.000000\n", + "Name: 'survived', dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BnwZ2T+E8kqSzGPkB2SuwGTg6tH0M+FunD0qyD9jXNv84yRdXeL5LgD9c4bHrlXN+a3DObwG5dVVz/mtn2zGNcB9LVR0ADqz2cZIsVNX8BEpaN5zzW4NzfmuY1pynsSxzHNg6tL2l9UmSZmQa4f47wPYklyU5H7geODSF80iSzmLiyzJV9XqSfwL8d+A84JNV9dSkzzNk1Us765Bzfmtwzm8NU5lzqmoajytJWkO+QlWSOmS4S1KH1k24j3pLgyRvS3J32/9wkm1rUOZEjTHnm5M8neTxJIeTnPU5r+vFuG9dkeTvJakk6/5pc+PMOcl17Wv9VJJfmXWNkzbG9/a7kzyU5NH2/b1rLeqclCSfTHIyyZNn2Z8kt7d/j8eTXLnqk1bVX/obgz/Mfgn4duB84HeBy08b84+Bn2vt64G717ruGcz5+4B3tPaPvRXm3Ma9C/g8cASYX+u6Z/B13g48ClzYtr9treuewZwPAD/W2pcDz6913auc8/cAVwJPnmX/LuC/AQF2AA+v9pzr5cp9nLc02A0cbO17gZ1JMsMaJ23knKvqoap6rW0eYfCagvVs3Leu+HfArcBXZ1nclIwz538EfKKqXgGoqpMzrnHSxplzAd/a2hcA/3uG9U1cVX0e+Mo5huwG7qqBI8DGJJeu5pzrJdzP9JYGm882pqpeB04BF8+kuukYZ87D9jL4yb+ejZxz+3V1a1V9dpaFTdE4X+fvAL4jyW8mOZLk6plVNx3jzPlfAz+U5BhwP/DjsyltzSz3//tIa/b2A5qcJD8EzAN/Z61rmaYk3wT8DPDDa1zKrG1gsDTzvQx+O/t8kr9RVa+uZVFT9jHgF6vqPyZ5P/BLSb67qv58rQtbL9bLlfs4b2nwF2OSbGDwq9zLM6luOsZ6G4ck3w/8NPDhqvrTGdU2LaPm/C7gu4HPJXmewdrkoXX+R9Vxvs7HgENV9bWq+gPg9xmE/Xo1zpz3AvcAVNVvAW9n8KZivZr427asl3Af5y0NDgF7WvsjwIPV/lKxTo2cc5L3AT/PINjX+zosjJhzVZ2qqkuqaltVbWPwd4YPV9XC2pQ7EeN8b/9XBlftJLmEwTLNczOscdLGmfOXgZ0ASf46g3BfmmmVs3UIuKE9a2YHcKqqTqzqEdf6r8jL+GvzLgZXLF8Cfrr1/VsG/7lh8MX/VWAR+G3g29e65hnM+X8CLwGPtduhta552nM+beznWOfPlhnz6xwGy1FPA08A1691zTOY8+XAbzJ4Js1jwA+udc2rnO+ngBPA1xj8JrYX+FHgR4e+xp9o/x5PTOL72rcfkKQOrZdlGUnSMhjuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUP/H4RPWD9J/JtfAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'survived'\"], bins = 2)\n", + "data[\"'survived'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "f0130c76", + "metadata": { + "hidden": true + }, + "source": [ + "__Gender:__\n", + "\n", + "There was more male passengers than females passengers." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "988a7dc3", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309\n", + "unique 2\n", + "top male\n", + "freq 843\n", + "Name: 'sex', dtype: object" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'sex'\"], bins = 2)\n", + "data[\"'sex'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "d7768b75", + "metadata": { + "hidden": true + }, + "source": [ + "__Port of embarkment:__\n", + "\n", + "A majority of passenger would have emberbaked at Southampton, then Cherbourg. A minority of embarkation port is unknown." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "de80b033", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309\n", + "unique 4\n", + "top S\n", + "freq 914\n", + "Name: 'embarked', dtype: object" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMmElEQVR4nO3df4zfhV3H8edrVNjYMmBwYaxtPDLqFpzTsQaZRI2UxAHLyiJbMCp1qWlMmM6xxHXuD/zHCEbFLVNMs7p0ybKxIEojRF34YWKWEa8MwZZNKsLaBsZBgDknOtzbP+7DPErb+7b3vfv23n0+kuY+v77fz/vyzT3v2899v3epKiRJvbxq0gNIksbPuEtSQ8Zdkhoy7pLUkHGXpIZWTXoAgLPOOqump6cnPYYkrSi7du16uqqmDrXvuIj79PQ0MzMzkx5DklaUJI8fbp+XZSSpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJamh4+IdqosxvfWOSY+wojx2wxWTHkHSMvCZuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQyPFPclHkuxO8i9JvpDk1UnOTXJfkr1Jbkly8nDsKcP63mH/9JJ+BpKkV1gw7klWA78JrK+qtwEnAVcDNwI3VdV5wLPA5uEmm4Fnh+03DcdJkpbRqJdlVgGvSbIKOBV4ArgEuHXYvwO4cljeOKwz7N+QJGOZVpI0kgXjXlUHgD8Evslc1J8HdgHPVdWLw2H7gdXD8mpg33DbF4fjzxzv2JKkIxnlsswZzD0bPxd4E/Ba4N2LPXGSLUlmkszMzs4u9u4kSfOMclnmUuDfq2q2qr4H3AZcDJw+XKYBWAMcGJYPAGsBhv2nAc8cfKdVta2q1lfV+qmpqUV+GpKk+UaJ+zeBi5KcOlw73wDsAe4BrhqO2QTcPizvHNYZ9t9dVTW+kSVJCxnlmvt9zP1g9H7goeE224CPAdcl2cvcNfXtw022A2cO268Dti7B3JKkI1i18CFQVdcD1x+0+VHgwkMc+wLw/sWPJkk6Vr5DVZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqaGR4p7k9CS3Jvl6koeTvCvJG5J8Ockjw8czhmOT5FNJ9iZ5MMkFS/spSJIONuoz908Cf1tVbwV+HHgY2ArcVVXrgLuGdYDLgHXDvy3AzWOdWJK0oAXjnuQ04GeA7QBV9T9V9RywEdgxHLYDuHJY3gh8ruZ8FTg9yTljnluSdASjPHM/F5gFPpvka0k+k+S1wNlV9cRwzJPA2cPyamDfvNvvH7a9TJItSWaSzMzOzh77ZyBJeoVR4r4KuAC4uareAfwn/38JBoCqKqCO5sRVta2q1lfV+qmpqaO5qSRpAaPEfT+wv6ruG9ZvZS7233rpcsvw8alh/wFg7bzbrxm2SZKWyYJxr6ongX1J3jJs2gDsAXYCm4Ztm4Dbh+WdwDXDq2YuAp6fd/lGkrQMVo143G8An09yMvAo8EHmvjF8Kclm4HHgA8OxdwKXA3uB7w7HSpKW0Uhxr6oHgPWH2LXhEMcWcO3ixpIkLYbvUJWkhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDY0c9yQnJflakr8Z1s9Ncl+SvUluSXLysP2UYX3vsH96iWaXJB3G0Txz/zDw8Lz1G4Gbquo84Flg87B9M/DssP2m4ThJ0jIaKe5J1gBXAJ8Z1gNcAtw6HLIDuHJY3jisM+zfMBwvSVomoz5z/xPgt4HvD+tnAs9V1YvD+n5g9bC8GtgHMOx/fjj+ZZJsSTKTZGZ2dvbYppckHdKCcU/yHuCpqto1zhNX1baqWl9V66empsZ515J0wls1wjEXA+9NcjnwauD1wCeB05OsGp6drwEODMcfANYC+5OsAk4Dnhn75JKkw1rwmXtVfbyq1lTVNHA1cHdV/RJwD3DVcNgm4PZheeewzrD/7qqqsU4tSTqixbzO/WPAdUn2MndNffuwfTtw5rD9OmDr4kaUJB2tUS7L/EBV3QvcOyw/Clx4iGNeAN4/htkkScfId6hKUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqaNWkB9Dymt56x6RHWFEeu+GKSY8gHROfuUtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktTQgnFPsjbJPUn2JNmd5MPD9jck+XKSR4aPZwzbk+RTSfYmeTDJBUv9SUiSXm6UZ+4vAh+tqvOBi4Brk5wPbAXuqqp1wF3DOsBlwLrh3xbg5rFPLUk6ogXjXlVPVNX9w/J/AA8Dq4GNwI7hsB3AlcPyRuBzNeerwOlJzhn34JKkwzuqa+5JpoF3APcBZ1fVE8OuJ4Gzh+XVwL55N9s/bDv4vrYkmUkyMzs7e7RzS5KOYOS4J3kd8JfAb1XVt+fvq6oC6mhOXFXbqmp9Va2fmpo6mptKkhYwUtyT/BBzYf98Vd02bP7WS5dbho9PDdsPAGvn3XzNsE2StExGebVMgO3Aw1X1x/N27QQ2DcubgNvnbb9meNXMRcDz8y7fSJKWwSh/Zu9i4FeAh5I8MGz7HeAG4EtJNgOPAx8Y9t0JXA7sBb4LfHCcA0uSFrZg3KvqH4EcZveGQxxfwLWLnEuStAi+Q1WSGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNTTKb4WUpJFNb71j0iOsKI/dcMWS3K/P3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDW0JHFP8u4k30iyN8nWpTiHJOnwxh73JCcBfwpcBpwP/GKS88d9HknS4a1agvu8ENhbVY8CJPkisBHYswTnkpbU9NY7Jj2CdEyWIu6rgX3z1vcDP3nwQUm2AFuG1e8k+cYxnu8s4OljvK2Wjo/L8cfH5DiUGxf1uPzw4XYsRdxHUlXbgG2LvZ8kM1W1fgwjaYx8XI4/PibHp6V6XJbiB6oHgLXz1tcM2yRJy2Qp4v5PwLok5yY5Gbga2LkE55EkHcbYL8tU1YtJPgT8HXAS8BdVtXvc55ln0Zd2tCR8XI4/PibHpyV5XFJVS3G/kqQJ8h2qktSQcZekhlZ03JN8IsnuJA8meSDJK15Pr+WV5I1Jvpjk35LsSnJnkh+Z9FwnsiRvTfKVJA8l+YckZ016phNdkjVJbk/ySJJHk3w6ySnjPMeKjXuSdwHvAS6oqrcDl/LyN09pmSUJ8FfAvVX15qp6J/Bx4OzJTibgl6vqx4CvAL8+6WFOZMPXyW3AX1fVOmAd8BrgD8Z5nom9iWkMzgGerqr/Bqgq33k3eT8HfK+q/vylDVX1zxOcR0BVfX3e6inAM5OaRQBcArxQVZ8FqKr/TfIR4PEkn6iq74zjJCv2mTvw98DaJP+a5M+S/OykBxJvA3ZNeggdWpKfZ+4X+n1m0rOc4H6Ug75OqurbwGPAeeM6yYqN+/Dd7Z3M/X6aWeCWJL860aGk41SSVwHbgfdW1XMTHkfLYMXGHeb+O1NV91bV9cCHgF+Y9EwnuN3MfcPV8edNwPNV9cikBxF7OOjrJMnrgTcCx/oLFF9hxcY9yVuSrJu36SeAxyc0jubcDZwy/MZPAJK8PclPT3AmzXkW+OikhxAAdwGnJrkGfvA3MP4I+HRV/de4TrJi4w68DtiRZE+SB5n7wyC/O9mRTmw193bn9wGXDi+F3A38PvDkZCcTcBrwa5MeQi/7OrkqySPM/YD7+1X1e+M8j79+QJImKMlPAV8A3ldV94/tfo27JPWzki/LSJIOw7hLUkPGXZIaMu6S1JBxl6SGjLskNfR/Zb61uodEM50AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[\"'embarked'\"], bins = 4)\n", + "data[\"'embarked'\"].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "7e97726a", + "metadata": {}, + "source": [ + "# Analysing" + ] + }, + { + "cell_type": "markdown", + "id": "3be8bd75", + "metadata": {}, + "source": [ + "Analysing data often requires using more than one statistical observation or column of a dataset. Analysing data can therefore use more than one technique to discover patterns, relationship and possibly make some predictions. Consequently, we use more data and complex techniques" + ] + }, + { + "cell_type": "markdown", + "id": "5d40aa6b", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Comparison of data" + ] + }, + { + "cell_type": "markdown", + "id": "913c48c9", + "metadata": { + "hidden": true + }, + "source": [ + "We sometimes want to compare data grouped by a certain category or compare several datasets with the same observations (or columns). A comparison becomes informative when the centre, the spread, the shape, and other unusual features are computed. We introduced those in the previous section.\n", + "\n", + "We are going to discuss and show how data summaries and boxplot can support us in communicating comparison of data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c19b5b7c", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fares = data[\"'fare'\"]\n", + "fares = fares.replace(\"?\", \"-1\")\n", + "fares = fares.astype(float)\n", + "data[\"'fare'\"] = fares\n", + "data[\"'fare'\"].dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "3bb86c93", + "metadata": { + "hidden": true + }, + "source": [ + "We create a table that compares the fares for each class. We discover most unknown fares in third class, which will negatively impact any analysis and comparison. It appears that some First and second class passengers may have been travelling free." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6748eb4e", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 'fare' \\\n", + " count mean std min 25% 50% 75% \n", + "'pclass' \n", + "1 323.0 87.508992 80.447178 0.0 30.6958 60.0000 107.6625 \n", + "2 277.0 21.179196 13.607122 0.0 13.0000 15.0458 26.0000 \n", + "3 709.0 13.282715 11.498791 -1.0 7.7500 8.0500 15.2458 \n", + "\n", + " \n", + " max \n", + "'pclass' \n", + "1 512.3292 \n", + "2 73.5000 \n", + "3 69.5500 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[[\"'pclass'\",\"'fare'\"]].groupby(\"'pclass'\").describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "41cccc83", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KiDgcWAdMT/NPB9al9ivTfGZmNgB1GQJRaEujw9IjgA8Dt6b22cCpaXhaGidNP1GS6lWwmZnVT03HBCQ1SHoKWAPMB/4ArI+IbWmWlcChafhQYAVAmr4BOLCONZuZWZ0MrWWmiNgOvEfSaOAO4MjerljSDGAGQGNjI5VKpbeLHPDa2tqyeJ058LYcPHLfljWFQLuIWC9pIfABYLSkoenb/jhgVZptFdAErJQ0FNgfWNvJsmYBswBaWlqitbW1xy9ib1GpVMjhdebA23LwyH1b1nJ20FvSHgCSRgAnAc8AC4HT0mxnAnPT8Lw0Tpr+84iIOtZsZmZ1UsuewFhgtqQGitC4JSLukvQ74CZJ/w14ErguzX8dcIOkJcArwGf6oG4zM6uDLkMgIp4Gjumk/Vng2E7atwCfqkt1ZmbWp3zFsJlZxhwCZmYZcwiYmWXMIWBmljGHgJlZxhwCZmYZ69YVw9Yznd0/z9fPmdlA4D2BPtYeAA0NDVxxxRU0NDTs0m5mViaHQD9oaGhg27ZtHHPMMWzbtm1nEJiZlc0h0A8WLFiwx3Ezs7I4BPrBiSeeuMdxM7OyOAT6wfbt2xk6dChPPvkkQ4cOZfv27WWXZGYG+OygPhcRSGL79u1ccMEFu7SbmZXNewJ97OSTTwbg7LPP5s477+Tss8/epd3MrEzeE+hj8+fP5+yzz+aaa66hUqlwzTXXAHDttdeWXJmZmfcE+lxEcNlll+3Sdtlll7k7yMwGBIdAH5PExRdfvEvbxRdf7IvFzGxAcHdQHzvppJOYOXMmAFOnTuWcc85h5syZTJkypeTKzMwcAn3uvvvu4+STT+baa69l5syZSGLKlCncd999ZZdmZuYQ6A/tH/iVSoXW1tZyizEzq+IQ6Afjx49nxYoVO8ebmppYvnx5iRWZmRV8YLiPtQfA8ccfz89+9jOOP/54VqxYwfjx48suzczMIdDX2gPgoYce4qCDDuKhhx7aGQRmZmVzCPSDW2+9dY/jZmZlcQj0g9NOO22P42ZmZfGB4TrZ08VfixYt6nS6f3bSzMrmPYE6iYjdPpqamnaZt6mpabfzmpn1J4dAP1i+fDkRwWEX3kVE+PRQMxswHAJmZhlzCJiZZazLEJDUJGmhpN9J+q2k81L7GEnzJf17+veA1C5J35e0RNLTkt7b1y/CzMx6ppY9gW3A30XEUcBxwLmSjgIuAhZExBHAgjQO8FHgiPSYAcyse9VmZlYXXYZARKyOiCfS8EbgGeBQYBowO802Gzg1DU8D5kThYWC0pLH1LtzMzHqvW9cJSGoGjgEeARojYnWa9ALQmIYPBarvibAyta2uakPSDIo9BRobG6lUKt0sfe+Uy+sc7Nra2rwtB4nct2XNISBpFHAb8KWIeLX6QqeICEndOsk9ImYBswBaWloii1ss33u3byU9SPi24INH7tuyprODJA2jCICfRMTtqfnF9m6e9O+a1L4KqL46alxqMzOzAaaWs4MEXAc8ExFXVE2aB5yZhs8E5la1n5HOEjoO2FDVbWRmZgNILd1BJwCnA4slPZXavgZcDtwiaTrwHPDpNO0eYCqwBHgN+Hw9CzYzs/rpMgQi4kFgd3dHO7GT+QM4t5d1mZlZP/AVw2ZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGRva1QySfgx8HFgTEe9ObWOAm4FmYBnw6YhYJ0nAVcBU4DXgrIh4om9K7z9HX/qvbNi8tS7Lar7o7l49f/8Rw/j1t6bUpRYzsy5DALgeuBqYU9V2EbAgIi6XdFEavxD4KHBEerwfmJn+3att2LyVZZd/rNfLqVQqtLa29moZvQ0RM7NqXXYHRcQDwCsdmqcBs9PwbODUqvY5UXgYGC1pbJ1qNTOzOqtlT6AzjRGxOg2/ADSm4UOBFVXzrUxtq+lA0gxgBkBjYyOVSqWHpfSPetTX1tZWl+UM9PcqB/Xalla+3LdlT0Ngp4gISdGD580CZgG0tLREb7tJ+tS9d/e6Gwfq0x1Ur1qsd+qyLW1AyH1b9vTsoBfbu3nSv2tS+yqgqWq+canNzMwGoJ6GwDzgzDR8JjC3qv0MFY4DNlR1G5mZ2QBTyymiPwVagYMkrQS+BVwO3CJpOvAc8Ok0+z0Up4cuoThF9PN9ULOZmdVJlyEQEZ/dzaQTO5k3gHN7W5SZmfUPXzFsZpaxXp8dZJaTUaNGsWnTpp3jI0eOpK2trcSKzHrHewJmNWoPgObmZm644Qaam5vZtGkTo0aNKrs0sx7znoBZjdoDYOnSpVQqFZYuXcqECRNYtmxZ2aVZDwwZMoTiMGZBEjt27CixonJ4T8CsG+6///49jtveoT0Ahg8fztVXX83w4cOJCIYMye8j0XsCZt3wjne8Y5dvizl+aAwG7QGwefNmKpUKmzdvZsSIEWzZsqXs0vqd/4LNumHHjh0MGzaMq666imHDhmXZfTBYdLxfUK73D3IImHXDkCFD2Lp1K+eddx5bt271nsBerOP9gnK9f5D/gs26YeXKlUQECxcuJCJYuXJl2SVZD0hiy5YtSGLy5Mm7jOfGIWDWDaeddtoex23vsM8++3SrfTDzgeEa7Peui5g4+6L6LGx217PsuRaA3v/KmXVfU1MTixYt4oQTTuD888/nhBNOYNGiRTQ1NXX9ZBtQXn/9dRobG3nhhRd23kr64IMP5sUXXyy7tH7nEKjBxmcu989LGsuXL2f8+PEsWrSIRYsWAUUwLF++vOTKrCc6OzD8rne9q5xiSuQQMOuG9g/83H+IZDDI8QO/Mz4mYGZZ+853vlN2CaVyCJhZ1r7+9a+XXUKpHAJmlq3qmwHmyscEzCxby5Yt4/TTTy+7jFJ5T8CsA0ldPtovMOrqYQNbQ0MDV1xxBQ0NDWWXUhqHgFkHEdHl47AL76ppPhvYtm/fzgUXXMD27dvLLqU0DgEzs4w5BMzMMuYDwzWq25W69/ZuOfuPGFafOsyM448/nvPPP58rr7xy51XguXEI1KAet4yAIkjqtSwz673qW4DkyiFg2Tn60n9lw+atvV5Ob/cO9x8xjF9/a0qv67Dd68kZWrt7zmA90O8QsOxs2Ly113tkvhng3mF3H9wHHnggr7zyyhvax4wZw9q1a/u6rAHFB4bNLDtr165lzJgxu7TlGADgEDCzTK1du3aXaz5yDABwd5BlqG4/EuQfCCqdj+/0nkPAsrPxmcvLLgHw6b714OM7vdcnISDpI8BVQAPwo4gYGP/rzKjPKb8+3dcGi7qHgKQG4J+Bk4CVwKOS5kXE7+q9LjPLm7v2eq8v9gSOBZZExLMAkm4CpgEOATOrq8VnLu71MnLfq+uLs4MOBVZUja9MbWZmNsCUdmBY0gxgBkBjYyOVSqWsUupi8uTJNc2n7+55+sKFC+tQjfVGvbYleHuWzduya30RAquApqrxcaltFxExC5gF0NLSEr09Ol+2Wi4pr8dZCNb3vC0HD2/LrvVFd9CjwBGSJkjaB/gMMK8P1mNmZr1U9z2BiNgm6W+A+yhOEf1xRPy23usxM7Pe65NjAhFxD3BPXyzbzMzqx/cOMjPLmEPAzCxjDgEzs4w5BMzMMuYQMDPLmAbC72ZKegl4ruw6+sFBwMtlF2F14W05eOSyLQ+LiLd0bBwQIZALSY9FREvZdVjveVsOHrlvS3cHmZllzCFgZpYxh0D/mlV2AVY33paDR9bb0scEzMwy5j0BM7OMOQT6gaQfS1oj6Tdl12I9J6lJ0kJJv5P0W0nnlV2T9Zyk4ZJ+JenXaXteWnZNZXB3UD+Q9CGgDZgTEe8uux7rGUljgbER8YSk/YDHgVMjwr+fvReSJGBkRLRJGgY8CJwXEQ+XXFq/8p5AP4iIB4BXyq7DeiciVkfEE2l4I/AM/v3svVYU2tLosPTI7luxQ8CsByQ1A8cAj5RcivWCpAZJTwFrgPkRkd32dAiYdZOkUcBtwJci4tWy67Gei4jtEfEeit9CP1ZSdt21DgGzbkh9x7cBP4mI28uux+ojItYDC4GPlFxKv3MImNUoHUi8DngmIq4oux7rHUlvkTQ6DY8ATgJ+X2pRJXAI9ANJPwV+CbxT0kpJ08uuyXrkBOB04MOSnkqPqWUXZT02Flgo6WngUYpjAneVXFO/8ymiZmYZ856AmVnGHAJmZhlzCJiZZcwhYGaWMYeAmVnGHAI2qElqlrQ53RoASct6uJyzJF1dx7qWpX9HpFNN/0PSQfVavlmtHAKWgz+kWwMMOBGxOdX2fNm1WJ4cApablwAktUp6QNLdkv5N0rWShqRpH5H0RLrP/IKOC5D0CUmPSHpS0v2SGlP7n1ddRPakpP0kjU3reUrSbyT9WXUdZmUbWnYBZv0pIv60avRY4CjgOeBe4JOSfgH8EPhQRCyVNKaTxTwIHBcRIekLwFeBvwO+DJwbEQ+lm8xtAWYA90XEdyQ1APt2UodZaRwClrNfRcSzsPPWHh8EXgceiIilABHR2e9AjANuTj8ysw+wNLU/BFwh6SfA7RGxUtKjwI/Tjef+d0Q81aevyKyb3B1kOet4z5Ra76HyA+DqiJgIfBEYDhARlwNfAEYAD0k6Mv2g0IeAVcD1ks6oS+VmdeIQsJwdK2lCOhbwlxTdPA8DH5I0AWA33UH7U3yoA5zZ3ijp7RGxOCK+S3FDsiMlHQa8GBE/BH4EvLfvXo5Z97k7yHL2KHA1cDjFveTviIgdkmYAt6dwWENxi+FqlwA/k7QO+DkwIbV/SdJkYAfwW+D/AJ8BviJpK8XvTHtPwAYU30XUBrX0M5B3RcS7O7S3Al+OiI+XUNYbpOsGWiLi5bJrsby4O8gGu+3A/u0Xiw007ReLUfzI+Y6Sy7EMeU/AzCxj3hMwM8uYQ8DMLGMOATOzjDkEzMwy5hAwM8uYQ8DMLGP/HxUZ0ub0LtqdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class_fare = data[[\"'pclass'\",\"'fare'\"]]\n", + "class_fare.boxplot(by=\"'pclass'\")" + ] + }, + { + "cell_type": "markdown", + "id": "cd23b504", + "metadata": { + "hidden": true + }, + "source": [ + "If we wish to zoom in our data, we could apply the log of 10 to the fares. However, we need need to consider all fees paying passengers. We can now compare the fares against each passenger class. It appears that first class passengers may have generally payed more than lower classes." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a01a9b3f", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class_fare = data.loc[data[\"'fare'\"] > 0, [\"'pclass'\",\"'fare'\"]]\n", + "class_fare[\"'fare'\"] = (np.log10(class_fare[\"'fare'\"]))\n", + "class_fare.boxplot(by=\"'pclass'\")" + ] + }, + { + "cell_type": "markdown", + "id": "cf427131", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Changes over time" + ] + }, + { + "cell_type": "markdown", + "id": "dee8228f", + "metadata": { + "hidden": true + }, + "source": [ + "Some datasets record the time when some observations were made. We sometimes want to explore and discover pattern of changes over a period of time; i.e., increases, decreases, or no changes.\n", + "\n", + "Line graphs can show variations and compare different behaviours over time, if some values have similar scale. The titanic dataset has not data over time. So, we use a simulated scenario of temperatures measured over a year. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "bac023da", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\",\n", + " \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", + "temp_C = [5,2,6,13,14,15,18,25,20,14,10,9]\n", + "plt.plot(months, temp_C)\n", + "plt.title('Monthly temperatures variation')\n", + "plt.xlabel('Months')\n", + "plt.ylabel('Temperatures (C)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f65cc4b7", + "metadata": { + "hidden": true + }, + "source": [ + "Sometimes, the variation in values can vary a lot and have different scale. So we simulate two countries with varying GDP and demonstrate this graph is not clear to read." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "246a545b", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "years = [2000, 2001, 2002, 2003, 2005, 2006]\n", + "GDPs_A = [1.3e7, 0.9e7, 1.6e7,1.8e7,1.9e7,2.1e7]\n", + "GDPs_B = [2.1e10, 1.9e10, 2.5e10,3.1e10,3.2e10,3.3e10]\n", + "plt.plot(years, GDPs_A, label=\"Country A\")\n", + "plt.plot(years,GDPs_B,label=\"Country B\")\n", + "plt.title('GDPs variation')\n", + "plt.xlabel('years')\n", + "plt.ylabel('GDPs (USD)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a5337270", + "metadata": { + "hidden": true + }, + "source": [ + "We use again the log 10 to show a comparison in one graph. It is worth noting in this case, the increase and decrease is not accentuated as much. The exponential aspect of the data has been removed. So, an interpretation needs to take into account this factor. However, we can show a lot more cleary both variations in both data." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "6abddbdc", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "years = [2000, 2001, 2002, 2003, 2005, 2006]\n", + "GDPs_A = [1.3e7, 0.9e7, 1.6e7,1.8e7,1.9e7,2.1e7]\n", + "GDPs_B = [2.1e10, 1.9e10, 2.5e10,3.1e10,3.2e10,3.3e10]\n", + "plt.plot(years, np.log10(GDPs_A), label=\"Country A\")\n", + "plt.plot(years,np.log10(GDPs_B), label=\"Country B\")\n", + "plt.title('GDPs variation')\n", + "plt.xlabel('years')\n", + "plt.ylabel('GDPs (USD)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "be9559b6", + "metadata": {}, + "source": [ + "## Relationship" + ] + }, + { + "cell_type": "markdown", + "id": "613d5f49", + "metadata": {}, + "source": [ + "Some analysis can help us discovering some potential relationship between observations or data. We aim to explore and potentially discover how data may be related to each other. For example, the land area of a country and its GDP. These two observations or data appear to be independent from each other. They appear to have been observed separately.\n", + "\n", + "We need to check our chosen data are independent, that is one cannot be obtained from the other. One example would be exploring the relationship between individuals’ weight and their BMI; the latter is computed using this formulae BMI = weight/height2. So, the weight and BMI are considered as dependent variables. We know an association using a known mathematical expression.\n", + "\n", + "However, for independent variables, we often hope to describe the association with some known expressions, such as a linear or an exponential expression, for example. So, we use some regression techniques to aim to discover those. \n" + ] + }, + { + "cell_type": "markdown", + "id": "292e928f", + "metadata": {}, + "source": [ + "### Visualising relationship\n", + "\n", + "Visualising relationship between two chosen observations or data can achieve with a scatter plot. The position of each point on the x-axis and the y-axis indicates the value of an individual point. A scatter plot let us observe whether some relationship may exist between data.\n", + "\n", + "So we plot the fares and the number of siblings / spouse in a scatter plot. The scatter plot is quite challenging to read. The second scatter plot shows a bit more clearly the match in the data with more details. It is the best that can be achieved. We can perhaps infer passengers traveling in larger families may have pay more each ticket. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "6921c485", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data[\"'fare'\"], data[\"'sibsp'\"], s = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "73dc16f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = data.loc[data[\"'fare'\"] > 0, [\"'fare'\"]]\n", + "y = data.loc[data[\"'fare'\"] > 0, [\"'sibsp'\"]]\n", + "plt.scatter(np.log10(x), y, s=1)" + ] + }, + { + "cell_type": "markdown", + "id": "c075f8d1", + "metadata": {}, + "source": [ + "We explore the relationship between age and fare. We may have some clusters of fare per age. Applying the log10 to both set of data shows more details in potential clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "8e44a9d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ER+BKKZUOrtrQ4cdWdPvgD7fh8TXjbwnwT7+r4W9e3hFzzYzzre3s/+g8q7Ye4TP/+m7oNU7Xw4g+Nnxc9prqEwnXV5l5fT6vPDqLsqK8fl2DI1WfPVjWERks56kGBld14AdOnQfgRFMby9fXWMvLHmVb3dmYe14+vXYv51o7APA2toZe43QJ2ehj7fvL19cktQxtT5atTZVUfXZ/nkNfGiznqQYGV6VQvvXQzSz9WTVHzrbxmVtLqCi5jk0f1tNxxcRMk3z5nnKeXruXkvyRjM4b4WgJ2nDh6RJ/S4DWQCeP33ND3PHjTt4H+nb4m+7tmZzBcp5qYHBVBF5WlMflTrjUcYXvbzrMxpp6dnqbuG7kMFZtPdIlanrtg1Nc6jB4fK3MnzaGsqK8pJZ5DT/WHlaYkz2UsqK8pJaKjf7MvozyUrVpxGDZfGKwnKcaGFwVgQO88PB0nl67lxcenk7p6Fwg/lDAYBHzABVjr8moCFSjPKVUKrgqAve3BHjzwBk+du0IrssZFoqWwiPi8CJUfk42H78+n5HZWY7fP7qAZT8GpCwy0ygvMS0kKuWMqyLw8M0clq+PvdB/9GzLWLMpE71/otmbOtuvb+h3rpQzrurAF1dO4IPjTWz8sIF7K4p5+2ADT67Zw4uLZ4RWGwxPT/x+/2mGDhEW3DgmVIiMVTyMtWyrvaRsW+AKj99TrumOPuQ0xaRroajBzlUdeEFuNju9fjquGF74Qy0A/pZ2nlyzh/efuzd0jB21fWt9DR1XDFsO+SjIzY67MUKsiG/lFk9oSdlli6ZpB9GHnC65q5G6Guxc1YEDvLh4RijqBiJuR/vnv7yFr/xqH//8l7cA8SO7eEvUdrekrOpfWgxWg53jIqaIZInIByKy3ro/SUR2iEidiPxCRNIeovpbAnxw4hx/O/t6bpkwilsmjOLhmeN5edtRPL7mLjuZ/8WMEj789iLuLC+KWYjsrkCZkz2Uqjmlro6+B3JBUIvBarBLJgJ/HPgQuNa6/11ghTHmVRH5MfAY8KMUty9C+BKvIORkZ7Fq61EAvvbaPnKyhzpeWjbR49095yYD5TyUUl05isBFZDzwaeBl674A84G11iGrgYfS0L4ICyqKGT9qJAB/9JxlavE1DM8SAD48fZGqO0q5e2pRaAGp4FopH/LWwQYe+fh4th72haLz4MzKjogCZXi0urhyAssWTXP95fmCimLunloUd/nbcAM5WldqIHKaQvk+8BXginW/EDhnjOmw7p8ESmK9UESWiEi1iFT7fL7etJWNNfWcPNcGwE5vE9/+XQ2XO4PL4V641MHqP3pDC0iBNexw6xF2HPWz8WA92+oaQ2um2Pte5mRnxZwhOVAuz+29OzfW1Hd7rK4DopS7dJtCEZH7gQZjzG4RmZfsBxhjVgGrILgeeLKvD2cXFtvarzBy2BDmThnD9/5QS0n+SE41tcXZ1CFAzenz/N1dkyM2gUi0t6bbo+5wyZzTQDx/pQYyJxH4ncADIuIFXiWYOnkJGCUi9n8A44FTaWlhlLZAJzUfXeCBGcGA/8yFS3zuton87O9nd9kTc/V2LyOzh/Dkwqn8aEudtcNOUKwIO17U7SS1kGgWp8fX3OPUhL8lwIoNtazYcKhHr+/p2i/9SVM5SjnTbQRujFkGLAOwIvD/aYz5axFZAzxMsFOvAtalr5lBq7cfDRUtl6+v4bi/FY+vhafX7mXTU/Mijg0veK7fdxqPryX0umR3yHFSCEw0izPevptOP9sej56TnTUoCpFaeFXKmd6MA/8q8KqILAc+AH6SmibF19TaDkCWwGduLeGaEcN4cs0envv01dRJ+KxKe0/LuVPGsPx3NfguXmbp/PKkP9dJaiHWXpn2Y0vnlzN7cmGPlpPty/HomTKzUWdiKuVMUh24MeZt4G3r9hGgTzd7PGKNIOk08P1Nh3mkcgL+lnZq6y+GptKHR29PLJwSeu2f3/QxvvPGQXZ5/cy8Pj+pz3UyM9AuFs6eXE/Z3LyoxwodD1+M9dlPLJyaVHt7KlMiX52JqZQzrpqJ+c0Hb+bpNXtpuHA5YjlZJ4XIdBfokimKZmqxMFPbFY/b2qtUqrluV3qPr5mv/2Y/N5Vcxxe7KbiFp1M21tQnfant8TWzfH0Nz95fEVEgTVb0pb5e+iulkhFvV3pXrQcOwSLku55GVr0TuQNPrJELq7cf5TtvHOTrv9nfo/HNy9fXsLnWF3O/zWTE21tTx1srpXrDVSkUf0uAomuGkzc8i5zsLLYd8vHiHw4yf1oxjS0BdnqbaA108MTCqfhbAqGRH02t7Sz5xGRaAx2hfTPtYYZgqJozKWYkHBwzXtNlfHm8toVH++FRf/Slfqou/Z1cIWi0nxz9vlSqpfN3ylUd+JrqE/yy+iQAzZc7abjYCMAbB8JnGUro2H0ng7vY15y+QPG1w9lc6wtt7BA+zDDeZg9lRXmOhxxGDxmMHjoY/v5Oi3Tdsa8Qotd+idUuux0qMf2+VKql83fKVR24PbOy+pgfgPyRw9hW52P+tGImFOQw0lo90D72VFMbm2sbuHvqGKruLI3YNzM4PC84zDCV+1za+3PG26czlZxcIWihLzn6falUS+fvlKs68KbWANvqfHgamrnv5mK+tGBqRHEyOi3y0K0lvOs5y0O3llBWlBca3gf28LwpcT8r+r2AhJdB4VG1/Tn2z3QVMZO5QnCb/kplpOrqSClbU2sgNEdkUKdQvrFuPzWnLwKwbu8ZLlzqjEhTRKdFfll9Iu5Mze5EvxfQ48ug6EuovrxMd2tKwK3tViqak1RnT7mqA59clMe2umDeO3/kUO4sK+SPnrPUfHQhtASsnTY51dTG52+bwPNv1vJ3dwUj6ORnQHZNsfTkMihdRcyefLZbuLXdSkVLZjBEslw1DnzFhtrQuiAAI4cNoa09uMLtskXT+MLcMh59ZWcoKi/IHYa/pZ2yolw2PTUvtCemfaxSSrlBvHHgrorAq+ZMoi3Qyd6T55k+/jr+rOQ6nnt9P/dWfCwUqT17fwXtnQeoGHsNc8pG8+3f1fDCw9MBjeqUUgOLayby2OmPvOFZ7Dzq58PTF6jzNfPWU3fzzKIbWVN9gvePNbF8fQ3ffPAmvvbpCiYU5jA6bzjfffNDPL7mpJdLTfWypqlYXjZddAlXpdzHNRG4XdQSwABb6xrZWtfIvpPnuWX8KF7adJiyolxr2dhgsWD5+hp2HA0OOVzy79Ws+eKcpKrAqS6kpWJ52XTJtKKhTqhRqnuu6cDttMfr75/gQH1wbe8x1wQn59RfuMSdZYX8/SeCu+4snV/Oyi0equ4opa6hmfbOK3h8Laze7k04dDDeZ95WWsCjr+zsdk2U7tZeiR4rnkmpnExLL2XafyhKZSLXdOB2+sP+Rw1wqSO4Jac9tPCTU4p45dFZoWLl3VOLONHUxp1lhZy5cJlg7J78Z14tjCYeBtRdhB1rrHimyLTxz5n2H4pSmcg1Hbjtc5UlvFp9ilvG5fHkvTfyj789wKxJBYy9bkSXf/ThsyLtiLgnnA4DyuQI220y7T8UpTKRk02NRwDvAMOt49caY74hIpMIbqdWCOwG/tYYk/YK2PQJ+ax9/yPG5ecwoTCHz8+ayOLKCTS1BvjSzz9g3KgRnGpq41sP3QzA1sNnaWy+zBfn3RBKZ3SXX42ehZmfk83syYXk5yTOxaY7wu5JXjhVuWTNSSuVeZxE4JeB+caYZhEZBmwTkTeAJ4EVxphXReTHwGPAj9LYVgCe/c1+Og28eaCByx0mlKp470gj2+rOho6zl4DdVneWbXVnKcwb3mWvSoidX03lLMxU6kleOFW5ZM1JK5V5nGxqbIBm6+4w648huDv9X1mPrwb+kTR34G8fbKDTSmMPATo7grvMv/b+CW6fNJrbJxWQnzOMPx5p5NoRWcAQrhmRxaTCHG4rLWDlFk/M5V0hMsKMnoXZ1BrgnUM+GlsCoeVoeyLZNVGin+9JXjhV+0vG2vNTKdW/HOXARSSLYJrkBuBfAA9wzhjTYR1yEiiJ89olwBKAiRMn9qqxT67ZE7p9BXjHExwiWFvfQm19C8sWTeO9I42cb+tg3d4zoWP3nbrID946HHd5V+gaYYaPVllTfYJ3PY2862mksBe52WTXRIl+vid54VTtLxlrz0+lVP9y1IEbYzqBGSIyCvg1MM3pBxhjVgGrIDiVvgdtDHlx8Qz+4T92E+gwlI/JpbQwh/880MBNY/NYUDGWxZUTWFBRTHvnASaPzgGE2vqLTB9/HZ+dNTFhYTFRpJqqneGTXRMlk9ZM0VEhSmWepNdCEZGvA23AV4GPGWM6ROQO4B+NMX+e6LW9XQvF3xLgS/+xm20eP1kCyx+6mTMXLgFC1ZxSCnKzef9YE0/+cg+fLC/ixrHX8M3f1jBvahFTP3ZN3J13etMeLeylXqLvVb9zNRj1eC0UESkC2o0x50RkJLAQ+C6wGXiY4EiUKmBdapvc1ertR9lmpU06DTy37gAdV4L/Ae07eY7vPTKDp9fuxdvYirfxGEOHCB1XDG8eqOfNA/Vxd97pKS3spUei71W/c6WucrIWylhgs4jsA3YBG4wx6wlG4E+KSB3BoYQ/SV8zg+yVB21zJuUzBCjMHcbmWh+rt3t54eHpfOza4VwzIou/+LOrBbc7ywpZUFHMig2HWLGhNuaaH/HWA4n3+OLKCSxbNC1hWkHXGEleou/VyXfeG/r3pdzEySiUfcCtMR4/AvTpdjAjh0X+f2MXMRtb2u1WMfP6fHKHD+XMhcv89k/BvTILcofxg7+a2e0+mPGiu3iPOykQasSYvETfa7on+Ojfl3ITV83EnDtlDP+6uQ47EC8rysHja+Xa4UN5aGYJVXMm4W8JcGfZaDqvGD5z6zh+tOUIT987NbRGSaJ9MMMLdeGTeR6YURLxfHeihyQm89p0cGPeOB1tdvKemfD3pZRTrurAf/DWYcKzKB5fKwAXLndwwt9KQW42K7d4+Pf3joWGFLa1X+HlbUetVQpJuJhVeHS3coun213r44mO4vo7knNjVJmONjt5T53Cr9zEVR340vnl7D7WxIVLHdwyLo9JRdfwVm0DkwrzQuuUhEdQwUknNSydX84urz/pCTA93bU+06K4TGuPE+losxu/B6UScVUHvsvr58Kl4NyhS+1XmD25kN/X1DNu1Ah+sesEGAMII7OzAMjPyeaW8aN4c/8ZRmYnt3dF+K71dmHL6eW8PWvSbWmLTJKOSFijazXQuKoDX1w5IXQJfMjXGhpG+OaB+i7H5liduJ0GCT7Ws2GE/bkGSSpkUluUUqnjqg783cO+iPt/8WfF/HrvGWZPymdK8bUcPHOBaWOvJT8nGAHvO3GOUSOHMXtyAU2tgZhrmXRX2PL4mnnnkI8ln5yc0jVI+rKw6CR14MZCp1KDnWv2xAT4yq/2Rdy3hwn6mgOU5I9kp7eJklEjeWLhFApys/n272o419bOTq+fHUebWPXOEdZUn4h4Dzs6jX7ctnx9De96GjlcfzGpjq27/Te7+9xUcrIXaF+2RymVGq6KwP/5L2/hS6/uwQA5w4Tn7r+Jl7cd5YWHp1M6OheIjDJfeHg6T6/dy3OfruCDE03EWsuku+jU6WYOycq0glqmtUcp1T1XReAl+TlkWS1uazf4Ll7C3xLgh28d5sEfbsPTcJHV2734rVTJmwfOMDpvONs9Z3lgRkkoLx4+2y5WdBr+fFlRHq88OouyoryIxz2+Zh59ZSceX3Po9vvHmkLPh+9AH2v2p5OouC9lWnvSwcksS52JqdzEVRH4E7/YQ4c1DtwAKzbWYYC3rGViT+w+BVwtYK565wgAO476OdzQHFpOFhJv0OBkRubVfS+DG0dsrvVx3N8aGm9uf0b4/pipXotFJcdJMVcLvspNXNWB3z6pgGP+1tD9sddm89GFAEW52WQPG8KdZYWMHZUTSgM0tgTYe+Jc3OVkk106NXzPy8bmy7R3mrDUSuzx5reVFtAa6MQY02UzhGQLh1po7B0naSLduEK5ias68LGjRkTc/+hC8DLX1xJg2aJpXSKmr33qxoj74RsRJIqu4o0Xth9fucXDqq1HWbZoGmVFwfe0d6ufeX1+xGes3OJhx9Hgmi0bayI3Q0g22tPosHecjAPXjSuUm7iqA39gRgnrPjiF19/G8CEwY2I+e0+e589vKu7T4lsyBb9EMzqTLRxqoTH99DtWbpL0hg690dsNHf7m/77HNk9j6P4nygqZWZofsVFDvH0nF1QUs7GmPiL9EL37fKz3ACJur95+FHsDifDnMjWl4TTtoukZpTJXjzd0yCTvhnXeAFs9jWz1NEYUB+PtOxleTAw/NtaCVeHvAUTcfmlTnXV8VsRzmZrScJp20fSMUu7jqg78s5UlvFp9KnR/mMCN466N2HHeLkLdVlrAig2HaAt08Pg95TwwYxyzJ9ezoKI4Ynf6WOmNWJfRocJo82VqTl9kQUUx+TnZEc95fM0sXx8sZm455CM6su8PTlMCmjoI0isR5SZOtlSbAPw7UExw9N4qY8xLIlIA/AIoBbzAI8aYpvQ1FXYdOxdxv93AvlMXInacB0K37Z92sbFsbh4rt3ji7j5viy52hd8uzBvOtrqjbKyp77JU7PL1NV2GE/b30EGnCzjpQk9BeiWi3MRJBN4BPGWMeV9ErgF2i8gG4L8Cm4wxz4vIM8AzBLdZS5vnPl3Bf/u3XVwBhgFF141gwY3FVN1Z2mWI4IKKYm4Z/xFOoutkJHq9PWszPAIf7BGt2+iViHKTnuxKvw74ofVnnjHmtIiMBd42xkxN9NreFDH9LQH+bvUu3j9+DoBsge99dgYfnb/UpUCZqHBpv5deJiul3CIlRUwRKSW4P+YOoNgYc9p66gzBFEus1ywBlgBMnDgxmY+LsKb6RKjzBgiY4OJWbe1XuhQoExUu7ffSy2SllNs5XgtFRPKAXwFfNsZcCH/OBMP4mKG8MWaVMabSGFNZVFTU44YurpzAI5XjIx57cPpYSgtzGDF0CKWFOdxWWhA6dtmiaXzm1hIKcocx7roREetbLKgo5u6pRd3Otkvnuhi65kZm0r8X5SaOOnARGUaw8/6ZMeY16+F6K3WC9bMhPU0MKsjNDs16tK19/yO8ja28caAeb2MrP3jrcOjYL8wt4/ubDuNvaecbvz0QsVSqPdtuY03XjSDCpXOJVV2+NTPp34tyEyejUAT4CfChMebFsKdeB6qA562f69LSwjALKor56daj1DdfZhjw1fum8v92HGf6+FGcbb7cZcnX8OVka+svdilQ9efQOi2WZSb9e1Fu4iQCvxP4W2C+iOyx/nyKYMe9UEQOAwus+2m1eruX+ubLAFwR+PEWD97GVqqP+fn7T0zmG+v280+/+xCPr5mVWzyUjs5l01PzmDdtTGip1N4UMLu7vA5fYra7450u36qX9PHpd6MGu24jcGPMNkDiPH1PapuT2Jawsd6dBs62tANw6twlnlyzB39LO9vqGjnccDFm8RKSK2DGm9UZ77X2OHCo4ZVHZ6WkWKoF1/jS8d3o963cxFUzMb84dzLLfr0/dL8gZyhDRBiaNYTbJxVw5sIlpo/P57OzJnQZF25LdIkcHZ2HLx+7cosnVPSMd3m9dH45x/2tLJ1f3u1nObWgoph3Dvli7uc52KUj3aEpFOUmrurAX952NOK+v7WDu6cWsbnWx7q9pyOWlI23FGiiGYfR0Vf48rFOorJdXj8eXwu7vH5mXp+fktmNG2vqedfTyLueRgp1tmSEdMwe1Rmpyk1c1YG/8PB0Hlu9k6bWDm4vHcXssiIemDGOW8afItZ+l8nqbiOH/ih6Btdr6SAV56eUGlhctSfmzOvz+Yd5NyAChxuamTuliLKiPJ5YOJWqOaWs3u6N2HsyVpEr3r6WidjplDXVJxIWzLorTPak6FaQm80TC6fyxMIpmj5RSkVwVQQO8E9vHMSYYPrk6bV72fTUPCD20rCxClLx9rXsruioBUmlVKZxVQfu8TUzsWAk3sY2Ro0cwgsPT49Y9yR6adhEy8IG97UM0N55JTR+PFEKJBXpES2QOaNr1SjljKs68OXra/A2tgHw3+dNYeb1+azYUMtLm+poDXTwxMLItbRiFaQi97U8ErGvZaICVqLnnHY4WiBzRq9UlHLGVR34s/dX0N65n4qx14VFsRL105lURsPa4aSWXqko5Yyr9sSEq/tYtgU6GZk9hAdmlMRcMjb6NbEi5Hj7Zzq5dI+3b2ZfXvJrqkGpwWFA7IkJkcVKcLbjTbwIOdmZlonesz8ib438lRrcXNWB+1sCtAY6WPLJyWBgZPaQiMtse0/KZ++viFi50B5L3RrojJjNGK/Y6eTSPRMu8zOhDQONXtUoN3FVBx6MvusiZlyGi16LxFaQm01O9lC+88ZBcrKzQq+NLiomU2TMhIJkJrRhoNGrGuUmrurAu4s47T0po5eVdfJapUB/T5S7uK6IaettEVEvlZVSbjFgipg2+1K3NdDBvpPn4y4f293rk3mNUkplkm7XQhGRn4pIg4jsD3usQEQ2iMhh62d+epvZlb3vJQiba33cdUNhqEgZS/Q6JPbrwy+V3bZBgNvaq5RKLSeLWf0bcF/UY88Am4wx5cAm636fsgt4VXNKWbZoGh+/Pp+XNh2Ou5dh9F6HsRaectt+iG5rr1IqtZzsyPOOiJRGPfwgMM+6vRp4G/hqKhvmlN0R+1sC5GQPjVt8clKcclsBy23tVUqlVk9z4MXGmNPW7TNAcYra02PRQ+rCF7l6fc9HgKFqzqQBVbDUYYRKDW69LmIaY4yIxB3KIiJLgCUAEydO7O3HOWanF64uGdv9rE0tbCql3KSnHXi9iIw1xpwWkbFAQ7wDjTGrgFUQHEbYkw9LNOTP42vmG+uCC1x9dtaE0LoodlrhttIC2jsN40aN4J1DPm4rLWCX1x/zvRZUFPPekcbQ3pdKKZXJetqBvw5UAc9bP9elrEUxJIqMl6+vYVtdY8zd6O1lY7fVnaWsKBePr4Uza/fi8bXEfK+NNfVsrvUxe3J93D01lVIqU3TbgYvIzwkWLEeLyEngGwQ77l+KyGPAMeCRdDYyUbEufInZWLvRh0fiP3jrMEvnl4ci8GQ+RymlMo1rZ2LGE55uaWoNsHx9TajTXlBRzMaa+tBPnYWplHKDATcTM55Ye14e97fi8bWE7ocXNrVYqZRyK9d34NEFzvA0yG2lBRz3t/Lle8p57YNTLJ1fzuzJhSyoKGb25GAkvnKLRyNxpZQrOZmJmdESzbDc5fXj8bXw2gen2FzrY5fXzxfmllFWlMcX5paxsaZeZzIqpVzL1RG4vyVAY3OAu24oDA39C4/Iw3egv2X8RzQ2B1ixoTY0oWcwFC111UWlBi5Xd+Brqk+wausRIDgEsGxuXtytznKys0JbsdkTegbDTEadnKTUwOXqDjy4VVonYLrdFi3WsYPBYLjKUGqwcn0OPCc7K5QSiZUusJdc9Z5tYd/Jczwwo2RQpRJirbqolBoYXB2BO9lV3n7MnokZvV+mUkq5lWsj8PAC5m2lBazc4mFBRTGP33MDjS3BYqW/JcCCimLuuqGQm8ddS2lhDkvnl4de3xebIeimC0qpdHFtBB5ewByWdThixcGXNh0M3QbYVtcYet0ur5+Z1+f3WXFPi4hKqXRxbQceLEp2AMIDM8ZFrIESXaxsDXTSFuhkZPaQboud6WhnX3yOUmrwcW0KpSA3m6o5k8jJzgKCnfTq7Udpag2ECpsQjICr5pTyxXlloYjcfr0dEaczxaFFRKVUurg2AofYmzaE71APhNIX4bfDUxma4lBKuZUrO3B/S4DV2720BTp4/J5yHpgxjlvGf0RboINL7Ve4fVIBjc2XmVM2mtLCHDy+ZkYMy+LOssKIzRr8LQFaA508fs8Ngz7FoTM2lXIfV3bga6pPhGZVLls0jbKiPJ5YOIWVWzyhaHrHUT+bDjbgbWzF29gaeq09YzP8fZYtmjboOy29ElHKfVzZgcebVWk/bhcs504Zw/f+UEtZUR4jhmVFFDHt48N/Dmb6XSjlPr3qwEXkPuAlIAt42RjzfEpa5UBboIO9J8/T1n6F+276GC9uOERh7lD2nrzAi4/MYOb1+QB866GbWb6+hmfvr6CsKBh5h6dgEGH19qMRRU9NIyil3KDHHbiIZAH/AiwETgK7ROR1Y0xNqhoXi78lwFO/3BMqVO446mft7hP4W9pDxzy9di+bnpoHBPfMDB57dQZmeArGZo9QGaxpBE2hKOU+vYnAZwF1xpgjACLyKvAgkNYOfE31CTbX+rh9Uj4gtHde4f3j55iQP5Kbxl3DjqNNPPfpitDxz95fAdRYP4OuplqCEfjIYbFTK4OJplCUcp/edOAlQPhOCCeB26MPEpElwBKAiRMn9uLjgsI7moLcbFZsOMT7x8/xmZnjycnO4s0DDdTWX2TetDEAlBXldVn7pCA3mycWTon5/oM1+hwMS+sqNdCkvYhpjFkFrILgpsa9fb/ojqZqTik52VmDPoJWSg0+venATwHhPeV467E+Fd2haxSplBosejOVfhdQLiKTRCQb+BzwemqapZRSqjs9jsCNMR0i8j+A3xMcRvhTY8yBlLVMKaVUQr3KgRtj/hP4zxS1RSmlVBJcuxqhUkoNdtqBK6WUS2kHrpRSLqUduFJKuZQY0+u5Nc4/TMQHHEvyZaOBs2loTqbS8x24BtO5gp5vKl1vjCmKfrBPO/CeEJFqY0xlf7ejr+j5DlyD6VxBz7cvaApFKaVcSjtwpZRyKTd04Kv6uwF9TM934BpM5wp6vmmX8TlwpZRSsbkhAldKKRWDduBKKeVSGd2Bi8h9IlIrInUi8kx/tycVROSnItIgIvvDHisQkQ0ictj6mW89LiLyf6zz3yciM/uv5ckTkQkisllEakTkgIg8bj0+UM93hIjsFJG91vl+03p8kojssM7rF9byy4jIcOt+nfV8ab+eQA+ISJaIfCAi6637A/lcvSLyJxHZIyLV1mP9+rucsR142KbJi4AK4PMiUpH4Va7wb8B9UY89A2wyxpQDm6z7EDz3cuvPEuBHfdTGVOkAnjLGVACzgX+w/g4H6vleBuYbY6YDM4D7RGQ28F1ghTHmBqAJeMw6/jGgyXp8hXWc2zwOfBh2fyCfK8DdxpgZYeO9+/d32RiTkX+AO4Dfh91fBizr73al6NxKgf1h92uBsdbtsUCtdXsl8PlYx7nxD7AOWDgYzhfIAd4nuE/sWWCo9Xjo95rgWvp3WLeHWsdJf7c9iXMcT7DTmg+sB2SgnqvVbi8wOuqxfv1dztgInNibJpf0U1vSrdgYc9q6fQYotm4PmO/AumS+FdjBAD5fK6WwB2gANgAe4JwxpsM6JPycQudrPX8eKOzTBvfO94GvAFes+4UM3HMFMMAfRGS3tVk79PPvcto3NVbJMcYYERlQYztFJA/4FfBlY8wFEQk9N9DO1xjTCcwQkVHAr4Fp/dui9BCR+4EGY8xuEZnXz83pK3cZY06JyBhgg4gcDH+yP36XMzkCz4hNk/tIvYiMBbB+NliPu/47EJFhBDvvnxljXrMeHrDnazPGnAM2E0wjjBIRO1gKP6fQ+VrPXwc09m1Le+xO4AER8QKvEkyjvMTAPFcAjDGnrJ8NBP9znkU//y5ncgc+mDZNfh2osm5XEcwV24//F6uiPRs4H3a5lvEkGGr/BPjQGPNi2FMD9XyLrMgbERlJMN//IcGO/GHrsOjztb+Hh4G3jJUwzXTGmGXGmPHGmFKC/zbfMsb8NQPwXAFEJFdErrFvA/cC++nv3+X+Lgx0UzT4FHCIYB7xf/V3e1J0Tj8HTgPtBPNijxHMBW4CDgMbgQLrWCE4EscD/Amo7O/2J3mudxHMG+4D9lh/PjWAz/cW4APrfPcDX7cenwzsBOqANcBw6/ER1v066/nJ/X0OPTzvecD6gXyu1nnttf4csPuj/v5d1qn0SinlUpmcQlFKKZWAduBKKeVS2oErpZRLaQeulFIupR24Ukq5lHbgSinlUtqBK6WUS/1/qaa2FYR34JkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ages = data[\"'age'\"]\n", + "ages = ages.replace (\"?\", 999)\n", + "ages = ages.astype(float)\n", + "data[\"'age'\"] = ages\n", + "x = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'fare'\"]]\n", + "y = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'age'\"]]\n", + "plt.scatter(x, y, s = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "230edc77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ages = data[\"'age'\"]\n", + "ages = ages.replace (\"?\", 999)\n", + "ages = ages.astype(float)\n", + "data[\"'age'\"] = ages\n", + "x = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'fare'\"]]\n", + "y = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'age'\"]]\n", + "plt.scatter(np.log10(x), np.log10(y), s = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "b86fc924", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Testing for linearity" + ] + }, + { + "cell_type": "markdown", + "id": "b3693a91", + "metadata": { + "hidden": true + }, + "source": [ + "Linearity is a relationship can be expressed as a straight line. A Pearson’s correlation coefficient measures the linear correlation between two data. Pearson’s correlation coefficient returns a numerical value that can confirms our observation made on a scatter plot. It could support our decision of choosing appropriate analytical methodologies." + ] + }, + { + "cell_type": "markdown", + "id": "ec49c911", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Is there a correlation between fares and number of siblings/spouse traveling on the Titanic?" + ] + }, + { + "cell_type": "markdown", + "id": "a6c6ba72", + "metadata": { + "hidden": true + }, + "source": [ + "The above scatter plot suggested a correlation was highly unlikely. The three correlations shown below confirmed our assumption numerically." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c8d521b3", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson : 0.16043996653488585\n", + "Spearman : 0.4458772417727718\n", + "Kendall : 0.3570445658158402\n" + ] + } + ], + "source": [ + "x = list(data[\"'fare'\"])\n", + "y = list(data[\"'sibsp'\"])\n", + "\n", + "print(\"Pearson : \" , scipy.stats.pearsonr(x, y)[0]) # Pearson's r\n", + "\n", + "print(\"Spearman : \" , scipy.stats.spearmanr(x, y)[0] ) # Spearman's rho\n", + "\n", + "print(\"Kendall : \" , scipy.stats.kendalltau(x, y)[0]) # Kendall's tau\n" + ] + }, + { + "cell_type": "markdown", + "id": "d1929906", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Is there a correlation between fares and the age of passengers travelling on the Titanic?" + ] + }, + { + "cell_type": "markdown", + "id": "467033a2", + "metadata": { + "hidden": true + }, + "source": [ + "The above scatter plot suggested a correlation was highly unlikely. The three correlations shown below confirmed our assumption numerically." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "d4e4ddab", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson : 0.1769386401456062\n", + "Spearman : 0.18947642093473568\n", + "Kendall : 0.12961133210331646\n" + ] + } + ], + "source": [ + "\n", + "x = data.loc[data[\"'age'\"] < 999, \"'fare'\"]\n", + "x = list(x)\n", + "\n", + "y = data.loc[(data[\"'age'\"] < 999), \"'age'\"]\n", + "y = list(y)\n", + "\n", + "print(\"Pearson : \" , scipy.stats.pearsonr(x, y)[0]) # Pearson's r\n", + "\n", + "print(\"Spearman : \" , scipy.stats.spearmanr(x, y)[0] ) # Spearman's rho\n", + "\n", + "print(\"Kendall : \" , scipy.stats.kendalltau(x, y)[0]) # Kendall's tau\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc47298e", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Is there a correlation between passenger class and fares?¶" + ] + }, + { + "cell_type": "markdown", + "id": "5c20ac8c", + "metadata": { + "hidden": true + }, + "source": [ + "In many public transport tickets to a higher class can cost more than a lower class. So, would like to explore whether this was the case with the titanic.\n", + "\n", + "So we first convert in numerical data both the class and the fares.\n", + "\n", + "It appears only one fare remains not captured and therefore unknown. The distribution of the fares appears to have mean value at around 33 (unknown currency) with a standard deviation of approximately 52 (unknown currency). So we could surmise the fares may be skewed to the left. \n", + "\n", + "We surmise the larger proportion of third class tickets may skew the distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "90ee97f5", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'fare'\"].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "2adc22dc", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 211.3375\n", + "1 151.5500\n", + "2 151.5500\n", + "3 151.5500\n", + "4 151.5500\n", + " ... \n", + "1304 14.4542\n", + "1305 14.4542\n", + "1306 7.2250\n", + "1307 7.2250\n", + "1308 7.8750\n", + "Name: 'fare', Length: 1309, dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fares = data[\"'fare'\"]\n", + "fares = fares.replace(\"?\", \"-1\")\n", + "fares = fares.astype(float)\n", + "data[\"'fare'\"] = fares\n", + "fares" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4266cae6", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1309.000000\n", + "mean 33.269280\n", + "std 51.747562\n", + "min -1.000000\n", + "25% 7.895800\n", + "50% 14.454200\n", + "75% 31.275000\n", + "max 512.329200\n", + "Name: 'fare', dtype: float64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(fares, bins = 300)\n", + "fares.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "5ef6160a", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fares[fares == -1].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "f7b80577", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"'pclass'\"].dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1b14fa0f", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1.0\n", + "1 1.0\n", + "2 1.0\n", + "3 1.0\n", + "4 1.0\n", + " ... \n", + "1304 3.0\n", + "1305 3.0\n", + "1306 3.0\n", + "1307 3.0\n", + "1308 3.0\n", + "Name: 'pclass', Length: 1309, dtype: float64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pclasses = data[\"'pclass'\"].astype(float)\n", + "pclasses" + ] + }, + { + "cell_type": "markdown", + "id": "851b0078", + "metadata": { + "hidden": true + }, + "source": [ + "The fares appears vary for each class. The first class spread of the data. However, there appear not to have a clear distinction within the data. None of the correlations suggest a strong correalation exists the values varies between [-0.56, and -0.71]. However, it suggests the fares are likely to decrease as the classes decreases. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "0d3944ee", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Fares \n", + " min mean std median max\n", + "Pclass \n", + "1.0 0.0 87.508992 80.447178 60.0000 512.3292\n", + "2.0 0.0 21.179196 13.607122 15.0458 73.5000\n", + "3.0 -1.0 13.282715 11.498791 8.0500 69.5500" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GgLR8OzC2gm02M7MKKSsEIqI1Io4DJgPHA3/Z1w1LmitplaRVLS0tfV2dmZn1Qo/ODoqIbcB9wAeA0ZKGpkWTgU1pehNQD5CWjwK2dLCuGyKiKSKaxo8f37vWm5lZn5RzdtB4SaPT9HBgBrCWIgxmpWqzgcVpekmaJy2/NyKigm02M7MKGdp9FSYCCyXVUYTGHRFxl6TfArdJ+hrwOLAg1V8AfE/SOmArcE4/tNvMzCqg2xCIiNXAuzsof5pifKB9+ZvA2RVpnZmZ9StfMWxmljGHgJlZxhwCZmYZcwiYmWXMIWBmljGHgJlZxsq5TsD6qKP75/n6OTOrBT4S6GdtAVBXV8fy5cupq6vbq9zMrJp8JDAA6urq2LWruOHqrl27GDp0KK2trVVulZmZjwQGxLJly7qcNzOrFofAAJg+fXqX82Zm1eIQGACtra0MHTqU+++/311BZlZTPCbQzyICSbS2tjJt2rS9ys3Mqs1HAv3s1FNPBWDevHls27aNefPm7VVuZlZNPhLoZ0uXLmXevHl8+9vfBtjz9/rrr69ms8zMAFAtdEs0NTXFqlWrqt2MfiGJbdu2MWrUqD1l27dvZ/To0e4SMrMBI+nRiGhqX+7uoH4miUsuuWSvsksuucQXi5lZTXAI9LMZM2Zw3XXXcdFFF7F9+3YuuugirrvuOmbMmFHtppmZuTtoIJx66qksXbp0z5lCM2bM4Oc//3m1m2VmGemsO8gDwwPAH/hmVqvcHTQAGhoakLTn0dDQUO0mmZkBDoF+19DQwMaNGznxxBN57rnnOPHEE9m4caODwMxqgkOgn7UFwEMPPcTEiRN56KGH9gSBmVm1OQQGwKJFi7qcNzOrFofAAJg1a1aX82Zm1eKzgyqkq4u/VqxY0eFy/+ykmVWbjwQqJCI6fdTX1+9Vt76+vtO6ZmYDySEwADZs2EBEcNjFdxERbNiwodpNMjMDHAJmZllzCJiZZazbEJBUL+k+Sb+V9JSkL6TyMZKWSvp9+ntwKpekayStk7Ra0nv6eyfMzKx3yjkS2AV8OSKOBk4APifpaGA+sCwijgSWpXmAjwBHpsdc4LqKt9rMzCqi2xCIiOcj4rE0/SqwFpgEzAQWpmoLgbPS9Ezg5iisBEZLmljphpuZWd/1aExAUiPwbuBhYEJEPJ8WvQBMSNOTgNJ7IjSnsvbrmitplaRVLS0tPW23mZlVQNkhIGkEcCfwxYh4pXRZFCe49+gk94i4ISKaIqJp/PjxPXmqmZlVSFkhIGkYRQDcEhE/SsUvtnXzpL+bU/kmoPTqqMmpzMzMakw5ZwcJWACsjYhvlSxaAsxO07OBxSXl56ezhE4Atpd0G5mZWQ0p595BJwHnAWskPZHKvgJcAdwhaQ7wLPCJtOxu4HRgHfAGcGElG2xmZpXTbQhExINAZ3dHm95B/QA+18d2mZnZAPAVw2ZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4CZWcYcAmZmGRvaXQVJNwJnAJsj4phUNga4HWgE1gOfiIiXJQm4GjgdeAO4ICIe65+mD5xjL/8F23fsrMi6Guf/tE/PHzV8GE9e+uGKtMXMrNsQAG4C/hW4uaRsPrAsIq6QND/NXwx8BDgyPd4PXJf+Dmrbd+xk/RUfrXYzgL6HiJlZqW67gyLiAWBru+KZwMI0vRA4q6T85iisBEZLmlihtpqZWYX1dkxgQkQ8n6ZfACak6UnAxpJ6zalsH5LmSlolaVVLS0svm2FmZn3R54HhiAggevG8GyKiKSKaxo8f39dmmJlZL/Q2BF5s6+ZJfzen8k1AfUm9yanMzMxqUG9DYAkwO03PBhaXlJ+vwgnA9pJuIzMzqzHlnCL6A2AaME5SM3ApcAVwh6Q5wLPAJ1L1uylOD11HcYrohf3QZjMzq5BuQyAiPtnJoukd1A3gc31tlJmZDQxfMWxmljGHgFkPjBgxAkl7HiNGjKh2k8z6xCFgVqYRI0bw+uuv09jYyLp162hsbOT11193ENigVs5tI8wM9gTAM888A8AzzzzD4Ycfzvr166vbMOuVIUOGUAxjFiSxe/fuKraoOnwkYNYD99xzT5fzNji0BcBBBx3EypUrOeigg4gIhgzJ7yPRRwJmPfCud71rr2+LOX5o7A/aAmDHjh0A7Nixg+HDh/Pmm29WuWUDz/+CzXpg9+7dDBs2jAcffJBhw4Zl2X2wv1i+fHmX87lwCJj1wJAhQ9i5cycnn3wyO3fu9JHAIDZt2rQu53Phf8FmPdDc3ExE7Hk0NzdXu0nWC5J488039zrdt20+Nw4Bsx6YNWtWl/M2OBxwwAE9Kt+feWC4DCOPms+UhfOr3QwARh4FUBu/cpab+vp6VqxYwUknncSiRYuYNWsWK1asoL6+vvsnW0156623mDBhAi+88MKesre//e28+OKLVWxVdTgEyvDq2iv885LGhg0baGhoYMWKFRx66KFAEQwbNmyocsusNzoaGD7qqKOq05gqcgiY9YA/8PcfOX7gd8RjAmaWtbvuuqvaTagqh4CZZe2MM86odhOqyiFgZtkqvRlgrjwmYGbZWr9+PUcccUS1m1FVPhIwa6f0AqK+Pqy21dXVsXz5curq6qrdlKpxCJi1U3pFcGePwy6+q6x6VttaW1uZNm0ara2t1W5K1TgEzMwy5hAwM8uYB4bLVCtX6o4aPqzaTTDbb5x44ol73QIkRw6BMlTqlhGN839aM7efMDP2ugVIrhwClp1jL/8F23fs7PN6+np0OGr4MJ689MN9bod1rjdnaHX2nP11oN8hYNnZvmNnTRyR1UoX4/6ssw/usWPHsnXr1n3Kx4wZw5YtW/q7WTXFA8Nmlp0tW7YwZsyYvcpyDABwCJhZprZs2bLXNR85BgC4O8gyVCs/EuQfCOo7j+/0nUPAsvPq2iuq3QTAp/tWgsd3+q5fQkDSacDVQB3wnYiojf91ZlTmlF+f7mv7i4qHgKQ64N+AGUAz8IikJRHx20pvy8zy5q69vuuPI4HjgXUR8TSApNuAmYBDwMwqas3sNX1eR+5Hdf1xdtAkYGPJfHMqMzOzGlO1gWFJc4G5AA0NDdVqRsWUe2Wirux6+f56VeJgUqn3Evx+Vpvfy+71RwhsAupL5iensr1ExA3ADQBNTU2D/tXdX/+B5Mjv5f7D72X3+qM76BHgSEmHSzoAOAdY0g/bMTOzPqr4kUBE7JL0eeDnFKeI3hgRT1V6O2Zm1nf9MiYQEXcDd/fHus3MrHJ87yAzs4w5BMzMMuYQMDPLmEPAzCxjDgEzs4ypFi6mkNQCPFvtdgyAccBL1W6EVYTfy/1HLu/lYRExvn1hTYRALiStioimarfD+s7v5f4j9/fS3UFmZhlzCJiZZcwhMLBuqHYDrGL8Xu4/sn4vPSZgZpYxHwmYmWXMIVBhkm6UtFnSbzpZLknXSFonabWk9wx0G608kuol3Sfpt5KekvSFDur4/RwEJB0k6deSnkzv5eUd1DlQ0u3pvXxYUmMVmjrgHAKVdxNwWhfLPwIcmR5zgesGoE3WO7uAL0fE0cAJwOckHd2ujt/PweEt4JSIOBY4DjhN0gnt6swBXo6II4CrgDJ+b2zwcwhUWEQ8AGztospM4OYorARGS5o4MK2znoiI5yPisTT9KrCWfX8v2+/nIJDen9fS7LD0aD8gOhNYmKYXAdNV7u9TDmIOgYE3CdhYMt/Mvh8sVmNS18C7gYfbLfL7OUhIqpP0BLAZWBoRnb6XEbEL2A6MHdBGVoFDwKwbkkYAdwJfjIhXqt0e652IaI2I4yh+9/x4ScdUuUk1wSEw8DYB9SXzk1OZ1SBJwygC4JaI+FEHVfx+DjIRsQ24j33H7va8l5KGAqOALQPauCpwCAy8JcD56aySE4DtEfF8tRtl+0r9wQuAtRHxrU6q+f0cBCSNlzQ6TQ8HZgC/a1dtCTA7Tc8C7o0MLqTql98YzpmkHwDTgHGSmoFLKQahiIjrKX57+XRgHfAGcGF1WmplOAk4D1iT+pIBvgI0gN/PQWYisFBSHcWX3zsi4i5J/wdYFRFLKAL/e5LWUZzccU71mjtwfMWwmVnG3B1kZpYxh4CZWcYcAmZmGXMImJllzCFgZpYxh4BlS1KrpCck/UbSDyW9rYu6l0n6XwPZPrOB4BCwnO2IiOMi4hjgj8Bnq90gs4HmEDAr/BI4AkDS+em3AZ6U9L32FSX9raRH0vI7244gJJ2djiqelPRAKvurdB/7J9I6jxzQvTLrhi8Ws2xJei0iRqT7xNwJ/Ax4APgxcGJEvCRpTERslXQZ8FpEfFPS2IjYktbxNeDFiLhW0hrgtIjYJGl0RGyTdC2wMiJukXQAUBcRO6qyw2Yd8JGA5Wx4uh3EKmADxW0DTgF+GBEvAURER78NcYykX6YP/XOBv0rlDwE3SfpboC6V/Qr4iqSLgcMcAFZrfO8gy9mOdGvhPcr8DZGbgLMi4klJF1DcK4qI+Kyk9wMfBR6V9N6IuFXSw6nsbkmfiYh7K7cLZn3jIwGzvd0LnC1pLICkMR3UGQk8n24zfW5boaR3RsTDEfG/gRagXtI7gKcj4hpgMTC13/fArAd8JGBWIiKekvR14H5JrcDjwAXtqv0jxS+MtaS/I1P5N9LAr4BlwJPAxcB5knYCLwD/3O87YdYDHhg2M8uYu4PMzDLmEDAzy5hDwMwsYw4BM7OMOQTMzDLmEDAzy5hDwMwsYw4BM7OM/X+ea1N5UHkppgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_vis.boxplot(\"Fares\", by=\"Pclass\", grid=False)\n", + "data_vis.groupby([\"Pclass\"]).aggregate([\"min\",\"mean\",\"std\",\"median\", \"max\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "79d80127", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson : -0.5588100663539586\n", + "Spearman : -0.7091345258216657\n", + "Kendall : -0.5916856604501165\n" + ] + } + ], + "source": [ + "print(\"Pearson : \" , scipy.stats.pearsonr(pclasses, fares)[0]) # Pearson's r\n", + "\n", + "print(\"Spearman : \" , scipy.stats.spearmanr(pclasses, fares)[0] ) # Spearman's rho\n", + "\n", + "print(\"Kendall : \" , scipy.stats.kendalltau(pclasses, fares)[0]) # Kendall's tau\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1e670ad", + "metadata": {}, + "source": [ + "## Analysing the strength of a relationship" + ] + }, + { + "cell_type": "markdown", + "id": "a888ef06", + "metadata": {}, + "source": [ + "We sometimes need to measure the strength of a relationship between data. Otherwise, we cannot establish and demonstrate the relationship." + ] + }, + { + "cell_type": "markdown", + "id": "370bb5a8", + "metadata": {}, + "source": [ + "### Model fitting and prediction with regression analysis" + ] + }, + { + "cell_type": "markdown", + "id": "619b93b8", + "metadata": {}, + "source": [ + "Regression analysis can help us establish whether some specific patterns may exist in the data. We aim to find some values that can help us writing a mathematical expression. Those values are referred as predictors. A two-process compute first some predictors; it is the referred as model fitting. The second phase create some predicted data using those predictors. The latter can then be used to generate new values, that can be compared against the known data. The outcome of this comparison then suggests a strong or weak against a specific pattern in the data.\n", + "\n", + "We may need to explore the strength of a straight line, a U or N shape (quadratic expression), exponential growth (i.e. sharp increase). Sometimes we need to try several regression techniques before selecting the most suitable set of predictors. \n", + "\n", + "Some regression may require splitting the data into some learning and testing dataset. The model fitting uses a learning dataset and prediction phase the testing dataset. You will experience those in your practical task.\n" + ] + }, + { + "cell_type": "markdown", + "id": "82258078", + "metadata": {}, + "source": [ + "#### Linear regression\n", + "A linear regression attempts to describe the strength as a relationship as a straight line; that is 𝑦 = 𝑎𝑥+𝑐. This equation has been adapted to a new equation: $y ̂=β_{x} x+ β_{0}+e$ . The constant c is now obtained by adding error e and the constant value $β_{0}$. \n", + "\n", + "Let's have simulate a terrible example. Please, ignore the math, but look at the shape. We have a Pearson and other correlations suggesting it may be increasing linearly. However, the graphical representation suggest otherwise. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "540e3842", + "metadata": {}, + "source": [ + "##### Misleading correlations " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "6b49a185", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "years = range(2000,2023)\n", + "temp_C = [5e-2 * x + 0.01 for x in range(0,23)]\n", + "temp_C = np.exp(temp_C)\n", + "\n", + "plt.plot(years, temp_C)\n", + "plt.title('Monthly temperatures variation')\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Temperatures (C)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "8195e532", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson : 0.9893400063012718\n", + "Spearman : 1.0\n", + "Kendall : 1.0\n" + ] + } + ], + "source": [ + "print(\"Pearson : \" , scipy.stats.pearsonr(years, temp_C)[0]) # Pearson's r\n", + "\n", + "print(\"Spearman : \" , scipy.stats.spearmanr(years, temp_C)[0] ) # Spearman's rho\n", + "\n", + "print(\"Kendall : \" , scipy.stats.kendalltau(years, temp_C)[0]) # Kendall's tau\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ded6fcf", + "metadata": {}, + "source": [ + "We test for a linear relationship. The model has a low standard error. However, a visualisation suggest the relationship may is not linear. The next step is likely to test if there is a sigmoid relationship between both statistical variables." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "6cec1963", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope : 0.09044893291948816\n", + "intercept : 0.8536183941457415\n", + "r - Pearson correlation : 0.989340006301272\n", + "standard err : 0.0029052346237728446\n" + ] + } + ], + "source": [ + "slope, intercept, r,p, std_err = stats.linregress(range(0,23), temp_C)\n", + "print(\"slope : \", slope)\n", + "print(\"intercept : \", intercept)\n", + "print(\"r - Pearson correlation : \", r) \n", + "print(\"standard err : \", std_err)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "8430a93e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = range(0,len(years))\n", + "\n", + "def myfunc(x):\n", + " return slope * x + intercept\n", + "\n", + "mymodel = list(map(myfunc, x))\n", + "\n", + "plt.plot(years, temp_C, color = \"blue\", label=\"observed values\")\n", + "plt.plot(years, mymodel, color = \"red\", label=\"predicted values\")\n", + "plt.title('Monthly temperatures variation')\n", + "plt.xlabel('Months')\n", + "plt.ylabel('Temperatures (C)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c6ecc272", + "metadata": {}, + "source": [ + "##### Finding about a linear relationship between fares and siblings/spouse" + ] + }, + { + "cell_type": "markdown", + "id": "1852f595", + "metadata": {}, + "source": [ + "The above correlations obtained above suggested no linear relationship existed between these two statistical variables. This analysis confirmed the finding. In fact, drawing a regression line with two statistical variables is inappropriate." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "925d1761", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope : 0.0032295944388510164\n", + "intercept : 0.3914078069464819\n", + "r - Pearson correlation : 0.16043996653488599\n", + "standard err : 0.000549584972426517\n" + ] + } + ], + "source": [ + "x = list(data[\"'fare'\"])\n", + "y = list(data[\"'sibsp'\"])\n", + "slope, intercept, r,p, std_err = stats.linregress(x, y)\n", + "print(\"slope : \", slope)\n", + "print(\"intercept : \", intercept)\n", + "print(\"r - Pearson correlation : \", r) \n", + "print(\"standard err : \", std_err)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "d17cc4bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1309\n", + "1309\n", + "1309\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = range(0, len(y))\n", + "\n", + "def myfunc(x):\n", + " return slope * x + intercept\n", + "\n", + "mymodel = list(map(myfunc, x))\n", + "\n", + "print(len(x))\n", + "print(len(y))\n", + "print(len(mymodel))\n", + "plt.scatter(x, y, color = \"blue\", label=\"observed values\")\n", + "plt.scatter(x, mymodel, color = \"red\", label=\"predicted values\")\n", + "plt.title('Monthly temperatures variation')\n", + "plt.xlabel('Months')\n", + "plt.ylabel('Temperatures (C)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a47575fa", + "metadata": {}, + "source": [ + "### Clusterisation" + ] + }, + { + "cell_type": "markdown", + "id": "92bd1221", + "metadata": {}, + "source": [ + "Previously we have shown that some clusters may be present in a relationship between the ticket fare and the age of the passengers. As, more than 200 ages are unknown for third class passengers, the analysis results needs to carefully considered and perhaps treated as invalid. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "949453b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ages = data[\"'age'\"]\n", + "ages = ages.replace (\"?\", 999)\n", + "ages = ages.astype(float)\n", + "data[\"'age'\"] = ages\n", + "x = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'fare'\"]]\n", + "y = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'age'\"]]\n", + "plt.scatter(np.log10(x), np.log10(y), s = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "e6306b54", + "metadata": {}, + "source": [ + "We apply Kmeans an unsupervised learning algorithm to explore whether some clusterisation exists between the fare and the age of some titanic passengers. We apply the log 10 view better the clusters. \n", + "\n", + "We surmise between 25 and 29 cluster shows some suitable clusteration between fare and age. Across the age some passengers would have paid some low and high fare; the passenger class would have influence the price. However, the older passenger the higher clusterisation appears on our visual representation." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "f4e4121e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 'fare' 'age'\n", + "0 211.3375 29.0000\n", + "1 151.5500 0.9167\n", + "2 151.5500 2.0000\n", + "3 151.5500 30.0000\n", + "4 151.5500 25.0000\n", + "... ... ...\n", + "1301 7.2250 45.5000\n", + "1304 14.4542 14.5000\n", + "1306 7.2250 26.5000\n", + "1307 7.2250 27.0000\n", + "1308 7.8750 29.0000\n", + "\n", + "[1037 rows x 2 columns]\n" + ] + } + ], + "source": [ + "\n", + "points = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'fare'\", \"'age'\"]]\n", + "print(points)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "208d40e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 10.75286155 25.38760504]\n", + " [238.60859375 37.046875 ]\n", + " [ 68.43788652 36.93262411]\n", + " [512.3292 41. ]\n", + " [ 27.20516934 10.47992701]\n", + " [133.55552093 33.88178372]\n", + " [ 21.63756078 45.91176471]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create a KMeans instance with 3 clusters: model\n", + "kmeans = KMeans(n_clusters=7).fit(points)\n", + "centroids = kmeans.cluster_centers_\n", + "print(centroids)\n", + "\n", + "plt.scatter(points[\"'fare'\"], points[\"'age'\"], c= kmeans.labels_.astype(float), s=50, alpha=0.5)\n", + "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "b1a59cc3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 'fare' 'age'\n", + "0 2.324977 1.462398\n", + "1 2.180556 -0.037773\n", + "2 2.180556 0.301030\n", + "3 2.180556 1.477121\n", + "4 2.180556 1.397940\n", + "... ... ...\n", + "1301 0.858838 1.658011\n", + "1304 1.159994 1.161368\n", + "1306 0.858838 1.423246\n", + "1307 0.858838 1.431364\n", + "1308 0.896251 1.462398\n", + "\n", + "[1037 rows x 2 columns]\n" + ] + } + ], + "source": [ + "# Import KMeans\n", + "\n", + "\n", + "points = data.loc[(data[\"'age'\"] < 999) & (data[\"'fare'\"] > 0), [\"'fare'\", \"'age'\"]]\n", + "points[\"'age'\"] = np.log10(points[\"'age'\"])\n", + "points[\"'fare'\"] = np.log10(points[\"'fare'\"])\n", + "\n", + "print(points)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "9c01ea69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 5 ----\n", + "[[0.97209054 1.430804 ]\n", + " [2.05659298 1.53497137]\n", + " [1.3682321 0.10860429]\n", + " [1.44247983 0.91894097]\n", + " [1.49045016 1.54880327]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 6 ----\n", + "[[1.42787795 0.90628152]\n", + " [0.95544882 1.41886317]\n", + " [1.3682321 0.10860429]\n", + " [1.3838504 1.55436296]\n", + " [1.83302542 1.53135721]\n", + " [2.29848765 1.5354806 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 7 ----\n", + "[[2.30556205 1.54972964]\n", + " [0.94151488 1.331857 ]\n", + " [1.42151973 0.82778043]\n", + " [1.84299534 1.52755759]\n", + " [1.43178973 1.53720992]\n", + " [1.37146037 0.03273315]\n", + " [1.01603146 1.56689721]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 8 ----\n", + "[[0.93974973 1.33310048]\n", + " [1.83631363 1.65225111]\n", + " [1.38064182 0.79817257]\n", + " [1.42210148 1.54661206]\n", + " [2.30157113 1.54880316]\n", + " [1.37146037 0.03273315]\n", + " [1.7919822 1.29713274]\n", + " [1.01743254 1.56644855]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 9 ----\n", + "[[1.43715471 1.57969041]\n", + " [1.37146037 0.03273315]\n", + " [1.18085059 1.37075029]\n", + " [1.78052608 1.3009551 ]\n", + " [1.38949171 0.76894029]\n", + " [2.30157113 1.54880316]\n", + " [0.90538927 1.34068089]\n", + " [0.98114867 1.60205795]\n", + " [1.84605288 1.65443232]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 10 ----\n", + "[[0.92698571 1.31567607]\n", + " [1.84605288 1.65443232]\n", + " [1.37146037 0.03273315]\n", + " [1.12802177 1.51847748]\n", + " [2.30157113 1.54880316]\n", + " [1.38949171 0.76894029]\n", + " [1.45693997 1.6525995 ]\n", + " [1.38628597 1.39107186]\n", + " [1.80703707 1.30644108]\n", + " [0.90167746 1.56722066]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 11 ----\n", + "[[ 1.1326325 1.46045817]\n", + " [ 1.84742571 1.32602249]\n", + " [ 1.40562415 0.9261374 ]\n", + " [ 1.41529031 1.4095289 ]\n", + " [ 0.91987042 1.58952554]\n", + " [ 1.39976299 0.44733823]\n", + " [ 0.90892288 1.32659942]\n", + " [ 1.34160064 -0.11361058]\n", + " [ 1.83885332 1.65664513]\n", + " [ 1.45428576 1.66417078]\n", + " [ 2.30157113 1.54880316]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 12 ----\n", + "[[ 1.41565275 1.43286932]\n", + " [ 1.39976299 0.44733823]\n", + " [ 1.84321073 1.65103501]\n", + " [ 0.89942994 1.33270412]\n", + " [ 1.12812066 1.54076113]\n", + " [ 2.30157113 1.54880316]\n", + " [ 1.41500314 0.90875237]\n", + " [ 0.90143654 1.5789671 ]\n", + " [ 1.46043168 1.67152489]\n", + " [ 1.82314245 1.31094 ]\n", + " [ 1.34160064 -0.11361058]\n", + " [ 1.12823673 1.28877212]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 13 ----\n", + "[[ 0.89995779 1.31965587]\n", + " [ 1.83885332 1.65664513]\n", + " [ 1.20541499 -0.15385375]\n", + " [ 1.40936769 1.4311165 ]\n", + " [ 1.43277293 0.97638729]\n", + " [ 2.30157113 1.54880316]\n", + " [ 1.45994808 1.67029912]\n", + " [ 0.89857295 1.55200963]\n", + " [ 1.65164913 0.2099079 ]\n", + " [ 1.12566779 1.62806088]\n", + " [ 1.83670143 1.3226783 ]\n", + " [ 1.30161443 0.54953963]\n", + " [ 1.11743005 1.37715898]]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 14 ----\n", + "[[1.84541569 1.67123292]\n", + " [0.89899388 1.52161784]\n", + " [1.34010668 0.62922567]\n", + " [1.40636687 1.44379361]\n", + " [2.31192636 1.65632239]\n", + " [1.32089189 0.02655219]\n", + " [1.10462903 1.65450153]\n", + " [1.44289907 1.0183598 ]\n", + " [0.9002739 1.3050732 ]\n", + " [1.80271938 1.36421698]\n", + " [1.46166941 1.6770069 ]\n", + " [2.23450756 1.35552777]\n", + " [1.12015904 1.38563178]\n", + " [2.10072422 0.41086712]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 15 ----\n", + "[[ 1.1159541 1.32186485]\n", + " [ 2.42099992 1.62882855]\n", + " [ 1.65164913 0.2099079 ]\n", + " [ 0.89485682 1.33085048]\n", + " [ 1.80216566 1.36581069]\n", + " [ 1.82258602 1.67986152]\n", + " [ 1.41024125 1.44469155]\n", + " [ 0.90143654 1.5789671 ]\n", + " [ 1.20541499 -0.15385375]\n", + " [ 1.46904805 1.00190933]\n", + " [ 1.12886041 1.5584954 ]\n", + " [ 1.29829336 0.56761446]\n", + " [ 1.4636198 1.67773671]\n", + " [ 2.2535288 1.24840496]\n", + " [ 2.09691842 1.5854795 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 16 ----\n", + "[[ 1.39845798 1.46959529]\n", + " [ 1.85070527 1.35138302]\n", + " [ 0.90208057 1.28476124]\n", + " [ 1.3314826 0.47023674]\n", + " [ 2.25954816 1.6666925 ]\n", + " [ 1.4019703 0.90344303]\n", + " [ 0.90793647 1.66822688]\n", + " [ 1.17006992 -0.16747776]\n", + " [ 1.12765492 1.36932979]\n", + " [ 2.35760176 1.40169391]\n", + " [ 1.84154463 1.66835158]\n", + " [ 1.6913445 0.04473158]\n", + " [ 1.46602267 1.67976564]\n", + " [ 1.1386463 1.60378565]\n", + " [ 0.90370939 1.45474121]\n", + " [ 1.53315835 1.23758797]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 17 ----\n", + "[[ 0.89681314 1.46722525]\n", + " [ 1.46543589 1.67848329]\n", + " [ 1.83696695 1.34453269]\n", + " [ 1.32676677 0.61731162]\n", + " [ 1.4316264 0.16812414]\n", + " [ 1.41398698 1.44328569]\n", + " [ 2.41208412 1.40509662]\n", + " [ 1.83322478 1.66230417]\n", + " [ 1.17426305 1.19644959]\n", + " [ 1.10530539 1.4214022 ]\n", + " [ 0.90059907 1.29743171]\n", + " [ 1.14683111 1.61076894]\n", + " [ 1.51075297 0.99825627]\n", + " [ 2.22049866 1.63393478]\n", + " [ 1.15904916 -0.18036066]\n", + " [ 2.10072422 0.41086712]\n", + " [ 0.90844706 1.67193152]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 18 ----\n", + "[[ 2.26005056 1.6477488 ]\n", + " [ 1.12055483 1.38556449]\n", + " [ 1.34020268 0.46176514]\n", + " [ 1.39927163 1.44808301]\n", + " [ 1.97442036 1.31900702]\n", + " [ 0.88917651 1.63140575]\n", + " [ 0.9033812 1.43948757]\n", + " [ 1.9266714 1.66301411]\n", + " [ 1.12342511 1.62845806]\n", + " [ 1.70565819 1.3371146 ]\n", + " [ 1.6913445 0.04473158]\n", + " [ 1.5133154 0.90611787]\n", + " [ 1.44601119 1.68178172]\n", + " [ 1.20650656 1.00286682]\n", + " [ 2.45574453 1.40645018]\n", + " [ 1.71209536 1.65364543]\n", + " [ 0.90105176 1.28064383]\n", + " [ 1.17006992 -0.16747776]]\n" + ] + }, + { + "data": { + "image/png": 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cyrkMecfm+NwiT5+f6YR7erhh6wilKwwCf/P0UaZWawyXi2v3wAsj/vSxI/z8O99IT34jLXj+/CLPPfACyzMrRGHMV//iESzHQiUKlSikIZmfWKCrv0S74XHs4RNc9+Z9dA92cfyxUxx/7BQf/jffzdD2ga/xNoA0JEPb+pk5O0+73sbJuSk9s+XjZJy1fSRxQm25gTQlL56QORmL6lIDw3p1E7dz3glO1x+jaPUjOzM8pRUTrUPkrW625g68LMfZNPivEExTEkSXU/G01tCJyT5/Zo5ifv2jG+gpsLBap9b0qdQ9/CDGNCUZ12L7SA+ffuQ4lYbPu+5IjU7Gsdgx2svsUm3DMQwhMQKBKggMW3ZmBgLLMImVXjPYi40mXhhRzrhUfZ9IKUzDIFKKSClylsVCs8XnTp2hN5elFUb8/pOH+OnbbiFrWzw3N8/HnnkWQ0iytsVMrcYTU1N897XX8IatY3/ve1cNm3xp4RATrVmkEFjC5Pbea+kyC6ivQW/UaJ6vnCHSSScxKxAIEh3TimMW/FWGM70kWrEaNMhbGTKmS9Z06LMzrIRVpFxme2544z2VGYJk+SWP3Y7neGH1N/GTRcAkTJaphDGGcLFlLwqfYLWCI8s0o0kiVSPRIXHcpMkETjhAr3uQWLfpy95O1hzuHFuys7+H3QO9PD89jyUlrUB1rncjlE7vgQBOLS7z1j3buf/5kzx9YYacbWMZBp9frvLw6Qv85JtvWavSBVhutjg5v8zQJawggIxtUfN8Dl+Y4R37d60tf+7BF/jcH30F0zJxcw5P3P80lYUaA9v6aNc97ExH20fDiSfP0D3cRaGnwMpchYGt/fQMd1NfafDFP32AH/q/P/g1w4VOxuGaN+zBdCy8hsfyzCrSEIzuHsawDG56R6cwUYDt2IR+hFZ6LaQJEEcxbs4lDuOXPNa3GmebT5IximvGHkAKSdbo4mzjCcay178s4dNNHv4rhAO7Rqg1g8uW11s+Az1FSnl3jUq5UmvRaAcYUnD9rhEGugr4YQRaU8pn2DbcQ9a1Ge4p8MTRC1Tq7bX9fd87bsB1LFZrbeJEEcUJvh8jBZQsm5Fykf58nuFikZLjUHIdvOiSl73zUtV8H63BjyOiJMGLIo4uLLLYbNIIArTWuKbB8cUl/vjpwzT8gL989ijdmSwDhTwFx6E/n6c/n+eTx46z2n7p0MvV4CUBfzn1RabbC/Q5ZfqcMlnT4csLh3ho5VnES+v2AVCNmlTjJqGKCFRErBMUGg20Yg8/CWnFHhpN3sgQ6Yh+pwshXFwjy4K/SKhiItXET1aIVJNItV6yYlZrzYX63wEGiYrx4lliHXZmVnVa8QRBvIwpXCLVpBFOEKo6juwla25B4NCOZ6iHZ9lW+n625N932Qe/a6CX3nyOKFZX4fx0zgVoBiEFx2amWuex0xcIo4SjMws8dX6axXqTmufzd4ePbagPqLV95FUS9K5lMldrrP27UWnyhY8+QPdgmZ7hLnKlLO2Gh5O1qcxVUR3SAYDpmHgtH8OQWI5Jq7b+bhS688xPLNFY/fpqTN78wTvIFTMUuvPc8NZrue6N1+BkbAbGejn41usAMAyDHQe34uYc4jghDCLCICIKIizHZmT3ENnC5QSIVwpaaxrRCra8vIjSli5+0iDR0RW2/Max6eG/Qjiwa5hnT80wt1Snq5TBlJJq00cpzXvfuY9GO2Cx0uTZUzNkXAu0oJCzGegucG52BSkEtZbPYqXJ6cnF1OD3lejvKjC9WF2ruB3qLfIr/+i9/PZfP8TkQhUpBdmshdldwOuGqXqdRCls02C8q5st5eKal9yfz1MPAmbqdSptj1htNCRhkiA7QeKZeh3HMOnKZHng3AR5yyZMYlxrYxLONgwUcHxxkTvHt37D9+1E/TyNqM2Au568sqVFj13igcXDHbP90riY1I10jCVMDGRnmSbWCdWwQaQTMoZNTELRzNHjlEBIlN6FwdPMtZ7Glm1AoHWCEAZb8u+96jH9ZAkvnseV/SypRwCJIUy01gjS8IsiohFO4CfLgEYr8PQCQmiEUBjYNOJz+NEyseVjSndDLPfWbVv43PMnWaw3kXBVoy+AvG2z2vIQQhAnCVIIMraNbRrUPI+VZot2ELLa8ii4DsfnFnn87CRnllZAa7ryGWzDXDPaQRzTV1h/1hPPT+K1fPyWT6PSws05CDqhRDQqUem1C7E2qEhDEocx2UuqxYUQCMEV4/KXIvRDHrn3KR6790naTZ9yf4lWvY2bc3nT997Ogbv3ky1kWJmrcPTh43QNlomjhHwpi52xU8ZZnIaZ3vXjb8H6BpLQLzeEEGTNEpH2scXGgSdSAbbMYoiXx1RvGvxXCK5j8cP33MKhFyZ5+vgUDT9g3/gAbziwjb5ynj+49zEE0FXM4AcJGcek2vA4M7mM4xgkShF0PPEk0mgdcHZ6hZVa67JjbR/p4b/88+9kerFKveUz3ajxm4efYLXRRJPyuKNEcW51lbxjM1JMcwY3jQ6z0Gyw1GphdJKAl0IDiQYShU9CrDSNMKAdhnz86DFKrsto6fJiIykEXvT381DONWfJGpfnPqQQBDrm8iDG1aGBWCc4Mh1QDSEIVUzByqFRhCqiyyoymu0DralGDcJkkDBy6bVWUdpAEwOSjNHHQvthut3rLmPVACgdAoKENiDRJCQdQ5dy8BUag3Y8i+JiNW0LtIEjepDCAq0IVJXnV36NTH2InDXKaO7d9GfvQAiDka4iH7rtAEem5l/SwzekwDQM6p5Pw0vlB/JuGl7Juw4Z2yKME6Yr6UD/l08+y7nFVYIoYbHW5OzCChnbYrBUYFtvF335HEprbtq63pZ66uQMZ56ZwM06mJZJbamO6sTT3ayDk3OIghjLNQm8iGJ3YS2MsvOG9Xh9u+FR6ClQ6r16IWC76fFrP/Q/mXh+EjuTstYmX5ihZ7iLX/qzn6NvtAeA40+c4r7f/SJCCJyMzZbdw5w/NoU0JLZj0jPSzZs/cDtv/fCdX/vl+RZje/4Wnqt8Dsty1gZ1rTWtZJVrSndvJm3/ISLjWLzphh286YYdG5afn11lerGGbRm4tkW14bFUbRCGCUKAbbr4QRspIfUwFWgoZE1Wai2SRHHywiLPnZ4hihV7xvvZv22QscHUK25Oxsw1WqSRe9AijesnSnF+tYLdYTO8adtW/vjpw5Rcl3YY4l0lrKmAnJHWByw2m8SJIlaK2VqdgXyO0VJpQxhAKc2Wv2fi1pYWib6cdmkKA0ML4quYukw75K4vn2JkpsrMSJkH3robL2ujSHnseTMDGqQI+YGt7+BTs48w0ZwlVPPM+ilDKWe6BImH1kU0CdfkpnENE0vmCZNVlrwnUasxWXOIHvcABXsnsuOJOUYPQhj40RKapFOI9eJBL0EhEMjOTEUACZFqIJAkhEBCrD1a0SRR0qAWnKQVT7G99P0AXDcy+DW6UYBWmkYQECtFzrbSwi0Ni40WidKUsi5mZ4B/5PQET5ybpu55rLZ8/CgiUYpWELLSaFFt+wyV8vzcO95IfzEPQBRGPPfACximQe4Sb31grI/zL0zhtwN6SlmSKKFVaWPaJuPXjrI4uULfaDel/iJaa9p1j/pKg+/65+9ByqsbuE/+1meZeH6S3tFu5CWGcP7CEr/2I/+T2++5iVJvgae/8By9Iz04HY++3F+itFDFa/qM7B6m2FNgdM/Ia6LSdjS7n3q0wIXWc53HmRbnjWSuYTx348t2nE2D/xrAwmqdidkVgjCm3vRoeiFaQ6IUUsLcco30G00j1kIIoiTBjxIcy+TjX3o2TeY6FlJKTk8u8dhzE/zIPbdSzLl84dRpbMPAiyJilaQvlEhwDJNYKZ6anuXO8TFyts2unh6qfsC51coGit+l0ECQJMRK4cfpqKC0IlGaB8+d55Yto+zq7UGTcvuHSwV29nydVLsXYX9pO8fr5yl1wgEX0Up8zCuoYALsf26G//Bv7kUoTcaP8VyTn/ntB/l3v/adHLt+BEMYKBJirei1y/zx+fswMChYWWpREy8MMIRBpGMKhkevtcxSKDlOHweLy0SqiUaRKA9L5khUi4r/HGV3P9uKH0QKC1Nm6HVu5IX2b3HRr78aDJEl1o21dRQb8x1axyBMLFlEaY/z9b9lKPt2MlYfp+aXaPnhFfaaIhv4vOeFI4xXlpnp6ePU3W/nfBCh0NiGQaXtkXUsvDBisJjngZPnqbQ9bNNAaUXOsdFo2mFazHXD2BCJ1hs09qdPzWGYBl39JeqrDUzTQMg0Pl/uK5ErZSj1FdFK0z3Uxb7bdzK6a5iuwS6OfOV5jj54gkQlDG0f5Ht+/h523fDSVN+H/+4JCl35Dca+WWvht33OHJ5gbM8oR75ylOlTc9zwVhcnY7M4tcLk8WnyXTlUosgWMvRt6eYrf/YI5b4Se27e8RJH/NZDCoP9pbczljvIsn8BjaLX2UrR6n9Za102Df5rABMzqyyuNgmjiJYfcSnxZEMo80WMlDiKcSyDuZU6t+4fW3sxSnmXxdUmX3ryFN/9luuZrjcI4oii66ztRgpBohTNIKDqpUlf1zTJ2TbjXV28sLBAq1NVGySXUz2DZN3rNoUgURopBQXX4dn5eeYaDTKWyU0jI3zk5hsxXsJjeylszQ2yv7SNo7UJCmYGS5o04zZagxdfngTPtEP+w7+5l2x73ZvO+Omg9B/+zb18/8d/Ej8rMESWXfktLAYVpJCU7DxBElKLmh0WT4KftNli17FkjI3NUpjFV1ksOpx0YZLoAMfoxpZdVP1jrNq76c2kUsS2USZrjuInK4DGbCYM31cldz6gNe4we0+ZOE865ULCVQrIEtooFZHoNpbM4yULLPtP0aPfwW9++XGk0Gmo7UW4ceocv/MXv4fUmmwU4tkOfPGT/OKP/ixPbxlHkKC0pumHjPd00V/K8cS5KQqOQyMIgDRhKxC4pkWUKLZ0l5mt1phcrdKVS+PNQTsAAeWBEjOn5wnafmfWIegZ7uK7/tl7uPtDd64Z2ovv6bnnLjB3dgEn56CVplVpMXN6ju3Xb8Vot+Ev/xJOn4Zdu+BDH4JCGubxWwG58vpMIo5jKvNVHNdGxwon55Av57Eck1OHznLTuw4wfXKGOEqYO7dIFEScffY88+cXGRzv5/FPHXrVDT6kjlzR6qNovXQV9zeDTYP/KiAIY46cmubp49MEYczhE1MEUUQQJqniLYBYt+/ZyOcd548wWl9mutjLF8YP0rZcgliRNANu3FsgjhXWJVzinnKWo2fneM8b9pG1LJRmjdFy0WGQAhI0eTsdCBzT5ObREZ6amiZjWTSDgFClXuI9zx5h6/IyF3p7ue/AQVpOSh81RZqYi5RCK4gThSUlCBgo5JlrNPjMydN84Lr9X5fRXw3rHF49yZnmNI60uL68i7cO3MyO/ChHqqdpxz7Xl3cx4HTz6NKxy7a/68unEOrK3rRQmrd85TQPvv8mNIpm7GEIiSUNYhWzFFQBsGT6WUhCPCVxDI0pNEJAO5YUTYXWClNYKB2R6AApHGyjzHzrQYJkhYp/lFp4BlO4mGQoHqpw+0cmEEpjepo4I7j2P87y+B9uY/UlperX51l+soTohIxi1eTI5Cy1todrOUTBxsEvG/j8zl/8HvlwfXmm8/f//Mf/k/f94n8icDNYhmTXQA+2afLe63bz9PmZTnBJpBV7a9CXcNg3qm/2DHdTW6qzNLXCwHgfWmmSOA1HVpcbmLZ1mVbN4uQSf/sb91HoyjN8CV/+8U8/Te/Eca79j78ASkGrBbkc/MIvwP33wxvfyPCuQaZPzCJNSavWJvQjkihGa7BcC4Emk3cxDIMkUVQXatSW67RqbSzHQitNoSsdEKZPzZJEyVpC+fWOTYP/CiMIYz722UNcmKvQVcwgDcHMUqqKGV/izl809gcWzvHrX/p9BJpsHNI2bX7uqU/yc2/7SZ4d2E6sFE88f57uco7BniI7RnvJuhaGlGggjBP29fXx0MQF/DjCMQyEkGnVaRjRnckwUMivHfddu3cxVathCEGi4caJc/zBH/0eQmtyYUjLtvmlT9/LT/74T3Fo23Yu2oVYpSwML4oYLhUpuxm2lrtQWvPk1DTbu7u5ZcsIL4bSmqlqjWYQsKKWeGD1SaSQDLhdhCrmSwtPcbJxgQ9seQt7i+ssn7PNGbLSovGiEP7wTHXNo38xMn7M4Ey1o0mkWY3qJCoh0QlSpPfLEAaxVh2teYVt2CTaQKIQOsISTTTRGvvESxZYbD+KKfNkjEEa0WkiVceWJQxh04jOIZptbv/IBFZr/WRNL93D7R+Z4HOPXUOSs7gyz+ZibF8jkIRJBYFBwd7O56bnGSzlU/YNG4NG73nhCPIqNQqGhu88fZS/vv4W/CjGNgw+8sab2Nnfy7aebs4sLeNYJrTTxKEGokQxUMoTdRg+l4Z0eoa7iMKYpNNrIQpTZVeVaPKlDDOn5y47h6e/+ByGYZC5pO7EMA2Ge7Ps/r//GUSXSD23OsSE974XZmd554/czX/7yG8jzVT7XmtN4IV4rYBSX5FnvnwUIQVxHBOHMXEU4zV8hCGIwxgnY+NkbQQCwzSoLde/LYw9bPLwX3E8f3aWybkKw31Fsp1S7oux94uv3MXPNBv5/PqXfp9cHJCN0zhtNg7JxQG//qXfJxOlHlsYJzRaPkuVJkdOTlNteJydXqbaaLOwUue6wQFuGB7CMS1aUUwrCmlHMUPFAgeGBunPrxv8vGPzT+64jZ5Mlmzg8wd/9Hvkg4BcmB4/F4bkg4Df/6PfIxsExEoTd0TVFFDxfc6urDJbr3O+UiFWiu5MhofOn7/sXsw3mvz3Bx/hNx57mP/y/Mf57yf+kiMr55lsL3CiMYkUMOh2M91e5Hj9woZte+wStnm5vzI7UsZzr+zHeK7J/EiZMIloa5962MSWJlJIgiQiSMI1tUyFxpYGttAoHLLSZ29+Ble2OgnWBK0jXNmLKfMoHbLoPYbAxBAu7XgegURiMXJf5SVnHSP3VdmYeX2x8bkY8oEoqZO3x+l2DyCFYLSrTMa2MF5ksLZWlslGV47tu2HA1soyffkcb9i5lf5Snt2DfUgpeN/BvQyUCmilsQxJKwyJ4pi8azPaVWK+3uBd1+6i4K4zp5I4oWughJ21uXBskskTM0yemGV5ZoXBbf0sTS6nwm5KMXlihof/7gme+swzSPNyI7vzzFNwtepppeAv/5LQjxi/bguGYRK0ApIwQSWplILj2uSKWRw3NehxmNBupoNH6EU4WZve0e6UMqo1SaJwC+7rpifE18Kmh/8K45mTMxTz7noRiiHJZRz8TlLsUrzj/JEOk/lyCDTvOH+ET+66jbRYV+OHEX4Y8dAzZzENg63DXfzZZw/T31NgJF9gIJ/rJFoTSq5DO4q4ZXSU7uxG7q9tGJxeWeZ9zx5BXOVDEFpzz7PP8Ne33r5huQa8OGZiZZW5eoOBQp5bR0c2xPwhFW77vSeeIlKKID+H1CEmAp0YNNoxZj7ghdp5Dnbtomhleb56hgPlnSRacb41y9HqObiCV/bAW3fzM7/94BXPWUvBV96yi5B0BhCRsBo1yAgHxMW4fYjRuet5q4yn5ikYLXrtFbqsACF0h1a5LmQgOvE3RUiQVFn2n14TWlOEZM63Ox795TA9Te5C2NnXxf3JTm1sytaHVGdHE+OYPewu/QKHzy8wW63zwtwiewZ6eC6KqHnB2ttyoauXtmVf0eh7tkN1eIQbx1M5hcolRXFv2r2N6UqNozPzVFoeE0sVan6AJiRJFO8/sJc7d45v2J9hGvitgJWZVQzLwDDNzqMRvPDoKa5/8zWEfsgnfvMzXHhhGsMwmJ9Y4vzRKcavHWPr/tG1cGN+ZQ47vkoSutWCM2d4rppwzR17GNzWz4Vj0+mx51axHQu/nRYFSkNimAal3iI/8Evfy1/950+wOl+lXfcIWul90krTPVBiy57hbxsPf9Pgv8KI4mRN+wZSmeOR/hLVRvuydUfry2ue/YuRjUNGG2lpvwC8IKYdRJhS4tgWB3ePMNhTQErJcqXJeLaLRjFhulbHtUz8OOaOsS3cs2/PFfffjhO2Li+vefYvRi4MGVt5aWmBRGtW2x5PTk3z/ms2as4cW1ikEYSUiuCpFg5OxyOWVFseUSMEQ+E2sowN9hIZCYlWfGbuMV6oTZAxHLqsAlPe4ob9elmbf/dr33kZS0dLwb/7te/Ez24UyIp1Qlv7lIwcLaVQQjHi9rKrMEagIvxolV2ZBcqmgRRuSq/UEkEqM+0niziiB4GBITJEqkHWGlwz+CZZWuM2cUZc0ejHGUF7a7azvuxQOFN6JkgkLq7RR97ahtIBI7nv4/e/Osnz0/PU/YBKq818rcFgMY/WmrofooHPXHOQf/3Fe6/4XLQQfPm6m+lteSSJYrR7vXbCNg1+6PYb+LMnnuXjh54jVoq+QhZTSi6sVPjrp47Sk8uxb3hdOE0IQWWxRtAOyZXWG8ZrrWnV2lSX6zz8t09w8umzhF5IZb5GFIQEXsCF41MUe/J0dyjE87LINbaLGV7evYtcDr1jB8FDHlPHZ2hUW9iOiVYKKSR+O0DFmvPHJjFtk+Edg/SP9RH5EXd/+E4evfcpMvkM1cUaCOjqFGvd8q7Xh1z014NNg/8KY994Pw8fmVgL5wDsGOnh2VMzl607XeylbdpXNPpt02a60AtAPpsW0dTbAUIKeso55lYanJpcxJAGveUcfhjxb37s7cy3W1Q9j/GuMn2XhHIuRcVLPb4Lvb20bPuKRr9l20z29F71OhPA0oo4Saj5ATXP5w+eOsRQocDtY2NM12rYpkFIGp81MNEJtNteyjgxTBAwvbhMtdbiQ7fczZnGFMdqEww6XYQthdNy0WnBLLom0ZMmYlvI0WtG+PBf/SR3f/UUQ7M15kZKPPCW3ZcZ+/VzVeStDFsLg8x5K+wqjGFJE03I1lyVPrsPS2bQOkJg0Y5nO5LFNpaRo8e5EYMMk617O4qXG73F2Xu6ufY/TnMlaqaWgtl7+jCEQ6IDBCYmGWKaGLgUnX1krSFA0eXczhMnyjx06jksw8A1DQaLBSqex1S1jiklrmkQJoq24/KPPvxTG1g6bctGC8Ev/ug/YcKPuHB+hpxj89037d9wTnO1BocvzCCEZLS7tJZsD+OEmUqdP3/iWb7/9gNopdne14PUivpKg1wpSxREa+ESrSFXzrEys8rj9z3N7Ok5hJA4WQfDMmjVPCrzNU4eOsveW3elbJ9b78Y4ci9cyc+QEvHhD2M/9aeszFXoGUoHicBLJSuSUOHmbQa39aNVyuuvLtYwTINb33MDM6fmuHB8isKWtKq2veKx8+A2bnz7dVd9j19paK1oJVXQkDPLL1vB1UVsGvxXGDfu3cLTx6dZqbboKmWRQtDo0NpejC+MH+TnnvrkFfejEXxh/CCmBMsyCaMYp/Pn7GIV0zQwZEqpawdpq8PfeeAJLgQNtNbkbJu37drO23buwOx80IvNJn979AXOLKf9QO87cJBf+vTVvcT7DtzQSe5eOVzhJwpTKRKtue/kaQqORaI0PdlneOuONOFsX3wFBcQrBspQIDqFSEJjZgRhPcGayfBs/xlk3eD5xxdoLAQ04jY6yqO7YhiLYdWAsok+J/AGJJ9967V0CmPBfen6pKWwyu7iOLf1XEvZytNOfIbdLFF4knp4DC9aIMFH6zDtq6XBwCJj9mMbRZQOEVhIw0TpEEk6uMTaR+UzPP6HO7j9I2c3sHS0FDz+h9tQOTuVsBYmQpvk7DF2lX8cx+imEZ1FYNDl7idnjPPvn/sbgiSh4QcovV7/oBJNIwyxDJkm7JXi2bEdvP0XfoV3Pn+YscoSk119fHb/QWShgFCKBEEQx5xeWOHg2Lo43HPTczSDACHYwKyyTYNq2+OLL5zhodPncUwD1zL5ruv3AdAz1MXqfJXGahNNKl1smJIkUR2uvombTWP/hiEZ3NbP6lwFgJ0Hx9l23Rg7b9iG+O69aYL2UpaOlClLJ58nCmLsjIXf8kEKkiQh9EMM08AwDNxsGjKNwpjFC8t0D5VxMg53/ex+Hp84y9LKAgLo7x3m9m37X1VZhUux5F/gucrnWA4uAJpue5Tru97JQOYbb7F5NWwa/FcYpXyGD7/rRv7PfU/x5SdPk6iEvOsy2J3nTHtj4+W25fJzb/vJy1g6GsE/f9tP0rIcLA1+GGFIiWlI2r5K489ZJ2WjaE3bD2mHEZ995hRGv7lGuzu3WqEVhHzPdftpBAG/8/hTzDcazNbrALQcl5/48Z+6jKWjheDnf+afEmUya01DrmTyBRBrkErRk81QdByU1tSDgE8eP8Ge3h5KSQ6pJSsLFYKZGCVNNDENy0P0aMy5LNfEWznjLbC8d5ljn1rCtizyPRZhaKAvCFixwdFgKggExAI8CTkNGkQg0UYCLhutvl4/8YCILjvPOwdvJWemOQ2lIw4t/B9i5RHrNpokbUSCQuOj0ERJkyhpEaoatlGkYG3HTxaIdQu0xjbKFMVOVm8O+Nxj1zByX5XchZDWVpuZe8okOQOBjynyWDKt6O1xb2AgeweGdOnJrMvi+lHE1EqVOElwLAsJNLyQRGuSJEHpdGwLVJLOeoC6YfE3B29Ds87QyGkQEixDYpsGn3n+JB+8Zd3LbQZp4d+L49peGDFXa2AZkoFinkLGwQ9jPvbUs4yMlpg7MkMQhCiVqr+GfsjyTMDw9gG8hkepI/u99n4IgZN1yJey3PPT71j/4Y1vhNnZlId/5gzs3Jny8PNp2EpIwZY9wxx75CSBFxJ3aJXIlPUVxwlJlBAFEYPb+1iZrWAOeByufYpSb5G+gVT0LkhaPF39BHdYH6bL3qiG+kqjEs7yuZnfYDWaXtOHWvDPMNs+zntH/iV9mfGX5TibBv+bQK3lM7VcxZCCsb4ucl9HA4W2H/K3X3mW4xMLaJ3GHherDWpXiOEDPDuwnfd88JdTHn5zmaliL5/fdhDPckBDpEBbqVZIybZptH0MKdaEzwwhCOOEUCnClTaiYCOERGlFMwj5syPP8fZdOzg8M8d8o8mFahV9CaPk6W3becP/9f9wz7PPMLayzGRPL/cduIHQddPib31l+TLJ+iAgBOTs9N5IISi7LvPNJkOFAittj+BEhmZxhTgfotsaqTUqlOh7XWpLgufyU0z3VEkmfFYrTZyMSagcYjM1bLgaTjuQaHRNIXaG0CY1/BowNSIS6XqkJ6YViIt5ZAOoGIyao2S3r9MEBWm1qYFN1BFBoJOwFRjYsowWEOoaQ7m7Gcy9ifn2A/SYN6WaO52ipUpwFIFBkjOY/L6ey+6VRpMxhzBFhkg3aMezXGh8gu2lD29YL4wTwiTBkhIhBEG03sowiJPOvRZrGYA1KY3O9he5Lxe1dYSAhhcwS33Dcbb39mCb5za8BwCL9SZaaxzTxOnUfLi2SRcZlgZddMtHK4XZ6aCWCqFpQj9Kxc1qHpadhrxs10JISRIn9I9dITSYz8NP/MRli4UQlHqLvPDoKfrGejGkpL7apLZcJw5jLNtEJYpCd47hnUP4zQCtNCfrj2DLDLaxXrDlGDkSHXGm/ji39H7P5efwCuLxpb9iKTyPLTLITh2IUjGr0QwPL/0fvnvs/35ZjrNp8P8eUErzhSOneOT4+fRj0ulH9O4b93DbnrGXzPg/cfQCTx2dQqPp7jQht0yD1fqVDb4GPMvh3t23oYwX/SAgyUFDRPixZqnRRAmNR4Lnex0vLf1IEqkxVKqhozSY0iBWCZPVKieWlnlhcYmlVpOaF9C+VOhMa9qWxV/fctuGY2cNg5LrIqRgudUmTDrGN1GgQJkCZGp8sradds8yLtX6TkNBP3X9jfzWg8vkVrZyZuE8Mq/QTQnTFrQliaGoVjxMyyAvXay8RNqCZssnVklq2WxgRqZe/VQaCmJLjE5U+oZHIBYtsEK0zbpnL+nUEQiKy2W+evg4O/uH2bkr9QAj1cCSBVyrlyCoIoWB0mkrQVf2pt67s4v+7B0M59+G1gmJDln2nly7ToHEMXoR2GuiaZfz7ROCZIVEZMlaQ+TNcarBC/jx0obeudW2T18+x2KzhaEVUaduI7qEAZXWQ2x4RS5D3Q+xDbk2OBTYKE63f6Sfse4yC/UmLT8g2+mXUPN8LGmQd1Md/YtwbYtVQ5AvZZCRIonS8zFtg3J/mTAI08Gl0iT01oPz2UKGLfuGueU935hWjGEZqCShsdrEbwUkUUzkR5iWQam3xC3vOrjW/zloBQxs7+FCOEvBvLxDV8YoshScf1ULr7TWTDSfwhT2mrEHkNLEVDbT7WNESYB1BRHBbxQvi8EXQvwh8D5gUWt97RV+vxu4F5joLPpbrfWvvBzHfjXw5OlJHjh6juHu4lqMM4oTPvXUC/QUc+wavnoy88FnzhIl8YYG5Bq9ZgAvhb7kT2VwWdWEUGlVfhCnmjqx1oQCTCkwL/L6BfhxjFYQOgltL1izBLZhEAvNdLVGxjKZqtbROtVYASBWECmE0ohYo22JNiVozRZlctf1u4klPHzuPNPLq/ixwmrEaRhFQFKwiO1UwkF2PsB43iepRSgR0nuNi/AVg/kcTx9ahFXnstmC6tiyaqVNEmuSFQO9JcHIQNCM0guZl9CSYGtoS/ShDBxXMBgj9oRoV6NjAdMmDMWsKc1KMLRJvpmjUC+QKzg8/shpdu4aRGvN6nLA1BkDy9qO1dPAtkxAYAgXSBDCRKMwOpK2QhiMFd7HQPYNtKIpBJK8Pc5zS7/JYquHVtwiZ0Z0uR6GEXOp4Q/UMiEWUmTSAQyBn2w0+JZhsK2vmyhR1DyfIIxIOvmEzqMmStSGe3jlUJsmVhpTQqw0OWfjzDRjW/zUXbeSsW0ePjPBUqNNkiSYSLLSpDeb22ActdIkBmTyLj3dRVodBdd8OYft2sxNLNBqeBS78ximscZ59xoeUki6+kucfOoM/Vv76Oov8bVQmatgZ2wq89UONVejtSKOIY4i4ihBJYqV2Qo3vv1auge6kHMmigTjRSZP6QRT2q8qLVNrTah9LC7X5JeYRLpNpMLXjsEH/hj4LeCjL7HOQ1rr973E7/8goJTmwaPn6C3mNiS0LNMg7zo8ePTcSxr8ZjtAvEhi4GL8Xelk7eO9aAoUrHmiG6BBd5YroOYFa/wQrVTKaOh49yIV18TPkCbCOtvHOjXEecdmsFAgiGMylonUYNQizHqE4SfISJE70UBlTWrXFQm7bVTV5+Th81x3xy6Sqk/iK2SS4NRjZNzxZZsx8aBLICJ0M6b2wAJJI0ZpjQQen3qWHT+YY2mpide+ugDYxfPt7s6xvNwkOQt2wcJoQVSL1m/OpbtoGNAw0KddyCpwdFpUtSVGj/qIvgRiQdbP0697SRIYLHcxN1cjCCLu//QRTp+coxGVCZMGWFu5+e0NhsbSiuJYt8ibOwBNyUmprUESc6FWJVaKLcW9FGyH87UKHz3qMtvcTaJbgKA/V+eNIyfI2SodGLUgTFy0loTJBFpn6cqMdAaWdQwU84z1lMnaFkdn5gniGBXHaxryoiN5oFRC1InnI1KNJD9a1xXVnXdER4KsNDCuUOeUMUzu2bmDW/sHefjZcywuNFhKGpyfq1BZrlDcVsQupMnOiucxsK2f5DPnWGgsppr3QKvWptCdJ2gHbD+wlZ6hLk4fnqDd8LBsk0JPnpkz8/zN//g0CHCyDje+/Tre8cN3rYWFroT6agOtNGP7RomCdDZqWAbLM6s0Vpssz6yQK2V5yw/cyc3vPIAUBqPZa5lsPUvR2ujlt5JVtudvfel371sMKSWuzBMmHvJFRl/pCEu6uMbL06DlZTH4WusHhRDjL8e+Xuvwo4imHzLUVbzst0LGYbZSv8JW69g20sPEzApk10dr2zLWvXsJ8cXxQIMSIAzWg7Kd5QJQ1voyISAWGmkCcSoopTv6OUIIYkejE4EO0+PozoxBSuh2MuQyNn25HEvzVYzJJm4Yd9rRaYrP1xA6ffkycz7KNWjWIiZWZnCzDpnVmOypKu5MG6OdYLYTtCGJCybFnEljf5HKUggtBa4AaTBUKCAV/PmfPoplSeL4pRteCCkwDEkYRPh+RKsZdgbHS7MFl22V/tGW0IbEBHnSQh8zwQEdg5cxCe5I2FMeQ0WaQsHhS58/yumT8/QPlOjRe1nxD9Nsuzx+v+KuD82SK8aYMosQMJS9m4zZzzPzs3z85DGCjhqpEHDb4BaeXpghUd3YxnlsIwQNy60CgbLJEqGUwIstYp16qokSROoYOWucnLVl7UqCJGa+2eTOfePc+9QxEqXZ1tfNheUKYZxgdzznqBM6c6QkTDrKqGrt1UrlI2KQIWRDcEKNV29y/5ee5513XYNhSB4/fI4HHz2N0przU8s0mwH7dg+yc8849ThktdYmOlWhsLtIW6fssA/u2MFfiSfS0J2ZOjBJnLA8W8HJ2OQKGU4fniCOEmzHIgpj6isNIj9i7txC2mdZaZanV8gWXO764NU16kVHNgShkU5HWFqAUzApD+X5qf/6Q5R6ihtCiDsLt7EcnKcWLeDKAqDxVZOC2cu2/E0v+e69EthbvIsj1fuJEh9TpgOpIkGJhN35OzeEer4ZvJIx/DuEEM8Cs8C/0lpfrnz1DwC2aWKZBlGcYL1IR9sLozUFwavh/W/czwNPn6HZDsh1enwu1VppA/JErRt70fHWTdAWGxglF39PPf/UqEkhUUKhLI02UmMmNChDo62URmm0AKnWdqAcgWFqIi+m1FVkV+ISP7REqyhRtsBeCnHnPYx2gkAQdllYKyFmn0PsK7SfcPqJc1iTTUrTDYQA6ScoCUIq3HaMLLtknm3hlLsRJRvHNOnOZsl2wiPLSw3yhfzXLG2PwoQzp+fXJihCgGVJokjxNQXhLyIGlEBKgQ40UoLyFcXlEsVyntXVJm94424eeegk5a4caDBllr7MLeTMOWa9aZZO2wy/wabs7KY/eycFazsT1Qp/+sIRejNZes00KdiOIv7i+HO4lokmQxSXsGSIIRMKdoQhFH5s0o4c0iCLgWtESJF2xPrShUFuG0o7ZD06M8n9504RdVRLo1xMLmPjGiZZ26I3nyVWKT0z0QqJoJx1Wag1UZHC1JJEpr8bCGRboYzUK845NgPFPM88P4ljWfT3FfjSgyfo700F+cIwoVh0OTOxhOvYvGv/bk7OL3FqchGaCe+4dRcfuOlaPv9f7+P6u/Zz5sgEfiPt5GZYBqVCBsexOH14gkJ3fk1Ebf7CIvXlRsrS6cohpUwLx1YbfOb3v8wd778F+yokiK6BEpXKMvOLUwhTo0RCFEa4eZvsQI4nmn/OTYV3059Zl1l2jTxv6PsBZtovcKH5LKC5png3o7n9WPLy5u2vNG7v/T4W/NMsBRdIdFoJLoSk1xnnjQM/8rId55Uy+IeBrVrrphDivcAngF0vXkkI8dPATwOMjf39m15/K2Eaktt2j/FgJ4Z/MfantKbS9HjbnZdd1gZsGeziX/7gW/itv3qQ5VoLraHpBZi2pGV2PPq4Y9/T4ss1ukU29HnX6SOMVZeZLPfyud030LbTlzW5xBJqA5IXScULpbBqMUIptAEi0mjbBNeg0mhze2ErPLdCcSkiez4kCCISRyI1HVlNjfQTTEuSP9XATMCqx6hmhbiZIF2J9BJEcjHV0DHg1QAbAyOv2dZzOUPFMCWGYWBZBkny0o2kL23ApTW4ccDb559lxFthJtPDV3uvwzOvHue8GOKyTEkYxmiVcuFPH1tgYbrB8EgXh58+z4njs7iujWlJxsZ6GRwqkbfHGS4PkvcK3Dxwx4b9fnXyHK5p4ZoWrSjkxMoyK36blXabUCX0uBlKzg6qvkHGbGHKAC+2mW8VeWZ+nGboYkhNwfa5tneWvlydx6cN5ltNZpt1Pn7yGAO5HI6Rfq5V6XEsWeSu0SGU1iil15wPL4goZVzyLYlebLNgRMQyIZcx0ZZAewmhSENJvWYGxzIZ7+2mr5Dj0LPnyWVtSsUMpmnQbAUIkTJ6bFszNbPKgWu3cO3oIGOlEqNDXXzgXTcRhRGVxSpbdg/TM1RmcXKZdt0jW8zQN9bL059/jiRONnjcrXo7fa/EOv1TCEG+nGPhwhKrCzUGt15ZJnjLjb1MVI8wsqWL6kwDPw4o9TsIW9EzksPOmBxa/QRv6Pt+yvZQ533R+EmDee8MraSCQHCh9QwZs8hg5qW/2VcCrpnnu8d+mZPVhznXfBKNYlv+ZvYU34Rr5r72Dr5OvCIGX2tdv+Tv9wshflsI0au1Xn7Rer8L/C7AzTff/JpVM7rr2u3MrtY5M7uMaRjQSYLdvGuUA9uGvub2t167lf+1/YM8eWyS587McvjCDKttj9VqGo/UV+jrccPMWX7rU7+fVk12+Pj/6uFP8rPf8VM8M7z9qkGNixAarHqE2Y7XpDg1IdqWfP43v0Tue9oUq4pZIUjaETLWiESl3OaLbBaVin0VjzbSQciSGLFCJyCbydokZIO/HSlcLWhXL2/FCGnv0uHhMo16m7NnFq+4zpVwoDnJrz73xwgNGRXiSZt/fO5+fmn/j3C0NH7FbS42ldFhOitQSpMvOLiOzf79I0ycW2RhrophGWSzNkmiOrMKxfBIN0EQUe66vNH0+XqFou3gxzFPzs2gtKJg2cSuYqHVoOJ7KDIo3cc5P0OsFFHiESibWEkKdvrcvcjh8dlt9GU8NCaL7SafnThNTyazZuwBytkMPaUszy/Ms727mxdmF5HNkGbVw0ti/JbFYqTYMlAmU22zZEcEYUxCGvIRhqAQSBKtGO0vMlQupEwupVhcrjM+lhpayzI6FbMa2zJptNblDsIwpquU3gvTMsnmMwReCJZJ4NqstkIqrZDW1AqYki37RliaWunkGwRJmGCaBpZtpgnhjhCeFKmnr+KrD/7FN7RwDhk42+uMvL8CEoJJTf2oycC7EhwjQ6wDzjaepN/dzrnmU1TDeSrhDEVrgD5nHCEkYdLm0OonuLn7u14TRt+WLtd1v53rut/+LTvGK2LwhRCDwILWWgshbiV1AldeiWN/K+BYJj/8lhu5sFjh9OwyhpTsHe1jpKf0dWf7C1mXt92ymx1b+3j6T+eorlxBO6SDbOjzW5/6ffLRurb5RbmF3/rk7/H2j/w/aXOLS7ChvqhjhZWxvlRGaSY3tgSh1Dz9lReoztdwYvCTtJhKxBplgNRpEs5oRVirEQZ02l6pDce60pUbhsQSYPgBjYZHoZBJZZS9kFqtje/HbN/Rz+pq8+s2+Jk44Fef+2OyyXqWNqPSv/+nYx/l+2771/hXYzR0CpIuhpC6unOYhpnOkITAzVjEcYLvh2QyDtmcw+SFFXp6C0RRwnUHLp95lp0M9TBgsdUkVgmFzrPImhZSSIq2y0wjlT7Q2gKheHpuJ6OlJYqOz0Irj0ZQtD0SZfLo9FZuGEgL56q+z3D+8v6u124d4MiZWZSGsBlRqbYxTIETQGOphSUEUTbGUZJdokDdCKl5IY6VQbVTaunewX52DvWms0Kd3piMaxNGMbZlks3YFPIubS/EMo0OSwmiKCFOFNfuS+WuhRDc/O6DfO6jDzLTaOP5EXGcJrcrs6vYQ2W6xvoY3j7I6lwVlSTEUcLybFpoeGk7w3bDo9RbotxfvvLj0xqvOMfuf6pZmQyoTGmElrhjMX1vFFhdLTQK1yhwuvE4c/4pskaZWMUkOqEaziE7oRLbyKKBk/WHGHB3vOwyBq9FvFy0zD8H7gZ6hRDTwC8DFoDW+neADwD/WAgRAx7wYf0PXI/UkJLtgz1sH7w8TPGNYLSnSMsLX9JDf9fpq2ubS6159+ln+MT+2zeG+C8mc+GShLCARCE77ZG0TEP6hpfQv7+bhelVZs4uIqVAddYxEtCXtFP6hshrAqQhaVTb7LxhnGqUMD25TBhExInGMCV79w1z6tQ8QfD1Nzm/e/n5jb05Lj2khruXnuezg1fvKnJxADSkYHW5xTX7R6jXUuaIaUiCMMa2TZpNHykl7XbA3GyF977/BoaHu0iU4mx1lblmg6xlcdPACH97+hgLreYGTzxUCTvL3cy1UqmBMIkxpERqE4RgojaAJWOyZogUsNDqIohNXNNkvNzNzlL3Wmeyy5rHGHDTnlHuyI2werbKzuESC1NV2lUfJU1sy2BmroJppYa623GxY0F/ucCF1RW01mRMk6jTOGe10mLb1l6GB8s8/PgZBgfScOWenQM8f3yGSrXFlpEuFpbSyfq77t7PwCWVsze943ru/7snWXj+AkiJFKlxNvtKqOFezizWuH7HEOZoN0rDWN6lsljFtMxUq16mtF0hBW/6wK1kC1fOhQkh8FUDmU0Y3T9IZtsMUpsYliAhJkw8BBIvqdCOq4xk9iGEpJ1UcGQOISSVcJaSNYAlM9gyQ7s6RXzvb2Odnbqsu9brDS8XS+f7v8bvv0VK29zEi9DyI8b6ysyu1K66zlj1pVUzt9QuV628kvy67IRqLv6oMybWqk9xNO032ztYwnYsAqVRlxTzZFXIm/0zDCc1Zo0SD7o78eTXripe89w0ECt2FW3ONtost0OypmBsSz8jW7pBw8JcDcMQJFfq1fcijHgrax79i5FRIcPe1zF51JAk6UzD80Ms00QrjRIaxzY5cMNWllbrLLfb2E2LD//gnezZO0Qt8PnD555mulHvVLGmdM+hXJ6Tq8skiSIyDRKlKDouN/QP8ZXJc9QNg4xhYRsGhpTMNOuAIFIWtTBlZVzMw1vS4IN79pO1bW4cGOLBqfNkLZui7ZC3bVa9NpONKj+w7yDhhZARM0vQjGlEBtl8jpWohRQSaSikgKWVBuVSBs+POT+1jO/HGIbk+Mk5Tp1dYHSoi7HRbt7ztmvJuDbTsxXOTy1j2xZozfBAif27h9i1fYBSIcOeXYN0lzfGlaVhUOkrUbh5J0bDS+mg/WWsrhyNZsBiEPHg6VmkNBBSkEQxxWu2MJzPEPlpNayTddh67Sjv/vG3veSjS6WiUwaaa+cIVQuEnapmSotYxVSDOXJWN0JIEh2idIIQChObRCfUo0W6nS0UnzjFHT/w31J+/hW6a73esFlp+ypDo8k4Fi81m5wsv7Rq5lSp92vG8El0WgFry/TvlkQGCdnJBpm9aQhCGJLuwRJJnDB7bgmA/eEcv1K9D6E1GWI8TH66+Sj/vnwPx+yXzleoRKEShWFJJk/OMbqjn+6Ci6k19ZrHyeenODW1irQM4kQhpcA0DcIouaysPz1BQMNMpgdP2lc0+p60mc1cedbVYZm+aJlgYb7GyGg3URQTR4LxHb2cpsapripBPkYOCr7YukBXq8C9p48z32oyWlj3bv04Zslr86G91/LpMycpOS79uRyWlBxemO14+BCj6XYcqr5H3NEgunhZF0UbDCkxhOCjx47w5QtnObq8xKH5GRKVZkhU2vwA1zS5cPKLlKclmbak0fTTFpda0/aC9N4rkEYq7xAlCSjYPt7Dnh1DxIlieaWJ54UoNB94302Ui2lM/kPfdQsXppY5eXYBKQR7dg4yNtKNYVz9JVVKs1ptUxrqwhjdeP+lhFaccPude6kv1kkSRbm/iC8E1wx3040m8ALG94+x4+A4tvPSYmYFq5dQtQhUC1u6RKpNqFoY2BjCpB4vULaHCHWbBe80jXiZIPHwVK0jQ61ROqJVmeaDP/CHmK0Q6IRLX9Rdi6soyv5DxabBf5VRyrq0g5AwurRByEar9LldB/lXD11ZtVIJwWd3fQ09b62hHZG70EDlLLQhsCo+1mqATDRz55epLNRpt3zcrE3/SDez55bIqJBfqd5HVl/SEJyUQvQr1fv4wd4fxZdfW2lQa400JWeenyI0JHUvIjYMtGMhgxgjYxMEafN2xzXJFxxqtVSiOY7SPIFlp9ovYZDwQP91/JOJz1yxI6AW8NW+y+VupUyF5Nb/fZH1o2m3Q+Zmq+QLbtrouhByLm7ihoKsllyzf5TZVoP//tQj+HHE1mJ5w75d00QiKDkub926g9OVFdDw1NwMoUoo2naqihqGLLaaNMMQg/XTF6SSF1orlNb0ZHMULJs/OXqEdhzR5biYlsFCq4EGsqbNaDuLN9VmSSbkDQMLTbMZrAlvXeyWmc6Y0muUQtDTVcBxTOorTWoNjzhOMCLBM0cneefdqUyyaUh2jPezYzwtUlpcbvC5rx7jwtQK+ZzLTdePsWfn4IYBQEhBPu8ShBHZzMb8SaXWxnEsugfK9HR07wHiOGGi0uQ7fupta/mBrwej2f0kOkRg0IiXcY0CBia+apI1S9zS/T24Rp57p/8TSiscI4fEpBmvkJDmLwwshj/5wkbq16XodNe6kp7PP2S8/rMUr3EIISi4DuuRDIUpY3KOh2MGgKZtO/zsd3yEtm3jd6Rc26ZN03L42e/4STzbQYqEvNumnGtQyKReigAKbsiWriWGe2uYKiF/tkbhVBV3ycfoHLSyWKPV8DAMA9uxmD67AMCb/TMv2fHqzcGZq16XlALbMbFdC93xNN2sTWOpkdYbCJFyr5ME00x1XaRMQzqWZeC6NvmcQyZrUypnKBQzOLZJNmszum8r//ueX6BtOmuhJU/atA2bX9r/IxsStoYpWQt/X+FSiqWUgthuhXzow7fzY//4LcxkfYpYDPWVOXDDVkrlLL2ZLAutJudrVRZaTZbbbeaaDZbaLWKlyFgWK57HTxy4ie/bey0rvofSmm2lbq7vH8IxTAwhSXTaEvJiE3BB6tUn6FSmWqcG+nMTp2lGYeqdK0XF99CALSVhHNE672FlLfI5h4YVE3fYNAI6tQkboVWqInni9BzHTs5y8ux8Z5DVVKoen/rcs8zOVy/bbmJyiT/884c5eiJtbr5abfG39z/DfV98DnXJLMw0JAeuGSWMYqp1j2YrwPMj2u2AKEoYHigRx4rl1SZLKw08P8Q00ybjnvf1528AtuVvwpZpo5VBdxcjmX0U7QF63a28bfBnGMruRukES6bCSUon+EkzLULszKc0mtKFJpZ3FTZQp7vW6w2bHv5rAMVsyqV3TY/xwUVqrRxe6GAaCaWsRzHX5MBbz/Nn3/8Gdj64QGmuzbn8AL+euYe6kaOUbTDUXWG4a5Xx/kUcK2JqqY++UoOcG5DEGqE0zX7JY384Rqu20QPTGoa39jG4tZfzJ2dpnE8HjOGklnr0V0CGmOH48ryDEGklpGlJTMvsGIWUAWLZJqrT8UtphaFAmgayY/wRadVxveYTxwleorBtkxtv2sbIaDePP3oGIeDGm7djWbv4lcFt9H75foa9FWYzPXy177qN7ByRSgikShMbrb1SKYMoiTXZnIOQcMNN41REyM72AEOXMGOU1pxeXeF8rULV95ht1gmThKLjkLMsLGkylM9z69AIjmFyx8gYD0ydxzEMzlUrKK3J+D53P/goQ3MLnOop86mbD5C4GcyOoqbWmkinipxT9Rr+xRyKVtTDYE2VNFQK6aftLJ2ciYHEzJi4RZPGUtrB6tJnkf4pME1JGCUsrzYxDINyKbPGKLNtg1zO5f4vPs9P/OAb15bHieLTX3iefM4lm0kHVtdJB5nnj89y3b5Rtl2idHnbTdt4+MkztNseYZRW+WazNt3lHLZt8uThCdaUfjT09Rbo6cqTyXxjevRZs8Qdfd/P6fpjzHkn0Cj63G3sLtxJyR4AoB4vUrKG0ZamGs7QiitodKfKFvJmF+GOUaLMySsb/VwulWV+nWHT4L8K0FoTRMdp+l8kjqe5abvPiSnFcPcit+w+w3yli5V6ASEV3fk212yZ5PTcMNVmnmffOg5oClmPd68e4olTe/m+Ox9hpZnDCzI0vCztIOFtB55lsVpmYnEQUJgqZmhokTd/+Cyf/d19aL3Ot2lUmxz66gsopXAyDgOjXawu1Jk1Snid/ksvhofJrLkudOVmLZJYIU1J6MeEfkwcpWX2TqdiUqm012i+nCNoBiRxjJFzSJRmYKBIpdLC8yIsy0DKlK/tuhYnjs8SxQnlrixdXVnm56sszFVpthIOvQQbx7YMECmN8MWwLINMxsKyDaq1NubOLL959EmW/DbnqqtpRbCbMkWm6lXO1yvkLYtmFBImCRnToh1FlB0X0JyurLCjtB679uOIF5YXKbsZ9p84xb/91f+MUBo3CGg7Nv/ubz7Fv/i5f8wTO8YJVbJmCDOGJLwkzJCgL2ckGYI4UQT1EL0Sg6doigit1Yakt9ZgdKi4UkqEUARhgmGk99YJPA4ee5Tu5Tky1+3jsH0HK5UWvd1p3HphsUazGTDQf7mOvW2bHDs5s2bwoyjh8UPn2LG1j8WVBmGYvjMaTamQYW6hRnc5l+YZSL+BC1MrbNvS8w2Fcy4iZ5Y52P0ertfvTK9PbCxeMUUaRitag5StQerRMkrFaBIiHdCME46/a4Ab/8NVeGdSpmyd1xk2DX4HcbJMnMwihIttbkOIb10XnLb/IPX2x5GiiCG7Kefn+OCdj2FbPgJNcXieds8qUmiK2TYZJ2BL9wpogdICP7LRCt52/TF2DCxgGJBzYpIkRghNMeORaEEx2wYUAk0522Swr07ODund0mJpcj0ZlUQaK5tqn7QbHlOn02Tog+5Ofrr56BVDIVoIHnR3IoxUsKurr8jSXJUkSql1gjTuniTpv/tGuqks1ikUM8StAMeUZHvKFPuKWLZJuxV0pviaYjFDoeh2KJEhaM3OnYP0DxT52J88jGFK8nmXQsElm7Npty5P3qZOqiaTcSkWJZ4XoRKF73fEtgyJlJIwiqntlqiBBI1mpFDkTGWZR6YvsKu7l7LjcGp1haxp0QgD8pZFJA2CJEEpzXyrSX82z45yD+frFfb29nWOn6qf5nyff/ur/5mMt15nkQ3S8/2NX/9/eet/+1UCQ64xdOJOa0DJxhRFJ1+NAoSV/h5NBmmyX6TSE1eKvimlMYxUSE8KgTTSoqmh08/z8/f+DwQaNwqIjn6ZN/DbtPb9Hbz/XUDq4V+trMQ0JEGw7ghMTC1Ta3hs29rL1i09eB0Z5GzG5onDE/T3Fmh7IUG4vs3wYJlmKySKkrWB4BvFiw39RfS54wgEiY4whIUlHRpJHaHT+J4hLZrZkHv/95v4jp95CAsL0Wpf1l3r9YZve4OvdEC9+dd44SEussylzFHK/TCufeUG39/U8VSTuvdJTGN4bVDpyvfRDjS9xQpRbNAOHKLEwLVCLDNGCs1QTwXLitFapOEJDVknYN+WGc7OD5KxA/zIwpSKvmINU2qkFeFaITk3YP/IDBkjwM3ElPu9DQZfdGQ2L0794zjBMCVebPPvy/dcxtLRQvDvy/fgCyuVTjZl575JojBeiyWHfoxpydSbzjnsuG4LN9y1l6cOnWe54bO80sL3Izwv1UsPgphiyaW7J78WVigWMywvNfjCZ59jYLhMo+GjVGq4u7pzlMs5fC8VUrvU4AmRJiyVUhSLOQ4cGOfkyVmaDY8gjOnuzmMYkmY2Ie5P6Lcy2MLAkpI93X18ZfIcj0xfoD+bY7HdJGvajJfKNKOQfM7BjyP8OCbRirvGxmlFEdONdeE8CQzm8tx4/+euzJEFLAQ/feIsf3DrQbKmxVSjRtLRZRewlmR+sYySLQRGQ6MEGAJMJVGoNIT1okNpnRr61KhKxkd7GcxJ/uWf/DpOtD4IWUH6d/sHP7jGTunrSStw4zitir0Unh+yY3xd+mC10lp7ZlIKcjlnw7rDQ30M9Bap1j2UUhTyLvmsw+JKg7YXUrJeHjXIi3CNAteU38rR6hcxhN3pNQwa1dHOSc919qYePvXEP+N7HtsLZ89t6K71esS3vcFvtD+JFz6FaYysVdop1aTa+F16y/8a07i8acI3gzA+BzpZM/ZaR2TsRbrzKa/bthIsM22G0g4dTJngmKmht9oJA5+vUZxto7Yb1N/nkuQlXfkmNCUj3ZMYMqHhZzBkjG3C/q2T9OQauFbq2VpuzNDOClMnSgQtE60lhjA6yay0R6qU61Vbx+whfrD3R3lzcIbhuMasWeJBZycqm4Ggo8gpwXQsDFMCBlEYA2kS1nZtBrf2kS9m+On/7/eSK2S4490HOP7CLE89fpbZ2QqOY9LbV+BLXzhGd/e6sYeUyVFveKnUQawYGi4TxwmV1RatDhXRsgziOGW4CMA0DZTS2I5FIe9y/cGtaeWn0pimiWEadHXn085gI5reQowdSUzTIEhiTleW6c1kWfbatMMQU6StAA0pOzlVTca0kEJgSwNLGnixR292XXahP5cnSBIO1JtkgvUK6Uvh+D77ag1GC0WiRGEZBkZnRhQmClfKNfVNBWQMgz3dfVQXm8RxE6No0G/lWFluIqXENCVKpWEdrTVKpwOfaUjcjEUpn+F73ncDjd/4X6mE9hUgLmGnZDM2d9yynQceO01fdx7bTnMyq5W0H/PeXeu03GLevaoAnm2baC1wHIuBvvWZc5ykHd9c91szm96aO0jR6mei8Qwz7WO4sogQEOmQSPnYMoMjc8hsGX7iJ7nqdOZ1hG9rg69Uk3bwGKYxtKGsWso8iWrQ9h+nmPuOl/WYWq9/aEr7tP0nSdQqlinXG1kIKOfbmF7SEfyC7KGAvT82h9Aa2QaVhf5frTP1x93031ynp9BMGR+GopxvkigTpSTd+eYa/1wlIA04+I4ZZk6WqSxkaVUcErXelEIIiTREWgiTsQjaEb60+Hxm34brEGGCNFIZXGlIFiZXiMIIISSmaaaKlEASJySJonuwTK6QQSmFYUgOHBzj4A1b1/ZXWW3y3JFJPC8kl3PXjH6z6aMSRbGY6VRiQi7nks061Optmk2fIEjW7hsdb9YwJZmsRbGUZW62yuJCDSS02gG5nE0SJ+y7fguBWWGlVWFwpB8hYLZep+L7+HGUxp9dFzOKaAQBC7JJdyZDO47ImhZ+HLOrt4cwSVBaccvg6Nr1vGl0nN979hDhtu2EGRfbu1w6w3McDuVcut0sp1aXsaWBbdpoNMrUNKMIpyP7YAnBd+zax2CuQMVq8ph5nNHuLlxpUK96aK3XaJKmKegq51hZbRInikIhw5bhMm9/8zU8e2yaN9QWcaMrD0IvZqfcectOLNPgsUPnqNZSR2TXjgHe8eZ9uJfw5beP9+HYFm0vXEvwprsLGB0qY0hQSq0V42mtWVlpcOP1W3Hsl88Maa3RqLVQT5c9TKl7gFn/OLVggUo03WnQI4l1hC1zWPKbbyzyDwXf1gY/Viudh3/5bZAyTxRPvOzHtM3UyIXRNF74BEmyiiYGgjUHI9Wxh2I2SKfkTcWWH1vFaF3CI28DaLb82Cqnn+xH5umUs6cetyFjLtKk1xwv0ekgpeEN33OBL/3pDoZ21jn99DCBl8aADVOSxArbtfFbVzYKqTSxQRwnxHGCVAohU8MvpcTqfMBaa5IoYWl2ld0HxvjdX/44zzx4gsCPGNjSw7t/4A3cec8NSEMyOblCFCWsLLdYXm6Szzu4rkWz6eNmbBzHZGW5Qb3mkcu7FEsZ4khRrbTXzkl2kpFaQRIrvFZAXE64cH6JXM7Btkz6+gpIKWnUfZ57dhJ3ew6rxyYqSb46NcFss0Ej8MlZNqaQFByHouOSNBRLXpuxQolGGDLfajKYK6A1LHstvmfXNYxcUoy1r6ePt49v54E3eNz1G1f2HJWAT990AFVZoT+bVq6ueB5+kvaqzZkW3Zksq75HbyaL1jDbbBC7McWci98MWax7+J0aBtNIdeIdy6DdDrEsg/7eIr/8r97H2GgPv/vRB8i4NuzaRfSYuxbG2YAOOyWMYp46cp6nDp+n5YX09xY4eO0o1+wa3hCuuQjXsfje993Axz91mHrDwzQN4ijBcSx+8ofexMkzCzxxeAJDirTSNlaMjfZw1xt2X/HefKNQOmGy9Rznmk/hJXXyZjc78rcykt2PFAa2yNCIl7BlhoA26LSnbawDFK9ee8NXGt/WBl+KHKCu2M9Saw9pfHM6OVeCYXQhZTdN79PESY1UpH0ji+TSUxECip/2X5IPX77Pp/bh7Nr6L353O13gUikdQCWSQk/A7ptXGNu/SrYY8fRnxxGaTtL1chrjpdA6DbWYltGJ8SriTsMUgUo98Yuy0SrNNzz15WNYjkWhnCVbcKku1vjT/3ofC1Mr9O0d5fHHz7Blaw9RFJMoje+F5PMuvb0FFhfqtJoBxWKWIIhTOQQvHQwvJoXROg1L6Ytyu5pEaXbuHmR1tYVjm5TKWYrFDJ4XsrrSpFpp80PvuY3/fuFJji4v0JPJpMlSrWlGIQPZXNolCxjNF5lpNrhhYJjv6+7BkgZ+ElG0Xa7tG6Ans1FFsxb43DI4yvV9Qzz/x3/I9T/2EVSiyAQBvuOgpOD/+sV/QdOx6bVs7h7bzsMzF/CThKJwMIUgSBKkELx5yzjft/daGkGIKSX7evv4q3NP8MnPPddpSC6JYkXcoWRaWXNtxlYuZnj22DSWadJshQz0Fzl3+9u49f/85pUfrpQkH/ggf/Ppw5w7v0ShkCGbsVhaaXD/F48iENx8cPyKm45v6eWf/NjdnDw7T6Xaprsry+4dg2QzNqNDXVy3b4TTE4tEUcz4lt6vWb379UJrzfPVzzPVOkrO7KJoDhApjyOV+2kmFfYW30SkAgLVRGBgpj0kO9x8iBKPSPmvCV38bzW+rQ2+afRiW7uI4gsbYvVaxyjtk3Nuf9mPmag6SVLBMQ+QJF+6eCasN7i+HNb5uOPRXw7ZTn/fCIe1UvGLEKBTQUiiwCBbjLnhHTMMbq8zfbwnFUvTKXvFsAzajZduOSg7XtrFwVKQFvdI2yQOE1SnmbYUAss18ZoBA6PdyM4HXujK0ax5PPrZ5yhPrjK2ewjDkGQzNhcuLNMyJc1mwJ69g9SqbfKFlLUzMFCiVmtTr3lEcRryMgyBaZrE8bokgxBQKmWxTMnWrT24lzTTyOdd8nmXTKaOl9V0Z7JkTYuZTiWrkJKcaeEnSdqOUQj8JKHkuLxr+0729lw9rzPXbPC3p44xUUs11/OWxXve+EZ+7/578T72MXauVFgYGuTRN96B7zpkvJQG+tFjz1B0HLaXulBoqoFPRqcCb9+9+xreODq+dow4Vpw4PU8x59JsByilN8hGtP2Y3i6XfXuG2DLUxcmzC/T15teU7+JMli/8f/4r7/gvv4jQCivwid1M2lbw/vs5Xwk4eXqBetPj8POT+EGEEIJsxma10mLPrkEKuSsbx2zW5obrLlcUFUIw0FfcILj2cqEeLTLdPkbJGlgLzdpGFlM6nGs8ydbsAerxAjmji1hHxDr9NmyRxZI2XtIg1hEWmwb/dY9S7sOsNn6bKJ5GCAetYyCmkHkPlrnjZT9eFE8gBNjWVtpBBoEFwkTrKlylyCkaN1FZrmj0VTb9fSMCXkzsEwIMMy042nXLClKmBhohcDLDdPUVOolOkyhICDv0xfUGFZpL0g/EcRqLN0wDw5AEfgRaEwYRjmthdFgRhiGor7bIFzJIQ6LtBJVJEKEkkzgsLdYxlusY+0ZIdIxTElxzYAipTJaXGti2wdZtfSwvdTpqSYnjWAyPpJ7nqdPzhEGEVjpNqkrWKIi9fQV27Rni+LGZDQYfUmqiZRuc82rkLZttpS7GimXO16ocX1kkUAmRSqj6XtoARBqMFArMNZsYwmBHV/datexFVHyP337mCWKV6uFLkVJW//yFZ9HAs295I/3ZdfbHUrtFvRNWMaXEkSbLXouym+HuLduQAiq+z1J7Yy+B81PLLK+2QEKp6CJlmrdodypWCwWXd75l/9osyzIlC4t12u20+jXjWizsPcBf/PYn2PbYl5HnzjL25lvZ8gv/CPJ5jn3uWS7MLFOpptXCGddC65Rtc35qhb++9xAf+YHXjrDYcnAegbxM3vhiHH85mCRREYawyZpda3k0ISSJClNNHvHysoReq/i2N/im0UNv6Rfxg2NE8RmkzOPaBzqsnW9hXE9IDKOEUo1OyOXq5eX197v0/UqdKxHitRDU3381z8QkDeJs1JC5SOtMBKgY5s92oRU4V2JLdKSEQSDkutE3DIlpGxiGgdap8Y9VgpACyzaRhkRIgTQkca0Npia8dpVorIWgQzSfc+BLJhrNjDfFcrDUOaSmYBZx6QIMBofKjG7pYXmpThwlFIoZunvyrK62UGheODpDHCeX0AIlhiO5556D3HzbDk68MEuz6ZPPp/cpjhNWlhq89Z3XctZoEiQxh+ZnWPU8NBovjnFNE0ca9GRy9GWzvLC8yFyrztlHv4zWsKVY4l/d8kYODKwzVR6dvsB0vcaK7xGrNEznmhbbS2Vmmw0sYdCOIjKmSazUmrEXQNX38TpNPyqBz5ZCaUNO4FLESULbD8ll7DVGlTR0WsGsFEm8HlJL4oTJmVWmZitpQ5eJJYaHyuza1o92Mjx5w1uwb3sHN/3Qm6CThF1YqtFshWitsDthOyHoJGUDjp+eZ3m1uVag9drAlb/Vi29+3uqhEsys8fIBtE4ItU/J6r8qn//1hk0tHUAKl6x7E6X8hyhk78EyR79lxt4yx0njKxLTGMaQJbQOuZp3D6Dzkpk/6SbJCVQnVKyykOQEM3/Sjc5d6TE6pAY/yxUfcyfEE7RtWlWNvKQ5ShTF5Luya9+QVinN71IPP5N3EDbofIw2NGYnUVsoZxFGqqdvSElPf5GRnQOIO5qE4w1E00Q0bKhZRF1t8u/zCcshC+15HOniGhkyMkszajDdnuKGW7fitUNyOYet433s2DVI/0CJJFa4jsmtt++g3JXd0ERDCNi5a5C73rqfnt4C3/f9t2OaksWFGktLdWrVNm9+yz5uvmU7u7v7eHZpnlrgU7BtSo7LlmIJP44JVcJwvsCx5QWqgU9fJk+3m6Evm2W+2eDfPfQFpurVteN+ZXKCqUYNUwoKtkPBdlBa8cLKEoaQbCmVyJomjTCk4vvEKg2JSSGwjbQGwJYGYZJweGGWWCn8JOba3oENj65czGF1eiBfhNlpH6g6AnTpg9OcOJMa562jPdxw3Rhbt3QzN1/lyWcmmFuo09Od5we/97YNjBvDkERRstadasNrI1Jtoou6+K8mtNa04xpZswul4w0MOEgTuQLocbYwlruOLnsE0ASqRahaxDqkYPawq/iGzaTtJr41MGSJfOadNLz7sI0teEkVIUqgW6QhmMtlAAC8W23OHuqn+Ckf63xMNG5Sf797FWMPAgeND4QI8mgadNTWAReQCB2jVAz4KOWklaheRCbncPCNe3jwk4eJgqijd5N6S0mSUOjLYtzokdsfIwzQkcZ/zkQcSrfLl7LEYYKbc8gVXZYaKxzrnyacUdiuQhqSKIhRbUnfDVmSrS1qT+eJSho7I4hCRVA1KO/S9F8rGZ7oYna2Qk9PPu212vBptnzuumsfDz14gne++wCtls/SYh3LNBgYKlFZbXH+/BJ79w0zuqWHn/pHb2NpsU4UJfT2Fda4334c4Rqpx51oTacGjb5slm43y1u3bufJuWkksNBuAuAYJr2ZLPXA5+Mnj/Hzt9yJ1prJenWNl38RjmESJQmNMOAtQ9s5X6uw27ZZ9TyeX0oHksF8WuC00m5jSIEhBH4cc2x5kTtGtrC7u5dLYVmSLSPdTEwuo5XGto1OU5fUE+8qpl3FqtU2M3NVRobKdJVTsbHdOwYZ39LL1EyF97x1PzdeP3aZsRseKJPNWFTrXqciWZAkaVK4kHMwzLRRzKuJlWCaF2pfphEtA5p2UsNPmvQ4W7CkS6g82nGVnYXbyZoldhXuYMk/j2PmMLFRKBIdYkmHnYXbXtVreSWxafBfBeQz78KQvbT8z+MwTpwskIQ1oPmS2+mcXGPjvDRswECQZa0jOiaS7rQlFAopHKxsAa1a9I/lmTwRYtkGw9v62H/rDrJ5lzvvOcizD5+k3QpAafpGunj/T72FR9tfodLTJiJGC43Ugq5xE7HTwlgy6HpRYs6zmmy/ZpRoXjM/uULoR+SKGXZctwVzS0IzaTJW7mLicIPGUoSTM9j7pjKlPTGraokPfOgOnnriLIefPk8YxAwNl3nv+w4SxQkXC7wKhQyFS7okmZbJ9NQKe/cNA2mSeWCwxItxrlbhur5BGmHAZL1KWykKts2+3j5ipTiyMEfFb5O3nJT2qCFKEmabdYq2w9HlVFm0HUdkLYta4F+B9ZXWJPzMwVt4aOoCD02fx5IGppSUbLejxwOmEKz6XpoDsA22l7v40WtvTFU0SUNRTzxznieePocfxuRzDlGU0GqHmIZkbKSbbMaiv7fAwmIdzw8ZHCixd9fQhvOxbZNCwaXVDq7o2e7fO8K2rb2cOpsyauI4peF2FzIYUlDKu4x1mua8GqiG8zyx/FfYMkPBTKt9bZllJZyinVQRCWTNLg50vYfR7DUAFKw+3tD3A5xuPM68dwqBYDizl53F28mZXS91uNcVNg3+qwAhBFn3ZrLuzWidAJKW9yVmVv4RqYefNmm4GmvnCntkQ5xeFBDCwRBlFAop8sTJBIbMIWURMNA6IFFNMjmTH/uVGm/9/hzPfbVM4m9FSsnKfA3TNvnVP/9Ztu4ZIgwTMhmbeW+Ox4/ch1nXOCqHIQwSHRMbCaW3SzIPGCzMrFIs59BKUa+06d3XhbXVpm9f/1pT7DT2LJhuT2EKg5F9OYb3ZlMmkUzvUTVaxTEcMhmbN9+9jzfdtXdNGwbg3NnFqxZHqkSR6RQARSrmqeUJHls+QyMK2Jrv4a7+PWwv9JMzLZTW7Ch3s6PcvcFYTzfqnKmuIEX6PJpRSJSoVPdGpEY8Z6XHMKWkJ5OjGYZM1qtps3AhcAyDnGlzoH8Q17R4x7advH18B0prPnX6BP/j6Udox9FaJ6yS43Kwf5Cc5XDL0AjPLc7zwNQES+0m/qk21qpm51AvB/ePcuT5KTw/YnxLL+Nbumm1Q7aP9/HB99+EEIJTZxf4u888c0Xqo1b6qhWuo0Nl3nT7LuoNn9VKC6czgwijhL7BMkODJf73Rx8iimJ2buvnDbfs+Jawb66GM40nMISFa6wrmrpGgW57lLzZzW29H+wkcTe+HAWrlxu737chafvthk2D/ypDdJJFGfdODFlCqVwn/HIx2fpSvawsIGLd4Buk4RoHtCLW8whcLGcvmiqOeSOJnkMpD1AIoREij2VuZ9u1TbqHznHuGYe5U7vYe9M2brxrL30jqSeXyaTneaZ5CvKKAbufVsMjCmMydoZcMYNntrjpR3cgny9w4vAEhmlwy9uvY+eNW/jkyl8TJAGO4awnFHWCazhkzQx+4uEaGS7mzpRWxCphW26dKSWEWFN/BBjd0o3tmHheuGbcIS26Ukqze88QiVb8xcQTnKjP0WPn6XXyzLdr/MGZB/ng1lu5cXCYh2cukGiFIdaNRCMMyJgmRdslY5pUwwBDCGQnsRElCX4S8+aRcSAN3ews93B4YZaMZSHjhESn1NRQKe4cHdt4HULwnp27+er0BMvtNl4cUXJcxoplyo7LXLPBXKPJ5yfO0O1mKCQW5y9UISvItG22d/Vw88FxZucrTM1W2bW9n/e9cx/X7B5a073ZtrUXyzQIggjnkhh9HKdhw13brkwvFULwzrv2M76lly888AJnzi1i2yb7dg9SrbaZnq3QXc6Ry9qcOb/IqbML/OAHbmN06FvvKWutWfLPkTMvr5FxZYFKOINGX8aguhTfjob+IjYN/msEAhMpM5iyG0SM0m2SpIbSC1fdAhIkA2Qzt+MFT6C1QggbrVt0JF9AJAgSStnvJ4yPY4hxlA7xgyeRIk/GuRkpLSRddPflKb5tjnd+4Gcw5JU9tnpUT1vs5RzcF1Vc+mGL0Aq4+913cvu7r9/w25vEW/jK4hfxVAtHZohUSKhDri1dT6hCHlt5JO1OJB0MYeAYDjd23UKPvTF+fSls2+R933Ejf/fxJ2k1fbJZBz+ICIOYN961h77+Iqfq85yozzOS6UKItOtVWlgV8LunvsK/3n8Pd20Z56uT58laFq5h0IhCJIIf3H+QP3n+MD2ZLNXAX8uuKDSKVE/nUu85UjGOkeoSlWwXDQRJjCkk0RV69TqGyc8cuIU/ev4wQkDesvHimLlWg9uGRnlyfobRQgkpBIuzaX7AdWzOVFcZLhRxXYvt4/1kMw633bidA/tHN+zfdSzuecf13PvZIxjSJ5Ox0/sTxrz9zfvoelFf2kshpWDvzkH27hxcW/bE0xN86aHjDA6sh8Z6uvLUGx6f/+oL/PiHv/XJz3SwtNKZ8YsMt0YhWNeFejG01lTC2TUaZ587Tska/LZJ2MKmwX/NQEoL17qFtv9F9MWuPFqz7sVDGuoxENiAixAS29qCKftwrGuxzDHiZJo4WURrHyEySOGSy7yFUu5DhPEp2v6jtIOnMI0hXPs6pFz/6FNBN0UYnSPjHLziefY5/WvndumHonQawui1+6643VhuK98x8t2cbJxgOVj6/7d331FyXPeB77+3Qufu6Z6cB8AgkwBBEMwgRVIiJVGBsiSLCitLWtlav7XX6+e3e3a93rfxvGd733nBu/auVyvJlmytRUlUoCWKEiVSDGICCAIgwIA8gxlMDp27K933RzUmoHsQiMHMAHM/5+AA011ddbum8avqW7/6/YgbrcSNBAfTB7C8Mhk7TcHx882TgXoaRSMpM3XB/4y961v4/G/ew/59fQwPTdGZrOeGHT10dftngAenBgjrpn9HsOdycHqAaauAEFB0LP6/t37K/W3X84923MzLQwOkSyV2tLRzW3sXTZEovckUr46cYWOqwS+mZtvomkbYMLmhqYWDo0P82satlByb/myae7rXMpLPMZzPoQnBxphfYvnA6Bk+snFL1fi3NDbzv92ymxcH++nPTLM2GeG29i5Op6dnpo78340AMfvzVKlEW6ySXiiY981nrq0b22hIRXnt9X5GxjL0dDawc3v3OzobP/jGaRKJ6nz1eCzEyFiGTLZEXY3nF1tXdDsnc3tIaPOzl/LuJJ3R62umWLrSYf/kjxkuHfUPCgKOZF+gI7KV7ckHVk1apgr4K0g0eBv50s/w5+7n3igUJGD0AhIhQgh0XK8AlGit/7/xvDzTub/G0JMYenLeOm1nCE34FSiD5iaC5iYMvYVc8edoWtQvK4xXmfOk0hN14WsHG+IbaQg2krHSmJqJrhk4noPjOTQGmlkTXYPjOX4FznOCdSpQz20NdwCQd3I8evoRonqU0ZKfkpkI12F5FobQaQq18Pz4MzSFmkmY1Rdb52psjPOeB66v+Zz/3vxxnMiNMW0ViBmz00oNwRjPjx2lZ20jn7v+xqrXf6B3E999+w1cKWmLJZBISo5DMhSmM5Gk7PrptH5zcQhoOt2JJN1z+t5arkvZrZ19BdASjfGRjVvnPdafSc/bf4lGP5CebSt4tnSC63oICT1dC5cBaWlK8L77au+fS3G2+uZC5HmnHxfPuthNjJaOk7FHCOn+N9GSmyGsJ1gfr313/KncPoaKR+ad0UvpMZA/RMpsoye2Y0nGvtxW72TWCiOlpOQcJh7+IAFjM0IYaFoAQ2vH1HsQIkAosANN8/PjDSNBXewzBM0NlYJsonIBeP46wSEU2Dzv8aC5CU86jBaHOZw5yMH0ft7KHmaiNIpAEDDWLDjOZCDFnQ130xhqwtQCSOkR1IM0hhrZlNjCz0Z+wt/0/RXf6v9bDk7vx/Fq319wMncCDw9HOpTcIoFKb9qAFqDslbG8MhI4kTt+GXsVtiTaKXgWjudypjhN1AhUpnY8v/F4IELCCPPC2NGar++uS3FnVycWNgOFSYaKU8RCOtc3NpGxSuxo9m+8Chsma+qSTNcoSDZRLHDjnBu0Lsb6VD2e9GYCezAaoHVjikK6jFNyiZsBsrkSw6Npbr95HfXnmZ5ZLFs3tZHJVL+/QsGiPhmhLr40d6sG9Si3N32SzXV3Y2oBDGGyMX4ndzR9mvCcC7lnSSk5mXuVmFE/7yAqhEbESHIit3dJxr0SqDP8FcPD89KYRheG0QTcCYDrpSmUXsL1MuhaC4beguuNAxp10Y8CfkG2aOg+cqWfYmjNaFoET5Zx3BFCgesxjXXztmToaxi0gpSs19H1RkwRwZV5RkoHKIbeS5t+/pS7mxtuJWEmeHHiV0xY49SZSVqCLQwWBoibCRoCjViexd7Jlxktj3Jf83sqmS6zck4WXRg40q7kj89PY3SkgylMMs7l3eCzqa6Vrkg9J3NjuNKvv257DkXXZmOiBUPohA2TqXK+5utP5cbJGNPouqQjESdkaBTcMi+MnmB7qpO7u9f4IxaCD/Ru5r++9jKyVCQV9Ofwxwp5gobB3V1rL2ncXfE6bmhu5bWRYVoiUYKGQao3QVqz6Jw2sYsuDakoD9yzdd48+5W047ou9h86zfhEllQyiqYJsrkShYLFJx7ataRz4QEtzLrYzayL3XzBZT0cLK9AyKw+GJgiRNYZrVlA8VqkAv4KIYSOoTfjell0bfaDqWt1BAPX4zincb0zgCAUuJ5Y+EEMfXYOMx75ALpeT774ZKUuUIh4+H1Ew++uykoYt8Y5XFpLtxkhyltoFNBEmIy4mxOFKBucHFFj4dvmp6wp9k/v83OZQx2UvBJ7Jl+iJ7qOsO6f5QW0AA2BJvrzpxgpDdMWbp+3jqRZT8H1L+BKOadiqQSQBLQAJbdEQ+DyKpaamsFvrLuTp4bf4EhmmLRVIGwEWBtrpCHg7+ecXaY7Wr0dKSU/OXOQ1miE5t44b49OMVEoIdCIBgW7uptoDM+eWa9Npvidnbfyo+Nv8+bECEIIbm7t5P1rN1ZV07wQIQSf2nIDbbEEz50+xXipQNwM8ht338wdHd3o2tJ/OY/HQnz212/j2RePsu9gP67rsaa7gYfet2NeM/OVRsMgrNdheUUC2vxvIZZXIG42rYpgDyrgryjR0ANM57+OJsIzNfqldBA4NNb9C0LB7QgEQgSqXiuERjR0J5Hg7UhZRojATMrnuQaLAwgRIK/tIC+3I3CQGCA0JOOMlkZYu0CLN096/HL0F0igIej/J/dsD0MzGSoOkjDrZoK+n1FhMFDsnxfwx8qjHMocYLAwAEDJK1F2y9QZSSwsonoMgcAQBmuj66rGcKkiRoAPdu7AFDrfPPUSJadMf36CU7kJEmaIVDDKp5qr77bMOiWGCtO0huoQAcFtPa0zfV5t6dJXHJu3vJSSKSdN1hwn2eggpGBCGyXrdtLMpdedMXWd+9es577udViuS9AwzptuuBTy+TLDo2lMQ8c0NKamC0xM5ljT1bBig6YQgvXxWzkw9QSGGZi5QOtJl6KXZkvdu5Z5hEtHBfwVJBzcheuNkSs+yWz+vSAWfpBw8OK+MguhIS6i8t/MmoSGnHOB+EJbGC+PkXXS1J+TLqkJ3S8CZk0SDnfMeWam+hoAaXuaJ4Z+jCFMtiSu41T+BAAZJ41tW8TNOhKBOjw87m99HxFj8eami65f0dOrNJhBSsbLOeqDEdoiyarla6X3nS0pYLnOTE7+WYfTg3y3fy+NwTjJgH9Gn7NLfP3E8/yjjffSEXlneeq6phFehjP6c41P5vif33sF0zRobUkghMCyHJ54+jC6rtUsi7xSdEauI+9OcyK7p/KJ9PsnbI7fTVt48XtXr1Qq4K8gQgjikQeJhO7Ask8AgoC5Fl1LLup2OsKd7J/eVzVv6Uq/VEFzaOE54bJX5tzDQrQSlAVgebNVP6WUeLh0RWYDwZuZN/CkR9T0X7MlcT15N0fBKWBLi/c0v4+IEaY11I6hLfzxTFsFLM8lFYhgaNXfZBzPZdoqYGg6htAYKWXYM3GCWxrXUXYdsnYRIQSpQISxco43p8+ws2HNvHXEjCAdkRST5fxMAD9ryspzT8tsmqWUkp8PvUHSjBDUDAqOhQCiRpCSZ/P86BEeXnN112x59UAfUkoS8dnqrIGAQX0qyrMvHmXb1s5lr7GzECE0Nifuoie6g6nyYOV330FIX0kVP6+8RQn4QoivAR8ERqWUVflfwo8qfwY8CBSAz0sp9y3Gtq9FupYkHNx5xdbfFGymN9rL0dxREkadP1/uFck5OW5K3TwTwGtJmAn/tqM5BwtDmLSH2jmRP059QEPPFmh57BnM431s2nQdzb/5MGd7S5wpDhDRZ9evCY24kSBuJJi0JmgNt543DXOkmOHvB/bTlx9DIIgYAd7dupVdDWsRwr8/YN/kKZ4ceoOMVeB0YRK7cmAYLExTdm3WxJqIGLPbCGkmx3OjVQFfCMH7O7bz1WPPMlHOkQpE8KRkrJwlGYhwS+PsdFPRtZgo5zAQHJweoOz6B76oEaQ33syx7OhF/35WqhN9YzMlpucKBU0y2RLZbPG8N3OtBGE9Tjiy+cILXqMW6wz/r4E/B76xwPPvBzZU/twK/LfK38oyEEJwZ9O7aAg2czjzOpP2BEkzxT3Nt7Iuev6mL3Vmkp7IWvoLp0iZs/O2USNKd6SHTQcmuPNz/xIhJWahjIz+CvHvvgKPPw67dxPUgmTdLH755ll+82mJUaO/8Flpq8BXjz2DJyVtoSRCCEquzfdP70MCtzSu47XJPh7tf5WGQJThUoaSa6MLjdFSFk1onMpPYHkuW+pmryk40iWq125k3R1t4Esb7uGXw2/xdmYIXWjc2riOu5o3ETdng58hdDJWkcHiJGE9QMwMIaWk7Dm8NtnHjvqVO91xsUKhANlccaZu/lmeJ5GexDTVhMFKtyi/ISnls0KINedZ5CHgG9JPKn5JCJEUQrRJKYcWY/vKpdOFznV113Nd3fV+Tvol1Be5s/FuGIe+wik0NCQeMSPOryc/RMMXtkN+Nldb5Cvpjg8+CGfOsCm+hWfHniasR+ZNJ2XdNO3hjvPO2e+dOEXJs2kLJWceC+kmzcE4vxh6g23JTp4cOkxjMEbeKZO1i8RN/3pGzi5heWWiIsJwMU1PtJGIEcCVHrbnsi3VteB2OyIpPrPu9pmc+FrXUkxNx5EurpSYlakoIQQhzSBrlzCvgTs5b9rezWNPHCAaCc7bB1PpPOt6mojVaG6urCxLdUjuAE7P+Xmg8tiiBnzHHSZfegHLOYYukkRCuwmaW1Zs9sBKcSnBHiCoB7mv5X7S9jQZO814eZxD6YO88t9+n3c7ZapziPB7Kz7yCGu+8DlOFU7Rlz9JWI+gC42iWyCoheiNbuCXo0+RtqeoDzSyJbGVxuBsqYa30mdIGNUXpIO6yZSd51RunLxbpl6L8nZmmIlynpxTJm6ECGgGQgjyrsVYOcNjA68R1Exawwk+1r2LvFPmb068QNYu0hNt4JbGXppC8/O2z/c5KroWcTNEuGwyXJzG8vzmGwHNoCkUx5ILN7i5Wmzd2MZbx4Y5enyESCSIrmvkC2WikSD331NdNkJZeVbUdzAhxJeALwF0d1/aV+CS9TZT2S8DAl2LY8kpStlDxELvIh75qAr6V0CdmeR49hjfG/wOSEnniQECxQWan+fzcOwYhmZwb/O76c/3cTT3NrZnsyVxHbZn8+z4UwREkIAW5FT+BMdyR9jd+C42xDcCEDIC5Jxy1aplJfMmYgQoOjavZE4yVc7jSRfbhTE3iykM6swQk+Uclutg6Bqu9DhTmOLbp16mK9ZAwgwT1Az2TJxiz8RJPrv2TnoTCzcsn0ufd9CsVNWsfOQ8KQlqtUsRX00MQ+ejH9jJkePDHHxjEMtyuHnHGrZt6VBn91eJpQr4g8Dc78ydlcfmkVJ+GfgywK5duy66MIeUDun836JrcTTNv+quEUXKJPnSs4QCNxIwLz+fW5mv7Jb4+6EfENSCftnargTlsEmwWKM/bzQK69cD/nTS2tg61sb830nWzvDdgUdImfXolTn8kB7C9mxenHierkgXIT3MTfU9fKdvD3EjNO8APm0X6Iyk6I42kLNLFB2LhlCMwYKNruloUlJwSuSdIrowSJhhuqL1aEKjYJc5nh+jIRinJ+qnmkaMIDmnzKOn9/AHW95XMwvoXEHdRAI5p0xreP5F55Hi9Lz5/quZoWts3djO1o3tF15YWXGWKofqMeA3hO82IL2Y8/e204fnZWeC/VlC6AhhUrReW6xNKXMczR3BcsuE9BBFt8DB925CLvBFSmoCHn645nMDxdMgmQn24N/g5TdXcRkqngHgumQnmxJtDBanyNhFCo7FcHEaD8kHO3YwWsoQNgIEdQPLdYgZIYquRdH1K1yWXAddEzQEYzPTWAXXwkDjVH583phiRpCsXaIvPz4zd38+ZddGAHEzRM4uYXsOViX9syEYJ2sXL2KPKsqVtVhpmX8H3AM0CiEGgH+LX9cXKeVfAo/jp2Qew0/L/MJibPcsvwn4QscuA0/WrpOiXJ6yW5rJyPfwcKNB/uovfo0v/M73EVISLDo4kSCeEJR/+G3iC9y9W3atmTP2kltiuHSGaXsaAB2NCWuCtfRiajqfWnsbh6YH2DtxiqJrcUdyAx6S/+PQY5zKTZC2CzQF47SEEoRcG5Bk7RKW6+AhqTPCRI0gludQmprg9p+/TNvgOCOdzVif7iJQl8KWLv25CY5mh/nPb/2c3lgz97Zu5vrkws3tXelhaga76tcyXJpmtJxFQ9ATa6AhEKPo1fjWoyhLbLGydD51gecl8DuLsa1aDKMdkEjpVpUTkLJE0Fi9ebdXUndkrV9M2fMbl+TdPH03dvB//vxLbPnJIdYPa4gNGzj5wV18dPMDC66nOdSMxKPkFDmaP4InPUJaCCRk3DSvTe1lbXQdDcFGTE3nxvoebqzvAeCJwYN8+ejTCCloCsYouxbjpRwZu0hHJEVQN6kPRMk7ZQYKU0xZeVw81u4/wh//679Ck5JwyaIYCsD/+Anf+8v/yNPrG5gu5zE1nTWRRsquzd+depkPdZa5vWl9zfcQ1gM0BeMzef5rYrMXm8dKWTbXXVq1TEW5ElbmbXGXSNfqiIbuxnYHkNI/k5LSw3ZH0PUGQoFtyzzCa1NTqIktddeTdqYJaEEEAsezyYQ8XvvYDRz/oy9w4GM72daxG/08aYmtoTaag62cLJyotD3057uLXpGmYDNhPcK+qeoStlm7xPdP70NDIxmMoms69cEYhqZRdh1O5caJ6UFs6WHoBhsTrTjSJTc5xh//678iWiwTLvkXmcMli3ChzEd/+99QnJ4EAd3RRkKGScwM0RJK8OTQIUpu7TN1IQT3t21l2i5QdPx1SinJ2EVc6XFXy8bL3d2KctmuiYAPEI98iHj4/bjeJI4zhOOeIWiupyH+O2ja0tTpXo0+0fUpbkzeRNkrY2oGDg5BLcDaSC8ekjsb72JT/PzfsDSh8e6WBxAIpPQouUXKXommYBM9kTXEjQQDxdNVtfUHCpNMlnOEjdlE0JgRoikYx0NSdh0yTpGwbrIz1cMtjevYXNfOfc++jpALNHnxPG5/6lXWRpvpnXOWHtAMHOkxWJha8H1sSXbw8JpbcfAYLqYZLqUJ6wG+sH43beHkhXemolxhKyot83IIYRCPPEg0dC+uN4WmRRa9Bo1SLaAFeG/bB2gPd3Aqf4JkoJ5N8c3UBxpImHXnrYczV0gP0R7uIKgH8aRXaWzhv9aT3oJF3USl3WLBKZOzS36vWT1AXA9S1mw2JVrJ2WWOZodJmGE2JdpYM5onUqp9ph4uWdwwYaHH57dqtD2XsWKWH/S/SnM4wQ2pbrbUtc3cZHXW9lQX1yU7GC/l0IV/gVilBCsrxTUT8M/StLA6o19CI6Vhfjb8uD/vroeZsiZ5duyX3JC8kZ2pXZe0rt7Yeo5k3yJ1Tg38rJOhO7Km6uDRHW2gIRjjSGYYD4kmBAJBwclheQ5B3eRIZgRT0zGETtou0p+fIN7VQjkcJFiszum3wyHGu1rm1QoquzZ7J06ScUp0RlMM5Kd4Kz1Mb7yJf7D2DgL6/HHpQqMlXLsJvKIsp2tmSkdZeq50eWb0FwS0IKlAA2E9QsKsIxWo5+D0a0xY4xdeyRzX191AUA8xZU3iShdXuqTtKQSCG1M3VS0fNYJ+9UvPxvVctEpOqCclutD8s3/8JiimpmMKHcdzOfTe3bDA3cW6blD42EcYLE5Tcm08KTk0PciUVeSGVBf1gRjJQISOcJLj2VH2TJy85P2mKMtFBXzlHRsvj1FwC4T1+aWDdaGjazoncscuaX1xM84H2h5iQ3wjOSdL1k6zJrqOD7Z/hFSgdttFT3rc3LCO+lCcsnRwPJf2SIrtyS7CuklvrBkB5J0yQd1kR6qHVEMrx771V9jRCOWwf4eoEwnjxWNoP/kJv37dfbyv/Xps6TJUnCLnlrmlcS3t4dl69kIIGgIxXh6/vJ67irKUrrkpHWXp2J5Vs0kIgI5Bya1ueH0hcTPOHY13cUfjXRfVZ7To2nRG6tmYaPVvkBL+vP7RzAi65k+trIs3zVvXmeI03u47MYdHkN/6FvLYMYwNG/wbw2IxAsBdLZu4q2UTebvEnx5+nOZQ9RSNqemkHXVDlXL1UAFfqTJpTfBW5g1GSsNEjRhbElvpCHdVFVlLBurxkBScPBPWOHknh6kFaAo2YUmb1vCl335fdC32T/azf7IfiWRbspOdDWuIGrVrtfTGm3l5/DgRIzDv4BDW/cydkO7XsDn7XLYyj/+jwf28YEaJ3LeZ9J09BAyTneUxtodD8+bkI0aQ+mCMnFMmds4Y0naR3vjF1dpZDQpFi4NvDHD47TNoQnDd5na2bekgHKpZTk9ZBmpKR5lnoNDPY4Pf51juKI7nMl4e42fDT7Bn8qWqEgMxI0ZTsJnX0weYKI/jSUneyfNm9k2ydoaeSM8lbbvglPnqsWf58cABcnaJgmPxs6FD/PcjT5Oxap9J39K4Dk1opK3izPhsz8HFZXOijYlybubxsVKGZ0eOIKWHKz2eHDrEt06+wuH0GaZKeb7fv4+vn/jVTPMS8A8U72nbyrSVn/d43iljeQ53taye9njnk82V+MYjL/LU829TKFrkCmWefOZN/vY7L1EoLFBQT1lyKuArMxzP4bmxXxLVoyTNFEE9SMyI0xBo5HDmEGPl0arl0/Y0zcFWv7+ptPCkS8pMEdJCM+URLtYLY8cYLqbpiKSImSGiRpD2cIppq8hTw2/UfE1DMMY/7L2LiBlguOTnvqftIh/ouIE/vP4D9MQaGSqlOVOcYt9kH52RFDc3rGMwP4Wh6TSHEoyXs3hIOsJJ+nLj7Jvsm7eN6+o6+Gj3TRRci+FimqHiNAj47Lo76HyHfWqvNb/ac5zpTIHW5gTRSJBoJEhbSx1jkzle2ndiuYenVKgpHWXGWHmUslcmGphfB14TGoYwOJk/QXOoZd7ytmezNrYOx7MpexaG0AloQdL2NCfzJ2g5T3/cc+2ZOEnjOdsGaArG2D/Vzwc7d9SsXNkZreefbHoPo6UMtufSFIoTrEzlfL53N5PlPEcyQzieR3e0AdtzmCjniBp+Iw+t0vM2GYiQDETYM3FyXgkFIQQ3Naxle6qLkVIWXQiaQ4lzSiKvXp4nOfjGAPWp6uY1DakYr73ez713blL3I6wAKuArM1zpcG6D8rM0NCyvXGN5n6GZGHNqvmtCx3Kr89zPp+w6xI3qMsK60HClxDtP1UohBC3h2r1w64NRGoIxzMrBwjunc5WGwPFc/30Ibd7UzVymZqgz+ho8z8OxXfQaDcx1XcOy3GUYlVKLOkVRZpy94cmbKTswG2BtadMR7py3fH3V8sxZ3qpafiFn59g3xFuYtgpVz2fsEp2R1EzAvlhzrzmcPRi40iOgGwSETrlS88aRHvUB/+x02i6wMXHx30oUvzFKZ0eKTLY6KyudLbKup1Gd3a8Q6gxfmRE1omyOb2XP5IsU3RIlr4iOQdSI0hnpoisyvwtZxIiyNXEdh9IHSZr1GJqBJz0yzjQJo47u6MIXbaWUHH9riJeffouhgUliiRDdt7XxZsImoxVnmpzknTI5t8THW3ddVNDwpMf+yX6eHT3CeClLQzDG3S2buLG+m9ub1vPD069xpjDFeDmL5bmEdZP2cD2NwRjTVgFNaNzRtOGy9+Vq867bN/LN776MYWhEI35DnHyhjFW22X1r7QqjytJTAV+ZJ27EKXll8k4eXejYWORdWek/W/1xuan+Fgxh8kbmdRzHBSRdkR5ua7gDU1s4He+1F4/z88f2Ea+L0NyexCo7HP7pCbqvS1DYKRgupQFIBiJ8Zu3trE+0LLiuuZ44c4jnR49QH4jSHk5ScC0e7d/LcDFNfTDKsewIJdciIAyEJih5DmeKk/QXUvTGm/lQ546qXrbKhfV0NvCJh3bxs1++wchYBoD6ZIQPPbCdjjY1DbZSqICvzCi7ZV6d3ktvdD1CaNiehS4MDGEwUh5mqHSmappGFzo763exLbmdvJMnoAWJGJEFtuArFS2efeJ1GlvrME3/IxgMmbR0pBg9PM3Hbt1NojeGlJL6YPSim6yPl7K8OHaU9nBy5oJq1AgS1k1eGDnCYGmaOjNMZySFKz00oaEJwXAhTWMozm9vvFdNPVyG9WubWdfTxHTGn5ZL1UXU/lxh1By+MmPcGsOTLoZmogudkB7G1EyEEJjCpC+/cN0YUwuQDKQuGOwBhk5P4nreTLA/SwiBGTQ4/uYQDcEYjaH4RQd7gFO5cSTnNhT3s4yKnsNAYYqEEUITGqZmzNTbSQUiHJjqV8FpEWiaoD4ZpT4ZVftzBVIBXznHwpkw8jzPXdIWJIjzrEp672w7Ernw8FXsURQV8JVZjYEmNKFXNRqRUmJ7Nj2RNYuynbbOFEIXOPb8dD0pJbblsH7rpZdkAOiJNSKozhrypCSsGbSF68g61amiGafEzQ1r39E2FeVqogK+MiOoB9mZ2sWUPUHRLcwE+glrnPZwO23hjkXZTjgaZPf91zM2lKaQKyGlxCo7jAxO07O+me7ed1afpjmU4ObGdQwWpmfaDBYdi8HiJDc3reO3N9xL2bOZLueRnofreYyXskSMIA/33LIo701RVjJ10XaVSk/lOfDyCY6/OUQgqLPt5nVsvqGL6xLbiOpR9k/vY9KeIKgF2Bq8AXk0xt9+5ymkhC03drPtpjVEorULmgFYZZs3Xuvj9VdP4dgeG65r54ab1xFP+nP8u3ZvIF4X4sWn3mJ0aJpQKMDu+7dy0+6NMzfwjA1N89pLx+k/PkqpZIOUhCJB1qxv4cbbe2lorq5g+YHOG4iWAvzkl68yfnwKvaTRFq5jtH4Ys8flsy238ujUPo67o+ia4IZkN/90x3vpiNYuvwyQnS5wYM8Jjh4+g2HqbN+1hi07ugkEzQVfs5wcz+WN6UFemThJzimxPt7CrY29KvtIQZxbEGul2LVrl9y7t7pxtXL5xobTPPI/nsEq28TrIriuRzZdoHtdM7/2uTsJBAyklLjSpZSzeeQrzzA1niNRCdaZdJFkfYRP/tY9xBLV3cXKJZtH//o5BvsmSSTDaJpGNl0gFA7wyS/dQ33TbOCRUuI6HrqhzbvId/LIMN//xq/QNMHwmWnGhqYBQUtHkqaWOkDw8X+4m66181sRjg1N88hXnqVctkhP5jnTN4lEUt8YJ1YX5tTREXp6m2ld24BVtillLHbcuo77P7Kz5kXGibEMj3z5GUpFa96+6uhp4GOfv4tgaGUFfVd6fLdvDwemTlNnhjE1nYxdQgCf772LnljDBdehXN2EEK9KKWu2m1NTOqvQUz/aj5SSprYkoUiAaDxES0eK/hOjvLm/H/AzZgzNYM9zR5ieyNPSkSIcDRKOBmlpT5KZKvDyM2/VXP/re08y2DdJa2eKSCxEKBKgqS2J7bg888TBecsKITBMfV6wdR2XJx7dS6wujKZrpCdypBrjpBqiTI1lMYMG4WiAJ767F8+bna+XUvLzx/YjkURjYcaGM9Q1REk1xMimi5w+MUosHmZ8JE1QN0jVxWjpSHLglZMM9k3UfC/PPH4Q23bn7avWznoG+yY49Oqpy/xNLL6jmREOTg3QGU6RMMOE9QAtoQQh3eR7p/fWvCtaWT1UwF9lsukiAyfHqaufX+hKCEG8LsLBPbOpl1JKDu45Oe+M/KxUU5zX95yaF3DPOrjnJHX11emZyfoYJ94apnSBcrlDA1MU8mXCkSBjQ2n/gFAZo65rjA+nicZDZNIFRofSM6/LpYuc6Z+gLhVlajyLEPh9boVACMhnSoTCJq4ryaT9XHFN0zADOm8dPF01jmK+zMkjIyQbYlXP1aWi8/bVSvHaZB8RPVD1bSVhhpkq5xkpZZZpZMpKoAL+KuM6LkJQc/pCNzSs8myGztmsGW2Boliu69VMobRKNrpeXfdG0/zA69+RuzDHcWeyKF3XQ2izYxWawHX9g4yovJ+zbHv2vTmOO/89itnaOgLw3Nlx64aGVaoumHY2i0jTFtpXtYusLaeSa2NoC/+3dmocoJXVQwX8VSaejBCNh2qeZWfTBdZvaZv5WdM01m5sJTOdr1o2M12ga20TulEd2Hu3tNd8TSFXpq4+SiS28MVegKbWOoTwA3uqIYYzp9qi43gkG2I4touma/Mu3NbVR4lEg5SKFnWpKN7cg5EHgZCJWwl4sfhsVU6raLNmY3XBtGgiRF0qQiFfncqZnS7Su/mdpY9eSZvr2sg51UXMbM/B0HR14XaVUwF/ldF1jd33X8fURI5S0Q/6UkqmJ3OYpsENt/bOW/72+7ZglRxyGb+jlJSSXLZIqWBx5/1ba25j553r0TWNzFR+5qy6WCiTmcpz93uvRzvPGShANBZi1+6NjA5OUZeKEAiZ5HMl8rkS4UiAeCLE2NA0t92zmVB4tl6Prmvc9d7rmR7PEQqbxBIhctkihUIZI2DQva6ZydEs9c0JQpEAnucxPpKmvik+70B3lqZp3P2+baQn8xQLs/sqPZVHaIKdd6y8omDbU10kzAhj5cxMGeiyazNcynBvy+aZlo/K6qSydFYhKSVvvNbHcz87XMmDh46eBt79oR00tSWrlu87NsLTPzrA5FgWgFRjnHs/dANr1i9c0Gx4cIpfPPYawwNTICCRCHPX+7exeVvXRY3RdT32Pn+EV555m3y2xJnTEwghaO+qJ5oIc9u9W9h5e2/VwWP2vR0iM1VgaGAK23Lo6G4gHA2SSEXIpgu4jn+mv35rO/d+YAfxuupso7PeOniaZ594vXLQg7aueu770A5aO1ZmUbCJco4fDx7gWGYEIQRBzeDe1i3c1tiryh2sAufL0lmUgC+EeB/wZ4AOfEVK+SfnPP954P8CBisP/bmU8ivnW6cK+Fee63pkpgoYhjaTH78Q/8zWv9BZl7q4olhSSrLpIq7jkUhFajbIuBDbcsimiwTDJkg/5TORjGCY56+NP/PeTB3D0CgWLGKJEIGgOW+d0Vh1w5XzrU83NOJ14asicGbtEmXXpi4QueReAsrV63wB/7JvvBJC6MBfAPcDA8AeIcRjUspzm5A+IqX83cvdnrJ4dF0j1VidgVKLEIJA0ODooQFGz6Spa4iyaVsndXPa2kkpGTkzzdFDg5RLNt29Tazd2IoZmP8x8zyPwb4Jjr85hOd6rNvcRte6JnRd8587NcHxt6qfA4jGqwN0MV/myOFBRgenSdRH2LSti2R9dN57C1duEnNsl/7jo5w8OoJp6my4roO2rvoLBvC5+6pcsjn2xiBn+iaJJkJs2tZZ8yaw5RY3Q8TNizugKavDZZ/hCyFuB/6dlPK9lZ//EEBK+cdzlvk8sOtSAr46w19Zhk5P8uhfP0e55GAGDBzbQQh4/yduYfO2LqSUPPP4QfY+fxTd1NB0Dbvs0NRax8e/cNdMoHYdl8e/s4e3Dp72q2UKcCyHng0tfOATt/Dzx/bz9uvVzz30mdtr3tk6PDjFd//qOcpFe2ZcAO/7+M1s3TG/YUsxX+bRb/yK4dOTmAGj0prP44Zb1vKeh2684LUFgKnxHN/52rNkpgsEggaO7eJ5knc9uJ2bd29chD2tKJfnip7hAx3A3CTmAeDWGst9TAhxN3AE+F+llNWJz8qK5NguP/zmC+iGTnP77FlzuWTzk2/voaOrgbHhNHueO0Jze3JeGufY8DRP//gAH/yk/5E4uOckb+7vp3XOWbWUkr6jo3z3r59n+PTkzHNGqUDPq8+hP32C/kM3sv7f/wHEZ7NMXMflh3/7Ipqm0dyenHncKtv89NE9tHc3kJxzv8HzTx5mZGCSljlz757nsf+lE3T3NrN5+/mvL0gpefw7r1AuWfPW4dguzzx+kK41jbR2LlyiQVGW21Jl6fw9sEZKuR14Evh6rYWEEF8SQuwVQuwdGxtboqEpFzLQN04+W6oqoxAMmXie5MjhQV576TjhWLAqZ7+hOcGRQwPkc36q4Ku/OkqyMT5vCkUIQX1znBefepO6+hhCCJqOv87H/+hhbn70v3Hryz+k+8//GNnRAc8/P/O6wb4Jcpli1QXXQNBESjhyaGDmMatsc+jVUzS0zG90rmka8WSYfb86dsH9MDGaYXhgirr6+dNghqljmDqH9/VdcB2KspwWI+APAnNPjTqZvTgLgJRyQkp5Npn5K8BNtVYkpfyylHKXlHJXU1NTrUWUZVDMl1mooLxu+HVyMlOFmnVlzk6TnE0BzaSLBEPVXyzNgFGZltExSgXe/V//FWa5iGn5B4qAXUZks/Dgg5DL+eMqWAvWuddNnez0bEP0ctFGSlnzwnEwZNa8b+BcxbyF0ETN+f5A0CA9deF1KMpyWoyAvwfYIIRYK4QIAJ8EHpu7gBBibpLzh4E3F2G7yhJJ1sdmcvDP5dguzW1J2rrqKeSqb/hxbBfd0IlXvh20dqTIZ6uXK+bLJOujlAoWa/b90u+SUovnwSOP+ONqiIJXe1x22Zk3zROOBgmEzJp3x+ayJVq7LjwVk6yPIj2vZjmJUsGi7SLWoSjL6bIDvpTSAX4X+Cl+IP+2lPKwEOI/CCE+XFns94QQh4UQB4DfAz5/udtVlk5rZ4qOngYmRjPzgmt6Kk80HqJ3Szs33tGLY3szZ/Lgz4+PDae56Y71Mxdcb33XJnKZErY9W8LBdVymJ3M88LGbyGVLRIZPz5zZV8nn4Zg//dLclqRzXRMTI/PHlZkuEI0F2bB1tn6/Yerc+q7NTIxmZkozgD/VY5Vsbr7rwhdc48kIW3b0MDaUnre9Qq6Ermtct3PNBdehKMtpUerhSykfBx4/57F/M+fffwj84WJsS1l6Qgg+/Onb+dG3XmLg1DhCCKT0z7Af+sztfgPy9hQf/vStPPG9fWSmCohK7Zqdt/dy+31bZta1bnMb7/nwjTz7xOv+mbL06+Pc9cD13HrPZppbk4wefhrLDBKwq0saEI3C+vUz4/rgJ2/l8Udeof/E2Mw26+pjPPTp2whFAvNeuvOO9eSzJV594SgC/0uEaeq8/9dvpqOn8aL2xbs/tAPHdjl6aACEACSRWIiPfeGumfLRirJSqTttlYsmpWRsKE16Kk8kFqStq74qldGyHM70TWDbDs1tyXl5+nOVChZn+ifwpKStq37eDVClkQkCvWvQ8rnqF8bjcOYMxGYvnM4dVzjqj+t8N3ll00WGByfRdZ2OnoZ3VNN+YizD5GiWYMiko6ehZk0hRVkOV/xO2ytBBfxV7vnn/Qu0nudP40SjoGnw+OOwe/dyj05RVqwrnYevKItv927/TP6RR/w5+/Xr4eGH553ZK4pyaVTAV1auWAy++MXlHoWiXDNUeWRFUZRVQp3hrxJSSgb7Jjh6eBDbcli7sZU1G1qqCpudLQx2+uQ4kWiQTds7aW5LXvHqkNl0kbdfP83YcBrH9rtVBUMmvVva6eltYmI0y9uHBshnS3SubWT9lvaZWvhjw2me/vEB+o+P0tic4I77r8NzPfqPj2Jbfvqnpmu0dqToXtfMQN84Q6cnideF2bStk8Zz7r6txfZcjqbHeXNqBF0IttW3sTbRgHYVVM1cCaSUnMxOcmhyCMt12ZRqZlNdM4EandGUK0ddtF0FPM/jyR/s4/U9p9BNHV0XWGWHlvYkH/vCXUQqlSSnJ/zCYOmpgt8dynZxXY873r2V2+/bcsWCfv/xUb7/Ny9glR2GTk8wNZ5F03V61jcTjgT8VoaeJBAw0E0dq2QTrwvziS++i4FTY/z5f3wMq+xgBnSssk0uU6ZjTQP1jTH6j4/heZKW9iSxujADJ8dp72kk1RDFth2kJ7n3AzvO28yk6Nh89a1XOJWdJKjpeEgsz2NnYzsP99543paCCnhS8uiJg7w82o8hNHQhKHsundE6fnPLrcTM83dAUy6Numi7yh05NMiBl0/Q0lk/rz/r2NA0z/30EO/96E1IKXni0b0UCvMLg7mux69+fpju3mY611xcrvqlsMo2j/3PlwhFApRLFoV8mYaWOlzbZWRwig3XtXPgpROs3dxGe3fDzOumJ7L86Fsv8dLTb6LpGo0tfnniidEMmi4YODlGIVuirj6KpgmmJ/Nk0v79AROjaXp6m9B0Dcd2efrHB+ha10RTa+0z/ScH3qYvO0lHJDGv4NurY4P0xhu5rbVn0ffLteTgxBleHDlFZzQ57xvRUCHDT/rf5Nd7dyzf4FYZdWqyCuz71THiyUhVM+765gRv7O+jXLKZnsgzeGqcVMP8LBhd1wgETV7fe/KKjK3v+CilokUkGmSof5Jw2D+jN0wdz5P0HxsjFAkwOjQ9r0dtXX2Mw6/1kc2UZvrTuq5HMV8mGDRwXY9spoiuawjhN0+fnsgTS4SxLYdM2q+zY5g6miZ460Dt4q225/LSSD/N4Vh1wbdgmOeHT1yR/XIteW7oJHWBcNX0V1Moxqtjg5ScldcM/lqlAv4qkMnULmym6xrS8+fti4UyohIczxUIGWTmFCJbTHObqZfLDrox+5EUAopFi0DQxHM95JwaNkIIbNubV+LAcz0QVN6DmFdCwX/eb4YuAceefc4MLFz4rOw6ONKr2TEqpJtML1QCQpkxbRUJ6dWTCYamIZEUXRXwl4oK+KtAe1cDuUx1YLLKNsGQQSQapK4+CpKqIAl+lcj2roaqxxdDsiHG2WNMLBHCsmZr7EgJqYYYxUKZYCgwr/Sy53pEokE0jZlm3bqhIxCVbwL+nP/MugDdNPxlJYTCswfActGivbt24bOIESBhBinUOAvN2CW6Yyuzr+1K0hNLka1RJqPsOoR0U83hLyEV8FeBXbs3YJVtyqXZoOW6HhOjWW5512YMUycaC7H95rWMDaXnTZ0UcmU0TbBt15orMraOngaa21NMjGTo6GnAKjs4jkuxaBEOB+hc24BjuzS2zM6fe55fSuHOd2+he10LU2NZPCnRNEGsLkyxUCYQNEk1xikVbRzbRdME7V0ppsaz1CUjMx24zva23bRAc3VNCN7duZGxUg5nzjeMsuuQty3u61j4Yq/ie1d7LyXXmTd143oeI8Us93asV/12l5DK0lkl3jp4mp99fx+25bcmlMDNuzew+4HrZ+rhWJbDU4+9xuHX+iuFyCASC/LBh2+la92V60+QTRf58bdeZrBvnKmJHGdOTxIMmXT3NhEKBdi0rZO+4yMU8tZM0bMtN3Txno/sJDtd4D//ux/Qf3IMgfCnfQR0rWsmFDbpPz6GY7t0rmkgEgtRyJcJhQMEggZSShLJKB/69G20dix8pi6l5OcDR/nF4FE8/G8Ipq7xkTXbuLn5/F2yFN/+8UG+e+Igluf4vyfgXW29vL97s0ptXWSqlo4CVAqb9U/g2i4tHamqDlZnTU/mGR9JEwyatHfXL0lhsLMF0DLpAqap+1NLEtq66glHg7iOy5n+Scplm8bmBMk5F5c9z+PYG2cY7JsgWR9l281rKOYsRoem0Q0NTdOwLIe6VJTGlgST41mmxnOEwoELFlqbK2uXGchNI4RgTSxFyLj0omurWdl16MtO4UiPzmgdiYBqsH4lqICvKIqySpwv4Ks5fEVRlFVCBXxFUZRVQgV8RVGUVUIFfEVRlFVCBXxFUZRVQgV8RVGUVUIFfEVRlFVCBXxFUZRVQgV8RVGUVUI1QFGUKymbhUcegaNHYcMGePhhiMeXe1TKKqUCvqJcKc8/Dw8+CJ4H+TxEo/AHfwCPPw67dy/36JRVSE3pKMqVkM36wT6b9YM9+H+ffTyXW97xKauSOsNfpaSU4I2AOwgYYPQitBjSK4B7HGQZtFbQOy66ebmULrinwJsCEUNqzQj3NOCA3oXQmy/weq/y+kkQUX9MInC5b/UdkVIykE8zXMgS1A3qzCBHM+OMl/J0ROqoD0UoODYhw2BDonFe5cyxYo78V/6SLtelZp1Rz/Oneb74xSV7P4oCixTwhRDvA/4M0IGvSCn/5Jzng8A3gJuACeBhKeWpxdi2cumktJDF74B1cPZBoSP168F9A6QNCMADYzNEP4UQtUspz6zTm0Tm/xrcEb9gvTcG3ghSXwda1F8msAsR/jWEqP7YSW8Kmf86uEOzD2oxiHwOYXRf/pu+BAXH4m+PvMrR9DielJzKTnI6P42BhqnrZO0yAV2nN95AayRByDD5B+t3sjHZxPdPHeLlkT7ev+8l1hQWaAuZz8OxY0v6nhQFFmFKRwihA38BvB/YCnxKCLH1nMW+CExJKdcD/y/wp5e7XeWdk6UnwToAWjvoHf4fGYDC1/xgrXeA3g5aBzhvI4t/f/71SQ+Z/wZ40/5rtTB440AIvCHQW0BrA+sVZPmXNV4vkfm/AW9idjx6B0gNmf8a0lva6Y9HT7zO0cw47ZEElucyVszheBLbc/GkRBcajusyXMwS1HSiRoCvH9nLj/oO88LwKdoiCdzeXqzwAvXeo1FYrzplKUtvMebwbwGOSSlPSCkt4FvAQ+cs8xDw9cq/vwu8W1zsPIGyqKQsg/UiaC0w91fgjYEw/Gmes4Twp3XsfUgvu/BK3T7/zFxr9H92+kCYfuCXNrijIDR/m+XnkPKc/rDuaX9qSTTOf1yLAyWkffiS3uNkqcAvBo7yzaP7+MXAUSZKtRuU1zJdLvL6xBBt4QQecCozSdlz/SbcQpCxSgQ1HUPTKTk2fblpIoaJJz1+cOowzeEYmhAcee99/nuuRdP8bJ1VxJOS4+lxvnfiIH937DUOjA9Sdp0Lv1BZVIsxpdMBnJ7z8wBw60LLSCkdIUQaaADGF2H7yqXwsiA90M7p1iQzQNh/fi6hg6f58/LaAumE3vQ568oBZ+feNZCVgCsC4Fn+zyI55/VT/sGl1jmANOcfhC7grakRvn5kL64nCekGByfO8OTAET678Sauq2+94OunykU0IdCEoOTa2NJFVvrlgsSVEoRAR8OV3kxzblPTmSoX/QMDYEcj/OAv/pSP/M6/AOkRKJb8M3tN87N0YrHzjOLa4noe3zlxgD1jAwQ1HV0IXh09TXu0jt/aehtx1cR8yayoi7ZCiC8BXwLo7l7aedtVQ4v60/PS9YP5WSIKTIB2Tm9X6QHewsEeqp8TkUqQ1/3Xnp3/l7b/LUJEql+/YOc1C7T6C70rwJ97/5ujrxI3g0SM2Yu9Rcfmm0f38Uc730PUPP9F4HggiIuHlBJT0zGEhkDgSYmU/oEA6Qd+TWhEKhdrbc8jbgaxXIdAJeif2bmd//GLR+n68c9oPTPCLXfc55/Zr6JgD7B/4gyvjPbTGU3O6187VMjweN+bPLx+x/INbpVZjCmdQWBuJ+fOymM1lxH+Fbs6/Iu380gpvyyl3CWl3NXUdOWaZl/LpDeFV3oGr/hDvPIrftbNHEKEwdwF3vD8IKu1VjJz2s5Z4SiYWxDnHgjm0tf4B4qzZ/p6t78ur+wfVLTmyoXcUQjcCphI5xRe8XG84o/8KR6twc/OmbftIggTYW6/qPd+ZHoMy3XnBfuMVWIwn+ZYeoIn+t/C8bzzrqMxFGVjoonRYh5daHTFkpi6juU6gCRmBrE8F6cyzdMTS1F2HSSSB7s3M1LMMbdtaCkc4pfvv4fAn/4nPytnlQV7gF8NnyQZCFc1K28Oxdg3PkjRsRd4pbLYFuMMfw+wQQixFj+wfxL49DnLPAZ8DngR+DjwlFypzXSvYp51EIrfqpyVmyBsZOkJiH0RoXfMLCfC70d6Y+CcwD/mV4Jg+CP+XLp7hpksHb0dEf7oebcrhAHRzyHzX/NfLzUQdSDHQOv1L8bigbERgvcii3/nXzRG97dTfta/gEy5kiZ69puBCeF/gNASF/X+87Y1L9i+PT1KX24aDUHBsfj+qUMMFtJ8cfOt5z3T/8T6G/jqm68wkE8TM4OkgmHOOBamZmAKjYJnEdB0msMxXOkxWS7wid4dbG9oo+g6vDE1ggacHcndbevY3tB+Ue/hWpS2SgT16lCjaxoSScm1CauG8EvisgN+ZU7+d4Gf4v9P/ZqU8rAQ4j8Ae6WUjwFfBf5GCHEMmMQ/KCiLSHpTfrAXSdDmZId4aT8DJv7PEZUpHCHCEP0tcE8gnT4ggDA3I/QmP73SfhNkEaF3VXLhL/wxEXobxP8Z0n7LP5MXKX8qxh0AHISxBvQ1SOtlsPaB1jU7Zy8leIMQvAf0Tn/OXiQR5haEdvFnxM2R2Mw9A6PFHKeyUyQCQX9KBo+18RQDuTQ/6nvjvNMIdYEwv7ftLo6mxxnIT/PhnuuImgHemh5ltJijK1pHUzhO0bGIm0G21reSDPrTVl/YdDN9uSmOpccxNI1NyWZaw/GLvpfhWrQ2Xs+bUyM0hef/LkuuQ0Q3iZsLZDMpi06s1BPtXbt2yb179y73MK4aXulZKD3up1Oeyx1ExH4LYVx+KqD0cpULuFHERc6tzxtn5j/hn71X5vG9HHjpyty+iUj87zMHpkvleh5/9vpzjJVy9GenyDs2QU2n4NpEdJNbW3qQUjJayvNvd90/b+pHuXL6c1P8l0PPUx+IzJzJO57HmUKaj6zZxt3t65Z5hNcWIcSrUspdtZ5bURdtlcsgp4AFvhYLAXKBm4AudvWy7Ofj26+CFICLNDchwh9DaHWXNk7R4mfrWC+Ad9qf+xAAQWTkYYS55R2NUdc0/uHmW/jm0X28Nj6IQGB7LnWBENvq2/w5ZOFn2xQdWwX8JdIdS/HZDbv47okDTFlFwP91P9C5id1ta5d3cKuMCvjXCq0D/xLJOaT0/4hLPxufXYVEFr4D9uv+xV1N99fpHEfmvwqxf4IQFzkHq3X6F2etVyp31Ub8VEXPAcqQ/iO8+q+hacl3NNZkMMw/vu4OQHJ4coS2SJyYGZyZUrFcB1PT1TTCEtve0MbmZDP9uSkc6dERrVPpmMtAFU+7Rghzq59yOTcnXkqQI2Cs9e9cfae8kUqwb59N5RTCv5HKHQHn6MWvK3ivf2HWmxPspQThgN4GXgYKP3nnYwWEEHywMu9u6sZMsPekZLiY411tvQT0dzZtpLxzAV1nfV0jm5PNKtgvExXwrxFCiyCiX/RvbjobUL0zoPcgIp+5vIuGbuXGp3nrkP5cfnoE+d//GPkv/hl85St+NcjzjdPcAsaGyn0Ajp++ie1f4NXifmaOs/+dj7WiJ57iU+tvJGuXOJPPcCafYbiQYXfrGu7tUGUNlNVJTelcQ4TeDvF/7leclHl/GucSql0uvOJz78otg30AXjiJ+PTL4AlEwUFGw4gL1HsXQiADN/kXmLUE/gR+aM5NYK6f0rkIdjZ1sjXVwsnsJLbn0Rn1q1wqymqlAv41RggdjN7FXanRCyJUuREqDPYhyE4hPv0KIufObjvvX5DjwQfhzJmFbzIK3AJaxJ/KqVTSBMBz/TP/0AOLNvSQYbIl1bJo61OUq5ma0lEuSIgghD/pXx9wTvrF0L5/BrwFUnrP1ntfgKZFIPr7QNHP2fdy4E76N2qFHgBz55V4G4qy6qkzfOWiaIEtSP33kYXvg3MKcUoiCm7thS+i3rsWfjee0QWFb/sNV0Q9hD8EgbvRNHUeoihXggr4ykUTegtEPoR0TyLXByHyJqJgVS94kfXeNXMj1P3rKzBSRVFqUadSyqXR2v3yB7/WA9oCF4NXYb13RbkaqICvXBIhBCL6GahrRH77Y8hYABnxs3hkNAzx+Kqr964oVws1paNcMqHVQ+z34T3HkCc/A999AU4VERu2rsp674pytVABX3lHhDD9OvmNW+C3H1zu4SiKchHUlI6iKMoqoQK+oijKKqECvqIoyiqhAr6iKMoqoQK+oijKKqGydK5i0h3xm4gIE4x1fq9aRVGUBaiAfxWS0kIWHwVrf+URAcJEhj+OFrhhOYemKMoKpgL+VUiWfuoHe619timJLEHhfyL1RsTldLdSFOWapebwrzLSK4D1ot9ecG5jExHyz/LLLyzf4BRFWdFUwL/ayIz/t6jx5UzE/PaGiqIoNaiAf7URUcAD6VU/J4ugNS75kBRFuTqogH+VEVoczO3gjcx/Qjogi4jgbcszMEVRVjx10fYqJEIfRrrjlekbA/D8P6EHQF/kfrYLkFKCN+w3NNeaEHN70yqKsiKpgH8VEloMYv8YnGNI5wSIMMLc6nekWgLSGUAWv+33tkWAEMjAXYjQA34TdUVRViQV8K9SQhhgbkaYm5d0u9KbQua/DBigtfmZQtKB8lNIBCL8viUdj6IoF++y5vCFEPVCiCeFEEcrf6cWWM4VQuyv/HnscrapLC9pvQrSBi05mxYqKsHfes5PG1UUZUW63Iu2/xL4hZRyA/CLys+1FKWUOyp/PnyZ21SWk3O8kil0DmGAlOCNL/2YFEW5KJcb8B8Cvl7599eBj1zm+pSVTkQBu/pxKQHXvwFMUZQV6XIDfouUcqjy72FgoauGISHEXiHES0KIjyy0MiHElyrL7R0bG7vMoSlXggjcArJQfR+AnAK9E7Sm5RmYoigXdMGLtkKInwOtNZ76o7k/SCmlEEIusJoeKeWgEGId8JQQ4nUp5fFzF5JSfhn4MsCuXbsWWpeynIz1ELgTrF8BYb9Sp8yDiCAiv46YW+5BUZQV5YIBX0r5noWeE0KMCCHapJRDQog2YHSBdQxW/j4hhPglcCNQFfCVlU8IDcIfBvM6pL0PZA709YjATv+mMEVRVqzLTct8DPgc8CeVv3947gKVzJ2ClLIshGgE7gT+02VuV1lGQmhgbkCYG5Z7KIqiXILLncP/E+B+IcRR4D2VnxFC7BJCfKWyzBZgrxDiAPA08CdSyjcuc7uKoijKJbqsM3wp5QTw7hqP7wV+s/LvF4Btl7MdRVEU5fKp4mmKoiirhAr4iqIoq4QK+IqiKKuECviKoiirhAr4iqIoq4QK+IqiKKuECviKoiirhAr4iqIoq4QK+IqiKKuECviKoiirhAr4yooipYv0ckhZo8mKoiiXRTUxV1YEKT2k9QKUnwYvD8JABm5DhN6DUF20FGVRqDN8ZUWQpceh+AMgAHo7iBSUn0Pmv4GU7nIPT1GuCSrgK8tOetNQfh60DhBh/0FhgtYOzjFwTizr+BTlWqECvrL83NOABKHPf1wIwEQ6R5ZjVIpyzVEBX1kBdGCBXriixoFAUZR3RAV8ZfnpPX5Ql9b8x6UH0kEYW5dnXIpyjVEBX1l2QotC6EPgjYA3BdIBLwveAARuAb1ruYeoKNcElZaprAha8Dak1oAs/xLcQdCSEHgfIrADIRaY7lEU5ZKogK+sGMLcgDA3LPcwFOWapaZ0FEVRVgkV8BVFUVYJFfAVRVFWCRXwFUVRVgkV8BVFUVYJIaVc7jHUJIQYA/ouYxWNwPgiDedaoPZHNbVPqql9Uu1q2yc9UsqmWk+s2IB/uYQQe6WUu5Z7HCuF2h/V1D6ppvZJtWtpn6gpHUVRlFVCBXxFUZRV4loO+F9e7gGsMGp/VFP7pJraJ9WumX1yzc7hK4qiKPNdy2f4iqIoyhxXdcAXQrxPCPG2EOKYEOJf1nj+80KIMSHE/sqf31yOcS4lIcTXhBCjQohDCzwvhBD/ubLPDgohdi71GJfSReyPe4QQ6TmfkX+z1GNcakKILiHE00KIN4QQh4UQ/7TGMqvtc3Ix++Tq/6xIKa/KP/htko4D64AAcADYes4ynwf+fLnHusT75W5gJ3BogecfBH6C32LqNuDl5R7zMu+Pe4AfLfc4l3iftAE7K/+OA0dq/N9ZbZ+Ti9knV/1n5Wo+w78FOCalPCGltIBvAQ8t85iWnZTyWWDyPIs8BHxD+l4CkkKItqUZ3dK7iP2x6kgph6SU+yr/zgJvAh3nLLbaPicXs0+ueldzwO8ATs/5eYDav6CPVb6SflcIoVonXfx+W01uF0IcEEL8RAhx3XIPZikJIdYANwIvn/PUqv2cnGefwFX+WbmaA/7F+HtgjZRyO/Ak8PVlHo+y8uzDvxX9BuC/AD9Y3uEsHSFEDHgU+H0pZWa5x7MSXGCfXPWflas54A8Cc8/YOyuPzZBSTkgpy5UfvwLctERjW8kuuN9WEyllRkqZq/z7ccAUQjQu87CuOCGEiR/Yviml/F6NRVbd5+RC++Ra+KxczQF/D7BBCLFWCBEAPgk8NneBc+YcP4w/L7faPQb8RiUL4zYgLaUcWu5BLRchRKuoNM0VQtyC/39iYnlHdWVV3u9XgTellP/PAoutqs/JxeyTa+GzctX2tJVSOkKI3wV+ip+x8zUp5WEhxH8A9kopHwN+TwjxYcDBv3D3+WUb8BIRQvwdfjZBoxBiAPi3gAkgpfxL4HH8DIxjQAH4wvKMdGlcxP74OPC/CCEcoAh8UlZSMq5hdwKfBV4XQuyvPPavgG5YnZ8TLm6fXPWfFXWnraIoyipxNU/pKIqiKJdABXxFUZRVQgV8RVGUVUIFfEVRlFVCBXxFUZRVQgV8RVGUVUIFfEVRlFVCBXxFUZRV4v8HudhHgKa6SokAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 19 ----\n", + "[[ 0.90291561 1.45489916]\n", + " [ 1.84098083 1.69611539]\n", + " [ 1.48615202 0.42064316]\n", + " [ 1.39683691 1.4786554 ]\n", + " [ 1.86086875 1.33435117]\n", + " [ 1.17006992 -0.16747776]\n", + " [ 2.29363283 1.25022025]\n", + " [ 1.50939982 1.24892965]\n", + " [ 1.69437075 1.52803152]\n", + " [ 2.09807475 1.59152685]\n", + " [ 0.90208057 1.28476124]\n", + " [ 1.12481061 1.3710907 ]\n", + " [ 1.13611639 1.60364932]\n", + " [ 1.45560453 1.68988935]\n", + " [ 1.41005201 0.92197227]\n", + " [ 2.42099992 1.62882855]\n", + " [ 1.18718905 0.56500583]\n", + " [ 1.70164347 0.01625398]\n", + " [ 0.90793647 1.66822688]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 20 ----\n", + "[[ 1.85532512 1.7003296 ]\n", + " [ 0.90086701 1.28735764]\n", + " [ 1.17006992 -0.16747776]\n", + " [ 1.3943879 1.4784627 ]\n", + " [ 1.54366826 0.91498718]\n", + " [ 2.34560088 1.3529474 ]\n", + " [ 0.90793647 1.66822688]\n", + " [ 1.35565256 0.36950993]\n", + " [ 1.1053155 1.42355581]\n", + " [ 1.86381455 1.32443611]\n", + " [ 0.89616821 1.45407095]\n", + " [ 1.17426305 1.19644959]\n", + " [ 2.09807475 1.59152685]\n", + " [ 2.43505822 1.66830196]\n", + " [ 1.70164347 0.01625398]\n", + " [ 1.139604 1.61667271]\n", + " [ 1.70494728 1.56158741]\n", + " [ 1.31710911 0.68400167]\n", + " [ 1.45560453 1.68988935]\n", + " [ 1.51757806 1.27123505]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 21 ----\n", + "[[ 1.82258602 1.67986152]\n", + " [ 0.89404151 1.28859351]\n", + " [ 1.17762089 0.77053328]\n", + " [ 1.46700038 1.68080918]\n", + " [ 1.17006992 -0.16747776]\n", + " [ 2.43505822 1.66830196]\n", + " [ 0.89597995 1.45106414]\n", + " [ 1.11082273 1.29274219]\n", + " [ 1.39732095 1.47707062]\n", + " [ 1.80807957 1.37568696]\n", + " [ 1.11853365 1.47682984]\n", + " [ 1.48123221 0.90580295]\n", + " [ 2.09139153 0.28843907]\n", + " [ 2.06086348 1.20102628]\n", + " [ 1.37927175 0.42764745]\n", + " [ 1.49271087 1.24714577]\n", + " [ 2.10024531 1.58643246]\n", + " [ 0.88898972 1.64257327]\n", + " [ 1.11411931 1.67683477]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.56481133 -0.0167102 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 22 ----\n", + "[[ 0.89837018 1.29986048]\n", + " [ 1.7058403 1.65881975]\n", + " [ 1.17781456 0.58953762]\n", + " [ 1.39768082 1.47402 ]\n", + " [ 1.80733166 1.372591 ]\n", + " [ 2.13986651 1.60852773]\n", + " [ 1.5333774 -0.01462142]\n", + " [ 0.90844706 1.67193152]\n", + " [ 1.44569902 1.68492723]\n", + " [ 1.4514876 0.41329382]\n", + " [ 1.11003614 1.44333643]\n", + " [ 1.44789468 0.90637285]\n", + " [ 1.13949048 1.24012714]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.15904916 -0.18036066]\n", + " [ 2.10072422 0.41086712]\n", + " [ 0.8958101 1.46670708]\n", + " [ 1.91053174 1.67741573]\n", + " [ 1.52047663 1.26127384]\n", + " [ 2.43505822 1.66830196]\n", + " [ 2.05420342 1.31713678]\n", + " [ 1.1447665 1.63419834]]\n" + ] + }, + { + "data": { + "image/png": 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CJLC3+hWq4SRFs3/xHoTK5+n5z3P30D8jZ5aX7T85Xef5F0aZW2gSRgkPPXYYy0zzJUqpNMQ4XadcztDxIvYfHueay4ep9OR54cg4L7w4wQfecxNrXyZk1v0kyJgjdMIzxKqFITJoUnqmFBlcK/1stI6JVJULQ5FCCIRwiFWV19sUNoJ9zHW+gmusQ3Tps1onLHiPYhsD9GRuflXGWTX4rxFMKfF1fNHrWmu0BiEF+09OLIvLD1YKTFUb1No+tWYHL4ixzJQ2uHVtL1948hDVlsdbb9gJQMax2Lq2j4n5+rIxDClBSAw0hiG7E4QgY5pEsSaXsdMdkxnQHhglUDXS7pVW92cMIgtqBvwvg+wFOuj2JyD38wiZRYX7ofM3ICSQhWQMHT6Nzvwo0rn1Fd+7hVaHL+w5zNHJOaQQ2KbBnZdvpjefuWTyc+n+wrMnRokSRZgkGAiE6NIe44SJhQYjfSUSpZlrtSlmXHKOTc6x6XNc9lSbnF6osX3N8qV1JutQnX7ptg8zY/P8yW99hrnJKqYlqU43iaMYJ2vTM1Ak8CLqc01KlQJjJ6Zo1T3CIKRd9xg9OkXv2hKX37QNvx1w7Rt3Mbi+FwApDPrd9Qy4GxnvHO16996l7gCaBLRmNjhLT/4GXqg/xtnOQWyRwRAm840JTrT2clv/uylaS9fZiqtM+6eWGXsAW7p4SYuz7cPsKi2tUJ5/YZT7v3YA05RkHJtvPnuShVqHNYNF2l6IY1sIkX4oh49O0VfJUyy4zC20GBwo0VcpUG94PPjwQX7mx2992XChIV2KznUIYZOoFkEyg0CSsTYhMKm4t3X3FEhpkyh/Mf+RPhsaRYwhsmgdXXqg1wAL3sNYsmfR2AMIYWDLPhY6D1F2b3pVwqerBv81wjVb1/HFpw6Tv0AHp9HxWVMpUM65aVLPD/HDCNs0yWdsrt6yjqOjM0zON8hnNKVchsGePFnXpreQ4alDZ7h55/rFAqz33XUNTx85w0Kj3Y39a/wwQgrIZxwGewpo0gkoihMs0yAIuxORYEkGTzVIvUWf1MOPIH6hu9EAKoAL0WF058/QmQ+A90mQPSDOrWQKoEPwPoe2diLkt19h2glCPvHQM3SCkKFyASkEQRTzxedeXDrnl8F8yyNKFI5pIATpKkhrpJQ0fB8vzBFEEVpDwXUI44StgwXsnE0u5zA6s8DG/h6iTkjoRzgZiyiMWbPx0j0btNY88JePp6G0OGF+spaerhQ0a21adQ/LNtl8xTCtZofRY1OYlsHQxj5KvXnmp+pMn5mnUM7xoV9/D9uv3XjRF37A3UDe6qEZVVkeu78YgfYoiT7q0SzNeCG9L9E4SsdkzRIZo8jz1a9yR//7Fsfx4hYCuaKhsYVDI1pqUtRs+Tz48EF6e/JY3XBixwtwHZOFageNRpipobUsg3a9g5Qy/b2zFJYrFlymZuo0mj6l4sszxPpz9+LFpxHCJGttRmtFqBZwzAHKXa9YCIO8tYumPoDS4bIaCEPauOZ6TOP1E9HTWhMk0zjyYvkLQ2bxkwk0EQL7Ox5r1eC/Rrh6y1qePz7OxHydSiGLaUhqLQ+lNffddBnNTsBMrcXe4+PkXBuNoJCxGeopcGJyHikE9Y7PTK3F0TFJ1rVZ11tioJxndLa+aPDX9hb57Q/ex///s49zpqu/k3XtlIaIZmKuQaxT47d+oMLaviLJOS9ZDoBqpuEYXSX17M8PmUQs1uolE4ANogf8R0DkQEVgXhC2EulDqqMXEa/Ay99/Zop6x2dtz1Luw7FM+os5vrT3yMvZufSsu0ndME6wTQPTkERJgtIKlQgWWh2iOCFjm0SJoifnMlDKI6Vk4KphTjx+lL3fPIJqhiAESZJgGAZves9NlxxzYarO7NgCvWvL7Hn4EEIIDFN2q5zTOHsUxpw9MkV1pp5ehoD5yRoq0WitsRyLMy9OUpurEekAWzvLYrkbc1dyqPZ41+BfmLA9HwJHZOmoBkQSpWOEENgygyEsOkmTVlwjUj6dpI4rc0x6JznZfp7Z4CwqgYwoY1nGYvgw0gF5c2kCP3F6Fs8P8fyIZssn4y7x/TUalah0JStA6TRCbch08l1cYZKGWRAC9TKhOqV85jpfZa7zNWLVwZIVYt3CMDIMZH+IUuZGTJkniGeo+3uwjD6UjjFFESnsbnZDAQlD+XcjxXduTF8p0s+il0R3MMXyiSdRPobIIV4lU71q8F8jZGyLn37LDTxz5CzPHB2jEwTsWj/I7Vdsor+U5+P3fxOASiGLH6XGp9byODY+h2MZJEoReDGClIuvdcCJiXnmm20u/KJvWdvLf/nFH2Zsrka97TM5V+ePHniK6WoLrQWGEESx4vT0ArmMzdrerjG1r4NkGvQsqWG/0ICco2VC6vnHoJtAGzqfAllm5VbFAnTnFd23I5Oz5JyLv4xSSMIkWeEcU2RDn3tffJ71tTnOlvt4YOc1dGyXuFuDgABDCMI4oZx1UDqVW+gvZNnQ35Ny3Fsd9MYSjedNrLMtXNMgiRMQgt6RMk9/9QV2XL/5ouQ5QBhECCkI2mEaQkqSLm0y5ehrpTFMyczofLov4DU10pT0DBQxpERnFMbOeb7c+N8ceKGH/sogV5TuYHPhWqQwKNsDXF+5l9HOi5e8D+ndlxjCoBO38JI2AolrZBFIHCOLLVxiFVGNpmjHDZ5tPMCcf5YwTDixT/DY0WkM1aSSL7Pzqgzrtws0mvW5XYtjnB1f4OiJaVzXwjQNao20yM/3I1zHxHEsoijBsgzCMKFQyBDFClBs37wkM9HxQkoFl1Lx0rz4WLV5cfZXaIVHkMJFIPA4gS372dXz33DNIQAa/j4mWn+b1i5ol6y1mXZ4BCEMpHCw5SD9uR9iMP/2S471WqGSeSOTrU9iiMyykFOoZleTtv9UkXEs3nDVFt5w1ZZlr5+aWmB8toFlGTi2SbXlMVttEsQJAnCyLnP1DlJANgp487G9rG/MMzcwxAObryFWihdHZ9h3YoIoTti5foDLNw6xfqAHSA3ZdLUNwLn8rWGkCd6z01Ws7lIb+w3Q/jOQJUg6pEZ9JSggl/5UM6BjIIFkEuQgGOtSV+68/YXx7VerQurNxyt4e5YhMYBohRj+tWMn+din/xCJJhuFdCyb//Prn+Uj7/p59g5vRmlNsdv8RMqYX3jzTfzt4/s5OjVHMDPPmbkasVIUXZe259Fam0EliuzhBVzTIFfKUJtv8Pyjh4mjmIHhXi6/cSsbd61dZNr0DBSRUjI7XSVJNEmSEF9AKEqLumIMI/X80RoVK9r1DoYh6XtPjcob28hihtmmT5RpMuWf5Lpwmhv73gbAuuz21Cu+4DbY7ZjLHxincqbNwoYCR+5dj8pJHNxF49GMF1A6IWMWMISBrxJONPdyurkfL27z4uM5FsayGIUO0gzxlObpJzzmagbvf8u9FKw0pxBFCc+/MIphSnLZpZDlYF+R06NzeEFMX9YhSUJa7QDTlGxeX2F6rkV/b4FyMWWjdToh9abHe95+/UuK+o03/oJ2dARbDi3bz48nODz7K/Rl78IUFWr+49jmIIZIw6WWqmAZFRLVIWNtxJJlsvbmV817/k5Qcq/HT8aoeU8Bortg05Sca6lkbnu5t3/LeP2vdBVMLzQ5OTVPEMbU2x4tP0TrtLBICpiYryMEXDV5it/74scRWpONU0P24Yf/gT8vasZ3XknWsZBScnR8lm8cPM0H33IDxZzLQ3uP4VgGXhARL1LYktSYJoq9xya4add6hMyizW2g6pCc5NJhAg0EpB7+uUkhNV4Ej4B9A5jb0v30DBhrwdz6iu7NtRvXsv/M5HmhkBQtP8Q0LqYxZkOfj336D8lHwdJrUWppP/bpP+RNH/51TNciUZpYKdaU8vze/U9gSEEx41Bre3SCNoZhECUJRjPEGG3QMAXJpgLW6RatuodWGq/tky1k8FoBR/acYts1G3j7B9+IaZm4WYcrb9vOn/7Hf0SgceOQNwYnWZM0mDSKPOJsxsMGAW7Ood1YWgH57ZDMxpjyHU2ULwlije1KXJEnFgHPVR9gV+lmClYvM95pArU8YTvy3Dzv/8jToDSOlxBkDN7yOy/wtx+7heCW62lEs2itMDDpJA1sI0OkfIpmH8eaz9JOGoQLRRoTGUp9GsgRKh9hJWxbuwnvjEUh2bA43ujEAqYhqJRzNJoeRlfozzINyqUs+axDuZRBaegt57hsxxpG1lWolHM8d+AM+w6Oo7Vm7VCJ9/7wbrZvGXzJZ2Ku81UMUVpm7CPVJE46xOoQWXMzQfIkXnyKsrwVw3AJ4km86DimKIJUWG2Lvs+fRZ/4P/Evu4/MB/41FF4/eREhDAZz76Ts3kInPIZGkbO24pjrXtVal1WD/z2Ak5MLTFdbhFFE24+Wmdhz5jkb+vzeFz9ObgVD9tP//T/wl3/+eZJsugwu5Vxmqk2+8txR3n3HVUwuNPHCiHzWOec4pLIBStHyA+qdrrERbhqLtzZCdAhokyZo08rN5QjO+93kXAUuFCHaD8lUejznesh+cBn74NvB1qFert24ludOTVDMONimQcMLUo8wvJhZce+LzyMvEd6QaO49spfPXnULhazBFcP9jFdbSCGo5LP4YUyt7YEQxElCu+1TnPURocLFJCg7iEKEN9MEITAtk9CPKPcV0L15ju49zaErRrjq1u0AFHtyrNnQy5qxF/n1hfsRaDLEeJj8QvtJ/n3xXg6KIYQAISX6PEpt6XoPYULcEHj4xGGM1wnIFTI0o3lOtQ6ws3QzD8/89bJrtNsx7//I0zjtJUaY46VP0U985Ek+/c3baJkGSZcxprQiSNr02usoWL2cbu/HkVlmpkyEIRBCAxJL2CgdU3GHmGrUmZisUe6GXfwg5f+XixlGJ6oEQUoHFUBvJce73n4dd9+xE6U02cxS4d/xUzNMTNXJuBZaa5qtgNGJKls29q8YJjsHpToYYsk4Kx0TJbMY0iHRCkNmsHQRH4dWcADTvZ1OfAqlY0I1Sv7pBXb8zNdAg9FJUNl98G9+F+6/H26//ZLjfrchhMA11+Ca373eBasG/3VAEMU8d3yMPUfH8MOYPUfHCKKIIEwWi1sQi9R5AN5y4nnEJRQWJZrNj36VY/f+yOJrvaUcB05Ncd9Nu8ja1qKRh6VoixSpzECuG94QwkHbuyF8BmQmTeByQZy8peCzTcSpCL3Jgh8pQ16TJnQ1KX2zm7AzhtIwT/AAOvPub8nozzXaPHH0DIfHZ3Atkxu2DvO263ayc10/Tx0fpeWF3LBlHWsrJR4+eOKi96+vzS1OhBciG4VsqM2TtVNeeMMLMaTANk2iOGGq3kRrcEwjzVZ4MYkpUVp3770mMkWXU55g2y5xGBMGEbZjUerN89QD+6hO1zny3GlOvzhO3tT8ysL9ZFmanDKkxva3Gg/w/spP0qx2LmIbmQXdLSxKK0q1ThPBpmmACYHyGOu8iKea2NIlUGnI7vIHxuFSVFWl6fns1xl711Z0tzDJEBYD9gYMw+by0h2c7RxMn0HJsgcwTSqfx2E/z7vuq+SpNTrMzDUZ6i8urk4FUG94WKYk4y7Pw0zPNviHz++hUHAXOfdJovjmsydwHZPbbrz0ijBjbaATnUIkkli1UDroslh0mpBVIEUWKSSahEjNEakqsW5itgSX/cwxjPbS5Co7IRDCfffBxATkvz+0hlbCqsF/jRFEMX/x1T2cmV6gJ5/FkCIN2cCyWPWFtn2kPkf2wiBwF04QUHv+AAe238iWtb1kXbsrAqaJooTtI/08cegMfhDhWEbqTSpFJ4io5DP0l5YecOG+BZ2MgpakYZrzTuQpD/GBcVAgOhqyAn59Dv2XI3BTprtvB8Q6MMpgrgetIHwGbWxGOBe3E9RapXx91WTvaMJfPnYawzBYWy4Qxglf2HOYF85O89NvvJ4r1y95PkcmZsnZBrULcsFny310LHtFo9+xbEZ7epFSorVittEmUoo4URgyNaqmIQlilUajdCpg55mSWGmSMCFu+GniVqeT5exElT0PHSRXyNC3rofThydo1joUKzls12Lksa8gLrHiEGjeGJzgQXcnQohldMHOCQvu8buhrNTblYakUW3h9kO/M8xo5xBFs49OvFR3UTnTXvToL4TtxZTONChafV1GTpNYBxiGxa1972TAXU+vvY7ZYJTyGpMzB+zuOSkUCUWzQhwnSCEYWbvE0Omr5IjjZLEILo6TlAWjIJd1GOtSUs/Hs8+fRhpi2URgGJL+3gJP7jnJDdduvGRHt8HcOzky/3+DlhgyLbZKdAB4mLpCPXwSgUDpGKUikiQmUW3Qkt7PTyEuld9WCv7u7+Dnfu4SO/zTx6oe/muM/ScnOTNdZW1viaxrAwLXtjCkXHTyVnoeR0t9dMyVqWMd0+ZUvrJI66w1PY6Pz1JtekwtNNi1fpCrNq/BtS28MKbjh3TCmMFKgSs2DdFfPs/gyzwi/89B9HGhZy8+MI5o6dTYkxp90VKID5yFdkQ6QdQgOQHxBMRnSD3+Hggfu+i8dTKFbv0u9dnf5w+//Gn+n09/iROTJzk9M8f+0SmEEKzrKXF6tsr+C3Tp+4s5LOtiqd8Hdl6DugQ5XyH48o7rCKOYlh9Ra3s4poEhBX4Y44dxtyo5pQ46pkQmGiNjIlsB2cMLqJqHSjRJnBCHEZXBEtlChiCIeO6hQxiWxMnYzI4tIASsiWpkVii4g9TTX5OkxVvL4rQC6s/ZhDMmZkkhHY00BNLW+LToc4YZye5ECEmPPYQlXURXH2ZhQ44gs/JKKsyY1DYUUCTYRoaC2cOW/LUUzV4GMxsRQnJl+U6KVh9u2advU5v6nCLwNDZZ8vE6Zuaa3HnbDvLnicIliaZczuFYBqfOznFmbJ7ZUxNc8+SD/PTef2Toc3+PbjRQSnNmbJ5Hv3mUJ587hbEC88SyDKJI0WxdijAAioCMuR0hDBLdQdMlDZCyb0yZX2TvQIwW6epHE5A5HWF0LkH5bLfh+PFLjvv9gFUP/zXG3uNjlHJLPVbNblOT+TC6hB+Y4stbruFfP/HZFbdpIXh4x3XIMMILIx45cALTMNg41MNfPvQcQz0FhioF+ks5YqUI44Ri1qHtR1y3bZie/PICFyFstDq2fJDPNpdT8s+H6m5/f4l0kvBAnYKgy9qxb0j1d84/Z+2h23+EUgl/9ewgo9W04riSDdCiiheY7Ds9wY1bRyhlXJ49McYNW0ZIlOLY5Bx7To6vKD/csV0+8q6fv4iloxB85F0/T9OyIU494CBRzDU75LoVoLFS+FGIlAKVaHqKWbxajaQeYE62MdtpcZZWKb0SeX6ITBNFEY35Fi88eaz7mmDSKuNhLoZxzoeHyaRRTEkZkq7nKdJkZCAZ/d8l1v10E2dNglsQKGKcVoU3Df4zxrwj1IJZpvwTDDobiZKj+LrJwXvX8ZbfObTy5yQFR+7dgKljcmaJ3sxaXCNPJ1mqGN5WuJ5aNM145xhqS0zjrGDyiERgktlg8a77LuOGazcuO6xhSAI/Ym6hjWlKrpw/xa8//AdpziIOCWwH7v8Tvvp//Q7PFkaQUjI1VefkmVk2b+hn00jvYrhIqZQh7zqX1u2v+89QzlxLEA3TiY+htIcXz2DgoMW5RjcSMLCMHtaXPsJo/eOEySzR5ogkO4HRWWEVlMvB1ldGLvinglWD/xojitUydoFlGqzrK1FtvTRPvWO7/NJ9H+J/3v9HSywd00YLwb96xy9SxUQ32phS4tgW121Zx1BvASkkM7U2Gwd7COOY8bkGjmXihzE37hjhh3bvuNSIy/4Sp6JFz/5CiI6GUytNWAmoBQifhsxySV4dHgTV5Gx1LRM1RW8OjgtQ2iQKWtRrBkFiUNQmIyO9RHGqm/MPTx5g3+kJso5Nbz7LyZlq94BLo+8d3sybPvzr3Htk7xIPf8e1ePbF4lhRomgFET05l6YfkCjY0FfisuEBgijhyHib4GSdnDAQjkPS1c2XhkQIyfxkjXJ/EcOUuFmHVr1D/7qlXsHHdt6MPvuVFZdtGsEjzhasruKplBIVKxApV799wmL0f/QzckeWdTcN0BpT3PHGu9hX+xrj3lH8uE07qVMP5yhavehQEeQ6/PXHblzG0gkzBloKPvX7b0Tnc1jCwo9btI0GCQk91hIrxpAWN1bezhcPPc1zX9pPdT6g4KQriNHxKp/78j56ylm2bV56jxBQrXcIgoiKpfn1r/8B2XhpgnfCAMKAN/72v+TxX/k4U54miGKCIOb02TmKBZfennSVuVBtsWPL4DJ657J7pjWJ9mn7J0h0A4GN1qqr/++hVEI7PIaUFq6xHtdciyZgIP825tpfJXrPRvjNrjDghZAS3ve+Fcf9fsGqwX+NsXP9AI8dOEX2vGKirWt7ef74+CXesWQpDmxYz9t+8aO86YUXGK7NM1rq48tbryVbKZMXgkYnQEpBfynHbLvGyeo4OrLpzRUIwoiPvv9uZpptqh2Pjb1l+oorJ6e0ql782iYLsmJFo6+zIt2+DAmLOjyqDkkD1foEGGsRzk2gxgGb+XS1jWuBIRT1RoBtKixLESjJmbF5qrUW/+ztN3N4bIa9pycY7ikSeTE5bdDteoLhK6xGTFC20LbAM20+c0W3EvZlaG2xUhRcl81DvYzN17l8eADLNInDhNKkh93fQybrEEcxpmUwPTqf0g5tk2zR5cpbtuFkbb78N9/AMJYLdAWGw6/13MdvVO9H6CWWjkbw74v34gsL15QYpkHoh5iWgZO1aTd9nIzN5m3rGbQq+M9oLrt2I3LHLMfre5DCxJQ2RdGHlzSoRdNIaWAqm7HrBvjdh97M5Q9MUDnborq+wOF7N+JnFUbcIO7qxjTieWyZ5Zrhe5bdj6nZJs8+OUvUthmspBXHkPLtJ6frfOb+vbzzvmvRCjas711MzuZyLrccevSS5AKdKDY99RDz19+DYQjanYBqrcORY9Ps2i4IgpjeSp43v/GyS35WafN5m1DN4BhpwZbqNuvRxBi4uNYwoFG0idQ8QphUMm/Ei07T4jijf/1uRt7/KYQC2YnQuRxCypSl8zonbLVWRMk8Go1t9L1qBVfnsGrwX2Ps3j7CnqNjzNXbVIpZpBA0vQvilbKbLFXphy2MhMJQi8rmGloLnrtxmIfO7qIxVsCUBpZlEkUxrmWgZEg4cIxoICQnJWhBY7rC+FiJP3jsKc74DbTW5Cybu7dt4e6taV9X6Orhe59BRSfSxGzXbgkB/EgBfn2WFSG72y+CzyJlM/giRHnQCt2pgH0XEJPtzntSCHrocLJjgSOI49SWG45B2I4pdDTPnBjDDjXHvn6S9kKHesenVO8QuYKoZGJ0FKajkFVFnDdIXIlQGi0FWGLpgi40SEIwVW9y5foh7rxsM735LE0/YMhxeeKJCY5Nn2F2YoGgExKHcSoNoMFyLHqHyuTLWaIwxrRMTFMTBRFWt8dA4IeM50f4gPwp7vCPpTx8WeQRZwt+l8202OzENDEsydpNA7z3l++lZ6DI6cMTGIZk+3WbGN46wF+c+VViFRLrdlcZMk2oKq2ItI+BhUQSZ232vWsjAtmVENBpIpOERMVpfFtArANmgjOM5HYu3o7DRydpdZ0HeV4HMMsyqDc9Hn3yGE89dwrHNrFtk3vvugKNprcnx3BnnswlyAVuHLCuPc9zUiClwZrBEgvVNmjNts0DbN7Qz7bNg8t6PKwEpSOkcImVBwiUTlAECAyEMLvVqgKlIoJkAtsYwJAuPe6ttKPjVK/X1J97BwNf9KiMD+PsvCn17F9nY98KjjHV/CTt+ASgyZqbGCq8h4Jz6Qnw28WqwX+NUcq5fOAt1/CpfV/nmP80yogRbonhoQJHRj0MN8YwUwogWhB2THL9HZLIJGiZGI5GGpq+7QtIqWhNVPDDCENKbFuQ3TGJldWYcZE0OKxwhmaZ95p8aZ/C7DUXi5hOVqu0w5B3XXE5WjXR7f/NQrvOgdmI2/sWi/2IFeiMgfkX6zB+aomlo7MCpCD5i3XI3EqeSJo0AwmiF2QhZe2oJvhfAGMbm/tCbGly4GDE9KjGUiG1QOLHPiprUkoSrhoa4sQLk5xxAk4+cgrXNsmUXDpSIeY0bkehTYGWIJRGKpBxqpMjACPWxILU6F9o7HV6kUEYUSlk+JHdl1PoCtxFYczXOw/hd3y8VoBKFIZlIpUi8CN0ENFp+XRaPs2FNsWeHOu3r2F2okqn4aNFysXfuGsdR/ae4kFnJxdBQOBFZAsZ8qUsKlFcfss2rr/rcpyMza7dS1XZkQqohhMkKsE0HASasKsAmZAyY0B0ZZHPXV68fLBzK0YBhrAwhcXB2mNcX/mhxb3anaDb6GX5ysj3I2ZmG5impL83Tz7n4ocxn75/DxnXZmKqzimrB8+wySQXG33fcpjtOT8UJHAck1ze4Yd/6JoVnp+Lce7ZzZibaAR7UdpHdynBGoFGoXWM0jGaCMccJoynSVSb8eZfYMkeMpkRyED7p5rUdZMNpTeTsV5fY+9FZzky96v48anu5wjN4AXq/l529P3/KLjbX5VxVg3+d4B23GI2SCVZB91BXOPl1f38xOeJ1pep9x8kn6Rdito9C7jlAHt+PUks0ElXtlJoMj0e0lTEkaSwpoNpJ4u+2tDVs0wqSaXHJe+6ZM0ss06MClzIhCAUJCbthkFmTY35gwXIZhZbJbaCkL9+fh/3bN1Cnj00/Sp7JmNylk+kBIbQxDptI9GOLbimSPE5B/HZJvqUIt7gkvxwHjOv0JHANlN+jNIChYkhEqToVhbIriiUkKmWfjINxlocPc9w2+b5OZfYTug0TPwgnZTyh9uoEA6e6jDZP0cnK1iYb+G4FtkoJkm64xngLsToRGGEmqDHRIQa6XTVuqRIPf3zjX13Tk3PCcyO4jJRIH8+TdBM6auWY6F1J+14pdSiNEWxkkejaS60ueWtV3PTW67iyQf3ceWt24ijlL4opOTFPSeRhoFKVmDr6NSIDY5UcLM27brH9Jk5Hvyrb/DDH7pr2a6Jiol1jCFMBIJYhYBGCpO4q4cvuv9Sr/6c/OlSQ5R0n1RbRyDwkw6wfOW2YbgP2zrRFTlbwtxCC6U1tm1hd71w1zahkKVaa+H5IV9dcwW/uO8fV3z2NYJvbLyOwAuxLbOrMaQZ6i+uuP9KEEJgyQoNfy+OMYQUkiipEyZVNDESG43CNMpkzPUo7aFRzHW+jCHymOf1bjBlAZWEzHW+ykjpZ7/lc/hu4EztD+hERzFEDkOm91apGC85zZna73HF0P96VcZZNfivAEor9lSf4WD9AItfIiG5oedmdhUve8lS6EP1F3ix+QIaKFql1BZqi7rdoLCuSe1UGYw0cScNjZ2LiH2T/EALy427MhsaFQnsfMiWe05jqgymsEh0hDNvYvV30E6c0j4SiZV18JsGTi4iktmUby4NYpVwtlrjxdlZdve8yKlqRN0PEGqBM80SUSLJmAklJ+BErYRjKiwZM3PnCJe9KySijNaSHj1GECe0E40tFWFidHXPJSUnwjCypDH981cBEkioR/+chbOf56ZcwJefjIgsiRtEWM0EI9IkBtSqHqZlkiWD6ZgIS9Ju+sRx0o37CMxGgow0uhGlXZ2KBokCbQKxwg3Bk+m+QNfTT8M8hoa1nuTRLx1g29YhtuxI+f7tuke2mKV3sEyz2kZIQRKlche9g0UKlTwbL1vH9Xdexm1vv44kUQRByL7HXkwL3RAIKagMlrBdA5RKi7bOGVINypSoQpb5SJNt+AwOlhjZPsSx508zP1Vb1ju3kzTImz204iqStFo2rFt4Cw6JIcgO+kg7WfQQLyWoFug2MjFTlpMWcIE08I6tg6wbKjM716LjBYtc+UbLwzINclkb67zuXK5r4YcJjmMi3BL//g2/wG89+vFFlo5v2mgp+a03/XPO1EK0DhFANmuzfl2Fm67ffMnvy0oQGMRKMx0qpgOB0jlKpseA6+MYFSqZO7p1DYog8XDMNXjRGRxj7UXHsmQP7ejoRdIdryW01lT9R5HCRsolkyyliVYu9fBZ4sTHNF55O8hzeFUMvhDij4G3AzNa6ytW2H4n8FngVPelT2utf/PVGPv1wIuNw+yvPU+v3YfsJlViFfPk/DcoWkWGs5cWCttX30usEnJGfrG6UhoKwxQM7pqlPZ0n8ozUFhma2Dcx7IS+HQvYhbBrSFl03gSgZIBQNonSuH1NJCZSW+mX2dJoo0PWMJgwNC0/ZU8I0u5XidaM1RrsrrhMNBaIEhdDmpxtljjbLKLRXFmZoxY4xL4kTEw6sclXx3fTU34DZafNTeVPMtao4UUJGwp1DKFRWtKX6ZC1YpTQWIZAKTj4gsvkpEWpEHLtrf0sVB20GGLvN09hzSsslocCzkn/1KptEqUxkpi4ZIEt8fy0pN9sxsgorRWTMWQnQtw5QZQz8PtMtCUQCdhaEeVTxsq5m2ADlcRkIJDkBl2efPQIW3asSVtL1jo02yGltT1Uqm0sO03IOhkLpTSGaaASjdNllBiG5J733sLuuy5n4vQshiEZ3jrIJ37jMxi5LFHSQSqFTFSqhFjOQ7kAAuYQmIaJfS6ZLwQLU/VlBt8QJn3OCEondII2Z54oUz+Vg67Yr0Cw5uYZipub6Xx6CfuVLm4SlDbQJNhy+crUdSw+8J6bybjP8dTeU8wvtNPrlZKMa9FTyi0zjmkTH03Gtan05JgsXMX/Z9N/4c7R5xlqznLcKLJ3182ETpZB41yBmcDzQoQU9JSyHD42yVB/kZ7yy+vS18M6j81fScU6Ts7wQUAjcnihPswd/R592QilFWEyS0/mFhxjACEsNMlFQmmaOOXsv469mbXWJKqD5OJrF5gkuoUmAL5HDD7wp8D/Av78JfZ5TGv9+uuQfodQWrG/vpeSVVo09gCmNHGNDAfqz7+kwe/EHQRy2ZdRCpkqVpZjhndPMne0l6BpgpGGdAYun8cthksO2wXhcq0VgWyCIZASiNOwg5TpF0slAtuN8U1FHC6R6eNuy7m8Y9NIrsISn8M0MtTDHNVOSCc2KdkBz84O8bH91zGYTSVvpzs5+nM9jHSmuHFkmEPzBXLGPF5scLzeiwEIoaj6DlkzxFIJ/oLLf//dfqanLYRQaF0k97cd3vNTM8zONPDbly60OYeeSo752SZyIcTJO+imImjFS0GL7qUJQIYaM4zJVGMSS6ANgWUIwpykXTRIcgZSQ7+22GA4CJEwtLaHqYkagR/xwD/u4ejhSWpBQnO6QVsJei2DXCatPvVaHht2rgGt2Xpl2kovDGMmxqskiWLDZcPkcg7j4wu8ONPEL2ZJDIMEEGGMihPoKUCUgBRI00BpzcmJOo5lMJJ3cLLLC+2KVi8VZw2WdNi3p0r7dIxbaaEjCVKjQ4upb4xgF8/g9rVASoTQmDhEy/SQ0gkiJyKyhsSVFxdYlIrwUz9W4Z47s3z2gTkmJ1sUFhxGJ6pMzdTp680vcuXrDY+hwRLz8y2mZuoIBG0h+NTQtRS3ulTrHa4YGaJSyXHs5AydTohpGhQKLuOTNT752WdTETnHYvfVG/ihuy5PJSQugf01l7MdmzBzM45MaV6BylKNmzy1MMVIYQZT5hnIvZ1K5naEMCi5u6l5T12kUxMmc1Qyb3zZZ++7CSklpiwRJ20ky2WhlY4wZBZDvPxE+K3gVTH4WutHhRAbX41jfa8jVCF+4lOxey/aljEyzAfzL/n+dZl1TPrjcN4HawoLRQJSURppUBxuoJLUfU8TuOC0I3Z/+TQDYw1mhos8+5aNBLlzmjVpmEefs3imQichsdKp/r0JQSTIFnzac8u9BFMpKtkMM0GRCW8zA85x5n0TpQVD2Q6xlnzqxV1E2mKsfa6wStAMfI7MRmQti2eD7QzYEWcaRUJtUPNdDEOxJtsid/xy3r7hON/84xKzs4JKxU856sYgrbbB3/7JY1i27GqjXxrnGoiEQYTvxbSbAW4c8NbqQdYGC0w4FR7puRzPuJi/bUQaIo2WYAcKczZCGKmksGEZJOsstl8+jFKKfMHloS/t49jhSQbXlCiVMrzwzWOoKGa65hN1QqSGTN4BIbnlvmvoXVPm0MFxHnzwAFG0xIC5+uoRDr4wTr6SIzmqkN12ltq2oJRLw1FCgEylG4RK0AIOnZhl/XUjrN201FErDGPmZpusi2/mUPRlFo4WWDdkMNNM0DLESFy0qYjNhNrhXtbcKNFGiHQSkkh0i8RSL8MRCUXLY8AM6DdDHKNBs/135LPvAkw8/yE63v1oFI9+M8tC1WbThm1s2rCJthdSrXaYmKrR31cgCNIewPe96Qr+4h+eRCcgTTCkIE4UcwstHNsgm7U5dmKGOE661bQJ9aZHFMVMTNcWJaJn55pkMjZvun2FBHcXx9tDFKwTKF2gHqWrE0tKciaM+1ewrvRBClZpmXZTX+ZNtMNj+PE4pkyf40g1cMwhKtk7XvLZey0wkHsbE82/IU48DNltGkQMIqIv85ZloZ7vBK9lDP8WIcQ+YAL4Fa31wddw7FcNlrQwhUmsYswLPoRQheTNl87239J7O3tre+jEHTJGmkCtRVUkkoTkXFgZQy7FX7c+P80v/fLXEBpcL8bPmLz3vz/D//wfd3P8mpT1sPj+czAShNH9iiuBVJJErbBs1Ro/illbLPLE9C00Og7X9o3imiH7Jwd5anotC/75S/7UcARxghCKfROTRFrQibbiyhilBZZMkDHMeTlMYXLw0Bo2T3kM9sdIaaX9cEWGfAHmZhrkC/mL2TMXIAoTThyZTEM8Aq5oneW3jv11GidWEZ60+MWxL/OrW9/PwfwlGounqRFMQ6C7OU2VKIrlHKVylvm5JrfduZNvfP1Fyj25NEyRc7j69h1Mj85z6ugkGQHD68psuWKY3XdfwfrtaxgbW+Dzn3uOnkoOx0k9Mc8L+eIX96XJTdPALbh0oqRbpatJLAPCBCzZrSWgS6rSJEJQt22MbrP5vc+d4dFHXiSKk7QPqxhG+jPYZgyNIq7hoo0IZIJQgmSySKado6Xn0uR9ZCEKCi0TLA39Zh0vsdCmQyJyWLKCH3wDIVxMYy3tzmcwjHU0miYnz0Bfb0IUHcJxMtx56w5OnJ7l2Mlp4ljxxpu38fa3XMXnv7yfa69Yz7FT03S8CKU0pmGQKdo4tsnREzMUCi5utxPW1EyDRqOD41jk8y6yqyXUaPp88Sv7ue3GrZekZxqyHy+aZiKYINGSSKlu68Ics5HBfzv0JO/beB07y0OL7zGNIhvLH6HuP8dE+zkSLRjO30nZvQ5DvjzZ4ruN9aVfoBkcoB0dR3XrJAQGeXsHG3t++VUb57Uy+M8BG7TWLSHEfcA/Atsu3EkI8QvALwCsX3+JL+3rDEMY7Cpezv7a81Ts3sXYn9aaVtzguv67XvL9g5kh3jfyk3x67JPUozqg8BIPU1hEKzRSdtoRv/TLXyPTWWJ4uF76+y/98tf4v770YwRZa7mxvwAKTZIIGo2LY4AxMN/pcNvGDdTDiBera9g/20/QFcLK+T7v3fcUG+bnONPbxxevvoa26xJrjQCqwbmKSkknST0TXy0VYQnAwqKtNNK6uAesaRqYloFpmyTeSzeSPhfPz8QBv3Xsr8mqpXh/RqXv/e3jf81PXPmv8I2LdYfS7nmi23Upgq4K5fEXJ5iZqrFmuMLep0/y4oExMhkb0zIY2djH4NoyI9uG6Bkq0TtQ5L3/bLmE7tNPncBxLRzHotMJOHlylnqtQ63WJooSSuUsQ1sGmWSaJIhRUZy2HLfNxSSy6LJ1kCnLZnKhzdxck+npBg8+sJ++/sIiM6Ze95gf02xes5UFbxql9WIfWd8LKRazVL9icGZinP67DyNNyPWWcMoRRauZxu6VpIcsriGpuMMYsgfPfwQpCkjZixAWjaZGivSZVzhE8QlcZ4Cd24ZYO1RieE0P73nH9URRQrXeYWRdhd6eHNOzTTpeQC5r099X5Jm9p4mTBOO8CvN2x1+sizhH/xRCUMi7TM82qNbaDA2UVnwOBjNlnpjOMehuAj1PK/LxdS/V0GFdLoctJH924ik+svMNjOR6gPTeTvsxnzsrON3aiBCCHrvDfcNVruh5/Q2+aRS5fPD3mW09yIL/MFprKpk3MpC/dxmz6Dse51U70ktAa9047/f7hRAfE0L0aa3nLtjv48DHAXbv3v3SLt/riKvKVzMXzDLujWEIE4RCacWOwi625C+axy7CZaUr2JjbzOHGQU62jnGseZRW1MKLL5ZXuOErpy+p7ic07P7Kab7xIy8/ZpJITBPiFZiBf7Nvf8rjNw0MoN019tefOskn/jiVcsiFIW3b5qNf+Bw/97MfYs+mzctpjpeABoy8RaMYrLg9SRRr1lZo1DxOHp1+2eMBvLF68KUVKKsHebDv2ovPpSvbqwLVVVPU5PMOjuuw68phTh+fYXq8immZZHI2KtEcPzKJUpq1IxUCP6Lcc3EsdXy8Sj7vEgQRB/aPorQml3OI44T5+RaNuofW4PbkWZhvkZhG984sJWO0JE2y02WuSsn8XIvHHztCTyW3aOwBSqUMvZUcR49MMbK+wtEjU3gCmg2PIIhZWOigtWZ4uA9v72WUbzyFXwsRVgyVgFhZ9MUancSUrRGKVl+aVyIhSSawrFRuI5NZLGRGCAelljR3wjCmp5SGJU0z7bEchDGuG7NuzQRxMgkI4ngQ1zFZM9jLzFwTIcRiI3nTSJuYK6UW9e+FSEUr4+TSIT4/DjGkwZinWAiyQBZTGIsTSs5y8ZOYr08eZWd5iEemjjHarnKmtcC6TJntpQGkELSigD8//jQ/vfUmLu/57mnQf6swZZY1xXeypvjO794Y37UjnwchxBAwrbXWQogbSZ/0lw52fw/DkjZvHrqXaX+KMW8UiWR9bgN9dv+3nO3Pmlmur9zAcHaEE63jtJLWivsNjDYWPfoL4XoxA6PNlx0rDCWOE5PNevj+xTHuOEl47PRp5tveYhepnO/ziT/+I/LBkqHOhalH/Yk//iNu/dVfo+OsrHdyPgwhIG/gD5q0ZnzyhbTdnNcJqdfaBH7Mpu2DLMy3vmWDvzZYWPToL0RGRawNFi795u48oVLeJD29BUzD6IbTBW7WJo4VvheRydrkcg5nT83S258nihRXXrvhokMWCi7tdsD8fIs4UeS6SpJut9lHPu8yPVXHMCTSMiBRxMkKBWDn7pkhKeQdpBQ0Gh6Dgxd7ujt2rkknF6UJgohqtYNhCAxDUKu1ME0D34+Iohz+s1dj9Fapnq6z7SaDNYUZOi2XgdwIQ5k1QJc5o0m7nukAIRx6e2CoHxaqUCrGCNEtSIsS4kRxxa60f7EQghuv28RXH9tLT+kAWreAGK1hdj7klhvq+P7drFszssj4SZKE2flWmmAX53v+IaVShkpp5SSl1pqxTg1bmjQjP214giQRCRYWXhyhtKZkZ/ja5BH2VyfodbLESZLSkDtVpBRsLw6Qt9Lr+dL4QXaVh1YU4/t+w6si1CCE+Bvgm8AOIcSYEOLnhBAfFkJ8uLvLe4AXujH83wN+XH8r7uH3MKSQrMms5YbKTVxfuYF+Z+AVUbv67H485XEpzvTMSBE/s/K87GdMZkZWbssmFv9JlDLQWuA4KxtJDawtFilnXJphiATetu/5S2qiCK15277nX+bK0nCOISX1IGD9lWuIkoSx8QVOnJphbHQerx2ybdcajh+exPdXLsdfCRNOBU+urKboSYsJp7LitnM4R8E3pGB+tknvQIFGtYNlm7iuje2Y2I5Ju+UTBBGddsDEWJW77r2CNcMVlE6oBicZbz/JdGcf1+4eolH3mJ9rLos7x1HCho29i/rwUXd5ZZgGhnnxsyLO5W8MybrhXkbW9yKl6Pa+vWhvrr5mA298w076+vNcedUw5Z5UE8Z1bbJZh/HxKrVqm6AFyfQA3tH11F/YjeMLZMugt5igu2FEpWew7Z1k3beQJNOLvPQfehM4LkzPBTQaa5mebbBQa/NDd17O4HkFUzdcs5FNI5NMzdSZX4iYW5DMzhls3TTJbTcepqf8BKWiz9VXTHLd1WNcc6WLYUhM06DjR3S8tN+tlII7b9lONruyFLgQglroobRiQ65C0XIpORnKVhZDSJqxj+zuM+e3Gc6WyZkOs0GbvOlStBzOtqp0uvIPOdNmPmjTjF6eJfb9gFeLpfMTL7P9f5HSNldxAXzlMeAMMucvVTuePxc+fc8Gfuy/PbPie7WAZ+7eACHdbOR5284dQ2gsC5JEEIYrG8li11MfyudxTRNPazbMzy169BciF4asn5tbcdv5kOe0GYA4Iyj+2AZaT4/jna5jFrOMjPSzLtuD1jAzWccwBUn88n7AIz2X84tjX15xm0bwSM/lL3MEjVsKEVITdsDrhFh2Wt2rtMaxLa7avZFqtUYnWqBThx//4B1sv2wdYdLkUO3vaEVpyAI09BnsvGknp/42Ik4SbNskiTX5gstll63lqSeP02qZ3QpVA9MUTE01VjwzIdK8xlvvu5JMxuayy9bx7LOnyGTS5GY261CrtZmarPG2d1xHxwvp6y3iBxG2bVKp5FmYbyNl6u0LIZida1EuZQjDgNOnahSKeXbfeBrFWTq+iWluwja3ks++FyFzRMkpougoApdCTvPjPxowPrmTWn03hVyWHduGqFzAlzdNuOuOh9m5LWZ0fAAhYMNIh8F+H63hqsu/yeTU6ZShJaCQ10Th5SxUryIIE5ROaZmbRnq5754rX/LTM4WBIp2QCpZLKw4wZEprtYVBnMSMthYYyOSRQhCqtDmLEgITk0gnTHh1Nuf7zj0OmPKVteD8p4bVStvXGRpwZNqsQSndZdV0N0oIshb/47/czS//ytcQCbhBjO+aaAn/43fuptM20TPpclwWQHb7lui06h4hBYapiLVJtbryMjlrp96UlIKBfI44UZzp7aNt2ysa/bZtc7av72WvLdGQaIUpBcfn5tm8vYf8+hK6ZNGod3jhzCRnx0exI0EcJ2lRk2sQhgl6pTZ9XfvqGQ6/uvX9/Pbx5SwdjeBXt75/xYQtpMY0P9Rm0xsmyJQDEAKRODSOKkx/C1EUQwibtg0gBo5Q2XWEQpLq43uDWTrx2zjV/DLtaJa8tRTzjVVA/xWHeNdP3Mn9/3iSYiFDpTePZUoOHRxndjZtnaiUIpfP0qh7JIlePKdzP5VKvXspJf/4mecYHDzO2YNn6P3qFxhszTGZ7eVzg1fhmQ62bXHy5INII63haDYDLCtdsHc6fjdsoheNfhzF5PM+t9ze5rrrrqFQ2EKcTKJUKl5WzH8Iw0ipxqX8h4miowTRfkBSLFzN2sGtL9miUmsFepq1Q70Mr10eUksSSS47y5ZNdzO/EJIkiko5wxU7qzSamrOjV+GHEZvX97F188AlO12dw1C2QCsKaEQ+WdOiHQe0ohBHGtjSZMxrMJzroZ2EHKxNMuk1aEcB1bCT1s5oTaQSxtt1+t08N/StJ3eJ5kLfb1g1+K8zckYOP/EIOyEYqbLjuXourYAAjm7v51/+9bu54YunGZxtMlUosOedGwlKFiIBVdOgIGmD6CGdMJJucZcC7UOwAKYB4QrRgdFandl2m3YYkjUt1lQKfPHqa/joFz634jlrIfji1dd8y9eolcawJQdnZpCdAG++gzEb4TRiwoIk0xBpSEcLHNciX3Cpd3sXxlF6wmmVK4RBgpDQvmY3H167mWtOPrOMh38pYy8NQabH47J3nCYODIJ6liTWZAqC4dtOMv64gWkOEUUJxW1nUb3HCBpZSPLsvGqYTjzN3vk/IlEeBWt42bFN6SASyZbr2twyvZUzp9P01P79o8RxQi6XxuPb7ZD5uRbtdoCUS0zUc2EcrdOCuZ5KjmzW4sgn/oH/8FTayMVNQjxp86HDn+c3bvgQMzuupF7zCFr+YkK31QoWV3bnJpT0p6bTCTDNmFKxSLGoiZMWSlXROkJj4AXfoGC+p3s+JrZ9GbadqjTG8QStzt8TRceQsojr3IFjX71sAhBCIGUZpTxgeZgxSeYQMkMhV6RUOL9CN0Ol52m2bfrhxfzAt4LrezcQJAmGEEx7TUp2FktI6pFPxcnxwa03U7Rd/o+n/oFEKQq2g4Vg1m8RqXQ5bEmDMEmohR3es/HiBP/3K1YN/usMIQROJ0s0qxFFkHmN8gS6o9MiljwIA5pnJPdHm5BDYAwJzHmwC6SfoAVJUyOzmuisIDypcHeBdAVqQdDYmxDbArfYQc+YEGviXhftGCAFs60WPdlsypowJKcWqrRdl5/72Q9dxNLRQvCzP/sh2o7NOcndlSAB0zCQQJikQmIZ02JCN3FDhdmIwZEoKTBNI/W8hCaJFdmcjevaaaFVGGPbJoZpEAURpmmybkMF0zSoW86KbJzzYVoSpELKhLXXzqG1JIlM3HJqHPOZHpSn6LtilNs2vJN8weCg/7+J20X6B4qsWddDNucAOWb9wwRxHUO4SGGiiJAYOEYZU7qELPDu99zNwYPjfObTz6C1Zt1wBcsyOX16FulHxFGSruREuqKTMjX2SulFow+aZx8+wB889Ydkk6WkeaZLQ/21Z/6In6v8OlYuj2WZNJseppkWLiEEUbgyRbfdFjz4pYSdlx0AZhAiA0hUMk+r9Ze4zg1Y5vKkdBAeodn6AxAGQpRIklma7T8hjG6kkPvJRb12IUxc+0ba3kMkyTxgIYSJ1gmaEEtuASKSpIpGIUUJKbNoFaNUG2OFgrlL4fbBLexbGCNQMVf0rEEgqIYdBlTCh3fewUiuh5ONOUpBzPVffYL1k/OcGCwzftN2Go4FXbUhKcA1LB6dOsZda7b/QCRtVw3+9wCsMZdoVCGHNOqYxuiVyEz68MWzmuBYTPOzCgJAgXTBHJZEVymyN3djz3XoPJUQvqBI5qHdm6A6QACJASITU5w7iVPMok1BkrfwtpbxNxfQGVjfU2J9qczR2TlG63UA9mzazK2/+mu8bd/zrJ+b40x/L1+4/uqUndP1ILXWSL2U+xekk5gtZZf9otFJ2vTbNgxiNEKlXr8WElOlYSdpCEBi2QaNmkccK5JOgu2YXHvjZtat7+Wpx46CgOtv2oppGezfc4rquQ4qK0BaYOdCogDsfEzv1hqGG9O7pQ5CYxgGhWKM7vQQeyY33LGBWDTxF4bIWQOLx9FaUQtP0QxHCVSddjyNIsQSBSyZwZAWWTnAQOZqbNvk2ms38OwzJ7Ftk9HRhUUDbxqS5FzxlGJZnD19TSGEYHKixj2nn3zJpPlNZ/bw1OV3ggTbNnBdm1qtg0qWjP1SuEh08yOCWq1O4M/gZkqLJAMhbIQs0Wr/DeXivzmvtiSm1fkrhCghz3HBRQahSwThM7jOjdjWUsc0172bjv9llGqiu5pIQuQxZT+IDJ7/MOfLlBrGWgxjACm/PdmAipPlIzvfwFcnj7BvYQylFTtLg9yzdhfDuTIAza9/lT/4iV9BaIXjBXQcm1/4OPzKr32I/Zdvos/NU7Rc2nHIwdoUzcinZL/+fPzvNlYN/usArTWnDpzlyS88y8zZOcZPTeKvTVAvKPynFMagwOqT6ASiSUVwUGMOgVFIG2xrWxOeUHhHNMmcoP6XMbQBCzI3SERR4R/SGGUw+yRmFlRbw0KMGfsEwzlErMk9P4dzuk7t7Rv4+vRJ9BhkhMW6XJGZdhpS6Tg2n7zxRrTQaEchIsE55V0tFLgaAkFWW8Qqjdf7SYKfJBhaYQpJxjLRpFRIwxSUe7K0xnwiFNmORCWKgTUlqvNtfC/CsgykFAjHwslYHHlhnDhKKPXk6F+bQQ4dQ/WcYOtIB6/XZ/y5Pjpz576sS8nqYq9GI4kCULGB6SaU17eIOjZSW9iWjUgsYnuGXE+eg/U/w0sWaIRnMaSNa5QBaEYTNKIxTJEloo0ixCBDojs4ogBaU49OUbKXvOMgiDl+bJpiKbPIMS8WMzTqHeI4FU9zXYsgSIjjGNXNWThOKjuw1ptf9OgvREaFDLZm6XQCwigmChOCIE5FuM6je56TcU4llA2UUNhOkzi2zzPqISCxzC0kyRSJmsE00urtOBlDqQamMQxNH/sz+5An5lBb+vB/eB2B/eyiwdc6xA++hmVdRpKMd48LGoUhe4njM0g5gOzKBiiliOKj2NaObyuccw69bo73bbqO92y8pqv+eh7hsNnkip/8EEbHW3wpG6Tn819/4xO86RP/hpluHL9gOlQjj0hdunDx+wmrBr+L6kydubF5bNdi3bY1mC+TOPpO8NxX9/OVv3iUfClLqa/I3MQCrU8l6LJOk3dVTWQk6ATUNGCAqoGR7yb6EghOa0QCrc/FcI6KH0DwgsIcEQhboyMgq9EdCA6l4QPaMTLSKFegbYk97eONtpA7ioSxpiVCTjTOJd3O6aeDMjXa0mmsRqfJQKVTeqrIanrJMt1qEhrpPjIGy5AkSmFIk3WlIrPtDoVsho4fYRZseoVNX5+DZZu0WwGmKVGJpFjKkC9lkFLitUO01mzdNcTmay2ePPo3JP0RRlTGMYqs29Wgsukkhz6/nuZEjnMSolZWYedjZJKn3KtpLpjk8jamKYh0qssjpUhDRuUI6TbRKHLmILXgFJOdZyjZm3FkkVp4ClO4RLqNJXMkOkbpAKUVnWSWrNFH0V5PMxyj4qRNsOViyGYpTCBEWlBkWQa9vXnm5ppIyaKxl0a6XWuYyPbiSXtFo+9Jm8lcH+32UrgnipIV1SnOFTWlqwhJuRwhpYc6V8cgTBz7OqTMkiRVOK/aW+tUatr45ikKP/YJUBrRCdFZm8xHwfuUC29J9w2jIyRqIZ0ArG0oldaVSJmn4z+MYaxF61Y3xp/CNDaiVD2VSxavLGlqiOVChAD83d8hV14cIZTmrd84wNfeejuN0KcWegxlilirLJ0fDIRBxFf+/OsceuJo+qVAk8lnePsvvpmNl19a9fKVotP0eOSTT9A/3LvYvHrNpkGMhyzCsRAsEC7ohKU+4gk4UxF3zo+xVjWZUAW+bg3jKYvkgla4qgZhXYOdVmwG+zXn6pyFUMg4xmh2lTcThYgUmdkO4fZSqqzYFb1KQwBw7tskRBqGScW+dJoT7soBSJEaWV1URDpJNWtCSZCAJQW2Kcm4JpcNDHDb8AjPP3KcxlCV6lgDP4rodEKEEIRBTLGcoaevsOiBFkoZqtUae154jN6753GHppibcHFdRdbqwTV7CIwpLnvHKPv+dgsqNlCJwHE1gSfI5mIqazLc85M54kGXWObIlT0cUUZgIEyfWEpcWcYQNoa06HG2MNZ+Ai9+Flf24ql5TOFStIeJVIeMzJHogDjx0UKxNncTsfJoxZNLH4SAvv4CzYaHZZtImSac0ZDNOlx3/UZ8L2Jqqs7Zs3O4GYupqToqSUM9jw5dzYeP37/iM6SF4OG+K7sVsGml6zmu/kUNvXR6vDCKsSyDgf5N5PIxplFECHtRRkFrH4SLYSyFskxjLaIZk/+xTyBaS5OL6HT17N/zP2HiVyGfJ0lmFyuFQSLlEkdfqzamtQHLXEei5kErpCwhZZE4mUCpFobx0rUT3xaOHUO0Vw71ZYOQTVPVxe+6UmmRVsH6zqWH/yngB97gP/LJb3DwG0cYWN+/6I11mh6f+u9f4IO/9eNUhnpe1fHGjk6QxGrR2MdRTLPWRp0rromWOVkAXK7n+E88jog0GRI8DD4c7uOj3M5BsQI9UgNB1z9fcqhSiYZIIfwIChbaMTBmPKTSi/FelaQxX22BUF2Rsa6cLud49ULgWgaBikm0xjIFZh6kD5YSRIkCSyEU2MpipFSkULD56F1vpOA4vHnrVl58YYxnnzjO+NgCjmPSP1DgoS8doKc3v6yATemYUNfxmibaiFm7sUypKJmf7hArk95NCUWnQRzH5HoDOgsZGmdKNGddsgXJup0+u28eItvfYrLjEysNuBTsIgKDRDvEykGKNMmYqJBaeIqMWcGLF4h1B4mBIWwE5yQRwBQOwkgbahvCwldVMsaSgmpvb54wjImihJmZBkmsGBws4XkBR16c5uSJWUrlLFu2DhKEMXEcY5kmiUgQQuJpl1+77oP8xnN/gtSajArxDBshJf/59n+OLxwcS1Iu55ifb6USu6ZEKUUcp3FypZYYQPmMTbHkctttVyDkYaL4BFL2YQkHjYNKZsjn3rfM05YyR/6LcknE6EIoAX/3d/BzP4chy1xKA0QIt/szk4aHzj2mOk4nXZld8X2vGNu2QS4HKxh9z7UZXdOLF4dkTZui7ZAz7Uu1Dvi+ww+0we80PfY/cpj+4d5lS+9sIUO73mH/o4e48723vapjns8vD/yQA48dpjHXwLJN4uBiCYWMjvhPPE72vN6kma5Q2n/icd6n344vvr2P0VwISEo2MlIYfkS8sbhoHIRIw0FKgJ2VeJ20RF5LDWZ6/lJCIGKkKbCkRCIZ9WuEOk4ThJbs0g41gYiIshEDPVkKjpPG8S3JVddv5OrdmxbPqTrf4sBzZ/G8gFzeXWT/BHGLJIbKoIPSCiGg3GdS6isQJHWCpIVWTUxgqMcnnZXGEJi4Rpms0Y82TzDjT6O1INIelsiidMJg5gpqwSnCpEnRXo9A0IymCZI6sU49WtsoEqsOkW7RiedwzTKx8jCFS6wDeu1NJDpCoxjIXLV4Pbt3b+KTn3yKwcESAwNFtNaMji5w9kwaLms0OjSaHuNjCwwOljh9ehbTlGQyVprWVJpD5mY+8MZf5Y7J51nnzVPafRXzd99HvpngPHyIdet6sG2TRsNLV1rdXIHrCso96USQxIpCMcPwugrv/rH1rN/0GRR5oEkcHyeOj2Ja2yjlP4zr3HLRs2KfdhCdlaU9RLsNx48DYFk7ESKDUq2lBC+kOQBzY9p2USeLVM606cckGecOpHj1vGutNeq9P4bxr/7VituVEHzl9quRQhCohKLlkrkElff7ET/QBr8+20gTWys0W8gWMkwcn3rVx1yzJU2ITZ+Z5YXHD1Oba5JEMWGwsuzBnYy9pFDYnYzyAJtW3H4pyEDhnmgS9Vh4W/M0NgiwA3DB8E10onENk04coW3QtgJHL8ZKtehWNGpFjEahkEikEBgI7K4ej0YTacVEu8mVvYrfeuarfGPyDH4SM5wr8r5tV3Pvhh0YQnI0nGf8BsFkM8TwAgbmTCrzBn7sUahIRq5v0InnCFQNW+ZxZJlERwSqung30tEFCoUmJko6KCOmHp7GkjlMaZEVfUhpEKgGU97z5DpZNv3jKD2jo9TWP8rcvXm8rI9JDikMHJnHkXla8TR+skDBXkuk2nSSObJmP1orvHiBLcW3kreW5Hg3bxngllu38dSTxxFCEAQxR16cSKttHZOZmSaGIdP2gH5EpTeHRqfc+m4CNpOxKQ0UeCx/G+VyjsuvWIduRMRxQrGYoeOFTE3V8f3ovARtmvjtdNK+seX+HP/u37+DTZt6qTd/G8hgykEwt6J1gFIBSlexzPUXdLEK8PxHUOteIJs1kSsZ/VwOtnZzFjJLMfezNNp/RJxUAQuIECJDT+nfEob78YKHEBhoJOgIy9pGNnPft/XsXgqxUjw1e5pHp49TCztc879+k/f+i19F6nRiCjIuMZp/95sfJsxlkEDBcvGSCIV6XTtevZb4gTb4bt7tNqW+uJ9l4IWUvo3myt8qipUCxb4Cj/7DN2nXOiTdZtyXsOmspbno0V+IDAlrWVl0bSWcG0LlDDAlSY9N8/ZeEtfA7HKTw2yEEYuU1WEqtKnhvPkw5wW8/an9bJqeZ2xNH1+66WoarkOok0VKZqK7ISKRJiS11jw8fgrXNCjZLnnTZsZr89+ff5yxVp21uRIPjx9neEsfyaGYyFbM90Q46xyGF7Ko3Fl6dizgGEUSHRApjyhJvVq92CtXL16jQHanoYSKvR1fLWAIB9co48gisfZSb/2bh7jxZ/4GpULMTsy6rMllv6X52h9ezuxuTUb2LvLMc8Ygbabod66gXNyIxCLWAY5RoNfZjmsuD/2FqsmNtw2w67IhTh6f47nnTne14AWZrEMu59Bs+ARBTCZjcdNNW9nz7GnCMC3UMrs1CEJIdt+wmbe+9WpabR/TMNiyZYC/+9tv8vnPP49hCExTEkVqMY5vWtbiSrJcznBg/xjr17dQurEspCKEk/LfkzZ+uJe8mW7TOqHR+mPC6BDynVeS/dWvrfxASQnve9/in7a9nYr5HwjC/SRqFkMO4NhXIWUOy9yE69xAGL2A7hp7y3zp6t1vFVprPnXmeZ6dO0O/k2NdpsTpq3fx7z//v/mJZ09y5XyHz5tNfu+KQYKsi9sdsx6m8U4/ifDiiIy5svTI9xN+oA1+z0CJ9bvWMXFimt41S1/YJE4IvICr3nDZqz5mu96mMd9k+/VbeOaBvSmlzDTQSpGsICcwQQEvJQFetM3DYIKLtbJt1yL0V1gxnIvTuwbCkLSvK9O5utQVxu1KGUuB5RjUVXDR07H76Gn+9L/+aVqIFYS0HZt/+1df4Of+9Qd5evsGFGALQaSSlH/fLcxyDYtWHDCS719sC9njZGiEPg+OHmXQzbOt3IdRkOQtm7OnZrFbPrPFkN3DPfRd9ST5fA9CSPLmIH5SI1B1FDHo1LOXmOjueuMct8iVZQxpUjY3YJ7X5MImj9sxuexnfgfZChcVBM2uF3v3zx/kHx+/i6QQorVCCIkixDGKrM+/gYq79dKfbzTDieaXaISjCCSWzLD5mrs4etQhjBIqlaV+sOWeLNVqh9HRBT77j3vI5R2Gh1NtoWbDx3FtpBTcc88VXHf9xsUx4ljx4ouTFIou7VbQ5fkvJWy9TkRvb55dl61j3boejh6d5srRmJ7KpbxYC63qi39F8VGC4HnipEZsHCP68yEGfmosle/oKHQui5AG3H8/5Jc/f1Lmybi3XjSCEALTXIdprrvkvXulGO/UeW7+LMPZ8mLxVN5yyPSt4a/uzPNvrnwzn3vy78kFLcwkJkhiNJqC6eCaFjXfI1AxGVYN/vc97v3ZN/F3//mzTJ+ZxXIskjhBxQm3v/Mmhrdf3OX+O8XYsTRMtHbrIHbGxrItDFPSXGiRRBdrxn+dYT7MvhWPpRF8nYuZRKEfIaRYli/QEjAFSdEk3JxDC0nn6hIIwXC2iKdilALHMAhVgt/NJ3QddXJewJ/+1z8l758nl9zlNn/iv/4JN/+P/5uW6xCoBNdYeqwMIVgIOxRsBylSmmasFVIIcpbNZLuJI00MKYlUgpE32X71MBaSSa9JuW+OgeIIXjzTNe4SU7rYsoAlsswHR4jPVaQhEUhkVyc0a/XT6+xkzj+4zOADFD/zTcSlJNcVbPjSNCd+bC2BaiCFiRQWOTlIJ55FBiYle/2i938OflLnQPXPUSrBkrmubovkaP2z1Dppy77zV5LVhTbNVqrSKA2JbZlUqx2KxQw33Lh5UR55YWH5Ku7MmVnm51sIBMWSi5SSKIrxOukkXyhkePNbrlwcy7Ikp04EFMtNhOggL0iSanxMc6mngh/sIYyOo9QcoAlv7Gd8bw/Zz85ing4xd9xF7oN/fJGxf71wrDGDFOKiSlmjy80/0ZwlVAmONOl1cqiUiYAUkjCJqcc++VUtnR8MlPqK/Mxvvo/jz59m9MVxssUMO3ZvoX+k77sa1xNCUijnaDdSaeQ4Wjls4wmLj+rbU5YOSywdjeCj3H7phK1I+7WqJEkZmAWDuNdBhArZjulcViLu7YqmCYkGXPPiY3WVhHnbU/svWfkpteYdTx/gb96wGwnY0uh+ASWGEDTCALRm1mvTWJzU0jzBuZXAkdoso83aYh1mr5Ohx82CEBSsNZTsETrxLEqF2EaJrNlLJ55HC82sf4CEpJvo1QgkQjhsL72DdbmbmQsOESYtbCM1UErHGCfOYHRWLmyyvITCGR9TZMkYFRyjhwX/OB1meHr2vwGQt9dybeUX6c/sWnzfZHsPzWiCIKmjdepFmiJD0RpBls9iWWV8L8J1LeI4odWlOopzzUu6q7Jmw2PNUJnBoYt18CFtGtPphGRzNrJr1AxpIGWcTqhdITpIufRhdJy2N0Gipgmjg5jGRhz7CsBG6zmkLOHYSwqVcTxG2rNIA90irZxF+/1rUKqF61g4mTbmCqvL7z2kz+xgtsDp5jyhSrC7nPtEK9pJxLpsKeXz/wDgB97gA9iuzWU3b+eym7d/18dat3UoFZoyRDqpTCzQmEtphZfCQdHH+/TbuZNR1tJigjxfZ+Ql2Tm2ky5PpWPhhyFCSkSkEKGmvbNI466+xdp7XyXI84hpoUrosVxqkZ9WyAIbpucWPfoLkQ1CNs+k7JOSk0mVIUmrbvvdPH1uhhONKhqBI41FKYFmFOAYJq0wpB74FO1ub1M01cBjxm/zUzuvIFBfoWCtwbaXSvBj5WNKl+HcrTSiMYKk2s1CAAgq7nY2Fu7Gkhku73k/x+qfoxVNp76/kGR33obOPb8iXzvOmjQ3OChCMuYAc/5BQt0kZ/RjSBeBpBPN8OTs73Dn0G9TsNOV4Hj7CdrRFLbMI2VaPZrokGp4jDU7Rpg7MkKjHtNuBQRB1G37JxFSYJmpXpAU4PkRBw+O09uXJwhitm8fWnZ+xWIWy5IksULaXYNvSCCl0Z7rG6u1xg/2EQYLbNlSJuPcRhAeJoqPo/xZbOsyTGsTxdwHlnn9QhhoHXb1Zs53es7lugRxPLZYkft6QWtNNfToc/MkKpW2Pv98k27j4s2Ffm7o24DWMOU1aEZBWk6CYE2myD1rd64mbVfx3UG+nOPWH97No//wFIMb+2lWW+R78nitAJUkqAs7IXXhC/PbYuPYrkXgRURhjNOfJ0pCRI9DcGMf3DVIubuc9ZKYQEVp1yCt8JKInGVz6+AGvnjmMIFKMIRkbKiftmOvaPTbjs3pgQp5w+LWoQ2UbJdIJWRNm4LtMNqqcapZI1IJphRILQlVjEBQcbIpC0mkTAvbMIiVItGavGmjkwGK9jqa0QQZsxeJSaiahKrFhvxdnG09wrbifYRJi3Yyg8Qibw7hqyq14BT9mcso2eu5vu8jqQaOjsma/Zg/HcFHf3/Fe6eFYPodl1Fy+hnJ3c6Mty818sk8JLqbAK4QJk1ONL7ENX0/l05g8QRSWEhxfkjLJtExbt80O3bew9nTDTIbber1DkePTNFo+vT3FRFCUK21MQyJIQVBGHH8+DTXXLOBjZuW9wK2LIPh4QqnT8+htcayzMXJTog0Uau1xverTE0tsGVrjnXDBiBw7CuxrW3E8QnyuR/DdW6/yNiZ5kYQedDzaNJEMySgY6QsIbo1C68nTjbn+PzZA0x6aVXhQtCmHnbYUugn05VMng86vGnNdipOlnvW7ORofYai7WJ3tfPDJMY1Ld605rvv6H2vYNXgvw645YdvoDxQ4pufe5a1W4aYn6zSrLbwmz7qXJHLd9APzHQMpGHg5iRJlDaXkLaBeccw/u0VLFIhs4FMjnroU3YyjLZqWMpgY7HCDQPD5Cybe9fv4InpM7TDkAdvuYZ/9zdfXHE8LQRfvW03RdvBEpK+zHIxrHrgs6tnEKUVo60asY4pWi6XVwZRWlMLPUbyZU41F2iEPq5psrOnn4xhcarV5N1bfoLx9pNMes+QqIC8tY5txR9GEUHXY3fMIo65xKqS2qQZjdKfSRPvQshlGvYUXLj/fpK3vgVUgtEJibMmWsCeP3sbvf3XoXTMjP8CgaphihyGMLqaQBHteBpb5pgPXgQg1ik3P9CNxTDYOQgAoXj3u25jz7OnefbZk5immYq3FVwKxZSHbhiCet0jiDX5vMnISC/vfNfuRbplHCc888xJnn7qJH4Qk8u5xHFMpxNiGIKRkQq5nEN/f5HpqQa22+CW23yuva54gcSDi5A9KN1c0bN17etx7J2E4X6UDhEohHAQRg9gYMgerPNi/q81RttV/vDoE+QMi7WZ9DPPmzYnW3MshG0IodfJ875N13Fd73oAhrJF/sWuN/C1iaMcqI4jhOCqyjB3r91Bn/tPITT16mDV4L8OEEJw2S07uOyWHSRJgpSSpx54jv/43v9GHKd/a61XbgKy4gFZNkHkijksx6JYyaOUwtpc5nSnRrKrh6F8EVNI/CSmHYU4psmmYoXhXIlIJawvpEyHaa+FZRj82d3vY3uplzBJyOy8E/XWtxJEIZkgpOOkcsm//G9/kY7r0G9aWIbBRLtByXa7xtxnTa5I2YlYXyinMfrzlt5HarOYQjKcL7EuV1w0lkIIpjpNcqaFJTNsLNzFhvydXWndNAZbDU5wqZlRa4Uh0kRtoiOmOs8x0XmaSLUoWCOM5G+nfPvtnDnyV6i/+0vyZzoEGweo/ehu3EL6vlY0SSM8k/L7tSbSncVJBp0K2pvdUIjEJGNWiFSHZjSO0hECiSFsTJlhwN2F41jcets2brl1K0ppHv7aIf70zx5LBeNsM62ILbjs3LWGbNbmiiuGefHFCZ55+iQLCy1q1Q5KazZt6ufqq9ez7/mzeF7Iho0l1q+v0GlHbN4ywLvevRshBFG8j2b7WUxjpXCFQrByhatpbiKXuReVzJOoOYRw0hWEDjDNjRjGBqr1/4jWIbZ9OVn3HkxzeMVjfTfw8ORRbCGXqVuW7Awb8730uwV+fvttGEJcNJkNZoq8f8tulL4euDBc9YOBVYP/OsPoFildc+cVFHryZAou7bqHShRxFL+kp29YBkmUpIycJK20tDMWlpvysOcnq1iOyWV9G5lvRKy/fDMTQQsvjhZDAHnTZlOxh3YUcaZZxU9icpbNtX1ruWPtJtbmUg8qYxhw++185rEvc/zjH2PzzAInByo8eMu1JLksG22Hehhw89B6CrbL83MTGELypuGtXNE7yO/sfYROHJE1rcUvYqwUGdMibzq0ooC85Sx6xolSxEpxTd8SU0oI0ZU3SFG0RjCEQ6Q8rPNYOEqn9Mw+dydKJ7xY+xQLwVEyRg8Zo5d2PM2BhT9ne+lH6eu7gX0/cYC8ObSMdRMmLQzhYss8hnAIdBOhUxYQgCIi1AHrsjemn4W0KdkbmPEOYAqXREs0aS9bpWPWZG9Ydh2GIXjDnTt5+pmTVKstgiAmX3BZs6aHYtFldrbB7GyDb3zjKOVSlkzG5tDBCaQUZLMO69f3cv3uTUxMVBkfq7JlywD3vf1ydu1ai9ktJBRiB0JYKO0hxdL9SUXR6CZuL4YQglz23ZjmdjqdTxNEBxHCwbauRelZ4uQkhhxAiAJheJAg3E+5+EtY5rdXAPhKoLXmxfo0Ayt45WUrw5nWfEoHfokk7A+ioT+HVYP/PQLTNHCyNn19vagowW8HtGpt5ieqK79BgE4UlTVlrnrDZbzw+IsopTBtE7+dSgwsrhIaMW/90N3sN5psLPQQqYSnZkbJmTbXDQxjSYOyY5CzBpj12nzkylso2iuXu09Jzf333MZQNu1qdH50uR76tOOId225kjePLF/yv3/btfzZkT00Qp+8ZePFMX4Sc+e6zXhRzGdOvUCiFDnTxpCSjGlx34YdDOdXZqpAamR3lH6Uw/VPEiUtLCNLrHwSHbI+fyc5a4CF4DgLwVHy5ppuslgBijBps2/+T7mp/1+yLnsz450nMUUGU9qESRshZHrs2j/gGj2EUQPd7T2pu1wiQ2SWxeuVjjGFAwIco4BGk+gwrRG4UCAJsG2T9/34TXz6U892jayNH0TMzja56qr1HDgwytBQGSkFk5M1TMsgl7UZPTvP4GAR17XYvHmAbNbhxpu2cNVVyym6UmbJZ3+SZvvP0JgIkUPrDpqAXPadGMal21QKIXGdq3Gdqxdf63gP0e58FtNcGscwBklUlVb7U5SL//q7nvxMey0YqQqrsdyoJ1pjCHlJg6615kx7gWONGQSSHaUBhrPlH5iELawa/O8ZmJbJZbft4KkvPJd6siJ9QE3bJA67nHgpkKbEsk2cjI00DIY2DtAzVGbLNRtZs3mQ6TOzzE9WCb0QN+fiZCxuuPcafujNb+RYfY4np87y7Mw4Q5kCV/YNLevlaUkDjeZUo8rVfWtWPM8NhTIASqvFIipIGRECwfp8ecX3Xd47yL+65g6enDrL2VaNzaUsvU6Wh8ZO0I5DZjtt6qEPaIZyBYbzZdZkCi/7Zay427jW/AUmO8/RisYpWusZyl5L0Uq16ee8g5jCTTtM6Zg5/yB+Uuf/be8+w+S6zgPP/89NlbtC59zIOYMgwSQxSKJoWVkmLY8syZY1Mx6v48zaY+/ueMYzY4+9z8zIab2yrB3LspUs2aIkUhQpUWIGAYIEEYicOufK4aazH6rRjUZ3Aw0CRDfQ58cHD9FVt+49dVH91rnnnvO+IHD9Eq+O/gWd0fvZkPg5hsqvUfFy1AU30BTaTshIEbc6GS4fIm4up+yN4coSAh1DC1IXXMdI+QjLa96D61fIOb20RG6n5I5QcIfRhEbEWI6l1Uxud6kVKxr59C/cy+uvnaO/P01be4otWzvp7xtHICbH3ieLmWjVWUyZTImGhgsLheSM4HdBMLANQ2+gVHkBz+1B11cSDN79lnrjFXsPmjYzmaAmEhO588evb9bLOeyq7+LZgRO0hhPTHh+u5NlZ2zHrFEvH9/jqmVc5NN6HjgAheKrvTbbXtvORzm3T8+nfwlTAX0Q237OOl769Dynl5LRKJJhBg/Y1rUhfYoUsdF2jXKxglxx+629+mVKuxGN/+SSxZJRYMsrKrVO/zMM9o4RrQgghWJ2oZ3WinsZQlB/2nJwM9hdPZ5Oy+vNcbmtspzUaZ7RUwNINLE3D9j0qnkdnTZLNtU3V2ThCmxGsm8IxPrh8AwDpSok/fPUZ4laAM7kxIqZJfShCyXUwdZ2uaIKvnnyD9pok9aHLV0QKG/WsmCWYVk+fP5mILWOfpexlMMXUateQnqKv8BJxs521iY/OeH1n9H5OZh9H4hExG5H4eNImoNUQNVvwJpKsXej968IiZrUSs6ZWlHrSwZ2jmAlAXV2MB981fXilr3eci+NWPF4db/d9eXHRqIl0CoKOjrl764bRSsz4mTmfn6+JwoCX3eJGuLdxJW+mB+gppklaIQSCtF0iboW4v2XNrK95YfA0B8d6p/XofSnZN3KejkiK3Q1v/3DUYrA0vtZuAlJKTh04x70f203Xpg40Q0O3DOraUzQvb8IKmKy5bSWhSBCEIBIP8/Bn7qdjbSvNKxqrOWw8f8Y+Pddj2aaOaY+vStbjSJ8zmTF+3Huap7tP8Hz/WXryaQTQFZs7JXRjOMYjKzfTFo0T0g18IGSYtMcS7G7s4PNHXuF3XnyC33/lKX7YcxLbm31B2YGRfjzp4/g+eccmpFe/4EKGSdG1KU0sf399pO+azmsqsAZXlvClS94ZwBTham8fH4FGQI9j6TH6SntmfX2N1UpzeAeerJBzeig4A5giRCqwGsfLUR+sfoEZIkjMasP2szP2UXbHqZvYbr46OmvxPTlZlDwUsmhvT5HPV3Am6v7m82UGBzLcfscKUqmrKxP4VgSs7fhybMbjUubQ9Xo07e3v3UM1bcK/WnsPD7dtJKhbmLrBu1vX8Svr3kFiljKFUkqeGzxJfXB66m1NCOoCEZ4dPHlD2r0YqB7+IuH7PvnxAk1dDaQaE/DOao8vny7wxrNHyKcLpFoS1LYkSQ9mEJrg/p+7F6gmZLvtvVt56bF9JJsShCJB7IrDaN84K7d20rpq+vBMZzRB2XU5NDpAbSBEjRWk4FbYM9jDuztWkwxevrbnTy9bT10owj+ePEhvIUNjOMqyWIqj6WHqgmHaonHKrst3z7zJuew4n1q3c8a46miliKXpE/Pxp6ccEAhszyOgG4yU5q5ZOx+p4CpixTbS9ll8qrnmPengyjJJayWa0DG0EGU3PevrM/Z5Su4YAkHEaEQXFo5for/4CvWhDbRM3LQVQrAs+gAHx76ERBLQ4oCk5I2haxat4duvqt3NzQnWrGvh6JE+auuiWJZBQ0MNhUKFYMDEtl1SqSgPPLiBNWtmH3673oKB3ZQrL+F5/WhaA6DhyzRS5qkJ/csbOhYeMSze0bSSdzTNndfoAkf6FFx71i+DkG7SV8rOmkDxVqQC/iKh6zqp5iTFbIlwzdQHM5qIsHLbMgbODDHSPYYQsGLbMu758O3TEr7d85E7SNTX8NJ39jF4bhgrZHHXB29j18PbJ5ffX9CdzxDQdTbXNtFdyJC1KwQMne31LWQrJdKVEonA3EG/v5DlB93H0bXqMFHBsXnszBE21zYRNasrTIOGQXs0zqGxQU5nx1gZr522j+ZQlEy5QsSw8ORUxlI58V/IMMg5Nq3Ra8tYqguT9cmf5Vz+J4xXTlLxsggiBLSVGKJa3enCVM1LSSk5k3uKsFFH2KgjbZ+l4mWqK6VFkLrABkLGVK+2xmpnU+qTnMn+iMHSKYQQtEY20RV7x4xsmlcihOB979tKY0MNe/eeZny8QDgc4GM/s4tt27rmHLN/O+lanETsVykUH6dsvwDSxTTXEAl9EstavIuXTKGRDIQpuPa0e1YAebdCU6hmSQR7UAF/UbnjfTv47l/9gEDYmszR77keru3y6f/8KKt2rEAIMK2ZWf00TWPLOzey6d71OBUHwzImp3xe6tj4EKam0xlLsjxeiy999Ikx9958lrO5cbbOEfB9Kfm7Y/uREloj1Rk0nvQJGAYnsmPUh6OTQV+IaoGUN8cGpwX889k0Pz57jqNDowgg79qULJeGSJiS55KYGJe1dJ0tddeewM7UQqyseQghLfYMfYferAWUkPIcqZBJV1ywNnHHjNc5fp6800/EqA6ZNYY240sXEPjSIeOcm7a9lJJjo/Cdky2UnRQIQSIQ4WNrYdVbGO0wDJ3dd65i1+0rcBwPyzKmLaBaCL7M4fo9CCwQFq43gucPIOWqRRs0hRDc17Sab5zdT1A3Jm/qur7PuF3ip9o3XWEPtw4V8BeRDXeuIT2U4eXv7JsYu60uHrnnI3ewfveaef1CaZpGIBS47DbiokUp1SRnF30xzFGm7oLufJqRcmEy2E8eV1Sz8fQXcqxKTD/+xe0eKhb4f/a/gqXr3NPSyeuj/UghGSkUKbsuddEwDaEonpT80vpdxOeYHvpWvDZQx8mxBhqifRNtkhQcn/3927mrcba0vWLyhu/U+6z+yvgTBb4v9sbwIP9w+AD1kQjJYLVHn7MrfP71ffzqzt2018w9xfRydF1bkB79pVxvkEz2L0AE0PX2iWmuFfKFrwM6oeD1rQ53Pe2s62CsUuAnAyemMi4JeG/bBjYnr39W3MVKBfxFRAjB3R+6nS3v3EDP8X6EgNZVzcSS13fp95pkPU+ePz5j3NL1qzNalsXm7o4WHHtGoKv2yKsrZMve1HxzKSWO77MuOVUY+8Wec3jSJxGs3mS8p2UZ4+USOadMyfH4zNbtxAJBVtTUTlbOmk26XML2PFKh8KxT6lzfZ6xcwtQ0DKExUMjxYnc3LbG7KVcKaNoYoOF7DQzlKhwcHmRXy/TVoqYWIWI2U/HGCejTg3XZS9MeuWfae/3+6eMkQyGCujF5nqKmRdl1+fH503xi47Y538/NoFx+DvCr9WsnCBFA0xsplp4gGLh9wXPszEUTgofa1nNHwzLO5UcRCLqitXOuN7lVXZd/HSHEF4H3AUNSyhnL90Q1qnwOeBgoAp+SUu6/Hse+FcWSUdbd/vblKumIJthe38q+4R7qghFCukHesRmzS/xUxxrigbl/CepCkWpK44u+LCxdZ1W8jtdGemnTEtXEXZ7LcKnA1voWltVMfYEcGxuZtn9NCGpDYWpDYfryOZbV1FIfnnvGyUA+xzePH+ZMenwi573JQ8tXcUfLhR6nZG9/L4+fOka6UuZcNo3jeaRCIbqzGUqey4pECl3GJvcZNDxOjI/MCPhCCJbH3l3Nce+OEdQTSKolDQN6nObwjslti67DcLGALgSvDfZTcV0kELMsViVrOTY6Mu9/n8XKdt5EXBTsL9BECM8fw/fTl13MtRgkrBCJ1I1LA7HYXK/rxP8FPHSZ598LrJr481lg9jSFyg0hhOCRVVv48PKNSCnpLWYJmSafXLOdB9sv/0XTEIqyqbaJvmJucsogQCIQZEOqia5Ygt5iFtf3+dCKDXx89dZpM3TCpoXrz5yqKSe+RK7Uq/+L/Xvoy2VpicZoicUIGjpff/MQL/WeB2Bvfy9fOfIGhq7Rn89RdhyQMJDPowmNM+lx3hwdnrZf1/OJmLMXwKix2tiS+jRxq5OCO0zZS9MU3sHm1Ccn8+sDGJpGplzm9aF+kBCzAsRMi4rrsa+/77JrG24WmgjDLCuGpfQn0hksjSIiN7Pr0sOXUj4rhOi6zCYfAL4kqxHiZSFEQgjRLKXsvx7HV66eoWnc07KMe1qWzcgjfiWPrNoCJw5waGwATQh8CalgiN/beT9t0fhl97e7pZ2/P3KAmBWYNpw0UiqyKll32auLPX3dlD2Hlotm7gQNk4ZIhO+fOcG2xhaeOH2M+nCYvGOTrZSpmdifZ1eoODZR06Qvn2VZIknEtPB8H8f32NY49zhu1GxmffKRyS+42e6lWJqOI6tlKi98aQkhCBg6GbuMuQjG4K9VIHA3+cLfIcT0FdC+HMEy16Fp178GtHJ93agBt1ag+6KfeyYeu64Bf6RvjAPPHKL7WB+xVJRt929i2aaORTt7YLG42mRSIcPkU+t2MlTKM1wq0JvN8uOzp/m1H3wPXQiaozE6auJsrG/k9pZ2EhfN69/S0MQbQwMcHBkgZgXQhUbOtomYJjuamvnyodcYLBZojdZwV1sH7TWJydceGhma9SZu0DAYK5U4lR4j7zikggZvjgwxUiqSs21qrABBXUezAhQch8Finm8dPUzQMGiKxvj4+i3k7Qp/c2AfmUqF5Ykkd7Z20BCZfu/kcp+joutQYwUImya9+SzOxIKzgG7QGI5ge3PVU7x5BAPbsZ0D2M7BatDHwJdZNC1GNPzhhW6eMg+L6g6LEOKzVId86OjouMLW05093M03/8d3EQIi8Qi50QFO7j/Dzvds4f6P36OC/tugIRRlf18f/23Ps4Cg5NgUHJsjo8O0xeL053O80HOeX95+O03R6pi5qev8/KZtHB4e5JWBHiquyz3tXZRdl3848gZBwyBsmhwYGmDfQC+PrNvEbc3VMdeQYZCzZ9b9lRNlEsOmSdGxOTY6zFi5VK2d63sMlfJYmk48EGSkVMT2PAxDw5WS3lyGvzv0Gp3xJPFAgIBh8FJfNy/2nueXtuxkVWp+Y9K60CbTVGtM1Vetlg6RBI25h6puFkKY1ER/gYp9kEplD5IyIfMdBAO3qd79TeJGBfxemFZtu23isWmklJ8HPg+wc+fOeQ96eq7H9/76KSLxMOGJXOahaJBYKsqrT73Bml2raFt1Y1YjLiVFx+Z/7nuRiGHhI8lWysSsIFJK+vMZ1tXW40mfbx4/zL/ZPjXP3dA0tjQ2s6Wx+m8yWiryhy/9hKZIFHNiOCRiWlRcl28ePcy62gailsXtLe18+dABai4ZDhovl+ioibMsniRnVyi6DnWhMGXXqWZPRJBzHHK2jaFp1ASCdNVU8/4XbJsT46PUhSMsSyQnj523K/zDkTf4vTvfOa/EWkHDACnJOTbN0di05/oKuVtmNogQBsHANoKBm3vG0VJ1owYWHwN+XlTdAWSu5/h9/+nB6grV2PTFQpquYZgGx145cb0OpVzklb5eSq5DxLLIVioYmlatFapVq1CdSo9SGwpzJj1Oulyacz9vjgwBTAZ7qC7wMjQNV/qcHB8FYHN9E+vrG+jOZslUyhQcm/58Dgl8aPUGBgo5woZFSDewPY+YGaDoOpQ8BwNBaeILoD4UnuyBF5zql8Dp9PQcMVErQM6ucDo9Nu3m9FzKbnVBVo0VIGtXsD2PiueSscvUh8ITmUAVZWFdr2mZXwHeCdQJIXqA/wCYAFLKvwIepzol8yTVaZmfvh7HvcCpOHMO2RimTimvftneDkV3ak6+L+W0+fmaEFRcb6JoiZgziRpAyXMn//0Kts3J9BhDhTwSMDWN3lyWrY3NmLrOpzZu48DQAC/3dVNyHe5pa8aXkv/z2ac4mR4jXS7TGInQJQx2vLCHRHc3p+pq+c6OzUjDIB4IEDUtKp7LWKnEaLmI7XqMizIF2yZiWbiez5nMOEdHh/mTPc+xOlXHu7pWsqWhac7PmSer9XjvaG2jL59jMJ9HaILliRR1oTDFyxSpV5Qb5XrN0vnZKzwvgX9zPY41m/r2OpDVVLGXrkgslyp0bpiZJ0W5dpvqG4GJylWmSaZSRp+oSOX6Pi3RGGXXIWwapEKzl9MD6KpJ4PuSgl3hlf5efCknpklKRkolvn/mOFsam2iLxTF1nZ3Nrexsrq6MfezEUf5s/0sIoCkcpeK6tB04yJ9+7q/QJIQqFcqBAL/11W/x2f/tlzi8NoAnJQWnmq44OHE1YAjBnv4ebmtq5c3RYcZKJSxdZ3kiScl1+NuDr/HhNeu5p71r1vcQNkwaI1FKrsPyRIrliam1B0OFPBsmzpWiLKSbf64Y1QRj29+1maFzw9WygFTzho/2jZGoj7N6x/IFbuGtqb0mwd3tnQwVC0QMEw2BPVErN2QYdMQTDBWKPLR89WXHwZcnUixLJHl9cADXn5oTn7Nt2mvi1FgBvn965rBctlLh60ffQBcaqWAYXdNoQ/CXn/srIuUKoUr1Bm+wUiFcLvP5P/trzGKRwUK+OmQkNHzfRxeCVCiM5/kcGhlkrFQEAV3xJEHDJGYFaIpGefzUccruzHnoUJ3B897lqxkvlyk61W2klGQqZTwpua9DfQaVhXdLBHyAd3xsN3d+8DYyI1mGe0YZ7hmhfV0rj/72B6+YW0Z56/6PO+/joWUrKTgOlqFj+z5hw2J7YwsSycfWbuCOlstfYemaxqc3b5+oSiWrN14dm46aBJvqG0mFwhwdHZ4xLHQ+m2a0VCJsTF2ovueV19DmGHI3gF88egrH96l4LkXXIRoI8t7lq2iMRPGRnE6P40nJykSKVamphG+WruNKn+5sZs73sbG+kZ/fsBXP9+nL5+jL54iYFv9y6y5aY2oWi7LwFtW0zGuhGzr3fPgOdr13G9nRPMFI4LrnoFFmChoG/2btbewYi3L4VB8NqRhbdiynriNFfTgy7Ubs5URMi1WpOsKmgS8lQd2YfO2FVapzTaz1qd58zdoVoufPT/bsL2WVy2xI52iORllTW0dzpIZksFr+sCOeJFepsH+gj9W1dTNm2jiex2A+z9ffPEhjNMaOxhY21DfOWBm8ramFzQ1NDBcLaFr1BrGaEqwsFrdMwL8gEApQ36Z69DdKz5lhvvmFn+D7PtFIgPTIOE++8RK737WB5ndfXdrZHU0t7OnrnhFsR0pFNtY3zvjy6IonqQ+HOToyPFnA+lxDHcWARbgys6RgKRBgbyRIxLRIWEFSoemzuvK2zf1dKzibGZ+WK6jiurzc102mUqYjHud8Ns2RkSFWJWv5hS07COjTf410TZtcd6Aoi8ktM6Sj3Hie6/G9f3iJYMiivilBJBYiWR+joSXJnqePMNgzflX7u69jGWHDpD+fw/U9XN9nsJBHQ/DQspk5fqKWxV0tnZRcF8/30QQ8vWsH/hw9aikEP7xjJ8lgCFPXGczncX0f1/foz+cImSaPrNvE2tp6enJZSq6DLyUHhgYYL5XY0dRKbShMMhiiLVbD8fFRXu7tnvVYirIYqYCvvGX93WMUsiUisemLinRdQzc0jr5+bo5Xzi4VCvOrO3ezq7mNsXKZkWKRLQ3N/NrO3XP2mD3pc2drB3XhCBXPIxOw+IPf/beUQkFKgeqVXikQoBgM8of/52+zsWs5LdEY71u5li2NzYwUi4yVy+xqbuNXd+6mMRLlk5u28dMr1+J6Pr25DDm7wp2tHdPG4YUQ1IXCvNBzde9RURbSLTeko9w4TsWpVpGYhWEalAqzj6VfTioU5qNrN/LRtRvnVWe06Dq0x+Osrauvbo+AjVv57Y0baH/i+9xZcih0dnLswftIRappl3tzWSKmxc+u38yj66rDThcfJ6Ab3Ne5nPs6l5O3K/zH539EQ3Tm/SBL10iXr/49KspCUQFfmWG4L83rL5+k98wwNYkwW3avZNna5hm1cWsb40hfksuWGOodJ5cuYAVMmtpT2BWX9hUNcxxhbiXHYd9AL68OVFMKb2ts5rbmNqLW7Kl3V6fqeKHnPBHTmha0jVgNj7/zXsSKNegXlQXMVSqczYzzz8eP8Gz3GcKmSbpcJmAY7GpqY2tT87Qx+YhpUReOkLcrRK3p94bGy2VWzzPXzlJQ8ioczpziaPYsAlhXs5z18WUEdXVPbbFQQzrKNKeP9vF3f/okR/adwbU9BrrH+NYXn+XH33ltRoqBmmSE5o4Ue595k6GeMXxPks8UOfDyKTKjeVZsmK1s4NwKjs1fvraHfz7+Jlm7mjrhu6eO8af7XiRTmX219O7WDjQhSJfLk+2zPQ9PStbX1TNSKkw+PlQo8MNzp/F8iev7fO/kcb506HUODg0yWizytaMH+evX902kSagSQvDQ8tWMlcvTHs/bNrbn8UCnml8PkHeLfPXc93l2aD8lt0zRLfPj4b18/fxTFF210n2xUAFfmeQ6Hk98dQ+xeJjaxjjBsEVNMkJja5L9z5+g/9zojO3HhnK0dtUhNIFdcfBcn7qmOKGoxdhQ9qqO/+z5s/TlcrTV1BCzAkQti7ZYDeOVMk/OsvAKoD4c4V9v30XENCfnvmfKZT6wej1/cM+DLI+n6Mvn6M1m2dvfQ0dNnDvbOjifTWPqGk3hKIPFPD6StlgNp9Nj7O3vmXaMzfWN/Oy6zRQdh75cjr5cDgF8ZsvOaembl7I9o4fIOHkagynCRpCwEaQxUMuInebVsSML3TxlghrSUSb1nRuhXLKJJ6eXGNQ0DdPUOfbGeVq6poYw+s+PYldc1mzpwLFdyiUbw9QJhqrB/tiBblq76ud9/Jf7uqkPz0zB0BCO8OpAHx9es2HWFbsdNQn+3e33MFDI4/geDeFoNXsl8NlttzFaKnJkZAjX9+hMJHE8j+FSkZhlIRDoQmMgnyMZDJEKhni5t3taCgUhBLta2tja2MxgIYcmNJoiUfR5ZNFcCnzpczhziqQ5c3FZyqzhQOYEd9dvU+sRFgEV8JVJnuvNKFB+gaZrlEvT0wq4jguiOlxiWgamNfVx0g2dSmnmXPjLKbsOsVnG6nUh8KSPJ32MOS5KxUThldnUhsLTFoFNLeSqvldNCBy/WqDE0DRK3uyJzixdVz36WfhS4vpetSbAJXSh4/izp6NQbjzVRVEm1TUnAIk/UZ1JMjVmb1dculY3Tdu+viU5mbTuUpWyM2P7uVwYY19bW8/YLGmUM5UK7TVxLO3qiohcfM+hORpDAp7vY+kGAU2r1rsFXN+jbiK523i5zPra+V+VKGBoOq2hBnJuccZzWSdPZ6RF9e4XCdXDVybF4mG27l7Jj7/7OoVcmVKhgmnpROMhlq1pYcX66XVfozUhtt21in3PHqOuMY5pGfi+z9hwjmRd9LI3baWUnDrSx54fHmGgZ4xILEjb7e0c1NyJQirVIid52yZvV/i59VvmFTR8KdnX38sz508zVMhTF45wf+dybmtu4972Lr5x9BC9uSxDxQIVzyVsmLTF4tSHI4yXS2hCcO8cGTGVud1Zv4VvnH8KQ+iEjWoRnKJXxpYOu2uvbsW18vZRAV+ZJl4bpVS0KWRL1WGZsouU1cVV+ixl+u59eAtW0OTVZ4/huj7Sl6xY38IDH9yBFTDnPM7rL57g6W+9SiwepqElgV12ePMHx1i1LkV2ffUGrACSwRC/sHkHq2vnN/3xOyeO8uPu06SCYVpjNRQdh68ceYO+fI66YJhjYyOUHIeApiEwKHsuPfkMZ9PjrKqt4yNrNsyoZatcWXu4kQ+13cczQ3sZqlRXWCfNGO9pupPmkLpiWixUwFcmlUs2z3//IOu3daJpgkrZwTB1TMug9+ww3aeGZgzT6IbOXe/exG3vWEsuXSQYtohcUnlstuM8+70D1DXFJ8f9AyGLprYUg0fHeeTuzUTba/CBuouqU13JULHAcz1naY3WTN5QjVgWIdPk2fOn6c5lSQaCdNYkcH0fTQh0TdCbz9IQifLrO+9UQw/XYFm0lc5IMxknD0DCjKnzucioMXxl0kD3GJ7rYVoGuqETjgaxAiZCCKyAyYmDPXO+1gqY1DbGrxjsoTq7x/P8aTd5oXrj1bIMTh3poy4coSEcmXewBzg9PoZEzpg9owlB2fHozmaoCQTQhMDS9WpJRgS1wTCvDvap4HQdaEIjadWQtGrU+VyEVMBX5kkg5cybs2+JlMyZ7FhMzaK56t0iQc6138scU1GWCBXwlUlNbSl0XcN1phcakVJiV2xWbWy7Psdpr0XTxKzHcSouK69yhe4FyxMpEHLGF4YvJUHDoCUaI2fPzH2TqVTYfYUiLYpyK1ABX5kUDFvc9Z7NDPenKeSqqQoc22Wwd5yOlU10rLw+dVlDkQB3PbSJ4f40xXx54gvFYaBnnM7VjW/5OI2RKLtbOunOZibLDBYdh+5sht2tnfzazt2UXY+xcglf+ri+z3CxQNQ0+cTGrdflvSnKYiYuzY+yWOzcuVPu27dvoZtxy8qMFXjj5ZOcPNKLFTDYtGsFa7d2Ylo6x9/o5qWnDjM6lCEYstiwcxmBoMnxiTH8dds62XjbMsLR4Jz7tysOR149y6G9p3Edj5Wb2thy+0piiep8dyklxw6c5+Wnj0weZ/s9q9lxz1qsQHVsf7gvzWsvnqD71CDloo2UklA0QNeqJrbeuYraxviM43q+zw9OneSbbxyibzyHJgVt0RhdySRddSnKwuFrxw7SW8hhaBo7Gpv5nTvfyYpUasa+LkgXy7xytptDfYOYus5tnW1sa28mYC7OOQ+e9DiRPc+BzAmKXpnOcDNbE6tJBWaeL+XWI4R4VUq5c9bnVMBfeob703z9//0RdtkhFo/geR7ZdJGOlY186NP3YgUMpJR4rk+5VOHrf/UM48M5aiZSLmTTRRKpCI/88gNEa0KQy8HXvgYnTsCqVVQ+8GG++bX99J0boSYRRtM0cpkCwVCAR375flL1U0vwLxxHN7RpN/nOHOvnn/6/Z9E0wWDPOMP9aUDQ2JakrimOQPDRX3oHbcunZ+Tsz+T4/POvUHFcRgslzo+NIxHURcIkw0GODY6wsr6W9lScsutRsm3uWNbBh7aun/Um41Auz+ef20vRdoiHgni+T6ZUprM2wS/cuZPgIgv6nvR5ov8FjmbPEjNCGMIg7xURCD7S/gCtoavPYKrcXC4X8NWQzhL0o2/vR/pQ35ycnEbZ1Jai++Qgb04ULRFCYJg6e398lPRonsa2FKFIgFAkQGNrkmy6wJ4fHYHnn4fWVvj1X4c//mP49V/H6OqA55+nqS1FOBokGLaob07iOC7Pfu/AtLZcOM7FwdZzPZ78+ivEasLouk5mNE+yLkayLsr4UA7LMglFLL7/9Vfw/akbyVJKvn3gCFJCNBBgIJsjFQlTFwmRLZc5OTxKTSjAQDZPwDCoi4RpTcTZc6abs6PpWc/V9w4ew3Y9muMxwpZJLBigLRnn3GiafefmnrW0UM4W+jiWPUtTIEXUCBPULeqsBAHN5Mn+l/Cv14135aakAv4Sk8sU6T09TDw1PUGaEIJYIsLBPacmH5NScvCV09N65Bck62s49txh5MMPV3v4hUL1iUIBvVjgo//0xxiV6WkSErUxTr/ZR7l4+Rw7/d1jFPMVQpEAw/1pDNNACIEQAt3QGBlIE4mFyI4XGO5LT74uU6pwbjRNKhxiJF9AINAmXgeCXNkmZJp40iddqqbs1YTAMnTe6Omf0Y5Cxeb40Ai10ZkJ3ZLhEHvPLr6AfyRzipAemHG1EjXCZJw8I5X0wjRMWRRUwF9iPNdHaGLW4Qvd0LArU4muqjdtPTR9lqRYusbKwy+AP3uPUeDT+dpPpj2mTRQicV1vtpdMtdHxJgtpea6HuKiAidAEnls9phBi2r4cr/o6IcTEwqqL2zM9t47vT/1d17Rpue6n9lc9zmxrAQxdo3yF97EQKr4zaxIzqJ4DTy6+Nis3jgr4S0wsESYcC87ay85liqxYPzUlUtM0ulY3kR0vzNg2O16knSyiMPM5ANOuEBvum/ZYMV8mURslHL18BaRqErdqUrZEXQzHngrGruORqIviOh6apk27cZuKhIgEApQch2Q4hHdRgJdIAqaB5/sIIBacakPFcVndODN1Q00wQDIcolCZea7SxTLrmxZfyoDlkTaK3syCI47vogudlKVu3C5lKuAvMbqucc9DmxgfzVGeSF8spSQ9mscwDbbcsXLa9rvftQG74pDPlpBSIqUkny1RLlVoeucuiERmOwy2GWA4XDvZqy4VKmTHC9zz8OYZpRIvFYkF2fmOtQz1jhNPRQgETQr5EoVcmVDYIhYPMdyf5vYH1hMMTaVT1jWNh9avYiRfJGSaxIMBcqUKhYqNZRgsr0sxlCtQH4sStkx8KRnM5qiPRVjfNPNmpqYJHt6whrFikaI9da7GiyU0TXDnis75n/gbZG1NF1EjzJidnVyPYPsOI3aaO+o2EdBnLxWpLA1qls4SJKXkzf1nee6JgxRyZUDS2lXH/R/YQX1LYsb2504M8Mxjr01WsErW13D/+7fR2Ryp3rDN5Wa8xo9E+cff/3t6hqvj+LFEmHsf3sKaLR3zaqPn+bz67FFeeeYohVyJvnOjCE3Q0lFLJBbi9gfWs/3uVTO+PKSU7D/fx5NHTjBeKtE9nsFxPTpSCaIBi0Q4SKZYxpMSKWFDcwPv27yWeGjuKaYHevp54vBxcuUKvpS0J+O8f/M62pKLs7ectnM8M7SPs4U+BAJLM7mjdhPbkmtUuoMl4G2flimEeAj4HKADX5BS/tElz38K+BOgd+KhP5dSfuFy+1QB/+3neT7Z8QKGoU/Oj5+LlJLMWHX4Jp6KTAWO55+Hhx+ujuUXCtUev6bB448j77qLXLqI5/nUJCPos9wLuBLHdslligSCFiCplB1qEhEM8/K58T3fZ7xYwpzImVO0HWqCAQKmge16ZEplQqZBNDi/AtsX9qdrGolQ8KYInAW3RMV3qDEiGFdZS0C5eV0u4F/zJGIhhA78BfAuoAfYK4R4TEp5aSHLr0kpf+Vaj6dcP7qukaybvUrUpaoJ1AxOHOxmqC9DvDbCms0dxO++G/r64GtfQ544QbauhUPLd1EaNug41MOyNc0zkqT5vk/f2RFOHu7F932Wr2ulfUUDuq7h+5Kzo+McGRjC9yVrm+pZUZ+ayn45S3K2QsXmcN8gPeksqXCIzW1N1emY0anhpkigOpTheB6nhkc5PjiCaehsbGmkPRm/YgDXNW1yf2XH5UjfIGfH0tQEA2xqbaKxZvGlVI4YISJcOZmdsnRccw9fCLEb+H0p5Xsmfv73AFLKP7xom08BO68m4Kse/uLSf36Ub/7NT7DLDqZl4EzMpHn40TtYs6UDKSU/+e7r7Hv2GIahoek6tu1Q35zgo595x2Sg9lyPx7+6h2MHzmOaOgiBY7t0rW7kvT+3m38+eJQ3egcwdR0hqgF6ZX0dn7h966wrW3vGM3zxxVcpOQ6WruN6PhL42I6NbGufXrClULH5Xy+9Svd4FkvXJ9Mr7Opq54Nb1k/OIrqckXyBv3lhH+PFMgFDx/V9PF/yUxtXc8+qZdflXCvKtXhbe/hAK9B90c89wO2zbPcRIcS9wHHgN6SU3bNsoyxCruPx2JeexzB0Ei1TPdlK2eGJr75MS2cdw/1p9v74KI2tyWnTOIcH0jzz2Gu87+fuBOCNPac4+tpZmtprJ3vVUkrOHh/ky995iZNGeVqPW0rJieERnj15lnetm35D2fV8vrzndTQhaIlPrRWouC7/uP8QnakEqcjUUNUPjpygJ52lNTG1rS8lL585z4r6FFvami97HqSUfG3fQUqOM20fjufxvUPHWFaXWrTj+ooCN26WzneALinlZuAp4G9n20gI8VkhxD4hxL7h4eEb1DTlSnrPDJPPlqtpFC4SCJp4nuT4wW5ef/EE4Whwxpz92oYajh/smbg5DK8+d5xE3fTCGEIIahtqeObwKWoj4RnPNUQjvHjq3LS58wDnxsbJlMszbrgGjGpqiIO9g5OPVRyXV8/30hibPvSiCUE8FOSFU+eueB6GcgV6xjOkwtPvd5i6jqnrvHq+d45XKsricD0Cfi9wcW7ZNqZuzgIgpRyVUl7IS/sFYMdsO5JSfl5KuVNKubO+fvHNcV6qioXKnKnkDVMnly6SGS8QCM0saahpGgKoTEwBzaWLBIIztzMtg6LnYs1yY9cyDMqui+NNXzRUtJ0Z2062S9dJl6ZW+pYcFwkziqMABA2T8eLMueuXKlTsORetBQyD8cLMAuyKsphcjyGdvcAqIcQyqoH+UeDjF28ghGiWUl5Yu/5+4M3rcFzlBknURsGvzsG/NNi5jktDa4JKyeHYgXPT5sVXn/fQDY1ovHp10NSeIj2aJxaf3ksuFSrUB0MUHYe4Mf1jWajYJMMhrEtq6qYiYaRk1nbZrkubrsEXvgAnThBdsYJYsoWK6xK4ZP+5coXl9ckrnofq8ar59i9dfVu0bTpSiSvu45ZzSeI8HnkEYvObCKDceNcc8KWUrhDiV4AnqU7L/KKU8rAQ4j8B+6SUjwG/KoR4P+ACY8CnrvW4yo3T1J6iZVkdgz1j1DZMja9nxgpEYiFWrG+jtjHB4VfPUC7Zk0Hf932GB9LsfnDDZEHzXfet41tffJZgyJqcveO5HunRPB987yaeyw4SsiwsvRrcXc9nrFDkZ3ZumhHUW+IxVtSlODc2TkMsOvl8ulhi3cnjbP/kz0xOFzUiEf4t8Pl///vYd+ye7OlXXJey63DvPG64JsJBtrY181p3Hy3xqRJ++UoFQ9PY1tFyhT3cYmabkvubvwmPPw53373QrVNmoRZeKfNSyJX47pdfovfsMEJUqxQmaqO8/+fvpq6peqPyxKFunvz6XuyKgwB8CVvuWMF979+GPtE7l1Ly+osnePZ7B/B8H2R1nP6OBzdw+/3rePlMN48fOj6Z1VEIwQNrVnDfmuWzDqXkyxW+uu8gp4ZHEUIgkTQCv/qJj6Ll8zO2dyIR/stf/z12KIREYuo6H9qyft7Buuy4fPO1QxzsHUQTIIFowOLjt21lWd2VrxJuGbncnIvuiMWqU3Wji2+q6lKg8uEr14WUkuH+NJmxAuFogOaO2hkrXe2KS9+5EVzbpb4lOSMr5wXlok3fuRF8X9LcUUskNnXjtWg7nB9L40tJRzJ+xcVRUkr6MznGiiUilkXnt76B9pu/OZXB82KRCMU/+b8584EPYWganbXJt5TTfiiXZyhXIGQadKaSGG9hUdlN7QtfqKbEnuMc87nPwS/+4g1vlvL2T8tUlgghBA0tSRpa5u7JWgGDrtVNV9xXMGyxfN3sveqwZbL2KhKTCSFoSdTQcmGq5KlTswcigEKB8PlzbGi5tnKNDbEoDbEl3IM9ceKy55iTJ29se5R5WWLdEmVJWLVqzqRuRCKwcuXszynzp87xTUkFfOXW88gj1Xw+s9G06vPKtVHn+KakAr5y64nFqjNFYrGpXmgkMvW4upl47dQ5vimpMfwlQkpJ79kRThzsxrU9utY207W6aUZis0rZ4eThHnpODROOBli9pYOGlsTbnh0yUyrzRu8AA5kc7kSlqaBlsL6pgRX1tQzl8hzsHSBXsVlWm2R9cwMhqzrVcyCT47sHj3JyeIymmgjvWrcSr2sFp594mpYnHifa3U2howPvYz/Dsq52zp3p5vx4hngoyOZ5Jj5zPI+TQ6O8OTCMoWlsbGmkqzY5r/w7t6yLEudx8mR1GOeRR2YN9lJK+krDnMidx5EuXZEWuiItmJoKQTeSmqWzBPi+z1Pf3MfBV05jGDq6rlGpODS2JvnIZ95BOFqdIZMezfONzz9DdryAFTDxXB/X9bjz3RvZ/eCGty3onxoe5W9ffg3bcTk/nmY4V8TQBSvragkHLZDVnDeWoWNqOhXXJR4K8pm7d3JmZJz/+L0fYrsepq5jOy6ZSoWu2iQN0Qgnh0fxpaQlUUM8GOD06DhdqSS10TCO5+FLyU9vWnvZYiYl2+F/vbSfc2PjWBNpG2zXY2t7Mx/bvmnpzdC5Sr70eXpwDwfTJzGEji40bN+lIZjiw233EzbmrkWgXD01S2eJO/5GNwdePkVTW2paj3SoP81zT7zBez62CyklT359D6VChcbW1OQ2nufz4pOH6FjZSNuy65/uouK4fHnPAcKmSdlxyFccGmuiuL5HbybLxkgjL54+z9qm+mkrWUcLBf5h7wGeOXoKQ2gkJ/L8DGbz6EJwZmSMfKVCMhJCF4KxQpF0sYxAMJjLT6ZcdjyP77xxlOV1KZris68Q/eGxU5wfT09bbCWl5LXzfayoS7FrWfusr1OqjufO88b4CRqDtdNWKA9Vxnl++HXe3XzHArZuaVFdkyVg//MnqImHZww/1DbU8Ob+c1TKDunRPD1nRmbkx9d1DStocPCV029L204Oj1JybCIBi/NjacKWiRDVhGS+lJwYHiVimfRncpMl+wBS4TCvne8jU64QC1Xn6Xu+T8F2CE7Urs2UqitghRAIql8S8VAA23VJl6q5c0xdR9MEr/f0z9Y8HM/jlbPd1EcjM5K6pSLheSVdW+peGz9KzAzPSEdRa8V5M3uaijezZrDy9lABfwnIpYuzJjbTdQ0pJZWSTalQQZsjMZgVMMml55hzfY2KtoOYyMxWcT2Mi2Z+CFEdTgkYBp4vpwV8IQSO5yOZeszzJWLiORDVlbwX7cybzLYpJu8TAFi6wXhx9sRnFdfD8XxMfWbFqIBpkC5fOenaUpdzigS0mbV0daEhgYo/dxI85fpSAX8JaOmsJZ+dGdDsioMVNAlHg8RT1Rtt3kWB8IJSsUJzR93b0rba6FQStZpggIrrTv7sy2rCsqLjEDIN9Iu+jDzfJxIw0dEm0yYbuoYQTHwxyGnJ1qSUmBMVtSSS0EU3q8uOQ0dq9jz2YdOkJhikNEtmzly5Qkcy8Vbf+pLRHKoj787y+fMdLM0krKsx/BtFBfwlYMe9a7DLDpXyVNDyPJ/RoSy77luHYepEYkE2376c4f70tLzzxXwZTQg27Vr+trStK5WkJRFjKJunM5Wg4rq4nk/RdgibJitqUziuR0PNVHI0X0r6szkeXLuS5fUpRvJFfL+awbImGKBQsQkYBnXRCCW7mlZZE4L2ZIKRQoFkOEgsUB0GypTKBE2Dza2zFz/RNMH9a5YznC/gXnTFUHFdCrbNO1erKldXsjO1jorvTBu68aTPqJ1hV+16VW/3BlKzdJaIo6+f56lv7sWx3WqSMSnZee9a7n7vpsl8OHbF5Uf//CpH9p8FqhnSwrEgP/Xx3bSvaHjb2pYplfnK3gOcHU0zWijQPZYhaBosr0sRskw2tzRxcniUgm0D1bZvaW/mw1vXM14s8x++8zSnR8YQCHzpIwQsr6slZJmcGh7F8XyW1SaJBCwKFZuQZRAwTCSSRCjEz+3actlKVVJKfnTsND86dgpfVoeNDE3n/VvWsbOz9W07L7eSo9mzPD2wB0e6CKpJ7nam1nNX3RY0ofqd15NKnqYA1YDef24E1/VobEvNqGB1QXo0z8hAhkDQpKWzdjLT5dvpQgK0dKlcrU070ZtuT8aJBCxcz+f8WJqy69IYi04bCvJ9nyP9w5wdGycVDrOrq418xaY/k0XXNHRNUHE9UuEQjTVRhvMFRvJFQqZJRyo+a1GU2eTLFXrSWYQQdKYSbynp2lLm+A59pRFc6dEYTBE1wld+kXLVVMBXFEVZIi4X8NW1lKIoyhKhAr6iKMoSoQK+oijKEqECvqIoyhKhAr6iKMoSoQK+oijKEqECvqIoyhKhAr6iKMoSoQK+oijKEqECvqIoyhKhAr6iKMoSoQK+oijKEqHS/S1RUkqGKlkGSmkModEVbSBiBCi5NmcKQ9Ui04E4zaHEvIuXe9KnuzBC2ikSMQLUWzX0lMbwpE9rKEldsOayr/d9SffgOOPZEuGgxbLWFOYNyNQ5Gykl/cNZhsbzBEydaCTImd4RxjJFmutqSMRClCouActgWWuKoDVVUWw0XaB3KIPQoKs5RSyiCnwoi8N1CfhCiIeAzwE68AUp5R9d8nwA+BKwAxgFHpFSnr0ex1aunuO7fLtnH4fT3TBRXlAXgvU1bRzN9eH47sTjklWxJj7ccTtBfWaJuouN23m+cvZFhssZpJSMVHIMV7J0RRoIGwFAsjXZxU+1bp+14EUmV+IrT+5naDRXfUBAJGjxyHu209aYuJ5v/4pKZYd//OHrnO4ZBSk5NzBO/3AWQ9fQDY1CsYJlGnS2pGhIRglaJh9+YDMr2uv4/gtv8uqRbmT1LSCE4IFdq9i9Zdm8vzgV5e1yzUM6Qggd+AvgvcB64GeFEOsv2ewXgXEp5UrgfwD/7VqPq7x1Px48wqF0N03BBM2h6h9TM/jy2eeQUtIcStIcStAUTHAyN8j3+w5cdn++9Pna2ZfIOEWaQ0lChsWYnSeomQyW09QHYzQGE+wfO8sLw8dmvF5Kydefeo3xbJHm+prqn7oaNE3wD0+8SqF0A4pc53LwhS/Ab/82h3/3D+g73UNzXQzb9RjLFPF8H8fzkL5E1zRc12NoLEfA1AmHTL7x1Gs89dIxXjl8nsbaGC0T76MuGeHJl49x4vzI2/8eFOUKrkcPfxdwUkp5GkAI8VXgA8CRi7b5APD7E3//R+DPhRBCLtZk/Lewiuewd/QUDYGaaT3O0UoOQ+gMV7LUTwy9CCFoCNbwRvo8DzZtImrOPjTRXRxjsJymOZSc+HkUU9MJ6hZ5p8xIOUdTKEF9MMbLIye4q37NtF5+71CG/pEsTbWxafuNhgP0j2Q5enaQHeva5/0e07kSB0/2MTyWpz4ZZePKZpI1lym28fzz8PDD4PtQKLApEGSz0Hj8d/+E5ysxbMclYBo4rkeuWCEatvA8ScV26R5Ks21NG5lcie+/eISV7fVo2tR5NXSNWDjAi6+fZnVn/bzfw63Glz69pWGO587ieC7Loq0sj7ZiauaVX6xcN9cj4LcC3Rf93APcPtc2UkpXCJEBagHV7bnB8m4ZT/ozhlVybomAbpJ3y9Me14SGBmSc4pwBP2MXEUwFubxTwdSMidcLil4FAEszGPMLFNwKcWsqAGfy1QLXsw15mIbO8Fhu3u/vxPlhvv6D1/B9ScAyOHJ6gGf3n+KjD25lTdcsZRpzuWqwz00dI1CpnoOH/+u/49u//N+RyMkg7vsSgUDTwPd8CkV7sp2ZXJmANfNXKhKyGLyK93Cr8aTPUwMvczhzCksz0NA4kjtDfSDJR9ruJ2zMXnlNuf4W1SwdIcRnhRD7hBD7hoeHF7o5t6SIEUBQ/SW8WFgPUPEdQpeM1ftS4iOJGHPfeIyaASRTF2shw8L1vcnXXxj/d30PQ2iEjOnHiIYDc+7bcf3L984vUio7/OPTrxOLBGiqi5GsCdFYG6MmEuBbPzxAsTzL0NDXvlbt2c9CSJ+7ju6dqJVbfX+aBhJZLZquaYQC1R6q4/lEQgFsx5vZropDap7v4VZ0LHuWQ5mTNARSpKw4CStGYyDFaCXDc8OvL3TzlpTrEfB7gYuvt9smHpt1GyGEAcSp3rydRkr5eSnlTinlzvr6pXv5ey3SdpEXh4/xRO9r7B87TcmdHuSCusXWZBdD5SwXj6g1BGpwfJem4PRi3iOVLKtjzSSsuQNWR7iOhBUhYxcBaAulqPguFc9FExp1gRhSSoYrWXaklmMKnfOFEZ7uf4Mn+w7gRCoka8KMZ0vT9luqOBi6xvrlTfN676d6RnAcbzIISwm5QoX+kSxn+kb50SvHcb1LgvuJE1AozLo/s1JmRSWNaejYjotEEgkFcFwf1/WwLIO2xkT1OSl54I7VjIznp51X3/fJ5Ers3rJsXu/hVvR6+jgxI4x2yRVcyqrhzewZyt4NuEejANdnSGcvsEoIsYxqYH8U+Pgl2zwGfBJ4Cfgo8CM1fn/9HU5380/de/HxMYWOM+bxw4FD/Itl90yOrwM82LyJETvPufwwmpjovQp4b8t2Bkrj9JfSE4/7NIeSvK91x2WPa2g6j3beyd+ffb76WqDGCDFq51gWbWCskkciWRFt4u76NXyz+xUOp8+jCw2B4KWR4zStTWEfEvSNZDE0gedLTEPnY+/aOu9pjaWKPXmlIWX1C6BnsPpeShWbJ148ysBIjo8/vINwcOIqY9UqiERmDfoVK0iupY14NERpzMHUdQxNo1i2sQyD+kQEz/cZy5Z4/zs2sn55E+WKw4lzwwjB5DXPHZuXzftL61aUd4oEtJmzvHShIZHYvnPFWWDK9XFdipgLIR4G/ifVaZlflFL+FyHEfwL2SSkfE0IEgb8DtgFjwKMXbvLORRUxvzppu8ifH/s+cTNEQJ+6EZZ1ipiawa+seQhdTF3Q+dLnXGGE7sIIlmawsqaZukCMcTvP8Ww/Zc+hNZyiK1I/6zTK2VQ8hxPZfkbsPHEzRNKK0l8ax5EeneE62iO1vDp6mu/07ac1mJwcs5dS0l9Oc1dqLa2VOobSeeKREKs664mG5h7uudTp3lG+/N29NNfXMDye59CpAWJhCyEE+WKFTatasG2Pzatb+MA7N1VflMtBa+u0MfzJcxSJ8sITz2Ml4oSDJie7hxkZL9LcEKcuHqZkO0RDAVZ3NhCPhibfS89QmrO9o+i6xor2ehqS0SU9JfN7fc9xJt9L0pq+DsP2HcqezWdXfhhdLMx6i1vR5YqYX5eA/3ZQAf/qvDR8jKcHDtF4yZAMwEApzSeW38uy6Cw3La9S3i2TsasLqxJW5Kpf/2fHnsDzJeGJcfyCWybrlNCFhqkZ/Nv1Pz3ti+lqeL7PF771EqOZAr2DaYoVB8vUKVVcQgGD7WvbkUhGxov81ifuIxSc+GK8ZJYOkUh1sP7xx+Huu99SW5Qp/aURvnr+SWqMyGRP3pMeQ+Vx7m+8je2pdQvcwlvL5QK+Wml7i0jbxbkDpYDSNY6TVjyHJ/sPcGD83MRN3+qirPe17aDGnP8si7RdpD4Qw/ZdXhk5SV9pHCYGYoKayQfbbmNNvOUttVHXNB59aDvf+uEbHDzRjxDguB41kSDrljVOzLSpLigr285UwL/7bujrq97APXkSVq6ERx6BaPQttUOZrjlUx0+13M3TA3vIuoXJ+Vy76zazNblmQdu21KiAf4toCiVwRmfOEJFSgoSEefW98Yv38e2efbyZ6aEhGK+OvUrJmfwQXz7zHJ9d+cC8h31aQknG7QL7x84wUE4T1iw0TeB6HmXf4b8e/if+dOenp03bvBrxaIhPvX8XEsmJ80M0JGNEQtbkkIrteBiGPnNmUDQKv/iLb+mYypWtjnWyLNJKf3kEX/o0BJJqOuYCWFTTMpW3bm1NKxEjQMYpTj5WnRmToyNSS3Mo8Zb3PVzJ8mamh6ZgYvIqQghBfbCG4XKGU/nBee/r7vo19JfGGbwo2EsJHpKmYIKsU+KHAwffclsvtO3dd6wlZFmYhj4Z7H1fMjyWZ/fmrgXL0bOUmZpBR7iJrkiLCvYLRAX8W0TIsPgXy+7B0gz6S2kGyxkGymnawik+2rH7mm4aDpWzgJi2D4kkYxdJ2wVeGj5O2XPmta/VNS0sjzXgSR9Xeti+iyM9ElaYqBnEFDqH0t1X3tEVtDUm+ND9m8kVKwyMZOkfyTI4lmfXpk7u2rr8mvevKDcjNaRzC2kKJfiVNQ/RXRih6NkkzMhVZbuci6XpXLSQlorvcrL3OBuffIkV3QMUl3Xwlx/o5wPr7mdFrPGy+xJCsDXRxdP9h4gZQSQQ0I3JKwcfSew69f42rWphVWc95/vTeJ5Hc32cREz1LJWlSwX8W4w+ker4euqM1BPQDEqeTUi3sH/yDH/0G59D+JJg2cYOBeBz3+Arf/l71D36G1ccf9+RWk5It5AwOVsHqrNsPOnzzqYN163tQctc0jlsFOViakhHuaKAbvKhtl1knRL9Q9388m98jlCxQnAiVYFVqmAVSjz6r/8zR3rfvOL+gobFv1r5ICXPZqSco+CWGa8UGLFz3Ne4ni2Jjrf7LSnKkqR6+Mq8rIm38K9WvYtj//0PEP7sazeElFjf+Bb875dfmQtwb9N6WiIpvt29lzP5IZKRCA+1bGV33Wo0TfVDFOXtoAK+Mm/1wRpCo/Zkz/5SVqlCfc/QvPe3MtbEb63/6evVPEVRrkB1pZSrElm7ESc0e24bOxSgdsPWG9sgRVHmTQV85aqIRx/FMGa/MNR1g8jPffIGt0hRlPlSAV+5OrEY4vEnkLEYfrg6G8cLh5CxGPoT31fpCBRlEVNj+MrVu/tuRF8fYiL3jK5yzyjKTUEFfOWtUblnFOWmo4Z0FEVRlggV8BVFUZYIFfAVRVGWCBXwFUVRlgh10/YmNlzOMlBOYwqdrmi9KgStKMplqYB/E3J8l8d6XuVQ+jwgEICh6by/bScbE+0L3TxFURYpFfBvQj8aOMzB9Hmag1O57iuewzfPv0xtIEpzKLnALVQUZTFSY/g3mZJrs3fsJA2BmmmFTQK6iakZ7B05tYCtUxRlMVMB/yaTdUsgmbVoeFgP0FceW4BWKYpyM1AB/yYT0QP4gC/9Gc+VPZuUFbvxjVIU5aagAv5NJmoG2RBvZbiSnfa463uUfYedtapAt6Ios1M3bW9C72nZylglT19pHF3oSOnjA/c1bmBZ5PrWs52LlJKhcoaK71IXiBE2AjfkuIqivHUq4N+EokaQT6+4jzOFIc7lhwnqFmtqWqgP1tyQ4/cVx/nnnlcYKecQojot9I661dzXtAFdqItGRVmsVMC/SRmazqpYM6tizTf0uGm7yJfO/AQdjcZgHCEEru/x3NCbaEJwf9PGG9oeRVHm75q6Y0KIlBDiKSHEiYn/zzoBXAjhCSFen/jz2LUcU1lYB8bPYvsucSs8OS3U0HQag3FeGjlOyZ293q2iKAvvWq+/fwf4oZRyFfDDiZ9nU5JSbp348/5rPKaygM7kh4joM8frDU1HSsmYnV+AVimKMh/XGvA/APztxN//FvjgNe5PWeQiRgDH92Y8LqXEk5KApkYJFWWxutaA3yil7J/4+wDQOMd2QSHEPiHEy0KID861MyHEZye22zc8PHyNTVPeDttTyyh59ox1AGmnSEs4SW1ArQNQlMXqit0xIcTTQNMsT/3exT9IKaUQQs6xm04pZa8QYjnwIyHEQSnljBwAUsrPA58H2Llz51z7UhbQsmgDu+pW8sroKYKagakZFN0KYcPi/W07p6V7UBRlcbliwJdSPjjXc0KIQSFEs5SyXwjRDAzNsY/eif+fFkL8GNgGqKQvNyFNaLy3ZStra1p5I32WgmuzPNrA5kQnUTO40M1TFOUyrnXA9THgk8AfTfz/25duMDFzpyilrAgh6oC7gD++xuMqC0gTGitijayIzTWCpyjKYnStY/h/BLxLCHECeHDiZ4QQO4UQX5jYZh2wTwhxAHgG+CMp5ZFrPK6iKIpyla6phy+lHAUemOXxfcBnJv7+IrDpWo6jKIqiXDu1Dl5RFGWJUAFfURRliVABX1EUZYlQAV9RFGWJUAFfURRliVABX1EUZYlQAV9RFGWJUAFfURRliVABX1EUZYlQAV9RFGWJUAFfWVQ86ZN3y7izFFlRFOXaqPJEyqLgS59XRk/y/NAxil4FQ+jsTC3n3sb1BHVzoZunKLcE1cNXFoWn+w/yRN/rWJpBUzBB3Azz0sgJvn7uRbxLqmspivLWqICvLLiMXeSlkRM0BROTvXlT02kKxjmTH+JsXpW7VJTrQQV8ZcH1lsYQgC6mfxyFEBhC51R+cGEapii3GBXwlQV3aaC/mERiqDq5inJdqICvLLj2cC26pmH77rTHfSlxpc+amtYFapmi3FpUwFcWXNgI8J7mLQyXs6TtAq7vkXfK9JXG2Z5aRksoudBNVJRbgpqWqSwKO2tXkLSivDB8jP7SOAkzzAPNG9mY6ECoIR1FuS5UwFcWjRWxRlbEGhe6GYpyy1JDOoqiKEuECviKoihLhAr4iqIoS4QK+IqiKEuECviKoihLhJBSLnQbZiWEGAbOXcMu6oCR69ScW4E6HzOpczKTOicz3WznpFNKWT/bE4s24F8rIcQ+KeXOhW7HYqHOx0zqnMykzslMt9I5UUM6iqIoS4QK+IqiKEvErRzwP7/QDVhk1PmYSZ2TmdQ5memWOSe37Bi+oiiKMt2t3MNXFEVRLnJTB3whxENCiGNCiJNCiN+Z5flPCSGGhRCvT/z5zEK080YSQnxRCDEkhDg0x/NCCPGnE+fsDSHE9hvdxhtpHufjnUKIzEWfkf/rRrfxRhNCtAshnhFCHBFCHBZC/Nos2yy1z8l8zsnN/1mRUt6UfwAdOAUsByzgALD+km0+Bfz5Qrf1Bp+Xe4HtwKE5nn8YeAIQwB3AnoVu8wKfj3cC313odt7gc9IMbJ/4eww4PsvvzlL7nMznnNz0n5WbuYe/CzgppTwtpbSBrwIfWOA2LTgp5bPA2GU2+QDwJVn1MpAQQjTfmNbdePM4H0uOlLJfSrl/4u854E3g0rJiS+1zMp9zctO7mQN+K9B90c89zP4P9JGJS9J/FEK035imLWrzPW9LyW4hxAEhxBNCiA0L3ZgbSQjRBWwD9lzy1JL9nFzmnMBN/lm5mQP+fHwH6JJSbgaeAv52gdujLD77qS5F3wL8GfDPC9ucG0cIEQW+Cfy6lDK70O1ZDK5wTm76z8rNHPB7gYt77G0Tj02SUo5KKSsTP34B2HGD2raYXfG8LSVSyqyUMj/x98cBUwhRt8DNetsJIUyqge3vpZTfmmWTJfc5udI5uRU+KzdzwN8LrBJCLBNCWMCjwGMXb3DJmOP7qY7LLXWPAT8/MQvjDiAjpexf6EYtFCFEk5gomiuE2EX1d2J0YVv19pp4v38DvCml/O9zbLakPifzOSe3wmflpq1pK6V0hRC/AjxJdcbOF6WUh4UQ/wnYJ6V8DPhVIcT7AZfqjbtPLViDbxAhxFeoziaoE0L0AP8BMAGklH8FPE51BsZJoAh8emFaemPM43x8FPjXQggXKAGPyokpGbewu4BPAAeFEK9PPPa7QAcszc8J8zsnN/1nRa20VRRFWSJu5iEdRVEU5SqogK8oirJEqICvKIqyRKiAryiKskSogK8oirJEqICvKIqyRKiAryiKskSogK8oirJE/P8IsXNoQgvLrQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 23 ----\n", + "[[ 0.91519025 1.44129714]\n", + " [ 1.91536409 1.67760229]\n", + " [ 1.37006336 0.42006429]\n", + " [ 1.40643162 1.45882194]\n", + " [ 1.4436774 1.68399031]\n", + " [ 1.51665218 0.86882727]\n", + " [ 1.14537962 1.44769959]\n", + " [ 1.97735366 1.20790334]\n", + " [ 0.90206454 1.28853374]\n", + " [ 2.14040844 1.58015698]\n", + " [ 1.81104702 1.40788395]\n", + " [ 0.88305117 1.62427974]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 2.36924334 1.35079315]\n", + " [ 2.43505822 1.66830196]\n", + " [ 1.70779183 1.66243591]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.11005838 1.65875158]\n", + " [ 1.19767868 0.7931237 ]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.60160402 1.22952547]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 1.16371093 1.22968532]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 24 ----\n", + "[[ 0.90147689 1.50927251]\n", + " [ 1.91435825 1.52948602]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.44533825 1.68648357]\n", + " [ 1.17290219 1.21846842]\n", + " [ 1.52259528 0.8754644 ]\n", + " [ 0.90295894 1.24915262]\n", + " [ 2.35814408 1.3535665 ]\n", + " [ 1.40312544 1.47574255]\n", + " [ 1.14175176 1.6230182 ]\n", + " [ 1.75501334 1.3930785 ]\n", + " [ 1.37006336 0.42006429]\n", + " [ 1.17006992 -0.16747776]\n", + " [ 1.12059535 1.42939702]\n", + " [ 2.43505822 1.66830196]\n", + " [ 1.7033831 1.6584991 ]\n", + " [ 1.9072837 1.73193673]\n", + " [ 1.19767868 0.7931237 ]\n", + " [ 0.90221891 1.36861687]\n", + " [ 1.95022083 1.26241716]\n", + " [ 0.913518 1.69653352]\n", + " [ 2.13958159 1.59479742]\n", + " [ 1.51730245 1.25686525]\n", + " [ 2.18055594 0.13162861]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 25 ----\n", + "[[ 1.39811147 1.48882574]\n", + " [ 0.88376195 1.62137972]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.34020268 0.46176514]\n", + " [ 1.82565623 1.46521385]\n", + " [ 0.88996949 1.28247814]\n", + " [ 1.0940037 1.30235811]\n", + " [ 1.11411931 1.67683477]\n", + " [ 1.91840399 1.71353963]\n", + " [ 1.21035666 0.9429326 ]\n", + " [ 1.56904163 0.02300733]\n", + " [ 1.4505195 1.6913631 ]\n", + " [ 2.05003687 0.89193299]\n", + " [ 1.50697659 0.90548543]\n", + " [ 1.8383714 1.27507552]\n", + " [ 0.9008796 1.43622693]\n", + " [ 1.70973682 1.65964371]\n", + " [ 2.32097649 1.69820241]\n", + " [ 1.4748616 1.29084954]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.12578172 1.47484367]\n", + " [ 2.12856239 1.51435151]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 26 ----\n", + "[[ 1.75171795 1.40561388]\n", + " [ 0.91294698 1.23507827]\n", + " [ 1.37742755 -0.0297365 ]\n", + " [ 1.4505195 1.6913631 ]\n", + " [ 0.91190886 1.69223927]\n", + " [ 2.4364436 1.38415035]\n", + " [ 1.43685824 0.35521192]\n", + " [ 0.8994729 1.35593643]\n", + " [ 1.4544567 1.34754606]\n", + " [ 1.9072837 1.73193673]\n", + " [ 1.14994063 1.64532631]\n", + " [ 1.64289541 1.14922964]\n", + " [ 1.34879431 0.96763294]\n", + " [ 2.38024781 1.69188302]\n", + " [ 1.11220748 -0.33593541]\n", + " [ 1.45923267 0.71653399]\n", + " [ 0.8969837 1.4973079 ]\n", + " [ 1.70605735 1.65565024]\n", + " [ 1.15674729 1.3056172 ]\n", + " [ 1.13116562 0.5316725 ]\n", + " [ 2.10072422 0.41086712]\n", + " [ 1.39162127 1.49990428]\n", + " [ 1.10571128 1.47083892]\n", + " [ 1.91435825 1.52948602]\n", + " [ 1.95749549 1.28859257]\n", + " [ 2.13822689 1.58154057]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 27 ----\n", + "[[ 2.70954911 1.60196665]\n", + " [ 1.13193865 1.46877583]\n", + " [ 1.74032868 1.39395558]\n", + " [ 1.4514876 0.41329382]\n", + " [ 1.39956624 1.4900887 ]\n", + " [ 0.8881674 1.26558213]\n", + " [ 1.90942493 1.48927173]\n", + " [ 1.48693689 0.90259355]\n", + " [ 1.91717228 1.73347296]\n", + " [ 0.90242297 1.51480622]\n", + " [ 2.12867045 1.50665908]\n", + " [ 2.31285118 1.69823439]\n", + " [ 1.20357155 1.15381139]\n", + " [ 2.10072422 0.41086712]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.4505195 1.6913631 ]\n", + " [ 1.12003571 1.67142426]\n", + " [ 1.71091444 1.65701699]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.06562032 1.31798214]\n", + " [ 2.3818225 1.35135118]\n", + " [ 0.89435638 1.38820112]\n", + " [ 1.47636612 1.28974781]\n", + " [ 1.17781456 0.58953762]\n", + " [ 0.88886349 1.68259781]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 1.89571407 1.22929399]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 28 ----\n", + "[[ 0.89010097 1.36263576]\n", + " [ 1.87557157 1.74173119]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.80938941 1.37014599]\n", + " [ 1.4505195 1.6913631 ]\n", + " [ 0.88863261 1.66340823]\n", + " [ 1.60067732 1.12610378]\n", + " [ 2.43505822 1.66830196]\n", + " [ 1.10907708 1.42989169]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.12543181 1.64828638]\n", + " [ 1.46091739 0.58598115]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.38516179 1.49839192]\n", + " [ 1.70296748 1.62487235]\n", + " [ 1.92413028 1.5555631 ]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 0.91098249 1.24839489]\n", + " [ 0.90358653 1.49280778]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 2.09139153 0.28843907]\n", + " [ 1.46580626 1.35239443]\n", + " [ 1.4548838 0.90297502]\n", + " [ 1.96126431 1.17631988]\n", + " [ 1.17426305 1.19644959]\n", + " [ 2.12904391 1.6956735 ]\n", + " [ 2.13522376 1.4726429 ]\n", + " [ 1.15096728 0.5876111 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 29 ----\n", + "[[ 1.40684242 1.36015723]\n", + " [ 0.8994389 1.44185088]\n", + " [ 1.18886295 0.78072995]\n", + " [ 1.92852558 1.54822597]\n", + " [ 1.33038208 0.35605851]\n", + " [ 0.90004288 1.28711887]\n", + " [ 1.14249351 1.56989695]\n", + " [ 1.77204758 1.41613663]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.50149849 0.95691405]\n", + " [ 1.91601384 1.73628609]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 1.39271997 1.50447813]\n", + " [ 1.1015649 1.41348199]\n", + " [ 1.44524155 1.74977098]\n", + " [ 2.15477759 1.50717728]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 0.88346657 1.61045849]\n", + " [ 1.17290219 1.21846842]\n", + " [ 2.31285118 1.69823439]\n", + " [ 1.51285422 0.63013393]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 2.38585908 1.3398846 ]\n", + " [ 1.05394644 1.75685246]\n", + " [ 1.94499483 1.25450518]\n", + " [ 1.7329023 1.66271322]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.48347351 1.62172934]\n", + " [ 1.62585305 1.25025199]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 30 ----\n", + "[[ 0.89538145 1.3488648 ]\n", + " [ 1.70107058 1.66708143]\n", + " [ 1.50632883 0.61993308]\n", + " [ 1.12003571 1.67142426]\n", + " [ 1.92257561 1.53741748]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.27230146 1.48341384]\n", + " [ 1.43373264 1.47432363]\n", + " [ 2.14373821 1.12148462]\n", + " [ 2.44054562 1.67451365]\n", + " [ 0.88988358 1.65769911]\n", + " [ 1.51119635 0.94919616]\n", + " [ 1.20563126 0.9353896 ]\n", + " [ 1.44722634 1.68747951]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.72950977 1.45359879]\n", + " [ 1.85874417 1.31422529]\n", + " [ 1.57234695 1.24309768]\n", + " [ 0.88388193 1.23279361]\n", + " [ 1.8928394 1.73662523]\n", + " [ 1.09909597 1.46665804]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.08619845 1.29513416]\n", + " [ 1.16581829 0.519515 ]\n", + " [ 2.12233035 1.64249444]\n", + " [ 0.89812173 1.47761266]\n", + " [ 2.3040495 1.40450127]\n", + " [ 1.34577327 1.256041 ]]\n" + ] + }, + { + "data": { + "image/png": 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6r+6lbXcXpmuitCLWMdvzfWQtl/dueQWj1Xn+z8E7mfXKpA0HX4Wr3XsgIEZqQS3yOFmdI2+laLEzZCyHI+Upnl4YoREHbMl1c0XbBvJrTAIT5U/TCE/jmn0rE55qMFb+J7a2/TK2sfreNsIJFr0n8OMZlAqZrt2JFDZaK7SOEULSCMexRBsxdYre07S61+KYHRT9ZykFzzJceB9p65snyqUU9HUWGJ8pUmsEuI6VrEb8EMc2GehM7m0UK4oVD0OKFWMPgMC2DIrVBuZFMqjfLg6cnOa+vSfobc8tr/qUUjxyYISOQoZrdwxdlOOsG/wXCKYh8YLovM+11midLNufPTW5Ki7f3ZprTgYeS9U6jSDCNiUpx2JLbztfevwgS9UGb7gm8bRSjsXW/g4mF0urjiFNSZwxSc15WFosTxBuyiCKomWDvTBbxm+E5AppysU6cRRjWAZhFBFHMam0zeJsmYfvPkBLexavHvCZf3iAH37fK3DTNkf2jfFvn3gMKQWptMPs+BL7njjJq3/wKq68aeu3fe0Wa3U+v/8QR2fnEULgmAav3rqJzkwKpZ6f3qiBPaNjBEoRRjFSJq98FMdU4piJUpmh1gKx0szXauRdl4xtk3VsOiyXvaUK2WKJbV2r8yypjMPSTGnNY57B7NgCH/r9TzM/sYRpGyzNlIiCCCft0Nqdx2+ElOYrFNqzjB+boVqq4euIuchn6t4xWi/rYMsvXk7Nibi+YzN9qVYADCHZlOtia76bA8VxLCGpaZ903ee1Dx1kcHKJsb5W7r71Uuppp8nWhxPVWW7t2sbdk/vZuzhK2rQxhcHp2jyPzh/jJze9jO5UYXn8fjxPJTyCa/StnvBkijAusuQ9TXfmNSv3qbGHyepnEViYMsVc7QECtYBr9BPrKoZ0AInSUAoO4JodWEYLfjxLyurDNbsI4iJT1S+yqeXnvmm40LUtdm/uxzZMal7AXLGKFIINPa2YhuT6nclkLATYloEfJkSElf1qoliRcizC52G8vRD4xr6TtOTcVSE+KSXt+TQPPXuSa7YPXpTw6brBf4Fw5eZ+vvzEIbLn6OCU6x49bTlaMm6S1PMCvCDEtkyyrs3uTf0cGZtlcqFMNqXJp1P0tGZJuzZtdlJ1e/22oeUCrHe87AoePzLKQqVGIZ1KknhhhDAEbnuGHltCDIYlCYMIyzLxvPC88VbLDbQG3wuIwpgojDl2YBxN4uEW2rLYrsWJQ5N87iMP8eZ33cxXP/UEhdbM8oohk3MJg4j7vvgMm7b3UWj798cja0HA3z6yh1oQ0JPPIZvVsV84cBj41gpJFmoNQqWwTQMZJ4JqyZJfUvY8GmEGLwxRGvJOUufQ15nDTttk0g6n55bY2NZKWA8IvDAJgwURvRsvHHrQWvPVjzyAlJI4ilmYWgJASEGlWKVaqmM5JpsuG6RabjB2dBLTNugZ7qJgKOaXysw9MUPmkyd532+/nZ0t/ee98Jtz3bS7WRa8MrufG+MvfvdfEFqT9kLqrsWvfvDr/MLvv4Nndg5Si3y6UwWmG0XmvTIgGKnOEWlFq52mxcrwxfGn+ektr1g+ThiXEKydoDdkCi9aqVEI4xJT1S/gGJ1IkeSEIl1D4hKoObROJioAIWyUWkDQjRQ2kVqJtVuygBeNE6oittH6Te/tbddcwumZJUxTsqG3FaU0xUqDjpYM124fao5VcslQF4dOTRNE8aqFkG2ZDHa1kHkR9am01swuVeleI5GfciymF8qEUXxRpFTWDf4LhN2b+th7YoLJhRJtuTSGIRMuvdLccd2lVBo+s8Uqe09MkHZstBbk0jY9rTlOTC8gpaBU85gtVjk6LsmkbPraCnS3ZhmfLy0b/L72PB949+38xRe/wehcESkEadsi76ZIT9SYma8RxzG2bdI33EFPf/syn7+9K0e90mBmskh5qUYUxZwdMQnDFQ95bnIJyzYptKZ58oEjpDMuURgtG/szsGwTjebk4clvy8t/dmKaUsOjr7CS+3BMk65shjsPHuFb8cvOJHWDKMY2DCwpCeIYpRRKCxaqdUIVk7ZMQqVoSbt05bJIKem6cpAT9x9h7+IRVMkHKZKVjyF51dsvzHZZnC4yN7FEe38rT91zACEFhmmc5WFqQj/i9JFJlmZKy0ZoYXIRpUBojRNJpj57iuptc3iv6cI1rVUJ3WvaN/K1yf3UFuf4i9/9FzKNlXxGujmJ/8Xv/guv+9gvYuXyLAU1BIJIxUghSBk2tjQphQ0W/CqN2GcpqNFi25T9Qyw2HqUaHEcpgWO2IMWK+masPRxjZdVTCY4SqzoN1SBUJQyZToIn4kykKeZMZfCZh0pgoHWIIVccASFEM3X+/HfWCyIeeOY4D+49Qc0LaMunqNcDXNfiVVdv5eptg2RSNvPFKs8cm6QtnyaMFbm0jWWZoCHWGqU0b7plJ5b54lUhCyFozaWSHIO7mgbtBRFp18ZcQx7k28G6wX+BkLItfvK2a3ni6Gn2HB2nHvjsGOzm1p0b6Sxk+duvPApAWy6NF8SkHJNitcGxiXkc2yBWCj+MEECsNLrhc2JqgcVKjXODt5t72/mT972Zsfki5brH7MgC//p/7mZ+rgI64ZqHYcz4yXky2RTd/W0AXHrVMAszZRbmKkgp0TpefRIaVKyBGN9LqnZrFY9GLeDuzzxJrmVlX2dDCoFXf/7k6oVweGaOjH1+LYCUEj86Y0TOR8b3uH3/M2xYmGe0vYM7d11BzXGJlMIxTWwBhhD4UUxL2kFr8KKIjkya4fZWtNYs1OrE21opP2viHC3jmgZxGIOUtPe28MTd+9h27eZVy/AzCLwQIcCv+QgpiGOFVgltUsUarTSGKZk9vUDgJ9s2qhppSFq7ChiGJE6ZzG+I+MCpO+l84An6Ozp4fd8ubujciiEkfalWfnjD9Tz2ic8hLlC5K7Tm9Q8e4oE7bqIcNKiEDQwhyZiJoFzWdEkZNoGIGG8sUQvLLNU/Ty04Qax8/GiWWngCQ6RxzG4y1kZs2YVG0eJevXycenCKcnAQQ6SQwiKIF9FaE9PAEC5SpFA6QGKj8LFkC4oItCZnriT+I1XDli1Y8sLefd0L+K0PfoUT43PYlokUglNTi3S2ZPif738j3W1JEd2Bk1N89v59CClwLIPhnlaOTyxgGgGmadDdkuXV11zC6697/uTzC4GbL9/I5x/cj2tbyyFXrTXzxRqvv2E9afsfEinH4uW7NvPyXZtXfX5qepGJ+XJCL7NNFisNZosV/ChGAI7lMl+qIwVJQFIpAiCXMpkr1YiU4vDYLM+cnCSMY3YMdrFzQw8bupKXZt9MjfnpUpKwkgKtdWJQopiJkTlMO/Eern7ZNj7/kYfJFVJ4dR+/6SWmlM/LKofpC5eYtFp5ML8dI1NAKc3CbIU4ioljxfxUkY7uAt39ravCAEopegbPnwi+FbimmYRgzoFlSAwB4Rox/KtHT/LBj34QoTWZMKBm2fy3r3yB9/3E+3hqwyaU1uRcp0k6ifjZW67nn5/ax/TMPF64wOhSkShW5F2XWr1BbVOWWGmyT8+RMiwyBZfiXJlnHjhEFEV0DXSw84atDF/aj9H0FFu7C0hDMje5SBwp4jgiOmf+jGMFIsIwJVppQKMiRa1UR5qS4usdatelyOYV40vzVKyAI+UpxupLvGP4BgB2tvQzPrm47NGfi7QX0j+1SDXyibQiLe2m8dDMeRViR1Gw0phCEinFbP0RdPQYQVwmVEvEuoHSMVpXEZFJGJdwzV62tv3SMptG6YAlbw8SE1MmxlYKEKZJNTxOjIctc8QqItQVDGmSMbfgq2lcoxtLtiWTg64SxkUG8z+OEBcO1n3qnr0cH5+jqzW7XIRWAKYWKvzO33+FWy7fRGsuxWPPjdLVmsWxTbTWtObTtJbr1L2QDd2tdBqa6x65G+OeT8HWrfAjPwJrVFy/ELhiaz9T82WePDzWJGol7+nuLX1cf+mFiQH/Xqwb/O8BTC9VODG9QBBGFGsNql6A1klhkRRQnJrhLceeYbA4z3hrJ3dvvYIqDn4Y4Vgmn3pwH4YhydgWUkqOjs/x8MERfuo115JPuzzyteewbQuv4RNHarldje1YRFHMc0+e4sqbtpJK2wxt7aJaajB+cg4h4NL6OP9j4tMINCkd0hAW75+7j98f/hEOOP34XpgswJVGRTF7HjjM5dduYmhrN1rDwmyJrr5WhrZ0f1vX5qrBPp4Zn0Kn9apJpOIFay5zM77HBz/6QbLBSjFZJkxWFx/86Ae55dd/D8O2iFWSsOvLZ/nT+x/BEIKWlMNivUG9VsOUBlEcY5QCzBNFSrYg3lHAPlihWqqjY2jUPdI5l0bF58iTJ9l65TB3/PQrMS0TN+2w6+btfPh/fJq0DrjJO05vXGZK5nnA2khDWMuLEzftUCvXl8fr1XyifpPKNVmEr4iLIaZrkTNdPBXwubE9vLpnJ51ujuOlaU725Km71ppGv+5anO5tJdQRpjDoT7cy45VQGmxpUAzqZAyHehzQncqz5N1HWiwhhIMmxhBZDKGJdbKSbHGuRBOTtoZXjhGOgDCxzHbCuIjARAiJEDaObMOUWSyjDYTGNjoo2LvJ2MM4RgeLjccp+k+hdUzKGmKo8G7yzqXP+0zct/c4+bSzSh+oUvdp+AGHR2fZ0NPCk4drjM4scp07hGObTC9WODW5QC7loJTi0onjvO+v/wCtFcL3IJOBX/kVuPNOuOWFVxY1pOSNN13KNTuGODExD1qzsa+d3vb8Ra11WTf43wM4Nb3I7FIVPwqpeeGqIMWuyZP85Zc/iNSadBRQN21++aHP8/N3vI9DQ1twTJPJhTI3bF8pgipkXGaLFe5++ihvu+VyZiaK+F5ANp8wgM6wglSsqFd8ysXE2DiuRSbjMjDcyYmDk1Au8z8mE4N1BimdGJXfHfkkP7bp5/CkjWFKVKwQhiSbdzm8b4zZ6SKua3Hp1Rt5y0/dsmbY41vB1s52rhrq48nTkxRcB9swKPt+kuD2zzdwt+9/5nnDG7cf2Mtnr7mRvDS4vLeTiXLC7GjLpPHCiKV6A4EgjGMqdY/WyToyUNiYeB0utAQ0psogBKZpEPghLZ15dIfm6NOnOPj4EJffsg2AfFuaG/M1fvLQJ5IJk4gGJu/39/Dbqdt4zuwmjhRCCoSU6GhlJePvctCmQFY1Dd8jCiIaVZ9MPsWsV+LJhZO8onsHf338XkZu3ckvf/Bra56zFoK7X3YpCuiwsnQuZagcrxISE/ZqojZNJfLYkGmnO5XHZRRDZolUFRDNZ0pgkEIRkrYHaYQTNMLTy0nVWHugwa3nyH/6MdxTZRrDKWbf1I6R72Ww5cfpzbwBjcIQmeXntOIfoRGNIYWLFpowLlEPR8jZ2xDiwjHruheSS68kWaNYsVCu41gmSoWkHJtcJsI2TQ6OzHDDzg2cnl4iihUT8yWMeo2f+vDvYwdnic3Vas0H6HaYnITs+QnU7zaEEPS05ehp++6tMtYN/osAP4x4+sQ4Tx4dxwsjnjo6jheF+EG8TKFDQMr3+Msvf5BsuOKtpqPE+P7llz/Ia37y9ygpuKY1RxSrVYmn9nyG/SPTvPG6HaTS1io62hmHQcgkJp/JJvUBtmOx85ph9u85hZuyuHrs4IWNJ5qXVQ5zT/sVoCEKY7TWRFGMaRkIBO09Beamijz01f289oeu/ZaM/ly1xsMnRzk4PYtrmVy3YYA3X7adHd2dPDYyRtUPuG5DP/2FAl8/euK8329YmF/26M9FJgwYXlggYyU6OmUvQEqBY5iEccLX1xps00ADqh4ROjK5Bs3rEFoiWc2oGNtxiYKYwAuxXZNCe47Hv7qXpZkiR546xeS+4/w/T30Yl5WJKUVCzf2Dxtd5Z/bteFhUFmvnNQlVmZV7pXXS+GZxpohpGSAE1cjj2aXTlII6OpflF37/HeexdLQQ/MLvv4NGyianLYyHFKdnZjAMDQrsIxK9QbD1td1Ypskbei+jVjc4k1wVZ7keSRXymQdHJA9PE47Rhf3oQTa/606E1hj1mDhtsPUPx3juIyBeY2LI1RLcjWiS0+WPYsoCaSth02gdM1+/HylSdGVeecFnZKi7lZHpRQwpqDaCRJsqVqCNZsGhJu2YGDIpyloq11mqNKg2fGzT4NUn9iIvpFaqFHzyk/De917w+P+RsW7wX2D4YcRH73mKkZlFWrNpDCmYWCg1k7ErHp7W8Lrjz1zwwZRa87rje/n8pTfwyKER2gsZelvzbOlrP6sKVxNGMRu39/HkQ0fxGwGWYyKlRClFoxZSaM/Q3rXCgLn5tbuYHltESkGvv7js0Z+LlA7pCxOqIYKE0aM1Xj2iuz9LriVN31AHSmkO7DnF4KZOLrtm03n7UUozNV2kXveZKFb4woGDSMuguzuPH8V8Yf8h9k/O8J4brmZ3f+/y7w7NzJG1DIqN1fsbbe+gZtlrGv2aZTPa3p4kpJViploj0oooToTTkspNSRSpJEGtNa5l4ZmSWGliPyIqNZKwWJPhMTe+wFP37CeTT9HR38bIwQkqS3XybVmumjkAF+B3CzQvD09xl31J8n8hVkkmm2MRvmiKwTWNqzQE5aUKos1iU7aLvUujdLsFFoMaz+4c5LUf+0Ve++BBBqeWGOtt5e6XXYqfspGAe8pCzGiiFuhJFWjEAaWgTnwyIj1m8qOvvoUtuS72hRupBccxpEOoAK0RaLQOccwelA4RiFUhHbuWYsuPfwWjtlJnYtSThMXOdz/DyMFjcI6CwmL9EcDAPGsiEMLAMXtYaDxIR/rmZXrnubjjph38/ofuRkqBa1tJwj0IqfshrdkUew6NI6UgimPCKKm/qPsBUgiiWDFUmcc9y4la/ZDU4Pjxtb/7PsC6wX+Bse/UFKMzS/S3J2XujSDEdSyCcOVlOfPaDxbnlz36c5GOAgZL80BCNyzXPQwpKTc8LtvQw1ypShQpphbLbLlsgB1XbODUkUQLRzSpcp29BTZe0k3bWQY/nXV4x8++iqe+cYRJq5WGsNY0+g1hMWm1JsbvLJSLNQIvwPdCsgWXrt5WCq0Znnro6HkGf26+wue/8DTTsyXGxhYYq5aRlqStkKHWWmHz1X0MFAqcWljimfGpVTLF3dkMlnm+wuGdu67gv33lC2teMy0Ed+26iiCK8KMYP45pz6TRKqlVUEC2yQhSWuMaEiOMMNImcrpGdqyGXghQsUpWM0FIz4ZOnLSN74U8fe8BOgc6cFI2c+MLbK/N415owiSi9yz+eUJHXKmpdw74eAsKVZCYgUAKibYEpdjj8mwvl7cO8uzSafrSrYzU5qlHPo2UzRded8XKPgFXWsRKYY9J4lyTIKkVGdPBEgbDwx0MjBXY2mye0pN5E+Pq4/jREhKLSFcRWBgii2v240XT9GbvwJJnhR0+9YkLsyg1uJ+9F/2Lvwpo6uEI1fAE842H1mTiSGGjdEAYl3DM80UFAfxQsaW/ndMzRTw/6QSnFBhS4FoG2ZRNrNSy51/3E3abF0XkUw7V/iE828EN1jD6mQxs2XKBk/mPj3WD/wLjqePj5DMrPVbPNDVZCFbH7gHGWjqom/aaRr9u2owVVppTJx1/Qrwg5IH9JzClwXB3K/9079P0tOToGGqjvTtPHCkCPyJXcKnXAnZdu/G8gijLNhk5Ns3p3HbeP3ffmuehETyYW4POpsFrhIyfmmNuqkh7d4HLrt2If0683fNC/uVTjydFSfNVIq3RaQNUUmxlVg2OPTbOpa8YpuC67Dk9zvXDg8RKcWR2nifHJtZs6lFzXN73E+87j6WjheB9P/E+yrbNGbqMHyvmqnUytoUQCWumEQYYQqDQtOYz+AtLxCUf+3QVu5RUamqlknvV5NOLJtk89CPKixUOPHIEISQjQYprhLWm0W9gMnXGaDYjJCLZKVIKjABaPlGh8tYccaeByJnEKDr9NP9l82s5tHecpSMljrtTbG/p5lkjpBw3oALWCBgLApkVhBtiZF6QXbKwu20Woxqhimm1M2wodJA3U5SWasvj6szcihePU/L248dL1MNTRKqU3Fgd05t5E+2pm1edizh+Ellfe2Iz6hHmiXmU9jld/idq4QkEBvVwHKWPkbW3krG2nKXvn2h+Pp84296jY+za3Edvewunpubx/JDZYhXbMmk0Q4tSSgwpacmleM8d1/HRO/cwX65R8wIe2nYNP/JvH1t751ImbJ3vU6wb/BcYYaQwzuLUWobBQEeBYrV+3rZ3bbmCX/3G2t6qEoK7tly5/P9GEFH3Q0wpcWyLq7b1M9SZAmEwtVhj09U9mMcrTI8vYjsmXiNk9w2befkdV6y5f78egXT4nf63ncfS0Qh+p/9tePL5tfJVrCkv1jjwxCle8abVxzl2fIZ63cd1LKo1HzdtIT2BgaBc8fD8CBFqrAMOvVvbCWNFrBSffHo/z0xMkrZsOjJpTiw0w0pnhUOe2rCJW37997j9wF6GFuY53d7BnTuvoO6cr2UUKkUtCGlxXWLtE2sYbi1wWW8XXhRz9FSFaP8SWSTCtomNRB5aGhIhBQtTJVo6FYZp4GYcqqU6nQNtSCE5nL4GRu5Oao7OgUbwgLURSCZYSGoLVJywqFSsMUdCOj9UJXdjJ/3X9RNNerz5lTdzz4ee4ciBCaqex9JlFeZbywy0FJiONOFDAYQaHYGINOYRSG12aHgB9dGAjGGT8yx86VPur6Gyip6BluVxSWEzmHsX6I8zUflXIMIxOhHCpBaNMl79FLbZTt45SzRvy1bitLEcxjkbUVrS2GAxW/8aZf8AsfIJ1DxKB8Tao+qfwJIty968H89ScHZiyrWTpmdaGZ6aWqRcT2LycdPAe0GIUpoTEwtYVvJe9XTkCcKY1163jfufOUHatVgqN/jwf/493vPXvw9a45xh6UiZsHRehITtC4V1g/8C49KhLh48cIq0s2IsN/e2s/fExDlbauq2w8/f8dP85Zf/fpml07AsFJKfv+N9NOyEqZBN2QghKNeT1oWXD/vcfNlXSNnTxNqiq3Uzh09fzi+8900sLpUo1WsMdHbQ1rK2NG0prKELGqpwINXPOzf9LC+vHKYvLCY8/Nz2NY29JvFUNSJpRtLMIVQqHuUw4FOffoLOzhxXXbGB6ekilmnQ8BLRL0tIUFCpexCDcAAB4yMLzMQN3nPbtTw3NcvT45MMFPKEfkwGk2bXE2QAVlUT5AXagrpl8+mrrj9/hE0v+uwkaagU+ZTLlq52xoolLuvtwjZMIj+m7VQdp7OVVMohCiNMy2Dm9AJCCizbJJ1LsevmbThph7v/+SGMs3oF+KbD31/1bt6z58OrWDoawW+nbsMTSUjKMA0MyyBo+JiWgZO2qZU9nLTNlh0b6M61ow5oLrn6UkrFkD0PH8OyDGzTZOhYC/MdNWbalkidNDEik9CMiQ2NEGDMC+KJgMZ2A56LUSpCOUn4cH62RCpt89of+KFVV8mPpyj6TyOEIGUOLjNmlA5ohBOcLn+cofw70Wgy1ibE29+C/uWfXfuBF4LJOzJka/dTD0cBiSFdhDCJVYVAz1PyD9AiLifWDRyji57MHWvvi6ZUtGUwX6zR0ZKsTP3ATGjMscK1Lfo6CmgSFtdiuY5pSG66fCOnZ4qcnEwKGacu3cUf/fHHuO3EM1xnBxiXXJJ49i+ysVdKs1ipg9a05TMXreDqDNYN/guMa7YOsufYOPPlGm25dFMX3zvL/igsI6kgDWOTZ/o2cft7/zs/sXAfV+pTFHvTHL65Hz2hESc1hhDYpkkQRbiWwVDXOG+8/gFy6RilE4NiG0tsGzrJZ08ZnIwT3Xd3wuaW4DJu6rh0ubn1gl/mq1N7GK3NErxJIUKQ+6FxyOKuwuXLIxQInJSJ34iQhkgqRw2BMiXKMtGuAZFGhxFSaPysxT2PniC9fwKlFJ9tyXDDDZtXmEXNkzcrMQqN0MmDL5RGZAzCRki2YfD46Dh2JDi8Z5xq0aPkeeTLitDWhBmB4WtMXyDKmigNygZU07gbJNVAcNZqQCz/NVWucHlfD6/auon2TJqK79NrujzWM8axiVPMLS7iN3yiIG4Ktmks26K9t4VsS4bQDzFNA9M0CL0Iy0lercNuH+/t+nGurx6lNy4xaeR5QA4vG3tIxO20BJmyMQ1J36Zu3v7Lb6S1K8/IoQkMQ3LJ1Zvo39LNb7z/I4R+RK2S9OiNY406qUhFEPg+lmVgaoM4UmgDZEqgQo04qKAhiImJjEQiI1Zg+BEjJ2a4dPdKfqToP9ukZcpV9EgpbMJ4ibna11lofANDOEjh0Jd5C+WPXMPOdz8FWmHUFVFKgoTnPnwlcVpQC0YQ0sQQSahGSANXDBLEswDk7B1krUvIOTuaImsXRhgpbNtMNKfMCNMICaMAwzAwDEnKMRFCEEYx0wsV2gsZXNviukuHODm1wMRcInq3oaeVjl/5BYzBb6+h/MXGiYl5PvfgPkYmF9Ek43vzLbvYvuHijW/d4L/AKGRc3vvaXXzj4BdQ+iFMw8cwhtjUs4mDYzUKmSqWqQCN1oJiNcPA0Dx3DV3J1MY8KSfCMiJu3XUAQ8bsO3EJXhgipSTtaN72svswzJAoboOmJollFbGcElP+VzjlbUk8cSGYqM9Tjzxe33ct1ajBP4/cw5xXYqaxhCwJlNTE1wFSYxwSq0ITTspOkmVagZSoM9WiVjMjbBtEaYvYlNhS0NKSJptLJZr0VY977jvIxuFOOtqySCmYninhFQNMFJHQ1CsR2pZkaxZXZ3s5cWiakVSNo3vGcW2TdM6hISLEgsbxQBsa1ZRgEQqMCLQpEBqkgkiQKK2dMfaaZtA8Ga8fRrRnUrx1986kChcIg4gH63fj1XwaVQ+lFIZlIJXCb4RoHVKvNKhXG1QWquRbswxt72NuYpF6pYEG8m1ZrF2b+OqzgtAVaFMiwhjRiJERYEnqhsLpzmL359CNiA0/cQ1XvX4Xrmmx47qVBKLvhUyNLxJFMXazJV+jnugxqTiZiIRINI9IFDCwlzxeUXyOXn+RKbeNB1p2AjZogWVLbNvkwbsOcPtbr1k+TqxqyYU8hysa6zpePIUQFo7oxjLyxMrjdPmfMK7t4uHHr6TjSzOkRj0aG1xm7sgTZwJSwiTWNWxWV1sLIZAyjWXkGMi//Vt6f3Sz29fuzQ4nxkeo1BMqcE9bgFYO5UaBSCXMqzBS9HfmmS9W8fyQT97zDC25FP2diSJote7zz197mp++4wYGulq+peN/tzA+W+QPP3w3ozPF5Wf00Ogs+09O83s/9Vo2D6ydwP73Yt3gfweI4iWCaBQhDGxzI8YF4o5nI1Y1Iv03XLbxYaI4UaTc0H2STf138UeffDNRJIniMxxsTX/nPI4V0ggcdgxNkHICtBYIFG++8XEavsNgV5L4zbgpCpk6DT+Pm/URKJQ2KXsmTqpER3yKcqVvmQJYjRp8ceJRbu68jOdKI8z5JSYaC2itUC0aUQJdAXUlyDGNCAU0QAlNrdEAW6AiiI2mIJaQIJKQhrCMxADLJNadarb6E0KQz6WYm6/Q1ZGjWGqglKJS8ZrUzrMUMH1FdKTKgfHTTHYuUc1pFos1bMci0+wLDKAk2CUQMcShJsgDkUAokBpoJkTPpj2iWaFDCTB9zU6zlexZobZE8kBhuTa6VE+um0rkrA1Dkm/PooHKYo0b33gV17/+Ch77yl523bKNKEwEyoQUHNx3iuCSHFElwKjFCJWskoIeC38oixlr+oa6cdMOjZkqp0SdTx48wLsvX8nRQDIBhUESVpJSNCcdMC2Dup+EaURT811pzWXV0/zBiY8j9FlV0uN384HLf5Lj7ZsRQlCteMBqmeeMtQkpHM6l3njRLBqNibvshRvSxaYVP54nTPtM/EgBSXvzEms0CqUDbKONSFUQWCDAEIlUstYRKaPvm743ZyCEYKhbMz59mMs3OUSxRbEWUW8EKF1jtmShVAuFTIrBrhYafqKCeu/Tx8mkrOU2iADZdKKMev/e47zrddc8z1G/+/jQvz3OiYkF0q61XEEexYrT04v87Rce5Y/+85svynHWDf63Aa0VpfqXqXj3L38mhKSQ/gGyzi3PWwpd8R6g6n0DtMY220k0M6pk3BPsGj7FE4e3Yxg+ArCMiLZclUotxbaBMbIpDykSg+OFJh2FMr/2w58milsBGyk9lApJux6WESGERmuJMB1qGhwdNUXRNIY0iFXMRH2ek9VJjlcmWfTLVMI6jThAXQpiFAhA2xBv18hpIALdDuaSRTafQpiCpckGvtQo00CHEm0kTRR1syOXm7ZRSnN23ZUhk/68b/+ha/jzv7qHYdvg0OHp866XihXFYgPTMUiZKUzbQJqCWt0njJqerAFmDWQIZj3x2sNsUw1RAgrchqSR1UloZ+WuJcYH6PVd7n3gMJcMdbF1UyIDUSvWSRcytHfnqSxWEFISNxVD27sL5NqyDO8c5OpX7eTmN19NHCt8L+TZBw82q5kTA+XeNEB9Psav+ZjTSSjGnvJAaLy8gWp1GMsp2merdG9pZ2i4m/1z08zWqnRlVpyIcqlBW2eWxbkahpnwy4UgEXRrIgqTcGAq9vmDEx8nrc6pktbwm/s+zLuv+q8ElntewRdA3tlJyhzEi6aJVLVZHasJVQmJhSEzCM6aGGUKFXpIUk1nImGcGcLCNtpR2kdrQRiXUKxQIU2ZJW1tpD196wXfl7Vw4/ZRPjoqsY0Krl2mkI7xfYtaI8ebbxhnybsRkCilaQQhPW1ZxmaX1qxgbcmlODGxcI5O/gsLrTWPHBjBsYxVciGmIXFsk73HJvCDEMf+zhutXxSDL4T4R+AOYFZrfdka378C+AJwqvnRZ7XW/+NiHPvFQM1/mLL3dWxjYFVCq1j7NJbswLUv3Pav0vg6SoWYRgtn3jbLjDANePVVz3BsYoBiLYMhYmwrpFJPkU55vGzXAToLJYRY8VKl0M2q2RphlCaKJGm3jpQmaDshDAqFkDVMJXi00kI1XKlUsmTSTWmqvohrWEw2FpufG1AAvROIgArog4mhxwSdguxb0txy7S5UVbPnMxNMLhWJdAAjFqK5QpEiyanGzVCDUopaPSAMIqp1n9aWNLGCzs4sTz45sub1OlO3VFysEwGGjonyICyJ5yVerVkDGZHEwWNIzWuckiZKgZ8XaFOgtcKqQZimaYmTy29rQZuy6YwcsmmHh/ecYOumbrTWFKsepTAmP9xFa7GObZkIkYSzVKwxrKSAzWmuXgxDctuP3sQ1t13G5MlZDEMysLWHDzz9MOrxSapZkF0OUgvql8ToWkDQ7aKBhhSMb3DZ3J1iiGQFMFOrrTL4pmUwONxJFCoq5Qa+F6CaxVE0T+tM0djLi889b5X0LXP7uaf3GuJILa++zsCQKTa1vp/RYpoF7yGCeJY41kQNh8jLkWrrQpgrxvEMldI00jhGB2GzvsCSeaRwaIRjxKqGZbYAxvLKKtY1JIKC2UIYPINhDCCNDr4Z+tqO8OYbp/j6052Uii4gMI0ar71qLwOdLuXTPnXPYq5Y4/pLh+hoyWIaBrHSmMZqox7FCscyXtTezGe6dKWc882xKSXVRoAXRN87Bh/4MPCXwEefZ5uHtNYXTr//B4HWinLj61iy67yElhQ5yt49z2vwlWqqVq5yrQxcC3rbKrzt1kd4YP9lLJSzGDJmsHOO1129l86W8irZhbN/LWWAY88gkCROdQxCIZGARAswRMyxWhtRU/JYkLAapBCkTYfOVIFARbjSSh7+M4lOA7Ah/iFQJZ2EvFshSEUcqpxmd8smAiskaiiwQVpJ3Fw1/wD4fkgcK0bHl/CDCK01bujR+Mu/wew32bQgedIc5ps9jm0taeaWasjFCDvrENUEosGqPq5nzk2GYIbgljXKTDx9G0FsCSqtijiVjLNLWGwwswih6ekuMDVbwvdDvvS1fRw5McOcbVAt1qi1ZujWkDeSFVKjVmfDcD8ozZbLE2kAP44YLRaJhGLwykFytsNIcYn98zNUCwnrR0RJq0ltSlR3Jsk5aFBWkrg9srSAa1sMt7SSMldfj46uHH2D7aTSNkcOTBB4EUEQgRRIIRKFyqYUc9/zVUmrkG5vITF2jsVaRJDYzyIX3k4+eBUHDz3D1OkKpfISpeoYqZTLDW+q0DmY7D9URVJmP4GapR5OLBvPSFWxRIFYe2Tt7ThGJ7XwOSJdRwiLFiNLjqP4tb8jQINIYzu34qbfhhAXNm5KLbG1b5otvQHz5UTauj3v0/CrlOsVpuY9HFvw+uu3ccNlwxhScuXWfp48PEZP+2ovf6FY45bLz68AfyEhpSSXdql5PmlntYZQEMWkXYuMe3EatFwUg6+1flAIMXwx9vW9DqUbxKqKbZ4fdzRkjiAaf97fO9YlNMJjqyILSbw0xjRirthygt2bTxBEiTdpm4kXK6uKli81sEcigmGT4ptSqKxECEgkdDRwZvmugRDdTLwJLakohzarxlhwduJMg5a0OlnShkubnWPOKxLqiFQ94FX3H6Z/oshEXwv3vmI7jQ57eaaphh4nKlO4kYPsiYmPK4gFsRmDJ5d1V6RMvM5Tp+aJYoUQcHlplD/c8w8IrXEjnx7T4c0afuPKn+JA6/Ca102IRL8+CELqQQRNkbkzturM36nQ49WTz9JfXWAi2849fbtp4JJEdjRGqMnXAZmwgYQZEvVE7Njai441uYzL3Q8c5OjJWXo687RkHA48chSVDpmoekTFOkasE69YCG684yrae1vZOz3Jpw8/hx8nktZCCK7vG+Cp6UnytkOMRjR1XnSkUHYzvmUKtBBEJOJuGs3emSk2FVrYUGhZPn8/jpiuVrn8dVt54BN7iZVmYGMHk6OJyqptJ/pAUaAwDYP5XBeNeYuUWqPoS1pMOW2YTfmNpYUqd39xL696w+UYpmTPw8d4+J5DKKU5fWqWSqnB9l0DDPZvxq4tUqmUeOhzKW77iQp2roQhXPqyb+VU6W+b+jsGohmf9/UMQru4RhrifWQI0VJiUCdLkUAFhNFpDJE0h1HxFEJkcdNvuvBLdObZMjRdLaXm82EhhSabyvBLP3IzaTfflBdJ8LIrNnFicp6p+TK55oqmXPPpbs1y42VrP3MvJF573SV85v59eEG4rIkVx0ny+dVXb8U0vz3xwXPxQsbwbxRCPAtMAr+mtX7uBTz2RYMUDlJYSTOHc7Q+lG5gyufXfW/NvJ1y4y5iVUaKXEIfi2dIXOmgGfcF116RWkg/4TP87gXQGqMOcRp6/6DEyEfaqV93ZuaXrK7wOfPvJJhsoAj1WgqEGi8K6XJb6U+1M9NY5NL9Y/yv3/wsUkPKC2m4Fj/3N/fz/3zgrezfNQBAoENELDi0eJoYA27xEiqMB0xY6KMOIpSYplxOLKZSFnkZ8Ydf/wdS4YpSoRslcd3/tfdDvO1lv4lnrvZmYkCHiuOn5ogky9IQlpk0bT+DXQun+OPH/jEREIsD6obNzx/4Mv/1hvewvz0pckIIzGaoSZCEmQr5FIVcioWlGrdev4WHHj9OSyGNBlIZl9237mBmbJ6Tx6dxvZANrRk27xrimtt2MbStj1PFJf7pwLN0pNN0NOUe6kHAvxzch2skWuxpy6ISKLQtSMc+t+95luHZeUa6OvjyVbupue7yBBZrTdEPMJv5lkfGT3PniWOEcZSsZHaZOGMuLglnvyWdJY5iVKxRSiGE4FjfreiRr6z5DGoED7ZehmFJMlmXjq48zzxxEtsx6ewpcN9X99PVXSCKYgI/It+a5uSRGRzXorv9taTNI0xMjDJ+uIUbX34d/dm3MVn7NG3utVSCw0RNlo8UJobIYEqbKDyAJQsII9HOsdUIsVoCkcaQ+WYrRY1SRbz6x3FSr0GI8wvlAKTRSRxPEUdHm+GsiMTZcREyRAd/hDLfhWGvyCzn0i4/fccN7Ds+ycGRYxhSccvl29i9eQDX+c5DJd8p3n37dRwameHExELzmdYYUrJloJP/9IM3f9Pff6t4oQz+08AGrXVVCHE78HngvH53Qoj3A+8HGBq6OF3aLzaEMMk6t1L2voZlDKwqCY/UIu2Z25/39461kd7W32O6+P8Sq5nEq9FlBDaaxnnby6pi+N0LGLWVoIVRB9AMv3uBw0/2oDJnjP25wY0mdEyobcYahfO+itEUgyrXOJdQizzyfsz//s3PkW6cpfDY1Fn/X7/5Wd72qZ+hkbKJUAgEZVED14SqhDkDxkyIBXRGMGURhglnWkrBhqEObth/H/ICXaqk1rxqZh939l+7MnQSAx8bEKNALYsZrHp6U6HHHz/2j2Sis5RF42TF88eP/SNved1v4VlOsiIyDYIwoikLxrGTs8zOVejraeGpfaMcPDqJ61pYpsGG/jZ6ugsMbu2lpa+VzrYc73zrdavGff/oKVzTwjUtakHA4YU5FhoNFup1AhXTnkoxmC9wulRk17Fj/PWf/20i+xAE1Gyb3/7MF/mpn/tp9mzeuGz0x8pFpmtVJitlPn34ObozWRwzMZYlVWfM9NmxY1NTuVNjNZvYePWAXCGFZZv8n2t+ml/b88Gk6EuFeNJGCfidLT+Gbzp0t2awHYuBDR20d+V4+rETpDMuLa0ZTMugVvUQTRlo29GMjy7Q1jFMwd2F6NhM3mvjkvYbUTrALy+QsTfimJ140RSRqmKKLI7ZS9X7BqGKMaWxvBIzVIW42f9KnLVOkzJPHE+g4nkMc2DN58SQA4T6GxiyhzheImEZZQCNkC0IbdOofRApfwnD3JA8R1rjmHPs2vAlLu0/2TzWExjiB4DL1zzOC4l82uVPfuEH+PqTx/jGvpNoBTftGubV12wlm1574vt28IIYfK11+ax/3ymE+CshRIfWev6c7f4O+DuAa6655gL6pS8+culXE8RjeOHhJNaoNZqIrHMjaefqb/5792WkOq+g6j1EI3iSireHOF4k0qXzti18qbFKNmAVtKbwpQZL78jwfD1AJeDFBo4RU4nO//5LE48ipcSWJi+77/AFk31SwyvvP8Kdb9iVHJ4m6yWlEidrXkAgwdHgaHRWQcXAts1lLZ2OpWmcCygVplRAX33lkdAkiVh0wq3XBsvazoYpk45RTbx68tnn1cF/1cSz/NuG61CxJlDJRVA6CeE4jsXObb2cGJ1neqaEaRpkUjZxrDl6apZYawZ6WvH9kNaW9Hn7HyktkXccvCjkiclxFJqcbRMpxUytwlKjgdKa9ijmr//8b8n6ZzVnCZJJ6UN/9fdc94Hfoe44SJKGGLO1Gl89eZz2dBrnrHh+oZAmt6HAoZFpNg21cezQFF4DyqU6vheRWqiigb7LruLXOv+Q3Scep6Myx5Tbzv2FHXiGg20aRJFiaFMLnT0FpEz6I8zPlNiwOSn0Ma0kzKK1xnLMJoUzQeBFtDSbbifiahli5WEgcGhg6TnQc8RRGUdosvYwpXA2Me4CDCIcIXGlSeKsJOendbOYQq/xoJ55LnQdtImiQmLsz3j5yaQnjCzEHn7j65j2TgLvHuJ4jDg6iTQGMMwdSCFRqkqj9vfA+7DsXRc83gsF17G54+ad3HHzzu/aMV4Qgy+E6AFmtNZaCHEdiQ1aeCGO/d2AFC4duffhRyfxgkMIYZKyd2IZK01IvhlMI09L5o2k7B3U/Kfw9dKa29kjUdOjPx9GPfl+bayMoxRbFCyfTqvMfHS+nEKgYvYsHqUY1rh5YmnZoz8XKS+kf6K4xkA05DW0aWg0ja4C0gqjbmKZkiAQVKoN5lt78C1nTaPfkDaT6bME4c66lAmPnmV2zdlJYYD+6sKyR38u0nFAfzWZSM785Mzc0NaSSRq4qKSgx01ZRGGM50ekXItMyuH0+CIdrVnCSLH70vO9zhbXpewHzNaqREqRc5KQVNpKmo7nHZeJaoUfeeSx552U7njqGf71pusxpKRgJ6uRotegL3f+Pdt44yDHv3YSpTR+I6C4WEtkHRzJUqmG4RjUCYlTaQ5f/3rqNZ9auUHesXDDCIFgy/ZeNm7tXq7L0BrclE0QJDmBdMYhm0/RqCWSD3ZT8ycMI+JYsbNZnSuEoCN9C1OVz2FEJ9HUQEdoDUG8QJth0Wa2kLcGqEULaGKsuI6lJpo3ZCU+rXUNIdsRF2DrJNXFp5OOXKpMMlkkK1whrGQyQCFkC75/N1H0LFK0o1UAOkLFpxEYSGs7Umab1++LmNbO522r+P2Ci0XL/ATwCqBDCDEO/C5gAWit/wZ4G/CzQogIaADv0PpCbut/DAhh4Fpbca3zIlP/LljGELFOaGxxCCqGeskkCgS2qygMmcRp1jT6cRqC4TO3cCWcozXEoYlWIA2ItIUkpM1qsEbUCCmg221hprHISG+ehmutafQbrsVEf8s5PyZ5ikoaBiJYMMCXSX8MJZBSUql6bN3STbXq86XsNt58gTuvhODe7mR5fUbyZhWWC6WSwqKzMZFtp27Yaxr9umEzkT1bWbTJ5DEE80tVLtvWR7HcwLKS0vxASmzLoFpLtIlqjYDJmSJves1u+npaiJXixNIiU7UKadPi6p5+PnvkIDO16ipPPIhjtrS2MVWrorVmYGZ22aM/F5kgYHhuHkFCxRtubWVLSxtSJE08zk5AAihbsPX2zdwa9zAzWaR/uJ3RapFqxcMyHcyMyfjUEpZp0OuY5PIJR76zp8Do6BxaaowOm1ApbMNgca7Kxq3d9PS18OgDR+jua0EIwSWX9vHcM6dZWqwyONTB7FQRENz2xt109bYsj6c9dRPF6icpx5NnUqpoIGMY5KQgig5i29cv88y13EzozyCEida1ZHYXSf7Bdd+IvEARo6hWMT/yJPaJadTmNhp3ZNBZN0kSE0BTEkLHC+h4DmldgxASreZA5BDCII5HMMxBhMggRBalJtC6jBAtax7z+wkXi6Xzo9/k+78koW2u4xwoXcExNzJ9cgFpW9QWDYK6AQiE0Pi7BP3Ly9ZzIATFN6YI/aS61TBjEBBH0Cg3ox8CTCtCO5JS7J6zn+TVzJiJvkm7U+DRV+/iZ/763rXHKuC+V2w7/4uMhqqRxF2yCoImFbRmkkhEJJtt3NTFyZOa37n+p/kfj/89hgAn9Alsl1hp/vvVP3VewnYZZ8I5irMdwmXc07ebnz/w5TV/qoXg3v7dqz8D4ljT8AIaXsKMSKSPNY5lcuVlg8wWK8w1alh1i3e99Xq2b+2l5Hv84zNPMV4pL2vYG0LQm8lyZGGOWGlC0yBWirzjcmV3L/eNnqRsGMz39VF3HNL++aubmm0z2tmRFNxJgx/evpO0bXNVTy8Pjo2Qti3ylkvWdlhs1DhdKfHOHbupH6vTsrlAxYygaNOatiiPVZCGQFgSv9dgslilK0jheQHHF+fxrAhpSfaPTXFwaoZN6RY29HbwmjddQSptMzm+xMjxWRw3ESXr7m9l+65+tmzvI1dIccml/bS2rzbIAoN2ZkhbWTxtAYK04eAIG61LqHgG3/sqIrmRaB0hZDumNYTWAWiFkCkM8xLczAXkib/xDbj9dtJxA1GP0OkJUr8D5Y8NE93QBkohpI1WEVE8ijS6E2OvfTQRZ7wTrUPiaALD2nLW+F/8xO0LgfVK2+8BRH6Ou/5skNf/6kFCTyKbNEsVQ7Hh8uwf9bD7N6ZRIViBJnQEwoAnf6+P0riLNARaC5xsRKErSNiWSqO0wDA1VhxRbbgcrXad4zUnMZKU0SwcQpBu7eCP/89P8Wu/9o+rWDpKwP/zgbfSSK0hiSyBQnNprQVYGuYNVF2jUBiGZHR0gcGBdtpasyxeeiXv7/5Drj7+BP31BaZzndzfs5sqJo4hCYI4ccNVM6zT/COb7BytNEixiqXTsFz+6w3vOY+lo4Xgv97wHhqm02wXuHroQkimZ0sM9LURhjEhMZs2dHBCLnKsbQE/jJES7vFP0tbI8YXDh5muVRnIryTAvShirlHnR3ZcxpePHaWQculKZ7Ck5OnpSaaqSUz9K9dexS9/8jNrPgNaCL589RUYUmIIwUcPPMu9oyfYX5rhqdkJYtUUKmheF8c0OfzELG5DkMrBkvJJtUlSviKKA0pdkmqXQNgasVEwV/PJHQ3pv7qX3Vf0EfuK0qkSjYbPUrvJL/zAjRQKifrkD73rRkZPznHs0CRSCrZe2s/gcMfztqjUWqH1HCmjjbQwz/lOAFVM683AEhAhRAdae9j2FUijA60bGOY2LHsnQqwx6VcqSb/ZSmUlxVtPKLD5Hz/FwtMGZNIgbFR8GmkMAVXCcB8qnkKpClotkXgOikiHxPE40ujEsm9AyMz5x/w+xLrBf5FhyBaOPyqZGxEsTdk4aUUUiCZlUIASzG1N85W/3Yz6G0V71eNULY//Lou2oQBRAdtNDHejbJHKR6A1cZTsI9KCqGFw1yf6sV4ekO/0qCkblCDQBgqDcjjJaHWSpVCRN13ULTfwtk/ZvPIMD7+/hftecQleysIRPhqJRBFjEOqEebFsSD0Q4yb4KxRQrRVSSo4dnyEIIyoVj1iZfHXwekwjkRmu1/0khuyYZLMO5VJSUeVrBRIsy0RI8INEHmJ4sD3pVVpaiVHtb9/IW173W7xq4ln6q/NMZDu4t383DdNBykQH5wzOVAGjNfVGyOT0EvlsiiCKmcnXGY1L2IHExeCybX1M1cv8ybMP0KhoNuRbV91D1zSRQlBwXV61cRPHFpP01J6pCQIVk7dtpBBUjICf/y8/y5//f3+1iqWjheB9P/c+Go6D1Jr2dIacZfPhI3tpxAEtjpvIMtcraCBt2nSm08z7dSpEFCKJEWmK+Cz1gJm2qaVilAHK0YhIU8yEFG8QbNlVwMxZzMsqY0MhodIYIuSRudO8pZAkCw3TYNMlPWy6JOmCNVWr8NkTz3G8tEDOcri5bwOXd/SsCjMlQmgFlPIQYrX3r+IFhEhhmt0k6bwzz0VIFB8ll/3xtY382fjkJ5N+s2tAKEH631rxf6wTKdtJ5/4TiAKVpf+E1jFS5pP4PnNAEhtV2kHEPkot4abf+fzH/j7CusF/kSGE4PQzAzQqR9nz6U5e+f4p4ohmWAecTIRhwwMf7aHjG3XqosqsSHHkE63c/j8ncLIRkSdRMViO5vQzGZ79ShuXv75IpiVi/nSGhz/VycjhPBsunSe7QVCPLGaDPBl8vNiiFi9AmCdlCLpSVUbrdeI03Hv7JYTaJG4S59rMGpZU1GKbUJlYIsIQCq0FUd0mftbBGLFRYVLxa5rJZBA09Wdcx2JhoUIUK+JYYRiJbr5lJqXtSdcpTS4nUbGBaUpqDYXpmhi2gR8k1L7BgVYM26RYOT8h0TAd/m3DCm3SNCRSJXIDywlbKxFYU2harQzC09QbAe//8ZfhZCz+5NCDZCOb9o4s/T0tpJurmmfnp1hq+Li2iSklkY4xhEGbnSZlWiw0Grz3iqt5emqSD+17GqU1Gwut2IbBscUF6lHIU1s2ccMHfpc3Pf0Mg7NznO7s4KvXXEXFsTGFQKmk9ParY0eoRj6mkERKUQmDRIRUGvhxyLxXxzVN3JRJKaiTnxUIqVFpyVKvRguBDAENyhU0UoJYap6dn2LRrzPn1XGNZKJa9Dw+fuRZru7qZyjfsup6Hl2a44MHnsQQgrztMN+o8ZFDT3Nd9wDv2LZ7ueuYECamfR1+416U8psiaSZaKyDAMDYDISpeQqOQooCQaVBxkqj9Zgb/2LGk3+xa71A9xhwVKPMS0rn/gmFuIAyPg3ARuorWEUoVObOiTR4CjZAGQriE3j047m3rSdt1vDAIq63Mj7rMj9i09gVsu7VEvjNJmtaWTBb+weS37n4s4VPrmIY20IcF/99/u4Itv10j2x5Smbc58ViePZ/poL7k8Ng/NyuBmxIJwlJUvhTTsSMm1+qTtz3m/CyIJBy0OVvgknyGkn8CX80y7QlibSAI8OIkFi9kIujWImMasUk9dpBCkZYRxeMZ5GkLaTYjpUJiSCMJQTTFvSzLII4TkaqkB2kiopZ4h4ngWltLhNY1TENQrQpaC5qbb9pEKt3PI0+cACm4+sqNWJbB3gOnWSxegMLESmFVEgFKzL3XpfCGFFoKTClYzIW0V1xyYwZX79pAUXps9bvoTa8wY5TWHCvNMVpdZKnhMRUUCVRE3nRJmza2NOmxClzX149jmNw4MMQDYyM4hsnJ4lIzwSwwpCSKFTXH5hM3XocUAhPRVOGMCZsCXqerRTzCJPqgFaXAJ1GrgUAl/2pEEWkrUZ40MxZWr4k/WUOFijgHIMFNTJw8c2wVM12rYkiDdje1zCizTYOc7fDJY/v4tatuPUsaQfGJo/vI2w5ZK5n0UqZF3nbYMzPBNd0DXNK6kgy37VcReF9DqwqqWfUtRBYhO9EiReDfz9n0YSn7EEYXQnwL4ZStW5OuVGsYfZ02YctW0rlfwjAT5pCKxzGMYTAUKhoBPU/C721pHrsTKQtoXSMM968nbdfx3YPWmlP7T/PYl59k9vQ848emiP1E9+auPx1k7xc76N9ZQyvB/D6Hvzx1L2lW6JepZhXtLx59hh/58dsJUwIVSLRaw0NpxlsMF4hh+uMGQz8XkbECMuYiAk3amCBUhzhQVNjSpdftYi41T6AkptBYUhHGgoZyWAzSyRwiodsq0ZcqIZXBwVovgSswU4LqfMJ1l012ieOaSfxZJe0BC/kUs3NloljhWiaxUnR35WnUS8SxR2dbSHtbremLpagWD1OuSFpa03T3pdjYvZ/e1kPsHqrzQIfmkf2DzCzmODexbdkGApHowwsIc5r6sMLyBJY0SLkWTmgylarSsTXN/z32MHN+nZPlBRzDpM1JOPdj1SIjlSUypk3VCAjiiJRhUY9DClYKreFYbZbNbSsVkV4YcXB+lhY31ZQwgFbXZclrEKjk/qUMEz+OCVS8bAZdSxKIaJl0FXNGIG/56QEg1DHVMMCPI/w4xrMi1KAkPMugKsA8U7cgJUIpfB1jNidZgFAl67cthTZmalVmGzW600lIZqJaouL79GdX00KFELiGydOzE8sGX+uAMLgXw9yBiidhWRVTI0QbcTSKNLqWNXK01kTRUVzzkm/u3UPSjepXfmXt74wUzrv/CsyVUJIQSd9owxhGGxuI1dQyNRM8tFIoHYLIA4ugwjXJAN9vWDf4TSx4NaYbZRzDZEO2LVGM/C7h6a/v42sfe5BsIU2hI8/85CLCEOhYo5Vk5liGmWOJ1/MGfaqpT3I+BJpXMM5XvY3Ln5wHBcICt0uRGVY0piX105LUBo0hFK6MQYLEJSbGVzVCNUbONFgKU8tHbnHqFGjQatdACNJGjC1DhJAYxGy8NOL4PDjpBk5BUZ5KYeAQxwrL1vR2tLK4VCeXdQmjhOedzdgUCmksy6RR9+gdqpCyK7z5NbNs6NcU8iGNhkep4uLkh5gpX8LM+Efo62jghe0gC+zaOs4lQ0/zia/tYmy60FQIBdOElK0Qhks+69LwQsJNMaZKqnUNSyKlJAgjVA4WCj4K6M8UOF6a4+HpEbYWOmixUxwrzZE2LSqhT8a2CSOFH0XEWjHdqNDl5tjc2sZobYntbUnRkhCJRIJxlrUWQhAphS0NurJZZqrVZQ68oKk7ZMRopTEExGfd9jOk2zMkJYkgLpV5y55n2TAzz2h3B1+8djdharXxVFpjiGT1ZDQ1Kbw4ohwkBtmUkis7+0iZFkJ4yz0GIGn9eKG6ElNKvGjFCYmioyi1iGVtQ1tb0aqaiPyJLIF3H9LoBapovRKGM4wNaEpoHSDEGmSAs5HLJf1mb789ieXXast9aMUafWhNazuJ3v6ZfTvAYvPqCTQ26BLoJaTsA7nO0nlJIIgjPnd6H3sXxpDNUvu0afOOjVeztXDxW5/VKw0e+NQjdA60Lzev7t3YjeVYBPXzOdp9VJY9+nORIqaPGmsa+jOmWoLdoShcrjAMMISGUkzKiDGW5ZXPFLcLnBpc8pURWkY8xvoKPH7bMHFWJtwGAa4R4RphEoYBhIgBi9adZbalKsRxSBwJrIPt1OYy6GKGdF5S6NZs3jjAVVcO8+RTp5ifrzA3X8XzIhqNANPUpOwqt1xbYtcO1RyXSSqdJeXMAP9CrnMQNxjn9HQO21lEGu0g25Byine85gAf/Pw1BJGJUhJIqJGuGZHPZbli1xCP5yawigYBMe2tGQwp8YyIRlrRkklhGwaWlGxr6eL+yRM8Mj1Cl5th1quRMi2Gc61Uw4BcysGLIrwoItKKlw8OU4sCxqvF5asvEfRkshQ9D6eZ1PWiRAsn5zjcOrCBWhQwUS5zrLxAxjQZq5WJVaJgqs+07yJhKp095VsIdh89yYf+LGEkZfyAmmPzW5/8Mu/+pZ/iya0bl7dVgCUEQRxjG5KtLR10pLLkbQfbMGh301hN4+0YJp2plfBKbybXnKTi5TaYy89xFLC9baULk4rnOOMiCyRCrqwKtKpjWMOYZh9KLTYTqS0ImUPFU2hdRYjn16AC4JZbYHIySeAePw5btlywD62ULbipt+I1Po3AgWXaZQwi03zeNZoYYRQQYu3+zt9veMkb/DvHn+PphdP0pQrLCaha5PPh44/xSztfRad7cZsajx+dJI7UsrEPVUzRCoht0M1w9Nnme4IcDYw1jX4Dg0nWGp9e3pF0Na1XxBgpvSwDn8opTLliQs5wo/ufWuQtP/MkQoHdiLkyZfCmP9nHP/7FTUxeVSBlBhhG8lobwm7GaZP9dOUapC9dIihL/DCmpb9KdSpPfbKDNqefga013nXLq8lkHG65aSuHDk/x+J4TTEwWcR2TTcM21269k+GB1OrkmY5AlUBmMGXAUF+OTMZiZr5GqAy2DAXk3RlUFNHd9iCzi1lOTHSx52AfmhT93RF9g4MEhiKONRQkKUvS2prGEJJqOkQaJpYhMaWBH0ccK83T4aaZ92rUohBTSBxpYpw1LtdsauMbJpZh4PkRHWcZy65MNgnXxBET1QqxUvRnc1RDn/0LMxwszdLupNnR2UVDhIRxhBlIZLMbVqhjXGngq4R6qICUNNnW1kFtYYEP/dk/kvXOkmjwE2fhI3/2IW74//0WNddeniRMKUmbFq1uip/YcSVfPnWEU+VF2t00rmFiGwZzjRpv27IL+6wGHBnL5tWDm/jKyFF60lkcI1HknG3UaHcz7O7sXd5WygIXkvcQ0kEIjRApDKN/5dbqiKRD2vlyFRdENgvvfe+3tKnt3oJh9uN7DyGDPcS0kTQF8tCigSCDEHmkyLG20/T9h5e0wa+GPk/Mj9Lr5peNPUDGdCiHPnvmR7l94OLqWpxNDfTikKfmT1OeK2PkbKJi8tIus0lsuC8a4GfjZ9fcl0JwH4NrfidMjZEGu1WhlEaHECyBmddkNq5+MQUCsx7ylp95Eqe2MrHYjeTf7/kvj/A397+c8KwGDZoAgUBiIZDUoiliEWIWBFZTHjffs4C4Yok2p0FPahuZjJM0IrEEuy8f5IrdKwJ5Op5HFx2Ia6Czy5o5nCmfly2AQkpBZ5uks9UFPQtxhYTbDe0tSZUlHGZito0PfeU2ujtzHG4UmayVsW2YdwIyBZt6e8w1nX0cK82xVG2wMdOFBCZrZZb8Bl6cyC+32C7VKKAS+szWq7S5aepRQMqw8OKIrYUOgjgm1oprOlfuxa1DG/jg3ifpz+XpzeXRWnOyvMjx4gIYmqJfpxQ0OFVdZCBT4GhxDtuQ2LaVFIBhUQkDHCPJcVhC8AObd9CTyTH08NPI55FoeMtT+3nwda9mppFIPbQ4KTYWWvmBjTt4YmacjGVTDnxOlBY5VlxgS6Gd9++6lut7zn+WbhvcgiUM7h0/yYKXhGN2tnfzg5t3kDJXwiCmuQMhUihVXVUlq1QFwxwmCa/Eyz0ktNYoNY3l3HxBVcxvB0kBv1o+jmFuJJUZJAqfgnAMFZ9sbijRwkOSR8jURTv+9zpe0gZ/KUhc6nPL1gGyps3p6uJFP2bv5qR93mS1yJOLp1kK6sRExDsczPkq8kxDDxPiVot6SfAb4lb+V/0hJJoUMQ0MlBD8etcrqc+azajkWRCa9KaY/DZF69UxTpdGhVA7Leh7XXReuFIRsuXOCcQF9NeEgm1fnebAD53RkUkMvSJCNSsYZdP8CyTmWYk5RUwlmGUotQVV+n8hfAS0h5b96PQPI9zXAgY6OgnKBz0D8QyQA5GGyhJ8oYw4dQS9EXhzCvIFkK2gA1YkmZp0pGaVVl/nIq+4Yj9f2Xsbp6oLZG0bo27T2yuJ2gVF3+Px2dO0OWlSho0QggemTjBZL1MJfNKGhSkkWdshZ7tM1ErM+TWGcq2UQ59yWKUnlUNrmPdrvHXjLvozK8VYO9o7uW3jZu4dOYkUgkYc8uzCJGnLwrVMJusVTCFJmRaNIKTLzaCFZsFr4MURGk3GtGhz0yx5DdpTiVzzRK3MrtNjpP0LSDT4AUMz81SjANsw6Mvk+YtX3MHmlg7+95MPkDFtetI5NhfalhO+Rd9jMNeyKl4fhBFP7B/l8X0j1BoBA+05rrh8gJ2besjZ5xtoIVO4mffg1f6+qWBpkRRYuWRyv0EU7iPw72veIwlEGOYWXPeNaz90/05oHRH4jxD696LUElJ2Y7u3YdnXIoSJIINSk81wTqU55nySU1Dxt6yB9R8dL2mDnzZsVFMN8Nwb7sURrc7Fr77Lt+VI3dDF/V94nFI6IrI1SsXoSkT9hhZS+yqIhiIccNApiak1+8xefnDD23jV4ggDfoXxTJ57chtopFxM7aMbSWNsWU8stpERbHxPSHqDojGWpPuyGxVdLwN5gdxYy2h92aM/F3YjpmX0bPqjRhEhMUkSYDExIclaQSw3XkHQ5GFDPvw6+AGIApAFNQfVP0fHEyB7IXgAzM0QhkAMugGPlxDvfCopma8rSBvwu6D/+RK4rk5TIIHz6RVJ4vbmy0c42tJG+bhJSlrkOlLkOlyqccBso8qi1+D9O67nL597hOeWpmmz0wid6PTU4pANyuC2rz5Iz8QMU71dfOb6S7mio48fzu/GNAR+FJO3HXa29dDurn5WSoHHtX19XN7Vw5HFeR6eHCVVtZLKUMshbzuUfI96FJF2LF4xvImHJkfx4mSfphT4cdII/WUDG3n71l1UQh9DSK66+RX4n78Tx/M4FzXHZqK7E9VMBLe5aR6fGcc2TcqBz8BZjBvHMHEMk3oY8Mzs5DIbJ1aKf71rLydOz5HLpEi7FvOLFe78+gGMVwiuvWzDms+JZW3FyP82UbgPFc8nVazW5QiZwTA3YjnXEgXPgQ4wrK0Y5hbO7hr37UJrjVf/JGHwGFJ2IeUgWtfw6h9Fx7M46TtQ2gNdRmsJJBOWjosJRRgPrRovCU//JW3w290Mm3MdnK4t0XVWrD5SCk9FXNtx8TX5K6HH+JU2G8Wl7Pm3vchyjGFK6pflKd6UIbrXwT5SR8ikF6tlxNw2e5Jev85Ea46P5K+ioQyMpQh/l0tcMPB3ZMAUmNM+6fuLFDYZpAcaBPMCaQnyO2MMJ2HrXAjFDSmClLGm0Q9SBsUN58ZZk1rblWKWZGIRSGIdoZvMcYGk0xB0yiWQG2A5Dt4CqgLe10B2grkVhAEiA/EJKC8i3vk1RHVlPKLe/PePHUU/uwvSZ45tNv80K43OsM9lK6RN+ne0kz4r/JA3XPK2y5RVJtKaDjdNxrSYrJebzBLBzcfG+PMP/D0G4Ho+DdfhJ/5OMPeZT7LhDRduuj1VL/P5Uwc4VVlEAGnL5vbBHeRci0DFdLrpZeeiLZVGNWqcLC/y0cN7ydkOm/KtaDRF3yNlJsyat2y+lFv6h5ePEf14F8Gv//qax9dC8PlrdpG1LK7o7GNjvpUDCzP0ZnJrtjKEJMZfPku99NT4AodPzVCqNHjquTG8IEnSp12bxaUa2zd2k8tcoDmJzGI7N533eUKR7MdI9a/xq+8MKh4nDJ5AyqHl/I8QWbR28f17sNybEpVM2QnaB+2RcPJzCOEmiWQ8BOsG//sePzR8JX9/9GEm6kUcaRJqRawVr+3bzsZs+0U/3kh1ESGh68YhvJ557EAgHRM/bIAKqb6hHXlDAVGJuaI0xd/9yb8iI0U6iqhLk18Uj/Or217D01v78XZmMaf9xEhVY6IOm9I7e+isTONXTFQ9JrMxRoUCK38un3sFObo5fXsW/vfRNb/XEo68vnf1Z0SJzCwGAoOoybuOCTBxWNYmFwYGC1jCSoy9jlnWLhdpUNOAnRh7HYKwwbocvvwUqHO7eDWhgC9K+PGdEB4kabOllo+5Ir7TTVd2I8dnZlcZfEhaBtrSZLSyRNZyGM61MZRtZaSyyOjUaf78A39P5qykaKr57/4ffifHj+xnU9/wqrwPwJJf56+fe4RYKbKGjZRJkOsTx/cSh4mQz9kryblGjbKfeOmGkLjSYN6r0+K6vGJgI1IIlnyP2cbqYqOjkc+f/tef5c/+KJFoSPsBdcdGCcH7fvmnsVtaeOuWy5bDfJaUTFTLVIKAuh0mBVtnwYsjNhdWWDIHjk8xMr7AUjnR8U85Fhpo+AEnJxb45Fee5qffdr5Rf7EQRYcB47xKWdHU9InCYyTOgIthdCyvOs8Iq6FKwOpet9+veMkb/DYnzS9e+koOFac5UZknaznsau2jN5X/rsb1pBDk7BRV6WMIRShUkqg0JarTJpP1+bvf/Rey3kqsNt1s3PF/TnydW3/1lwmzLuFwCukpiDU6LdG2pGSG1FsFYd3CylcwWwK0ubbGPYA0JGHO5CsffBVveN+9oDR2IyZIGWgJn/2bqwkzZx6VJFkoMZCYSJFU00qM5Xi+IawmNU8sR/cBiOehKQWd7CdFouGsIToK8VjzK404dgpRX3vMoh7D6Ra00Z/sJ3yGZBI5c78MwIL027ghu4NHZ+coBx75Zuw5VDHTjQo/OHwZk/UyXhTy5Ow4S0EdjebWB/dcULc+jEI+979+mwNvuZ1f3f0KLm9f6W386PQo49USi36dSMXJGZoWG7NtTPkVLMOkHoWkTYtQqWVjL4Ci38CLkkTxkt9gMNuyKvxyNmIV8+iWIe746w/wmkefZmBmjtGuDj539WVUHYuMUstXIlKKk8VFTpWWiLTi4OIsG3It7GzvwpYGC16DguOyq2NF42Zmvkyl7qO1wj4jewE4tkW94XPwxDTzS1U6Wi8ug+27g+bzavSg1MlzOP+JrEMilfzdq7v5XsJL3uBDEsu8on2AK9rXbql2MbEh08aZWHdPOs9so8JSUCfSqumYJq/qGx597sLNMtDc/uQhPn3b1ZCTqHOck0Uzj5mv4acdFkWaolenVVfoSK2tRZLE3yXT13TxsW/8EMP/dpTCaI35IZMjr+9pGvvVYzFJERMS68QDl5goItwmQ0OjkNhkzDayMo3N3mQ5TdL8G60T4y8cULWEjSNzyyEfvdGEtLESxjkLOm2jN7WCSIH9MohGQS+uHqO1A1K30yXTvG/H9Xzy+DNM1ssIBIYQvHFoB7f2buKZhUk+dPgJHGmSNZPk7c6FygWToik/YMtcka/VK/zWnq/yf29+C4O5REzt/onjjNeK5CwH10oKoPw44lBxhjY7w1CuQCXwKQc+Xpxw+A2RrAIsaWDKZHKsRyFPz07Qk87SiCJ2tfesGkNbKoMpJRXD5MuvSqp7lYaG30BrRbqpya9J9H9m6lVu6RumNZXi8MIcx8sLzHt1trd2Mlxo5Z3bdq9i3EgpCaOkZebZTs+ZXJcQMD1fftENvtaaUqVBFA1jodBarfLytU7OwTS3Yts3NOWvJ9HLTofEMPuw3devJ23X8d1B3na5re8SvjpxiP50C+WgQd5yqUcBsVbLTOYNUwtk/LU93IwfMjR9YQZRNXJYCtpocxaohhZp08WLfZTysaWBFBYSSaQDYgIiFSCERKGop0KOvX0HQ+krOVJ+gJgQ2aztTMx4jEuWCB/d7Eeqml1mLVJsSF+BY+aIdYQtXRyZQcZjKPVswqkXJkmvQj8x/LL1LE2rCLCBEH6wB37nwNonKIEf7AfnFvDugtRbk8kjnkoSFXIg0U6JjoN9OZvy7fzGla9iql4miGN60rllA+eFIa5hEWmFQmMgmOrrpuE4pNbQra87Nkc6CmRMm1Lg8ZlT+/mly1+G1prR2hKWNFZVaTuGSaQVlcjj5d1DjJaXuMSyWfTq7J+foeh79DQLnBa8OoZIJiQvinhucYYbe4dW6dUAWEKyJd/G4aV5Yq1xDaPZDSyp0Gp1U2itmffqjJSLbMy30JFKcgeXdXSzpbWdk6Ul3rZ1Jzf1bjjP2PV3F0i7FqWqh2kmvRZilQje5TIupimXG5m8WBidXOSuhw8yu1AFNPn0Nl5+9XE2D7QiRAqtqig9j+28Fmm0YadeRxQdRssCSdVtDPgI4eK6r3lRz+WFxEtAPeJ7D6/q3cY7N11Lp5tlMNtGu5MhZdjIs8iVo73t1Jy1s6w1x+J0z4UrE20MTpa6maj2kzINMqaPwESpLmyZxpQmluFQsHsSD9xsI9IekfJptfrZlnsZBbubrbmbSctWDGwkBgWzj1d3/WeyVhcCi0TtPgIUJjaOzCKFScZsJW914hq5ZrehclLqbm4kMeq1JH5vXw/GIMgsmDubMf4KoKFlJ/pT70RnXXQmYcDotIXO2ujP/Bx0/SzC6EyMnJAgC2BtT5g+sllZGY8sXxMpBP2ZAhvzbau82ZOVBXa197Al306gYiqhz55X3oi4gPa7loK7btzNZL2MFILnFqcBmqEam3gtCd/mwuNndl3HG4a3IYTAliamIWlxXFocl4Lt0J3KIIUg0kkXqk2FNt694+plLZ5QxXz99HH+/NlHqMcRecfBQCxr6mzOt3FVZz9bCx2M1yos+g0GcwV2d/SuMuquYdLquFSDYE3PdteWPjYPdjb7+iqCMMKQkrZChkzappBNsaHvW6iM/S5hcrbEx770BNV6QHd7ju72PDGX8Ln7dzE2HRPHYyBM3PS7cFIJ7dMw+kjnfgnLuoxEIjnAsq4ik/slpNH5vMf7fsK6h/8iQAjBle0DXNk+QKwVEsGDM8f5pcc/TRyHSARfvfky/tuHv7rm77UU3HnzZSv7Y3XAJWO7mNIgiAc5XR6gNx1TDKcYSJfIWVmkkEQqJFB1TGHT6g6QVz0oFVFwehFCUg0XMKXF24b+J+32BmICbCPFgjfG00ufQwhwySOlgVIRMRGWYWNIk0o4g2Pk0VrhqQq9VidZS4M5mIxU6xW2TnQ0Sdia/aD7WHb3hYCbHPTJjyG/WEpK6Tdvhh/5YWQu4bvr8MjzXOQ4CfmQtBp8bHaUb0ydpBz4bMy38er+rWwpdJCxbJTSbMq3synfvhy2+OL//SPe/J9/HZTC9XzqTqJb/zO/8R7mTIERhWitSTdVJE0p6XAzVAOf0coSoYqRQuIYkoxpc3n7EK5p8doNW3nN0BaU1nzx5EH+dO8j1KMQxzCTXraOyxUdvWRsm2u7Btg3P8X9E6eYq1VZ8OrEWrO9tZPrewZ5bOo0NRmyNdvB1kIblTBge1sX79l5NRLBgYVpPnpo75p1JkprXHNth2Kgp4WXX7OFUrXBQqmGY5mgIYhiOtsK9HUW+OtPPkgQxmzd0MXNV26ip+OFkyb4xtMnsE2D/FlMoVw6hYoHefzQdt59yTUkSdzVk5lh9JLKvhv3rKTtSw3rBv9Fxply/es7hslbKdKGRTUK8NMp3v/f38Xf/c9/QihNxg+pORZaCt7/399FPeVgIojQywbfQOBIA0caaDRzfgXbMLnU7iOmxLZCG/V4jjD20CJp7GrJFK3WAKGqUwyniLSPQ4re9HaGM1eRtxI9IaNJWZv0DqK0osXqw1dVYh3imGlskcXXVQbTu3GMDFONw0hhsDl7PX2pTcjqnyTcepFaqaLVUfJ/I5f0IpVZVvTKY9AhovV6eG+zWfa5F8/cSKIBXE9WDGegI9AKYe0iVoqPHX2Sg0sztLsZOlMZJmsl/vrgI7xzy1Vc2dHPN6ZPEWuViIw1x3bksm388Z3/xJav3ENw9AjHOlu466Yr8FwHQeJte3HErb2Jbo1jmGzOt/P03ARp08aPQ2KdEFMDpbi5Z3h5eKIZtrl943YeGB9hrlHDiyMKToqhXIEWJ8V0rcxkvcJdp4/R7qRIWxZPzRYxpSBnO2wutHFL/zCny0VOVZbY2d7Fj27awRWdfcshpUtaO7GkgReFq4x7qBL10J3ta2tFCSF43S2XsnGgnbsePszR0Vlsy2Dn5l4WyzXGZpZoL2TIuA7HRuc4fGqGn3jzdQz2tH6rj/23Da01x0bn6Gw9v0Ymn3UZm14ijgWmeeGY/EvR0J/BusH/HoEhJCnTpMXKE2lFIw4YuyLNrR/8NW5/+ABD04uc7mnjzpsvo95URFRoOp0M13YM89TCaZTSWIakHjVj/1qjhKYRS354+EpinqPF7iNWERON/3979x0e13kf+P77njYdMwAGvRDsnaJEihLViyXLii3ZsRM5jpM4aztlN5tNnC337t67uzd3s9e72U2eZJ111nG8cYpt2Ypjy7ZkR83qEkmJTewdvWN6Pee8948DAgRnQIIiCZDE+3kePgTmnDnnxcHgN2fe8vvtxxIB2gLr0DUDXauhQQ+Ss8e5Lf5JfHr1RWeZ8jgIsHQ/lj5zLnbRzlB286yPfYAVke0ztrmBT0Lub0CkgNDkXOgC+O4FNw+F73tBXgS9fn4RAP8HQZ99IF0ICxl4AnJ/DSI987j+hxF6M0cnhjmYGKI9FPUW2UiJKyFTLPCl917j3295iHubl/HTgZOEDBOfbpAul9CE4FMbt/PXlsnB8Zs5kR7zMlpO1uh1pSRgWJjnzO4ouy4+3cuxU2P5vPz1jo0uNMqycvDZpxv8+sZtfO3gO2hA2LTIOzaD2TTbmjvYOdRLR9jL8dSTTmDpOmHT4kRijLZwDUHDZE1dA2HL4r62ZWw7LzVCwDD55KpN/O2RPeilImHDJGeXKboOjy1bOyP3z/k0TbBmWTNrlk0PGL+19xTPvXmYlvj0iuJ4LEQyk+cnrx3isx/fPn1XnU57Sc6OHfNy2T/xhJfx8jIJIbBMHduRWOfFbceVXhroWRYcSCnpHUxwoncUTQiWd8RpbYwumgFbUAH/mmHqOjfXdfDS4FFvIqPwSvKVgwFvNg6TS4yERlDT8WsGmiZoD9US94dYG22iPVRLfy7JSCFDwSkTMCz8usE9zSt4uHUzY6V19GT30Zs7QMSI0xRYiXnO6kJdeHnrx0u9tASqFCsHYpa3cMbFnRzMZep7IQVRq6Xq8zRrLVL/bWRphzf9UlsGog6KL3l36M6IN1NHSNCawWgHrfmif4zecX8HWXrbO65YivBtA30ZAHvG+gjo5lTWxz1j/YwX82h4/e7/ZfdLfKhzDb+29nZ2DHeTLBXYHG/jtsZOGgJhltXU8+5oL6tq4owUs+TKZQxdI6CbbIq3sGe8n8eXbqBgl+nJJLi3dRlD+TRDuQyaELTEGohZfvaM9fN414aK9q+tb+RfbrmLN/q76U4nWeoPcHtLJ93phJes4mxFqcnPN5rwkmWPF/K0hrwAKqmeHgRgc2MrjcEwbwx005dJsTxWzx2tS+iqufS78T2H+4iGKhcn1YT8DI6mSGUKRCOBqWLjM9IYf+ELXnrju+665POe7+a1Hby59xQt53UjjSWzbF7dVvVa2LbDP7ywl0Mnhya3S17edYyNK1v58H0bMS5Qr/dGogL+NWRrwxJeGDiCg4sljKk+DEtoLIvEcQG/5vX15u0yBcfmD7Y8RrZc5BsndxGzAsSsmX+Qg/kUIcOHEIK4r4u4r4uQUc/JzNtesJdeYWxNiOmullny7wN0BNcTNZvIlhMYmokmTBxZxnFLRH2tNPlXUXYcb4rhecFa6M2IwGPeGdwEMv2HXqoF5wxoQdDj3h26MEDvhMK3kUYHQo9Xa8o5x22aOu75zqYYADieHGO8mCcyOf1SAvFAiJcHTrK0po5fXr214vk/07mW757cjyNdWoM1yMl88jGfn45QjILjrY3w1hVLLE2nM1xLZ3g6oJYmc9bMpikY4WMrZibpO5NKzOjDqvMHpn4eYLL+lZcGQQArYrMvEmwN1/CJlZVvNpeqWgqSKWIycdk5xcannK1S9eijXnrjKumML8X2m7o4emaI/pEksXAAISCRKRAN+7lry/Kqz3l7/2kOnhiktWH6jt6Vkj1H+mhrquXWDVd+Vf21aHG8rV0HpJQcTgzySNs6VkUaMYSGqek0+2voCNVh6QabalsJGt4gYcT08fNLb2Z5JE5nuA4BONKtOKYtXVadl9c/7luC7dqcHh3ntWOneenISd482U3/RMKbIGO2MpuwWc+G2CNEfU0YwgfSxdT8RK1masrb+fMX9/Hvv/ccf/Cjl/jp4ZOU7OqBTpb2T666LXv992eXtQu/18Uj8974brl6ptC52lDbTN4pY7sufdkkYcO723cm3+RiVoAa08erA6eqPr8zUsv2eBfFnEvPWJL+8RQh12J9tHny04B3rQK6SVekjkSpMr/NeDHH5vilpRRYGaufyvMEXoqGZVEvcVvJcYiYXi6enmySB9qXz8hjf7WsX9FCMltZRzibL1EXDXl39xcoNo7retsvUyjo4zMfvZ2Htq/B7zMwTZ37t63ksz+7nWi48hOIlJK3950mHgvPeMPShKC+Jshbe6v/7m9E6g7/GuEiSZcLtAVjM3Lwp0oFdo6dIVUq0OAP0+iPMFbMIoTgIx0bAYhZAe5pXsGLA0eJ+0IEDYuSYzNUzLA22kzXeSkiYmYL/cNBhgrHCWgxIqaPslPg8OhxVpW2E2i78IyLtTX34qOeXYMvkyyOEtLrobiSt09IakMObbEohbLNs/uP0j2e4NO334x2fr+qHAdML+PljE8XTH5d8tIsOKOXc1lZW9tEZ7iWE8lRb0aU0Ci5Dnm7zJpY41Su+LFi9UVpp0cmSJ0poiNos6L4MMilS7yeP8NNHc3c3bJsssmCRzvX8uWDbyCLklrLqxY2Usjg0w3uaVla9fizOTudcvdIP02BMH7DoC1UQ6Zcwm94qZmbAiE+tmI9m+LNFz/gFXDz2nbePdTD8ESG+mgQTQjS2QLZfIlfuHeLF0wvUGycbNabbXUFBP0W2zcvZfvmi19X23HJ5cvUVOmO8vtMBsfSF/70cgNRAf8aoQuNuD9MplwkbE6Xqaux/KyLNtObSzKYTyMErI228MG2NTQGpgfBPti2jjorxIuDR+nLJfHrBg+1rubeppUVOV96JtKcOrWUpsYg0n8SKfKY+DHzW9l3NMYHlxWIBWfPUT6QzPCt18bIFlfhN9eRLZbZ2zPAhrYmwj7vE4jfNGivreFQ/zCnxyZY1jBz3rYUTYxkHWwnQtwEy5RTC4e8VUQBkBkwZv+0MReWrvO5tbfxk+4jHEoMkyjlCekWK0JxGn3eG2u6XKIrUjmvXErJM7sP0+SL0OgLc7Q4zJiTQ/ggVLLY4usgfk6WzKU1dfzT9XfwozOHODQ2hBCCW5s6eKRzTUU2zYsRQvCpNTfRGqrhlf5TjBZyRCyLz224lTtblszaZ381RUJ+PvP4bfx05zHeOdiD47gsbavnYx+4iaXtk91uFyg2TijkVamaZ4auEY0EyBVKBP0z08Vm8yUa68KLItiDCvjXlAdaVvGNk+8Q0M2pP2jHdSlLl99Zfz8bYi0gBFaVeruaENzW2MWtDUsouTamps+o0HSuY0OjaMLEKK1FllbjrTr0ipa4MkX3eIJYsPpdo+tKvvn2XqSUtMbOptPN4DN0ToyME4+EpoK+EAJD1zk8MDIj4PeMJ/juOwWGxqKAwKdt4APLerm9s4zQct4iKjQQFsK86X1fz7OChsXHlnnVnL6+bxeFpE23SHBGJogEfdTW+fmVKv336UKR/vEULTFvJey24BJs6RX9LlsOp/sScMv0/lJKxkfyJI8ViRYn7/BTOdLRIo2BS++3NjWdh5as4IGOZZRcB59uVLx5z7dMvsTgaApT1zENjfFUjtFElq62ei9oXqjYuKZ52+eZEII7b1nG0y/tx2cZU39btuOSSOd5aPuaeW/TQlEB/xpyc10Ho4UsLw0cnRo2FQI+2LaWm+va53QXogmBX79wQeazyYy9r8+mTZjediG9E0nGMrmpYH+WPtllM5hMs6LxnC4kOTNL50g6y1df2YllGLTUbQJ7P8WyyfcPt6CJk9zWKUFvAOEggv9kRm3Uy1UadtHSGo4p8SZqSEYLGeKjIdoC0Yr9xXTatynG5DTMEk5F9tEDPUN85619NERCxIJe90GmUOSvfrqL33j4dtrqKs8xF7qmEViAO/rzjU5k+Nsf7PB+dw1ecsFS2eZHLx9AF4Jb1ndesNg4VYqNz5fNq9tJJPO8sefkdEJvIXjw9tWsWzE/XWLXAhXwryFCCB5uW8ttDV2czowhECwJ1xG1KvseL8fKpjgvHDxe0W9pOy5CCJbUx2Z9bm4yN/q5ogE/Z4uOFMv21ONSSmxXsqZ5eun6Wye89QLec/yg34HfTNBg5Hm+9ya2rFuFaYTAWHZOVsNKyVyBkm1TGwpWnVJnOy6JbB5D1zA0jaFUhp3HerizdikFbJJOHk0I6vQgo4kcB3uH2bJs5sBq2G/RVhdlPJMjdl7/70Q2z/3rp2eESCl5bv8xakMBfKZBruilLQj5LAplm1cPneaTd17+p5WFtPO9blzpLXA6yzIN4rEQP911nE1r2r3fxSUUG58vmiZ44PZVbNnQQe9gAoDOltpZ8/rfqK5IwBdCfA34MDAspayY/yW8CPEnwKN4iSw+I6V890qc+0YUtQLcVHf1Mnd21EW5qbOVd8/0Ew8H8ZsGmWKJRC7PwxtWTQbj6urDwYoqYZZhsLyxjr09A7TFvPqthbLNaCbHpo5mltRPT1E8NjRGzYzj66DVE/BDIpki5ay54IKgoWSGH+w6yOmRCa8oh2Xy4MYV3Lrc+wQkpeSdk308v/8YyVyBnrEEJdulNhSgbzxJoeywtLGWkDl9t+03DU4MjVYEfCEEj96yhq++sIOxdJbakPezj6QzxEIBtq2YXuiUL5UZS3vJz/Z1D1AoecVhwn4fy5rqODZ4eYPP14KTPaPUBH0Vj/t9JslMnlSmQF10csXzJRQbn0/RcIDoihu/0MlsrtTnxL8CHrnA9g8BKyf//Rrw5St0XuV9EELw8S0beOzmNbhS0p9ME7RMPnXbZh5Ys+yCz22IhNjQ2sRAMj01ZRAgFvCzrrWJzvoY/ck0tuvykc1r+PlbN86YoRO0TMpV5qRL6a1gtS6QhTGZK/DVF3YwMJGmJRahJRbBZxr8w4732HHCy6X/7ql+/v7t/eiaxmAiTb5kA5LhVBpdE5wZmeBo/8zga7suIV/1TxOd8Ri//tBtLG2qYyiVYTyb47aVnXz+wW1EAtPBz9A1krkC+3sGkRIiAYuw36Jo2+w+3Y/rzr624Xrh95mUncopl2dXL1vm4sgpfz27Inf4UspXhBBdF9jlceCvpRch3hJCxIQQLVLKgStxfuXSGbrGnSu6uHNFF64rK6dNXsDPbvEWCR3oH0KbTFdQFwrwrx65h7bamgseb9vSdr69cx8Rv29G19BYNseKxvrz7v5n2nWil0LZpiU2PTvJbxo01oR5Yf9xNnW28Ny+o8QjIbLFEql8kZrJoJzOlyg5JUI+i4FEiiUNMYI+L7tlyXbY1Fl9hTBAW12UT999y9QbXLWxFFPXcVwX15VYhj61n88wSOWLWMbC98Ffrq3rO/mHF/YSDlgzrsFEMsuKzjjhKnf/yrVlvvrw24Cec77vnXzsigb8oXyat0dOcyo9StQKcHvjUlbXNC6aKVfv16UEe4CAZfKL2zczks4ymsnSP5Hi1aOn+NffeQZdCJqjEdpro6xra2JrV/uMKZ4bO5p5r3+IA31DRPw+dE0jUywStCw2d7bwrbf3MpzO0Bqr4fZlnbSfM9B5qG9oKoCfy2cajGfznB6eIFssURcKcqR/hPF0jmyhRMRv4TN1NGGRLZYYTmV5+p2D+AyDpmiYn9u+iWyxxN+88i6pXIEljbXctqKDhpqZfc4Xeh3lS2Uifh8By2BgIj31KcYydBpqQpTsWRYjXUfWr2jm0KlBjpwaIhzwoesamVyRcNDHB+9cu9DNU+bgmhq0FUL8Gl6XD52dl7bU+VhymL86/hYAEdNPIpPnYGKAu5qW85GOjSroXwUNkRB7z/Tz3597DfCCXq5U4tDAKG21NQwmM7x1opvP37uNpsngaeo6n7ptM4cGhtl1uo+ibbN9eScl2+bbO/fjNw2Cpsn+3kHePdPPx7dsYEuX17fuN00y+cpKVHJyTCHgM8kVyxwb6CGRzU/dvQ+ns1i6Tk3Qx1gmR9n2Uj9Y+Tybd76CePobvNe1lMH7HkbURNh5vJcdx3v4lXu2sLx5bnWNdU3z0gtwdh3ZZA6cyU9Avhugu8MwdD7x8M0cOTXE3iN9FEs22zYtYdOqNnV3f52Yr4DfB5ybyq998rEZpJRfAb4CsHXr1jl3etquy5On3yVs+KYWLYUMi6jl5/Xhk2yqa6tYbapcvlypxJdeepOQZeJKSbpQJOL34S/kufWFH3MnZVLtHTxjCH71Q/dPPc/QNTa2N7Ox3ZsON57N8d9+/CpNNWHMyT78oM/r//7e7oOsaWkg5LPYuqydJ9/cRyQwszsokS3QXh9lSTxGplAiXypTHwmSL5fRdYEuNbLFEtlCCV3XiAR8PJgY5Pe+/EWElPhLRQo+H9o3/pIf/r//ncENN5EplHjq7f383ofvmVNiLZ9pICVki2WaYzOzQg4m0lU/mVyPDF1j/YoW1q+YvQtMuXbNV8fi08AvC8/tQPJK9t/3ZCfInrdCFbzVq6bQ2Tde8d6iXAHvnO6jULYJ+ixShSKGprHh1HG+/Qf/ht999inu+/53eOQv/ye/8PGfIf38C7Me58jACMBUsAdvgZehaTiuy4kRr5zj+s4m1rQ10DueIpUvkCuWGEykcZF8ZMtahlNpApaJ39Qp2jYRv4980SZfstE1Qd4uexlGTcHvffmLBIoF/CWvjKG/WMTK5/jw//17GPkcYb9FOl/kzMjEjMHp2Zydjhr2W6QLJUq2Q8m2SeWL1EdCpPKV5RIVZb5dqWmZ3wTuA+JCiF7gPwAmgJTyz4Fn8KZkHseblvmrV+K8Z5Vce9YuG0PTyNnVC1IrlydXKnN2qZYrJcFSgf/8tT8lVJxOIGYVvK+tj30UBgaqzsUulKd/f9liiZMj44ykvaX5uqbRP5FkU3uz1x1012be6x5k14le8qUy21cvQUqX//T3L3BqZIJktkBDNERTTZiCaSOlnAzANq70ZhPdv+/NWQvEI12WvfQcz265i6MDo/zJs6+xvKmeB9avYENn06yvM8d1sQydW5e3MzCRZiSVRQhYEq/zPm2UqtcnVpT5dKVm6fzCRbZL4J9diXNV0xKIIpFTVYvOVXDKrKypXtlHuTzrWpsAieu6BE2Tu998BW2WQCqk9BbiVJmb3VEfw3VdssUSu0734UqXoM9ESBjN5njuwAk2trfQVlvj1Q1Y2sbNS71+/Wd2H+Z/PbcDTUBjJESxbDOazpHOF2mtrcFvmtSFg2SLJXrHkoxn80R6e6bu7M9nFQoUDx/hVMc6TF2nq6GWYtnmG6/v5rHCOravXlL1eQHLpKHGO//SxjqWNk6nkhhJZVnTtnjqpirXrut/rhhegrE7GpbRn0t65dvw7jiHCmnqfCHW16r+xquhoy7KHcuXMJzOEjBN2seGCcwSSMUFMiUujdeyJF7Lvt6BqTnxAsgUS3TURqkJWDx/sPK56XyR7+04gKYJYqEAuq5RFw5gaoJCyeb0yARhv4ntOJi6zqrWBmzH4WgoSt6sPu++YPk4VeMF6yXxGH7TJOz30RSN8JN9xyics5J4xs8nBA9tWslErjD5yccbTE7lCzjS5Z61l5YtU1Guhhsi4AN8qH09D7auYaKUYzCfYiCfYnmkgc+vuvOiuWWU9+9ff+hePrBuBblymaHGZnKzBNILZUrUNY1f2n7z1ErZTKFErlSmvS7K+rYmakNBjgyOVOTW7xlLMJbJEbSmf78Rv494TQgpJcVymWS+iN+yuGVpG7et6GBNeyPPrNqMO1vXDPDKpm0sa6pjedP0QL9l6DiuQ99YctZrsa69iU/esQnHdRlIpBhIeGMKv3rfVlpq56/It6LM5pqalnk5DE3jg21rubd5BYlinoBhXvEcNEolv2Xw2btvZWVTnCPN9RjPPAXlKmMmF8mUGPJZrGisJ2gauNKb9XJ2EPfsKtXzY7QQXv4eKSW5Ypl0vujVmvWZhAMWxbLD6tYGsoUiRwdHqQn4WNPayMBEmq/87r/nn/7pf0JIiVUoUPL7kULjv33+X7J29VKaojNn2pQdh+FUlu/ueI+maJjNXa2sbWvENGZOt7xpSSsbOpoZnUyzUB8JqinByjXjhgn4Z/l1k+aguqOfL6dHJ/jfr+3CcSWhSIS//b/+gE/9/r/FEGDk85eUKfHmzlZ2nuql+bxgO5bNsa61ccYsHoDO+hj14RBHB0ZwXYmuebWA8+kSRdvBZxocGxjF0HUMTSOVK9A9kiDoMxm7aQtf/8YPWPnyC9T095Jqbef1zbfR3NrEmdHEjFxBxbLNzhM9pPJFOuqj9I4lOdw/wvKmen7pnpuxjJl/Rrqm0RRduERhijKbGy7gK/PHdly+tWMvftMk4vemxOa23cbXnvw+Tc8+w0M+Qc3G9XPOlHjPqqUc6BtiKJmmPhxCCC/Y60Lw0PqVFfuH/Ba3r+xgf/cAmhCYuoEEnMkpnQIxmZ9Hm5xLL8naZeKRIIah0V92KT78M0gJo+ksAcvkY9s28MzuwxztHyUeCWKZBu/1DDKRK7BlaRt1IS85WDTo58TQKDtO9HLX6q4reFUV5epRAV9533onkqTzRVrOy43vhsLsefhDBJZ38uimuReXqA0F+M37b+flIyfZ3T2AK102tbdw35qlNESqv2E4rmTb8g5OjUwwnskhhKCtLkpdJMDp4QmWN9UzmEyRKZSJ+C1WtTRQdhwe2byK3rGU92ahaWxZ1sbda5dSFw7yqbs28/axHt44eobR8RSZQonbVnTQeE6qBSEEdeEQbx3tVgFfuW6ogK+8b0XbnrViiqnrZIqXvv6hNhTgo7es56O3rJ9TndF8qUx7fZRVrQ3eAinhFS45NjCCoWk0xcIsa6qbcaz+iRQhn49P3L6Rj9/mZfM+9zyWYXD32qXcvXYp2UKRL37vpzOC/dR+uk4yX1m0XFGuVSrgKxUGEmnePtnN6dEJosEAty/vYHVTQ0WStaaaMFJCqlCgfyJFIlfwatnWRSmWbZbFLz2dRb5UZnd3P7u7+5FSsrG9ha1dbbOmL17RXM+bx7oJ+mZmcAxYJhIvmyZMB/RMKkff6RFePvEm+wN+/CEf6WQWyzJZf9ty1tzSheWbHgMK+izqIkEyhRLh8+qhJnP5OefaWQxyhRJ7D/fx3rF+hBBsXNXKplVtBPxqTO1accNMy1SujCMDI3zpxTd5t7ufkuPSO5Hkr15/hx/tO1yRYiAWDNBRF+XlI6foS6RwpCSZL/DWyR7GsznWtV7aYqNsscRfvLKTH+w9RCpfJFss8+P3jvLll96a9U761hUd6EKQzBWm2le2HRxXsqatkbF0burxsZEkb755BHO0gMyUePVHe/jB11/h2N5uEuMZ/vGbb/LdP3+RUmF6VawQgoc2rmQim5tRzStb8AaG71l74foBi0U6W+B/f/ctnn/rMLlCiWyuyD++foivf/8tslUS3ikLQwV8ZUrZcfjOrv1EAz4aI2GClkltMEBbNMobx8/QPZ6s2H84laGrrhZNCIq2je26NEfCBC2L4cn0CHP1+rEzDCRTtMWiRPw+Qj6LtlgNiVyh6sIrgHgkxD954FaCPpPBZJqBRJpEvsDPbFnDv/vYA3Q11jKQSNM/kWLfvjMsCYa5o7mZwTOjGKZGvDnG+FAK6UqaOuvpPTnEgR0nZpxjfUcTH79tI7lSmYFEiv5ECgT88r230F7//urU3mhee/cEiXSOlniUUMBHKOijpSHK6ESWN/ecWujmKZNUl44ypXssQa5UnirAfZamCUxDZ3/vwIx6tz3jSUq2w8aOZkq2Q75cxtQ1AqbJcDrL/t5BuuK1zNWO0z3Ew5XlDePhEHu6B3h887qqmSs76mP89ofuZCiZoew4NNaE8U125Xzmvq2MZ3KcODrACzuGaG+KUy7ZTIykCUX8CCHQdI3RgQQ1tSGidWH2vnGUm++ZHmwWQrB1eTs3LWlhKJlB1wSN0bCXElnBdSV7DvdRH6v83dXHQrx7sIcHb1+l1iNcA1TAV6bYrluxuOksXWjkz0srUHacqUFby9CnKj2BtxAuX760hGHFsk3EV5lGWNeEV01Kusz2oVQIUZGW+Ky6cJBkIIA1+VzXlQim+/U1TWCXvVW8uqFTLFRvt2no6o6+Ctd1sW2n6hugrmszusKUhaVuUZQp3oInL7h6pvvsi7bNqsaZA5QtFfszc/+m+JzOe7aPfVVznIlcvmJ7qlCkvTZasfBqrscFaGiNAeA4LpbPwPTpFAte37JtO8Ti3iyc1ESGZevaKo6lzM4wdDpa6khlKsdZkuk8yzvi6u7+GqHu8JUp0YCf25e288LR/QT8I7hkEFiUSg201nSypmVm1tGagJ87lnfy6rHTNEXCmIaO60pGMlnqw0HWtsyepVRKyXjxKL3ZV8mU+7G0CGs7N3Oov0wqX5iqeZstlsgUivz8rXOrWua6Lgd3nmLHC+8xMZwi1hBh24MbWL9tGbfcs4bnvv02Qz3jjA8nKRdt/EGL5o566hprSE1kEZrGlntVub5Ldd+tK/ibp3dg6BqhoM/LiZQvUSiVuWdr9RxKyvxTAV+Z4eaVWU7ljnG4J4h0Q7jSoTXewz2bNEyjMuB+cMMqfIbBq8dPYzsuUkrWtjTykc1rp/rRqxnI7+Rk8hl8eg0hoxlHFsnrr3D/TcvYf7KLgVQagTcT6JfuuIWVc/y08PLT77LrxYNE68M0ttdRyBZ59u/eYKR/glg8wunDAxRzRSyfgdAEpUKZwZ4x+k6OsGR1Cw9+Yht1Tarb5lJ1tdXzC49u4dnXDjE4lgIJdbEQj99/K+1NsYVunjJJzKWaz0LYunWr3LVr10I3Y1Gx3QK7Rv4YS6/BcUwyBYnfFAQsyNoDrKv9NLW+5VWfWyzbJPMFAtZ0moULnWfnyB/j06PoYnqOtpTSO0/s07h2C66U1IeCcy6yPj6c4n//5+/T0FqLds7gruu6DPWMMdg9Ti6dJ1QTwLFdNF2gaRojfRNse3gDv/n7n1BdD5fJdSWJdA6A2hqVOG4hCCHekVJurbZN3eErU9LlPlxpowsL3YC68PQfqy4sxguHZg34PtOg0ZxbwrB0uRcpnRnBHrxBVE1YTJSOsKym+nkupPfEEEhmBHsATdMoFWwGu0dp6YqjCQ3Nmt6npj7M4V2nVHC6AjRNUBetnK2jXBvUoK1yntmCnuDKfRac/UgC5lRDtvph5axHnn2Ld9Jr83OuolxZKuArU8JmK0LouHLmtEQpJbYsUu9ffYXO04YQoup5HFmm7n2ep21ZE0J4XTjncl2Jz2/S2FZLNlU5CyiTyHHTHave1zkV5XqiAr4yxdQCdIbuI2sPU3IykwG4RNYeIGYtJWpdmTJ9phakM/SAdx43653HLZGxB4hZy973eeqbo9x05yoGu8cp5LxSi4VckcHuUW66YxW/+HuPUsqXSU5kMEs5tpx4nfvf/g4PTuzjwz+7+Yr8bIpyLVODtotUwUkwmHuH8eIRdGHRFLiFBv8GNGEyVjhIT/YVcvYIhhag0XcTuuZjvHQYKaHBv4Gm4GZMbfa+WsctMlzYx1B+D1La1PnW0By8BZ/uzYCRUjJaOEBv9lVy9iiG5qc1sI3W0HZ0zUtSNtI3we7XjtBzbJBCroSUkkDYR9fqVjbftZr65srZNI7j8uaP9/GP33qL4b5xhBA0tdfStqyR1mWNlEs2J778DX5z99fRpMQvy7iBIJqhe0Va7rqr4pjpiSx73zzG8b3dGJbBxttXsHbLUqxrNCmYYzsc3dvN3tePkssUWLKqhZvvXq1mHy0SFxq0VQF/EcqWh3lv4us4bhGfXoMrHUpukhprGetin0TXLKSUSBzKTp4Dib8mb49NBeuik8Kvx9hY9xksvXJ1q+0WODjxDdLlHiw9ioZG0U1hiAAb6z5DwJhewHX2PAJ9xqDpqUN9/MNf/BRNEwz1jDMyMAFAU0cd8ZYYAsEnfuNB2lc0zTj3SN8ET/7Zc5QKZRIjKQbOjCKR1MVrCNcFGXzvNH/87h/hK1cpth6JQH//jGIt40NJnvwf/0ghVyRSG8JxXNITWVqXNvLxX38AX2CWGr4LxHFcnvnb1zj8zmnC0SCmzyCTzCGAT/zmB2hbNvvaCOXGcKGAr7p0FqGT6WdBuoTMJgwtgKWHCRmtpEqnGCnsB87OmDHoz79B3hknbLZgakFMLUjYbKboJOnNvl71+MP5PaTKPYTNViwthKEFCBlNOLLM6fTzM/Y9e55zg71jO/zkm28SiQXRdY3kWJrahgi18QgTQyksn0kg5OPH33xzRn+9lJIX/n4HSEko4md0IEE0HqE2XkM6maPn2BB3Z48i3VluclwXnnxyxkM//f47lMs2DW11+IM+QpEAzZ1x+k+N8N55SdauBacP9XP4ndM0d9YTiQXxByzizTF8AYsff+PNivENZXFRAX+RKTopUqVufPrMpGZCCCythqH87qnHpJQM5XYT1CtzvgeMeoby7yJlZQAZzL+DX49VPkevZbx4lLJbOXB6roEzY+QyRQIhHyP9EximjsCrV6sbXqKzUE2A1ESGkb6JqedlEjn6Tg0TrQ8zPpxCaAJNiMli54JsMk9zKYHfmSVdbzYLx6ezcuazRU4d6icWr6nYNVoXYv+bxy74cyyEAztOEAz7KqaYhqNBkuNpRgcSC9Mw5ZqgAv4i40p7KnieTxM6jjy3q8MbtBVU5rAR6LjSQVIZ8B23hFZliYcQZ+vMXjiZlmM7U5NDHdtFnLPwSggNx3Ym2zCd9AygXHamgrtjOzN+Rim8N7CxUJyCNks3TCgEK6bTANglGwFVF35dKMnaQioVy+jGbDmHZl4vZfFRAX+R8elRLC2MXeUuu+ikqfNNT08UQiNmraDoJCv3dZNErS40URnY63yrKLiVzym7Wfx67QUHewHirTEQXuCPNUQol6aDlJfoLIJdttF0jfqW2NS2aH2YYNhPIV8iWhee2X0hJZbf5N2WTcyaElTTvILrk0LRADV1YXJVkoKlEllWbOi44M+xEJatayebrmxvuWRjGBr1auB2UVMBf5HRhE5n5H7yzvhU0JdSUrAn0DWD5sDMsZ7O8D04skjJSXsDrFJSctLYbp5OuQW++lX4N//G+z+dBqAldBsaGgUnMV2Fys1RcBIsiTyAEBd+2YUiAbbev47h3nGi9WF8fpNsJk82nScQ9BGJBhnpm+C2hzbgP2fQVNc17v7wzUwMp/AFLcLRIJlUjly2gGmZdKxsZmC8xN88+M8o+wKULS8FRNnyISMRb5bOOQO2mqZxz2M3kxzLkM8Wp65VcjyDpmkzcuZfK9Zs6SISDTI+lMSdHKsoFcqM9E9w+wc3XXODzMr8UrN0FiEpJSOFfZxJv0jZzQKSiNXBssiHCJlNFfsniqc4lf4xeXsUAL8RZ8W+emo++jlvoDOb9bpDNG1qamOm3M/J1LOky/0IBJZew5LwgzQE1s+pjY7j8s5LB9nxwgGyqTz9p0cRGrQuaSBUE+C2hzdwyz1r0M7LwS6l5ODOk7z2oz0kx7MMdo9SLtm0dsUJhv3U1IVJJbLo2Ryrjr7JUjNPxyN34f/VX5oR7M91+N3TvPKDd8km80gpaV4S58GP30pTx7VZzzYxmubF7+7k9CGvtqwVMNn+wU3cfPdqlT5iEbjq0zKFEI8AfwLowFellF88b/tngD8E+iYf+pKU8qsXOqYK+FefKx2KTgJNGFNTLmcjpaToJADw5XREe/vUHf0M50xtlFJSclO40sanx9DEpeWzB68rIp3I4QuYIKFYKFNTG8IwL3wsx3FJjWcwTAPD1Mlni4RrAlh+c8YxQ5HABY9z/vF0XSdSe30kBcum8nO+XsqN46omTxNC6MCfAQ8BvcBOIcTTUsqD5+36pJTyty73fMqVowl9xpz4CxFCoGsWo4VD6F//OnG3XLU/ULoOY1//jyR/6QGi5lJq/SsqkqRJ6ZIq9zBePIKULrW+lZPjAfrktm7Gi0entsUauqbeLEI1lQE6ny1ydG83w71j1NSFWX3zEmL1EWobpmfXBEJe941ddug+Osjpw/0YlsHKTR20LLl4gQ5d16aOV8yXOP5eD/2nRgjVBFi9uavqIrCFFqoJVL1eyuJ1JbJlbgOOSylPAgghvgU8Dpwf8JXrWLrcx8GJv8Nxiyw5sh8tWzkwCCCyOQqHdzCSjzOY20Uw28j62k9j6V53iSttjiW/x2jhAJowAcFAbgcxaxmroh/jRPpZxqpsWxP7OXStMu3yUM8YT335BYr5EqbPoFxyeP2ZvTzyi3ewbsvMFA35bJHvfuVFBs+MYvoMXMdl5wsHuOmOlTz4c9squoeqmRhJ8dT/fJ7URBbLb2KXHN788T7ufXwLW+9fd8nXVVHm05UI+G1Azznf9wK3Vdnv40KIe4CjwO9KKXuq7KNcg1xpczjxbTQM/GYt7opOnKAPPVe5WtUJWsgVS6c+OWTLw5xK/4TVsY8DMJR7l5HCe4SN1qm7aiklidIJ3pv4O7J2f9Vtfdk36YzcN/NctsP3v/Yymq7R2F439XipUOYn33iD1q44sfrplcCv/Wg3g91jM/reXddlz+tH6VjZzJpbui54HaSUPPu3r1PMl2Ycwy47/PR779C+vJHmzrkValGUhTBfs3R+AHRJKTcBzwFfr7aTEOLXhBC7hBC7RkZG5qlpysWkSt2UnMxUGoXER2+F2YqSaBqJj053HwaNOGOFQ5ScLAD9ubfx63UzulCEEASMOL3ZV/FptVW3DeR2VCzy6js1QiaRIxILznjc8ptICUf3dE89ViqUOfD2CeLndb1omkYkFuTdlw9f9DqMDSYZ6B4jGp+ZTsIwdUxL58COkxc9hqIspCsR8PuAcycktzM9OAuAlHJMyqkVPV8FtlQ7kJTyK1LKrVLKrQ0NDVegacqVUHZzM753I35Ofeuf44T9OEFvmp8TtHDCPk5965/jhv1T+wqhgRDY0psC6uXUqeya0YWF7eYr+vvPbnNksSKdcj5bnDV9v2HqpBPZqe+L+RISqi5K8gUsUhOZ6geacb4CmlZ90ZrlN0mOX/wYirKQrkSXzk5gpRBiKV6g/yTwqXN3EEK0SCkHJr99DDh0Bc6rzBO/UQt4c/DPBrvs7Ss5+N5/xf/3z9M6ECfTGeD0z8TxR9tmPNeVZTR0fJp3Vxw2WynY4/j0mekKym6OgF5L2c2ja2bFNm+Wz8w55LF4BOkyo11nlYplGtumu3kCYT+Wz6RUKFdkucwk83SurJyOer5YfQTXlbiuW9Hfn8+WaF2iunOUa9tl3+FLb538bwE/wQvk35ZSHhBC/L4Q4rHJ3X5bCHFACLEX+G3gM5d7XmX+hI1WaswO8vbojGpUuUCBxKfvxfdf/ozQb/w77KCO7U4P5krpkrWHaQlumxpwbQ/eSclN45xzt+5Km4I9wfKaxyjL6tvaQ3dXBPXGtlo6VjQxNpCY0a7URJZQJMDKTdMfPA1TZ9sH1jM2lMRxpruGSoUyxXxpTgOukdoQ67YsZaRvYuZ1SOfRdY11t156WUZFmU9q4ZUyJyUnw5Hk35MqdXv5cJAEjDrWxH6OoOGl3B0rHOJ48gfYsji1T3NgC0trHp5KwSClZCC/kzPpF5A4k7UFBe3hu2gP3s1gYRdn0s97OXomt3WE764a8AGy6TzP/M3rdB8bRBMCKSXR+jCP/eq9NLTNTBDnOC6v/XA377w8/QHTMHU+8HO3sW7rsjldh2K+xD8++RZH93QjNJASQhE/H/6Ve2hfrlIPKwtP5cNXrggpJTl7iIKTwNSCRMz2ijQJjlsiVe7BlWVCZnPVrJkAZTdPutwL0iVstmPpoTltm61dI/0TJMcyBEJ+Wrri6PrsH17TiRxDPWPoukbr0ob3lW5gfCjJ2FASX8CibWnDBRKWKcr8UgFfURRlkVAFUBRFURQV8BVFURYLFfAVRVEWiSsxD1+5DkgpSZd7GCsewnHL1PpWEPMtr1joZLsFxgtHSJbPYGoh4v51hIzmq54dMmPnOJY+zWhxAlt6BU/8usXSUAcdwWbGS0mOpc+Qc/K0+htZHu7Ep3uDraOFCV4Z3cXQyElu/sl+Vg85FJct4fCjWygE/V7VKqHR6K+nPdBMf2GYocIoYT3IykgX9b7YRdtnuw49uX5OZfvQhMaKcCetgUa0i+T2VzxSSk5PJNg/NETJsVnd0MDqeBxLV4Pd80kN2i4CUrqcSP2Iofxur2A4Go4sETJbWF/7qakKVHl7nAMTf0vRSaALH1I6uNh0hO6lI3zPVQv6PdkBfjjwEiW3zGBhhEQpjSY0OgMt+A1v/r4rJaZmYAidklsmbIb42baH6MsP8b9OPEnnuyf4rX/xDZASf75MMWCBJvjSn3yK45s7aLDqCBtB+vJDtAYaiZo12NLGxeXe+DZuqp29mEnRKfF0/4v054exNBOJS9m1WRVZysPNd6K/j7TPi4krJX//3nu83duHqWnomqBoO7TV1PC5W7cStlRRlitJDdoucqOFgwzm3yFkNBE0GggY9YTNFnL2EGfSLwLeHdjx1A8ouznCZgsBo46g2UDQaKQn+1PS5auT667klnlm8GX8ug8pJXmnSJ0VI2KEGC6NYWBwKHWSklsi7qslZtXQ6K+n5JR4duAVvnryOwRzJX7rd76JP1fCn/cWbfnyJXzZIv/st79Bc9lPqpxhsDCCQDBWSlJjhoj7aqkzo7w8upPR4sSsbXx7bC+DhREafXXUWjXUWTEaffUcSZ/kYOrEVbkuN5J9A4O82dNDW02E5kiYhlCI9mgNA+k0zx45utDNW1RUwF8EBnI78Gk1FXPmA3qckcI+bLdAwRknVeomoNfN2EcTOrqwGMrvvipt68kNUHBKBHQ/A4VR/JoPIcDQdFwp6c0PEtAtRooTuOd8Go2aEQ6lT5C1c9z2/BGEW/2TqpCSTT/ZAwISpTRhM0jZLZO2vTw7hmagITiaPlX1+bbrcCB1jFozWpHULWpG2Ju4eNK1xe7VM6eJ+f1o56+UDod4p7+fQvnaKwZ/o1IBfxEoOsmqueQ1oSOROLKI7eYRaFW7bXThq1rI/EooOMWpc5bdMvo5b0oCKLhFTM3EkY63+vbsNiGwJ4uU13eP4suXqh7fly9R1z2KAFym3xRsd7owuqWZpMrVE5+V3TK2dDC0ym4bSzPJlLNVnqWcK1ko4jcqhwsNTQMkedue/0YtUirgLwIRq52SUxnQHLeILnyYWgif7iVIc6VTsV9Z5gib7VelbTGzhskcCoSMACV3+o9f4t3JF+wSPs2Hds7L1ZUuQd2HQDDaUe/12VdRDFiMd8aRgCF0XFci8QaEzyq4JZr91bOz+nSLkB6g4FTm/s/aeZoDKqvrxSyJRUkXK69f0bbxG4bqw59HKuAvAm3B7ZN38dN/dK50yDmjtAfvQhMGlh6iKXALOXt4Rt75sptFoNEU2HxV2tYSaKDRV89YMUGLv5GyLGO7DgWnhE+3aPM3UZY2cWu6S8WVkpHiOLfV30R7sJlXH1yGnGVAWQrB7oc3eT+DP86EnaLGCBHUvdJ/GTuHTzNZGemq+nxNaNxat4mJUgrnnDfDklsm7xTYUrvhyl6QG9A9S5eStx0K5ek3c8d1GcpkuW/pMkw1U2feqFk6i8RI/j1OpH6EI0te4JSS1tB2loQfmOrbd9wSJ9PPMpLfh9ehIjG1EKtiP0vU6rpqbcvYOX488Ar9hWESpTSDhRF8mkV7sBmfZrEy0kVPdoC8UwCv6ayOdPFA0+2ky1m+fPyb+N7cMWOWTilgITWNP//TT3P0pnbaAo0E9AB5p4hfMzF1EwnUGCEebbmXRv/stX2llOwY38fO8f3IyU8jutC5r3Eb62pWXLXrciPZ0z/AUwcOUHa8N00J3Lu0iw+tWlXRt69cHpVLRwG8gJ4u9+JiEzZapipYna9gT5Czh9E1HxGzfSrT5dUkpWS0NEG6nMUUhtdnL6DZHyeg+3Gkw0B+hJJbps6KEbOm2+66LiezPQyOnmbJD16lrT9NadkSBh97ABGJoKFRljY1Zph6K8ZEOUWilMKnWzT7G2aMG1xIzs4zVBxDQ9Dsb5haB6DMTdG2OZNIYLsu7TU11Pj9F3+ScslUwFcURVkk1Dx8RVEURQV8RVGUxUIFfEVRlEVCJU9TFGV+pNPw5JNw7BisXAlPPAGR6hMHlKtDBXxFUa6+116DRx8F14VsFkIh+MIX4Jln4K67Frp1i4bq0lEU5epKp71gn057wR68/88+nqme1kK58lTAVxTl6nrySe/OvhrX9bYr80IFfEVRrq5jx6bv7M+XzcLx4/PbnkVMBXxFUa6ulSu9PvtqQiFYodJTzBcV8BVFubqeeAK0WUKNpnnblXmhAr6iKFdXJOLNxolEpu/0Q6Hpx8PhhW3fIqKmZSqKcvXddRf093sDtMePe904Tzyhgv08UwF/kZJSMjqSZmgwiWFodHbFCQZ95PMlus+MUi46xBsjNDVH51y83HFc+nrHSSXyBEMW9fEw/X0JHMelpTVGffzCi2xcV9LXO05yIkcgaNHZFcc0FyZXupe9c5REaQJTMwnqQfry/aTLSeqtOBErTNEpYWoWrYFWLG06c2aynGCk6NXPbfG3EDRm6b9ebMJh+OxnF7oVi9oVCfhCiEeAPwF04KtSyi+et90H/DWwBRgDnpBSnr4S51YuXbns8OMf7uHwgT6kACEFmi5YtbaF40cGsW3Hy/ouYdmKRj780S34/eYFj5mYyPLdb+9gdDQNrmRsLMPoaIbOJfUEgl4w3Lipg4c/dBO6UdmTmErm+O63dzA8nEJIkAJCQR8f+/lttLbVXoWrMLuiU+Sl4RfoK/QhpWSoMMhocRRD6GiaTsHOY2gGLf5Waq1aLM3ivoYHaAu28+bYGxxJHwJASoEmYEvtrWyMbprzG6eiXC2X3YcvhNCBPwM+BKwDfkEIse683T4LTEgpVwB/DPyXyz2v8v69/soRDh7oo7E5SnNzjKaWKJal851vvI0Emppj3uPNUU6dGObF59674PFcV/K9p3aSSuVpbo7hD1hMTOTw+QyGh1LE4xEaG6Ps29PD228eq3i+lJLvPbWLiYncZHu882ua4KlvvU0uV1ke72p6Y+w1RkZPcstTu9nyxadY/eRrWNkCtrSRUqIJDcd1mCiPYwgTn+bnhZHn2DH+NodSB6g166i34sR99UTNGDvG36Y33zOvP4OiVHMlBm23AcellCellCXgW8Dj5+3zOPD1ya+fAh4U6nZnQZSKNrt3naKhITLjjnNsLINhaoyOpKceE0LQ0FjDwf29ZDOFWY/Z3zvO8HCKujqvP7avdwLT1AkELGzbYWwsg6YJ6uNhdr59EtueWTd3oD/B0GCCurqZXR+hsJ9iscyxw4OX9DOmy2n2Jnbz0+EX2ZvYTbqcmvNzM3aG3E//kU9u/1ds/I9f4+a/eI6P/OFL/D8f/Cu6dveTc7KYmomu6ZTcMsPFIXy6D+lK3hp9g6gZQzunoIoudIJ6gH3JvZf0M9xoXCk5MTbOdw8c4Fv79rF3YICiKl4+765El04bcO7tSy9w22z7SCltIUQSqAdGr8D5lUuQzRZwXYlhzOwbz2SK+H1GRWDXNA2EIJXMEwpXr1CUSuU59907ly1O9b1rQpDPlQCwLIPERJZcrkRNTWD6+ckcCFG1y8M0dEZH5h6we3LdvDj8PK50MYXFqexJdid2c3/DAywJdV30+bmJQR76J3+KmZ2+Dr68F5h+/Z//gH/7409DTQhNaLiuQ97JA6ALg4yTmdGXf5ZfDzBeGp/zz3CjcVyXp947wM6+Pny6hq5p7OrrpzUS4fO3biXi8y10ExeNa2paphDi14QQu4QQu0ZGRha6OTekQND743KcmUvdg0GLUskhEJgZsFxXIqUkFJ79jzIU8nFu4TR/wJy6i3elnOr/t20H3dArxgNCYT/MUnitXLaJ1c1t0NPre3+RgBakzqonYkaos+oJ6kFeHnmJgjP7p5SzIt99FuFWb4yQki3PnQQkrpQITcOve2+CLg4BPYDtlqu2q8aomdPPcCPaMzDI2729tNVEaAyHqQ8G6YjWMJTN8OyRowvdvEXlSgT8PqDjnO/bJx+ruo8QwgCieIO3M0gpvyKl3Cql3NrQ0HAFmrb4pJI5drx1gud/vJ+9u8+Qz5dmbPf7TTbc1MHoSJpzy1s2xGsolmwammYGprGxNCtWNlETDc56zvaOeqK1QVJJ7263pa2WUtGmVLTRNI26eNib9TKaZvMtSzBNnd6ecV5+4SAvPX8Au2wTjQVJJnIzjlsolNANndVrWuf0s/fle7FlGZ8+/eaUc3KMF8cYyPfzzvhOHOlc4AgQONmLma8+ZuDL27T0ZrGljePaWMKkwWqk7JZxkWyt28ZEOTnjurrSJetk2BjdNKef4Ub0RvcZav2+imLlTaEQ7w4MkC9XvkkqV8eV6NLZCawUQizFC+yfBD513j5PA78CvAl8AnhRXqvFdK9jhw/28aPv78aVEtPQKZcdXn3pED/3qe00NUen9rv3gXVMjGXp7h5FExpSenf7D31wI0ODSQYHE+iahuO4NDVHefhDVYLVObnN9ZUr+dlHPsR3fniAocEkQkA4EmB8PM2SrgYS41lcV7J0eQO3b1/JD7/3LocO9KEbGgLBzrdO0NhcQ7lsMziQQNe1qW6nj358K+HI3IpdF5zCjE8afflehgvDCCEoOkXeGn+TsfIYDzc9MnVnXmHlSmQohKiS+6UYMBjvrKPs2pjCIGbFcHFI22nuit/N0tAySm6R7lw3AoEAJJIN0Y10hZbO6We4ESULRXxGZajRNQ2JpGDbBMwLzwJTrozLDviTffK/BfwEb1rm16SUB4QQvw/sklI+Dfwl8DdCiOPAON6bgnIFpZI5fvT93dREA/h803886VSe7z21k8/95gPouveBzu83+flf3E5P9xh9PeNYlsGyFY3U1YdJTGQ5eXyYQr5ES1stnUvildMoq+Q2b9C+wOe/9zTHm9YzPpahJhogVhtksD9J2Xbo6Kynrb2Ove+e4cB7vbS0xKb67KWUDA0m2XbHclpaahkdSROpCbB8ZROh0Nz7d2NWjLM3kYlygqHCEEE9iBACKSWNvibGiqPsGH+bexrurX6QJ55AfOELVTfpuoXvU7/MJ+s6iZpRSk6JgBGkI9hJ2PAGrD/Q+DDDxWEG8v1oQqM92EGtWbuop2R21cY4PDJCw3lBv2DbBAxD9eHPI3Gt3mhv3bpV7tq1a6Gbcd3Y+dYJXn7xII1N0YptQ4NJfv4Xt7OkK37Z58kOjhJYuRStWg7zSMRbTXmB1ZN/8T9fwHXl1FhBLlcklSqg6wLD0Pmt3/3g1BvTpXKly9P93yNZTjJSGKbgFjCFSdEtYmkWqyNrAO/N4FOdn57R9TNDtWIdmqaKdbxP3YkEX3rrLeoCgak7edt16Uul+Ni6ddzd1bWwDbzBCCHekVJurbZNrbS9QSQTuYqZN1MEFM7ry79UpaLNi8+9h/jLv+T+UpnKuShM5za/wGrKZDJPPB6hVLJ5d9dJBvuTgNf14bNMPvzYLSxf1fS+2qgJjQ80PczLwy9xPHMcATjCJqiH6AotnZouKYCSW5w94Ks0AFdUZyzGL23ezHfeO8B4vuDN6BLw8IoV3LlkyUI3b1FRAf8G0dQSZfc7pysel1KChGhs9kHXi5FS8uwPd3Pk8ACP5EexSrMshJpDbvPmlijJRI59e84wNJgiELDQNIFtOxRLZf7ov/6Q/++PfoGamvfX3rAR5tGWDyORdOe6qbVqCWiBqS4V2y2jC4OAfpHjqzQAV9TG5mZWNzTQnUjguC6tNTWqK2cBXFPTMpX3b8WqZoIhi1QqP/XY2ZkxbR11MwZtL9XYaJqjhwZoaoqSaeqg7JtlwHMOuc1vv2MlgwMJhgdTBINesJcSXEfS2BQlkyrw8ouH33dbwVswtq3udvy6D0MYU8HelS4T5QQbohsxNHWvM98sXWdFfT2rGxpUsF8gKuDfIAIBi5/75O2Yls7QYILhoRRDQ0la2+p4/Ge3XNag4chw2su5IwSntz0A4v3nNl++somuZY24rqRcdiiXbOyyQzQWJBT2YVg6hw+cP6v30jX6G7m34X7yTp7x0hhjpTEmyhOsq1nPpthNl318RbkeqducG0hjc5TP/cYD9PWOk8+ViMaCl5TtcjampU9OMgTbH+TZ3/wDHv7S/wlS4reLFA0fQtcY++rf0XKRfm4hBBs2tfPyCwcJh/1e373PnBqodRyXcOTK3P0tD6+gI9jJUGEQRzpelkvzwhk7FeVGpgL+DUbXNTqXXP5snHN1dsaxfAaFQgmf3+RVp4EXn/hDbu99l+joAIGN6zhxy30kT2l8LpWfkTahmptuXkIgYCGRBIPTwd22XVxHcte9a65Y2y3NoiPYecWOpyjXM9Wlo1yU5TP4mcdvJp3K09szzvhomqLh56XO7ez5+K/T/eDjmLVRHMfl0Hu9Fz2e32/xmc/dQyFfZmw0QzZbJDGRZWIsw133rGb9xvZ5+KkUZfFRd/jKnKxY2cyvfP4+nntmH73d48TjYZqaY9REp+/mfT5jRrbNC9l+92qaW2M8+4O9nDk9QrQ2yIMPb+DW25Z7CdsURbniVMBX5iwej/DAwxvo7RmnsammYmygVLSpq5/7XPWly5v4p7/z8JVupqIos1C3UsolaWyqobklxsTEzFwzxUIZIQRr17ctUMsURbkYFfCVSyKE4CMfu4VQ2O/Npx9KMTiYIJ0p8JGPbSFWq+q3Ksq1SnXpKJcsVhviVz9/L2dOjTI8nCQU8l9yojNFUeafCvjK+2IYOstXNrF85fvLe6MoyvxTXTqKoiiLhAr4iqIoi4QK+IqiKIuECviKoiiLhAr4iqIoi4SapXMdGx1NMzyYwjQ1OpbE8ftVIWhFUWanAv51qFx2+MmP9nDoQD8SiUBgmDqP/MxNaqWroiizUgH/OvTqTw9x8L2+Gbnui8UyP/jeO9TVhy+rupWiKDcu1Yd/ncnnS+x+5zQNDTOTl/l8Jqap8+6uUwvYOkVRrmUq4F9nMukCSNCNyl9dMOhjaCAx/41SFOW6oAL+dSYYtJBS4rqyYluhUKa2bu7piRVFWVxUwL/OhMJ+Vq9trSg04tguhUKZzbcsWaCWKYpyrVODttehBx7ewMR4lsHBBIau47ouUsLd966ms+vK1rOdjZSSZHkMW5aJGDF8+oXr2CqKsvBUwL8OhUI+fvFX7uLM6RF6zozhD1gsX9VEPB6Zl/OPl4bZMfY86fI4QngfEldFNrMhehua0OelDYqiXDoV8K9TuqGxbEUTy1bMb3rirJ3m5eF/QEMnasYRQuBIh0OpXQgh2BjdPq/tURRl7i6rD18IUSeEeE4IcWzy/9pZ9nOEEHsm/z19OedUFtbp7CFs1yZoRKamherCC/5H03souYUFbqGiKLO53EHb/wN4QUq5Enhh8vtq8lLKzZP/HrvMcyoLaLjQW7W/Xhc6UkrS5eQCtEpRlLm43ID/OPD1ya+/Dnz0Mo+nXOP8egBH2hWPSymRuJiatQCtUhRlLi434DdJKQcmvx4EZutQ9gshdgkh3hJCfHS2gwkhfm1yv10jIyOX2TTlalgaXk/JLeBKd8bjWSdNrdlIxIgtTMMURbmoiw7aCiGeB5qrbPp3534jpZRCiMrVQJ4lUso+IcQy4EUhxH4p5Ynzd5JSfgX4CsDWrVtnO5aygBp97awIb+J4Zj+msDA0g6Kbx9IC3Fr34Ix0D4qiXFsuGvCllB+YbZsQYkgI0SKlHBBCtADDsxyjb/L/k0KInwI3AxUBX7n2aULj5tp7aA8u53T2EEWnQKO/na7Qavx6aKGbpyjKBVzutMyngV8Bvjj5//fP32Fy5k5OSlkUQsSBO4H/epnnVRaQJjSa/B00+TsWuimKolyCy+3D/yLwkBDiGPCBye8RQmwVQnx1cp+1wC4hxF7gJeCLUsqDl3leRVEU5RJd1h2+lHIMeLDK47uAz01+/Qaw8XLOoyiKolw+tdJWufrSaXjySTh2DFauhCeegMj8pIFQFGWaCvjK1fXaa/Doo+C6kM1CKARf+AI88wzcdddCt05RFhWVHlm5etJpL9in016wB+//s49nMgvbPkVZZFTAV66eJ5/07uyrcV1vu6Io80YFfOXqOXZs+s7+fNksHD8+v+1RlEVOBXzl6lm50uuzryYUghUr5rc9irLIqYCvXD1PPAHaLC8xTfO2K4oyb1TAV66eSMSbjROJTN/ph0LTj4dVwXVFmU9qWqZydd11F/T3ewO0x4973ThPPKGCvaIsABXwlasvHIbPfnahW6Eoi57q0lGuKa50KDi5qkVWFEW5POoOX7kmuNLleHofh9PvUHQL6EJneWgD66K3Ymq+hW6eotwQ1B2+ck3Yn3iDdxOvYAiTmBknqEc4kt7D66PP4EpnoZunKDcEFfCVBZez0xzN7KHWbJi6m9eFQcyMM1zoZaTYv8AtVJQbgwr4yoIbLw0h8QqrnEsIgS4MBvPdC9MwRbnBqICvLDghNKB6LVyJrHgjUBTl/VF/ScqCi1st6ELDdsszHpdS4kqH1sCyBWqZotxYVMBXFpxPD7A5dhcpe5ysncKZnJo5Xh5iaXg9dVbjQjdRUW4Ialqmck1YHt5I2IhxOPUOE6URgkaEjdHtdIZWIkT17h5FUS6NCvjKNaPJ30GTv2Ohm6EoNyzVpaMoirJIqICvKIqySKiAryiKskiogK8oirJIqICvKIqySAgp5UK3oSohxAhw5jIOEQdGr1BzbgTqelRS16SSuiaVrrdrskRK2VBtwzUb8C+XEGKXlHLrQrfjWqGuRyV1TSqpa1LpRromqktHURRlkVABX1EUZZG4kQP+Vxa6AdcYdT0qqWtSSV2TSjfMNblh+/AVRVGUmW7kO3xFURTlHNd1wBdCPCKEOCKEOC6E+D+qbP+MEGJECLFn8t/nFqKd80kI8TUhxLAQ4r1ZtgshxJ9OXrN9Qohb5ruN82kO1+M+IUTynNfIv5/vNs43IUSHEOIlIcRBIcQBIcS/qLLPYnudzOWaXP+vFSnldfkP0IETwDLAAvYC687b5zPAlxa6rfN8Xe4BbgHem2X7o8CzeCWmbgfeXug2L/D1uA/44UK3c56vSQtwy+TXEeBolb+dxfY6mcs1ue5fK9fzHf424LiU8qSUsgR8C3h8gdu04KSUrwDjF9jlceCvpectICaEaJmf1s2/OVyPRUdKOSClfHfy6zRwCGg7b7fF9jqZyzW57l3PAb8N6Dnn+16q/4I+PvmR9CkhhEq2PvfrtphsF0LsFUI8K4RYv9CNmU9CiC7gZuDt8zYt2tfJBa4JXOevles54M/FD4AuKeUm4Dng6wvcHuXa8y7eUvSbgP8BfG9hmzN/hBBh4O+B35FSpha6PdeCi1yT6/61cj0H/D7g3Dv29snHpkgpx6SUxclvvwpsmae2Xcsuet0WEyllSkqZmfz6GcAUQsQXuFlXnRDCxAtsfyel/G6VXRbd6+Ri1+RGeK1czwF/J7BSCLFUCGEBnwSePneH8/ocH8Prl1vsngZ+eXIWxu1AUko5sNCNWihCiGYxWTRXCLEN729ibGFbdXVN/rx/CRySUv7RLLstqtfJXK7JjfBauW5r2kopbSHEbwE/wZux8zUp5QEhxO8Du6SUTwO/LYR4DLDxBu4+s2ANnidCiG/izSaICyF6gf8AmABSyj8HnsGbgXEcyAG/ujAtnR9zuB6fAH5TCGEDeeCTcnJKxg3sTuCXgP1CiD2Tj/1boBMW5+uEuV2T6/61olbaKoqiLBLXc5eOoiiKcglUwFcURVkkVMBXFEVZJFTAVxRFWSRUwFcURVkkVMBXFEVZJFTAVxRFWSRUwFcURVkk/n+Nxu87r7XCWAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 31 ----\n", + "[[ 0.8980475 1.47930592]\n", + " [ 1.60067732 1.12610378]\n", + " [ 1.92107623 1.73099045]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.46003065 1.62817739]\n", + " [ 1.09198681 1.42691972]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.44832457 0.59405178]\n", + " [ 0.89537979 1.34741053]\n", + " [ 1.11626371 1.65347794]\n", + " [ 1.83297409 1.31528389]\n", + " [ 2.09139153 0.28843907]\n", + " [ 1.40489571 0.91234874]\n", + " [ 1.70806091 1.48280328]\n", + " [ 2.36195329 1.6879083 ]\n", + " [ 1.16974698 1.22400204]\n", + " [ 2.14181769 1.57736152]\n", + " [ 1.26727333 1.48599754]\n", + " [ 1.447884 1.75608664]\n", + " [ 1.45488793 1.33677054]\n", + " [ 0.88969806 1.6615903 ]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.13589149 0.55407966]\n", + " [ 1.42494403 1.49061468]\n", + " [ 2.06086348 1.20102628]\n", + " [ 1.72727059 1.6843859 ]\n", + " [ 0.90445074 1.23478658]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.91252532 1.52154066]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 32 ----\n", + "[[ 0.91424176 1.46634039]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.44304993 0.6725149 ]\n", + " [ 1.43386504 1.60679178]\n", + " [ 1.90996721 1.6836847 ]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.14921994 1.42996698]\n", + " [ 1.80019993 1.40984455]\n", + " [ 1.43310766 1.34262594]\n", + " [ 0.88084961 1.61545418]\n", + " [ 0.8888024 1.23159648]\n", + " [ 1.49787164 0.93685274]\n", + " [ 1.44628246 0.34505281]\n", + " [ 1.04633651 1.76292035]\n", + " [ 2.11626348 1.51312678]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.19578657 0.9484928 ]\n", + " [ 1.04068032 1.30565763]\n", + " [ 1.39774399 1.47934651]\n", + " [ 2.32097649 1.69820241]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.70605735 1.65565024]\n", + " [ 1.94248508 1.23103085]\n", + " [ 0.88896706 1.34897163]\n", + " [ 1.45382447 1.74625663]\n", + " [ 1.21919512 1.23168647]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.15393832 0.5015695 ]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.62029715 1.20878181]\n", + " [ 1.13382651 1.58513774]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 33 ----\n", + "[[ 0.88969806 1.6615903 ]\n", + " [ 1.91916447 1.31401939]\n", + " [ 1.44233843 1.59601081]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 0.8942516 1.35499707]\n", + " [ 1.35895926 1.25168098]\n", + " [ 2.12904391 1.6956735 ]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.48969621 0.91682378]\n", + " [ 1.0989188 1.31775045]\n", + " [ 1.70886205 1.65807335]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 2.40052904 1.30929315]\n", + " [ 1.44832457 0.59405178]\n", + " [ 1.73447694 1.4173942 ]\n", + " [ 1.12003571 1.67142426]\n", + " [ 1.45382447 1.74625663]\n", + " [ 2.70954911 1.60196665]\n", + " [ 0.8978801 1.48820297]\n", + " [ 1.2185312 0.92340697]\n", + " [ 0.89799443 1.22763444]\n", + " [ 2.21039899 1.47093709]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.11249343 1.48065641]\n", + " [ 1.64727632 1.18832596]\n", + " [ 1.8928394 1.73662523]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.94433742 1.54671814]\n", + " [ 2.04032208 0.80720131]\n", + " [ 1.13506841 0.50428597]\n", + " [ 2.37329474 1.69265041]\n", + " [ 1.31437852 1.48835843]\n", + " [ 1.4504254 1.43528306]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 34 ----\n", + "[[ 1.15690427 1.29390383]\n", + " [ 1.91668799 1.54423416]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 1.41684703 1.57214188]\n", + " [ 0.89770648 1.48315417]\n", + " [ 1.51080174 0.93364662]\n", + " [ 0.91171792 1.2390755 ]\n", + " [ 2.36195329 1.6879083 ]\n", + " [ 2.10177555 1.31531487]\n", + " [ 1.76551188 1.24720834]\n", + " [ 1.14017617 1.63224477]\n", + " [ 1.58875555 1.65314529]\n", + " [ 1.13506841 0.50428597]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.50779122 1.26247851]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.74700617 1.69082136]\n", + " [ 0.96114454 1.78791464]\n", + " [ 0.89920034 1.35626089]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.43818583 1.7299543 ]\n", + " [ 1.44002077 0.60632508]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 2.13664708 1.60238827]\n", + " [ 1.10933672 1.45889768]\n", + " [ 1.39721692 1.43513348]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.72154313 1.47610713]\n", + " [ 2.04032208 0.80720131]\n", + " [ 1.21042132 0.97212859]\n", + " [ 1.91708301 1.73230639]\n", + " [ 0.88932509 1.62236648]\n", + " [ 1.87981224 1.35661986]]\n" + ] + }, + { + "data": { + "image/png": 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UbUhRwA8P4QWPoLSHbe0g61yDIVsuOIdy/V+I4rOYxsBK/kE1WKr9DR3mf8E0OlatP+PPc7hynIVgiUglPDD3BJY0SVAonSCEZCaYo0iBxQccnpw+yGW9m2nJFDj87DiHn53gHW+7jr7eC4/luTCkoLe3hfGJBeqNgIxjoTX4fojj2PT1pNuIE8VS2UMaq0ORQghs22Cp7GEYL22IcsI7yvHKw5SsLqRI83VKK07Xn6RgtbEh943V3FwM6wb/RYIpDfw4vmC51mnUVTbZNOfHdvtyBSZqVRb9Bgu+hx9HWIaBa1rsaOvgEycOs+B5vOnECajX19xvJggYnJ5FCIlEY0oDQ0gQkJUGsVLkmzOH2cU6fhRScl3KdY8oUThKEgpFnCiyOYvZxTr3PnmStmIWz4/4hzsf513ffw2uY3H49BSfuPdpDCnIZiwmZyvsPzrGbTfs4ppdQ9/0tVsI6nx29GmOV2YQAhxpckvPDjrsPGoNg5/1Al73wGGGJuYZ6Wtn/ystKq5NpBJk6qMSq4RKlDDeWGIo10aSKOardYpkyWYc8sKhzchy8PQkZ7NLbBtYLdft2hZz5cbzHvd4tcLvP/wAU7UqppTMNRqEKiFrWnS4WfwkZt77Km2uy+nFRSphQBDHVIKAU4sLdOfyXN3XTz2KuHFoAwPNgi0hDBxrO461Ey84QOrd15p7XTH6oqYofraBdSYm2hjS+IHDZDteS9X7DF7wKELkEcIijE7T8L5CW/HnsMy+5d/HySxBeATT6F9lKKXMkqglvOAxCtnblpcfLp/g3pmHMYRBRto8uXiIpahKl9OGl/hYsklSUHD02Smys/2UOh0WWaTTaqWjvUC54vGlew7xrnfc8DXDhY5jcdnOfixL0miEzM3XkEIwONCGYUiuujJlVwnAtk3CKEYpvRzS0VoTxwo3YxHHz+84fLtxsvY4WaO4bOwBpJDkjBZOVB9lKLvnBQmfrhv8FwlX9/TzqROHl8My57AU+PTni7Q6LqDJG/O0mgGhzlGng6u6+zi+OImlzzJQyOBa7bRn82QtUBT46vgZXrVhA/lcbk2j7zkOZzrbkQIKlkNvvojWYElJkMRYhonXTNpaFVDNHGOlHqDR+EGcMliqimdnZtBaYxiS1oKLY5ucGJ3lX770JG+9dR+fvv8ZWgsumebUOO86RHHCFx5+lu2DnbQUvnEJg3oc8FcnHqARB/S4RaQQBEnMZ8eeQeg0yHA+rjgywp/95j8hlSYbRDQcC/U3X+S9/+0dHL50M0JArBNAI5FUIx8viQi8GK2haGYIiNhGD45tkndtxmaX2NDdShgnBFGMY5lEccJAZ8tFj1trzceOPIMUglgppuo1BGlIZMn3qAQ+lmGys6OLatPAW4ZkqFiizXWZrtUYrZZpmXf4Lze+gj3dvRe88Bn7EiyjkyiZ5bnevftYSP+7FhBaIxugsg34zVnm/rmT2nXT6aCXPI0mQsp2DNlGuf4x2ovvOy90tQRCrmlopHCJ4pWWFrW4wX2zj9JiFbFkev8biY8tbRajCmgwm0wzUxjMnRUUsxpTmDQSb3k7xUKGqekylapPqfi1n5ebb9rB2PgCpmEwNNCOUpqlcoP29jxX7E2dDMOQbN3SxbFjU0RxQhyvXCvbNujrbSXrvnT6VCmFeo6ieWEPEFtmKEczJDrGFN86Y27d4L9IuKq7n8enxhitlulws5hSshh4JFrz5q270LrMa3vuZbb6DK7pIISmlnQxHV7Cjp57ibuqDGQbuLJCLc5zxhtiNrwErS/j5Gtv5fL/vHaMTwvBgzddT79loQSMVcvESuEYJptKrQwUWlBN6mGfzrNoBIzVyiw1PJIkQdlg10DUJZFOkBKEEkzPV7BMk5aCy0PPDJNzHaIoJmOvpopapoHWmuOjM1yza8M3fN0OLoxRDhv0ZVuWlzmGSZeT587xQ5xv6LJewJ/95j+R98KVZUE6mP3Zb/4Tt/zNL5JkXUwkEQqlFUpr5oMacahwhU1EQqvI0S1KGEIy2NXC4TPTPH5slChOEEKQJAppCF53zc6LHvdMvc5ktUpvLsf9ZxtIITClTBPDTQMaJgknF+eZqdcBjdCaqWoNJdKzyhgmxxfnma2nwnmuZa2iemadG6gYtxMn06TcrfRaiJqi/10LGPWVayMbGkjo+NEvMbL/Zei8hRQ5BDYqWSJJ5lDKI1HzGLKIFx6i4X+VIDxOYggwOrGb1d4ASvuYRvfy9s/Wx/GTgCAJqMUNMoaNOG8wTppEVEGzdiAB6QhineAaK6HOlbDR83vcQRjzwEPHefDhk3heSGurS70ekHFtbrpxB/v2DJLNOszP1zh4eJS21hxxkpDL2ziWiQJUolFKcdv37cayLpTfeLEghCBnthBpH1usHuQiFWDLLIZ4YY5v3eC/SHBNi5/dcw0PTY7w8MQI9TDksvZuXjm0hZ5sDq/6hwy4ZSZqPZT9mJxlUjQm6M8/zDNLm2ixGpSsWTSCTnOerBlSjSeoBlVU7mq44w647bY0ll+vo3M5tBCM/fM/8B92buePDzzKZKMKGqQUhCrh1NICecthIJ/mDPZt7md2f42K30AYgliB1RAYdRBpHolEgdYJfghxoqk1Ahp+yO1fPUQxl6Gv88JkoJSChh9dsPzrwbHKNDnzQu9LCkmoklWO7eseOIy8SDGaVJrXPnCIz7z6ShzDAi0whCRMYlqsLDqCcEHQbbSyIdMOjmah0cAxDfwwYrGmcSyLuEmP7S0VefDQMLs39VzAqoG01kIIqJ8bJLRCJalZVlqjgGLgc92dj9I/PcPZzg4+e8VeKq5LRzaHISVKCBY9j1+9/x4GCiUGSy28Ydsl3DS0AUNKLHOAlvz/g7e4H84LbRU/6zdnP2tAKTKfPkbtRwdASqQsIEUWrUPi5AxxvMCS//eE0QmCJOKZmuRYY4RELFIwO9lTzLEtm7KBsplrljc77k0zXBvFMWxMYVCJqmg0gQqxpYUjbWIVYwmTSMe0DmWJTmuklbDZXfFsG15IsehSKl6cvdNohPyP37ud02fmsG0TIdJiq7b2HL/287fQcc+d8KkTTOQ7+ZixkcjNYtsmgwNtDJ+ZwzAktm3Q3p7nFS/fwatu2XXRfb1Y2JK/igOLX8SynFV1FvVkgV2lm9eTtv83ImvZ3Dq0lVuHtq5ankQnieMRDGFzZfscZW+eauRjiwZZI+TK9jka0TShkoAgBmzZIEzauar1CTZkZ4mvbSE6/rsY//4wxhmQ269Hvv2dbM/nmZ8YYapRRaim4dYCwxDEWnG6soDdNFjXX7aRf71rP10yg5oPqTTW9rKUTqfCWmlml2okiSJWiun5Ct2tOXo7SqvCAInSDKwxEHw9cKRFvEac3pISqSE6j4Y4NDG/7NE/F9kgYmhygQSN0oqilUkHPwE/230zn/jSIU6MzTFsLzFqVEkSRSGboe4F+FFMkihqjXA5zLNY83js6AhxouhpK3D5lj629K1UbXZmcxhCMlOroHR6fc4/i6tODfO3f/ZXCK3JhSF12+a/fvJ23v3en+TIjm1p6CqOSbTGj2OGy4uUg4Bn52YYLe/hx/fuA8B1LkcgVgV0rDMx8iLpBdnQWGfSI4mTaUxiDNkKwkDriLr/FRr+Q0RJmQcqLYwFeQqyiik8lFI8uJBjKTJ4Vc+PYxo9AEQq5kjlOIaUZJe9dYtO22C0MUlASM50SXRCPWlgCpNNl7QxOhbT4rdSbCmgtabRCKlUPd78piuX4+xr4dOffZJTZ2bpbC8gzxtsS888Sf6SnyISYAU+7bbDz0mDu37x95nevpuWliylkovnRwz0t1IsuAz1t2N8B1TaDmQvZSma4Wz96eWZkUbT7+5iU+6KF2w/6wb/OwBJMoGKj6N1AMkiJbNGyVRAAhhofRyUw0KYxRQaKUASUYkV24sBdvSXeLGByORI3rEBrWtIuYibjZHAF8+cwMHAj2JCkQACoSCDSWIpHpse48b+jWQzNpt626k2AoYnF5cLtNZCGCYkShGEMYiUqqiU5sGDZ7hyxwCb+tvRpIng3vYim/rbv6lrc0X7IAcXR9F2dtUgUouDNCZ8nn0f6Wun4VhrGv2GYzHSmzJyLCFJmka4L2zj//zbIxgIijmHpZpHw69jGAZRkqA1xFFCRIJtmiQqNfxKB3hBSM61qQchh85MsWuomx9+xZ40sW5ZXNPfz/96+Aw0E/PnkPN9/vbP/op8sEIpzYVpGOpv/+yvuPZ//Co1Z2VWEymFISQlx8GLY/7lyEFes3Ub3bk8fngUpVfrxkQbTVSWNY2+yhrEm2xAIYRNrBaQIo/SdUyjj5p/N0rNsxC3MRHm6LAUUELpOpaI6c1eynBkExuXLG9z0pvBwKBkFalFNQxhIITAlCYlO49ruJSsPApNm11iW24TfbkuSkOtPPHIGIfHh9FC0Z1t5y23Xs22rd0XHvh5uP/BExTz7ipjHy8s8p/v+VMy5+koOWH6+dUf+iX++L/9PWdHlijkM2ilcV2brs4C99x3hJaWLJds733efX67IYXB7tKr2Jjby6x/Fo2mwxmiZHW9oLUu6wb/OwBxdAKVTKJ1CFRZnYBLQwNFq4EtQyItmkZYsKswRrtjkiSjWPbLz3swWkiSScLG58jkf4yJcpkgjCmZDjSNuBCQJJq6F7Lo+wA4tknOddjQ08qxM9M0wlQeIIySCwh/YZwsfzakWGY/FLIOh05PMbVQxbFN9m7v50e/74o1wx5fD7YWu7i8bZCnFkYpWhlsaVCJfZTSNKJmrF5riOHOq3bxn/76i2tuR0nBF25Mqy7rSURBSC4r9jL7SKqw2FrIEoQxSzUPhCBOEmpekHKzBRSTkFc+8wib64ucybdxz/YrMV2XMEpoK2TRec3hkWm2n5rgyu2DALQ6WQaLJeYa9VViaa/f//RFQy5Ca16//wAfv/7a5WX1KCJMEhpRRMFxmKxVeWRslDds28Bc5fdIufcr96Pyhgydv1HhQpomIAXVN7ajVYgWqdRyIitY5mYs2UsjeBApC0xFNoZInxOQCDJoIjLWEJVgnil/lqKVVhsHKr0PJTPPlDdDoMLleH2bXeL7+27mho6rUFrhGpnl5/RMbYzy7tO07vDQGpQ5yXRLK5t1Z8okuwg8PySfW4n7x3HCroMPIC8iVyG0ovveOzmw4Vomp8qEUUxyepap6Qq93UUeeuTkS27wIY3lF61OitaFydsXCusG/yWA1j5R8BhR+BAonzB4BK19Ut70uVdlddWkEOCaMatTOguAhZDXkrq6K3x4KbuIoqdw1A9RiG0UKwmxc+OCFKlUQkmlv3Msk8u39/HUsXHcjEXND0j0arORjXxePXyAgcocY8UO7t2yj7rMECUK4pTzbJrpy9rVWmBmvsY9T5zgDS+/9Osy+guzFZ766nFOHh7HcS32XLuVN169h52lXh6bG6YaB1zdvpH+bAv3Th5LkwpeGqrypM17/9Pb+bPf+xekPo+lIwXv/W/voOHaGAhcw0IB1UaIEVjYpkkcJ0wtVtEabMsAnSqLJoli9/gpPvy5jyC0JhuHNEybn7//k/yXN/8c5e5WwijGNg1achnuP3ia+UqDQ2emOLYwRzFnkZUWoVrxPDfMzi179M9FLgzZMDu3ct9Zuf7TjdpyyKgaBnjBEyi1iCCLZkUpU+cl4x9tew5LJ7Xe4x/dgM6m8eE0dGBjmzsxpEUh+ya88FFSBpO+wHyeCzUIBPK8hGybXaISVZkLl+h02tDo5fqIalzHwCRjrM7DzAYLfG7yXgpmlp5syuVPdMKTC4dwhM3V7Xsu+owM9LcxMjaPIQX1ekAYJtxcniETr31NrcCnMD1GtW0vlmVgaYNCIYNlGoyOLxInelUy/bsZ6wb/RYbWPl71IyTxKaRsQwsTpUZIvbTzefrnvW41hXO7hxyOUZtMgje6kD9nPCPi4H5U3Ik0+jGtHQiRQwgDkabN2J3p5F6GaaiIDAZSSpRS1IjowqX3vL6ur7xyOxOzFYQQKLXa2O+dPs0f3f1Xqwzf+x+/nQ+8+qc40L0Z0PhhRE9bkVLeZbA7ZQDtPzbKhp5W9u0YuOB6KKWYGl2gUfOZHlvk3k8/iTQNuvtbiIKYez79JMeeHuGHfuoV7Glb+f3R8hQ5YVGup4lQ0ZwYPbV9iFv+z/t57aOH2TC8wMhQG3fetJtG1kY2r6qhJbommJ8J0dWEOFZpzFiDaUiSqCltpjUtxHz4cx8hd15Fb7ZpWH7nk3/KO/p/j4eimHzGobs1x8mJBSpeQCnnkjEMTozOI4IE0QK6SbQ429lB3bbXNPp12+Zs50ox0/nEU0NIFjwPQwi2tbXjhV/BMPuI1Tzo84cG8K6xOfVEF8XPBk0efoHaG1vRWYll9qJUgzhZAjyksGgt/Accawe2tYUgPE6v4/BMPdOcTSrQEYbZS6wSBIJedyXs0mqViHSC0ulMMNJJM4ylyRoZJoOZC87z4NJRDCFXDQSGMGi3W3hq6TCXt+7EkmvTEF9z66X87h/ciRSCTMZEa81IphXPsHGTC69paDuMue0Imc4GbNvEaSZ7DSkolxvfE8Ye1g3+i44o2E+SnEIag+lDphuAC5z/oK68uOZjAYV3ziOURjRAZyH762Wq/9BOfM25lyVAqSXAIFJlTHsvKplFE5LE4+we7OaGs/08JWeoqgjRnDgMGEUuV510tqwIgeVcm594w7U8/Mwwk+dpimcjnz+6+6/WNHx/cNdf8rq3/nc8y2Gx6uOFMUEUU8g69LQXaSm4PHLozAUGf26qzGf/4UGmxxYYOz3D9Ngihilp7SiwNFtl+55BegZaGRue5dmnRth73Upv1q5MgfyYxJ5RqKwgKaUerDbAkxafvWQvRrdCxBAkZmqAEoEVauTxEHUaAjuh1NJGQwr8MEbp9Px1lKor2qbJLUcfed7wy21nnuGxvlcTxDGPPDtCT2uRjGUytVDBSETqUdYlRh3ipqrG567Yy6988vY1t6mF4PNXXL56P7DsUVcCn6t6+9nX00e1LrGMISJxikTXWe0wgM4ZlH+kHUiQ5NAIhNBonSBlHkvYWOYWTLOXjJ1STAvZN6PUR2mJF9jkwEk/S85QZGSRWGxmLlzk5Z1XkTNX5poJiha7SC2qM9qYwKr5XHfPcfrHqxhbt3PmDe3owXQAmPBmGPOm2L94aDkkdD4saRLFMbW4Qau9dqI/DGO2bGhnZGIJ349JlOLLPXt575HPrbm+QvLg0D6iMCafz9DRnjZ311qjlMbN2N85Hn61Ch/7GJw4Adu2wdveBoWvT2ri68G6wX+REUePIkTLeQ+XhZAF9BrFM9QUhXfOI2vnVU82QKApvHOexf09kGv6rVqjtYfWDQLvLoQ0MYyt+PW/ZGv3ENsKO+nzNuPlFZ6OaRMZ/HLEvu0DFxREWabBqbG5VctePXzgeQ3fq4cPcPv2NO7sBzFnJheYmq/S1ZZn37Z+wihZ9RvfC/nER+4ljhIWpiskscIw0yKfRj3AtAye3X+GvddvpdjicuixU+y9bgtJojhzbJJDj5+m9JWYjJPWCsiKBkvQuNzAmEnDVbIBKMgeiInaBToD+UdinNMaESqQkjhTx7ysA1EyUYnCD0KkFGilaSu49MzPLA9sz0U2Dumem17Oi4RRwlK9wf6T4yt88lARhwmWWjH49UyGd7/3Jy9g6Wgh+In3/iRBJoOZTjGWI/OGTAu42t0cH7z6Ro4+cpKx4Zi2jcOU2i9H5h8FFqnMWpx6soXZkSyFVsXGy8u0DwgkBTLZDIo5tA4xjHZsczOGLJGoheVzKrivJE5G8IKn2ZKLGAngmGeicdiQc3ht79Vc3rKaxmgIiZ+ELEQVLnl6kg984JMIpcn4EX7mKcTvfYLo89v5/JaIMW8SiWQmWGCkMclQto/BbO/yLEbpdHblyIvLFT/9zBg7dw7Q3dPKmZE5fD9iTmt+46af4Ve/+hdIAZkowDcdtBDM/u0/s30Y5hdqNBohfjOpr5SmpSXH0GD7d4axf+CBVdRqcjn4wAdSyvWNN74gu1g3+C8ytA4RrBRRCGFhGhuIksUL1nVu9xAX4ZULpXFu9wjeniP1ARto1QAMhMhgmtcgjf6Uv6vG+dFXFvnUQ5cyMVumKC1CnXD1ziG+79oda27ff46BHqjMPa/hG6yuHiDO8c0XKx77j4/xmutWFymdPDxGverjZm3qVY9MU9TKMCTVxQaBF6I1ZPIOg5u7iMKUGvmFf32EI/vPkM06dOksY0/EaAOSvAArjVUnHSIdGGNo6lAhEsjfF+EebcapJICCeoDx5AzFG/tYMgVKw1BHiZ0bugiihPmuXhqmvea5e5bNqWwLNS/EkALXsag2Qnpai4gmrTDnWIxXysjn3MYntmzi2v/xq7x+/wE2z80z0tnJHVdcTtWxm9mbNFTlmiZd2Rxb2toI4oS3b7+Mez98N8f2nybwalz1Az7t/afJF3cgM6N8+e9aiCOIA0kUSA5/pZWtV8GeW+dYmqkS+Z2UJ/swDEnfloCuDQu42cGV+yZsWnLv4XDji3xx/iEWIh/bcAGLCX+Gu6cfpMUqsil/3m8QVKIKslrlAx/4JG5j5Vpl/AiIiF//eka+8j+pZwyWoiphEhKqkLHGJAUzR6udjoaLUYUtuSGy5tpVtlprgiBiZHSOai3AtgyUSkNyTxUGedutv8KrZw/R35jHH9jA6A2v5vv3XsUtQ3UeePgEWddmcamBENBaylJvBFxz1Tfe4OYFR1MAkep5jKtzlfO33QYTE5B/fknurwfrBv9FhmntJvTvwWClItWwdhCFj12wrhyOERfhU4tG+n2KIqmJXQRstOimVpnFbwyTL8Y4bge24fGe17+V0UWPit+gv6WDtvzaU8WlmnfBsrFix0UNX8O0GS2sFtFKNJhaEScJlUZApebxj194gp62AlftHGJ6bBHLNvEa4bLqIRpq5UaTRWSB1oydmmVprspbf/ZVnDw0xuEnz9Az0EoYxDiujVmDxAQ3CniFf5T+I0ucvqyDL12+C8910CZoC+yRmMxRldYhNMPdkvSy6XpE70zMhhsHGZsrs3OoC9s0iWLFY3uu5yfv/via10kLyVNX3MAV2/pxbZPbHzp8Qa+AczpJlpBkDE2cJCRoNJKGY/Px668l22yB6CcxFuAaJrUowjVNLu3qor9QItGKvUM9RA9N8PhdB7EsE8PO8MwXL6d3x1m6t41z7MHNKBVgOj5GLkEImD2TY+SwRbalhpA+z9xTIvJSh6Otb5LLX1Pnxlt/dNV5zYaLPLY0QUNlaLVbkE3GTKRipv15vjD1FV7T8wpAp6qXWlCJG9z05eGLOigqSej5zJd5/AevwZEWhpHBS3yWogqn6mfZJjYSqJBWq8TLO69ecxtwTvDMZH6+Tnt7kyUUpLIYSaLRuRyHtryWg81wTYuvMAzJdVdvZmx8gTNn53CcNMRX9wK2bunmyn0bL7q/Fw3PI4CIUun373nPt7ybdYP/IsNyricOHkapGYToQAiJVhXOpeaUgjhKP9sbTXSWNY2+zoLaZAImQlqgQ7R2qVXgwS9nOXu6FSE7kFKz96p59r3sEE/5/8bhRoDSCid02BfuY1/rvuWy7bmlGp978AjDE/Pn9tL8X3DXpst5/+MXjzvftenyC5YHUSq6lijN3Y+fIOdaJApai/t55eZB4jjBslJ9f4RIVSGVRohmab3SmJYk9GNsx+Tgo6cwDMHhJ85QKzeoltOBaXdtjN+c+mTqEesIb8bifffdwy/d8haeaenHHlEYc3rZyxb63HGv/L1wep69b9jFy3dvpq2YpeYFdJZyfElpfutH38ev/PMfgtZkozRZrYXgl970s8hSkWI2QxgnWIaBaRgEccraAfDDGMswiM0YywgwDY0fSpLlYilNxkyLwVxTI4XBxlIbP3vF1XRksxxfmMeQkr1dPWwqtfDBX/yfREFEvdxAK02SKM4eKRCH2wiDENPKpyyZKMGwNG5BE/maz/yvTWTyAR2DPi1dDYQU1BZN7vyTfmR9idvevXLfTlTPUI+9VHDvPHqkJU2qYZ1H55/mqcUjWNLCNixu7rweoTWDE42mR38hbC+gc3QRt5mklULS5bSzGFXQGjblBhnK9rEpP4h9kWTtOURhgu2YeH6IIK0BCcMYw5AY0iCTSUXaoihmerpKe1sOx7G4ct8mzo7MMzG5BMDgQBtX7dvwksoqLON5BBCp1+HkyRdkN+sG/0WGlK0I82c5+OgneOLBcXwPNm8Pufblm5DyEEuLDkmcPoALV7rsFOXn1FGm0ELgvd5NmSfaRyAJQ5fP/dsQ1Uqejk6NkDFxBI8/2MFSo4K69m5Gg42pfykk0/40vvK5seNGao2Av/3cYyz4i5TNGVbnExQN2+YXbv3JC1g6Wgje9+qfxLMulD9IX0YwlKK16FLIOmnXp3rA5w+fYk9sUShmkFIwPb6AVw/QKlUwjIIGUgpMx2LXlRs5eWiM2cklnt1/FsexyBYyhEGMq0J+c+qTZPWKoXF1BAn8r3s+zjuGfhZ/jXiwfs7n0ItoK7i84WWXUmgKaUVxwu2PHOFQ9ybe9BP/g1uOP8FgZZ6xUgef37SH0Mkw5Ic0/JByzaOYy7Cpt42ZpRp1PwQNpbzLtlw7z9ZGiGWEIRVSCLzQJtYmGTPCNj0Kjk1vQeHHgrfs6uKVm4YwpcMVvf3Lxxn4IZPDM8RRgp2xwdB49QClFCpOUukGRxD56czPCmJeVhumT1WYEEXudzaxNFOis99ECEngZbAdl/v//VFue/fNy/vxEh+andXOh69C5sIFTGHSZraQM11CFXLn5L240mG4z+H6jEnGv1AV1nctFodWS0kLIXAMm5zp8uqery9GrbVGSMHQYBuHDo0RhDFxrJpsopReGSeKJFZEUUJvT4m5+Rq+H/Gp25+kVHTpbcom1+oB//apJ3nn26+nv++FbTTyjaK+qRPbtbC8CwdMlcsit25d41ffONYN/reAarnBxOgCUgr6N3SQzX1txT2vEfDPf3GQA4+ZBF4PQmoOPeVxx79JPvDrpzHMGGmlUztVFIz/SS/9Pz+ZhiAamthJpY2f/OAOzGlJOGqwtLgBQZFc3mVpsUF7Z0ilkieOBZmMorWrzFP7u7l69yh13bnMUGjEDe6dvpcrWq7g6ZNTzNfLLNqTxIEAoVIXuOkOC0tzcHCI1731V3n18AE21OcZb+ngS5v3UsNOg+RN6QcEGLI5Q202csg2e4YKISjlM8ws1CjtGaR6eCatXl1qEEfx+ZIwKKWpLtZ55tGTTAzPEUcxCzNVHNfC9yOSOOGm+tELDNM5COCm+jG+VLhI+zq9QnkUSnOJcMmf19vUkDJl61gG85bN5y59WRoCIvWsW/IuWmvKdY+b9m7hxj2buf/gKa7YOkCsVMqukYJT02fY1jPNtLKo+hmUthESerNLDLUuYEhBp7sZU2SYayikeZpj5c9xaesPrTrcKIiIghjTNpBSNPMcGtMyl7WKhBQIATvDKX7Lvyed9RDjYfIz4eP8SvQqRhY2Y1pm6uUvNUCvzr/0u91Y0rqg7+5CsITWGlua2E1FTFvaFKw85bDCV27ZxNs/fJG7ISSPvno7KvZBgCUtJIJEJ3Q6bRe5g2tsRghaSi4PPjxGZ1faCrJcbVApe8SxWm6IUihkGOhrxfcjtNbc/+Bxsq5NNrvyjuZzDlGU8ODDJ3jrD13zPHv99uP+W2Je+dxETxMJMfJtb3tB9rNu8L8JKKX46pcO8cSDJ9IFWiOl5BW37WHftVueN+P/5EMneeqRUwCU2goIAb4nmRgpc/DJXq676QyBnxZeRZFBY2+W2Ud7CP9JExyS1LqzTFzXjlHSiFn42N/sw/fbsSxB4Gt6+uc4dSxDrWKjlMA0NaVuE7vTozqbQXamJTWGMEh0wkwww0hjhOMjZapigXpUp1ZT2KUEDNCxJAkM7BYfEkmSF9y1cReWV6SYt2kzBNFkQEyM1glaCYShl2PkKjRxHQulFIY8T+tbCkLH4Aff8wr+/Nc/jb3N4NiBsQuuV6IUNb9GsUNjB3msRYmUgnrVI44S+qKl1KNfA66O6IuWAC6QiTj/DgkJpZYc93/mAFt39LFlV+pVV72AQtahs5SnXPORTaaMQNJZzNOSy7B1oJPrLhniVfu2LUtNPH5slHMMeikFl++uE7ceZ7puMlUpolGML7WhgazdoDUbkLUEs+U+Nra0MpjvYs4/RiOeI2uu5EYq8zXaekosTJUxTIMoTDnxSZQsE/bjMMHVEb/l30P2PJqm2/z8W/49vGOpg8DOIM+V0j7ncd2S30B3poP5cBEv8cjIDAioxXVMaeIaGUyxYjoy0mY2iaCQ50N/8GZ+8QOfTKU7/IjAtVBC8Hd/+m4WrIQwXODc/Mo1XPrcLva1Pn/nrOfCMAQJmsUkoKYiYkcR24oMkpaSyzVXbW7WkWj8IKK7s8j4+CJdXcULtlUqupw+M/eS0jK11pzkENW/uIkf+Jn7QYHtxYSuCRI+/Rcv5wddi29dHPkFMvhCiL8BXg/MaK0vW+P7m4HPAMPNRZ/UWv/GC7HvlwJPP3aaR+87Rld/y7LwUhTF3HP7U7S25dm0veeiv330vmeJo4RCS3a54jUKDdAm93z+EnbtnaFQapDEkqRuYlqKmnbZv2WAWkcGrQUsafSSoLWtQeBbVMuKzh4DKRXHD7fguJJMRiAkJIlgakSSqwhGkxyNpLH8cJvCRKGYC+bI2C4z3hxGJsaUNnaLj11MPUh/zsVwEoQRI6VGWIodL6tx02WXEDYkd39xlll/nlAHRGUHrQVCauKGRRgpFCp9AVFQWkRnPaST0NZ2KVppOnpK7H/gOEiN3RNjFBSJLwknJNjQ8fYlZLFCcrIdRyjiSQdRNwi8iAmrBU9Yaxp9T1hMNLncF9MEMk1JruhSaMmSL2R49J4jbNnVn/Zx1fM4+Qm25S2qvosh0thwxjJJtMZsFrC5TvoqGlLy+ut28bJdGxmdW8KQkg3drTxdfprTVegqLNBVWAJgR9c0M9Usrdl60+Yu0Fc6SUdmCLgWBDTi+VUG37RNBnf0EUcJ1cU6gReuau8nEMRxzK3h8PPPeqJh7jZ2pI3Q4wQ3v7oTm2PY/NDAa3EnHZ5aOsJiVEHpJI2PC4cWq7g6Ma01Wmgy0qF63T5+5QubueJLh+gbr7C0oZsv3zKIkS+SFwIDo1nDq/FUgIGkZBU4WT1Dp9NOyf7anPO5SoNoi0VltI6RgNQQW5pyp6YUKuI41XWaX6hy5b6NtLXlMUyDJElnAOcjSRIcx3hJaZlaa0LtM3VlH3/31bew9Y4zlEaqlIcKHH/tEPVsRKRCLONb1+x/oTz8vwP+BPj751nnq1rr179A+3vJoJTikfuO0tqZX6WyZ1km2XyGR+8/9rwGv1ZJPcXzny9pCOLYZnayxOf+7TJufOVpCi0NwsDk8IEWLCuhNmGz5elxinMNyu05Jq9vp1LOMD+bRwPTEwlSSpJEohLVLLCRGAaYdkJ1McPZOIfWTbqlTkvZJRLXcNm+pRX/QELRNZGuxtIh0lQkvkGup0HHlTPEjdSwWbkIJ9vG6fppdhR2YJTqxI0YYSky7QECgUrAdBLiIEOoQ0TWJ955CDIeSmvcITjWVmWrfCNzU2Vip0HXO8pYHfGyskS8aLD05SzRjIkRGPRszOGNe2Qv87BUBkZjHjy9jZ+ev2/Na62Bh4e2QkOBArdbEE4LkjDdvhCQLWboHmglSTTdA21MjS0Qq4Bj5c8yFxyjb9MClUbApW7MzMhuwkZnGg7zIvp729Ga5TaHiQqpRONoJ2Hnhl5sI0c5HGMuOEaiz7GbUgOdsQKG2gISBUpLTKkQAsrxSSY9i5I9gCFWv+Adfa30berGzTkce2KY0AsJlQIhkIZsPleC/rC67NE/Fy4xfapCkii0AMe11lSmtEOLayp72SY2sb9wmPl4kfloiQlvhplgnja7BcdIw1+VuEaX085CWGbGn0c4gnvesBMB5Mws1ajG9kwHrVaJ041R/MTHFCYFI8ekP8vnJu4BBLa02dtyCa/ovBZTXjyROmMHBELQs7uDxE8F7jocyVS9xvR8wPx8lWzO4VU37+LqKzdhGJI9lw1w4OmzdHWtLuaaX6hz/TVbLrKnFwdSSlyZJ0g8olyGZ9+ybfm7KAmwZIaM8Y03D1oLL4jB11rfL4TY+EJs6zsdgR/RqAd0rdFzM1fIMDNxIZ/+fGzY2sXI6dWl5rZtkSSaJJYcebqHI093EfoGQWAShSbvuPRhfuzT94ICK0yIbAl3CH539xvROh10lBLEkW4eY9poQ8pUeC3BJLYVtTEHt2d102+BoGAWyPRY9A3YjI14aDtESIgaFlJoWnctYlhglKJl4bVG3GA0GcWWNq07ysybNeySj5BgZmK0lkRVi7ZLDSonSvjbRpBWiG6kCcOutgJSwp3h7UROC6XbllBWQrxkgNBIV2P3hrS9LiapSzIDGrczIVOpoXtrkFHkL5GoWPO7972OD95xJ0JrXBXjCQsN/OqGH6AeOAgNwtbIUky2E+rHLKQh0AlEBARBxI49Q6hEkS+6nKx8ibngODmjh23d7Tx1ahzHrNA5tJ8Tz1xF4GfJZiyEgJv3bqGzJc+0d5gTlTtJdLisOdPj7mPGfwZbFEgV8AVWLWLznXMUznhUN7qcfl0HUd7k3ECggdngMCVrkJK9UpmcqJB6PMtN77qEz/7BIyRJwsCOXiZOThMGEbZjoZQmjhKmrRa8wFzT6HuYTMgiCJq6PILF6Qpf+sev8sq3XY9hGjz+pYM8+JknUEozuX2aeqfPjo2buHxwJ17ssxRVmQrm6LBLBDrGFha3dF3Pv499AS00EoEUkkQnLIZlbGGlSd3GKImKMYWZtphUNeIkZsqfwxBpo5z5cJGM4XBDx5UXfYeiNoEY1WBB0ky5SAkZYWBtLfFTb72Z1kJ2lUP2suu2cubsHFPTZQqFVBq7WvPp7Ch8R/DwLynexFNLdxImAWaTpaRIUCJhR/4GpHxhfPMXM4Z/vRDiaWAC+P+01odfxH2/YLBtE8syiaIYy1p9+QIvpNiau8gvU3zfm67koS8foVb1yeUdhBAszFVSffoI5mdcTFOhNKhE4iYhb/74w1hqpRDKClPj8IEnP88TO7bgG3ZzStok+mmaeYDm8ZkgpGatDmkaTaQi2p12duwzaBTnWRq3IRK0FsvcevQZev9+ienBIo/dugk/lxq6SEcILThdPY2yFK3bPFScqvfoJC0dsrojbMOk0O9hYaP9HE7RpDWfJeOYCAQL4QJtb06oDSfEixKjJSYzGCNMjVFQyF2apAF2CyzFJ8j1KOIlk3DKRHg2SgfM/1SOX/nRN3HZxyZpOdRgqlDiy/FOwoKJgYIkNe7SVlgdCmfWIl5qhq+dhO6rbFq7MswOV7npBy9hwruDjGwFoXEdm6u2DTKxUGGyMgJbq6jqRrYPdHHDpRvZ1NNGORzlaPnTZGQbrpEmICPlcax8O4bIoIXGlC5tjy3wfT/9DEJpLE8RuZJrf2eYL35kF9NXlc67J4ogqSFIjeCE9yTD1ftQOoI8bHsXjI04iNDByTq0dBdJIoWKFUopDujd0HjsfO2987YtuN/aiNBgWCa5kktHXxsHvvIsdsaic7Cde//tEboG2kiyitENkxTqOU4fHMVxHa7v2MeZxjhn6qPEWnFt2+Xc2nUD98w8yKWl7Zypj+IlARqFIUxcM4NlWJxujFEwszhmaqFngnmqUQ1H2uTNbFPTX1OL6twz/SBXt+25KD2ztS3PzFyVU9PzaAGxThk5tmviCs0fP/kob9+zh0s6V1QnC/kM7/zRl3H4yDhPHjqLlvCqa3axZ9cAmcwLER3/1vCyjrcy7Z9kNjiL0nGzfkPS5Wzkpu53vmD7ebEM/n5gg9a6JoS4Dfg0sO25Kwkhfhr4aYChoW++6fW3E4ZpsO+6LTz6laN09a1IJCilKS/Wue3W509A9W1o52c/+Hr+5g+/wPxMqvhVr/hYlkngx4Akjlc8k1eUjz6vpMErykf4YtvlaSx3tX7WCmINlsYYvLBoSqEoR2V2lXbha498b0Cmu86mA+O8/z/ejdCajBfjuyZv//Dj/OGHb+XE5d0kpAnDum5yhyVI+9zO9fKBxERY0sKUMNByoSa+KU0ymzX2hEVS9HG3RKhQYGQ0wkw3Y7aRsmNEKs5llCLclhi5NcFfSBCOIrItHnrlZpbIIzJgWJpCq0+8YOCdtJGuxsxrBJJMm6ARAELR+oY6lU1HOGmO0rG5m5kNVebrJ5AiiyFNupx+2uxONna30dthkdvRyd621606h9H6IxhkMKVDpBosBKfwkyW8eJFER7hGC21+O6/56S9g1c8buL3UIr/mp4/wz1+9hjh3LowhqEYTNOJZqvE0J8p3kjU6MZohFNVRxeiYYNvGK1FJ2qbRavYR9usBLZ1ZjvzI27j8Q/9CHGmcJMGXJkoLfqv1dQTKRmho6ShguxYD23po72ll/5cPky1maOkoYFomnlVDaIFpmNgZxdiJKfZ07WBbfgM9Tge9bhff33cLkYpZiqr0u1202kXmggW8xMc1MnTYbTxdfhal0vDhOTRi/7wWiCsqnDkzy2ywQDms0JlZu4dCb7HAA85ZWvsLVMsNPD/BKtqENvRkXWwp+Zv9+/n5a69lqKUlfSK1ZinyeTyeYbjbRwLl6ghuOcvuzPPr778YyJh5fmjoVzm29AAna4+h0WzOX8UlxZeTMS/e/esbxYti8LXWlfM+3yGE+D9CiA6tV/PBtNYfAT4CcNVVV10kzfbS49pX7GB6fJHhk1OYZlq1lySKPVdvZuferz1QXXH9VrZf9h6eeuQURw6McHj/GcpLDWpV/4J1e8OF52Wh9IZpCOliSckUGuEoRLj2iHDf7H0Y0sCSVvqC1z3e/767cRsr+814aXjg/e+7m/ff8VaCrLWGgO7asKVNqNaWZUhUQle+k2TPEmdGRtFJagZkJtUHkm7KooGmrLMAkUm9H7ve4OpHR2g7VWV+c4EDrxzE7kmYu70IaJQnUJFAWBqzPULL1OkNwwijJChc26DUb+EWTLb1b2LWHuZo/Sw9toEtXRSKcW8YrRXtTg8JAa5xIV+7Eo1hG3liHTDlPY3WGkvmUDKmoebwVZmNn5td0+OGlBK6+Y45jr/lnOFJ2T2NeJ4ztfvJGK0Y59USFEoF+vfkGT10kr6tGzj19DAtbQG5/BzVhYQ3vL1MoTPi2DU/Tvgvz1LdX2Muk+VL8TZGpxyk1Jh2Kgk9tKGPzoF2pBCoRDE3vsCGS9JQkpmYIFLRM8uxqC2tFAaFOqZkpQlWUxi4hkOoIogMvCmD2XkFeNQ65sEV9Od7mA8XOSeynOgEQxiY0kwTws3iv3OefqxXS3ucDy+KMKWgnITM21Ea2jE0RpOKlWs2irn39Gl2dnVx7+nTjFUqDC8uMlAosKOzEykEtTDk7/bv591XXMFl3S+90bdlht1tt7K77dZv2z5eFIMvhOgBprXWWghxDSljb/5r/Ow7FrZj8YM/fgPjZ+cYPj6FYUi2XNJHz0Dr153tzxdcXv7qy9i0rYdnD5xd9TKdjwm77WuwUFpXSeiegxAr0rq6oFAVg3DKxOy50PAmOuGZ8jNUogqWtLj8ruHnnVVcc9cwX33T9q/rPCUSE5NIRNTjOjkzl4o2Jz61uEKoIjZk+4m7Zpm2T5MsiZTa6WikvWLsmxtbPs8NT03znp+7H6E1jpcQuAY/8OED/OWHX86p9yb4p2yieYmZVzibYqpPOdSfdkhmTFRdUHpFneyukO5Li5iWRDoxIpBIsoQ6wqSBIXJkZJbpYJyS1UqiI3rcvRecoyOLhEmdRjKH0jG2TEv+TemCEtiigH3qIJa3thGzPEVxZEXOQmBgyyIIQZBUyJsXGqMrfmCAr86dRc8qunqXSIIF5msml17VYMO2cWxHQn+JI2/civ2WHPp0zI21Og9+qYXKYtpnd+vejWy6bCA1sk0540wus5wTyAQOWS+L7/gYdQO7GfqIVEyiE3YUN6fHKwSXt+ziyxOPMHq8judFxHE6G6tQxljIs/XqFnqKnSxGZZRWJEqxEC016zRWbrKnfIpmnhb7QgolpJ76aLlMxjSphmGqoyMESiks06QRRSitaclkuPvUKQ5OT9PuukRxTKwUI5UKhpRs7+ggb6cqmZ8/doxdXV2rGsR/t+KFomX+C3Az0CGEGAP+O6S0Ua31nwM/DLxXCBEDHvAjWj+/T/qdDsOQDG3uYmhz19de+XnQM9BKoxakHX8AJCSZVDtdRHBfaSc/M3X3mqEaLQRfKe1KjWJTTn/Z+FsrD690QHugFi7CfNDQ4XQwH8wz5U/RPVZd9uifi4wX0zVaXfO7tSCR1FSNjbmN1P06k/VJIhWirATTMNie38BkcIowCZAmyI6VcNDF4NQj3vNz95NprByj0zSmP/W+r/Ibd70Bc8/qbbTd2qDlxgbxokG8ZOJuDDFdSVUvsMHcTiOuYgoLKSR11ULeDIhVBYFBohqUowl2tryeot2H0gnlcIRaPIMlXLoyl3Gy+iXq0dwqZo0iosXcSD2ZpboxS+TK5TDO+YhcSWUow7nhzBAmJbufkjWUMp50ghSr752d17zy/91E1/QeRp95BqU30N5+iq6+eYptBrZtozmFbXeCsNi8yyGXV2AWue/TZUwjoX8ToCMQNgtTS2zaNUDPxk4e/vxTdA91IIRg08QAxweHKVNlYHMPs8EioHlF57WrCqb2tuzkzif2M+uPpOwwq1m3N5ElOpXnmL3E5Vf2wlQBlKav1aJMFUMa+EmwLBgnEVzXvg/XWE0VPQchBEu+T6I1G1paGF5cTPsbC0GYJNTCECkE857HXKPBFX19SCGYbTQo2DaGEJxdWmKgVCJrWeRtm4lajWoQUMqsvc/vJrxQLJ23f43v/4SUtrmO58CrB/RtaGfUWyIhNfbKPufxaCqOwy9vexu/feJjIDTZJMKTFhrBf93wNjzDRtikLn2zPZUSoM3zqkgDiTQURsvaMQW3qUzYardiS5vZgRK+a65p9H3XZGbwPK70WtMLBTrRiFg2OdoQTSUUPt+Jt3GCoFTHqFv0tfUw8OouQLMQzyGQTTbL82PvF0efdwZy+ZdGeewHN69eLsBwwXATnL6keZgJoQoIlY8pLTQKrQVSOJScy6mHs5QbVSI8dve9k97sdoKkyqHFj1OLpjhXVQyCnNHJoj6N0glK2GhibFmgM3Mpo/WHOfu6Aa79nVOsFdfRUnD6tjTBKBCYwuCyltuwjSxd7mUseQ+Tlw4hJWJZQCbz6GScwcIP0leaYdMGC3QZ4gBEEVQASIQ22LmvzonDkzSqnVhmgEhOUmq1ybiCyswRDOsoYbyJlq5BXv2Ol+PmHSZOTXPm2Qkc10JraBtuYes1G9m+fSN5J8fW/IYLPHCpJdNfsbBlH1Z3+tzYlTxGI0NVB0w8HrE4MoqUEqEFMQnFXR0M7sgT6QitFY5pM+j28qrulz3v/TelROm0/WbRcaiGIXZzmS0lcZIwUi7TlculzeCTtP+yEgLTNImUYrxSYUtbc8Bq1lR8L2C90vYlhtbglDIs3pghe2eMNvWyMdNCkOThaT3IW53/yHXuMQYr80yYrXzV3Uk9ayMQqRSvAOGAuaSbksCpoRUKdCgRBYW9M1jzGDJNb0oIQckqcer1V6E//OjaxysEj966ccUJ19BkHKYNJUKNrkA8pohPxQhXYPeYHLv3LFfkr6A0l8ec0jTiKsMLJyhvGcZuE8s1ARKTmOh58wMdI9Vlj/65cLyE9pHa13PpmxAshHN0Oj0kOibRMd2ZQQ6NRhwazRAlBtCKWljkh/bWmAo/TyOeI2+t9ECNVYCXLLCteBvDtXtxZIGs2Y7EYsY/RCOZRecN7vmrq3nVTz4OSmN5CZEr0VLwxY9cSpKzcEiwhUFGQN77KCrsY0v8LCFPoZMEITRRpKlj4AiLlsaTIMx0dKcM2M3xpNa8KQnZrGT3lfOE0QK1ssGWPf285p0bCTzNsafq+DWfwW3TbLv+XTjZdCD/of/4Os4eHefEU2eQUrJt30YGd/Stojk+F0ppFpY8SsUi5tTq2YiQUJuNuf6KbVR1jUQrSmYBb1RzSVcHnTsMQhUx6PayKT9w0U5X59CTz1MLAipBQNayqIUhtTDEMQxsw2C0UmGopYV6GHJoeprJWo16ELDg+xhNWZEoSRivVOjM5bhmYICcfXH9/e8mrBv8lxiFkst8a0xNaewCiJCVuyIBIdCmptJt84lLrkTE4HdB8ViCvahSg98k6CRZMGqkxrcZ71YGIAXll5k4SQZVFbh2hEBjmQmWAbPBLOWojJ/4OIZDe0cff/jhW3n/++5qlsjH+BkTLeH3/9sraXgmelEhsyDzAqEFSU2jhSJ8RmEUBXoJjE6B8jS1j4e4psvJ42ewd0VE7TXsbQlmJwTDmky7tZzUtaSNK3LUk1RBNGlyyQ0sBCnrZ36oQOha2GsITQWuwfzQhbrh55KBF/6tCZXHfDBN1swTE3N6wubZkRpZN8axBRuy25msVPnTBx7g+p2n6ciujqmb0kEoQcYosSH3MhbDM4BgynsaRYwlckhTMnulxSceuJWBzw9TGGlQGcpw+rZO4pxJFtHMu2jyVgtKF5CNf8TAI0MrkbAQehpLKFyZw6QVoedBV4FzYaTK8tmtdL9KkDIh41TJdEk6+m2wDEim6ekZS9fTEngYeFN6rU2DzZcNsfmylIAwM1fli/cd4czYPIWcwxV7hrhkS8+qAUBIQT7nEAQxZna1wV9aauA4Fq1ukTaxQj+NzYSTByu8/tpXYltfvym6emCAIEkwpWSqWqWUyWAZBmXfpy2b5SevvJKi4/D/fv7zJEpRsG0sKZmp1/GSBITANk3CJGHJ83jr7otoLX0XYt3gv8QQQiAvyRId05QvtWh7IkIZGm2mwmUy1AglWLjaRg8qXv/oQTY+Ns9YtpUnou0EiYW2BIkEGYPXK6jsMCid0Bi+Jmk3Wbpc0BgUeLPtOGaCY8Z0FGo0IpucHbBkLFGwChjCwBIWk0uTjPR28vMf+WGu33+W7pkqM/0F7m8fwgtMzFmFLEpUWaOD1IOLJxXhkRj/SYWuasyMgWEbqBlBWItx2iCTtZk7sUC+V2H2KVRVEnsaQ5jNdo8pTdQQJrbMIDGIVYghLQxppp+1ycwbb4QPHVzzemohOPCaleYcEgOVQIJAihgkxLFBvZZFaU1PMYthhATa47autyNVlg8deoyWnKZkd9LudONIl7wFx2dHefCEye6+BMsUBJHGNgV9LRJTuPjJIpe1vpVp/xCHFz+BFpq8HCIK8yz6wygrQmU9jr2luxm+Smm4EgNLRphIhNaYkUQHd4GoAwaCCJtqOl0TFuCDmAMygJ2Gc2oCPjOPGA7RmyS8qXBe32NIp2IRxAeBBVDz6ZQQCXoJvI+h7X0IczXLbHhkjn+9/QmMpkFfWKrzyTueYu/OAV7/6j3LVbqmIdm7a4CHnzxFWE6wTIlhpOJzYZQw1N9KHCvKVQ+tNflcBjeTaix5XvQNGfyXb9zIgclJgjhmd3c3QggWPY+uXI7/0KRinl5YoBSGXPXAAwzNznKqrY2xSy+lYqWzB90MCWUsi/uGh3nl5s3rSdt1vDjIdbiE05KwYGFWNbmzMYZHKn/gCmq7TS7RY/zd+/8+bYkXhNQdG63v5JeufDtHjEFUVlAbNChfaqLyBpUr0m2nRMy0y1M9cIgSRT3QNEKH1nw9nQW40JXpolN3cvRfhpmcWSQciolcg7sGNyMvb1LpFjR6ThM9qzE6NOYGifI1alLjPxHjP5JgbzaIhhU4EmEbCKHA0iRzCmubRVxJkGYab1WhwMyl3Vplk4JjYtJIaiQ6ISEt2Npe2EOH08Ozlf2AYFPnHp76pzb2veO30CpZZuloIfirP305YbYZEkgkQWKASEhiA8NUzM8UmJpuBy0QQjJr5BnsihgYmGdr4TLmK4r+zAw9xZU8hdKak7PznJirksxKHj/tUQ81nQVBa1aSsQQ3bA+4brATQ9r0Za9grP4Yfq3E8bMVlKqhaSVILNziEvmuOkopNAY6trEE5O2AAEGiBX16GB0voExNak8rpCGaptFGp1l4kU1v8CMh4sdOglKIhoasgF+bRf/jAFzrkk4VTSAAPZN2jJFtLOt7aBtEEe39O+Tft8w0ixPFZ+86SCHnkHXTkEfGscjnHA4eHWf3zn42Da1o/Vx3xSYeePwUjYZPGCUIIJu1aWtN9egffeoMK1wNTWd7gfbWPK77jRU+tbkuP3fdddx98iQHJidJtOaSzk5es3UrA6V0BlG55x7+8ud/PmVx+T4N2+ZnheD9P/dzPL11K525HEXHoR6GHJ6eXk/aruPbB601R5dm+fLYScbrFYbL82kY1pTM32hT3WXiTKdxmqDHxHJD/u59f0/eX4nB54I0BPL7T/0r13zoP9FwM2BeGGNtihsDaTPnKBGYUuFFNt5impRLDswyN7wf/9kYs2LRe2Unc/ctgAXa12mf9QyYfQJnb/ORUYLGl2Ma98TIBJxMBjGuERhgJIRhSCLSuLxZTTn7SikMYZCzCsR6gdhPyO1IBdxarA5qcZlQB6nHj8QWNqZ0GPFOkBCTt4rkjRYWghlO7gx46L6fpP9zj9E+UmN+KM+B7xskyFqpPdSgIzc1mFKhJVQqOcYmOsi6CbYpcKSNJTSnJwV1r4c/mdjPbK3B8PwijmnSmk2T2WNLZc4uLuJaDpXQJlIRpYxB2YOegsCUCfcckdzUv1JLWK9rTo1O4drFZujDQesslWoBz/GwnRBDZ8FsoESArxRaCgbCDEeH2zlrldi7ZRyQFHMBlvHcnEYMugbVBuLHjiNW9T1ufv6xMfSB7ZBLh9T0OfBBGOcZ+yidohmbIJkGNQtGyjybnqlQawT0dK5O0AohcGyTQ8cmlg1+FCU8vH+YrRs6mJmrEkYpLVNrTUvBZWK6THtLdrk6XSnFmbEFNg12fEPe/Tl0ZLP8yJ49vOWyy9CwOularbL73e/G8FaortkwfV8+/Kd/yit+4zeYbsbxC47DgucRJRfn/X83Yd3gNzHn1ZlqVHEMk43FVqznEW/6VvHA5Bk+efoQBduh1XGZMu2VIllDEnZKwpWqcH7wvmeel5Xy+scP8/GbrlqpvlpuVCVW/taAkfZsTYXDNEoLgsRgybMoKUk8mRBlYkaPTKAqGmNQgCXQGqLDCdFxCA+mswU1BaqiEYZExYLCQJFAzROMxs19CMysJPYUpm3Sva2TxeklsoU89ZMx7maLzkuytPcXMYWFnzQwhIHSCTkjj2vmkcLAT9IQQH9mAy1WB3fN/Htasm9kMYttHHzzpfjaW+kO19S4V7FJFJkUXBOtCmijwchcD7aZIIXCIE14xzoiCBxOjebYvh36SkVOzs7z0JmzbOvooOhmODE7j2vZ1IIAkzwZu4bSIYaAsaWQ7T2Cot7AkSmfgWaIeupMB6bzDMZ5FCYhBIbTYPTZQbZuAwpnQRk0YpOd+RhvrsQ9j28njGBjzyL7to5T8wwaQYGe1hqWGZN6+ud4uFX4TPmiBV0o4DNL8KPt6U1HAgaokOV4vzDB2gvSBbUE5+nvxElyXjXsapiGJAjjtAfrxz5G5fEDbKoYFL/v9WwYaKfhhSAgm7F59KlhutoLNLwQP0zS51RAX0+Jaj0gipJvuuuUsRa75mMfY42l6elqzeueeop7br6Zsu+z6Pv0FgpYxndA16sXAd/zBj9IYj55+hBPzowva6bnLJt3bLuc7a2dX3sD3yBqUcDnzh6lN1dYHlSGCi04QuLrtd/cjdNzyx79c5ELQjZOziHCJlnDABKaipNNy5+AWQOEIC5BhCRtKShAgSE1BGknIcORJDJBzwqSjEZ2CMhodJxuUy1okmlw44ib9Rh9usqUWeSILiAMSRxETfllCCoRpmVg2SaZfIat+zax73W7OW0cxB9coJ5dIFQBAT5CCyIdkTULFKyVAracmaccLfDEwv202V14cR2FIlQBBbNE1izS8CKiRCC0Rho6VQpVBsKIiGKTnGgh51+HblRxrQWSJKDg5DCkpO4LktigaOWxDQNLGuzo6uS+U8M8dGaEzlyW2Xod17LY0JoyP/JWD7EOcIyAMIaB3HaqfsL40nJBOXMTbZhd/ZCfTmNmWoL0UJ5k4vhWBo3LcFtmITfM6MgC5ShHC2fwAklPW51G4HB8rINLhmYIY5OqZ9FWOGeMLc6FdsRwtOLRPweioWE4JO1dFQE2GFvB6ABZTPMBsh0wQQeAA3IlRNPZXkBIQRwnF8gKe37Envlh6L8ZlKK9XufVdgY+/Vfc9cHfZ/qSlQI1z4vo7ynR3dHHUqWBUppCPkMu6zA7X6XhhZSsF0YNEoATJxAXaReYDUM2LyxwD6TvQJJQymQoON+69PD/DfieN/ifO/MsT8yM0ZcrLidt6lHIXx99nF+8/Ca63G+9U/z5GK4skqhk2djHsaKx4KeO1flG+px3rjRnutqpO/aaRr/u2Jzp7kAoMOqpI580w7YyBnsWlLOyWd0AlEQ5Ir37UmNJEHaz2tIWiFigFjXJjMbo04iSQBbSGX8SwqV6jt9WDyDQuCR4sQFnnubXMrdwyGknClKJYykldsamd0s3uZYsP/Ohd5ErZrlRXcNI4yTPVp5iPpjCkg4lq439iw9QMFb0iSCtAq5HNTKGS0JCm92N0jHVpIyXNPDrRWZnDUzHY3S0A8NQtLYEuK6HoV0afgd9Lfuo1CwankdQbSWTCckXXUwpCGKLoqkxpYlhSII45sTcPB3ZLHP1Bo0oxJASxzQxpFyePJnCIdEGBcdECgsv9unIrWietBaKTI1dTUvnPOSGQcbo6mZmjhZYmFvg2JlZWopZetqvY2l4gtlYUanaXLl1lCjJYBk+jx3fxnS5wCUDczhWREsepLkd1BzoSSCD3twB2cU1jb7OCvQmG7BAuCBaIfsOCO6AeBhEO5gZEHYaynHfjBAr9MSsa/OyKzfzlUeO09mWx7bNVGd+sU6nDdt+/t2ph9+EHabSIK/+3V/iX//s08SZ9HrYtoFG4Dgm3eeFh+Ik7ZPwgouXbdsGudyaPWIbjsNIRwdeHJOzLIq2Tc62L9o/4LsN39MGvxYFPDo9Sk+2sCpDn7NsKmH63Rs27nxB96n0SqVSGMQ8c3CUhbCBnTHwjfPiiFqnTpmEz12zh//2L3esuT0tBJ+/cjeG19yqJu02mNWYDYFV1yQKEkekxVgxlA5BbbMmzgu0oSnGIcagwBoyiM9o1DQQC2zLIjgbcn7lq6sjfpsHntNNKQGV8N8bX+bt5hvQVtpNSStI4gQVKzp628gVsyilEMpgc24nW/K7lrdRjZY4XX+WMPLJyOyy0ffiOhpFziygdUpDzZhZHMOlFtWZXoRyQ5JJTGbnC4ShzZnRNKbblnXpLuY51vCZrMymQZAgRmuHyqjk7SePwokTPFtsYfy1r0UimKxUWPI8/ChGoyllXMwwohoEzNRqtGWzNMII1zLx44RtHe2ESUKiNFcOrvSfvXbPBv75c7MUsoMY3ga01pydXOD46SmEgErNo1LzGZtcpKezyPDYHFUvTxC3kWiFEArbWGJivpPpxVZmFgt88F0lpN2Vjrz+HWAMwZsl/PeTq+7RMqSAN7Wm3ry5CZw3QPQkiAJQg+Q0JCfB3AK5n0DYF7b5u+HqLViWwcNPnGax4oGG7Vu6uO30Iwi19oxUaMWmh7/MiVteT60RMNDbitGUP5DNEIzWmrmFGlfuHsKxXzgzpLVGveUtGB/4wNrfA3ddeWVakBXHFByHrPm9Ywa/d850DSz4aVJnrSq7vGVztvr82vbfDDYUWgDN1HSZwwdHKS95RCRIkcCgBqdZvZmA4YHKQEM4vPs/vou//aOPpiydMKRu22gB/8/7fpwgcRBGswbKALOusepgL2oyMyATiLMarweUCVYFSkdh8TKNXdXU/qSCb0SoQCMCiU40tmvh1y8s1LqZMdZqqg4g0NzCGPc6lwDpy5dECdOjc2y7agt//ot/x/67nyH0Qro2dvK697yKl7/5WqQhOf3IBCP/GjI3uYDRskDXTVnar8ngqTq2zBD7OSYbHspcwJFZWl2XMFHMNzwMGRMnJnHkIEXKsI+VohaEtCeKU3ML5B0byzTpyefYffokv/a/P4REkwkCbnYcxCc/xm/+wi/xSHcf1cAna9mYUpJ3bAoZh4myYq7eYKi1hWoQUK0FdBfyKDRz9To/uHsnfaUV73XrUCc3XrmFh546jRACP4x49tQUrm2RsU2m52oYhiDjWPhBRHsph9bwyJEOsk4Ny0zoabfZPpiwVPfYMmRgGgrUBKgEZImaFzO1FGH8r0vZ+IEjaUjLU+isACnQ/7gNChug5Q/A3Ay1D6WevtEF5sY0jKNDUEsIc3DVzCqMYh4/cIbHDpyl0Qjp6sjz8ku3smt7L7msAx/82JoeNIAV+JhnTjM5UyHjmPzUO27k2MlpHj0wjBSpMFwcazYMtPGK678+TaavhVgpHhkd5SvDwyz6Pvt+53f4kQ9+ME1V1+sEmQwx8F9+4RcIXRcJFB0HP45RL2F7wxcb39MGP2umjZrX6mfpJxHtzgsnS3oOLY5Lby3Lnaeexav5qESRCEXiQn4YGv0aZYFdFogQ4paUfn2wZSMv/6//mR/Yv5+bDx8F4P4dOzhR7MWZ1RgRBO1gxtD2NMig6embEOfAaEDxOMtVsdKH7AS4Expt51ALS6AFSZykpA0h13Qa+6imHv0acEnoVVWU0isRqabcw+N37MfO2OTb8mSLLotTS/z9r32c6TMzdG/o5JHP76ertQ89FBM1EmY/E2BOG7S+qY3pRplqTZC1SsSyTqR8pqphmvuTkoytGB7uRjdTjKKZjFFasau7i7l6g4xl0uq6dGrFT33g53CCFWVSN0gHtv/64d/j0V//n5S1ph6FdOZyy0nBvmKBiWqVvX09/NDeS7GExE9iik6GS3u6aMutflYqdZ/Ld/Szc3MPp0fnePzwWTJ22iIx5zrksw6Vuo8fRLiOy8t3dKM/9q84Z84y3dbF03v2MbtkUA8chvp7uPmGTZCZBEyEdQlHjnyaVuPvMIRNeGUbJ+68gq77phFnQsyteXJvK6XsHNkK0RPNsE0VjL6VgxROk4vfQIdPI9z0uyRR/Pvnn+LU2VmK+QyuazK7UOXzX34GIQRX7d3wvGGTxM3Ssm83t73yMnZs6Sbr2gz0trJ7Vz8nhmeIooSNA+0M9bc9b/Xu1wutNZ84dIjHxsbozOUYKBQ4c+ml/NePfpR3HD3K7lqN232fDw8OEmQyOM17WvbTZ6ARRXhRhGu99Lr43258Txv8DjfH1mI7Z2uLq2L1sVL4ScI13YPP8+tvDrWaT/JUnStLPdyrT6OyaXFQbiYm8yxUlsDvAbRGKJDzaTLWLgt2T03yi3d8Cak12Sjk6uGzvP9LX+IXb3wXh9o3UjgNdgVcYRCGCYmVxu4TSHXla2A2wOsGI4LiaY09B8I20bFCazBMiWEb1KtNSttzFJUnKOBhrGn0PQymzBJxGKMShSYtbrEyJo2aT/eGLmTzBS+2Fagt1Xnw04/R3tvGhksHMQxJNnKZNsex3TpzT9TpvXYXsXWWgpNBIJFxH0IuEJlL1D1JFDlMjHXgN/IIVFP1MeWjtGWzWIZkS0fb8su883O3Iy6SHBda88qnnuSfL7+KnGUSJMmyZkuQJBQzGV69fSs7ui+ezJ+Zr3LnV48wOrWIQJB1LW65djsF1yGKFe0tK+Gq1mKWhXKD4tOP86YP/SQSyEQBvuWgH/g3/tcP/yKnhnbwqut209e7GdgDpHmfD/1riV39N/GaK5+kJV9HZ+Dpa7fzsWAP12+Y4Z35cbD3grERokMgu7lQ9OgczGbFboozY/McPTlFpeqx/5kR/CBKzyVrs7hYZ8fWbgpvextcJGximAY7f+V9kF95p4QQdHcU6e5YWwXzW8F4pcITExMMlkrLodm8bZPp7uYfcjn+yytewac++1ly9TpmHOMnSVr8ZdtkTJMlzyOI43WD/72At27dw58ffoSxWhnHMImb0q2vHdzO5mLb197AN4jxsUWEFmwRrRx/ysDIpKIBlarCjxQtRyA+m7Zus9L+KFR2gZnz+T8f+yj5cCXMko3TJO4ffOWjvPW6/0zQbHIckqRiYWH6z2ywTFcEyE2ziqrZM9BCGIUkicLOWERhTNgoL+9HNKsptdbcpwf4WZ5e89w0grujXoysxDBT3rdhSqpzNXKlLNJIm2YncYI0JG4hw+zYArZjYxiSKIoRdYsBewtmi8lcZYGFpzvI7M6grLOkZ2BgaBcrbqE8PcT42SW8MG2ubkhBKpqQyhR0F/Jc2tvF0+PTyy9zaWwM27+w7wCknv4VXp0v5bIEcUyUJJQ9H1NKbNNgoFRkqlrDlJJNHW0XVGaWqx4f/cyjJIkm59pIKZBC8Ol7Di5f8PNnkvNLdeLFMr9991+QjVbua6b5+YOf/EP+5Pf/hfnnSGefGZ9nbrHOl+e28NXDW2nJ+wShotZIiBOJood3vvEqVu64Bclk6uGLEsjnzFy1D8aK2NzhYxOMjM+zsJRSYt1MWtvg+SHDI/P82+1P8hNvvwHuuANuuw2USj39XC7tNXjHHauM/bcbx+fnMYS44H6cC9WenJ8nShIcw6A9m23m0VgeyCtBQH5dS+d7A22ZLB+4/CaOLExzqjJP3nTY09FDX7b4bY3rSSEo5DLUGwGJIUlCtfJ61tN/kL6yLYfg+8oHkWrt2LnUcMvMM3yh96pVy9PSdnVhaGa52DEdBWQ9RCmN467x0J+nhimEwBMWv6xv5Ld5AEHaFNvDRAO/zI2EpkXWsZabaktDUo9itNYsTi9RW2o0N6txsjaq6W0NHxphcni6WTSlaeksUezIIwAr3EAh6CcwptAixFSt2EkXgeNxSZfFgfGpZcYH6GYzF4MfvvxSbtqyiYMT01T8gGLGoTwwQJjJrGn0PcfheKkVP4pxTBPH0LTnsnTksjw7PcNEpcrJ+QVQMNBS5P0338CevpWG9U8eHmFytsJSxSNuFvJkbIuh3ham5muYpoHnh2QcizhR1BoB3zf8JPKiNRaKnY/fR3zpplXLY5XQ8MPlQaXSyKF1hGnWiWJBzRcrN404ZeQkZ0DHEB9Nk73WTlJZhnmQJYS90qlterZCtR6gtcKyjPS6CnBsi4YX8OzJKeYWanTceCNMTMDHPgYnT8LWrfC2t72oxv5r4dyV7c7nGV5cJEwS7CbnPtGaRhTRXyqtzef/LsT3vMEHyBgmV3T2c0Vn/9de+VtEf39rk7Io6OwqIueqlMsecZKGGdYaYqSGjXPzyx79c+GqkD5/dT8Z2077A0ppEQQRaxMq0nhP2AiWNVEA4iCm0J6jupDyPLXSq0I7h0UH72l9C9fVTtOrqkzKAg9aG1lsJBRb86A0KlGYlkV7Tyst3S2MHR1HkzaPEU3Fwka5gZ2xaVR9qos18i05pJRoNOX5CvOTC7z1Z27m7vI0rdkSZrwid5DGXE1u2ryJs4tlFhqNZt1Zeh47ezq5bdclZC2L91x7FR8/8AwTlQrzV1/LDRcbyIXgwI03MWCajC2VkVLSW8zz9MQUZc+np1jAMU2kEExVa/zaF+7hj37w+xlsbQHgoadOMzlbppB1yDR53WEUc/zsLK1Fl/6uEvVGQM0L8IOYOFH0lWdwL3JfrcCnMDVG9+bVYm2lfBbTSGWA7eXm1un/iYLW8+1t+AyoabBvAKMVwmOgTkEwB+YOMDcism9DiBUevJSSKEqaM6XzrlUzvCVEOih0tOVT4/6e96x9Pb/N0Fqz6Pt0ZrOp/HHz+M4hUakTtaW9nWsGBtDAVLVKNQzTMjQh6Mnn+b6tW9eTtuv49iBfyPCyG7fz1fuepaenRLXqkS84eF7aveciTDfG3XY8aeOu0SrQkzYTz+n/adsmQRgRRQlu1qFeCzAMgWkaWLaBFJIoCAnqAWEUI3VqpP1GQCafYd+rLuMrH3uYKIiQZuqta53KI+RLOSpeyBfkJoQh0CrNN7iFDPteuZtCS44oTHDzDrlSlsnhGcZPTBKHMaZpIA1J5EcgBMWOwnKRQBIlSEeShCmVM1fM0onFUGuJ0aVUytYyJBU/lcZ93c5tfPHoSd6851Kqvs9ktYYlJQOlInONBidn59nT18Om9lZ+6ZUvZ7JSJUoSxB13wBvfuByK8BwHLQS/+4EPEmQyEMe057K0ZrPcsnUTT4xMIIVgplpPu0KZJu25LBU/4FPPPMt/vOl6tNaMz5SxTGNVkZJtmURxQt0L2bO9n7GZJTZlbJYqDY4NTzOW78S3nOUwzvkIbAdj+3Y2DXSsWm5Zkg39bZwamUUpsC0D0Hi+hWOH7Nqk08SPnodkBIwNzQIrAfYu0FsgPgPZH0TY119g7Pp6Wsi6FksVH8NMm4skSpEkinwug2kYmGvIeLyYOL2wwGeefZbJZh3AfKPBku+ztS3N19TDkPlGg1dt3Uqb6/LqrVs5PjdH0XFwTJNEKcI4JmNZvGrz5q+xt+8erBv8lwAvu2EbLS1ZHn7oBH2NNubna1SrPr6XhlbWwn2de3jv6Ytx8eG+rhWJV9OSSCMtaEnidHumKSkUU3EorVND1N6Rp7pUp0jI5DNnsWyD/m09XHbDTrIFlxt++BqevucwXi2N5XYNdvADP/86Pv+Ru5k6M40XpKEaIQVu1iVbzGBaktbullXHV1ussWXvBlSimRyeJvFCcqUs267YjFaK8nyV3s09jB+fpLZUx3ZtNu/ZQCabYerkND/xzpu4//QwDw+P4McJgy0l3nL5ZUQqWfbUSq5LyV3xUi3D5MzC4nLIRQpB/zna5M03L4ciDn/1QU6UWnno2us55YdEQUDBcdjZ3UmsNE9PTLHgNcjbNqZhoLUmTBImK1UKjsPhqWkAvCDCdSwqNX8N1lcqT/Fjb7yaRw+e5bGDZ7FME8MweHL3y9CPf2rN+yqkwRW//v9hNhPdcZzwyNNnePjpYTw/opDLEEZpeMeQkr7uDnraFDfsnUvpm7oBRj9Yu1cK+SBl5sgS6Pqanu1lO/rYvKGTY6emiaKEWKeDSrHgYkhBsZBhqP+Fz299vRgtl/mLxx8nZ1n0FdJZX962ObmwwLzngecta+1c2Z/O2nsLBX7+uuu4+9QpnpmeRgCX9/Vx65YtdORyL9m5vNhYN/gvAYQQXHrZAJdeNkCSKKQUPPrIKX7r1z5FHEfL3vT5oV3PdPjly97Fbx/6KEKnYRxPplz8X77sXfjGSml4LutgWSbFYgalNVnXZnx8kWzWIZ/PIKUgDGO8RojjOvS/ah/dV2wjnp6nr6uINCTzE4vYlsXv3Plf2XDpAGEY47oOo8fG+eQffT71ztsLGJYkjhLiMMZxbUzLYnpklmJbAaUU1fkanYPtFNry9G3uSWcJWiNlmlwdPnQW0zLo2dBJ91DHsrEUQjA3vkAmnyFrW7z2ku28Zsc2lNbL8dZjM3MXJZ4kSi0nasMk4dGzozx4+izVIGRjWwu3bNvM1ve8h5PXXM/jI+MMFPIMkF7zc0ZwvFzh1Pzi8v7qQUjUnIKljTRYbpxhGpLWUpZ6I2R8eokoSXut2paBm7HZtaUbx7a46aqtvPzKLSitueuho/zVJx7iD976i3zg4x9CaE0mCghsB4Tk0B/9JcZMjUfuOsT8Yo3FSgOlNZsHOti3c5Cnnh2l4UdsGmhnU38bNS9i61AHl+2+Iq3LiA5D459SsbQLoEjllS/EQG8LL79mG+WKx8JSA8cy0EAYJvz/7d13mFzXfdj977ll5k7dmdlesSiL3kiCIEiCRSySSEmkKilZ1ZKtJI5iJ3rf2E7sxM/rOHkcJ28S2Y5fR7EVSbYKbVmFsihRYhM7CYBoBIiyaNv77vR27z3vH3exi8XOopDA7gJ7Ps+DB7szd+6cuXvnN2fO+d3fqW2I0VQf43/9zQuUbIeO9lpu27ZyxhW0V9vTJ07g07QZ1S2rLIsV8Th1oRBf3L4dXYhZH2YNkQif2rp1xqTtUqMC/gI7m4e89YZlRCJeznM2V8Z1XGx75vjOm1XtPLLjd7l76CBNhVEGQjU8U72RouEH6c0L+PzekI2UktGRDKbPoL29lnS6wLr1zYwMpykUSlNlaq2Aj9bWBNnqMP2mQUG4BDTB2h2ruOn+LdS1esMJgYAXNN586SiO49K0soHMRBa7aBOK+gjFQmTGs2x910aC0QBHXjuObujc8r4bWXXDcv7P732HQraIFfKjT77RHNvBClkEoyFy6TzBSGDqTeo4LnbZZu32VVOvXwgx9ViA5YkYlmGQK5UJ+qZT6uzJ8dxNjfU4rsu3du/j8OAQNaEQNaEgfck0X315Fx+/cTNbm5t46VQXjuuia9pURzhdLGIZBhGfn4BhkCwU0Sc/pMD7ECnYNjuXLwO8b0ztTdW8ebwfy2+ilW2cyRRR23a5eeOyWa/jnltW8+q+0wzUVvHbX/4Lbjv6Ou35cQpty3h1zXZWtLTz1tMHiEcDBCyTg8eT6JogFPCxrKmamzcto3dwgu6BCVa3Gzy8Yy0bOhqnh5TMDqQwvSwccU5wl97CMcKcvtL5XEII3n3Xetpbq/nF82/ReWoI06ezflUj48kcPf3jJGJBQkEfnaeHOXpikE995BZaGuMXOtWvCCklR4aHqavQK49ZFqcmJia/dc495LQUA/1ZKuAvEoau4bdMaqNRHMelUCiTyeQZHZmZklfQ/VPZOJoGsViIHVvbePNAN67rDecU8iWYnBiVrovrSh5431ZOnhiiqSWOXXY4sL+bQNDHho3N6IZONBoguK6JsdEsH//SfYTDlXt/40MTCASBkEUgNHObzFiWfDrPfZ+6k9seunnGfe/7J/fzoz/7KZmJDMFIkEKuSDFf5PYPbyHaOsHjX3kZOeIQjFo4RRMnH+K2h26loX3uReJ9hsGjN2zim7v2kS4WCfl8FMplCo7Nu9d00BCNcGRomMODw7RUVSGEwJUSiSRdKPIXL77K799/N3esWMbzJ88QNE38hkGmVEQTgo/fsJm/3b2PRCjoLZztetUK5OTFegGfOTXcAlC2ncnxdC8DC6BYtjE0jbIze3LGZxp86gPbeOynexE1UYbWP0JX0aZYstm6tpn9R3tpqq1C0wR9QxOYhkYo4Od07zj1NVECfpNVbbWEAn5u3bKcretaZuxfiAAy8Ajkvj3Zyw95dfQpQuADCL16VpvO0jTB2lUNrF01nYX02huneOqFIzTWT/fmq+Mhkuk8P//lYX710duu+uSnEAKfruNIyfnfW5zJb39zBXQpJacnJjg+MoIQgjU1NbROnhdLhQr4i4Rh6mzY2MKrrxz3hjQQSOliGNpUT18I0HQN09Tx+7zJz4bGGPFEiFWr6mlsjjMwkGRsNE2xaGNZJn6/yfbtK3nPg5s5c3qE/XvP8OabPdTWRuhY3UAgOJ2KaRg6UkJP9xhr1zVVbGfTigakkDPqooDXIxeaoPG8jJKzVm1dzmf/8FH2P3eI/pNDtKxpZNVtPkTDT3HcLLf/1glOvGIzdtpPVUsNa3fG2XqjddE347r6Ov7lXbfx2pluuseTLK+Os72thRXVXm9zf28/AdNbUct2XPb39TOezyMQ5Mol/uTZF3lgbQe/tuMmdnX1kiwU2NLcwC1tLdSEQyyvjvNGTx8dNdWTxdTKGJpGwDTY2FjPgb4BHtq4jkKpTN9wkh1blzMylmF4PIMmBCtaqomELA539vOe22fXZVq1rI4vPnI7bxzuoncoSWtjkBvXtdA3NAGIqeyps6mR3u+SZLqAVT35rUZKdL3ycdJ8m5F6LbL4qjeur61E+HYgjGUVt7+Q/W/1UBWd3RGIhi0GhlOk0gWqolew6uUcbmlt5blTp2iJzhxGGs5mubm5uWKKZdlx+PaBAxwcHMSYPKd+3tnJjY2NfGzTJrWIuTL/Nm1u5eUXjyFxJ3uKHtPUaG2r9iZb/V7VxkKhTKlk83//zvvI5Uo8/sM9hCMWqyIWdEwH3eHhNIGQDyEE7ctraV9eS3VthFdfPu4Fe+mtiCUmV5xCynNWJZpt48611LfVMjGcxOf3YfgMysUypaJN08p6OratxJVlBMasYF3TXM29n7wTgLIzwanxP0EnSsE5RVW9yc0frsJ18wiRIWqtYTDzdwR8bfj0mkpNmVIfCfPQxspF7lwpp4ZhToyOMpbPE/H5pnL2a0JBfnnyNO2JOJ/atnXW4x9Yt5ofHDyM40oaoxGklBQch5hl0RqLUbC9InLS9XqQPkOnuT5G8zkT16WyQ6kw9wIbtYkw79k5c3ildyjJOZmyxCJBkGJqUv/s38hxvYVy2pvm7q0LvRER/NCc918q6c5dc+b8NYOvpjvb2zk8NERPKkXcshDAeKFAPBDg3pUrKz7mxTNnODAwQGt0+voaV0p29/WxLB7ntra2io+73iyNj7VrgJSSEyeGuPNda2lfXoum6+imQU1tlMbmOD6fwZq1jQQsH0JAOOznwfdtpW1ZDU3NXv1457xhAyklju2yYsXMYZH29hocxyFXOsVY/nnGcs8wUXiJXLHXW5iiee6x2JqmBA984V4altXhC/hwXRcr5KdxeR33fmEVY+IvOT7ye5wY+0NGc8/gyso55uniAaS0QZSxZQZtMg9c0wI4MocrvdIO6cK+d3BUYUNDPflyGdt16U2mCJtesHekRAiNWCBAld/ihVNnKj6+LR7jtsYWnGSJ/p5xhvomqCrpbKipJVUosnUyC8jyG7Q2xEllZl/QNZ7KsXFV42W1u70p4c0BTAb2gGWyrDFOOlekbDuEAz7S2QL9Qyluu2EFidjVzzRZv7qRVHr268vmS8RjQaoiV793DxDx+/mNW27hfWvWYJkmpmHwno4OvrRjB7HA7DZIKXn+9GnqQqEZH1iaENQEAvzy1Kl5afdioHr4i4TrSjLpAg2NVSQS01fOZNIFDuzvIpMpUl0dpromwsS4l05377s3AhCNBth+y0pefukY8USIQMBHqWQzNpJhZUc9zS0zU+gam+LUtfVy/NgA8biF3x8hny8xMH6EHbetparqwkXj7n70Nmrbw7z8j08zOjBIdVOEzffHia19HVdWYxnNuLLASPYJCuUumqKfQYiZfYuyM4YmfJMfCDMzKgQCV5YQwkfJHeWdWFtfS1u8ihMj4ziulx1Uchzy5TJr62onh2dMxrL5io/vOTOKvW8Cw4AWK4yFTn6syOvpU6xf38zOFe1em4Xg3h1r+MaPXkNKqIpYSAmjySx+0+CWLe2X1e6muirWr2jg0Il+amJh/D6D+poImXwRy29QLDvUxEK8Z+c61q1ouPgOr4CtG1vZd6iH4dEMiVgQTROkMwWy+RKPPrRtXsfCQz4fdy9fzt3Ll19027Lrki2XiVVYszZgmvSl0xULKF6PVMBfJHRdo7o6TC5bJBiaTrEMRyxWrW5goH+CkeEMCFi1qp477lpLdfX0B8Mdd3mB+pWXjzM0kMLnN7j9zjVsv2XljKtoAUpuF9vvOUUsUc/hAy6ZlEsgZHLHvT5WrN9H2XkAU4/N2daSO0Bw9Y+4+19m0YSFK0dIFZ8CuQlD89qkCQu/3kKmdIh8+RRB38yv2j69AdtJoWkhpHSm33CTk6q6CGC7Gfx65bmES+XTdT5/yzZ+cfQ4R4aGmcjnCRk+OqJx6iYX6MiUiixLzP5WI6Xk2ScP0hAIUR2O8FZxglGnAAGNqqxgh1lL9TlVMlsb43z2g7fwzKtHOXZyCCEEW9e1cPctHcSjl1d5VQjBB+/dTH1NlNf2n2Y8lSMU8PEr79vGTRvbFqQUQCRk8emP3sLzrx7nwJFTuK5DS2MdD793K8tbLzzstpBMTSNuWWRLpak02rMypRINkciSCPagAv6isuO2Vfz4R2/gt8ypdE0vPdHl8792Nx2rGxBCVFz/U9MEW29cxuatbZTL3hWtc5WezZaOYZo6N95qsnW7xLbBML19FGxJwT4zZ8CX0qU/9bdICZbhXdRSchw0/OTtTnxG7VTQ9yafDTKlt2YE/Hy5i4nCL8mWjwAC283gunlMrQ4p8phaDImGJnxE/VsqNeOyBH0mD29aj083+NYv92AP5RmgQL8cIVRlEW6KVBy/z2aKDPYnqWvwxn1vD9ZjS+9yfdt06Ds6DO8999hIUgNpMsfHqMo6CCEYPzZMdnUzNfHLry9jGDp33LSS225YTrns4DONWR/e880IjLLyphdo2DSM63qL5ITCPqSsXrRBUwjBPStX8ncHDmBNrlwGXvruWKHA+9euXeAWzh8V8BeRDRtbGB/P8erLx6fGboUQ3HnXGtZvaL6kN5SmCfz+i5V5FVMXLGm6wHcZ6zcX7G5KzshUsJ/ao/DSEYv2AIZv1cz7zrk6qmQP05P8Xwj8xKydpEv7vcW93BEct4hPr8bUawGHlqovYOhX7oIe7WQWa6jMeFDzKoBKyUgmS8NpPy3h2c8jpg/TFGNyaKqMM+vvcexwHz/5/m4SNRGik8Ni2UyB7/3NK3zy1++ioSn2ttqtaxq6f+Gn2/L2MMfGv44mfIR8DZNzIUXOpB9HCI3awM0X38kCubm5mbFcjudOnfLeW8I7K9+3ejVbGuZnSGwxUAF/ERFCcMeda9h6Qxs93WMIIWhuiRO5wpNhId9qRnM/nzVuKaU9uYRg+5yPdWQOzhuPN7SY93jAldOTel5tepuQfzqDZrzwspeFpFcBEA/spOyMe718maM5+gVMPULAXIEm5i5Zm0rmKZdtYrHQZCnm89ppuyQnchiGV6p5ZCjNvtdPcUdTCzkcJpwSGoKaiJ/xgTTH3+pj0w0zUxWDIT/1zTGS49mpAH5WcjzLrXeumfFaX3zmLaKxED6/QT5XQghvH8Wiza6Xj/OBjy7egHgphnOvzfjbAejCj6XX0pd5hmrrBjSxOEOKJgQPrF7Nra2tnJ6YQADL43GiFcb1r2dX5K8jhPga8H5gSEq5scL9AvgK8CCQAz4npXzjSjz39SgSCbBu/dWr3GkZbUT9N5Iq7sbUatGEhSMzlN1xaoIPYJ7zhj6fT6uBc8fcAU34CJqrSBX3IfSWyfIJBUruMBH/VgLnfIDkSscwxPT+BRo+vRqfXk3R6SfoW37BNMyRoRRP/WQ/PV2jCCEIBHzcfs9aNt/YPlWF8829XbzwzGHSqTz9PeOUSjaxeJCBvglKJZu29hrC5nRWi98y6To5PCvgCyF413s28djXX2DczlAVD+G6LmMjGaJVQbbePD1hWMiXGR/LoOsaR97soVQsI4FQyE/bilpOdw5d6p9n0UqWOvFps88NXbMo2ROUnBSWsXA1di5FLBBga4VMnqXiSn1P/DozRjNneQDomPz3ReD/u0LPq7wNQggaIh+jNvwhpHApOr3oIkhT5FNUB++94GN9Ri1h/2ZKTv+MfH1dixH2bSDgW0bR6UViUxf6II2Rj8/I0NG1IJLyrP16tYNcNC7cq//u119kaDBJXUMVdQ1V+PwGP/vRXvbvOQ3AoX3dPPHDPeiGzvBgimLB62mPDKfRNI3eM6OcODY4Y7+O7RA4Z6L8XM2tCX7lC3fR2l7DyFCK5HiOrTcv5xOfv4PQOVcj64ZGJpXnyJu9SCTBsDXVuz+0r9tbl+AaZ2gBXFnpb+etNKaL63/FqGvdFenhSymfF0K0X2CTh4FvSi9CvCqEiAkhGqWU/Vfi+ZXLJ4RBIrCTRGAnUrqz0iYvpCH8UQaATPEgQmhIKTH1OK2xf4NlNCNTScTf/T0c/0foOOotijFZ1bDK2sFA+jvoYmZmRNkdIeTruOCY/cE3TlMslKlrmO5l+i2T6toILz37Fus2tvDC04dI1ETIZYtk0gXCk8NhbrpAqVgiGPIxPJCkpa2aQNCH47iUSy5rN879jaqhKcaHPrFjxrzK+UxTx7ZdXNfFNI2p7fx+g0y6gFFhov1aUxvYzqnU9zC08IxjUHTGqPKvwtQjF3i0shjM14BbM9B9zu89k7dd0YA/MpJm394zdHeNEo0GuOHGdpavqF202QOLxeUEewBdC9Ac/TQle5iSM0zB7mU090uODP0rQq+PseLTzyNcgZYrIUNBxJe/7C17t3MnEf9mMsWDpEtvYogwQhg4bhpNCxHx30hf6tuUnEH8RhNx6zYsc3pd4c6jA4QrXNrv95skx3N0nx4hlytR5Tc4eWyAidEMuWyBUNjC5zfQdEE+W2J0JM0vfrIfv9+gpi7C+z60jXy2yPe//QqZdIHmtmq23ryc6pqZAexC51EhXyYUsbAmTIYHkpTL3pW1pk+npiaCXZ77SttrRcLayHjxEMnikcmgr2M7aQw9Qmv4wYVunnIJFtUMixDii3hDPrRd5qXOp08N8w9/9zoIb9y0N5Xn+LEBtm1fwb33bVBB/yrwGbVM5PdyJvknXomGVIaNn9qFnp0evhBZb0lDHnwQ+vrQwmGaop8iXTxMqrgLVxaIBW7HdQsMpL+DJix0ESRTPEiqsIeGyCNUWV6xOL9lkK1wJaucvCLVH/CRz5c4fWKI5HgOx3UplxzGRjKYpk4kajE+lqVUsrF0geu4DPYn+fE/7KKxJUEkYuHzmxzYc5r9u0/zkU/uYNmKuYu3nUvXNS+jZ3JJSDG5MpRA4EgXn39RvdXeFk2YrKz6OBOFI4wU9+LKIvWBW6m2tqje/TVivs7CXqD1nN9bJm+bQUr5VeCrANu2bbvkwhyO7fKTH+8lGPYRDHpjsYGgj0jUYs+uU6xd10RLy+KeTLoW2W6O7tRX0EUIiUvsH0+cXbxqNtf11j79whcQwiBqbSZqbQag5Ixyavw/49Mb0CbHgXVCuLLIYOb7hHxrMbQwm25o5yf/sJtwZGZRtdREjsbmOC1tCXKZIoV8iXh1iGJfGV3X0HRJLlsklymgGzqRaIDGljgBu8j6t17GevMMxrq15N7/QWzLRyDoI5ct8sQP3uCLv/XuillA5/P5DSTe89TWz5zYHBlMTg0tXes0YZAIbCQRmJWboVwD5iu593HgM8KzA0heyfH7/v5xcrnSVLA/S9M0DFPjyOFZny3KFZAq7MJ18+haCEemsE6X0HNzTE5ms95C15XuKh0BKaaCPXgTgQIDKW3y5RMArNnQxMo1DQz0TpBJ5cnnSgwPJHEl3PvgZkaHU1gBE5/foFSyCYX8FAplivkymi4oFG00XSOWCLFiqJN/960v85Hdf8dHel7gPc98g4/+zseo7TwIeOmU2UyB7q7RCxaTO6tUtBFSEIpY5NIFymWbUskmkyoQrw6TTlUu3aAo8+lKpWV+B7gbqBFC9AB/AJgAUsq/BJ7AS8nsxEvL/NUr8bxnlUoOc43YGLpGIT87s0B5587NyZfSpdAewAlqFYO+DAURq1bNuh3Alfmpi7McN0uufIKSczaN0aBg9xLxb8EwdB5+dDtHD/VyYM9pCvkyN926Eiklf/bHT9B9ZoR0Mk91bZiauihFq4xEks0UKRdtpCuJRC3ipsuv/fS/Y5Wnh4cspwROiXv/7Hf57n98jDNDOU4dH+Lr//Mplq2o49a717JmfdOcQ4OO42L4dDbf1M7wQIqRoRSaJmhpqyZeHVLnoLIoXKksnU9c5H4J/PMr8VyV1NVFkdJ7051fTqBYsFm2vPZqPfWSFvZt9GrfuDa6FmT0A3Ha/8PpyhtrupetU4FlLEPiYDtZUsXXkUg0EQIJZTnCSPZJIv4tWEYzhqGzYUsbG7Z4czzPPnmQb//18wgBNbURSsUy46NZrxBdUxy/3yQW92oUDfSOMzGe4/YTe5lztXjXxXr8+3Q3bMP06bS011AslvnRY69x3/u2cNMtlcvvWgGT6poIxWKZlmXVtCybLlc8OpJm1ZrLq5apKFfDwl+vfQWEIxY3bWtnaDCFbXvZEK7rLfEXiwdZvWbpXDo9nwJmK1WB2ym5w2gEccMGb319DXZIwwl6p5YbNJHhIOKJJyBcuZ5M0FxBwFxOurQfFwddCyEAhwyW0YquRRnJPjnrcdlMgZ//eB9CE0RjocnhmjC6oVEslOk+PUww7Me2HUxTZ3lHPY7tEB3q9Xr0FZilAuHBXu8q59YEfr9JKGxRWxflhacOUyxU7qkLIdh573pSE3lvxTG8yeRMKo/rSLbf3vE2jrCiXFnXfurApLvuXo9pGux6/STSlTjSqwN//3s2XUJtGeXtWpX4t5ya+O+M555FEz5SN/vZu/sWmn/qxzpTxr/mToKf+jdTefiVCKHTHP0s47nnQbo4bhoQWEYrIbMDEGRLR3BlaUa5hb6eccbHsgQC07eFwn5AMtA7TqnokkkVqIoFWbG6gWDQh2nq9HQnyGs+Au7soF/QfQyGamhdXkNL2/QVv6bPwHFcBvomWLai8jfGjrWNfOBjN/Pcz99kaCCJlFBdE+b9H715xrUDirJQrpuArxsad9y1lu07VpJK5rEC5hWvQaPMpmkWrVVfJGh0kCkdwNTqidfvwP+lRnxG7YyJ2AvRtRBB3yo0EQRcNGFNPVZKF6+M2czxcyG8r6hSQj5XIpsp4LqSQMDrlZdKZZZ31JHPljjVOUgkGmDlmgb29tyK1v0kVBrVERqZBx6irXVmULdth9HhFE8+/gbVtVE2bGll5ZrGWZVL121qYfX6JsZHM2iaRrw6pFKClUXjuhjSOZffb1JbF1XBfp7kyqc4Pf7fSJf2oAkfZbefgfR3SJcOIi6zPxG1bsKWKQwtMuODouyOEvZtmPXh0dSSIFYdYnhwgpHBFMVCGbvsMD6WJZctomk6pzuHGB1JUyyU6e8ZZ9+u04holH/41L+jbAUp+70Lucp+i6IvwC+//CfkNN+MzJxS0Wbf66e8WjxFm4HecR7/+118/9uvUCrZs16HrmvU1EVJ1IRVsFcWleumh6/MPylt+lPfRhcBDO3skE0EKR3Gck8R8a2fcaXsxSQCd5EpHqToDGBqNQgEJXcUgUZN6N2ztg+G/Nxw8wqOHOxF0wW6aYKUuI6LpmsI4V0HZZgGhqF53wSyRRI1YQZbWvjqv/4aW0/uIjzcy2CghtPb3sVH/8n9pJ88yMnjgySqw5g+g6OHe0kms2zcuoyquFd0LVIV4MzJYQ7sOc22WytnHynKYqMCvvK25e1ubDeFZcxclUoIHSEMUsV9lxXwTT1BW+xLjOWeI1V8A4lLxL+F6sC78BmVr3h1XJctN7fTc2aU8bEsQoOGpjhViRC9Z0ZYtryW4cEUuWyRUNhieUcddtnh7ndvYKBnnNd9AcQ6wYYtrTxyewexeIiHHtnOvl2neOO1E4yPZshlimzdtoLq2ul5CCEE8USIva+fvL4DfjrtXTB3/Dh0dMyoi6Rce1TAV942KYvMdQGEwMSR2cvep6knqI98mPrIhy9pndFCvkxjc4IVHQ0zipud6hycGlppW147Y1+D/RMEgn4e+NBNvPeDN0495iyfz2D77R1sv72DXLbIX/zXn84I9mcZpk46NbvUw3XjxRe9khiu6104FwrBOXWRlGuPCvjKLEW7j4n8q+TsU5hajJh1KyHf2llF1nx6PUiXspOi6PRSdie8BTGMVhxZJGhUzlm/kIJTYv/4GQ5MdCGly4ZYK1vjywkZlcsXL1tRy97XTxII+mYE7UDQy9zxW964/9n7MukCfd1jPPPEfna/3Ekg6COdymP6DDZtbWPtphZ8PmPGfuKJ2WsNA6ST+UuutXPNSae9YJ9OT9+WnfwAn6yLdH6are3mGMnvZax4ABBUW5uptm7A0NR82mJx3U3aKu9MpniE0xN/SrK4G+mWKJS76Ul9jaHsj2eVGDD1OJbRxnjhWQrlHpAOZTvJROFVbGeUkG/DZT13zi7y9RO/5Gd9+0mX82TtEk/1H+SvO58mVa5cmmDrtuXoukY6lZ9qX7nsYNsOK9Y0MD6Wnbp9dCTNay8ew5UutiN58ZnD/Pjvd3HsrV6S41l+9vhevve3L1MqTk/ECiHYec86kuM5isXpHPxctkipZHPLzus0v/6xxy54cRqPPTbjppKT5q3xr9KTeZKym6PsZuhO/5Sj439N2b38b3rK1aECvjLFlWUGMt/FFFX49Xp0LegFdb2ZifyLFOwzs7YvOUMEjHaE0HBlEYSNT29A18KU3ctb5em1keMMFiZoDMQIGxYhw09jIE6ynOO5wUMVHxOvDvPIZ3ZiBXwMDyYZGkiSTua4971b+NK/fh+tbdUMDSQZ7J/g0N4umpoTbL1pBQO94+iGRnVdhLGRDNKVNDTF6O0a5c19XTOeY/X6Jh744I0UJmv3DPUnAfjIJ2+loTl+Wa/xmnH8+HSP/nwV6iL1Z5+jaE8QNJswtRCmFiZoNpN3hhnMvjQPDVYuhRrSUaYUymdwZB5TnxnEhNAQwiRdPEDAbD9n+y5cikT8W3BlabImjokmApScIVLF/TO2v5jdYyep9s0eK6/2RTgwcYYHm27A0GYvJNLYEudXf+MeRobS2GWH6trIVDnij376NibGs5w8NoBtuzS3JiiXHcZHMwTDfoQQaJrGyHCaaCxINBZk/+5T3HjLinNev2DTjctYu6mF0aEUmq5RXRuZVcbjutLR4Y3ZVwr6oRCcUxdJSpfRwl4sY/bSlJZew3BhF83h+1WK6iJwHZ+xyuWSzM4pP0ug48iZwyreUoXT69oaWhW6FpysBW/gysurEFl0bXRt9impCw3HlbjMXbVSCEFtfZTGlvis2vOxeIh4dXjqIqmzyw1OrcmriakFSgxdp1isfBxMU6ehOU5dQ9X1HezBy8ap8LcAvNvPqYskcXCljWD2h7F33hSvViuVy3Sdn7XK5fDpjQgJUnrB79zw6soiIXP1jO39ehNCyqntmbF9geB528/l7Bh7R7ieiVJu1v1pO09zMI4pLm+ZwHPnHGrrq2CywJ7PZ2BaOsWyV1rBsV1iCS+/PpXMsXJ1/WU9z3UpEvGycSIRr0cP3v9nbz9nwlYTJmHfMspuctZuSu4EVb4O1btfJNSQjjLF1Kuosm7lzNgv6Bn2kcw7+A1Ba41DU7yVsG/9jO0NPUoscDvj+efx6fVowoeULiV3GJ9eQ+QCk7ZSSo6l+nh+6Ah9+XEipkVHpIGiWyZdzhM2vEVOsnaRjF3kQ623XFLQcKXL/vEuXho+wmgxTcIf5vbatWyNL+PGHSt44sAbdLWPMfyBFOWCTXjAT/vJahLVYVITOYQQ3Ljj8rOLrks7d3rZOI895o3Zr1rl9ewrFMFrDt3D0fGvIYSBqYWRUmK7GRxZpDF09/y3XalIBXxlhlP9W3j1xBGaa46RCIMjXQ51NdDVv4P2nbPr4tSGHkQTFuP555HSRuIS9q2nLvxBNK1yKiXArtFOftK7l6gZoMGqouja7Bk7RY0vghAwWPR6izEzyCeW3cbKyKX1un/Rf5CXh48S84VosGLknRI/7N7FYCFJ4oYQh+mjkCvjLxvoUpBtKXGieYSGEwlWVNdz34NbZq1lu6SFw/CFL1x0s4hvOatin6Y7/RNytre2kaVX0x7+MOHLuPhOubpUwFem5Etlntx7gqrQTkbGbkXXs7iuH8cJcHggxcnBMToaZ07MCWFQE3o3icBdXh6+FjynzEJlBafELwYOUmdFMTXvFLR0k0YrxkBhgk+230HCH8ZFkvCF0C5xkfWRYppXR47RGIhNPSZo+LF0k1eHjtJbGCcaDNAcTeA4LprmTdgO5CYIrw7yqS13qaGHdyDmX02VbxVFZxwAv55Qx3ORUWP4ypSe0SS26+AzdKT0YdtxXNebhPWbBoe7B+d8rKb58Rv1Fw32AD25MVzpTgX7s4QQmJrBsXQfCX+YGn/kkoM9QFdmGAmzHqMJjYJr058fJ2IEEJrAMHU0XQMBMX+QA7kzKjhdAUJoWEY1llGtjucipAK+cskuZW3XS9oPEsncwWCOy30uYb/nF1CefX9lgiv00hRlUVMBX5nSnKhC1zTK9sysGyklxbLN+tYrk73SHEigCSi7s5+n5NqsjTbN8cgLawt5w02unPmR4UqJpRk0WDEyzuzaN2k7z7bqFbNuV5TrjQr4ypSg3+T+LasYmEiTyRe9AGzb9I2lWNlQzYr66ovv5FKex/BzT/1GhgpJsnbBex7Hpq8wwcpwPcvDb68+Ta0VZVtiJX35cfK2l3KZd0r05cfZVrOSX191LwWnTLKU81ZFc11Gi2mChp+PtO24Iq9NURYzNWm7RI1n8rx+vJsjvUP4TZ1tK1vY3N7IbWvaiQYCPPtmJ/3jaQI+k51r27F8Bn/55CtIYEt7IzetaCFk+ebcf7Fss+9UH3tO9lC2Xda31rN9VStVIW/BkVtrVhM1Azw/9BaDhSSWbnJP/QZurV2NPjkGP5if4PXRE5zKDlGwS0gJQcPHykgDN1evpNaKznre9zZvJZIv0/eNvyRwqovBllrsB++m0x+i7Do80nYrj/fupjM7iC40Nle18Ztr30tzMDHna0mWcrwxdpLDyV5MzeCGeDub42349cW5dKYrbcYLhxku7MJ2MkR9K6kN3kLAqLw0o7J0iCs1Lnulbdu2Te7evXuhm3FdGphI89dP7aJYLlMVDGC7LslcgRX1CT5z9434DGMyj9olXyzztad3MZLOUhXyqh4mswUS4QC/dv92ogFr1v4LJZtvPLebruEJqkIWutBI5goEfCa/fv92aqKhqW2l9NYf1oU2Y5KvMzXAt8+8hI6gLz/OYGECgaAxEKPOqgIEn15+B8vCM4PY2NM/JfTwR8B18eeLFCwfrib48//2m4xs20RndpAVoXqaAgnKbpmcW+LmxEre13xjxUnGkUKKr598jpxTosoI4uCSLOdoC9bwyeV3YC2yoC+lw8nU9xgrHMDUqtCESdlNAYI1sc8S9i1b6CYqV5kQYo+Uclul+9SQzhL0k91vIaWkIR4l4DeJBPw0J6KcGhxj/2kvh1oIganrvPjWacYyeZoSVYT8PkJ+H02JKBPZPM8fOllx/3tO9tA1PEFzdRVhy0/Ab9IQj1B2HH629+iMbYUQGJo+I9jarsOPenYRNSw0oTFeylDtixD3hRgppvFpBkHdxw97ds0Yr5epFOGHP4o/m8ef9y7ntwolgrki/+LLf0r/UA8RI8BgYQJLN4j7wzRaMXaPnaQrN1rxtfy8/wAl16HBihEwfIQNi+ZAgu7cKPvGTr+TP8NVkSx1MlY4SNBowadHMbQAAaMeXQQ4lf7B5PrAylKlAv4Sk8oVOD08Tjw8s0a5EIKqoMWuzp6p26SU7D7RQ3UkOGs/NdEwu0/04rqzvyHu6uyetX+ARCTI0b5hcueUGa6kNz9O1ikRNPwMFiYwhA7Ca6MuNIYKScKmRbKcZyA/fTl//tt/i3Rnl3kAEK7kxqdfx9JNXClJlr0SDprQMIXOoYmuWY/J2UU6MwMkfLOvLI2ZQd4Yr/yBt5BG8m9gaLMXTvfpEYrOOHl77tRa5fqnAv4SU3ZcBKLi8IWuaxTL04XDpISS7VQsFKZrAsd1cSsMCRbLTsUiaJoQCAT2HEH5LNt1ptIrHemindNWIQT2ZC9VAPa5dXw6j0/17M9nFUo09k734p1z2m1oGkVndsG0s1lEWqVjJSo/ZqG5slSxiBmAQOBeoECecv1TAX+JiYUswgE/+Qq97FSuwLqW6QwZTRN0NNYwkZ1d9XIiW2B5XQKjwofB2ubaio/JFErEwwHC/rlLLgDUB6oAL9gnfOEZ6ZuOO32bLjTqzpm4tdasoxSovO+C5WO4pc6rlCkgYkzPPRQcu2LphohpETNDZO3ZHyKpcp41bzN99Gqq8q/GrrDgiCvLCHQCupq4XcpUwF9idE3j/s0djGay5Ete0JdSMpbJYeo62ztm1j25Z9NKimWbVN5Ln5RSks4XyZfK3Lu58uLdt61Zhq5pTGSnV6HKFUtMZPO8Z8tqNO3CV2CGDYvbalYzkJ8g5gvh102y5SJZu0DA8BExLIaKSXbWrcXSpzOFtI9/Al2vnHgmNUH3B+5luJim1hclYPhwpctwIUWNP1wxeGtC477GTUyUsuQm0zyllEyUsmhCsL1m8S1enrA249OjFOzhqfF6xy2StwdoDN2Nrs2eZFeWDpWlswRJKdl3qp9f7D9GplBESmiri/GBm9bTEJ9dGuHEwCg/2XOE5OAQm179JS1jw7Tt2EbdP/k1r1xuBb1jSf5x11v0jCURQDRo8Z6tq9m0rPGS2uhIl1eGj/HS8FHSdp7e7CgIQUuwmogR4M76tWyvXjWrjIJ84QXcBx/AcWx8k1k6UhP8zZ//DsM3b6HKDJIq53Emg+GaaBPvbdpK1Jx73dU3J7p5qv8AabuARNIcSPBA0w00BRfnalcFe4zuzE9IljoRCHRh0Ri6m7rApVUcVa5tF8rSuSIBXwjxXuArgA78lZTyj8+7/3PAfwF6J2/6cynlX11onyrgX32O6zKRzWNo+lR+/FzkCy8gH3wQISUim/Vqo2uaVxt9587Kj5GSZK6A47rEQoGK4/oXU3ZtkuW8l/4ooeCWiZnBiitfTclkcL/7HQpHjyA6ViEfeYRcwPtm4NfNGfsMG5fW43Wky0Qpiy50qszANRE4y04aRxbx6V56prI0XNWAL4TQgWPA/UAPsAv4hJTy8DnbfA7YJqX80qXuVwX8RSSdhuZm7//zRSJezfSwVwO9bzzFoa5BimWbFfUJVjfVYhozg7PrSs6MjHOkZwjHlaxprmVFfQJd03ClS1d2hKOpflwpWR1toD1cN3UxViU5u8hbqV76c+PEfCE2xFqIV8isAW9C+GRmkM70AKZmsK6qmebA5VV1LDhljqZ66c6OEjYDbKhqqXgRmKIshAsF/Ctxpe12oFNKeXLyyb4LPAwcvuCjlGvHY4/hOk7FCR/HcdAfewz5+c/zs71HefGt0xi6hq5pvHasi/p4hM+9axuRyclU23H53isHOXimH9PQEcCrR8+wsrGGR27fxBMDb3Ao2YMpdISA10ePsyJczyPLbq14ZWtfbpy/Pf08BaeMKQxs6fDs4CEebrmZzfG2Gdvm7CLfPv0ivbkxTM3ARfLS8FG2JVbwYPMNl1SZc7SY5m9OPU+ylMen6djS5ZeDh3h34xZurb20Fb4UZaFciYDfDHSf83sPcEuF7T4ihLgT79vAv5JSdlfYRlmEnGPH0HOzlx4E0HM5Cm8d4UzfCC8cPk1jIjJj6GZwIs0Tbxzh0du3ALC7s4cDp/torq6a6lVLKensH+Gb+16l3xqkORCfcd+JzCAvDx/jXQ0zV9CyXYe/63oZDY0GKzZ1e9Ep83jPLlpDiRk9/WcG3qQ3N05jYHrs3ZUuu0ZP0B6qZeN5HxDnk1Lyg+7XKThlGgPTz2e7Dj/v309bqOaCJRoUZaHNV5bOj4F2KeVm4BfANyptJIT4ohBitxBi9/Dw8Dw1TbmY0bpGiv7KY91Fv0V3rIbXjncRsnyzxulro2EOdQ2SKXipjS8dPU0iEpwxhCKEoLYqzC8HDpMwQ7Puq/FHeH20c1YVzO7cKKlyftaEq183cZEcTk5fRFZ0yuwbPzMjjRO8TJyoGeC10c6LHofhYoq+/DhxMzTjdkPTMTSd/eNnLroPRVlIVyLg9wLn5vK1MD05C4CUclTKqaXr/wq4qdKOpJRflVJuk1Juq61V+cKLxeB73w9zjXELwam772M8k8fyzf7CqGkCBFN5/8lsAcucPTTjM3QKsoRZYTLWpxkUnfKscsp5pzRnm02hkypNXwtQcMpIZMW5AL9uMlGu/A3mXDm7OOdFa37NYKI0O/9dURaTKxHwdwEdQojlQggf8HHg8XM3EEKcm4v3EPDWFXheZZ7EG2r55m//IaVAkJLl9fRLlkUpEOSbv/0fqGtupLUmRqbCVa5l27vqNhr0HtdSHSVdmL1dtlgioUfIu7ODeM4uEvOF8J23QlbcF5q6NuB8Jdeh/pxhl5Dhx6+ZFJ3ZF5xlygVaAhdPsYz7wkjkrG8aADmnRIsazlEWuXc8hi+ltIUQXwKexEvL/JqU8pAQ4g+B3VLKx4HfFEI8BNjAGPC5d/q8yvxprq5C7ryD/7ri2+zY+ypV/b2kmlrYdePtyFCQtc111FWF2Xuql3ypTMDn9eBdVzKYzPCujSvxm96pduf6FXzzl3sI+syp7B3bcRlL53johhvYWz5OQPdNLX9ouw5jpSwfat0+q2fdYMVoD9fRlRuhzheduj9ZyhEy/KyLNk9ta2g6O+vW8GTffhoCsameftEpU3TL3Fq75qLHocoXZFNVGwcmumiwpucgsnYBQ+hsjre/g6OsKFefuvBKuSTpfJHHXtrPmeFxBAIpJdWRIJ+44wbqY97E6OGuQX7w+psUyvbUNjevauXBm9ZOlWCQUvLasS5+tu8YrusipTfsc/eGldy1YTm7x07wi4GD3oVR0hvDv6tuPXfUra04lJKxC3y/6zVOZ4e950QS94V4pO3WGT188HLpn+4/yKujnYjJtpiazvuab2Rz/NLKBhecMj/u2c3hZC8CAUIS0v18dNmtLAvVXHwHinKVXfULr64GFfAXHyklAxNpxjN5QpaP1urYrDIJJdvmzPAEZduhMR6tWDUTIFcs0z0ygSslrTVeGeWz8naJ7tyod1VrMHHRi6OklAwWkoyXsgQNPy3BxAXz9lPlPH25MXRNozVY87Zq2o8UUgwX01i6SWuw+sIXginKPFIBX1EUZYlQC6AoiqIoKuAriqIsFSrgK4qiLBFXorSCcg2QUnJmeILD3YOUbIfVTTV0NNbMKmxWKNm81TPIqaFxQn4fm5Y10BiPXPXqkMl8gQM9Awyk0tiOl+dumQbrG+tYWVvNUDrDwd4B0oUSy2virG+sm0r/HEim+ccDR+jq6uGOPa+ytZil2N7Onh13kLcsBAJNCJrjVaysTXBmdJyusSRVQYvNzQ3URysXWjtX2XHoHBrlrYFhDE1jY1M97dXxi9b2VzxSSs6MTHCod/L8a6iho6EGn6Emu+eTmrRdAlxX8qNdh9jd2YOh6+iaoGQ7NMUjfPZd2whZ3iIio+kc/+eZ3Uxk8/hNA9txcVyHezau4l2bVl61oH9iaJRvvLqXkm3TNTbBcCaHoQlW1VYT9Httc6XEp+uYuk7RtqkKWPzazm2cGhnn//nHp+k4doT/+NX/gZCSQKlIwe8HofF7X/wtDq7ooKkqSpXl5+ToOO2JONXhIGXHwZWSD2xey20r507LzJfKfP2VNzgzOo7PMJBSUnIctrY28rEbN1Vc9UuZ5rqSH+45xK6TPZiT51/RdmiKRfjVu7YR8vsuvhPlkqlJ2yXuza4BXj/eTWMiSn0sTE00RFMiysBEhp/vPwZMFgZ77U1yxRJNiSjVkSD1sTD1sQhPH+zkzPDEVWlbsWzzt6/vJ+jzFhfPFMvUR8NUBSx6kyn8us6eM94FXQ1VEarDQZpiUQrlMt9+fT//6afPEi4V+U//+38QLBYIlLyreK1iEauQ54+++j9oNXTGsjnOjCcRCAbTGWJBi4aqCLWRED8+cISBZIXSz5OePnKCrrEJmmJRaiMh6qJhmmNR9nb18UZX75yPUzxv9gzw2olumuJR6qvC1ERCNMejDCQzPHng2EI3b0lRAX8JeOXoGaqC1qzFuGurQuw71U+hZDOWyXF6aJzqSHDGNrqm4TcN9pzo4WroHB4lXyoR8vvoGpsg6DMRgKnruK7k+PAoIZ9JfzI9Y8H0RCjI3q4+kvki9x3Yg5jjm6qQklt3v4IARjNZqgJ+So7NRL4Ak8+jCcG+nv6Kjy87Dq+f7qY2MruoWyIU5KUTqmDaxbx07AyxCudfXTTEvjP9FMpqYfX5ogL+EpDMFSoWNtM1DYmkUC6TK5bRxByFwUyDZIVFya+EXKnsXbEKFG0H45xqm0J4wyl+08CREteV59wnKDsuEkn94ABWcXZ9HvB6+nWDAyAEztTjxdQ8AYDPMBjPVX59Rduh7LiY+uyxZr9hTH1wKHNL5QtYZuXzz5WSQml2fSPl6lABfwloq42RrlDYrFi2sUyDkOXzrogV3rKH58sVSrTUxK5K26rDQSbjPVHLT9Ge7u250uvJ50plAoaBfs4EqeO6hPwmOhoDdQ3emH0FBb+fofoGJBJT9wKMlJLAOQGoUC7TFq+q+PigaRINWFMLvp8rXSjSloi9jVe9tLRWx0hd5PxT5ocK+EvA7WvbKZZsCqXpYOq4LsPJDHeuX46p64QtP9tWtjAwPnPoJFMoITTBTStbrkrb2hNxmmIRhlIZliViFB0b23HJlcsEfSYrqxOUHYe6aHjq24crJf3JNPetW8WK2gQ/Wb8FOceEsovghRtvQROC1niMkUyWeNAiMlnKITnZ+9zcUnlxdU0T3LNmBcOZ7IxvBUXbJlsqcffq5Vf4iFx/dq5pp1i2ZwzdOK7LUCrDXeuWV/z2pFwdKktniThwpp8fvXaIkuNMFRnbubad+7esnkotLNk2P971FvtO9U3Wv5eE/T4euX0Ly+uvXunfZL7Ad17fz+mxCUYzWbrHklimwYqaBAGfyebmBjqHR8kWSyC8omxbWhv58Nb1jOcK/MHjTxF4/bWKWTp/8M++zIH2VSyvjhOyfGSLJQKmgd80kVISCwb45PYttMzRwwdvQvuZoyd55sgJXCkRgKHrPLR5Hdvam+d8nDLtQFc/P9h9iJLteB/cUrJzTTvv3rRapbZeYaqWjgJ4Ab1reALbdWlKRIkGKhclG8vkGJzIYJkGrTWxeUk7lJO99ol8AZ+uY08OLbXGqwj5fdiOS9fYBAXbpj4S9oaCJrmuy+H+Ybp7euh45mlWTIxRaG+n6/73oEUiU2mAiWCA+miY4UyWkUyOgGnSlqiatUrXXDKFIj0TKYQQLEvEKo5LK3Mr2TZdIxOUXZfm+Nznn/LOqICvKIqyRKg8fEVRFEUFfEVRlKVCBXxFUZQlQs06KYoyP9JpeOwxOH4cOjrg0UchElnoVi0pKuArinL1vfgiPPgguC5ksxAKwZe/DE88ATt3LnTrlgw1pKMoytWVTnvBPp32gj14/5+9PZNZ2PYtISrgK4pydT32mNezr8R1vfuVeaECvqIoV9fx49M9+/Nls9DZOb/tWcJUwFcU5erq6PDG7CsJhWDVqvltzxKmAr6iKFfXo4/CXOUrNM27X5kXKuArinJ1RSJeNk4kMt3TD4Wmbw9ffE1h5cpQaZmKolx9O3dCX583QdvZ6Q3jPPqoCvbzTAX8JUpKyUj/BEM9YxiGTuvqBoJhi0K2SNfxAUrFMrWNcepaE5e8eLnjuPSdHCI5liEUsUjUx+g/M4xjuzQuqyFRP3cJYvCqXvadHGZiNE0wbNHa0YBZYaWu+SClZKAwxkgxiU8zCRsBzmQHGC+lqbcSVPlCFJwSft1kWbAevz69iMdYMUV/YRSBoC1YT9gMLMhrWHTCYfjCFxa6FUvaFXk3CSHeC3wF0IG/klL+8Xn3+4FvAjcBo8CjUsrTV+K5lctXLtk8+e2XOfrGKZisjW8YOh1bltF5sAu77AAS6cKKDS08+Jk7sIIXXpVoYiTND//3M4z2J5FSMjY4wehAktaOBgIhC5BsuGUV9z+6A92YveBFaizDD//3Mwz3jnN2CaxgxOLhX38XTe21V/wYXEjeKfJ470uczgzgAr25IQYKYxhCRxcaWaeIT9NpDdZT66/Cr/l4qPk22sONPDWwm30TJwAJUiAE3FW3le2JtZf8wakoV8s7HsMXQujA/wQeANYDnxBCrD9vsy8A41LKVcB/B/7zO31e5e17+af7OLLnFHUtCepbEzS0VmP6Df7hL36BlJL6lgT1LdXUtyY49VYvz31/1wX357ouj//1s6THstS3JrCCPiaG0/gtk6HeMaobqqhtTnDwlU5ef+rNWY+XUvL4137JxEiG+lbveetbE2ia4Pt/+RS5zPyuG/vz/t30D51m549e487/+i02f+85zGyBsmsjAV1o2I7DcGECn2YSNPz8oOcFnh3cy97x49T5q2iwEjQE4lT7ojwz+AYns33z+hoUpZIrMWm7HeiUUp6UUpaA7wIPn7fNw8A3Jn/+HnCvUN2dBVEqlNn3wlFqGmMzepxjA0l0U2e0Pzl1mxCC2sYYh3efJJuaexHzvlPDDPeOE6+LTv1u+A2skB+77DA2lETTBDUNUfY8e3jyG8S0/jMjDHaPEq+dWVclFA1Qypc5fqDrsl5jspThlZFD/Lj3JV4ZfpOJ0qVfyZkqZ8k89wt+4+4vsf0/fJXb/88TfOYrT/C1D3+FNQe6SJdz+ISBrumU3DK9+REs3YeUkqcG9pDwRdHE9NvK0HTCRoBXRw5f1mu43riu5OTQGD/ac4i/f+0AB7r6KZ2zfrEyP67EkE4z0H3O7z3ALXNtI6W0hRBJoBoYuQLPr1yGbDqP67gY5sxhlWwqjxX0kU3nZtyu6RpCCFLjWULRymPR6fHs5JKInlw6PzX2rmmCfMZbwNr0m5RGMuQzBSLx6bzs9FgWARWHPAxTZ7R/4pJf34l0Hz/sfQFXuvg0kyNuNy+NHOLhltvpiFx8Xd702BAf/af/GV92+luFVfAWMP/93/4un/neP4eqADoajnTJ2d52hqaTsrP4dXPWPoO6n+Hipb+G643junx/1yHeON2Lz9DRNY29p/toiEX5/F03EbYqL0CvXHmLKi1TCPFFIcRuIcTu4eHhhW7OdSkQshBC4NjOebf7KRXKBIIzl51zXYl0XUKRuZejCwmHTW8+x9Ynvs6q154kasqpXrzrSPyT4/922cEwtKnfpx4/xwcJQLnsEKu5tIqKeafIj3pfImRY1FlxYr4wdVaMiBngx70vk7eLF91H7Ac/QbiVV4ETruTOZ4+ABBeJLjSsycnasusQ0i3K7uxea8EtEfMt3aqQB7oG2HOqh6Z4lLqotzxlc6KKoVSGnx04vtDNW1KuRA+/F2g95/eWydsqbdMjhDCAKrzJ2xmklF8FvgreEodXoG1LTmosw9G9Z0iNZ6htTrB6cxtWaLoHZQV9bNi+kgOvHKeuOT7Vq65pinH6aB+1zfEZ+xsdmGDFxhaiiTnS5158kdYHH6SxUMIsFyn7/NwoBX+64VP0NHSg6xqJ+iqklIwOJLnpXesxfQa9J4c48WY3riNZtqaRqpoIydEMVdXTz1PIFTFMndVbl13Saz+dGaDs2gR0L7hKCVk7z2gpxWgpxfND+7mv4SZ0bfak8VmhU92Qr/zBECiUaetP8by0caUkZFg0BWoouTZMTs6+mTxFgzV9XF3pkirnuKfuxkt6DdejVzq7qApaaOd9g6uLhtjf1cf7tq4h4Jv9zUi58q5EwN8FdAghluMF9o8Dv3LeNo8DnwVeAT4KPCMX62K617Cje0/z02++gCMlpmlQLh3lxcff4CO/cR/1rdVT293x0E2MDaXo6RxA0zTcyT/FvR+7hcHuMQZ7Rr3bHZe61mruf/TWyk84We1QpNOcfbuaJS9YfunNv+HLvi/TsL6NieEUriNZvr6ZW+7fyBPfeIEje06hmzpCwO5nDlHXmqBUKDPYPYquaziuxDR1PvD5uwhXBSs//3nyThHwXouUcCrbT19+BA1BwS3xi8E3GCyO87HWuwkYcwwjdHQgQyFEhdovBctkqKWWkmtjagY1vioc12WilOGBhu2sqVpG0S3Tme71Rri8RB1uTqxhTbR19nMtEel8oeKC77qmIaWkWLZVwJ8n7zjgT47Jfwl4Ei8t82tSykNCiD8EdkspHwf+GvgbIUQnMIb3oaBcQamxDD/95gtEEmH81vSbJz2R5fGvPcfnf/9D6Lo3gmcFfXzsS/fT0zlI38lhTMtg+fpmEnVVTIykOXWoh0KuREN7DW0dDRXTKIELVkE0DY0/+mANJ2+5A7vs0LKynqYVtRx46RiHd5+koa16qhcspWSwe4yb79tIQ1s1owMTRGIhVmxsIRS59Bz2hD96NqOTsVKK3vwIYcNCIJC2pCVYzWBhjGeH9vJg047KO3n0UcSXv1zxLl03Cf3KZ/iN6maq/VEKTomQYbEq0kzU9OYkPtxyB335Ec7khtDRWBFupMZftaRTMttq4hzrH6YmMjPcFMo2ls9UY/jz6Irk4UspnwCeOO+2f3/OzwXgY1fiuZTKju07gyuZEewBIrEQgz1j9J4YpG1149TtmqbRtrpxxm0AsZoIN9y1bs7nyabzpMeyBMIWVReogqgX8iRSwyTu2TDj9j3PvUWsJjIVAHOZAumJLIapcfDl4+x8/w2suaH9cl76lNZgHXX+OCPFJP25UXyaARLybpGgYVFlhpDAoeRp3lV/AwG9QqA5e7n/+Yt1aBrmE0/wyU0XXqxDCEFzsJbm4PxeO7CY7Vy9jDe7B8iXylM9edtxGUpleOjGdRj6oppKvK6pK22vE8mxDIZR+Y0jgHz24hOWF1IqlHn2B7s49NoJhBC4rsu7Rm1uCIYQuQpBf44qiKmxDNUNVZRKNnt/eYSBrhEmRz/wWSYPfHonKzdePJumEl1ofKT1Tn7c+wqHkqfRENjCIWIGWB1pnZEuWXTKlQM+qDIAV1hrdYxP3LqVH+w+xHguj5j8GnbfhlXcuurS5meUK0MF/OtEfUuCfeXZGSJSSiQQq377WSJSSn72rZc4vr+LmqYYuu6Nvb7irGOj41LxGtw5qiDWt1aTHM1w8JXjDPWMEQj70TSBXXYo5ct85f/6Fn/03X9ONP72gmvUDPEry+5FSsmJbC81/ipCujX1jaLs2hhCJ2TMnXUEqDIAV9jG1npWN9bQPTqB40qa4hE1lLMA1Hep68SqTW0Ew5aXEz/pbGZMy4o66loTb3vfowNJju8/Q11LfGoeQAhB1bIGvv/+L+OGQpdcBXH7/RsZ7B5luHc62Evp1eGpbYmTTmZ54cdvvO22nm3bPQ03YOk+TGHMyJgZLibZXr0WU1N9nfnmM3RW1lezurFGBfsFogL+dcIK+fnIP7sf028y2DPGUO8Ygz1jNLbX8P5fvesdTRqO9Hv1bc7dh5SS1FiWw1YjP/iDv6H8X/5f+N3fha98xRsOmWNh6pUbW1m2thHXcb1efdGmXLKpSoQJRQOYps6R3affdlvPagrU8P6m28jYBQYL4wwWxhkuJrkpsZod1edX/lCUpUF1c64jdS0JfvX3P0jfySFymQKx6shlVbuci+kzprJfAIqFMkffOE0mmaOQK1Iq2PStqOWhz/8Llq1tuuC+hBBs2L6K53/0BuGqAHJyolmfnH9wHUkkdmWqS26oamdVuIme/DC269JgxanyqXF4ZelSAf86o+sarR0NV3Sfrasa8PkNCrki/qCf4/vPkMsUCIb9CGDlhmYQgh/+1bN8/vc+OKNsQiWbb1tNIORHSkkwPD2WbtsujuNy24M3XLG2+3UfK8PNV2x/inItU0M6ykX5LJMHP3MH6fEcvScGGRtKApJcpkDbmkYCYYtAyI9jO7y15+RF92cFfXz6dz5AIVdidDBFNl1gYiTN+FCS2x/cyvrtK67+i1KUJUj18JVLsnJjK5/53Q/w1N+9Su/JIarrq6hrrSZ6Tm/eb5mMXGKhs1vfs5nGtmp+9q2X6Do2QFVNmHd9eDvb7lmPNtf6p4qivCMq4CuXrLohxt0fupmeE0Mz6vCcVSzYJOouvKrVudrXNfNP/+iRK91MRVHmoLpSymWpa/EWTJkYTs+4vZgvIQSsvWn5ArVMUZSLUQFfuSxCCN73uTsJVQUm8+nHGeweI5PM8/7P3XnJpYwVRZl/akhHuWyxmgif+Z2H6DrWz1DvGKFI4LILnSmKMv9UwFfeFsPUWbGhhRUb3l7dG0VR5p8a0lEURVkiVMBXFEVZIlTAVxRFWSJUwFcURVkiVMBXFEVZIlSWzjVsMJ+mL5fEp+msiNQQMNRC0IqizE0F/GtQyXX4h1P72DvWCxKEAFPofHT5VrZWq8qQiqJUpgL+NejJ7rfYO9pLczA6Vc+m4JT59ok91FohmkOxhW2goiiLkhrDv8bk7BKvDJ2mIRCZUbzM0k18ms7LQ6cXrnGKoixqKuBfY5KlAhKJUaGEcNj00ZOdmP9GKYpyTVAB/xoTNn1IwJVy1n15u0yNdeHVphRFWbpUwL/GREyLTYlGBvIzyxPbrkvOKbOjrn1hGqYoyqKnJm2vQQ+1bWK0kKUnm8TQNFwpcaXkPc1rWRWpmZc2SCkZyKcoOg61gTAhwzcvz6soytunAv41KGL6+Y11d9CZGuZkepSAYbI+1kB9YH5q0fdkJ3js5F6GCmk0vInjOxtW8u6WtehCfWlUlMVKBfxrlKFprI3VszZWP6/PO17M8dUjL2MIjaaAlxZquy5P9x1DCMF7W9bNa3sURbl076g7JoRICCF+IYQ4Pvl/fI7tHCHEvsl/j7+T51QW1p6RbkquQ8wfmEoLNTSNpmCUFwZOkLNLC9xCRVHm8k6/f/8u8LSUsgN4evL3SvJSyq2T/x56h8+pLKDO1DDhCuP1hqbjSslIIbsArVIU5VK804D/MPCNyZ+/AXzwHe5PWeTCpkXZdWbdLicnji1djRIqymL1Tt+d9VLK/smfB4C5BpQtIcRuwAb+WEr5w0obCSG+CHwRoK2t7R02Tbni0mnu++nzNO16EWflSjofuIdyKAjAeClHcyhGrRVe4EYqijIXIStcwDNjAyGeAhoq3PV7wDeklLFzth2XUs4axxdCNEspe4UQK4BngHullCcu9Lzbtm2Tu3fvvoSXoMyLF1+EBx9Eui4im6Vo+UHT+M6f/SFHNq4maPr49TW30RiMLnRLFWVJE0LskVJuq3TfRXv4Usr7LrDjQSFEo5SyXwjRCAzNsY/eyf9PCiGeA24ALhjwlUUknYYHH4R0mrPVe/yFIgC/8pt/wGv7XmbrsjVETGvh2qgoykW90zH8x4HPTv78WeBH528ghIgLIfyTP9cAtwOH3+HzKvPpscfAdSve5UNwxy93q2CvKNeAdxrw/xi4XwhxHLhv8neEENuEEH81uc06YLcQYj/wLN4Yvgr415LjxyE7R/ZNNgudnfPbHkVR3pZ3NGkrpRwF7q1w+27g1yZ/fhnY9E6eR1lgHR0QClUO+qEQrFo1/21SFOWyqevglYt79FGoUI4Z8G5/9NH5bY+iKG+LCvjKxUUi8MQT3v+hyfLLodD07WGViqko1wJ1lYxyaXbuhL4+bwK3s9Mbxnn0URXsFeUaogK+cunCYfjCFxa6FYqivE1qSEdRFGWJUAFfURRliVABX1EUZYlQAV9RFGWJUAFfURRliVABX1EUZYlQAV9ZVBzpki4XKy6yoijKO6Py8JVFwZWSlwZP8Vz/cTJ2EVPo7Khr577m1Vi6udDNU5TrgurhK4vCE92H+dGZg/g0neZgFXF/gOcHOvnm8V04snJpZkVRLo8K+MqCmyjleWHgBM2hKAHD682bk4G/MzXMydToArdQUa4PKuArC647Mw6ALmaejkIITE3naLLiQmqKolwmFfCVBacJjam1E88jpcQQ6jRVlCtBvZOUBdceSaALjZJjz7jdlRJbuqyPNyxQyxTl+qICvrLgQoaPD7RtZCCfYayYw3Yd0uUC3dkJttcuozUUW+gmKsp1QaVlKovCrXXt1PhDPNd/nJ7sBHF/iPe2rGdrdTNCzDHeoyjKZVEBX1k0Oqpq6aiqXehmKMp1Sw3pKIqiLBEq4CuKoiwRKuAriqIsESrgK4qiLBEq4CuKoiwRQkq50G2oSAgxDJx5B7uoAUauUHOuB+p4zKaOyWzqmMx2rR2TZVLKiuluizbgv1NCiN1Sym0L3Y7FQh2P2dQxmU0dk9mup2OihnQURVGWCBXwFUVRlojrOeB/daEbsMio4zGbOiazqWMy23VzTK7bMXxFURRlpuu5h68oiqKc45oO+EKI9wohjgohOoUQv1vh/s8JIYaFEPsm//3aQrRzPgkhviaEGBJCvDnH/UII8aeTx+yAEOLG+W7jfLqE43G3ECJ5zjny7+e7jfNNCNEqhHhWCHFYCHFICPFbFbZZaufJpRyTa/9ckVJek/8AHTgBrAB8wH5g/XnbfA7484Vu6zwflzuBG4E357j/QeCneGtM7QBeW+g2L/DxuBv4x4Vu5zwfk0bgxsmfI8CxCu+dpXaeXMoxuebPlWu5h78d6JRSnpRSloDvAg8vcJsWnJTyeWDsAps8DHxTel4FYkKIxvlp3fy7hOOx5Egp+6WUb0z+nAbeAprP22ypnSeXckyueddywG8Gus/5vYfKf6CPTH4l/Z4QonV+mraoXepxW0puFULsF0L8VAixYaEbM5+EEO3ADcBr5921ZM+TCxwTuMbPlWs54F+KHwPtUsrNwC+Abyxwe5TF5w28S9G3AH8G/HBhmzN/hBBh4B+AfymlTC10exaDixyTa/5cuZYDfi9wbo+9ZfK2KVLKUSllcfLXvwJumqe2LWYXPW5LiZQyJaXMTP78BGAKIWoWuFlXnRDCxAts35JSfr/CJkvuPLnYMbkezpVrOeDvAjqEEMuFED7g48Dj525w3pjjQ3jjckvd48BnJrMwdgBJKWX/QjdqoQghGsTkorlCiO1474nRhW3V1TX5ev8aeEtK+d/m2GxJnSeXckyuh3Plml3TVkppCyG+BDyJl7HzNSnlISHEHwK7pZSPA78phHgIsPEm7j63YA2eJ0KI7+BlE9QIIXqAPwBMACnlXwJP4GVgdAI54FcXpqXz4xKOx0eBfyaEsIE88HE5mZJxHbsd+DRwUAixb/K2fwu0wdI8T7i0Y3LNnyvqSltFUZQl4loe0lEURVEugwr4iqIoS4QK+IqiKEuECviKoihLhAr4iqIoS4QK+IqiKEuECviKoihLhAr4iqIoS8T/D/RzlIuatQCTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 35 ----\n", + "[[ 2.12468724 1.71007558]\n", + " [ 1.16577287 1.41033515]\n", + " [ 1.47234654 0.59505281]\n", + " [ 1.65376816 1.16100798]\n", + " [ 1.47768101 1.61714718]\n", + " [ 0.89829991 1.48890144]\n", + " [ 0.8884574 1.36379222]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.4173776 0.92020053]\n", + " [ 1.72522019 1.67436101]\n", + " [ 1.06631173 1.74709432]\n", + " [ 1.17598902 1.21845865]\n", + " [ 1.74694986 1.42612721]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.39032245 1.49526565]\n", + " [ 2.37047213 1.68391027]\n", + " [ 1.17090556 1.61566737]\n", + " [ 2.18055594 0.13162861]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.34851163 0.32812096]\n", + " [ 0.88541349 1.65548686]\n", + " [ 1.90923618 1.53186623]\n", + " [ 2.13221486 1.50152827]\n", + " [ 1.4544567 1.34754606]\n", + " [ 1.93792991 1.2879509 ]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 0.55229195 1.12763625]\n", + " [ 1.15804743 0.6559763 ]\n", + " [ 1.08731117 1.49562843]\n", + " [ 1.01293025 1.32658872]\n", + " [ 0.8954373 1.24869778]\n", + " [ 1.44524155 1.74977098]\n", + " [ 1.90046889 1.73692318]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 36 ----\n", + "[[ 1.72727059 1.6843859 ]\n", + " [ 0.8923755 1.35614663]\n", + " [ 1.34851163 0.32812096]\n", + " [ 1.31503877 1.48006656]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.72185952 1.47346314]\n", + " [ 0.88541349 1.65548686]\n", + " [ 1.13708226 1.42627238]\n", + " [ 1.47234654 0.59505281]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.68482449 1.16923882]\n", + " [ 1.45790351 1.61117224]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 0.8876228 1.48958358]\n", + " [ 2.13986651 1.60852773]\n", + " [ 1.86373701 1.33838404]\n", + " [ 1.22047865 1.23968028]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.4794439 1.28146701]\n", + " [ 1.44627527 1.75192856]\n", + " [ 1.03926754 1.28431952]\n", + " [ 2.37047213 1.68391027]\n", + " [ 1.07740051 1.74448213]\n", + " [ 1.23431837 0.95245811]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.14299933 1.57847783]\n", + " [ 0.88065387 1.23290423]\n", + " [ 1.43892374 1.45646394]\n", + " [ 1.91708301 1.73230639]\n", + " [ 1.15525823 0.63496278]\n", + " [ 2.09972153 1.29041285]\n", + " [ 0.99926235 1.46076166]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.91659416 1.53961149]\n", + " [ 1.48969621 0.91682378]\n", + " [ 2.0208925 0.69010562]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 37 ----\n", + "[[ 1.49787164 0.93685274]\n", + " [ 1.14770197 1.43443994]\n", + " [ 1.72050705 1.47096542]\n", + " [ 0.89110367 1.33058009]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 2.35613241 1.50177837]\n", + " [ 1.43909028 1.63167307]\n", + " [ 0.89400494 1.55141182]\n", + " [ 1.15393832 0.5015695 ]\n", + " [ 1.41995784 1.33900323]\n", + " [ 1.91312744 1.54753203]\n", + " [ 1.07740051 1.74448213]\n", + " [ 1.44628246 0.34505281]\n", + " [ 1.85759503 1.33892851]\n", + " [ 1.11609631 1.25947398]\n", + " [ 1.62905243 1.20593391]\n", + " [ 2.0540776 1.24331378]\n", + " [ 1.44304993 0.6725149 ]\n", + " [ 1.88007587 1.73330283]\n", + " [ 2.39913911 1.28041143]\n", + " [ 1.68923284 1.66518837]\n", + " [ 0.89030165 1.22106345]\n", + " [ 2.18055594 0.13162861]\n", + " [ 0.88056921 1.69806597]\n", + " [ 2.12435099 1.71818323]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 0.89106649 1.43800229]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 2.12961561 1.52300161]\n", + " [ 2.3747529 1.71963506]\n", + " [ 1.39747524 1.49096213]\n", + " [ 1.13697314 1.58521814]\n", + " [ 1.45359909 1.76653944]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.22269742 0.96389377]\n", + " [ 1.00685165 1.4086655 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 38 ----\n", + "[[ 2.12468724 1.71007558]\n", + " [ 1.15797922 1.2566329 ]\n", + " [ 1.20946803 0.38907563]\n", + " [ 1.45382447 1.74625663]\n", + " [ 0.91743515 1.43527189]\n", + " [ 1.45202118 1.43501317]\n", + " [ 1.26255752 -0.05484602]\n", + " [ 1.92038603 1.30982851]\n", + " [ 0.88284159 1.67817361]\n", + " [ 1.45625259 0.67031031]\n", + " [ 2.38585908 1.3398846 ]\n", + " [ 1.140128 1.58782007]\n", + " [ 1.38811737 1.27273786]\n", + " [ 1.91184713 1.54225166]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 2.13221486 1.50152827]\n", + " [ 1.01275745 1.32302203]\n", + " [ 1.70389125 1.66423348]\n", + " [ 2.04032208 0.80720131]\n", + " [ 1.32065768 1.48297376]\n", + " [ 0.88733795 1.23117369]\n", + " [ 1.7425622 1.43448983]\n", + " [ 2.36796315 1.67435322]\n", + " [ 1.22222564 1.00445815]\n", + " [ 1.47260019 0.35385737]\n", + " [ 0.89648649 1.53004758]\n", + " [ 1.49781296 0.9461733 ]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 0.88305672 1.34115537]\n", + " [ 1.05565524 -0.12673057]\n", + " [ 1.8928394 1.73662523]\n", + " [ 1.13615833 1.43559926]\n", + " [ 1.43968261 1.59671989]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.64963824 1.21551177]\n", + " [ 1.15097648 0.68006035]\n", + " [ 1.07740051 1.74448213]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 39 ----\n", + "[[ 0.99874889 1.40003021]\n", + " [ 1.91141805 1.52604335]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.40498394 1.3218609 ]\n", + " [ 1.31030666 1.48903948]\n", + " [ 2.3818225 1.35135118]\n", + " [ 1.13674956 1.44529274]\n", + " [ 1.19446118 0.7629153 ]\n", + " [ 1.90046889 1.73692318]\n", + " [ 0.98092163 1.78595098]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 0.88792291 1.63096542]\n", + " [ 1.72950977 1.45359879]\n", + " [ 2.12435099 1.71818323]\n", + " [ 1.58289139 1.65985583]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.84861925 1.30719604]\n", + " [ 0.89060012 1.23200469]\n", + " [ 0.89420809 1.49105299]\n", + " [ 1.44442344 1.76939748]\n", + " [ 1.16037141 0.47963611]\n", + " [ 1.65114578 1.01870411]\n", + " [ 1.44008927 1.48518882]\n", + " [ 1.47234654 0.59505281]\n", + " [ 1.11781379 1.26421853]\n", + " [ 1.13322545 1.62916097]\n", + " [ 2.09797061 1.30242055]\n", + " [ 1.73934314 1.67477619]\n", + " [ 1.58230355 1.23515762]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 2.37047213 1.68391027]\n", + " [ 1.47141172 0.91413154]\n", + " [ 0.887518 1.3549538 ]\n", + " [ 2.04032208 0.80720131]\n", + " [ 2.12642978 1.53595104]\n", + " [ 1.43714655 1.63330142]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.21597899 1.03053098]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 40 ----\n", + "[[ 1.31114184 1.23965247]\n", + " [ 2.37389469 1.45173507]\n", + " [ 1.00551397 1.47344801]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.74044262 1.68188115]\n", + " [ 1.43714758 1.57285425]\n", + " [ 0.89006363 1.3560046 ]\n", + " [ 1.72245417 1.465237 ]\n", + " [ 1.49877004 0.92098259]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 0.88790116 1.6286754 ]\n", + " [ 1.22860946 0.59123969]\n", + " [ 2.12961561 1.52300161]\n", + " [ 1.12992035 1.65824397]\n", + " [ 1.57845339 1.65756792]\n", + " [ 1.03466486 1.29558161]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.83716709 1.31446151]\n", + " [ 1.45553647 1.41581624]\n", + " [ 2.37025289 1.70174027]\n", + " [ 2.12435099 1.71818323]\n", + " [ 0.88927347 1.48558484]\n", + " [ 2.0540776 1.24331378]\n", + " [ 2.39312147 1.23359209]\n", + " [ 1.91141805 1.52604335]\n", + " [ 0.88733795 1.23117369]\n", + " [ 1.31808684 1.48976466]\n", + " [ 1.0545495 0.30103 ]\n", + " [ 1.43818583 1.7299543 ]\n", + " [ 1.19578657 0.9484928 ]\n", + " [ 1.13295027 1.49073868]\n", + " [ 0.94026021 1.80295415]\n", + " [ 1.90046889 1.73692318]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.58929161 1.18206517]\n", + " [ 1.49635147 0.59365137]\n", + " [ 1.15061617 1.35722241]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 41 ----\n", + "[[ 1.3013445 1.52323364]\n", + " [ 1.91549226 1.29277023]\n", + " [ 1.4032662 0.30103 ]\n", + " [ 0.89523138 1.26510188]\n", + " [ 1.70048242 1.65017906]\n", + " [ 1.46724185 0.93012922]\n", + " [ 2.35613241 1.50177837]\n", + " [ 1.44357208 1.61234908]\n", + " [ 1.07740051 1.74448213]\n", + " [ 0.89852481 1.5003669 ]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.36023403 1.38202721]\n", + " [ 1.91184713 1.54225166]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.16451563 1.2511008 ]\n", + " [ 1.54406491 0.46689075]\n", + " [ 1.75146292 1.4130806 ]\n", + " [ 1.13648109 0.70093579]\n", + " [ 1.45359878 1.75192856]\n", + " [ 0.88371839 1.66102612]\n", + " [ 1.88244776 1.73572981]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.49593721 1.31265068]\n", + " [ 2.39913911 1.28041143]\n", + " [ 2.11877542 1.66028204]\n", + " [ 2.04032208 0.80720131]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 2.3747529 1.71963506]\n", + " [ 1.14366933 1.60381428]\n", + " [ 0.88451396 1.3814045 ]\n", + " [ 1.00934515 1.32638065]\n", + " [ 1.08243273 1.48735475]\n", + " [ 1.63289436 1.14941208]\n", + " [ 1.15235666 1.40970948]\n", + " [ 1.43071537 0.67838614]\n", + " [ 1.44373212 1.4749932 ]\n", + " [ 1.21597899 1.03053098]\n", + " [ 2.13980689 1.44706294]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.17439276 0.41842417]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 42 ----\n", + "[[ 1.58787803 0.90040196]\n", + " [ 1.44543991 1.63104677]\n", + " [ 0.88273977 1.30144929]\n", + " [ 2.37025289 1.70174027]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 0.87776413 1.60053069]\n", + " [ 1.45782529 1.34168902]\n", + " [ 1.70806091 1.48280328]\n", + " [ 1.15149797 1.40276822]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.14493529 1.65298309]\n", + " [ 1.22222564 1.00445815]\n", + " [ 1.23447559 -0.07876055]\n", + " [ 0.88736779 1.37708069]\n", + " [ 1.89482014 1.73361967]\n", + " [ 1.91107168 1.542736 ]\n", + " [ 2.13980689 1.44706294]\n", + " [ 1.69471338 1.22046032]\n", + " [ 1.39005298 1.49032729]\n", + " [ 2.39312147 1.23359209]\n", + " [ 1.17723897 1.25268521]\n", + " [ 1.20467511 0.61916298]\n", + " [ 1.46934473 0.97481759]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.00535918 1.48329688]\n", + " [ 1.01249556 1.32748567]\n", + " [ 2.11877542 1.66028204]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 0.89567143 1.47558116]\n", + " [ 0.95467467 1.76385128]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.85961573 1.35175138]\n", + " [ 1.45343938 1.76895263]\n", + " [ 1.48411296 0.59441579]\n", + " [ 2.0208925 0.69010562]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.7089022 1.6758885 ]\n", + " [ 2.37389469 1.45173507]\n", + " [ 0.91035846 1.21890931]\n", + " [ 2.0248171 1.19808066]\n", + " [ 1.14132194 1.5122414 ]\n", + " [ 1.04245989 0.10457227]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 43 ----\n", + "[[ 1.7089022 1.6758885 ]\n", + " [ 0.89572443 1.23654923]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 2.13223678 1.46634051]\n", + " [ 1.15966794 1.61429771]\n", + " [ 0.890456 1.4373182 ]\n", + " [ 1.35064209 1.2852646 ]\n", + " [ 1.71787718 1.48168929]\n", + " [ 1.8928394 1.73662523]\n", + " [ 1.35601078 1.49683956]\n", + " [ 2.38088928 1.73623073]\n", + " [ 1.49565373 0.90460348]\n", + " [ 1.15257828 0.46433312]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 1.14622257 1.39408573]\n", + " [ 0.88056921 1.69806597]\n", + " [ 2.03571675 1.18463518]\n", + " [ 1.447884 1.75608664]\n", + " [ 1.00258108 1.48363291]\n", + " [ 2.39913911 1.28041143]\n", + " [ 1.69197192 1.26814563]\n", + " [ 2.18055594 0.13162861]\n", + " [ 2.12241972 1.67761197]\n", + " [ 1.19849765 0.73993597]\n", + " [ 1.86754504 1.33959474]\n", + " [ 0.88532678 1.33830807]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.12566788 1.4992308 ]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.25596751 1.01174714]\n", + " [ 1.44839069 1.61624064]\n", + " [ 0.88784391 1.55233447]\n", + " [ 2.35361081 1.52287512]\n", + " [ 1.47234654 0.59505281]\n", + " [ 1.07320214 1.74675774]\n", + " [ 1.00542075 1.33546429]\n", + " [ 1.1224724 1.23910798]\n", + " [ 1.91312744 1.54753203]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.45655778 1.42816574]\n", + " [ 1.57931045 1.13523214]\n", + " [ 2.0208925 0.69010562]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 44 ----\n", + "[[ 1.59327744 1.65490387]\n", + " [ 0.92965966 1.22630392]\n", + " [ 1.46110234 0.94006563]\n", + " [ 0.9990834 1.48793735]\n", + " [ 2.35613241 1.50177837]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.93123292 1.39617611]\n", + " [ 1.32208457 1.47486063]\n", + " [ 1.90248553 1.7375706 ]\n", + " [ 1.15393832 0.5015695 ]\n", + " [ 0.89379953 1.41511011]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.58318183 1.22609177]\n", + " [ 1.06366553 1.75075282]\n", + " [ 1.17755681 1.24295988]\n", + " [ 1.53231499 0.43309844]\n", + " [ 2.11877542 1.66028204]\n", + " [ 1.13339969 1.49013934]\n", + " [ 1.78727898 1.32716913]\n", + " [ 1.09829486 1.37536891]\n", + " [ 1.7288829 1.49210409]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.43392515 1.6358701 ]\n", + " [ 0.8835917 1.68055216]\n", + " [ 2.3747529 1.71963506]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 2.39913911 1.28041143]\n", + " [ 0.89426637 1.32044242]\n", + " [ 1.44304993 0.6725149 ]\n", + " [ 1.4032662 0.30103 ]\n", + " [ 2.18055594 0.13162861]\n", + " [ 2.15634323 1.45166934]\n", + " [ 1.44442344 1.76939748]\n", + " [ 1.42877661 1.34593858]\n", + " [ 0.8848938 1.52730152]\n", + " [ 1.20896426 0.93661203]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.74699168 1.6943841 ]\n", + " [ 1.96330939 1.19816201]\n", + " [ 0.55229195 1.12763625]\n", + " [ 1.44128496 1.49821984]\n", + " [ 1.90850727 1.56093179]\n", + " [ 1.15962937 1.62014645]\n", + " [ 1.62808237 0.97050074]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 45 ----\n", + "[[ 1.89200996 1.341684 ]\n", + " [ 0.89061146 1.41020253]\n", + " [ 1.19060624 0.53066643]\n", + " [ 1.43789355 1.60772616]\n", + " [ 1.19882153 1.19643932]\n", + " [ 2.38088928 1.73623073]\n", + " [ 1.24810187 -0.07216366]\n", + " [ 1.31030666 1.48903948]\n", + " [ 1.59440919 1.1706861 ]\n", + " [ 1.8985484 1.74026142]\n", + " [ 2.39913911 1.28041143]\n", + " [ 1.03882301 1.76399452]\n", + " [ 1.48969621 0.91682378]\n", + " [ 2.10892102 1.55501077]\n", + " [ 2.18055594 0.13162861]\n", + " [ 0.89056399 1.3236412 ]\n", + " [ 1.00620305 1.45378532]\n", + " [ 1.74531559 1.68999413]\n", + " [ 1.44488045 1.46279254]\n", + " [ 0.88509487 1.64173384]\n", + " [ 1.41031009 1.31343648]\n", + " [ 1.05455556 1.30289213]\n", + " [ 1.47234654 0.59505281]\n", + " [ 0.8966653 1.2262326 ]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.13399123 -0.5451352 ]\n", + " [ 1.1393744 1.6017317 ]\n", + " [ 1.59327744 1.65490387]\n", + " [ 1.91312744 1.54753203]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.06057302 -0.05103434]\n", + " [ 1.7387156 1.33771377]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.98368887 1.17691339]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.13908906 1.44223789]\n", + " [ 1.2155014 0.84523198]\n", + " [ 1.73322658 1.50066883]\n", + " [ 1.43895931 1.75126732]\n", + " [ 0.89082762 1.50758704]\n", + " [ 1.0545495 0.30103 ]\n", + " [ 2.12435099 1.71818323]\n", + " [ 2.35361081 1.52287512]\n", + " [ 2.15938603 1.41516161]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 46 ----\n", + "[[ 1.00949275 1.33438461]\n", + " [ 1.92464256 1.5463408 ]\n", + " [ 1.00836795 -0.11484378]\n", + " [ 1.41904829 1.6275091 ]\n", + " [ 1.14222703 1.56739832]\n", + " [ 1.47234654 0.59505281]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.2288629 1.46102077]\n", + " [ 0.88775397 1.50374439]\n", + " [ 1.58318183 1.22609177]\n", + " [ 1.13708226 1.42627238]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.8985484 1.74026142]\n", + " [ 1.23431837 0.95245811]\n", + " [ 1.23447559 -0.07876055]\n", + " [ 2.04002446 1.23230169]\n", + " [ 1.31597816 1.47565999]\n", + " [ 0.94026021 1.80295415]\n", + " [ 2.18055594 0.13162861]\n", + " [ 0.88973131 1.32303369]\n", + " [ 2.37329474 1.69265041]\n", + " [ 1.7279303 1.47547152]\n", + " [ 1.17439276 0.41842417]\n", + " [ 1.78603876 1.2642845 ]\n", + " [ 1.74044262 1.68188115]\n", + " [ 2.40052904 1.30929315]\n", + " [ 1.24867088 1.23442913]\n", + " [ 0.88790116 1.6286754 ]\n", + " [ 2.11939319 1.65499959]\n", + " [ 1.55019302 1.61533509]\n", + " [ 1.11733055 1.70365571]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.65114578 1.01870411]\n", + " [ 0.89061146 1.41020253]\n", + " [ 1.45359909 1.76653944]\n", + " [ 0.55229195 1.12763625]\n", + " [ 1.15097648 0.68006035]\n", + " [ 1.00535918 1.48329688]\n", + " [ 1.40912995 1.31617988]\n", + " [ 1.47961417 0.91474496]\n", + " [ 1.10692981 1.23675355]\n", + " [ 1.87767117 1.36725305]\n", + " [ 1.43944723 1.46578379]\n", + " [ 0.8966653 1.2262326 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 47 ----\n", + "[[ 1.13769114 1.25518608]\n", + " [ 1.92467512 1.55605199]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.44026435 1.54518565]\n", + " [ 1.13868907 1.65111059]\n", + " [ 0.88294768 1.31501424]\n", + " [ 1.4729227 0.7740731 ]\n", + " [ 2.39913911 1.28041143]\n", + " [ 1.80756391 1.23748744]\n", + " [ 2.38088928 1.73623073]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.17990956 -0.06448027]\n", + " [ 1.67188884 1.59745661]\n", + " [ 0.88242847 1.59685875]\n", + " [ 1.15050862 1.41834048]\n", + " [ 1.58195461 1.23639241]\n", + " [ 0.93837289 1.7645878 ]\n", + " [ 1.90248553 1.7375706 ]\n", + " [ 1.08390582 1.50106136]\n", + " [ 1.19578657 0.9484928 ]\n", + " [ 1.44325749 1.64490664]\n", + " [ 2.13522376 1.4726429 ]\n", + " [ 1.74244856 1.42293259]\n", + " [ 0.90494698 1.22237378]\n", + " [ 1.44899494 1.42813816]\n", + " [ 2.18055594 0.13162861]\n", + " [ 0.90546286 1.48148442]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.21995872 0.57424773]\n", + " [ 1.51325748 0.97569847]\n", + " [ 1.89678362 1.36673366]\n", + " [ 1.56089918 1.78793523]\n", + " [ 2.03571675 1.18463518]\n", + " [ 1.04431692 -0.42867059]\n", + " [ 1.31625965 1.4910178 ]\n", + " [ 2.12904391 1.6956735 ]\n", + " [ 1.34577327 1.256041 ]\n", + " [ 1.010125 1.33375362]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.42994102 1.75104946]\n", + " [ 2.35361081 1.52287512]\n", + " [ 1.75290253 1.69717558]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.50381975 0.54851482]\n", + " [ 1.0545495 0.30103 ]\n", + " [ 1.31333984 -0.7780644 ]\n", + " [ 0.88912648 1.39671025]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 48 ----\n", + "[[ 1.21919512 1.23168647]\n", + " [ 1.89849541 1.7535764 ]\n", + " [ 0.899833 1.4189053 ]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 1.1167886 1.69079068]\n", + " [ 1.92834671 1.40920196]\n", + " [ 1.45089175 1.55538178]\n", + " [ 2.35361081 1.52287512]\n", + " [ 1.48969621 0.91682378]\n", + " [ 0.88659076 1.33280441]\n", + " [ 1.15910638 -0.09078829]\n", + " [ 2.18055594 0.13162861]\n", + " [ 0.88757225 1.63185823]\n", + " [ 1.60336843 1.14584012]\n", + " [ 1.4452378 1.77187057]\n", + " [ 1.14503298 1.41090779]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.16398734 1.57744073]\n", + " [ 1.58875555 1.65314529]\n", + " [ 2.15806701 1.73682236]\n", + " [ 1.90675438 1.57901174]\n", + " [ 1.07797528 1.49009685]\n", + " [ 1.11239693 0.38907563]\n", + " [ 2.38088928 1.73623073]\n", + " [ 1.8161704 1.29889236]\n", + " [ 1.23585117 -0.62761454]\n", + " [ 1.36885711 0.5187675 ]\n", + " [ 1.0411336 1.3031154 ]\n", + " [ 1.41890497 1.65376191]\n", + " [ 2.70954911 1.60196665]\n", + " [ 2.0208925 0.69010562]\n", + " [ 0.89572443 1.23654923]\n", + " [ 1.74539046 1.68646093]\n", + " [ 1.20702481 0.98256111]\n", + " [ 2.39913911 1.28041143]\n", + " [ 0.89527006 1.51433633]\n", + " [ 1.50647308 0.6227839 ]\n", + " [ 0.94026021 1.80295415]\n", + " [ 1.39953604 1.29445924]\n", + " [ 1.58005729 1.33051174]\n", + " [ 1.17257537 0.69232172]\n", + " [ 2.15852295 1.43148456]\n", + " [ 1.32020952 1.47575426]\n", + " [ 0.50116885 0.95424251]\n", + " [ 2.09441654 1.60045158]\n", + " [ 1.43941275 1.43748424]\n", + " [ 1.73267178 1.47884246]\n", + " [ 2.0248171 1.19808066]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----- cluster 49 ----\n", + "[[ 0.88933917 1.35580454]\n", + " [ 1.7279303 1.47547152]\n", + " [ 1.42497593 0.30103 ]\n", + " [ 2.38088928 1.73623073]\n", + " [ 1.42744809 1.35397718]\n", + " [ 1.4246939 0.95822895]\n", + " [ 1.142595 1.64217543]\n", + " [ 1.95967205 1.21201659]\n", + " [ 1.89769625 1.74348545]\n", + " [ 1.44092103 1.62224135]\n", + " [ 1.03440015 -0.02639954]\n", + " [ 2.39913911 1.28041143]\n", + " [ 1.14125405 1.39932656]\n", + " [ 0.88716044 1.615237 ]\n", + " [ 1.56481133 -0.0167102 ]\n", + " [ 1.30145784 0.57656541]\n", + " [ 2.14705312 1.4792952 ]\n", + " [ 1.82399826 1.35148641]\n", + " [ 0.89435921 1.23620881]\n", + " [ 1.44021135 1.75564396]\n", + " [ 1.1892012 1.20746429]\n", + " [ 0.88971432 1.48293292]\n", + " [ 1.72703725 1.22931152]\n", + " [ 1.61768053 0.98869149]\n", + " [ 2.0208925 0.69010562]\n", + " [ 1.31333984 -0.7780644 ]\n", + " [ 1.23447559 -0.07876055]\n", + " [ 2.70954911 1.60196665]\n", + " [ 1.92174197 1.5681959 ]\n", + " [ 0.87797895 1.76524896]\n", + " [ 1.18123084 0.84265617]\n", + " [ 1.02774201 1.30644247]\n", + " [ 2.18055594 0.13162861]\n", + " [ 1.4359553 1.49201557]\n", + " [ 1.49156709 0.77356333]\n", + " [ 1.54717582 1.234643 ]\n", + " [ 0.50116885 0.95424251]\n", + " [ 1.31030666 1.48903948]\n", + " [ 2.12904391 1.6956735 ]\n", + " [ 1.04431692 -0.42867059]\n", + " [ 1.04183486 1.76481617]\n", + " [ 1.00551397 1.47344801]\n", + " [ 2.35361081 1.52287512]\n", + " [ 1.50381975 0.54851482]\n", + " [ 1.74539046 1.68646093]\n", + " [ 1.10397623 0.46007041]\n", + " [ 1.13082254 1.50908626]\n", + " [ 1.59327744 1.65490387]\n", + " [ 1.94176433 1.40788171]]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "for cluster in range(5, 50):\n", + " print(\"----- cluster \", cluster ,\" ----\")\n", + " kmeans = KMeans(n_clusters=cluster).fit(points)\n", + " centroids = kmeans.cluster_centers_\n", + " print(centroids)\n", + " plt.scatter(points[\"'fare'\"], points[\"'age'\"], c= kmeans.labels_.astype(float), s=50, alpha=0.5)\n", + " plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50)\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "0e43b851", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=27).fit(points)\n", + "centroids = kmeans.cluster_centers_\n", + "plt.scatter(points[\"'fare'\"], points[\"'age'\"], c= kmeans.labels_.astype(float), s=50, alpha=0.5)\n", + "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ad6cda7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Fare: 13.614171854854613 - Age: 0.2850130887063675',\n", + " 'Fare: 25.36471172685712 - Age: 30.293405573569075',\n", + " 'Fare: 7.944066666299346 - Age: 19.36952032062056',\n", + " 'Fare: 86.63545327107583 - Age: 40.17981186013159',\n", + " 'Fare: 15.649884940963089 - Age: 3.4057614775612457',\n", + " 'Fare: 12.375641189742817 - Age: 26.26450324609141',\n", + " 'Fare: 243.14149617919765 - Age: 21.871803700126893',\n", + " 'Fare: 30.63035425013231 - Age: 2.634166108404895',\n", + " 'Fare: 32.26372256366604 - Age: 7.8311241325145025',\n", + " 'Fare: 14.127716251860619 - Age: 37.33152706209799',\n", + " 'Fare: 49.53291155846338 - Age: 34.87283832428549',\n", + " 'Fare: 68.22759569082812 - Age: 21.630924681699998',\n", + " 'Fare: 28.54989586339707 - Age: 48.965404992118565',\n", + " 'Fare: 205.51862107756992 - Age: 49.91538131100203',\n", + " 'Fare: 36.71227791460026 - Age: 0.9622541701420471',\n", + " 'Fare: 65.49684549737087 - Age: 53.944875912043095',\n", + " 'Fare: 111.94462739272556 - Age: 13.3833872067451',\n", + " 'Fare: 15.229805799621921 - Age: 19.13690669926506',\n", + " 'Fare: 7.646568241278432 - Age: 40.78105797748848',\n", + " 'Fare: 151.55000000000004 - Age: 1.354031018847058',\n", + " 'Fare: 11.322607218303759 - Age: 57.12845192984917',\n", + " 'Fare: 139.5880160191939 - Age: 31.58978041091118',\n", + " 'Fare: 32.90807271660342 - Age: 18.06613514087649',\n", + " 'Fare: 7.933026402324783 - Age: 27.65991733973642',\n", + " 'Fare: 512.3291999999999 - Age: 39.99140347914063',\n", + " 'Fare: 16.915562736496383 - Age: 9.1568683675919',\n", + " 'Fare: 15.13246096357654 - Age: 0.8620247358054882']" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "[\"Fare: \" + str(math.pow(10, centroid[0])) + \" - Age: \" + str(math.pow(10, centroid[1])) for centroid in centroids]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea0f1454", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a53dd549", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Data engineering and science/Simple analysis/population.ipynb b/Data engineering and science/Simple analysis/population.ipynb new file mode 100644 index 0000000..3dda9c5 --- /dev/null +++ b/Data engineering and science/Simple analysis/population.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Population, GDP and Internet adoption\n\nThis notebook expores all those subjects to answer the following questions: \n\n[add questions here]\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"We will simulate a data pipeline based on the following steps:\n\n- extract : ingest data from various data sources.\n- transform : clean and prepare each dataset for tra\n- load : merge the data sets together \n- visualise: create some meaningful graphical visualisation.","metadata":{}},{"cell_type":"markdown","source":"# Extract\n\nWe upload the data and the libraries required for the notebook. ","metadata":{"_uuid":"caf4ec4f-f5cd-44f6-bd87-a42bb5998fbe","_cell_guid":"2e322c9f-5725-4093-ae2d-3e06fef613fe","_kg_hide-input":true,"trusted":true}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nfrom sklearn.cluster import KMeans\nimport seaborn as sns\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"0704d808-a7ec-4a50-aaa8-fe2134a807d4","_cell_guid":"cf1cf904-4fba-43b6-9f19-322dfa1cbf68","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.344153Z","iopub.execute_input":"2023-10-26T20:28:51.344497Z","iopub.status.idle":"2023-10-26T20:28:51.359482Z","shell.execute_reply.started":"2023-10-26T20:28:51.344472Z","shell.execute_reply":"2023-10-26T20:28:51.358458Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n/kaggle/input/population-dataset/World-population-by-countries-dataset.csv\n/kaggle/input/internet-users/Final.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"path = '/kaggle/input/population-dataset/World-population-by-countries-dataset.csv'\ndata_pop = pd.read_csv(path)\ndata_pop.shape","metadata":{"_uuid":"28a62502-7432-44e5-87eb-877d25cb754f","_cell_guid":"6d1ceed1-1929-4c1f-aa9c-cb9bf17b5a17","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.361232Z","iopub.execute_input":"2023-10-26T20:28:51.361709Z","iopub.status.idle":"2023-10-26T20:28:51.380139Z","shell.execute_reply.started":"2023-10-26T20:28:51.361677Z","shell.execute_reply":"2023-10-26T20:28:51.379042Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"markdown","source":"## World population\nWe transform the datasets from a wide to long format, so that we can merge more easily the datasets together. We aim at having a country name and a country code; both uses the ISO standard. We aim at having a year and the population. ","metadata":{"_uuid":"f352bd71-3a53-4d05-b1a6-15a3391dc608","_cell_guid":"12af0be9-5050-4d14-b22c-036f7f2f79f1","trusted":true}},{"cell_type":"code","source":"data_pop.dtypes","metadata":{"_uuid":"5213c597-6442-4ae6-aeed-299cd30630c5","_cell_guid":"1795b98f-3391-4877-8d9c-702da54af9d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.381506Z","iopub.execute_input":"2023-10-26T20:28:51.382276Z","iopub.status.idle":"2023-10-26T20:28:51.391171Z","shell.execute_reply.started":"2023-10-26T20:28:51.382247Z","shell.execute_reply":"2023-10-26T20:28:51.390268Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop.describe()","metadata":{"_uuid":"0daf98d8-5114-4c54-8a4c-e6bbf6311a74","_cell_guid":"c0f42347-4411-4bab-b06d-1b0e75605146","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.393035Z","iopub.execute_input":"2023-10-26T20:28:51.393739Z","iopub.status.idle":"2023-10-26T20:28:51.516296Z","shell.execute_reply.started":"2023-10-26T20:28:51.393706Z","shell.execute_reply":"2023-10-26T20:28:51.515206Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.172174e+08 1.187633e+08 1.208717e+08 1.234910e+08 1.261315e+08 \nstd 3.695745e+08 3.739180e+08 3.804316e+08 3.889142e+08 3.974401e+08 \nmin 2.833000e+03 3.077000e+03 3.367000e+03 3.703000e+03 4.063000e+03 \n25% 5.022802e+05 5.109642e+05 5.206540e+05 5.311622e+05 5.421252e+05 \n50% 3.718330e+06 3.826398e+06 3.929109e+06 4.015834e+06 4.124521e+06 \n75% 2.636053e+07 2.721235e+07 2.808607e+07 2.890669e+07 2.972333e+07 \nmax 3.032156e+09 3.071596e+09 3.124561e+09 3.189656e+09 3.255146e+09 \n\n 1965 1966 1967 1968 1969 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.288372e+08 1.316853e+08 1.345256e+08 1.374350e+08 1.404490e+08 \nstd 4.062000e+08 4.155171e+08 4.247722e+08 4.342805e+08 4.441772e+08 \nmin 4.460000e+03 4.675000e+03 4.922000e+03 5.194000e+03 5.461000e+03 \n25% 5.533362e+05 5.647475e+05 5.823645e+05 5.981078e+05 6.100030e+05 \n50% 4.242788e+06 4.326013e+06 4.387887e+06 4.474171e+06 4.550402e+06 \n75% 3.055227e+07 3.134845e+07 3.200449e+07 3.244145e+07 3.277149e+07 \nmax 3.322047e+09 3.392098e+09 3.461620e+09 3.532783e+09 3.606554e+09 \n\n ... 2012 2013 2014 2015 \\\ncount ... 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean ... 2.874902e+08 2.912969e+08 2.951160e+08 2.989277e+08 \nstd ... 9.017511e+08 9.129343e+08 9.241050e+08 9.352101e+08 \nmin ... 1.013600e+04 1.020800e+04 1.028900e+04 1.037400e+04 \n25% ... 1.539939e+06 1.574621e+06 1.609909e+06 1.645868e+06 \n50% ... 9.824808e+06 9.948838e+06 1.001582e+07 1.022085e+07 \n75% ... 6.057984e+07 6.120753e+07 6.174243e+07 6.182699e+07 \nmax ... 7.089255e+09 7.175500e+09 7.261847e+09 7.347679e+09 \n\n 2016 2017 2018 2019 2020 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 3.027560e+08 3.065980e+08 3.103591e+08 3.140425e+08 3.176734e+08 \nstd 9.463321e+08 9.575052e+08 9.683483e+08 9.788967e+08 9.891628e+08 \nmin 1.047400e+04 1.057700e+04 1.067800e+04 1.076400e+04 1.083400e+04 \n25% 1.689616e+06 1.716772e+06 1.740174e+06 1.751950e+06 1.767996e+06 \n50% 1.036160e+07 1.040671e+07 1.045548e+07 1.047907e+07 1.052565e+07 \n75% 6.187352e+07 6.191725e+07 6.193141e+07 6.150589e+07 6.157091e+07 \nmax 7.433651e+09 7.519371e+09 7.602716e+09 7.683806e+09 7.763933e+09 \n\n 2021 \ncount 2.640000e+02 \nmean 3.210893e+08 \nstd 9.988295e+08 \nmin 1.087300e+04 \n25% 1.791783e+06 \n50% 1.054019e+07 \n75% 6.295547e+07 \nmax 7.836631e+09 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02...2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02
mean1.172174e+081.187633e+081.208717e+081.234910e+081.261315e+081.288372e+081.316853e+081.345256e+081.374350e+081.404490e+08...2.874902e+082.912969e+082.951160e+082.989277e+083.027560e+083.065980e+083.103591e+083.140425e+083.176734e+083.210893e+08
std3.695745e+083.739180e+083.804316e+083.889142e+083.974401e+084.062000e+084.155171e+084.247722e+084.342805e+084.441772e+08...9.017511e+089.129343e+089.241050e+089.352101e+089.463321e+089.575052e+089.683483e+089.788967e+089.891628e+089.988295e+08
min2.833000e+033.077000e+033.367000e+033.703000e+034.063000e+034.460000e+034.675000e+034.922000e+035.194000e+035.461000e+03...1.013600e+041.020800e+041.028900e+041.037400e+041.047400e+041.057700e+041.067800e+041.076400e+041.083400e+041.087300e+04
25%5.022802e+055.109642e+055.206540e+055.311622e+055.421252e+055.533362e+055.647475e+055.823645e+055.981078e+056.100030e+05...1.539939e+061.574621e+061.609909e+061.645868e+061.689616e+061.716772e+061.740174e+061.751950e+061.767996e+061.791783e+06
50%3.718330e+063.826398e+063.929109e+064.015834e+064.124521e+064.242788e+064.326013e+064.387887e+064.474171e+064.550402e+06...9.824808e+069.948838e+061.001582e+071.022085e+071.036160e+071.040671e+071.045548e+071.047907e+071.052565e+071.054019e+07
75%2.636053e+072.721235e+072.808607e+072.890669e+072.972333e+073.055227e+073.134845e+073.200449e+073.244145e+073.277149e+07...6.057984e+076.120753e+076.174243e+076.182699e+076.187352e+076.191725e+076.193141e+076.150589e+076.157091e+076.295547e+07
max3.032156e+093.071596e+093.124561e+093.189656e+093.255146e+093.322047e+093.392098e+093.461620e+093.532783e+093.606554e+09...7.089255e+097.175500e+097.261847e+097.347679e+097.433651e+097.519371e+097.602716e+097.683806e+097.763933e+097.836631e+09
\n

8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(data_pop['Country Code'].unique())","metadata":{"_uuid":"da9b4784-3633-4778-a860-06055fe06ef6","_cell_guid":"dcc7e57d-b1d5-4413-ba23-9097a88df7f6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.517814Z","iopub.execute_input":"2023-10-26T20:28:51.518099Z","iopub.status.idle":"2023-10-26T20:28:51.524362Z","shell.execute_reply.started":"2023-10-26T20:28:51.518075Z","shell.execute_reply":"2023-10-26T20:28:51.523200Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\npop_long = pd.melt(data_pop, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.525929Z","iopub.execute_input":"2023-10-26T20:28:51.526268Z","iopub.status.idle":"2023-10-26T20:28:51.547676Z","shell.execute_reply.started":"2023-10-26T20:28:51.526243Z","shell.execute_reply":"2023-10-26T20:28:51.546791Z"},"trusted":true},"execution_count":93,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"pop_long.columns = ['Country Name', 'Country Code', 'Year', 'population']\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.548744Z","iopub.execute_input":"2023-10-26T20:28:51.549003Z","iopub.status.idle":"2023-10-26T20:28:51.559081Z","shell.execute_reply.started":"2023-10-26T20:28:51.548982Z","shell.execute_reply":"2023-10-26T20:28:51.558029Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## GDP\nWe repeat a similar process for the GDP. Similar standards are used.","metadata":{"_uuid":"d3617312-9971-49fc-a30c-357d2d407eea","_cell_guid":"98e1e4ad-2241-4d7a-b701-a07fe5aa0509","execution":{"iopub.status.busy":"2023-10-19T16:22:33.566429Z","iopub.execute_input":"2023-10-19T16:22:33.566852Z","iopub.status.idle":"2023-10-19T16:22:34.617823Z","shell.execute_reply.started":"2023-10-19T16:22:33.566821Z","shell.execute_reply":"2023-10-19T16:22:34.616873Z"},"trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/countries-gdp-2012-to-2021/GDP.csv'\ngdp = pd.read_csv(path)\ngdp.shape","metadata":{"_uuid":"a8992442-7dac-4c03-9f3b-13cdc8d45e7b","_cell_guid":"be74ad63-eebe-40b5-844b-021624380077","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.560206Z","iopub.execute_input":"2023-10-26T20:28:51.560460Z","iopub.status.idle":"2023-10-26T20:28:51.580073Z","shell.execute_reply.started":"2023-10-26T20:28:51.560438Z","shell.execute_reply":"2023-10-26T20:28:51.578585Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"code","source":"gdp.dtypes","metadata":{"_uuid":"1e5fa2f6-ab9d-4115-aab3-47f9d5952d7c","_cell_guid":"c161c56d-19d2-4157-8122-4e4bff8f0fb1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.581561Z","iopub.execute_input":"2023-10-26T20:28:51.581877Z","iopub.status.idle":"2023-10-26T20:28:51.589480Z","shell.execute_reply.started":"2023-10-26T20:28:51.581850Z","shell.execute_reply":"2023-10-26T20:28:51.588251Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp.describe()","metadata":{"_uuid":"cc48f68b-e22c-470d-8d9d-fe7c874e4ff9","_cell_guid":"39bd124c-d5d9-4541-be4b-affa418221ee","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.593301Z","iopub.execute_input":"2023-10-26T20:28:51.593702Z","iopub.status.idle":"2023-10-26T20:28:51.713345Z","shell.execute_reply.started":"2023-10-26T20:28:51.593675Z","shell.execute_reply":"2023-10-26T20:28:51.712285Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 134.000000 136.000000 138.000000 138.000000 138.000000 \nmean 473.490078 486.392600 510.248600 541.649901 587.373909 \nstd 612.439366 635.127847 666.405011 705.754944 772.265425 \nmin 40.119192 26.318449 26.983496 28.434172 20.018579 \n25% 107.452258 110.089913 114.582873 122.509292 123.574875 \n50% 217.932654 197.938953 202.801243 210.677240 232.182537 \n75% 476.295836 485.401860 538.891433 586.773416 639.414205 \nmax 3007.123445 3066.562869 3243.843078 3374.515171 3573.941185 \n\n 1965 1966 1967 1968 1969 ... \\\ncount 149.000000 152.000000 155.000000 160.000000 160.000000 ... \nmean 648.068814 703.235758 718.916647 735.345411 796.539042 ... \nstd 849.994333 921.818962 954.791908 982.957313 1060.025132 ... \nmin 16.577652 12.786964 12.900238 20.395642 20.682296 ... \n25% 140.756742 145.396584 152.410537 149.457032 151.634207 ... \n50% 251.239040 266.219488 252.252422 292.642193 293.802194 ... \n75% 681.131112 768.852316 763.567965 760.566852 826.288906 ... \nmax 4081.915955 4229.254573 4336.426587 4695.923390 5032.144743 ... \n\n 2012 2013 2014 2015 \\\ncount 258.000000 259.000000 260.000000 258.000000 \nmean 16248.249264 16768.974417 17083.306427 15423.701141 \nstd 23882.158473 25383.007646 25945.938982 23375.375304 \nmin 238.205949 241.547671 257.818552 289.359633 \n25% 1986.934959 2110.418190 2173.282618 2097.331179 \n50% 6454.612266 6755.073675 6904.579093 6192.562429 \n75% 19638.711935 19792.134135 20277.795912 18210.359455 \nmax 165505.178100 185066.578100 195780.006900 170337.924400 \n\n 2016 2017 2018 2019 \\\ncount 257.000000 257.000000 257.000000 255.000000 \nmean 15582.736498 16383.403010 17344.572407 17231.399427 \nstd 23586.086580 24397.646814 25978.513510 25791.905913 \nmin 242.065671 243.135809 231.446476 216.972968 \n25% 2079.448266 2088.500117 2269.177012 2186.046581 \n50% 6079.088736 6436.791746 6912.110297 6837.717826 \n75% 18575.232030 19743.954910 20614.898860 19809.323135 \nmax 174610.637000 173612.864600 194280.822100 199377.481800 \n\n 2020 2021 \ncount 252.000000 245.000000 \nmean 15773.923985 16882.053955 \nstd 24065.495555 26113.837043 \nmin 216.826741 221.477676 \n25% 2139.636129 2304.844567 \n50% 6034.203335 6621.574336 \n75% 18652.166725 18751.026510 \nmax 182538.638300 234315.460500 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count134.000000136.000000138.000000138.000000138.000000149.000000152.000000155.000000160.000000160.000000...258.000000259.000000260.000000258.000000257.000000257.000000257.000000255.000000252.000000245.000000
mean473.490078486.392600510.248600541.649901587.373909648.068814703.235758718.916647735.345411796.539042...16248.24926416768.97441717083.30642715423.70114115582.73649816383.40301017344.57240717231.39942715773.92398516882.053955
std612.439366635.127847666.405011705.754944772.265425849.994333921.818962954.791908982.9573131060.025132...23882.15847325383.00764625945.93898223375.37530423586.08658024397.64681425978.51351025791.90591324065.49555526113.837043
min40.11919226.31844926.98349628.43417220.01857916.57765212.78696412.90023820.39564220.682296...238.205949241.547671257.818552289.359633242.065671243.135809231.446476216.972968216.826741221.477676
25%107.452258110.089913114.582873122.509292123.574875140.756742145.396584152.410537149.457032151.634207...1986.9349592110.4181902173.2826182097.3311792079.4482662088.5001172269.1770122186.0465812139.6361292304.844567
50%217.932654197.938953202.801243210.677240232.182537251.239040266.219488252.252422292.642193293.802194...6454.6122666755.0736756904.5790936192.5624296079.0887366436.7917466912.1102976837.7178266034.2033356621.574336
75%476.295836485.401860538.891433586.773416639.414205681.131112768.852316763.567965760.566852826.288906...19638.71193519792.13413520277.79591218210.35945518575.23203019743.95491020614.89886019809.32313518652.16672518751.026510
max3007.1234453066.5628693243.8430783374.5151713573.9411854081.9159554229.2545734336.4265874695.9233905032.144743...165505.178100185066.578100195780.006900170337.924400174610.637000173612.864600194280.822100199377.481800182538.638300234315.460500
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8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(gdp['Country Code'].unique())","metadata":{"_uuid":"a6200065-a3c5-4d03-a714-a2fbc8ced590","_cell_guid":"8bfa6fe6-9143-4ca0-a2e7-4d741a6989a9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.714469Z","iopub.execute_input":"2023-10-26T20:28:51.714720Z","iopub.status.idle":"2023-10-26T20:28:51.720357Z","shell.execute_reply.started":"2023-10-26T20:28:51.714698Z","shell.execute_reply":"2023-10-26T20:28:51.719732Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\ngdp_long = pd.melt(gdp, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.721044Z","iopub.execute_input":"2023-10-26T20:28:51.721267Z","iopub.status.idle":"2023-10-26T20:28:51.745227Z","shell.execute_reply.started":"2023-10-26T20:28:51.721248Z","shell.execute_reply":"2023-10-26T20:28:51.743851Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp_long.columns = ['Country Name', 'Country Code', 'Year', 'USD GDP']\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.746701Z","iopub.execute_input":"2023-10-26T20:28:51.747001Z","iopub.status.idle":"2023-10-26T20:28:51.757774Z","shell.execute_reply.started":"2023-10-26T20:28:51.746978Z","shell.execute_reply":"2023-10-26T20:28:51.756806Z"},"trusted":true},"execution_count":100,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Internet\nThis dataset is a bit simpler. Less transformation is required.","metadata":{"_uuid":"e6be91b8-d8c6-4212-ad69-2ce46452ef6f","_cell_guid":"d299a245-fa89-45c1-9623-569d3e7bcf7a","trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/internet-users/Final.csv'\ninternet = pd.read_csv(path)\ninternet.shape","metadata":{"_uuid":"ad3170c0-5705-4a57-9f17-853844f08e72","_cell_guid":"5a26b58f-e229-48ec-81f7-dd8ab7a51338","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.759207Z","iopub.execute_input":"2023-10-26T20:28:51.759490Z","iopub.status.idle":"2023-10-26T20:28:51.783791Z","shell.execute_reply.started":"2023-10-26T20:28:51.759467Z","shell.execute_reply":"2023-10-26T20:28:51.782480Z"},"trusted":true},"execution_count":101,"outputs":[{"execution_count":101,"output_type":"execute_result","data":{"text/plain":"(8867, 8)"},"metadata":{}}]},{"cell_type":"code","source":"internet.dtypes","metadata":{"_uuid":"bb11a84f-3d44-4ab2-9864-dc1c4a8df688","_cell_guid":"dedee6b7-ac31-4647-8b00-2a8bed20650f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.784729Z","iopub.execute_input":"2023-10-26T20:28:51.785074Z","iopub.status.idle":"2023-10-26T20:28:51.792523Z","shell.execute_reply.started":"2023-10-26T20:28:51.785043Z","shell.execute_reply":"2023-10-26T20:28:51.791485Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nEntity object\nCode object\nYear int64\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"len(internet.Code.unique())","metadata":{"_uuid":"4906007d-31a3-4312-89f1-25d4bbe6bbdf","_cell_guid":"6d9ec685-820d-4cca-a721-afe2a68bddd7","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.793712Z","iopub.execute_input":"2023-10-26T20:28:51.793961Z","iopub.status.idle":"2023-10-26T20:28:51.806167Z","shell.execute_reply.started":"2023-10-26T20:28:51.793940Z","shell.execute_reply":"2023-10-26T20:28:51.805509Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"216"},"metadata":{}}]},{"cell_type":"code","source":"internet.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:36:30.135197Z","iopub.execute_input":"2023-10-26T20:36:30.135549Z","iopub.status.idle":"2023-10-26T20:36:30.162264Z","shell.execute_reply.started":"2023-10-26T20:36:30.135525Z","shell.execute_reply":"2023-10-26T20:36:30.160983Z"},"trusted":true},"execution_count":136,"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0 Year Cellular Subscription Internet Users(%) \\\ncount 8867.000000 8867.000000 8867.000000 8867.000000 \nmean 4433.000000 2000.151799 39.989614 17.043606 \nstd 2559.826752 11.812151 51.981410 26.883498 \nmin 0.000000 1980.000000 0.000000 0.000000 \n25% 2216.500000 1990.000000 0.000000 0.000000 \n50% 4433.000000 2000.000000 5.501357 0.855662 \n75% 6649.500000 2010.000000 82.231594 25.449939 \nmax 8866.000000 2020.000000 436.103027 100.000000 \n\n No. of Internet Users Broadband Subscription \ncount 8.867000e+03 8867.000000 \nmean 1.089138e+07 4.440695 \nstd 1.248841e+08 9.755705 \nmin 0.000000e+00 0.000000 \n25% 0.000000e+00 0.000000 \n50% 1.004700e+04 0.000000 \n75% 8.664195e+05 2.007603 \nmax 4.699886e+09 78.524361 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0YearCellular SubscriptionInternet Users(%)No. of Internet UsersBroadband Subscription
count8867.0000008867.0000008867.0000008867.0000008.867000e+038867.000000
mean4433.0000002000.15179939.98961417.0436061.089138e+074.440695
std2559.82675211.81215151.98141026.8834981.248841e+089.755705
min0.0000001980.0000000.0000000.0000000.000000e+000.000000
25%2216.5000001990.0000000.0000000.0000000.000000e+000.000000
50%4433.0000002000.0000005.5013570.8556621.004700e+040.000000
75%6649.5000002010.00000082.23159425.4499398.664195e+052.007603
max8866.0000002020.000000436.103027100.0000004.699886e+0978.524361
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"It appears the number of countries to be smaller for the Internet dataset. Therefore, any merging of this datasets with the GDP and polution will reduce the number of countries by 50. This confounding factor will limit the analysis. It is a bit disappointing, but it will be enough a first exploration. ","metadata":{}},{"cell_type":"markdown","source":"# Tranform and load\nWe merge the datasets based on the year and the ISO country code.","metadata":{}},{"cell_type":"code","source":"print(pop_long.dtypes)\nprint(gdp_long.dtypes)","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.807624Z","iopub.execute_input":"2023-10-26T20:28:51.807989Z","iopub.status.idle":"2023-10-26T20:28:51.817447Z","shell.execute_reply.started":"2023-10-26T20:28:51.807963Z","shell.execute_reply":"2023-10-26T20:28:51.816385Z"},"trusted":true},"execution_count":104,"outputs":[{"name":"stdout","text":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object\nCountry Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"data = pd.merge(pop_long, gdp_long, left_on=['Country Code','Year'], right_on = ['Country Code','Year'])\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.819009Z","iopub.execute_input":"2023-10-26T20:28:51.819327Z","iopub.status.idle":"2023-10-26T20:28:51.840904Z","shell.execute_reply.started":"2023-10-26T20:28:51.819299Z","shell.execute_reply":"2023-10-26T20:28:51.839816Z"},"trusted":true},"execution_count":105,"outputs":[{"name":"stdout","text":"(16492, 6)\n","output_type":"stream"},{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nCountry Name_y object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['Country Name_x', 'Country Code', 'Year', 'population','USD GDP']\ndata = data.loc[:, cols]\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.842213Z","iopub.execute_input":"2023-10-26T20:28:51.842569Z","iopub.status.idle":"2023-10-26T20:28:51.852162Z","shell.execute_reply.started":"2023-10-26T20:28:51.842539Z","shell.execute_reply":"2023-10-26T20:28:51.850921Z"},"trusted":true},"execution_count":106,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.columns = ['Country Name','Country Code', 'Year', 'Population', 'GDP']\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.853458Z","iopub.execute_input":"2023-10-26T20:28:51.853809Z","iopub.status.idle":"2023-10-26T20:28:51.864097Z","shell.execute_reply.started":"2023-10-26T20:28:51.853780Z","shell.execute_reply":"2023-10-26T20:28:51.862937Z"},"trusted":true},"execution_count":107,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nPopulation float64\nGDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.865117Z","iopub.execute_input":"2023-10-26T20:28:51.865438Z","iopub.status.idle":"2023-10-26T20:28:51.883869Z","shell.execute_reply.started":"2023-10-26T20:28:51.865413Z","shell.execute_reply":"2023-10-26T20:28:51.882903Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":" Country Name Country Code Year Population GDP\n0 Aruba ABW 1960 54208.0 NaN\n1 Africa Eastern and Southern AFE 1960 130836765.0 162.913035\n2 Afghanistan AFG 1960 8996967.0 62.369375\n3 Africa Western and Central AFW 1960 96396419.0 106.976475\n4 Angola AGO 1960 5454938.0 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Country NameCountry CodeYearPopulationGDP
0ArubaABW196054208.0NaN
1Africa Eastern and SouthernAFE1960130836765.0162.913035
2AfghanistanAFG19608996967.062.369375
3Africa Western and CentralAFW196096396419.0106.976475
4AngolaAGO19605454938.0NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We apply the log normalisation on the data. The range is really large. It will allow us visualising in more details the distributions. However, any patterns through the years will appear as linear. It would be incorrect to interpret it as a linear growth, when it may be instead exponential. For that reasons, the non-normalise data may need to be used. \n\nThe distribute appears to be guassian. However, it includes the population for each year. So, it should be only used as a tool to explore the data.","metadata":{}},{"cell_type":"code","source":"data['log_pop'] = np.log10(data.Population)\ndata.log_pop.hist(bins = 100)\ndata.Population.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.885011Z","iopub.execute_input":"2023-10-26T20:28:51.885246Z","iopub.status.idle":"2023-10-26T20:28:52.385634Z","shell.execute_reply.started":"2023-10-26T20:28:51.885226Z","shell.execute_reply":"2023-10-26T20:28:52.384400Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"count 1.638700e+04\nmean 2.131655e+08\nstd 7.006673e+08\nmin 2.833000e+03\n25% 9.660195e+05\n50% 6.749849e+06\n75% 4.626525e+07\nmax 7.836631e+09\nName: Population, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"data['Year'] = pd.to_numeric(data.Year)\ndata_int = pd.merge(data, internet, left_on ='Year', right_on = 'Year', how = 'inner')\nprint(data_int.shape)\ndata_int.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:53.274864Z","iopub.execute_input":"2023-10-26T20:31:53.275211Z","iopub.status.idle":"2023-10-26T20:31:53.587275Z","shell.execute_reply.started":"2023-10-26T20:31:53.275181Z","shell.execute_reply":"2023-10-26T20:31:53.586389Z"},"trusted":true},"execution_count":120,"outputs":[{"name":"stdout","text":"(2358622, 13)\n","output_type":"stream"},{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\nUnnamed: 0 int64\nEntity object\nCode object\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"# Analysis\n## Has the population increased since the 1960s?\n\nWe discover the overall population may have increased since the 1960s. A boxplot shows the non-parametric distribution of yearly population across the world tend to increase. We would need to complete some further investigation to explore further this trend.","metadata":{}},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:59.444689Z","iopub.execute_input":"2023-10-26T20:31:59.445040Z","iopub.status.idle":"2023-10-26T20:31:59.453385Z","shell.execute_reply.started":"2023-10-26T20:31:59.445015Z","shell.execute_reply":"2023-10-26T20:31:59.452330Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop['1960'].describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:32:02.772369Z","iopub.execute_input":"2023-10-26T20:32:02.772824Z","iopub.status.idle":"2023-10-26T20:32:02.789620Z","shell.execute_reply.started":"2023-10-26T20:32:02.772781Z","shell.execute_reply":"2023-10-26T20:32:02.786617Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"count 2.640000e+02\nmean 1.172174e+08\nstd 3.695745e+08\nmin 2.833000e+03\n25% 5.022802e+05\n50% 3.718330e+06\n75% 2.636053e+07\nmax 3.032156e+09\nName: 1960, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"years = range(1960, 2022,1)\nrows = data.Year.isin(years) \ncols = ['Year','Population']\ndata_graph = data.loc[rows, cols]\ndata_graph['Population'] = np.log10(data_graph.Population)\n\nsns.set(rc={'figure.figsize':(15,15)})\nsns.boxplot(x = data_graph['Year'], y = data_graph['Population'])\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:23.655917Z","iopub.execute_input":"2023-10-26T20:34:23.656234Z","iopub.status.idle":"2023-10-26T20:34:25.959859Z","shell.execute_reply.started":"2023-10-26T20:34:23.656213Z","shell.execute_reply":"2023-10-26T20:34:25.959148Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## To which extend, the GDP and population may have a relationship?\n\nWe produced a scatter plot to explore the possible relationship between both the GDP and poplution. It challenging to interpret a possible relationship. A 3D scatter plot shows some level of complexity in the data. Some advanced regression or some clustering analysis may help identifying some relationships. ","metadata":{}},{"cell_type":"code","source":"data['log_gdp'] = np.log10(data.GDP)\ndata.log_gdp.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:32.865676Z","iopub.execute_input":"2023-10-26T20:34:32.865982Z","iopub.status.idle":"2023-10-26T20:34:32.876696Z","shell.execute_reply.started":"2023-10-26T20:34:32.865958Z","shell.execute_reply":"2023-10-26T20:34:32.875423Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"count 13156.000000\nmean 3.315185\nstd 0.752098\nmin 1.106767\n25% 2.734065\n50% 3.268659\n75% 3.879667\nmax 5.369801\nName: log_gdp, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.scatter(data.log_gdp, data.log_pop)\nplt.xlabel('GDP (USD) log-values')\nplt.ylabel('Population - log values ')","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:36.264746Z","iopub.execute_input":"2023-10-26T20:34:36.265081Z","iopub.status.idle":"2023-10-26T20:34:36.706387Z","shell.execute_reply.started":"2023-10-26T20:34:36.265056Z","shell.execute_reply":"2023-10-26T20:34:36.705531Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population - log values ')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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BX7XEgkgUh7dLx666+/eQ/aLt0c/+/qOdPxn/7hkND2xqsEHY6chWoP31uJmorSsQEMjrFlrfXzQ6xAIyIiMojBGhEREZFNXtzfgj3HLubktqysEjMTjFnRoF+pKb5IQ32v24mheCLtb9Ly2sxljZ1d/TjYdAk3++Jym8rSNziCbTvTb1ta1nn3orFqr1cPfSgckkmBaK6WuIYCHvzBvVVwOh1YVBnOyW0SERFNds6J3gEiIiKiySCRSCLSHsU7Z64g0h7Fu2ev5CxUA8bCMKsCMakyzAgpFJNCMDWZ0ZnDAWxcWaHaFF9agpm5f+GgFxtXVmSFasBYIPbcy6fQ2Nw5vqzx7kW3YE5ZACOjSeH7Jkda1tnY3IlEIom9x/U951KlWy48tb7WVGVa5jGeSOh/7KzYBhERUT5hxRoRERGRSXJTOHPJ6ioxkcowB4DUSMThADasSA/FlPqQSf3OMiOVZBLYfbQDC+ZM1wzXpMqzaGwIsf5hTJvmwj/vP6d6H1MHNCgNeTBq+75W+L3yQybUSMMAjD5vIgJ+N57eWGdqiqfcMR4KevHE2hoE/W6hwQZK25CbbEtERFQoGKwRERERmWB1QGNEaihmRSAGWBeKZS6/LPF78LPXzgIqAVRqACZHmrDa1HoN75y+mjYUQo209LK2otTwkAe1bUfao7quIwWiIkGmEcU+F9Yvn4vNq6pMVaodi3RmLYEFxqr1lJbGZh5PSq8Tucm2REREhYTBGhEREZFBiUTS8oBGTa6rxKwKxVKnSkbao5qVWakTSlMlEknsOnIBe493GG723903ZHrqqSKd2VVqIKr0vPk8RUgmgaH4p8MuAj4XkkifFhsOevH42moE/R6h6jHg04BS7fLHIlfxo1dOC98nuaBM5HWiFaYSERHlKwZrRERERCoSiSQ+aLuOjo9vIuh3j4cPiUQS+4535GT550RWiVkViklEG/VnXq6xuRPPvx4xPT2ztNhr27CA+ooQDgevaD5GSlVdmc+bFHYBEPqbnlBKZFnmWKWaeKiWKvV4EgkytY4bIiKifMVgjYiIiEhBY3Mntu9rRVdG+HDXwnK8e7bT9lBt8z3zUT8vNKFVYqmMhmKpRBv1p17OquW20tLLlo5u09uS23b9/JDmks4tqytVl2amPm+pRP8mQmtZ5tYtS3D5Wi9ePXzB0PaB9OPJiuOGiIgoXzFYIyIioiktdTlcid8DOICe/mF0dvVj56ELWZePxoaw+2hHTvbtlpnT4HQ6JqxKLJORUCyTSKP+1GEMVi63lZZeiuyDz+2Ez+tCd++wrm0rLekMB714Mg+a9CcSSTz/ekT1MnL91IyQjicrjhsiIqJ8xWCNiIiIpoyRkQQOvHcRnd0DKC/1Y3rQi5cOtE3YNE8tvX1xOB1iy/usrhKTozcUkyPSqD+195gV/dAyQy2RffjG5kVZyzJjA8PYsb9NMzBTWtI50f3DEokk3jj2kenltKKk48mK44aIiChfMVgjIiKiSSmtEm2aB2+duITjkWtZfcryWXCaZ0KqxJToDcWU6KnqMhqqBf1uPLG2BqGgfKglug+ZVYDLassnJDATGTSgRq6nmp1SjyerjhsiIqJ8xGCNiIiIJp1chwh2kUKhXFeJqbFqqWNqVVc0NoRY/zACxW4U+9xIJJLj+xLrF1uKmelrG+s098VIZZlSD7RUSoMB7l86G+Vhv+5gTGTQgNb1rehRp0fm8ZTvS2SJiIiMYrBGREREk8rYJMPchgh2cLuc4+FLrqvEtKhNr4y0R3WFVH2DcfzmrXOKoVGg2C28X5Itq6uE749aUGakSkxtMMDOQ+fH/1s0GNMaNPDsI0tUt2FljzoRAb8bTyuEmvm6RJaIiMgMBmtERERU0FLDj6s3+vHqkQsTvUuWKErJGuyoEpMLNvQESZmBlJGqKpHQKBzwCd23VDND+q8jt29674+eEEskGBPZntxE2FRW9KiTU+xzpfVqK/a5sH75XNWJp4BYxR8REVEhYbBGREREBUcKgJpar+Ht01fROxCf6F2y3GA8kTbp06pqH6VgQ2+QlBrCqU1QVQqPRKZTbt/Xir/65j1ZIY6WF/e1wesqMry80GiVmJEQSy0YE9meVRNh9di6ZfGE9ZojIiLKNwzWiIiIqKDkS/+0zLDH4QCSFk9GyAxFjFT7ZFahVc+ZjrZLN9MCkabWa7qCJL3PgVx4tOvIBc2wrCs2hLZLN7F+eUXaMkotsYG40DJJOWaqxIyEWGrBmOj2rJgIKyJzmScrz4iIiBisERERUYFIJJJ49fB5vHr4wkTvCgDA43Ji6xN3oKd/GD29w9hxoM3y2xANRUZGEjjw3kV0dg+gvNSPNXfOhcvllA3AMgPAUNCL4fio6vZTgyQjjfAzw6NEIom9xzuErtvdN4TNqyqx93iHrqq1zP0WZaZKzGiIpRSM5WoirBbRZZ5ERERTEYM1IiIiynuNzZ34p9fOon9IPQDKpWjvMJwOB+5edAveOXPF8u1rTfqUvHigFW8c7UBqsdyLB9uwdMEMnGi7kXX5zKo6kcBFCpJqK0oNN8JPDY9aOrqFQ7LSYi+cTge+vqnedKCndz/1Xs5oiKUUjOVqImzm9h5fW42g38NlnkRERAIYrBEREVFeSu2jtvf4xYneHVlSuGLlcjvJk+tqAABnznch0hEFkmNL7+rnhcZDjh/+5qRieCb3dzO6+4ZMNcJPfYxEw6tin2s8NFIa4KBF7/JMM1ViekMsQD0Yy8VEWIZoRERE5jBYIyIiorxzLNKJF/Y0I5bnQwmkcEWkski0Ab806RMA/viHv0+7zq6321Hsc+FrG+txsTNmeXimprTYa7gRfmZ4JBperV8+Ny3kSR3gcKa9C7uOtGtuQ2/oabZKTG8AqBWM5WoiLBERERnDYI2IiIjyyksHWrH7qFj/rYmUGq4YqVSShAIe3H/HbJSHp2kOEwCAvsERbNup/3bMkO5rS0e3oetnhkci4VXA78bmVVVZf5cGONRWlOLwB1dMLZOUY0WVWGaI1dnVjzdPXEZ373DavokGY3ZPhCUiIiLjGKwRERFR3nj37NWCCNWA7HBFqbIo4HejdyCuWK32xLoarKifNf7fiUQSv9rbYt+OGyDdV709xJTCI5Hw6umNdarBkVXLJOVYUSWWGWJtXlVlKhhjKEZERJSfHMmk1YPhC9PoaAJdXX223obL5UQoVIxotA8jIwlbb4uIcouvbyJzEokkfnv4PF7Jk4mfQPb0TIlWuCL1huvuG0LJNA/+cdeZtEolue19b+uq8ZDlzPku/ODFE1bcBV0CfjdcRQ7NqiqtqaBbVlehPOwXCo/kppbqXeJoxTaUpD6XXDo5OfHzm2jy4uubzAqHi1FU5NS8HCvWiIiIaEI1Nnfi+dcjwlMic0Xpp8fH11arBjaplUWR9qhqqAakT66UHouJ8PTGOqHlhlb1/JK2ZXaJo529w1glRkRERFoYrBEREdGE0ap+ykcv7m/DstpyoeBGtNl/d9+QbY+FtBRVklmJlxmIiQRJVoZZVoRXDMCIiIhoojBYIyIiogmRSCTx632tE70buqVWmGktFezsGhDaZonfg5+9dtaW/X1ybQ1CQe/4PlbPmY62SzfzIhAjIiIiKnQM1oiIiGhCtHR0CzfBzzddvYOyvb1CQS+e+qT6q7G5EzsPndfcVjjoBRyw7bEIBb1ZARgDMSIiIiJrMFgjIiKiCSG6TDIfvbCnBYPDo1l/j8aG8NzLp7B1y2Ls2N8mtK0n19Wgp1+9D5tR4eBYRRoRERER2UN7vAERERGRQYlEEpH2KN45cwWR9ihGRhLj/92j0dQ/18JBL7ZuWYJin/bvjnKhWqoX9rQIVaBtWV2JZXXlKC32Cu+nHk+uq+EUSyIiIiIbsWKNiIiIbCG3VDKzcf5E87mdWLu8Agvnh1A/L4Sm1muWTCeNpQwLUFMengYAqK0oRSjoVQ3jAj4XegX3zciETiIiIiLSj8EaERERydJqzK92+c6ufuw8dCHrMvkUqgHAYDyB373djiOnruCJtdXCyzetIlWqOZ0OPLWuRnUq6NOb6gEgK6wMB714fG01gn6P6YEEZJ7e1w0REREVNgZrREREU5wUBHT1DqK3L47gNA+udffjzROX0Z2yXDO1MX8mueq0fBLwu9GrUkUWjQ1h287Tlt5m0O9WrVzL7H+2rK4czz6yRDY4S60+a6gpm/LBjd3hldHtaw20ICIiosnHkUzm22/HE2N0NIGurj5bb8PlciIUKkY02oeRkYStt0VEucXXNxUqI4HYs48sSQsJjkU6sW2ncqXVRAv63fj+1lV4/d12vHr4AnLxxWesiqxG9XHJfBwlU6niych9tTu8Et1+5r7HBuKGnm+aWPz8Jpq8+Poms8LhYhQVaY8mYLD2CQZrRGQGX9+Ub5QCC5HlmlqC09z4m2/dC5fLiWORq/jRK6fzbolnpmKfy5LeaaKkEEUupGH/szFqAZZSVV5jc6fqclmz4ZXo9o9FOvHCnua0ikSt/oHhoBff27pq0oakhYqf30STF1/fZBaDNZ0YrBGRGXx9Uz5RCizuWliOd892WrJcM+B3Y/VnbsHuox2mtzWZyIVmU6kCTZRWgOXzFKVNXg0FveM98NSOXzPhVSKRxHe3HdHc/or6Muw5dlH39gHgz55sQP38kKHrkj34+U00efH1TWaJBmvssUZERFTAMvujXe8ZxL7j2Sf90diQpSFY70Ccodongn43nlhbg1BQPjRzOh0MU1IkEkn8el+r6mVSQzVAvAdeV2wILR3dhh7vlo5uzdC5KzZkOFQDgO6+/OxBSERERMYxWCMiIspzqRVPJX4P4AB6+ofR2ZU9YIDMkXqj7dgv3nfuaxvrpvyyTlGJRBL7jnfYOuTCaHiVi9BLmgJLREREkweDNSIiojyW79M2zQoHvVhp4RJVs6RlnMtqP+3xNaPEh4TDiZ/u/ABd7JUmTK65v57A0iij4ZXdoVfmFFgiIiKaHBisERERmWRlDy0rhgsUgvXL56Khpmz8sXr0gWrsO96BHQfaJmR/HA7gmYc/bXyfunxT6tGysGI6zpzvYq80ARMVCJsJr2ID9lZ+PrmuhscLERHRJMRgjYiISIfsKpzhrIbq0mRDvZVMk706TbJldSUeWn1b2t+cTgdKAp4J2iPgmYcXY0W9+vPFXmlitAYT2MloeJVIJLFjvz2hLisbiYiIJjcGa0RENOlZVVEmGnxFY0N47uVTePaRJcIn0xMZRuRaeXia7N9Fl+IF/W7EBuKW7AtDD2uJDCYwQ6kHntnnUWRwgag/WDUfC+eHWdlIREQ0RTBYIyKigiYXmgFIW06Z2eDfSEWZkeBr+75WNNSUaZ5U2x1G2Gnjygrd/dGUArTailKEgl7VbYWDXvzVN+/BK4fO43fvtOve39KAB/9u8yL09A8z9LCBlQGVHLkeeFY8j1YNLvC5nXh49W08poiIiKYQBmtERFSw5CrIin1jH219gyOK19NbUWY0+OqKDaGlo1tz+aDdYYQdUiuEHn2gGi0d3ejqHcSL+9pUq8nUemA5nQ48ta5GNcB8cl0NXC4ngtPchvZ7eCSBgaER3L3oFkPXJ3V2TdbMrEgzuyw3M5AvmWbNMuRvbF7EUI2IiGiKYbBGREQFIZFIItIeRaQjCiTHTqxfPXwh63JqgVom0YoyM8GXSNBgVxhhlyfWVGPd8orxxy015PC6ijSDMbXHe1ldOZ59ZElWYJoZrASKjQVr/YMjupfpTmV6l1F3dvVbevu3Lwhj48r542FspD1qy5Lu0oAHxT6XrvePTFtWV/GYIiIimoIYrBERUd5rbO7E869HTJ30yhGtKDMTfIn0DRPtLZYPwkFvWqiWSTQYy5QZ4Pz1N+9B26WbiiFKOOAzdT9EQ9VCY+WEWrkASm0ZdWNzp2VTbIN+N76yoRYr6mep7ssTa2sQ9LuF76/Sku7UpeJGhAIebF5VaWobREREVJgYrBERUd5IDQVK/B7AAZxsu469xy/adpsioZnR4Ett2WMqkd5i+SKz4kwuyFlWV46GGvEeWGoBjtKSTbOPmWioWkj0BmFa25ILoJSWUZvtExgKePCNT3rflUzzAEmgZ2AYkfYoYgNxbNspvy+Zf1e7vyL76PMUYXB4VPf+P7W+dtKFtERERCSGwRoREeUF0YmbVhMJzYyGOFrLHiUivcWstGFlBfYc7VD8d7mBBKGgF/cvnY34aAKR9ihqK0rR1HpNNcgRCa30BjgSp9OB+5fOxs5D5zVvQ0khLMEVrUAz+jgq3aZWAJVZ8We2T+BT62uxqDKMxuZO/Ox3Z9O25dCRV6ndX5F9NBKqcQkoERHR1MZgjYiIbCcyudOqJWR6iFaU6Q2+Aj4Xnt5Ur+tkW1pC+fPdzehVaf5vhsMBPPPwYqyon4XqOdNVl2tKAwlSJ6umhlhK/aj0BDlGApxU5WG/1l1Wle9LcEUr0BKJJJ5/PaK6rZ/vbhZe+ioSQGVW/BkNKVPvj1I4mEzq367ccWNHkMoloERERMRgjYiILJcapEmhTGoPI5HJnbkgWlEGfBp8/Wpvi2Y/Jre7CA01Zbr3Z1ldOYbjCfx01xnd1xXxzMNLsKK+fPy2li6YiQPvXURn9wDKS/1Yc+dcuFxOAJ8OJFDqm6X13In0MDMS4KQyE4yJhqp6WdXjTE8FWuSjqObz0TsQR+SjKBZVhjVvWzSASr2ckediy+pKbF5VBafTYXopaSa540Z0H4N+t+pk21RcAkpEREQM1oiIyFIiSzonOlADgIDfrTv8WlZXDr/XhR/sOKF6uaiJ/l2hoPkqqsw+UcFpbty9aBaCfjcSiSScTofs87TnWEdaNZSZsEOkh5mRACeVmT5rcqGq2VDMqh5neiv5Iu1Roe3ub7wIp8Oheb9EA6jUy9VWlApP1QwFPHhqfW3aY2J2KamczONG5HgJB714fG2NbE+3zMupDeMgIiKiqYPBGhERmSYFEk2t12wdNCDC5x6ruBqMJ1Qv1zsQNxR+9fSLTQ80uuxMz8n/jv3ySzmlwQFNrdfw9umriPXHsff4Rew9fhGhoBd3LSzHbpkea5nVUGbDDq3HwEiAk0pkia7P7Uw7FpQCEbVQ7K7F8gMUMq9vVY8z0Uq+8Qo0weyvqfU6mlqva4Z9IiFZwO/WXfHndTvx7S/djvp5oaxgz45lmpnHjcjxIh0bTpnJtkG/G3cvnoWGmjJT01aJiIhocmGwRkREpkzU0AElfp8bX7rvNvzj785qXtbIybzZMEiLnpP/ZbWfTt5MnaTY0tGN2EBcNuSMxoZkQ7VUUjWU2bBD6zEQCRGDfjeisaHxgQmZYYa0RFfpGByMJzQDEa1QzFnkxIP3VCnuo9lecZlEH/dtO0/h65vqUV8Rwi60C10HMBb2ZclofNbS0a1ZrTYUT8DpcMg+Blb3u1Na6qt0vGQGrnon2xIREdHUxWCNiIgMUwokJlI0NiTc/N/IybxoRZlSNY/IckPRk//UPmhmJilmkpZx2t3DTCREjA3Ex3vOKVVaSSHIriMXZKeESiGj3GMtEor96o1mrL2rUvHfzfaKyzwmSqZ5VLcl6RscwXMvn8LWLYuFl2GmUgr7REKy3sERQ8MLzCzrdTjEBxmo9U8UDc2k1xcRERGRGgZrRERkiNXNxq0UKHabCr/UiFaUyZ3U6+nBJXryb+UkxVTdfUNYWT/Lsh5mSoGiVsVZKq1Kq7dOXla9vlyQJBSK9QzhzIc3MHeG/BRSM6GS3DFRGvDoCspe3N+Gr22s1+wLlkkp7LNzeIGZZb3PPLwYQb9n/BiKDQxjx/421fBZCUMzEmXVQBIiIpq8GKwREZFuiUQS+4535M3yz0zhgE93+KXn5Em0oiyVkR5cWif/doabpcVeobAjMwAa6/9WjWKfG++cufJJABLP6geXGiimhohdvYN4cV+b6lRGwwGZTJAkGiJ19QwqBmtGQyWlY0Jr6mzWvsWGEPS7hafWpt2WzP03OrzAbJht5HW1rLacoQfJSn1PL5nmQWI0iZZL3UASqJ8fku31l8mqgSRERDS5MVgjIiJd8q2nWibp5N3pdAifpBs5edLTg8nqHlwSOyYpAukBiFbYkfkY9PQN44U9LarBGJAdKEohYqQ9qnldMwFZ5uVEQ6RwiU/x34yESlaHot19Q7h70S3jz8eZC13Y9bZ23zW5+2/k/pip5Eylt7cZK89IkhqkdXYN4K2TlxWP4V1vt6PY58LXN9UrDiexciAJERFNbgzWiIgoTWblVvWc6Wi7dPOTk5V+7Dx0wZLbyZzWaJXUk/dldeVYumAmDrx3EZ3dAygv9WPNnXPhcjnHL2/m5En0pN5sDy4ldkxSBLIDEK2wQ9rnlw60ag5GyJQZKNodkGVeTihEKvFi0W0z0HOzX/bfjYRKVoei0v2SjsnailIcPnXFUAWZ0ZDMSMWZHIZlJJE+j6KxIcT6hxEodiMc8GWFrUZ+8JF6FMoNJ7HrxxAiIpqcGKwREU1RIyOJrMDp5LnrWScnehqG67F2WQV+9474JMNMmfsVnObG3YtmodjnRiKRhNPpkD3Z2nOsY7wSLVcnT2YbuysxM1xg48oKvHu2UzgAUQs7Eokkfnv4vO5QDcgOFG0NyGSCJJEQ6csP1qFIoNJKT6hkZahm9H5pNfg3EpJxmiZZRS0sCwW9eGJtDYJ+NxpbO7H/+CXDtyM3nMSuH0OIiGhyYrBGRDQFvXSgFXuOdaQFUy8eaINcfmZHqAYADqf2ZdRIjcybWq/h7dNXEesfm/y49/hFhIJe3LWwXDboSa1EK/a5c3LyZLaxuxIjkxRTg5FHH6jWFYDI9aFrar2mu69XptRA0c6ATClI0gqRVtSLVVrpGTixfb91y0CN3i+tCjKjIRkrzsgsrYnT0diQ7mEdSuSGk4j+yPH7k5dxvLlTthpaCYchEBFNPgzWiIgmucwv8SfPXccemcDJpvxMUX1FCIeD6kvVAj4X3O6itMsU+1xYv7wCy2rL0dR6DXuPX8y6XjQ2pFk9tX1fK750/wKhfTW75NKKxu5yjExSTD2J0xOAyFWP6JlcqSY1ULQzIFMLkqyqtNJ6TLUCA73WL59r6/1iSEZyrA6H0gYN+D05nzidOpwkkUiiR/CHgrfPXB3//zsOtGHjygo8tqZG8fIchkBENDkxWCMimsTyddBAOOhF/fyQZoDy9KZ6NNSUYdeR89h7/CL6BkfQNziCnYfO480TlxAfMd6jreuTnj0izCy5BKxr7C7Hqr5WapTCICtCNblAcaICMrtDJDumuDbUlGlehuEYqfXO1BuMWR0O5cPn1GtHzqMISfT0DWHH/jbD+yL9oCMXrnEYAhHR5MVgjYhokrK6MsZKUoikFKCknqQ1NnfKDkwws/RQEih221JJJsfOAMzOvlZ2hEGp1KrP8jUg06JUzbPryHnbe6vRxLFziZ+ZbcsFV5lLxEWDMavDoXz5nDpzvgtnzndZsq3dRzvwxfsWpC0L5TAEIqLJjcEaEdEkZHcYYlSxz4Wvb6rPOvFKZjRyk/7b7vsRDvhsqySTY2cAZleYZPX0SolIoDjRAZkRStU8Sj3/zLDy2CxEVgZZZrdl5xI/M9tWCq4ye2eKBGNWh0P5+jllhX2NHdh41/zx/+YwBCKiyY3BGhHRJGRXGGLW1oeXYFFVGMDYSdWuIxew89D5rMt19w7juZdPYcvqStvuh1Tt43Q6bF9KmarQwiKz/eXkPLGmGuuWV0y6UEitmsdoqOYqcsDncaF3ID7+N7uOzVywKgyzMsgyuy07l/iZ2baR4EotGBMNh/Yd70BJwKP5/Obr55QVWi/exMa7Pv1vuyZDExFRfmCwRkQ0CeXjyUqxzzUeKDU2dwpNkpQbTGCV1GofOyvJCoFa2GG2v5yc4DTPpHts7aq+2XxPJTavqszrY3NkJIED711EZ/eA6nREq8IwK4Mss9uyc4mf2W0bCa7UqqZEQ58dB9rG/7/a8zuZQySfuyjtv+2aDE1ERPmBwRoR0SQk2pQ/l9Z/UqGkp6eO2eb4G1dW4N2znUKVaIVWSWaEXIDW1HpNNewQmWiqVz4en2YkEknsO95heaAd8LmweVVlXh+bLx1oxZ5jHWlLC1882IYNK9KnI1oVhlkZZFmxLTuX+EU+iprattHg6nhzJwBkBbhGQh+157ezq9/Q/hWCVUtuSftvuyZDExFRfmCwRkQ0CQWK3RO9C2mkgCCRSOL51yO6rlvsc6kGbAGfC253kWJ49ugD1Xld7ZMrctVCSo9t5snw/Utnyy7ZNSrfjk8Ro4kkzl7owo2ewbTjyM6Jhk9vqp/QY1WtkjGRSOInr57G0Uhn1vWSyfTpiFaGYWaCrMz7k0gmTQVXiUQSZy6INbzXG3I1NncKv1dK9yHz/jW1XNN1m5ID713CgfcuZVWbxQaMB+KZz28ikcSbJy4b3t5EyBz4oMTnKcLCynDa3+ycDE1ERBOPwRoR0SQUDvgs21bQ70Yspb+TEVJAsG3nB7qr0NYvr1ANdZ7eVK+6jDOfq31yRalaSOu5kE6Gy8N+odtpqJmJptbrmpez8vjMhWORTvx6bwtu3Bwc/5tdAwmA/OihprZsEwBeeKMZN/vU3xd2H+3A4sowHE6HZWGYaICZGWQpBctGtqW0PTWXr/ch0h6VDfYz72NsII5tO8UnZW7f34qOzlhWda5ZqQF7Q00Zduxv076Sgsznt6Wj25LJzrm0YUWF0Ov9G19YqDjpOJf9PImIKHcYrBERTUJWLt/7yoZa7NjfprmE5fG1NdixP/vEdf3yCjTUlOFY5CqORfRVUISDXmxeVYk5ZcWaJyNTPTxTYqb3l3QyLLoEbO2dc3HhSqwglzspVWfZMZBAzpbVlSgPT5uwqsrU+9/Z1Y+dhy5kXUYKWvT4m5dOGg6w5MKrgF+s2jH1mDUaLMttS217anYdaceuI+1ZVWBy99Gh86nvHYjbEvBKtu9rhd/rMv150t03NH6cSctNC0HqZ82COdMVA1WRfoFTvZ8nEdFkxWCNiGgSEll2oqXYW4Svf34hltWVw+lwaC5hWVZXjmW1Zdh15Dz2Hr+IvsER9A2OYOeh83jzxCUMDunvlyYtjeHJiHFmJ+919w1hZf0sof5A9fNDBbPcKT1IGsBbJy9nVWc9sbbaVJWOnMwKULuqVUSmb0qXaWq9hndOXzVdmarESIClFF71CuxjanhrdqhEZhBsdnupVWAAZO+jyHLDXOqKDSHSHjW9nc6uAXx325G8HK6Tav2KCjRUz5R97aR+FnX1DqK3L47gNA9CQfHPJFZRExFNPgzWiIgmqWV15bh3ySwcPnXV0PU9HhcaasrGt7V1yxK8sKdZNRRoar0mW+mid8mP3C//PBkxpqnVWJ8lSWmxV1d/oEJY7iSyjC8aG8K2nactvd1w0Iu/+uY9aLt003RArBacyd2/oN+Nr2yow4p65UqpiWRlGJYa3poNljODYLPbk/x6bwuGRxKmt5Mr13sGtS+kIuBzWdqn0U7vnL6Cxz9Xrfi65GcRERFlYrBGRDSJLawKGw7Woik9cRqbO7Fjf2taqBbwu/H42urxoMTsybDk3iW34N98Xr5HDenT2NyJvccvGr5+wO8eDzv0BGa5rDAUqcxKZWQZn1WeXFcDl8spdFKuNzhL7X8md/+kvl3nV1ZgwZzpE/YYKDESholU/4kODcgc5KEUBBudtJkpWmD9xd45bexzRBJP5L4ML+Bz4elN9QCAX+1tEf6BJ9YfNzTFlYiIpi4Ga0REk5jZJvHHmzvR0tEtW2nQOxDHtp2n4XxkrErJikoOn6eIoZpFrAg6ewfiaGq9Nh4u6AnMclHVoRYwyVXGWRX+6iVSrSeyNFUtOJOWGGr1M9t9tANe98RNYxQJsETDq8fXVSMc8Kkei6L9Abc+vAROp0NxAqr03Jz+8IbQ9ijd0PBoTm6noWYm5pQVo74ihPr5ofHnsKGmDPuOd2DHAbGl3VYFqERENDUwWCMimsTMDjE48N4lzctIkyOtOBFhqGYdq5asSc9vvk1ZVRsqIPWwygyyrHpMRK1fPhcNNWWaVXTHIlfxyz0tqv3DRIMzkX5mQ/HchBxy1AIsiWgYFg74NI9FkffAUMADOCBcHUj2eeCO2XjzhPHgd/3yCtljwul0YN3yCux6u12oT5/oMUhERAQwWCMimtSsGGKgRe/kSCUbV1aM938i86yquOhKWRKcL0QqzzIDQcCeKpSNKyvw7tlOmX5mtVhWO1bJeTRyVTFEeulAq66JjqKDAPKRNOBCKzwXCcOKvUVC02VF3gOHRxL4wY4Tn27b58L65XNx68wAtu3MnyWzPk8RBnNU+TVRqium43jzNaHwK5PWxGGn04GvbqjV7J2Yr5OLiYgofzknegeIiMheUm+sUDA9+Ar43ZbdRnff0PjJsJqAz5V1maDfja1bFuOxNTWW7Q9ZW3GRb8uiIh9FNSuIpEAwlZHHJBz04o+++BnMmO7L+vvWLYtx+4KZuLNuJvzeovF/iw3E8YvdzfjjH/4e39vehJ+8egbf296E7247gsbmzvHLHYt06grVCt0frlmAfcc78MIbzXjj6EcYUWjeL4VhavqGRvGbN8WW9Sm+B35S/ZcZVo5NM76AHxkM1XyeIu0LGTA4PIqNKys032fl+NxiX/k9rok9NQgHfPjqhlpD1xWZOLyifhY2rqwwvR0iIqJUrFgjIpoClHpjNbVes2SZk+jkyKc31eesqf1UZ3YZcKp8WhbV2NyJf3rtrNBlM++7kcfkyXU1WLloFtbdU4V3T17EjZ5BlBZ70dM3jBf2tKQ1z08lV1mWuky1oaYMv9wdEd6PQhYOejFvVgA/efUMkik97F882IYNKypkQ/WGmrKsfmyZdh/tQNXs6UKVrpnvgSV+D3722llAZftG2+0/uKICrx6+YPDa6o6e7cRffzJZ9nhzp9ByfQAYjItNILVrUqnX7cSQxj5IlWJOpwPnV/YIh84BvxtPb6wTnjj82JoaVM2enj3lusSLJ9fmx+RiIiIqLAzWiIimCLneWNLJpp6mzpkcDqB6zvTx7YlMjsynZYWTlVXLgEN5tCxK70TPWH/6FEA9j0nqMTsyksCu33+IC5e7MbPEhwsf9+CNY8anrW7f14qOzl70FvCyTlFbVldhYHgEe2RCkmQS4+FJZrjW0tEttOz1hTeasay2THc4f/Fary190wJ+Nx66twrD8VFbqhG7YkNou3Rz/D1UNFjTw+d2Cgdxov7tFxbh/OWbqo9JaqWYUviVSlqyu3lVle7nf0V9OZbVjgWtsYE4Km6djtkhHxITML2UiIgKH4M1IqIpzul0oCTgMXz9ZBJpJ3p6JkeSvaSg8xe7lU9OtQzHR9Mmg04UIxM9A8XZy52Vwt9QwIP7lt6KkVGgq2cQoele+D0ubN/fin3HOgxXL8npig3ZVtGUb95suoib/erH3p5jHfjifQvgSlmGKLr8ONYfx85DH2LR/LDq+0zOhhB8UpL32JoaeN1FeMWG51l6bPRWYPo9RRgQ6NG2YeU8S/db6p+5or5cvlJMYWpuavglVRnCAfT0D1vyuSL92ORyOREKFSMa7WOwRkREhjBYIyIi00v9Mk+C82VyJI0FSUsXzMSfPHfYUEPwvsERxSmbuWRkomc48GlftEQimRb2SsvppP8e64sWSauSeu3tjyzb/6mqu0/7mEsmgQPvXcSDK+eN/03Pe9KuI+3YdaQdoaAXT8kENHorHc3oHRwZH/Yxa8Y0W25Demz0VmD+4Zpq/PgV7cb9f3BvFYY0Ku42rKzAkQ+upAVkDqQvn5WGeKyonzX+t8ywTCsg42cJEREVAgZrRERkuh9XPvXgomwulxNPb6wzFS7ITdnMJSMDFGIDY0tB5aqV3EUOrKgvx9c3LcTJc9fzavpjPhGtcjKrs3sg7b9rK0oR8Lt1hcGpPeykcM1IpaNZ0rFqx/ti5sRKqQLz+dcjqktnpYqw9o/Ve5dJyzEV+5ClVJf94QPVaQFZ9ZzpaWG1UmDGsIyIiCYbBmtERGSqH1fmiR7lJ9ETcCXSlM2JOiE2ElL80+/OIpkAfvRqdpVOfDSJI6ev4sjpq/C67ZniONHcRQ7ER40tbVtz5xwsryvHsUgnDjZZ38crU3mpP+2/nU4HvrqhFtt2qldYyUkNgY1UOpolHatWDhCRyE2slJbf7zpyAXuPd6S9vjOXWYoEZhKt6jK5gIyBGRERTUUM1oiICIBy7ymfpwiDKhUrcid6NDkZqRqzipGQYjCekA3VMg3F7a/IMiJzKmYo4MHwSEI4GE0N1RwAPAKTGSW9/XHUzw/hwpUeXftshMMBrLlzbtbfV9TPwtvVV3Ci7Yau7aWGwFYds35vEQaGtI+T1B8a9P5gMdaMvwK3zpyGHfvbVAfAZHI6HXhodRU2r6rUXGapZzkmq8uIiIi0MVgjIqJxSoMHmlqvaU76pPxmRZ8pvVVjmX3NzDQbdzoduGthuS2TFnNF6jn1yz0tQksct25ZAiSBSEcUidEk+odGcK17AKcvRHXfdhIQDtUA4FhzJxrOXsGeo/b3mduwoiJtcIEkkUii/WqvoW1auRwzHPTirz7pydfUeg17jytPhM38oUFtUvLja6sR9HtkXx/LassNvXZEgzAGZkRERNZhsEZERGnkTrg46bOwWdFnSu+SX7m+ZgG/G1/NaGauZ3uFHqr9zbP3jgdIWkscw0Ev+gZHsGNfK6K91lUKOgAUOYERjYwtmQR+/MoZy25Xdl8cY6HaY2tqZP/dzDJOPcsxAz4XejX6k7lcTtTPD6F+fgi1FaW6fmgw8v7J4IuIiKhwMFgjIiIhPNErXFb0mXp8bbVwkKpUHdc7EMe2nadxfmWPYpgiZyIa0FvtaxvrxkO1FfWzcH6lehP5lQvLbRmokARQeWsJ2i7Zv8RTzecaZuPJtbWylWoSo8s49S7HfHpTPQAIh2UMyoiIiCgVgzUiIqICp7Xk0oo+U7/Y3Qynw6G59FckBNt9tANVs6djRb3YMuKJaEBvlYDfjac31mU9blIT+V+8fhZ9Kb27in1FuGfxLTjwnv0DAyZSTUWpaqgGGF/GqWc5ZmpwpicsY1BGREREEgZrREREBUApPBNZcmlFn6m+wRE89/IpPPvIEtVwTTQEe+GNZiyrLROqgpvIoQlG+dxFePaRJVhYGZa9j4lEEo2RTvRnNMTvGxzFvkZ7Q7U7ambi3OUeJI0NDFUU9LuwvL4cB5sua15W5JjUO7DC7HJMhmVERERkBIM1IiKiPKcUnt37mVuwR2Y5YeaSy+o50+FwwJIgZfu+VjTUKAdioiFYrD8+PrlRiRQmXr7eZ2hfJ9JgfBRFRU7Zx6mxuRP/uOuMrmECVnE4gAeXz0Nvf9zSnnUPLp+DJ9bVIZFI4kTbDdUwTLRfn8gyTp+nCJ+9/VY01JRxOSYRERFNCAZrREREeSSzMi02EJfttdU7EJcN1VJJSy6Dfrdl1UldsSHVQExPdZwUwslV48lNoi00ciGjFdNZzZAmcEo97vYc60g7NhwO4MEVFTh6tlP1sfd7inD34lswK+THmjvnji/rFAnDMpdqqlFaxlnsc2H98rnYvKqKQ1SIiIhoQjFYIyIimgBSmBSNDSHWP4xAsRvXo4N46+TltADBbGTwwhvNeELHoAARalVptRWlCPjd6B2Ia26ntNiLY5Gr+OWelrTLF/tc6FOZ0lgopJBReq67egfx4r62CdkXuQmcj62pwRfvW4AD711EZ/cAyks/Dcmq50xXDcf+7RcWKi4JFu1pJopTiYmIiCifMVgjIiKykNYgAUB+aacSs4Vmsf44Yv3DJreSTq0qLZFI4vbbwjhy+qrqNsJBL062XcOeYxez/m0yhGqhT5Y76nmurVZXMR1zygJpgVkml8uJB1fOy/q72XDM6jCMyziJiIgoXzFYIyIissixSCde2NOMWEb1VeqStYlYCljsdwtXkWkJB72onjMdkfZoVmDy0oHWrKWFSubNCsiGapPFcHwUv3mzzdI+ZlruXTILXo9LNUjTw2w4xjCMiIiIpgJHMmn1TKjCNDqaQFeXvc2RXS4nQqFiRKN9GBnJfcNiIrIPX9/00oFW1RDF5ynC05vq8dKBtpxXLz2xphqhEi+27TxtelsbVlbgyAdX0sLDUNCLirJpeP/DqOntG1XkAEan6DcahwN45uHF41NgiUgcP7+JJi++vsmscLgYRUXaP1Sa+ymTiIhoCkkkkoi0R/HOmSuItEeRSIwlOcciVzUrkwaHR/HjV05PyJLAq9F+BP0ebFhZYXpbe452pIVqABCNDU1oqAYAFeXFE3r7E+mZh5cwVCMiIiKaIFwKSkREJECuV1Yo6MUTa6vxi93NE7hn2g42XcbBpssoDXhkK84mgwtX7as6dzqARB5Xw7GHPxEREdHEYcUaERFNSUrVZ3KkvmiZ1WbR2BC27TxdMM32u3uHsedoB1YuKofPUzTRuzNhAn4XnnloMf7kD5cKXT6fQzUA2L6vVfX4JSIiIiL7sGKNiIimHKXqs6dkph0mEkn8el9rrndRSDjoxcqF5bob5O9vvGTTHuWv+opS3NcwO6sB/8aVFaqP3+0LZuD9czds2ScHzE99BYCu2BBaOro5KIDIoNFEEmcvdOHazQH09sURnOYZn+xrdJKtRGRSNBERFTYGa0RENOmoncgoTeWMxobw3Mun8OwjS8bDtUQiiX3HOyakL5qWLasrxyeNXunqx4k2e8KfyeK+htm4e9EtWX9/bE0NAMiGaxtXVuD2BTNtC9asrDHr7su/Y1QEQweyit5jSbr8yXPX8fbpq+jpG866jNIPLqL0/IhDRESFi8EaERFNKmonMg01ZZrVZ//02llc6x5AV88Q3j1zdUJ6kbldTsQ1plf968mP8fm7K/GTV09P+lBt86r5+OhqLz44dyMtjHI4gBV15Tga6dTcRmmxV/HfHltTgy/etwAH3ruIzu4BlJf6sebOuXC5nEgkkggHvegyGK5uXFmBd8922h7Oqt0/O1gRiDF0oFRyxxQARNqjiHREgSRQPz+E+nmhrGNN77Ekd3k5cj+4iNLzIw4VLj3vhfwhgWjyciSTSTblADA6mkBXl32NjwGO+yWazPj6zh0j1WiSLasrsfPQhRztqTEP3VuJ2rml+MGLJzQv63ICU+Fw+7MnG1A/P4SRkURW+OV0OvDdbUdUT5DDQS++t3WV4ROYprbr+PvfvK/473LhWTjoxZOfnNQnEknsOnIBOw+dN3T7WszeP720QgyRk0et1ypDh/xmtDpM6fJyx1Sxz4WR0QSG4ulvcsU+F76+qX78+NB7LGldXo7e11gikbT9fYkmntxx6/cWYcHs6VhSFR7/gUbpsvwhwX78fk5mhcPFKCrSHk3AijUiIjIll7/Aqk3mLPa58fzrEdXrv/7uR7bsl5VumTENPQPZS5LkTIXviOHgp5UrLpcTD66cl3WZp9bVqJ4oP7muxtQxuaK+HH/x9Ar8+F/eT6tcSw3PHn2gWvV18NbJy4ZvX4vZ+6eHVhVOfUUprkYHEO1VPnkU6Vu4fV8rGmrKGDqksPO9Vs+2ragOS7280jGlNBSmb3BkvOJLpAo59Vgy2jNTbx/Dlo5uzWo49kYsHJmvj+o50/HaO+2yP5YMDI3i1PkunDrfhRcPtmHDigosmDOd1YtEkxyDNSIiMszqX2CN9kbbtvO00PYzKx/yUa6X9Onhczvh97lz2nNOJDRaVleOZx9ZknUspgZfZq26fTbq5pTgzPmurONTK5QQOckW4XAAqesMrLx/IsGKSCgR6ejO+lvmyeNkCB1yvfzLzmoXPdvWu7xR6/JbtyzGjv1thvb71/ta4fe6dB1LZl6LevoYil62UHsjTiXHIp14YU+zobYQyeRY/06vR/2HFf6QQFT4GKwREU0xVlU9WN0/Ru7kLuB346sbarGstjxvJ3NaKbU6KxT05izACk5z477bb8Xv3lGv6BuMJ/BHX1wIp9OB7r4h9PQOY8cBYyfFknDQi8fX1mDHfnOh2LK6cjTUlNlaPel0OrKCHrnjNuh34+7Fs9BQU4bailLLTp6feXgxgn6PofunFVpr3Qen02E6IPzFnmYsXTDTttAhV9WzeoMos4GYnb269A5zsbo67IU9LYb7WEZjQ4i0R4UuKx1LZl6Len70EL1sPv+QQsBLB1p1T92WMzQ8qvrv+f5DAhFpY7BGRDSFWFX1YPVSLqWTu96BOLbtPI36ikt5OZlTryIHMKrS2TS1OuuxNQvw41fO5GS/vrahDvFRsWq+noHh8emaiUQSe46pT00N+FzoVVjSBWA8PFtWaz4Ukwu+7KR03MYG4th7/CL2Hr+IUNCL+5feqmu7XncRhuKfnojpCRmVliztPd6RtrROet0DELoPT62rET5GlMT64/iT5w5j3bI5QpfXEzrkqn+RniDKikDMzmWzerett9JQ5PKmh8MI3mXpWDIaZKX+6CGitqJU88cRvduk3Hr37FVLQjVRrF4kKmwM1oiIJiG5yo2m1muWVT0YWcqlVE1idHlZISop9uCzS2djf+PFtJBDLjh5871Llt9+sc+leLuilR+pJ6ZOp0Ozv9nTm+oBQHOZZq5DMTl6Kp5GRhL4xe5mzW1GY0PYeeiC0KRXiRSqFftcWL98LjavqpLdj8z9jQ0MY8f+NqEQWnrdF/u0vwpKl92yulJo/9X0DsSx89CFrGMxk57QIVfTF/UEUQAsCcTsXDard9t6Kw1zERTUV4RwOHhFOMASCbzk6O1jKPLemMveiKQt9f306o1+vHL4Qk5vn9WLRIWNwRoR0SQjV7lRGvBontTrqXrQe4Kltswz6PdMimo0EdHeYdTPC+Ghe6vQ0tGN67EBnL/UA8CBGzcHMTKSgMvlxEsHWtHccdOy2y0tduPLD9apLpU0WmEh2t/M7mWaWoxMJQz63fjKhjrcs+SWtG01Nnfi57ub0auj2kY0VEvVNziCnYcuYE5ZQGiJoRFq4Vamt05ctmyJstYzLxo65HIQgp4gCoAlgZidvbr0blvv8kbRywf9bkOVa6GgF/XzQ7oCLJHAK5WZPoa56P1I+ih9Dlj1fmoUqxeJCh+DNSKiSSKRSGLXkQuyU6q6e7WnTOqpetBzgqW1zPP2BWGhbU0W3X1DcDodeP/cdew51pHWjP7Fg21Yv2wO9jZaU612+4IZ2LhyXlqIpPT8mqmwEOlvlquKtEQiiUh7FJGOKJAcu799g/GsSi6RqYSxgTi27TyFC1dj+NajdwBQro6yU2YwNBH7AIwFw1tWV8m+x+jVOziCLaur8NbJy6ZCh1wOQrAj5NK6rJ29uvRuW2/4Lnr5x9fWYNtO/cfzU5+8H+kNsJQuH5zmxl0LyzFzuh/BaR6EguZ/AMhF70eSD8wAZFT0xrN6eYaCXty1sDynSz7lPL5W+YeEXE5eJyLjGKwREU0Cjc2d+NXeFqEATY3oCaHoCVP1nOn4T/9wSHVb75/r0rWPha602KvYEDmZBN44bi5Um1nixbrlFVhz51y4XE5d1zVTYZHrpZxKy52ffz2SVoW16+122evrmUr4+tvtuL26DAsrpk/IEI2u2BD2He9AScCDEr8Hz78eyfk+SMrD/rFjZG8ror3mqjvKw358f+sqUyeNuZy+aEfIpXVZO3t16d223vBd9PLL6srhlHnfCfhciI8msqY5B/xuPL2xLu39SG+AlXr52EAcFbdOx+yQD4mEShNMg/JhmXshEg2U5KrNpCXuWhW50dhQTkK1jSsrUDV7uuJ00edfP4vfn7yMJVXhtM/uXPWOJCLzGKwRERUItSUMVlWvSCd5Wl9oRU+YWi5261pqls9um12CDy/3mN5OU2sn9llUkZbK4QAeXFGBx9fUmNqOmQoLs7+sy1Wb1c8LZW1D6UTKyLEmOpVw27+8j61blkzYUiGz01etUlo8tvyuoaYMP371FI5FrpnaltnQIZfTF/UGUVYEYnb26jKybauqwzIvr/S+A0DoPUG6P3qOJenyLpcToVAxotE+W4K1qUxumErbpZuGwjK5QEnp+0++fO9wAPjMbTPg87iwY3+r4mfNwNAoTp3vwqnzXXjxYBs2rKjAgjnTc9I7koiswWCNiCgPGOn/FAp68cTaas1qG1HSSZ5aP7QV9bOEt3fu0k24ivRVTOWjsT5btWiMXLMkWNtrsiJNzmMPLMC65RWyFWpGwi4jYYfZX9Ybmztlq82KfS58fVO95qRFoydSor2devqGceb8DUO3IWfzqvmYPbMYPb3DExqaaU1tTZVZvbR1y2cw40Br1pJmAHAXORBXGYFrVU+hXE5f1BtEWRWI2dmry8i2zVSHqV1e6X1nUVUYi6qmVsuAyUDuM8EBIPVdQU9YlhkoifRXnGhJAO9/eAPvfyj+2ZFMAruPdsDruax6Oat6RxKRNRzJZOZXoalpdDSBrq4+W28j9RexEQNNjIkof5l5fWsFErnqp/TsI0sAQPW2Nq6swGNrapBIJPHdbUc0q3furJ2J91quC91+5hfuiSSFHtIJ4G/ebBNaLuL3FGFgeDQHe/gp6TmRk6tlJFrH6JbVlYqTLUWuD4wdnw01ZULHnV18niIMWvT8/tmTDaifHxJ+LdlFet2LNO5WqpAYGUngwHsX0dk9gPJSP9bcORcnz11XfU6trLbQOn6sruyQe10pBVF6LqvFzl5LU7mPE7+fW0d6L5Cqr0SlhmVa74fhoBff+2QJ+fe2N1mx2wVL+hwhZXx9k1nhcDGKBAoFWLFGRJRDmScvUnP0THr6P+kR8LngdhfJnuRJoYWa3Uc7UDV7OoJ+t1AQ8IGeX2mFL2m/RfPD419WR0YS2HNMrAdLsc+Vs2DN4QA2rFAP1cwuIxE52RapGth56ALeOvmxbKCXSCTxq70tqtcHxoIfv8dlSwDl9xZhYEj7ebMqVJMqqKTHd3ldGfYev2jJtkUVOR24dcY0nLnQhcc/V4Pvbx2rKGpqvYa3T19Nm3iqFQS5XE48uHJe2t9yOREx19MX9VRsWdm83s5eXewDRma9pFC9KkKqvtIzjMSKvomFjo8BUf5gsEZElCOyyyI0zq1+ubtZeJmWiKc31Sue5EXao0KhxQtvNOMJwT5e8ZF8isvEZC4bO/DeReEThes91nzJdQBYv7wCexuzT1Iqyotx75JbVYcTiIRdWstIRKvdRE6EAOVAr6WjW2joRjQ2NNZnyWLhoBePfa4aP3r1tOXbVvLkuho0tV6TfT+weh1B5jadTiCRAEYTSVy81oeL1/pwsOky7qiege88uhT180N4fE2NJUFQLici5nr6op4giqEVTRZKP7YoDeQRpTcsk25/quNjQJQ/GKwREdlI+hLa1HpNtiJF6yTaqlAtMxCRO8kT/UIb64+j7VK3JfuVjzL7Hh0+9XHO98HrduKxNdV49IEFWUvsRCZ9iv7qL02azAwh9FS76f3FPDPQ03V9G3JaqaLpwpUe26fDSf36APkl19L7wfrlczGjxGeq91qRE3j2kc9gSdWM8WbhBxovou2SfJ/AE2038MPfnMR3Hl1qaRCUy1CJARaReUpDZOR+DAgFvXhszQLhqm41esIy6TNLq7+iqA0rK/DWicuWVSXnglW9I4nIGgzWiIhsIlfxMxG0+ltJ9PzyebBJvaluIZJbNjYykkBHp739N+UMxhNo6ehG/fxQ1hI7EaJhVWpwI4WvDTVluqrd9P5iLlUmSAGInuvXzw/h8KkrlrymMp/vx9bUoGp2Cf7v7yIYittzchUbiGP7vlbENfq8NDZfw1998x7sOdZh+L6OJoAf/n8fjFeiDQ+P4ievnlG9zom2GxgeHoXHU2ToNomoMKhNGZcbIuN1OzEUz37fisaG8ONX1N9XRImGZVKgJDJMRIvDASxdMANHz3YWVKgGAPctvXWid4GIUjBYIyKyQa4GDmjZsroSD62+TeiytRWlCPjdab2VJoNw0IvH19Zgx/6MX9oDHtx/x2zMDPnR2xdHcJoHxT43EonkeAh54L3c9r1KZaZ3ipHlIVI12pbVlcI9burnhwxVDaTet+o504WWQJYGPKifFzJ0IjU21bYOQb9bdZngivpZKPa58YMdJ3RtXw+RZa9dsSG0XbqpeV83rJiLw6euqr5mpUq0UFDsmHjxYCu+uqFe6LJElP/kertmfR4GvbhrYbli1a5cqGalUNCLRCKJd89cxaxSv+rnSWZVebHPlTUV2ucpQpHToTktOpkce4/MV8W+sVN1ufuh1ruUiHKPwRoRkcXyaQR8eXia8GWdTgcWzA7i5DnxSV4TzeEA1i2vwF6VZShSVdKy2uz+S0pLW6Qvqp3dA7m4G7LM9E4xs0RGtIm+FI4ZqRpIvW9tl24K9RV74I7ZcDodWFZXjvXL5wrtZ0PNTKxdNhf180LCvbbq54UsW15kRnffEO5edItmU/6H770N3/pf/6q6rRNtN1DsE6tCuxqduGOeiLJlBmPVc6aPL+/W6iUoWjkfjQ3ZvhRezXB8FD948YTm5TaurBgPkdR+wBwcHsXWLYsR9HsQjQ1h+/7WgvzRUArUVtaX4WjkWta/6xlGRET2YrBGRGQx0WbuuaAnnDkWuVpQoRrw6VTM6jkl+OWelrQvzsU+F9Yvn4uGmjIA2f2XRPqIzZzus/9OyDDSOyXz5GvlwnLsMXCipPULvyT12FpWV44tqyux89AFzesF/e60+yZamZcaEjfUiE3RXL+8QnfPLSNBody0XbOkx1erKf8/vyXWh61vUGyZ06yQ39gOE00hIhOTrbiu0tCj1B8j5IbKSNfNh8p5EaKfO0fPduLRB6oBQPMHzBf3t+F7W1ehpaO7IEO1VMeas0O1VFrDiIjIfgzWiIgMyPxivKgqPP73/Y32Lh9cUhXGqfPaAZhSOCP3pR4AfrmnxeI9tY/D8WmoBowt4VtWW45dRy5g7/EO9A2OoG9wRHGphOjUzK9tqLP1fijJXOqiJpFIpt1vKzigPifA4RhbwplKtDry7sWz0u6bnmbVEj19eIxYVlcuWymm5OlN9Vi6YCZ+vjuCw6euGLrNVJn7rtaU3+oKs8c/Jzbxl6jQmAnDUolOTDZ7XaVgLLPCV65qKZ8q560ktSEAINyywExbhXyhVdWd2buUiHKPwRoRkU5yX4yDfjfWrazAm40XERXooWTG5++ej/vvmJ3VYDiTXDhzLNKJF/Y0I5by620o6MX9S2cXxC+6iypDuP22GbLTMZtar2HnofNZ15E76RCdmvnWydwOaZAboKBGrtG0FbRWZiaTY0s4U7/EiwZkUgWhxEhIJlJVpieclJNaKRYbiOPmQByvHz6f9vqWni8A+PMfv21ZxZqefZ8V8uPMhaglt1syzc3BBVSQtJZLKvUVe2pdDe5afIvw7eiZmGzmukaCsdSqpVxXzoeCXqyoK8cbx+1fTqonKNMzabTQTYYAkaiQMVgjIhIkVQbJhTexgThefutD2/chdRpWQ02ZbKWSUjjz0oFW2R4q0diQ7H3Kpc33zMOutz8SuFyl7C+yohVo0kmH6BfQptbrQpczI+B3457Fs9BQU6argsKuZT5yjaDlZD6GRqvIjIZkSlVlesNJNVKlmMvlRChUjAeXzcWZ811pFS+NLdewbaex5yGzMtDIvj/+uRrLpvT29Mc5FZQKhhSmNbVewzunr6b9YCQyEEUKtJxFTjx4T5XQ7en5nDFzXSPBWGrVUq5DluH4KGoqpgNI4I3jl2y9rRK/R/hzsqd3GMtryy3tm+l1OwGHA0N5NkV0qgSIRPmKwRoRkYDG5k78am+L0EQ/O6UGDE6nAw+trsLmVZWay1uORa5OaGNiNetXVGDLZxfg8Kmrql98i30uJJLJtKmdEtEKNOmko2Sax5J9N2tFfRm++dASzZMEuWoMu5b5rF8+V6hXWuaXeDNVZEZDMq3+Y1bLXJJ5LHIVP3rltOHtJQE8saYaJQGP4X33eIpwR/UMyybbcSooFQKtpvwiA1Ekv3qjGWvvqtS8nN7PGTPXNRqMSdez6zPOXeREfDR7Qmjf4Aiee/nU+BRLO/3stbN4Ym21UFi240Ab9hzrUJ14qpc0IdXtciI+Yu+0VIlWUGym9QERWaMgg7WDBw/ihz/8IVpbWzFjxgx86UtfwrPPPouiIv7CSkTWy4cGwGr9W9T6LwFjocw//e6snbtn2PzyABqqZwKAZijTNziCH+w4IftYNLWqN/aVjJ+s6DjpskNmjzg1cieQAb/b8qW7xT4Xvr6pHg01ZXjr5MeG+peZqSIzGpJpHf9WSCSS+KDtOjo+vjk+fKGp9Rq27TQeqklKAh7cvUh8KZqc7zy6FD/8zUlLwjVOBaV8odQTzerP5K6eIZz58AbmzlAf3CEadsldTu91jQZjl6/34dVD5/HmCXuqxuRCtVRWtyWQE40NYdvO09i4skIoLJMmnm5cWYF3z3ZaVrmWq1ANAB5cUaE6jMhs6wMiMq/ggrUTJ07gW9/6Fr7whS/gT/7kT9DW1ob/9b/+FwYGBvDnf/7nE717RDTJ5EMD4C2rK7F5VZXhL02Rj6IYjOfuC6Ae7Z29+N72pvGwTKRhfGY/mkQiibdPXxW6PanKqrt34nqRPPbAAqxbXpHVI06O0gmkHf3wtj68ZHwIh5n+ZWaqyHIRkunV2NyJ7fta0ZVyTE7zFiFhUThrdPlOZujwR1+8HSMjCfzlP76DGz3Gj++Z0zkVdKqzqsm/mW0pNfl/Ym01duwXm4SrR1fPIGaHfIi0RxX3Ve+gldT73iNY7V5a7B2vkDdi15F2Q9crREfPdmLrliVZvfPULv8///3d+O62I2nLhvPdxpXS9PPptrY+ICJzCi5Y+/u//3ssXLgQP/jBDwAAn/3sZ5FMJvG3f/u3+MY3voGZM2dO8B4S0WQS+Sia0wbAqaz6whRpt6axuZ1Sw7Lvb12FSHsU2145pfrrt9SPpqWjWyhoCk4bqzRS6jWXCxtXVmDj3fM1L5dIJBFpj+L51yM52KuxYy010DLbvywfAzIjlILN/iFreutIx6ReSqHDYw8sMBWqAcC/nryMnr4hfOfRpZYGLDRx9DyPZiZeWrUttSb/VlSJymlq6cTPXj2luq96+kjK3XeR5XyxgWHb7qNECiiLfW5s26n+OZvPumJDCPrd+P7WVdh3vAM7DqgHrl2xIbx54lLBhGpBvxtf2VCLFfWzAOS+9QER6VNwwdrZs2fxpS99Ke1vq1evxl//9V/j0KFD2LJly8TsGBFNOtLExVy6d8ksLKwKIxzwWfeFyYbvXHYsRQTSmzdrfdmX+tGILrG5e9Es/ObNtgkJ1cwu/bSb0nCAqfwlfmQkgV/sbrb1Nr7yYJ3q4ykXiDS1XlMMHX782zOW7NeJthv4/z1/FDf74qpBw/DwKF482Iqr0QHMCvnx+OdqOPhggqgtmxQNt8xMvMxkdFsTVSW+/5j8YJ/UfRXtI6n0GtXq+/b42hrs2K9930UGM6j5xucXYlFVGJH2aMGGapLuviE4nQ6UBMSWznZ25+dS94Dfja9uqEXQ71H9vJ0sP1oRTUYFF6wNDQ3B40l/85T++9y5cxOxS0RU4PScvJrlcIz1yjia0efDznL++ooQdsG65SHhoBd/9c170Hbp5vhjdvLcdbxxrMPUl31Af1gm3b6Iz1TNwN/95qSZ3UtTX1GKSEe34r/XVUzHnLIAykv9WHPn3Kyln7k87rxuJzasnId/PXk5bQCH1nE3Vb/ENzZ34ue7m20JjyUbV1ZgRb161U5mIFIa8OSsr8+FK71Zf0sNGg5/8HFaT7czF6I42HQZd1TPwHceXZqTfZzsRCvNlMIzpYbtcuGWmYmXcvttdFtGpmHaLXVftap5G2rK8N1tR1S3lxmMSdct9rmF7vt/enQpXC4nzlzowq639X+2RzqiEzI51A7S57/o94DyUuuWugd8LvSaDCa9Lgc23T3fVLsPIsoPBReszZ8/H++//37a306cOAEAuHnzpqlti/S7MaOoyJn2v0Q08Y5FOvGrPc1p/ZMCfjeSCXtOXpNJ4M7aMjy5rhbNH0XR3TuM0oAHdfNCtn2pWrJghiVfACVf3lAHn8+FJQtmABh7DNWa6uoVG4hjRolP6LIzSnyomxdCOOhNew4zhUu8+Djabzr4kzgcwJ99+U7881vnsPud9rTtOhzAxrvn4/HPVY8/x+ev9KQ9x3LHXSjgQXzUnqkKKxfOwqOfq8YX71+Qs+OuUCQSybTHJNYft3VYSXCaG1/bVI+7Fs5SvMyxiHy1z0RPJZb86JXTGFVoMnei7Qb+/l8+wH96bHKEa5nHh5nXjJ5tyb1HhINefHlDXVogq3SsSA3b1Wzf34oVC2fB6XTg7IUuoamV5y7fxMLKsOrlRLfVeukmnA6kPR75uEwv837ftfgWrFg4S/a5FLnvySTw5NpqlAZ9add9+9QVof1pu3wTX7x/geHHateRdhz54Arub5hj6Pr5IlzixaKqMJxOBxZVhYW+Bzy4ch7eONahejlR/2bzIgDAP/3ubNaPMD6PE3Xzw/jw4s205yngd6F+fhizZ0zDwvkhLKwMT/nPYLvx/JtypeCCtaeeegp/+Zd/iZ///Od4+OGHx4cXmJ0I6nQ6EAoVW7SX6kpK2BiYyC6jiSTOfHgDXT2DCJf4sOi2GShS+NJy5P3L+PvfvJ/1dzurVAAgnnRgxowAVs0I2Ho7qb79eAP+58+Pmd7OUxvq8eA9VeP/PZpI4tcGmywrqbh1OhbdNgMzfnsGN24OKl5uZqkfdy2diyKnA9/84u2q9++bj9yO99uuW7aPj9y/AGVlQXzr0Tvw77bcjtcPn8fHN/pw64xibLq3CsfPXMF/fu5w2v7PmO7Dhrsr0ds/jFd//2HWNqM2hiYrFt86/hmXy+Mu3x15/zJ+svODtOfJYeM5zr97aAk2f/Y2xfckwJ7XlNWUQjVJU8s1+Ip98OfRslA9nw0SueNjxnQf/sOWz2DV7bN13b6ebSl9NnXFhvD3v3kff/H0Cqy6fbbpY6WrZwiXo4P4TPVMxM+L9eKMJx0omT5N9bEU3dZz//JB2uft2Hukdg/KiRBPZp8nyL2Xit73377djm//4R1pz33FrdOFrvvq4Qs4fOqKqceqKzaEl//1QwSnuRHrz78wU8Q3H7kdM1KeA5HvAWVlQc3LpQpO8wBIpj1GM0v9+PcPLxl/7tbeVYkPzl3HqbbrSAK4fcFMLKmeiSKnw9D7DtmD599kt4IL1r74xS+ipaUF3/ve9/A//sf/gNvtxh/90R/h5z//OcrLjS+hSiSS6Onpt3BPsxUVOVFS4kdPzwBGNcZVE5F+cr/wB/1ufO3z2dUhiUQSP/6X7BOXXHA7kohG+3J6mwsrpuPbj96OF3ZHTAU4N6J9aft++nyXavilV7jEi9khH3pu9uOp9bWyJ5eSJ9fVoOfm2Pu2dP+yKjxKvPjyg3VYWDEdL79pzSS5htoybFldlfY43Hf7LeP//81j7bL7fePmIH69J7c9+yTeIuT8mMt3xyKdss+TVVWNctxOjB+zSs5esPY1lWlGiQ/JZNKSig01P/7nJjz9+YW23gYgVgUmWv2VeR2l1/H//PkxfPvR21WX8hrdlshn049ffh91c0rQ/FHU9LHy0eVuxGIDaLnQJXT5cx1R/N9XT6k+lm6H2Iso80essffIZtt6eJrxYUcXbq/SXhYvfN/741nP/eyQT7PqSmLVY5W08w3PJsFpbqxacgswOoobN3rHX+8i3wOi0T7Vyz25vhYlfnfa+wkA2feY1M/U+TOnYf7MeeP/nfo+P3eGH3Nn+LP+TrnB828yq6TEL1TxWHDBmtPpxH/9r/8V3/72t3Hp0iXMnj0bIyMj+Lu/+zssXWpu2cFIjvqWjI4mcnZbRJNVZu+Z2EAc23ZmL4eJDcTx3P/3Ac6t7E5rHB9pj9p+YiknHPRiwezpE/Ie0FA9E0u/dS9aOrpxpr0Lu47o782y52gHqudMx7K6cluGOzy5tgaJRBKJRBIN1TPVe9lUz0x7HBuqZ2LpbTNkexINDo5YNh21qeUa3j51BUG/O+t2EokkXthjb8N7vYLT3BN2zOWriXqegn635vNwo8e+UE3qfzY8PIpn/vYt224HAD7u6rfkmFPrMybSmF+pib5U/SXXRF/k+PjVnmYsvW2GUL8xPdsS+Wzq6hnC7nfaLWnE/sIbLcLBTMDvxsv/ml1tm/lYLpg9XXN6pqo8DHv+5V/P49YZxZp9UPXe98zj6EmN4QhZTD5WvQMj2LK6Cm+dvJx3ve0yzZ8VQFfPEGL9cew52oE9RzuyXu9q3wNEvy+kSnxSnVsztzTtbwmNql3KPzz/JrsVXLAmCQaDqK+vBwD87//9vzF37lysWrVqgveKiHJBdoy9xnV2H+1APJHAsppy1FaUTljTXrnpi7kkNaM3c/+372tFIpnEtp2nLdsvpSb6eidTZjbbHxlJYN/xDrz+rnXDGwDgR6+cSjufkb7cizaftkqxz6U51U1r8uRUNBEN0sPBsWNXi2gTblHzZwVw2+yStImdHk8R7qiekTZ8wGqzQtrLbrSa86sFZwA0p0421JQZaqIvcnx0xYaw73gHSgIe1fclvdu6fE2ssnTHAWsqcPVUO2lVN6U+llrTM1X3aVA+7BFdtvjAHbPx5onLhm5bjcjwBr33XRrYI31uKQ1HUKL0WOlRHvbj+1tXYd/xDsuOK6uVl/rQflV9oIr0/UF06M5UHc5DRPYouGDt/fffx9GjR7Fw4UIMDg7iwIEDeOWVV/DTn/7UdJ81Isp/StUHIr8d7j9+CfuPX0LA78aaO3PbtNfOqZ9GmDl574oN4Zd7zPeAWlFfhobaMt1hmaiXDrRijwWTSuVkblP6cr9++Vzrb0zBltVVmFNWrHoCpzV5cqqaiGBdNFSvrSjVrHgJ+FyIjyYwFNf+9f3BFfNwz5Jbsv7+nUeX4v/9xXGcu9yjuQ0jHv9cjalqM6X3eum1VuxT/wq7fV8r/F6XUKi189CHWDQ/PL5/osdHagiRWTkjMbKtfBMOenHf0tnYeei86uVSQyKlgEjkxwAA6BuM4/tbV6UdP129g/jH357VvK6ryGHpwB5JZgimRLrvz78eEbqvmceI9IPSzt9/KDT102wwVlrshdPpwLrlFdhzrCMvK9c6u9UreUUn1hIR2aXggjW324033ngDzz33HABg6dKl+OUvf4mGhoYJ3jMislsikdSsPhDROxDHq4cvwO1yIm5TWXgo4ME3Ni9CT/+wZnA0EURO3tWY6ekS9LvxlQ21WFGvPBURGKs2O/DeRXR2D6C81I81d84Vnt780oFWzYl4dnjn9FVLtuOAelgcCniweVUlnE6H7Mmr6GM8VVldFaZGb6guUvFy25zpeP+cWLVZrF++p+Lw8CgqyovRfjWGEYun0d5RPQMfnL9huNps65bF2LFfPSDQCiy6YkPY33hRaH93HWnHriPt4/tn5PiQq5wBcnusWW3zPfOxqHIscDwaEXtvSw2J5CqOE4kkfvDiCc3t7D1+EbUVpWmPpehy/nfPduJzDXPwW4FQSi/RoHRZXTn8Xhd+sOOE5mXljhGn04FFlWGhYM1MMBYKeJBIJvHOmSsoLfbiibXVllaj54po6ElEZJeCC9YWLlyIl156aaJ3g4gmgNXLt8yEal63U7Va5Kn1tVhUGTa8fRFay6jUmF2qo1fQ78YTa2sQCnpx260lePPEJbzwRrNiYCZXbfbiwTZsWFGR1itPzshIAnuO5T5UA8Z6+gX9bsRMNt7WijmeWl87/lzrXS5L5oNlLXcvmoXbq2cYfi6Uqn0cjrFqSdFQDQACxe6sv/3wNydtXQbaevGm7PZFq81e2NNi+jUEAE2t+iYBpwZ7Ro+PzMoZu481Ow3FR8eDCtGAsLMrve9bZsVxIpEUfjzkHkuRZv2x/jiu2zQARO5xSCSSiLRHEemIAkmgfn4ItXNLkUgkNb8rqC0RFzl2zAZjwyOJtPAvFPTijuoZOHnuRtrnrwOAJ+O+SO9HEq/biZo503HqgjX9TPWaqBYfRERAAQZrRDR12fGlyWjVmtvlxIaV87C/8WJa5US4xIsn19q/5FOkabeWhpoy4WU5qUT73KT62sY6LKsrx0sHWvH9HU2qgZlStVkyifG/q4VrB967aGj5Z2mxBxvvmocH7piDv/jpO4ZPhO9ePAt7j4tVyuiVb0uK84meoNnuYHlmqQ93L8pefqlHamDa1HoNe48bO67DAV/af9sdqgHa1WRa/25FqGbGi/vb8MTaGtmBOFoyK2dy/SPGkqowllSFLVla+s6Zq3h8zdgS5tqKUpQGPOjWmCr91snL49W0cvQ8HnKP5T2C76+D8VHNy+gV9I/10Iy0R8fDsF1HLuD1d9vTAieRKjPJioXlph4ruWBs48oKvHu2UzaUl0hLZTNfi9HYkOxnXxLAUDyBLaur0DcYl30/GoonJixUAwq7OpSICh+DNSIqGHZ8aYqPJPDQvZU4+N4lXSdzvQMjqJ8XwkP3VqGloxuxgTgqbp2O2SGfqWlRIuGAVu8huSl3clo6unWHasBYM/wd+8UaK6cGQSKB2RfvW6BZbbbnWAe+eN8CxWWhRiflfW/rqvFt3i/QT0hJQ00ZipwOS/u7Fftc2LplCernhWSPB7Mha6FrbO7Er/a2pJ30lwY8+PL6WtXHYJq3CP1D1p+A11dYsxxJCjR+uuuMoeuHgmNL794+dQWx/mF4fE7bQ7XJoCs2hKDfrauJfCq5nllGtwUA65fPxYwSn1BY1n41hu986XbsPvqRZgimJdYfHw+2nE4HHrhjNnYeuqB6najAkrxldeVYv3yuUECW+Vg21JQJXa9m7nTdFYtaYgPx8ddisc+FEcE+h2r2HO2AA8o/FikdO2rB2O6jHdi6ZUna5OrqOdPRdukmuvuGUOL34GevnQUMfP6/deLSWEqXZ0SHwxAR2YXBGhEVDNFlIHrdMmMa/u7bq9HS0Y0zF7qEf23u7hsaX+bicjkRChUjGu0zHKyJBCQifeZEm/jqrQAMTnPjaxvGKs+cDvkeSalW1pfjPzy0eHzMvUhgVjLNoxlGJZNjVWkPrpwn++/lpdrTCDNtXFmRFtSVh/VvAxj7ch8bGLa8v1vf4AicDkfWc3os0ilbVaM3ZM0HRpc2KwXN3b3Dio+B0nWsEPC5LO3zY2YJ/HB8VKifFWVrar2GJ9fVpi2z7ukdFgq3MpdDAtlLtkW3BQCNzdfwV9+8B7vebhdaBtl26aZQCCYi9XOiPDxN93WUiAZkPb3DSCSSupbWBnwurFtWgd+93a77xyOf24lBgbDMyI9SSnYf7UDV7BLFnpiZx45IMPbi/lZ8b+uqtPdQ6X0p0h41/J4SNRnW2mWiJ64TEYl1gSYiygNOpwNf3VCreTm9X62kxr/180PY8tnbEPBn9yRSup4ZUl+Wd85cwauHzuO5l09lfdmVApLG5k4AYifZ0vIZLXr2P+h342++de94QCH9iu51K3+MHI104jdvjp04iizPTCbHTmZFqFWlrblzrvAP6g7HWKiWWS1g9Ll9fG2NZtN1ozJPVo9FruJHr6iHQ9v3tZqqoMyVxuZOfHfbEXxvexN+8uoZfG97E7677QiORa6Ov0Yi7dGs+5JIJPH86xHVbf/89Uja9awagqLk6U31lp7gmVkCb+XJ/1Tzzpmr44FO/fwQ7l50C9Ytr0BpwKN53bdOXpZ93WVuKxQUe5/pig2h7dJN3LNYbBhJd9+QcAimJfW9UPR9UeRyUkCmZceBNnx325Hxz0BpeaSa3sERnDx3HV/fVK+5/VDAg0fuuw1/+uVl+POnGlBUNDGnRi/saVF9r049dpxOh6nvAZOpF1lwmrugfkAiosmLwRoRFZQV9bOwcWWF6mWe2bIYf/ZkA9Ytn6u5vczlA6LhndllB5lBgtayQykgEf1CLHI50RMbYKxHWubSy6ULZmJYoz/dnmMdGBlJCC/PFI2A1KrSXC4nNqxQP0YqyovxxJpq/Pg/PyC7BEfPYwOMHQ/PPrJkvAePHVJPVhubO7Ft52nNsFI0ZLVDanAsF4pJpOoxuVB5287TWWGbdIINjFVeaIVHvYMjadMErR6CInE4gK1bFlt+glcyTTvIIetJyyBTScshtUQFXnciAVGq7r4hLF0wU+iyPb3DuHytT3jbSjI/50TeFwM+l9Bno577n/kDU0NNGbyeItXr/Hx3MxpqyvDsI0uywlCf24l1y+fiz55swPe/dS8eue823H/nXMDhmLAwOjaQfbwpMfs9IBe9yLasrtIcUmJWwOdK+8GPiGgicSkoERWcx9bUoGr2dLywpzmtL1pmY/f6+SG4nA7VZXlyywdW1M/C+ZU9wtdLJJI4e6EL8fNRuB1JLJg9XbVixcgyNCkgsbJiQE8T6f3HL+JsezRtiqdoFdqB9y4KL8+8o2Ymzl3uUd2uwzFWlaZGCssy+5w5HBCaLCry2Ny75BYsqgwjFPx02eL2fS2q2zUq4HePn6zqrbiaiOoE0b5veu/Lp1Mbl2BFffnYFD4BkY4oFlWNTem16/FIJoGg39oQTOodRxND7lixcjnksrpybFldKbRks7NrAP988pzQbYsuMZX6dCnv39jyQ+n9TeR9sXdwBE2t14TCDr2956Q2B5GPohgaVu+N2DsQR+SjqK6pyWcvdGnug51E35vMfg8wM6k2FPAADvWKuXDQi82rKrF5VSUiH0XxZtNFHG+2tt8dMFYdrNRrlYgo1xisEVFBWlFfjmW12l+WRUO4TKLX09s43swytO6+Iaysn6X5hVhPNd2yunJ88+FF+PEr6s3RIx3diHzya7o0xVOrWk3S2T2AJ9bU4MWDbZqB2YPL56G3P64aam5YUSH0ZfqxNTX44n0LcOC9i+jsHkgLBUU01JRhy+oq7D3eIVvFcPjUFZxpj+KpT0LWRCKJt09fFdq2Xr0D8fGTVb0VV1ZUJ+jpf6ZnuIbR6rGxJbCLxUscUy5nZ7WGlaGdnX3gSIzcsWLljxsAsHlVFd46+bFmzzCjw1TUPP3JUsnMzzBpguTe4xex9/jFtM80kWnSon0+gU/7h+073qEZCEo/MKVWoKqJtEexqDI8vowy34keMyLBmNr3ADOTap9aP1bRr3bd1B8eF1WGsagyLPtdScn65XPRUFOGm/1DeP53ZzE0kv5GH/C58PSmelaqEVFeYbBGRAVL9MuyaAgnd72G6pmKwYyR6ZxmlqFJveC0vhDrbeJ7M6avGbE0xbOuYrrQ5ctL/ePLM0UCM7PVZqlcLqfikAM1oicBqc91sc/4YI1Q0Iv+wbjqhDlpaZOe8Ebp5EpvUCYaHusdrmE0iEomgW07T+OheyuFLp/6PmGmWkOLntAukUjizPkuHDlzBUPDo6iZOx3rlo29BuzuA0djfSPVJkErvXbMhhqZRN7T4zb0Sbyjesb461eq6GpqvYa9x7MrkTPf57SWS0oBmGiY5XQ6UCLQuw74JLwW/XjT2epw4fwQXj18Qd+VLGL1MaP1PUCpWjAc9GLlwnK8e7Yz6++pPyoqXVfpB8vUysGu3kGcPd+FptbraVOZ5a6/om4WIh9Fx8JUx9jEZWlKLRFRPmGwRkRTgpFfrOUChT3HOvDUuho01JQZms5pNEhI/dKt9IXY7ynCgjnTcePmIEZGEsKVWaL9zzI1d9wcr2xQkrpsU09gZrbaTIRSuGSkUmj7vlZ86f4Fuveh2OfC1i1LgCQ0pzf2DsSxbecHqJlbKrx9uZMrPUGZ3vBYz3CN+vkh09Vjvz95WbN6JuB3o37ep699M9UaavScGDc2d+Ifd51JC1KbWq/jnw+ew4aVFbh9wUzbevVNBVrTo8NBLx5fWyM7UVfy+Npq2ZN3O37cWFZXjo0rlX940Fr2aMSJths4FrmKFfWz4HQ6UFtRip/uUq9c1vM+p/ezTk8lYInfg13Qnt5dX6HvM39hZVjz/cQuRo4ZveGW3DaUlsk++kC16o8vepbYSlK/h61afKvQDzxOp2O86o2IKJ8xWCMikqEVKDx0b6WuAEFiNEjI/NKd+qV299GP8MG5GxgYHsWp8104db5rfLmmSHWXaP8zOXVzS8eXiMrJXLapJzAzWm0mQilcemJttaGpnl2xIcT69VX+AcDXN9VjUWUY75y5InT5xpbraGy5rhloAsBD91YiPppApD2qGRqmBmXScRWNDWH7fn3hsd6m2marx6K9w9iyukp1mdzTG+uyTtb09nYSMXvmNIyMJODRaKquFtwmMVYNevlGvyX7NBWJhGZPftI0Xy1E2bG/DU6HIyfLzRKJJN4926l9QYu9sKcFy2rL4XQ6hENx0fc5vZ91eisBNQN1n0v3j2lOpwNf31QvFLr7PEUocqYPO/C5nVhyWxgPNMxF7dxStHR048xHXWjr6Eb71V7ZimQ9QVgmI+FWJqUfHUV+jDS7xLZQlugSEYlgsEZElEFkGZbocpHMoEFvkKBWheZ0OvD+uet4/9yNrOtJyzUBaIZra+6cq9n/TMnssmJU3hrUtWzTzsBMhFq4tG3nacPbDRS7hZ/bzJMpvSehWs9Vsc+VdowW+1xYu2wu/vXkZdXr/fz1iK6wKTM8LhFs3i9dzorqsfKwXzYkU+t1CGQvTdqxr83wUl4AOHU+imf+9i3cUT0D33l0qexlEokkXnijWXNbH8i8pmmsEm31Z25RXVK+cmE5VtSXw6lSzQOo94gClKsy9S53FmHXpFot0iTK+vkh4VBc5H3OyNRsvZWAWgHY05vqDS0XlEL3X+1tQXdvdohY7HNh/fK52LyqCgBUQ61FVeHxoSlSdVZX7yB6++IITvOkDb4xiuEUEVF+YLBGRJTBypOcy9f70iqGRE4etqyuxIcfxzSr0EZGEthzTPkEExhbdvnF+xaoLqF0uZyalWdKykv9eHDlPNuXbVrFzt5V4YBP87mVmjJnnkzVVpQaWoKUWbnm8xRhcHg0azt9gyNCYXDv4Aigcx/STsgN9D4yWz1WWuxF/fyQocoNp9OB224twbGDV+H3FJkK1iQn2m7gh785KRuutXR042af9m0k8elzSUCRE/iDVZXYvKpq/DlVCtd2H+3AgjnTFat5AOC7244I33ZmSKZ3ubOIiZjcm3nbouG+yPuc3mWNEj3LG5UCsFDAg6fW15qqNMwM3dWCMD195BiAERFNXgzWiIgyWHmSs+tIO3YdaU+rntE6eTh36aZQFdqB97KbTMtd58B7FzUrxGaXFesO1lL7p010FZoouypDwiknXEb63jidDiyuDOFo5Jqu200mgSfWVONGzyDePnVlLBjLsdQT8h7BZWKZl5MLQXr6hvHj355WPcZTK2OMnLj+8DcncaLN+uqwE203MDw8CpfLmXaf9Bx71XOm49T5Lsv3rRD5ve7xUE1k2WRqGJZ5TETao7qeh8yQTO9yZxF2TqoVvW09SzGNvs+J0LO80YqlkEoYhBERkR4M1oiIMthxkpO5rEjphCCRSOL/qPQGAsbCtS/et0B46IDI5Yz0Wcvsn1YI7KoMSa3QMHKyl0gk0XLxpqHbvtEziL3HLxq6rlmZS770NCDPJHci63BCdXnuHz6wwPBJtF2hmuSvftWIm/3xtOAh4HcLX396sVuzf9xU0ZuyZNFsxZiR94DU65g5xpWIhFoBnwtud1HaZXyeIiSTybTeXSL9FyWZwbSeSrR8CbUYgBERUT5gsEZElMFsQ3U1qZUUcicEbxz9SOik6G9ebEJDTZnQbYqEZnr6rKn1T8t3TS36KsK0KFVoKJ3sKU1Ba+nolu3nI+Kd01cNXc8KmUu+9DYg17KifhacjzgUl4n++Ldn8O7Zq4o9zZQMD4/aGqoBwIWrvVl/07PU9PCpq9i6ZQn8HicGhrObnk81UrhltmLMyA8nqdex+hgHxEKtpzfVKy5tTf1b9ZzpaLt0E919Q2hquYZjKlWwckNx9FSiMdQiIiIaw2CNiKY0paDDbEN1JVIlRfWc6Vk9yf7lX8+pNuVO1dxxE3/8paWaYVjqck01LpcTG1ZUqN5+RXkx7l1ya972T9Py7tmrqieZItYtm4M7a8sNVWgoTSJ9al0N4qPGghOf24mYBX3B9FI70ba6/9KyunIcev+yYpBxou0G/t9fHMdffGWZ8HZfPGhPnz2rvbi/FXfWluHwqYkLT61gpH9gJincMlsxpveHk8yQzI5jHBAPteSCrMy/Sf9996JbsDxyFb/c05IW6qot2bSzEo2IiGiyYrBGRFOWWtChdJKjZ5mNkt1HP8IHH95I286OA226t/Ov71/WDMP0LNeUKtD0TPgsFMciV/HjV4xP/JTcWVuuqxJNojaJ9LmXT2HL6ipD+zMYt6aSKRTwYHgkoRp+BKe58fjaaoQDPtUT7cMffCz7d7fLif/wB4t0918aHh7FyXPqvcbOXe7Bn/6fw/iyYNPyq1GxZdR2cjqAhMZ7SVdsCKVBX072x4r3NiVbH16CSEcUu460G7p+arhltmJM7w8nciGZ3souUXaEWivqZ2FZbbmubbISjYiISB8Ga0RU8LRCDTlaQYfUCy3zJCc2MKza80mE3GACIzq7B/CVB+sAWBeGPbamJq8mfBp5bjM1Nneafs4A5ZN1rYBWZBLpWycvozTgMbwcVK9Q0ItvfH4hegaGxx/XptZrqmHD1zbUZQUGw8OjePFgK65GBzAr5MeNniHF4zs+ksDhDz4WDh2k537XEbEeY929w2mvXTWzQn6cuRAV2q5d3C5nWm8sJY4cFQo98/BiBP0enLnQhV1viwdgWoFcODg2tdXpdBgO1lLDLSsqxkQm0WqFZHZVdtkRajEoIyIisheDNSIqaFqhhhyRoENtqlzb8psT1iw+ldQ7zeowLF8mfBp5bjOJPNeinlw3FlJG2qMpQWsc22SGTaQGtMU+t+ays2hsCFtWV2LnoQuW7KuWp9bVYFFVOO1vSmFDKOjF/UtnIz6aQKQ9Oh4eZDb/FwmqpGmZHk+R6uXknntRqa9dJY9/rgYHmy7r3raVREI1AKivCOFw8IotPR+B7ABJb3N/rapZKeQy0rtSKdyyomIsMxgr8XsAx9jEWtGQjIEVERERAQzWiKjApFYwdXYNyE7My6w6yyQ6VW7noQ+xaH446wSroaZswoO1zN5p+RKG6aFWjSZaUahF5LlO5fMUwe91yZ6sA8B3tx3JWhqsZvu+Vnzp/gVCt10enoa1y+dg//FLwvurV3CaW7byTJIZNnR29ePNE5fTXmehoBfTi924cCW7Ob+IFw+24qsb6hX/Xem5F6U2EVLi8RThjuoZtg8wUOIuciA+qr3ustjnQv38EO5aWC7cf1GE2rJe0R5mQb8bX9s4diwtmDNdM+QSqTTbumUJgn63UAWYFRVjDMaIiIjICgzWiKhg6K1i+bVC5YpoRcauI+3YdaQ9rUpqZCSBC1d64HWLLeOyi57eabkkunRTrRqtoaZMV0WhGr3VN//m8wuxrDb7ZF1pmaRWT6qu2BBi/WLLO0uLvVhWU25bsBb0u/E337pX87iRwobG5k7ZCrpobMhU9ZRafzOrKgyPN3cCgOLxl0gk8eCKeYjGhtAuM73TbiKhGgCsXz4XTa3XLA3VACDWH0c44JMNlUQqy4LT0o8l0ZDL6t5kDMYKl9Fl/la0ByAiIrIagzUiKghGqliisSHsOnIeD62+Le3vohUZqdt57uVTuKN6Bk6eu2Fbg28R+TxIQHTppnYj/0qhikKtqiRA33O9cWUFVtRnT94zG/YEit26mq3rXS4n6msb64TDWCuX0Ga62tWv+G96KwyVHHjvEg68d0nx+Ms8Tl1FDowIhl254vcU4fN3V+LPf/y2LdtXCp1FKsu+tiH7WBINuTh1kowu81e63hNra4QrHYmIiOzAYI2I8p6Zk/ydhy5gTlkg7cu6kV4/ACZs2RgAVJQX494lt+ZkkICdwyBEnkvRZbYi1Wiiz/U3H16MuxbOApB9/xPJpKmwJxzw6Wq2rmdioagtq6t0VQNZFXDJudEzpNhnTW+FoZbM40/pOJVCNa+7CEPxUUO3tXFlBd4922nZ47b69lvRdummbc+DWuhs19RLCSvNpqZEIoldR84rVsKqLfM/FrkqO4QmGhvK6nMZ9Ltx9+JZaKgpY8hGREQ5wWCNiPKe2ZP8zGWDIhUZ+SLXFWp2D4MQeS77BkeE9lWkGk2sr9NirKgfC9Xk7n+xz/hHZSj4aTApF1QEp7nxlQfTe55Jocbzr0eEHwvVfQh4sHlVpa7rWB1wZVLqs6a3mlTU869H4HMX4Vd7W1Qv5y5yYCiub9vFPhe+vqkey+rK8egD1Wjp6Mbx5k4ceM/ckt6GmjLbnoeA3y074TYVK8vISqKtHOSW+R+LdOJHr4hPdo4NxLH3+EXsPX6RIRsREeVE/jXoISLKYPbkUlo2mEoKL0JBe07klTgcQG3FdKHL1lVMx4//8wM5CdUSiSRePXQez718KuvER6okaPykb1Um0WEQ0gm6CK0wK3XppBal5zoc9OLZR5akhWpy999MuHX/0tnjJ3LL6srxxNpqBPzu8X+P9cexY39r1mPbUFMGj1t9cqaop9bX6j6ZtCvgkij1WZMqDK3WNziCv3npJLp71fvd9Q6OwO/V97hv3bIkrUF//fwQlpus6JKOb7ueh96BOHYduYB3zlxBpD2KREJ+Gax0f+5edAvq54cYSpAhSu+tcjI/rxubO7Ft5ynDLRikkO1725vw3W1HFD/HiIiIzGDFGhHlPStOLuUCndSKjDMXurDr7XbTt6MlmQRaOm4KXbblotjlzGps7sSv9rZohg5KAwNEwzKp6kXE+uUVshNfJalLJ0VoVd/Y1VOsPOwf//9jJ4jyS5kyl0AZqdL0eYowOPzpMkYzy/aMLpcW9dGVHvxg+3vYsHI+FleF86qadMHsEpw6HxW6bDjoRf08YwMA1EjHt+h2QgEPlteX65pWnDnpVau/FZERRt5bpc8Uq9+XRadKc0ACERHpxWCNiPKeFSf5SoGOVJFRW1GKw6eu2BYkGJFMAgfeu4gHV86z7Tb0DIVQGhggGpZJJygijfw3r6rEnLJiS3s8qfV1squnmPTYJBJJPP96RPWyqcGlkSrN//VHq/Hhxz2WnAyKBFxl0324dnPQ0PZ7B0dxpr0bZ9q74S5y4j88tGj8OV1WV46tW5bgRztPYSLGCZSV+gGIBWtKAa/I4yfXky3z+BbZzkP3VqK2ohSRj8T2WY5o4ECkl5H3Vul90673ZbkfiUYTSZy90IWjkas48sEVDAx9+iMFg2ciItLCYI2I8p7ZKhaRZYP5UCkjp7NbfsmcFcxUEqQSDcukoEe0kX8uezyJnrwV+1y6loa+1zK27CjyUVTzeqnBpZEqzQ8/7rG0IfxYwLUYv9zTgt6BTxuPSeFPsc+N721vMn078dFEVqizor4cl++txCuHL5jevl63zZ6OE203VI8JhwN45uHFqifaIgMApJ5sase32nZWLizH79//GK9a9DgpVaUSGaX3R4JQwINEMol3zlzB5Wt9tuxT5o9ExyKd+PXeFtxQ+KGAwTMREWlhsEZEBUHr5HL30Q7F64ouG5Ru4x93ncFQPGHJfjscQN3cUkQyeryJKi/1a19IhdqSFiPVAJ1d/Vl/0xOWAfomDuZqemCsX30ZrGTpgjDKQ9Ow9/hFoYBtX+Ml7GsUb2IvnYQaqdK0utF9Y3MnduxvSwvVgn43Hl9bMz7h1crlor/OCHX+4N4q7GsUe5ytNKNEe4rrMw8vwYp67RNsrXBY9PiW205sYFh2abEZSlWpRHpJnz2Xr+sLx4ZHEvjBjhP27FQK6f1ST9U2g2ciIlLCYI2ICobaSeqCOdMNLxscGUngwHsX0dk9gMvX+iwL1R57YAHWLa/AjgOthoO1OTOL8c6ZK4aqtbQmfBoJYt46cRmbV1XpqqqRew7ybeLg9ZtilYHHm69jeMSa40OOVKlmpILSTC/CzAA2NhDHtp3Zty393flJ5YaVVZ7RT0Kd2orS8X3R6rVntbDGFNdwiRdPrtW3JMyqcDh1O4lEEt/ddsT0NuUovS+w7xSJEp0AmsrjcmJ4JJGzIL202Cu0RD8Vg2ciIlLCYI2ICorSSaqeoCYzSGu+2K1r4tjSBTNw8twNoX11uZyGq868niL8zUsnx/9bT58XpV/hU5e0GAlior3DiicWesOyXFWjSZSCgcbmTuGqMjtDtWKfK23JslJYKUfPlNRMcifBDo28RKrckPZRZPiFiKbWa/jprjMT1usws7JSOp5jA3FU3Dods0M+xQmauWRX7ykAaO3oRtvFmygv9WPNnXPhcjk1Q3oiiZ4KsFR2vrdmkt4vRZboZ7K6MpiIiCYHBmtENGmIBDUvHWjFnmMduoK0TP2Dce0L4dP+aGvunIsXD7bpvs2hlAmPgL6JZlq907bva8VfffMeQ0v51E4sch2WiZILBop9LqxbNhf/+v7HE7hnn1q/vEK2ErChpgy7jpzHzkMXFK+budxZtLpI6SRY61jtig3hH/7lfTzz0JKUfbxgurpMz1RLKylVVkrHs8vlRChUjGi0Ly+CNTtP7g82XR7//y8ebMPSBTNwoi37hwT2naJMdk1Xttrja8feLyPt+gd+WDGlnIiIJh8Ga0Q0Zbx0oFW1F5so0dNqqVLN5XJiw4oK1duePWMabvQMCi1D1erzIlLN0hUbQtulm4aW8uXLiYXZ8KhvcGRCmuPLCfhc2LyqUvbfnE4H5pQFZAcnBHwuPL2pPi3YEK0uMnsSfKLtBp7527ewYHYJ/uIry/DQ6irMKSu2tEeh3TbfMx+LKsMFt6wxV6/BZBKyoVoq9p2a3PQsARatpHQVOTAyOnEBddDvBgCMJvW9T/k8RYYrg4mIaHJjsEZEU8LISAJ7jpkP1QDgjpqZOHe5R7Wqx+EYq1STPLamBgBkq+XuWDADJwSWlkq0+ryIVrN09w3h7kW3CC83BMwtObRSrsKjXHl6U/34yapovzMA6M0I2kSWAEuPT+SjqCXLCc9d7sHWv3kLdfOmI5EE7lk8C9dvDuLUef3VILkUDnqx5bO3FWQgZGS4hV3Ydyq/memNp3cJsOhnz0SGasDYfiYSSVy+lj2MR82DK7KriomIiAAGa0Q0RRx476Kp5Z8ShwN4cPk89PbHVSvQNqyogMvlTPvbY2tqUHlLCX6+J4KBoU+XeZ78UDxUk6idwIhWs0iXS+0l1dR6TXU5nuiEVTvpCY+s7kUlVzVmRuYSRDP9zgAILQFuqClDU+s1XU27tcRHE+NB2hnLtmpO7dwStFzsUfz3fDiWjTIy3CLTE2uqURLwoLWjO235pxHsO2UtqwZFmOmNdyxyVXbqrNoS4HypZtbS2TWA7247onu4wkP3Vtm4V0REVMgYrBHRlCD1OzNLCsyUKtAcjrHLSP8OfHqSpBRaGQn81E5gRKpZMivPpF5S9fNDqK0oNTxh1U6JRBKR9qhmIJS6NM3KE/4tnyx1NDsFUwo0Mk+YzfQ7a/lk6qzIEmCtfm2TxZbPLkDfYDwvj2UrKA23CAW9GI6PqgbA4aAX6z7p6dfacdP0vnR26av8mUr0hmRWDYowEox9et1O/OiV7OumklsCnE+VlEq8bqehXpDf2LyoYIN4IiKyH4M1IpoSjE7mlMgFZo+tqcEX71uAfY0daLt0E9MDPqyoK0sLrOROkszSWo4pUs2iVq2jd7pnLuh5HFOXpl25Yc0JfyjgweZVlXA6HbJhRjjoxeNrq7Fjf5vqPjocQKjEixX1s9L+bnbJalfvIK5cF7uvegYEOBzGgt+JJr1GnE5H3h3LVlJ6rTa1XtPx+jf/BO88dAFzygIFH1Za7VikEy/saUZs4NOBN0ohWSKRVAy99Q6KMBqMAWPvtUpLz1PJLQF2Oh14Ym2N0PUnipFlqA8ur8CKeh7bRESkjMEaEU0JRiZz1lVMx5yyAMpL/Vhz59yspZ0AcPLcdew9fnE8TDn43sXxEycApqub5IgsYVOqZhGt1tGa7mnVUiURSpVcaqQeOgcarZky+dT62vH7pxY8Oh0O1X1NJoFtO0/D+Ygj7Tkwu2T1xX1taSfvavQsZX3m4cV4+9QVzQb2+Sb1NZKvk2qtInf/9Lz+q+aUmF4KCnCIgUR6b9x9tB3vn+vK+ne5kEz0hwORx9hMMKY34JerCJYGA+Qjd5EDcZ3B2tIFYTyxrkb7gkRENKUxWCOiKUFkMqdErjotldbSTunEqdhn7Vus3iVsdlWeWbVUSYTRSq7SYi9aOrqzmvur8boccLtd6E0JqJQec7WwZprPhX6N2/313hb4vS709A+jtNiLrt5B4f2UIxqq6ekRt2V1JVbUz8LS22bimb99y8zu5cxkWeZpBa3Xv/Q+1tllzTJ5DjGQr1BTktrvUPSHg67YECIfRbGoMiz772aDMb0Bv1xLgsnUb2/Dirl4fG3tRO8GEREVAAZrRDRlqE3mzKxOczodiLRHs05I9SxJtKrJ/frlc9FQU2YoFBOp1tFTfaZncIAVjFRyScsAj0au6rqe2+3C3z57L9ou3TQ8QU/0BDnaO4wf7Dgx/t+5qvJYv7xCuL9QeXgaAMDjKcId1TPytmpty+pKlIenTbplnlZQev3bsUQd0O7xN5m9dKBV6IcbSVdsCGcudOEXu5t13c62nafw9U31su+zZoMxPaFYZksC6XPk8vU+4W3kSjjoxX1LbxXuLTl/VjH+8qsrZKvUiYiI5DBYI6IpReqLduC9i+jsHpBd5qlUkXXXwnJdJ056ZfazykX1jZ7qM5FqCKuXgxmpfpCWAeqdUNc7EEfbpZuGKm7M9kgTrTgzyuEYW9a5rLYc+xovplXlKUl9/L7z6FL88Dcn8ypcs6tKshCYWYptZGm1qO37W+FxO6fcc3IsctXQZ8P/efkDDMYTuq7TNziC514+hS2rq8b7PkrMBmM9vcPC109dbm0kqHXAiu5+yjKHxOj5oWXJbTMYqhERkS4M1ohoynG5nHhw5TzZf1OryLIzVAPGgo9inxuR9ijgAOorQrYuqzoWke/Fo1R9JlINYfVyMD3hWGYQaWRCndFlTJGPonldrZNMAkG/B06nA1/dUCs7LTCV3ICM7zy6FMPDo/h/fnkcl65NfFXK0gXhKRfgAOaWYpsNgLX0DsTT3jty2YtxoiQSSfxyT4uh6+oN1VLtPHQe+4534Ksb67Givjxnwdh4SJ/SH85IUJsE4PMUYXB4VOjyPrcTTqcD/UNily8JeHD3olvG/1vPZ8nCefJLbYmIiJQwWCMi+oTdJ51KpEAIAH72u7PjJze70G5bVc6xyFXdU+NEQycre+yIhGPFPhe2blmC+nmhtJN2kemombROvuSCgqbWa3j+9YjwbUwU6XlZUT8L51f2qAbFSgMyPJ4ifHldLb63vcm2/RTVerEHkfbopAxrlGgtxd66ZYnq9EKzQzJEbd/XikQymTUldzJWGbZ0dAtVgNqhd3AE23aewtvVM9B+tTcnwdgzD396jJn9zBQJ1TbfMx+LKsPjQf++4x3YcaBN83qZ7+WiP7QU+1xTuk8gEREZw2CNiOgTVp90Bnwu1eb5Uu+06jnT8do77bK9r+zoXTY2NU49VAOyq89Ef/HXuwRTjUg49vVN9YrNvJWmI8qRq9JKDdI6uwbw1snLadvRMwxgoqU+L4+tqUHV7OlZjdalkLehpky2xyBgrBLQDpeu9+F725smZVgjRyTE+NErpwAsxor6WbL/nqvG8l2xIdn3GLt6MU6kfGjWr2eJttFgTK41QS6C2tllxWlB17rlFdhzrEP1duXey0V/aPn6pvopE9QTEZF1GKwREX3C6hOkr26sh9OBrFAn9QSlsbkTf/ajI+jWWMJjVe8yM1PjRAIVuRMas5TCMdEedNJ0xF1Hzqs2r06t0kokkth15Dz2Hr+oGpwVSqgm97ysqC/HstrsqZFNrdfw3W1HFCuNjFQC2ilfwxqrl0GKLDdOJoFtO0/D+YhD9rEQDb31LNEzwupejBPJyh8S7GQmGHtiTTXWLa/Ier7MfGYGp7kR69fX6xEQC8iUKm6lz5LnX49kvXcH/G78my8sREP1TMF7QERE9CkGa0REn7D6BGnH/lY8ta4G39+6SvYEW88SHNHeZVon83orDDq7+sf/v5kTGrOkcMxoUOF0OvDQ6tswpyygGdA1NnfKnnjlM6/biSGVfk1Kz0vm1EjRqa96KgFzJZ/CGjN90JS2p2e5sdJjIRqO/49/fzd++UYE757txMio9S3mre7FOJFOnrs+0bugyWwwVhLwyL6uzHxmfuXBOuzYr/7+ofRDjZkfW6TPkshH0fF+posrw7inoQI9N/sxMmK87x0REU1dDNaIiD5h9RI3tUoaI71ptE6CRE7m9VYYvHXiMjavqho/qTJbPWZGZghkRGpAF40NIdY/jECxG8U+NxKJJBpbxJbJ5psNK+fhjWMdWVVGAZ8LT2+qF3pe9E59lR7Lnb//ELvebje1/1boig1h3/GOtEmAExGyiYaTohVtRprDKwVXIuF49ZwS/MVP37E9MM2HJZRatJ6jY5Gr2GPzUBsrmA3GlC4n8pmpNu3a6YDqsbhyYbnia9jMjy1OpwOLKsPjLQRcLieK8iCQJyKiwsVgjYimvNSTp/uXzpbtdSbZuLIC757tTDuRyDxxyCRXPWKkN02J36PY90r0ZF5vhUG0dzjrBN1s9dhEczod6BuM4zdvnUt7Dqb5XBgooCo1ScDnwquHL8j+m1qPv0xGpr5KJ6j5EKwBSGtqPhG910TDyUQSWdU6cvtrpjm8UnClVW14NHLN0O3ple9LKLV+qDAzDTTXzARjasv7RYLaZx5ejKDfI/tZsayuHBtXVigOUtl9tAML5kyXfQ3LhZ4AEGmPoqt3EL19cQSneRAKyn8+pV5/RokPd02fpngfiIiItDBYI6IpTe7kSc2COdPx6APV41/Ie3qHNSeUyVWP6K3WCPhc+NlrZ2VP8hpqyoQrjYxU5cntqxXVY3ZTqjZRCiH7CzBUAwCthXqiSyRFj8l/+f05BI97UDN3OtYtq8ibYQaZpFB5y+oqbF5VmZPgVzSc3LZTOwQX3Z6Snt5hvHPmimzwLYXjP3n1NI5GOg1t3ww7ejEqMdLrTuSHimKfe8KmgephNhjTWt4vhWN7jnWk/cDkcAAbVlQoDtIAgJGRBA5/cEV1/+Xev+Q+t32eIhQ5HbJL+DNDa7nrz/jtGTy1vpY91oiIyBAGa0Q05UgnWk2t17D3+EVd15W+5Euh0jtn1E8KJJmhhd5qjd7BESDjhOHT4KBSV6WR3sbz+V5ZIkep2uSJtdXYsV89CM03DgDL6svQdvFm2pCLcNCL+5beqjqQARDvZyX6PLdd7AEANLVexz8fPIcNKysMDzOYW1aMvsGRrGXFf/jAArRc7MbV6ABmhfz4w/ur8Zc/e9dQyLTz0Hm8eeISvry+1vbqtaZW89VeqUGC0eWSDod29V5jy7UJCdUA63sxqoXoenvdJRJJ/GqveiXa9n2t+NL9CyzbfzuJBGNmlvc3NnfKVpwlk+oVZ43Nnfj57mbNcDJziXdsIC4bTKsN20gNRAH55ac3bg7i73/zPrasrkJ52J8Vwlo9jITyl/RcS+0iiv1u9A3EESh2IxzwZT33PDaICGCwRkRTjN4KtUyZIYXRHjVWVvmIhoPSSbqexvO5rCwxI/WLbWfXgOxy3mhsqCD7pyUBHI9cw9YtSxD0u9O+vB+NXBXahkhAY+SYTGLs5HnjSmBJVQinzkeFrwsANXOn48vr62RPSu5afEvaZc1MIu3uHTY0OVQ6rmIDcVTcOh2zQz7FyzY2d+oO6uVI7zG1FaXo0ZgWrCRzabpcf7cX9jSb3lc1csvmzfZilDuBbWq9Jhuerawvw55j2c+H1hTZXUcuaE5p7vrkhDufBP1uxFJCKj2PtZHl/YlEEpGPoppDNeQqzo5FOmXDMSWpIbHDRF7x670tmhtI/ewo9rmwfnkFbp1ZLLR0m/KP9J6htjx4eHgULx5sxdXoAJwO4OK1PtX3gNTn3upBNURUuBisEdGUYaQJuJzUkMJojxqRJTiiRKdXpoZ70onUriPnVSue7JryaSWzYanVnA4gYf0gRby4vxXf27oq7fkw23w8lZljcs+xDiycV6r7eo9/rkZ4WbEVk0j1TA6VO66UwgozvdDkNLVew093ndF9P0X7PUphoVkBnwtud5HiY5S6bN5sJYfc81Hsc8m+/0VjQ7KhWiqlJYZqPTZTBYrdebMEOhz04q++eQ/aLt009FjrrbjR856b+mNUIpHEbw+fV+wJKULt+NYS1RlU9w2OKB4PWgEt2UPtWM38t9hAXHHyrBR+Hf7gY5xou6FrH6Tn/vYFM/D+uezr8tggmpoYrBHRlGDliW9qSGGmR41SUBAKeDA8khAOzADlE0yJUrj30OrbMKcsMCFTPq2gt/IhF+wI1QD5JZ1mm49nMhpeJZPAUDwhfHkAuKN6BjyeIl3Xyays6Y4N4aWD54SvL7osVimE71I4YTLTC02Onsq35XUzccuMYkzzujQfC+n+WzWRc82yuaiePR0tl7qBJFA/P4T6eSE4nY60SpGe3mF0XIlh15ELKCpKon9wFB53EWaFpqGheib6hkfSGtDLVaXJPR963iMzZR4Lej8jwgGfZT+OmHXf0lsN971UCizXL6+Q7U1o5Aeq7r4hNDZ34p9ejxRsL0slesL6iSQantq1rNHodjOr0d86eVm2OgyArs8tKfwyQy5US1UoxwYRWYPBGhFNCVad+MqFFMvqyvHtR2/Hr/e24MbNwbTLaoVTmUFBid+DjzpjusICAFi/vEK10kKt8qxQp3wei1zFj14pvKWdZmQGInqCXdETm9Tj4XhzJw68d0lo32bP8OPc5R6hy95RPQPfeXSp0GUzpQYIr/xe3+sE0F4WKzrZM/WESTSoWrdsDhpbrqu+F2lVnWVe7njzdQDXUewT+0onPf9WyKw8OnzqCu5fOht9g3G8c/qqZlXc2fZuvHni8vh/yzWgn+ZzIT6iL7QV1dR6bfxY0vMZEU5ZSvbsI0vwi93NllQAGrXz0AW8dfJj3cvPlEIyqVJr7/EOfH1T/fg2jf5A1dnVr9kLslCJhvV2U3p/TySSePXwebxxrCOtD13q8CMpAD9zvgvvtVzPutxnb78ViWQyKzwXZXS5pEhlpBUBmV3y5dggotxgsEZEU4JVFRpKAdWK+nKsvasS7568iBs9g7oaH0tBQWNzZ9bkTxGhoBebV1ViTlmx4cqzQpjymaqxubMg+6WZJReIiDQf13tik3o8iAZrl28M4I7qGarLatxFDvz9H9+nu1JNTiKRxL5GsX1L1dnVr/rvopM9jfRavLO2HHXzQqongqJL3TIvJ1q9dflaH0r8HluWMUZjQ8JLKeXINaC3s8Jp7/GLqK0oxbK6cl2fEamfA8vqyrF0wUz8yXOHJ3RKqN7lZyIhWd/gSNo2jfxAFQp68cax7OEGE6E04IHD4bD8uFc6duxuap86iOnt01fTjr9Q0Iu7FpbjYNMl2Wpi6XjRqnaPxobSAvRdb7ej2OdKC1zViEzYVRpuka+BmR5WffckovzHYI2IpgSzFRoiAVWR04GFlWGMZFRXiIQaZr5E3r90NpxOR8FWnulldllvOOjF42trFHuv5Cu1JZ1qz73RExtgbKmpaM84t8uB7zy6FP/xh79HT798wBAfTeJHr57SVa2W2lh6VsiPxz9XA4+nCC0d3YaWAu48dAFzygKyfdKkKj0Rmb0WSwMe1YbXqVVOG1dWyE5SzIVdb7ePnxzTp9WHop8RW1ZXZh07LpcTqz9zy4Q9p6lEl5/pCcmkbRoJCT57+62meqpZ6YE75mBOWbHlgU3msZNIJLHryAXsPd6R9h5lZVN7rWquaGxI6Hg08h6aGbgqMVL9K3q9QlGIU9WJyBh+qyKiKUHPxMOx4KUaQb/HdEClFWpsWV2FmSEfXtzXJnNtMeVh//j/L7TKMyPMLuutujWIjqsxfPYzt+DVI+0W7pm9tAZJyD33Rk9sUre5qFJs2uetM4oxPDyqGKpJTrTdwPDwqFDV2g9/czKtAu7MhSgONl3GHdUzsHLRLM3rK/n57ua0+2xkAEbqCVNT6zXN5YqpS3LfPSsW3tlJOqF2u5y2LbUsBFL1YWxAu7H9WHVwVdbf8+U5BcSXn+kJyaRtlkzz6NqX5XVl6Ojs1XUdO5WH/VhWV44tq6tMVVamcjiQduw0Nnfi+dcjikM1rGhqny/VXL/WCHGNVP+KXq8QFMpUdSKyBoM1IpoSRHpRrV8+Fw01ZZY269UKNaz4cj/VfhE1u7SisWWsJ1WhMDNIwuiJTao/euR2PPO3b2ne1h3VZdi+v1lov3YcaMHXNi5UvUxmqJbqRNsNxDQCPDW9A3G8cewjPLhinmJjfDWpJ0xaJ7kBnwtPpyybysVJo9byrlRTOVSTRGND+M1b2v36nlgrH25H2qN5FQSIvEfq/dxobO7E4VMf67rO8eZrui5vN2kZ+OZVlXjzxCXVClNRySSwbedpOB8ZOy5E3kvMNLXPp2quqMZnh+hndeblJsvyyUKYqk5E1mGwRkRThkgvKqskEknsO95h+8nWVPxFdLIHiaGAB9/YvAg9/cOml/MaPbFJ5fEUafZOA4C/++eTcDrF9uv8xzHZv0vLMW/cHNS8vXOXe3QFSJleOngOe49fxHA8u6+XltTqM62TXLe7CH6PC++cuYLSYuv7msnZ+vASOJ0OnGnvwq4CqsqcKLH+YaHnJeh3Z/2tsbkT//TaWTt2CwAwzVuERFK+/5ySkmkeRNqjqhXXeqq4AWC/YK9FK0hTSQeG4thzTHxCrpa3TlzG5lVVcDod+PL6Wkurvn69t2WsfE2Amab2+VbNpfbZIfpZnXm5Qv+ML5Sp6kRkLQZrRDSl5KIPmZFlZUZNxV9E9Z4QFpqn1tdiUWXYkm0ZPbHJ9J1Hl6pWkEkSgsVP02T6exl53cyfVYwz7TeFL59J7zHkcADPPLxEV/VZNDaEH7x4Yvy/AzLhjJyg3502aVK0N2A46EX9/LGpfZOl8sNO4aAXgWKx5yTzcbdiSV7A50KvSjjcP5QeqDkAqLU8DPjd+MddZ9KqseR6e4lUcU8Et8uJr22sR0P1THznh7+3dNvR3uHxQEvphzaf24kkkNbwPzjNrVkhG9VZ/Wb0tZlvr2m1zw6Rz2q5Hwdz8RkvhV+/e/sCLlyxZrmy1aseiKiwMFgjoinHyj5kUoVNbCCOilun4/LVHltOVByO9CmAU/kX0Xw9IdRjRX0ZWi/eTDv5teM5NXpiI+c7jy7F8PAodhxowaEPrmBkVHB8pYxF88NpFTWxgTi27dT/fLZc7BGqprNKMpletWTkJFdkcmQ46MVfffMetF26mfUDgNOhvtwsNWwv9MqPXHhyXQ0uXesTuuz2/a3wuJ1oqClDpD2Kf9x1xvTtP72pHgAU+3Jl0nrVyR1fSr29pHBJ9LZzIT6SwLadp2zr/Zf6mm2oKYPf60JLRzd8fg+qyotRM7cUANJ+fOvqHcQ//tbaqkSjr818ek2HND47RD6r5X4c1PMZL31uApBdjSD1y+3qHURvXxzBaZ7x/ZaGPr195mP8311nMZpxuPncTtRUTEfrxZ60ilF+HyMiOQzWiIgMkquwEVwJotszDy+2ZJhCoZGCy8z7nY8nhKI2rKzA42tqFO+blYye2CjxeIqwcuEtePOEvl5LmV5/tz2tEsfo62ZkNIkTbTewYWUFjnxwJa3Cyy6pJ+Z2neSuXFgOl8sp+wOAniXtk72604zUE3LRXpe9A3E89/IpTPMWZVWSGb39ZXXlSCSS+NXeFl3Xzzy5DwW9GI6Pqr4fyvX2kqq4dx05j73HL6Zd3+8twoDJ+2mUXb3/pNes3Od3cJobdy+alVZ1JLV1sJKZFg759Jp+SuCzw2gLDqXrhQIe3H/HbJSHp2V9bhpdjXDPoltxV/0tiHwURaQ9CjiA+orQeOVv5md19Zzpsj96ENHU5kgmk8Z/cp5ERkcT6OoS+8XSKJfLiVCoGNFoH0bYLJioYCUSSew6csGyqWJqpvIvoXInPqlLmhKJJP70/xy2pAF1rpmdCqeX3GNp9Nh658wV/ORV85U6Vsqs8Ors6sfOQxdsua3/9IdL8ZkFMwCMvRd8d9sRy09yw0Evvrd1lerJmmgwmy8TBPPBE2uqURLwjD9eAPDHP/x9TsL50mI3/t0fLJbtnRhpj+J725t0bzP1/iQSybQlx0r+7MkGxYrtzGPqWPNVHHzvsu79ylfS+8Rr77Rrfn6Hgl7ctbAc757tFHp9hwIewOEQumwup4J63c60Za16uF1OOJDE8Ej6qWLA78bTG+t03QejPyTl4gcomtx4/k1mhcPFKCrSbiLMijUiIh0amzvxq70ttoY5Qb8bT6ytSVuuMNUonTikLmkq9rkLMlQDzE2FM8LK3oL5tBRJ0hUbQtulm2mBQdulmzh1Pmr5bTW1do4Ha3YtSxZpbi66pF2p8mOqCQe9WLe8Iu2YP3O+K2cVr919cTgdDty96BaZfzP2vJQEPOPbe+fMFcH9UL6tzGPqo6vyQ0YK1cqF5fizHx0R+tyIxoaw+6h4pdpT62sBqC/TNhJIyRF5TYcCHjy1vnZ82XKkI4orN/pwtr077ZiXKsBmhvzo6R1G3+AIHCkVWwDGr48kUD8/hPp5Id2fHUZbcFjZuoOIyE4M1oiIBFlV+XHvkltw+JTySdDXLPjiXQiUfokWmbS4fV8rvnT/ghztabZw0IuVC8t1nXilMjMVzii9JyiJRFJ2aczJc9dt3EvjMgODP3rkdjzzt29Zfjud3YNp/21XcGVlk3IpWI18FMWB9y7ivZb8fA7tJLfkOdJhffCq5rV3xia0ZobaVvTbEt3G5Wt9iLRHhYL1NXfOxYsH21Doa1v8niLcf8dsw+/XajIrf+XeC8amnM4dn0hqhcwfS0r8HsAB2YrIRVVhLKoaG4hjpAIs9fo0uek5PlhNSJSOwRoRkQCRsEdEOOjFv/n8QtxRM9OypXmFSG2ZZ7HPrRlQdMWGEOvPbbVacJobj6+tRjjgG/8CuWDO9Kz74XUXYSiu3ZcoV9PdjHz5bWzuzOpftwvt8HuLMDhBPZe0ZAYLHk+RLYMNZoX8WX/LOsmd5smazKiX1ZWBTa3XCqonocflyFqCpuX2BTPQ0dkr/r6a48Do1PkunDrfhdKAB19eXzu+T0b6ZmX26RLdxq6327Hr7XbZSaGZXC4nNqyosCWQyhWHA/ibb92Lv/zZu5Zv+4k11VlVkLmYPC4xUs3FCjBScizSiRf2NKf1Kg363bh78aysaadarTqIpiIGa0REAlo6ui2pRpGqJnL55dsIO3+J1FrmuX75XKHtBIrdOW3i/LUN2ZWEcoHKtp2nMCTQQz8XSyq1vvwOD4/ixYOtuBodwKyQH49/rgYfnL+hWJk5UY3MtSg1A//Oo0vxw9+ctDRce/xzNbJ/zzxh/fL6WsMVrmaam8spxD5rekM1ALjt1hJ850u3C7931c8PYdfb7WZ3Vbfu3uG0KZ1GlhRnVuDp3YbSpNBMj60ZO973HOsoyMq1DSsqcOFqzJbPiZKAR/bYYnhF+UZrAMPJc9exRyZAjw3Esff4Rew9fnH8uwMgv+RZ9D2FaLJisEZEJEBvdZHIOPZ8/fJt5y+RIpV/75y+KrStcMCH+5fOtn2IhNZ9T30eI+1RoYqg4DS3pcEJkP3FOTYQx7adyl9+K28J4MKV3vG/n7kQxcGmy3AX5Ue4q8fja5Wn033n0aXjAeK5yz346Gqv7OVENNSWweMpErqs2jQ8rWXEeia1arGq2rYQvHXyMjavqhR+X62fF0KxzzVhVXw/fz0y3muxoaZMaF+k3lly70dGliWL9Ht8bE0N5t9agv/7u7O2Teu0msMxFqo9tqZGuP+cXvnYb5JIIn0naGztxJEPrqT9MJb5HVWE9N2h2KceH+S6hyxRvmCwRkQkQO8X6GceXoyg3zMh1Whmqs1EhgaYCddEKv9iA3EE/e605QiZpIoeu5dTblldqasvjuj+3L1olqXHg1wY6tDYfGqolio+WnhlKUG/W/XfPZ4ifHVDvdAUT3eRQ/YxuGvxLDz7yGd0TRVTq0yVW0Zsx3Jwq6ptC0FUZ+9Cp9OBr2+qn7Bqvt7BEUTao1hUFUZLR7dQwLd+RQUaasoU/z31mDvT3oVdR9Qr8kT6PTY2d+LHr5zW3LeJdvfiWZjmdaG81I81d86FyzU2xc2OAMzqqlIiK0jf/5par+Gd01cVv0eZqT7Vep+aiB6yRPmAwRoRkQDR/jXhEi+eXDtxPSbMVJuJDg0w80ukcPC0eBb2Hr+o+O9SRY9dFQNGAw7R/VE7MdZLKQwtxGVbRokeVyLL5f7DQ4vxmaoZaUtkv7y+DrNmlSAa7dO9b0qVqblaDp6rXn75Qu/9VaryCgW9eGJtDTquxmxdLhrpGAvWRPf7pYPn8MaxDjxwxxyUh/1Zx03qDyui3mvpBADZ47CQKh7vu3227GvNSA87LVZWlRLplfo6lwZXnGy7rhqm5dJU+9whAhisEREJETkhf2pDHR5cNheJxMQkGlrVZltWV2HzqkrFkwGRyhazv0TqCZ5qK0o1K3qsPmFac+ccLK8rNxxwiOyPSKWDaNVhIpHEr/a26N7PXMpc3hYOevHY56rR3Ts03szdrM6ufuHLqi3RTD22vrqhfvzfpMoXq1m1HFzteNHz2EwGRsJ2tZAz6Hfb24ftk48LPfvd3TuctgQ+tffRr/a26B6asf+9S9jXeClre8vqygum4lHtfVXk89vvLUpbKict2X73bOeUHTI01alNLpebxloyzQMkgZt9w4j1DyNQ7E4bdqS2Ta3bTu2J1tk1gLdOXs7r1yWXSdNUxGCNiEiQ2gn5lzfU4cF7qhCN9k1IsCZSVbDz0Hm8eeJS2jS6VKK/MJr5JVJP8CT1HVL7Eup0OvDE2mps22nNMqXldeWmgg6REzitSgc9VYf/84VGU5Mnc2Hrw0vgdDpSer8NY8f+NtVjwAF9AxvfOnFZ15LdfB8eIkJtyU9q0LLz0IUJ2sPcM7M8Tynk1BPeh4Je9A/GMRQXXy4sHXO1FaXweYowOKx/QIj044lRmdWtqUv/46MT31PNir6EWp/fa++qxLsnL+JGz2Da+8GjD1QX9PsE6ZdIJLHryAXsPd6R9qNQKOjFXTJhq5Zinwvrl1fg1pnF2LFf+7NdqbVDoVShc5k0TVUM1oiIdFA6IRdtaG4X0aqCzGl0qUR/YTTzS6Te4EmroqexuRM79rfJ/lvA50J8NImhuNiJasiiL4OiFVFytKoOP3fnbER7hjAcT+BqtB83evL3F2tgrPdZ/fzQ+PPZ2NwpFILqPX+I9g4rVlIqVQjk6/AQEXInXqlEm0xPNnYszxN5z1q/fO54lW1T6zVdIderhy+gojyAZXXlqJ4z3ZIKTqts39eKf/v5hZZvN+BzoVejT9OW1ZUoD0+ztC+h2ud3kdOBhZXhrB6Khfw+Qfo1Nnfi+dcjsn3EorEh1XBXSd/giOKQpcz+tYXQ2kHr9Xvf0ltzuDdE+WNqfeMiIrJAPn7R1ltFJtcrzapljFrMBE+plL6ASr66sR7FXhd+8OIJoe0tmh/C0chVS6oSjFREiVQdHnzvsuF9mgh3L56VtgTGzl5Ncq8BOyfc2k0pENQ67lNN1LTLXLNieZ7aEi3pPUvuhLvY50JtRen4Z8KyunJs3bIEP9p5Sjggfv71CPxeFxZVhvIqWOv6/7P3p+FRnWe6P3pXqUZVlaQqkBgkGQk0IYgBS8hYVtsOg4GEYDnttrHjBHend2yS7t67T3c6+3/Ouc6H/9VX73Q6nf/uIZs4ce/OYBvszu5gm8QQBtsJk0FMDojS4IAQowQqoZJUKpVUdT4Uq1i1ag3vmkol8fy+JBZVa61aw7vWe6/7uZ9wFLDA8HyyrRuTZdZi+9PrsmHrxjrJrqd6naa5eP8mzEeqfJN/Dp0I9ol20s4Gb+7vwrJFs6dFnuHaxjLMm+3NcN9x7Dp0CR+dvT4t7rMEYSQkrBEEQcwA1OYoiWWlGVHGyIreCRKLSPPWgS58++VHMjK+pDh87gYOn7sBwBjxRe0EbrpkGamB36TB7N8ndFKq7XCrp5uu0UgJglvWVGFHDk68Hqmfg8j4JM5fHMhq6SDfKabnWLEKsGLjyMjYRMb5tLKuBGhdwlyiPjI2ge/uPIN8Z57qMmizGRodV7wvsCIUQFdUFyN4OYRgTwiwAHXl/jSHqxgkjE09LGOlWeOpluUqOXz9Piea6orxa5mGSWYTCkfxj2+dnhbPALsOXUrdj67fGhV14xnVSZ4gphMkrBEEQUxz4vEEfn1CfXmCmMPHKDcZC3omSMHLIaZGC91X72BdY7lkGYYUU/FQ+L9/1Z6V9ajFZbdiTEVmFIfQ3WhmlzDhutR2uJ0qZ5vYJFGqlDAUjhqWJWgUFgvwylNLsbKuBO8e+j3OdN/Kynq9bju2bqg15NiwNH353KoFqjsmr6ybA+vTFsmyMjFGo+rz1cymyONE3QK/6H2BlU2PLEB9RUA0I7O+IoD6ioCRm0yYyIlgH17f2yGa6dhQWyKZT+Z12/Hl9bVJ0VkjWsZpFodvKBzF3hNTJ6pxdPTemepNYIa7HynFDejtJE8Q0wkS1giCIKYBcm9pdx+5qGlCJpWVluvB7lwGCguDI1Fsaq7IeMhn5Sd7gqmHwvHxSbz1QRduhiKY43fjuc9Wa8rWEzuWf/vTE7h1JzffVH91Uz3iiQR+trcTw7zJlMeZhxGZ807objSzS5hwXWo63I6MxRSdbQ8vmWvYtso1HSjyOjDOmAmYC7zy1BKsrEvmAmWjSYLTnoeND5eralQhB2vTlz0f9yiKywPhKPa39WJtY3laCanbwV6OnmvwBWux+8Kpzn7sP6ksSMwv9pDLbJrCv1+d6byF48G+jM9wY+WGpnL89pProvfa4UgM23edw8WmcjzzRBWCPSEEe0NAAqhb4EfdA/JORbUOZG7bp0Np5XRG6blKbyd5gphOkLBGEASR48i9pV1RXYx9GsoXlLLSsl1uw1reoSZfCkiKOVarBS9trNNUyjQcmcC7hy/i8s0wznTfTv29/VIIH5y+huVVs/AXzyxjXp7YsSz02HFnJCbzranDac9LCYsNNSWi7ipWd6Oa7oqsSK2L1R0XCkfx848+lf3Mjv1dWLl4juZt5KNUkpTrHV75tLZUYGXdHFMnr89+dhFGoxPMk2+1sJYnszo2dx7sxp7jl9M6L9ct8Bt+3mcLoWAtdl9gEdbMFNUJ4+GL/0fP30x7oSIHS7D/nuO9OHjqCsYn7hU87z7aA4/LhpcksvXUOpA5ZmK8wnTETLc6QeQSJKwRBEHkMFJhuvfKlCo0ObHM6J6nFdbyDrUTeKfdioo5PgDSJa4svHv4kuS/nem+jX/6jzNY37RAsyiYq6IaAERjk6m3zfxJNTfxik3G8dXPLRYNghbCkuHHgs9tx5Y11akurmLrYp3Ih0fHmZxtHZdDaJ7l1bS9HGpFYTPRWt7L4fc5sam5EoC5k9cinxMbHl5g6DLj8QSCPSFc6Amhozdk6LKBzM7LRp332YS19D9bDW9yATMzGFmXzZ27apxealES/42AL6pxiGUVcqhxIPOFXxJ0cgMS1on7BRLWCIIgcpQTwZv4wTvymUpq3WpmZKXpQU15h9oJfDQWx9f/529SrjJhKdMnn97GsfM3df+Gs58O4Oyn9zr5GSEK5hLCyYmcEKrkctQjcHKEIzH4fc6MdaV1fct3IN9lw6iM6Ox12eD12JnWef7iAGIJC+yWBBbNL1Q9kc214//1pz+Df38/qPkYvMAT5tVMXltbKlEScGNoeBw7D3Yrfl5uQqYlQD0cieGne9gzz/Twk/fvlZEbcd5rxe9z4vFl85lyJqWy0KQwuuGNWOfGO8PjCI+Ow+uxJ8+HBDAUGUdBviP1/7UIXfx1zSpw4eHCfMnPmpnByLpsLgKBf+4qOb20bMtUC8BvijjPWMcY4edI0Jl6ZoqwThAskLBGEASRg5zs6GMKKmedIDbWFmP1Q2U5lZXGIjb8ZE8HVlQXIx5P4Ldnr2laz5nu2/jnn5/FXzyzLM11VeB2GCKsCeGHnm9qroDVajG9JMXlsKJyXgHm+PPhddux+2iPYcvmT0605NwI4QTO/W29TOKKGCxin+JZbrGgKJ9t4vXu4UvAXeeilgl1LpUkBXxO1FcENLmoxIR51slra0slNrckXW7xeAJ7jl+WLX2Vm5Cd7OjDG/s6075f5HWklWBmw3kjx/DYBII9IdRXJoP5ufP+1ycu4+0P5MuP9bKpeQHmz/akBCcA+OjsNUVnWesfLJR0SkmJmFob3oiJnjsPaD9enIBYEnArCm1i58as99rxwroarKianfFZvWOeFKzLlhO85JxeasgV8T8k4jxjHWMK8h0I9oRS59Sd0XFYLEAil9rszjC8LhuGZZ5Dc6k6giDMhoQ1giCIHEPtA67HZZMV2LwuG155aqkpDzd6ymNYxIbhSAzffuMkPr02pOvh+Ez3bYyPT6Y1G6hb4Ffcd3rYdegiPjxzFV9aV4PYpPayOxbGxuPouRHGEytKsfOANrFKDL64oTXnRgyr1YICr0PzdrGIfUqny3AkBlgAv9eJ0DD7ZF7LhPp0Vz/z8s2Gm+g01JZgXWMZk+t19UOlaKwtEb2+WcoB/V4HNjVXpP77dFe/YpMGqQmZ1PHmSjDXNZbB47JlpZmCEsHee8IakDzvi3zmu2jqFwRQt8CfNj4rudbk9reSo0ptwxszRM9QOJr2+6QEcKnz5/adMfzLzz9Ju66NHPOEsC572aLZeGNfp+LyxJxeasgl8V/44oRljHE78vDa7vZplVM5E9i6sQ4AstJJniByHRLWCIIgcoh4PIH9bb2qHnDXNZbLTpi2bqzT9LCtJJrpLY9hLe/ovjqketvFeOuDLnx5fV3qv/U0NWCFm+xvfrTCtHVwjEYnmVyOauBPtrXm3IgRjycwpGMCFI6Mp5ajx2VxtvsWIuPahFXWCfXJjj5NDUaMxpdvx1fW16Zdm6zNTxprSzKO6cREHAdPXUHfYAT1C4pw+Jy0+/OFdTWp/aRUbuZ12bBVJsRcqSNwLuzrFAJ1V+95zwInhrOKV163HVs31IrubzVuLdaGN9kqNxTbRrVCmZFjnhDWZR88dYVJLBJzeqkhl/LIhA41lpLjyPgkIuPTo6Pyovk+3AyNMTeFyEWEwlkud5IniGxBwhpBEESOoOUtfjJEvAKlxR5D3xgqiWZGlMdkO//kZiiS8beG2hJsa11iuCAl5N3DlzS742YXOnHrjnmTnnyXDRaklxWLnTtac26EGOFWeetAd6pLqZ7l6BFhWCbUuVJeBQDPranS1KnV47IhnkggHk+kJkpvH+zC3hO9aS5SCwCH3YoorymC8DxiEcYSSE7SxAj2hLKSj2YU/HMjW6Wpz6+txumufmbxSmpyb4ZbayquBz1CmVFjnp7v9A1m3reM3A6OXMkj45rSCF/sxRPK7vxcw+XIw5iI2PfptTDyXbkzBbcg/R1AwOfEc2uq4HM7Uvu/qrQQ3VfvSApn2e4kTxC5SO5c1QRBEDMQ1lJJrW/xH182P1XWZdQbQyXRbFvrEsVyQ5YJV015UVYflOf43aJ/X1k3B2i1iHZfNRLW31kx14vYRALFRS58bdMSXLoZxnd2nDZtu7iA/82PVmDurHzJc0drzg1/WUa5VbjJr56JpBHZO0rrN7u8atWSOZhV4MIvGTL1Al5Xxt9YnCAjYxP47s4z8LpseHjpHFy9OYJg72DG5xJINgxprCvGXH8+YAHqyv1pky0WYWxEkE3G54PT2XGjSU2I1eB121H3QPK3m+HSEp6/nIi5oroY39x+RNWyxMZrM9xaU1FuqEcoYx3ztIhSrN8pKRK/bxm1HRwsIrteWK6rx5fNx1sHu3Ds/E2Ep7GjCwDGxiclf7NcY51s4XHZsK6xDJ9bVSErmnGQcEYQ8pCwRhAEYRKspZJ63uKXBO49dBvxxpBlW17f26n4wCs14Rofn8RbH3ThZiiCOX43PruiVHfQvtMGRBmeUZ/7bLXkv62sK4FVJoD78O+u40z3bV3bmdxWC8YnErL5X0MjMXxnW3PqwbaqtBA+t930ScZ7Ry7hlaeWSJ5DLBMvr9uekXPjddvxpSer4XM7FN1KauAmAVoxItBaav2coN7W0ad/JTI89uB81JQX4ci5G4rB9FWlhaKCJ2vHyuGxCRxou6q4TW3Be3lyu9GTNuYFe0NMv+uD01cyhLWTHX042XmL6ft6cdmt+LMvfgZnu2/hyLkbmsT/rRtqYbVaVI3vAZ8TKxeX4DdnryESlRcgEglgy+oqFHgdaccz2BNSLY6IjddmuLWmqtxQq1DGMuZp7XrIuuzVD5UpNvoA7jm9tMIisvNxCtypUjxUMxvzZ3tSIvvprv6M7qYA4LBZYbdZmbrXTif0CvR68XsdeHz5fJQE8lOddodGMzvpkmhGEPohYY0gCMIE1JRK6nmLb3T5Bsu2sAo8wknUP//8bJo41X4pOcnOs1owGdeuckQngAcXzcInn0oLX8urZqU1LhBDzvXXUFuSIQrWlBXh1ffaVW6r8u/kT3I5cTYbb+4TCWD7rvO43jKa6mbKh2XiJVZWNhyJ4dV31O0nFrjjo8VlsbQygHMXB3StX2pCna2SP279LMelaXEJvvXqUVmRP2FS6zz+mKfYUeIun3x6G+2XBlD3gF+1OGUEgyMxWC0WPL+2Bs+trsbuI5eYJ/zC/co6vm9ZXYW1jeWwWi0on+PFa+9dUPxOgdeBVfVzBduu7bwTfs8Mt9ZUlRtqFcpYri2tXQ9Zl22zWfGldTWKgtcLBnRfZBHZ/V5Hsnsq717ZNzCKD89cSxP/pKIoGmpLsGzRbPz4/Qs4EexDbDI5KIxPxDE+YW6Tn/sFn9uOVUvmYEV1MWWdEUQWIWGNIAjCYNRm02idCGl9Uy6HkY4C/iRKKKrx0SOqccwqcGJ51SzRdSyvmoW/eGYZ03LkXH8OR15a8wMAsOZZ8LO9nYaHEJ/u6sfIWExX+ZgwN4WVXYcuYl9bL5qXzs14MG+oLcGGpnLRfK28PAsmJs0RZ4SoEZXEKC7KLItUi9iEOlvB7GLrFyur9rrtaPnMXOw53pvxfU7w2tBULvrvRrNjfxf+eGMdk0M1NpnAd3eeSYlUHpc96yWE3FhotVqwuaVSMsdSmEUknMiyjqkFXkfqe7dCY0zfEROqtIpXwu+Z4dbKRrmhEL1CmZTYZETXQ9Zlc58Tc3rJNZ/Quk180YzV5bSpuVL0pZQwDiMcieGnezJ/Ry7D6s6bCtY2lmH5otmSx4ggiOxBwhpBEITBqM2m0ToR0vqmXA7WbWFxme093oO6BX6Mj08ylVEWeR1pb7z9d/+bRaq5GYrgr7esyHCVPffZakWnmlZOdvRh54FuUzp77Wu7gt9+cl3XMjY/WoF3Dl/S9N2RsQnsa7uCfW1X4Pc675aSuNE3MCoqwiSArIlqQPq5z1rKyOeD09cUM9Z8+XZ8aW0N3vqgm2lCbYaryuuyIYHMfD4PL/haTswbjsQUz6O9J8wX1YDkmGexWOC05yEaYyuP4sS/dY1lJm9dJsKxUGuOJeuYOjQ8jmPtN9A3EGFyx0mJWlrEK7FlmeHW0iqE64FVKJtd5E5m1FXNzliGkRmmWpfNfS7YE0qWVCeSwhbn6jQS4QsmvjjW2TuYsX1SWbLZcu+aTa6KahuayvHsaumIC4IgsgsJawRBEDqJxxO4cGkAsYsh2C0JnOrqV/4S7jkZqkoLVQWpG/GmXArWSRmLy+zspwMpoYuF5VWz0LR4btrD+f/8jzM4d1E5l4lrTCDmKtOD3ITB7MmhnmyWgM+JLzxaibISr6jLQQ2h4WjO5N54XTZs3VgnWl7ETU5Pd/UzdftUut6+sj7pAmmsK0HX1Tu4dHMYkcg4asqKRF2Nu49cNHwC2fLgPFEhc2RsgrmRiNKxN6kCVJShyDg2PvyA6vNJr8isFinRSkuOJWuTlp0H5Y+jEClRS4t4JbUsM9xacsvku/84p9Sd4XGER8fh9diTImUieR4V5DtS/19NKSK3DXwxa1aBCw8vK8PQnVFMSJQjmtn1kHXZVqsF9ZUB0cYeRiB2vzvd1a/YIVzs3x9eXJIVJ+z9gPD50Oe248X1NcnGSwRB5AwkrBEEQehAzxtZzsnQffUO0+R20yMLUF8RMNXmb7SjgHOPsdA3OJYxufizpx/EK9/7SPG7co0JtCI1Ydiypgo/3dNh+PqMhD9Rnk4lN1JYAHzh0QpsfrRS8tznJqd1C/yoKS9ivi6FkxZhRtbprn7s2N+FAZl8spMdfdh16JLm3ye2TS9/YQne+kBebGFpJJJLFHmcaKqbg31tvarOy7HxSdjzLKk8JrMxww1sJK0tFbKilpR4JdVJVGlZRru1zFimVCmiFHwxy2azIi+Hj7cZiJVo7jyQfr5ICcJKZeShcJRENUaE+1isvLyqtJCpaydBEFMLCWsEQRAaiMcTqgKthfAdEawZPPOLPVnp3KSltE4KriSTa1QgB+c64+Nw5Elmp3EsWxRQVe4p5ULjI9d8Yvuu88zrMhtht1D+RDnbge9m8krrElVv57mJ+/62XkUnkFDU5of4szQhWVFdbPh+TiSAweGoYY1EcgG/14F4IoHjwZtY21CKdw6r6wacDVFNqxNLbkzZfeSi4eJ2SSBf8TNi4pXWCboZbi2jl2mmoywXYbmPSX1OzIUmhtJ5m60y8pmAmID2/NpqZoH5fjq3CWK6QsIaQRCESk529OGNfZ1pZSdq4TsizOi+phf+pGz3kYto7xnUtBwu5+yD09cUPyvlOvuLZ5bJNj+43DeCkx19TJNhKRca331klCBlt1kRM7HLWcDnxLdffkRyoqyn22yuoKfkzGq1oMDrUP29weFx5jLLHfu74HbamPezmpLvvkE2p+d0YXwiju/uPGP6elyOPOYyar/Pia9+bjGGIvKh32LiBIBU6fGx8zfTRE5uTAFgqJORg/VeICY20QR9eiAnnLHcx6Q+x1KWzEo2y8inC1KuUDkBja5JgpgZkLBGEAShAiOytdY1lqU9/JrRfc0IuEnZ9l2/07yM5z5bDZvNikXzC/DptSHJzy2vmiXrOvuLZ5bh4/M38Op77Rn/xncPyQkwSu6jba1L4XPb0d4zYIggtWheAYK9g7qXI8Xza5P7Vuqh3MgOr9nmieXz0LR4ru6SFz1iNEuZ5UA4imCPshuT45WnliA0FGXK0yopynRwiuF12TAsM1FW+nejsFiAr22ux9sHP027frj1Z6Mk2eu243vfeDQlNvcNjMoKWy+src7Iq2ItkQOkHT3cmMJvMmEUU3EvILTD6izjf1ZJrFVy0XLZZ2KfmwnRALmGy5GHP3hwHlZUF8u6QklAI4iZDQlrBEEQjBjlZFpRXZz232Z0X1NDPJ5A+8UBHGm/gej4JKrLCrG2oRw2mxX//POzCEe0PYjPKnDidxdvK5acLK+ahb94ZpniNr794aeyn9mxvwsrqoslHSdKx+4H75wz7A28055nmqjmc9vxlQ21ii6ubDocjaZp8VxDJiFaOiRyMJdZMl6WrS2VWFk3B/F4AntP9Mpuk89txxPLSxU/53XZEFc4abduTDbz0OuyVWL9ynI8vHguVtbOSQkJBW4H/u1XF4AsTea3bqjNEJtLi73M4fusmZms4oQZIkau57/dTyiJZqzOMqnP8mEVa3fs78KyRbNnTAxALiF0+3lcNqxrLMOm5vTsTxLQCOL+hIQ1giAIRoworZNyG5jRfY2Fkx19eG13e1o7+dNdt/AfH3yKtQ3zZbPNlHA7bbJi4dJKP/7s6QeZ8tFY9v1AOIrO3kHRh1qW7xtZ1mIxad7ry7fjH7/+KGw2q+JnWbsRZguXIw9WqwWjCtvjc9tRVVpoyDqNbsYhRl25H4d9N2TPL7/XgU3NFczbFI7E8N9/eBSr6ufIhoArOdX4HVSd9jx87+2zCr9GPRZLUlR7dnXSTWO1WlBTXoTO3kG0XzLG/amEx2XDSyLdYgH2oPxsdPrVg9n3AuIerDmcSt0yWZxl3LJYzz2l8XwgHMXBU1eyct2pKW2fLnhdNiQAXXloBEHcn5CwRhAEwYgRpXVybgMzOqXJIfcwnwCw76RyLpoc/QoZUddujTIJRAD7vpf6XDbLIl12K3PGk1q+sr5Wdp/xJ4R9A5GcEdWAZGfHv35uObqvDsqW54UjMXzr1aOirg4t6GnGIWwOISTgc6JugV9RKHthXU3qOo7HE/C47FjXWIaj529iWGL5g8Pj2HO8FxuayvHxhb70CbzXgdHoRJogLiSBdHds55VByc9qYWllAEsrA1j9UFnaOamnU7JWtj21NKOkk49SsH2uNvpobalASSCfJvE6UVOOyeIyU44VYMtn5K5Po8+9bOUzPrmyHHtlhP8NTeX47SfXc+o+xEesAyc/P5HKOQmCUAMJawRBEIzoKa1jdRtkq7NZPJ7A67/uMHUdcpN+QN5hJkRvg4dslkUmGGsD1bjJWM6fqRA01DIUGcfmloWi5Xl8WHPzWBGK1gX5Dry2u122NDI56arG9l3KJdqsjlMtjU8OfXId3/uzlrTcnonJuKL7bGRsAsGe0D3BSaOzxOu2p4l/UqVsejsla4UTN/Uw1Y0+pDoG3q/uNC25ZEaUY54IGtMFmDWfsfNuXIDR5x5rPiMrUg1Bjl/okxTPlHIIs42UiCZ1XpGARhCEWkhYIwiCYIQ1r8nvc2KLige4bDA+Pom3PujCzVAEc/xuLK8qxp0RxgwpDcwudOLWHeXJAquTTE+Dh3g8gXg8Aac9D9GYOU4y4F5IO+s61jWWy4oQYm4VqUlkrpexcXACZ0NtCSbiCfzwnfOyeo9cbp5ahKL1l9bVKOYaNtSWwPr0UuzY34UBhRJtJcep1mM0PDaBzt7BNEfWf/5GPm+QI9h7T1irW+DH7qM9qtYtbAYgVxZndoabFM+t0Z85drqr36CtUYfXbcfWDbX3XYmZER0vWT6rphxzMp7AG3vlXzaxdgFmzWc0w0kd8Dmx+qEyxXxGNUg5sEPhqGSp+sjYhGwZu9n4vQ48vnw+OT4JgsgaJKwRBEEwwpKN9PRjC/H5VQty6gHun39+Ni0rrf1SCB+c1lfmqQSLqAawO8m0NnjIlotredUs9NwcZg5pD/ic2NRcgdJij65gdU7E3ZGDZWxC+MLnyY4+vPrOecXvDISj2N/WiwKvw/DJEavLrKG2BCsXz8G10Bh6r9+Bz22X3A4px2k8nsCP3w9q3la+QAaA3X3G+1zdA37VmXtizQCETLWo63PbdX3/ZEcf9rVdMWhr2BALPZ8pDhk9DjKAreMltxwjyzHbf387TTwXQ20XYCW0OKmVuvw+t6YKNptV8X6ZS/mbRkBCGkEQUw0JawRBECqQnIwXOPHy0w9icXkhJibkSyDNhu9OuzkwittDU1PipBRsLOUwk0Jq3/vcdry4vkZUiMrWhN/vc6pq9PDcmqpUCaGeYPVQOIrtu5QFqlyAEz7V5lntPHhvcizlXNEK6/63Wi34TNVslM1yK17fExNxHDx1BX2DEZQUubH6oTJ09g7qm8QKriNW9xlfrLFaLXhpYx3TNSG2n8UEE8D4fCi1DI5EVZUO8sl2ttrqh0rRWFsy7Sb9rPtXr4OMpeMlay6ZmnLMpYtmYWBoTPazKRgPG0s+I3cNqelc/OUNtbBaLJIvjHYe6IbVIl2ezmG3WdFUV4zjwalxa+pBbVknQRBENiBhjSAIQiVik/H6ygBmzfIiFBqZsu0aH5/E//tHRzEQzn45lhhK3cLkGjlI0VBbgngCeH1vR2rSEo7E0iYTQHYnzBYLsHB+oSoXILe9LKJOPJ7AG/s6jd5s1ejpALehqTx1bPTkWRmdvQYYm2v49sEu7D3Rm7af3vqgG5VzfbqWK9w+FveZ121H3QPJ73HCSGwyjtaWCnx45lpa2WaR14EnZNweUoLJ48vmm+IG9eXbER2fxDjDS4q+gVF8c/sRptJBIVrPRa/bjkQioVosbawtyTlnmlEZZUY4yFg6XrLmkqktxwwUuJg+z9IFWE0+IwBVnYt3HujGC2ursUVi+cIxMp6A6OcGh8enjahGbjSCIKYDJKwRBEFoQDgZn+qHPGG5Z66wrrEMbR39aZMQrgyK37GQlZMdfUyTCSPCyLmSwE+v3pHNilm/shyzGCdlwu0ViiPchJUvuJ3//cCUZFcJeeWpJSmHwLX+EVV5Xccv9OGZJ5IuPSNyhX6yp8Ow7DWjePtgl+h5kkgAv78e1rxcvkDGweI+e2TJHHT2DiIcGcfOA90ZwkhrSyVKAm6mLolSgolZjQrCo2yiiNuRJ9phVkmA5cSkto4+pvWsbSjFQzUlGW69/W29aY5KOdQ6dI1CT54Za0YZy4sMFgcZC0bnknHlmPULZyHgc8qWg7J2AX5uTRVW1iXzGX/8flAy2J+Dc5eJfVaIGnffskWzsfNA7scE8PG67fjy+hpyoxEEMe0gYY0gCGKa80//cQZnPx2Y6s0QZUV1MZ5bXY3dRy5hX1svRsYmMDI2gV2HLuGjs9dVlfWxTN64UiEtky+/14GvbqrH0Oh42sM8t31CJ5LFkhTVnl1djXg8oSmzRvh5KcFtqmltqcDKujmp/w72hFQJa/wOsEZ0aB2OxLD7yEVsblmoe1lGMDERx94T5gR1b91QKzqplCr14pyF+9quSGaHcaLYN55eKuugynappFriChZKfukgJy71DYxmOPaUeKhG3Gm2trGcOSRei0NXDCWHGf/f+wYi+OjsNU15ZmoyylheZBghqgHqcsnUlGPmWS340vpa/MvPP5H8vFIXYA7OkQyIu/BGxiZEhV81Yz6Lu+/gqSs53SWaj1juIEEQxHSChDWCIIhpzMfnb2RVVMuzWDDJWA/ITVpOd/WLOlvUlvWxTN44AUeLePPCuhrUVwRE/+3Z1dX44mOLsP9kL7qu3IHTnofmpXNTn7daLVhS4TestCaXRDW/z4lNzZVpf2PtkMuHEzu1fFeMX5/ozcokLB5P4MKlAcQuhmC3JLBofmFGh9au3kHNZbJS5Dvz8OTKcsQm4wj2hERdG/yy9NNd/djXdkXVdvzbLy/AactDfWVAdD8a4fw0k2hMvlR0IBzF7iOXMsQlNcg5zViaqnCdP1nGOL1lmSzNWlgdT2oyyszobimGmlwyteWYALCyTto5JuYykyqzNCMzTgt9gxHDl6mWDU3l+PhCX0aDGMpIIwhipkHCGkEQxDTlZEcfXn2v3fDlLppfgE+vDYn+GyeqeVw2RSHp+buuCFaXmdJDNevkbXAkiqa6OczijVQXTiFnP72FfW33HADH2m+ml25eucO0fbmCL9+OVfVzkO+04Z3DlyQ/11ibFG74Ex8WQUEIJ3Zq+a4Yo9HJlAuOFbUh91JCxsOLSzImi0YTGZ9MK3OUaygwMDyGY+dvql7H2PgkvvcfZ+Fx2fDSxrqMayBbgokQv8+JRCIh6ypjdXXqLVdVcppJuZdYHDis7jKWsswNTeWyJetClPadmowy1hcZSg4ypY6XanLJuDGdtRyTD4vLLB5PKJZZGpkZp4WSIrfhy+TD3TsByHZXfuaJKk3NRQiCIKYTJKwRBEFMQ8wq0fK57Vj9UBk2P2rH6a4+dF8dwpX+zIYMI2MTOB7sl3wbzT1QB3tCzC4zJYGEdfJW5HEyiTdczptcKRU3CTjd1S87qW1tqciJHDRWtqyuwtrG8tTvLivxZpYUItmMkispFAo7DbUlzJN5oetHsruuz4mHaouxX6KEUYga4Yc1hJ3/ealjrkbA0IrQeSZ0eLK4k1iRKk1jveZcdivGFNxjaqhf4Mfy6tkK12+5aRlvALvgDog3tKkqLUT31Ts4HrzJ7D4ToqYs06xSZCW436b0IsPvdWDL2hpZB9kjS+dicDiKE4IXNlzZPf9YcOOPVIm+Uokl/5x/eMlcAOriBoxyc5olXgd8Tqx+qIy5VJlleXIuM7lGPEY2iCEIgshVSFgjCIKYhphVohWOxPCj3UkXXJHXgZhCV77jF/rw9y8/gu6rd0QfqNW4zJRgmbzxBRw58UZqwiw22WXZD1JZVrlIwOdME9WAe8LA7iMX8f7HlxGNxSGsKLwnIlZiU3MFAODjC2zh72KuHzExoqa8CJ29g8zC2tDwOOLxhKL7gTWEnSOXs8V27O9CPJHA9l3nDV/2G/s609yjrNfcc2uqDN0epyNP8fpdUV2sq8RTiqWVAXxu1QJFV42YAM+JByc7+vCtV4+qdp9JwVKWaXQpMqDsMPN7Han9pPQiY3wiDqsFomIYh9Q4mkgAe473YlFpYeo6PdnRJ9ksZM/xXrgcefjcqgomoWzl4mR+ZMdl9hdBRgliWjMnWdx9NptV8bgYVapJ4hlBEPc7JKwRBEFMQ7JRosXiwBoIR9F99Y7kA7Ual5kSLJM3oYDTUFuCZYtm4+CpK+gbjKCkyI3VD5XBZrNmfFdqssuyH3IpE02Jx5bNE3XRJLPwLil+f9ehi/jwzFU8sbyUSdRobakULV+UmrCpyWDbebAbe0/0yjbBUONC4bYjl7PFBsJR/GxvpynLHhweT2sKwXrNNdSWILEZePXd8xmCrBa4EjYp8ZW1HFALPTfCaeuQcrBKuR8B/U0BhBgV/K8GFofZ+EQcp7v60VBbkhJCX9vdLpp9xznE9KAmk2zXoUvYe6IXkeik7OcGwlH850efYtWDpbIdQfmoKX+VQ01mnJCtG+sAyJdgAmwvmKhUkyAIQj8krBEEQeQwUiKEEQ/1RiEn8ql1mSmh1oUm5kATE2KMcCjlWidPIV6XDQlANLdrRXWxqt8/ODzOXIZXEriX88NSjmm1WrBFhQNKqQmGmqYXnEBstnD97GcX4ZdHezSfL8MmCi27Dl1CabFXcWLuciSbK6yoLsbJjj689UG3IaKaxQKsfqgs9d9yThi58eCxZfOYhGIh4UgM+9t6UeB1iOaeSV3nRjYFyAZK4xXnMJMK8wdEcscSCcWGEnpQm0mmJKpxvHv4Et49fAm+fDvT51nLX43MjOMQ3uvkhGcOJYGa3GYEQRD6IWGNIAgiR5ETIVZUFxvSWdEI5EQ+LS4zJZQmCRwngn2yHdv4QowRDiWlzKdHl87F4XM3dK1DC8lGEwEcD2aWbfIz4sw6l/oGkp3p1JRj+twO1euRaoKhpRzZTOHal2/HkysfQHGR23C3lVG8KSgJ5USTf//lhVSW2tj4JN49fAl7j182VExZv7Jc1FEqhdR4AAAfnb2u6bzeeVDaUaYkhhrVFECIUlmmxaK+HHRDUzl++8l1WdFsW+sSOOx5sr9rx/4uLFs02zQnJR8zRe/wqPKx4V4Esdzb1jaWYd5sD3Ye6FZ8ESQlEvu9Djy+fD5KAvmi9zpWUYzEM4IgCHMhYY0gCCIHYREhHl8239TwbhZY3GZass6UUJoknAjexA/ekXc88YUYvZO1gM+JTc0VKC32iP7O59ZUo/dmWNc6tJJsNCGfhfb+x5dNW/9HZ6/hc6sWqCrH1HI8pJpgaClHVlOOqpZV9XNgtVokrwuuaYQUvnw7kwCgh9DweNq+PNnRJ+kgNEpU40Lnn11drfq7UuOBGaWiUwE3hsiVZa6sLZbt0ixkZGwCe473GuKwGwhHcfDUFVOdlBxT7dbmvwiSuoY5dh26BJcjD/UVAaxrLECB14GA1yVZasn60oggCILIPUhYIwiCyDFYM6H+8PFFWdoiaVjdZkZOGJQyuuREAD58IUbvZI3bD2K/MxwZx84DxnRvFDIn4MbNu44wPZhZvhW6O+lWU46p9XiICXJaypFZ3Cha4TKigPTr4nRXP46dv6koYrz4ZK1p5xMfbl9mq5HD2sYyTaKaHErCx1Sh5D4T8tiyeWioKZb9LceD/fC4bJiYjKu6no1y2PUN6h+HlOBfp0Veh6mdmL1ue5pQKPUi6F7jl0uiL7rGxidxqrMfpzqTx+eljXWwWi2S9zFylhEEQUxPSFgjCILIIeLxBPa39TKJEOFR8yYVfLwuG+z2PN1uMyMmDEoZXWpFAE48YM3LYdkP/N+ZLEfV3y0x35mHUV5ekNdtw5eerMXbMiVruQTrpFvN8RBDTJDTWo5shijjtFsRTyTSOplarRaMjMUUO8vyzzWrRTwg30i4fZmtRg77TlxBnsViirjGiZf7T/biVOctQ5evFp/bji+tq8EP3mUfF3YduoSPzl7HC2ur8Q/bmiVFnKnMeOQaTpgJ/zp9Yvl8TRl6zOtaUw2/zyn6AkcoilWVFuKjs9cUl8mV14p14hRmTRIEQRDTCxLWCIIgpgDWTnNyeNxsQct62bqxLmtuMzlYymM9LrsqEYATD1jEF7X7gaUcVQlfvh2PLp2Ljy/0pQlrdlsebt4ezSkXjhysk241x0OIXFmylEjmy7djVf0ceFz2NLGL/714ArIleGqIxuL47s4zqsVgn9uOb7/8SCp7jPs9P93TYUoQvt/rSO3LbHQg5th7ohdffGyRqow1FjjxMhuimsuRh7Fx6dD8cCSGtz7ols03E4PfVZRFxDESJYddwOfE6ofKsPeE8kshLYi9wCgJ5Bu+Hj5+n1P0RZDYyx2hu02JPcd7M/6m1ISFIAiCyG1IWCMIgsgyYg/mWjpKBntCRm9aGsLJjNluMznMKI8VCjGsWXAs+4G1HFUOr8uGL60Vd7aEwtEpz9djhXXSzXo8pHh+bdLpFOwJiQqfwrLLo+dvIjyadIrta7siei7G4wnsPGB8GaRaMTgciaH76p20c6+htgTRiUm89t4Fw7fvhXU1qf2WzUyrRAI4eOoKnmx6wNDlZqucFUiW/nG5ZVJjeigcZco3EyPbXUVZ8t2eX1sNm82qKIZvaCrHotJCxVxBv8+Jx5fNR0nALfkCo29gVNPv8fucSCQSsmWkUiK91MsdI7PlpJqwEARBELkNCWsEQRBZRKpTpVpRzeuymdJhsrWlQrT7mB6nGaCuI6QYLOVoastjpcr/9LrzjJrEr2koxVsfTF2p52cfmo+2C/26J/Gsk+7n1igfj76BUXx45lrapJgTPgHgm9uPyAq3cmWXZnWLlUONGCzmHAt4XYZuj9dlw9aNdWnXopmNHMQwI6srW+WsHNx4ruRe01K6abSo5nXZMCyzHY8tm4fJeBytLRWS1x53vkg6Q912vLi+Bivr5gBAKpNsX1svRsYmUqKax2XDusYybGqulB1zT3b0aS4DfeHuWKG2NDxb4qxUExaCIAgityFhjSAIIksYURrIIdc1UAsWC/DKU0uxsi5T3NLjNAPY3WZyb+lZy9G8HruiCMD9Vqlt15sFZ9Qk/p3DPbqXoYc5Rfm6JvF+rwOPL5+P2GQcwZ4QVlTLh6/vPNAFqwUZx0V4PDY1V4qWUbMIt2rPRbPLINWIwWLOMa3ZgD63HQ8vmYOAz4mRSAwWqwV15X7ULfBnXINmNnIQw4ysrmyWs/KRE9Vyha0b6wAgs7zRZUMCSBOw/D4nWlsqZZ1kLC8nTnf1S2bE7Tp0CaXFXsnxWavAJRQBRd3JBU48v0b8vpZNcXaqzleCIAhCOySsEQRBZAEjSgOB5OTgsWXzDA9t/kJzBSbjSQGEPwnS6zQD2N1mcm/pWcvRAl6XogjwylNLRAVEo9AyKcqzWjAZN1ou1U7A54TXoz7Dz+3IQ8uD8+Bx2fHR2WsZk/IX1lZjy5oq0WuB9ZwSCm1qxDK152I2yiBZxGCp0jQzsgHFaKgtwbbWpXhtdztiE+Z1kAWAQp/D8GVms5w1G6jtKiqGUGhKd4ZGRIUvrgT9G08vlX35IPdyQu+LFi0C15bVVVjbWJ62PL4AGI7EUD6vEPP9LsQlxuFsil0z7XwlCIK4HyBhjSAIwmSMKiHhJgfHgzc1fd/jssEhcK5wroR3D19K/Y0TQFZUF+t2mgHsExK5z7E4czjxwWq1MGWlSaG37FXLpCiXRDUgWQrlcakX1lyOPFSVFYmWO3PCmVKulNqMITVimdpzMRtlkCxisFhpGoeR2YBSnOzow0/ev2C6qAYAr77TDpvVqjrAXeq6jccTiCcSmnIscxGWzDMgM7eTc5CKlfoD98SweDyBb24/IrtsPTlgel+0aBG4CrwO0W3lfrPNZoXf70EoNCIprLGO60rlv0rINWEhCIIgchcS1giCIEzGiBKSgM+ZeuOuNbR5ZGwCL39hCa7dHkHfYATR8UnRnDZOAGltqdDtNAPYJyRyn2Nx5vDFB61ZaWrLXsUm89nOpFKDxZIMiJf6b375ZjyRQJHXIRvyLSQ0PI7X93bIfkZJ3FCbMaRGLFN7LppdBmmUGKw3GzAeTyB4OZRsiGJBWlmolGvVTH6yp0NWuBFed+FIDDsPZF63Dy8uwccX+nLyWgSU883EeP7uSw8lodBhz8O2p5ZiKDKu6nwwwmEsh94XLVpeXBjhAGN9ufPtlx/Br45dwvsfX0Y0li5Ee102tDw4T7QrKIeciE4QBEHkLiSsEQRBmIwRJSTcw3Y8nsCHZ65pXs6r751ndm2IBbyLofT71LjN5GB15nCwZKXxJ+hy5U9iJYpyIlw2M6nUsKahDLMLXPB67Ah4XagqLUT31Tup3y8s39TStdCIcHU114wasayqtFDRUeJ12TK6k/75Mw/izX2duH1nLPV3oSipBSPEYA6t2YAnO/rw4/eDaePCbvTA47LhKxtqsfNA9htoDEdi2H3kIja3LEz9jbtWuY6uSp0Yuc6buYxSvhn/mPDHuWBPSHEcD4WjsFotWFU/V9U2GeEwlkPvixa1Ly6McoCxvtyx2azY3LIQm5orJcVqsc6orCI6QRAEkZuQsEYQBGEyet6WCx+2O3sHVTmIhKgphWL9rNLvU+s2k8OIrp0cYsKYHPzyJ5bsObmgfr1w5WBCl44UnAi0/65YygmANpsVdQv8d7vsiYeJA5llZWaj5pphFW7DkRj+5gdHlMu0LJnn0sq6Eqx5uAIfn72C20NjGBoex86D2gUnPWKwkci50UbGJgzJhdTKvrYrqe6Qaq/VXEcu34wb0wBIjnNmil9GOIzl0PuiRa2L1EgHmJqXO1arBfUVAdRXBESXY9R9jCAIgsgNSFgjCIIwGa2lgb58O7798iOw2aypv2W7W5iSoMLqBlDrNpPDCPFBS3kbV/5UU17ElD33nW3NaZMnPWKM3+vAVzfVY2g0vazLaoHs73hw0Sx88untDGcVXwBkydJz2PPw2PJ5eP+Y+Q4gtQ4Tlol20+ISxUwqjuFITLTMLc9qweKKACYm4jjWnllCzcK6xjKsqC7OyiRarryT+3cjsh/lcNmtGItpy2UbGZtAZ+8gRsZiOen+1EprSyU2NVeI5psJ0dvMRYv4ZZTDWAojXrRI3U+E22iGA8woUSzbIjpBEARhLiSsEQRBmIzWnKbwaAzdV++kPXxnu1vYusZyUScThxo3gNiEhCtFPNZ+w5S39mIZaAA0CwqDI1HVGUTc8YvHE9h7oleT6+aFdTWizgdAXPz0umz4MkMZ3479XXA7bYrbFApHUZifnXNPi8NkRXUxWlsqsa+tN6N87rk1VarLGZUEbLXXoddtx9YNtVkr85Ir73xpYx0aaksMyX5UQquoxhEKR/Hzjz41aGtyg9+cvYZNzRW6lsEqflWVFiLYE1IlABnpMJbCiBctwvtJQb4DSEB1ppwWSBQjCIIghJCwRhAEkQVY3rCLIZzg15QXqQ6U10rA58Sm5gqUFnsMy4PhT0hOdvThW68eZW4UoBapDLTHl83TLCgUeZyay7C0CKxy+0POdTc8NoHrt0aYBMBgT4hpW3z5DnjddsVsK61oPafEjnO+Mw8rqmejvmIWQkNR1cdbqUEIqwvV47JhXWNZqqQxGyiVd37/F+ew+dEK9PYNZ2V79BAeHZ8x5Z8ceoL/OVhdmmLj6+PL5qMk4JYVn4x0GEthhPOLBC6CIAgiVyBhjSAIwkSEjqnnPluFH7zLnlskdMac7upHJMqWdbVqcQmOXehTtb18OFeCGXkwLBllWiZv/IBzseYLoXA0LZxfDT63HTXlRejsHWT6vJirSY3A2tpSISnIsJTxvf8xY9km42H0+5z48voaxdwtLv/t9b0dzI0MtqyuSnW9VSK94cSo6PEcjU7i8LmbOHzuJtP6hXx05pqsGMYibDTUzMZnV5SllV+aDWt557uHL5m/MQJ8bnva+WABINf7IeBzwuuxm75danHarRndHtViREm/nPjVtLhEtHFDcvy750CWE+6zkQNGwhhBEAQxUyBhjSAIwiTEnDQiueiSCHNs1OaCRSe0Tf6kgpiNmADF4wkEe0L48ftB2c/xGwWwYnbA+aolc2C1WnRnEPEnrKe7+nHs/M00wYHFFcJSxheNKYT03yXPotzhkvs9VqsFF5uGZLstctu+omo2/uKffsNUDljgdTAd62yF2IeGxxVdRVLCBrcvT3bewsnOW4a6MAH57DQzyjv9Xge2rK3BT/cEdTWw+IPl82CFJbXNv7t4G3sVzqOr/SOa12cWekU1wLiS/hXVxXA7bAj2hoBEMpOtpqwI33r1KNP3lV5kkPBFEARBEGyQsEYQBGECUiKYnHghhJ9joyVofGSMvWSvtaUCJYF8U7Np1Igi/EYBLI4JLc0I1LKiuhiAMRlE3IS1boEfz62uVu0KMaqJhduRh3cO9yh+jv97nl1djcr5hRmONKEgaLNZseHhBbIZfRxDw+OKOXvZOMZ8WPaxUCTd13ZFtlEEi7gmlgvI7Q+l7LTYpH7RR0jT4hL43Ha8sLYGd0aiuHwjrMkJ+6ujl1P//wPXVVmRbkNTOVNTjWxjRHdcPcH/fMTG08PnbmgqddfyIoMgCIIgiHuQsEYQBGEw8XhC0ZElBz9gnEOLE6Wz947iRNBiAV55ailW1ulz08iJAYA2UeR0Vz9+tLtdMYMtG90NhZPhqe5yapTjJa6g9FoAvNKaKQitrCtBQ41ymdim5oqMhgIZ67AgrVvqVB1jIaz7mHMx/mh3u+znWMQLqVzAF9ZWA5DuAMtlp7W2VDBtsxr2nriCvSfulVV7XPofHZXEqY8v9GHpwlnMY56e7qNqWF41G4fPaesIy6E3+B+QL6XXUupuRO4bQRAEQdzPkLBGEARhMMHLIV2uhm1PLUV9ZXoHSK0OJaXp2ytPLdEtqsmJAQ21JZpFEamMNKH7R2v5W2tLJT46e43pu2KT4WxkEEnBGp6vhFJZWwLA9VvDALSXia1rLJOd7Es5vFpbKrGpucK0Ekc51LqKWDvF/vrEZTy58oE0J6pSXhy3P1gErQ9PX4Xf60Ro2Lx9pdexxUIoHMW7DE5Hjrw8K8AorLmdeYhEpcukXY485Fktor/z8LkbTGXTz62pxs4D5gT/myUyG+WCJQiCIIj7ERLWCIIgDIa1y6IYAZ9TVKxQ6lIoxfDYhKiAZNQkj6UJgcdlN1wU4bt/tEwIuY6nm5oreMJGRPV+mqoMIpZyVCW3otOex5TDtq/timiQP4tLUa70V0mg2HXoIj48cxVfWldjaInjltVVuD00Jirccqh1FbGeg29/8Cl+ebQHL22sAwBVeXEsgtbgSAytLZVM5be5TueVO8yfVSP2NX9mLg60XZX8969+fjFWVBdj95GLokKnUjk/N16wODq1YJbIbJQLliAIgiDuR0hYIwiCMBodcyexCX08nsCHZ65pXmZJwI1/2NZs+CSPxTmxY38X/vDxRbrWIwa/dEnLhJC/n/nCGF9oy6YDTQtK5aiAdOkgwN5IY2RsAvvbelHgdaT2ycnO/oyMNb5LUan099Glc5lK6gaHxw0tcQz4nKnuozXlRZL7bkV1MYI9odR5IHSQClFzDnJlm2ZRXOTGN55empHFNtNxOfIwNq4sFDdUl6Cu3C9bxh2PJ/DR2euyyxEKw0IR3izR3QxnmVG5bwRBEARxv0LCGkEQhMHUlfuxG8qB8EI2NJWLOqM6ewcxODyueXuKPM6MSR7XnVOPgMRa/hYe1b7tcnATTDVlkbnqQNOKUjmqmPDmc9tR+0AR2jr6mdfDz0Cz51lFHWScS3Fb6xLsPNCd8e98znTfYl43AHx45pohpa+PLZuH48GbKPI4saK6WHTfne7qxze3H8kQXV7+4oNYXF4outyzn6r7PWYSHh3Hk00PJMXByyF8ePoK2jpyZ/vM4smV5Xj38CXZz/C728pdNyxjWyKRdD/yBedsiPCsIi6r0AgYk/tGEARBEPczJKwRBEEYTN0Cv6bucccv9OGZJ6oyJjh6HAoWCxCOpAtbSplorLBul9djN0QUEcJNMFnKItc1lmFFdXFOO9C0IicGcsJb8HIIH5y6ivOXBhCOxFSJakKUyjJf39uZ5mQTQ+21MTg8jqa6YhwPSm93a0slSgJu0ZJep90Ki8WSVtonds5LOe0GwlH8j5+cwJ998TN4qKY47d9OBG9i7/FeVb/HTMIRXofVsiL82y8vTPUmmY7HZYMjz4p5s9y4fjsi+TnOycl/qdBUNwdWqyXtZcO1/hGm9V4PjeJmaBSABZdvhrH6oTLYbFYjfpIkLC8SuJy37bvknZFGRQIQBEEQxP2OJZFQSou4P5icjGNggO1BSis2mxV+vweh0AgmJszvXkUQRPYQXt9aumACwF9vWY76ivSys2BPCN/ZcVrX9nFh/0rbxW8KoATrdv3N8yvw4ekrsqKIWgI+J76zrVkx04smjsn9kotlgVrE5w1N5fjtJ9czvifspBuPJ7D7yCXFjqTAvXM+Hk9kONWEJLukLsHKujmp9fy3fzmEYQUhcarwuu05u23ZxGIB1q8sx6LSQtGXCg8vLsHHF/p0i//cep5dnRTwlHIItcI6jouNiT63HauWzJmxLxq0QM/nBDFzoeub0Esg4Ek2SVKAhLW7kLBGEIQexK7vkx19eGNfp6oyTqFAAIBpwq9EwOfEt19+BN969aii00EoWEnBsl1J50QVtu86r2m7pZASAM2ayOplqrZLq8CbDbSE7HtdNgzLCGXbWpfC47Lhg1NXcbKTTcjlzvnO3kFmAZs7/4wQvQk21HTxnWo2NEmLeEKXpNaxgfVFQq6OiUYi9xtZfr/NZkVBYT4+PnsFt4fG0j4n9n0gWS48MDyG4ZEYfPkO+H3G7dv74ZgRRLag+TehF1ZhjUpBCYIgTKKhtgTxBDJC3uXggs35whFLqaMSA+EoDp66wpSJxjUFUOJ0Vz/GFbpKPremGjsPyDc4kMMCgP/2ZzpmpBlVequEcDK2cF4Bfrqnw7Dlq0Ep34nrylpa7FHlppMT1QDgB7vOQe3bQu6cV1Ny/ea+TqyoLjYlSH4m4Mu3IzxqrFMuMj6Bf9jWjODlELbvOpdzDkw+e4/3IoHM8mB+t2QpRxnr2KCUr8hhRr6mGswUiaScqX6fE1vWVOP6rRHRfxPu3xPBPry5rxO374ylfa5pcQmO/O5G2v3b40pOncTOP5/bjhfX1+rqCCt2TnhcNqxrLMem5oq05Qj3bVVpIbqv3iFBjiAIYgogYY0gCMIkTnb0KWbcSLFjfxdWVBenHoqlOkCqoW9QOnuID4tYoOSE8rrt2LqhFh6XXZfDZPOjFah9wD9tJwpS++le0P9SrKzTL66JTcamCpbQdC4sfUV1Md7c32WYSKLVgs+dX6yEhsfR2TuoqSNtLiMnGrDwp59fjECBCwvnFWDb//MRjKyJ2Hu8FwvnF8Lntue0qAYon4c79nchnoDo/UEovsmh9kVCtkT+bKxPrsQ9FI5K3nvFxE2pMVosO1Hu3AtHYti+6xycjjxEeWMg62+W2paRsQnsOnQR+9p6U452sX0r7FRr5rElCIIg0jE3YZUgCOI+JR5P4M392p1anIuGT0NtCf5hWzNaWyo1LbOkyM30OSWxgOW32fMsuh09XpcNX3i0EnUL/FhVPxd1C/zTSlRj2U8/eOccTgRvyi4j2BPCsfYbCPaEMDERT/vveDyBE8HkZCwXRDUAyFM4Rl63HSuqkw0AWLovZgNOtOWEJRYGR6KpIHk1eF02fOPppfjG00szvuvLt2NlXbHEN9NxO/Pwp19YjE2PLFC1fjlGxiZ0iVaBAhfqFvjx++tDhopqHK//ugMDw2PKHzSYTc0LsPqhUsOWNxCO4vW98m7SHfu7EI8btxM50UZ4vXFC08mOPsPWZfb6uGXrOVd37O/CxERc131aiqjgxQLLb2a5X3CO9rcPdonuW+E1Z9axJQiCIDIhxxpBEIQJGCEYSIlSH529pnpZAZ8Tqx8qw94TvYqZaFyGjBjxeAL72+SXARjj6Nm6sW7aCWn8spx4IqG4nxIJYPuu87A+bclwFbA4ErQ0ADCTlXXFOKHQpGI4EkuVG+dCKWWAl420rrGcOfetyONUVabdUDMbn11RliYQxxMJ/GxvZ6rBQHg0hvZLIab1R6KTCHhdCHhd2H20R/HzbkceIgpOQr1wx9Os4xoejWF4JPvNGOoXJBvKHDx11bBlKsUDqCnLV4JFtBG6pHN1fXpfWnGwxiMYidxvVvPMsPeEui7ERh5bgiAIQhwS1giCIEzAiImlUJRiFbXEeH5tNWw2q6IIwJXoiaG23HBwJIrGmpIMMUiJ6djJUyoXh5Wf7u3AskWzYbNZU8sTO07C/ZgroloyW6gGk/GEorAG3Ls+cqGUkn/Ob2quYOok6nXbMDA8hmBPCCuqi7GtdalivtvF62Fsa70nqiVLxTObeqg5poMjUTTVzYHf51S8LiPjk6aLa9zxNPO4ckHx2RJE+C8bsrlewDiBkkW0MVLIM3N9RrpcWeMRjELuN6s51mrdoEYeW4IgCEIcEtYIgiBMQO/EUugc05qhZbEArzy1JCVSSWW1CcUsofuKy45RQ5HHie6rd5gmAVtWV6HA68hqjppRodpyuTishEdj2Pa9j/D5RxZg0yMVppQnmcHSygA+t2pBat8Fe9jcVtz1wZVSTkU5qJiAa7Va8NLGOkUH2nBkAq+9dwFAUmx5fNk8xVwt/uTWKNeNWtecmaIaf8xiOa5qBXcOLjcqW91u+cIr63qddiuiMenucz63namhjVECJatoY5SQZ+b6jHRDssYjGInU9pv9kiEX3MEEQRAzGRLWCIIgTIDLa9LqKHps2TwcD97ULGpxfKG5Aivr5qT9TambnFQJohq4SfZxmfwwPgVeB1bVz1W3Eh0YFaptlEACAJPxBN49fAnvf3wZsWnSEn7Dww+kuSBYBBW+AKO1463w2hJ2j5XC67bjkSVzsKK6WFJIbagtwYamcuwRCS4XIxSOYtehS0yf5Sa3RrhufO5kYxDONae3uYle+AIUy3F95akl8LkdqTHozmgUr77TLrsOftmumt/LiajxRAI/eOc8k6AnJrwqNZGxWID1K8uxqLRQ9re/uL4GOw906yrLVwOraGOUuGPm+ozaRtZ4BKOR2n6zXzLkgjuYIAhiJkPCGkEQhAmozWvi8LpsSABpE3W1ohafubPyJbdPrCyEtQRRCW6SbfQEywiXmVKnTpZufBzByyHDJ0LTRVQDgNd2t+NL62pS+4tFUBGWG3NixU/3dDC5eABgbUMZDpy6msomYzk9W1sqsKm5UvF8iccT+PiCOWHf3HluhHskHInhR7uTQhQnCv/Dtmbsb+vFzoPdupcvhVDElCrdZnXH8um5HpYVNPnnDv8FwcDwGO4MjaO3P4xoLI5FpQVYUOJDeCwmMk5YZF9UrGssUxReufXeCkdw8eoQAAvm+N1Y/VBZqpxb6bdbLRbNZflqUSt45/L6jBKgWOMRjETuN6t5yaAlXsGoY0sQBEGIQ8IaQRCESbDkNXmcefh8cwUKvA7cCo2JCnF6uuupeUtthPtKOGk2coIl5jLzue1YpeBA4mNkqPbJjj68+m5mRtb9xODweIYYqUVQaagtwbJFs/H/+v7hlFgmhcdlwzuHLzFvo9dlw9aNdcxiqVmdSvnnudHuEU4Ubm2pRLHJ5W2vtKY7zeSuOyV3rJBnV1ejcn4hXt+bLrJKnTtSLwjkWFlXAqvK81PIvfX60bJ0vuhnlH67lutEK1oE71xdn1aXK4fXbcfWDbVp+9frtiuOO3ycjryMzp8sKP1m7pz48ftB2eeG9SvZHbUs6yXkiccTCF4OJaMOLEBduV+2S7nwBWBVaSG6r96RHQNZXhoaFV9BEIQ5WBIJMxqiTz8mJ+MYGBgxdR02mxV+vweh0AgmppEjgSAIZaSubyl3lJAirwOxibihYfQBnxPf2dbM/OAV7AnhOztOa17fltVVWNtYnrE+pX2wrXVJRrmqEJb9yFLKyfobV9XPwewil+QDNOtxnY5klFkyuCPEzjUtkwCW/ao0qfXl2/HYg/NgsVoUJ0BiHGu/gR++K1+SqAW++BiPJ/DN7UdUCXiffWg+2i70K7r6jOgUu6GpHB9f6DNM8FF7LmRjApkrk9RsbofYywkzm8WYuT653FGucQz/OvC4bFjXWJbmWj0R7JN1LwqvJa5JS0NNCTp7B3Gqsw+HfncDY7zxiHOd87+n9jfH4wnsPnIR+9quSC6HpWv0dGwElE3i8QSCPSEEe0NAAqhb4EfdA0mhnnPCtl8cwIkLfYhNpt8EnXYr1jc9gJryIoTCUXx6dRCh8Dhu34ng+u1RTMrcM4XPKizRFGKf8brt+PL6GsXnp/sdmn8TegkEPMjLsyp+joS1u5CwRhCEHuSu75MdfYpvoM1ATUkjABw9dyNVWqYWJRHv7YNd2HuiV1SgURLE1IoQcr9bi2jicdnwEs/xpEUUmQ5YALzSuhQNNekum4mJOL73H2cVv/83z68wpOvcyY4+vLa7XTb83cxt0SMwt7ZU4qOz15iEBLXi7JbVVYaVeAZ8TvzR6iq88evONKcOf1uNbO5hRJ4hYQzZFhTNXJ+SMNbaUomSgFvUNXRndBw/fFc+b8/rtuOVzUswFBlX5SICYMhvVtp3WpxRRBKp5zKnIw82qyUrz2vfeHop4gnInsPfeHopAMjeKzY0lePZ1dWGb99MgebfhF5YhTUqBSUIgjAB/gNv30Akq6Ka1rfU4dFxzeuUKzU52dEnW7ailG2mtjRPrJSTK+X4pPs283I4RsYm0rbPrFLBqeaV1iVYWZfc/3xR6lj7Dabvn+7qN0RYW1FdjHyXHdGY9n2sJ8NMa4ZTwOfEpuYKbGquYJpUKwXhC5ft9dhVbY8c3Piw8u75LLatWsos+SRdN5dEy9u15BkSxqD3uObK+uLxBHYekC/r/+jsNfzDtmac7urHt149qvqaHo7E0H11EJtbFkp+Rur3GfGblfad2L9n89gaCffMNDA8huGRGHz5Dvh97OKgmlLK01392Nd2RXQ50fFJZOvu/uP3gxiNyj8bvrmvUzFod8/xXlTOL0zdvwmCmBpIWCMIgjAYuRIVs1CbNSaGlom7koinJrdNKttMrUgyEI6is3cwNcEwyjH45r5OrKguNiR4PpfwOPPw0ucWSx5D1jywfW1XUFNepFsoMUK41JNhpjXDiS8us05uuRyu3UcuynYWfX5tNTwu9denz22XzSozS/Q42dGHN/Z1YnBYXqxnzTMkCCEs40QoHMWr757DiWC/5vXsa7vC1PQk1zHDOWimq5WDxd2qtZRyqmF5JgkpjKEcr/+6Aw01NJYSxFRCwhpBEIQEah4a4/EEftd9Cx+dvIy9KkKF1SCXdRWOxFLChtYHq4DXperzrS2V2NRcIbs+NV0zhYIYhxaR5PzFAdSUF+F0V79hWWih4XF09g7qDp53OfIwMRnHhFwIi4k8+9lFGB2bYAphBtQ5uIwQSvQKl0Z0wJNyk3nddiQSCV35SUKsVgs2tyxEabFXNo8qHk+odtI9t7YKAa8rq1ll4UhMtrSJj9Q1TxBKsI4TekQ1ICl+dPYOoqa8SDaPK5fLL80oxzZqmUol8UruVpYu34B8KeVMIDwao7GUIKYYEtYIgiBEUPPQeLKjDzv2d2HA5DehX9tcjzd/3SUbXq5H2FBbAvebs9ewqblC8t85p5gaxCZLWkrzfnmsBx+cvgKLQgmFWgZHomiqm6OpVJCDC7peWVuMM923EZvMbuZHkc+JJ1c+kJoMcpNGqXPGarXg4cUlTF3ojBBK9AqXRnXA43d1DEdiKJ9XiPl+FyYm4qZMpJW6SGpx0gW8LlMnWlIB6mqYaQ5QIjsY3VlXjpOd/fjn//NJWpOC3Ud7RPO4ci0/kEV4UrutRi1Tr6Od5fsspZQzBRpLCWJqIWGNIIgZjZGdCcUeGo3uDul12WC354m6Vjwuu2JHQL6wofa3q524y4koWveL2GTJarVgy5pqZhcMx2hUunOkVoo8TlVCkxyfXhvC9//yMXReGcSBk1dwuuuWQVspT99AJKP5gtxkMB5P4OMLfczL1/twzyKkyl0naieJctcJVyrJDz82M6NKadlqc9n0OveECLMjxfLT1LbEyqZAQswctGYhauHASfY8rlzKD2QRntS+jDNymWrK/sWeN5jKgRlLKXMVv9eB2GQircmMFDSWEsTUQsIaQRAzFi2lCmoeGgEwv21lZevGOknXCmuI/OBIVHOZBjdxZ80kExNR1LyF5iMlBJzs6MNP96hzvqnF47Ip/l6/14Ga8iLFZgx5VgtseRbFrpYD4Si6r95BfUUAXb2DmoQ1r9sOW55FMcuKw2m3qg6TV5t5pvfhnkXglbtO1DAdO1aqyWUzsiTNjIwiM8Q/4v5AaxZitsiF/ECWsVuty9jIZap9CSP8/P3g0HphXQ3iiQS27zov+zkaSwli6lHuG0oQBDEN4RxTwgdATkA42SHuwFHz0Ghkd8iAz5kSNTjXyqr6uWn5V6yCRd/AqKbfztFQW4JtrUuZ1iW2TVr3CycExOMJtF8awH9+9Cm+/4vf4fu/OGd6V9V1jeWKn3l8eSmTaFiQb8eXn6xlWi8ngr5z+BLT54W0fGYuvvv1R7FldRXT55XcRDv2dyEeT/+QmsmLUQ/3nMDr96WfXyzXCStax4hcgMtlU9pHaojHEwj2hHCs/QaCPaG080BqX+nFaPGPyC5y50w2aKgtQWtLZVbXyQr3nDCVsI7dasZ4I5ep9iWM8PPT2aHltMtPwS0WYFtrchxfWTcHG5rkn1FoLCWIqYccawRBzDj0lCqY8SAqhdpOnuHIuGwDAyDptvnwzDXZ5bC8Sa97wK9YZiMloqjdN/wSPqM6eKrB73VgU3MFSos9suvedegi9p+8oliSERoeZyrbAIACtwP/9qsLqreZ4/iFPjzzRBXWNpZj74leReFjfELZRSd0GqiZvBj5cK+UOaaHeDyhmP+XC44TJZT2EWs5uJxzb0V1seHO3Fx3Bc5EjO4KmU23p9y2b2quwIdnrjK7drPJVDuqWMduNWO8kcusKS/K6FwshS/fnvG8wVIO7Pc6AIuF+aWAy5GHPEFunpG4HHn4488thtUi31DhlaeWYGXdvevo2dXVqJxfiNf3dsh2eiYIYuogYY0giGmP8KE7nkhoLlUw40GUT2tLBUoC+aonNic7+hRLAQDg8WXzRUv9+LCUabCU2UiJKKz75vOrFmBJZSC1H04E+1TnqBnBC+tqYLVakp0XE8DP9nZICmOsgll+vo1JmIQFulxA/GNpVFmUcDLIMnmxWIBXnjI+U0hrnpmSiLD7yEXFidN06VgptY9YhQ+lTMnWlgpDnWqtLRXY1FyZ04IlC0YLVWau32gRzIxAfLl1yW271WrBqvo5ujMvzWCqHVUsY7dal7GRy7RaLXhxfS3Tff/FJ2szzm+W55QX1tUAkBexGmpmY94sT0an14HhMfzmzFV09g5lfOfBRQE82fhA6h7+6+M96O0fFV2+w2bBZxbOwmdXlKW5q8XyMuWEspV1JWioMedlE0EQ+iFhjSCIaY3YQ3e+M4/pu2IPhmofGlnDk/W8VWRx13DCxmScrcMky5t0qaB0pd/Cug+ffmxh6oHwRPAmfsAgHGrB40re6oRCitdlw9aNdWmNKIwS9kZHJ5iEyaFR/S4L7lgmy6IqZHO3WBBOBlkmL8K361OJ0kQ8Hk9gX5t4GLmQqXacCFHjQGMRPljcvaz7SojQXcsfN7gSQuHv4H7fwPAYhkdi8OU74OeNt1omlFoFMLnviZ1jQgcyt72hcBTh0XF4PXYEvC5VE2GpbVDbtdpIEcyMQHwpWLZ9RXWxquYq2SIXMq/0vCDL1jJX1pXgYlO5rDC6oalc8v7C+pyi9lmGe1nRvGQeJibiOHjqCvoGIygpcmP1Q2Ww2dJLOR/9zL3P3QxFACRQWVqA2T635DWvxZVtZvMcgiD0QcIaQRA5g9oJkNRDN2s3yLCIqKH2oVHps+say5hLPaVgcdckEsmJHSusb9K1Pvip2YesbjytvHQ36D54OYRgTwiwAHXl/rQ3xxMTcfx0T4dh6/TlO5ge+N899Hvd6+Ify5JAvq5lSU0GtYqsRsIyPrBMxD0uO3OZz1Q7TviwiilqhA+WPEStJVGvPLUEPreDWRR6eHEJPr7QJ7o9YuI4i+NKq1NL7nvxBEQF+HAkbIXGmAABAABJREFUhn1tV7Cv7YqkmA8ARV4H/mDZfNwKRTAWm8Si0gI8UOLD8FiMeT+JiRBiQpkZIpgZgfhisG6722HLSmdQteRK5pUZY7fRy0yWORbgZ3s701zhPrcdL66vwcq6OYrbo/ScoidawGaz4smmBwz7HB8Syghi5kDCGkEQOYHaCZDWzpN8vB5xIUrNQyP32R37uzBgguCgxl0TCkdR6HEwdbgMR9idUloe/Fj3oRHHkdWRVl8RQH1FIOP7Jzv68JM90uWfWvD7nIjHE/C47Hjm8UWijpV4PKGYh6eEUAjrGxAvRWFFbjJoZuaZEizjA+tE/A8fX8S0To/LZrjjRKt7So3rSI3wwerIYxlTOOTGPrnfIedYEVu3kuNKucS1EiUBt6gbTe57LGe73L4aHB7He7xmJcJuwErimVLJ45v7OuF22jA0Oo6h4XHdIpjwnB0YHpNdHodetyfreRzsDelajx7sNiscNmva8c7FzCszxm6jl7mybg4aako0L4/lOYVELIIgzISENYIgNGFkvoyWUhUjOnIGvC7Jf1Pz0NhQW4KVi+fgWmgMvdfvwOe2GyY4dPYOMk9odxzoYhaH3jrQjYaaElNFEZZ9aMRxZHGkSSF17ukh4HMiHBnHN7cfERWCuG3q7B3UHbjNF8L0CHWsk8GpmJiwjg+sE3Exp6oY6xrLDb0+tLqn1LqO1DRgYXXkrWssl81uZMmONEJEF0PMccWyLv7vKfI68MTy+Zjtd+P1vZ2y3zO77yWLeCb7/eFxfHfnGVXfkTpnpEpeWZA6t1ifHVjP41uDEabPGYnLkYcnV5Zj86PJjqT831NVWojuq3dwrP1GTmVgmTF2G71MEr4IgpjOkLBGEIRqjAxC1lqqovdtOEv+iZqHPKvVgs9UzUbZLDcmFDovqkHN71TjuMpWMLvSPtRzHFkdaVKwZNdpoWlxiWhpKycEceXBegTFZKbekrTrTatQt2V1sqtoLkz+hKgZH1jPJa/HrpgB6HXZsKm5QnKb1L5U0JNzpbb0Tk0DFtY8RK5rrp7SLyNEdDHExjK16xocHtedTTidETtnpM5Zlg6OUvdXqWeHLWuqMsqGWc/jY+3ZyVeTE4+5c+9kRx++9erRtN/ndduxqr4EswvdqcxATnxj7eQLaMsXJAiCILIHCWsEQajC6CBkrXkterOPciX/RI54PIEhnY4mOXIhmF3tcWysnY25szzMjjQh/EnL+d/f1pwfJcdvP7ku++9cDpNXRSaekGSmniPtb1qOZ8DnzClRTU+HX9ZzKeB1KWYAbt1YJ7pPTgT78PreDlUvFfTmXKlxoAHqGrCoyUNUW/olPJZm5mAJ91EujG3TBTERTK+7UOz+KvfsIHwRwYltrM2BzMTrtmHrhjrF5xqp3zcciWH/yauy3+V+7/VbI9jXdiXtvmTPs8BqtSAai6d9XmzMmepOtQRBEPczJKwRBMGMGUHIaieNHCyTR6/LBrs9b8rC1vUg9mbfaHIhmJ3lOHJsa12qq/OkEfvU67IhAfkMJVaxTm+mm/Ca0HI8c0lg1tPhd2BoDEVep2IWGF9QUhu+feSTa/iXn3+S8Xellwp6w97VONAA9c1DVlQXo7WlEvvaehWzolhdvGLHUo+QrIRwH+XC2DZdEBsDWB1/Xrc9bRyTun7UCnWc2La+qRx7dZTF6sXntuMfv/FoRgdIIXqFSDFxkSM2mQAmExmfF445Ytec025FY20xtm5YrPgbOEicIwiC0Ma0FNYOHDiAH/zgB+ju7obH40FDQwP++q//GuXl5VO9aQQxozGjG5jaSSMHy+Rx693srenykMg90J7u6mduWKAVllLYbMByHAFgW+sS3aKaEVlqn20oSwsen0qE14QakVJr6TYHy+RLzQRNb4ff1/d1Ymxc+bN8EUGNAyseT+CHu34nu2yplwqsLw/aLw2Irp/luPry7RgYHkOwJ4Sa8iLm5iFik3GPy4Z1jWXY1FypaayUc+6YgdhYpuZauF+RE5FZz9nn11TD73MqXj9ay4B/faIX65vKceR3N5hKUI3mKxtqmQQps8qcleDGnNNd/aLXXDQWx+FzN3Hk3E2sbyrHs6urZZdnZMwHQRDE/ca0E9Y+/vhj/Nmf/RlaW1vxl3/5lxgcHMQ//dM/4U/+5E/w3nvvweWSDiMnCEIfWt1lcqgpWxLCOnmcDmG42XCo8cklpxJ3HH/8flCxs6cWjAxNP3jSHMHT57anTRwtlmS5pxRi1wSrSAkACbmFK8Ay+VIzQTPi+CiJalIiAqsDq+NyCLfvyHdDHAhHEbwcysj5Y315sPtoDw6fu5Gxj1iOa3g0htfeuwAgfT/LCYdSAtjI2AR2HbqE0mKvKZmZRiM2lqm5FqYrQrcYC1tWV6HA61AUulnPWb/PyXT9aL2vJRLA3uO9WNtQqlhOaSRqne1TVXo8EI4i2BNSvOYSQKohhpS4ZnTMB0EQxP3GtBPWfvnLX2L+/Pn4u7/7O1gsyQeCQCCArVu34ty5c2hsbJziLSSImYtWd5kcasuWhJjRRj4b8N08fQMR2Y57RpKrpbDccdTS2VOOeDyB/W29hgmWZmSyAcBza6sQ8LpS53A4EsP2XeqvCSmxWcjg8Di+/4tzeHTpXNRXBOD3seVlSbkp+ZMvAKomaGa6PTwuG7a1LkXdA9LnEYuzjrUpxPZd5/CSQAhW456S2kesx1VsGWLChxll/QD7sRQKyUo47VZYAIzxcqaUxjJun722uz0tn2omEPA58e2XH0kF4Be4Hfi3X11QfEHFmqeoxSUpt1zWTrxSHPrdDebPqj23+KxvKseyRbNVP0NMZelxsDfEPH7uPdGLLz62KMOFZ9Z4QBAEcT8x7YS1iYkJeDyelKgGAD6fD4C+N/AEQSijx10mB6vzTIpcb9EunLiHIzHsPJA9d5pcN7Ncwmq1qO7sKYdZLkCnPQ/RmLRDSouTJOB1ZZzDVo3XBF+k3L7rnKwYePjcDRw+l5y0SjnK1OzHN/d1Ju12MggnaGa6PUbGJhC8HILVYhE991mddUXe9EYRcusTCmNa3FNik1j+S4RQOIodB7pkzzO5ibAZZf0A+7F8bm0VijxOxfPTgqTbhhPGfG47Vi2ZgxXVxRnHU0wgjScSOSeq5VktmIxLP68GfE40LS5JOYzEeH5tNWw2a9qx0fOCSmzfaXVJiuH16MvXGxufZBpX/V4HtqytkX0pIUagwImXn34Qi8sLNXX1ntLSYxVTn0QCOHjqCp5seiDt72aNBwRBEPcT005Y++IXv4h33nkHb7zxBjZv3ozBwUF873vfQ319PR566CFdy2YN9tRKXp417X8JYjry4vpa0QBvji+tr4XDwRY4zufhJXOxcvEcdFwOYXB4HEVeB2plXCa5htT1fSLYhzf2dmBgCh64AwVOfOnJWl3ZZLlOPJ4QPWdOBI3JVBNDTlQDgD/+/GIAwP/e3c7kcPO67aivDGSc66zXhNg+sNmssNusqhx2nNPpz595MHXOqN2PIQZn10A4ik+v3cHiuwLqrAJzIxx2H+nB7iM9CPic+NL6WsXfJrYf6itnYVahS7EclGPHgS6sXDwndaweXjIX1jwr81gg3Ed8li6ahQuXBhRFBrllsDp6wpGYqmcj1mNZXOjG4ooA/mRTvez9RKgZhCMx7Gu7grqKQNp9Rmyc9XsdiDDk7rHgcuQxZfixMBlPoLWlEn2DEYyNT6C6rBAPzCnAcCSWdo1XP+DP+E1yY7rUOaZ0HxDbd9y18ufPPMh0zopdM3yKC92K+0WJugVFaAv2y37mxQ11WFlXApuN/Vp7+rGF+OLji1BUlI+hoYjm7VN6NjKDQIETSxbOwu6jPczfuTU0lnFNmzUeEEQuQPNvIltMO2GtsbER//qv/4q/+qu/wv/9f//fAIDFixfjtddeQ16e+sk8h9Vqgd/vMWozZSko0P+AQRBTxZOPVMLrceKHu36XNsmcXeTGf3lqKZofnK9r+c2zvHo3cUrhX99SXQTN5IX1tZg/24tAgQv1C2chb5oIk1o48sm1jPNwVqELf7p5adI5ZQJK2We+fAfWPFyBPKsFc2Z58f999YjiMjc/tgizZM57uWtCah98rfUziCW0Hfsd+7uw5uEKADBtP57vGUTzimTDoYcL8zHrvXZF0Uq47wu9DtxhLNEEkkLTv/z8E/xfW1fi4aXzFH8btx+4a+hrrZ/B//jJCbZ1DUVxLTSGz1TNTv3tyUcqsebhCry5N4i39yvv11hC+rkkdjHEtB1SyyifV8j0/fJ5haqejViO5ewiNxo/U4qOSwNwOu14YX0t3v3NpxiOsIvAP/5VMHVsJLu1qjg3xHA7bVjbVI6PTl3F0Ii+ZQk5dO4GXvv/rJMdn7nzpf33tzEwNMY0prN+ZzKeQPvvb+PYuet497e/z1gO/1r53/+/9Wj//W3cuhPBa+/8DkMj8i5J/jXD8XBhPgp+cU7XflxY5se6hyvwr/9xBuHR9G3w5TvwZ3+0LPX8IdwP124NY8/RSxgYuie0iT2z6Hk+l3o2MpOXn34QDy+dh1m7lcdPjor5RRnXtFnjAUHkEjT/Jsxm2glrp06dwt/8zd/g2WefxRNPPIHBwUH8r//1v/C1r30Nb775pubmBfF4AkNDowZvbTp5eVYUFLgxNBTB5GRulSYQhBoWlxfiH7/xqKiTJhQamerNmxKE13c8nsCr/2mOqLbxkQX4+NwNRVfC0B1zxzSzkXKjAUmXhdhk+vadMfz9z9pM2yalxIHw6Dg+PnsFiysCKJvlRsDnlHVNeN02PNlQmnbdyP1uPnL74H/85AQaNToVbw1G8PHZK6llmcG7v/09Fszxps7XF9bVKIrQ3L5f31SOh2qKUV1WhL/+/mHVbtBXf/EJ4hOTir+N2w+LKwLIy7Oi+cH5+G/PLsOP3j3P5ATsvX4HZbMyH+QXzWV7eWC3JCTHU7uFrf5Lahnz/S7FczNQ4MR8v0v0+3LnqNKxXLm4BP/lb3+dtm6F6uEMwqPj+Ml75/CF5gr869tn1H2ZkSceKsV7vzUn+5J/bilRNsuNslluxOMJfHz2CpOjm/sOkHkfUOOifvUXn6C2tABls9wIhyOyoprS73pkyRzslSlvVaKyxIPF5YX4l//2GC70hHDh0gAAYPECPxZXBESfP7j98GClH082lEk+sxj1fM5/Nnr/WA/OdN/WvCw5uPv94vJCDN0ZZRo/geR11lxfkrGf9I4HBJHL0Pyb0EtBgZvJ8TjthLW//du/xapVq/Df//t/T/1t+fLleOKJJ/DOO+/gueee07xsLbkKWpicjGdtXQRhJtVlRan/H48nEJfJjblf4K7vYE/I8PJPfr7WH/7BQtHA9ZkytshlX62oLsbreztMWW/A58Rza6ozMvD8PidGx2JMeU23h8ZSx+F5hZyirRvq0q4d1syveDyhuA/agn2K2yr3G7Tg9zoAi4Upa+iNvR1YtnAWrFYLVlTNZg7nP3GhD3/0RBUA5f0rxsBQFOd/zzbh5R9LAHiophjbnlqK7751RvG7Prdd9HpcNL+QKaty0XzpvCcjlqG0755fUy06riudo1LHkssOe1+kbE1LRO7uwxfx/rEew0o0OVyOPPzx5+qw80C3ocsV8rtPb+P20BhT9qWaLrtySHV+lGJgKIr2iwOoW+BnHhOE1wzHskWzNQtrXpcN1WVFqeXWlhehlpflyvr8ofTMYtTzeXVZEaqfKcKJYB9e39shWWrpcdmwrrEc82Z7FHNX3Y48tDw4Ly1fkNtW7ppTatSxfmXSJSz2G7WOBwQxXaD5N2E2005Y+/TTT7FmzZq0v82dOxd+vx+XL1+eoq0iCIJIx8hA9nWNZRlh3bnesEEPUpM/Lsfn0aVzTQuJ5oTLhpr0TrPxeIJJTAHSO8StqC5Ga0sF9rVdSXM5+X1OPL5sPmKT8VRXvdNd/czdNM3spCn8DWp4uH4OFpUWMk3ehWHYDbUlcDtsivuZ/z013TLTYHRIie2HugV+XU1c9HZCNmoZWprGKF2brS2VKAm4UeRx4u95XSuLPE5UlRbiW68eldwetcQmE4hNGiuqAckS0HyHzfQgen4ulpxIprTPhR1kpWDp/CjG4Eg0WdXBWForNXboCfjfurFu2uSt8llZl34vKXA7AAswNDqeIagKPxdPJNB5ZRBIJMccuc7GwL3mJj989zxOBPvS8gktlqSo9uzqatnv62kiRRAEcb8z7YS1+fPno729Pe1vV69eRSgUQmlp6RRtFUEQRDpahQk+9+MDLcvkj+tiqQdhXpdwXwuFy2Pt7OvsuHxPKBNOUjwuG5ZU+NF55Q52HbpXZlbkdSCm8CaV3+XRzE6aPrcdoXAUhV4H8p15GI2yixfHL/ThmSeqsK6xDPvarih+Xvg7hiJsk3f+9/jdMtsvDTAFedeV+3HYd0OTODZVopZZy+D2HV8A6756B8fab6RN/lmuTf45zYlFq+rnAgCCPaGp6ZqoklA4in81qfGJ3DrFRDKWfS7X/ZWPVjG+b2AU39x+hOm7egVl4Xjj9zrwwrqaaX0PZH0JJva5pQtnqV7XK61L8acTcRw8dQV9gxGUFLmx+qEypqYDYuNBLncSJwiCyCWmnbC2ZcsW/N3f/R3+9m//FqtXr8bg4CC2b9+OWbNmYePGjVO9eQRBEAC0v51vbalASSB/Rj/QxuMJyQd3s51YHK88tQQ+t4N58qBGKH3n8CW8//FljIsIZSNjEzgu0tlukLGbJufUMkK4lSIcieFHu9uVPyjCQDiK/W29WLZoNpOwJvwdrL9L+DluUlpTXoTD55QFs7oFfl3imFmiltprvqG2BMsWzdY0iRajszeEH753Pu185AQyj8uu6toUikVmisFGo9T51yyEIhnLeCh0fkqhZf877XnYdegS8+f1Csok6hiDzWbFk00PaPruTHbDEwRBmMm0E9a+8pWvwOFwYMeOHfg//+f/wOPxYPny5fif//N/wu+nGwFBELkBy9t5PveLO00pK8ioybdUVprW/axWKBUT1YyA2z96yqrMZufBbvh9TnhcNtmQfzF3C8vvMqrMUq84plcYkxOYWRG7nvae6GXO3hL7vhBOIFvXWKZq2zg4schMMTgX8bpsSABMjS44OJGsprwInb2DaOtgy0lkGTe17H9WgdFIQZlEHYIgCGI6Mu2ENYvFgueffx7PP//8VG8KQRCELHIT9+fWVKlyTE1X+OJB30AkrVSMg+9sMWryLZWVpnU/qxVKzYLbP7myPVKwCH5i7pZsllnG4wl4XHY88/gihEfH4fXYEfC6VJ0jLO4OMQFNqkx4XWM5NjVXMK1fb/aW2iD7Y+dvMn+WDycWVZUWZpRgTwVmboPPbceqJXNSmZjxeCLlJhwdm8CxduV9eLqrHz/a3a5KNGcZN80S47esrsLaxnJV4yq5ogiCIIiZxrQT1giCIKYT93NmCYsbhs+O/V349suP6Jr8KWWl6aGhtgRNdcWipZzZQOjU0hzcL8Dntkt2rdOLy5GX0bWRC9KWEn3UOMmkXF9K152cc9KIa5PbrtNd/Th2/mba/pVy8o2MTWDXoYvY19aLlzbWyYpierO3tATZhyMxzefK4EgU3VfvTLmoBiTPvT0au1NK4XHZsK11aVrAvNrxj4OlhJqPnIOTD4to7bRbmTof8ynwOqbsfqbF9Sn2HYIgCILQCwlrBEEQJnM/vp1X64YBks6Wg6euYMuaamzfpd6JJeecMKrsbqpENUDcqcUXkE539aualD+4KIANTQsQCkc1Z6opIRTVgKRbaM/xXiwqLUxzj/GPz4rqYkVBWqmsWOy6i8cT2H3kkqJzUk9JtpKgolQaODI2obgderO3tGYZrloyR7XwAyQdVdnMWHPYLLBaLBjjiUR8YXZRaaFuQZrPSxvrUF8RSP23lvFPK0oOTj5yovVjy+aLXhdKTFWJr9L1z+oU9fuceHF9LZ58pHIqfgZBiGLEMwtBENmFhDWCIAjCULS4YTi4fK4NTeX4+EIf88Q34HNKimpKEzAW9PwmtQgdTUr5RZyAxIX3swgGK+uK8fLmpbBaLQj2hAzdflY4R5XUZFfu+GgpgzzZ0Yc39nUqNopg6bIoNekxUlCR2w5WkUrqc1pFLq7E8Sd7OjDM6FzjHFWdvYOa1slHKbePY3wiAeCePc7rtuO5NffOJ67pw1/802/SxDe1SLkoszFWaM2LlHJzHg+qL/VldcsZjdL1L3b/EHPPct/5l59/Aq/HicXlhaZuN5FkQqZrqdTYapQ7keU7wZ4Qgr0hIJHM/OOcqHrELpblhsJRhEfH0T8Uwcfn+9LGWK/bjkd4ZebcekmAI4jcgYQ1giAIwlD0dvYMhaPYc7wX21rvde7sGxiV7U4ndG3wS/HEHDZq3UnZ6lYKAA57HrY9tRRDkXHFB2X+Q3WB2wG305aRGbZwXgF+9usOtHX0pcq8TgT70X31CF6424lvKhohDISjmtxjWsog1QheSl0WTwT78PrejrSSSL/PiS1rqrDzQDfTOvRuh9buqWq/z4cTUaxWC5Ytmo2/+v5hprJQ7tpkyfjyumwYVhH2z8pwJIbtu87Byjufuq/e0SSqCXPUhNem2WPF6odK0VhbomsCLebm1HJOqHHLGQXL9S9W6ismqvH50Tvn8N2vN6fWQWKFObx9sAt7T/SmlYW/9UE31q8sF3WS+tx2NH9mLo4LhFKHzYoNTeXY3LKQ+YWaz23Hl9bVoMAjnm97sqMPP34/mCbe7z7aA4/Lhj94cF6GWMvygi4eT+Ddwxfxq2M9mJi896PllivGcCSGfW1XsK/tSmq9AHS/NCQIwjhIWCMIgiAMxaiSr7cOdOM725pTD72lxd6Mh14AcNmt6OwdhMdllyz3kYLFnQQY95tYCIWjsFotWFU/F8C9N91i7ii538k9YP/u4m0cPndDdD2ceDVVjRD2tclnXYkdH7VlkFocRFLHe8eBLrx/tCfj76FwFNt3nVe1Dj3bobd7qpYge76IYrNZ8ZUNtbLnjNdlw1ZeVhxLxld9RQCFXkdGLp3XbcdwJKaqw6YYP9nTkTqftF7T4bsTXCmxxeyxorG2xJRoATXnxFR2sTZLuLw1GEHH5RCGRsZJrDCJtw52Ya+I6MnFAwCZ/xaOxES/Mz4Rx7tHevCr4714+Qv1acdG6kVKOBLDD95NH6f5IpXU2DQyNiEq1iq9oDvZ0YfXfnkBUQlRV2q5SnDrlfs3vZEGBEGoh4Q1giAIwlCMytwZCEcRvBxKyy4Sm1iPxeKpN7mspWL8dci5kziynSPUfmkg1Un1o7PXMiZ5Dy8uUXwg5x6wPS75W/2O/V34zrZmQxohqEXpWIkdH7VlkFom4tzx5pwr4UgModGYqKhmJlLnnd7uqWq6ykqVO3pcdqxrLMPR8zfTSpbkups21JZgQ5N084Djwb7U/+dKn5ZVzcZrBmUADkdiqTFF7zUtJcqbOVaoKb1kdV3xy9DqF/hFRXiOdY1lkk69bGGmcHmqs19UxOHG0taWSuauvfc7wvMvNBIV3bd6mZiIpwlJal+kcMc235mneRvExoJs5iyybhNBEOZCwhpBEARhKFrcMFJs33UOL22sw4rqYqaHZS2OFv5ETSoHxcjfxMJuGQGHK5VlhVW84rKXdh36PXYf0Scg+X1OjMcmZdfNKoIKJ9JqyyDVTsT9d8ULrV0djUJJRFHTPRUQbxAh9n2/z4nHl81HScDN3DSCj91mRWmxR1LE+fhCn8i3MuFKnzwum2IunhqCPUlhTe81LSXKmzlWsJZengjexM/2dqYJnmKuK7XneE15UcoFKuaiBfSXUSp930zh8qMz12T/fdehi9jf1osXn6yVLCdk+Q25QDyeQPByKJmxaQHqypM5nUZs51SMnW/eFZK0OhpHo/KlwnIIx4JsZrKybhNBEOZDwhpBEARhKFarBU2LS2TfTj+6dK6sM4KD65DY2lJh2kM6N1GTy1d5aWPdlJVLZgNOfLJaLahfEFAtrImJMae7+mX317rGMtncPA7hRFptGaTaifjjy+Yrbns2YBFRpILohd+Ta+DxD9uaM74PIPW3zt7B1N+kMvH4DA6PS5YiaZn0aulCKsvdXaPGtSeFmGhrxHKFqCm9fPtgF1PZmhZHzY79XYgnEth5oFv0XAL0ZT6xNJoxU7hUymEDgOGxCclywobaEmZRk8NMEU6uyUrGvQ737nV6uyJPxdgZuiskZTO2gQ9/vdnMZJVjqvYFQdyvkLBGEARBpDDiIf9tiRwVjg1N5XjmiSq094SYHz4Nn1zfJcBzJ8nlq3AT0taWCiYxaLrBF59qyovgc9uZguk3PbIg5f4RK/uTc1StqC7GR2evq84JU1sGqXYiPtvvMtVtYLEgLbhb7N9feepeWZPS9SgWRM9HbQdVMXHD47IhkUiocnWIlSJpmejpzVUTksfbfVLnKCtSoq1SySsLrS2Vkq5BKU4E+xTXuWN/F5Ytmq3pHB+QyBI0IvOJ9Tw1Q7jUC7eNy6tm4Uz3bcl/b22pwKbmyrSwfLPy3KSWLRcjwL/XaVn/VDu1uHFyKuCvN1cEranaFwRxv0LCGkEQBAFA+kFcrixMyIngTcWJ3fELfXjmiSpVkyOjJ9ccz6+tRjyewI/fDyp+9s39Xfj7lx9RFIOmG0Lxymq14MX1tdi+S/7YBHxOtP6BeEc2DiVHldacMDVlkGon4sMjMVOO74MLA6ivCGDnQfnOoYlEsnudEZNutR1UpcQNLdefWCmS1ome2uxEOX5z9jq+8Oi985Y7R4OXQ/iX//NJqnOuEnKlumpKXsWWq6UxQDyewOt7OxQ/NxCO4uCpK1kfw+Qyn9ScpwBS+X7CJhcBnxNNDPmTZiEmqvHZdegSPjp7XTYs34jweTmRkmXfvLmvU1M+11Q7tbj7C+uLIaMQjgW5IGipyWMkCMIYSFgjCIKY5hjhMpN7EOeXfimVtPxsb6fiuviZXt94eqlop08xjJxcc5NXAPir7x9mWm4oHEX31Ts555bQS9PikozzZWVdCS4qOG5Y857kHFVqc8KE32Upg+Q+u611KX7wzjlZt1jA54Qv36H4m7Twye8HknY0Bk539Yu6NNVOutV0UK0pLzLcbSJ0boQj44qOPTHWNZYrlqCyEhoezxD8rFYLrBYLs6gGyJ//agQGv9eBr26qx9DouK5SQK7JBgt9gxHVy9eLXOYT63n67uGL+ODU1YyOsY8smZPWVKFyfqHitT5VqGkqo0XcMsI1JnaNsDCVTi0uG5P1xZAQr8uGYY3PF8KxINuZrCzbRBCE+ZCwRhAEkcMoiWbZcrVwyE3sO3sH03Jl5OAewBtqS+B22PDdt84ofmd8gn3SK8WDi2ZhQ9MDTBlgYgyORLGqfi6TSDNd4ByEwofwZ1dXo3J+QUZekFZHjRRqBDIhSmWQaeupKcbm5gq8c/iS5GeeX1sNj8vOuumq+eRTeUcLx6HfXZf9d9ZJt5oOqma4TfjOjZMdfaJlhEoEfE5saq7A2PgE9p7oNeSaE9svrPuKJYdKjcDwwrqatM7HWlGzzpIit+71aUFqG1m3/V2Ra5drcsEfM3xue86PzVo6IrNg1HWsRSTT49Ra31QOC6DZbfgCT0hieTEkZOvGOgAQfdHnddvR8pm5+PhCH9MLoGyVK3tdNtjteapfShEEYQ4krBEEQeQIQhEtHBmXDImWC582w9UiRG9+Ev8BvG6Bn+ntbswAYe1K33CqPELLW31uu82YuKl18mxoKs940NeC3ARuZd0cNNSUmN7hTo1ApgWlLnX8yUg8njDVbWABoHSYIwpZZqyTbjUdVI12m/BLkfS4aJ5fW43TXf2GlveJ7RfWfbXtqaWor5QXwliX1dpSadgEmHWdPrcdqx8qw94TvVl31EhtoxGlc/x7Uq5kXOlFy+8w6rdrOSZanVovP1WPhxfPBQDRlzlyuBx5+OrnF2dcR1IvhoT3WaEQtaK6WLQzuNVqwTNPVDHfC9U68rWw9W7H9FzvQEsQ9wskrBEEQeQArO3pOdFsW+sS7Dwgn9VktKuFj9jEvm+ArbzIl2/PyPTKVnklt90AVD/8+3lCgRkTt/Ur2d6w8ycC3IP+rXAEb/66E2Pj2sRHud9jhuhlZic8IUpd6oSB4larBVvWVKsuJWLFKD2W5RxU00GVuy6Mgl+KpEW85ze5+Ob2I4Ztl1T2EOu+YrkWWJbl9zqwqbmCZZOZqCotZMqWenF9DWw2a9ZL2uUyn4wonePfkwrc5pRzZxst4pYRIqXf69CUz6X2Xi7mrhK+zOkbGMWHZ65hcHg89Rl7ngWV83z4QnMlFlcEJO8dYi+GqkoL0X31juS9x2q1oL4yICqeq70X8vMbL/SEcPvOGGYVOrH4gQBGohPYeSAzAqFpcQl++8l1WTFOuN/MfClFEAQ7JKwRBEFMMVra07++t1NxAjUQjmJ/Wy8KvA5Z8ULrgzh/Yn+yo485A+nFJ2vTtiMeT0iGUZuBVlGMX2piZDgx/yF5UWlhZmmv14HHl89HSSA/4zharRaMjMXwi48uahbVgOyGLZvZCU8Ii1PqN2evY1NzZdrffG7zykEBYF1jGdo6+tP2gVrHIssxU9NB1ahcILHJ8umufqbvbmpegPmzPWnneVBF92AWpLKH1HablSMpzlbJlr6+sK7GMDGZu6aUxs4NTeVYWTcHwD1HzWu/vIDoOFu3V7cjDxHGzwqR23dGvVwZHImm9sV0R2v4vBHXsZ5zUy4387k1VfC5HYovVIQC1qbmSs0vYsTEsGwKUVarBfUVAdFy74YacbfZM09UIXg5hGBPCLAANaVFsOZZdOcwEgRhLiSsEQRBZBGhU6fqrpCiFlbxid+BUEq80Pogzk3s1ZR5JSd299YvJrK4HHkY0zh5Y0GtiOR127F1Q23afmPZZ0phyOsay9ICtwH1eWNaRFkhwgmcmW4yo8qXWVET3s+fbJldSuZx2fD3Lz+Cg6euoG8wgkQigQ9OX2P+vtdtZ550szaI0CpuPPXoAlSX+yUnffF4AsfO32RaVv2CQMakV8uxcDny4HLkpblcWBtiiO0rn9uOF9fXMJ+bJzv6JB3Ffq8DL6xjXxbLupSOmS/fjhefrE0bewFg2aLZzKIaALz0ucX46R51pW0+tx1f2VCLFdXFaL84gAs9IQwMjcFf6ET9A8njbbVa7jYYWZJRuqemaU3fwCh2HbrEvG1Tiddtly131Bo+z3Idb2gqF3VFeV02bFXID2RBT26mGGZHBUwVUr9LTowjCCJ3IWGNIAgiS4iJSEoP10YiJV5omVD78u2oKi0EwF7m9dSjC/DUHyxK/bfUhNBMUc1iSXYmbKgpURTGXI48fL11KepFSk1Y9hkXhvzGvs60Cb7SxFrsYVtM7AK05cQJ4U/gzHSTsQiwWjvhSaEmvJ+P2Q6+fW1XMsqb1DAxGcfH7TfTOuHJwTrRlRKWvG47EolE2kScNSSbtVulT0Is1HIsxsYn8eTKcsTjCcAC1JX7UwKOEg21JYgngNf3dqS2OxyJYeeBblgtFsXfqyR0GdGEhYPlmvK57fjHrz8Km82a8W8HT2V2nhXDnmfBn2xajJV1JbBaoOpesWVNsvvyf/3n32YIOb86ejnVDAIAdh7oTrsf+tx2fHbFfLx7pEdxPb58Oz48wy5Oc7ideaguK0Jj/VzM8thx4XIIuxnWp5etG2oBQFNHZCVYxHShK0rNNcLCTBXDCIIgpCBhjSAIQgNCoUMpzFpqspUtUY2PmHgh9SAuRXg0hm+9ehQvrK1GbJJtojhnlif1//WEmeshkQC27zqPbzytLIx99fOLsXThLMl/Z3UC6X1zLyV2Pb5snq5yH1++HV9Zf8+JZ7abTKt7TA9qwvv5GFUWKYXeMOux8Un8aHc7AHbhk3WiKyXCAdB0HrOKmwvm+tDZO5ixXK3Hgt9B8rDvhuQ+ymwaExPN12O5DljGtZGxCcPcmSzXVDgSQ/fVO6JifQdjrl5sMoEfvtuO0x23sLxmNlpbKrC/7YqsI5ejfzAiGxPA7Q+pbWcR1QDgsytKRbuGKhGJTuKTT2/j8y0Lsbi8EHdGtYndYrjsVricNlnnpFnh80piOrmipp5sZo0SBGE+JKwRBEFIIPXQIyZ0BHxOvPzFB7G4vFB0ObmU+SIlXggfxG/cHsXBk9KTJ26i2dpSwbTevoHR1P/XEmbOitNuxXgsLhsSv2N/F76zrZlJGJODxQkkJ2goPVjLiV16Sp6ELhaWc/RNnW4yre4xPagJ7+eTzYYaejGjjFbqnOX/LR5PINgTUpwUsoqb5y4O4NzFgQyh0IhjIbWPxMZyi8LpLeeqVDOuCZejZZLNeq0MDI+l/Tdrsxw+iQRwPNiH48E+AECR1wG7zSrbrdnjzMOHZ64yr0MrG5rKMXdWvq5l/Oidc/ju15sNdat+dVO9rvuDXsg1Zg5SDnJ+N9Ga8iJYAHT0DiKRADxuWzLvNt8JWICz3bcyMmX5Yx+JbgQx/SBhjSAIQgQpl9DDi0tEOzcOhKP4Hz85gT9/5kGsqJqd9m9mikhakZqQcQ/iJzv68NtPrjM5Ej46cw1+rxOhYfnf+NGZa6nui2ZmWG18eIFiIwVOXDQiC0br5EWp7NJMQfYrG2rTSsNYztFQOIrdRy5ic8tCTevU6h7TA2sgPYAMkUiti3OqMbqMVo4Twb60UklAelJY4HaocpyJiWBGHQv+PpISrZUaSHBNYdY2lmfsazXjGn85p7v6JVyp81EScEuOSwX5bN0v39rfDactDw21JYbkMgJgKmOeTAAjGsudWfC67fjy+hqsrJuTLGnUwa3BCDouh1BTXqQq100M6to4cxEb+zwuGyYm44jGeCLzUfXlxNzYt6GpHB9f6MtKgx+CIIyDhDWCIAgBci4hMVGNzxu/7sCyhbPSJkBGikgWC9BQW4wLl0K6HvzlxAu1E6/Q8DgeXToXh8/dUPwc55RjFU98bjtzowZuMsNamsodl6l4q69UdrmtdSlCQ2O6BR1hp0kpNx7rObrr0CWMjE1kNF1gQat7TC9KJbsA8M3tRyQnMSuqi/HptTuITgI3+sPoG4xgfxtbNlU20VpGq9YZ8fbBLtFxUG5S6HGpf9wUCoV8Ebz90gB2a5i4DoSj2PXb36NugR9v7OtU/X2OnQe7sfdEb8ZEl1Xo4i/nvSOXRMfypCv13gsC4cT6ZEcf828IR2J3x5Ulkk0VzMDMvEwAeGXzklQMgxHl24PD47BaLVjXWM7c5RqQ79xMzBykxj69pf1C5MZXoxv8EARhHCSsEQRB8NDrEhoYypzcGunASSSAtmC/rmX48qU7Cmr9/U5HHtPnOAGHVWT59suPoPvqHQyORNE3MJoR+O5z27FqyZw0oYfVuWDEcdFSrsGyj3/wzjlF5wwLrzy1BD63w7ByPSAZvL+v7YrqN+is7jEzJqRSzsTTXf1MuXKLKwLw+z0IhUYwMRFHbXkRfrqng1n0zRZKAqlYntjOA5lOqS1rquFz2zPOmxPBm4ovF+Qmni67FWMxNuFbzBnGieA15UU4fO6GJhFl99EeTaKcEOE5okbo4sM6KeevD1DXQIDj9b2dOXfO6mEocu9eYETJcJE3KYxuaq7AvrZe2WPjc9uxZU01cwMRYnrDMvZlA72RDARBmAcJawRBEDyMKNsUTm7NDkJXy4tP1ko+lGn9/SVFbqbPcQIOq8his1nTRMpNzZWKQla2nFFaO2iy7GM1otrmRyvwwamraRNmtZ3ltJyj/Iw9VqcGa8MHrcgJnUJnYjyewI/fD8ou7yd7OrCiujhjHR6XHY11xfjgtPouhGai5ERlKaUMhaMZ4f1Jsa0KP9ur3eUFAGOxOLxuOyrm+nDu4oDi56WcYbmUgbdjfxfiiQS27zqflfW9ub8LCY2q+1SIal6XjSlSQAvC811qfPH7nBiPTcoKZbOL3Kh9wI94PAGr1YKXNtbJnl9f2VBLzqH7hHg8oXvsMwq9kQwEQZgHCWsEQRA8jCjbFJYD5dIkcENTOVbWSU8GtPz+gM+J1Q+VYe+JXlVilhaRhaVsU48zitWBpqeDppGlwV63Hb85ey1twux12/HcmipVkz495yi/iQKLsGhErp0YaoXO4GXlcurhSAzByyE8eDc3kcvXyRWRnI+cWKw3VyspthkjHA1HYkyiGn/dYtfUiupi3VlYRjAQjmZ10p2L554UAZ8Tz62pFu2yasSyxc53te5Ujv/y1FJYrRbE44nUcsx8CUDkHsJsSFiAodFxDA2PT0kHdyl2HbqE0mIvnYMEkWOQsEYQBMHDiPLA13a340vratIeeqY6CN3ntuPFuyHPcmj5/ZyzTIuYZZbIomVSxCrMsJRyygXJG1kaLPawPxyJYfuu87A+bVH14N1QW4LWlkpV2UJCWHNgjM610yJ0spYMB3uSwtqRT67hX37+iartEmbcmYmcWJxLXYk51O4b4TXV2Ts45aIaRy5NupVQk1upF26stT69FD9+P2jo8ZIrGxcbX+TuCV9aX4vmB+cjFBrJ+I4Z9yfiHmZ0v9SyTC2dcqeSbDarIQiCDRLWCIIgeBgVgCw2mece0ncfuZjm8jGT1Q+VorG2JO3BUu6hU83vF4pUWt/waxFZWB6c1UyK1AgzLKWcckHyRpxjLKKElgfvTc0V+PDMVaaOf0avWyuahU7WTbMk1/HDXb9TvW1miGpClxZ3fa2oLs7obGq1WnKyKzGgft8MhKPYdej3qF8QQE15kamdhWcqZjjIXHYr3C677JjPjcXBnhAu9ITQdSWEzitDkstc11iGFdXFCEfGsfNAt2GOMal7gkMmI3QqmtvMFCYm4jh46gr6BiMoKXJj9UNlad2otcYpyKFlmUZ1ys0mXAZlgddBgi9B5AgkrBEEQfAwsmxTbDJvtVqwuWUhSou9WXk7mu9MH+aVHjpZfj836dErZmmF5cFZKLw11c2R3Aa1wgzrhF7qc0acYyyihJYukVarBV9aV6P7/NfaoVILWoXOunI/dkM5xL6u3I+OyyHcvjOmaruc9jxEY8Z3RXTYrNi2ZTmGRsfTytykOpuydsmdCtY1lqGto595HNx9pAe7j/TA73Pi8WXzTN46Nnz5doRHlR1g2XSKScF3kIm9AGlaXIJj7TfThPU8qwWTcekB56ub6pnGfE6gslotKC3xoL4isxmNmGjWUFNi6P2EhLLs8PbBLuw90Zt2r3rrg26sX1mOZ1dXK77Mam2pxKbmClXHWmmZmx+twNxZ9/JAgWQkgFLWZq6y8+C9Dr96BUmCIPRjSWhNQJ1hTE7GMTAwovxBHdhs1rSuYgRB5C5i4o2WEOa/eX6F5EN8PJ7Av/+qHYfP3dS1rSz4fU48vLhEtqsV35Ul9vv5kx4zyjdYUHqzzHXMU5W11RPCd3acVlw3dyzfPfR7Jseh3LEHxPex0aWDX9tcj1X1c1V/z4iyGK3rlkLqnHtzfyf2t11R/P7ahjK8sK4m9d8TE3G8/N0PIbe7LQBe/esncLr7lik5UVrhn1tK10RjXbHuTsJm8TfPr0BNeRH2t/WmTRJZUcpY83sdgMVi6guMba1LMzqrCjEza0wMKVejsKRd7HoScxmd/fQW3tjXmSaC+b0OvCCIPJBD6oXI48vmoyTgnlLXDT2fG8vbB7tknzVW1hajvUc547LI60jFasTjCQQvh5Il/JbkCw9OqAWS57Pw5YIcLrsVCQBRxi7F0wWlGIb7Ebq+Cb0EAh7k5VkVP0eONYIgCBHkApDVCA5K7qbzl9hynvQSCkcVW8XzXVlyzjMzyjdYhDoWZ9lP9nSI5h3JZW2pcaCd7OhjEtVYuo6K7eNwZNzQ7oJa89z423a6qx/7GIQrPeuWOv7c30939ePY+Ztpjh9OLGYR1QDgyLkb2LLmXi5T99U7sqIaACTufq7I61D4pDGwhvFz5yzLNZGrohp3jVitFqxtLFdsfqKFF9bV4NOrdxTHPjm8bju+vL42Qzzji1VWCxTzJTmn2E/3dBjmXBMK8fyyYFYHGR+xsZ3ryvrdrz/K/DJFeD2HIzFRUTEUjmLXoYv4xtNLyUU2A4jHE2i/OIC9CtfbiQ62MYmL1djQVI7ffnI9bWzcjR647FY8+uA8xOMJDITHVI0fYzNMUOOg7DWCmDpIWCMIgpBAKgB5RXUxs8NCTlzo7B3UnWVlJMJyOf7v5wscYiILa2i9GKxCHUvJn1KIOP+hk/tN126xuZUL8h34t19eYPqsXLA2H7FzrLvxjiYhSwiLuCcHt211C/yoKS9SJSirWbfU8X94cQk+vtAnuU4WsZjPaHQi7fxWI6g2L52HWYUu1eWganA78/D48nn41THl38SNK7man8bC82urASCVC/f4svmqG2eMjE2gqa4YXVeHREUvALpENQBAIoGGmmI01EiLVaz5kg21JYhOTOK199jGEYZNw5bVVaI5S2qFKj2djoXLEXPiykFiQPYRip9VpYXovnpH9PxmefFlZvi/1DU8FovjwMmrhq9vOpPNGAaCINIhYY0gCEIlrA6LgC/5sCoWKA6wT+yzyeBIVMRtkBkgLYXaCZKayZwR+4t76BwZi6kWipAA0+dbWyp1lWKsqC42RFhjFfdYELrr+gYisiIIf93886kg3wEkgKHIuKKTRbcgIgL/HGJ11BV5nLBaLfjTzUvx9z9r07Relz0PYwp5a5HoJJOoxhctjRpDNjUvQHR80pDzTgm+6PXX/+tw2suFfJcNFkBV98jjwX5sa10Cn9uRNs4CwH/959/q3t7hsXuCrNxklTVfMuB16d4mPgVeh+6Sa72djjmkxnOl8vb7XQwwMlpBqWQSYIsh4Mp0R8ZiGW5hj8uGZVWz4HLYUFLkRqHPgVffade0vYTx5OKzJUHcD5CwRhAEoQGWAPqmxSX41qtHJZ1YBfnZKS9TQ9/AqKqcEiFqJkhqJ3NayxqFaCltfH5tNYYibO7CkoBby2al0Ns1VG32EStCd11psUfRoaPkYlByshgN/xxi2c+cOH7+97fxsY6SytWNpfjV0cuav8+HL1oadU3ULwigboEfeVZLRuC4UXhcNmxrXYq6B/w43dUvOnaO3hXUWlsqMDGZwO6jys0lAOCtA934zrbmNPGg/eKAKoFODtaJqtVqQU15UUok6ewdzBBJWM47NVmLRpwDejsdA2zjuRyhcBTtlwZw4dIAbt+JIo4ELBZgls8Jb74DBfkODEdi8HrsCHhdKYfVwPAYhobHMTI2AQtPSOJ+l9CRFQpHER4dR36+DaOjE/C67RiNTmBusQ/OPGDR/MI0R7NWB5cc/O/3DWQ2cfD7nNiypipDLFYqvT3V2Y/fnL2GcV6O1G70IN+Zhz/+3GI01JYwi59cma4YI2MTOJKFbFhCG0bdFwiCUAcJawRBEBqRK/9pkmgUwHdiuR3qh2Cjw+35eN12pvwwJVgnoayTueDlEKwWCwaGxwzprHfsPPuEgC8UBXvY8vD0PtTq6RqqpZOaVoQOHb4bLdgTknSj8clm+yRheSqrOP6X/3pIl0AT8DlR/0BAt7Dmc9vx4vp0wVSvCMttX015EU529JniEuR4aWMd6isCiMcTil349hzvxQtPVjNf7wPhKPa39aaVRAZ7jcuv7BsYTftvKWGFpayd5bz7L5vqMTgcxTuHLsqGq7sceTh/6Tbi8USGK0kNrGN2W0cfAIiKPHrLkn+2N6gq90rqXrgbPXDarbDlWdOuW9Z7Z5HXgYfr5+DI725kZDq+cNdpqSdjlKVkMhSOZmRtSq2DZXmj0Ul8/xfnsK11CX66p0NxG4npi94ICIIgtENdQe9CXUEJgtBKPJ5A19U7uHRzGJHIOKrmF+Lf9wQVnTB/+Pgi/Gi3cvnE5x55AFZYAAtQU1YEJIBX3ztvmBuDw2m3GtIhS6kbJsex9hv44bvKv5810J0F1on6pkcWoL4ikOFSUHLzBXzODOeMVqQ6szaJZI+Jdf3LJtnocKoXqYwouf1shNC0rXUpPE4btr9zTvd5LDa5VuoKqsQ3nl6KFdXFupyqcrjsVjwwx4fqsiIsXuBHPJHA994+a/h6+HjddhQXunDxRtiwZXLnj1wmoJquyz9+Pyh5Pmi5djwuG17aWKdpDGDtdMxR5HXgieXzURLITwmLx4M3mcbzmYxcDl08nsDuI5dU5wjKrUPtte+0WRGl+ce0YUNTeca93uu2y2bJUlfQTGj+TeiFtSsoCWt3IWGNIAitnOzow479XRhQOSndsrqKqQGCy5GHsfF7+UxKD1ZTiRphKdgTwnd2nM7CVt1jXWMZUxno1zbXi+YWKU1kjH6oVeqWaUQmj170Cjtm43HZsK6xXNbJJxbk/c3th3FnRPt1xtKAQSvC8+xEsE/RHSi2fS/w3JjZuhZteRZMTE6/R0+uPE9r115ubJQqgzUKtWOQEddvMo9rniGO5+mM1P3vZEcf3tjXaUizIm4dAEwTw4nsInx5yH9RJnavF+tOP9Uv13IZmn8TemEV1qgUlCAIQgVpQexuBzqvDOLdw5c0LcvrsTOVcfFFNUC58+VUoiYwv6a8SJcbzee247Fl8/HLY8o5TD63HV/ZUAuPy84krEmVc7J2/zMKsa6hcn/PNnpzlYxgfVM5jgvEK5cjD4lEAtFYHCNjE9h16CI+OntNslxL2AH33391QZeo9ujSOXiwarZmEUYJYZC81aLe2dm8dA48Ljvi8QRzKWBNeQGu9I1gNCrfiEGO6SiqAcnyvH9n7AosxkA4imBPyPTr5c19nYpNBrj72MDwGN7ar/xyR4lkHtclOO15iCo06ZjJiOXQGf3igVsHwNZMZzqT77RhNGqsMz+X4PJQ5ZqeyHWnz5WXawRBJCFhjSAIghGj28kHvC7NWVq5htIbVjMe+MKRGHP4/ZY197aNJbBeLqOEHmrvoTdXyQiWLZqNP3qiKi0MXMw5I9Zplk+yVOsi3v/4sq6SaK/bjq0bFuNbrx7VvAwl+BN4rRP3Xx69jF8evQyPy4blVbOYvtPZO6R6PTMJNRlgYhz63XXTr5fQ8Hgql1JsfDL6PsYn281IchG+SG3Wi4f7oeujz23HP2xrxv/1o2NTfo/RggWA3CsEYR6q2hdlufJyjSCIe5CwRhAEwYDRb5058cZqtYg6oKYL6xrLsKK6WFV4N0dn76D+7DTGiZzfl3SgsQSHs7ju6KE2SS5M8ELhaOp4xOMJ/Nd//q3s54VuL0A580oNWzfUproPmsmvT1zGR2eu4uynt3UtZ2RsAoenSYc/I/MWp4Jj7dnZz9t3pef58YP3zXyRI3RX34/w3c5mvXjQ0yDHZbfqFoizwVc21MLhyNP88jHgc+K5NVXwOO268i3ryovQ2z+c9n2HzZrWeVVIa0sFPreqAr861oN9bb2SZZ4EQcwsSFgjCIJQwIy3znzxhu+Aau8ZwO4jyqWNZuB12TDM+PAp9nAoJT4KnUKco43rMKeHunI/DvtuqHKgZbuccyajtwOqEfxsbxAOuxUNtSXYfeSS4gRKWK6lJZ9MDP75c6z9hu7lKXGmW5+gNh1Z21CG2gf8GByJYmh4nCmj8n5EeA1wY7DHRY/9ZuJx2dLuNWa8eODfz9R2BPa67fjy+hrTStSNQHgflrpfi7G00o+H6+diVoErzaX50sY6WXHOabciz2pJK3H3ex3YsrYaPrcDoXAU4dFxeD12BLzJZZ/s7MPP9namRXMIt33zXVcaudsJ4v6A7rAEQRAKGPnWWfjgJSybnBfwqFqe25mHiI68Iz5bN9YhnkjgB++cl+1IZ7EAz362Kk2AYhEfd+zvQjyRwM4D3Ybsz4DPiboFfk0ONCrnNIaa8iLVkzujGYvF8f1fnMO21iXY18bWxbO9ZyA1OfqBjklma0sFSgL5KMh3AAlgKDKOYE8I+Q56vDKD33xyHV94tBJAsvmJUZ2Mpwt6O+1OZ7ffdGBdY3naPcSMFw/8+5laN9fWDbVoqC2B9WlLRjOFfGeeruxEPXDjqNR9mH+/Pt3Vj2Pnb6Z19+bcaT63A7eHxnAieBMnOm5iTlE+Vj9UlhLnXtvdLjpeTCYAm6COeXwijp/u6RB1flqtFqysm4OGmhLFZwhytxPE/QN1Bb0LdQUlCEKKY+038MN32zV9t8hjx59+YQmGRseZsm7cjjxEGMtp/D4nnn68Ev97d1DTtnHwxT413QH5WVVT0eGTv36xfUkOtOyQK11B7TYrYirurcJuu1r4xtNLAWDalnJPR1pbKvDRWfOzynKRDU3l2HOcTTwmsovHZcM//cUfpAkr8XiCqXNnsqvqfJQE3CjyOBGOxLDzANv9jCUzTyyOgbXbJB8lYddlt2IiAeY5jtdlw9aNdRm/SS6nNR5PIHg5hGBPCLAkXesj0YmM/cXf5vUrywHAsGvH6O7fhLnQ/JvQC3UFJQiCMIiCfIfm7z62vBT1FYGMv0uJEayiGgBUlxZgts+tabuk3hCrKV3hZ1WZlbW1oakcHws6PopNMFgdaEY2VshWkwY9ZGsbcyH7So2oBhiTB/WT94PM5dOEMYg1ptCDWheY25EHpyMvze2jpoxeC/wxb1FpoWgeoFnuPa/bjtjE5IxzBrodVqyoKcbZrtsY4XWe9LrtSCQSafuXRYR/aWOdqFtJyVXW2lKBTc2VmS6tGjZHNf/ex5Us5ufbMDo6AV++A34fu5NKeB8tcDsAC1IvBqtKC9F99U7y3/IdqfsLJ3Bxy2u/NIAj525gLDaBIo8Ti0oLUehxoLdvGN1X78DpyENz/VzUVwYyhMjdRy5l5JLxMwKFwt8Hrquy955EwjhBjUMsp5MgCIKENYIgCBlOdvTh9V93aP7+gZNXUF7iVV02ycL5SyH86aYlqkrxlFxcakpX+FlVRpe88Es7ykt8GfkmWgQzNY0VlDByWWaRjW3MFbfaVEGi2vQnkQC2rK5C32AEB09dVfx8y4Pz8Nzqaia3jy/fjvBoTGZpmfjcdmxZV43w8Dj6BiMoKXJj9UNlsNmSb8s58UPo2gGA7751RtW6hHhdNjy3phojkVhKlKkqLcS3Xj2KaGxmOQT/5PP1qczPT6/dQSxhgd2SwKL5hQAgenzFBE0p1xWH1kxPNSWERpYb8hvBiN1ThetZujDZUVj4+T/dVJ/mzv/394Npv7/j8mDavUiugQyXESjGVLzQEeZ0EgRBAFQKmoJKQQmCEGKkaGBW2eTfPL8CI2MxxTfinDuN/8ZZTIBiLV3hWNdYhufX1qj+nhSrHypFY22JaCmMlCjEIiApHUs1pR1GLssssrGNRh1zgphqvra5HkUeJ9O4/DfPr5CcUAvFBU6Uki3T8zrw1U31KVeQmrFPuG6912NrSwU2tyxM+1v7pQF8d+cZzcsUw+u2o+Uzc2WdREaUaoshJmixPp+LlSHWLfAzOZemg8OZQ+ye6nPb8eL6WqysyzwHxT7vcdmwrrEc82Z7ZJvDtLZUYmQshn1tV4z9ESbztc31WFU/d6o3g2CA5t+EXqgUlCAIggGph12jO4G+ub8LbocNQ5FxnOroN2y57T0DmD/bg9aWCnx45lpaeZJwAnGyoy9jkiecsLGUrvA5dv4mnltdrfp7UjTWlmBkLCb6IC7sLsr9JrlOpOsay7Bs0WymxgospR2sTRqmskwkW9toZFMPgphKuLFfyf0r7DAsRMzRozQuvrCuJhUXcLJDvEOt2Ngntm69Y3BJID/tv08E+/Dvv7ygeXli+PLt+MevPwqbzYpFpYWSgkxxkRs/2q2cbapUAuv3OfHVzy3GUCQz51QtVqsF9RUB0XgHlu/yz414PIFgT4hJaNMiymkV8qTuqeFI8r58sakcz66uVvz8yNgEdh26CKU17jp0UXGbcpFc6IhNEERuQcIaQRD3LVJOpy1rqhAaihoqGoTCUd1lOmLsPtKT+v9etx0NNcWYNzs/4026kgCl1cEUjsRSJRFSJS+s+UMBXtmRHJwoBEBRQNrXdoXpTThraQeLmKS3TESvsyEb2wiARDViRhDgZVBp6TCsxIrqYrS2VGBf25W0sjWxDtF6BXGpMdjntqd1UZSCLxa8fbDLlEYJ4dEYuq/eSd0zpLIxgz0hpuVtfHiBrDjzwtpq1FeqF8JYkXs5JzWOqynT11LSrzUGgOUc3HO8F5XzC7Cybg7T52diWZSSwE4QxP0JCWsEQdyXyAlN23edn4It0s9wJIaTnf1AJ3DYdyP1EK1mwgYoi1VC+I0LxMKP/+1XFwAGYe35tdXovnqHWRQCjBV3WBowsDZp0NrMwYhcNLO3EUhu544Dxjk6CYIFtY0GWOCLZZww9ca+zjT3r9/rwAvragzJYuQcWZuaKwAkSy2DPSHcujNmiCAuJlZVlRbiL//1kGweFV8sOBG8aWr3Uf7YI5UPxuIgdNis2ND0AEqLPaZ0hVZ6ySE1Xj+8uCSj6Q4/gJ/1JZeWF2J6XqKxupBf39uJhpqS+9a1rEVgJwhi5kPCGkEQ9x1Gl3nmIvyHaI/LbqpYJSyJ4E+Ugj0hpuW1tlSgobYEx9pvMK3TjC6kLKUdrOUfWspEjHIVmrmNADUsIKaOZYtm4Uz3bU3fFYpyfOGFL6D0DYyKf1kFye6GF0U7mHIlcmPjE/jtJ9dVh6+zjH1CsepkR5/iejixIB5P4Gd7O1Vtk1r4Y4+UeMXiIByfiOOV732EDU3l+IdtzYZmmJ3s6MsQWIu8DnzprsAqN16LiZLcOO5xyU+91LzkEjoY9boeWe+rnFPdrG7guYDXZUMC6c0ROFGcOz4EQRB8SFgjCOK+Y7q9ZVXKkJFjx/4ufPGJhcofBHC6qx+V8wtULV+pJIL1wZvL9jFbFJKCtbTDiBwmMYzMRTNyG4Vh3TWlRTNelDYDn9uOVUvmYNmi2YAFONXZz9SBcibislsxJjOeLa+SFs/OdN/GhqbyDDeQFNx+X1FdjIXzCvDhmasZnTbFXEdClMTtdGEugo/OXENoWH77tDrC1I59LGOL12VLiQWdvYMYZigb1Qp/7FFy6K6oLobTkYeoQhODPcd7cXtoDC9vXmqIk0hKNBscHsf3f3EO21qXYOeBbk3LVhI41bzkEjoY9cYAqDm3OAFzprGusQwrqotT5+juIxdTZdycKP7R2Ws51QGcIIjcgIQ1giDuG7jJT1tH31RvChMBnxNNi0t0leQMhKMYHmGbJB07fxPLqmarWr5SSYRaoUytKKT0WVZYSzvMymEyMhfNqG082dGHH78fFEwEeyQ/T4iz6ZEFaP2DhWn722qx3LfC2lgsDpfdigSQ9sIg4HPiuTVVioLF8Qt9+PuXH0H31Ts43dUvm6EYjiS7DeZZLfjR7va0a2zviV48rHJ8fVNE3GYR5oxCi2jPMrYMj02kxhazXUjc2MPi0L3aP6woqnGcCPaj+8oRvLBOneARjydw4dIAYhdDsFsSWFDiw78pNGz4918FTelYyqHmJRf/eOmNAagpL1KVx8dyv55uHOU1ZDrZ0SfqOtWbTUsQxMyEhDWCIO4Lsjn50Qv3xpQlyJ8FX76D6WE5HIkBCTaxijVzSK1QplYUenzZfF1dxbTk8EgFhOvJ9DE6Fy0ZmF6JfW29GYHpz62phsdlx7H2G6n8pe6rd9JKqE539VO5Jw+XIw8Oex6GRu6Vhfny7QiPKk9A6ysCGSJmrk1It6yugi/fgR0HugxxKym5yviONX7mGKvA3H31DmrKi5i6RgLi7jCpkj05QuEo3j18Ea1/kHQBZ7ssWotozzpmcC+cCtwO1dvFgtdtx9YNtcy5n2/u68T4hDqndmj4XjdoznUkt7+0PheYKaoByaY7rS0VTJ/lv7zS6/i2Wi14cX2taGdaPmoafnCYkY9oBsORGHYfuYhNzZU53wGcIIjcgoQ1giBmPNMlE8piAdavvNfKnjWfTAm/z4lVS+YwdccciowrPii3tlRiU3OFaQ4vNcLVyJg6EcDvdeCrm+oxNDquK4dHrpudFlgnRAX5ypNe6cD0Msyb7cHOA+n/ZkF657YirwMxlRPamc5/2bwEax6uwMdnr+D20FhKkPzWq0c1ldyqmZCaTcDnxNrGclitFjjsVt3b1NpSgc0tC/HFxxbhr75/WFHQ58qrSos9iE2ynXeDI9EpK+l/9/AldF4exH/7o2VZK4uWe5HBObEHhscwPBKDL98BP0/4YB1bDp66ioOnrsLvc8LjsqnOfpOCG3s2NVemxkeWYxfiZZuphesGLdf4JdefCz46c011Sb8RMQAr60pwsalcVnQWa/iR6W5O55WnlsDndqRyDMWcYLnCvrYrqCorykp3bYIgZg4krBEEkbModeRiXcZ0yYRKJJLOikWlhWioLTGkJMdiAapKCwGASVgr8jhRt8BvqCNLi8OLRbiKxxM4ev6mqm15YV0N6isCqr4jhVQ3Oy2wOphe292eCs8WQ2qymBQvLol+R2giGNQxoZ3OeJx5gMWS4fB7fm01VtaVIM9qweKKACZ4oqOeklup6yLbPLemKmOSLHatcmWactvq9zmxqbkSANB5ZZCppIxjx/4u/MnnFjN9tsjjxMDwGPOyjSbYO4hXvvdR1tb31U31ouOWnOOKE5WWLZqtyi1k5Ln42RXz8fyaGths1rS/Zyv0Xqpkz4jnApc9D2Mx85xroeFxrKwrxolgv+RnmhaXpJeYGxQD8OzqalTOL8DP9namOVil7tnc/ZqfR6b0ndJib8a563bmIRI11w3IwsjYRDJXlIGZ3MCBIAh1kLBGEIQmjBC95FAKNWZlujUqAO6VFxgRDJxIIFU2peZNttGOLC3LUxKu1IRsazl3zEBPBzzgXni2sNQpHk8g2BPCj98PZumXzCw4FyYAVeeonFtDqfsf9/0V1cUI9oSw/Z1zhrmE1OATlP7JXatWi0X2HH2Bl5+l9lwcCEcBi3IpOjdO7W/Tnj053RgazRS7lRxXnKjU2lKpqQTP67YDiQSGeeekz23HI0vn4DdnrzOVQ35w+hrOdN/OGHuzHXovLNkz4rngyaZyvHv4kgFbJ02bjKgGJPMGn3miSrPjW46VdXPQUFPCPB5arRZsblmITc2VTN/hjzOnu/px9PxNU5tmqIbxUWcmNnAgCEIbJKwRBKGKeDyB3UcuZWQ3GSlcsIQas64n194mrloyB8cUXFZceYFROUyhcFTTm2wjHVlGLy8eT+D8xQGmzz5UMxtfb/3MlOegKInFahxM/FKnhxeXMHdJJNIRm2xqOUfFBLGRsQmm8YoTVqdCVAPEx0ipa5Vl0q6nxG5oVLkUnRunfAxl0TMF4eRdjeNqn0YBUihyeN12vLi+Bivr5uDBhbPx3bfOMC1H7L6d7YxBYcme3ucCr9uOzY9WIhKdYHKCa0VJD5UqRTTqxZjae7baF67JcS9m6j7USl25H4d9NwzvAE4QxMyFhDWCIGThPyj1DUQyBDUOo7oksUwY1ATG5tLbxIDPiaULA4rCGpB88Dcqhyl81+1gRuj+VKA2cLqmzFg3JQvCCcad0XG8+s75jM8Jr5uG2hK4HTZVk1Y9XWOzictuTQutnypaWypQEsg3xGlr1Hhl1AsALdlYasdIuUm73hK7oeFxFHgdaG2pxEdnr8mOU35f7oztZuJ12TIm72ocV0YJtsORGLbvOg/r0xasqC5WLYzxr4OpyBjkX2N6nwu2bqiF1ZrcD1MtCkmNHUJRjHM151KVQa5GdQR8yUgMMzqAEwQxcyFhjSBmIEaVaWrpmKW3SxJrVzjWwFjWEsjn1lTjp3vkw3f18vzaZDdGFrgH/4baEmxrXYofvHNOc0ctr+feOo0u8cw2Wtww/N+fDfReN2c/vWXi1k0dX15fh7Pd/TiuUN5kFr58O76yvtYQAZkbY9t7BgwZr/RO9F2OPPzx5xajoSZ5bR881Yu2DuXzyJdv1+S4kHKy6Cmxs1iAnQe7U/9d5HVg86MViMcTgOWuQG6xpHWzzaXOqqZhyRyb1QqxRjYj4MYqtcKY8DrgXvT8dE+Hqiw+rfCvMaMcc7nQ3ffarREEe0Ky93GjojWk0FplYHZUx+dXLUDtA0W40j+MW3fGUFLkxhPLS7HneI9s8wROMJspLyMJgsgOJKwRRA5hhCBm1AOU1nIevV2SWCcMrJ9jLYFsqC2Bx2XDd3eeYVqu3+vA48vnoySQjwK3A3k2KyZhwae9IXxw6kpaADz/ISweT6ju2rWyrgTAEmzflel4YmF4JIZ4PJE6l4wu8cwW8XhCU4ZYwOuSXaaRIqOe62Z/Wy/8Ba4pd0BwCLuFSqEUsM1R6HWg6+qQ7u3Sgs9txz9+/dGMEHUtaBFOlT5bVVoIn9suKzB4XTbY7Xmiyxobn8TOA12wWoAV1cX44XtsY8WLT9YaKqrrcd4JXxwMDo8LMqx60v6dK4OeLo5NrQxHYtjf1pvq3AqwdQfms66xzLAujNw9XkvzDeH50VBbgujEJF5774Ih2yaF8J6q1zHHfxEy1d19dx/pwe4jPXA78/Do0nl4qKY47T5mZLSGGHpcu2ZGdQR8Tjz92EJYrRYsXTgr7d82tywUbZ4gJphN95eRBEFkDxLWCCJHMEIQM+oBSq89X8/DEqtzQ43Dg/WtY90DfkXRy+OyYdtTS1G3wJ/2YGWzWeH3e/BgpR+fX7VA8iFMa9eulXVz0NVwB/tPqhdddh7sxt4TvTkR3q+H3UcuqnZdyGWgGP0WX+91s/NgN2teclZQEtW462dFdTG6rx5RFIuRMLbjoBq+sqFWVFRTK6yeCGoTTsMi4fMc3Hmo5NrZurEu1XlPTCS5F1ZfwdTZtamu5K5obxxanHesAq4Qrgx6ZV0xLvSwNzKZjmSM4Sp2GNepVUxI0Ap3j+dEh/1tvWluQym484N/3Q2P6DtuLrsVLcvmY7/MCwlh90xu27V25eW/QMyV7r6R6CT2n7yC/SevwOOyYV1jOT63aoGh0Rpi6KkyMDOqQ6lMU41gNl1fRhIEkV1IWCOIHEBJENvWuhQ+t1325m9kNplee76ehyW13StZYXmIYhG9XtpYh/rKgOy6lB7CtJYXzC6Udl4pYdTbaSMREzUA8a6M8XhCk5NL6uHajLf4RpS1aKz2NRSLJdM9xEdMXGYRi4ciymIPh9dlg81mZRKI5JC7ptQKq5PxBN7Y26FpOzxu8XJkFoej0PH60dnrsp9nvU6W18xm+hwr8XgC8URCsezQ48yDxWpNCWF6z3mhWzJXsvyMhj82xSbZf9/jy+anytqE98BwZBw7D3SrHrf493ir1YK1jeXYe6KX6b4tdt0pjTlyjMXiqC0vgs1qkXQv7jnei0WlhRnXNbdPuq7ewff/83eqxNm2jj4ASN23Elp/gAmMjE1g16GL2PNxj+K1MJVVBnpLaTc0lWc07lFTpkmCGUEQRkLCGkFMMSyCmDBfS2zyZ2Q2mR7Hmd4uSVodXazLVvrt2crU0FJeYERWmN6300YhNrnyuJK3JLFusx6XXZVbTep4xeMJBC+HFEtKuf0EiAt9YpzumprsMCOpKStE55U7sp8ZGZtIBZBzsFw3wZ4Q83bcc2ddwq5DF1X/Do/Lhs8/sgAFXgc8LntaKTSgTVht//1tDGicAL51sBsuR17aMlnGfp/bjm+//EjKbccyzrNeJ1pfgIgJ4qe7+pkdOyPRSQCTmtbNwkwU1fj8dE8HHls2j/nzE5PxVC5dTXlRxj1wRVUxfrIniLaOPkQZ9p1YMwWr1YIta6qxfZf8fft0V7/odaekSTkdeYiOS58zb+7vUhS2+Pc+4TlssVpUOx4PnrqKg6euGppfZzSs18JUVRmwPO8piWfPPFFFZZoEQeQEJKwRxBTDMlESPi+KTf6MzCbT4zgzokvSVAfGZitTQ+3bUrmsMFb0vp02AilRQ67b7FIFlyCH027Fn//hg6h7wJ9xvNRkYw2Eo9h95FJGZ0IpR9PJjr6cyUbTAleOpySqcYiNI0rXDYs7wWIBXnnq3ri2qblCshOxEm9/8Gnq/3vddjyyZA5WVBejqrRQk7t3YGhM9TZwDEdiGWM2y9gfjsTQffVO6nplHeeVJvtiL0C4roHB3hCQAOoW+DOuIylBnOX4+L0OjE/Ec1aEmC6EIzH88thl5s/vPnovm87ntmPV3euAE0R//L66pj3DYxM43dWfNgae7OjDzgPi15TPbceL62uworoY39x+RHbZQudawOdEy4PzBFl7mbCO6Z29gxgZi0m+1NHCTDifp7LKgOV5T048m66uM6PzXY1eHkEQ6iFhjSCmGD1vCvmTPyOzybTY871uO7ZuMKbjHjD1gbG5+LBmVAcyMwODlZiYiOOne9SX0527OMD0ucbaEklRTW02lphTSkzU1putZgZKQfhC1BYxSY0jctcNizthc3MFJuPxVJe75ESYfeLqddswHJnI+M5wJIZ9bVewr+0K074RE6ADBfqFbf6YreVlCOs4v66xXNbpJ3wBcrKjL0Ng2X20Bx6XDS9trENDbYkqQZyPy27FmoZyeNy2NLGTyD5h3nXgcuRhTMYFJgf/PFYaW8ORGHYe6Mb1WyNMLxG3rK5Cgddxt1Q1hp/uUd+wRorTXf2iL0BmgjimFZ/bjlA4qthZVAojqgyUnvdy8XlMD0oxBGpFMrO7vhIEwQYJawQxxeh5U8if/FWVFirmlFgsye5zSqjpdJUMyS3DpubKKXd0zXSM6kBmZmCwHCc7+vCTPR2mhowfPncD7T2htAdKM4Qv/sTSiGw1I3jq0QWYTABIJIWyX/KcKkaip9xbyp3gduTBarXgHZ4zxe9zorG2mHnZmx+twG/OXlP8HKvgKBS+6hfOQsDn1FwOCqSP2VpehrC6QzY1V6C02MPk+pUTRkbGJvD9X5xj3rdijMXi+OUxc85Fo/B7HdiytlpT3th0RauoBtzrYuz12PHWfuWmBaFwlLkraYHXgVX1c3Gyo0+2tFQLv9V4Ds9kwpEYfrS7HYB2McaIKoP75XlPKYZArPQ16fqsFW02Y3bXV4Ig2CFhjSCmmLCKQG8xuMlf99U7ijkliQTSyorkkHpQ8nsdeHz5fJQE8sluPgXIPcA+t6YaOw/IlzrqzcDTihbHmFaED5RmCF98gSRbDkCvy4Yvb6jLOMYBnxNNi0vwm09uZEUU0Fvu3VBbgngigZ/t7UyJrBGRSX4oHGUur21tqURNeZFiyZgaCvIdaf+dZ7XgS+tr8S8//0TXcrnzRUsJlRp3CIvrNx5P4I19nYrbbOR+zSa15QXo6B1S/NxXN9WjviIAq8WStXFqusPSBVQLQ8PjOHL+OpNgx4elJHmm5+/JweJQ1CPGTHWVwXSA5SWfWAOOcCSG7bvO4WJTOZ5dXa1qebmSq0sQ9wMkrBHEFBKPJ7DzgL6H06HhcRxrv4Fr/SNMn1cjAtCDUm4id1ysFpjS+EEP8XhCsVmAGfx0bweiE5O4cWvUlOVz11K2HIBcoL/HZUs2ArAAdeV+jEQnDHd2SKEni4gj6UQ5z/x5JSeu3+vApuYKHA/e1L1taYisc2WduLDty7fjsytKmQQo7nzRU0IlJiJ4XTZsvVu2yaHkAunsHdTdeTWXuXh9mOlzQ6P39kEuh9HPdCwW7YJd3QI/TnbobyDjtFuZmjhMN77+1FLYbFYMDI/hrf3dss5drWLM/eI600rwckjXy689x3tROb8AK+vmADC2aRlBEPohYY0gphC9ThotD6FqRQB6UMpNpI7LVDd+EBKPJ/Dvv7owJRPV8GgMr713wbTlc9eSUdl3UnDHDgC+uf1I2noOea8jNpG9SSBXGqi1vERLWa6SE/eFdTWqciZZGZJwE4s57sKjMfz2k+uqmwaovV7lnJ/DGq6xqcxbVIJrqKGHccZro8jjzKqrlhBH6VqXY14g35BteKqlcsZlAXpdNtRXBmC1WhDsCWnKmCT0weVY6uX1vZ1oqCnRnNNJEIR5kLBGEFOI3pud2ofQqSoDJLJLrjgNT3b04Y19nTPSEcO/lozKvuPjc9vx7OoqjERi8OU7cLV/RDSMfqr2rZyjQS54WevLhHWNZWjr6JcVn8KRmKK7TQ1SQp2U447ld4k50FivVzPKfqYqb5EFgw6jIh6XDVWlhfjWq0eztEbCaAI+J+oW+NM6oGpaToETaxvKsa/tyozK2tu6sS41JkylGHO/dq40UrQPR2Ip0VMYVyBFLo/zBDGTIGGNIAxGzYNDtm92U1EGSEwN2XIaSp3vM939IbyWpJxHvnw7wqPqmzU8+pm5+M/f/D5nJ3dSjgal7mRaJ2srqovx3OpqybHV6KBzqZcQLOKW122HLc+SJnoqOUZZrlczyn5qyotQ5HXMSPGblXWNZei+emdKrjVbngUTk9mSEGcuTYuT3aCVnMNet122ec6XnqyFzWY1/EXJVPLo0jlp447apilGiWH3a+dKM5onDY5EUy8ulaAX6gSRPUhYIwgDUfvgYHYJGcdUlQESMxup833Lmird2YFTCb/0Uk1JrZjziHPCsF7jAZ8TKxeXiAYY5xoDw2MA7k28Tnf1izYb4Adia3mZwE0MpMSniYk4frqnQ/0PkEHqJUQHQ0bOcCSGv96yHFaLxVBnhhlOE6vVgi+tq5kxIoJavG47NjVXGp/PxwiJasbw8YU+PPNElaIgtnVDLQBkOKn9Pgde+eIyLC4vxMREXPJFyXTDYgG2blic9jc1TVP0imFq7g0z9fnUjOZJfQOjzF12H5jjpRfqBJElSFgjCJ3oeXAwo4SMY1PzAsyf7bmv7PZE9pBr8a4mmD7X2LK6Cmsby1PXi9qSWjHxR+kab22pSHXZvTM6jh++Oz3238793bhycxgfX+hjmjjs2N+Fb7/8iOqXCXJO25MdffjJng5ZF4paWlsqJCd5rM6uodFxrKqfa9g2xeMJDDGuW6142VBbguVVs3Cm+7aWTZvWbN1QC6vVgr6ByJRuh9/nxLJFs/DhmWtTuh1KKJVa+9x2PFw/Bx+330zL8fL7nIhEJxS7UmoldNepyZJZeLKjDxaLcDzJHF8aakuwbNFs/NX3DytmkvHxuu1o+cxc016OrGssg8dlF40GELJ+ZTlsNmva31ibppzu6pe8x7OIYWKinBQzuXOl0SW1fp9T1Thxpvs23j7YldZNlCAIcyBhjSAUkLPBG/HgYNab0foFAQqeJQyFuxa4rmIzjYDPmSaqAcoleixlMqwB9Sc7+vDqO9NDVAOSziw1k8eBcBTdV+8wv0xQctqaUW7s9zmxqblS8t+LvMZk2qgpr1Jzn/G57QiFowj2hJhfqLx9sOu+FNXWNZYlG1HEE9jXNrUO0VA4ikKPPSsOdj0sWyQvwD76mbl4dnU1tqypTjl12i8N4HRXv2miGgcnYMhlFsq9EPofPzmBP3/mQayomp36e/fVO0yi2uIFfrgceaguK8TahqSYtai00NDnOuF4WFrskVy+xZIU1aTEFKV70orqYnxz+xHZ7ZETw9SOzTO5WQJrDhorWgTqvSd68cXHFmWIrARBGAsJawQhg5wNHoBhDw7CB8Gh4XHNLecBylQgjEfN5H66ojaDUE2ZjFJAvRk5LLnI4EgUq+rnik7q/F4HHl8+P+XekxOGzNpfnCAgJXjVMuQ4WSxAWKKjKKDuvFE7QQ1HYvjR7nbRZcbjCQR7Qgj2hoAEULfAj6r5hdh7IvfLjvkY1aBiVoEruU8uh6aka7GQdw73YENTuSan0yP1c3C0XbmcdVPzAtQvCGBoZBw/UOGMDfic+KPVi/DGr+WvueMX+tDashAfnrmKcxcH0HE5hFiWyl35YrbYCxGWMeONX3dg2cJZqoP+L/SEAACnu25hX9uV1HXHjfmhcBTdVwfxwWl2p5HLbsWTK8swd7ZXdCziL/9WOIKLV4cAWDDH78bqh8oURRS5e1KwR7nkfSAcRfByCPUVgbS/ax2bZ2LnStYcNDVoEagTCeDgqSt4sukBQ7eFIIh0SFgjCAnk3mx+/xfn4HGpv3zkHhz4D4LxeAJ7T/RqFjGoSQFhFPF4AruPXGIqOzEKp92KaCye+u98pw2jUfMmvnLOKLXNGZRKv6XeyJuRw5KLcCV3ejvXat1fXpcNX95Qh/8/e38e3tR55/3jb+2SLdmWwAa8YBu8COMCjtlinKUsARIKJm0DWZqkyzShaTozbdN5Or9rnnnm+1xXp5Pp8nSalKRJ2pClkHQjxAmbIRu7TVgCRl4IGIMBGyTbsi1LsqXfH+YoWs5yn6Ojzb5f1zXXNPjo6Eg6y32/7/fn/dm2L9KpsXBWTsS/p+vVWDG/AHV3zgBAVkbl9wObt5+Fcr2CWChjO2+iFQ+D9+nzA398/1zIpKz+cAc0KoVsXVRjiV6rwiMry2Ax6uF0eWVpVLFtfzt2N3aieJpJhiOUh2PnurGprhJv7G4hLj+0mHSYPdNCJKzlTk4P3INUKkWEwKvXquD3+0PuvyaDBgtm5eBPe9oES67tTjc2/eqjuHV0ZTATLCYSNf/oD10AlZIJGX4tM/taVDEFJ9tvEt236mqLsKamWPB++MUzxYzaylzRx8r1TCIVuTZvP4PHV1tD7nNS783jrXNlsjVw6u5NbLk7hTIRoMIahcICyYRGygo36cCBZPK2amFBRLYRbVJAkRNmtTWeHQM31c1GdVlOILfw8NnrsuZnhfPAl2dieo4J/S5PRPlcNM0ZxGbGjMfVeja2H7iAwWEvqkqzUVaQJbn0R/L3pVCgqmQy0nXqEOfW4LCXNRtwcHgE2w9cQENTJ57eUIVZBZmoLs/BprpKvPDOGV5RKvwcIHmu/Glva+A1comtL9c3hwglwcTLTRQt1WXZWDxrauC7VMoUn+BwumMqaFtMOmy4db8geR+70w2TQYOH7inFi+80E73Hg8tLka7XEG0bPAYJF7e5AtGdLi92i3DRJeKM2risRPBeS3rPeP9IB4CxkP9oGkyFX/8k47oxIT8fOZY0tHb2Jiwfl3SsOjg8ErEgIOXerFAAJXmZol8Xb0hL+H0+P17daUvAEXKTk2VI9CFQKOMeKqxRKCzEwj1CsqIaDEku09fuLpGlDTplfCImxymceK+2Gg0afGNlOUwGDY7ZrqPb7mJtBiInGpUCe5suiyr1Jm3OIDYzpts+RHjUqc/epsvY23RZVHe5cKTm1gy4vPjh8wdDxNoDn12Fd4RdeAq8bngkJIPJZNAIOr3CzwGS54pjwIM/vn8O37x3lmxiK5eolkocPHMNzR2OwPlSXZ4Dnx+yONdiRXAjFKVCQXw/5WqEFI5Rr8Zjt9xCPp+fuNNjMIxjyefzC2ZqJZpwJ3Mw2/a1Q6mIdIgGQyoWnblgx5kL9sD9SWqDKbZnANe4Ll2vxuwiM9qu9IeIm9HcI6OhrCAL6Xo18QJysIgoxXnm94/l2cUzY03s+EhMCX/9oQtE3x3fOS0nCgWw9Lb8mL8PhTLRocIahcJCLNwjHu8oTrT1iBogVZVmw6ANdVZYp5tDVkDHY9grJXrEDALDiUfe1323Tx/rzBbkGAovw4s13lF/xPtFU+odDul9xOfzY08Mcq70WiWGPckrqjDf9Yr5+QEHG4nwG21uTbgDUowjk8lgIv1tg7ezDwwTvebgmWs42X4DK+YXEB/XRCC4xK6qNBuv7UouR0g4GUZt4HyuLs9BXW0xUUn94bP8JZ0KAF9ZUoS1S4pFuaH4IiKSuRSdeW5VlWZjx8EL2HHwYsQ2JJ0qywqyYDJoiEtsmX1uqpstSmQKhu0+wVYGz5V5R9qBU25OtPWI+rzBIqJUl1/zRbukxWEpC4hix0diS/hJFwVVUS6EGw0aIkf/PSzdYSkUivxQYY1CYSEWWQ9slnk+2B78B89cS8jqJSW5EBpISsn/CiYekyyTQRtwcxxv6SZygcUTOcLMSe8jOw5ewJBb/o55SoU87tVYr6qLcbAlOreGyWAi/W2Z7Y63dIvqpMuUoEqd0I9nXtvVgsNnr8X9e5lXOgntl/uJS9PDz5E1NUX48OQVXiHXlKaBc4h//34gZIGNgbT7MBvJWooenDXm8/nxyemrvNvzleArlQosnj1FtBP6jd2tks81JlOS7ViYRdFG23W8+C7/809stEA0RNt8gETkZaP+cIfoMa6UBUSx4yOS7yP492nt7CU+X6J57ltMOvz8idvx/pGLrCXcDPNKJmEDR3dYCoUiL1S+plBYYFbcYsHWhjb4fPw1RMyDn8tNc7ylOybHRkl+jrd045nNh/Ds1hP4/Y5mPLv1BJ7ZfChwTpAOAvnOwXhMsrbtb8ePf3cQjbbr47IbJmln3kbbdVYHhhzIJdatXlTI+3c53H2A8P0tWTqn9g66iZ4RzDnA3M9JnTLBKGQSR5OdpdV5+OHX58KgUwlu63R58WnrjTgcVSjLbyvAY6vKibZVAOgbChXQlEoFFldM4X1d8bQMov1z3aOry3Pw35tq8JMHq/DdtRX4yYNVeHZTTYRQYOtw4EjzNdg6HPD5/EkXHK9QjOVtrq2dERCTiJoP3HJOcTF35mTRxyLlumX46FQX77OWWVQiLSuPB1IX1oLL8xmRV+w4WswYV8o4Wcr4SOx5Rzp+0mmE73V8PLi8FGq1EmtrZ7B+1watCk+sq8APvjY3qvehUCjkUMcahcKC1BU3EoSyl8SujlEmDiQrrel6DfEgkO0c9Pn86I1TSVDvgCfpnGpywVd2xTgOSbOUEs20yWl4an1lRCMLs1GLh1aUYe7MyRG5ZdEQfH8Ldmf2D3iSolwtK11HXHoHIKoQ6wGXF3W1RdjbdDnmDq1/+uocPP/OGcG8uViw//gVOAc9cMXAuSkHRr0a1sIxl9hT6yvx6k4b7+/hB/DiO2fRcbUfD9xyi/h8fhw9xy8YXOjqJzqe/gEPfD4/pzOLa3zB3ZClVHJIfyzw+8dczcFIKb8Ohvns8cQR9qwNvp9lGLSijudEW09coj+kLqyd67BDqVAEHPTV5TmYO3My9n96OZBbR8qWXS28Y1yp42QxIhnzXYs970hF6vnl2Th45hrRtsEoFMCT60JdddF22aZQKPJAhTUKhQPSzm9moxbfXlOB/iEPum4Mov5Qh+C+my/aOR9+Uh78lPFHeLlnSV4m0UDyq3fNJNo/22DxeEu34ISRFJ1GCY1aFdOOnolkrNtfaUQunFDZFdvENtl5a187nt1UExi42weGMTDohSlNi3S9Bq2dvbL+zsz9bXDYm3TflSXjCyciSendjgOfR3095VjS8Jsf3IH6Qxext6kzJgKbXquCWq1MiKjG0GjrSdh7C/HYamtIXhpf3lcwu451ojg3AwusU4ie7U6XF0a9GgMCv/G2/e3Y3dgpumyOuyHLGcwrmZRU11r4M0ps+XUwiSwhdzjdsHU4cKKtB0fOXpfsgNvbdBllBVm8v3c0DYsYpLoX3zt8Ce8dvgSzSYe75uZicNgr+fMOuLyoP3QBa2tnsP5d6jhZijgr9rwjyZgz6tV4bJUVzR0O0dfck+tmY4E18hygmcsUSuKhwhqFwsPYw2s2r6vmoRVlqCiyAABsHQ4iYa3+8BfbhOdBkD5kk2kATJEXNvGFJKTW7nSjj3DgGD5YlHvi4fb64Pb6oNcoMZyCXQmFJreMcFJdxr1KHD7Jcbq8Sd3FkIvgCcrgsBd//fDziI52cpOsbr6H7ykPmajyOQXEhFjz0T/gwTHbdZQVZOHexYVov9KH5ov2kOdItAx7RnHyfPzLK5MdxpUZLmYolQpYp5uJyrjf2N2K6rIc4km9d1SgLvAWYoLtSRw+p87fJHrfeBH+jCIRLNhK8KMtIY825/BPDdIz2sLhq1aIpmFRMFKbDzA4nG6iJh1C7G26HMjXC0eqe1GKOCv2vCNxMz+22gq1WimqMoYkK5FCoSQWKqxRxg1yrNSxscA6Bcr1CqJAYCkDkvDBsXOIrEMd6XaU1ECoPJDUEfTe4Q7BiUD45MPn80fVZZGPVBTVgLGBLwDB655rlZhtkpPKcVn2gWHsOPA5a0hyLBxURwQ6I8abdIMaP3igCrMKMjES5uriOgdsHY6ovxuFYsydFDgOvRor5ucjP9sIhQKC2UxiaEhCITORVJdNxqa6L3GOI0gn906XF7YOB/Gk3u0VVw7LJbaILaOW81yKFjaBTGrn02ib8XhE/h7hyHl/5KpWiLZhUTCxjEIRw+DwCGdlhlT3ohRxVsp5R9pIhG+7DctKYDJoaWknhZJCUGGNktIEixHhlnMpK3VckOYXRDMgYQbHxnQN0fak21GSAz7hV87yQJJBfPggsLWzl7dT3XhDAWDlwgLMvFVeyzfwZa57h9MN55AHxnQN0vUaznwjgHuSk0wTV7Fsa2iPW1mvyaCJKjA8Fvv6/v1zUDMnFw7HINH2TFl1tISfM2PdQi+K2odeq8KwR97ssoXWHDR3OMZtqTcAHG+9gRNtPZxjCDElc5vfOYNHV1lj0uWVTWxJxZLzYLgyKqV0Po22GQ+pgzBehH+eWOTycn3P8Ybrt5PqXpQqzko570jnDTQfjUIZP1BhjZKyCA0cpazU8UGaXyB1QMIMji1GPdH2pNtREg/buTrmPCnAtMnpMSkPNBo0UKsUIYIZ1yDwRFvyZhvFAj/Gso+eWp+J/95UwzugVSoVGBz24i8fnScqsUmWzpVyE08BpXCqSVTQNRd1tUW4d3ER/uXFw7z3YiHnl8WkwywR2TVylFVH60bTqhWYM3Mypk5KQ4/DJRiaLwajXo3vrKnAOwc/x3uHL8m232SET5BwusgXIwaHR2JaBh4sQCQyT4wEg06Fx1dboVSQVQKEI1R+Hf7vydbxNFqCP4/P50dDU2dMcnmZ77n+0AXRYr5ccP12UgUyQJpIxrxOrABGOm+g+WgUyviACmuUpIOkpFPMwDERHTTDH8CkTQ16B91YaJ0iaSWOkpxwnatjzpPoc0i4GHB58eON86BUKHivpUZbd8KzrGLhqCGBuTeUFWQFrtXWzt4IN6GYEptoy44mMgqMiZ6kotrGpSW42T8c4VYOnyAJTcDumZ+P3Y3c1wBfh9dw5BBWv1yViw9OdEW1D8+IH00tsRHMa+dMExQrxwtcgoTP58e2fe0cr4o/jAARzfknd2kxFw/fU4YF1ikAINmpwyZEcHc8LUmqjqfREDz2E+tKlOrc++jUVUmvixahca5UgYx5rZRzjwpgFAqFDyqsUZIKkvBVsQPHRHXQDH4AkzY1yErXRbUSR0kuYuVe0qgUROUp/UMeLK6Yyvn3Rtt1vPAOd2OOaN5bDN+814pt+9p5JwimNA2cQ/K6puxON+oPXcBHp66y3nOqSrOJS2yAMVHtmO2arMc43jDq1dBoVCHft06jhNvrg5izymLSYfn8AiiVCmxYWso7QRJyER+z9WDVwgIcPdctOEHz+fywdTii6uoshCKJw/iKpxmx61hnog8jrpxo64kYPySTgG7UqwMCRDTHtXJBQVx+26x0He81JAX+jqdnsWphfD5brGHGflJciVKce4k8z0nGudGUUVKRjEKhyA0V1ihJg5AzZFNdJUwGDZo77KIf9NFmbESL2DyIaFbiKImHcV1KOVdJIBW2wktGIjtUihPVxLx3OEa9Ghq1Co4BdtFcqVDwThQeuacc2/bxr86bjVp8e00FTrXfwCenuogaJ7CVuDD3nLraIqISm/pDF/HRqa6kmWgnM4+ttoZMhDIMWrzy/jm4veK+u+BJF8kEqbo8B6OjfrywI/Kcdzjd2HWsM/CM4ZqgHTrdhRf/dhr2MBH2rrm5yM4ywDnkwfXeIVGfg42cLEPU+4gVF64OJPoQ4s7epssoK8gKee4mekwRQpAQK+W4gscVozJ1suXCaNDg5frmkIgCKXm4IyM+7P/0Mrp7XZicqcdeHscpABw7141NdZV4bZctJs1WYk3wdzQy4sMr750T9Xou95dQhUiizvMV8/NFNVugAhmFQkkGqLBGSQpInD0vvHNGcplCojM2pHYVooGmqUejrRtv7G6JuchCkgvFVzISb1NMuKASfj6TiMlKBXivoYdWlKGiyIKKIgvmzpyMX7x1Mqpj3nmULD8qliW9bDAlk6lE+OQ52M0r5loJX1xgJtjXHWOC1szcTGSka3G5ZwA3+oaRk2XA0tvycaK9By++yy8kv7WvDc9uqmG9xzbauvHbv5yO+HeH0y3r72/QqpCWpoZeo8JwlN0IKfLx5p6WkEiJbrsrwUf0BQMub8CVTzrW2bi0BBlGbcR9uKo0m0hY27i0BKY0LbbuaxOVv8i2rdg83Lf3t2F3Y6eo8aDd6YbJoMFvfnAH6g9dwN6myyklsH373lmoKLbgeEs3XqlvFt1tm839xVchwjyru26QNWuRG8YFTqFQKKkEFdYoSQGJ3VyqqJYseWRSXGh0JS61OHS6i3XyHQuErgehkpF4dagMP7/5zmchMVnMNWQtNEedq+MWOXmJF6kmqtXVFmFNTTFruDjp77P0tjzML88JOR/YJths2WTb9pNlYfHlab25u4VoH9Hi8oziD/XRdxOlyEvv4Bfi1fGW7riL6UIwziISd7xRrw6UUYdfjyV5mUTueub1Wo1StkYJJHm4b+9vk1zS2TvohlKpwNraGVhTUzzmKr9oR/1h4ZiORNM75MaOAxdEn3dMk6RwoUqoQiQW3WtJSZYxO4VCoYiFCmuUpCCWdvNkyiOjLrTxi8/nx++3fxbX91wxPx9NLT2cIlOiOlTW1RYhx5Im6fxmE5PDJ3//9cTtaL/Sx3sNkbhEUw2LSYfqcjJHSTJg1Kvx2GprQPDk6o5LwvzynJDzIpoJNh9sz6LWzt6Q8k/KxKR30J20XX8ZpxrJfW9geCTQCZrNsbRoVg7vtbVhWWim4aa6SsEyfRKE8nBHRnzY3Sj9mg928zHPmbKCLBw8cy3py/jf2N2CYY/4hR6mSdJHp7oCjmGScziRbr5kGrNTKBSKGKiwRkkKYlGqmax5ZNSFlpoIZZG0XHLgZt+w5P1L6chWVZrNG9we7+Bhs1GLh1aUBQbvY2H+16MSkPnKVdgaM4T/TpvqZuO1XS0pVfbDxn23F2L9HTPQ2tmbMsKaRqMKOCX4uuMKEe5giHaCzUf/gAc+nz8pcoYoyUVWui4m91S9Rslb2qdSKjDq4344hF8fVaXZgo6jLbtaOMsydx3r5GzmsXBWToSIxnTeNBm0cDjdostDg+G71vZ/eln2yoVYL8CU5Wei68YgBoJ+CyaXMcdiQEaaNiJzjg0polowweW26XpNQoVEi0mHDctKI86jZB2zTxSExrgUCkUYKqyNU1LtBklSvkCCyaDB4tlTUFWanfSfmZI6kHSrFRoYC/HkutkwGbQhge4kzS74hNp4CgIbl5YEyoNIvi8ShMpVwjN52N6X6TiZ6swqNEOpVMh2r4wHjlsOlLKCrKhcPuEOhmgm2EJs29+O3Y2dIedqojM6KYlHqx4rm+wdku+6S9ersamuEtbpZvzlw3bO3DA+UQ2IvD5aO3sFBWsh4evYue4IZ7DT5WFteMN03nxqfSXMJp1kUQ0AMtK0nH/r7pWea8fngqouz8HTX5uDP+1tDVkcMxu1uGNuLrpuDOLM5zdF55oBQOvlPgBjTRtu5xibPryiLG7O6q0NbfjqXTPj8l5cMOJZdRmt3pCLaOd8co3ZKJSJjqzCWmdnJzweD2bOTOxNe6KTijfIaFcN19xeiIoiC30wU2SHVNzJMnJPCPjgWqUV2+yCjXgKAhlGLW+mm9iAapJyla0NbZg7czLar/TheFs39jVdidhmPIhqAPDSu2fxyD3lqC7PSakS195Bt2SXD9dzK5oJNgnh52pZQRYsJh0tB53AeEb8+MVbJ2EyaGTb5+OrragosgAAHlhaivvvnIktu87h4JnrRK/nenbIIbrbnW60X+kLLNr4fH48s/kQ72tkEW14NEQp3XJJXVALrDlYtqgIR09dxs3+4YCQuG1fuyzf54DLi71Nl5Gu10Q456pKs1FXW4ydRy/BHeOGJXanG86h6BYBpWI0aPDYqvLAb0GrN+RBaM4nJLrJNWajUCgShbXXXnsNJ06cwK9//evAv/30pz/F9u3bAQCzZs3CSy+9hEmTJslykBRyUvkGWV2eg011laK7f1pMOtTdMYMKahTZIRV3qkqzUT7djEmZelHloGtuL+Q8d6U0uwiHxN0UXoJqStNgccUUpOs1+OhUF/GkIitdJ+r7ErpeScQYu9ONHz1/EM4oHBKpQt+gN+QeznZuJCP9Ax74JWqbTCe8cKRMsKUQfK4+vLI8bo1JKMmLHPcarnu4UqlAc0cv72tNBg02LiuF2cTuSjne0o3Xd8vT/OJEW09A+CC9H0cr2vS7uF+/9LZ8vPVBu+D48EcPzIVz2CvauaNSKjCryIKRER+Ot3SzuvOiZfuBC9jTeAn3LCjAmppinGjrift93Jiuicr1bNSrQ0pbhdBpVFi9qCDQwIYiH0JzPraS7pBSZINWtjEbhUKRKKz9+c9/xqJFiwL//cknn+Dvf/87NmzYgLKyMvzmN7/Bc889h3//93+X7UApwvh8fry6k39Alew3SJNBI7rEhwadUmIF6WSitbMXlTMn4bt1X8J/bmkk3n9FkYX33I222QWJE5QpQT3R1oPDZ6/DOeQNZHilEQbLM2WpYr4voZVq0jLWiSCqBcPcw8PPDdKsnnizbX87jBJdPlyTbNIJdrQEn6sLrDn46WML8OLfTktyrq2pKcTQ8Aj2fxrpqqTIh9mkw7fvnYW+QQ/6Bt2oP3QRQ+7YuoBIWTE/nzemguT+6XR54RzyYFHFFFZRTU4n65Gz17Fh6dj4ivR+HK1ow+eyVquVWLmggLexwqqFBZg9I7pFfZIFIgVCzXUmgwZlBZk43npDcP9D7lFsP3ARO491wu2J/7lpMeqjcj0vrc7HjoMXBbcb60iaTwW1GEFynrJdKw6nW1R3WdIxG4VCkSisdXV1hZR77ty5E/n5+fiP//gPAMCNGzfwzjvvyHOEFGLqD10QzNWIxQ1Szjw3MZlQCgXw5LrkdeBRUh/S85HZrmZOLp7+2hy8vssmKHCQtpSPtlyCxPl2vKWbNRB/iHBVuro8W1S5H8n32m2PbblfqhJ8Dw8/NxZXTIlJp8xokZq5xJW3pFYrcc+CAuyOw2cNPldr5uSiPC8DzRfscDjdaL5oR1NLN1G5cUXhmPOOCmuxZdGsnIDL0dbhiKmoZjRoQs5toQY0x1t6AkIVG6T3z23721F/uAPfWFmGBdYpAMYaemwRWFgVi9PlDdxrSGMFohFtSJ6JDywtBYCIPDqFAli5oCDwd7H4fH581n4DnVf74OgfFvwtgn9mo0GDR1aWYdTnJxLWGBIiqgU5HaW6nqdOSmN9rdmoxV3zciV3BKeQ4/P50dDUGTenI23gQ6GQIUlY84eNHA4ePIhly5YF/jsvLw83bpA/XCjR4/P5iTvFyXmDlDvPTUwm1JPrZmOBlYpqlNhBej4Gb7fAmoO5Myah/tBF3lXBeDot+ZxvJKueQuxtuoy9TZeJnUlC3+vxlm5RK6oTDbZ7uM/nx9Fz3Qk4mtjxcn0zHr7VZTacuTMnEwtrCgArFxagODcDr+9uFSX0hZ+rSqUCg8Ne/OWj88STmmDBIFUaT3BhNKjhHfElbXbhrmOdmJmXierynJhPBh+8VZLZO+hG/4AH2/a3827Pt7B5vKUbW/eR34cHXF5s3n4WFxb2Y2Ze5liXzxh0Pma+Q5JYASHRRqiEkPSZyOTR7f/0Mrp7XcjJMmDpbflQq5Uh25Eu+h5v6cbWhjbJGYrMb1FXWyTp9fEk+DsOHxeQnMPA2D3RWmiOyk1PkQ7bvCvW0AY+FAoZkoS1oqIiNDQ04MEHH8Qnn3yC7u5u3HnnnYG/X7t2DRkZGbIdJEUYki5QDHLdIGOR50aaCfXkukoqqlFijpjJRDBKpQJra4uRl50eVUaanHA536QGy7NBIliYjVr4/H4cab6GrHQdSvIy0X6lD/b+YZzv6oPfP/4EIrlhc3LJ+TsmC70DHs5niRjR5J+/PheVM8fKw6rLcgLf1dZ9bbznLNu13WgTX27HTGZ9Pj8qCs04eOaaqNcnGqNejdsrp6KqNBtOlxev7bIlrbAGfFEuHevJoNmkC9xTjzST/aa9g+4Iwad/0IMXdkjL8xpzqMbOucl8hySxAnyiDSO8sGWKSXkmqtVK3LNwOuvfRkZ82LLLFuEmZVv0lbN89qOTXUkrnPPl+gU3qNjdyO+CCr4n0uYD8Ufucm8SSKsrKBSKRGHt29/+Nn70ox9hwYIFcLlcmDlzJmprawN/P3r0KKxWq2wHSRGGdJKRrlfLcoOUM6Q8mBNtPfAIdEVKlFNNzpJXSmogdjIRTrQZafEg3hZ/z4gPv9h2MvDfQuVTlEjYnFz2AfKmGakG27OEVDQxpWlCGiAETwa1GqWoa3vU58ebu1tEHbvRoEFVaXZCXAbREt5teyzQPfm70TLOsLKCLKTr1cSLjmJgJpvMuKCrZ5DodWc/t+PNPa0xOSaxCH034RNqsQ112ISXWD4TfT4/fr/jLI7Z2Bdmwhd95XBrh+x/wIO62uKkc1tvXFqC5fMLBL/jaMc7lNgi9/lKyp1zc+P+nhRKqiJJWLvvvvuQlZWFjz76CBkZGXjooYegVo/tqre3F5mZmVi3bp2sB0rhh3SSsYLg4UqCnCHlDEIrMeGtuuOJ3CWvlNQh2u6cyb6qGytXh8mgCWkswJQBhU/kklVUWzE/H5My9ESlMeFZS3zMmTkJp8/fjOrY2Jxc/UnWtEBO2J4lJG5SAPhyVR6O2a6zTuCry3OwamEBZ15T+LXd/PlN0eViAy4v/vj+uZRzqek0SqxdUoz2K304ZrsOo06DLbvEiYqJpHfQjeOtPZIFLJKyRbEdHRVAUpwHzNgFgGgRRQ5hjO2ZyLZwCYD4fY63dOPl+mYiJyUj1MfC5ZtjMSSVuGYx6YhENQY5upFTYkOiXOnbD1zAR6e6WOc71HBAoYQiSVgDgCVLlmDJkiUR/56VlYXnnnsuqoOiiIdkkmHUq7GmpkiW9xMb6i4EyUqMRqVAVWk20f6iIfxB4XR5WVfpoyl5paQWqeA8kwqpQCGWO+ZNQ8V0C/pdHmQYtHjl/XNAErg0gjHq1fADIZPv4AkEaWnMhmUl2LydrJTr867+aA87ADNBPNHWg/pDF2XbbzIS/iwhcVfoNMqQ7nXhiyHHW7pZmz34/aFZXQyHz3RJOvZkEFPE4vb68M/PHUgKZ5UUrt0cwrsir4m62qKQ4HW+skWAX5RiIxnWEJZUTsU3750VeHZJEVGkLhZxTcLZFi7Tb3WkDj7/uBYzxZbHMUJ9LNzaWek6LLROwYcnr8jSoVmvUaK6PBsHz1yX9HopDrPxPN5JZWJdXWA0qLH0NvaOr2zzHWo4oFAikSysAcD169fR2NiImzdvYuXKlZg6dSpGR0fhdDphMpmgUqnkOk6KACSTjMdWW2V7MEoJdeeDZCXGMeCJectntgeFQuArk1LySkk9kt15JhWSe4eUcqr3D1/C4TPX8dCtgX2ylcAx5TFApCsCGOso2Dvoxl1zp2H7gYuc+3lweSmqSrOxdskg64A0GFOaBs4hMmebVq2AZ4R/Km53ulF/6ALv8Y0X2J4lXO4KvUaJYW9kwD4zOairLUJ2VppgWHzwvf3oueuo/yQ5XCjxIlVFNaNBI3gtBmM2avHQrdLqkREf9n96GU0t3cjJMuA//2ExPr/aH3F/eGbzodgcfAwx6tUhohog3NhGrLgiRjwzm3RYNCuHVdxmO/fYJvdSy+PsA8OwGPWiX8dHcPOGh1eUyZKF9Y2VViyqmILmDv4xcnikQrQOs/E63kllSOdTBq0KLgkdZxdXTMUnp6/ybhO8mCd3xjaFMh6Q3BX05z//Od58802MjIxAoVCgrKwMU6dOxdDQEJYuXYof/OAHePzxx2U+XAof8bRwSw1150JuB5wUGm3s+TFCpWpiS14plGRD6N4RPPHKSNPi5fpmotV4ZpC1Yn5+LA9fEqY0bWCSGHztkrongp0rz2w+RCQcLpo1BQ3Hhbs3q5UQFNUYdh69RLRdKsP3LAkXBhh35LCX+/cgFSKZe/vgsDfugdEU6Yjp+rqmphB1tTOgVCrw9v62iLLgtz5ox8oFBfja3SVo7ezFMdt19A94km6hgARmIYEhXARbaJ0SuCdKcaOIFc8cTjfrvwsRLHhLLY97q6Edj6wsl9WtHd68ge2ZKhazSUe0+PXkutkwGbTUYTaOIZl3mdI0WFiRg31NV0Tvf3Kmnijix3bJgVd32ni327KrhRoOKBMSScLayy+/jNdeew3/8A//gNtvvx3f/OY3A38zmUy45557sGfPHiqsJYB4WbjlDjmV2wEnlkbbdbzwjrSuXED8A+ApFLkRuncEi09iV+OPnJVWxhJLnEORwiBXSREjqNXVFiPHYggpFSP5HhgR7mYfWYOBERENF5O5O6NcCD1Lgt0Vtg6HrKLHp209ON7SI9v+KMlFRaElIKrxlQV/cOJKwq61eSWTcLI9ulxGIDQrCQCncAawl7nyuVH4usRLEc/4YCb3SoUCTS3SOkgzER+rFhbIcnx1tUUR3wmb6A8F0D/kIVqgCl5QoNlnFJJ5l3PIGxDVxDSGsph0MKZriLbd/+llQTfzgMsL2yUHKoosvNtRKOMNScLan//8Z9TV1eGHP/whHA5HxN/Ly8vx8ccfR31wFGnEy8It54NebgecGMY6nUkX1YDYCX4USjwhvXeIXY13inCQkMJVnkpatho+iCQpKfr4VBee3VQTKJMS2l6vVWFZdR5mTbfAWmjG1n2tgsdF+YLwZwlJaZrcixwNTcIOQ0p80KgUUCoVsglczJhiZMSH3Y384kqiRLWv1BSiLD8Lg8NenL/SD1/YRDlNr0ZlkRlnLzpC7nt6rQrDLOVgjDjGBvM3xqHLRXj8RSK6FW7efkaWUuVj57qxqW423trXHtKYxGLSYeGsHHxy+irR++RY0lj/ne+ZKrRAFb6gQLPPxg9SQ//FjL0YUW3F/Hyk69WCkRbpejJh7bPzdqLtbB1UWKNMPCQJa1evXkVVVRXn3w0GAwYGBiQfFCV1kOtBn6g233IMCGMl+FEoyQxz7W//5HPUH+6I2/syrorgrm7OIQ+M6RpYjHr4fH784q2Tgvu54XCF/Hf9oQuiOh2TlCANe0bx3uFLeO/wJaTp1RhK0cyqeKPXqPD9r34JZflZaL/ShyPN19BtH8KHJ7tCHB5spWl0kWP84h31A6PytQBgxhQNTZ1J2Z1YrVRgT9NlvHuI+/46NDyCY7YvHJXpejWWVefj41NdrMIaCUJCUnj8RSK6FcqV/2d3umEyaPGrp2vR5RhGR1cv+pxumNK0MJt0qCi04Fd/PiW4nwyDVvR7S1mcptlnqU+0of/V5TmYO3My9n96Gdd7h3DkzHXeTLXjLT14dlMN8rKNvOeaz+eP6ObOhneUcJGB6r2UCYgkYW3SpEm4epU74PDs2bOYNm2a5IOipBZyPegTYXWXY0AYC8GPQkkFlEoFKoosMRfWNi4tQYZRy1ueyuDz+Ylyc7YfuIi8bCOqy3NwvKWbOHerd9ANn8+P5otkq7YME11UMxu1uM2aTZT9MuwdRfvlPrzy3jne3zG8NM3n88Pn80tqtkGZWDyxbjbS9Rocab6Gls7eRB8OKyM+P0ZEimODwyOiGjdIJdgZmupRGL2DbiiVCjiHPPjz/lDnmtmkI7qfvPL+OUndEKkLbWLBVzJNGvrPJszxwQjhQueaUqnA4tlTsFcml7a1gArAlImHJGFtxYoV2LZtG+6//34YjUYAgOJW68QDBw7g73//O7797W/Ld5SUCUO8BxliBoRyd12iUMYDJGXc0WAx6bB8fgHxPYDE/cqwtaENc2dOFgziDabbPkTcrIAyhkGrwgPLSvCnPeTu4O0HyDtwbm1og8/vx7Z97RPidxGTnZMI9FoVvnnvLHT1OPHOwfi5WUnRaZR4Y3cLFV+jINgZmqwuUbVKgRECh2NWug6Ntm789i+nI/5Gej+JphsidaGlFmLLOJnt7QPDeKuhnXff4WXW4XAJc0Iwcx2hc62qNJtIWOMqNWcw6tX0nKZMSCQJaz/4wQ9w9OhRrFu3DvPnz4dCocBLL72E3/zmNzh58iRmzZqFJ598Uu5jpUwQ4jnIEDMgpF2XKBT2QSWpkCUFKY7Q6vIc1NUWC4ozdqcbv/nLKeIJtkGrIna2jSdUCkCjVmJYYs6UyzOKF99plvmovsDudEedk5lKrK0pTErBiuH767+EimILjviSs7GG2+ubEE0/YoXZqA2Jv4h2cWXVwgIcPdcd8nqjXg0/Qks+xTpRjQYNvCM+3tdYTDqU5GXih88fFNiXGiqlAn2D/GVyQsIIJTXgEs/ElnFKdZdxufGlRteQznVI8643LCvF5u3cY77HVlvpNUCZkEgS1kwmE95++2384Q9/wO7du6HT6dDY2Ijp06fjqaeewne+8x3o9Xq5j5VCkR2Sh4hCATy5rhILrNSZRpnYcA0qNy4rkb38LlpHaI7FQLTd2YuRDXi44MsxGc+sWlyIoqmmmImnFDKYa63zevwybBUAxJjjjAZNYFKYrE4mSnTcNS83ZNJM4hJmE8+C7/Ffu7skontm76AbA4PeQN7Z51f78JcPPyc+Tr6OmwwblpXg/SMXMSCQKzXgGkFN5RQcOsPf4ZpPGKEkDjEuM65xzqJZOawdZLncitG6y8KRGl0jJgeaNO+6ujwHyvWVeHNva2juqVGLh1aU0UoeyoRFkrAGAHq9Ht/73vfwve99T87joVDiCslD5Ml1s6moRpmQBA9Gu+0uVgeYQwa30FjpmFWSI5RrwEwn9fIxq9CMiiILnlpfiVd32mgJHQ9fu3sGhj2juNw9gJPtN4lft3JhAXazTNoY9BolFs7KiXu5q9iK0wGXF3/5sB1fu7sEPr8fOo2SusPGGWwdMEkycoPFM7Z8J2uhGcdbuvHK++dYHUFDbmn3nXSdClqtmvW62drQhqFhsq7VQqIaQ6pnzsUDqV0xpSDGZcaXgcYmqgUT7FaMhbtM6nkl1vVPmndN8wEplEgkC2sUynghEU0TKJRkR2wJg1S0aiX+5wd3QK1Win4t34C5qjQ7ptlvXOg10ksmkxG9VgXr9DH3BTOQrj90AXubLssusFlMOtw5d1rKltsqFBDlqGFYtbAADywtRUleJv6408ba6GLY6+MV3mLJivn5aGrpIb6Wdh3rxMenujDknpgOz2SGGdcAiLh3knQEBLgn/iTh6NZCc0BUOWa7HlFmxxfsPr98spSPjEH3KKxFZhxviTx/SVxtYhnvizrRimLRdsUUg5hmAdGIYYD4ruFs8LnLxJ5X0cxhSEUzmg9IoYQiSVj76U9/KriNQqHAz372Mym7p1DiDl15oVC+QGoJgxSW3pYnWVQTGjBvFMgBiQXV5Tk4eOZaXN8zlgx7RnGirScwOFcqFVhbOwNraooD98sTrT1otPVE9T7WwiysWVwEJ6F7JBmR0lDgiXWzsaA8B7YOBzxeH5SK5HvmVJVmY8PSUrR29qK5w476Q8L5blRUSz42Li0JaQQTPuYpycvEv7x4WDBfia+sTGiizRcnsG0ff7D7ORFl+5Hve0Pya8XA9v2EC1EleZlov9KXUmNN5jOcaOvBkbPXQwRYMaKYHF0xxRyzkFAW7DKTKoYFw7jKYuEuI4muMRk02LisFGZT9OcVFc0oFPFIEtaOHj0a8W8+nw89PT0YHR2FxWKBwUCWb0OhJAv0IUKhRL9qK5ZdxzoxMy9T1GCa5Bhfee8cfL74t048eOYa9FoVlEoFq/MoFWEmHwBYFx8WV0zFtE8+x46DFyW/h62jF7aOk/IccIqwqW42lApFUneZtQRN0KyFZlrmlqKwdVdmG/OQ5CtJnazziSokcQKDKSDWhn8/bEJieFdfvVaFx1aXY9GsqYL7J3GLyV1mKeReDxfFuN5frNAVLSRCWbDLTI57G+Mqi4W7jCS65tFV5bTKhkJJIJKEtf3797P+u9frxVtvvYUtW7bgD3/4Q1QHRqFQkpt4ZmRQ4occq7ZieXWnDQbtWHt2knOI5Bj5WsHHGua9F1pzUFliwR/rbaKzqpIJu9ON+kMX8dGprtDOfQYNlt6Wh6mT0uCTYteaoASX4yV7Q4gNy0KFgm77UAKPhsKFTqOC28t9zyMVxGIVjRHvBZt4w/b9cAmJ4bfK4Vtdk4+evY4ffG0u6/59Pj/qD13E3qbOkBL8cLeY3GWWYtzrf2pog8/vj8iBZN4/Xa8RJXRFC6lQxmwXbQlvsFsxVu4yGl1DoSQ3smasaTQaPPLII2hvb8f//b//F7///e/l3D2FQkkS4pmRQYkvUldtjQYNfH6/JJfW4PAIfvHWSeJzKFVcM8ds3ai2ZmPlwgLB4ONkh61xxYDLG5VLLVnRqJXwjsifk5euV2PF/AKsqSkCADyz+ZDs7yE3JoMGwNjE3tbhwJ7G1D6Pkx2VUoEpZgP6BjwYFAjrD5+Yn2jrkWXCHYtojEQs2MSDNTWFqCi0RHw/UoTEk+038db+NmxYWhry78dbujmbxjBusU11lbh6Y5CzwZCUMkuxn4HLeci8/4r5+UT7kev5TiqUMduRiGF8BIvXsXSX0egaCiV5iUnzAqvVinfeeScWu6ZQko6J5tyKZ0YGJXZwnbcZBq2k/Q0QhF4LQXoOpVI49B/ft+G5f7oTALC7sTPEraBQAPPLs6POJ6PISyxENWBMQN5+4AIGh70YGvamhNDQO+iOWyOTicyY6JqPNTXFgbK5+kMXeBt5hE/M5Zxwyx2NkSqLIWLJnZzO+j1JFRL3NHbiq3fODOSOkjrGXnjnjGDGo9gyS7nF0CNnybqqyvV8JxHKgl1mJGLYqoUFOHqum0i8jqW7jEbXUCjJSUyEtUOHDtGMNcqEYKI5t+KdkUGJDXznrUEn7rFgNukwNOyFW8ZOmELnULQry/Fk2DMKW4cDDywtxf13zsT+Ty+ju9eFnCwDlt6WD6VSgfYrscvYCs/zCSddP/Z7y93hk8LN3qbLiT4EYrrtQynbpTUVuO/2QswuinQ8MU1C8rKNoibmyTrhTqXFEDFwfS6pQqLfD2zd14rSgixkGLTEjjGSSnyxZZZyi6FOl1ew86xQcwwxkAhl4SXSJGLY1+4uIRavqbuMQplYSBLWnnvuOdZ/dzqdaGxsRHNzM7773e9GdWAUilji7RybiM4tsWGwlORD6LwlLddYelse5pfnoOWSHe8cFO4SKAahc4hkwEyCyaBB7dyp2HkktuVtH5y4jIpiC9RqJe5ZOD3kbz6fH3fNzWUt4ZHKxqUlyDBqkZWug9Pl5e2M+vhqK4Dkz/qixB+zSYcPT3bJvl+LSYfcyek4c8Eu+75TCaNBg/V3zOAdpyTDxFyOsVUqLYaQwicCRSMkfnCiCx+ckP+6A8SJZbEQQ4tzM3D6/E3Ovy+clSPruS3FNSZ0zYkVr5NV7KZQKPIjq7CWmZmJgoIC/Md//AceeOCBqA6MQhFDvJ1jE9W5JTYMlpJckJy3pOUa88tzUFaQhef+dlqOQ4tA6BziGjCL4ZGVZXD0x/5cPd56A8dbukPuRVxh1NES3v3veEs37/ZXegYwODwCvUaJYRldh5TUR4rgazbpsHFZKbbtC70uTQYNFs+egqrSbJQVZOH/vX1S5qMlp662CDmWNGSkafG7v3+GoQR1m3xsVTnR+CCeE/NwEc3p8kb8lsxvbDJoiMU2pVKBjctKiLp/pgp8DSGSVUgMFsuY39o+MIyBQS9MadqQIP1YfIbWSw7evx87142v3V0iu7gmVpymYhiFQpGCJGHNZrPJfRwUimQS4RybqM4tsWGwlOSC5Lx1urwwGjS8mWnMSn1rZ2/MJqUk51DwgNk+MIw3drcSdQM1GjSo/dLUiO5lsSRYaOcLo46WDctKAxMIklIiWuYnjeKpJly45kz0YUSFQavC5CwDbvS64Aq6bhg3h3dUvNDKLGZVl3FPZH0+P8539cn2OcRQV1uEtbUzAAC2DkdCRLVkiYsgEdHYGAupDx1zkXwmk8T8TlLqaovQfqU/5k5IkowsuVzVchLssOPLTQwWwTcuK+V1PItFaPEmVuPmiSCUiXGWTrRsaAolXsQkY41CiReJco5NVOeW2DBYSnJBej4Oe/gFH2al3j4wLMdhRcB3DrENCK2FZoyM+PDG7lbBfa9dUoS8bKOskwUS7E43mi/Y8fnVvpiIWRaTDgtn5RBNjCnRk4qi2rolhSifbgmIrlAA/UMeZKRpAT/Q7/KETLJsHfzukmDCxQa+iWxrZy9cnsS4I3MsaYH/Hc/nc7hjL9GTWLkbUpAsZMbq+1YogCfXVWKBNQe2DkfMhDWdRoWn7/8SrIVmot9PDle1nDDPbaGGCE6XF3ubLmNv02WYDBqsXFiAY2GB/bFkvI2b4wHb9azXqnDPggKsXVIccr5OtGxoCiWeEAlrXV3Sav1zc3MlvY5CISVRzrGJ6tySEgZLSR5Iz8eRUfYkZKNBg8eCOtENDIrrBLq4YgomZ+qhVCqw4+BFzu24ziGuAeGiWTk4+Nk1IrdaWX4WXnn/nKjjlotf//kUCDKmiTEZNNi4rBRmkw5Ol2dclVlRyNFpVHB7hc/9KZPGOhgeb+nGK++fY51YBT8nSRZS0vVqbKqrhHW6sNjAiOJNAuXJsST4HpiRFlsH1QJrNuaVZMM55IExXQOLUR9XUY1tEQIA6g9dlDXTMZg/7W3lXMiM1XjoyXWzscA69kyKZQmm2zsKl2dE1O8X7Kr+tK0HB09fDXGHxgOjQY3HVllRXZ5DtBgdjNPlxe5jnVi5IB9mkx7b9rfH8EjHGG/jZrFwucm4/p1LKB32jGLHwYvYfewSvrOmAtXlORMyG5pCiSdEwtrSpUuhUIgfCJw7l5jJC2XikCjn2ER2bsWyhTgltkQ/6fDDPTLW5bKsIAsmkRPTOSWTsLhiKgCgIEdctzu+AeGuY+TNB2ydjoS5B+QU1YCxSU9muhZlBVl4ZvMhmfdOSXaY66Wze4BXqGbISteJmliRLKQ8vtqKiiKL4HvL7ZCSQvgz2eeT+4oMpdHWg+aLjpCSb7mcIUKlXFwOFp/PD89I7NyCjgEP6g9dxNra4oi/yS16sT0vYl2CKbYCgvmddh27xBvaH0s0KiWqSrMBkC1Gs7G78TKWEzY2iobxOm4mgSt3lVk8PBrmGjSbdLhzzjQ0HOfvMu32+vD8389gU91sbNvHL4yOx2xoCiWeEAlrP/vZzyQJaxRKrEmUc2yiO7eSoVMZRTwn2nrgIXC2cDHgGsHL744tmJhNOtw1d5qo1wdfh2LOIbGr7LzEdi4ddza/cwbLqvOTotSIEltWLizA3JmTIxxIJNdGmlaJkVEfXq7nX/AMn1iJXUhhE3xOtPVEJXR8uSpXli6J4c/k1s7eqPcpRHiOohzOEDbRzGTQ4JGV5Vhg5XalkDh65WD7gQvIy06P+Hxyil4mgwY/f+J2qNXKiL9Vl+egrrY4Jq48u9MN2yVHiJjM5yRKtJgMjImdTNVGNIvMB09fjfpYjHo1BniyRcfzuJkPvtxVrsVDh9ONdwgWVBhe29UimOs6HrOhKZR4QiSs3X///bE+DgpFEol0jk1059ZECIMdTwjlqojF4XRj+4GLSNeriUL42a5D0nNI6io72zFYC82oP9wR9b6ShcHhESK3EiW1+d0/3Qm9PnLI1nzRTnRtDHl8+NXbpwS3Y5tYkYrgbEJCllELb5QOqZn5mTjZflPyPYDzmZzA+XuwgEkaJM44WtgEI6fLi83bz+DzBfk4ZuuJx0fghcv5woybXnnvXFRCn9PlRfuVPs7nx5qaInx48gp6BzyS34OLzdvP4PHV1kBpHVc8gRgndaxhBLVoFpldnlGYDBo4eRobCXWXfmy1FQAm7Lg5FgsPpJA2S6IZdxSKdGjzAkpKk2jnGHVuUVIBWR1fYZCe6dFch3IN9B5cXgrrdHPMMngoX2A26bBxWSltpiADc2ZOwsXrTtZyv1d3yt+l/URbT4RgISSCcwn3cggbWek6yU6njUtLsHx+Aeu9x1pgRj2ERfav1BSi/nAH/DK6XRkBc3DYSxQk3mjrxuu7bLxuH2CsZC8Z4HO+VJVm46k6Ff7nr6fh5cjzJIHvvnKirSdqQZeLweERPP/3M1i1sIDTSZRMohrwhaBWkpcJhQKSz+XFs6dgbxP3OfbtNRUAhIWzWI6bY9HxUuo+g1/XbXfho1Ndsi88yM1Ez7ijUKIhKmHt+PHjaG5uhtPphM8XemNQKBR46qmnojo4CoWERDvHqHOLkuzUH7oQM3FjYHgEdbXFEQNGBjmuw2gHehqVAgtnTcHcmZOhVCqwcFYOdifZxGe8ESwMxLsD63jj9PmbOH3+ZkBwqSrNRv2hCzHpLgsAe5suo6wgKzGl2iy8XN+Mh1eUSeqwmGHURhxzYLI75IZeq+J1Thn1aqyrnYH8HKPszUFOtPWwihRMuWhdbTHW1BThLx+2J51QQwLbgoic5ZHOIXbRVm53Nhe7G1PjNwl2i7df6YtKIGa62kYrnIkZNweLU8HdjEmzBaPNNZS6T5JzPRaOymgwGjQTNuOOQpEDScJab28vnnjiCZw+fRp+vx8KhQL+W3dq5n9TYY0ST6hzjEJh53hLd8wm4Aw5FgP+e1NNoGRT7i540YZee0f9OHjmGg6dvYZ75ufj0JnrUR0P5QsUCI2tMxu1eGhFWaAD3bZ9sRNcJhqM4EJafh0NW3bZ8Obe1pCJH9dkUq5SbS56BzyBXLJv3zsLv3jrJPFrw0V5scLOY6utUCoVWGCdAuV6BWcOkhQOn+W/D20/cAG7Gy/B5Y5vF8lgzCYd3J4RDEk4BrbvXk7BKy1NDVuHQ1LmoBzI6WCMJcFu8Wjc34xAp1QqZBXO+BC6Xk0GDRbOzkF2hgE3+oZZg/yjyTWU2kUzXuKu7KTKSU2hJCmShLVnn30WLS0t+OUvf4k5c+Zg+fLleOWVV5Cfn49XX30VJ0+exEsvvST3sVIovFDnGIUSSqydJAxZ6brA9Re8uhx+LFKFb7lCr/3+5CmVGi9EDMODGh3ZLiWuA+t4JtaiGjDWqCQcrslktKXaRr0aGrUKjgH+/WxtaMNX75pJvN/wXEcxk90soxYP3xKIGapKs/GnhjZZvn+jQY0BnqwqhniKamaTDg8snYk+pwfdvS7kZBmQOzmdKJuPjb4hT0D4yjBoZX8W/Xnf+ZC8L6ahTirec6SK5XyvY3OLR+P+Dhbo4jHeJrlenS4v9jVdIdqflI6uQucs2z7jNe6KBQPDI7R5AYUSBZKEtY8//hgbNmzAvffeC4fDAQBQKpUoLCzEv//7v+P73/8+fvazn+FXv/qVrAdLoVAoFHKiFTbS9WqMjvp4w4iDJ698Qc5sreJJyjMYQc7j9QkGI1MSDyO+rFpYgE9k6CJHST7CJ5PRlmo/ttoKg1Yt6ESz33LDkhIsBMgx2ZXTmecdSS5nSF1tEaZNNkZkIqazNMwg5fc7zsbUABMeos801Ek1Vi0swMy8TFELR6Y0DR5dWR7iHBMqkwSkub/lilXhW1wL/1tJXqbs4pTYjpck1zvbPmPt4I01tHkBhSIdSU/M/v5+lJSUAADS09MBAIODg4G/L1myBL/+9a9lODwKhUKhSOF4Szf++P450a/buLQEGUYtcccqZvLKVzLBFfDMV0rBdMDb29QZF4fORIQvGy9aUjETSgpGgwZqlSLpsnJiSfhkUmqpdvCE/UjzNaLXGNM1gu+lUABPrgu9r4id7AaXnzL7kXPC6fYmrrwznHS9GsOeUdYsxGjuvbSqjB+jQYNvrCzDAusUACDOEDQZNPjl95ZArVYCgCh3Ean722TQYPHsKYFMtWjjHLgW3e6am4vBYS+OnL0eIpQaDRoiR6dYxFzDpNuGbxcLYWqhNQdtV/riItjR5gUUinQkCWs5OTm4ceMGAECr1WLSpEmw2WxYvnw5AOD69etQKGi2FYVCociB2DJKqfkeFpMuooMeSXOQaNwg4e6XMUHtAnYevQQ3dafFDKNBgzU1RVhTUxTUtWwIH57siqtIpFErk64rWk3lFOIcvgGXF/Ot2Wiy9cT4qJKL5g574D6kVCpw19xcbD9wgei16Xo1NtVVoiw/C+1X+nCk+RrOfn6T6LUWo15QGHhy3WwssIaK9VInu8H3p/E64RwcHkkZIdxk0EQ41aLFYtLhoRVl2LqvDTf7hmXdNx8qJaBUhD5rSRqTPLqqPCCqSYHrmR6NmMbXYMDp8rKKtmMOQ/Z7RixENUCcaES6bfh2ct8nzEYtvrt2NgAEvuMTrT1ojMEzJ7x8nkKhiEOSsLZgwQIcOnQImzZtAgCsXr0ar7zyClQqFXw+H7Zs2YI77rhD1gOlUCiUiYjYjlTRiFzBpVPBCDUHiab0Idj9crylW9ZwcAo3IyNjjpnwbDznsBeHPrsWt2ynZBPVAGBypgHL5+ejgaVjIxvHJ5ioBgD1hzpw8LNrgftQjsVA/NrBWzk+r7x3TnRJGnPf4RP7q0qzI0Ltu+0uUZ+Pwe50w3bJAaVCAfvAMIx6NQYE7k9pOpWksH8KO6Y0DTYsK4HFqIfD6cZL9c2y7v/B5aVYWDEFX15UhL/uteH0+Zs4f6UPLp5usXLQN+iNcEUqlQqsrZ2BvGxjzDrd+3x+pOs1+OqdM3C+qw8AMMWchqW35UsS7IQaDCSLz0IBoCQvk3h7EicumxAVbbOlcB5aURYYazHuxMUVUzHfdh1/fO8ccTyGxaTDwlk5vCI61xiQQqGQofD7xZu1W1pacOjQITz88MPQarXo6+vDP/7jP+LIkSMAxoS3X/7yl8jJie7mH09GR32w2weFN4wCtVoJszkdDscgRpJwMkGhUKQj9/XNlELyuUDYyihtHQ48u/WEqPdi8lqkDtiPNF/D73dIn+ysqSmE2zOKvYRCBkUefrxhHiqKLaI7JVJSm7E8rXT84b1zsrhCn1pfiXS9RvR9R8r7BN+j2Jy8J9p6Is7laLuo6jRK6p5NIMG/O+nzja3MPbyDcbBQdaL9Bv60tzXEsWY0aHD77CmYO3NywIGVkabFc387jWGPfOeDxaTDs5tqoFQqeJ1fUkoyw68Rp8sbkaPHQJp7Gkyqdb/8yYNVokpnhT5ftF1BmXMQgCQhlXH47226HHKPS9ersbw6D6UF5ojzh+15L5dom6zQ+TclWiyWdKhUwgsPkhxr5eXlKC8vD/x3ZmYmXn31VfT390OpVMJoNErZLYVCoYxLpJRyvrm3VbAkj60jldiSp/C8FilEW/pQf6gjqtdTpGHrdMDlGSGeGDHOkax0HV6ub06pXDHa+GKspOihW50uj7d0yyYWvba7BV9fOhN6rQrDMXL51NUWR0z6wjsTck1mo3XAUlEtMbBN9kvyMgXLQS0mXaDMPVh0YES1dL0aK+bnY01NMQBg+yefY8fBixH7GXB5sbfpMtL1auRY0gKh+hVFFnzaekO2z8m4tgeHvZzudCldGsUumAjlnoaTit0vxY6PSKIwxLzObNTirnm5gfMpeCzIVxXABeNwXFNTTPxaoQoECoUiHUnCWnt7e6B5QTAZGRlRHxCFQqGMJ8SWcopZAWbrSCVW5Io2rwUYK32IRfYNJbZ03RjCwc/IQuMBwDnkhcWoh7XQjIdXlKWMUyFWQdiphFatxH89WQO1Win7hNg55MUf6m2y7Y+NkVEfjjRf45wEpuIkf7yiUPA3LjAbtfj2moqAAwx+oN/lCXFoBf+7rcMR4kYUes4EN9RhyyobHB7B9gMXMewZxSenrwoKr8H7EPpsUjnR1sPq2BYrdjFE4yRjW7BjI17dL8OdhtEgZRFQqhAl9nXhCwViEPvaaN6LQqFwI0lYW7NmDUpLS3Hfffdh9erVKCwslPu4KBQKJSkR4z7j65TJNliWMjkMX4ElzfeQUvbBhVKpwKKKKWg4PvFKOTVqJbRqZUrmwp3rsIvOUmPON2ZFfsuulqQXrSQkXow7PCM+tF/pg7XQHLcJsZzUH/7C1Rp872Lux2cv3ky5zzQeYcqMN28/y7nNQyvKUFFkCfm34Odqt92FP39wXnQ5r9iGOlIaNsTqVnLkLH+zFFKxC4heZGZbsGMjFt0vw7GYdPj60hK8+A73+SRmX1KD+aUKUVTAolAmFpKEtf/zf/4Pdu7cif/5n//Bb37zG8yaNQv33nsvVq9ejby8PLmPkUKhUJICPvfZotlTQ7YlGdyGD5alTHjDV2CVSoVg17y62iKsqSmW1fo/OVMv275SCe+ILykD+EmQ0qAg+HyrLs/B3JmT8cPnDyaluKYAsMCajWMTsLkAG8xEOB4T4ljCLEysWliAo+e6qaCWBCgUwFdqipBjSYPJoMWmusqILC+zSYe75ubCO+oLuNC4Mp/CERLVTAYNfv7E7QH3dSqJx3qtStCFRyp2AfJ8dpJ7RDy65DJCqVqp4CyrnGw2YGDQi+aLdpz+3M67L1ruSKFQYokkYW3jxo3YuHEjbty4gV27dmHnzp345S9/iV/+8peYM2cO7r33XqxatQpTpkyR+3gpFAolIQi5z5QqJe65vRg+nx+2DgeaO+yCg9vwwbLYCS/XCqyUXBCxOXDhGNM1oo6dknoEn2/M+XKirQcjo8kpLPqBuIhqFpMOC2blYE9jZ8wcLXLATISldshMNqQ4jsY7iSp79vsRklNmNumwcVkJTAbtLRfaED482RXSjMd8q0vhbhl+R6fLG3BkAsklHgvlO5LmEpKKZXJ8dhLRTGz3SzFltOFjFZKyynsWTkej7Tpe390acg2M92B+CoWSPEgS1hgmT56MRx55BI888giuX7+OnTt3YteuXfiv//ovPPvsszh7NnrrLoVCoSQaEvfZm3takGbQ4qXtn8EuYrW4+aI9MFDMSNOKOi6+FVgx+R5ic+DYsBgnpmNtIlE7ZxoA8aHY4x1m0vbVO2diyy4bDp4hz62LF4wo6vP58dGprkQfDiVGPLaqHK2dvQnvsOxwurF5+1k8tb4SGpWSNevM4XTLIqoxBAtK3fYh2fYbLd+8rwKv7bJFHRfgHCJrFhOtk4y0ZJLEHR/Mk+tmRwitwQ1wTAYNFs+egqrSbNaxCklZ5QLrFFSX5dBgfgqFkhCiEtaCyc7ORmlpKVpbW9Ha2gqXa3ysiFIoFApJaYW9343/er1J9L6Ds4OyjFqiLBlS0YtkICo2B44LsavXlMRDcq4Fs+PgRew+dol2SeRArVbi22sqMK90sqzCoxzdNhkR3tbhiNs1SptGyItOo8TT98/BoHskoszSlKbB4oopSNdrMLdksmRhTc6geADYsssGtSq65jikMIKSz+fHhycTLx6n69V4fLUV6XqNLBmcpK7waJ/FYkomudzxwXA5xsR0shQDzTWjUCiJIiphze/34+jRo3j//ffR0NAAh8OBjIwM3Hfffbj33nvlOkYKhUKJK+FlkfGaiAav3nIhZz6alBw4LsSuXmvVSnhSNJtsvLCsOj+kfIuE8Siq1dUWI8diQLfdhY9OdYVc70a9Gt5RP9xebmHrT3tbYdCp0T/kQVa6DlWl2QG3qH1gGOcv9+GDE+Im+jWVU3D15hAudw9ELaoZDRpUlWYDiG+JnEoJrF1ShK4bgzjz+c2QcjidWgH3SBLXzSYh31lTgYrisdD/6rLsQCn24bPX4RzyYm/TZextuky8QMPGPQvyUZKfFSGUSO2GOeCKT1OXYJdVa2cv0bM01myqq0RFkQVHmuVxsJK6wsU+iwP7l1gyGe6OD+7uyieYUQGMQqGMNyQJa01NTdi5cyd2796Nmzdvwmg0Yvny5Vi9ejVqamqgVstmhKNQKJS4wlbmZjTENz/MqFdDo1ER56NJhciJJyI0ubo8Byvm5ye8DIlCxq6jHbI7VJKZcOcX2zW1pqYoIFgcOXtdMFQcABwDHvxi28nAf5sMGjyyshxKBfDXDz8XJczrtSr4/X4cOsPfJVAMAy4vGpo6kWHUojeOjtK+QW+EcKvTKKFQKKIWCycSZqMWD60oCzlPlUoFBoe9rPfaaESlRlsPvv7l0ogYAafLw9vpM9EEu6ySIV/NYtLBOn3smSlHyL/YjpZ8OasbbmXfOZxuOIc8MKZrYDHqo3KMUZGMQqFQJAprjzzyCNLS0vDlL38Z9957L+644w5oteKygSgUCiVaog3cD4erLDLe5UwDwyP48bpKKJWKmOaEkE5AxExUSMuQqFst8XgmmGPI5/Ph3tunQ4mxSaB1upk1x4dLsCDF6fJi83ZxbhFgzKUmp6AWzLb97ZJeZzHpUJKXiWO2blmOYzw6HmNFul6NTXWVrOcpiduYbYFGiOCFlHChRLk+sjNjojHq1XhstTVEdCQVsupqi7G3qTPC2cfsE4Dkzxss9JGUZhr1agzwOAyldLQUk7NKoVAolOiRJKz95je/wd133w2dLvatlikUCoUNksB9McIbyUQlWtbcXggogPpDHYLb9rs8WFwxNabHQ9oswaTXwNbhIGqC8ObeVrkPk0KRBc+IH+8fvgQAOHjmGuu9wj4wjDd2J+YcPtV+MyHvy0ZV6WTkZafDWmBGWUEWzl60y5ITlSrIkQ+30JoTlSA5ODwCpULBeq8lcRuHL9B03RgkevZwLaSECzWn227gyDnhz6fTKGMmqGo0qkCZMwOJkGUx6bCmpghraopgu+RAa2cv9AYtinPSUZr/xfMt+PN224dYmzCE7zfcBUtSmskl5EXrVKdOMgqFQokfkoS1lStXyn0cFAqFQgxJ4D4QOUjlC/0nmagAgF6jwjBP3hIXFpMOdXfMQGtnL9HkRo7yEUEIDUsv7DgbMqlm+x4bbdeTulSIQgkm+F7h8wNv7G4hKvuMJckkXJ1ou4ETbTdQjw5kGbUozc/EySQS/mLNY6vKAUh3LKXrVPjOmgq0XemLyuHFJXKRuoiDF2hsHY6onz3BQk1GmpZIWFu5cLroLEdSHCxRBSRC1oZlJSGLbvffNROTJhnhcAxiJMhNHS5M5WUbI84JoW6WAH9pZrBwRh1mFAqFkrrQMDQKhZL0BDvPMgxaQWfZll0trG4Dvk6XpBMVRlQTm03FlHKQrqaLyVORSr+LLIsnfMLPfI91tcVYU1OE4609eIGKapQYYdCq4IpRJtcr751LirwvnUbF2yAhkfQOeCaMqGZK0+DRleUhQsfP3jiOz7v6Re1n0D2K9it9kkLkg+ESuUgXXoK3k/vZY51uFmySYDRosHZJMQpyIgUpPjbVzUb75T6ikmy2ZzefkLVwVg627WuP+Pcn7p+DWQWZvO8VTXklyWupw4xCoVBSFyqsUSiUpIat5FMIoRIetk6XYh1ipKJa+Io0yWq6lDwVMTBCZdeNwaj2s/3ABexp6sRQEjltKPEhfEJt0Krg8/tDSr6kdhIMxmLS4edP3I73j3Rg+4EL0e2MhWQQ1QBgZDQ5jmOis2hWTsiiy4m2HtGiGkPvoBuLK6biqfWVeG2XeEckn8glRSST+9mjVCrw+Gorf4njqnIolQpWUcnp8mLbPm4Hl8mgJRLWuJ7dXO/Jln9od7rxn1sa8fTX5qCqZLLg55YqflHhjEKhUMYvVFijUChJC1fJZ7SwdbokmaiIZc3thai7Y0bERIW0LCQWSBEq+aCi2vjEoFVh1OePaDKRrldjxfx8TDYbMDDohSlNC3PQBD54EluSl4n2K31ovmhH/WHhEjQ2mIn+R6e6ov1ISQkjPo7STP+koOH4FZRPN6O6PCfq3E1G8KkqzYZeo8Jzfz8jypXIJ3JJFcnkfvZw7Y8tLoBNVKou43ZwyeGwC35Pn8+PZzYf4v08b+5pwdwZk2j5JYVCoVBEQ4U1CoWSFIQ3GijJy4xpM4Hw8hGSiYpYKoosnAP0eHTsCv9OpXYrpEw8gksv9RoVZhebkZ9txMenroYEeJuNOtw1L5fzHLYWmkV1lf1iv1rcNS8X3lEfGpo6k6oToZxE6+ibCIgtuw9HrVJApVQQB+gzjmbS3E02GMFHykIGqcglVSST+9kTzf74HFxyO+xIfk97f+SiG4VCoVAoJFBhjUKhJBy2yYccXdn4YCsf4ZqoSIEkqyaWZSFs36mCLsJTJDDsHcXx1hs43noj4m+OAXdIiSabU4W0zHrj0hJkGLXotrvw4ckrgh34KBMDRlRbPj8fruERnGjrwZCb3Pk1MurHyKgfC6zZaLT1CG5vd7ph63AQZ1Cy8eDyUpxo6yFaqGFE5BxLmmiRS6qoJfezJ1bPMjkddqQCP9d2YrqMUygUCmXiIUlYa2xs5P27QqGAVqvF1KlTkZMTu5ImCoWS+nCVe0YjqgkFKvOJXsETleYOO1EXNTZinZPGB9d3muzuGL1GiWFCVwklOWFrEEJS0mU26ZCfbcSp8zeIcpUoE49PW3rw7KYazGmdJKkD8fkr/VhenY+G48Ln1+Z3zmDF/AIphwkAGPH58fb+dt5t0vVqbFpXCWuhOapnxXjP7ZLLYSel4QMD20IVX5dxCkUKIY260rSAf6zJFN85TwVfCiV5kCSsfeMb34CC0PpQWFiIH/zgB7j33nulvBWFQhnHRJthEw6zig0gqvIRZqJSVpCFg59d4xUEwgPaLRk6PLgscYNtub/TeEJFNW6mmPW47hhO9GEQE9wgRKlUYOOyEl4xxOMdxS/eOhm/A6SkHHanG7ZLDmyVeH+zO90Y9ZHdYwaHR7D9wAWk6VSi3HEML74jLPwNDo8Erg/KF7AJBaTbMrmOTAdxKID+IU/gb1lGLXoHuJ2IlozI9+NaqOLrMk6hsMF1brd29uJEWw+OnL3O2eRkLN+0AGtqigL3DCr4UijJhSRh7eWXX8YvfvELeDwePPDAA5g+fToAoKOjA3/+85+h1+uxadMmXLlyBW+99RZ+9KMfQalUYtWqVbIePIVCSW2iybAJx2jQYMOyksBggq18xJSmwSP3lBMPOEgyXp5cNxsmgxZOlxcF0zKRa9bD50ucNUzO75SSPKSSqAZ80SCkrCAL9YcuYm9TJ+t2TMk3n8OUkppoVErMLs7CyXa7bPvc/+llXmFEiA9OiGuCMTIa23t580W7LE4ULoHJPjAc0mQkWHiS4m7hOibSfxd6fzahIF0/NlUJvkeYTTosmpWDo+e6I+IOuJzZ6Xo1RgSezQ/fUx5yPCQLVWxdxikUBuYaYBPO2M5tLhixf29TJx5fbQXAvoBMBV8KJXEo/H7xxUH/+Z//iZMnT+L111+HVqsN+Zvb7cY3vvENLFiwAM888wzcbje++tWvQqfT4a9//atsBy43o6M+2O2DMX0PtVoJszkdDscgRkaoM4NCOXzmGl6qb5Z1n8GDiUbbdby+uzWkrFTKah7bYD884yVZru+tDa0JKaXTaVSiOt5RgPKCTLR09iX6MGLG/PJsnOtw8E4a0vRq2ll2HKNRK+Gl4x1e2J5JXALTivn5WFNTLChGcQlM4f8u5nnI5Y5hE7hIha/g949VF3ASjAY1nn6gCrMKMkOe37YOB57dekLw9T95sGpcl+OKJdryRD5nF9c+xYq48SihlLsLezAkkSfPbqqhgi+SZ3xOSV0slnSoVErB7SQJazU1Ndi0aRO+8Y1vsP799ddfxwsvvICDBw8CAH7/+9/jd7/7HU6ePCn2reIGFdYolPiz59glbBPIoRELM5gQCo4Wu5onNAhLxPXNHJPD6YZzyIOefhf2NV2Jy3sHM2fmJPzgq3Ngu+RAQ9NlnGyPDLmnfIFCASyfX4C9jewuLgqFMvFgnklCAlO6Xo3HV1tlFaOEnoexFr021c3Gtn3tCXNbWzJ0+MO/rUR/31DI8/tI8zX8fofw4t9311ZgccXUqI5BrDAkN3IITT6fHzsOXsDuY50hC21Ggwa3z56CqtJsQYcmqbMrXJQVEpeFtpe7hDKRQjEDFXzHoPNvSrSQCmuSSkFdLhdu3OCeOPX09GBoaCjw3yaTCUql8MFQKJTYk0xBp8Z0jez7ZHJ45C7fiHdAtNDvFMuVULGsWjgdSqUCLvcI2q+MXweWXPj9oKIahUIJYWtDG+bOnIw/7rTxbjc4PILn/34Gm+oqsW2fPHmafM/DeOR2vrG7lTNbKh7Y+91o/vwm8icZQv5dSsMDUrdV8L9124fw4cmukDLnaNyFpPh8ftguOfDhics487k9JOfUaNDgGyvLsMA6hWhfx1u68fJ75+D2RDrXB1xe7G26jL1Nl4kdmsGwObOYksdVCwuw61jk8zTcNiK0vZwllMmSdUvaCZdCociDJGFt0aJFeO211zBv3jx8+ctfDvnb/v378dprr2Hx4sWBfzt37hzy8vKiO1IKhRI13OUloYGo8cJi1BNvK2R7D8bW4RAUnJgMqGRczRNaTU2GlVCGdL0aPr8fjbZubN6eHMdEEUalVECvVYVcUxaTDgtZyrcoFErssTvd+NVbJ4lLo9/Y0wLnkDxiFN/zMB65nYkU1Rjs/cMRwhpJR+PgLuOkGXEk2VpcwpBcwk/9oQvYefQS3BxNgwZcXmzefhYHZ17FPQumBzpUhjeFKCvIEqwQYPsct5VOQu5kI5RKBXYcvCj5s+wWuUgltL0cmXnJknVLKgxTKBR5kCSs/e///b/x6KOP4nvf+x6mTJmCgoKxluSdnZ24fv06cnNz8W//9m8AxjLXrl69iq9//evyHTWFQhENlxgTHogaz7BTkkErg1atxH1fnom3PzgvvGPC8VAyreYFl0GwZaQxg9F1SwrRcDz+5Z5cDA6P4BfbToKwUTQlSTDoVPjVU7WspUZfu7sErZ29sA8MY8vOFpqRRaHECVtnL/G2colqDFzPw2R6TsYSS0bkQh9JAyOmyzjfGIvk30iJVvg53tKNV3faiI/h9Hk7Tp/nbkCi0yghpV/Tp2038WnbTfEvDENsoJHQ9kKLriSNOrp6YhstREKw4EuhUOKDJGEtNzcX7777LrZt24YDBw7gypWxSd7MmTPx2GOPYcOGDUhLSwMA6HQ6vPTSS/IdMYVCEQ2JLZ0pL5E7e4wPkkErg2PAg+k5JqLVY2uBGfXoENxnsqzmiSnrfOeg8OdKBOLTOilywNcFj48B11jZLtvkIbjsWadW8XfFXTsbl28MoP6QtPNSjBOVMrFRqxQx79I5keF6HibLczKWWDJ0qJgxCf19QxFjmqrSbDy1vpJVjNJrVfD5gZERH7bsaonLsbIJP6TjsFi43bkcb6lMsJgc/N1221346FQXUaOORHPn3FzRr0mmqBgKJRWRJKwBgMFgwDe/+U1885vflPN4KBRKDBBjS2dbDeV62MoRAFtdnoMV8/OJOln2uzxEq8fWQrOo8o1EkkxlnZTUobpsMpZVF6AkLxOtnb3Y/M4Z0QIViROlujwHT62vxJ/2tsExwH6dZ3RoJQtry6rzoyoDokwcJmXocd3hSvRhjEvYnofMc98+MAyTQZMU5Zqx4u55uRj1+bH94/PYfawzIiR/0awc1vvrsGcUm7efgVIBSa4tqfQOukNc7ofPXhfsfp4suV+pACMmkyx6Opxu1sy2eMG1uLb9wAV8dKor6q6/cuf6USjjGcnCGkN7e3vAsZaXl4eSkpKoD4pCociLmFKO4NXQsRyOi9jbxD7QlCsAtqo0m0hYy0rXwVpoHpvohw0ALCYdHgwaAJCWbySSeA90rQVZuOYYCglITkbStEoMecbfKricHG+9gcWzp0KtVqKi2ILHV1tFC7SkTpTq8hxUlWZzrmSLKekO510qqlEIoaJa7Lhz7rSQ/06m5jhyYdSr4fX5WcP1//bxBfz9kwusAgWJcBJPUQ0Auu0uPLP5EOfvwzYOqz90YVz9nrGCEZlTZdHT7wc0aiVrZANzHqyYn8/bkZXrs8qZ60ehTAQkC2sNDQ34+c9/HhDV/H4/FAoF8vPz8b/+1//CsmXLZDvIYL7xjW/g2LFjrH/71a9+hfvuuy8m70uhpDJiSzl6B928ORwkA00xOSBiA4KFJvrMNiQCXCwRstXHO+CWye+ZmWvC+S5n3N4XAHRqBdwjwrOPGdMycN0xBIAKa0IEX2Nc5zsXYh2bQl1x75o7DdsPXCTeHwMt7KOMB+aXT0be5PSkLdUXYvuBi/jo1FXcMWcarvQM4nhrT6IPSTKmNA3unDMNUADpeg0y03Uwm3RwujzYvP0s5+tSJc7AqFdj+4ELRNu+trsF7pFR3HC4JN2fJyIPLi8FgJgtepoMGkyfasTZCw7Z9imUg8rXkZVkgVeOhg4UykRAkrD20Ucf4Qc/+AFyc3Pxz//8z5g5cyYA4Pz583j77bfx9NNP44UXXsCdd94p68ECwL//+79jYGAg5N+2bNmCPXv24Pbbb5f9/SiU8YBYR0m3fSjqQZiYrptiAoKDXyO0bxIBLlaQ2OoTFQodb1ENAOZbc3DwzHXB7T6/2h+HoxkfhF9jwec7VxMMBrkcm+PR2UKhiMV2qTch91UhdGol5pVNxsiID8dbb/Bu63C6x0VZ9tDwCN47cinw32aTDhu+XIJXd9kSeFTyIUb/cw558fK752J2LOOJ4EVXks7yYlhTU4jcyemBMWhrZ6+swhopbA40kgVeMeN5CmUiI0lY+93vfofy8nK8+eabgSYFALBs2TI88sgjeOihh/D888/HRFhjKzX90Y9+hCVLlsBiscj+fhTKeEBMkwCzSYcPT3bJ8r5ihKNYOcyEBLhYhLWS2uonQig0g16vFhR3pQbxT2TCrzHmfC8ryEK6Xo29TZdDXKfRXk/hQc58zolciwFd9vFRvvdk3WyM+BR4eUfylwZR4s+AawRA8jXhcI/4cLS5O9GHEVdGw+oyHU43XtjB7VRLNWizF3lZelse5pfnhIz95F70rCi0hIxDmeez0G+55vZC7P/0MobckeXL0RDsQCP9rBOlOzCFEg2ShLWWlhb88z//c4ioxpCWlob169fj17/+ddQHR8Knn36Ky5cv45/+6Z/i8n4USqrCCFdCbdbvmptLXGYghFjhKN4Os1iEtYqx1UeTTZVq7Gu6glULC3hLiKmoJh62a4ztvNZplJhfnoPHVlmhVislvZdYd9p4EdXmlUxCTeU0ZGSm4W8ftME+Aa5XCoVCmQjML8+JWHyVc9EzmkZZH564gpL8LJw+f1O24wFCHWjd9iGi10ykhWAKRSqShDWdToe+vj7Ov/f19UGni88FWF9fj7S0NFky3aRONkhRqZQh/59CiTeLZk/FgllT8M6BC9hz7FKokyVDh4fvKceIQFYDKZYMHSqKLZJEscqZk2Q5BjZ8Pj9aLjnwaWsPdvM0X3j6a3OwwEourjHXdduVPiJb/fmuPswqsmBx5VTsPJyauTxiOfDZtUQfQtRo1EqMjPiSIhuM7RprtLG7Jd1eHw6euYamlm7cd3sh1tbO4Lw2mWukd8CDLKMW5dPNON7akxJBznKzavF0PLS8DAqFAs2f38TCiinYdfSS8AspFAqFktRwjVMrii2wmHSyLKI8vLIcWq0q5N/OXrATOQ8HhkcColq4o9+UpoFzSHqnXtslB06dv8E6Dg4nmvF8MkDn35R4IUlYW7RoEV577TXccccdqKqqCvnbqVOn8Prrr2PJkiWyHCAfIyMj2LlzJ5YuXcrqnhODUqmA2Zwu05Hxk5FhiMv7UChcfGvdl/DYVyrR/PlN2PuHYcnQo2LGJKiUCnzWzp/DQsoT6+dg0iRjxL+P+vys7xsPDp3uwu+3f4abfcOC225taMOyRUWij40koB8AvH4FMjLTcKxZOHcsEcSiLHPAJX0QmCzct6QY2z86n+jDABB5jY36/HhzTwvva9xeH/728QXsabqMp78+DzVzckP+znaNWDJ0guHI45FJmTo8+dV5OHrmasR3olTEvxMgJXlRALBk6omeLRQKJTngGqcCwBP3z8F/bmmUvO+MdC2e+tpcLKqcFjLm7Rt04/m/fSZ6f8x4bO0dM7C4chpKppux4V/rJY/TxOQp3n1bPuf3lErQ+Tcl1kgS1p555hls3LgRDz30EObMmYPi4mIAwIULF3D69GlMmjQJP/7xj2U9UDYOHjwIu92ONWvWRL0vn8+P/n4yO6xUVColMjIM6O93YXR04k1SKMlH/iQD8ieNPWj6+8bO/1yznnilbvXthTh65lrItozzbVZBJhyOwZDtG23deHN3S+j2Jh0eXlkuyh0mhUZbN377l9PE29/odeHoqcuYVUSW3chc3zo1mRD34fFL6LreL3oipteq8OiqcmxtaItqtZIPS4YOG5aXYvPfJp5DSYj3D8pTJh0NRoMaj907CxgdxfufnA+4ynYc+Bz2frIV9oEhL/5zSyO+f/+XYErToHfAg+v2Qfzt48jPR7rP8cbNPjdee/cz1u+EimqUYPwY64zLdq5QKJToWb1oOo42XxflIrNk6PDQ8jL8aW8r8TiVYVZBJp7+2pyIMWu6XoXBYeHMs4eWl2Jg0I1v/X+7ZY0POHDqCu6/oxgnznbFLT7jw08vY21NUUo71uj8mxINGRkGIsejJGGtoKAAO3bswIsvvoiPP/4Y77//PgAgNzcXjz76KL773e9i0qTYlXIx1NfXIysrC7W1tbLsT64SOCFGR31xey8KRQoPCjQ6MBo0eGxVOarLc/DVO2awZqKFn+Ncgf52pxu//ctp1NUWY02MHtw+nx9v7OZ38rBxs39Y9LVampeJLKMWvQMe3u2abD1osvWIPqZhzyi67UMxE9UA4MFlpaguy4GSpZmETqOE2ztx71+eJLh3D7hG8Op750JKSXQaFdxe8QHHz//9M5ptxwNJmQyFAgCTswysDXjkYNXCAhTnZuKP75/DsEfeIHPKxCVdr8bjq61o7ezl7SLNIPU5Ew0KACsXFuDrXy7BjNwMUZEEDy4rxW1l2ZhXMplonBpOVclkzJ0xKeS1Pr8fv9h2UvC9r90cxPYDF4mPlRR7vxvNF+xxbSbAvGeqdwWl829KrJEkrAHApEmT8K//+q/413/9VzmPh5jh4WE0NDRg7dq10Gg0CTkGCmW8wtWhM12vxor5+VhTUxwQwIS6bgJkgf7bD1zAhyev4OEVZZIbB3BB0k6cDSlhrUqlAnfPy43JgIqBZAAshWDBFIhsJtFtd6Hh+OUJLawlC+H5LFInO1RU44d24BufLL0tD70DbnzaKk/0AQB024ewtnZG4J65t6kTJ9qi3//KhQV4YGkpgLGJ/j/+9hO4ZO4SSJmYbKqrREWRBel6DdG44umvfgntl3uJxjcGrRIuj/SxwljDnWw8tmpWIAOba2waHl0R3v2aZJzKRfhrfT6/YNMps0mHD092SXo/Ek609aCqNDtm+2eDdgWlUISRLKwlmv3792NoaAhf+cpXEn0oFMq4RM4OnaTCVu+AB8///QyeWl8pq7gmZUAQTSenHEt0mY9CyD3ZZxNMGZhB5fGWbtm6xVIoFEoiyUjTYn55jqzC2kcnuwL3UGuhGT6/P2phbc3t01F3x8zAf7df6aOimozotSqolArRz9SVC/Jh0KljuoAWaywmHazTxwQjkg7lzPYVRRbkZRs5nZmMqBU+fnS6PNi2rz20C7tRi7vm5SLHkoYMgxZQAP1DHt7xZlVpNgw6NWwdDkABWAvMKCvIQvuVvsB7leRlorWzF3/98Dxu9rsCx2VM0yIzXQezKXT/Pp+feKyrVCqwaFYOb4fzu+bmxnS89PGpLlQWT4Jeo8RwnBY6aVdQCkUYImHtpz/9qegdKxQK/OxnPxP9OlLeffdd5Obmorq6OmbvQaFMdKJZ5QtGrLC1taENVaXZspWFShkQPLi8VPL7J/MARKEAvru2AplpOmLBlMRxGEy6Xp2STh+NWjkhQ/rlQK9RonKGBU0t8gkVFEqs2H7gAjbVVQqKCWJwDHjQ2tkbeGZap5ujLp2rP3wJB89cx0O33DfUNSIOvUaJu+bl4pith9N9DwDnu/pwtqMXHzR1whnUZCeiE6NBg0dWlqG6LAfPbD5EfBxGvRpenx9ujjJejVoJrVoZVt6vxOioHyMEgY5SnrnBYxylUoGHBCJAgrcPXni1DwxjYNALU5o2QrAKHz9Wl+VECFgAQv5toXUKp+DldHmxbV+ooHfQdC1wfQBjsSP//NwBwe/DbNLhoeVjTtBwkZD5G9sC7/GWbl5RbdXCAuRYYhuS7/b68Os/n4rpewRjStNIXmimUCYSRMLa0aNHRe9YoYhdwGFfXx8++eQTPPbYYzF9HwqFIg9ihSa70x0yQYkWktVYhvASAimU5GVCr1UlZRbOk+sqWRtF8K3YkjoOTWkaPLqyPDDgbr5oR/3hDtk/Q6xQKRVI/b6liWHY66OiGiWleGtfGzYuK8Hm7Wdl22ew8KVUKrB60fSonSsOpzvg5E7mRZtkQK9VYd7MSZiUacCsQjOshWYolQp8/culvI6kWUUW1FQV4P47igP5VYzzKdgJxbzO1uEgeiauub0QFUWWgChhu+TAuYsO3Oh3QeEHJmXdOs5bzjE20an+0EXsbeoMEYrMJh3umpuLHIshsO2Jth68utMmKChxjXG4yiy5tpey8Br+muMt3ZyiFhApeLERfH0AIM5gY14ntM/gz02yyHjsXDcqZ8Q+ZzyePHJPeUIaFwg5CcU4DSmUeEAkrO3fvz/WxyGKzMxMnDlDu9VRKKmCGGGLQc6VeZLV2BXz81FVmh31g/nouet49b3kC5hWKLhFNb7BrRiXxIZlJYFBqLVwrDzj4Jlrsgd5y41eq8KqhdFPgCkUSupgd7pxrLlb1n2GC19raoqw82iHLLmUf2powzdXW1PWERwPhj2jOHKuG2aTDkXTTKKyYLm2Y3sd6TMxNzs95PUVRRZU8HQaZ3uvtbcaOwkJCIyLzHbJAVuHAz74YdRrYErTYtDF7igLR84IECG4GlrxCV58/KmhDT6fvI7z13a1YO7MyYGMN5JFRrvTDd+oHwqMdQtOdVYtLGAdN8YSn8/PKSgz41KhcSuFkghSNmONQqGkDiTCVjhyr8yLXY2Vwh/fPYO/fXg+6v3EgifXzeYU1fgGt2JcEhajPuS/pfzuiWDYM4rBYepVo1AmGsdbxXdm5oItl1OpVGDuzEk4JqEDdDgOpxu/eju68i+9VoW75+Xi6LnuqBc8hMobYwVb2WQ4XI4juSB9Jso1jhEjDAqJd3K9FxckLiKx8RIkxGIBz+ny4kfPH8Sjt5o6kQqqrVd6U0pUMxo08Pv9IdcUU/a8wDolbsfBCGpcixHMdb1qYQFrOW6sr3sKRQgqrFEolLhQXZ6DutpiIldQrPIcYrEaywwiT7TfwN5G7twNOdBqlPCIdD7wCYckg9utDW342T8shtGgwYCLW3ziavZQXZ6DTXWz8cI7Z5O6A+WR5uuJPgQKhZLCsOVyHm/plkVUk4tv3FOO2yun4mt3l6ChqRPb9rdL2s/GpSVYPr8AwFh54wefXsHZi/YQpzbbZD0aF49eo8TKhQX4ypIZgffdvP0Mr8Amd14rA2nYP/NMHBnxYf+nl9Hd60JOlgF3z8vD51f7Q8YhPp8/ZJult+UHnFIMbKIVEFlCyvZ5o3ktKaQuIqmd2hOB0+UNiDUZBi3Ra7p6BmN8VPJRV1sUyBpMZFnl8ZZuolJmANjNk3EHxO66p1CEoMIahUKJG2tqivDhySvoHfDwbhfLPAe5GjIA7INIsQhlsdXVFo11zErT4vm/nSba5723T0d+tlFwcERa1vDM5kO8ohrA3+zBZNASi2rWgizYOnvJNpYR59D4cqypVQqMjHJ/6RaTDvPLc7CnKbZiMCW2xLMr3ETHbNRi4/KyiPB0rsWLWLhy2EjXq3Hf7YV4+wNht7TZNOagUioVyDCSiQThWEw6LJ9fELjfMw4pEuHG6fIQZdpZTDpsWFYCk0HLO9EXmoST5rX6fH581n4DnVf7YDJoiLKcSMP+397fht2NnSHPwHBBU3dr0Sz4jv3WB+1YuaAADywdyxtjG2+k69UR3wObkBXNa0khcb8z+03FJhxbdtqgUauItm3ucMT4aKKH7b4l19hYLFznDhdCw0m5c5opFFKosEahUOKGUqnAwyvKeB+gichzkILYgQAbpjQNfvm9JTh1/oZgiaqtwwGXh2wCXVk0CdZCs2BJBung1ingVBMqpf20lTzH6MK1fuJtKewwpVIjo9yTzgXWbOyOscOSEnseXz0LL9c3E3UOnGho1Up4ZOzy+9CKMlSX56C6jMz1TOrKqamcArNRj33HOyWJpIPDI5g+xSTooErXq+Hz+eHz+aFUKiSXKVaXj33+8M/NtWgV/m/K9YqI553RoMbS2/IxdVIaUUi50+XFa7tsRMcr9Jw73tKNrQ1tsHO4rLhcWItm5bDm3RkNGjx2q3Tw7f1tvB0kGdjK3vx+BF47My+TdbzBJiyGC1lcYxWS15JC6n5nXESxaMJhNung8/nQNxibRbKB4REAZNmGyZaxy1BXWxzS8CIZHF0+nx+v7iS7lsWQiuItJfWhwhqFQokrXFlnichzkIpcToRHV5ZDrVYSlaiSDhLS9WqUFWQRlWREO7g1pWnw8ydujyhXCeZY83U0HL9CvE85Qr4nOt4RH7wCgsKexstxOhpKLHljbwsV1TgYlSnIPF2nwuP3zgrcN0ldz6T37MoZk7C4YiqKppkkL9bYOhyYX56NvU3c1/Xg8Ah+8dbJwHOgqjRbVFMhhWJM7NnbdBl7my5LdjdVl+fA5/fj9d2tASf0gGsEn5y+ioeWl0Z8t9E6w/mec0IuK74sJy7BjPlMIyM+WRYvdjd2IuPsNdGv29rQhrkzJ0saq4gtpSN1vzMuorKCLJgMGt5FO7EwnUSTPdM1ESRzqL/tkiMmzVhoB2VKIqDCGoVCiTvx7DwVC6LNBwle0WYQmqxlpJGV7SyrzseJth6ikgwp3VqDcQ550X6lj/O4SVfrKfGHSjHjgwEX7Q7JxagMutraJUVYu6RY0rOJ9J7NTACry3OwamFBRNkgCfWHOwL/mxHAuAh+DgiVM9bVFmFweAR7my5H7FOqu+l4SzdrOSjb/qJ1hnNlfwJkC2RShbGtDW3o6XXJkivq90OSC8vudGP/p5clPd/FltKRisjMdkqlAotnT+EVgsUwr2RS4Jx5an0lXnznLF1wuMWXq3Lx8IrYxatEiy0GZbN81z2FEku4bQYiGBoawnPPPYfLl+kKOIVCIYMRkhZXTIW10Jy0D302pFrMq8sm48cb5+H/PV1LPBHx+fywdThwrsNO9iZ+H1FJBlMOxKzySuVEG3swd6PtOhXVKBRKSmIx6fDU+krU3TGD89nE3JuPNF+DrcMBX9BE/nhLN16ubxZ8H4UCcLo8gdfsOiZeVAuHeb1Owz/EZ1xJT62vDGSvMTCff01NMZpa+JsvMM8TEkjErD/tbUXzRTsOn7mGLbtaiPbLBV/2J8kCmdTfwu50o+1yn7QXy0h3r0vya8WMc0jdQcHh/+l6jehj4uJk+028vf+L8yoVRLWZuRnxeZ/8zKQZX7PeM2NwaHzXPYUSS2RxrA0NDeH5559HdXU18vPz5dglhUKhBCBp3x5PpFjMZ+Zm4Kn754h6jZQSmD2NlwVzeuxONxqaOmFK08I55MGy+Xk4drY7pCyDtExjb9NllBVkhQiFPt9YmQ+FQpl4CDmmkpkV8/NRVZodcDvYOhwRzx2fz4/6Qxewt+kya/A7QF6O5vcDm7efhW+dH2/ukbfRgVBZPeNK4nOQ2zocokr8hCARsxwDHvxi20nBffGRrlfj8dVW3gWsWGcw6bRkQfexJCfLIPm1YsY5pO73V94/F7hGSDrEi2F3YyfqamfEpWGIHCybn49VKmXUDbCEsBj1Mds3G1zjda54kjvnTJXtvdmqQSiUeCJbKag/VUdRFAolqSFt3x5PpJRQzioS151IagkMafh1eFcyYKxDaUleJiqLLbh7Xh5++tIRos8YnsfS2tkr2EWUQqGML3QaFZ6+/0sYdI9g8/bUyjkKn5BxPXcWzsrBRye7WMPJmVJGptuiGH7/TnNCyrPtA8MAvnCQM5PiY7bryErXBf4uBJdIFT7JJt1ftDzxldlQq5U4dPYqBga9MBo06B/yYMDlgb3fg0mZekm/kxhqKqbiaPP1qEVmhQLISNOILgc1GTQw3vo/sc9jsaV0SqUCi2blCLrUo7lGhPD7gbc+iK1IJSdZ6TpYC80honZGmhYv1zejd8Ajy3vEuySS675ZmpeJY7bIJlYOpxvvHOyATqOUJV9Xo1KgqjQ76v1QKFKhGWsUCiUhkLjQxLRvj+VxhMOUUIoRvqwF/MJa8HFkGLQJWXUd9ozizAU7zlywY3djJ6bnGIkGqeGOhVQZ2MqNAqHZZSaDBotmT8GRs9ep0EiRhEatFGxEkSy4vaNweUbGujrXVeKFd86kjHNtwOXFlZ4BVJVm82ZU7iYob5cSxJ2or+mthnbo1CrO7pdGQgGEzd3Etj+TQb7yPy6MejX+uMsmy3NIqvvSYtKhotiClQvYmx+IYeWCAs6uoHw4XV68/N45Se8ptpSOKWMmJRZh9QDweVdqdBUPFrzC83UfXlFG/FurlAqM8pS9Cv2OYsa/QtvyjdfZRLVgNCp5hDXHgEdUNiCFIjeyCGtKpRK5ubnQ6+NrN6VQKKkF82A+0daDw2FiQ7gLTWz7dilE44arLs9BXW0Rth+4KPg+eq2K90EfbdezcOTotuVwukUdD+NYON7Sja37UqMUQ27S9GpsqqtE/5AnZOBZXpA1oTqVGQ0afHdNBQbcXmSl6zA66sMv3z6V6MNKSbwjvoC7I1aTUTlh7slXbwykjKjGsP3ARXx08io8I5FutPGK0+Xl7X45QHDOsbliuCbZcnaB5GJgeASQ6VopnmbC511O0a9jBI0Hlo6VPUppSKFQjIlqzD7Yuqkb9Wr4Id+9wahX4zGBEtpw5OqSLgcd1wcSfQhECAle6Xq14G+q1yhROzcXQ8NeNNl64AlagLGYdHjwVudfppw9w6AFFAiMT5wuL7btIxv/Co2Voz0HBoZHUFdbjA9PXonarRfrEm8KhQ9ZhDWLxYL9+/fLsSsKhTJOERKPwl1oYtu3i3WeyeGGW1NTjI9OXRU8zrvm5XIeS7Rdz8KxmHTYsKw07qVYWek62T9LqsEMhLPSdegdHDs3S/Iyka7XYMX8fBz47Cpc7vE/aX9sVTkqZ04K/LfP54+q++xEhzmvqkon4+wFe8gEKtmwO92oP3SBaMEhGXEMjJ9z1KhXEwljgPTulwCwYVlJyPMtUUJLmk4Fl3tUVvefkKgWLoAwgkbw2OGBpaW4/86ZaDjeiXcOXOB15qTp1FhUkYMp5jQsvS0favUXDSi4svCAsfgF+8Aw3mpoj0q8VKuVokvpou2SPh5RKIC5Myeh4/pAyHdjMenw9aUluNk3jDf2tGByph4F2UY4h70BsYt07Dbs9aHhVldVs0mHe+fmIsdiCJwXJ9p68MzmQ6J+GzGdeYO3Tddroj4HciwG/OJ7S8bO5f5hvLG3lbXkXggpGcgUilzQUlAKhSILfMKWGMGFcTyIad8u1nkmlxuOtCR097FOlORlRhxLLCYgzKBeybK6HUv6htx4e//5uLxXMvPbv54OmTilcpC7FNiyc6SUTlMiOdF2I9GHQMTeJtohPhl4bLUVALBlV4tgKXo096ht+9qhVCgCz7dECS1DCVi00GpU2LSuEv0uD++CnlqtRNHUDMFytyH3CBZYp3A63MPLBhmshWbYOhxROwJ7JZTSUYfQF2Rn6TE5U4+KIjOyjHqUFWRhcHgECsVYHMhnF27i9zvOcl5vUltyOZxubD9wAU+tr4S10Bz1Iicz/gVANFb+6l0zJb8XQ1a6LuT81mlVoj9DvDPlKJRwqLBGoVCihk/YqirNFiUeMS400lWnbruLtbsUn/NMrBuOj+ryHGyqm40X3uEeLAHsQp2cExCmbIT5rMGr2w6nG4fPXMWZiw5Z3ouNP+1pi0uZT7ITPnGaSKIaMOauYq67cHfFE+sq8PsdzRPuO5lopELJ6ngmvCOmx+vDS/XNMXu/4GdtVWk2zly8GbP3SjYcTjeUSgUWVwh3NhSzWCgFuQQusfuR4hAS46ZMJiwmHR74cgne3NsaMt5h8lV7eofR0zuMcx29Ea/dq+kUFFajfTRubWjD3JmTo16wZca/gHBmrt3phnMouvJNNkGsujyHtfyZD7HZgBSK3FBhjUKhRIWQTbyutki0eNQ76MZC6xTB8jGzUYuPTnXx7otN0JJ7gGsyaAXFArvTje0HPkdFoSWwqi3nSq/fD+w61omZQc44pVKBwWEv3v6gTXRHMbFQUS3+WEw6DLg88Iywn3yJdMtt2WljDS6notr4Jk2nSohziPIFm+oqUVFkCfy32RSf0qhX3z+HP2lTpyujXJA+x0kFKKmlbHKVwIndj5Qu6YybUqyrvq62GJYsHY58dg3NLOJVrFkwKwdvfRBZbkvyWJMjnF8Iu9ON/Z9eluUaFDM+NaZroop74BLE2MqfnS4Ptu1rjyixDS/BplASARXWKBSKZEhKGaWUBTGWcKHysbvm5Qpm+bA5z+Qe4JIOQOoPdaD+UEfAzReLLIhgIXGiZ56NV4IHkSENQc5cC3EBJFLEYgsQp+Lr+KeqNBsHz1xL9GFMWCwmHazTQ13WUoQPKQy6RzE4AUVVrud4eDxGSV6m4O8QTSmbHL+z2agV/f5KpQIbl5Vg8/azgtuGO+sZ0eREWw8Onbkm6Hb9+FQXnt1Ug8kmA5o7Tog6ThLS9WpUFJnRdrmPNUSfpCtwounudcmyHzHjU4tRLynugaRZGFv5c3VZjqhMZQolXlBhjUKhSIaklFFsWVDwwJLLCs4IC95RshXAcOGLZAAqZoArViBj3HwrFxYIuorMRi2+ee8svLjjLNF3yQiJJXmZeOW9c6KOK1oMt8KjKbFBAWCBNRvfXVsZGEQyrkSaa0VJBiqKLGjucCSNa2lmXgbOX+kX/TqjQQO1ShF1h7p4w+b8oBmHsYNrnMAVj7FoVg5r91WGaErZ5PidH1pRRvz+wYs6R85eJ3pNuLOeEU2shWbMLZmMX2w7yft6xvlvLTDHRCweHB5Bo60HKxfkY3djaj5T3Z7oS2yDz2vSsbJSqRBVullXW4Q1NcWSzneurEEKJdFQYY1CoUiG1Kml06jg9pIJLuEDS65OWEqlArYOssywcOGLZAAqZoBbVpAFi0kHu8hBHsnq50MryqBWKUUJlJ+29eB3f/9MUkelaKCiWmzxAzhm64Elox0PLC0FEPsOfHqtKu7nUbRoVAp4R2nNaSJgHAjJIOLMyDXh//eN+YIdqdl4bFU5SwmSF9v2xa7UcfHsKUjTqTE5U4/dxy6JKt8XKoWqLs9BXW1RynZrTVbYxgl88Ri7jnVi1cICHD3XHZNSNqHFSAB4dactYjxh1KvxWFAunxBSrqlgmCyw9it96B10IyNNi+YLdqLX1h/qQD06WBvlyMWeFF6oOnvBHrXoGHxeixkrh4/Xu+1D+PBkV8gCBS3bpIxnFH6/tGKR8+fP469//SsuX76Mvr4+hO9GoVBgy5YtshxkPBgd9cFuH4zpe6jVSpjN6XA4BjEyEvtaewol1tg6HHh2q7Adn2RyLuVh6/P5BduJW0w6PLuphlUkYxscSn3on2i/gd/+5bSo1/ChUABPrpuNBdYpOHzmWkzDpymphUIBfPM+KyabDPD5/YKr/NGSiuIaRR6YUG4SzEYtvr2mAv1DHly/OYiG41c4FwQUCmBBeTbmlWVj//HLaJfgKhPixxvmoaJ4LGsspGT67HXOLplC9//g8r5uuwsfneqKidCWrlfzLqYsr87H5Ew9jOkaWG51HxRaCDrSfA2/30GfI3JRV1uEtbUzAv/t8/lh63Bg8ztneH87i0mHnz9xe0BUikUpG1+Xdp/PD9slx9jC5K1uldZCM+/7h5/3bA2jxGI0aAS71VKkUVdbLOk34rr/RTNW5jsX4wWdf1OixWJJh0qlFNxOkrC2fft2/Ou//ivUajWKi4uRkZHBut3rr78udtcJgwprFIp4fD4/vv//PiaadNfVFkdMQkxpGiyumIKq0mzJD1uhHDG2rqDByPXQV6uVONfZhxf/dlq0c42LjUtLkGXUYcsuG1xU2KCwIDQBp0wM5BQ/tWolygqyUFlswdLb8nHq/A0id4qUc3FT3eyIIGo5MOrV+H8/uIP1Xh58z88waAEF0D/kIb7/h7w+TQvfqB8vvktWqi+W8O80GrcH6UIYRRhTmga//n5t4FwR6+D6yYNVSV3KFi8BmRIbvru2AhqVkvectJh02LCsBCaDlmj8mwwCmVTo/JsSLaTCmiQf7XPPPYdZs2bhpZdegsViEX4BhUIZt5Bq8zkWA/57U43sD2ah0gehCYicWQ01c3JRnpeB5gt2NF+0o/5wR1T727a/XZbjooxfqKg2/iEpbR32jGLtkiLs//RKiAtESmfYx1ZZcXvl1MB/V5Vmw6BT44NPr+D05zfh5ZiYSDkX39jdGpOmFo+ttnI+W6K557MJKEaDJmbXoVajwqZ1leh3eQIB+O1X+nCk+RrnM5RrAhyvJgYTgeAsMilNguTsCE4KqTASbZknJfFkpetgLTSHlGVKWUQIhuaaUSjCSBLWuru78a1vfYuKahTKBKe1s5e4hTjT6TMWD2a+HLZ4w3zGsoIsHDxzjQ5OU5T7bp+OhqYrxNmAFEqs0GpU8I4KCzc+nx+/empJSImZ0+XF5u3iO7UxxHqSHQtRbVMdv0tZKlwCCmk525yZk/DZ5zdFCZ0OpxuXup2YnmPCibYebN5+JuQ7Mxk0WDz7C9f3ibYe1tD8h27law0N09I7OchM0wKQnnEZi47gfDTauvHG7paQc8ds0mHDl0vQO+BGd68LOVkGZBl1eGGHcHdPSvIS3HiAimEUSnyRJKyVl5eju7tb7mOhUCgpBumqa7peLbmFPCmkA4h42dlpJ7bUptfpoaIaJSkgdUPVH+7AwTPX8NDyUiyuGHOcHW/pFlWiGTwpk+LESTSb6mZjgVV+UU2OJiGnz9+U9Lq3PzjP+Tena6wj8N6my5y/M9OFmiIfjHBJ0hk9HFOaBiV5mbE4LFbe3t/G2onU4XRTEU0GzCYdnEMejMjcMMdi0mGhQBdZNqLpLEuhUKJDkrD2v/7X/8I//uM/4s4778Rtt90m9zFRKJQUgXTVdcX8gqR40LO5L5jV/OryHFlENybAmNkHW4aQ1AETJX4cPHMt0YdAoYiGEVGeWl8JAKIFFWZSFutus8GYDJqonWtmkw53zc3F6K37rxwLJsHPg/4BT9K7j2lZePxwDo11OZRS0ukc8uJfXjwcGHfIAdfYpdF2PaHjjHCx15SmgXModVyT4fcmrlyyze+cwfGWHsnvw5d3NjMvk7X83O/3y5a/SKFQ5EGSsPbSSy/BZDLh4YcfRklJCaZNmwalMjTQTaFQYPPmzbIcJIVCSU5IMluMejXW1BTF76DAPsg80dbDOslkJqKrFhbg6LluTtGNhEOnuyKaF5hNOmxcVgqTQcM6YHptV0tMyqEoFMrEZctOG3E3TyByUibFiSMFhQJ46J4yvPgOt3OGLcA/eBLabR/Chye7Qrrgsd27w5sOwA/0DrkxMOiFKU2LTOPYv/W7PDSwPUaMl2YrxnQNAOklncECeLRCCNeC4cZlpXh9d2tU+44WjVqJutoi5FjSAhmB//Li4ZS5rjYsL4HFqBdcbP3yvDxRwtqmutkwGbSwDwwH7j8mg5Z1/1xRJwCSIv6EQqF8gSRhrbV17EY9bdo0DA4Oor09MmBboaAXN4Uy3iEpd+QLkY4FbIPMLKOWM3CbgatUgnTw22jrxm//cpp1H5u3j+2DKc9iqC7PgXtkFC+/e4533xQKhSKGAULxYk1NISoKLRGTsniFq/v9Y3lVfA1o5s6cjP2fXg7kQC29LR9q9dhi7vGWbmw/cDFiv8H37qrSbNQfuoi9TZ3jQtRJZVbMz2f9vaIlmq64eq0KtXOn4dBnVzE0TLYPi1EPAHC6PJIahDBsbWhDVWl2yLUnxjnPVa7NjDsSTe+AB9sPXMRT6ythLTTjeEs3PDGOWLCYdLhzbm6I0C55X0Y9UcSItdBMJBoHC/7HW7rx1w8/J1rM5Yo6oflpFEpyIUlY279/v9zHQaFQUpRou3LKmXnGNcjsHfBI2h8D2+A3GJ/Pjzd3t4jeh8/nx8AgdauFE80kiUIhRa1UYMQnby5OqpE7OT1icubz+dEf5T1TDM0X7Vi7pBgGrRq2TgfgH5swWqebcaKtJ8Lh8u6hi1gxPx/3Li4SLFfdstOGV3faqKCWBFhMOqypKUZetjFivBCNOLVqYQG+dndJyDhixrQMfHjyCs5dsuNUu5339cOeUZgMWvzp/96Hwyc68dzfPuM9X8wmHXw+P7Y2tGJv02VpB30Lu9ON1s7ewDUoFFcRTDzLtaPl1Z02XO4ewDsHL8b0fTYuLcHy+QUAELXjNDhvUgilUoHHV1t5F5nraouwpqYYSqWCVxCVy8lIoVDij8Lvl/ooG1+Mjvpgtw/G9D3UaiXM5nQ4HIMYEXDPUCipBptABvBb1cUMIkne/5nNh2JWYvCTB6s4VwdtHQ48u/UE8T58Pj/qD13A3qbLdMIXhAIQVb5GoVCiY+PSEmQYtSEl87HsAspFuLBiNumwSCCHUq9RYpiwKzVFPnQaJb5136yI7FAhgsUCZrxgHxjG+ct9+OBEl+jjMGhVeGyVFQsrpnBuc6T5Gn6/o1lwX+kGNd78/+5Ff98Qjp69xiuQkJazkj7Pvru2Aosrpgo2CwkXW0jHHRMFi0mHZzfVBMaY0TZfkSJuHW/pxpt7W0MWc81GLR5aURZy7guNVcM/CyU66PybEi0WSzpUKqXgdpIcawzHjh3Dhx9+iK6usQdibm4u7r77bixcuDCa3VIolBREqVSgrCArIKTVH7oYsWIYboOXc8Uu1plAfKVRpO/rcLqx48AF7Dx6iXacZIGKapRUJFVzoxQKYNv+L6I8Evk5wpd4HU63YOg6FdUSw9NfnYOKIguUCgWRcGEyaPDIyrKQ57lSqcDgsDeiFE4MLs8o3vqgHSqVgnOsQJqBNugaQf0nn0OjHDveTXWV2LYvMjB+wOUlvkZIn2dZ6Toi91m46z1e5dqpQng3TK5qCpNBg8Wzp6CqNBtOl4e1uZTUJgBceWjBx0UyVg13MsYLMRUkclabUCjjBUnCmsfjwY9+9CM0NDTA7/cjIyMDANDf348//vGPWLFiBX75y19Co9HIerAUCiV5YXOfhcOIZkynTD6Eyi/DifUgk2uAfrylG1v3kZVjvL6nhZY4UigyoNOooFAg5HqymHTIzzHi9PmbcT2WTesqceDMVRw5e51o+5K8DEzONOBIM9n2sSJczEpFcZAiDCMUAIh4Rqfr1ZhdZMYxG1nwusWkg3X62GS/ujwHm+pm4/XdrRgIasCTrldj1OcPXJtOlxfb9rVDqfhCAIvWTcQgtBBXVpBFLBi/vOOL4xkL//+iQUZGmhYv1ws738IRem+m3FCK2CK1cQIJWrUCnpHkWupiuqmHN5niE8JIhK7qshxZBSKuPDQG0rFqLMe0IY1cDFpAAZxqv4EjZ6+HNNMaE8XLscAa+t3KWW1CoYwnJAlrzz//PPbu3Ytvfetb+Na3voXJkycDAG7evIk//OEPeOWVV/D888/jn/7pn+Q8VgqFIjNyrTiJHSS/sbtVsBOm2BW7WA4yubI2xH5uKqrJT5pOhSE3/V4nAgus2Wi+6MDg8EjA8Wk0aHD7LfdBSV4mfvj8wbgek8Wkg7XQjHMdDuLXtF/pR/uV/hgeFT/R5FlRUgNTmgYblo11NAx+rnOJDAsIFsaAUFfQ8ZZubNvXHiKq6bUqViEpvJmE3NlgXAtxSqUCK+YXiA6yHwv/PxtoOmTrcEjKah0L7OcWLRnBs/kifw4cQ7DYQtI4gemgK6Zsd17JJCz50jRZhM9oMBu1uGtebqCjKHOuhufpCY1bhYQuob/LDelYVeqYlk006x/ySCr3d7q82Lz9DC4sLMADS0tvxZhcZL2eaD4chSJRWHv33Xexfv16/OQnPwn590mTJuGZZ57BzZs3sWPHDiqsUShJjFwrTlICdIVENQYxK3ZlBVkwm3S8g4V0vRoKhSJkIkBCeIkBkFrBwfGmssiMC9eccXHA6DQqrKkpwtsfnI/5e1ESxwJrNhpZXDUDLi/2Nl1GWUEW2q/0ib62o4W5N6QbokrWiBt3zc3FR6fE51lRYktWugbf+cps9A950D/gCSnRlcKjK8tZn+NcIkKws+dEWw8+OdUVUWqbrh87x/km10KLR1sb2mDQqWWPbeBbiFtTUyS5I+xru1vgHhnFtRtDko6LEdXCBbBgF6GYbFhGbDne0o3N288Kbs84uYJdWd32Iexpuowhju/jZPtNLPnSNNTVFsWkgysXZqMW315TESICsQlm8RbC5IZkrCqmcUIwQpUjaXo15+/Ox65jnfD7/Th6rltQYBZbbUKhjCckjQR7enowZ84czr/PmTMH7733nuSDolAosUXOfLNYZpuJWbFTKhV4aHmp4CqrmIk3X4kB6edO06kx5I69wGTUq+FH4su5FADOXCR370SLY8CDY+e64/Z+lMTAJqoF8+KOs5hflrhV8gyjNmHvLQYqqsUHnUaF1YsKcO/iIrRccuDdQxfRermPc/uH7ylHRZEFwJhwtbuxU3DivWFZKV7bFdnxlBHAxMIIFoPDXtZul4PDI3j+72eicgnbnW7YRLg7xdB80R4ixgQ7d6S41gDAOeTFy++ei/rYGFFtxfx8VJVmB5xDYlxhjNhCsqinUABPrvtiHBcsRvl8fnx06iqvwPKnva345r2z4pq7+NCKssA1MJ4hGauyLeYycFWakFRQSBHVGHY3knXATVQ+HIWSDEh6+k6dOhXHjh3Dgw8+yPr3xsZGTJ06NaoDo1AosUFKSC4fUnMgTAYNr3NNyoodV1it2NBhYGxysmFZCafASPq5F1XkSOp4BgAatRJeng5GRoMGd83NhUIJWAvMcA578eI7wqvYsUSrUcLNEypu0Krgh7xlsRevOWXbFyU1GRn148i5+GeWMfdKi1Ef9/emJJZ0nQorFhQgOysNfYNuDAx7ocSYgGGdbg5MdvkcJGyLN6QTb4B9IYURwKSUZJGMD6IuvSc0slQUmpGRpsERwoWT+sMdOHjmGh7iyZMDErv4dLylBxuWfnF8YmDEFluHQ3BRz+8fG2OxQbIo6Bjw4FdvnxJ1fAyLZ03BZxduEn/P0TQLSFWqSrNRV1uEPY2dIdcT00G0qjR7rPw4SDwDgPpDFyPcl3qtCiuq83HgzLV4fwxOegfdtLkBZUIiSVirq6vDb3/7W5hMJjz++OMoLCyEQqHAxYsXsWXLFuzatQtPP/203MdKoVBkQO6ORFJyINL1ajyyshybt0tbseMjPKxWaujw4PAINm8/C+V69o5jpJ97ijlN9Hsz8IlqADAy6sN7RzoAAPXoIJ2vxIyC7HR09gzybuPyjEJN0LKaQkkF7E43Gpo6sfS2fMHyHkrqM788G1MnpcFaYIa10Mz7jBJykNTVFmFNTTHrPrgWiRgRoqo0G89sPsR7rMELZGyTXOCL8YBzyANjugb9A56Yn8PWAjMOmq4Jvk+zBGcb47pngxEj6mqLkGNJk6XkNpj55dlo4slTA74YWzHHSkK48ES6qHf24k2cvWCHvX8YWRlamAxaZKbr0HVzgOj1UpmUpceK+fm8ZaTMbzDeBRe2644340yhwPkrfRF/16qV8PnHxnzhDHtG8e7hjlh+DNF0210RJc5yNTeggh0lmZEkrD355JPo7OzE22+/jT//+c9QKscmST6fD36/H+vXr8eTTz4p64FSKBR5kLsjEUleRDgr5hdggTUHSp6JQzQP3+CyB6mhwwxc7j3SnIylt+ULlvVIJdz1JUceeUGOEZ3d0gbeQqIaA9vgkJL8mE06uD0jtFlEGNv2t2P7gQvIzjJQYS3Fua1sMhRQ4OxFe0THWTHPJRLn18enrmJNTTHn3/k6GpK4lhgRZ3DYG/Gc1WtVUCkVcXdvmdI06B1y444507Dj4MW4vjfDx6eu4tlNNQAgy7OZKcvt7CZzTotx+a+pKURd7YyQ8Qfpot57hy8Rv4+cvHdL5GFzCCbKnTYy4sP+Ty+ju9eFnCwDlt6WD7X6iwU+n88P2yXHWJmyAkTCuRBsblWh0lqH041dxzoj/t0jsMiaTOi1Kt7mBsHl0GK/X9qNlJLsSBLWVCoVfv7zn+Pxxx/Hxx9/jCtXrgAA8vLycOedd8Jqtcp6kBQKRT7k7khEmm3GYNSrsaamCABZK/RoibZlOZd7j7RcR61Wivp+EoVRr8Zjq62oLs8RLF+iTCx0GhWe/uqXYJ1uFp0LNFEY9oxKFqQpycOnrTcC/zu446zY55JcznCuoHbS59qJth7WvLREdaiWK7MsGoK/d6nP5jU1hcidnI6sdB2cLg+27SN/Xmal6wKuNSEqCi2SFvWSgS8cgsXIsRgS5i56e38bdjd2hjSPeOuDdqxcMNbpstHWjT++fy7kmqhHB9L1ajx+a0xE6pJituO67hKdgRsPhDpO7226jL1Nl1kFMb7vWc5saAolVkTVxspqtVIRjUJJMWLRkYirbIWNx1ZbQwYkse7wJLVleTBckxihch1mQJau12DF/HwcOXuduCNqPKgum4xpk9MjVmeDBc9PW7vRcPxKgo80tdCqlSm1wiyE2zuK4y3d6B10w2LUY1PdbLy2q2VCTBIoE5fgjrNixYATbfwlgQxSF35In2uf0GYVrDDfO/MM39rQBrsIoUqtVGJxxVTi7pwMFpMOTpeXqJEC1zhM7GJmovn4VBee3VRDFMafYdACCnB2BmW2DS5fthj1rNeoz+fH73ecxTFbZEaf3z/W6dJ2yYGL19gXRJiswpULC3Dos2shYzdGFApeGO62u/DRqa6kFzxjjdtLJtqHC2J8brSq0mxZs6EplFiRGv3hKRSKbETbkYiLYDHmRFsPDp+9HtKBM1ElAHKs7vJNYpjPfb6rD16/AhqFHzNzMzmDqxkXxNCwFwfPxD9snWHlwoJAiDIbwR3iDp+9TkUUEYwnUY3hgxNdgSYcZpMOj66ywqBR4XfbP8MwT7MKCiXVETth8/n8OHyW7N4udeGH9LlGr012gr/36vIcLJg1BV2OYVzq6sXru4UXDT461YV7FxeKbkBQO2catja0Em/LhZjFzETD5FBmGLWsLiS+z2A0aFBWkAmtWgXPiA+tnb2snd3D3U+Ntm68tvMcBgViC7hEtWB2s5RmMqJQPLumjle27GqBz+9nFaiZ77mutkiUAzjc+VZRPP67zVKSA4XfL2TaHHOmKZVKnDx5ElqtFlarFQoF/wBDoVCguVl8YHiiGB31wW4nyweSilqthNmcDodjECPjcOJFSS3YBjRyil/JFDBK0oacC5NBg18+tSQkj4ON8Otb6D1XzM9nLRWIB0KiGkM03xtl/PPU+kq0X+ljnXhQKMmEXqOMSmT6yYNVxM5qW4cDz249IbidKU2DX3+/lrekjO/5uePABSLn03hEr1VJLme1mHSBjDXmO56Uoceiufno7xvC3z5s5w3eZ9i4tETW5gdsBJcjsuHz+dHQ1Bnz4+BCpVRg1Ccu3ZURwQDIPr54an0lzl/pY80poyQvRoOGVTBlIBUwv7u2AhqVknVu88T9c1Cel4HmC/akmJdQUguLJR0qgsZrRI61p556CgqFAmq1OuS/KRRK6hLrfLNYl3iS4vP5caVnEColICUz3+ny4l9ePCwqHJUkuJrU0SAneo0S37xvFhZYpwhuS/IZKBObLbtsgnkqlNiigDxNS8Y70Tq3xJRsEjf+yc9k/XfSgO7sLAPxMVG+4MHlpaydGSe924yHVpQhx0LWyfvMBXusDjEAU47IlR+lVCqQYdTG/Di4ECuqAaFuL7l5pb6ZujRTED5RDSDPpuu2D7GK4nanG/+5pREatRLeIGMLbXxAkRsix9pEgDrWKJTxQ3CA7Cenr8oW1LyprhImg4ZViAy+vs+cv0nkWIg3P94wj9gST+q64CJ8AEOhAGMO0GTKGUxVjAYNllfni3IsfbkqF022nqT5/o16NQZSpIyKzbHG5SprvmDHL946SbTf8ImdkEv4qfWVqCrNRv2hC9h19NKEFhGsBZmwdfYRb8848gF+p1RdbRGRYy0a15xYzCYd/jvMZcecc62dvUk53qCkJlJciMmA2aSD3+9H74BH9Gtp4wOKELI61sJ57rnncM8996CsrIz1721tbdi9eze+//3vS9k9hUKhCMI1qYllR8sX3jkT4tDhWu2KthNpLLCYdKIchNF+BiqqUYJZU1OIikILPm3rQUOCSqBTmbvn5SLdoIYCY05g63QzjtnEuV4XWKegosiSNOXd3107G3/caUv6jCgzS4g8l6ts0awcHD0XGZbORXCAN0lA95ZdLfjj++cwJJAdxTCeM6A6uweQla5F7yD/RDpNp8L31o91NQaAZzYf4t3+o5NdMBt1cAxwn5fxXiBwON2oP3QxIhzfbNJh47KSlOgSSkl+Vi0swNfuLglqyDCED092SRKr4s1dc3Mll8bTxgcUuZAsrBUWFvIKa88//zwV1igUiiyEi2hOlzeivT0zqYlltka4vzd4UrRo9tTAv8vRiVRuxDakSMbPMJFRKoAUXEQGABh0KtTVzsCJCSaq5U5OQ9eNIVn2ZUzToK52Rsg1LOYaNRk0KMnLxIl2sm6V8WBg2Ctbd0OSkliFIvIeToJr2Iu39o9NvMoKsnCirYf1mB1Ot+Tnz9aGNhh0akFxRKhkKpzHV1vHbebUoHsUS0qzcfDMNd7thtyjUCoUUCoVsHU4BL9jx4AHeq2K/72H4+/6ZBMNHE43Nm8/i1ULC8blb0yJLSqVAgXZRiyclYPl1QWBLOHgRdg1NcUhnU+TLdeRcaJ6pWS93CK48QGFEg0x6Qra29sLjUYTi11TKJQJBqkDLZpJTbRsbWjDgllf5JbJ0YlULthcdSTB2Mn0GSY6C63ZOGZLHkFELDWzp8HW4cCrO22JPpS4IpeoBgD1hzpw8LNrIdeymGvU6fLiJy8cSir3Uv+AB8vnF8jS3ZBELxMS1XRaFdwspX3DXh/2Nl3G3qbLMJt08HjlL/+zO92wdThk258pTYNHV5ajujwH1eU5KJxmwpadLXErXYwXOgEBjME+MAxbhwNNLWRuQqHvKdkWOY6d68amusqIBUcKNxqVMioxJtm4d/F0fHDiClwEbtZ0vRor5udjTU0x54Jr+DhxoXUKlEoF8rLTk6ob7YZlJaguz4n6/pmMlSaU1INYWGtsbMTRo0cD/7137150dHREbOd0OvH+++9zutkoFAqFQUjgSZWulHanGy2XHKiZZAQwFigslxMjGiqLzPinB+ZFfKckwdjJ8hkowNmL8k24E8HRc9ex79OJ41SLFcEOWeZavWvuNKI8KABJV86zbX87djd24qHlpfivJ27HD/nTR38AAQAASURBVJ8/KNqRJYVw55rFpMOGZaXY2tDKKqwFE8vJpJyJx8xkExi757+9//y4E9UAIIewgcNbDe1Jky0YC+xON0wGDf57U01Cu4SmAjWVU/D4qlk4df4G7/hGp1HCnSIZhhaTDvffORPF0zIEu9Ezzlu+CoZGWzfe2N0Scs0EjxODG5+daO1BYwIX/t7a147qspyoF4NplQZFDoiFtaNHj+K5554DACgUCuzZswd79uxh3bakpAT/9m//Js8RUiiUcYmQwJNqXSnDJ63V5TmSnRjhuThjE78SmAxa9A66YdRr8Os/n+KdiCkA/OBrc6FUKkKaOexlKcVjJuzhg67q8hxsqpuNF945S7s/JpBkchlJIR5iCR96rQrfWFWGl3ecGxcdNLc2tMHnR1zdKXqtEiqlUvZzkbn31NUWx+088fuBjUtLkGHUhgTAJ1p4/PDkFdn2de3GEGwdDjhdXmzeHt/FEaUS8MVBj7CYdFh6Wz52N3YKXgfJLKoZDRpZzv3eQTeUSgWWzy8g+k4mIqsWFuCBpWMNLLjGaGNurgKsqSli7R5rMmjwyMoyKBUKbNnVEpP7ll6jFNWYZMOyUrR29sI76kNdbVFELprZqMVd83KJOt6+vb+NtfojPA8SAHx+P2wdvbz7C1/IEPvZhAgu45QaB2NK00TkaFIoUiDuCjo8PAyXywW/34+amhr8x3/8B+65557QnSkUMBgM0OlST/WlXUEplNjA5krjyqhheGp9JdL1mpTqdPXTR25DTVVBxPUd/Pl7nW68/cF5wX396IG5UKmUvKWaXIMfhgXWbFSVZUsKnw0WOKPtDkqhJANajRKeFHEfJCvzSibhZPvNmOxbp1HBHYMSSy6+u7YCiyu+yMU80nwNv9/RHLf3jxdSc+VSgU11s7HAOiVlnO1cPLW+EgDw6k5bhHA91v03j8iVGty5NhHfSTy7pIqFEcMWWKdE/E2ocoLv7yMjPvzo+YOyC7cr5uezLoKGYzJoUJybgQtd/SHHYDRosPS2PEydlIZuu4u16cVdc3ORYzGEfKZG23Vs3n6W9z2NBg3UKoXkhYg0nYq4+Qop311bgYXWKXhm8yFJgvKmukossNKuoBRuZO8KqtfrodfrAQD79u2DxWKBwUBmwaZQKBMTNldallEr2DFya0Mb7r97RqwPj5Nwx5jQ5MRi0qF8OnvoqVKpCAx2jzTzhywzOIe9IZM+NphV192NnRHHplEp0GiTbs8PXpkcTxkklIkLFdWiJ1aiGoC4imoAkGHQhvx3spcBmU06uL2jGBLpGByvohoAbNvXDqVCEZU7PJEwoetMyW5VaTZsHQ7YOh2AH4HuvwDw0amrvJ/NEta5lus7MRk0cHlGMSLzc33F/Hx8/e6SmIhM4Ri0KszMy0Rmupa3cUVdbRFyLGmci5MMwWM0sX9Xq5Wo+dJU7JY533duyWSUFWQJntNOlxenz0felwdcXuw4eJFzMcThdIc0IWC6y76+u1Xw2KJ16MktqgFj9+/Wzl5J1//KhQVUVKPIhqTmBXl5eXIfB4VCSXHYOneylaCQrHLZnW4MDCaudGNweCRkUOZ0eXhX8Ug7bpJO3ki3e2BpKe6/cyb2f3oZZy7Y0drZC8+ID95ReWZTWxva8K37ZsmyL1LS9WoMe0Yxmmzp0BQKgCVfmoqDn5EJ5JTk5ZX3z0U0gsgyahNeDsrFt++dhfYrvcR5ehOB8MxBRpja/M6ZpC2f12tVuGPONNacK6VSgYpiCyqKLRGvE8o7ZRuDhGdhZaRpcem6k8g1L5aq0myo1Uo8uqqc9ziNBg1qvzSV122/amEBjp7rjhAEF8+eEvG9zSudHCE+hQuWUmFzqgEI+be+IQ/2xKBp1sv1zXh4RRn+e1MN6g9dkHzdky6GMN1lUxFGVD5muy76tSsX5GPDrUVqCkUOJHcFtdlseOONN9Dc3Ayn0wlfWKCCQqFAQ0ND1AdIoVCSHzZnmkJYZ+LFlKZNaFfKj09dxbObagIDOOV6RdQDOJJw1fCVZyHUaiUmZepx5oKd+DWk2J1uwI+4/g6DwyPQa5RUWKMkHbeVZ+Pb91Wg+aIjpZwxlEjCRRmlUoG75+UmrXDVO+SGd4TeE9nY2tCGqtJsKJUKKJWKpBTVdBoVVi8q4O3CyEdVaTbqaouwt+lyRP4q3xiEcVsdb+nGK++di8l9K3jMwp9b9kUXypl5mbzjqa/dXSLYuZx5v2DxUMidJkRwHu3hs9dD3Fnp+rEpczzOr94BD57/+xlsqqvEhye7Yv5+qQwjKotxHfOVBlMo0SBJWDt69Ci+853vIDMzE5WVlWhubsbixYvhdrtx8uRJlJSUoLKyUu5jpVAoSQhXlke0JShMzleislOCA1EBeQZwJJ02+dxvXCuosWzy0O/yxP13kDPYlkKRi09bevDj5w9KDkimxAadRoXy6VmsJVFCBIsyJMHeiWK8d7WMhuBnde9gcgnejKB07+IitF/pwzHbddFjB7aFy+CAfaH9SMlbW1NTCGuBGVAAp9pv8OZ9hY9ZSMZKQtsIlWcGE76tz+eHrcMhepx2vKUbb+5t5XStJkKwfWNPC5xD9Lpnw5SmwaMry0Ncx0KLwOkGNTatq4R1ulmy+Eqh8CFJWPuf//kfFBQU4O2334bH40FNTQ2eeOIJ3H777Th16hT+4R/+AT/+8Y/lPlYKhZJkxKpzJ7MCqlQq8NT6St7BDkN4LpochA/SxQz2uOBa0RVaeebqonrX3Gkxdc90211YW1uckhk2FIrc2J1u7DrWyVquZDRo4Pf7Q+5DZpMOHu9oUrpokh3SMPSn7/8SlEqFJGHN7nSjoakTy+cXJHXOGhXV+GGe1Yn+DfUaJWYXWzBtUnogI+1EWw/+5cXDvB3QwwUmn88fiHhgc6MPDo9g+4ELyMtO5xwzMALTqzttoj6D2aSDtcCMfpcHWek6bFhaypr3xTdmIRkrMdswn1+K6Mh8xnMdDtj7h+EeGUXrpV4MBN1v0/VqzCuZjFnFZmSl6QAF0D/kCXmvZG2AMd5FtXVLirDv0yshzkCSZ6bJoMEvv7cEavUXYfIkC9ffuq8CFUWRpdYUilxIEtaam5vx9NNPw2g0oq+vDwACpaBz587Fhg0b8Jvf/AZ33XWXfEdKoVCSDqlhoUIsnJUTGFgxK5v1hy6GhK2G8/hqKwBEDP40KoXkzLFYDdLFut+4Bn1jAbQXY3KMDMGDd+aYT7T1EHWsGq9o1Ep8qdiCT9tuJPpQKAni2Llu/NcTt6P9Sh9vBo/P58cv3jqZ0GNNVYY9ozAaNLxh2RaTLjCBl1qyvm1/O3Y3dmLjstKExg9QpMM8q0lcK6TMK5mEU+dvRrjvLSYdhtwjIaKvTqOEAmNu6+OtNwDcwIcnu7CEI9ieKUVmE+hVSgVxFMIr750LOC6DBTop3cAZPN7RkHsWIwL+96Ya2UouGRpt3Xh9d0uEsPIQQcTG8ZZu1k6q4QwOj+DgmWusjQ7MJh3umDMNu49dkvYBJhg6jRJunooCk0GDjctKkWnU4uX6Zt7zz2LS4StLivGVJcUR59WJth5egezRVeUhohoD58J1hg5PrJ+DWQWZGBFonkahRIMkYU2lUiE9PR0AkJGRAbVajZs3v1gpLCgowPnz8odjUiiU5CJWZRfHznXja3eXhJQF5GWns7rSjHo1HlttDemsxQh+ziEPnC4P3jssftBkFpl1JhZS91usXIFiCC6XKivIwnN/O53Q40k03hEfznU4En0YlARid7rRfqWP9RoO/jfSTsAUdm6fPYW4DC2akvWx8O4xoSOeZb6M68fn9+P13a0hAoNWrYSHTgIFMRo0gWc1iWtFCItJh4U85d52pxub6iphMmhuiVgu1kW/AZdXsFsk23uIyRcd9ozixXc+w7TJRuxp7CRyeHLBiNjhY6zwPEJSfD4/bJccsHU4AAVgLTDDWvhFCd7b+9tYPz/X+wULh9duDmHHwYvSPmjYe8mxn1gSi2oMKdTMnoKqsmxBwYv5zR5eUUYcexL+HJVa2cG8NnzhuqLYgkmTjHA4Bok+K4UiFUnC2vTp03Hx4kUAY00KZsyYgYaGBqxduxYA8OGHH2Ly5MmyHSSFQklOYuXoCs5M8fn8vG61geERBI9Dx8KLvfjLR+ejWrW+a25uUmQwxMoVKAbm9ygryMIf32+OSbv0VMMVxQSGAmjUiqQJY1copGVCkiwsdNtdEo6IwsB0ASSZYHFNxsRw7Fw3NtXNxtaGtph2CE3Xq/HEV2ZDqVLgVPsNHAkLSgdARTUAZqMWQ+4RXpdM+MXLdR4QlZilafCzf1iMn750hPe43trXhmc31QAAntl8iOCTxI7GlhtAS3Tu6Qe+PBN7GvlFwC27WgILbFwEh/9/cvpqiNBXjw6k69V4fLUVIz6foIAd/H5sURgTAYtJhw3LSpKiY2fFDIsowSsacYx5vdRc4/CF62QYy1MmBpKEtbvuugt//etf8aMf/QhqtRrf/OY38dOf/hT33HMPAODSpUv44Q9/KOuBUiiU5IOk7EIBwM/z31z0DroFw2QZXnjnDIDZWGCdIltWRo7FEPU+5CBZwphPtPXgpfrmCTewpcSGZBHVAOmNVtgWFkLLsdidLOOZcJFSr1FKbkZi1KsDEynSCVb4ZEzsb2B3umEyaPGdNRX4xbaTko6bhDvmTMMfd9km7P1UoQC+u3Y23t7fHip+GbW4a14ucixpY6XUfr/g7zAwPBLSaAhgn5STlGU7h7x46wNhAYdZbPL5/Sn/G1pMOkzPMQmOswZcXtQfuoC1tTNY/04ifg0OjxCPzwZcXvz948+hUSsn3H2U4cHlpbc6wg5h59EOfoFZBFJccBajHoA4wSvapl9y5BpTKPFEkrD2ve99D48++ihUKhUAYP369VAqldizZw9UKhWefPJJ3H///bIeKIVCiQ9sQbpcD0GSsgs/gLraIuRY0tA/4MG2/e1ExyFmQuT3Y2xFrw7Yto9s/0IkOgSZgfQ4SIO+pTKRM9Uo458V8/PR1NJDPEm2hJWKjzlrL2BPY+e4dXSSOPv8fmDj0hJkGLXIStehJC8zIridlIHhEZxo60F1eU5UXQLzstOJFmgYYrmYYTHpsCCFusqajVp4RnyiJ+GFU4zouD7A+feVCwqwaNYULCjP4R1vkJZSB/9m4WOYhdYpUCoVxPv64EQX0XYn2npwiCW3K9V4cHkp+l1k18bepstYU1McMSaMVfj/e0c6ZN9nIhHKKGNgXF3AmCMy+P6p0yjh8/klZwcDwEPLy2A26dA76EaGQYtX3j/He48Of95Fcz+mUMYzkoQ1jUYDszn0Ilm3bh3WrVsny0FRKJTEwNV5ki9Itqo0W3D16+NTV/HzJ27HjkNkQlmaToWPTpENboN5Y3erLN3TwgcRiYTEFWg26XDnnGl4J8mzQihjGA1qfGOlFTq1Er/922mM0oqvhFNVmo0NS0sDpdfNF+2sYdcMG5Z9kQFJGqItBa1aAU+SuPuW3ZaHhuNXBLfLMGqxuGJq4L+jybwKzneMBoWC/PX9Ax7k5xijej8Gs0mHikIzdFoVcrIMMBm1ePndZln2TYLUMueq0skoL8jC0tvycer8DdG/34alpTjV3oPdjaELMgrFmKj2wNIx0UBo0k26sMRsxzeGkXuxLNUXm4JL8myEmaGDLO7AZMiBTRUYUS38uswyanF3kFOTL8BfDteaOajpCyB8jw7OQ6NQKNxIEtYoFMr4g6/zJF9wbWtnr+CE0u5040fPHyQWvapKs3kntVzIIaoByTWIIHEFeryjVFRLEQxaFR6+pwzb9iV3Xky6Xo3RUZ/kMr5UghHSxeQzbtvXDqVCgRGfHy/+/9n79/go7vveH3/taq/SrqRdkARIQhLosggZjAUYCzk4XAw4xJbT1GCSGjdpa9OcNOckTfM9vz/ax/nnNG3Snqapg51LYztxjNM0AUexwQLHTkBgkMzFIFYXDEIgQAKtpNVttdLu7w95lt3ZuXxm5jN7kT7PfxKL2dnZmc98Lq/P6/1+H6Sf/4YLiQtOh/G7E+pcG1oqIkfDLcCzbGYiYY0vYIjl2nHazbJ9dnS+TTWocdLsf7cLuQ6L5qThSxZmo/fuiKqxTC0upxVffmwZhscnkZtlhX88iH0HlIuaZzrv4EznHRw+3YPdmysU5a7j3qfRiSBOtt3G0Oi9Z5ydacbSwhzi6yCt8ukfn5Sdw+xtWE6tYqhawTJV+GxdCaoWuzA8Nglvtw/lhTnE7Z3v6EyFPLCphlz74P5ty+qiSB7J6DmnnmKl0Max1nxoDAZjBiJh7ZlnnlF8YoPBgFdeeUXx5xgMRuIhGcTFnAOkYTOkopfDZkJ1qTuhi5HId9vN2BNV1SiZ8MNZtq4txjune2Ima1y+OrHJ8JJFTnzc60/I9TLIqCjOwUsHE+dYISXTmoFH1xQT7ZjPNjghXYkIwy3WabPxgUJkZ1rw/rleHDh2VfV5HDYTRjQ66LJsJmxZXRQJ/QqFwvLuWYcFoXAYJ9tuxYT2CeXaGRiewI9/d0n2OkjGGKEUBgBUL061FC7gFtUf3xxWfQ617N5cgeoyd8zfjBoKOkRvrH1nb10kMb1cpVaxvmNoNKiowqTRaMCuTRWy4uD+I50zN16CN452EZ2LhHQW1QDgt83d+G3zPcHe5bRieakbp7x9sp/lC+epkgdWD9QIqLs2lmPjA0XouD6IfQcuSIqVre392LkxfiNXT7FSbONYaz40BoNBKKyFVYwgaj7DYDCSA8kgLuYcoB1esWe7B3arejOtnAvCYTPBbM6I+b38BWQiEVoQnunsJ1oIyfWyTFRLPVL1mYwFpnHg2FV85cmayDvO7WLrFeaYbFwOC3ZvqURtVX7KhDNlZ1qoJOp+qGaB5lC10YkpHDh2FYV5jkieMzn3bCA4HZNsPjqVQHTYX2t7H3G+TfkiEWN472xvjBjmtJvx6QcKE+KkybRm4K+fvA9nu+7gSMv1pIguUs4SoQWzf3wS+492Ed8fbmPNU+KCp8QlWal1VUWebKVMJSG+TrtZ9hgfgRA6U5jCrKlyrNtpRW1VXtqHgfLx+QM45e2D2WREUKIardNuRjnPcZhtt+h9eUmhob4Uj60rRdeNIQyOBtDbP4pGAvdwtsMCk8kIo8FAFM0hNK/WKlYKzXNJ3GcsHxqDoQ2i1evPfvYzva+DwWAkiVAojKOtZImUhSaitMIvowf9tisDqs+xU2ZHes92T8rsygnlgyFNbpvOPLQ8Hy3efiphaunGCKX3RS/4C15uUf7iwY/Q0n4nyVdHly/vqEZ16Yy7JxXCmVwOi6rcktFEh23SWvxHtwmxkCEOfuGGe2F4NXDazYordbo+CVvihDQu/93ZrjuSi1b/eBBvJig8vqbMjdGJII62JlZscdrN2LWpInKPAMDb7RMc14QWzLWVM4UD2q4OyAoGfAFAyt3i7fap3qgTgqYjanA0gHXVC2KunbSo0kPVBVi/YiEQTv/8amJIiWrAzHv1rZdORMRybg4zG/nDuZvYUVcWaaPebh+RsMZtBJC2W6HjtG5Yp9I8l8GYS7AcawzGHKa1vU9RtTT/WOxxoVAY+49qn1TteKgEDQ8viQz6pBWq+HDCnFDoC3+3LpG7ckKutNaOvplKpjxmu6jmdlrx5c8sx5c/A7x5/Ap+e/yqrPOOIU+mNYNKNUqhBa/RaMDGB4qJhLXH15cmTNDQytDIZESI6L0zmuzLwYb7F6kO/6wpc+OxdSWRxRNJ2CYpXJuoLM5FR88ggtMhPLxioaLn/OKBC6re88ngNH71Xhc+uNSXdOFTjItXfTjl7U/49/rHg7je50fn9UH88vdduNE/ErNZIZQQPXphzYltagUAMXeLFkFBCJqu+N7+UXi7fagszo1ceygUxjunezAg075OtN3GibbbcDmtmvPvpTOcWL5tbXHaVLfl2PRAEY5+SCaK8sdCknx/0fnLSNttdJvk3k+S7yJxpTH3GYORWFQJa729ZDuqixYtUnN6BoOhE7HhM+SuAY7MzNgug5bLo7rUHTPhVzORbqgvi0wm9MoVISSQcecMhcL4qOsOem4OwWk3R/5NyJWWaTNhfI5OyqPzezQ8vASFeVmCAiNDGQ+vXITDlBY5Qu806aLi8fVlKM53KAq1spozEAhqFwWV8vrRzpRwEDpsJuzZ7kFQQ3nY8sKcODFUSzVOPu9+eB0/amxT3d+rFc9HJ6ZSfvGeTIHlbYl7MzgyGSPUilX4Vlp5M9HHEVXGdlgAg0G2fTae6Ebjie64EOUvbK3C9391nuh6UlXgTTSHT6f2eylEbVUePCW5xOkNosVfkj41en5DWnhDrE3KfRdzpTEYqYcqYW3jxo1EpcsvXZJPSstgMBKDkMCjlP86ehl2sykyMacRoiFUoYh0QhJNvtse89+kuSKkxLJohO4fNxECZsKlBnj/9uCyfMFF4dgcFNXE8nus8RTA+KRBcKJrMxvnRFVKGrxDcZHDd6YCyhYVtVX5CIXDxIJp/coFuHVnFBevDqq9ZFWkgqgGAGZzRmSBpJb3z/ViR11pTN8VCdts6oRvRFtf3dKeeEcWgy5iFb6VOnHkoH0+o9EgOpZy7N5SCQCKi49w92KNJx/f+rPV+MGvzhGnt7BZMmA0xIc/zxXSMZU2Fwp8vW+EqJI6aYVjofmN0s0Nfpsk/S7mSmMwUgdVwtr//b//N05Ym56exo0bN3Dw4EG43W584QtfoHKBDAZDO0qq3UnhH4+t6EUjREOoQpEat4Waa5ESy6InSGL3T6oyoM8fSHmnRSLIspmwt6EGnsUu0Z1UzmHoveaDt9sHGABPsQtDo5P4UWPqVdDUE4vJgMkpNQWD6F2DI0s4WTjpRH8mRFw+ZxFXce1oyw06F56m+KJCLdWGb/r8ARw49jGqS9xxmwOTU3Nz4c8Qhp9HUakTRw4t5xMr5iM1lm5bWxzpe5QWJnj1cDtWLp2PM1138IumDkU5Yycm2XuVbuRmWREKhfGH8zdlj3U5LILir5KICLmclELwc1oyVxqDkT6oEtY+97nPif7bX/7lX+Kpp56C35+alc8YjLmGHtXuuIFfy0JQrkKRkoqESna/OeTEMk48TJVqgamIy2HBwysXSeZaena7J5IgXgqj0YDqUnfMsd5uH43LTCtmNq2SawVwO2yi/1ZblY+VS+fj3Q+vo29wHPm5dmx8oAgmkzFyDGmIeDo6HvRicDSgOXyzsbkbjc3dMU5aWqGgs51Mm2nOOImF8ihy4y0/56rLYcGuzRWwW0z49R8uA+EZh4zURkn0+aQqh/ILLQhVw851WGQT6n9wqQ81ZfMwPD6J3Cwr/um5h/DWyW4c+qBb1vHsHwvib773B+aMngNw80TS8WnD/YtE27iS6pnR4lhb9wAam5UVCmEwGOkD9eIFmZmZ+NznPoeXX34ZzzzzDO3TMxgMhehR7S564FeyENy1sRzZDouiXTeSPBhrl+Ur2sEjEct+8Yl4mArVAlOV3VsqUVuVL5hTi6S0uxxahNt0JdnFK+REaiGX5+HTPTEuT5pV/OYKfQNj1M7FbQ5k2Vh9KlImJ+eGqMbRdnVAcAzmR6NMToXwn7+7FNMvNZ7ohs2SgYdXLIxssIm5dYTcNmc6+/HNfc0xfYhYMQCSwko+fwDffeOs7LnEYKLa3ODhFQsBkI9P+e5Mat+ttlAIaVQFg8FIDXSZdYVCIdy5I189jMFg6I9ei1zuvLVV+djbUIMXD16QdKC4nVZsXl1MLIApcYodOtWDpYU5xBMNErHM5w+gsfkK1cnVbIEvmtEIVxDLdUczAXs6kWUzwWAwEOcBo+Vzkwr5InV50hSJ5grvn+3FY+tKqbpj52rVQjXImKJmHY0nunH8wi3s/sQ91th8RbAirVgbmpicRlPLdTS1XIfDbsamBwpRMC+++ignKHD9+xvvdqKpJb4qI822yto9Q4iDx6/iSOt1bFldRHQ8zWq0Ss+Zm2UlHm8ZDEbqQFVYGxkZwenTp/GTn/wE1dXVNE/NYDBUosfkgH9eowGwWzIkk/gqydECKHfa8fPGSEEqNh44dhUN9aXE16AHVrMx6S4mjpoyNx5bVyIomgmFRtAoDKEmR8lsYHRiCn+7834YjYZPqviOCS58Oe5bOg/nL99V/X3OTDOe2VolOlEPhcJ4+W2v5DleP9KJlUvn4/dn5nbONDX4RiZxpLVnTrVxmtgsGQBSK++VzZKRUtfDh1ugmzMMCE6rl+VHxoMxieAddjO+8GgFcjKtkb7rvbO9RO4zBkNPRiemcODYVWRapeeratKLkEBa2KO8MAffeumE5LmUzHkZDEZiUCWseTwe0aqg4XAYixYtwj/8wz9oujAGg0GHyuJcxaERckRPOk57+7DvgLijyGE3Y8828QW7GEqddkryUigRG5tariclHNGZacamB4pw4NiVhH6vFI+tKyHO+yEllkW72/oGxgV/I39X1m4xxYT7zAUGxwKoW74w8t+FeeIht1k2syZhbeemcsl3tLH5imwfMuAP4N0Pr2NoNDWqbSYDt9OKnZsqsP+ociH4YAq96+lCls2ELauLsKOuDMDMhsyZzn788fzNpItapgwjcrKMKf8+aBHVhBgZD+Klg3Or6AwjvZCr5Kp0I5gU0sIeXTeGZMcPbs7L5Y1jxQ0YjOSjSlj7yle+Iiis5eTkYPHixVi/fj1MJpbbg8FQA6nLJ5lwk47T3tt48eBFyWPNGQasqshT/B1qnHZyYhx3bz/s6CM+5+jEFDbVFkkm6KeN027Gv/z1erQouE69UbKDKxfCoETo5XZlh8fnntvh8vUhrFu2IPL+S4XchkJhTQKwVMGCUCgsGL4lRN/guKrvny1wIdK1lfee0/DIJPa/K18lNVWcqenEptoi5LszIwtMT4kLnhIXVi6dn3QhfmQ8GHHSMRizBZfDAhgMmjcbV1fNR0/fKG77kjdm8Ktw08gNKwdJde2TbbeIznWmsx8/amxjOdgYjBRBlfr11a9+lfZ1MBgM6JOotKNnULVbzWk3x5Sfjx74W9v7sO+AtKgGzIQ4qalwpMZpJyXGCd1bUkKhsORECJgRgAYoudq+uLUKXTeG0HtnlMr5SFhfswDHL4hP5kh3cEly4yl5ptyubLbdQvyZ2cLvz/TibNddbFi5CPlue0RIE3qXtOSjs1kyJEVTJX1IIIVD3/TEYACef2J5pJ+ODo0OhcI4fDrxYZ5WkwGBqfQovbp1TREOnyYTb6OJ3vCIHisri3NhMCS/8iznmuOPZW6nFUsXOXG6neUjZqQXu7dUAlBfcTh6Hvnf713G705KV8nkQ7Nu9uRUGJXFOSjKcwhWuNYLuby0pBvLQhteLAcbg5E8NNvK7t69ixs3ZvKpFBYWYt68eZovisGYi+iVqFRt8QK304pvP/cQum4MCbpjlCTZVnMNRqMBW1YXE4dCijmqQqEwGpuvagupNMhPhNYsK0CvbwI9N4fgG54gcqgICZdrl+WrCiPTgttpxZ8/tgz3V8zXXN1TjyqqZzr70dLeT/Wc6YLPH4hpu0JCO+fEDE6H0FBfhvfP9cY8A7mFyMTkNFo7+rDGUxBzPq6dt3aSOSfNGUZJcVZPli7KxuXe4aR8NzAj4DhFxN9kFeHQS1Tj+qkPLvXFtjMVQpYz04x11QVYWZ6HJYW5mvq+6LEyy2ZOuqgWjcWcgb1P1GB4fBK5WTM5lL76vT8k+7IYUTgzzfCP3RuPU0GYTTVqytyRsUdp7tONDxRi9Seit9FoQGt7H94/10v02ac+vRS5Tmvk3em6MYQznf04efF2zBxKDR09Q+joGQIQX+FaT4Ty0nKQ5GKTa58sBxuDkXhUC2snTpzAd77zHVy6dCnm78uWLcPf/u3foq6uTvPFMRhzBRKhij9IkoaMqi1e8PTmCphMxhjXRXRok5LFj9pr2FFXiqaWHiK3jJCjqrW9D681dWhOmuwpnrkHUhMho9GA+8rno2ieHZOT07IOlWjh0ucPwD82iTtD4zh0qkfTtUbjsJlgNmfIPivu3omJhwDg7fYRhSfrUYWWNAwRmPnNI7O4KhxfaD/t7cPPD7fHLC5yHRY01Jci351JHIb40995kWUzY3QiiP1Hu+KEORKC08kJZXTYTPjfX6zFmc5+vPy2V3M+SYfdDFOGQXG/IdX2xcJ/aOe/5NCz6MnOTeVY4ynA5x8pj+Q0a2q5rkiEWLHUjY97/fCPBSPVJV1OK3ZtKofTbsHgaAC9d0bR2KzMzQLMjJV/smGp4s/pic8fgNFowLrqBQCAN49dYaG/KcTa6gL8//78QXxw7jruDk8gN8sK//gkkStfDTRdV4mkpswd+f/cfOFISw/RGLO6Kj8yfxLbSBbCYTfj0TWLY+YcXMj3zo0V6OgZRGtnH462aC+akypuL5LNGLn+VkneYQaDQQdVwlpTUxO+9rWvYd68efiLv/gLlJaWAgCuXLmCgwcP4i//8i/xb//2b9iyZQvNa2UwZi0kLp/oQVJJyKhfYW4qIYeSljBKLdWVjEYDnt3ukZxcCBVHoOJS485vMymemJAmqDUaDejoGSQWD5WyZ7snIpSd6ezHiYu3MSIQ2ruqIk9UOFManqxXFVpS9mz3AEDcNfPdgenO60c60XVjCIcFhNjBkUkcOHYVX3myBneHJ4jONxGcxnf3nxX8t1RfAO7Z7oHROJPL0W414dJVHzqv+9BxXZ2DbWQ8iL/ddT+MhpmKrBc/HiBy4sm1fSHhOhQK65ILTE/RZv+RTtRW5sNoNKCyOBc/alSeqP785YG4v/n8Aew7cBFfebIG66oXwNvtUyWsDXyyUaEVU4YRUxTFYk54DYXCePuDa9TOO1dx2EyoX7Ewzjmphgcq5iPDaMCyUjempu49c+OThrixhO8UcjutcGaa0X17hPj7Ur1PFcJgADY+UBTzN6PRgM2ri4k2Erl5oNKIhz3bqkQ38rjNTk+JC55iF1491E5lnH/lUHvS3V40KqLrsdHJYDDEUSWs/du//RsqKirw2muvweFwxPzb888/j6effpoJawyGAkgHv8HRgKKQ0VAojP1H5XcSH19figXzMgXdSEp2FoXQWl1JyunBVYPjXy8NlxoHt2indd3Redm+9u9/1EVQ44uj/N3daAHtTGc/vrmvWVA4A4TzqEjt6pKEMOiB027GuuUFyLKZUVmcGydgDI1N4odvXpw1oT0D/oCgqBbNK4faY4TU2ci2tcWRnI9xC2CoX8AOj01iXfUCtLb3EYlq/A2EUCgM7zUfvN0+hBFGls2MnCwrXM7YPva0Vz7MNtOaAaPRmDLP0jcyicbmq3i8vkyX0G9uUaulL/GPTapyA1pMRmy4fxFWVeShvDAHjSeu4p3TPVQqjPYNzCRpb2y+gkBwbuYipMVn1pXgyU8tgdFoiDgnlRQK4TM8KjxfEBLDuVBE7r/940HJyuizha1rigXzjynZSATI00U4M814Zit5RfnaqnwEpqbx499ekj9YhpHxIBqbr+Dx+iWaz6UFrv01Nl/BgWNXFX8+2RudDMZcQ5Ww1tPTg2984xtxohoAOBwOfP7zn8e//uu/ar44BmOuQDr4ZWda8JPfSU8aonfaSCcwnsUuQVdWKBTGy297ia5NiE2rC6nY6eXym3HXSsulBsxUvtq9pVLT9UdfNxfu6cgy40b/KLXr5FhTNR+rqvIlQzX5oawk1TulEMrhkYx8UjZLBvzjsSFl0Y661vY+vCRTvXY2kipCjJ4cO38TU6EwjgiEC2vRUDk3GamzYnGBI8blKRWSyrXPVRV52H9U/vybVxcntCoxCQeOXcHC+ZmYDtFXqqMXtWuX5csKyEL87qRaR1gYOzdW4ExnP7710omY8dNmzsCEBkFs5p5lKQptZwizvMwded/4hUJ+23xVsaDqzBIvjiOUAiL6+765r1nRd6UbBsOMqPbUxgrRY6Q2EnduKkeWzYyTbbeQm0UulO/cVK54/iVV3VopTS3X4zZuk8X7524q/oyWaBEGg6EOVcLakiVLMDAQb+PnuHv3biQ8lMFgyEOyM+92WoEwZCcl0YsSJU44IRqbr2hyVNVW0MtRwZ/chkLhSPhi38A43jt7Q7NLzWY2ombJPDyyqhCexS7VEyp+/jv/eBC/ev+yri6url4/1lYvIA5bpVG9M1VyePDdJNGOulUVeYrCThjpxcjElKCopgW3c8aVcqSFvJLn2a67OO29DaPBICsqc+2zob6U6Pwtl24TXUOiefHgRex4qESXc79zugc37ozitDexRUsmp8L44ZsXcUrASahFVOP4+eF2XVzKcwmDASgvzBH8N6VFjzjcTnXOHj0cm4nAYTPhz7Z5ZIuF2C0Z+H//ox4WS4bsOYU2QP3jwbjvcNjNRNeoRiSj6ZgfnZhKifmN2jamNVqEwWAoR5Ww9s1vfhNf//rXcd9992Hz5s0x/9bU1IQ33ngD/+///T8qF8hgzAVIrfTDhPnS3v6gBzvqyoidcELHhUJhTTvreu6Wacn5JgSX6HsiGEJLez8u9w6rrgzFJZNP9GRbadJdWgsCviirNH+KnrxyqB12iyktFz6M5LF2WX6cW4mEnx1qh9kkvwDlIM2z1ftJCGGqEQ4Dv1WRA42EscB0wkU1DpLwXLXMpjyPySIcBrpuDIkKHkqKHgEzc5WqxerEE7U5rGxmIyaSULxiXXUB6u9bCE/JzMah0SCc7oHjS59ZRiSqcURvgLa29wmGyJI4qaXmj1KFu2g75pU+X9KiYnpeg9JK7gwGgx6qhLWf/exncLlc+OpXv4r8/HwsXrwYAHDt2jX09fWhtLQUr776Kl599dXIZwwGA/bt20fnqhmMWYhcTq7aqnx4u31E5woEp+G95oNnsYvICSc0genoGdS0s057t4ybsHAV6GjgsJswMj4Vl+hbbWWo5vO9+P6vzlO5NrWQllinldSWL8qm0g7+yHgQvz+TPmFXpFVcGfqitjLvTDVa8j6TVYVMTUiCWx1286wOtc4wGnQJ86WF1PhFUvQoGv5cRYk4kp0pHkIqxZ9/ZhmMhvjCCGrhF1QQwuWw4C92VMf8FpJ5pxq0brCJzR9JiinRSPrPoSRHmdJCT7SvYcdDJagudVMR8+TQQ0BkMGYDqoS1jo4OAMDChQsBADduzJQ4zsjIwMKFCxEIBCLHcBgM7IVjMOSQyyVWWZwLmyWDKJHypW4fqkvdipLKRkMqvPAThOuxW0bboQYAazx56Lw+JHkMqUgFzEw0fnjgI1qXF4PLYcGG+xdhKhSWrZJHGp5JI6mtkCibalWoLl4hE6NTAa6Ka2PzVd0qxTIYDO089eml+LCjH2e77ib7UnSBVFQjnY9wFOc7MOgPaHbukVTglRNXhOYqQo5zSXFEhfa4bW0x1ngKACBmvpedaQHCwOBYAK++3Y7JKXLhnaQoz+RUCGc6++N+B0kOW6UQFyjgVeuWmj8qKdzF/03ZdgtefPOiIjFcSdSFkmtTCmmqmMfXl6HrxhBOeW/rKnbpJSAyGLMBVcLau+++S/s6GAzGJwglyo3+t8X5DnTICEIAcHdoAoD6HUlS4eXx9aWoWuzSZeeKdkGCaLzdg7KTeyU5xNqv+SL3nBZbVhdFquMZjQacbJOvTgiQiVskkzUuRFYMIVGWtN2ULXDiweoCPHJ/IbpuDOH7v/5Il0p5E8HpuMl7suG7C6Lfxdb2Pl3aO4PBoMd/vqW+qM9swWEz4V//Rz26bgyh7eoAGk/IhwU/vakClcW56OgZJP4MHznBg3PTBKdD+PJjywDDTJVfTrgaHp8UnKuIOc6lxBHS9Bwcj68vRcPD9ypNCs332q4MKBLVSBmdmBKsHB8tqK31FFCZv5FusO3cXA63wyY7fyRxwPE3Qvn39s+2Vimq3koadaHm2pRAEt4qlL5AD7FLTwGRwZgNqBLWGAxG8igvzCES1qKT8arZkSQRXhw2Ez67nl7VpOhJHq2CBGKQCi3EBSAoXqfDbsaebfFl5rXkzONDMlnjRDUpIYgPafLgK7f8eOyhElgsGTAaDbqIahzrlhekVCW+559YDqfdEvcuTk2F8Mqh9mRfHiPBGI1AiEWFMtKMPds9MJmM8JS4UFmci2Mf3ZQcBzlBjBM8KotzcfzCLcVOdCnBQ8pNU13qFj0nieNcSBxR4vx2feIqkuP3Z28Qn1MNv2jqwKqKPJzp7NfNeUR6X9wOm+TGJTcnbOsekG0nchuhazz5uLK2WDbUX2nUhfeaT/O1ySG1Qb52Wb7gb9IqdvFF1/LCHF0FRAZjNqBJWDt16hTee+899Pb2AgAWLVqERx55BGvXrqVycQwGIx5HJllFJf5xUk44IUiElz3bPdQGUD3CPWlALGY51OVaicZiMuCxdSWiJd5JQwJIdvO5ydLehhrZymCcqMZ30AmhJHkwNwnTO3x0ajqETz+wCL//sFfX75FDLszl1UPtszpvE0MYJqox0gmhfuxMZz+CMi4rviCmNNG8nOChxU1D4jgXEkeUVKHcTeCACoXCuHhlQPZcWvCNTOKHb17AKYHiIPyK2tGhlJzzjxNZum4MiW7UktwXp90Mnz8Ab7dPcE6hZk4oN5d4amMFyhZl42eHO2LGWqfdjHXLC2TnN3xa2/vw8ttk7lWt8xyhDfLywhx866UTkp9TI3YJ3XuSvJKpUimewUgWqoS1yclJfOMb38CRI0cQDoeRnZ0NABgeHsZPf/pTbNmyBf/yL/8Cs5lMAGAwGORkEwo4pMdJoVdiWw49ChIoQS5EUEmOjarFLszLsWkKBzUajaKi2sy/k1WPVbqb/9TGcviGJ3Dw2BXJ0M/W9n7s3Ci/OKitykdDfZlsSCM3CdOS700uXBUAfn8meYJabeV81HryJV2iYgtCBiNdme3J/ecaqyrmY80y4X5Mrv+yWTLw8IqFsFtNaLsyEBOKyc0xXn7bG5dTMstmwpbVxch322Vd9lrD8Ugd53xxhGRMFnOgC9HRM6goZ51ahES1aF552yspavFd7HynG8l98Y8H8aPGNsHPqx0TSeYSazwFqK3M15xTTuk10shry98g93bTd8uJ/S7S/jzV8uwyGIlElbD2wgsvoKmpCV/60pfwpS99CfPnzwcA3L17F//5n/+Jn/zkJ3jhhRfwP//n/6R5rQwGAzPWeZrHyaFHYlsg+Q41t9OKnZsqJHNuKKlsajQa8FcN9+EfXzmt+pomJqfR2HwFj9cvET1GrdgptZv/0sGLRNenZIKW77YTnXNwNIC1ngLiXX8O7vcCSGlR6tOrilBdJh2CpKV6GoORigSn9BcHGImjqjgX66oXxP2dpP+amJxGU8v1uM0zziWUZTMLFmoZnZjCgWNX8JUna2THHJJk+VLjF6njXEgcERuTZ4TBIsnNMj6pIkqMTEwBEsVz+MUSxAoIkFbn5Dvl1IyJSjZClUZw8FE6biu5NiUQpyqROC46iiHbbtE8H6EhIDIY6YoqYe23v/0tnnzySfzd3/1dzN/nzZuHb37zm7h79y7efPNNJqwxGDpAIxxQKVonIXxSwaHDiVBGio68uhWL8NXPr4irKqaEppbrshNxpWInTfGGdCKnJB+c0pAgp92MnZvuPR/SyXtSkFlPkVZPYzDSCTkXKSN9MBiAjQ8UCf6blv7LPx4kcqqThLJpFRhIHOdS8ypaG5DpLkrwn1VtVT5C4TD2HSDbvHv9SCfsFpOqNrVzUwX1DWAxlLZ7JZu0StCad5f2BrdLJwGRwUgXVAlr/f39WLFihei/r1ixAr/73e9UXxSDwRBHazhgskmkQ8dhM8FszpAUzWg78tZ48rFyyTx4u33Yd/CC4E68FKMTUzG76vycaNy1KRE7aYo3pBM5ohwrmWYMjEzA2+3Dqoo8YoHMPx7EvgMXYPxkd5x7hu+cvoZf/v6yot+jN8Nj0iFGqeJQYDAYDCG2rimGyWQUHIsS0X+ROKW1CgwkjnO5eRWNDUglOdtSEf6zCoXC2H+0S9HnvT0+Rd/JJfDn54rVoyomB2m7z7KZ8Ox2j26VMkk32ssLc+Dt9sW8u60d/YqqpJKwYeWilF17MBiJQJWwtmDBApw6dQpPP/204L+fPn0aCxbEW8YZDAYd9M59pieJdOjs2e4hEs1oO/I44UupqMbBTdqkKpxxz1hMeBM6n1aUOiE3rFwkmWfNPxbEj397CcC93/WdvXWRNvL60U7JvB7Ru+NnOvvxuxPdxNemFYvJiEmZZN2A/IJPq0PBbsnAVCgsmzicwUg35N4xmyUDe7ZX4YdvtsWFpjG0YzDMiGpPbawQHYs2rFyYkGuRG8NoOPnFHOeJnFcpdW9H48w048Fl+TjZ1pfU/IbRz0rVfI/wXd7xUAmqS93wj08KOuK0VsWUgnTc3vtEjWQqCK2QtJe1y/LxrZdOxDyHTGsGxgP0w/VJU4AwGLMVVcJaQ0MDvv/978PpdOLZZ59FSUkJDAYDrl69ildeeQWHDh3CV7/6VdrXymAwotAr95neJGKHmz8RTkSFolAojI+67qDn5hCcdjM+7JRODixFbpaVqMIZAFnhjTsfDUidkGrCC/iTYG+3j7gC1ehEMOGhxSSiGokQWVmciyybSZUIazMbMZ6ARNeMuY0BxGtdapDkwPzyZ5Yhy2ZmohplasrcqClzY+MDRTCZjJJj0YFjV1X3X0qQG8NoOfk5x3ky51ViG6dyhXrW1yzAoVM9ibhESaKflZr5nqfEheMXbsmKpA0Pz+Si/ea+ZsnzqamKKQepkJuIuafURvvaZfmCbWJMB1ENSP9QZgZDK6qEteeffx49PT345S9/if/6r/+C0WgEAIRCIYTDYTz55JN4/vnnqV4og8GIh7bTKhFoGXj5laj4WM1GfPVPVsCz2KXrRJjvEvOPB/HG0U4MUHDicbZ9uRLqrxxqFxSehHZpK4tzNVXpixYq5RxyshXizEZMSCwOuEkw6YTc5w/gF0c6yH9MFCULHCicn4WLVwYwNHrv3nC/9/KNIU0LlSy7GUdaeiILVNpI3UcGcP/SeejuG4lZbMj1IUqxWzLwxW2V6B8Yx4FjV0WP21xbiPvL8/D+uRs4LVORL9VIhm4VkwOzqRO+EeHNg5Ntt5JwdfRYtjgHY5PT6L41kuxLgc2SgW1rF0eqcBqNBqLUDXpLTqROaVpO/lSYV3Ebp43NV9DUch2jE1MRUY3fh3Ei9P6jylJsbFtbjA8u9cVuzDksmJwKqRZK+c9K6XzP7bTCs9hFLJLqURWThFRLySK00U4yj6SJw25m+dUYcx5VwlpGRga+/e1v49lnn8X777+P3t5eAEBhYSE+9alPwePxUL1IBoMxe1CSQ8TlsGDD/YuQ7878RMAStvxz/MWOalSX0rPdC4lIZzr7dU2U//TmCnTdGJI9v5xI9vqRTqxcOh9dN4ZwprMfARXOpiybCXsbaiJCpVxoKlGFOBkxiJsEk07I267eVb0I6L41guHRIHZvqYLTbo55zqFQGK8cald1Xo6evhHsf7cLb/y+KxJSxWfGcaev20MN62sW4PiF9BUttq4pws5NlYIiOM28MuOT0/jV7z/GZFD6/fqw4w7KC3PTTlRLBltWFxHnwDzTof1+Zloz8OiaYoxOTBEl06fJZ9cvQWVxLr72739MWj+QYTRgVeV8dF0fignd50I9ZceiiSnUVuahlcKzEEKJQJFqTn6SVA1inOnsFxTrOVFty+oirKrIQ2VxrqKQy2ih8fOPlAvOcdQ6wPnPSmnOOO7zpCIpjaqYatErJYvaNsMXhElERyXIbc6OjAdxprM/pVPRMBh6o0hYCwQCOHr0KK5fvw6Xy4UNGzbgueee0+vaGAzGLIRkpy96whiXD+1JQ0JyywmJSHKhGFqI/g00XBgD/gC+/sJxTblWnt3uiQiVJKGpWTYzlYnc4GgAaz0FshPyLJsJpy/1afounz+AfQdmrn+tpwAdPYN4491OHDt/k1qYZTiMiPONL66lYvECl8OCPds8aKM8MU8UWTYTyotyAQi7T4QqAWsJdyS5RwP+AH7yVhvR+TKMBkyHUiu+UYvjVSnzsm042XYrZlHJiQeDozPCe2VxLn71XhcVoXIsMB0JaUwknLvHaDTgmW1VxJUTaWO3ZqBF4D5yoZ4kLJyfCagzDouidlxPBccZQJYjVQySTarW9n7s3DgjRJGOIzvqStBQvyQyrxK6V2KCUTRCrjmhZ0WaM07o8yQiad/AGNHv1itEkbaQq6XN8NE6t3A5LPjyjmoMj01GHHD/6z+OSW4A6BF2y2CkE8SziLt372LXrl24fv06wp/0pna7HS+88ALq6up0u0AGgzH70LLTl4gdaTERSQ9Rje8KA+hNAtUuhB12M/Zsq4opkCA3yX/9SCf+ZMNSVd/HhwtDkpuQ03R4vPK2V1cnIgAcPt2Dz31qaUxYaCrmJNm9pRImk1F1Eu1kMzoxhRd+cwEN9WXIy7XDPzYJR5YZbocNlcW5MX3Imc5+NLVcT0i4Y3CK7FtSTVSbCTUrT5jws//de1UEXU4r1nrycPzC7Zj+LCfLjOFRukJfoh1j0e6e2sp8WC1eVc5irYyMa//dnmIXjjul82KR0FBfGnGoJ8ppFgqFcenqAIJXfDAbwli6KEfz95JsREnNc0gcaNEhjqTjSHWJm+i38edZ2XYLYECMyNJ1Y4hoDiY233PazVi3vEB0ExWQFklDoTDeO9sr+1tcCosuKYWWkKu1zfDROrfYvaUyJgLE2+2T7SP1CLuVQ4srlMGgDbGw9oMf/AA3btzAs88+i3Xr1qG7uxs/+MEP8Pd///c4cuSIntfIYDBmIVoEMj13pEOhMF5+26vLuYWIdoVxKA2foA1fkCOd5PvHJjV/d3SOllUVeWioL0NTS4/ui96RiSlA5+8Ih4F3P7yOR9cujvwt2c+az/qaBVhVkQeAzLmQyghVpI3e/a8szsWPGslcZHOZe/nODPjpW5cUJb7+9KpFOO3tVy3y+/wBHD4dH545RFlUSyROuxnPRG1cAID3mi8pohoNuCTtckL84+tLUVmUi3OX7+DExVihNFkVzWk6hDhIN6KknD1KQxxpVEXlIzfPUjIHo7UhGi2iDI9MYnBEfs6xYeWilBdaaLQZPmrnFgYD8PwT8SJeMsNuxdDj/WUwtEAsrB07dgxPPPEEvvWtb0X+Nn/+fHzjG9/Axx9/jCVLluhygQwGY/aSKiEb0TQ2X0mIc4HvCouGxK1ls2RgQseFWPQkjnSi5Mgyy07kHDbTjIglAufiUFNZNB3oGxyP+W/SUJlEcfzCLbR1+yIT09qqfITCoJqXTAyrJSNOXDCbjAgSVGAlhdv9b6gvRXlhbsq1rwwDMJ1ChrUVS+chy2ZGKBRGKAzF4dG1VfmoLnWnTPtOBZYW5qD7th92iwmeEhfOdPbjp29dUn0+p90Mf4LCdIWQy4vFOZM8i12oLM5FdZkbOzdW6OIyUeJeoe0Q4lDqNhOC1G3EHZdqyfSF0DrfUzsnyHfbVX+nXvDbaSgcpl6EQe3c4vknlmONJ77dK22TeqPX+8tgaIFYWLt58yZqa2tj/lZbW4twOIy7d+8yYY3BYKQ9oVCYevJqfi6SLJsJW1YXYUddmeQkV2yRYjVnwGCArqIaoC7MxO2wyU7k9myfKW4jtMu4YeUiBKdDePPYFUG30WwgPzd+ki8VGr12WX5c5TY5cdKUYcAUT50R+psY0RPTVRV5iqvNqUXIsROcCsFmzsCETHEApSQjpxYJeolqXFtSWuX2/OW7OH/5LrJsJlUbDj9ubMMXtlTiK0/W4MeNlxCg/BzTkbNdd3C26w4a0U0lb+fGBwrxh/M3dRWJzRkGWMwZMW1ALi/Wmc5+nLx4G/7xIJparqOp5XqMm4T2ppoS94oeDiEOGs4eNQ40vZLppwJy1calSLV0C0LtlHQsUuoGE2sTDpsJYUD2fY5GD1ekWvR8fxkMLRDPKicnJ2G1xnZOFosFADA1lXoVzRgMBkMpelRofP6J5XDaLap25rlFSmPzFTS1XMfoxFRCF6ZqwkyMRgPR5D46LKRvYAzvne2dtWIah8EAbHygSPDfpEJlhCq3/eq9LlGRREhAIxXVonn9SCfsVpOuC/YsawZgMEi+d7RFNY5UrMZKmx11JagucUeq/QHKhDUOtfdqcGQyItJ+9XP34btvnFV1nlTHlGHE1LRygYxG3s6Dx/UXiYPTYQSnp4jzYo1OBAU3qWi5SUir/Yp9Hw1XmRg0nD1qHWipVhWVBiQiihiJEnpIERMISftXNSKhWJsAoKidpJIrkvT9PXDsY1SXuFFd5pY8lsGghaKR+MaNG7h48V7yWr/fDwDo7u5GdnZ23PHLly/XeHkMBoOROGjmhtCySxy9aOgbGCeuzGYzGzFBscBCdJjJLpnk5TGJuAUmclyy4+hqf54SF1rb+4h/X7qzdU1xTOECPvxQmVAoDG+3L3IP13oKYDQaEAqF8YHGaqgkDPgD8Hb7iI5dsdSNnr5RRSLcro3lKJyfhX/55Tm1l8iQIzzTr3m7faoXpzR4/Ugnvv3cQ0kPW9QLNaIaTRIlEnPuM6lFuJ5uklAojMbmq4rzbvK/T898UbScPWodaKmYYkMp/Fxqajd3kh3+Go0WgRDQJhKKtQml7SRVXJGk72Vjczcam7vhdlrx3OdWYFlxjs5XxpjrKBLWvve97+F73/te3N//z//5PzH/HQ6HYTAYcOmS+pwRDAaDkWjUhgy4nVY8/WglCvOz0XNzCE67WdEuMV9Ie/9cr6qJJE1RLXoS19reh/1Hu0SPE5pQRU/kWtv78K2XTsSF6OzaVC563kRiNWfo7gTctrYYT22sID5eKqwpy2ZOXG4wwjXJ+csD2NtQA6fdjA87+nC09YZkpU230wpXtg0vvpmYSpNzlcYT3cm+BAAzIm3XjSF8cWtVQvL1MfTl1cPtCExNw+2wxVWI9F7zEblJvNd8cYV7pGht78PLb3tViYh895me+aJoOntmowNNDhr5VfUQerRWnyRxWUmRKiJhKrRJpe/lgD+Af3zlNL76+RVYVT5fp6tiMBQIa//4j/+o53UwGAxG0lFaRWnL6qJISIzFkgGXKwtF8+yYUpBsPVWT9EcXEZBaIOzcJD15lUowK+WASyQrl7pxytuv2/ldDiuWFpLvlMol5d2yWjicVA88xS4cd94iap8/f6cdu7dU4kjrDdlj1y7LT2mBxWAANq8uwskLt2elwyoZDI4GsK56Aa6sLVac642RWvjHgvjxb2c2z/l5REn5/n+fx1/sqI6MH1LChZYcWxzRLpfywhw47GbJarVaHEI0nT2zwYFGipbnvGtjObIdFl2EHhrVJ0ldVvx8lqmYIy/ZbVJtxdPX3mnHyiXzUkKgZMxOiIW1J598Us/rYDAYjKRDWkWJ1kSHxmKBNtG/jSR04Y2jnVhVPj/GscBNarWGPiQCh92sq6gGAL4R8rxCJPfs5MXbNC9PFLfTCk+Ji7iymH8siJcOyoulzz2xHL98Vx+nojnDgCCFCgDhMJioRhnOZfDUxgqULcrGzw53SAobjPRAjagGzOSX4/pFQLigze7NFVhVkUdlHOHaHyeSyLU9rQ4hLc4ere6odETLfMHlsGDz6mJd7hGt6pOkLqu9T9REqrHPlWevFLUVTweG1eVNZDBISb2SWAwGg5FExHaaSZI2KyEVRKcsmwnPfXY5jBkGDI9Nxk3iSBPEfv2F4zGLlKSELKokOJW4YhAkeYVI7rl/PJiQXFXcwrK2Kh9bVhdRq5h74fJd3dqFMcMITNN5pqkgqmVZM7D3yftwrusO/nCul0qy+2TAd/+s8RSgtjIf3ms+7DtwQXOOMKvZiFB4porsbMTttOJTKxfNygIvL791CaOB+HeWEy4a6ss09xdc+yPZzKLpEFLj7KHhjgJSS5wjuRYtoZKTUyGc6eyn7uqimS+QNPeep8TFhDQCxObqctDMpcxg8GHCGoPBYPBIRA4Jrfk2aDA6MQWTySg68SedgPB3/pMRsqiWRAoVJFXmSO/5uuUF1IQuPs5MM57ZWhWzSFlVkUft+45fuKX6s9G5+fghVksLs3FaZ/dhojEYjRidmNLtWSuFH6ZEipD7x2g0oLrUjWe3ezQ7dw0GA4IJrJicSHZtLMfm1cUAoDr/ZiojJKpF09SiPWz46c0z+S3lRBJnphnffu4hySIzSlAqbtFyR6kV5/QQ40ivZaZqsTpGJ6aoVJvlQ7N6bCpV1ZwtRM/V264OEOUU7RsYS8CVMeYqTFhjMBgMAfTOIUG7AunOTRXYf1R5rjap61BbzIEjUSGL6YTccye955xzUiiHz+ICB8523VV1fU67Gf/y1+vjFpZqc5rQhluM1Vbmx1Wd/V//cSyp16YHI+NB/Oxwe7IvIyYsj7vv2XYLYEDE7eofnxQUPOXcP7VV+WioL9VUHXhicnaKam6nNSbETW5hrlb4TGW0/B6n3Yxnts1sEni75Ysq+MeC6LoxRGXsVypu0XJHqRXnaDnl1FxLa3sflc2D1490YuVS4dQUaqBdPTZVqmrOJri5emVxLo5fkM8H+/7ZXuyoK2MCJkMXmLDGYDAYSUCraMURvcNuNECx80PqOrSKKYkKWUwn5J47abgIt1gQc1ae9t6Oy2Hldlqxdlm+ZOL4Z7ZVCbo11OY0oYXDbsaebfdcdHzh29vtm3WCAkey85A11JfGLESkRAe+4Em6qM13Z1K73nRi69piHJZ4Hz+1ciFOeW9H7qXcwhxQPgakA+YMI4LTytzFzszYTQKtIsnUVAjvfngdfYPjyM+145H7C/HxzWFFxRakxC0a7igSce4XRzpht5gwPH4v/cOZzn4qTjml18IJYbTSYgz4A/jGC8dj5hxaxEE9qsemQlXN2YjRaMAGgnB538gky7PG0A0mrDEYDEYSqCzOpeIuiN5hV5pzQq7q2ZnOfkxqDK/SM2Qx3SCpMqc0XETMWcnlsBKavC8tzFG1Y861r1cPtesuln62rgT9QxMITE6joigHm2uLJcOzWN4UfdhcW4jH65cQH6/W6Utro4EWDrsJzz9eA2+PD43N8uFFUqxY6sbHvf44kZt738oF3keHzYQwEOPii3YN2i0meHt8CIeALLsJ2Q4L7BYTYJipVn3y4uwqvKFUVAOAZ7bGbhJoEUl++W4nDp/uiSnUsJ9XgEVJsQXOeQYg0kf33hkluj6pvo5EnPP5A/juG2cj/53rsMjmJhRzyvFDR8sLcyJuseGRSSKh8LWmdqpOaH671yIOKtnoUkKyq2rOVvLddqLj2HyBoRdMWGMwGIw0J3qSEL0beqazX1LUEsvnEQqF0dh8lUqibLGQRaXMhLvG59ZKJ+Typ3CLlOB0CA31pXjvbC8GRyYj/84V0MiymREKhWPOJZYbR2jyrmXHvLYqHyuXzo8rWKEWvqORc9Ud++heSMeZzjt453QPHrl/EfLdmYLXm51p0XwtjHjm58wsVLj25fMH4B+bhCPLDLfDFvcc1OZoKi/MgcNuTro7j+PTDxShuswNT4kLxz+SDy8Sgl9hWey+rKrIg91qgrfbhzDC8PkDaL4QH0bPCQSkGzIOuxlLFjlx6aqPSqXcZGOzZBCF/IptEhCJJNnxIskv3+2UdPlyKCm2MOAPoLH5qqqceVICoRrBIHqMEWPAH8CBYx+jusQdabtCoaMGg/Iqse+fu6n0klVBWmQgGpYXLfWJ7luHCdoykHobOYzZAxPWGAwGIwl09AxSC13jTxI4QYXLO6HEndTa3ofXmjqIJttyiIUsDo9Mxu32i2E1Z2D7g4uxo64URqMBRoMh7UKdSEJRxPLbNNSXYXQiGHGgNLVcR1PL9ZhzCn1Wroqtmh3z6Ans5tpCTTmxOJ7aWI552baI4DA0NomXDl6MO25wZDLm+7JsJmxZXYTH1pXirZNX8dZJba4ihjDOTItg++KwWzOwvmYhHqjMw/DoJF5r6lAchsWdP1VENQB4t/U6Fuc7UFuVrygEWuy9E3vfpO6tGKTjxsh4EOcvDxCfN9WZmJyG1WyMKzjD9QViojsHiUjyhUerYj47NRXC4dPKiieQFltQs3Hlds64wrzdPkGRVk/BoLG5G43N3XDYzVh/3wLBEGaloloiIS0ywIflRUtd1Ii7ahyGDAYphnA4lbvBxDE9HcLAAJkNWy0mkxEuVxZ8vlFMzdKS8AzGXEXp+32y7RZ++Gab7HFWcwYCEuGYDrsZ//bVeiInlJyLRCwvjFq40AuhcJFvvXRC0WIyOoxDaDLFFXDwDU8Qi3aJgJ+fSggt933b2mJZN8XMwrM4Ik6qQXACC0DrBCI6d9pp7228ePBiSi/O5hqPry/Fm8evaj6PVML0VBbKueuWcy1tWV0kKmKLkeq/PZ1QUi1T6p4/+aklyMu1wWEz43r/CM509KPzxjDNS9XE1rXFaP7oVpx4vWtTOZx2C3z+AF4/mloidSrxV49XY131AlWf1aNiKkM9avtP2pVjGXMDtzsLGRny1aKZY43BYDCSAOnOskFu3kagQpC4k0gSDZMi56ZyOa14UCaJPp/oMA6pUMaTbbeo/AY5uBxIYu4R0t1srfedxE0xOjGFA8euoKmlB89u98Rck9hiIfrvfQPjgu4KGvrXyHgQL/zmApFAyEg8NEQ1QDgMi2afoxdccvUPLvVJHtfa3o+dG8lDwtLht6cTJGF+JPf8N3/4mPalUWPpomxBl5jPH8C+A/EuX0Y8Whx9LC9a6kDyLvOda/Nz7Xh6cwVWlc/X+eoYcxkmrDEYDEYSIMn3kmk1YSwgHfYzMjGlOLxBSEwhSXpMQrRDS6oy2qFTPdi2thgfXOoj+l5+GIfYJFfPUJgn1pegYF5W5J4B9xJPZ2dagDBiKq2RLLK13ncl7q7RiSm88JsLEXeNfzyI/UeFRU/S50ILpeFWjPRCKAyLVp+jJwP+AN798Lriao1y7pbG5isp/9sTiZrcXNGQhPmlQ3uT4nJv6jjn0hEWAjh7IHmXw2Fg18ZyZDssmJdtw4MrizA8NMYixhi6woQ1BoPBSAIk+V7kRDUOJQmLxRxkq6vyiM8hBN+hRbKjeOpSH/7puYfw5vEraDwhnyOL5HeSCJZKkXKfad3BTkZ1Ki5PmxCc6JloWPjn7Iff1tOlMlvf4DjRcdzvEetjo128NPITzibCYWB9zQIcv6DecSzXnlKhvbmdVnxq5cKEP3+b2YiJ4NwWFFiRgdkD6buc7bBgXfUCmExGZLBnz0gATFhjMBiMJCGWFFcpfJeWmFtCykEmVT1Ujh0PlaDh4SUxk1aSHcUBfwBdN4ZQXeomEtZI3GgkgqUcLocFGyQqUNKEVadizBX4bT3ZbZ9UbMjPtROdLzfLKtnHvvCbC9jbsBz7j6ZODkgpaORQVMKyMhfur5gfX4gl0wz/mHzOMLH2xI2Hvf365lGWYsdDJagudUccU++fu5lQ99xcEtX4VXNZkYHZB+nYkewxhjH3YMIag8Fg6IxUWFBtVT5WLp2Pr79wXFXCYYMBKC/Mify3mFti16Zy2QWd2nCc6lJ3nPBEuqM4OBrAWk+BrMtMSRiHmGDpsJsl77GaBORa0eqw0xpCxWAkivZrvph3q7I4N24RTAuS92LPYx788t3Lsv3OxgeKcPh0j+w7OjQWwC/fvSx5zM8Px1ZNTWW2Jjjv4choEHVrF8blzyQpdiM2PqipuqoHi/KyYtzNWjd/GOLsfaIGRqOBFRlIMnoWeyCZN7HQX0YyYMIag8FgKEDpZEEuLAgAum4Mqa7iFQ4D7354HZtXF+NMZ7+oW4IkubEagUZs8qJkR5HEZaY0jEOswMGZzn7BiqLJ2tHW6rDbuoYl/U8GJqMBU6H0UTRXV+XhkVWFuHbbj1/+Xlr80YuDx6/iSOv1SAENo9GALauLBQtjaEXuvdi2thgPLlsAk9Eo2++YTEbs2lSBfQek39FfvNMpK5qlg6iWZTNFntHA8AROefsT8r3OTAsA4fyZasaH094+2WeWKPjjIS23eqpjs2Qgw2jQRTwXwu20wlPiYkJakiGZ92pBjzkjg0EDQzjM9roBYHo6hIEBfW3iJpMRLlcWfL5RljyRwUgT+NUR3z/XKzhZeHD5grj3W64cOFf2+/UjHZpCMQEg12FBcCqkeQK7ZXURTly8TSz0iZUuD4XC+Oa+ZtkdxX/eWxeZ/AhNxvQQvfTcSVWL1G8HIJuzabYv0FKN+8vn4WzX3aReg8NuRoYRGBqVf1f3NizHGk8B0XuZCLh+IxQK42/+/Y8Yo7jwdtjN+Lev1guK6M5MM774aBXWeO71JyT9jrfbh39+/Qy1a6SJw2ZCgduOy71+Kuf7+lMrUbNkHkKhMP72B8cxODJJ5bxy/N3TqyRzVioZH057b+PFgxdTws3LH+eiCYXC8Hb7sO/ghYSJTzSRc5y6nFb803MPoevG0CdzqDEc+qAbE0F9HozYfISROEjnvbS+i6RPYOtvhlbc7ixkZBhlj2OONQaDwRCBRLDgcucYM4x49KGyyN9Jkve/fqQTK5fOx4mLtzVfK63Fz6qKPPzpI+X4xgvHJd0VBgPw/BPLRSdIanYUxVxmtEUvsYqiyUTqt7e294G/B8b/71CITRYTSaaN/vTJaslAYHI68t9yoct7tlVh5dL5eOWQVzbp+74DF3GzfhT57kxsWLlIF5eYEl5+2wu71QTPYhf+fLuHaljcyHgQHT2DxP0JyXGJTHzvsJmwc1MFRseD6B8ax/HzN2NyZOVmmfHIqsKYHJAAqAmmHT2DqFkyb+Z+qBxX+KG4cqG5JGFbpM+ztb2PyKENADvqSmAyGnV9H6ScM0ajAdVlbjxL+R3g47CZMKJSuNu8uggfXLwdMx8gLcLg+ySPavR4u6OuDI3NV9DUcj1GlHM5LKgudasqYMHyqKUGpPPeVRV5VOZ1iZozMhikMGGNwWAwBJDbdePz2jvt2PRgaeS/SZP3H2ntUR0GShtucWM0GvDMtirJ3//8EzUxrg8hxMJdpCbBqSh6JQqh3y7WDgdHJvHCby5gW4LzIDGALGsG2q76qJ5z29pifP6RckWhywBkc09FE70IzvpEGEyWS2Z0Ygrf3X8WTrsZX9xaha88WYOX3/ZSux5OCOPeKc6lesp7O2bxxXevrvUUCC7KEpkEu37FQvz6Dx+LPtfgdBiFeY6Y/jMUCtMTTD/5+VrERE5E4/JW+scnJcUu0rAtufGBZGEfzaL5WVhXvQDFC5z4RVMH7g5NEH9WDiVij9rQUNICE3u2ewDEO59JeKAiD7s2VsT1Tae8ZBuC/HZkNBrweP0S7KgrizsnALR1+xRdo9Nuxrefewgmk7ybhKEvpPPejp5BavO8uTxnZKQeTFhjMBgMHkon5wAwMBxA28d3UTRvpoIc6aLkYJKdI9FEL27UiGJCsB1FMoTCUwHItsPDp5moJofNnIGNDxTCkWlGdqYFI+NBOLLMyM2yorPHh4PH5SvSRrNlTbGsU0OKVRXzUVWci77BceTn2rHxgaLIopC/QFhVkQe7xQRvjw8Iz/y7Z7ELrR39mvJHcQJWQ30pRiamcERjKLpa/ONB7DtwASuWzsPehhqEpsPouDGIUCiMsYkpGAxAXq4dRXkOjEwEMTwyif3vylfVjBbCxPL9VBTm4OLVgVjXjEgeoPLCnIQVCpETykcnpvDCby5EQqpoh4J7imfaIA0xsbW9Hzs3zowrxicNuof6kyzso8n+JK/bGk8+Nj1Yig/OXcfd4Qk4bGZc7x/BnaEJTExOofmCuIj0QOV8LJqXFRnXhscmVY1ztVX5sFtM+O4bZ2WP5aqMcu+PGA6bCXs+yZcHzPQn3m4fvv/rjxAITot+jiN6s43fN2mtzCgmiCjNOeofD8a54hjJQUnRKgZjNsKENQaDweChdHLOcfLCTXx+wxIA5JPOQDBxIXxupxU7N1Vg/1GyxQ0tUYztKEojtvDfsHKRbDtMhRxCqc6XdywTzQP4k99dIj7PvBwbdm+pRGBSm6tqU20RqkvdsscJtYvjF25hTdV8vNNyQ9M1cPzh3E388946LC3MwUsHycLn9OD85bs4f/mubILrUCgsW6EzOqxQzPHp8wdwytsn+Pdo0Yqj68ZQyr1rvzjSiVA4TBz2SMpoYMZBrbViMRDrTknEJoviBXvUM80wGrCs1B3JwVSzZF7k31ZV5MW9i5zQ+mHHHXyIO3BduIXdmyuwrnqB6usfHicLvY2uMmoU2ADLspmwZXUxdtSVxt1fo9GAT61cSJTXVcpJqLQyI2luUzXuPSbUpAZaxVYGI91hwhqDwZjTCE321E7S3vzjx1icl4VMqwkDIxNw2s0pVQWOE89qK8kXN0wUI0NtQQSphX+y82DNBhrqSyMuDf6zIRXQd9SV4L4l8/DgyiIMD43hwmX1RQscdjM8i6Xfp1AojMbmq4LP3+cPUBPVgHvCx4PLCtB9czjpYcViwlb0+yUX8siJAWqcxxz8PECpuHD3+QP42eEO6ufdd+AijE8aUFuVr6liMUf0vdN7PFG6YD93+Q6qy+RF7mhR8ExnP5parscJrWJtVwlqhAkluefExEE+JE5CJXlUhb57Rvwrwo66MtG8hz996xJRzjUm1KQGSsVWBmO2wYQ1BoMxJ+EWr00tPXGhQBtWLlR93hd+81FSnQ0Omwlmc4akI42JZfegUSFUbWl5LQt/BhmjE1NxSd25ZxOcJnOLLpqfhWWlbmR80i60OHn2bKsCAEGhD5hpS681dSSsEiNwT/h4amMFyhZl46dveTExKR8mpifRwpbYohyIzRHH7+fUOo+BT/JftvRg8+piGI2GlF2465Wfk7v/UikBPkWY0y2R907pu3ny4m3s3FgR+W+uSqfQu2k0GlBZnIsfNbZJnlNLcna1woTcmC62gcPNVTbXFmJ+jh3OTAtcTvJxkCRlhNh3j05M4cCxq2hquY5no8JVo2nrls9l6XJYmFCTIqgpWsVgzCaYsMZgMOYcre19oomyZ5xCV2GzZKhaXJKIanIl6rWwZ7uH5TQjRK0gxj+HmONMzr2gZeHPIEMo3Il7Ng31ZQKfiIcvDJAsHvjvuNNuxoPV+TjbeSeu7+HaHABdKwOKEf371ngKUFuZL7jpoAar2agq3J1z0onlkIrOERddHZNmJc/973bh8Oke7N5cgVUVeZrDItMJkhBOAHj/XG9KuVNI3s1o/J9UkK1ZOg/N53vx0q/PY0BiPCBNzn7g2MeoLnErHnv1ECZINnA+7LiDf95bp2qeIOWYI/lufs5ADtLxccP9i9j8hgI0NhkBevl5GYx0hAlrDAZjziAVYsUng/JEzWk3Y9emCricVoRCYaIExUrgT1qYI00cuVA70nAeraXlUzG8LFUxm4wITikTaOSSzb9/rhe5DoukO0xMGJBbPESHjZ24eBv+8SCOtAqHcHJtjnNhJRIx98vj9WXYUVeKxuYrqgo1RN8Hb7cP//GbjxRvVHzY2Y+jrdJ5oLgccXpV8ozuD3ZtqtBUMII2eqcaIAnhTJY7RUoE4N7NHze2EYm6g6MBnPb24fu/Oh/3b/zxgLTPbmzuRmNzt+KNmujrpyVMkIqB3ms+GA0GVcKKWPXdUDhMLEbzx0rSz+W7M4mOY4hDY5MxGla0ijFXYcIag8GYlXAhHVw1PaPRgD+c6yUOsRqdmMJaT75ggms1+MeDcDmtkcmnnPvB5bQiHA5LXm+0WMcmLWSQhtqRhPNoLS2fquFlqYhSUQ2Qd4/6/AE01JdKCkdiwkAoFEaWzYw/eWQJRkaDgiFUoxNBogThHHq5WKWQEj5mBLYlKMxzxC26HDYTwoi9ZmemGeuqC7CqIi/mPlSXufHnjy1TLEqRVCqVer9oJN/n+HHjJZgzUqt//eLWqrhCNHy0VDIl6Z+S4U4hEQFqq/Jht5rw3f1nZc+XnWnBf8oUMeHGA6V9ttq8azSECU7kamknm8PsO3CBqEKuGFIh2yREv8ut7X14/ShZmgQ2jmpDi+teCpZyhDEXYcIag8FIa6J3rrPtFsAAnOu6gz+ev6k5T9DK8vm4cOUuxgJ08g1xu90k4R4koWHPbKuas7Z6NWELYhNIIaQW7BxaSsuHQuFPxBn9woLnKm6nFbVVeUSiVr47U7EwILWw59pgqufPUyJ8SIUCkr6Dazz5uLK2mLg4ggExBRslOdPZL/ieKg0LlCIQnEZAxBzGv1aDAXh0TTHsFpNIDs9FGBkP4mjrdeLfGI3DbsaeT/p+o0F6jFArqikJ4UykO0WJCOBZ7CLKV4YwYsI/heDGA7VirZq8a1qECaE+Sg7+OKREWJHKo6aEwdGAonGaJcLXhlbXPYPBiIUJawwGI2057b2Nnx3u0C2Bs39skpqoBsRX8iJZ0Kd7rgpaeTuiURO2oEbokBPO1JaWV7PoSSZCBTFSURDcsroo4pbq6BkkEtZys2ZcpGLCEZfIfF62DQ/mZOK0l2xhn4r586TykckhtshXsvDniiOQ9NlK9CAuAb3Q7xHrZ2mSk2XGo2sX487QBPJz7dj4QBFMJiMAYEddqWj/V1Gcg30HLoqet6G+FHm5mRgaDWB0IggDZp6BZ7ErLuwxzlFoN2saF5WGcKoVgZSMD0pFANJ8ZcPjZC72wdGAarGWZKOGFkqEKRLkhBWamwjZmRb8RMY9GA1LhK8Nra57BoMRCxPWGAxG2hEKhfHDNy/glLdft+9wO61wZJmJj5cLuRHaWSXZ6U/nXBW083Zw51QTtqBG6JATzkjcC1xOvZNtt5CbZRVNxp7KiBXEaO3ox4sHLyS1Ci4gLDQrra7HFwaE2q77zYuYlMnZxC1Cz3Tq1zeJsW1tMZYW5qS0EF9bmY8sqxneHh9u3R2D99qg5o0RLgG92MKP60Mbm6+gqeU6dUF4cDSI0gXZ2PZgSdy/SQlOazwFMD5p0Py8+GNEdqYFP5apXClGItuK0vFBjQhAsoHlJag8CdwbD9SKtYnIqamHU1ZOWKG1icC5B0nO5cw045mtc9exTwstrnsGgxEPE9YYDEZaIVXRkyZPb65Alo1MWNu1sRzzXHa88N8fSZ5PSAwj2elPZq4KtY4zPfJ2aAlbUDoxJAkxIXEvTAanYwpVGFJfD43gcliw4f5FCE6HImFQ0ffVaTcrFtUcNhPqVyzEH8/f1PQO76grwaL5WaJtUkt1PbG2OzAs34YG/AF4u304efE2wa+ggzPTjC8+WoU1npn3KVWFeCEhJddhiTizXj/aqVpkk3u/z3T2qyrCQOv7xaC1cRI9Rni7fcS5RF0OC768oxrDY5MJbStqxge1IoDcPa4szoXbaZUMB+WPB9HnbLs6gMYT3bLX1TcwRnT9WlAqcpE6j6XuPelzkasQ/PTmCpy7fIfoXDs3lTNRjQJqXfcMBkMYJqwxGIyUREjQOdPZTzXEQQi7JQNf+swy1FblExUZcDut2Ly6GBZLBrL32PDSr8/HTNBTyS2iFLWOM73ydmgJW1A6MSQNMZELxeIvWpLt7iJhy+oiZNnMeP9cb4wYwX/2asSETz9QiCfql+CDS32ahLXqEres2Cz2bLg8V8HpELzdvphFNg3Hh7fHp1u1RrfTip2byuG0W0SFmFRMGi0mpAyOTOLAsatoqC/V5FyTer8Tke9Oy8KT9vNSIqzs3lKJ6lI3te8mQe34oEUE4N9jrrgR9w7t3lKJ//i1so0x7pyVxbk4fuGW7H1//2wvdtSVwWg06JIiASDvkzc+UIjVn8xxSCqUS9170udiyhAW1qIrCL9yqJ3oXG6Hjeg4hjRKnd0MBkMaJqwxGIyUQ8zZoKYyoBJsZiO+9zcPR/LjKHW91K1YhKrCbLRdGUg5t4hStDjO1AhgJAsNLWELpEmn1YSq0gzFEqOupgCt7f2SO/5a4RY4gHBCdP6zVyMmNLVcx3Q4rCl0SEti9b6BMbx3thcHjl2JHBP9zKmENVEST80ZRjzxcClK8p3wTwTTtj8hEVKUVE/lI9ce9M53l2oLT/8YmVttfc2CpGz4qN0goSUCCIZ5O6343CNL8V7rdcUbY0ajARtWLorpU4TwjUyio2cQoxNB6ikSOEj75NVV+cQVyuXuKenYKraR8qcblyLLZsaBYx8TievOTHNKvW/pjBZnN4PBiIcJawwGI6WQcjbozZd3VEdENQ7SIgMcqegWUYpWx5lSAYzUGafVsSA3gWyoL404CpQS/dzfPPYxtfbqsJkQBtB8gX5oYW6WGX/x2eUxYWAA8M19zZKfe+VQe6RIgNIKeROT03jrxDUtly070RcSaT0lLrS29wmGA0YLhsFpbcKl2zlTDIEkNEyO4HQIv3rv48i7kK79ComQosW9KNce9M4PlGoLT9LcoMvKktOe1GyQcO/0aplqv3LPQjTM2x/Ar9+7jK/8yX3IspgUb4zlu+2yxwAzIclC168lRUI0anJLahVWtFbf/eGbbYqc3OuqC1LqfUt3lM5xGQyGOExYYzAYKUMiQnaEkJtApHMBATVorRSlRABT4owjXTSUF+bEhPlwz0rrBJLEVScm3pCSZTNhb0MNhscm0TcwLuuCAGbEt+B0SLGb7ZFVhXFhYN5un+yzHxkPYt+Bj/DpVUVYWpiNFh2LiERjMADPPyG98BQTaXdtKsf+o12S53/9SCe+9Jllmq7x6c0V8Cx2KRYcpeDehYb6MuS77WnX/5AWcjBAmdmP9L3VKz9Qqi48ScPkkhVOR/o8uJxkQu80v1gQybMg2jBq6sA/P1+n+N0i/U0nZHIvqkmREI0aoYyGsCJ2DmemGf4xmQrACh2+qyrylH2AIctcm+MyGHrBhDUGg5Ey6B2yk2UzYcvqIuTlZsI/NglHlhluh41oAjEbnGikaK0UpUQA+9ZLJyS/I3qhQbJoWLssH9966YSo+03tBFLOVcfl7Hn5ba/keeQwGAzwLJ5pZ3LOsSybCXufqIGnxKUq/2C+OzPub6TPvrXjDlo7yBJN0+L5J5ZHEvQrycHo8wew78BF2fMP+ANAGLJt12EzwWzOkFyEanFwiCEWvpqKcM9HzKEjBMn6etfGcmQ7LIoWfuWFOXDazVTy3jntZqxbXhBxbKbiwjPV8iaFQmF4r/lmqm8agMrCXLgcVvhGpPuappbrWDjfIVhFmRNjtqwuIn4WRBtGw9IVMMUgueckIpNcBU4puHcuOB1CQ30Z3j/XSyyU0RBWhM4xMDKBH//2kuLfIkaqhV3PJpI1x9Ur3yCDkQyYsMZgMFIG2iE7DpsJVYtzsXBeFjwlLngWu9iATYDWSlGku+ZdN4YUO+Nqq/Kxt6EGPz/cHrNQdjutWLssH4dO9cSdg+9+4yaQ3ITulPe25IROzlW3bW0xPrjUR0UUHhkPoqNnMHJ+KUYnpiKCo5hjQAqh55eK1b/4IpKeORiHxydl2+6e7R7ZRaia56EEWqFjeiD0fOQgrU6Y7bBgXfUCxdeiRVQzZxjx7HYPXJ9sBnTdGMLgaECwUm4qkEp5k4SreHfDzEu5IMToxBR++pa0KNPa3o+dG8l+i9YNIylI7vm66gIikVnN90tV3M13ZxIJFjSEFf45vN0+Tefjk2ph1wxtqC2QxWCkKkxYYzAYKQONRb3NkoE/e7QKLifb+VILDccDSXjJybZbRNcTvdBobe/D/qOxC2WH3Yw/3bgUv3z3suR5ot1vpBM6kvAhITFPCwMjEzAayNpt9L3hHAPeaz7sO3BBUqgwGCAoNlQW5xKLHImgob4Uj60rRdeNIZxsuyUaHksrp11u1kyONKG2Oz/XPlO9rnw+ABBVJeUEOJ8/gNePdmqqfCmE1tAx2oiJ0HJsWV1MFPasZIxQey18gtMhWMxGjE4EJd2w0ZC6MLjjfP6ArItaibMjUXmT+NcULTxKhbKTiuATk9OS/67E4aV1w0gOuXueZTMTCWvDI5M42XZLkZtaquLu3oblSXPbD4/S6ZdTNeyaoR4tBbIYjFSFCWsMBiNlIBF0bGYjJiRySX35M8vYYKwRWo4HufASpQsdsYnYyHgQLx2Ur8LJLcJGJ4LEEzrvNfmcY7R540gXNtUWER3Lv4dGowHVpW48u90j+fzCYWDfgQvo4oVSGY0GYpGDJjZzBiaC9xbR0RVK+WKGXkSLxfy2Oy/bhgdXFmF4aAxTCpxx0Q6Onj4/fRFWQ+gYTbSEQm9ZXYQddaVxoWt8lISBkQjiJqMBUyGyBE+vvO3FiIDYLNRnkIr2Us4+EpemnLNDqv+lEX5FkvssEZA6vIg2jLK1hRrK3XO57zcYgP3v3ssFKfeMQ6Gw7Du378BFoMEQCaMXg3ZIXigUxmtNHao/DwA7HipBdambbZLOMrQWyGIwUhUmrDEYjJSBRND58o5qhMJh/OxwR4z7g+1o0oWW40EqvIRkoeNyWhEKhdF88SbeOCKdfJ4Enz+AX71P5mw709mvOWeaGvzjQRw4dkXWOSbmOgNmnt9zT1TjhwfbJHNXNbVcR1PL9ZgF3I66UjS19FBxrWXZZqYZUudyOa34820edNwYBMKIhG2ryRunBb5YbDQaUFmcO7PYHJlE28d3scilLuF7a3ufpKi2xpOHtqs+Vfc80cIvHzWhn9Fwiyea4Ysk+bRIRTUAgqJaNNF9BoloL+emiz4egGpnh1D/K/S8uPyjpFWRxa4/0aIaEL+5ICYQkbSxLzxaBQCCxW9IERvzSL6ff//knnFj81WiPmPfgQswSrQTMeF2w8pFkYIpnBtxYGQCI6NBODMtkpEBHT2DmkKw3U4rGh5eolhYYTm7Uh+tBbIYjFSFCWsMBiOlIBV0aivz2eRJZ/SuFEWy0JgMTuO7b5yl8n0A4B+bJJrQNTZfTbhri49BJhyUc52hoSbOjdDa3ofX3ukkrrLIX8DJOd5IeXa7B4CwMMAxGZzGv/7Xuch/H79wi6iKp1LcTit2bqrA/qNkYrHQYlONgE+yO3/5xjCe2eYRTNQuh3+MTriVGrSGW/JdgrTCFwdGJlRfkxoG/AF4u31ELoyVS+cTV7/+RVPHjIIuc05SZ4fY8xqdmMKBY1fR1HIdz273aK6umSj4LkY5Z59oG8u24rknV2BkNIBv7mumnvMptrBAKd472xsTvi7n9OPaDRdmywldTS3kDtifvnUJdosJnpJ7uWZDobDoWOfzB2L+LnaNYvdHa87cT61cJJv/lA/L2ZUe6JnvkMFIJoZwOBn7S6nH9HQIAwOjun6HyWSEy5UFn29UUSgJgzEXSbddR/Z+SyP1PIUmww67mXo+KrfTis89soSoSlmq5Bl7Yn0J3mzullx0GQxcxcwCANrEDrfTin/eWwej0YDT3j787HC7qufgcliwe0ulZCibHs9Yir0Ny+G0W4jyWcndQyX5X7zdPvzz62dkj/u7p1dhdCKo2P31F59dhrrlC4mPj0ZLPxsKheNECKUI3UetfX9rex9ePdROpQqoEnbUlaCxuVv2uF0by2PC/ah8N0HInJLnJdW+SdszDeT6CH4ILuk7y29j1WVutN8Yxj++cpro80qQc4MNj0wStQebJSMm55za/jMny4yaMjeGRoPouj4omVpDCfz703Z1AN/df1bxeRx2M8LhcMz4S5LLsG9gDAeOXSW+PkbyUDIm0nCssfk5QytudxYyMuSL7jDHGoPBSEmSVfqbQR8SF8HKpfPx7ofX0Tc4jvk5Nhw+dY36dXAJpElIBVENAG4PTsiGV8041y7C+KQBqyryNLlJovPQvXqIX82PjJoyF/7nn94fVyWT/4zfOU033xgwUwnYbM6IaWtOuxlli7Lx88MdMWIL1waFksTTzP+iZHd+XfWCiEu07eoAGk/ICzVuh/rwVC3uDpJwHjGkXGha+n5aBQtUQbhN3Tc4Tv2rG090o/FEt+TzU/K8pNp3Il0ke7bNhGbKuRinpkJ45VC75Lmif5NQG/vhgY+IP0+KVIL2A8euYG/DcmJxjF/IQe2mxNBoEMcv3Fb1WSleedsbe38U2DZslgw8vGIh7FYT3jx+Ne7fSXMZSsFydqUONApkMRipCBPWGAwGg6EbJJWfgPiFkxpIQv1IEkgrcavxXQS0OXmRfAH0+pFO2C0mzffxTGc/UfU6MdbVLBB0gNF4xnLs2e6JCFNnOvtx8uJt+MeDOH/5btyxYvmLaOd/UVqkg1v0Vxbn4viFW1QT+t9zdwhXbFRSkU2twOK0m/Ht5x6CySS/+6sEPUMU5foEt3OmmiyJEJqfa6d5aTFIPT8lz0uqfdOo3i2HM9OMZ7ZWRX4DPyUBl++LqxR8pPW6rNAU/Zv4jjWD0YC7Q9Lhw1L3RMhlCUC2Pb548GJS8tLpwcjEFBqbr+Lx+jIAwPA4eYj6xOQ00ZjzyqF2yVyGUkg9/1SPiJht0M6pyWCkCkxYYzAYDIYukFQsE6u2pwZOPKutFM8LRzKh27K6SDKkJJqJyWk01Jfi/XM3Y11SmWb4xxIbijbgD8Db49N8nhMXbmn6/MhoECfbbkXufSKKEPAdLKMTQWJxkO9koJ3/hWR3PstmQigcRigUVtRWSRcferg71Aos/vEgum4MUXcka3HQATPvfUt7v6AoD0jnCXx6cwU8i11ELoxchxUGkBl6HHYTzKYMxb9L6PkpfV5C7TsUmmmjeofK79xUHiMMRjvMWtv7VFcKHhwNiBZvIP08H/FQz4Wy1zhbRDWOppYe7KgrhdFo0EWAHRkP4rfHP8Yfzqsbo8SeP8vDlnho5tRkMFIFJqwxGAwGQxfePH5FdvFFQ1TjT8TkQsnkJnSrKvLihDIppkJhfH7D0pi8XeWFOaoXf5rQuFBz2s2ac1NF5wvKdVgQ1CmnSUN9KfLdmci2WwADMDw2CW+3D+WFOYqcS3wnilKHmRwkAtnoxBS+u/9s3AKPxuJDTXgkiSOvsjhXtcCiRzih1nOuqsjDzo0VoqI8yXOQe85F+Q68+OZF4mv6wuZKXLgygOMKxW4hd87AyISi95vfvrW6The67bgzNIHgtHwnFR3erCSPlhwXLt9B88W+uL+TtuHe/lF4u30RR1pj8xXB65kJ9VR/nenK6MRUpN2RbCio4fCpHtV54cTajxKnLoMeehfIYjASDRPWGAwGg0Gd097bgrlSaOG0m7FrUwVcTnUTMbkJndwCOZrohOWcMGIyGRWdgxaeEpds+KAU65YXaAoD5RNd+U4pJKG9re19+MlblzQXRYgWZfTI/yImkPHhFnhbVhdhVUUeKotzNS0+tIRH6plLS06UVBOqpcUh447qR8TERLnnMOPkMmPL6qJICDIHV1FRKCRZjAVuO37Y2Kba1STmziGB76Ckkbvu5sA4Hl1ThHdOS/cv0e8W7RByIVFNCVwuO87hliq5OFOJtqsDkfdCjzFQrajmclrx3tleyWNYHrbEw/IpM2YTTFhjMBiMOYTeuUVCoTAam68K5m+iyTPbqjTvLMstovc21ODFgxcULWz5O99febIGrxxSV1lTKW6nFZ7FLlWLGafdjGe2VSHLZqYqrGlBLrRXbLGv5l5HizJ65X/hhBlvtw/7Dl6QXJQ3tVxHU8t1uJxW7NpUAafdrOqd1RIeKSdUzRS5UC4syImSakO1tDhkSJ+nWJ8hVvV2XrYV3bdHVIljtwa0FTnQ4u6KdlDu+kTcpsGJi7exdW0xDp8SL1zCPYukFqKQgQlq4jSe6MbxC7ci72tDfWlKuPc2rFwkOy9hediEYfeCwSCDCWsMBoMxR9A7t0hrex9ea+rQ5FKSI5H5N9Z48gEsx74D5KFbHNzOd21VPiaDIfyosY3+BfLgFqSk7qhodm26V9wh12HR9RnKQRLaSzNRvTPTHCf0iIZgZlvx9Cb17Y+rSEi6MPf5A9h3IFZcUPLOqnWdkTjy1J5bSsQiKXYi9rvVOGQMAJ5vUB7+RVIIYmQ8mBBBXYhch0XWnUOCUPvTgn8sCLtFfulBkp+TkbpEv6876srw3tleqmOKkpBmbjwJTpM53VgetljYvWAwyGHCGoPBYKQIeu4Kalmwklyv2OJSKy6nFV9+bBmGxyeTslNaW5mPhvpRvP3BNQQUhKBE73y7nPpX0eMn3+bcUUdaemJynonBXaPRaMAXtlQm1Cnicljw5R3VGB4jf8ZaE9VHs666QPD7okP//ONBFC/MwSKXDaGQtkR2WkMsxd5Zof5DbXgkiYNL6bmzbCZsWV2MVRV5gv9OIpbKhWpxguirh9qJFt6Pry/9REAnJ1EVbrUwMTmta7ViLbz9wTXJf3/9SCds5oxZ4wprqC/D++d6Y9oLFxqsN1vXFuPUpb6ktVXufaU5pnApAqQE35oyF0JhoMBlx85PV8BiyYC3m6ywD8vDdg895o0MxmyGCWsMBoORAui5K0hjwUpyvXqwe3MFqsvcun5HNHyxkL8gUsKZzn5dkzhHMzoxFTfRNRoN2Ly6GIdP9yjKF1ZblY9ta4txSCJciya7t1SiulTZM6aZ/0tM6AHuueVMJiNcriz4fKOahTVa1fKi31mx/mPXpnJV7g6SPoekXdvMRmRkGDE6MYXRiSkcOHYF75/rFezXSMRSkqIKtVX5WLl0Pr7+wnFJx5jDZsJn15dJfh+fVA5PjCZVRTUACASlr23AH0CzxsrEqcRj60qwo640RvT2jwdlnYDRFWn577Yz04y6mgWSIbXb1hbjqY0V+NNHyuG95sP3//u8os0hGnDv60xIaBmVzbfaqjzcvDMqecyFKzMiWttVH94724uta4pRtihH9twsD9s99Jg3MhizHSasMRgMRpKR2xVsqC9Dvtuu2rFFa8Eqd700SUbJddpi4cmLt7FzYwVRiBqXrN57zaep6AN/okuaLwwAvN0+DI4GkG234INL2pJ8kzDjYCqSFLbEIBWnDJAulKq0CAENtFTTjIZ7Z0cngqL9x74DF7Fi6TyipPk76krQUL9EtH8RcsTJta2JYAjgLebF3A6kYinJcSaTEXu2VUle257tHkV9aSgUxmtNHcTHM9QzISO+pRNdN4bgKXHFja1GgVBzp92MdcsLIoVLuPYpVjSDq4DMP8cXt1Zijadg5nuMBlSXuvEXO6qJx23OFXbzzgiaWq5r6qu493VHXSneO3tDdUgo5/JTmgM0HAYOneqB1SIfGv2pFQtxUGb8JZkrzYacZLTnjQzGXIAJawwGg5FESHYFo3d51bjY1C5YhSaHAHRZXKoJB9QC/7eROAiU4h8PEk86K4tzI+62P5xTn49GaKIrmi8syhHxzX3NCQsXspmNmAiGPnEwXcX7524qbtOklTv/dGM5XjooniNPTRECrbR29FMLc/P5A/jV+5clj7nSO0x0ruoSt2TuMzFHrVDbcjksmJwKSf5OvghMKpaSHifX7pWK9o3NV5Oae3AuUVGUgzOdd5J9GVQQG3+VVPsVK5qh5Bzc+8DPg+pyWLBrcwWcdkvMOc509ksWHagsykHH9SHZ38+9r0ajAeuqC4id0A67GRsfKMR4YApNLdc1h84GCFyc04RfIjWnmi05yWhudDAYcwUmrDEYDEYSUZorinN7cE4fEgFKzYJVbHJYUZity+KSJByQ1i6w0G8z6KStDI4GFIdUaM1HIySQZtnM+JNHlmBkNAhnpgUu573FU6JD2yYIHUxSkDrxaqvyYTIaqIgr06EwLl0dwN3hCdXt77T3Nl6UEPqU4h+blO0//ONB2XBQKeceSZ6d7+yti3k3Q+Ewvrv/rOR1DfgDONLSg2yHBblZVpQX5hCJpUochkqEByla2/t0r3TMmMHttGJzbTEOHruiOHQxkSHspEiNv1KVqUlRcg7S94FkzOofHFf0vra29yl6NiPjQbx5/Gpc/lBdIRTvxJ7pbMpJRnujg8GYCzBhjcFgMJKI2t2+ppbraGq5TrQTSuruKS/MgbfbhzOd/YLhFj5/AKe8/aquF4jNtxT9vSQCB61dYLGJr16JpHOzrIpDKsScNs5MM/xj8rmySATS3VG5e1IFpflaSB1JJItJOdH2tLcPv2jqwN2hicjflLa/1vY+VRVmxXA7rXBkmYmOLVuULRkOunZZvuB9VyIKRy/uT7aR5ciKLqzhclrx4LJ8ycW3GoehGvEiuj1k2y0p9Z4ohUbYMU2sZqOkYPb05gqYTEZsf3CxpGMqmuh3fmlhDl5+20vtNztsJoQBVedLRqg5H6G+Te59IBmzfCOTsnnTuPdVSxXnRLbdquJcHL9wS5W4P9tykpHOG5PdvhmMVIIJawwGg6Ejcgt2rbt9JDuhJO6etcvy8a2XTugaDvhnWz14sLpAsXuEJAfdjrpS2fOEQmG8/LZX029QAjfpPOW9TXR8tMgqJAaVF+bIPiO+Q0D6vpWmVGVDNflaSB0YUuKKnGhLw4WgZWEpxtObK5BlIxPW5MJBT13qw+cfKY+7b2rz7Kjp13z+AA6d6sG2tcX4gFfJUI+ci2J9c6pV/cwwGjCtoWCGKcOAtZ48TZsiNOFENX5lTP4z3lFXJpvfK8tmwt6GGngWuyJtl+sTvN0+XOr24e7wOABgXrYNdpsJv3rvY9lr3LWxPOKk5PpT7l14/WinZFGMaJIRah6N2g0p0g2/fLedaHOjsflKyrxPUvzkrUuy4apiz3S25SQjdYWng0jIYCQKJqwxGAyGTpBMamlVjJTbCZVyQdXVLEhI+IzLaVXsHiHNQffe2Rv4wpZKycVCY/OVhO5+c5NOtSEVQveKdKJLct+UJoEWw2E3ITgVlq32R4IaByd3nzih5JT3NjXRdm/Dcuw/2iXwyXuQuBBoLiwNBuD5J2bEvFAoLNt/kFQFFVvwqc2zo6VfO3WpD//03EPoujGkW/Lv094+/Pxwe8x9IXHMJRKn3YxPP1CoqZgJAAyNBnHK2y8oWEaTZTNhc20RrJYM/PL30nn7aMCJamJpDYxGA57d7pHs757d7hFMIWA0GlBd5o6rKE3qpMx2WLCuekHM3zwlLni7fUSimjPTjGe2ViU17E/LhoCSMctT4pLc3JgJo76q+nckksGRSdXivtacZKlY8IB2nkoGY7bDhDUGg5HWpOJkBCCf1JLsCpJAshNaW5WPUDiMnx3uiCwO/GNBvHNa/4Wk2pAB0hx0gyOTkouFUChMTUgC7i0G/eOT2H+0S3LSSTOkgnSiS3LftIqM0W4RWrnasu0WVZ9T48wgER9fftuL8YC0YCj37ildWDpsJoxIPJvnn1iONZ6Z30TSf6xbXkDU9oUWfFpEYbUi1YA/gI7rg7I5F/mQjgW/fLdT8Lo4x1wqsL5mAf78sWXYf5SsUAxJuCdfsMzOtKCzx4cjrTcwOjGF0YkpHEx0TisAre39kerJfGgv7LXmjSIVT3ZuKk+q6EDSt716uB2BqWm4Hba4d0XpmCW2YaaHUzcRRL8rAyMTkdykWTYzQqGwYFvV0rZSueABrTyVDMZcgAlrDAYjbUnVyYjSXBtiiwelyE36xXI86ZVfLBrSkAH+4vjDTmXhS2LuoY6eQSpuNaEFXW1lvuSkk3ZIBclEl3QBqCX/UrRbhGvDWnMb/eStS9Ty5sk5M0jERzlRjUPKhaB0YblnuwcAiMUEOfEhy2YmEtaEFnxqReFQKIwPLvXJfqcY+w5cwLPbPTG/NRQKw9vtg7fHB4RnHERcCCDpWHDaeztlxDMpjl+4hdGJIM52iefFi+YzD5XIuswG/AF03RiKCCCt7X04eLw77rhE52OTE6ZpLuy1bnL0DYwRfY/bYVN8bTQh6dv8Y0H8+LeXAMS/K7TGLKXFmYSQ22jQA+5dGZ0I4r/f+5hojqm2baVDwQMaRTYYjLkAE9YYDEZactrbh30HUnMyoibXBn/x0Dcwpjh8glsYCzk3AOieXyzLZoLFnKHaWUAjt5HWkDY5hJwIJJNO2s4Lue8k3T3fsrpYMvm0kpCYVRV5eK2JzGEjhtL3V0vCaFptAhC/30oWltEhngAUiQlS4gNJuKiYmKB2ge3t9ml6j0cnpmLaQWt7X5xo23iiG1k2Ex5esVDUgRZ9jlBoxq2bLpCKam6nFdkOMqcn1+ZTzUkk9y7SWthrEYxCoTDeO9sr+x0ule5smijt24T6XRpjFo0+ds92D0Jh4MWDFxKyCcghVcRJaIxS07ZmW8EDBmOuw4Q1BoORkkiF9Zz23saLB6Wr6yVzMqI21wZ/8VCY5yAWmriFsZhz4+EVC3V3Ijy73aPaWSC2a6sGLSFtchXr3jjahdpK4QqKciQypIJ093xHXSkK87Lic+/ZzVi3vAArls7H5z61lCjfVUfPIAZHJqlcP+n7qyVhtNbCIRxijq2OnkG0tJO7tqJDPAF5MYG02p9W98mqijw01JfGJZIXW2BzIhgNXj/SiVA4LFpNdXRiStaBxrWljp5B4qTz6YSSIhZcm6fhJKIJrXeRBDHBaH6uHU9vrsCq8vmCnyPt3zasXJR0EUTt/Xz9SCdWLp0f099L5TukVZxprScfnTeGRAW81vY+2C0ZGCN0ENPgxEXpokNCY5RSMXK2FTxgMOY6TFhjMBgph1RYDwDRRVY0yZyMaM3jwhGpbnbNh/fO3EBLu3hY5NObK0TzXPn8Ac0JsIGZyeHaZfmyDial95y2e0JtSBsASVEN0N6uEhFSwS12VlflSYYAcmJKtOB3prMfJy/ehn88iKaW62hquR559/jJvPkMjExQ+w2k91lLwmhahUNqq2bunZaqkg31ZVjjKZA8JnoR2zcwhvfO9sYs9KXC4NW6T4R+S5bNhC2riwUr8dIUyIGZdvDqoXbN5+Da9myjtnI+cRGLaAFYj1BxmyUDE5PKhQ+1+Te1wN/kmJdtw4MrizA8NIapKeExgPR9znfbaV6qKtT2bQP+AL7+wvEYAVqs/6dVnMnlsOCvHl8OAPBe88Hb7QMMgKfYBU+Ji3qfQkKm1SQrwouNUSQbaEo3Xmi6q4VI1VzFDEa6kbbC2m9+8xu88soruHz5MjIzM3HffffhP/7jP2CzJTevAYPB0IZcvgkliZX1noyIQTNZ/ZnOfslFOrcwXlWRh2/ua1Z7yZLwq7Z9/pFyqpMwmu4Jsft6prOfStVKIHntigShxY7BEJtHT0hMMRoNGJ0IKgp94TMyStcNRHKftYjYWguHGACEgYgA6bSbUbYoG+cvk4XwcbgcFuyoK5U8hkSs456TWJVFsQUfMBO6yX+fxfri0YkpHDh2BYV5WXE50PQIL6ThtBUL66KBnMtVTxbOywKg3JVI+t7sbagBwsC+gxckn4PbacW3n3sIb528GudsdNjNkiKFkvySNIne5DCZjMiQuIbW9j68fpSsbSfSfSeGlr6N/6yE+n+axZk23F8IIH6u04hu5DosCIoInRyZ1gz89ZP3YXQiKFhIqLwwB6e8yvI9PnRfAY623JA9TmyMktpAU7PxomebStVcxQxGOpKWwtq+ffvwox/9CM8//zzuv/9++Hw+nDhxAtPTibMIMxgM+pAszJQsspI1waWV+Fdup7ahvhQ76spgNBo05zQSQszNQtt1RVOoEsph0th8RXG+OilSYeEkhFh74US1mjI3asrc2PhAEUwmY8wxNHK9ODPVVfQUg+Q+axWxtRQO4af78Y8HFYtqALB7S6VkX6DUsREt9K1bXhAjsvHfXbFF1a5NFdgvIyREt4dQKIwjLT2K7uH6mgIcvyAdbkULubAutWxZXYTK4tyEO2o4op+lEleifzwYJ7jzcTutkeIQz273yI5nJpMRj9cvwY66sjjxVmiDSG1+yUSj5P1LhvtODFpFkTi49z0UCsu6SJUUZzpw7AqaWnoE53YkobdjgWkYDQas8RQIFhI65b2tSFjbtrYYK5bOJxLWlM4F1Ljv9GxT6VA4gcFIJ9JOWPv444/xH//xH/jBD36ADRs2RP6+devWJF4Vg8HQipqFmRTJnuBqTfxLInT84dxN7KgrA0BHnMqyZmDvk/dheGwyoeEANIQqoftKoxiC0PekysIpGpL2cuHKAC5cGcDh0z1xu9E0cr24nPQER9L7TEPEjnZy+fwBvH60MyG5uPhtVqzoiFoXGD+cd8PKRch322PEDrFFlVBhGD5cexidCKp6zzwlbrR165/ry5lphn9Mn+e5qiIPnhKXYF/vtJvh17EdOewmeBYrD0ObqQ4t/3yj3xsl45nQxksi80vSRKkLM1nuOw5+H7KqIo9a3zbgD6Cx+QqOtN5QHCbJPX+xTS6trlRu/iPU9kjnF3ZrBp7d7sEaT4Gmgi9iqHX06tWmWOEEBoM+aSes/frXv0ZRUVGMqMZgMNIbPQSQZE9wAW2LCaVCBw1x6tnHlqG61K35PEpRmw9mfc0CLF/ijquAyOVT0iP0KxXalRBKwmmFdqO15CrjoJWzDFB2n2lUr4tekFnMRt0dSA67OabCrJhzbMPKhVTup88fiKn+ShJiRYKW92x0PKgpFJeUddUFuvQFzkxzZGEt1NeXF+bgWy+d0E043LPNI/iOSDmKQ6GwbGGJmeq0y+PeG63iWCLyS9KGtF91ZprxzNaqpLp7SEL6tPZtSpzfQmPF++duqv5uKaTmPyTjktNuxr98ZX3EyU0r6iAapSkv9HZ0ssIJDAZ90k5YO3fuHCorK/GDH/wAP/vZz+D3+1FTU4P//b//N1auXJnsy2MwGAqhnZh2ZlGQOvZ1tYsJpUIHaUjczk0VeP1IR2zSc4cFu7dUJu2eqckHk2nNwJ5tnki1so6eQfjHg9h/VJtAy90j/nm4sLosmxmhUDjlxDU1jsXo3WgaBTfUPEd+cnS1iwkli365RM20Q6iEGBkPYt+BizA+OfO9Ys4xmiHM0dCq3qolxNKZaUFtVT4a6stiRD9a2MxGPLxyEeZl65N794uPVsW0G6G+XqtwaDYZ4wRQh92MPdvUiTiNzVdk3UHhMOC0C4d1p6M4pgXSfjVaJBeCRnJ4qXOQhvSJ9W12SwbGVRSekII/VuhViVbOOUYyLj2zrSouPQKNDZtoSNvSxgcKsboqX3dHJ43NNAaDEUvaCWv9/f24cOECOjo68A//8A+w2+148cUX8aUvfQnvvPMO5s2bp/rc/E6VNhkZxpj/ZTDmOqFQGK9TTnb915+7Dw8uk66upwe032/SxeC8bFuk7/ri1ip8/1fnRY/9wtYqrPHk48HqArRf82FwZBK5DguqPsmjk0weXL4AxgwjXn7rElHYVs3S+fj/XjqBAcoTdf49+rCjH80XbsE/di+szu20Ro5LFdSIBwP+AC73DmFZqRvVZW64nVbJ++nOtqK6zC3ZVh5cvgA3747i138gE0r+x+fug9FooNYWa5bOQygURvs1H1o7+uPOedrbh9cOt8f8TqHn+eDyBYDRgFff8uoazvfKoXaYM1JLpCVFa4jl/NyZvmvh/CyKV3WPiWBIt4IF2x8qwUM10lVygXv9Gr/NOewmjI5PxeXpi8ZhN+Pfv/Yw2q/5cKnbBwBYVurGshJ170goFCa+H/7xoGAexlQbN2ghNn6T9qt5OXbRNQRpnyMEd8+jxyEOp92MZx7zYE1Vvuw86vWjnVizrABGowEPLl+ANcvuzQGysyx46cBHVIU1obFCr370C1urYLFkSB4j9h66s634wqPiz4F/r7S0e9K29GB1AZYlIHJAzRwzXWHrb0aiSDthLRwOY2xsDN/73vfg8XgAACtXrsTGjRvx85//HF/72tdUnddoNMDl0mdyxyc7O/mluBmMVOCjrjuqhJHdWz04fPIq7g5NRP42P9eOv3yiBnUrFtG8RMXQer8fzMnEvN+2xfxGPvNz7XhwZVGkmtmjD5XBkWXFDw98JHtv6uY5VF/bdCiMto/vYmB4Au5sG6qXzEOG0SD6d1IefagMj6wpwRf//i2MB8Qn+RaTEafa6CYjn59rw18+cV9s+7kxjMOneuKOHfAH8P1fncf/3rMm6e2Ng6S9CBEM3xv7nvvcCvzjK6dFj33uyRWYR9BunvnsfTh8ugej4/J5c6YNRtSvKiK/4OjPftLe7gyOY3h0EtkOC27dHYvrG+bl2PBXDfcBgKDwLPQ8m8/34oX//kjVdSkhEbnc9OLTtcV4848fq/psdN9VvDCH6DPZWWZ84wur8dHlO2j848eYoOywIcFmNuJrux5A/SeVDEl49KEybHqwNK5vPHG+F//0sxbRz331qfuRl+dEXp4T9bWLNV/7R113iHNZFS/MiZkTN5/vjRtXuPcqVfpAGvDHbzXjcDTN53uJ+xyhz/LveTT+8SBe+O+P8PD9i2TnUQPDAfT6JnBf+fzI37g5wEdddzBIuaKz0FhB+p4L4cy0wGI2aprzib2HQs+NP5cRe75K0NqWaJNq15MI2PqboTdpJ6xlZ2cjNzc3IqoBQG5uLqqrq9HV1aX6vKFQGMPDYzQuUZSMDCOys+0YHh7H9HRySrMzGKnE71uvKf6MO9uKR2sL8WhtoeAuos83qsOVyqPH+717S6WkA+3pzRUYHortt5YV5+BfvrJet3sjtvv+YM0CfHDhlqpd+WhCoTAyDNKTuEkKuaGiWePJw96G+9B5fRBv/fEych0WVBTl4qVfi997AHjpN+dRVZidMq4NufYihNkQhs83OuOMuHJH8tiR0QBxG9q6ppjItcZ9vxJCoTDePPYxDp8SriTH5+7QBP7xldNw2M2Sx3HPE4Dss5/LcC4Ph82EN/+o7hzRfdcil03WLQkAz2z1oCw/C3cGRmHKMAJIvLD2Z9s9WF6Sq6ovLZpnR9G8mYXd8NAYlpfk4qufXyHqollWnIO7d0eoucR6bg4RHeewm7HIZYv8xtPePsF+hXuvvvr5FSnl3lWD1PitZhwGZvoptWOI2D0X4o9ne4mO67k5FGl//L/TwplpxrOPLcOy4py4d4T0PRfi2cc8qK3MozKv4b+HfLQ4DOVQ25b0ItWuRy/Y+puhlexsO5HjMe2EtfLycly7JrwYDwS0hQRNUV6siTE9HUrYdzEYqcrUVAjvfyhfzpzP05sqEArNBNBUFOVG/h4KhSN/TyY03+9V5fMlc3ysKp8v+l163BuxPC4D/gDePtEt+Pfv/+o89jYsxxoPWXiut9uHEY0VwpRSON+Bb7xwPOYeO+xm+cpnwwG0XRlImZxDq8rnY8vqIuJwL7fTiqWLcvDBxVt4ralDNu/Wa4fbsXLJPKKF/WPrSmWFL+77lbwvre19ePltr6oqcqTPEwD1EGOtPL6+FH88f1OguMEijE4EdQt5FGLnxpm+h6RynsEwk7OLQ6zvelomB9KKpW5kWk04ceEWUUVLvXBlWanO31aVz8fKJfMEc2d9cPGWZDJ6pXm7nDLCMsfKpfMwNRWKFIP5+eF2yeOV9AupjtD4rXYc9nb7iJxk/DGE5J6rwWk3C14nabsgOf+//PVMAQCxd0TuPRfLucn1N3rP+aTmON//1fmYgj9q0DKn04NUux69Yetvht6knbD26U9/Gr/+9a9x6dIlLFu2DADg8/lw8eJFPPvss8m9OAaDQURrex9eOdSuyHmkd4WkVEVLJTYaCZOjz6WmVDwAvHjwIgAD0W5vohPlOmwmwcTppCF6qZbYd1VFHrHI8vTmCpzp7CdOrq6kQpjRaMCz2z1Uq6qd9vbpLqqk2vPkqCzOxePry0Tf58riXNWCo1LeONqJ2so8oqTgzz+xHE67RbQP4vqo4HQIDfWleO9sb4zAawAQBnD+8gDOXx6AjJlVV+SSpKvFaDSgsjg38my5QixCbZ1LRr9tbTE+uNQnKLTmu+2C95q0au/xC7fQ1u3D7s0VyLKZWeVAqBuH1SaH1yPJv1TbpVXNWagAAB+5ggBaqs5qhWSOE13wRy1aq+vSJtWuh8FIZ9JOWNu8eTPuu+8+/M3f/A3+1//6X7BarfjhD38Ii8WC3bt3J/vyGAxGFELCjpKFPMeujeXYvLp4zg70aiqxtbb3SbodlKJlsh8OA/sOXICRYLeXtDolLbTudyfyekmEUpJFElc5d1VFHr65r1nRNSgRnpRWVZP6fae9tz8RaPUl225JaD9jNRsRCMpvMAyPTcb1A6FQGN5uHwZHA+gbGE+IqAbECikkzzj6uXb0DEaeq1gf1VBfFnHh8d/PcBJNyUqFYFKE7oOcgHhIIPfjTBXZe5sE/P5eSdVeTsDbspos/2GqCtI0UToOq620rMe9FGq70e/lhpWLVFfmVbrpKSfkJEugJZnj0BKRU626bqpdD4ORrqSdsGY0GvHDH/4Q//iP/4i///u/RzAYxOrVq/Haa68hLy8v2ZfHYMwZ5Bb5QouFXIcFQYU27EyraU6LamoQC2fgFktS4Qxiz5XGZJ9kt5dUGFK6wOYLGG6nFfUrFuLN41eVnSgKvRwsQpAKpaQuojWefHi7fYrF0r4BZflWSHfDpX4fAOw7oL+oBgAwkLVBklBhErY/WEK0oOUvvlvb+4jCd/Uiuj+QesZiz/XBZfmS4lCWLbWmp1tWFxEJB0pdwmJ9NQ0Bkevv9zbUwGk3R65pb8NyvH6kk6jtHDt/k+i7Er0hkg6Q9CNCY4jSPlYKMdFL6L3k3jk5gd7ltOLLjy3D8PikandTKgo5ah2GDAaDwZFaMxdC3G43vvOd7yT7MhiMOYvcIl9ssaBmEfjQfQVMVFOAlnAGqedKY+FEsttLIgxtXVMsuCgXQizExD8exKuHvIp/QzR6OVj4KBVKSZ1iahYI75/txY66MkW/W2wRxYkQZzr7BcNXud+XSJFlcDRA1Ab3bKvCjf4RHDh2VdX3uBwW7N5SiVUVeXj/XK+ixXciQmLl4PcHQs9Yqt3Kvb+JcN857WZsfKAQBwnE9VUV8Ru3fBHNPz6J/Ue7iF3CWsLrlfDiwQsxQl2WzYRpwtxU4wSVVx02U8I2GNIJkn6EP4aEQmH8/ozy3LN8Nj5QiNVV+aKbGELXxL1zazx5OO3tFz336qqZucNaz+yam6l1GKYSNNN/MBgM5aSlsMZgMOhBa4f93u74cuw/qr5CL5/airmVU00rasIZQqEwGpuvCjpnop8rjTwsJGIOiTC0tDBH8N93bioXzenE/d7WdmXChNNuhj/KnZTIfH9qhVISp5iaBYJvZFJRKIxY/yIk4oqRqBBHAHjjSBespgzxNphtxdObKiL3l58TjAR+aLvc4ru2Ki8SQtna0ae7e89mNmJCIjyVxKmZKNGIFJfTil0CfQMA/IFXFIKP025GeWFOzN9I22+0+M1/H0PhMPVcWkLw3W+036fklwxKXZSGw3f0DGJoVLsTdnVVvuhmhtx7efnGMPY21GD/0fjw5HAYaGq5jqaW65pSS6Qiah2GqQLt9B8MBkM5TFhjMOYwSgdikknZzw93xIgQWkjlSUyqojScgTSk7I2jXdi1qUKzU4ZUzJEThtQm3FW64Hc7rfj2cw+h68ZQUnaBteR9kQu3UZu0mrSNKQ0D1AtOcOW7iYTwjwdjXIBcG/OPB1G8MAeLXLZIJTqj0YAvbKlUnDMy22GJVFwkSdzPLWT5FfNowy32AWguOqFHAnYtfPmxZagucwv+m5yw6R8P4lsvnZB1ZEvxk99dgtFwCWOBew4wq1k60Xu6MDoxNeuLF0ghtzmpZKyiEWbokpg3kY4nTrsZ39lbF+Mo5gu0JKklkoUa55Yah2GqoCX9B4PBoAcT1hiMOYqagZhkUkZLVANSdxKTyigJZ1CyQOQm20K776Q4M82KhFI98rAoXfA/vbkCJpMxaYtGPfO+KElmHg1JG9MSBkiLzauL8EBFXmRRZTQYiH9rtAvQU+KCyWSEy5UFn280IqwB9xwpSipyDo9M4rS3L84R4nJasdaTj3OX7yAQDMU5gfQS1YTCxpS4bITQOw+R0jyLw+PiGwdirqJotDqyJwRCKkmKVqQLczXvlJLclyRjCI0ww4Jcm+i/KRlPuGq1P2pskzyWRqVMmmhxbil1GKYCiapmymAw5GHCGoMxBwmFwnj5ben8Uq8cao8biBM1eU7lSUyqQxrOUF6Yg2+9dELRuQdHA1hXvSBm9733zigam7uJPv/FR6uoTezUTp5J23CWzYRnt3uS3gb1zvtCIipEky5hgNvWFuOpjRUxf+N+6yuH2mULDyip/sY5UtquDOAHBz6SDKMEgP3vCgszPn8Ap7x9st9HG6GwMaWOUL5DJDvToumaHDYTRiSExOefWA6n3YK2qwNoPCHf/8i9H7VV+Vi5dD6+/sJxybZB05E9m0jlvFNqISnQRNslVF6Yo9mZ6u0Zwjf3NQuOhUrHk0RWyqQBjWei1g2fLNLtGTEYsxkmrDEYcxDvNZ/sxG1kPAjvNR+qS++Fz5BOyvg5qfg4bCaYzRkxkwGn3Yx1ywuwKsphwlAOaThD140hxa4z7vlH7757u33EwtrNOzNuH63PVsvkmbQN732iRjR0LJEkIu9L9EJCrJAARzLCAOVElmicdjO+uLUSazwFgv9eW5WPyWBI1oUBAAMjE/B2+zA4GsC8bBsezMkUPdZoNKBm6Tx8eUe1YgdgMpFqO6QuGyGR22EzwWrOQCAon/xeiM2rizE6EcTJi7cl8xtWFufi+IVbVN6Pt05elRVcmagWj8thmXUpG+Q2bvRwCXHfScOZKjYWKh1P0qlSJs1nkopVS8VIp2fEYMx2mLDGYMxBvN0+4uOihTXSSdlOmVxce7Z70mpHMN0gCWc42XZL0TnFFqdK8nQdOHYF7529gS9sqVTtBNM6eSZtw6kyqU5U3hduIeEpcaGyOFekMEQFsmxmnGy7pXueoGj+bFsVjAZD3DXZLRlYWpiNvFw7lizKwbxsW8w1iTlOXE4ycfWNI10xQsq837bNVPIsn0/19yUTrW1HTOQmFUL52C0ZMBoNMYVUHHYzHpLYdKkozMYpiSqGa5fly/7G1vY+1RVeZxNWsxFmU4aswBjN7i2Vs2rsJtm4sVtNVF1CavL2kcAfC5WOJ+lUKXOuOrfS6RkxGLMdJqwxGHMR0jkw7zjSSVltVT6MBHkqZtPkJtWQC2dQOskSW4AbjQZFyegHRybjdtKVJBrWOnlOxwTFic77ItR2/OOTgjnBtIQbkeK0W+ApcSkS4wVdVHYz/mxrJWor84nCrfjupLtDE/j+r86LOiJTIQSWFGemGc9srdLUdvT4veMCuchGxoNoarkuGIpHUnjl1KU+fP6Rcskw1nR5bnrzpc9Uw2iQLl7B4bCZsOeTcHmpPlxNInm9ib6mbLsFGSYjpq/4kIEwXmvqkPzsy297MTVNlh+PZJOBpP3ZLBn4SkMNum4M4eDxq0TfDQiPhUrGk3SqlDlXnVvp9IwYjNkOE9YYjDmIp9iFRsiH73mK44UJ0klZuuWpSFekFi2cC4k75pT3duQYUqeZXN6y1vY+VcnouZ30M539inKl0Zg8p2OCYr3fJ6F2xC3GWtv7sO/AxbjPiIUblRfmwGE3K3K9SNHWPRD3m6XavaiLajyIfQcuorywR1O4lZgjMtUqYUqxc1O55nae6N/7iyOdsFtMGB6fRN/AeIyrTQo5l0o6PTc92ba2GGs8M21CqH902EyoWpwLszkDbqcVDrsZgalpvHnsCt4/1yvYhwNQnUheL4REdyUo6TtINhlI2t/E5DQMRgM+u74MR1qvK7oGobGQdDxJp42ouercSqdnxGDMdpiwxmDMQTwlLlnHhsNmEl2IKJmUMVeafpAk8Jc6pqTAITmhr6spwLplCzASCMLb7Yt7xlqcHgP+ABqbrwoujqVypdGaPKej8Kv1fRITo6TayKqKPNln/JPGNly95YfBAGQYDPjD+ZvURDUAMTn8XE4rHlyWjw8u9cVd765N5ciymWULs3TdGNZ0PWJCTTo5IdwO8cqBpCT69/r8AXz3jbOqPit2raFQGG3dAxquShy7NQPjAXU55pRiMxsli2Y01Jci352JvoExvHe2N8bl57CZsKm2EAXzstB2ZQAwAMHpEL782DKEw2G09wwCBsBoMOCP528SCVJcHy71b2qS+2tFr5BLIUhdQqTv0b4DF/Dsdg+e3e5R9BvExkLS8SRdNqLmsnMrXZ4RgzHbYcIagzEHMRoNspOzPds9kiIDE82SC0keGEA4pEdq0RNN84XbaL5wO/LffNFOq9OjqUXa6SbkDFI7eRYTlWZzG47+zUILak6kEnIccm2kob5U3k0RDOF3BJUZaeDzB0SvV8hVpxdCi+F0cUI4M82RwgxaxOR0+b0ABKuUanUuSUGSa5QmE8EQGurL4pxjWTYTtqwuwo66sshz3lFXFukXbt0dw7sf3sDB48Lvr8EAhMP6XLPS5P5aIamGThNSlxDpezQ6MRUZ27/yZA1RGDQtISkdNqJS0bmVyDDodHhGDMZshwlrDMYchdvh4k/OXA4LdmtILs/QHxKn2C+OdCJMeUXEdxpodazIhbMIOYPUTJ5JnH3pihIXGh8xkSqatz+4RvuSZwXDI5MxRRyAmWdBkrtNDJfTitHxSUxOaXtv5aqo+seC+PFvL0W+U+17oKRwSdLh3VK9nUv1KxaitjJP0EWiF6MTQXxnbx0am6+iqWUm3Hl0YgoHjl3F0dYb+OLWKqzx5Ec2FH75bqfs+6+XqAYoSyRPQ6BobL5CpeKmHFk2E579JPccCUrfo9ePdOKf99ZhVUWeqOubg6aQlA4bUank3ErGvCMdnhGDMZsxhGmvvNKU6ekQBgZGdf0Ok8kIlysLPt8opqbIEp8yGHqTiomF05FEvt/ebh/++fUzun6HFG6nFf+8tw4dPYOqr4NUgPirx6uxrnpB3N+FJq1Ck2e5BXQywpFoITZxV1JMgqGdLNvMHqWSRfuMo6kcTrslpu9tbL6iuTol51ZVIujw3wPScYFEnEkF/urxaqz1FKCjZxADIxNxFV/1gBNYOBfJmc5+nLh4m2qYdDTOTDO++GiVpEtu29piPLWxAqe9txPq8BSD378LtTuleTiFCIXC+Mr/ex8BiXBZWvztzvtRXeYW/DepjRAlQu/fPb0qJgdmKghJqQTNea2ac83meUc6wtbfDK243VnIyDDKHsccawzGHIftcKUfyc7lxDkNtDhWtqwuIhIQhEK4ALKwBxJnX6LDkcQgnbxzx53p7EdTy/W4fydxoTHoQiKouRwWbLh/EfLdmZLPN9+dqfo6uPDDLJsZg6MB/Pl2D673j6B/aBwnL9wWrLjJ8fqRTqxcOh9dN4ZwprMfJy/ejhGehIQMtYVLonFmmuEf01fgAoC+gXF8c19zQt110aF7tVX58JS4sHNjhazLSC3+sSB+frhd8phDp3pQstCJ195JjSqo0WGQQgKR2AaM0jxtjc1XEyKqOe1m0fmUnIPpK0/W4OW3vUT9SfQcgIUAxkNrXqvGdZZO8w4Gg0EXJqwxGAxGmpEKuY0GRwNEYZn8hRG3k76qIg/vn5NPhP3jxjZ8QSQ0WW7yTJIDTkk4kl4ITd5tlgyUF+agpsyNjQ8UwWQy6poTiqEPWTYT9jbUwLPYRTXfEkdDfSnycjPhH5vEnaFx/Pxwuyon1oA/gK+/cFzUTcUXMrQULuHYtbEcjixzJCxVL2xmoy5CFim/4C2i3z/Xq9t3kTz7V97yShY6SBTR+b/EHD5yIhOJQBEKhWXzeQrhclrx5ceWYXh8Etl2C37y1iXZvveLWysFr4UkJ2ptVT7sFhNRgQ5+P8E2SOlD+sz4pMu8g8Fg0IcJawwGg5FmVBbnyoZSupxWhMNh2eTGauEm9nI5TaR20uVEOQAYHJlUXUGO1NmXTAeg2OR9YnIaF64M4MKVAbzx+y6sXDoPZ7vuJuEKGVoYnZiC0WAgdiaQukA51wSgLORTCpIQRU7I0Fq4BACyHZaEbBIkW0TyRS2iadw3rST7fnBw+b+0VpeWEyg6egZV5VbbvbkiJqRTbrzatrYYazwFcX9X4mDylLjmbGXLVEKL6ywd5h0MBkMfmLDGYDAYacaZzn7ZhQK36NYjQTd/Yi8XiiK26BET5YRQEzpBumhPlAOQC+McGJnAyGgQWXYzXmvqkP1cOAwmqqUxShZQJC7QhvpS7Kgrw5nOfl0T8AvBCRk0FoVcP5E2BRA0wN0vtpimX11a7p4qvedi+cnExisut90aT+zxXH/f1j2gyMGUapUt5yJaXGepNu9gMBiJgwlrDAaDkUaQ7u6HwmGs8RRgy+oiwVxcWhCa2KsNRSENf1ETOkGyaDcYAP+4Pq6+aFgYJxkGg76VCJOB0gWUlAt056YKOO1mfNB2G68fTU6eLE4814LbaUV5YQ46egaxuiqPeh+VagyPTCIUCuu+mLZbMzAeEM+lpxcuhwXVpW4cv3BL9tgvP7YsxgmmVWyUu6ek97xuxUJsWLEQSxfliApXpPnM1PT33H1IpcqWcxUtrjOSeQdzHTIYsxMmrDEYDEYaQbq7//PDHaitnFkE0Fq06jWxHyYUtpQuwEjcP+EwsO/ARdysH0O+2x5ZKAFQnAyaVsW3uYrTbsYXtlTixTf1qVbYUF+GfLedOF8SDdQuoIQW8P7xSew/mnxxlobTbM2yfHzjheO6V+ZMFfa/24XDp3uwa1O5bg49t9OKpz5drtv7AwCZ1gw8uqY4ktfPkWWG22FDZXEuTnlvEwlr/P5ei9hI8n6RtFWH3Yy/+7M1GB4ak60aKLeJpLa/j74PrCCBPpAWCdLiOiOZdzDXIYMxO2HCGoPBYKQRpOKSfzyoqHLn1rXFOHWpL+Y4h82ETbWFKJiXpevEXs/QidqqfOxtqMGLBy9IOqGik5tn2WaGxuhwW7lKYGLVw3ZtKsf+o12KrzvVkcvxp4ZntlWhtiofGRkG4up40extWI6bd0bR1HJdsGBG9LMjye9HAy0LqOgFfGt7H/Yd0E8wIcVqNkb6AZKcUx/w+hS304rFBQ4cTqHKtU67GQ8uL8ARnV1zPn8A+w5cxP3l83QR1rg2fvXWsG6Vgf+64b4Yt1k0avtxLSItyftF0lb//DPLkEFhbFObL05IIGQFCeiipMKnVtcZcx0yGHMTJqwxGAxGGqFEXCKt3LltbTGe2liBP32kXHY3l3THVwl6h05k2UyKwguFBB2pSmBS1cOSLYbQDg3jFgaA8vx9M6GMM0Kj1GKjtiofPX0jePP4VaLzOmwm7NnuiXx+R12ZbBtVkt+PI8dhQd3yBTjZdjtOgA4DsmKeWmhU4KRFIBjCmc7+yO8SElgddjP2fCKSfp7XpwyNTeKlg8kXCKN5ZlsVVlXk4cSFW9TFYiHOXaafL7GhvizyTJ7aWIGyRdn42eEOooIUpLidVkmhR20/rqW6NOn7JSd08POjqUVtvjjmYNIXpRU+abjOmOuQwZh7MGGNwWAw0ojK4lw47WaiECq5yp1Ouxlf3FoZqWRGEuJCuuOrBD1DJ1rb+/Dy217V18aHX0QhlUQPPm6nFd9+7iF03RjC4GgAfQNjeO9sr6JKsY89tBjVi90YHp+MWxgoFaa4hXBtZb7kYqO1vY9YVJs5b2XcoshT4oqIwKe8twW/J3rhc8c/jis3hhEOz+R5W7ooB7kOK2AAhscmMS/bhgdXFmF4aAx/smFp3PUD4qHDSsVo/vGhcDjp4Z/RvHKoHaFwWFQ0jhZzovuUUCiM//n9Ywm5Rj5i7jmuTXq7fZpFtac+vRTXbo/gZNttyeP0yCGY77bH/PcaT0HMe0Yj/FmuD9bSj2upLk1KIoQOpekKokVohj6orfBJw3UmN6fSY6OSwWAkDyasMRgMRhphNBrwxa1V2HdA2i2ktHKnHEp3fJWyqiIPDfVlaGrpoeb80SO3Gb+IQmPzlYSJHveXz0P37RFFQpbJZIyZ2HNurrbuATQ2d8ueo6Z0nmRV1/g8YMG4PGD8Zyi12FAjVN72jcX9jVQEvnctLtTXLIq7lo6ewbhzi12/0N+EriPXYcEj9y9Cvjsz7j0UOp4LTU4VRsaD+OnvLkkeI7RQ7egZpOqgIiG67fHdc9H3nUqVU6cVuU6rrLCmB2K5nqLbpNrwZ4MBeP6J5UR9sBYxQm11aSXoHV6pNF1Bot+HuYiWCp9ybVKLMKbXRiWDwUgeqTVbYzAYjDmA1l3KNZ58XFlbLJlHh2blTrU7vqSIiQlbVhdhR12ZqnPq6SRruzqAyuJcnOnsx4FjV3X5jmgMBmDrmplwXX7bIRGyouHaQGVxLo5/dEtz+K1Qm6qtVC/gqgmlevP4VRTmZUWclzREYKE2Oe+3bdi9pRKryucTvcNi1zE4MhnTbrjFFCAcXpuI8ESlTASlE7wLLVRpiFck1FbOR60nP+65SPV/NKp16l3xUwwXYZi8mOjlsJslBZ7nn6hRFCqpZRNHb+FLb4eQmnxxWsbOuYjSZ6ilwicg3ia1CGN6b1SmM7PJxTebfguDDCasMRgMRgKhtUsplkeHVm6n6AnB8Mik6h1fOcQmmKMTUzhw7CoK8xyqfovaXDckNJ7oxrGPbiIoUz1OC3XL82GzmpGfa8fGB4pgMhkB0BOy9Ay/1bI4Viu+cJVdH1tXokkEDoXCaGy+IiiY3h2awPd/dV4wrJD/DisRdrnFVKo507TS0t4HAJH2mCjhaVNtseL2p7XKabQIrVfVTzE2rFxE/J6KiV5nOvupJlpPxcT7UmPvg8sXUPkOkn6Vj9qxcy6iZv6kR3EkLcKY3huV6Yz4JmsxdtSVJu1+qBHImCNxbjK7ZnEMBoORYkQPyH0D4zHVJznU7lLy8+jQ2hETmhCQoFQU0XOCqbc7RkmeMqU8vr4UDQ8vIT5e7SI2FSuXaRFfDhy7EhdKLITYQpa03Qs5RfnvsBphV09n2vZ1xfjD2ZsJdb+9++ENvPvhjchiYlVFnu6ik9oiJ2oEkWiiRehEVZzl4OdXk0NQnKeQfywUCsN7zQdvtw+hcBhjE0EYDAYUuDKx8YEiGI2GeznfMi1AGPCNBPBx7xBgAApy7x3HnQcGwFPsgqfERXQtoVAY3m4fvD0+IDwTPupZ7MKZzn5JIcSYYcSjD5UR/1Yp1BRGSZSbM51RK2ZVFufKVrF22M3E/YbWeYuW0NTZBsn8eGaTdWZsfzaqSFEir+v9c72KBDLmSJy7MGGNwWAwdEKpQKVURNLDZq4lL5lSUUTPCWaywrK04rCZ8Ph6Ogs8ElKlchnXlgdGJoiLcwhBKhzxF7K08vH9oqkDdqsJhz64pvlcQhgAqMl9f1/ZfCxZmJNQwYcjejGht+jEd1kq6SM5QeTVQ+3E7c/lsGD3ltjiGTP5IkvR1HI9IUImrb5OLvehXMGRl9/2iv7e/e92wWrJQGBSukLx/ne7YM4wIjh9zw3ciG5k2Uyyi2qha2g80Q2bJQNT09Lu4tfeacemB0slj1EC168eaenB/ne7ZI9P1/FKT6LbXLbdoq/LS0FFEa3zFq2hqakOaZ+rdH48OjGVEFGK5LqkBDLmSJzbMGGNwWAwdEDNQl2JiKSHzVxLXjI1ThE9J5gkoV1yuYWSwZ7tnoRPtpIdtqXWIamF7ExL5P9PTYXw6qF2Kuf1jUziu/vPUjmXEOVF2ei8PqzoMzazEaFwGCE9ylEq4KdvXcL9FfN1ObeQy1JNH1lblY/A1DR+/Fvp4gwRDPF59YRCmaZDYUzIiEpqUOvQU4LcfSQd6+RENY6ggAgmt6iWugaS+z4wHEDbx3dRNE+Z+08Ko9GAzauLcfh0j+ZclnMNNWOC2Pypo2dQVuAemZginntpnbfoEZqaKpD2uVo2svQUpZRel1iRHuZInLsYk30BDAaDMdvQIlCRTNq4wZ8/eHO7aK2f5DZSipa8ZGrycek9wdywcqHkv4eTJDRsW1sMlzP2N7md1lkXHsCFZZ1suzUTHhaKv99ibVl3wve+/+svHFftkEs0nKhmUPCqTQRD+O7+s3jx4EWdroqMscA0mi/QrZiZZTPhb3fej3/eWye4cFPTR7odNuLvjz6f2HeOTkwRi2pupxV7G5bj755ehS2ri2SPV5sHkRS5+3jae1u3IjFC/KKpI64fCYXCeK2pQ/O5B4YnNJ+DDxdiLIXezzDd0DImCM2faG/gaZ23cJt+UqSj2CrfV8z0uVoLS3GiFG3UbLAJXctsdyQypGGONQaDwaCMFoGqb2Bc8t9TLS+ZlnxcJK4yNRNMud1ut9OKT61cJJjPQ0+cdjOe2VaF2qp8fP6R8qSHX8qhJdSYZOdaz8qtcvhGAnjz2JWEtwFacJrwiqVunL88oOgzs4lnt3tQXeaO+ZvWPlJNIYNfNHUoUzsF2FFXgob6JTAaDQiFwvhRY5vk8Q67Gasq8mL+RjM9AMl9/PnhjoSK0r6RyTinR0fPIJWcl713RrCijL6DJBVzWaYqWscEITGL9gae1nmLnoWDkgXJc3vx4AUAy+G0WzRvpA2OBqj2da3tfXjlULuqCAb+vHk2OxIZ8jBhjcFgMCgxHQrj0tWBSDU8NRw4dgWFeVmik+1UyEu2a2M5sh0WzZMZPSaYclb+hvoy7KgrxSkvXeeMHM5MM/7lr9dLVvdMJbSEGpMm7tWzcqscr73TjomgflVd1UCSg4rP9b5R7G2owf6jdEJpbWYj/myrBzmOmeTyw+OTyM604MeNbboW7FCKVFvU2keqKWTgo3Bvqkvckb6O5DeMjAcjv2Gmmu3VuOIdWtIDkFxDMpye/IUsLefHLw63Y57TilXl9MOVUyWXZaqjZUwQK0BAewOPxrxltomtJM8tHJ6p3k3ixJWjb2AM39zXrDkVCtdvatlgi04rAei3YcxID5iwxmAwGDzU7ISd9vbhF00duDukPZxEyk2R7LxkbqcVm1cXU1sQcBPM15o6YhbuQknB5SDZNf3DuV7sqCtN+G7hM1urIqJaqqOlopUSt1AyQyHUiGrzcmxYW12At09063BFgNlogNI7MuAPwGk34zt769DRM4i2qwNo1HB9E8EQXE5rnOD0hS2VSSl8IERDfSl21JWJ9kE0+kg1lR21wF9oKfkNUoUDtFShS9VQJX7fTbMvf+2ddqxcMk8XwSvVN1NSAS1tbmQ8iDOd/XHtXI8NPBrC2GwSW5U8t5MXtW1q2i0ZOHDsatzflfZ1re19cfNOVfCc4LPRkcgghwlrDAaDEYUapw6tioIcUm4KPW3myZwQGPhhVCrCqpQ4VdSEe6khHXaglVZfe+VQu6jwS/oMjrT0oCjPoem6E82ja0uwdU0Rlixw6iK4jExMoaG+DO+f61V07sHRQGTRTkMMEToHt5CUqvxIEwMAu82EsajvIn2XaPWRSis7amHtsvyY94n0N/QNjBO5LZSkB+D6g97+UaJrSGQRGJfDEuf0qCzORa7DQsVROTDMkorLoUc1cg6tIqlYO9fDIUZDGJstYquS5+YfD2qq/D0u4+om6etoztmHx+P7ndnmSGSQw4Q1BoMxJyCZDKpx6uiVJ0psgay3zVzJhIDGBFuLO4qPEpeHmnAvJex4qATVpe6U34FWU31tZDyIxuYreLx+SVwbGBghc2zuf7cLLqcVWTZTQoQaGrze1I5DJ65g95ZKfGdvHQ788WNN7jAh8t12xe6z6EUNDfeO2Dlqq/Jht5p0rXrKEQbw1w01MBoMivsXmn2k0WjAxgeK0HiiW1I8cjksgMEg+Z02S4ZoEYNDp3qwtDAn0teR/AaXw4L3z/XK/gaAPD2A0v7AYADW37cAh0/1EB2vld1bKuPagNFooOqoTFWnXiqgRzXyaLRueEm1cz0cYrNFGNOK0ue2bnkBmlqui/77Gk8e2q76VM0N5Pq6UCiMl9/2Kj6vGFLj5WxxJDLIYcIag8GY9dBKpK62tHY0pDt1YoN1IlxlJBMCGhNs2oUYlDpV9Ar3cjutaHh4SUpNoIRE0DOd/aoXo00t17FwviMut5fTbiY+R7Lyq2nBNzIZEXyrS93UhbXcLGtksVZZnIvjF24pEoi0LkzlBCfPYpe84OO04s+3efDSby9qEk2HxyaxrnqB4s+R9JGfWrkQp7y3ZRc7XD8n58jatbkCRoNB8jszZPqD6L6O5DdsuH+RYEiUGHKCkRoXRzgMHD7Vg21ri/H7MzcQ0ClvocNmwp7tHtGxhevLf9zYpvka5kJScTWbYjQ3wcSgseEl1c6ZEKYOufai9Lll2UyyG7hTUyF8/YXjVAoKRNPYfIXaZp7LaUUoFMbJtlui94W1t7kFE9YYDEbaIzXo00ykLrQTRrq7vfGBQqyuykd5YQ6+9dIJTW6KRNjMpSYEtCbYtAsxqHGq8EXE4ZFJzWFfic6fITfpFRJBcx0WBKfUL0BHJ6aw70B8G0hGMnMhhEIqnZlm+MfoXN/rRzrx7ecekm1vBsSlYBHFmRmbfFuNiK51YSrXdknOv3tzBWqWzsPDKxbikAYnkxaBQ6yPdNhMCAMxgpTYhoASoWn/0S7s3lwh2i+TVCHm93Vy/XxwWtn7K3U/tTqvT13qw6baIrx18prssasq5mNTbRE6e3x453QPxifv/Y7cLDOe3lKJLJsZ3m4fYAA8xS54SlxEfapWUc2ZaUZ5YQ683b5Z6zJRsymmZzVyPmLtnrT/ngvCaCIhbS+1VfnY21CDFw9ekK1AfeDYVXzlyZqIO1voXeu6MaQ6xFysDYRCYUmnnFLGA1P47htnI/9N073JSE+YsMZgMNICMfFAatBfVZFHPZG62tLaq6vyIwsmGo6zZNnMSSbYvzjSCbvFhOHxScnrol2IQa2bL1pEPNl2i+i7hDAYgOef0L5rrwS5Sa+YOJBKFR5pYzAAj60rwY660pj3g0TUJmXAH0DXjSHZ9vZXTyzHzw+3E+2Qr6suoJIbSO4zADQn3ZYrONLa3qdJVKNRNY3fR4rlI4veEOCOHxiZwBtHyAX26HMILRZJqxDz+zqpft7b7SO+Prn7qbVC74A/gIFhss8X5mWhutQNz2IXKopdogJadamb+PtppWRYuig7ro+YTYtltZtielYjF0Ko3Q+NBfDDN9skRZu5Vm1Rz3x3gPL2ssaTD2A59h24KHtubu4t1l7UhmRLtYH2a8rDS7etLcYHl/oE2z8/tJ+me5ORnjBhjcFgpDxi4sGDy/IFF2/c4NZQX0o8GVSb8FqtS4qG4ywZNnOSCbbPHyDaxdOjEIPWe6tlt/v5J5Z/MrGkD7/AAAzA2c5+HGm9EXcs1/4fX1+Ko630dmfThXAYeLP5CqpL4nPc0cyrNzgawLrqBbLt7fbdMaIE86sq8gT/rkZEl/sMDVFerOAIDZGDluuT6yNDoTC+ua9Z8thXDrVrrhLHFfbg98ta+jqxfl5J2K/c/aSRV2xeDtlv9BS7BMf0485bguPE1FQI7354HX2D48jPtWPjA0VxFZa1CoMcZ7vuxv1NSHhNRzebFteZntXIxYhu963tfXjpYJvsZ+ZStUW9892pbS9rPAW4WS8/5skJsWrnYlJtQEnf7rSbsW55AVYsnY/PfWop3jp5lTj0npZ7k5F+MGGNwWCkHNEigpTLQM4RQWr5HhwNYK2nQFXCa7UuqVRObCq1C6pm4iy2i6dXIQYt95bkmgwGxOya61Hpif8OKK0UCQBvHr9K7XrSjcbmbjQ2d8ctNGjm1YvO1SfV3nbUlaKppUdyp1yunasR0aU+Ey04dfQMEuUb45BzMZBsaEjRUF9GfbefRHihUdlyZDwI7zVfnNuKtK9TEoZIMvY47Gbs2Val64YCx7LFbrx3pleynTtsJoyMB/Him/GOFqFx4pfvduLw6Z6Y/vaN33dh65piPLWxItJ+W9r7NF8/v1/nIyS8ppObzXvNp9p1pmc1cjlIBJ5kuMWlSDUnmRq0uBTz3Xai75CaTyrNF0oyD8t1WIjOZTYZ4R8PoqnlOpparitOnUHTvclIL5iwxmAwUgo1VQrFILV8cwnD1YZo1lbl46ufX4FfNHXg7tC9qohyA30qJjaV2wXVMnHm7+LpWYhBy73dsHKh5M7k808sh9Nu0XXSrNU5w5hBaKEhJIT5x4NxhRik4AthciLWs9s9uhYcEUJN7j0auZa05LBxOSzYUVeq+vNiJLLSo7c7Xlgj6evWLstXHIYoJhRn2UzYsroIO+rKqG0oSOF2WuEpccm28/X3LcBLv5UOE+PGiV+91yW4eRYOz1RRvTUwhu7bI9SKoMjlhRISXtMl9Ku1vY+4EqLQu0LaPvzj9McsEoEnHJ5xGGkRtGiJYanqJFOKFpciDSGWpM9sqC9FvjuT+HlVERTfARAnoqmZi7HqwnMTJqwxGIykwZ/I+MeDggnRtZBlMxE7RbSEEa7x5GPTg6X44Nx13B2eSCkHGikku6CrKvJUL8CEdvESUYiBFDlRV+k1ce17YGQCI6NBODMtcDnlqxDSClVk3ENI1OULYbWV9/JrXboygOMXxPNiKRXCEt3O1ebeo5FrSUvFtd1bKnXpM/sGxqifUxSRy5dqA2tl0hpICTe03M8bCAosiBH9PgiNuVk2E3ExiwF/AJeuDuDwaeljhcI21WI1GzUVPkh26JeaAk5iiIUj79pUITs/e+NoF2or86neB1KB4kxnP37U2KZK0KIlhqW6k0wJWsQxWtEItMdNGlVnSWFFNOYmTFhjMBi6IzTpO9PZHzdY8lP20GDL6mLJxQJ/gaxlkZJhNGBZqRtTGqotkkI71EDJLqiWiYnQJDkVwmLlFh8N9WXYUVcac01yixkxkU5swh4KhYldBQxlkCw0osW2uuULcf8nxU9iJvTZVjy9SZ0Qlqh2ftrbJ7gA5hZ3exuWY/9R6cT8WnMtyW1oJCKcmiMUCuO9s73UzyuGp1i8jQm1Aa6ohhRywo2QUEw6RpBuKADSRS+k+tDRiSn88fxNyd8YTfOFW7IOMpo89lApfvOHj1V/PhGhX3oVcIpGSuxw2s2yn9fjPpAKFEJOWb6gJTYXJdlQlHuXSMZvLgdjspxkStAijtGMRqA9bmqtOkvCXCuiwbgHE9YYDIauCE36xBZdtCfSbqcVO+pKUZiXpWjHKxVDNKPRI9RAyS6oljxVYpNkmvecZEHJLwYgt/j4w7nemBA1oWfgsJvx0PICZNlMkqGkYrvX3m7lFasY5ChdaERP6P3jQRQvzMEilw2hkPqOSu++5bT3Nl48KB1q9/PDHfDL5BPTmmtJbkND73DqaDp6BhMWVm0xGWUXVPw24O1Wn/tKDNIxQn5DoTQmnFRsgUuyMaOkb5sITssfRAEuN9caTz6Otl7H8Kj6dqJn6JfaAk7raxYoGqMlE78noYABoC7vKZ/Xj3QiFA5j/9GumPOQ5M4iza3nJag4KZaDUQmJynenVRyj6TajPW4KiXWtHX04KlAMSg1zqYgGIxYmrDEYDN0Qm7QnSjzgBrdkOaL0SGCrV6iB0kkz/55mZ1rw48Y2yQVsInbxSBaUavL4RS9sxZ7ByCfJbknhu1C8PT7iz84m5BZFUjjtZlQtzkFL+x3ZY9UsNLgJvclkhMuVBZ9vVJOwphU5l+S+A9KiGgBZUY1Dba4ltRsaepHIXDeTUyH83YvN+MKWSuLfSFuwIB0jSMSwP5y7iR11ZZH/Flvg0qrKCcy0kYqiHJzplH+ntfL8E8thNAB/+8JxTaIaoF/ol9TzlAutPX7hFtF3ZNlMeHa7Rzrxe5IKGJAIPHLjx4A/INg3kgjupLn1vN1k47dQDkYl6FX0SQit4lgqRCOIEd2X/fLdTiqiWjLGN0ZqwYQ1BoOhCySTdhpsW1uMDy71yQ76iXah6eEq0zNprZpJM/+efmFLZcITtEdDsqAEoCmMlWa7jnOhJE+v0YzVZMSnHygUfBfF8kdxPP/EcvT0jaCxuZvou2yWDHxxayXcDhsqi3MRCoXhvXZcsqqjw25O+9AMmiFfJIjlWiJ1Mei1qFK6YZHoXDeDI5OKNjloChZKxgiauZpoipdPb67AyqXz8V/vXdYtHDQ6vJVGviW9No0SlR5g7xM1qC6TFnv0FnSk3mspgae2Kk9TwRQtxMy3SLs1jVMgPYs+CaG1H0/1CJDT3ttEuR/l2FFXgob6JSkhGjKSBxPWGAyGLtDcwRYiWjz7/CPlKbMjFgqF8eaxK4JhUFpdZXomraUxaa6tysfehuX42eGOGJEjEbt4JAvKXxzpRFjDSi03y0oUtqUEbkEaCoWRaUvfITnTbsbnHykXfReXFuZI7no77RZiYW3b2sWoW74QwD2xSUpUA2ZcB2c6+1NyJ5lEKJITjRvqSxW1S6fdLOlcc2aaUV6YI/hvSlwMYosqtW5eNRsWlcW5sjnf9IB0k4OmYKFkjKDplKMhXnJhmdwi/r4l83D+Mp0CBTazEVvXFqNgXlakvQHAN/c1Uzm/XptGJOGFWuEqusqhp6BD8l6LCTwdPYNJE9ai51ueYhcaIT+GSeVgJCXRxXBSXRxTSygUxs8Od1A5V3WJm4lqDCasMRgMfdAj/EastHaqDPrN53vx4n+fg08mvECtq0zPHCc0Js2t7X3Yf7QrRuRw2s3YqTLZuxJIFpRaBDG3c6Zq7auH6LoHcrOsqkJTpXDYTRgZT6yI4IteYAi8i3K73krEj3y3HYDyanfJrtwnBMmCkkQ0VrKwdDut2ClT4c8/FsS3XjohKliJJeHvujGEk223FCfLJ3Hz6l1xz+W0IjA5hbEAnfxeSjY55CpykgoWSsYImk45EnHQYTNhROL95sIyv7mvmVpfuK66APX3LYSnxBW5f5yo29Y9oPl7ODFQr/GNNLxQC0rEMKWCDo1Ng+j3WmiuR9L29IR75zwlLtkxzGEzUZurpnKYZSKgkWqlsfmK7KYcKf7xxOTxZKQ2TFhjMBi6oGUHO5EV47TCDe7nLt/BYUI7uVpXmd45TrTsgopNjv3jQew7cAFGCmXmpdA7j9LaZfmSQoQaOLFO63k31RYiL8cOR5YZbocN7dcGcPA4mfuLJnLPQEoANxoNsgnvOXKzrKpCchNRuY+PXE40kgUliWisxNXCvctGmQIkYoIV/zet9RTgTGc/vvXSCdXJ8uXEMS1h8B09g0T3Z3wiiIkg3YrOcu8EaUVO0r5TyRhB0ylHsjGzZ7sHgHhlUYBOWGb0ef9iR3VMe6C9ifH8E8uxxqPjvERHjUTtvIpU0KG1aUBSEVdLpXKtcO+c0WjAs9s9su8ATeErVTaVtaJUJKORaqW1vU+yyJRS3jjahdrK/DkjbDKEYcIag8HQBS27iImsGKcFLZN0NUJQIpLWqtkF1TP3Gyl65VGacfeUY//RLurnnjkvmTjksJsRDodjBAKhhVFrex+xqLajrgTF+Q6ihPckKH0G/Mn0Y+tK0NTSIymCcO1bbai50veOu8boqqCkaM2Jxr0zpNcs55bgu2tqq/Kxcul8fP0F6fx00e+ukirPapLlv/K2N6afUOIuEhNOSe8fbVENkH4nlFbkJEHJGEE7tI90Y0ZofAGAr/37H4m+hxT+tSt1uHI01Jfh/XO9SSnCQRpeqJSG+lI8tq6UyGEqhJygQ3PTgHRDxGo2IkDxHXY5rZgMThONRxzcOxBXRdRhwW4FBU3mEkpFMhrOZaUbc5lWE8YC0pszydi4Y6QeTFhjMBi6QDJp5y/IUtmZxkftJJ1DbYXCRCStVboLqmfuN1JohYO4HBZ8eUc1hscmY3K40Awz4dp5ls1MdN5dG8uxeXUxAEgKnkoni9Ul7pkQqScNml0cSgVdscn0wysWSiYS5tq3WoeikvdO6BpJ+ygaOdG4d4b0muUcf0Lumq4bQ7KhMNx1jE4EVVV5fvVwO1YunY+uG0Oyv3lkYgqNzVfxeH2Zqo0LoXaR6OIFHM5M8YIZaipykqB0jKCdq4lkY0ZofHnz2Meqc4mRONzVFp3hKtzuqCtNSsgdSXihGo60XMf7525SLa7EoWSjjbQfHxiZEP03rXOxaHbUlWDR/Ht5+M509iueb831EE0p+JtpYo59Kbc0jU1cpXO6h+4rwNEW+aqhiaxAzUhNmLDGYDB0Q27SnmqTD1I7utbKkFpcZYlOWkuCnrnfSKEVDrJ7SyWqS2Oro9G47kyrCU9vrsC8bFukXZ1su0X02WyHJdIOpYRJJZPF6DbILQSOtPRg/7vqnHlKBF0p0enQqR6iSr9qxBInrzKomjDNAYJdcZo50QZHA1jrKSByIe2oK0VhXpaivoG0bfv8Afzq/ctEx/LxjwXx9ReO46HlBUTHN7X0YOH8TFVOSqF2kawcTOuqC0TfCT03I5SOEbSFANKNGe79GxiZwKEP1FflI3G4NzZfUfX8o/u1ZDhRSMIL1TAyMQXwxDpauQqVtG3SfvyNI12wmjIEc7jRrIbMbTZxqJ1vzZYQTT5a8poJbZQYZD7KF8lo9ZtK5nTb1hZjxdL5RMJasjZxGKkDE9YYDIauyE3aU2XyocSOrtXBpNVVlmo7onrnfiNFbBJMglTy5Qsfa69MNxaYwrxsW0x7p33flEwW+W3QaDRg8+piHD7dI3nvtOY/JFkInbrUh3967iF03RgSbd9qxJJ1ywtihDOpMM2X35YuUiG1K04zJ1rvnVF09Axil0yxAe55SvUNQosi0rblH5vU1OeNjAeJxcTRiSlVldrENiySlYNpVUWe6L/pvRmhdIxItBBAI9eZEveo0lxKqeSer63Kx/OPL8eLb8oLzVk2E6amQ5pCIrWmbVDStkk2DYCZXK1Coh9NN7lY/5Eq8y0lG796XKuWvGZiG1VyRdr5IhmtfpN03HtifSmeeHgJQqGw7mlYGLMDJqwxGAzdSbXdO612dLWLHVqhFkBq3dNE5H4jRa37KrpyKdc+znT244/nb2Jikk6VwDOd/THPjPZ9I50sNtSXCbZBEgFCa/5D0h3nrhtDou2bez6rq/IUVcPkhA65MM01njxZ4UtqV5y0f7BZMmTbVmNzNxqbu+FyWgWdfM5MM9ZVFyDLZkYoFIbRaBDsG8QWRWuXyfdFbqcVjiwz0W+ihZpKbVIbFpzo/sqhdqJzO+1m+DVUi5N7bxOxGZFKY0Q0akP3Mq0Z+Osn74sJ0+c/b/7YXl6Yo8jR9Pj6UngWu5Luno+mtb0Pb/yebCyjETKqNW2DkratVPR+/UhnJKx8cDSA3jujRJ/LsplQUZSDs13im2RS/Uey3yVSUYtGUn+x71eb10yrqzB6PKXVb5LMvVwOCz67fiYUP1FpWBjpDxPWGAzGnIKGHV3NYkdNIup0IVGTDiU7sUoX5m8c7URtZR7OdPZTrRoXzR/P38TOjffuA+37RjpZ3FFXKvrveocaa91xFnt/5Xa+OaGDZJJ/2ttPdI18oZSDtH9QIthyYbJ7G2aEzTOd/Thx8Tb8YzNOsKaW66ILKKlFEUkl452byuG0W4ivNdGQtk2uWMM3XjguKZq5nVZ8+xPHJHefo/sT9yeCJEkuQDFSaTNCL4T6awCqF9mPrlkcF6YfjVDf4LCbiceC+bl2fG7DUoRCMp1JAqGZP0wJWtIf6Nm2B/wB/M2//1HxZtfGBwrx22bxIhDb1hanhDtRCPl8nWXId9vRNzAumGNTa4iv1rxmWl2F0eMprbZFMvfavaUy5vekYhoWRurBhDUGgzFnoGVHLy/MIXY0zJVBV+9Jh5YdWxIG/AE0Nl+hWn6dz8TkNBqbr+Dx+iWRv9G8b2omi0LoGfqiZcdZ7fsLALVVM78nFA5TE02bWq6jsjg37hnpmdPrjaNd2LmpXNCpJ7SAopGDyGm3oLI4l1oSdas5A4Gg+MLYmWmGf0y+b93xUAmqS92K2qbJZMQz26pkxWyTyQhPiQueEhd2bqwQfBeWfuKGUvPeznYHhFh/vWHlQlXvhcNmktwQEOsblGyw/OUTNZGQ6VSAdv4wJWh1SpK2bTW/UamolmXNQOMJ6cqqpy714fOPlKfc+xYKhWXTEkgVrIlGbYiv1rxmWkRavkhGs99UM/dKlbBgRurChDUGgzEnoGVH5xYMcqLaltVFWFWRN6cGXb0mHXI7to+vL8X/n70/j4/qOvP88U+ValVVCVWBBEYSSKClEMQYs1qWl4AxkKixnHbHS5zYPenumM6k5zc9neQ185rf/Dvdafd8e6Y7P5Lu9LSdxDbON53IDokhAmJ32AzCwjZLabGREGCQQCVU2kolVf3+EFdU3brLOXepRXre/yRGd6t7zzn3ns/5PM8Tjyfw2a1RnOm8qfk8PGGFes4hdi4aed+MEurMCn3RuuLM0n+9bjtsBRYMjUzO/pvgZhNcXR6XsZ89r74TSpusmJnTazASVc0/ljyBMiIH0dBoFFarBds3lBsiPO/avExxMvj843XYd1hZHA/4nGh+aEVG+ohcX9Dbb+eqA0JpvNbafl7YFUyrgCzc9yK3Q7cA9aWHq9Bw71KEw2yhhWYgdvgZuQiQjJqwbYRTUq5t+9x2PL+jdrZtG11xW4rRqLoQZ3bVci3E4wn8628uGFYRVutv1Osy1yPSylVdNWrc1DKGZzssmMhtSFgjCGJeoPcDrqjQgbePXlJdHQwUOfHstvydFOnF6I8OFkHl7WM9hpzLqA9YtXNIfdwaed9yeVVV64ozS/8dGY/hr565D1aLBe1dA2htu5LmZjP6GY9MTOHtY5cQXOZPudd6CmmonlNF1E+eQBlR0VaYGDU1VKG17Yque8havdRqgaluLqP6iN5+m8t9VQtGu6zEk+V4PIH9xy/pbofJ+L2OFBdxNpBy+Bm9CCCgJmzr7VuCQBibjuOhe+/BkQ+uzo5ZkfEYfnygA5/dHENTQ6WplcKF+8faTsy8Fl7OdPTjlXdChr+vtPxGvXnNWBbTeIsiGTluklBGGAkJawRBzAv0fDR53Xb886/O4/ao8oS2yOPAy3/+oObzEOlkYkU702TiAz6XPxa1rDiz3rPhsUlsCi7GP++/YNj1qvGrYz0p4m5yiPLalYvwl98/pikZvx6E+6W3Em+ye8VqteDFXUFdTjyW6qVAZtxcudJHcuU6jMCI8drntuOZbTXw+1LbhFliA0t4vJnIOfzMWOhhFbZ5SHba9Q+O470Prym2gdGJKbQcvYTWtj5sW1+u+beoYcHMwgcrRYXm5JHkrdJpZl49Le8DvXnNzCqKNJfGTWLuQMIaQRDzAj0TTNZJ8fDoJLquDKGmvFjzueYKUtXZhEpePKuLubSKbBR6xY65AO+KM8+qeabFWHFGJiFEeU/zaoSHoxkX1YC790tvvjexe0WrE09q0q42MZprbq65hJxYYMR4/bWddcwFOHgQV+HNhZDbTOdRe3pbNZOwLb5Gue205jQFZgQ2o9zmUvCIagDSB3IDUMsNOzUVx5EPrqB/aBylxW48el+Zae1Ba4ivEXnN5mrYO0GIIWGNIIh5gRY7ut/nxGRsmmvlODm/03yFpXIjawl4o0UocdEJt6MA45zJkPWQ75X+jIRnxZmp4qnPiXg8gQ+62Kp6ms0P3jrPVFghGfHkXwtil5napGjnpgq8f7FfUwJnuaqZQiVRCtPJLXjdM1IoiQWs43VzY1Was8njss3mJhVfsxFig9CvhPNku0p3PJ7Aoba+jC4C7DvcDavFwiyqKT1rQDlcO98YHjf22+10qB97W+Rzw95XvRAffnIr5R3x5pFuM/Q9APpCfI0QxmihhJgPWBIJ3s++ucn0dByDg+YmLbXZrPD7PQiHRzE1FTf1XARBpKO26r2nOdWOHo8n8PKbZ7nO8V+fv39eOtaECZuQ24oVtRLw8XgC39573JDJR8DnxF9/44FZ51z/4JipVUCl0FrynlDvv0ZVrcwm2zeU6y6iIdXGpCbIyZMiPYKLEWINYT6slZXVjqH8Dl3DVHjie3saAAD7j/egta0vpd+KrynUG8b33mhnuj4ekvsJ6/e5Ulvn6Qe8Ti+jxzbx8WaKCtRhYzC1wvZcH2+T+c6z6wwR8ePxBH517BLePtZjmkjGg5GusHwd62n+TeglEPCgoMCquh051giCmDfwrrqdvHCd6/iLit2oW+ZHPJ4Ln1OZQ084iFoJeCMrLD77WA1sNiuCy2ee0X/6P7/XfUxWKORBP3L91+u2Y2Q8xjXJ87pssNsKEB7JnVDj5sYq1FYUMwlrG4Ml6L46rMllJjUp0uMMyxdXWb5OCo1ArbIyi+DP4hx783AXntlWjb0t52W3EZwzZzr6JZPoi6/JrHQAau8egeRFI7E7M9m9xSpaaglr3ba+HMFlfhz+4ArOdOh35IrHysh4DHtbzuHSpgp8eWsN07POdVGNR/jzuGyIJxKIxxOSQmlRoQNIzLja1Bx+ZuQB1MozW6vx2IYKw8a5fBnrCSJbkLBGEMS8gseOzhuG+KdPrIHVaplXwpre3DcsJeDX1ZRgU7AUp0L9ms4hJTjsP37J9I/fZ7ZWo8jrmHeTeDMR99+iQgd+pKFQwcjEFDwuEy5QB6UBN3NOtO4rt/E3LzVw5S2cz5MiI9xa+QqLSMIiMoUuh1Xb5WAkCp/bobqAxXNNPO9hv9eBrzfV4932K2jruKl6rS2//xT1lQHUVwUAzNyrUG94tk9FxmOKDjxBBFT6W7JoqTWs9fcffYamBypNL8py4FQfqpYWwed25EzRIK3OuO0bKlSruAuMTkzh5X1n4XPbsWX1YnhcdsUiDFJjh5lFB7RS5HXQdwdBZBAS1giCmHewTjBZJ7l+nxPP76hDw71LEQ6bG1KeSxiV+0bJkXCmox+vtXZqyl0n5OsRCw7xeEJ3uJ0aAZ/T0JVi4i7J/TfUG9ac1zBXXAUCRYUzk6BH1i5VnRCGRybRffX2vBLKtDrOjHBr5TMsxTzUFjgEJw4LQ6NRbKlforiAxXNNPAU4nttei+Ayv2RuKyn2n+jF/hO9CPiceHR9Od49cwWDBgtKyaKl1sIq4UgURz64khGx66cHO/HMthrTz6OGIMQCfLnc/F4Hnttei7UrF+HQmStcxWMi4zGmbwNh7GhurERpoBBFhQ681trJfJ5MQYWSCCKzkLBGEAQhA0sYYnNjJZoaquBwFGTwynIDo6ovyn38aV0B9rpseGFXUHay3Nk3xCyqPLhmCY6d4wsJBvQlCibYyWbVWI+zAKNRAwtf3DG6lgbcTJvPlYq5LIKZVseZUW6tfIa1nchtxzsOFxU6ACgvYPFek5rYnDzmh3rD3KL5YCSKX7z7Cdc+PMcWREs9fbZ/aNzAq5InMh5DZCy7RZg+v24pVpYvgMc1U2yIx7X29aZ6jEen8N0fnjC9InOmc7TykKlCSfM5xJ4gxJCwRhAEoQCVCZfHiIm93MefFjfcuppF2HZ/OYLL/YofdqzXXegswAs7g7jQqx4CJUDtwhhYkoQPjkzgkyu3s3aND61dipVlCzTnFxQjVKVjdRnMBTcCi2Cmx3FmhFsr39HTnjS5khkyIbBeU//gmGLxmpkKnxVoaqicHR9yUXAWrklPny0tZhPcjcDjtqu6BL0uG0ZMcv3+rv0aftd+TdO+B96/jHOXBg2+otyiubESrW1XFMXGTCzuzecQe4KQgoQ1giAIFahM+AxiseP6rTHdx5T7+NPihtu+oYJpcsw6uXl84zLYbFYm12JpoHDetgsjiccT2H+8B789fRljSW6wYq8DX9leCyA9SXi2OHCqD998cgH+dk8DWn7/Kfaf6NV1vGs3RxHqDWPFPUWwQFmfsFiA6rIFus6XbVgEs3U1JbocZ3rdWnMBllBKuQUOLeOwIBDLMZPH7JbqcSxQdwTZbVaUlXhSnn2R28FymRnlw66b+Lj7FoqLHLPFVngI+JxY4HPAYgESGUjhGuodVH3vvbArCECiaIPXgeryBfjok1uIxlIrMNoLLIhNm/sDcl1Us1iAtSsX4my3eh+QwldoR1NDFZoaqrD/+KU0gY13cY9C7AnCOEhYIwiCYGA+J/4G9FX+lGP7hnLZDy/eiS5P2APLRNPrsqGpoRIAuRYzhVJFtaGRSVMTQzc3VqUlqy722DExOY0J0eQwmR8f7MDalYtQXxnQLaztP96L/cd74bRbVU0/iQQMybGmN4xHbf9kd+HIaAy+Qgf8Pieqyxao5uz65/0XsG19mS7H2Xxy/8nBktJAboFDi+CodC95qiayyC/CuLD7wUrUlhdjeHwS4du5J5K+f1Fb4R2BTatK8cO3zC1ckMyxczdwX00J03tPvOhYXbYAvznZg9Oh9OqlZotq+cBLT6zGxuBi/OxIFw6e7uMWSp9/vG62r+5uXIGmhirNYziF2BOEsZCwRhAEMQ/QM4E2q9rV2pWLZP/GO9HlCXtgmWi+sCuYcrz54lrMZL6U5HP1D44zV3AzmoDPiS9sWY7q8gUI9YYBCxCs8AMW4OV9ZxX3jYzF8JffP4av7qhlTrCuhtjlIYdel5XeMB61/ZXEeJacSZOxON452cf0W+TuhR631lxC6+IA7zjsv3MvpcaR9q4B08Txt4/1mHLcXOH3H32W8XO+3tqJv/3zB1Xfe8mLjmc6+vHdH57ICUdxruF12/HCzrrZvvblrTX40sMrceSDK+gfGkdpsRsLfE788K3zssfYuakCG4OpfVXroi+F2BOE8ZCwRhAEkSfosexrXZUMXQ4zV4PjRuHSeSqyasnnoWWiOdddi5nMl2KGA1Irm1aVpk0Gj/muY0NdCdP+I+Mx7G05j52bKnDgFJsQZAR6XFZ6w3jU9le7F0ZXZJW7F3rcWnMNLYsDtRXFXInjH1m7FO1dA2l9u9jrQGyKTTDOR3ZuqsD7F/tNG8+yUcE4PDKJ/cd7sLuxium9x7sA53PPFCaY68zkASxHU0NVWl+z2ax4fNOy1H+zWtL6j89tx/M7arExuNiQa9LrOOMJsafiBsR8goQ1giCIPECr6KF1Ap0J4WNYofIYT0VWrR9p88WFpoTw0dveNYDWtitpfzcqX0quuNOSKXTa8PDaeyQFoHAkKnk/lDj60WeSIoTLUYCJSQOrh2JmsqbVZaV3UsWy/8HTmRMY1Rxn8zmUW2pSmyySzOQ7C8sWCQn1hlHkcTALOyMTMckxe2gku1UmzcLvdeC57bVYX1eKNVUL8fKbZ7N9SYbScvQSyko8qn1ES5GLpx+rRsDrwtBoFMMjk9h3pFvPpeYMfp8TX//CKgyPT2r6psjEd4lex5lQ+VeN67fSi49QcQNiLkPCGkEQRI6iV/TQOoE2K/RTjJrjJhMT4rnuQlOCRzzVky8ll9xpyXz5sZV46997FLfhSRYuVyHPaFENmCnUoXWipXdSxbJ/JhKsC7A4zvJFRJdzd2hxfWgJ1RX+DoA5F1oyR7MQsmgEvkI7opPTmGRw1X358ytR7HOmPQe1og35yo8PdiA6NY2A1yXb7rQUuQh4XbPjSzyewMHTfTn1jvB7HXjkvqUoDRTOCEmJmWestjD03GM1qK8K6Dq32d8luou6MI7vUiHaVNyAmMuQsEYQBGEQ0/EELvYM4tbwhO6JmxGih5YJtJaVZy2w5jXKlwlxvsErnia3E55JfqZE2mRYQ4y6em/nlEDESnJhDS3onVTlUgVNpQIoYngmq+I2Xl22AN1XbzO1eSND9j0uG1ZXBtB19TaX60NrqK7wd62YISJngkfvW4pfHWcrPlLsc2JL/ZL0f5+jBTAiYzH86FcXAci3O73Fhlgc6pli+4ZyrKspUey3ZSWevHbAsrbV4ZFJxOOJtPtghIhMxQ2IuQgJawRBEAZwOtSP11s7cev2xOy/abW86xE9BOLxBC70spWdT/4o1rLyLHb1BHxObFpVqphjibfYwHx1lZmBVvG0vWsAoxMx5pDkTIm0Yp7fUYt/2X9R1X1ytvsm0/G2byjHyQs3EBnLjXxA4sIavOitlJlLAsK6Gvk8eFICVzyeSEkWvvX+cths1pT9pAQu8Rgn1+aNDtkfnZjCqVB6RUkl10euherqhcc1qv0c7P1Jrv2z5gXVgtdlk3XEJuP3OhCdimOMYVstIepy7c6IYkOCQ/211s6shA7zCGO5vODHIuyzttV9R7px8HRf2vhlxDuAihsQcxES1giCIHSiNxF4MlrFiGRxjDf0LvkjSYsb5aUnVsPndqR9yK0sW5DXq7pzieSP7eGRSU2Tv9a2K1whyVpEWj0I+Y7iiQRTSBdrmNu6mhI8+1gt/j//8HsMj2ZPXLNYgJee0B8+o7dSJsv+mRBDlK7xdKgfPz3YkeJcdNqtmIzFU6KY3vxdN3ZsrMCXt86EP8qN5eLfktzmhQm2mSH7cki5PrIVqusrtJsiPrsdBRiLmueEC/icWFUZYKos6nPbZducma6rF3YFAaiH5z7zWA1+fKCD6ZgTk9OwF1gQm+ZvDOJ2x1xsKCknnRSCYLX/eI+peTgDPiee3lYt+d3CSi4u+El9+/ncdmxZvTjFhcfTVqXGL6NE5FxyPxOEEZCwRhAEoQO9icDFaBUjBHGM1+0mnpzyrESqiWS5vKo7n8hUjjNxO8/4R7PFgngC+MnBTuZd1CoeCv3DZrPim0/dh//56mkjrlQTLz2xGhuDxuQV1FMpk2X/HRvZKqTu3FSBkxduaHKoyF3jz450SZ47GksXWxMJzG771KPV3ALXv/z6IlwONofNK++E4HbaEFzm5xbC5JByfWRrsvr843VpYqYRmCmqATPtaNVyPxYucKU4zqV4fket4vtLLi8oK067NaWdChUlhXF1RnS6hNa2KynjlvAu9rjsXHnxtIhqQHq7Yys2VIWmhkrV97/VasHuxirJcEu9eFw27HliDYLL/Xn9HSLlSmvvGpC8/5Hx2OyiWLJ7lretJr/fjRKRc8n9TBBGQMIaQRCEDvQmAhejZVIkTP61OB/Ek1OWlUiej9NcXNWdK7CEfGQyx5m4nWv9aG5urMQivxvDI5P49Yle5oliOBLF3ha+37p9Q4WiMyK5fzTcuxTfeupe/PRgR2qon9eByam4qkD39LZq7DvcnbKv12VDAqnuOanQaqNdnnoLg7DsL+VYldruqUer0dk3hA+6BnDso88wrhKeZrHMCHdS13g6dINJ0BNz8HQfVlcGuCfxE5PTzOF0oxNTeHnfWXhdNjywZsmsg0SvECbev39wXNfxeLEAeKl5DTYGS9F9ZYi7mq7m8xrgihRy9FmtFvxZ8+cUhfOdmyqwMbhY9ZjJC0rhSBS3R6OIjE2i9/oIHA4rPE4bzl0axO0k96vPbcfzO2qxvrYU+4/3oLWtD6MTUxidmELL0R689+Fns4LI7sYVaGqokhz7T164ru+GcCBud0YXGxLuY6g3jL//+YeY0igCJjM6MTUrDGUSqXc1AKZCJeL8jpHxGPYdTr3HxV4HYgwubbH7TLjHh9r6VKuyit/vekVk1jy7BJFPkLBGEAShA93VlURoESOEyX+oN8z8gSP3scuyEvnirqDuqleEPlhyOWUjx1lyO9cSLuJ0FKCsxIv1daUI9Ya5qxLy4HPb0dRQyZWIemOwFGtXLGR2CwgIx1pfW6o6weJJlK8HvY5Stf2T/z44MoGR0Rh8hQ74kyZUF3oGEeoN47PBMXRcHkoR1eTC1ASX2cqyBSnPJx5PcLkVxcc89nFmKlqOTEzNOkh8bjs+f3+ZruMlvzPi8QTe+/Ca3kvk4qXmu07KdTUlGRPWEgngma3VKPI6Zqs2fvjJTZw4fwMjjK655Bx9csK5r9CO5x+v43KLJi8onenoR2vblbSxurmxCqUBd0q/OdPRLyn0iwURuQWrTDqApM5ltEvdarVgfHIKTnsBpqaNeRdk2tEpV5QESF1Q8fuc2LyqFO9f7FfM7ygFr+P3lXdCcDtss4ujRV4H035SYqrwvC/0DGL/CbYCIABfnl2CyBdIWCMIgtCB3kTgYnjECPHkn/WDsemB5Wh+aIXsR43RK8+EdqRWr39zspdp8pXpHGdAqltGS7hIdHJ69jdMSoTuGYkQ1sU7GZSa1LL2GbkJsfjfMuXy1OsoVdtf7u9nOvpV80Wphan9+EAHJmPxWaGus2+IWVCR4kwXWzELI4mMx/D2sR7YCiyaHDli10cm+7xUviwjci8FfE6sr2MT6Iq8jpQKnfVVATy9tQah3jD2vnWOKcw7GTnhXE91b7n8qy1HL+GbT67hqsitllbCzAIKySi5jYx0qZvhuM6k+KhUlERMOBKVdNuakQ9xdGIKL795dnYxTs93rNVqmY2Y+F37VdXFMK1FvQgiHyBhjSAIQgd6E4GLYREjhJwrgrPl5IXrKPY4Z1btGaivDKhOFCg/WvYQxLT2rgGcPH8jJWcRy+q1MPnKRq6llqOXUFbimf1o1lrp7fVDXdi+odysy8QOUViXEZNB6jN3UQpTNmqyHBmP4Z/3XwAwM1nbUCdfIZQFllAqs9Aa5iZ2fZjd592OAjTee4/k+0d4xlpyL/kK7Xh6WzUCXtesSMoirMlN9OurAnhxV1BTHkGjhCFeocyItBIs999hszIVd1EiE26jeDyBV94JGXrMTIYfZqsqNg/CYtye5tWav2N5crg2N1aiqaFqXr4TifkBCWsEQRA60JsIXAoW98uZjn5894cn0vJssCZjZyFT+dHi8QRCl8MI9YYBCxCs8Od9cmGtqH2ksqxeC5Mv1lXoZ7ZWY2Q8xhXGoYSUq8Ji4XuW4UgUIxPmVOC8d0UAa1cuQjyeUGxjyeLQwiIXNi8oVD22uM/E4wmEesOKQhtLrjw98BzfiGtRClNeV1NiymQzHIlmLAQxF5BzD5vpxtn9YCV2P1g1G7Iofv8kO1F4cy9FxmIIeF2zfceIBatsO695hTKj0kqsryvFzk3yxUMmp+JM3wlPb6vGjw90pG0nhDGahTAGHT7TZ3gqgEyGH2bDMa6VNw9345ltNYo5SqXuHesiCUU7EPMFEtYIgiB0sr6uFN966l683tqZUlVMz8eEkvtF7mOGxRGUa3ktTodu4F9/fRETSWF/+9ELl6MAf/yFVYZUQcwXjAx7GRqNYlNwMdPk9LENFQCAY+euGzIRSJ4s6vlNVmhrp2quvo8+HcRHnw4qhqRIiUMLf3UBz22vxbrqRUzXwZIHj2UbPfAc34hrUQp9+/4vz6G5scrUyaYRCe1zAQuA5J/h9zrwyH1LURooVBQ8zQoF3LmpAs0PrQCg/oyFUHTh/dXW0Y8jH1xVPUeyYGTUglU2XaS8QplRaSXi8QTev9ivuI3ar3/2sRoA0iGLoxNTKc/ZKOLxRErhBiPJhrCTreq8WhiMROFz27mEaBZHnsdlw57mNWmVkAlirkLCGkEQhAFsDJZi2+ZKvP/hFdwanjDkA17KMcbyMeN12WC3F+R8frSfHemSXVWfmJzG3pZzuLSpAl/eWpPhK8s8RoeNFHuc3JNTLSFccgyNRnX/puByvyaxj1VYEQsBAnLCwa3bE/iHn3/ENKFkER8AMAkUWmEVQHi3lYPlebe28Vfs5GEuiGrAjKgmJObneZdoDcVU4+jH1/HUo9UAwBXeKLy/WIQ1sWBklOMsW5WpeYUyvS49wel1oXdQdcwcmZhCc2MV3vvwmuS9XVdTgm/vPa54DLV8bzyw5FzUwmMbynH/neq7mRZ2MpnLTUDq24+V9q4BPPtYLbMQzeLIG52YgtWS+SqsBJEtSFgjCIIwiAKrBasqA5gyMVcPy8fMyMQU/uqJNbBaLTmb6+l0qF9WVEvmwKk+VC0tSsmHZXboXDbYf/ySYS6T5MkXz+RUSwiXHMUep65QGL/PieAyP5dIoNWtlDxBNCKBOMsxXm/tnLlgHedRgud3AHxiiRysEy2z2b6hHG0dA6aHYam1N7HrjBdxYn5WjOzHAiPjMew/fgm1FX7uPGB6BKN8zlvI+7v1uPR48lwJlBS78bd7GiTvLUuFcal8b1rCztu7BkwJ497TvDrluyHTZKqQRDIv7ArO9pf2rgGuCrknz9/A01trmIVoo0KXCWIuQcIaQRBEHsH6kTI8PqlpUpYJ4vEEfnqwg3n7nx7sxPra0tkwWDND5zKJWROLTatKUyYzPJPTdTUlcDttOPrRZzh54Yam8wuTxVMhbfsDM+45oWKnnDD49LZq+NwODI1GMTwyiX1HujWdK3mCaEQCcZZjhBnCttXOowTP7wCg+zcD7GOTWn4nvduvqynB01truMIQeWh6YDm8brtqe9NrntPjeFlfV4p4IoG9Led1XsVdWtuuYJHfzbStkWGd2XKc6UXL715fV4o9zWvw04MdKUVrlFx6WsPtu68OYXP9YgSX+2ffRadCN1DsceJmZFz9AADaOmZCTmsritHeNaAr7Fwr4vGBx9FoxiJd8jEfWbtUsoK30Yh/c3D5TJ7ap7fWIHQ5jP/9/36oWmU5Mh7jet8YFbpMEHMJEtYIgiDyCNaPlKJCh2rS9GzR2TeUMmlQQ/jgi4zHJJPrGhU6l0mMnFiI+f1Hn2FN1cKUAhAsk1OjrunpbdWwWi2aPqi9bhte2BlMc9KpCYMnL1zXdc2CEGDEKryRK/Raj2WGm0BtW9bnvX1DOVqO9qhulxyW1tk3hMGRCbx5qFtx7BBEXd4wRFYCPieaH1rBLBprdc/xFJmREgcAYN9hbUKzHKMTUxgZZRu3zQrrzGWkngPv7z7T0Y99h7tS2rjXPVMxVeoe6Qm3/137NZwODWDF0iJcujacWn2a8RhHPriKIx9clRW/ecLOtbLnjjs/HIkiMjYJr8cOj8uuWpzGjEU6qWMKxR6S74/XZUNC9G8BnxObVpXi/Yv9quPFjo3lWFtdovp9Z7Va7uY3Y6g8zPM+MKLACEHMNUhYIwiCyCNYPma8bjt+tP9CSjGDXHJ1aRELPugawOEzyq4uI3O+mInREwsxoxNTePnNs1zP3Mhr8rkdAPhDYZKrDopREwb1rooL+xuxCm/kCr3WY5nhJlDblnWi1dRQhbISL/71NxcxFp1O+bvLbsVDa5dinSgvkvDsnbYCLgeQ0eFYwvFZ71uye25oNIrrt8Zw+MwVVQcea5EZOXHgkbX3mCLa+wod8zKsU45k1/HJ8zdSxKnk8Zfld8uNwSPjMextOY/PGsfQ1FCZsp/eypMj4zF89MmttH/ndVuqtWeesHMeAj4ngsv9aO8awM/f+4RZJDMipyTrMYV709xYmVJ8BIBkm/jSwyvxl98/phjCeTo0gD/6PNsY0dk3hGiMLT0Jz/vAqAIjBDGXsGb7AgiCIAh2hI8ZJUbGY2kVQoUPxjMdytXCMoEWseBQ2xXV/FnJoW1KxOMJhHrDOHnhOkK9YcTjmct4bnSRAiVYn7nR1yQIpyxtFZiZHH3zyTVofmiF5o9wQUDRQrIQwHIctVV4lmP4vQ7d51GC53cY8ZsBtuedPNFyOlLXdr1uO/74i/V49rHaFLdlMoIDSHy9QhsSJsNCHz8VuoFH1i5VvCYWxMfnuWeCKLylfgmaH1qB//0XD+E7z67D9g3l8LntiudRQpjIi4WVcCTK5AjUgiBWKMES1rmlfonsM85lkt8dbx+9hG/vPY7vvdGO1rYraU7K5PFX7XezjMEtRy/hr/5/x1LG83zJXyW8m/UKgWKefawG7V0Dsv1A6v0XjyfwyjshxeO+caiL67uA5fn9+4efYVNw8ezzl2sT3Vdvq+ZFY/3WAfhC9HnfN6zjMUHMF8ixRhAEkWfIhZf4fU5MxqYVV49zwdVlZlJftY/IbOdoM3piwYLaMzeycAKQKpzKtVWf244tqxenOZO0oqcaYrIQYMQqPMsxntteC0C6KijreZQwuiIs67XIjk1eBx65byli03G8ffSSZN6hkTuh3laVCZmaA4g1HIuVLatK8Sd/sDrl9+tpJ8KEWsiBpMXBlUmBXiBZKJzrYZ1SaA2Vf721M6U4itTzZn0vDI1M4vu/PIfmxiqUBtw4/+mg1p+TcYwUAbVULwVm3r+Hz/SpjgNqOSXFzzEeTxiSpxIwPoyfPUS/QtP7Zi46UQlCKySsEcQ8YC5WUZzvSH3MxOMJvPzmWcX99CRENwo9IogaSh+RZoR/8JINh4HSMz/T0W+ou8XjsiEeT6Tkt8nUh7dSPiOp3DVyQoDccRYVu2cmc9WLdF1L8jnNFCiMqAir5VrEz7t/cBzvfXiNuZ2xiP/i0GDBSSRXCCQ5HGtqOoH9J3qZf0/j2qWK7jk990xrYv5sCPTJQuF8mUzH4wmELofxbvtVtHUMaDpGeGQS+4/3oKzEI7uoE5vmqySeiYT4RqM1rN3rtuOrO2pnC9UUe5yoLluA7qu38Yvff8IkaO0/fgnvffgZV585d+kmeq4P4+btCZQWu7H1/nJYrRbsP96D1rZUcU4Q7tVgef8bHcbPlD7EZUNTQyXT8aTI1wIjBGE0JKwRRJ6gVRzLtkOHMA/xxwxrAvdcCB8RJqWvHuhgLgevhlK4GovDQ4ubj7dfaplcbN9QDo/Ljvc+vKZ5Mi31zHldL00PLIetwKIokMjld8vUh7fShP+pR6uZn5X4OAuLXNi8thzDt8cwNcU2CWYRH8wWKHiOr+dapPpBcLn/jnDLJwLwiv88TqJ///AzfOnRFczX4rQXILhM/jqyJTBlcgyXEwqzPZlWGnvlCjrwPKczHf145Z2QJpejGLk+ICzqNDdW6T5HLiO8m+PxBLxuO/M7PznvZnI+u70t57gKIGlZPPrNyb6U/37zSDcKCiyYkigCwNpGWN7/RhcFYFnIfGFXcM6J4gSRDUhYI4gMocc1plUcywWHDqEPnnaTb+XP19eVYu3KRfgv3z/G9ZEsh1K4GovDw4gJvVq/5AmDFU9omxoqEbocxt6Wc9yTPalnzuN6EaohWq0WlJV4VYUMljHGLCet3ISfVwhI3t5ms6JAw7WxnNNsgYLn+KzbJj87wZEm7gfPbKvWXJmSVTjiLboxGInikyu3mbfftXmZapvMhsDUPzhm6vHFidbNnnSLxwLBkTQ4MoGR0dhs0QTh35WKBQBgCgXWktzeLA6d6VPfKI/ZtKoU7V0DeP1QF9dCmtAuIuOT2He4O+MuzWQSgKSoxgqrGGZGUYD5UJWXIHIBEtYIIgPocY1pFcfMcugQmYO33eRj+XObzYqv7azTNYmxWICXnlAWiY3OW6K1X7J8NG/fUC6Ze8xqtaC+MoAXdwW57pfcM+dxvUiFgbGIfHJjDDlpcw9WoZPFIRaORLG35bzma2ER/7XmGftd+zWm7Tw6w6O0ovYc4vEE3j3L9ht4MXOiLfe7pNqTxQLJYjVy/w7cHXulkBqjksfqzauXpFxnpvPXjYzrd8XlMgdO9QHgFw/3n+jlCtvOZTatKmX+1jZDCJsv4dsEkU1IWCMIk9HjGtMjjpnh0CEyh5Z2k6/lz4WPyB8f6NDkXHvpidXYGFT+0DTSzadXtF5fV4o9zavxk4OdKav3rB/Nch/dcsg9c9Z70txYJdnWrBaLpiTQ5KTNPViFzkw4eVjFf7PzjL2YhfAolufQ2TeUVvVZDw+uWQKno2A2j5TNZk3bhlV05RHP/D4nNq8qvSO6pCInnqlVhtbCG4e6sHHV4tn/zkb+OmLuc+piP556tJpLXDNaCMt2+DZBzHVIWCMIE9E7Adcjjhnt0CEyh552k6+WfyEs9C+/f4w5VITnNxnp5tMrWp/p6Me+w90pv9PntuPpbezPJz1B/BjePXstZcKtdn9Y7onf65B17WgZY1ja9ivvhOB22BBc7s85ETjfYBFEWIXOTDl5WMV/s95dXrcdL+ysM3WslHou7V0Dis+hubEKX9iyHBd6jKsE6bBZcezc3dycB0/3SYqprKIrj3gWjkQl/z3TDEai6LgcRsNCLwD6JporeFw2bN9QkTOFHgYjUYQuh1FfGWDeh4QwgsgvSFgjCBPROwHXI47lW74t4i56200mLf9G5smy2ax4QSUsVGvuHyPdfHr6pZyIERmPYW/LOVg53Frij+6mhiquZ8FyT57bXmtoTj+Wti1XAIG4C6tgpiaI8Ij4Zjt5eMV/o95dDasXI7DABSSA4HI/gsvMFXSlnkux14GYSlGMlqOXDBcJJkXnFIuprKKr0na5IJ6pkbwgQd9Ec4M9T6xBcLlfV9Efo9nbcg4v7grSO40g5igkrBGEieh1jekRx/Ix3xYxgxFuw0ysdJqRJ8tMx51Rx9baL1lEjNdbOzXnPdTyzPXcEy1jDI8bRC401KyiB/kCS79TE0T2NK+Bz23Hhd5BJhE/dDmM4THjwg/FeN12PL2tmqt/8xQCUeJM5018/z8/nJE2JPdcjAztNII3DnVh7cpFTKIry3a5TrHXMfv/jWpXRuN2FGB8clp1u4DPiae3VcPjtCPUF54VjEcnYmkFAJx2K6IxtirH+YSv0I6hsZnFx2e21WBvS+YKUSgxOjFF6Q4IYg5DwhpBmIhe15gecSxf820R+eE2NDNPlpmOOyOOrbVfsjh+wiOT+NffXMQff2FVxvqm1nuiZYzR0maTw57nctGDZMGwyO0ALMDw2GRariq1freupkRV6PjBW+e48lXtbTmH7RvKeX8SAGDHpgqcutiv2PZHxmPY23Ie1ictXI5NuTBDHqKxabx97BKCy/ymirXZSIqvlcFIFEc+uMIkurJsl8sEfE7ULbu7IMEyrvHgdhTgP3xxFYD0aqUseJwF2PPk5xBc5kd71wBeeSekmNvy6W3VWF9bis6+ISxd5Elpz8K/i0OQtVxXLhMZi+FHv7oIYOb9sHNTBd5XGYMyCRUOI4i5CQlrBGEiel1jesWxfM23Nd/JdbdhJirOmum403tsrf2S1a117Nx1nO2+mdGQEa33hHeM0eIGEcKeRydic7bogVqlTb/PiWe2VWPf4W7F47xxqAtup031/vImgR+dmELL0R54XDbVghViTl/sx9984wF0XhnSXEVWing8gfcv9ituYwHA8lPfPtaDt4/1zP63GWJtviXF7x8aN3S7XOXhtUvx/oUbqLhnAkv9LgAz49rOTRWGhLHGEwlEp6YR8LrwN994AN1Xb+NCzyBztcvpBPBh901YLRasXbkIDnuBYh/6ycFOvHGoK8UFmdyexeP8+rpSuB02vPzmWU2/L9cRwpH3NK+Gz+24k5N0HK1tfWn3kXW80AsVDiOIuQkJawRhIka4xvSKY1RiO//IdbchVZzV1i953Fr5FDLCM8ZodYOEI1H8/L1PFLfJh6IHPEnrkwlHotjbcl71+IORKEK9YaMuNw0td3UwEkX31duaq8jKwTIOaZ0kmyHW5ltS/NJit6HbmY0w9gLpzjCvy4YEkNL+vG47EolESt665GMYlRsuGounuKeee6wGzQ+twLFz15mE1onJabS2XUFr2xV43XbV4j5Sf1drz8PjbKHITQ3LgQSYRcFc4s3D3fjenobZd0NTQyVCl8Mz46UFCFb4UVtRjO6rtxGORPFaawfGouqht1rJt/GAIAh1SFgjCJMxwjWmVxyjykL5Ry67Dani7Ay8/VKLWytfQkZ4xhi5tq1EZGwy74seyIWxTsYMnryZ2FRGJqbQ3FjFnRCcZyzIpfHFyP6XrdB9n9uOCGOlZYGAz4mt95fj4Ok+Vec0y3ZK6AnT87nt2LJ6MdbVlKSMvVLjcjyewJEPrqB/aBzRyemUaqgCg3cEKI/LnOlRssClZYGBtWK2HHLtmbVt1i8PoLaimFkUzCXEor3VakF9ZSCtSmdwuR+h3rCpohpARTIIYi5CwhpBZAAjXGMkjs0/ctVtmA854DIFT7/U4taaq84/oW2HLodVwwMDPie8HjvzsTMVGspTREEpN5rRBCv8OOYzb+JbGnDjb/c0oLNviDmkjWcskNtWfL+L3A7J7YzEyP6XjaT4M4ns+ZO3P/tYDWw2K5NzmmU7KfEseZHoqUerZ59te+cATocG0o6xMViCP21aje6rt2fyEBY6gIS020o8LquFWovhDXfm5Y1DXfjengbuBQa9yLVnnvQTRueg04vXbccDqxejte2K6rbtXQNMfVmvaO9yWDExKV8UggqHEcTchIQ1gsgQJIwRWsjFdpPrOeBymfV1pWhurETL0R7mfeaq809wDLy4K6g6efe42IU1ATPdfjxFFDKZtD7gcyK43G/qxLfY45wdl1jcK8ljgdZxQ+5+a8n5xotR/S8bgoQgXFklBByp8EixI5rVOc2yXbJ4Jhaik99zW+qX4E+n4rPustJiN7beXw6bzQpgxlF0pqMf//Lri0z9T07UziaCwLW+rhTxRIIpzNsopNozb/oJueftdhSgvtKPM503Va+D1UnpcdmwZGEhPr02nJIb0oIZsfXPdq9BZ98Qk7B28sINPL1VPY2G3kXBB++9B4fbrsr+nQqHEcTchIQ1giAIgotczwGX6zQ1VOG9Dz9jdinMdecfy6Q8Hk9oLnpgtDDNWxE3k0nrhX4nd08tFuXCBWp/FwtfvGOBlnEjk24/KYzsf8JzUavsqBcp4UvK/QxA1XXJ6pxW205qkUjO9WmzWfH4pmWSv42n/+VyJdah0Sji8YRqQRKjkWvPvOkn5J53PJ7AX37/mGLYKouT0mkvQDQ2jdGJKXxydTjt7wkAp0ID2LhqAOtqSpjyz0XGYkzvBL3u0vU1pQhW+HMylQdBEOZBwhpBEATBTS7ngMt1eJwrc9X5J55Qr6spUZ2Ua3H78LqN1MI7tVTENdJxKExI9x1W73dSE9/I+KSiO2bHRuVKiFLCF89YwLqt8BzCkSjeOKx8vy0A7DYLJqfUSxWIHW68QqIRrKspweutXRgFu7DmslsxEZMPLRPYvqE8Ld+YgJz7mUV4ZnVO8ziseVyfArz9L5crsfYPjmX8+nxuO6rLFgCQHut400/IhdyqCVxqTsqRiSlEGXNPvn6oC26HDZVLfDh3aVB1e5bxWI+71Fdon71nuZjKgyAI8yBhjSAIgtBEruaAywdYnStz0fmnZUINCGG0VSlV/NTgcRspXdfm1UsAAB2Xw9wVcY10PAkT0vW17FVYxUKH9UmLorC1smwBt2DOOhbE4wl4XHY89chKRMYm4fXYUVzoBCzA8NgkQr1hRMZjacKhEgmASVQDgD1PrIHVamEWGs3of/uPX0J4hF1MCfic+OtvPDCbW6x/cDyteES2FzTkBGm5f1dznTU3VqGpoTLt3vNWpM7lMPqWoz3YvsHcMGYxkfEYvvvDE9i0qhTHP76eEorpcdmwfUMFmhoqNbl8WUJu1ZyURW4H/uU3FwEON2c4EsXLb55l3p51PNZSZAcAnn+8TtGlSRDE3IWENYIgCEIz9OGoHWFSsf94D1rb+hRzHZkFTwJ+I+ANoxRTGnAznyvZbaT2O9Wuy1pgxeMPVGFoJD1RuhTJE3qWsCKv2w5bgUX2+OL2oKffqYlgWgVztWuSEi6F6otGhEWyuM+Cy/3pTi4VodFIznT0c+VXBO4WCEi+t00NlVlf0BD6VHvXAE6cv5HiUvL7nNi8qjStYIHPbcfm+lK8f6Ff8dgtRy/h3bNX8ZXttSnPgLdibK6H0Z88fyPj5wxHojgo4UodnZhCy9FLaG3rw4u7glxtn8VJ6Cu046+/8cBsrjyB5HEj1Ku+cKEHXgeqeCzsHxxT7L87N1VgY5Dc+gQxXyFhjSAIgiCyhNVqwe477oxMT5S1Ose0oiWMUgzPRFlwG6n9Tpbreu23Hdi2uRLFXrZKlMnXyRJW9MLOujTnhuDiMqM9qIlgvMJdPJ5AqDeMUF8YSMyEFwaX3RWx5IRLI/OMKYlqgLz7LFPOW958X75CO762o06yLxqxoKFHVFershmORCVDiiPjMRw6I5/UPZmhkck0sZ23IrWRlVifenQF3jl52dA2GxmPMSfxzxSjE1PcVZVZnISRsRi6r95WbLdmOwy1OFDFfa2sxJvW9n1uO57fUYuNwcWxR8SHAACylklEQVSGXStBEPkHCWsEQRAEkWUy7fzT6xzTAm8YlxSsE+U9zTPXz/I7PS67+nUNR3Hh01uoW+bXVNmSNbdYPro/T4du4F9/E8LE5N2cSPtP9MLjsuHFXcGZnGIZSiC/fUM52joGuN1nmeh/PPm0fG47/u7PH0xz9xgFr6ieLML1D45zhWPrJVls561IbVQl1oDPicrFRcyimtNuxZqqAFN1zC2rFzNVtMw0r3NUVeZ1EsphlsPQSAcqpcAgCEIOEtYIgiAIIo/hdZ4Y4RzTghGTL5aJssdlg9XC/jv/8JGVTNc1ODyB8oVuzRVx83VCNjUVx5EPrqB/aBylxW5svb98VvD52ZEu2WIHgvOlubEyYwna19WU4OmtNTl5j3ncOA9+bompohqPqK7mTjObZLFdS0VqrbmyxMccHmcLA//iA8vw5EMr0dk3xCSsCcUmpER3qUIlmSIciWL/8UvY3bhCdVteJ6EcRjoMAWDr/WXYUFeacccvQRDzExLWCIIgCCJP0RLOaYRzTAtGTb7UCj/wCDqDkSgiY2wT5kCRK+X8WvJy5duE7GdHunDwdF9KmOWbv+vGjo0VqFq6QLGCqECm3Dh+310Rzah7bGQOQh43zqmL/Xjq0WpDxYB4PIHQ5TBeeSekuF2yqM6SkD4TJIuSWvqfWNQeHpnEviPdqudNDscN9YaZrnV15UJud51SBUmrBVl7Bi1He1BW4lV1elWXLVANaWXJb2aUw1BgQ11pXo23BEHkNySsEQRBEEQeojWc06iwHV5YJpo+tx3VZQtUjyWEFyqFZrEKOl6PXX0CXORE/YqFGL49BiB/3WdyCKJLqDcMWIBghR8fX7olmeQ8kQAOnOqDw8aWK8vInFRKTMam0d41YFgIs9E5CFnEBwGjhW0e15lwbsFFlQuIRUkt/S9ZcI3HEzh4uk91LEoOxzUjDDXZXScnCLNWkDYLNfey0LbU2jVrfjM54dTvdeCR+5aiNFCIokIHfrT/gmIxGd5CBQRBEHohYY0gCIIgchQ5x4yecE6jnGO8sEw0I+MxfPeHJ1TFCxbXHeskNOB1qV7XVx6vQ4G4mmQOu8/E7aa6bAG6r96WFCHOdPSnTdr3o1f1HJNTcebrsQBQqS3AxM5NFfj9R58pOhWNyA9odA7CMx39eK21kytJvVHCthbX2dBolCsnnJnICSR6+h/LWPS1nXUp4bhGhqHy5vxaX1cKt9OGl/edZdreSJREXta2de/KhfC47IjHE8zimppw+pXttZpC8gmCIMyChDWCIAiCyEGUHDNMCfdlJkS8zgsjYcl3xCJesIoOHpdNUWBLDsVSmgBvDBpfJdUspNqNxZJaNVNoR0Bmwsz0impet322curJCzcUt9WTH1CobqoWLvnjAx2YjMVTwk/l0BpOOTwyiZMXrutyQ/JWIhUo9jhNr9Ao4LJbMRGTF2nNEki0hpTqDUPV+jyDDIVTzEKqLfC0rY8+uYWPPrnF5fhUE06NEi0JgiCMgoQ1giAIgsgx1Bwz2zeUMx1HakKkxXlhJOvrSrF25SL8l+8fU3Tw/PhgB6JT0wh4XWkTUVY33fYNFYoVDJN/Z66Ed+rJ6yXXbhIiZUtoRx5XZj8DxQKf121HIpFQdReO3GknnX1DiuFfgPYwSp5wych4DP+8/wKA1PBQKafgj+5sx0tyDjCtIahaXGeC2NzZN8S1n/gYm1aV4v2L/arn/3pTPQBkRSAR+vwn124jlrDAbklg5dIFiv1NbxiqAG8/Nzr/GA9S462WtqXF8al0n3JlzCYIggBIWCMIgiCIrCE1aQCg6gQ4eV7ZtSMgJ0Ble7W/++pt1bC4yFgMP/rVRQDpwgKr666poRJlJR7m35nt8E49eb20uJMynbMpkQCe2VqNIq8jpb2HLoext+Wc4vXwVHC90DPINdHWk6RfEAt2bqpIE5I8LhuiCm4s3nPwhqBqcZ0JYjNvhUZfoR1b6hfPVrm0Wi146tFqdPYNob1rACfO35gVSIH0PpgtgcRqtWBVZQB+vwfh8CimGEKc9Y4TWvu5ERVOebFYIDlW63E0srpKWe5TtsdsgiAIARLWCIIgCCILyE0aHll7j+qkKTIe012FLZur/XqdDjyuu3xxNejN65UrObHUKPI6sKV+Scq/WS0WVZGPp4Lr/hN3c8RJCRbJgnZRoQOvtXZy/AJppCqkGi1c8oa58uRJFAtdLH2subESpYFC2T4liB7B5X48vbVGsQ/OF4FErZ/vaV6jGHoujGeH2vqYKpvqJZEA9racg1U0/ujJwcniKjU6zyFBEITZkLBGEARBEBlGadLQcrSH6RhbVi9WrHzJEs6ZjcnsmY5+vHFYW7XBZGGBx3WX65N2FrfZK++EFEWVTOXEEti5qQIryxZwu2ekJuTtXQNM+7JUcBUjnojzhHzmGrxhriyuM4/Lhj1PrEFwuT+tbRnpbM31PpgJWPr5D1rOIf7EamxetVh2G6vVgiKvw+jLU0Qs6vI6GsUojVd6ivMQBEFkCxLWCIIgCCKDaE0oLkYIudIaOpgN9ITcAenCQr640ZSIxxM41NbHVOX0n94+h5eaPze7X/LvLirM3ER7x6YKfHnrTPED4f6HI1G8cbgrJdxPjJSL8kxHv6JAnLI/QwVXOd441IX4HfdNPsMjoLK4zl7cFUR9VUD273OhjwH6chcaBYurNAHgh2+dR+9nw7N9TIobt0YNvjplxGOv3pxvSo43lvukNZciQRCEWZCwRhAEQRAZxIiQPUGgaO8aQEKUmV7832bDOmE1SlAUCwv57IThdU+dCg1gfagfVkt6svdir0O1CqoYr8uGEQ3hiqcu9qO6bMFsWK5w/x12K1dRDJ42oVbBVY3BSBQ/PdjBvH2uwhuCZ4TrLBf7GI9Qpid3odo1hHrDuHh5ELduR7FwgQurlvsRXJbu/gP4RNEDp/pQtbQIG4PpzrWfHemSDD02G/H1a835ppamgPU+ZdqlSxAEoQQJawRBEAQhwkx3gxGTgWcfq0F714CkiDE0MpmxHDQ8E1ajcoDpye1jBlrbilb33iu/uYjxyem0f1erlinFC7uCM8d8J8QlyMnlORIm2lLHk6pAytMmBFEuHk/A47LjqUdWIjI2Ca/Hjs9ujuLXJy6rHkOtYEau4yu0KwoScswV15kAz7jDkqtr8+olaX+XKyzT2TeEwZEJXLwURltHf1pxil+f6IXHZcOLu4Jp18I7dv30YCfW15amPKfToX5DRLUvPLAMxz++zjVuSF1/ctsK38mDePP2OA6duSp7HLU0Baz3KdfeBQRBzG9IWCMIgiCIJMxyNwiwTgaaG6vw3ofXJF0m62pK8O29xxX3NzsHDW9yaSMERTWnQ6bR2lb0uPekRLVk7DYrYgyVDTcFS1IqMl7sGcT3f3kOEzHl4ycj18akRLrRiSl8/5fnUhLeD45MMJ1n+4Zy2fxoHpcN0/HMujSzxfOP12nuz7noOpNCTajmGXdY+tnrh7rgddsxbQmjIBHHdDyBD7tv4uT5GylCrCAMswjQQlsXj4G8ecki47GUcMd4PGGY63JN5UJULSliFveVxl6ptlW3zK/ZJcla9TmX3gUEQRAkrBEEQRDEHXgnbVocIKyThqaGSjQ1VEqeI9QbzmoOGi3JpY1wF7AUZMgUp0P9kvm6WKrWmVnBMzYVh8tRgAkVAa7rym3E4wlYrRZYrRasXrEQX29axeWiE7cxlnaRXJzD57YznWddTYls32R12qlV0c11dm6qUKwWORdQE6p5x53QZfVxMhyJ4m9eb1e9Ni0VXl8XjYFa8pIlL0h09g0Z0oa1hFXzjr16XJI8VZ8JgiByBRLWCIIgiJwlkwmneSZt7V0Dml1tvJMGKWEs2zlotCSXZhUUn95Wg32H9VchNJPToRv4wVvnFbdRcgyanRtITVQDgPDIJA619aHI65jtW+vrSrFzUwVXqJl44s8jGLKIBAGfE9VlC/DdH55gPq4Uz++oxb7D3XlXDdTntuP5HbWSubbmEiyLGh6XnWncCV0OYzw6hR/tv2DW5TIRlljcWF9Xij3Na/CDlnNg8VkmL0gYNW4kv1+SBbD2rgGcOH8jpQCJnrFXj0vSyIq0BEEQmYCENYIgCCInMTskUwyrWLT/eA9ajl5K+xuLU0lA76Qh2zlotAh7rILi+rpSrK/N3XxQZzr6sbdFWVQDlB2DuZIbaN+R7tn/7/c58cy2arx/sZ/rGEXuu9VIzRAMn32sBt1Xb2sWxHxuO762s26m0ILFoqsqLQ9uZwHGo+xhtcBMGO/Da+9ByQI3vB47Al5XTrV9oxAvmFSXLWBa1PjSwyuYjv8P//ZRWu6zbCHVJzYGSxF/YjV+qCLOJ4c7xuMJnP90UNe1JPeFZAQBLLjcj6e31uTM2JtLuQFzoaosQRC5DQlrBEEQRM7Bm7+LF6mPZFZRoLVN2c3DmttMz6Qh2zlotAp7rIJiruaD4s2NJtemeHMtZYJwJMokGKaR1Fz1CIZet13WKXPywnXNx31m2912tb6uFM2NlSnhqGbgcdnwtZ1ByVBhue23byhHU0PVnJ+sSy2YiJ+9FIORKF5r7WQ6R66IaoB8n9i8ajF6PxtWdIcKzrIzHf14rbVTU4GSZJL7ghy5NvZm63qSvxH6B8fT8p2auchHEER+QsIaQRAEkVNoyd/Fg9TEzue2o3ZZMdP+arl2eHKbaZ00ZDsHjR5hzwgXghb3gBGOgw6GnE3J9A+OSf67llxLHrcNo+P8eZ7MZnjs7mRfj2D47LYa+H1OyeejR7Dz+1L3bWqownsffmaqqLl9w0w+NKuEiOxz27G5vhSLFrjhK3TA75s/7he5BRM1UU1ArXBHruGXGQOFsWjZEh92P1iJw2eupLxXkkVlrdWD5a6HUEfqG0GMsMjX3FiF0oCbXGwEQZCwRhAEQeQWWvJ3qSFMZNq7BtDadiXt75HxGM50DKgex+OyMSWxNjuHFpDdHDR6hT0eQVEsiEXGY2k52NTcA0aFFfM6Rt47e03WhST3/KRYubQIn1wb5jp3pkgWvbQIhgJ+n1O2TWgV7KTEXavVgs2rSrnyyPHgddnQ1FAJILdC2bJNPJ7AK++Esn0ZGeW5pDEwHk8gdDmM331wFed7BlPyIPp9TmzfUJEm0OipHiyGqmiywStkJqeFIBcbQcxvSFgjCIIgcgqjE/OzrD6zsn1DOVMYWaZyaGVz4p4JYe90qB8/PdihmuReKURYT/VOMcVeh/pGyecYmVQUgNfXleJzVQvx0v96T/E4uSqqSU3WeQRDpeMko1WwkxJ34/EEdx65AqsFbqeNyVn1wq5gyjlzLbTOLOQcocK/H/mgT1NlzXzE67bjhaRcZmc6+vHKOyHZ3x+ORNFy9BK++eSalLZiZPVgqqKpjl4h06hUFQRB5CckrBEEQRA5hZGJ+Y0KoxHEonU1JaphZJl2BmR64i6eQP/NNx5A99XbGByZwMhoDL5CBzwuO+LxhOxEjiUs82dHurhdRa8e6EgJEdZbvVNM3TI/t3NKSQCOxxN483fGOFKygdxkXSz49g+OSxb8UDuO+JhSgp3XZUMCkA2lE6NFrPiDhko0NVQm/Z4xvHv2WoqDMZ+rFbL0R6Vt5Byhm1eV4v2L/TmVS1ArXrcdiURCUhxz2ixYfk8RasqLsWq5H8Fl/pR7w/oOEo9FRjifedul4KwL9YYBCxCsmClqkG+iXDyeQKg3jFBfGEjMVNcOLpt5T3b2DaW8r/x3Kg8f+eCKIW1VT6oKgiDyFxLWCIIgiJzCqMT8U1Nx/PhAh65raXpgOeorAymTyGzmNss2PBNoqbCYeDyB/cd70NqW6l4Rb3s6dENTqN7IeAxvH7uE5odW3HGq6aveKUaLc0pOADbSSWkkAZ8TT2+rSQu3FW+jNlkXC75lJR7d7kY5hyZwVzCLjE3C67HLiru8YoUQ2in+PU0NVXMixJMlTFppGwCyhWbMCrc1Cq/Lhlg8gahC7rZ7Vwawc9PylHYmFmXknj2vA0o8FulxPntcNuxpXpMi8qkh5azbj17YC4AvPlA12w+UUBNpWUVcsShWW16M7qu3ZR2RyRVmf3OyF++835tSxGL/iV447VbYCqyS4qjFAiQSTLdJFd5UFQRBzA1IWCMIgiByCiMS85/p6MerBzqYk2LLsbTEk/ZxnM3cZtlEqVKr1ARaHBajFA6VvO26mhL8668var7Ot4/1IDo5hd9K5NKTg0ds4Ql1lBOAjUxIbjRCG15fe1fAKip0AAlgeHxSs4hkVNiynENzdCKGn7/3iaq4yytWiEM71a4jn1CrvtzcWIXRiZhkXkphG48rP6cSzY2VaGqoAgD8+mQvWk/3pbwvfG47nt9Ri43BxSn78TxzLe7I5LFITzGQF3cFUV8ZYN5eaUyKTc/kEjtw6jL++AursDE4059481+yirhp4t6JXlgAJOteWhyR0VhctmKsUaKaQCbyrBIEkVvk59uQIAiCyChGVFTkQY94ZaRoITcJn29JyfXknnnjUBfiiQSTe+zVAx3o6x/BhMzkh5WDp9lFNYBfbBGe//7jlxRz7snl+DIqIbmRiPuWGcKRWWKUmkCUnPOotqKYuQjJ9g3lc1YoZykmoBS+K5CvedP+/cPPZguLPPnwCrzwB2vw/odXcGt4wrDxXIu4UlR4N48jyyKTy25NGS99bju2rF6sGo6fDOuYNDE5jb0t5/DppgpUly1gWlwQ+uDOTRWqCzCAtPsRSBXVhP1y2RGZqTyr+UCmvx8JIluQsEYQBEEoYlRFRV60iFeZrKI2FxwrrOhJoj0YieInBzuZth0Zj+HgqcuazqMVrTnxrFYLdjeuQFmJl1kAjscTONTWl9HwT+FaAKRdp6/Qji31i7GupiSnJztKEzOWPp+c88hqtWD7hgom0WhdTYkh158rJN/HoUg0b0UxIxCH6xVYLVhVGcDUlD5RPxkt4sqP9l/AV7bXzo4daotMwjuyvWsAJ8/fQGR8xmHY2nZF8T2d3BaGRya5xqSDp/pwEHyi1sHTytu/3tqZJp7lK/lQgVVvXkVWsvX9SBDZgIQ1giAIQhYeJ4gZ8IpXVEXNHPSGtfCE5MqF6piF3ufMKgBnMqea4FoRC2b56LJUm5ix9HmxiNLUUJmW509MPkyOWZnJbXgJrW1X5pSY5rBbMSkaL7wuGx5Ys0QyfFWM2eF6WkI5h0Ym096tamOMWriu+D2djfyOaqGW4aRCIPmO1ndKJsQu1jynRghi2f5+JIhMQ8IaQRAEIQmLE0RchTHb8E6U7l0ZwKfXIinCz1zPlaaFuRjWYrEALz1hzIe9mgCcqZxq2zeUK7rP8s1lyTIxi02zCbFDo9GUSen2DeXcYbz5yOlQP/71NxcxoZCgP1/xOG341pOr0NE3lFLBsrNviElYM3tc01LsREBcWVKu7/I6NnM5v2O+4XQUpBS+YPl2EMYgodBKYaENY2NTuHl7Au9fmHEcChgtdrHmOQXkC5KwCmK87ZIg5gIkrBEEQRCSsDhBRsZj2H/8EnY3rsjQVSnDOlHyFdrxtR11WF9XSvk/GODJSyXGV2hHZExfEQkzeOmJ1bNJuI1Eqkqd2TnV8lUMNiLE8z98cRXTufoHx/HtvcdTxjQh8X5yu87XeykmHk/gn94+j1OhflPP43IUZE20C49MoqDAii89sjLl342qLG0EQiinnKAhB2tlSR7HZm1FcU7md8w3XI4C/PEXglhfW8r07SCMc8nhuiwYKXaxCqqvt3bOrDopwCKIaXESE0S+Q8IaQRAEITnBZXV/tbZdmU0CnW1YJlQ+tx1/9+cPwmazAsg/F49ZKIkc7V0DmkPInn+8Dm8c6sQQQ6iPw2bB5JR6ph2n3ao5ZFRwqpkhqkk5CnxuO/NEiofdD1ZiycLCvBWDjQrxRAKqfd7rsknmVBPadHNjFUoD7ry8l1L9tr1rAP/6m4sYi5oveE1MTksKlFqFeF6k3lNGVJY2EiGUM3Q5jFBvGFdvjqK966bqfizvYNb39NBo1NBUCbxYLMrhoH6vAwmA6T2RLTwuG7ZvKE/53lH7djAi7Fav2MWTe5YlJJdFEONplwQxVyBhjSAIYp4jN8F9ZO09TPuPTkzlzKojy4TqazvrZkU1YgYlkWNdTYnqR7nLUQCXoyBlUpTs/Pns5ohi2J3AxmApjp27obrdt/7wXoxHpyQTem9aVapYLc4Ip5qcmCHV7swQ1QDg9x99huceq8l4vzMqobVRIZ7D45OqfV5Nqv33D6/he3sa8kZQm5qK48gHV3Du0iA+uXYb40kCmh7RWStSAlqm7qRS5WatlaXNwGq1oL4ygPrKAEK9YSZhjcWBzerSLvY4sypi7NgoXRVU4LnttQDkq4IagctRgAKrRbq9ioQ/n9uOzfWlWLTADV+hA34f/1hnVNitXrHLDEFVrS3xtEuCmCuQsEYQBDGPUZrgthztgdNegGhM3fWQS6uOuTahynXURI7mxkrVj/KJyWn8xy99DlaLRVJwKQ0UMl3LqqoALvQqTwICPieCy/ywWi2yCb1X3gm/NOP5S4mQhc4CxDNc0i4bCaB5c/xIiXAADA3xLPY4EVzul+3zD6+9R1XUzaWQJDXh8mdHunDwdJ+s+yfTopocIxlwq/kK7RgcmUCoNywpemipLJ0JjAxV5TlWZ9+QhqvVR/K4yzIuy4XNWpAqkAuLKO9f7E85nlggc9mtWLMigEfXlSO4bKZ/d/YNYXBkAiOjsVnRrLpsAbqv3jasnRhZoZwVue8wM77P1ASxXArHJohMQcIaQRDEPIXlw08l+mCWXFt1zNUJVa7B0gZYkoADwPDYJLbUL5H8G2v7CHhdXCFccmG8Rjz/ZIFjYZELmxcU4nRIWoTMRMidHJlKAM1b4U3JCWtUiGfyxEzumZ8KqTsggdxYHFATLn92pEvR9ZNvNDdWYngshiMfXNW0f2Qshh/96iIAeYE3F0P9jQxV5TkWq9jxtR11+Puff6R6bql9n95WDZ/bITnusozLs2GzvWGE+sJAYibcsra8WFL4eurR6rSclmoCmVx7MLKdZCPsVu49y/N95vc6AItFtyCWa+HYBJEJSFgjCIKYp7B8+E1MTqsmp87VVcdcnFDlGixtgDVPktLHO8/qtdVqMcRxKDx/QSA7FbrBLLBJCRyBt89j0iQ30PYN5Vi7chF+8Pb5lAq1LGTCbWVU5UHBCcsCS4ineGIm1edzOSQpWbztHxyXzAMnCJffeKIeB09nT1QrdBYYJiDP5DlcjY3BxQj1hjULa8lkw8GpByOd1azHYhU71qxYqDpeC/DmJ2R5L1utFtRXBVBfFUj5d6n9pI6XC+99I4V6vWIXy/tXgCUkl1UQo+gBYr5BwhpBEMQ8hfXD76F771F0LdGqY/7C2gbUEpGriau8q9fr60qxduUiHPngCvqHxlFa7MbW+8u5c+Pxhi4K+0hd5+Cw/omS121PEc7EE4yv7qjD3hb+nDwXegZNdWRmo/KgWogn68TMrJAkvbnmeJOa//idDsXk72bgslvx1R1B+H1OxOMJvPzmWUOOm0gAPrcDABAZV88f5fc68PWmetwemcQbh7sUxedMOTiNwEhnNeuxWMUOtfHa67bjhZ11JI7IYKRQr1fsYnn/ip+nkaIvRQ8Q8wUS1giCIOYprB9+62pKZifLtOo4t2BtA9s3VEi6aQRYxFWe1Wsp0eHg6T5FQUyMeu64GadFkdsBWGZCWYsKHXittZPp+Lz4fU78zTceUAxT2hgsxaVNykm+pdh/ohfHzl3nuj88ZLryIEuIJ+vEjFXUBYBQb5jpHFoEW/H+vEnNxxVcw2bx9ab62Xt/tnvA0GMPjUYRjyew73C36rbPPFY7m/RfzdGZS/nyWDDSWc16LNaQTKnxWqoyJpEOj0tMDqn8c1q/w3ifp5GCGEUPEPMFEtYIgiDmKbzhebTqOPdgbQNNDZUoK/HoFldZPtZ5c3lpTZCvJBSawSNrl8Jms6pOML68tQZVS4vwk4OdXGGhZobCsQqw/YNjhpyPJcSTBzVRFwC+vfc4k1DG2z7FZCOpOS9K98Uoij1OZhHW57YD4BN4CWVY+hS5jbTDIuhL4XPbsWX14tkFTSPFLt79SRAjCD5IWCMIgpin8Ibn0UfW3IOnDRg1yVJqR1pyeWlNkJ9pSgNu5m03BhdjfW0pOvuG8EHXAA4xFpAAzAmFY3VftBztQXNjJdMxmxur8N6H10xxwUqJrXLtt71rgFko422fUuhx9IkrIxrN1vvLsKGuVPG+GIGwYMNbWCKX8+XNVei9rx05Qd/ntmPz6sVYVORCYaENY2NTs9VJ1d6pep8HPU+CMA8S1giCIOYxlFyW4GkDLB/lenJP8eTyGp2I6U6Qn0l4J/tWqwWjEzGc6eALwTMjFI7HffHe2WvMLsimhkrutqLWvtTCNJPvC69QxtM+5e6/VjdVwOfExlWlOGhiVdANd+6P2a46QaznFcrMypdHEGZBrj+CmD+QsEYQBDHPoQ8/QmgDocthhHrDgAUIVvi5xRm9uadYRYdwJIqfv/cJ17VlEy2TfS15uASMCoUTi1i7H6zE28d6FPcJj0yiubFKMdT24bX3AOB3T6i1L94wTV6hrL2LTeRUuv9a3VSCyG3BTL5BowsZJLdRra66LasW496ahSj2OBEZj2HfYWWxnlco43VZE0QuQC4xgpgfkLBGEARB0IffHIfFRdbeNZAiWuxHryEJ2Xlyf7GKDpGxyZwI9XTaCxCNqSeV37SqlGuyr9cxZEQonJSI5XGxfTaWBtySLkiBlqM9eO/DzwwtRrGnebVqInxxmCZPzq54PIET59lCF5Xuv5ak5js3Vczepy9vrcGXHl45WzF30QIX9h/vwVhUX3GDZEFKqzC7bIkXW+qXzP73+lrlBRstQhm5rAmCIIhchIQ1giAIgpjDsLjIMpGQnSX3F6uDxeuxK54rE/gK7fi7P39wtsrn2c6bOBXql9z2wKk+rCxbIHsPxcJnPJHQLBwaEQon1x5GJ6aY9i/2OBFc7se6mhLsP94j6V7jEVxZ2tdPD3YiwlkxkicUsbNviKmYhK/Qrnj/rVYLNq8q5ar8eupiP556tHq279hsVjy+aRmAmUqmekQ1KUFKqzBb5HWk/DdrgnxeoYxc1gRBEESuQcIaQRAEQeQxSm40FsFsXU1JRhKys+T+YnWweFxswppUgnyj2FK/eLbKZzyewP/7O+XQVLl7qMcZJoXg8JGrlqomRuh1y3ldthRh6b0PryluzyK4srQvNVFNINmNxSrkVpctwNvH2arIbqlfrPhbznT0c4lqgHLf0eIua26sRGmgULYNaHHVAUDA6+K+FkCbUEYua4IgCCKXIGGNIAiCIPIUJTcaq2DmdtoylpCdZTsWB0s8ntCUIL9/cMywwgbrakpm/79WYVGvMywZj8uG7RvKsa6mRFGsSz623+fEM9tq4HPbDXHLAcDIxBTauwawvq7UMMHVqJxxQKobi0XI3bSqFN/94Qnme5LcLsToES3l7gGPu4w1tJunWIWAXqckCWUEQRBEPkPCGkEQBEHkICzVD5XcaM2NlUyiRqg3zHQ9RiRkZ91OzcHCm5specJeVuLFa62dGBqZZLoWKcQighZhUa8zzO91oPHee9B6ug8TsThGJ6bQcrQHrW1XJIU5qX8LR6LY25J6D/W45QQEF5pRgitru/G57YrONb/PiXg8gZMXrs+2KSUhdxNnyKbXrRwGqrUoACB/D1jdZbsfrMTuB6uYwyXl7oscVDSAIAiCmM+QsEYQBEEQOYZaXjQWUaa17QrbyRjnwnoTsvM6WtQcLFqTmK+vK4XLXoC/+9mHzNciRiwiaBEW9YgsAFDkceBXx3vT/l2L283I/YG7LjSjBFeW9mWxAA2fW4KDCkLYZGwaL795dva/k/uUWMitLluA7/7wBNP1C4yMx2bdelJodd4p9R0WkXnnpgo0P7SC+7zi+9I/OIZ3z15LEaWpaABBEARBkLBGEARBEDlDPJ5gSvbucdlVRRlWgSRY4ccx33VdopiW6n5GoDWJeUffkKbzBYqcePjepYhNxxHqDc+eS4uwqFVksViARALovTGiaf9MMTQaxabgYkMEV5b2lUgAB0/1YeemCrx/sT/lnF63HSPjsbQ+IS6gkCzkhnrDmoTPHx/owNqVi2CzWdP+prUogFrfkROZfW47nt9Ri43BxZrOC6QL3E0NVVQ0gCAIgiBEkLBGEARBEDnAmY5+phDFNw514Q8fWcl0TI/LpiiwBXwz1RuNEMW0Osj0oik3E4cO4HPb8ZXHazE8MYV3jl1KET2THU+891CryJJIaNot4xR7nIYIrkJIdGw6jt0PVuJXx3sU78Gpi/34m288MFuttajQgR/tv6B4rVIFFLQKn5HxGP7i//we62tLsKrKj4DXxSXAJsPTdzJVKZNyoREEQRBEOiSsEQRBEESWkcuXJsVgJIruq0NM227fUCHpfhMQRA2jRLFMTe71EqzwYz/SwyiliIzHcCM8jl/++6dpfxM7npTu4bqaEoR6wymhhloqL2YSsTAb8Dnx9LZq7DvczexC09O2pEKi1ZjpH7dnxZ9Qb1hVrJYqoKBV+ASAiclpHDt3HcfOXQfAJ8C6HQVovPcerKsp4e4780n0UspBqZafkiAIgiCMhoQ1giAIgsgiWpLY/679muo2QlXMshIPk6hhlCiWD5P74HK/qpsvmd+euqz491cPdGBdTYnsPWzvGsC39x5Py5m3mTM5fqbZ88QaWK2WtPZgtVi4XGha2haP2Cwm2W2mtYBCddkC1WIIrLAIsEJV16YG9gID85GZcPlLaUU6vG47Hli9GB6XLS0PnM9tx5bVi7F25SLAAgyPTc6K291XbyMyHkPFPQuw1O/Kxk8iCIIg5gAkrBEEQRBEFtGbxF6OZDcaq6iRD6KYEVitFry4K8gs3KgJcCPjMew/fgm7G1ek3UOl6q0HZHKC5QJCmLBUO9HiQhNCIYV22Nk3JNsO9VZMTXabsTrPhkcmEY8nYLVaZp1yRohqyQghp/ni7MwGYreZIH7NFE8YR2tbn2R/HBmPyRZsidz5m/jvQr5CASrEQBAEQWiFhDWCIAiCyCJacznJITU5nC+CGQ/r60qxp3kN9rYoi2uszrbWtitpbiMWgUjICXbkgyvYd6Sb7eIzgFTuM7HoIeQyuxkZx6WrwwAsuHV7AlNT8bTk/WqVbpPRIzYLYajCtYYj0dniBUrsO9KNX5/oVa0sqofkkFMloVEqlBGAqhAnFwLJGxqpZftQbxgXem/h0rURxKanEShyYnlpEYp9Tvh9qb/h1vAEPr12GwkkYIEFK5cuQKDIhch4DPsOp7YRsfhlJOLjDoqchUR+w9qP5P4tdDmMUG8YsMykDxAvNCQfv6jQgampOE5euIGJ2BSKPU6svBPuH59OoPPqEJCYcUsHl/kpbJkg5iAkrBEEQRBEFtGTy0nMM1ur8diGCvowZ2RjsBRoXo29Ledlt1HLUycwOjGVlqeLRSAScoI9tqECB0/3Zd25lizMJk/6+gfH8d6H19KqTgaKnGkVSvcd6camYCn+pKke3Vdvo71rQNJNJIRIPvHgcixe6JmdWOoRm599rAbtXQPcudmAGWeTWaKagPDb5ITGzatK0xyMLrsVCQDRWDxl22RRkud4coKm0nGUtn/lnVC6+HwVOHVxYPY/nXYrLBYLJian046hFNqejWIdUsUsCPMwUlwSjtXeNYAT52+kCOoe18y0N7mtSv2by27FVDyBqem7jW8/emEvsODFLwbh97gkjy/m3bPp7Xr/iV54XDa8uCsIAFx9jSCI3MaSSORLfSlzmZ6OY3Bw1NRz2GxW+P0ehMOjmJqKq+9AEETeQP2b0Eo8nkjLv6WVP9tdjy31Swy4qvmFlJiQXHTgP/2f3zO51sT3/+SF6/int5UrUibvpyevmBGsry3B5+8vQ3CZX7M4pZdCZwHW1ZTMJv7nwWW34o+/WK/qQswmzY2VKCvxGvacmxsrcc8ij6I4LIfYmaXW/ni3z2e+8+w6cvlmAF4hl/dY+Qq5Jo2Dvs8JvQQCHhQUWFW3I8caQRAEQWSR9q4BTMbSXRxaMNL9Np9Qy3m1Y1MFfvHv6q418f3nze8ll7vM57bj+R21sFospk4cz3QO4EznAFdhB6MZi05rEtUAYCIWx08Odhh8RcbScrRn1iVj1PEsGo1VPz7QgZ7rw7BgJixVLWw52ck1OTmNf/m1umicrxgdok+ko5R/UiokV87ZNjUVx6sHQprHjVyEXJMEkX+QsEYQBEEQJiM3ITDS8RFIymNE8KOUh2534wr8tu0KRsbkw378otxeQ6NR+Fx2pnPvO9KNg6f7Zl0a8QTw04Mds8nzZ3JPdeO5x2rwt3sa0Nk3hMNn+nCm8yb/D2UgW6KaEajlUssFjL6/WmNPIuMx/PrEnYq3J3pVtxdyxH30yc2crmZrBLRIYS4s+SdfeScEt8OG2opi/OZkb1rhCr/PieWLvTjbfcvsy804yfkYCYLID0hYIwiCIAgTkQt1eWZbNfYdNi5ZvVSyeYIdpTw/VqsF3/qj+/A/Xz0tu/9kbBo/f7dbc4VPwaWxc1OFpGiR7OJYV1OCf/zFR9znIAi9HDjVi48+Gcz2ZZgKLVKYD0v+ydGJKbz85lnZv4cj0TkR9ikHuSYJIr/IO2HtF7/4Bf7rf/2vaf/+p3/6p/irv/qrLFwRQRAEQdxFnPBdKvF9OBLVlBNJCqkqoHMVsyqoseT5abh3Kb711L34p7fOpSSRFxidmDLExaN2jDcOdaGvfwRjUWPChwmCh7kuqgG0SJEJSDRSh1yTBJFf5J2wJvCjH/0IPp9v9r8XL16cxashCIIgiMwmT96+oRzrakoME5dY0Stuad3fyCTX4uOq5fnZvHqmIEEinpAU1TLJYCSK356e22F4RG5S6LRhLJq/YcJiLJbUMNpAkRPPbjNvkULr2Ke2n1kLDmbSPziW7UvIacg1SRD5R94Ka6tXr0YgEMj2ZRAEQRAE4vEE9h/vkXSn6cXnts/m2gLYHWpmTLb0ilta9+dNcs0KS56fNw51YeOqxZiOJ/DqgRD3OcxgYpLcakTmWbG0COcuZdex5rRbmcXtgM+Jp7dVw+d2YGg0iiK3A7AAw2OTKPY4UV22AN1XbyMyHkPFPQuw1O9CPK4xYZ0KesY+6WImddgYLOU6bq4IcPF4ghYHVCDXJEHkH3krrBEEQRBEppGamLR3DeC11k4MjUwafr6Az4m//sYD6L56m2syZIa7S6+4pXV/VvFLSwU1ljw/g5EoOi6H4QtPIKJQvCAXcTusGJ/MrsOOyH8EMf/W7YmMC2vFHjseXVeG0kDh7PgXjydw5IMr6B8aR2mxG4/eV4ZPPxtGOBJFZGwSXo8dAa+LaawMLvfDZrPC7/cgHB41RVjTOvbJ7RcZj2FvyzmcqF4ombhffFxh4Uec/F8Q6NbXyldE1oKagLf/+KV5Hcruddvxws46AEh7T8+n1A4EMdfIW2GtqakJ4XAYS5cuxZe//GX8yZ/8CQoKCnQd02azGnR10hQUWFP+lyCIuQP177nP6VA/XjvYgcGkj+ACqwXTJjkcAOArO+rgctmwZuVC5n1Oh5Qncd966l5sDPJ9tMfjCbyhJm4dnnF2SU3I9Ox/sWeQSfz65NptrKrkc7JHGCtIDo/FEEvkn3tggkQ1QoTLYVVtF36fA3+2ew2GRydR7HWgbpkfVqsFU1NxvPm7bs1VSAWWLfbi8o0R2b8/+fAKLAkUppxbzBcaKlP+m2eMFGPm+1vr2Meyn1o1zFcOhHDt5gh+e/qKZCVaQaBzOQpSXLABnxNfueOIYyUeT6DjchgfdA7g+LnrKYsQyceLxxNobbvCfNxM8aWHqzA6MZV27Wq4HAVIAIiquIg3BEuwdKEHqyoDWLX8bpveuGoxOi6HMTQyqdjeCe3Q9zmRKfJOWCspKcG3vvUtrF27FhaLBUeOHMHf//3f48aNG/gf/+N/aD6u1WqB3+8x8ErlKSpyZ+Q8BEFkHurfc4PpeAIXPr2FweEJBIpcuD0axT/8PL0Ko15RbVGxG1/fvRo/euscbt2eSPn3P31iDRruXcp93a+3dipu88ahLmzbXIkCjo/3j7tvpgiKUgwOR3EtPIHPVS/CdDyBjz+5iXPdNxFPJBAZnWTa/+i5Gyj2OREocqF+xUIUWC2IXQozXWMscfc9Ln5+wrHEVNyzgOnYZYuLmLZTo8jjQO0yP9ou3jDkeGoIrdNbaMdInrntCOMRxpu/+XGb4nYvfWmt7Njz5CMr8Yt3P5Hd90uPrgQAyW3sNiv+8tn70XhfGY5/dA3/1PKxIeOeUZjx/uYdO3n2U2N0fAq//H2P6nbi0PLByMz77r++sJHpWUg9S7nj+QodkiJfJvAVOgAkUoQzcZvb80ep747qZX789kQPPrs1insWevD4A5XovhxOebcAwIVPb+Hkuc/w7gdXMDw6KXt8KRoWes35wUQK9H1OmE3eCWsPPfQQHnroodn/bmxshNPpxKuvvoqXXnoJpaXarLPxeALDw+Ym0iwosKKoyI3h4XFMT9MqMkHMJah/5xfC6rrUKrGUM81i0gLys4/VYPWyYvzdNx+UvJ5weJTreBd7BmUnNwI3h8bx/odXmN1d8XgCJz+6yrRt32e38dlABP/664sYYXSDJfOjt+867QSXg9fF9qlityQQDo9KPj85B8ZSvwsBn1NxAhsocqJiUSF8RW4EipwYHNY+2R0enUTbxRvYVL8Ypy5kRlwDAIfNiu8+tw5nOgdwKAfdIkRmEMabbz11L/51/wWMiAQOr9uOP/7iKqyqWCA79jQ3VmE8OoUDJ3tTnGsWC7Bzy3I0N1YBAJq2LEfr6cvo6BuCy1GABz+3FKurArPj2qqKBYaNe3ox8/3d99lt5u3KF7pn302nQ/2GXocWfvjLj1BXVqTooDp14Qb+8RcfMx/vy5+vNurymPG4bfiPX7oXq5b7AUC1zZUvdKN84YwIMzE6gYfvXTL7t4nRiZS/D98em93nqUdW4EsPVeVEmybuQt/nhF6KitxMjse8E9ak2LVrF/7v//2/uHjxomZhDQCmpjLT2aan4xk7F0EQmYX6tzrZTqCslH8MgGQYpd7QJzFCHpV11Ytm20tNefHs3+PxhGquH6n7eGtYWVQTuDU8wdROeaucfnZz1LACDoLLYU/zGvh9TsVrCPicWLl0Ad4/f13y+QnHkspl9OxjNZL7zP59Ww0SiQQKrBZ85fE6SeciL12Xw/B7nQiPmF89FphxxCQSwP01JSSszRHEFS2VEI8366oXYe1fPIRQbxihvjCQmMk1FkwK+VTijx5ZiScfrErJc7b1/nLYbNaUfbdvXIbtG5fN/rfUuMY77pmJGe9vn9vOtF14eAInzl3HvsOZqSrNwuBwFBcuDSJ4R5ASczp0Az946zzX8Yay8Nte3BlE3Z3cfID5bS6X2jRxF/o+J8xmTghrBEEQRO6gJJyZkVSfB7Uk0h5Gd5QWHlyzGE6HLWUSqhW5+1hTxhbaWOxxMp1DSXAS4/c68N7Za8zbs7LvcBcevvcevHWsR3abZ++IolqKHKyvK8U3n1zDlER6Y1B6W17CI5N4cM0SHDt3XfMxeBkajWJTcLGqSJlvrK9dhDOdNzN+3p2bKvD+xf60NrNpVWnav/u9Djxy31KUBgrRPziO9z68pukZ+Nx2bFm9GOtqSmYrWgrj7Ip7ivDu2avoHxrHogUuVJR4EZmIyS5eWK0W1FcFUF/Fl5dQwGaz4vFNy9Q3nOfUVhQz9bl9R7ozdEV8DI1KX/eZjn7sbWEX1QS8HruuMWhjsAQbgqXYd7g75Rhelw0JICXMlAoBEASRSeaEsPab3/wGBQUFqK+vz/alEARB5DVKohiL00yLG4y1qqQRv01NeDEj94sg1h07dzf07+DpPs1iopI4eIohfMjvdaC2olhxG5Z7JeaR+5ai5WgP1z4shCNRWVEteeIU6g0zFTno7BuadWAIbTo2HcfXv7AK8UQCnVeGUhw8YtbXlWJdTQm+9/oH6LzCFuYlRSZFNWBGTLVaLXhOxaGXLwjP3uOyZ1RYS25zTz1aLTkmyv27QFND5ezf+wfH8O7ZaylVhZ12KzbUleKrj9fh08+GZY8jdhKR0GU8eh3W+d7nhEWY5PtQ5HZwvx8EAl6Xpvvhddnw1Z112BhcDABYX1ua9lwAZNUNTxDE/CbvhLWvf/3r2Lx5M+rqZsoUHz58GD/72c/wta99DSUlJVm+OoIgiPxFThR7ZlsNPrs5ita2vhThSew00+sGk3ITGUln31DGnTrBigUI9aWLL1rFRC2Cl5hH7luqeo957pUgNMQynLukubESTQ1Vs79FzlkhRthOqr0nh9ftP9E728Y3r16Scoz2rgFdolqmCfjuTjzlHHriyoC5SHNjJUoDhSmT5ng8we2A4QmjTHabiSfrVqtFMkxO7t/l/t7UUCUrCCgdhzAXLQ5rKSFOrs/lOsK4wZsSQO14VqtF8n4k97UitwOwAMNjk5IimVwfo/5CEES2yDthraqqCv/2b/+G69evIx6Po7KyEv/tv/03fPWrX832pREEQeQtSqLY3hbpleVkcWhdTYluN5jYTWQ0rMKLEXhdNsQTCUlRLRleMdEIcbA0UKi6Deu9anpgOZofWgGr1YJQL1v1TqN47+w1VJcXz068itwOpv2KPU7Z9i4WW4Q2bi2w4vEHZpKyGyFuZppnH6tJC39dV1OSIgBMTcfxv372YRavcga5EEu5kC4tjqAdGytw4FSf7N/tNisevW8p1tWUZMT1oibEEcYiFr+kwmHVFoq2byhPax9SApTPbcfzdwqnxBMJTeGT2eLZx2rQ3jVgmNsueRySGoPIYUYQRD6Td8Laf//v/z3bl0AQBDGn0CsUvHqgA26nzZCVeDPFL5a8YnpZVurB/bWlzAn8ecVEI+4Py31gvVf1lYHZiRBrLiGjCI9M4uV9Z2f/2+9zwuOyKQq4AZ8T1WUL8N0fnuA612u/7cC2zZUAsuN8ZMFlt8LtsnMJUsnt7sKlQcOvae3KAD76dFDSHSZ2jbGEWMoh5whSOsfKsgXprj27FTs2VeAPHlxBE/w5ipT4FfA58Y0v3YtVFTM5Klneia1tV9DadkU11UFkPIa9Lefw6aYKnLqY/UqfYqRyk/kK7dhSvxhuhw1739IvqsmNQyQoEwQxl8g7YY0gCIIwjng8gUNtfbqEgpHxGN5tN6bSoJniF4vw43XZMKIjz9qXt9bgX359kWsfHrFM7/1JDglUguVeiY+V7VxCLG342cdq0H31Nnd7HxyO4sKnt1C+0J1R5yMPX2+q1+UAGR6fVN/oDo+tL8OiBW74Ch0YGErPEZY8kZ6aiuPIB1dwIzwGAFi5dAECRa605PssIZZKSDlglM5Bjpn5h5wLbTASxf989TS+9dS9WFe9iEs8Z011cFDBIZkJXHYrHlq7FGurFwGJmf4uzk3W3jWAE+dvIDIWmxUO9bC6yo8vbqmkfqUTscNSaVzL9LXw5uFN3m5wZAIjozH4Ch3w+9S3p3GayHVIWCMIgpinGJU3BQDOfarf7cIq+miFRfh5YVcQADTfF5YE+mJ4xDK9rjBxSKAcLPdK6liCc+iVd0KmFIJgweu2w1ZgkRV6Tl7QVjRgcHgC5QvdGXE+ylHosMJitabmOvQ68Nz22lk3iFYHCOvvam6swu7GqpR/U8oRplQ90mi3ipQgx5PrjMh9tE6yWVxor/22A2tXLNQknmdrvGNlIhZHa9sVVJcXw+e2p/zNarVgdCKmW0gT88UtldS/dKKWCxSYeec9cKdasJlFHNSKU4n/5nHZsH1DBZoaKhXDpQWKvQ48vHYppqfjuDk8AQuAyamZ/j4yHpvdTqiOLCUSk+BGZBMS1giCIOYhciv3WpmIxeFz2xFJ+vgRo+YGYxV99CAXMiYOVRGcLO1dA3yTDc7L5xUTWQQv3hxVcrDeK6n91tWUIHQ5jFBvGIkE4HYVYHxiCharBbVlxbAWWDA0GsWbh7oV24wWRsZj+Ktn7oPVYpGcWGgVxgJFLgAz4qZayKlZ/PEX9bnSlGARbf1eB5oaKtP+nQQqIhNITcrFooJcX2BxoQ0OR9Fy9FN4XXbF7XIVp92KaEy5iMwP3jqXIsrMFCiqxr7D3YZei9kLZXMNKcFYLr+dOLR+ZPyuw1BwTqaE9t4RovTkjFTLOSjF6MQUWo5eQmtbH168s2ip9O0yNDKJt2UqgCcTGZd2VKoVFiEIs7EkEqx1keY209NxDA6OmnoOm80Kv9+DcHgUU1OZrZ5GEIS55FP/jscT+Pbe44bnidq+oVxRhPrmk2sApK9qahF99MLjejjT0Y/XWjtTHFBSBHxO/IcvrMLLb55lvg7eqqDJ16R0H40MnTArDCPUG8b33mjXfRwp/mx3PbbUL5H8m5b2Hyhy4v/+f3dg+PYYpqbiePvoJaY8enpDi2fPn6E+oia4a22vBKFGPJ5AqDeMUF8YSMw4DYPL/ClOF7XFIKWJ9ckL1/FPb19gvh6eyrHZxuu2pzh6cgEaK6SJxxOzi06wAMEKP0ajU9h3ON0JNhmbNnwBR4v4ZNQ3Y6YWpMRtL5++z4ncJBDwoKDAqrodOdYIgiDmGWYlXxdWQ1ndYNnMl8HjsBEcWPuP9yiKKc8+VoPgcj9TqKbelVW1/FBGOojMciOZmausf3BM9m9acsF95fE6FCS10aaGSrS29akWSnh6W41sVV01GtYsxurKhYq5Z4xGq0uRIPRwpqM/LXx8/4leOO0F2LV5Gb6wZTlTgZ3kStXitsrrVM0HUW1DXQkeXVeGH+1nFwyNxuUowMTk9Ox/z7exgneRLq2do1dyW7MK5Cj1ETmM+mbMlMubt9o6QRgFCWsEQRDzDDMEjUDS5F9NOMvHsDGr1YLdjVUoK/Goig5qok1zYyWaGqp0f/Tl431MxsxcZa1tVxTvMW8VyY3B9Gp2L+4KquagW19XCqvEeVhW7o+fu4GLvUN4LgMh0slQUn8ikyg50aKxabQcvYQD7/diQiXEMRmpibXW/JS56lwr9jrw0hNrZvqpipvaLLwuG/7Xf2zMWiJ9JYx2WsuFakrlHHtk7VKUBtwp5zU6/YZeeMSnXC3YIwdvtXWCMAoS1giCIOYZZggayfnR8l3wUYJFdCDXDxssE11foR2RMf4Qp9GJKfzw7XPY0/w52W14q0hK7c/ynOXajNSkTIwWd4ERzOU+TOQO8XgCr7V2qm7HI6oB0hNrrVWLMy2qsY55j95XBqvVklXR44VdQdhsVkPHCiMEMaUk+0alXpBbHAlHoinOdrNy2OmFR3zKZsEereSbGEjMDUhYIwiCmGfUVhSrFhpgZT6KRSyiA7l+1GGZ6D7/eB1+erBDU1s9HRrAhtANbAwuVrwGniqSYlifs9R5kos87G05p+hgo9AWYq4RjydwqK3PNLeV1MRaTgzPBZoeWI76ygAGRybwo19dVN2+NOAGkB3RYybMvRoelx0nL1xPGff0CGNGCGJqSfZ5Fynkjsca1hiORLG35Tzz+TIJq/iktxp5NshHMZDIf0hYIwiCmGdYrRZsWb2Yr9qlCI/Lhj3Na1KSSxOpkOtHnXgiPUcPMJOM+4WddVhfV4ruK0Oa2+pPD3ZifW2pqW1Uz3O2Wi2wWiyqkzQKbSHyAVZRRUpAMRq5iXWyGH6hZxD7T0jnuMo0S0s8CC73zyS1Z0D4fUaJHkJOSHESfUFE87kds881Mh5L287jsmF1ZQBdV29rEsaMEMTi8YRqLj6eRQqW4+UzrOKTVrenGKOK+ahBFWmJbEHCGkEQxDxkXU2JLmHtxV1B1FcGDLwiIl/R6lD42ZEuHDjVJ/m35Ap3etpqZDymW5CKxxO42DOI2KUw7JYEVi5doPr7WO9JPJ7AhZ5Bpuug0BYil5ESy3xuO7asXjxb2CZTuabUJtaCGF5bUYxj567nhBOHRyhL/n1GiR6C83xd9SIc+eAK+ofGUVrsxtb7y2Gz3a2Gd6ajX7Igy+jEFE6F+tP+nUUYM0oQY0myz7NIYVahp1yAV3xSS30AIK0wg5gXdgUBpFeGN5pnM5yXlCAESFgjCIKYh2hd5U52EhEE62RazOnQDVlRTUCYSNVWFDMl+5dDjyClJTSJdR9e105RoQOh3jCFFhM5h5xYFhmPobXtClrbrhiWa4oljQHrxNooUUqJP3pkJVrPKIe88gpl4t+nN8S1ubEK6+tKJcekg6f7ZscuPQ4uJWHMKEGMdaw3ertMsfX+MmyoK8XtsSh++JZ8JVin3YqoSl5CLeKTWuqDmerpl9DadiXlfS1XGX5wZAIjozH4Ch0YGBpXrLTtsltRu6wYn16LpCy8JTMfU5MQuQUJawRBEPMQ3gmFx2XD9g3lhlSzJOYGLJNpj8uG+6oXYVWVHwGva3by+JOD6gnLhYnU6ERMs6gGaM+1oiU0iXUfXteO123Hj/ZfSJmcKwl8PI45ygNI6IFVbDEi15Sv0I6/+/MH0X31Ntq7BnDy/I0UkU3LxHpdTQm+9HAV3nm/D+NRY8PUAj4ndmxehtKA2xChTOn3CaLHxZ5B/OMvzyEam07bRgq/14Gmhkqmscvjsmt2GikJY0YJXaxjPesiRa7l6dpQVzp7/2xWq+Si1vM7arG+thSdfUNo7xrAifM3UoQoveKTUuqDmerpK9DUUKWpMnxTQyVCl8O42DOIm8MTsABYWOTGquV+BJf703L4FRU6gAQwPD5J7y8iJyBhjSAIYp6i9PEuzqlCHyyEQDyeQKg3jFfeCaluOzoxhWPnruPYuesAZsSgR9beI7viLCYcieLn732i+Vq15lrREprEus/alYu4XR9S90tO4NPjmGPNh0SCHCGQyXC55x+vm61CGVzux9Nba3S1Q7NzvQmCmdy7Vqm/raspgdthQ6gvDCRmiqoEl82IEXKiUHvXAH58oINZVAOA57bXAgDT2PWHj6xkPq4UcsIYq4Clth2LE59nkSKXkvaL32Vq7jGj+ogWtOYdtVotqK8MKKYZody1RC5DwhpBEMQ8hqpXEjzonYiGI1G0HO1h3j4yNqlrUqM114qW0CTWfY58cIX5N/l9TkzGppkrhup1zLHkQ9IqyGkV4/JdxJO6fgB5/ZuSyZTosGNjOdbXlqSIStVlC7iOkfws+gfH0XL0kiHXJg5Vl3MFJRIJxf8WkOpjx85dx+ZVpXj/Yr9k3wPA5YL1uGx4cVcQ6+tKEeoNM41dkTF9FVzlhDHevHJysDjxeRYpMhEqzIrUu4xFZCIhiiAyBwlrBEEQ8xz68CJYyETS8WR8hXZ4PXbN+wt5g7SgJTSJdZ/+oXGm7ZoaliNY4cfLb55V3E4Q+Gorig1zzMnlQ9IqyGkV41j3Mzr0VWk7HqFM6vo9rplP72QhRvhN4kWO6rIF6L56m/k6WMS5qak4Dp3pQ9eV23DaC9CwZgnqKwNpYVZSx5yaiqclttcrtrBy9OPrOPrx9ZT7ZrEAydpUctsQ/xapSpZGseeJNbBaLbL3Ta7fDI1MpvUbpT4mlZdS6HtCu+K55vqqGWcQ69jl9dg1O7iUhDEteeXkUHIH8ixSqB3PDJz2AtgLLCmVMylvGEHkDySsEQRBEAShiJ6k1Vp5/vE6+NzahbXSgFvzvlpCk1j3KS1mu6765QEugc9Ix5xUPiStlfv0iHGs7jsjQ1+VtgPSK9rJCWWbV5VKCiFSE/tkcYRFOJK6jmKvAw/fuwT9t6OYiE5hgccBj9sOq8UyG0b483e7cfBUH5J9Uicv3IDTbsXn15XJuqHW15XiZ0e6cPB0X8r1vPm7bqypykx1aKn7JjZ8Cfdx56aKtN9iFgGfczb/kxQ8/QZQD8mUgycPpXDNAqxjV8Dr0uzgUhPG1tWUoLmxKi2BvRZhScqJH48nmBcpxAuNycf7oHMAh85or6iuxJ80raIIAoLIY0hYIwiCIAhCkUzmUQKAnZsqsDE44zrR6pDQk3haS2gS6z5b7y/HwdN9TMfu7Btiut5ij9Nwx5z4eFrCY7WKcaz7xRPA3hbjQl/VtpNCTihTq3rLciw54UiKoZFJvH38suTf9p/ohc1mxdSUdKXAaCyu6Ia6r3ohznbfSvt7IgF8/Omg5DGziZZ7rxU1wYin3wCZCa0VXzPPeGe1WvDNJ9fglXdCTGIeizAm5+zUUzBJ7MQ/eeE6035y46hwvOByP2wFFsU25nIUYGKSPc+d+B5RBAFB5CckrBEEQRAEoQiraMNDc2MV3vvwmmRVs43BxQC057jRWrRAQEtoEus+NpuV+dg8E15WEY7VMScWJrWEx2oR43j2++nBDsVteEJftRSVyCfkRDUWpES1+YbYPcjqpGLtN+FIFJ/dGtVziar43HZ8bWdd2jXzjneCg2v/8UtobbuS6tb0OvDIfUtRGihkclzJidmjE1NoOdqDshKvIWGQRhVIAIAvb61B1dIi/ORgp2TFTcF1NjgygZHRGDxuO0bH7/6v123HyHgMXo99tlo2udIIIv8hYY0gCIIg5jEsuZr0uL+kCPicaGqoRFNDpeq5teS40Vq0gOW8ShNq1n1Yt+OZ8JrhmEtGy8RUixjHs19EpbosT+grT1EJYv7x0hOrNVXKZu03bxzuYq6WrAVfoR1/9+cPwmazSv6dd7yzWi3Y3bgCTQ1VqmO43DtGq6NVC0YVSBDYGFyM9bWlilU5CYKYX5CwRhAEQRDzFNa8UyyTEh6ShS+WCYhUzpzI+CT2He5mFr20IJz3k2u3EUtYYLcksHLpAsVJHmulXZ7tjBTheBxzyWiZmGp1iRgp5LKGvrJuR8wv9I4prGOnXlHN67KlJL0X87UddbKimoCWKuFqxY+U3jEel12To1ULRhZISD4mCWjGIhZhlQq4EESuYUnI1XqeZ0xPxzE4aK4F22azwu/3IBwe1WXJJwgi96D+TeQSLC40tSqf4qTyRlUFbW6sxO7GFbqPA2ivjMhLLvRv1t8qNZGVEgdYtxMfm6fNxOMJfHvvcVUx7nt7GtJyrKnt53PbVR1rAPDM1mrsO9Jt2HbE3IY3lJGFTFRU/uaTawCkF7XIZFVJqUqsUjkQBbZvKEdrm3ohgD/bXY8t9UsMuUYt4x6RGaSejVQBl0fWLkVpwK1a2Tl0OYxQbxgWqwX+Yje6egYxMTmNmrIFKC/xYnhiEiOjMfgKHfD7SLQj5AkEPCgoUF6YAEhYm4WENYIg9ED9m8gVWFxoWgUPlg9fNYycJGWKfOvfrCKcFmGSd2LKIsatqymZnQQlkIDHZcfgcFSx+t6OjeX4bdsVxbYX8Dnx1994AN/94QnVtv7X33gA//kfj3JVVyTuIq5omk88+fAKrKzwMzlStSLVb3yFdkTG2JxqAZ8Tm1aVplU7Ffc9LX1a2Cc5J1hkbBKj41OwWIFghV+x8qnc71N7N7CK4995dp2hzrBMLcgQd1G751rF5+RvG+Ec7V0D+P1Hn3EVkABmKit/ZXstCaxEGiSscULCGkEQeqD+TeQCah+nzY1VKA24MTwyyeTOkZrQpLsSJrG35TzzNRo9ScoE1L9T4Z2YKolxAJgrDAKA121H4+eWMFV+VKv2mbwdANNdRcBMyF4C0tVEcxW5qqACOzdVYGXZgozcPyMR2uDm1UtM799nOvrxWmsnhkYmZ/+t0FmAsaj65L+pYTmaG1fM5iUzUhSS6ptSeFw2vLgrqEk8V0JNXJNa4CGyg1LbU/qb2mIfy0KfGjs3VaSJzlp54sHlmIoncOt2FIEiJ7xuO4q8Dio0MY9hFdYoxxpBEARBzAFYEkG3HL3EdUypJPJSeWWsT1qYJ2fxeALxeII+TvMYpdxCUhMsudxN7V0D3BPykfEYfv/RZ4rbWCzAS0+sYS4Wsa6mBN/ee1zxmF6XDV/dGcS+w6nH8LhsGJuYgtIqtc9txzPbambDjQAgdDmMvS3nNAtsvE5RNe6rXojeGyOybqifHenCwdN9Kee0WIDPrViIe1cuQm1FseI9/uTqbRw81ad4n8wk4HPi6W3VmgoQ6EVOeGIR1QCgfnlg9jp58noZ6RIanZjC9395TjLcW0813S2rFyuGgxpRiIaQhkcoi4zH0sY+QRwD0kOQk/8m1cbCkSi+/8tz2L6hHGMTU7oFMZaFFlbeOtYr+zepHLQEIUCOtTuQY40gCD1Q/yayTag3jO+90W7oMdXcZckf30VuBzqvDOHtYz2qx823j1Nx/9brGtEarhXqDSPUFwYSM0UfgsuUw7PMvB4ppFw5cuE1RrgUlPirZ+6D1WJJ+U0AJH8na9/5zrPrUFtRnHaMM539iq7NPc1rsDForNPnG0+sxoLCGZGof3Ac77zfi2hM/t3jddtRv9yPjz+9ifHJu9t5nAX42q4gNgYXq7aDqak4jnxwBecuDaKjbwixpHed0KfFAmpy8nGv046+gRGcvngDl/tHEM/QDOSZrdV4bEOFbJvW8/5Wu2d627lWx5ZZLiG/14G//fMHZ69H73vnO8+uw+hEjPKemYxeoUwr+RwiroRYYCbmNuRYIwiCIIh5hJS7TA/iCo9i5CZuLCEZwmp1Pn6cslZSNXL/Mx39aeGS+0/0KoZnZer3JB9HSiQaGpmUfNadfUOmiWoA0txgSr+Jte8MjUYlHUMbg4sVXZv7DnfBakHaudWcdID8BPdnR7rx3GM1s/kKmxoqceHSII5fuI6J6BQWeBzwuO2wWiywWi349w+v4VSof3Z/j8uG7RvK0dRQxeyGstmsGBqJ4tylwbS/SfXpMx39afntMj3R9vucKC/x4lTohuEiOEvf0dvOtTi25Pqi8Iz2NK+Bz61ekVOK8MhkSpVOPe8d4R1jtVq4q5ESM8i1z+R/7x8cx3sfXlN93kL7MJK5KKoBwBuHurCupoTaKJECCWsEQRAEMQco9jgNPZ7ShE5p4nbgVB/2NK+Gx2VXDXUTf5zqcU5lIiH16ZDyhFVNKFSb8Ertr+RqkgvPYkXL9UgRjyfwyjshxW1efSeU8qyNFoLFiNud0m9i7Tv9g2Oyf1tfV4p4IiHpXFM6t1yY7GyfYDym1WrBmpULsWblwpTt5J7x6MQUWo72oKzEy9x2Tof6VUOuhD4tF+Zr9ETbabcqOvUmY9N4+c2zs/9tlAgOKIe4Cc+FtZ2LBUetji2W0MwfvHUO29aXcx03meTfpOe9k/yO4QlxnYuwOB+lQuml2udmiSIXhLEMRqIpAjNBACSsEQRBEMScoLaiGH6fU/fHtNqEjmXi9ubhbvyHL65SnUQLH6e1FcXYf7wHrW19zC6jZE6H+vHTgx0pSbCNDjedjifw2sEOxW2UVrFZ7puU0Phaa6fqtb2uYfVcy/XIEeoNqz7rkYkphHrDqK8KAACKCh3M12okUr+Jte+8d/ZaisMrmXg8gX2HlQuCyN1POVFBzzGF/Vme8dqVi2bDNeVE6Xg8gZ+qtH9gpk+HesO68m7x8CdN9YgnEvjJwU6MJPV/u82K2FScS2BNRk109riUp1DCc2EVnvY8sQZWq0X3wgCLQy6RAA4p5DVTI/k3sfQdcT5ALaKhUQsnWgQss11Jas5Hqb/LOT+FxS3CfMxeHCLyDxLWCIIgCGIOYLVa8NxjNZpCOZ7ZWo0ir4NpIsEycRuMRHGxN8x07g+6BvD9X34sO0lQmwT/7EiX5ETC6HDTC5/ewiDD75ZbxWa9b8n7d/YNpeQrkyOsYfVcy/XIEepje9ahvrvCWray2Ev9JqvVgkfWLlUt7iEOg0vGyPtp1DFZ9/8v3z+mKkp39g0pVm9MJtQXNtwtIyfOADMi1ojo2mIqudL0CpI8iwZqwlPA50RwuTH5Enkm+1oKYPi9jpQUAVarBc9sq8HeFvn3zktPrOYqGsGSD6zY68Cj9y1FaaAQRW4HYAGGxyYVhWG1xRujwuJ5UBNwd26qkHy/zdUQy3zC6CgBIv8hYY0gCIIg5ghyOZuUCPiciom9xbBO3G7dnmDajsU58X9/fRHRqem0cvenQzeYQ9P0TloHh9l+j9z94cnjxbsP77Zar0cW1sl50nbD4+qCIS9OewGiMfVKi1K/qTTgZjqHkc9XQM4lw3rMC72DkqIF6/5iwUxKlG7vGmA6FgBTRFMpcUZLVVkBvYIkC0JOPrUFDyMrX/JM9rWUr3tue21aZdF9h6VFSL/PiUfWLsX0nSoVm4KLNYU3SjE0MomWoz2y500Ww6RyVAqoCVhm5gNlEXAPns5v95m9wILYtHmrKAGfE5tWlWbcpaeWg5aYn5CwRhAEQRBzCHHOpv7BMdkJCMA/qWOduC1cYNxq7vjkNH70q4sAZqobPrB6MdZWL8KPD7CFphmRCyVQ5GLaTu7+sN635O14Jsm8q+darkeO4HI/9p/oZdqO9/w8PNFYiZ/97hPV7aTOrfd+FLnZQlvF+yu5ZFivaf/xu/c+WVToHxxn2l8OQZQGgJPnbzDt43PbmdsDK82NVdgYXJzybyx5/dQYGo0iHk/gYs8gYpfCsFsSWLl0gWEhXsLzUytSYaRgw5sSYPuGcrR1DKhu73IU4OtfXDVbUbSzbwjtXQNoVVgYiU5OpbhAkyvH7j9+Ca1tV1LELqMKWySLYYB0LjwxmVqgSYY1bDefMfLyAz4nnt5WLel+XFm2QFWQvWehG0sCHtSULcCn14bR1smxWCDCSDGcmDuQsEYQBEEQcwxxzqayEq9hkzrW0KZVywL49YnL/Bevwsh4DK1tVxQndGKMmCjXr1iIgM+pGA6qtIrNet+S96+tKEax16EaDurXsHqu5XrkCC7zq06KvW47gsvutklWAeAPGpZj8cJCvHmoWzEUMeBz4rH1FWhtu8L8m5IdM0WFDtV7LYTByYWqqSG+nyzVG3nFhrv7rsZ7H15j3k8KQZQG0l1tcjy/oxbBZX5D8j0CM/e8qaEy7d9Z8vqp0T84jm/vPZ4maj6ydqmu4wKYrboZ6g2jtqJYtUgFL3IuR6vVguWLvcz3fm31IvzRo9U48sEVfPzpLXRduY3JpDDaAiuwduVCbF1fgdryYrx99NM0QUyOsWiqe1Rom05HAaKT6c5So8MbX2/tnIl3NYDBSBSH2vqY3N2sedryPUeXUvXigM+JxnvvwdvHejQfW05Ek0Lcv7xOO67cHMHN2xNYEijEH24PYjQyjqmkti2Vm9UCZTHQDDGcmDuQsEYQBEEQcxytkzq5CQJLaFNwuXGTa70Y4Y4qsFrwlR11+IeffyS7jdIqtpaQMKvVgq9sr1V1XDynYfXcyBA1q9WCF3cFFY/1ws66tN+mdv49zWuwMTgzgXHaChS3XV9Xgu6rt/HlrSvxw7cuyG63adVMBU25hOBKTE7F8fN3uzVX3BPODbCFge07pF64Qo6fHuxkFsOU4Jn837syMOssU3u2rIKhOPRQgDWvn9L5pXLqhSNRtBy9pBrC5nXZMKJw/ZHxGP55/0w7dNoLsGNTBXY/WIXgcj/i8QRCvWH84vefIHw7ikCRC6uW+1PyrMmNvTO5wtLdXoIgGCh24sPuW8z34f/8/EMkEgkkR1AniwvTceCDrlv4oOuWqujAipSoZgZhhvyUPOw70o2Dp/sUc67x5Gk723nT0OvTi5pQpiR0SX1f/PL3nzKf2+OyYfuGcpQGCjWLzuIFRaFCss1mhcNmxaho+43BUqyvTb3u6rIF6OwbwsXLg7h1O4pAkRNetx1FXkdaKgqCEGNJJPLdZGoM09NxDA6Ku5yx2GxW+P0ehMOjKYo5QRD5D/VvYq6hpVKZeDVXrrBAJvEV2vH//MdGyWTWrEJjcv9+//x1Xe4/lvsmtY9UjiCv244XdtbpWj3Xcj08x1JL/s1z/jMd/XittTPFVSZOwM6SkF0un5KA025FNGb8OO73OfG3expgtVoQ6g3je2+0G34Oo/nOs+sAgOlav/PsupSJrdKzFYerv3v2WspzVWuDv3jvE13hpi5HASZ0CDxCmCFPPkunowCfv28pfv/RZ5Kiot1mxSP3LYXXZUu7H8VeB2rKinC2+5apOasINqRyrsk5UKX2yfa7UU0oM6I66j+9fR4nL7CFkH/jidVYUMhe4IIH+j4n9BIIeFBQYFXdjoS1O5CwRhCEHqh/E0ZjxIetVlgnCErXGI8n0sKsskGy60mAVwAS92+9z0bL/oLLJdQXRiIxs8Jv1Cq6kW1N629Takd3BZhxvHf2GsIj+tqUmvimpVoiK00Ny1G/PIBwJDrraMpVAj4nvrenAQBU+7KwrVYBm7fdXOgZxMv7zmr7YToQnGGlAfesw+U3J3tVK8oScwtxe2d53wn7xOMJfOPv3s1a/jS3owD/+y8egs2mLhTo4d/e62ZOByEec42sxkrf54ReWIU1CgUlCIIgCIPRK1Rocf7oRbjmcCSKN1TyRSUncpYrCmBUZT097NxUISmqKeW1Yqn+pvS7WdCyv9VqQX1VAOOTU4a3Db2/R++x5PaRyoFjBGoTWjMnvPuP92L/8V543XbzTnIHt7MA41F5V5ZaOOPDa5fiVOgGij1OPLOtBntb+MOGWdsDT7uJxxNAwjxnoRLT09MpIprHZQN5FOYf4qI4LO87YZ/LNyJZLUowPjmNzitDqK8MmHoenjyr4vsh9z7O5oIjQahBwhpBEARBcKL0cadXFDNC+OFF6pqVYKm0mc3EzD63Hc/vqJWsJKiW18qM6m9GkY22kS2yHSplNiMGi4ViLBYoimoA8MKuIID0cEav245EIpFW0XHnpoq0/HLJoZ2h3jDThDceTyB0OYxQbxiwAMEKtvxiAP9YZTTDY6lCpNEJ94n8Ifkdx/q+GxqNon9IX7VeIwj1hk0X1oLL1YvaqJH8Ps7GgiNB8EDCGkEQBDGnMXqFU8pFI3zcAdAlfGRD+FEL+5RDbSJhRMEAVvw+J77+hVUYHp9UfMY8rgKjHFxGke+iIA+nQzfmtKiWCZQcMeL8ZeJ8Zy1He9L2CUeiOHCqD3uaV6flZWrvGpCsrik14ZXKF7gfvfC4bHhRRujzue3YsnrxnYID6ddGEMkEfE48vPYezW1FzekpkPyOY33fFXucKC12a7ouQ8nAK4KlqI0awvt4dCI2bxaViPyFhDWCIAhizmL0Cqeci0b4uFOrKqgmfGRa+GERa+RQm0jUVhTrqgra9MBy2AosaUm8pXjusRrUV6mvvvO4CnKNfBYFlRAL39VlC/CTg9qrYeYjPredO9w14HNi06rSNAeZWn44n9uOv/7GAyn5lYQwTCFPlBJvHu5OyS3F46JUEvFHJ6Zk/xYZj6G17YridRGEx2XDnifWzI5/7334mab3z4u7gth3uFs1X1ptRfHsf7O874R9qssWYN+Rbu7rMpJgRWbeEevrSrGneY1iGLka4UgUP3/vE8VtXj3QMScWlYj8hoQ1giAIIqcQJtqDIxMYGY3B47ZjdDwGX6EDfh978uvIeEzyY07rCieLi0Yt5EFN+Mi08KM1D5p4UiGF1WrBc4/VaFqtDvicaH5oBaxWC5oaqtDZN4T2rgGcPH8jRYDgrV7J4yrINfJZFJRzjUoJ31633fQwyVzj6ceqEfC60NbRjyMfXFXdvqlhOZobZ/rHU49Wz97b4ZFJ1Ql7ZDyG7qu3JccgXvGWx0UJQLOITxAsvLgrmLLAwvv+SX6fWC0WxX3FOQVZ3nfCPlarBTs2luPg6eyIxV6XLaOLLxuDpUDzauxtOa9p/9ujUdVxaWQ8hv3HL2F34wpN5yAIIyBhjSAIgsgZWPLn+H1OPCMqEx8Zj2Hf4a4054YSPGFz8XjCMBeNkvCRaeFHqwgjl6hczPq6UnzzyTVpoV88xxfcNMHlfjy9tUZXWC+PqyDXyFdR8ExHP15r7UxxHbocBVi6yINPrw2nbZ9LohpPfiCPswB7nvzcrMA1Oj6FweEJnLhwQ3XfgNc1O9FlEdbqlwfS+gcAnLxwnela5fo9r3jLI8QByHoxEzVWlhXhk6vpbZLIDIKwBaSHA7P8TbzAIrx/pFzrj6xdikV+F0ZGpRft5PZVWszh2efpbbW4ER7H2e5b3PfJbrMipqO65Qu7ghl3dm0MLob1SUvavVFz2Hrdduw/3sN0jta2K2hqqCLXGpE1SFgjCIIgcgLWXF/hSJRp5VOt6hZP2Fxn35BhE34l4SPTwg+vCMPrEANmJhvrakrSkpWPRqfSxFC14xtRjZPVVZBr5JMoKDjU2rsGJEP4JianJUW1XGPPE2tgtVoUc48JvPiFVWnJwOPxBEIq4lPyM6utKEax16EY+iz3jOPxBIZVQqYF5Po9r3ibzy5KKb78+Wq0dw7g4Om+lPeHxQLYCvSJGcCMSND4uSX4/UefUdEDEU0PLJ91KQOpef/EiyhKfxMjvH+0LMho2Zdnn794ai1OXbiBVw+GUnK6FTptaFizBPfVLEJ8OoGOK2Hcuh3FwgUurFruR3CZH2c6B9JyvQZ8Tjx9Z9GxvWsAx89dT2lnfq8Dz22vzVouMql7ExmfVPye4/nuGp2YyrtUCMTcgoQ1giAIIuvoyfWlB9YJn1FOCzXhI9PCD4tY4yu04+ltM6FqWgs/WK0W1FcG0oSH9bXaJjx60OJEyAVySRTM5aqNRhHwOVMqVQJAWYlX0n0pl1uR95m1dw2oijdSz5jnniuNQZHxmKqDJHn/fHVRSrGo2I26ZX7UlBfjSw+vxJEPrqB/aBylxW5svb8c7d0Dqgs6fq8Df/yFVei8MoREPAG3y4bxiWlYLDMVEoPL/LPhu6HeMI60X8HHn9xCbFplFWgeUF8ZSGnXSosovAssehZktOzLs8+m+sXYECxVfA+uWbkwbb+NwVLF96dWh3fy2F5U6AASUCwKxFscSureSDnZ/D4nJmPT3AJ0voj4xNyEhDWCIAgi62jN9aUXYcKn9nEYGWNzgqjBInxkUvhhmfh/bUedaWKTXgeaVvS4GLJJptoGr3CmVhU3H5Hrq1ITPSHxvlTeRtZnpubY9bpseGFXULLKJs89l/tdZzr6mRKMJ+/P66LUU8zEbP70jjsxHk/AZrPi8U3LUv6+MbgYlzYNK+bZfG57LdasWIg1K9KFEOCOg7E3PNuv/rz5cwCA/cd70HL0knE/BoC9wJI3gl2uOG2zhdb3oHg/cfuqrShW/TuApIrA43jvw2uyfVRc+Mmo4lBS7+N4PIGX3zzLcTdmyAcRn5i7kLBGEARBZJ1srDJ63XbUVhQzfRx6PXad57LhhZ3pk2I5Min85KuDSy/ZEvX0Ynbb0CKcsVbFNQqe3Ge8WCzAS0+kC2Q8ifrFz0LtmbEc224vmC0CwHNNAkr9meU4M/dldcr+vI68zatKVQvAGAlrtdUnH16BhnuXIhweVdzuy1trULV0gWQIntpYqdSvdjdWoazEY6jTM19ENSCz4fe8Dqvk/UK9YYT6wkAi1YGYifNe7A1jcHgC/gVO1C8LoLaiGN1XbyMcieL2aBTdV2/jQk8YE5N3Q0qF9rWupgT7j/egta0vZdwUxmvWsTS58BOg/C7gLQ4lfh+z5otMZr4LtET2IWGNIAiCyDpZWWVMJHCmc4CpcmjA69J5LqRNitXIpPCTrw6u+YoZbSMeT8g6Z1iFs0zljdq+oYLJ4eNyFKRMNFl46YnVM1XsRPBWzBSj9MxYjh2WODar0/eZrdV4bEOFbH9mOU4iAfjcjrR/ZxXm4/EE3r/Yr3gO3om+Es9srYav0IF/3n9BddslgULm40qF4FWXLUD31ds4eeG65Ngp5ypMfs+sqymB22FDqC+MqwOjaO+6yXxN+YpUG1F7B2kVqADtDqszHf1pIeD7T/TC47LhRQkXqZnnBYDfnLgMCwA16VRoX067FdFYeoi51n72emunanUonuJQUmj5JszV/KjE/IGENYIgCCLrsIQUGc3IxBR+erBDcRvh47C6bAGz+0HuXLmeVDdfHVxEKlomnlKVO6XIhYTrzY1VaGqoVAxZAtjdSgJqriMzE/VrPTbrfkVeh2Ib0PvbWIR5FvFudGIKzY1VusMiPS4bHttQMVuNVI1ib7pgmJJryu0ALMDw2GRaiN2Zjn5894cnZEUTFjfgq++ENLnVtAjHvHhdNoxo6PdyrlK3swArly7AmqoAtt5fDpvNCoBNfNITesgibkodQynUWikE3MzzAuqiWjJSopoewgxFUniKQ0nB80041931RP5AwhpBEASRdVhCisxAbeI9GIli//EevPfhNc2imkB71wAJV4SpsEw8xcJbZDzGlFsrF/B7HWhqqGQaL7asXixZkVTM1vvLsKGuVFWANDNRv9ZjG3VNRhxHTZhnFe9KA25JB5zHZcN91Ytw7Jx6iNj2O+481hxwNeXF+Lj7Jvo+u31HkJ3EvsPdqrmmAPVwOI/LrioOjExMARrEK7fThv/45OcwPD6J4ZFJ7DvSzX0MJWaqTNZwjQ+CyCEIrYMjExgZjeHm8AROnr+BkfEYzl0axLlLgzh4ug/PPVaDeAKqznFAe+ih1jDueDyB11o7VX/z6zLuLLPPm+voSfHBMsZv31COdTUl5K4ncgYS1giCIIicQC6kKNsYlVT65IUbeHorhSoQ5sDijACQ1r9UInpyiue21872H7UQRI/LziSsbagrZRK8eRP186D12EZdk5m/TYBHvAsu90s64ADgQm9Y8Tq9LhuaGioBsE3ON60qxV99/xgGOd45rKHRbxzqwpceXcF8XF7CkSisVgu21C/RlJNKDcEFZH1yjWRIotdtx1d31MLndshWpgRmxiYp0U+4j2q88k4IDnuB4jZKoYdaw7g7+4ZUXbyAdJh2Js6b6+hN8TFf878S+QsJawRBEETOkBxSJKx0e9x2jI7H4Ct0wO+bcdjsO5z+obUxWIKDp9Un0gJ6Qju1EBmLIdQbhtVqMTSPmZ6cM8TcgDXcTCqkK5GDOc4tltTrkptIKYUgxuMJQ8Ui3kT9PGg9tlHXZOZvE+AV7wTHmfBsO/uGUFtRrHqdL+wKplyn0uR8k85iCmqh0YORKEZGzX3HCK4gI/OUivub0M+0JO/nKbAhx+jEFNO9lgs9NDvUWm7bTJw3G3jdNthtBaYK8QKU/5XIJ0hYIwiCIHIKllxf4uTRwodWdXkxs+Pt+R21iuE+ZvAPv/gY0Vh61S6tK69GlbuXE+cyIdqJzyEkApernpitD+zpeAIXewZxa3gi5z7uWZwRWvIkZRqnvQDf+tLnZivesTxnufHCDLHITAeF1mMbdU1mu0N4n4fS2MZznfF4Ah6XHU89shKRsUl4PXYEvC5Uly3Af/7Ho7p+EwvCgpBZ7xlBUNOTp9TvdeCR+5aiNFAo29+sVgvqqwKorwpwHZu1wIYRyAlSZoday22bifNmg6/uCMJqkQ7NFTCykADlfyXyBRLWCIIgiLxD7kNLvLrZPzieluQ8eQJmtVgymtctWVQDtJemB7QnRZY6jtQEdvOqUrx/sV+3aMd7brFbKTmfkREiohZOh/rxemsnbt2eYD63kSKg2rFy3eEgsG1DGQ63XZX9+580rZqduBsxkTJDLDLTQaH12EZdk9nuENbnwTK2/e2eBsXrFKrctrb1pbidhH77m5M9GSnGIZzPjPeM2OHHcp7mxkqUFBemiIxmLhJkcmySE6T0hFoXex2qYZl+GXeW2efNBjs3VcxWTaYwTYJIxZJI5GIQQOaZno5jcHDU1HPYbFb4/R6Ew6OYmjK2QgtBENmF+nfuklLdrdABJIDh8bvV3X7+breucCAjCPic+N6eBubJTTyewLf3Hlf9YFc7plrlMTm0CIFGndus65FD7Tqlzm2Uk5D1WKHeML73RjvXcXnw+5yYjE3rFiK+8+w6jE7EMi6QUrh0bqH0PHjHtng8kRaiODoxhR8fSM8JlozTbjW8WqLSdUr2Y68Dk1Nxzf1KbuyRyofmcRbgxS+syrjgYdTY5HXbMaKQukHtfadlHGfZT2lfs8+rhlrVWLvNihjj96rPbcfzO2qxMbg45d/zYWyl73NCL4GABwUFVtXtSFi7AwlrBEHogfp37iM1sSn2OhDTMbExku88u47ZpcM6WVE6JssEVg5eIdDIc5txPXJoETC1TqSkYD2W0fczmebGSjQ1VOFM54Cu6qFiMSRbocdEbsMzto1OxCRFpEzhddkUQ6zFfV2qfbd3DSj28Z2bKtKcw2quoHg8gdDlMEK9YcACBCv8CC5Xz4dmBkaMTV6XDV/dGVQcf1jGValvABaHlZxY6XXb8cLOuoyfFwAsAOQm8E57AXZtrkBTQxXauwYkK+xu31B+Z1zvx08OdqaIljPVYKtli1LkG/R9TuiFVVijUFCCIAhiziMnUBgRavHEg5V461iP7uPoTZTMu52e3DdKiaJZMDrvjt7rkYO3qhtLom6l6nXJ8B5rs85E7GLEE7/PbupbfNy0qnT2N0uFcmt1+ZEYl7/ICU0stHcNMFV9NZMXdgUBpIeoy4kmUu2eJTz2qUerudq41WpBfWUA9ZV8+dDMgDVEVYkXdgVnq5PqCT3UG2qtpXiDUee92BvG4PAE/AucqF8WmM1DGY5EcXs0itGJGCywpF2X2rk3BhdjfW0pjaEEYQAkrBEEQRBzGiOqkskR8DnxBw9WobzUi9daO3UJdXoTJfNupzf3jZ79zci7k81jCtvxCnFK8BxrdCJmiKjW1LAcSxd5JMPzWtv0Hf/AqT6sLFvAFfakli/QyJBbgEQ6Xnjul3hbqerOHpeN2X124tx1Q36DFsRijt68dGriR74nb5cTD9XCOz0uG168I6oJx9F7r7XeS63FG8w8L+vx1M6d7+2LIHIFEtYIgiCIOY2ZVcmEylfCB//+4z1oOXqJ+zi8pem1JkVORm/lMT37m1H1LJvHFLYzwknIsw0ADI5M4N/e/ZRpWzXqlwckJ1gz4p3+cDspt55Wl59RxTuSj6dVpDNCkMtVUU/uunjul9S2UrC2MbejIONVbnc/WIklC6WrZhohTMx1cUNOFFMLVZSqTqqU3iBXQmAJgph/kLBGEARBzGmMcDL5vU6ER5TDT6xWC3Y3VqGsxJO+Mq+Si4e3ND1LeI3aMVnEOTl4hUAjz23G9cjBK2Aa4STk2QYARkZjhtxHpXtolBtQyq2nxeVnZMgtoE+kM8I1l+kwWGG/wZEJjIzG4Ct0zFY2FIuXchWDpRySUvfLyCIlAuMKCdlZURuTBajKoXHIhcIaUYlWKh/ZfvSmud4IgiDMgoQ1giAIYk5jhJPp619cBavVwvThz7Myr2fSxpKbRwk9uW94hUAjz23G9cjBK2Aa4SQUYDmWxQLcHJ5QPRYLSvfQSDegWKTT4vIzMuRWj0hnhGsu02GwSu6x5P2Vrkst7Fi4XwBMC8PXi1x+NL/XgUfuW4rSgLQ7jTAevW49JfF2dGIqpR/lqjOUIOYC871/kbBGEARBzGmMcEcNj09iS/0S5u3NXJk38phK4tymVaXc1eh4r13q3BYLkFyvXDgnwJ4k3EjW15XiW0/di9dbO3Hr9l0RS861qNdJyHOsRAI4pDOBu8UCvPSEsgBkpMNQLNJpcfkZGXKrVaQzwjWX6TBYNfeYsP+e5tXYd7hb8bqUEO6XcMxcwuu246s76uBx2TE0GsXXv7AKBTYrpmGB3ZLAyqUL5tVEUA+5MIlmzaH6emsn4okE9h3uNiwnI0HkA5nqp0bnPM1HSFgjCIIg5jRGuKOMcuyYkUdH7zGVxDneanRGnLu6bAG6r96WPKfRwiQrG4Ol2La5Eu9/eAW3hicUz63XSSg+1p7m1fjBW+dTxEYxYjGSh5eeWI2NQfPcjclIufW0uPyMDLnVKtIZ4ZrLZBgsTxGXnx7sREQhqTwLZhQT0YOQt+ueRZ60ogkBnxPf+NK9WFWxAFNT8SxeZf6QK5No1hyq4ZFJ7G05n/7vs2LyGtVxkCDyDaV+mvw9VeR2ABZgeGxS07eV0TlP8xUS1giCIIg5j5zY4fc5MRmbVkyabVb+rlxCTpzLREJtqXPInTObCb4LrBasqgwwTbzX1ZTA7bQZkkTb53aoimZqf9+5qUK3+1BNMASQluNIjJRbT4vLz8iQW60inRGuuUyGwfIUcdErqgHmFBNhZcaVVguf25EWji/VzgYjUfzPV0/jW0/di3XVi7JwxflFLkyiBRdOW0e/Icf7wVvnAKzGxuBiQ45HEEYh5zgT/3t12QJ0Xhma/e6wWix4+1hP2vGEfqpUhZlHJDc652k+Q8IaQRAEMS9Qyn1mROgeQQDSK8THfNc1OzlYxZftG8rR1jEgK54Z4T5UCz0WKuO2tvWlfLCriXi8Lj8jQ261inRGuOYyGQabSQdZ8v3SEkLsdBQgylGg4MufX4mx6BSQmBHlg8vSRWyWyd9rv+3A2hULabxXIB5P4JV3QorbvHqgw/BJdLKI0D84jvc+vGZomHEiAextOY/PGsfQ1FBJbYDICeQcZ5tWleL4x9d1LYIoLYLxiORG5jzNd0hYIwiCIOYNcrnPjArdI+Y3Zjg5WMWXdTUleHprjazoZZTbT+k4QmXcpoZKbhGPN1+gUf1Wq0hnhGsuk2GwvA4yn9uuedKWfL94QoiFZ7eupgShy2Fc7A3jcFsfJmLyLtGAz4nHNy5TbV9Mk7/hmclfbUVx1nOH5Sqhy2HFCTkAjIzHcLFnEKtXLNR8HrOFNDlajl7Cu2ev4ivba+ndT2QVpe+JgyoFZIyCxWlmZM7TfIeENYIgCGLeY0ZhAWJ+wRoOsXblItkcclLwiC/ZDJVNRut18O5nVL/VItIZ4ZrLZBgsTwGKgM+Jp7fVYG+L/HWxhhcr3dunt1WnhWsKv7W+MoD6ygAql/gMcSayTurauwbwz/svZD13WK4S6g0zbfePvzyHP2lapemeKVWuzQRDI5PzKi8UkRski8lFbkdOVFRmcZoZmfM037EkElrT3c4tpqfjGBwcNfUcNpsVfr8H4fAoJUcliDkG9W+CmLuw9O9Qbxjfe6Nd9Vhetx0jSU4glkm7WjVHmgAag5bqaVIiAK9rjvcYWtuD2n7i/dWui+d+6alMZ8Q9Zu2fSmjpZ7lQOdNIfvHvn2D/8V7m7XnvGWsbZcXrsuGrO4NpBStYCPic+N6ehrx+XkR+fJ9nW0xW4s9212NL/RLZv8fjCXx773HVxZ587kuBgAcFBVbV7UhYuwMJawRB6IH6N0HMXVj698kL1/FPb1/QfA61CagR4gJhDkaIJ7zH0NoelCZwUvvnijCk9zpYJn9q1XV5J4e5UjnTSC5cGsTLb55l3t7ntuPvvvkgbDb1SSnLM2Kh6h4fVlcFUorGaBXsvvPsupxwARPaMfv7nHdsEm8fGY8puoOzDUsfmOuLfySscULCGkEQeqD+TRBzFyMda3KwTNpzReQgcgOt7UHYb3BkAiOjMfgKHfD75n57MsINxSq0zNWJZjyewH/6P79XzbOWjM9tx9d21qX9XnH7jScSeHnfWd3X6HPb8f98qzGtLZ8O9eMHb51TraKcjJpbh8h9zPw+lxPPH1m7FKUBd9q4LLW9BUCuijE8iwlzefGPVVijHGsEQRAEQRA64clhJQVLLpNcyaFG5AaZymU3V5DN91bkROPaMrz9+09Vj6GWqy0eTyDUG1atnMmSFDwXsVoteHFXkEugjIzH8P1fnsOe5jXYGJyZYEtNwj0uY6alkfGY5Fg6c+7V2NtynvlY8yEv1HyEZVFCbRul4gItRy/N/rfgUgUguX2uimoA8PQ2thyWAOUqBkhYIwiCIAiC0A1LIno15kPVLILIJlKTv/qqAK6FJ5iENSWhhSdPEouQnqsIAuWr74QwwuFc+8Fb5wCshtVikRwneVxwasiNpRuDi2F90sL0nNSq+hL5QTyewMfdN9H32e3ZasfinHviEG21MG6WYkUCQlVwo4TjTLLvcBesFnBV2c7HMc0o8u8JEwRBEARB5CByjhhWyB1BEOYjnvxZrRbUr1iIgM+JQc5qqwJawkzzWUhfX1eKtSsX4S+/fyylGIsSiQSwt+V8RgSG/sEx2b8J4ur+45fQcrRHdjvWirNGIXZIVZct4KognY9oCWfn2edMRz/eONSl2K+Bu+LXN59cA0DaWZa8jcdl537HGykcZ4rk35zv4ZyZgIQ1giAIgiAIg9Ay4QTIHUEQZqI2GS+wWvCVHXX4h59/JHsMOaGFx72STL4L6TabFS/srOMWFDMhMLQc7UFZiVdWDLBaLdjduAJlJd6s5IUSt8fh0Um81tqJSNI7Q1xMw+e2Y8vqxVhXU5JXIptc39NS3INnHy1i9+utnTM3XoE3DnXhDx9ZyXXcfCdfQ9czDQlrBEEQBEEQCohDSdQmNd1Xb3OJakDm3REEMV9gnYxvDMrkYFMRWjr7hrjdK3NFSJ8NCz3QwT3mmQ2LGJDJvFCCwNTeNYCT52+kiGhSiIssRMZjaG27gta2K4oJ8nMJub63eVUpDpzqS9teySGllNNMvI9WsTs8Mqm6zWAkisiY+nb5AGvhhHwOXc8kJKwRBEEQBEHIIBVKoraqzhPiNVeqZhGE2WgJG2OZjG9efbfqoxahRUtI51wS0gWX7n/5/jFVsSiTsIoBmcgLxZN/jwW5BPm59B5R6ntSoloyr4tEURahLHkfLWI3D16PXVexIhYCPic2yQiQRpEAsKV+MU5euKG6bT6HrmcKEtYIgiAIgiAk4FkhT4Y1xOuZrdV4bEPFnJlgE4RZaAkbY5mMv3GoCxtXLU75N16hhSekc64K6TabFV/TEBbKy+4HK3H4zBXmcNJcEAO0hCTykmu5sOLxhGplXCXCkSj2tnyMbesrUFtRzCSUhSNR7D9+CbsbV5j+3ANel+5iRUp8+fMr8fjGZbBaLVhZtgCvvBMyLYR6UbGLabt8D13PBNZsXwBBEARBEEQ2iccTCPWGcfLCdYR6w4jHE8yT8ng8PZCitqIYfp/yR2jA5yRRjSAkEPfH06EZYUI8sRbEhDMd/ZLHYZmMD0ai6Lgc1nW9LP3d47Lhr565D9/b05ATwocZrK8rxZ7mNdA7ou3YVJF2PwM+J7755BoEl/m5BIZsiwFaQxK18uqBjrR3ktT7zUzi8QR+e/qybiHoTOdNfO+Ndnx773G0dw0w7dNytAdnOvpNfe5CGPf6ulI0N1Zx7bumKsC0XbHPOfttsL6uFP/7Lx5Cc2Mlc+GPnZsq4HPbmbYNVviZvlfmQui62ZBjjSAIgiCIeYucE+aRtfcwTcqlQo2sVovqavZcCgUjCKOQ6o8qucRlc2mxulaGGPIqKcHS31/cFUR9JdukOp/ZGCzFtQcr8daxHs3HOHWxH89sq4bP7UgLxz154TrzcXJBDDA7JFHMyHgMocvh2bamxempB6NDXoEZAb217Qrz9m8c6sJff+MB00I1k9/dpQE3175rqgI4d2lQdTuxMCgU22hqqJoNU+8fHMO7Z6+ljF/JjtgvPbxStYhSwOdEcLmfvlcMghxrBEEQBEHMS4QQHSknTMvRHqZjyE3ehaTecs6LuepaIQityPVHcRJ3MYLALYbVtVLsdbBeoizU3+/yBw9WMTtrpJgJAzyP0YkYttQvQXC5f3ZSz+NEEosBmXZuAdkJRQ31zjgwld5vgtPTyHsid75MMxiJovvqbTyzrcbQ43pctrS+zBsGvvX+cl3uMCFMfUv9EuxuXIGX//xBfOfZdfiz3fX4zrPrUhyxQtVeJYQ+QuOXMZBjjSAIgiCIeYdRITpKH9aZrDhHEPmM3v4oJWAIIZpKE/2Az4m6ZcYkrqf+PoPVasGLu4K6809JORFZnqnFArz0RKoYkGnnlkBWQlEtbP3p1QMdeK21M8XxxJK3UKp9ZzrkVY32rgG0dUiHjwZ8Tjyd5IgcHpnEviPdqsfc88Qa1ItCOVnao8Czj9XAZrMa6g5TywcpCGYslY5p/NIPCWsEQRAEQcw7jAjRYQk1ykTFOYLId/T2RykBIxsh2dTfZxDyTyVXruRFKtSe5ZnubqjEdDyOUG8YtRXFaO8a0FSExgh4hBejCFb4mfqTVIig+J4kC2n9g+N478NrkuKkx2XPulMtGaXQ0ae3VWNj8G7Bkng8gYOn+1QFeKl+zdIevW47XthZN9vGeMQuI+ARzGj80gcJawRBEARBzDuMCNGhvCMEYQx6+qOSwJ3pSSxxl6aGSrx79qquHHZS7ULumXrddiQSiZT8bsVeB2JTccVzyOXoMwIW4QWYcdklhzz73HZ8ft1SvH28l+t8XpcNweV+nArd0HK5s7xxqAtT8The+22XYo4uQYjbvqFc1/kyyU8OdmJ9bens89YrwMu1R4/Lhu0bytHUUJW2b6bdYSSYZQYS1giCIAiCmHewhug0N1bh3z+8hkGalBOEbuRCyfSEzKkJ3BTilB2sVgu+sr1WV0ioXLsQP9Prt8bwtkTBBBZRT64IjVHICS8+tx1bVi/GupoSVJctQPfV2yntEwB+//F1LifYC7uCuvsTMHNPfvjWBebtT17QJ+RlEnGBB0C/AK9ljCGxa+5BwhpBEARBEPMO1vxLTQ2VaH54Ba6FJ9D32W343HaalBOEBpTyXK2rKeEOmfMV2vG1HXVMAjdNYrODnGDh9zkxGZvG6MSU7L5qofbCMz0duoFfHe/RdZ1mFxlgEV6k2ieL2w0A3I4CNNy7BB6XHfF4ArUVxXA6ChCdnDb0d8gRGZN3teUiod5wWpVevQI8jTEECWsEQRAEQcw7eMI/rFYLPle9COUL3ZhSCSsiCCIdoWKgmOScTqwigsDT26rJNZoHyAkWcrnPBFhC7c909GNvy3nd16jF4SXnvpRDi/AiJ0w6bFYkgNkw1/HJaRxuu4rDbVfh9zmxKViSMVEtl3A5CjDB8rtlHhOJY4QeSFgjCIIgCGJeQvmXCIIPXjFB2EetYuAbh7rwvT0N+OaTa/DjAx2IKOR1Egh4XVzXTmQPKcFiXU0Jmhur0NrWl+JcYx1/japEyVKEJvmcnX1DaO8awMnzN1Laqd/nxDNJ1SbFVTO1OqEEYXL/8UtobbuC0YkpTCos8IQjURw8LZ+8fy7z+MYKyZBgMcEKEs8I4yFhjSAIgiCIeQvlXyIINpRCOZVEEJYKhUKeq/V1pVi7chH+8vvHFJOm84ghRO4h1ZaUkr1LYURlZ4C9CI3UNScTjkTT3HPFXgc21y/G8Y+vp4lwav0mmfauAbQc7WHadj7icdnw4q4g1tWU4PCZK4ohxkKBB4IwGmu2L4AgCIIgCCKbCG6KLfVLEFzuJ1GNIEQIoZxiUUEI5TzT0S+7L2v+KmE7m82KF3bWKW5LFXnzF7m2NDoxhZajPWjvGmA6Dk9eNK/LBr8vNdwz4HPim0+uYRK35K5Z9RpHJnHwVF+aA5Ol3wgY5czLNhYTu+ueJ2aeo9VqwYu7gorbCgUeCMJoyLFGEARBEARBEIQkrKGc62pKJCesrPmrkrejMO3cQU8Yo9Sx9LSlZHjyor1wx82k5XeYKWy9fqgLbocNw+OTstdklDMv2+zYWIEDp/oMP27A50xxoAljx2utnSlVYf0+B557rJbGDsI0SFgjCIIgCIIgiDmCkUIIwBfKKRVixVqBVxzaSWHa2SG5/fQPjuO9D69JhGxWoKmhkvtZ6G1LydRWFMPjsimG/VkAvNR815WmJQTQTGErHIni5TfPzv63VIio2RVLjWT3g5X4/UefyYrhK8sWKIbTSuG0FyAaky9IIOVeTR47IuMxVNyzAEv9LsTjCf4fRRCMkLBGEARBEARBEHMArXnQlOAN5RTDU4FXal/Kh5Q51PKIAULI5iUcOHUZf/yFVVhfyy5+6m1LvBQ6C7C+tkTXMTIpbAkhonua12BjcKa/aqlYqobTUWB41VCvy4bdD1Zh94NVsu1BLJa3dw7gdEg+9HfHxnL80edrUgo3CKi5V4Wxw2azwu/3IBweJWGNMBUS1giCIAiCIAgizxHyQIkRJuus+aTEaAnlFEOhnbmPXPuRY2JyGntbzqWJNEpCrhFtSaCzb0jRrQYAo9FpJveb3msxmh+8dQ7AamwMLmZyfEqxY2M5ToUGUvbzue14fkctrBYL17MWjqdUbTQ5d5nS/U4Wy7fUL8HCI104eLoPiSTNy2KZCR398tYaAMDuxhVoapAX7AgiFyBhjSAIgiAIgiDyGCNzV4nRGsophkI7cxc9ecTEziclIdeotgTwud/0hEdrFbb0kEgAe1vOw/qkBevrSlUdn2J2bKrA01tr8Eefr5H93VJCt9NeAAsSmIjFZ/8tWfyuLi9Oz13mdeC57dpzl315aw2+9PBKHPngCvqHxlFa7MbW+8ths6XWWCT3KpHrkLBGEARBEARBEFlGz+TfyNxVYvSEckodiybHuYcZecRePdCRJuQa2ZZYnWT9g2P49t7jmsOjWa7ZLAQxXHB8/vhAR1qFUSnWrlwEQLm/yQndAJhDOY0Sx202Kx7ftEzXMQgi25CwRhAEQRAEQRBZRG9uNLNzV1Eo59zGjDxiI+Mx7D9+CbsbV6T8u1Ft6fbYJCwAlLJmed12tBztSft33vDo9XWl2NO8Gj9463xKyKLZJIvh6+tKEZ2axo9+dVF1P9bnKSe8sYZyEgRxFxLWCIIgCIIgCCJLGJEbzcjcVXJQKOfcQ3BJXrs5asrxD5zqwyK/GwGvy1Dn08+OdOHAqT7V7RIqKhhPePTG4GJcHRjF28d6mK7RKJJFsoDXxbRPNvLCEcR8h4Q1giAIgiAIgsgCRuVGMzJ3lRLkVtGGnjBfs2CpAKqXicm7DiuxA1NrWzoduqEqqlkswB80VKqKYLzh0UsWFjJtt65mEbatL8foRAyvHujAmEqRBSWSRbJM9XOCIPghYY0gCIIgCIIgMoBYYIknEobkRjMydxVhLHrDfM26pkznDNNbnRaY6T8/Odipul0iMbMtCzxhsKxOsO0bKmb76/raUuw/3oPWtr6UKqZetx0jKvnSxCIZ9XOCyF1IWCMIgiAIgiAIk5ESWDwutk9xlsk/5UH7/7d35/FRlvf+/9+ZrGQjCwENuwpJIBIiQmTVQpTFoIjyExHQIz0iVUR7rKKndTnF9bjUIlKsgNAKR5Slyo9FQcWiVMUFQdlEQUAhQgJZgZCZ7x90xkwyyz33zGSS4fV8PHxI7rnu+75mhjHJ2891fRqft0q0QCzzDeR87GPMdgANBLPdaaUzm+p7C6McDF7el2WTZirGLJYIXTWgs4r6dWrw3ny2q1izV3zt9lquQjI+50DTRLAGAAAABJG7gKXS4BIxo7/8sw9a43FXiXZpXqZap7VQcosYrwHWwrU7dfJ0bYM9yAI5n/qVccHoAOoLs91pJd+qy7Lbp+rDpEMBXTbpT8WYq6WvvbPbyHJNhM8hGZ9zoOkhWAMAAACCxN8KITO//LMPmv88VX95qkRbsfF7w/cor6pxuweZL3ypjDPbATQ22qKTNVZT59Zndg5GA+ak+Ghld0wNyrLJQFeMmQ3J+JwDTQvBGgAAABAk/lYIsWeSa8FsCOCp+iu/S0ZQllKaXR7qawOM4pJqU/MbXtBBKzbuNXVufWa7VhpZiilJ46/IksUSEbRlk4GuGCMkA5o/gjUAAAAgSIxW5yTERTktDT0b9kwyG44FsyGAt+qvUQM6BXUppa97kBkJbu3LLytP1PhUUWeXlhSron6dJUWYOr/+tcx2rTSyFHNYn/bqnf3L34FgLZskDANQF8EaAAAAECRGq3OmXJ0riyXirNkzyWw4FsyGAEaqv97ZfMDUtY3ydQ8yo8FtScUJLX3/O1NzsldNFvXrpPe/PKhjFafcjk2Mi1KFh70D/a3AdFeFltQiWuOHdlXv7DYNziEEAxBsBGsAAABAkBjtJJjdMTXkQVowl1fWZTYc83XZo6+MVH8ZbTjhD1/2IDMa3FZU1vhcaVe/atJiidCNl3f1WDF20/BsSQpq10o27wfQ1BCsAQAAAEHiTydBX/gbigVzeWX9eZoNx3xZ9hjMrpP1l+0Gmi97kBkNbpPiYwxdr6hfR2W2SnD7d8jovmXBDr6oQgPQlBCsAQAAAEEUrE3U7fwNxYK5vLI+f8Ixo8FXsLtOXn5xe5/2GktNjNGkom46XnFKi9fvVkV1jduxZrrAGgluE+KiDV0vu0OqunVK8zjGSMUYwReAswnBGgAAABBkRpev+Vp55m8oFuzllfX5E44ZDb6C2XXyzEb+ndQ2I6FBmOnOuMu7OsKqmGhLwKsXjQS3VqvNUEfNl1d+oxsv7+o1SCU4A4BfEKwBAAAAjcBbGOFr5VkgQrFgL6+sz59wzGjwFcyuk/bgq35QWlxSpfe//NFpY39XFYnBql70FtwaeW6SdKziVMCrFAEg3BGsAQAAACFmpvIsEKFYsJdX1udPONYY+9X5EnzVD0qL+nU2VG0YrM33vQW37p6bK4GsUgSAcEewBgAAAISQ2cqzQIRiwV5eWZ+/4Viw96uz38NM8OXL8shQLaXsldVaLWKi9PRrX3ocF8gqRQAIdwRrAAAAQAiZrTwLRCgW7OWVrvgbjgWr4quucN5DrKz6lPdBClyVIgCEO4I1AAAAIITMVp4FIhRrjOWVrrgLxyRpx75Sl4GZr40d4FpjVykCQLgjWAMAAABCyGzQEahQrDGWV7pSvyrMU/MGq036+9qdKq+uafCY0fnZg7mSihOqqKxRUnyMWqXEqaBlfOCeVDMQiipFAAhnETabzRbqSTQFtbVWlZRUBvUeUVEWpaYmqLS0UqdPW4N6LwCNi883EL74fCPYrFabfjf7I69Bx1NT+rkMyVwFUmZCsVBWhLlr3mCEkQ6Wrl4ju/SWcRp3eVflX9DK1P2bI2+vN11BEQ74/g1/paUlKDLS4nUcwdq/EawB8AefbyB88flGY/A36GiOyyTrVpC9tu5bp2o0X3gKHSXjod2oAZ3UOi2+2bx+/gpUIAs0VXz/hr+MBmssBQUAAABCzN/lmM1ts31PFWS+8tTB0kjHVbsVG/c6/uzrMtPmqDGaQADA2YBgDQAAAGgCzpagw59ln+64awBhpOOqK6XlJzVr+bawXxLZ3AJZAGiKCNYAAACAJiLcgw5fKsh84a4BhNGOq+4sWL1D+V0ywi7cBAAEjvfFogAAAAAQAGYryDxJio9228HSaMdVdypOnNbKj/YaGmu12rRjX6n+9c0h7dhXKqvVt62s/T0fABAaVKwBAAAAaBT+VpC5Mv6KLLcVZV3bpyg1KdavMO+dzftV1K+Tx6o1V3vG+bJPm5nz7c0fSstPqrzqlBITopWWGBeWy4cBoCkjWAMAAADQKPytIKtvWJ/26p3tPriyWCI0rrCLX3u6VZ44rXWb96vw4vYuA6tPdxRr9oqG1zeyT5vVatPKj/ZqxcbvfTrfU/OHpBbRGj80S726hv9+fQDQFETYbDZqjCXV1lpVUlIZ1HvQ7hcIX3y+gfDF5xsIHKvVpt/N/sjv5aBnwqOu6p3dxtD4QHQhdVVB9umOw/rLP76Wp9+okuKj9cxv+isqynkXns92FuvVd3bpWMUpj/dNS4rVU1P6OUIxo80fYqMtOlnzy3+zzoZOp0BdfP+Gv9LSEhQZ6X0HNYK1fyNYA+APPt9A+OLzDQSWt2BoWJ/2+nh7sfOyyMQYXdozU63T4k1XX1mtNr396Q9a8t4e03OX5Kgg86W7aWKLaN00LMsRavnaGfXeG/KV3TE1IMFkuHc6Bez4/g1/G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"},"metadata":{}}]},{"cell_type":"code","source":"\n\n\n# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data.Year, data.Population, data.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:40.474750Z","iopub.execute_input":"2023-10-26T20:34:40.475177Z","iopub.status.idle":"2023-10-26T20:34:40.996103Z","shell.execute_reply.started":"2023-10-26T20:34:40.475149Z","shell.execute_reply":"2023-10-26T20:34:40.995418Z"},"trusted":true},"execution_count":131,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## How has the Internet usage increased through time?","metadata":{}},{"cell_type":"code","source":"plt.scatter(internet.Year, internet['No. of Internet Users'])","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:46:20.003157Z","iopub.execute_input":"2023-10-26T20:46:20.003504Z","iopub.status.idle":"2023-10-26T20:46:20.415763Z","shell.execute_reply.started":"2023-10-26T20:46:20.003477Z","shell.execute_reply":"2023-10-26T20:46:20.414596Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data_int['No. of Internet Users'], data_int.Population, data_int.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:45:47.490885Z","iopub.execute_input":"2023-10-26T20:45:47.491210Z","iopub.status.idle":"2023-10-26T20:46:19.212868Z","shell.execute_reply.started":"2023-10-26T20:45:47.491185Z","shell.execute_reply":"2023-10-26T20:46:19.211734Z"},"trusted":true},"execution_count":141,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# create data\nx = np.log10(data_int.Population)\ny = np.log10(data_int.GDP)\nz = data_int['Internet Users(%)']\n \n# use the scatter function\nplt.scatter(x, y, s=z, alpha=0.5)\n\n# show the graph\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:42:51.380366Z","iopub.execute_input":"2023-10-26T20:42:51.380727Z","iopub.status.idle":"2023-10-26T20:43:12.249788Z","shell.execute_reply.started":"2023-10-26T20:42:51.380701Z","shell.execute_reply":"2023-10-26T20:43:12.248684Z"},"trusted":true},"execution_count":140,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\ No newline at end of file diff --git a/Data engineering and science/Simple analysis/uk-population.ipynb b/Data engineering and science/Simple analysis/uk-population.ipynb new file mode 100644 index 0000000..2c1bc99 --- /dev/null +++ b/Data engineering and science/Simple analysis/uk-population.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom scipy.stats import skew\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.preprocessing import StandardScaler\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-08-27T15:17:10.686884Z","iopub.execute_input":"2023-08-27T15:17:10.687301Z","iopub.status.idle":"2023-08-27T15:17:10.712128Z","shell.execute_reply.started":"2023-08-27T15:17:10.687269Z","shell.execute_reply":"2023-08-27T15:17:10.710915Z"},"trusted":true},"execution_count":230,"outputs":[{"name":"stdout","text":"/kaggle/input/domestic-growth/regionalgrossdomesticproductgdpcityregions.csv\n/kaggle/input/global-pollution-by-counties/country_level_data_0.csv\n/kaggle/input/uk-population-data-200120112021/UK Regional Population Data.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Introduction \n\nThis notebook provides a basic analysis of the UK population in last two decades. This work demonstrates some weaknesses in the data and further work may need to be completed. For example, the area of each geographical aggregation would bring more data for further analysis. Other factors and statistical data should be merged using ONS data. \n\nThis notebook only shows how basic Pandas, descriptive statistical methodologies, correlations and unsupervised methods can be used to show some patterns hidden in the data. ","metadata":{}},{"cell_type":"markdown","source":"# Import","metadata":{}},{"cell_type":"code","source":"path = '/kaggle/input/uk-population-data-200120112021/UK Regional Population Data.csv'\ndata = pd.read_csv(path)\ndata.shape","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.182313Z","iopub.execute_input":"2023-08-27T14:19:54.182822Z","iopub.status.idle":"2023-08-27T14:19:54.197405Z","shell.execute_reply.started":"2023-08-27T14:19:54.182783Z","shell.execute_reply":"2023-08-27T14:19:54.196180Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"(420, 10)"},"metadata":{}}]},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.199099Z","iopub.execute_input":"2023-08-27T14:19:54.199814Z","iopub.status.idle":"2023-08-27T14:19:54.208247Z","shell.execute_reply.started":"2023-08-27T14:19:54.199780Z","shell.execute_reply":"2023-08-27T14:19:54.206953Z"},"trusted":true},"execution_count":135,"outputs":[{"execution_count":135,"output_type":"execute_result","data":{"text/plain":"Code object\nName object\nGeography object\nArea (sq km) float64\nEstimated Population mid-2021 int64\n2021 people per sq. km float64\nEstimated Population mid-2011 int64\n2011 people per sq. km float64\nEstimated Population mid-2001 int64\n2001 people per sq. km float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"data.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.209549Z","iopub.execute_input":"2023-08-27T14:19:54.209928Z","iopub.status.idle":"2023-08-27T14:19:54.250065Z","shell.execute_reply.started":"2023-08-27T14:19:54.209896Z","shell.execute_reply":"2023-08-27T14:19:54.249047Z"},"trusted":true},"execution_count":136,"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":" Area (sq km) Estimated Population mid-2021 2021 people per sq. km \\\ncount 420.000000 4.200000e+02 420.000000 \nmean 3159.191891 9.891223e+05 1469.096318 \nstd 19377.016684 6.096465e+06 2290.766597 \nmin 2.889800 2.271000e+03 8.718600 \n25% 103.882325 1.087305e+05 193.382319 \n50% 338.689000 1.507605e+05 505.292988 \n75% 968.942600 2.852145e+05 1825.495254 \nmax 242740.869900 6.702629e+07 15794.496990 \n\n Estimated Population mid-2011 2011 people per sq. km \\\ncount 4.200000e+02 420.000000 \nmean 9.327738e+05 1387.300189 \nstd 5.746008e+06 2170.127582 \nmin 2.224000e+03 9.062238 \n25% 1.032262e+05 177.425985 \n50% 1.416640e+05 487.335747 \n75% 2.606608e+05 1765.935766 \nmax 6.328514e+07 13883.766320 \n\n Estimated Population mid-2001 2001 people per sq. km \ncount 4.200000e+02 420.000000 \nmean 8.711469e+05 1278.571770 \nstd 5.361023e+06 1946.069248 \nmin 2.140000e+03 8.144047 \n25% 9.678150e+04 168.879470 \n50% 1.346650e+05 447.797196 \n75% 2.457082e+05 1715.679416 \nmax 5.911302e+07 13378.671530 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Area (sq km)Estimated Population mid-20212021 people per sq. kmEstimated Population mid-20112011 people per sq. kmEstimated Population mid-20012001 people per sq. km
count420.0000004.200000e+02420.0000004.200000e+02420.0000004.200000e+02420.000000
mean3159.1918919.891223e+051469.0963189.327738e+051387.3001898.711469e+051278.571770
std19377.0166846.096465e+062290.7665975.746008e+062170.1275825.361023e+061946.069248
min2.8898002.271000e+038.7186002.224000e+039.0622382.140000e+038.144047
25%103.8823251.087305e+05193.3823191.032262e+05177.4259859.678150e+04168.879470
50%338.6890001.507605e+05505.2929881.416640e+05487.3357471.346650e+05447.797196
75%968.9426002.852145e+051825.4952542.606608e+051765.9357662.457082e+051715.679416
max242740.8699006.702629e+0715794.4969906.328514e+0713883.7663205.911302e+0713378.671530
\n
"},"metadata":{}}]},{"cell_type":"code","source":"data","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Regions","metadata":{}},{"cell_type":"markdown","source":"We can see some regions varies from a whole country to a city council. We will filter the data and focus on city counci.","metadata":{}},{"cell_type":"code","source":"data.Geography.unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.253089Z","iopub.execute_input":"2023-08-27T14:19:54.253429Z","iopub.status.idle":"2023-08-27T14:19:54.261052Z","shell.execute_reply.started":"2023-08-27T14:19:54.253401Z","shell.execute_reply":"2023-08-27T14:19:54.259788Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":"array(['Country', 'Region', 'Unitary Authority', 'Metropolitan County',\n 'Metropolitan District', 'County', 'Non-metropolitan District',\n 'London Borough', 'Council Area', 'Local Government District'],\n dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"data.Name.unique()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.262548Z","iopub.execute_input":"2023-08-27T14:19:54.262896Z","iopub.status.idle":"2023-08-27T14:19:54.275608Z","shell.execute_reply.started":"2023-08-27T14:19:54.262866Z","shell.execute_reply":"2023-08-27T14:19:54.274558Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":"array(['UNITED KINGDOM', 'GREAT BRITAIN', 'ENGLAND AND WALES', 'ENGLAND',\n 'NORTH EAST', 'County Durham', 'Darlington', 'Hartlepool',\n 'Middlesbrough', 'Northumberland', 'Redcar and Cleveland',\n 'Stockton-on-Tees', 'Tyne and Wear (Met County)', 'Gateshead',\n 'Newcastle upon Tyne', 'North Tyneside', 'South Tyneside',\n 'Sunderland', 'NORTH WEST', 'Blackburn with Darwen', 'Blackpool',\n 'Cheshire East', 'Cheshire West and Chester', 'Halton',\n 'Warrington', 'Cumbria', 'Allerdale', 'Barrow-in-Furness',\n 'Carlisle', 'Copeland', 'Eden', 'South Lakeland',\n 'Greater Manchester (Met County)', 'Bolton', 'Bury', 'Manchester',\n 'Oldham', 'Rochdale', 'Salford', 'Stockport', 'Tameside',\n 'Trafford', 'Wigan', 'Lancashire', 'Burnley', 'Chorley', 'Fylde',\n 'Hyndburn', 'Lancaster', 'Pendle', 'Preston', 'Ribble Valley',\n 'Rossendale', 'South Ribble', 'West Lancashire', 'Wyre',\n 'Merseyside (Met County)', 'Knowsley', 'Liverpool', 'Sefton',\n 'St. Helens', 'Wirral', 'YORKSHIRE AND THE HUMBER',\n 'East Riding of Yorkshire', 'Kingston upon Hull, City of',\n 'North East Lincolnshire', 'North Lincolnshire', 'York',\n 'North Yorkshire', 'Craven', 'Hambleton', 'Harrogate',\n 'Richmondshire', 'Ryedale', 'Scarborough', 'Selby',\n 'South Yorkshire (Met County)', 'Barnsley', 'Doncaster',\n 'Rotherham', 'Sheffield', 'West Yorkshire (Met County)',\n 'Bradford', 'Calderdale', 'Kirklees', 'Leeds', 'Wakefield',\n 'EAST MIDLANDS', 'Derby', 'Leicester', 'North Northamptonshire',\n 'Nottingham', 'Rutland', 'West Northamptonshire', 'Derbyshire',\n 'Amber Valley', 'Bolsover', 'Chesterfield', 'Derbyshire Dales',\n 'Erewash', 'High Peak', 'North East Derbyshire',\n 'South Derbyshire', 'Leicestershire', 'Blaby', 'Charnwood',\n 'Harborough', 'Hinckley and Bosworth', 'Melton',\n 'North West Leicestershire', 'Oadby and Wigston', 'Lincolnshire',\n 'Boston', 'East Lindsey', 'Lincoln', 'North Kesteven',\n 'South Holland', 'South Kesteven', 'West Lindsey',\n 'Nottinghamshire', 'Ashfield', 'Bassetlaw', 'Broxtowe', 'Gedling',\n 'Mansfield', 'Newark and Sherwood', 'Rushcliffe', 'WEST MIDLANDS',\n 'Herefordshire, County of', 'Shropshire', 'Stoke-on-Trent',\n 'Telford and Wrekin', 'Staffordshire', 'Cannock Chase',\n 'East Staffordshire', 'Lichfield', 'Newcastle-under-Lyme',\n 'South Staffordshire', 'Stafford', 'Staffordshire Moorlands',\n 'Tamworth', 'Warwickshire', 'North Warwickshire',\n 'Nuneaton and Bedworth', 'Rugby', 'Stratford-on-Avon', 'Warwick',\n 'West Midlands (Met County)', 'Birmingham', 'Coventry', 'Dudley',\n 'Sandwell', 'Solihull', 'Walsall', 'Wolverhampton',\n 'Worcestershire', 'Bromsgrove', 'Malvern Hills', 'Redditch',\n 'Worcester', 'Wychavon', 'Wyre Forest', 'EAST', 'Bedford',\n 'Central Bedfordshire', 'Luton', 'Peterborough', 'Southend-on-Sea',\n 'Thurrock', 'Cambridgeshire', 'Cambridge', 'East Cambridgeshire',\n 'Fenland', 'Huntingdonshire', 'South Cambridgeshire', 'Essex',\n 'Basildon', 'Braintree', 'Brentwood', 'Castle Point', 'Chelmsford',\n 'Colchester', 'Epping Forest', 'Harlow', 'Maldon', 'Rochford',\n 'Tendring', 'Uttlesford', 'Hertfordshire', 'Broxbourne', 'Dacorum',\n 'East Hertfordshire', 'Hertsmere', 'North Hertfordshire',\n 'St Albans', 'Stevenage', 'Three Rivers', 'Watford',\n 'Welwyn Hatfield', 'Norfolk', 'Breckland', 'Broadland',\n 'Great Yarmouth', \"King's Lynn and West Norfolk\", 'North Norfolk',\n 'Norwich', 'South Norfolk', 'Suffolk', 'Babergh', 'East Suffolk',\n 'Ipswich', 'Mid Suffolk', 'West Suffolk', 'LONDON', 'Camden',\n 'City of London', 'Hackney', 'Hammersmith and Fulham', 'Haringey',\n 'Islington', 'Kensington and Chelsea', 'Lambeth', 'Lewisham',\n 'Newham', 'Southwark', 'Tower Hamlets', 'Wandsworth',\n 'Westminster', 'Barking and Dagenham', 'Barnet', 'Bexley', 'Brent',\n 'Bromley', 'Croydon', 'Ealing', 'Enfield', 'Greenwich', 'Harrow',\n 'Havering', 'Hillingdon', 'Hounslow', 'Kingston upon Thames',\n 'Merton', 'Redbridge', 'Richmond upon Thames', 'Sutton',\n 'Waltham Forest', 'SOUTH EAST', 'Bracknell Forest',\n 'Brighton and Hove', 'Buckinghamshire', 'Isle of Wight', 'Medway',\n 'Milton Keynes', 'Portsmouth', 'Reading', 'Slough', 'Southampton',\n 'West Berkshire', 'Windsor and Maidenhead', 'Wokingham',\n 'East Sussex', 'Eastbourne', 'Hastings', 'Lewes', 'Rother',\n 'Wealden', 'Hampshire', 'Basingstoke and Deane', 'East Hampshire',\n 'Eastleigh', 'Fareham', 'Gosport', 'Hart', 'Havant', 'New Forest',\n 'Rushmoor', 'Test Valley', 'Winchester', 'Kent', 'Ashford',\n 'Canterbury', 'Dartford', 'Dover', 'Folkestone and Hythe',\n 'Gravesham', 'Maidstone', 'Sevenoaks', 'Swale', 'Thanet',\n 'Tonbridge and Malling', 'Tunbridge Wells', 'Oxfordshire',\n 'Cherwell', 'Oxford', 'South Oxfordshire', 'Vale of White Horse',\n 'West Oxfordshire', 'Surrey', 'Elmbridge', 'Epsom and Ewell',\n 'Guildford', 'Mole Valley', 'Reigate and Banstead', 'Runnymede',\n 'Spelthorne', 'Surrey Heath', 'Tandridge', 'Waverley', 'Woking',\n 'West Sussex', 'Adur', 'Arun', 'Chichester', 'Crawley', 'Horsham',\n 'Mid Sussex', 'Worthing', 'SOUTH WEST',\n 'Bath and North East Somerset',\n 'Bournemouth, Christchurch and Poole', 'Bristol, City of',\n 'Cornwall', 'Dorset', 'Isles of Scilly', 'North Somerset',\n 'Plymouth', 'South Gloucestershire', 'Swindon', 'Torbay',\n 'Wiltshire', 'Devon', 'East Devon', 'Exeter', 'Mid Devon',\n 'North Devon', 'South Hams', 'Teignbridge', 'Torridge',\n 'West Devon', 'Gloucestershire', 'Cheltenham', 'Cotswold',\n 'Forest of Dean', 'Gloucester', 'Stroud', 'Tewkesbury', 'Somerset',\n 'Mendip', 'Sedgemoor', 'Somerset West and Taunton',\n 'South Somerset', 'WALES', 'Isle of Anglesey', 'Gwynedd', 'Conwy',\n 'Denbighshire', 'Flintshire', 'Wrexham', 'Powys', 'Ceredigion',\n 'Pembrokeshire', 'Carmarthenshire', 'Swansea', 'Neath Port Talbot',\n 'Bridgend', 'Vale of Glamorgan', 'Cardiff', 'Rhondda Cynon Taf',\n 'Merthyr Tydfil', 'Caerphilly', 'Blaenau Gwent', 'Torfaen',\n 'Monmouthshire', 'Newport', 'SCOTLAND', 'Aberdeen City',\n 'Aberdeenshire', 'Angus', 'Argyll and Bute', 'City of Edinburgh',\n 'Clackmannanshire', 'Dumfries and Galloway', 'Dundee City',\n 'East Ayrshire', 'East Dunbartonshire', 'East Lothian',\n 'East Renfrewshire', 'Falkirk', 'Fife', 'Glasgow City', 'Highland',\n 'Inverclyde', 'Midlothian', 'Moray', 'Na h-Eileanan Siar',\n 'North Ayrshire', 'North Lanarkshire', 'Orkney Islands',\n 'Perth and Kinross', 'Renfrewshire', 'Scottish Borders',\n 'Shetland Islands', 'South Ayrshire', 'South Lanarkshire',\n 'Stirling', 'West Dunbartonshire', 'West Lothian',\n 'NORTHERN IRELAND', 'Antrim and Newtownabbey',\n 'Ards and North Down', 'Armagh City, Banbridge and Craigavon',\n 'Belfast', 'Causeway Coast and Glens', 'Derry City and Strabane',\n 'Fermanagh and Omagh', 'Lisburn and Castlereagh',\n 'Mid and East Antrim', 'Mid Ulster', 'Newry, Mourne and Down'],\n dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"rows = data[\"Geography\"].str.contains(\"Council Area\")\ndata_cc = data.loc[rows, :]\ndata_cc.shape","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.277186Z","iopub.execute_input":"2023-08-27T14:19:54.277556Z","iopub.status.idle":"2023-08-27T14:19:54.289306Z","shell.execute_reply.started":"2023-08-27T14:19:54.277527Z","shell.execute_reply":"2023-08-27T14:19:54.288507Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":"(32, 10)"},"metadata":{}}]},{"cell_type":"code","source":"data_cc.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.290138Z","iopub.execute_input":"2023-08-27T14:19:54.290423Z","iopub.status.idle":"2023-08-27T14:19:54.305659Z","shell.execute_reply.started":"2023-08-27T14:19:54.290397Z","shell.execute_reply":"2023-08-27T14:19:54.304420Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":"Code object\nName object\nGeography object\nArea (sq km) float64\nEstimated Population mid-2021 int64\n2021 people per sq. km float64\nEstimated Population mid-2011 int64\n2011 people per sq. km float64\nEstimated Population mid-2001 int64\n2001 people per sq. km float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_cc.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.309295Z","iopub.execute_input":"2023-08-27T14:19:54.309628Z","iopub.status.idle":"2023-08-27T14:19:54.328406Z","shell.execute_reply.started":"2023-08-27T14:19:54.309599Z","shell.execute_reply":"2023-08-27T14:19:54.327556Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Code Name Geography Area (sq km) \\\n376 S12000033 Aberdeen City Council Area 185.5661 \n377 S12000034 Aberdeenshire Council Area 6312.6326 \n378 S12000041 Angus Council Area 2181.4521 \n379 S12000035 Argyll and Bute Council Area 6906.8230 \n380 S12000036 City of Edinburgh Council Area 263.3906 \n\n Estimated Population mid-2021 2021 people per sq. km \\\n376 227430 1225.601012 \n377 262690 41.613383 \n378 116120 53.230598 \n379 86220 12.483308 \n380 526470 1998.818485 \n\n Estimated Population mid-2011 2011 people per sq. km \\\n376 222460 1198.818103 \n377 253650 40.181334 \n378 116200 53.267271 \n379 88930 12.875674 \n380 477940 1814.567414 \n\n Estimated Population mid-2001 2001 people per sq. km \n376 211910 1141.965046 \n377 226940 35.950136 \n378 108370 49.677919 \n379 91300 13.218813 \n380 449020 1704.768507 ","text/html":"
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CodeNameGeographyArea (sq km)Estimated Population mid-20212021 people per sq. kmEstimated Population mid-20112011 people per sq. kmEstimated Population mid-20012001 people per sq. km
376S12000033Aberdeen CityCouncil Area185.56612274301225.6010122224601198.8181032119101141.965046
377S12000034AberdeenshireCouncil Area6312.632626269041.61338325365040.18133422694035.950136
378S12000041AngusCouncil Area2181.452111612053.23059811620053.26727110837049.677919
379S12000035Argyll and ButeCouncil Area6906.82308622012.4833088893012.8756749130013.218813
380S12000036City of EdinburghCouncil Area263.39065264701998.8184854779401814.5674144490201704.768507
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"# How has bas the population size evolved in the last 20 years?","metadata":{}},{"cell_type":"markdown","source":"We use the estimated population size measured in 2001, 2011 and 2021. We show the distrubtion for the three statistical variables appears to have a long tail to the right. \n\nAcross two-decades, we can some variations. In 2001,approximately a quarter of city councils had approximately 100,000 inhabitants. In 2011, this number was reduced - the number of city council with a bigger size of population increased. The number decreased in 2021. \n\nThe median size of the population appears quite stable over the 20-year period. A skewness to right has impacated on the arithmetical average, inflating its value. ","metadata":{}},{"cell_type":"code","source":"#arg = dict(histtype='stepfilled', alpha=0.3, density=True, bins=25, ec=\"k\")","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:54.329690Z","iopub.execute_input":"2023-08-27T14:19:54.330016Z","iopub.status.idle":"2023-08-27T14:19:54.340636Z","shell.execute_reply.started":"2023-08-27T14:19:54.329988Z","shell.execute_reply":"2023-08-27T14:19:54.339600Z"},"trusted":true},"execution_count":142,"outputs":[]},{"cell_type":"code","source":"summary_2001 = data_cc[\"Estimated Population mid-2001\"].describe()\nprint(summary_2001)\nprint(\"Skewness\")\nprint(skew(data_cc[\"Estimated Population mid-2001\"]))\nplt.hist(data_cc[\"Estimated Population mid-2001\"], histtype = 'stepfilled', bins = 25, density = True,ec=\"k\", alpha = 0.3)\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:22:35.441163Z","iopub.execute_input":"2023-08-27T14:22:35.441536Z","iopub.status.idle":"2023-08-27T14:22:35.735975Z","shell.execute_reply.started":"2023-08-27T14:22:35.441506Z","shell.execute_reply":"2023-08-27T14:22:35.734891Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"count 32.000000\nmean 158256.250000\nstd 123861.472094\nmin 19220.000000\n25% 88807.500000\n50% 116235.000000\n75% 181867.500000\nmax 578710.000000\nName: Estimated Population mid-2001, dtype: float64\nSkewness\n1.752821244745742\n","output_type":"stream"},{"execution_count":155,"output_type":"execute_result","data":{"text/plain":"(array([4.18908291e-06, 1.39636097e-06, 4.18908291e-06, 1.11708878e-05,\n 2.79272194e-06, 6.98180486e-06, 2.79272194e-06, 0.00000000e+00,\n 2.79272194e-06, 1.39636097e-06, 0.00000000e+00, 0.00000000e+00,\n 1.39636097e-06, 1.39636097e-06, 1.39636097e-06, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.39636097e-06,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.39636097e-06]),\n array([ 19220. , 41599.6, 63979.2, 86358.8, 108738.4, 131118. ,\n 153497.6, 175877.2, 198256.8, 220636.4, 243016. , 265395.6,\n 287775.2, 310154.8, 332534.4, 354914. , 377293.6, 399673.2,\n 422052.8, 444432.4, 466812. , 489191.6, 511571.2, 533950.8,\n 556330.4, 578710. ]),\n [])"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"summary_2011 = data_cc[\"Estimated Population mid-2011\"].describe()\nprint(summary_2011)\nprint(\"Skewness\")\nprint(skew(data_cc[\"Estimated Population mid-2011\"]))\nplt.hist(data_cc[\"Estimated Population mid-2011\"], histtype = 'stepfilled', bins = 25, density = True,ec=\"k\", alpha = 0.3)\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:23:22.341515Z","iopub.execute_input":"2023-08-27T14:23:22.341931Z","iopub.status.idle":"2023-08-27T14:23:22.661166Z","shell.execute_reply.started":"2023-08-27T14:23:22.341896Z","shell.execute_reply":"2023-08-27T14:23:22.659831Z"},"trusted":true},"execution_count":156,"outputs":[{"name":"stdout","text":"count 32.000000\nmean 165621.875000\nstd 129741.384161\nmin 21420.000000\n25% 90540.000000\n50% 119445.000000\n75% 187090.000000\nmax 593060.000000\nName: Estimated Population mid-2011, dtype: float64\nSkewness\n1.6773899225579254\n","output_type":"stream"},{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"(array([4.10004548e-06, 1.36668183e-06, 4.10004548e-06, 8.20009097e-06,\n 5.46672731e-06, 6.83340914e-06, 2.73336366e-06, 0.00000000e+00,\n 1.36668183e-06, 1.36668183e-06, 1.36668183e-06, 0.00000000e+00,\n 1.36668183e-06, 1.36668183e-06, 0.00000000e+00, 1.36668183e-06,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.36668183e-06,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.36668183e-06]),\n array([ 21420. , 44285.6, 67151.2, 90016.8, 112882.4, 135748. ,\n 158613.6, 181479.2, 204344.8, 227210.4, 250076. , 272941.6,\n 295807.2, 318672.8, 341538.4, 364404. , 387269.6, 410135.2,\n 433000.8, 455866.4, 478732. , 501597.6, 524463.2, 547328.8,\n 570194.4, 593060. ]),\n [])"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"summary_2021 = data_cc[\"Estimated Population mid-2021\"].describe()\nprint(summary_2021)\nprint(\"Skewness\")\nprint(skew(data_cc[\"Estimated Population mid-2021\"]))\nplt.hist(data_cc[\"Estimated Population mid-2001\"], histtype = 'stepfilled', bins = 25, density = True,ec=\"k\", alpha = 0.3)\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:23:43.135571Z","iopub.execute_input":"2023-08-27T14:23:43.136659Z","iopub.status.idle":"2023-08-27T14:23:43.427455Z","shell.execute_reply.started":"2023-08-27T14:23:43.136616Z","shell.execute_reply":"2023-08-27T14:23:43.426283Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"count 32.000000\nmean 171246.875000\nstd 139003.691026\nmin 22540.000000\n25% 94377.500000\n50% 119070.000000\n75% 196042.500000\nmax 635130.000000\nName: Estimated Population mid-2021, dtype: float64\nSkewness\n1.7865526811927868\n","output_type":"stream"},{"execution_count":157,"output_type":"execute_result","data":{"text/plain":"(array([4.18908291e-06, 1.39636097e-06, 4.18908291e-06, 1.11708878e-05,\n 2.79272194e-06, 6.98180486e-06, 2.79272194e-06, 0.00000000e+00,\n 2.79272194e-06, 1.39636097e-06, 0.00000000e+00, 0.00000000e+00,\n 1.39636097e-06, 1.39636097e-06, 1.39636097e-06, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.39636097e-06,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.39636097e-06]),\n array([ 19220. , 41599.6, 63979.2, 86358.8, 108738.4, 131118. ,\n 153497.6, 175877.2, 198256.8, 220636.4, 243016. , 265395.6,\n 287775.2, 310154.8, 332534.4, 354914. , 377293.6, 399673.2,\n 422052.8, 444432.4, 466812. , 489191.6, 511571.2, 533950.8,\n 556330.4, 578710. ]),\n [])"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"diff = summary_2011 - summary_2001\nprint(\"median difference : \", diff['50%'])\nprint(\"mean difference : \", diff[\"mean\"])\n ","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:55.014010Z","iopub.execute_input":"2023-08-27T14:19:55.014438Z","iopub.status.idle":"2023-08-27T14:19:55.020395Z","shell.execute_reply.started":"2023-08-27T14:19:55.014394Z","shell.execute_reply":"2023-08-27T14:19:55.019628Z"},"trusted":true},"execution_count":146,"outputs":[{"name":"stdout","text":"median difference : 3210.0\nmean difference : 7365.625\n","output_type":"stream"}]},{"cell_type":"code","source":"diff = summary_2021 - summary_2011\nprint(\"median difference : \", diff['50%'])\nprint(\"mean difference : \", diff[\"mean\"])\n ","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:55.021450Z","iopub.execute_input":"2023-08-27T14:19:55.022404Z","iopub.status.idle":"2023-08-27T14:19:55.034193Z","shell.execute_reply.started":"2023-08-27T14:19:55.022371Z","shell.execute_reply":"2023-08-27T14:19:55.033068Z"},"trusted":true},"execution_count":147,"outputs":[{"name":"stdout","text":"median difference : -375.0\nmean difference : 5625.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Is there a correclation between the population per square meter and estimated population?\n","metadata":{}},{"cell_type":"markdown","source":"Over the last two decades the population per square metres appears to have increased. However, the Pearson coefficient indicates a correlation is unlikely to exists between both statistical variables. It is worth noting, only 32 points exists, which is not sufficient, to be conclusive. \n\nIt can be observed a small population size appears to have a smaller amount of people per square meter and the larger population size appears to have a larger amount of people per square meter. We surmise urbanisation may affect the concentration of individuals. ","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:19:55.037411Z","iopub.execute_input":"2023-08-27T14:19:55.037787Z","iopub.status.idle":"2023-08-27T14:19:55.047959Z","shell.execute_reply.started":"2023-08-27T14:19:55.037755Z","shell.execute_reply":"2023-08-27T14:19:55.046201Z"}}},{"cell_type":"markdown","source":"https://stackabuse.com/calculating-pearson-correlation-coefficient-in-python-with-numpy/","metadata":{}},{"cell_type":"code","source":"data_cc.describe()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:25:37.479501Z","iopub.execute_input":"2023-08-27T14:25:37.480211Z","iopub.status.idle":"2023-08-27T14:25:37.519364Z","shell.execute_reply.started":"2023-08-27T14:25:37.480169Z","shell.execute_reply":"2023-08-27T14:25:37.518268Z"},"trusted":true},"execution_count":158,"outputs":[{"execution_count":158,"output_type":"execute_result","data":{"text/plain":" Area (sq km) Estimated Population mid-2021 2021 people per sq. km \\\ncount 32.000000 32.000000 32.000000 \nmean 2434.404112 171246.875000 494.209598 \nstd 4700.084554 139003.691026 805.091051 \nmin 59.800600 22540.000000 8.718600 \n25% 242.507300 94377.500000 38.484986 \n50% 937.887050 119070.000000 171.704638 \n75% 2199.006625 196042.500000 553.280569 \nmax 25653.092100 635130.000000 3637.185154 \n\n Estimated Population mid-2011 2011 people per sq. km \\\ncount 32.000000 32.000000 \nmean 165621.875000 475.587846 \nstd 129741.384161 763.079423 \nmin 21420.000000 9.062238 \n25% 90540.000000 37.081753 \n50% 119445.000000 166.569417 \n75% 187090.000000 537.080196 \nmax 593060.000000 3396.263801 \n\n Estimated Population mid-2001 2001 people per sq. km \ncount 32.000000 32.000000 \nmean 158256.250000 461.945783 \nstd 123861.472094 743.616784 \nmin 19220.000000 8.144047 \n25% 88807.500000 33.345505 \n50% 116235.000000 162.025296 \n75% 181867.500000 540.320638 \nmax 578710.000000 3314.085968 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Area (sq km)Estimated Population mid-20212021 people per sq. kmEstimated Population mid-20112011 people per sq. kmEstimated Population mid-20012001 people per sq. km
count32.00000032.00000032.00000032.00000032.00000032.00000032.000000
mean2434.404112171246.875000494.209598165621.875000475.587846158256.250000461.945783
std4700.084554139003.691026805.091051129741.384161763.079423123861.472094743.616784
min59.80060022540.0000008.71860021420.0000009.06223819220.0000008.144047
25%242.50730094377.50000038.48498690540.00000037.08175388807.50000033.345505
50%937.887050119070.000000171.704638119445.000000166.569417116235.000000162.025296
75%2199.006625196042.500000553.280569187090.000000537.080196181867.500000540.320638
max25653.092100635130.0000003637.185154593060.0000003396.263801578710.0000003314.085968
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_1 = np.log10(data_cc[\"Estimated Population mid-2001\"])\ny_1 = np.log10(data_cc[\"2001 people per sq. km\"])\nplt.scatter(x_1, y_1)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:41:10.285436Z","iopub.execute_input":"2023-08-27T14:41:10.285869Z","iopub.status.idle":"2023-08-27T14:41:10.534102Z","shell.execute_reply.started":"2023-08-27T14:41:10.285832Z","shell.execute_reply":"2023-08-27T14:41:10.532822Z"},"trusted":true},"execution_count":180,"outputs":[{"execution_count":180,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"np.corrcoef(x_1, y_1)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:25:44.220956Z","iopub.execute_input":"2023-08-27T14:25:44.221342Z","iopub.status.idle":"2023-08-27T14:25:44.229544Z","shell.execute_reply.started":"2023-08-27T14:25:44.221308Z","shell.execute_reply":"2023-08-27T14:25:44.228347Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":"array([[1. , 0.6586611],\n [0.6586611, 1. ]])"},"metadata":{}}]},{"cell_type":"code","source":"x_2 = np.log10(data_cc[\"Estimated Population mid-2011\"])\ny_2 = np.log10(data_cc[\"2011 people per sq. km\"])\nplt.scatter(x_2, y_2)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:40:46.083626Z","iopub.execute_input":"2023-08-27T14:40:46.084106Z","iopub.status.idle":"2023-08-27T14:40:46.397931Z","shell.execute_reply.started":"2023-08-27T14:40:46.084068Z","shell.execute_reply":"2023-08-27T14:40:46.396807Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"np.corrcoef(x_2, y_2)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:26:01.031086Z","iopub.execute_input":"2023-08-27T14:26:01.031828Z","iopub.status.idle":"2023-08-27T14:26:01.040627Z","shell.execute_reply.started":"2023-08-27T14:26:01.031778Z","shell.execute_reply":"2023-08-27T14:26:01.039371Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"array([[1. , 0.65056536],\n [0.65056536, 1. ]])"},"metadata":{}}]},{"cell_type":"code","source":"x_3 = np.log10(data_cc[\"Estimated Population mid-2021\"])\ny_3 = np.log10(data_cc[\"2021 people per sq. km\"])\nplt.scatter(x_3, y_3)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:40:18.231489Z","iopub.execute_input":"2023-08-27T14:40:18.231917Z","iopub.status.idle":"2023-08-27T14:40:18.537127Z","shell.execute_reply.started":"2023-08-27T14:40:18.231886Z","shell.execute_reply":"2023-08-27T14:40:18.536086Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"np.corrcoef(x_3, y_3)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:26:07.849292Z","iopub.execute_input":"2023-08-27T14:26:07.850058Z","iopub.status.idle":"2023-08-27T14:26:07.858872Z","shell.execute_reply.started":"2023-08-27T14:26:07.850018Z","shell.execute_reply":"2023-08-27T14:26:07.857813Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":"array([[1. , 0.6777006],\n [0.6777006, 1. ]])"},"metadata":{}}]},{"cell_type":"code","source":"plt.scatter(x_1, y_1, alpha = 0.3, c = 'blue', )\nplt.scatter(x_2, y_2, alpha = 0.3, c = 'black')\nplt.scatter(x_3, y_3, alpha = 0.3, c = 'red')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T14:31:01.381299Z","iopub.execute_input":"2023-08-27T14:31:01.382117Z","iopub.status.idle":"2023-08-27T14:31:01.669678Z","shell.execute_reply.started":"2023-08-27T14:31:01.382076Z","shell.execute_reply":"2023-08-27T14:31:01.668462Z"},"trusted":true},"execution_count":170,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"Using the whole the dataset and various type of geography, we discover our previous observation may only be relevant for city councils. We cannot see any corrolation.","metadata":{}},{"cell_type":"code","source":"x_1 = np.log10(data[\"Estimated Population mid-2001\"])\ny_1 = np.log10(data[\"2001 people per sq. km\"])\nx_2 = np.log10(data[\"Estimated Population mid-2011\"])\ny_2 = np.log10(data[\"2011 people per sq. km\"])\nx_3 = np.log10(data[\"Estimated Population mid-2021\"])\ny_3 = np.log10(data[\"2021 people per sq. km\"])\n\nplt.scatter(x_1, y_1, alpha = 0.3, c = 'blue', )\nplt.scatter(x_2, y_2, alpha = 0.3, c = 'black')\nplt.scatter(x_3, y_3, alpha = 0.5, c = 'green')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T15:04:52.608346Z","iopub.execute_input":"2023-08-27T15:04:52.609058Z","iopub.status.idle":"2023-08-27T15:04:52.866016Z","shell.execute_reply.started":"2023-08-27T15:04:52.609021Z","shell.execute_reply":"2023-08-27T15:04:52.864774Z"},"trusted":true},"execution_count":209,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Are they any relationship between the estimated population and the number of individuals living together?\n\nhttps://realpython.com/k-means-clustering-python/\n","metadata":{}},{"cell_type":"markdown","source":"We applied an unsupervised learning algorithm - i.e. k-means - to explore whether some clusturisation with the data may exists. It appears the estimated population and the concentration of individuals may have some clusterisation properties. However, it is unclear if there is a clear relationship. \n\nThe concentration metric is likely to be dependent from the population itself. Without having access to the area in square meter, we cannot complete an more detailed analysis. ","metadata":{}},{"cell_type":"code","source":"Xs = pd.concat([x_1, x_2, x_3], axis=0)\nYs = pd.concat([y_1, y_2, y_3], axis=0)\ndata_to_fit = pd.DataFrame ({'x': Xs, \n 'y': Ys})\ndata_to_fit.shape\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-08-27T15:05:03.535117Z","iopub.execute_input":"2023-08-27T15:05:03.535558Z","iopub.status.idle":"2023-08-27T15:05:03.546339Z","shell.execute_reply.started":"2023-08-27T15:05:03.535522Z","shell.execute_reply":"2023-08-27T15:05:03.544986Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":"(1260, 2)"},"metadata":{}}]},{"cell_type":"code","source":" kmeans_kwargs = {\n \"init\": \"random\",\n \"n_init\": 10,\n \"max_iter\": 300,\n \"random_state\": 42}\n \n # A list holds the SSE values for each k\nsse = []\n\nfor k in range(1, 20):\n kmeans = KMeans(n_clusters=k, **kmeans_kwargs)\n kmeans.fit(data_to_fit)\n sse.append(kmeans.inertia_)","metadata":{"execution":{"iopub.status.busy":"2023-08-27T15:12:51.394915Z","iopub.execute_input":"2023-08-27T15:12:51.396100Z","iopub.status.idle":"2023-08-27T15:12:52.220065Z","shell.execute_reply.started":"2023-08-27T15:12:51.396049Z","shell.execute_reply":"2023-08-27T15:12:52.219105Z"},"trusted":true},"execution_count":220,"outputs":[]},{"cell_type":"code","source":"\nplt.plot(range(1, 20), sse)\nplt.xticks(range(1, 20))\nplt.xlabel(\"Number of Clusters\")\nplt.ylabel(\"SSE\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T15:19:04.933446Z","iopub.execute_input":"2023-08-27T15:19:04.934536Z","iopub.status.idle":"2023-08-27T15:19:05.268503Z","shell.execute_reply.started":"2023-08-27T15:19:04.934496Z","shell.execute_reply":"2023-08-27T15:19:05.267350Z"},"trusted":true},"execution_count":234,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"kmeans = KMeans(n_clusters=7).fit(data_to_fit)\ncentroids = kmeans.cluster_centers_\nax = plt.axes()\nax.set_facecolor(\"white\")\nax.set_xticks([])\nax.set_yticks([])\nplt.scatter(data_to_fit['x'], data_to_fit['y'], \n c=kmeans.labels_.astype(float), alpha=0.3)\nplt.scatter(centroids[:, 0], centroids[:, 1], c='red')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-08-27T15:21:06.998002Z","iopub.execute_input":"2023-08-27T15:21:06.999303Z","iopub.status.idle":"2023-08-27T15:21:07.252480Z","shell.execute_reply.started":"2023-08-27T15:21:06.999254Z","shell.execute_reply":"2023-08-27T15:21:07.251425Z"},"trusted":true},"execution_count":240,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Data engineering and science/Tutorial for basic Pandas and operation/.DS_Store b/Data engineering and science/Tutorial for basic Pandas and operation/.DS_Store index 13e87da..7bff742 100644 Binary files a/Data engineering and science/Tutorial for basic Pandas and operation/.DS_Store and b/Data engineering and science/Tutorial for basic Pandas and operation/.DS_Store differ diff --git a/Data engineering and science/Tutorial for basic Pandas and operation/population.ipynb b/Data engineering and science/Tutorial for basic Pandas and operation/population.ipynb new file mode 100644 index 0000000..3dda9c5 --- /dev/null +++ b/Data engineering and science/Tutorial for basic Pandas and operation/population.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Population, GDP and Internet adoption\n\nThis notebook expores all those subjects to answer the following questions: \n\n[add questions here]\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"We will simulate a data pipeline based on the following steps:\n\n- extract : ingest data from various data sources.\n- transform : clean and prepare each dataset for tra\n- load : merge the data sets together \n- visualise: create some meaningful graphical visualisation.","metadata":{}},{"cell_type":"markdown","source":"# Extract\n\nWe upload the data and the libraries required for the notebook. ","metadata":{"_uuid":"caf4ec4f-f5cd-44f6-bd87-a42bb5998fbe","_cell_guid":"2e322c9f-5725-4093-ae2d-3e06fef613fe","_kg_hide-input":true,"trusted":true}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nfrom sklearn.cluster import KMeans\nimport seaborn as sns\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"0704d808-a7ec-4a50-aaa8-fe2134a807d4","_cell_guid":"cf1cf904-4fba-43b6-9f19-322dfa1cbf68","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.344153Z","iopub.execute_input":"2023-10-26T20:28:51.344497Z","iopub.status.idle":"2023-10-26T20:28:51.359482Z","shell.execute_reply.started":"2023-10-26T20:28:51.344472Z","shell.execute_reply":"2023-10-26T20:28:51.358458Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n/kaggle/input/population-dataset/World-population-by-countries-dataset.csv\n/kaggle/input/internet-users/Final.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"path = '/kaggle/input/population-dataset/World-population-by-countries-dataset.csv'\ndata_pop = pd.read_csv(path)\ndata_pop.shape","metadata":{"_uuid":"28a62502-7432-44e5-87eb-877d25cb754f","_cell_guid":"6d1ceed1-1929-4c1f-aa9c-cb9bf17b5a17","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.361232Z","iopub.execute_input":"2023-10-26T20:28:51.361709Z","iopub.status.idle":"2023-10-26T20:28:51.380139Z","shell.execute_reply.started":"2023-10-26T20:28:51.361677Z","shell.execute_reply":"2023-10-26T20:28:51.379042Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"markdown","source":"## World population\nWe transform the datasets from a wide to long format, so that we can merge more easily the datasets together. We aim at having a country name and a country code; both uses the ISO standard. We aim at having a year and the population. ","metadata":{"_uuid":"f352bd71-3a53-4d05-b1a6-15a3391dc608","_cell_guid":"12af0be9-5050-4d14-b22c-036f7f2f79f1","trusted":true}},{"cell_type":"code","source":"data_pop.dtypes","metadata":{"_uuid":"5213c597-6442-4ae6-aeed-299cd30630c5","_cell_guid":"1795b98f-3391-4877-8d9c-702da54af9d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.381506Z","iopub.execute_input":"2023-10-26T20:28:51.382276Z","iopub.status.idle":"2023-10-26T20:28:51.391171Z","shell.execute_reply.started":"2023-10-26T20:28:51.382247Z","shell.execute_reply":"2023-10-26T20:28:51.390268Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop.describe()","metadata":{"_uuid":"0daf98d8-5114-4c54-8a4c-e6bbf6311a74","_cell_guid":"c0f42347-4411-4bab-b06d-1b0e75605146","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.393035Z","iopub.execute_input":"2023-10-26T20:28:51.393739Z","iopub.status.idle":"2023-10-26T20:28:51.516296Z","shell.execute_reply.started":"2023-10-26T20:28:51.393706Z","shell.execute_reply":"2023-10-26T20:28:51.515206Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.172174e+08 1.187633e+08 1.208717e+08 1.234910e+08 1.261315e+08 \nstd 3.695745e+08 3.739180e+08 3.804316e+08 3.889142e+08 3.974401e+08 \nmin 2.833000e+03 3.077000e+03 3.367000e+03 3.703000e+03 4.063000e+03 \n25% 5.022802e+05 5.109642e+05 5.206540e+05 5.311622e+05 5.421252e+05 \n50% 3.718330e+06 3.826398e+06 3.929109e+06 4.015834e+06 4.124521e+06 \n75% 2.636053e+07 2.721235e+07 2.808607e+07 2.890669e+07 2.972333e+07 \nmax 3.032156e+09 3.071596e+09 3.124561e+09 3.189656e+09 3.255146e+09 \n\n 1965 1966 1967 1968 1969 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.288372e+08 1.316853e+08 1.345256e+08 1.374350e+08 1.404490e+08 \nstd 4.062000e+08 4.155171e+08 4.247722e+08 4.342805e+08 4.441772e+08 \nmin 4.460000e+03 4.675000e+03 4.922000e+03 5.194000e+03 5.461000e+03 \n25% 5.533362e+05 5.647475e+05 5.823645e+05 5.981078e+05 6.100030e+05 \n50% 4.242788e+06 4.326013e+06 4.387887e+06 4.474171e+06 4.550402e+06 \n75% 3.055227e+07 3.134845e+07 3.200449e+07 3.244145e+07 3.277149e+07 \nmax 3.322047e+09 3.392098e+09 3.461620e+09 3.532783e+09 3.606554e+09 \n\n ... 2012 2013 2014 2015 \\\ncount ... 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean ... 2.874902e+08 2.912969e+08 2.951160e+08 2.989277e+08 \nstd ... 9.017511e+08 9.129343e+08 9.241050e+08 9.352101e+08 \nmin ... 1.013600e+04 1.020800e+04 1.028900e+04 1.037400e+04 \n25% ... 1.539939e+06 1.574621e+06 1.609909e+06 1.645868e+06 \n50% ... 9.824808e+06 9.948838e+06 1.001582e+07 1.022085e+07 \n75% ... 6.057984e+07 6.120753e+07 6.174243e+07 6.182699e+07 \nmax ... 7.089255e+09 7.175500e+09 7.261847e+09 7.347679e+09 \n\n 2016 2017 2018 2019 2020 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 3.027560e+08 3.065980e+08 3.103591e+08 3.140425e+08 3.176734e+08 \nstd 9.463321e+08 9.575052e+08 9.683483e+08 9.788967e+08 9.891628e+08 \nmin 1.047400e+04 1.057700e+04 1.067800e+04 1.076400e+04 1.083400e+04 \n25% 1.689616e+06 1.716772e+06 1.740174e+06 1.751950e+06 1.767996e+06 \n50% 1.036160e+07 1.040671e+07 1.045548e+07 1.047907e+07 1.052565e+07 \n75% 6.187352e+07 6.191725e+07 6.193141e+07 6.150589e+07 6.157091e+07 \nmax 7.433651e+09 7.519371e+09 7.602716e+09 7.683806e+09 7.763933e+09 \n\n 2021 \ncount 2.640000e+02 \nmean 3.210893e+08 \nstd 9.988295e+08 \nmin 1.087300e+04 \n25% 1.791783e+06 \n50% 1.054019e+07 \n75% 6.295547e+07 \nmax 7.836631e+09 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02...2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02
mean1.172174e+081.187633e+081.208717e+081.234910e+081.261315e+081.288372e+081.316853e+081.345256e+081.374350e+081.404490e+08...2.874902e+082.912969e+082.951160e+082.989277e+083.027560e+083.065980e+083.103591e+083.140425e+083.176734e+083.210893e+08
std3.695745e+083.739180e+083.804316e+083.889142e+083.974401e+084.062000e+084.155171e+084.247722e+084.342805e+084.441772e+08...9.017511e+089.129343e+089.241050e+089.352101e+089.463321e+089.575052e+089.683483e+089.788967e+089.891628e+089.988295e+08
min2.833000e+033.077000e+033.367000e+033.703000e+034.063000e+034.460000e+034.675000e+034.922000e+035.194000e+035.461000e+03...1.013600e+041.020800e+041.028900e+041.037400e+041.047400e+041.057700e+041.067800e+041.076400e+041.083400e+041.087300e+04
25%5.022802e+055.109642e+055.206540e+055.311622e+055.421252e+055.533362e+055.647475e+055.823645e+055.981078e+056.100030e+05...1.539939e+061.574621e+061.609909e+061.645868e+061.689616e+061.716772e+061.740174e+061.751950e+061.767996e+061.791783e+06
50%3.718330e+063.826398e+063.929109e+064.015834e+064.124521e+064.242788e+064.326013e+064.387887e+064.474171e+064.550402e+06...9.824808e+069.948838e+061.001582e+071.022085e+071.036160e+071.040671e+071.045548e+071.047907e+071.052565e+071.054019e+07
75%2.636053e+072.721235e+072.808607e+072.890669e+072.972333e+073.055227e+073.134845e+073.200449e+073.244145e+073.277149e+07...6.057984e+076.120753e+076.174243e+076.182699e+076.187352e+076.191725e+076.193141e+076.150589e+076.157091e+076.295547e+07
max3.032156e+093.071596e+093.124561e+093.189656e+093.255146e+093.322047e+093.392098e+093.461620e+093.532783e+093.606554e+09...7.089255e+097.175500e+097.261847e+097.347679e+097.433651e+097.519371e+097.602716e+097.683806e+097.763933e+097.836631e+09
\n

8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(data_pop['Country Code'].unique())","metadata":{"_uuid":"da9b4784-3633-4778-a860-06055fe06ef6","_cell_guid":"dcc7e57d-b1d5-4413-ba23-9097a88df7f6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.517814Z","iopub.execute_input":"2023-10-26T20:28:51.518099Z","iopub.status.idle":"2023-10-26T20:28:51.524362Z","shell.execute_reply.started":"2023-10-26T20:28:51.518075Z","shell.execute_reply":"2023-10-26T20:28:51.523200Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\npop_long = pd.melt(data_pop, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.525929Z","iopub.execute_input":"2023-10-26T20:28:51.526268Z","iopub.status.idle":"2023-10-26T20:28:51.547676Z","shell.execute_reply.started":"2023-10-26T20:28:51.526243Z","shell.execute_reply":"2023-10-26T20:28:51.546791Z"},"trusted":true},"execution_count":93,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"pop_long.columns = ['Country Name', 'Country Code', 'Year', 'population']\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.548744Z","iopub.execute_input":"2023-10-26T20:28:51.549003Z","iopub.status.idle":"2023-10-26T20:28:51.559081Z","shell.execute_reply.started":"2023-10-26T20:28:51.548982Z","shell.execute_reply":"2023-10-26T20:28:51.558029Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## GDP\nWe repeat a similar process for the GDP. Similar standards are used.","metadata":{"_uuid":"d3617312-9971-49fc-a30c-357d2d407eea","_cell_guid":"98e1e4ad-2241-4d7a-b701-a07fe5aa0509","execution":{"iopub.status.busy":"2023-10-19T16:22:33.566429Z","iopub.execute_input":"2023-10-19T16:22:33.566852Z","iopub.status.idle":"2023-10-19T16:22:34.617823Z","shell.execute_reply.started":"2023-10-19T16:22:33.566821Z","shell.execute_reply":"2023-10-19T16:22:34.616873Z"},"trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/countries-gdp-2012-to-2021/GDP.csv'\ngdp = pd.read_csv(path)\ngdp.shape","metadata":{"_uuid":"a8992442-7dac-4c03-9f3b-13cdc8d45e7b","_cell_guid":"be74ad63-eebe-40b5-844b-021624380077","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.560206Z","iopub.execute_input":"2023-10-26T20:28:51.560460Z","iopub.status.idle":"2023-10-26T20:28:51.580073Z","shell.execute_reply.started":"2023-10-26T20:28:51.560438Z","shell.execute_reply":"2023-10-26T20:28:51.578585Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"code","source":"gdp.dtypes","metadata":{"_uuid":"1e5fa2f6-ab9d-4115-aab3-47f9d5952d7c","_cell_guid":"c161c56d-19d2-4157-8122-4e4bff8f0fb1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.581561Z","iopub.execute_input":"2023-10-26T20:28:51.581877Z","iopub.status.idle":"2023-10-26T20:28:51.589480Z","shell.execute_reply.started":"2023-10-26T20:28:51.581850Z","shell.execute_reply":"2023-10-26T20:28:51.588251Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp.describe()","metadata":{"_uuid":"cc48f68b-e22c-470d-8d9d-fe7c874e4ff9","_cell_guid":"39bd124c-d5d9-4541-be4b-affa418221ee","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.593301Z","iopub.execute_input":"2023-10-26T20:28:51.593702Z","iopub.status.idle":"2023-10-26T20:28:51.713345Z","shell.execute_reply.started":"2023-10-26T20:28:51.593675Z","shell.execute_reply":"2023-10-26T20:28:51.712285Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 134.000000 136.000000 138.000000 138.000000 138.000000 \nmean 473.490078 486.392600 510.248600 541.649901 587.373909 \nstd 612.439366 635.127847 666.405011 705.754944 772.265425 \nmin 40.119192 26.318449 26.983496 28.434172 20.018579 \n25% 107.452258 110.089913 114.582873 122.509292 123.574875 \n50% 217.932654 197.938953 202.801243 210.677240 232.182537 \n75% 476.295836 485.401860 538.891433 586.773416 639.414205 \nmax 3007.123445 3066.562869 3243.843078 3374.515171 3573.941185 \n\n 1965 1966 1967 1968 1969 ... \\\ncount 149.000000 152.000000 155.000000 160.000000 160.000000 ... \nmean 648.068814 703.235758 718.916647 735.345411 796.539042 ... \nstd 849.994333 921.818962 954.791908 982.957313 1060.025132 ... \nmin 16.577652 12.786964 12.900238 20.395642 20.682296 ... \n25% 140.756742 145.396584 152.410537 149.457032 151.634207 ... \n50% 251.239040 266.219488 252.252422 292.642193 293.802194 ... \n75% 681.131112 768.852316 763.567965 760.566852 826.288906 ... \nmax 4081.915955 4229.254573 4336.426587 4695.923390 5032.144743 ... \n\n 2012 2013 2014 2015 \\\ncount 258.000000 259.000000 260.000000 258.000000 \nmean 16248.249264 16768.974417 17083.306427 15423.701141 \nstd 23882.158473 25383.007646 25945.938982 23375.375304 \nmin 238.205949 241.547671 257.818552 289.359633 \n25% 1986.934959 2110.418190 2173.282618 2097.331179 \n50% 6454.612266 6755.073675 6904.579093 6192.562429 \n75% 19638.711935 19792.134135 20277.795912 18210.359455 \nmax 165505.178100 185066.578100 195780.006900 170337.924400 \n\n 2016 2017 2018 2019 \\\ncount 257.000000 257.000000 257.000000 255.000000 \nmean 15582.736498 16383.403010 17344.572407 17231.399427 \nstd 23586.086580 24397.646814 25978.513510 25791.905913 \nmin 242.065671 243.135809 231.446476 216.972968 \n25% 2079.448266 2088.500117 2269.177012 2186.046581 \n50% 6079.088736 6436.791746 6912.110297 6837.717826 \n75% 18575.232030 19743.954910 20614.898860 19809.323135 \nmax 174610.637000 173612.864600 194280.822100 199377.481800 \n\n 2020 2021 \ncount 252.000000 245.000000 \nmean 15773.923985 16882.053955 \nstd 24065.495555 26113.837043 \nmin 216.826741 221.477676 \n25% 2139.636129 2304.844567 \n50% 6034.203335 6621.574336 \n75% 18652.166725 18751.026510 \nmax 182538.638300 234315.460500 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count134.000000136.000000138.000000138.000000138.000000149.000000152.000000155.000000160.000000160.000000...258.000000259.000000260.000000258.000000257.000000257.000000257.000000255.000000252.000000245.000000
mean473.490078486.392600510.248600541.649901587.373909648.068814703.235758718.916647735.345411796.539042...16248.24926416768.97441717083.30642715423.70114115582.73649816383.40301017344.57240717231.39942715773.92398516882.053955
std612.439366635.127847666.405011705.754944772.265425849.994333921.818962954.791908982.9573131060.025132...23882.15847325383.00764625945.93898223375.37530423586.08658024397.64681425978.51351025791.90591324065.49555526113.837043
min40.11919226.31844926.98349628.43417220.01857916.57765212.78696412.90023820.39564220.682296...238.205949241.547671257.818552289.359633242.065671243.135809231.446476216.972968216.826741221.477676
25%107.452258110.089913114.582873122.509292123.574875140.756742145.396584152.410537149.457032151.634207...1986.9349592110.4181902173.2826182097.3311792079.4482662088.5001172269.1770122186.0465812139.6361292304.844567
50%217.932654197.938953202.801243210.677240232.182537251.239040266.219488252.252422292.642193293.802194...6454.6122666755.0736756904.5790936192.5624296079.0887366436.7917466912.1102976837.7178266034.2033356621.574336
75%476.295836485.401860538.891433586.773416639.414205681.131112768.852316763.567965760.566852826.288906...19638.71193519792.13413520277.79591218210.35945518575.23203019743.95491020614.89886019809.32313518652.16672518751.026510
max3007.1234453066.5628693243.8430783374.5151713573.9411854081.9159554229.2545734336.4265874695.9233905032.144743...165505.178100185066.578100195780.006900170337.924400174610.637000173612.864600194280.822100199377.481800182538.638300234315.460500
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8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(gdp['Country Code'].unique())","metadata":{"_uuid":"a6200065-a3c5-4d03-a714-a2fbc8ced590","_cell_guid":"8bfa6fe6-9143-4ca0-a2e7-4d741a6989a9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.714469Z","iopub.execute_input":"2023-10-26T20:28:51.714720Z","iopub.status.idle":"2023-10-26T20:28:51.720357Z","shell.execute_reply.started":"2023-10-26T20:28:51.714698Z","shell.execute_reply":"2023-10-26T20:28:51.719732Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\ngdp_long = pd.melt(gdp, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.721044Z","iopub.execute_input":"2023-10-26T20:28:51.721267Z","iopub.status.idle":"2023-10-26T20:28:51.745227Z","shell.execute_reply.started":"2023-10-26T20:28:51.721248Z","shell.execute_reply":"2023-10-26T20:28:51.743851Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp_long.columns = ['Country Name', 'Country Code', 'Year', 'USD GDP']\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.746701Z","iopub.execute_input":"2023-10-26T20:28:51.747001Z","iopub.status.idle":"2023-10-26T20:28:51.757774Z","shell.execute_reply.started":"2023-10-26T20:28:51.746978Z","shell.execute_reply":"2023-10-26T20:28:51.756806Z"},"trusted":true},"execution_count":100,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Internet\nThis dataset is a bit simpler. Less transformation is required.","metadata":{"_uuid":"e6be91b8-d8c6-4212-ad69-2ce46452ef6f","_cell_guid":"d299a245-fa89-45c1-9623-569d3e7bcf7a","trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/internet-users/Final.csv'\ninternet = pd.read_csv(path)\ninternet.shape","metadata":{"_uuid":"ad3170c0-5705-4a57-9f17-853844f08e72","_cell_guid":"5a26b58f-e229-48ec-81f7-dd8ab7a51338","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.759207Z","iopub.execute_input":"2023-10-26T20:28:51.759490Z","iopub.status.idle":"2023-10-26T20:28:51.783791Z","shell.execute_reply.started":"2023-10-26T20:28:51.759467Z","shell.execute_reply":"2023-10-26T20:28:51.782480Z"},"trusted":true},"execution_count":101,"outputs":[{"execution_count":101,"output_type":"execute_result","data":{"text/plain":"(8867, 8)"},"metadata":{}}]},{"cell_type":"code","source":"internet.dtypes","metadata":{"_uuid":"bb11a84f-3d44-4ab2-9864-dc1c4a8df688","_cell_guid":"dedee6b7-ac31-4647-8b00-2a8bed20650f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.784729Z","iopub.execute_input":"2023-10-26T20:28:51.785074Z","iopub.status.idle":"2023-10-26T20:28:51.792523Z","shell.execute_reply.started":"2023-10-26T20:28:51.785043Z","shell.execute_reply":"2023-10-26T20:28:51.791485Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nEntity object\nCode object\nYear int64\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"len(internet.Code.unique())","metadata":{"_uuid":"4906007d-31a3-4312-89f1-25d4bbe6bbdf","_cell_guid":"6d9ec685-820d-4cca-a721-afe2a68bddd7","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.793712Z","iopub.execute_input":"2023-10-26T20:28:51.793961Z","iopub.status.idle":"2023-10-26T20:28:51.806167Z","shell.execute_reply.started":"2023-10-26T20:28:51.793940Z","shell.execute_reply":"2023-10-26T20:28:51.805509Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"216"},"metadata":{}}]},{"cell_type":"code","source":"internet.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:36:30.135197Z","iopub.execute_input":"2023-10-26T20:36:30.135549Z","iopub.status.idle":"2023-10-26T20:36:30.162264Z","shell.execute_reply.started":"2023-10-26T20:36:30.135525Z","shell.execute_reply":"2023-10-26T20:36:30.160983Z"},"trusted":true},"execution_count":136,"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0 Year Cellular Subscription Internet Users(%) \\\ncount 8867.000000 8867.000000 8867.000000 8867.000000 \nmean 4433.000000 2000.151799 39.989614 17.043606 \nstd 2559.826752 11.812151 51.981410 26.883498 \nmin 0.000000 1980.000000 0.000000 0.000000 \n25% 2216.500000 1990.000000 0.000000 0.000000 \n50% 4433.000000 2000.000000 5.501357 0.855662 \n75% 6649.500000 2010.000000 82.231594 25.449939 \nmax 8866.000000 2020.000000 436.103027 100.000000 \n\n No. of Internet Users Broadband Subscription \ncount 8.867000e+03 8867.000000 \nmean 1.089138e+07 4.440695 \nstd 1.248841e+08 9.755705 \nmin 0.000000e+00 0.000000 \n25% 0.000000e+00 0.000000 \n50% 1.004700e+04 0.000000 \n75% 8.664195e+05 2.007603 \nmax 4.699886e+09 78.524361 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0YearCellular SubscriptionInternet Users(%)No. of Internet UsersBroadband Subscription
count8867.0000008867.0000008867.0000008867.0000008.867000e+038867.000000
mean4433.0000002000.15179939.98961417.0436061.089138e+074.440695
std2559.82675211.81215151.98141026.8834981.248841e+089.755705
min0.0000001980.0000000.0000000.0000000.000000e+000.000000
25%2216.5000001990.0000000.0000000.0000000.000000e+000.000000
50%4433.0000002000.0000005.5013570.8556621.004700e+040.000000
75%6649.5000002010.00000082.23159425.4499398.664195e+052.007603
max8866.0000002020.000000436.103027100.0000004.699886e+0978.524361
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"It appears the number of countries to be smaller for the Internet dataset. Therefore, any merging of this datasets with the GDP and polution will reduce the number of countries by 50. This confounding factor will limit the analysis. It is a bit disappointing, but it will be enough a first exploration. ","metadata":{}},{"cell_type":"markdown","source":"# Tranform and load\nWe merge the datasets based on the year and the ISO country code.","metadata":{}},{"cell_type":"code","source":"print(pop_long.dtypes)\nprint(gdp_long.dtypes)","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.807624Z","iopub.execute_input":"2023-10-26T20:28:51.807989Z","iopub.status.idle":"2023-10-26T20:28:51.817447Z","shell.execute_reply.started":"2023-10-26T20:28:51.807963Z","shell.execute_reply":"2023-10-26T20:28:51.816385Z"},"trusted":true},"execution_count":104,"outputs":[{"name":"stdout","text":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object\nCountry Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"data = pd.merge(pop_long, gdp_long, left_on=['Country Code','Year'], right_on = ['Country Code','Year'])\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.819009Z","iopub.execute_input":"2023-10-26T20:28:51.819327Z","iopub.status.idle":"2023-10-26T20:28:51.840904Z","shell.execute_reply.started":"2023-10-26T20:28:51.819299Z","shell.execute_reply":"2023-10-26T20:28:51.839816Z"},"trusted":true},"execution_count":105,"outputs":[{"name":"stdout","text":"(16492, 6)\n","output_type":"stream"},{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nCountry Name_y object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['Country Name_x', 'Country Code', 'Year', 'population','USD GDP']\ndata = data.loc[:, cols]\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.842213Z","iopub.execute_input":"2023-10-26T20:28:51.842569Z","iopub.status.idle":"2023-10-26T20:28:51.852162Z","shell.execute_reply.started":"2023-10-26T20:28:51.842539Z","shell.execute_reply":"2023-10-26T20:28:51.850921Z"},"trusted":true},"execution_count":106,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.columns = ['Country Name','Country Code', 'Year', 'Population', 'GDP']\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.853458Z","iopub.execute_input":"2023-10-26T20:28:51.853809Z","iopub.status.idle":"2023-10-26T20:28:51.864097Z","shell.execute_reply.started":"2023-10-26T20:28:51.853780Z","shell.execute_reply":"2023-10-26T20:28:51.862937Z"},"trusted":true},"execution_count":107,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nPopulation float64\nGDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.865117Z","iopub.execute_input":"2023-10-26T20:28:51.865438Z","iopub.status.idle":"2023-10-26T20:28:51.883869Z","shell.execute_reply.started":"2023-10-26T20:28:51.865413Z","shell.execute_reply":"2023-10-26T20:28:51.882903Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":" Country Name Country Code Year Population GDP\n0 Aruba ABW 1960 54208.0 NaN\n1 Africa Eastern and Southern AFE 1960 130836765.0 162.913035\n2 Afghanistan AFG 1960 8996967.0 62.369375\n3 Africa Western and Central AFW 1960 96396419.0 106.976475\n4 Angola AGO 1960 5454938.0 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Country NameCountry CodeYearPopulationGDP
0ArubaABW196054208.0NaN
1Africa Eastern and SouthernAFE1960130836765.0162.913035
2AfghanistanAFG19608996967.062.369375
3Africa Western and CentralAFW196096396419.0106.976475
4AngolaAGO19605454938.0NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We apply the log normalisation on the data. The range is really large. It will allow us visualising in more details the distributions. However, any patterns through the years will appear as linear. It would be incorrect to interpret it as a linear growth, when it may be instead exponential. For that reasons, the non-normalise data may need to be used. \n\nThe distribute appears to be guassian. However, it includes the population for each year. So, it should be only used as a tool to explore the data.","metadata":{}},{"cell_type":"code","source":"data['log_pop'] = np.log10(data.Population)\ndata.log_pop.hist(bins = 100)\ndata.Population.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.885011Z","iopub.execute_input":"2023-10-26T20:28:51.885246Z","iopub.status.idle":"2023-10-26T20:28:52.385634Z","shell.execute_reply.started":"2023-10-26T20:28:51.885226Z","shell.execute_reply":"2023-10-26T20:28:52.384400Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"count 1.638700e+04\nmean 2.131655e+08\nstd 7.006673e+08\nmin 2.833000e+03\n25% 9.660195e+05\n50% 6.749849e+06\n75% 4.626525e+07\nmax 7.836631e+09\nName: Population, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"data['Year'] = pd.to_numeric(data.Year)\ndata_int = pd.merge(data, internet, left_on ='Year', right_on = 'Year', how = 'inner')\nprint(data_int.shape)\ndata_int.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:53.274864Z","iopub.execute_input":"2023-10-26T20:31:53.275211Z","iopub.status.idle":"2023-10-26T20:31:53.587275Z","shell.execute_reply.started":"2023-10-26T20:31:53.275181Z","shell.execute_reply":"2023-10-26T20:31:53.586389Z"},"trusted":true},"execution_count":120,"outputs":[{"name":"stdout","text":"(2358622, 13)\n","output_type":"stream"},{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\nUnnamed: 0 int64\nEntity object\nCode object\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"# Analysis\n## Has the population increased since the 1960s?\n\nWe discover the overall population may have increased since the 1960s. A boxplot shows the non-parametric distribution of yearly population across the world tend to increase. We would need to complete some further investigation to explore further this trend.","metadata":{}},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:59.444689Z","iopub.execute_input":"2023-10-26T20:31:59.445040Z","iopub.status.idle":"2023-10-26T20:31:59.453385Z","shell.execute_reply.started":"2023-10-26T20:31:59.445015Z","shell.execute_reply":"2023-10-26T20:31:59.452330Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop['1960'].describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:32:02.772369Z","iopub.execute_input":"2023-10-26T20:32:02.772824Z","iopub.status.idle":"2023-10-26T20:32:02.789620Z","shell.execute_reply.started":"2023-10-26T20:32:02.772781Z","shell.execute_reply":"2023-10-26T20:32:02.786617Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"count 2.640000e+02\nmean 1.172174e+08\nstd 3.695745e+08\nmin 2.833000e+03\n25% 5.022802e+05\n50% 3.718330e+06\n75% 2.636053e+07\nmax 3.032156e+09\nName: 1960, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"years = range(1960, 2022,1)\nrows = data.Year.isin(years) \ncols = ['Year','Population']\ndata_graph = data.loc[rows, cols]\ndata_graph['Population'] = np.log10(data_graph.Population)\n\nsns.set(rc={'figure.figsize':(15,15)})\nsns.boxplot(x = data_graph['Year'], y = data_graph['Population'])\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:23.655917Z","iopub.execute_input":"2023-10-26T20:34:23.656234Z","iopub.status.idle":"2023-10-26T20:34:25.959859Z","shell.execute_reply.started":"2023-10-26T20:34:23.656213Z","shell.execute_reply":"2023-10-26T20:34:25.959148Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## To which extend, the GDP and population may have a relationship?\n\nWe produced a scatter plot to explore the possible relationship between both the GDP and poplution. It challenging to interpret a possible relationship. A 3D scatter plot shows some level of complexity in the data. Some advanced regression or some clustering analysis may help identifying some relationships. ","metadata":{}},{"cell_type":"code","source":"data['log_gdp'] = np.log10(data.GDP)\ndata.log_gdp.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:32.865676Z","iopub.execute_input":"2023-10-26T20:34:32.865982Z","iopub.status.idle":"2023-10-26T20:34:32.876696Z","shell.execute_reply.started":"2023-10-26T20:34:32.865958Z","shell.execute_reply":"2023-10-26T20:34:32.875423Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"count 13156.000000\nmean 3.315185\nstd 0.752098\nmin 1.106767\n25% 2.734065\n50% 3.268659\n75% 3.879667\nmax 5.369801\nName: log_gdp, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.scatter(data.log_gdp, data.log_pop)\nplt.xlabel('GDP (USD) log-values')\nplt.ylabel('Population - log values ')","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:36.264746Z","iopub.execute_input":"2023-10-26T20:34:36.265081Z","iopub.status.idle":"2023-10-26T20:34:36.706387Z","shell.execute_reply.started":"2023-10-26T20:34:36.265056Z","shell.execute_reply":"2023-10-26T20:34:36.705531Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population - log values ')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"\n\n\n# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data.Year, data.Population, data.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:40.474750Z","iopub.execute_input":"2023-10-26T20:34:40.475177Z","iopub.status.idle":"2023-10-26T20:34:40.996103Z","shell.execute_reply.started":"2023-10-26T20:34:40.475149Z","shell.execute_reply":"2023-10-26T20:34:40.995418Z"},"trusted":true},"execution_count":131,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## How has the Internet usage increased through time?","metadata":{}},{"cell_type":"code","source":"plt.scatter(internet.Year, internet['No. of Internet Users'])","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:46:20.003157Z","iopub.execute_input":"2023-10-26T20:46:20.003504Z","iopub.status.idle":"2023-10-26T20:46:20.415763Z","shell.execute_reply.started":"2023-10-26T20:46:20.003477Z","shell.execute_reply":"2023-10-26T20:46:20.414596Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data_int['No. of Internet Users'], data_int.Population, data_int.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:45:47.490885Z","iopub.execute_input":"2023-10-26T20:45:47.491210Z","iopub.status.idle":"2023-10-26T20:46:19.212868Z","shell.execute_reply.started":"2023-10-26T20:45:47.491185Z","shell.execute_reply":"2023-10-26T20:46:19.211734Z"},"trusted":true},"execution_count":141,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# create data\nx = np.log10(data_int.Population)\ny = np.log10(data_int.GDP)\nz = data_int['Internet Users(%)']\n \n# use the scatter function\nplt.scatter(x, y, s=z, alpha=0.5)\n\n# show the graph\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:42:51.380366Z","iopub.execute_input":"2023-10-26T20:42:51.380727Z","iopub.status.idle":"2023-10-26T20:43:12.249788Z","shell.execute_reply.started":"2023-10-26T20:42:51.380701Z","shell.execute_reply":"2023-10-26T20:43:12.248684Z"},"trusted":true},"execution_count":140,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\ No newline at end of file diff --git a/Data engineering and science/data-preparation-for-ml-techniques.ipynb b/Data engineering and science/data-preparation-for-ml-techniques.ipynb new file mode 100644 index 0000000..e4d57be --- /dev/null +++ b/Data engineering and science/data-preparation-for-ml-techniques.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-01T14:12:24.884916Z","iopub.execute_input":"2023-02-01T14:12:24.885428Z","iopub.status.idle":"2023-02-01T14:12:24.912232Z","shell.execute_reply.started":"2023-02-01T14:12:24.885325Z","shell.execute_reply":"2023-02-01T14:12:24.911114Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/titanic/train.csv\n/kaggle/input/titanic/test.csv\n/kaggle/input/titanic/gender_submission.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Introduction\n\nData can be transformed into knowledge and then enhanced intelligence. We use the titanic datasets to explore first its features. The Titanic datasets contains the records of the Titanic passengers during its maiden voyage and tragic demise. \n\nWe apply some data engineering techniques to prepare the data for some various machine learning techniques - including _registic regression, decision trees, random forrests, KNN and artificial neural network_ - for the purpose of predicting survivors. \n\nWe also analyse and compare the predictions from all the classifier methods to explore further how the data and our data preparation may have affected the model fitting as well as the prediction on unseen data.\n\n\nThe notebook is structured in this manner:\n\n\n- __[Upload libraires](#Libraries)__\n- __[Data engineering](#Data-engineering)__\n- __[Survival characteristics](#Survival-characteristics)__\n- __[Data preparation for classification](#Data-preparation-for-classification)__ \n- __[Method: Logistic regression](#Method-:-Logistic-regression)__\n- __[Method: K-Nearest neighorn](#Method:-K-Nearest-neighbourn)__\n- __[Method: Decision Trees](#Method-:-Decision-Trees)__ \n- __[Method: Random Forrest](#Method:-Random-Forrest)__\n- __[Method: Neural AI](#Method:-Neural-AI)__ \n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Libraries\n\nWe upload all the libraries required for all the operations of this notebook.","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport seaborn as sns\nimport os\nimport random as rand\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\nfrom sklearn.model_selection import train_test_split # Import train_test_split function\nfrom sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nimport scipy.stats as stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\n\nimport tensorflow as tf\nif (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed\n print('Please install tensorflow 2.0 to run this notebook')\nprint('Tensorflow version: ',tf.__version__)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:30:21.088212Z","iopub.execute_input":"2023-02-01T14:30:21.088884Z","iopub.status.idle":"2023-02-01T14:30:21.739453Z","shell.execute_reply.started":"2023-02-01T14:30:21.088844Z","shell.execute_reply":"2023-02-01T14:30:21.738425Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Tensorflow version: 2.6.4\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data engineering\n\nWe explore the files in the folder, sets the paths and file names. These variables will be used in each section.","metadata":{}},{"cell_type":"code","source":"!ls ../input/titanic/\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:36.483134Z","iopub.execute_input":"2023-02-01T14:12:36.484288Z","iopub.status.idle":"2023-02-01T14:12:37.583058Z","shell.execute_reply.started":"2023-02-01T14:12:36.484239Z","shell.execute_reply":"2023-02-01T14:12:37.581624Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"gender_submission.csv test.csv train.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data_path = '../input/titanic/train.csv'\ntest_data_path = '../input/titanic/test.csv'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.584978Z","iopub.execute_input":"2023-02-01T14:12:37.586181Z","iopub.status.idle":"2023-02-01T14:12:37.591422Z","shell.execute_reply.started":"2023-02-01T14:12:37.586143Z","shell.execute_reply":"2023-02-01T14:12:37.590256Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Import and explore the data \nExplore and import the training and test dataset provided by the competition.","metadata":{}},{"cell_type":"markdown","source":"### Training dataset","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.825672Z","iopub.execute_input":"2023-02-01T14:12:37.827141Z","iopub.status.idle":"2023-02-01T14:12:37.862872Z","shell.execute_reply.started":"2023-02-01T14:12:37.827090Z","shell.execute_reply":"2023-02-01T14:12:37.861760Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.735534Z","iopub.execute_input":"2023-02-01T14:12:40.736625Z","iopub.status.idle":"2023-02-01T14:12:40.784900Z","shell.execute_reply.started":"2023-02-01T14:12:40.736575Z","shell.execute_reply":"2023-02-01T14:12:40.783854Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 38.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 ","text/html":"
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.786409Z","iopub.execute_input":"2023-02-01T14:12:40.786701Z","iopub.status.idle":"2023-02-01T14:12:40.803610Z","shell.execute_reply.started":"2023-02-01T14:12:40.786675Z","shell.execute_reply":"2023-02-01T14:12:40.802382Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Test dataset","metadata":{}},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:45.642106Z","iopub.execute_input":"2023-02-01T14:12:45.642583Z","iopub.status.idle":"2023-02-01T14:12:45.662691Z","shell.execute_reply.started":"2023-02-01T14:12:45.642542Z","shell.execute_reply":"2023-02-01T14:12:45.661472Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.221543Z","iopub.execute_input":"2023-02-01T14:12:46.222107Z","iopub.status.idle":"2023-02-01T14:12:46.261833Z","shell.execute_reply.started":"2023-02-01T14:12:46.222059Z","shell.execute_reply":"2023-02-01T14:12:46.260714Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Age SibSp Parch Fare\ncount 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\nmean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\nstd 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\nmin 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\nmax 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.919995Z","iopub.execute_input":"2023-02-01T14:12:46.920463Z","iopub.status.idle":"2023-02-01T14:12:46.940798Z","shell.execute_reply.started":"2023-02-01T14:12:46.920405Z","shell.execute_reply":"2023-02-01T14:12:46.939404Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":" ## Meta data \n \n| Column name | Description|\n|---|---|\n|Passenger_id| unique row indentifier |\n|PClass | Categorical data (1 = 1st; 2 = 2nd; 3 = 3rd)|\n| Survival | Categoricial data (0 = No; 1 = Yes) |\n| Name | Characters - Name of passenger |\n| Sex | Categorical data male or female |\n| Age | integer values representing age |\n| SigSp | integer Number of Siblings/Spouses Aboard |\n| Parch | Number of Parents/Children Aboard |\n| Ticket | Ticket number |\n| Fare | Fare in GBP at time of travel|\n| Cabin | Cabin |\n| Embark | Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)|\n\n\nSource - http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf (7/12/2022)","metadata":{}},{"cell_type":"markdown","source":"# Survival characteristics\nWe explore the survival characteristics using several combinations of columns. We hope to understand better some features that may guide the predictions of survivors.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Passenger and survival\nThe training dataset suggests a minority of passengers survived (i.e., 38% approximately), 62% of passengers perished. Some further decomposition suggests first class passengers may have been more likely to survive than lower classes. The percentages of surviving decreases sharply.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Survived\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:50.918803Z","iopub.execute_input":"2023-02-01T14:12:50.919222Z","iopub.status.idle":"2023-02-01T14:12:50.940405Z","shell.execute_reply.started":"2023-02-01T14:12:50.919186Z","shell.execute_reply":"2023-02-01T14:12:50.939284Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Survived\n0 0.616162\n1 0.383838\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp = temp.unstack()\ntemp","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:18:15.229152Z","iopub.execute_input":"2023-02-01T14:18:15.229539Z","iopub.status.idle":"2023-02-01T14:18:15.255184Z","shell.execute_reply.started":"2023-02-01T14:18:15.229507Z","shell.execute_reply":"2023-02-01T14:18:15.254098Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass \n1 0.370370 0.629630\n2 0.527174 0.472826\n3 0.757637 0.242363","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Pclass
10.3703700.629630
20.5271740.472826
30.7576370.242363
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Null hypothesis: Pclass means are equal (no variation in means of groups)\nH0:$μ_0=μ-1$\n\nAlternative hypothesis: At least, one group mean is different from other groups\nH1: All μ are not equal\n\n$p_{value} = 0.01$","metadata":{}},{"cell_type":"code","source":"\nsur_pclass = titanic_train.loc[titanic_train.Survived == 1, \"Pclass\"]\nperish_pclass = titanic_train.loc[titanic_train.Survived == 0, \"Pclass\"]\nfvalue, pvalue = stats.f_oneway(sur_pclass, perish_pclass)\nprint(fvalue, pvalue)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:29:37.721129Z","iopub.execute_input":"2023-02-01T14:29:37.721602Z","iopub.status.idle":"2023-02-01T14:29:37.732491Z","shell.execute_reply.started":"2023-02-01T14:29:37.721566Z","shell.execute_reply":"2023-02-01T14:29:37.731155Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"115.03127218827665 2.5370473879805644e-25\n","output_type":"stream"}]},{"cell_type":"code","source":"model = ols('Survived ~ Pclass', data=titanic_train).fit()\nanova_table = sm.stats.anova_lm(model, typ=2)\nanova_table","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:32:55.703937Z","iopub.execute_input":"2023-02-01T14:32:55.704329Z","iopub.status.idle":"2023-02-01T14:32:55.739204Z","shell.execute_reply.started":"2023-02-01T14:32:55.704285Z","shell.execute_reply":"2023-02-01T14:32:55.738138Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" sum_sq df F PR(>F)\nPclass 24.142900 1.0 115.031272 2.537047e-25\nResidual 186.584373 889.0 NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sum_sqdfFPR(>F)
Pclass24.1429001.0115.0312722.537047e-25
Residual186.584373889.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"__Interpretation__\n\nThe p value obtained from ANOVA analysis is significant (p < 0.01), and therefore, we conclude that there are significant differences among the classes who have perished or survived.","metadata":{}},{"cell_type":"markdown","source":"## Embarkment and survival\nThe port of embarkment appears to have less influence on the survival percentages. It appears most passengers embarked at Southampton (72% approximately), 18% of passengers at Cherbourg, and the remaining from Queenstown. Half of the Cherbourg passengers booked first class tickets. Other embarkment ports appears to be much lower. Half of the passengers from Southampton booked third class tickets. We could surmise the latter may have contributed to the lowest percentages of surviving the accident.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Embarked\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.612759Z","iopub.execute_input":"2023-02-01T14:50:09.613134Z","iopub.status.idle":"2023-02-01T14:50:09.626172Z","shell.execute_reply.started":"2023-02-01T14:50:09.613106Z","shell.execute_reply":"2023-02-01T14:50:09.625109Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Embarked\nC 0.188552\nQ 0.086420\nS 0.722783\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.631955Z","iopub.execute_input":"2023-02-01T14:50:09.632520Z","iopub.status.idle":"2023-02-01T14:50:09.652387Z","shell.execute_reply.started":"2023-02-01T14:50:09.632486Z","shell.execute_reply":"2023-02-01T14:50:09.651379Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Pclass 1 2 3\nEmbarked \nC 0.505952 0.101190 0.392857\nQ 0.025974 0.038961 0.935065\nS 0.197205 0.254658 0.548137","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Pclass123
Embarked
C0.5059520.1011900.392857
Q0.0259740.0389610.935065
S0.1972050.2546580.548137
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:10.164258Z","iopub.execute_input":"2023-02-01T14:50:10.164676Z","iopub.status.idle":"2023-02-01T14:50:10.185023Z","shell.execute_reply.started":"2023-02-01T14:50:10.164643Z","shell.execute_reply":"2023-02-01T14:50:10.183924Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked \nC 0.446429 0.553571\nQ 0.610390 0.389610\nS 0.663043 0.336957","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Embarked
C0.4464290.553571
Q0.6103900.389610
S0.6630430.336957
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:16.126671Z","iopub.execute_input":"2023-02-01T14:50:16.127079Z","iopub.status.idle":"2023-02-01T14:50:16.150013Z","shell.execute_reply.started":"2023-02-01T14:50:16.127043Z","shell.execute_reply":"2023-02-01T14:50:16.149263Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked Pclass \nC 1 0.154762 0.351190\n 2 0.047619 0.053571\n 3 0.244048 0.148810\nQ 1 0.012987 0.012987\n 2 0.012987 0.025974\n 3 0.584416 0.350649\nS 1 0.082298 0.114907\n 2 0.136646 0.118012\n 3 0.444099 0.104037","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
EmbarkedPclass
C10.1547620.351190
20.0476190.053571
30.2440480.148810
Q10.0129870.012987
20.0129870.025974
30.5844160.350649
S10.0822980.114907
20.1366460.118012
30.4440990.104037
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Gender and survival \nThe training dataset suggests that nearly two thirds of passengers were male, and a third were female. Women and girls appears to have a higher survival percentagers - three quarters of female passengers survived the accident, but only 19% of male survived.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Sex\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:21.461493Z","iopub.execute_input":"2023-02-01T14:50:21.461874Z","iopub.status.idle":"2023-02-01T14:50:21.474706Z","shell.execute_reply.started":"2023-02-01T14:50:21.461843Z","shell.execute_reply":"2023-02-01T14:50:21.473520Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Sex\nfemale 0.352413\nmale 0.647587\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:22.544412Z","iopub.execute_input":"2023-02-01T14:50:22.544835Z","iopub.status.idle":"2023-02-01T14:50:22.565483Z","shell.execute_reply.started":"2023-02-01T14:50:22.544801Z","shell.execute_reply":"2023-02-01T14:50:22.564390Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex \nfemale 0.257962 0.742038\nmale 0.811092 0.188908","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Sex
female0.2579620.742038
male0.8110920.188908
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:23.099261Z","iopub.execute_input":"2023-02-01T14:50:23.099666Z","iopub.status.idle":"2023-02-01T14:50:23.126110Z","shell.execute_reply.started":"2023-02-01T14:50:23.099635Z","shell.execute_reply":"2023-02-01T14:50:23.125241Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex Pclass \nfemale 1 0.013889 0.421296\n 2 0.032609 0.380435\n 3 0.146640 0.146640\nmale 1 0.356481 0.208333\n 2 0.494565 0.092391\n 3 0.610998 0.095723","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SexPclass
female10.0138890.421296
20.0326090.380435
30.1466400.146640
male10.3564810.208333
20.4945650.092391
30.6109980.095723
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age, siblings and parents\n\nThe age distribution appears to be multi-modal with some two peaks at around 0 and 25. Both training and testing datasets have a similar mean and standard deviation. However, some skewness may affect a normal distributions and any normalisation processes of the data.\n\nThe survivors and other passengers age appears to be of similar age at the point of centrality. We will need to complete some statistical tests to accept or reject the null hypothesis that the age distribution of survivors and non-survivors are the same. We surmise the values may have be unknown, without any data preparation the tests cannot be completed.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.246337Z","iopub.execute_input":"2023-02-01T14:50:24.247338Z","iopub.status.idle":"2023-02-01T14:50:24.633738Z","shell.execute_reply.started":"2023-02-01T14:50:24.247275Z","shell.execute_reply":"2023-02-01T14:50:24.632647Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"count 714.000000\nmean 29.699118\nstd 14.526497\nmin 0.420000\n25% 20.125000\n50% 28.000000\n75% 38.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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4C9VfWJecnVZVtIcmJ3/TX05ub30ivyy2eVrao+VFWnVdUivWPqn6vqd2adK8kJSV6/fJ3enO4eZvxcVtXTwA+TnNXtegfw6KxzrXAlL02fwOyz/QC4IMlru7/R5d/Z+I+xWb7wMMSLAZcA/05v7vSPZ5zldnrzWT+lNyq5it7c6Q7gceCfgJOnnOkiev88fBjY3V0umXWuLtuvAw922fYAf9Lt/xXgm8AT9P7J+6oZPqdvA+6Zh1zd4z/UXR5ZPt7n5LncDOzsnst/BE6ah1xdthOA/wDe0Ldv5tmAG4Fvd8f+3wGvmsQx5lvpJalR8zyFIkk6AgtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNer/AKGGVs0lKoXzAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.694278Z","iopub.execute_input":"2023-02-01T14:50:24.695165Z","iopub.status.idle":"2023-02-01T14:50:25.062419Z","shell.execute_reply.started":"2023-02-01T14:50:24.695120Z","shell.execute_reply":"2023-02-01T14:50:25.061338Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"count 332.000000\nmean 30.272590\nstd 14.181209\nmin 0.170000\n25% 21.000000\n50% 27.000000\n75% 39.000000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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y=\"Age\", data=titanic_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:25.141606Z","iopub.execute_input":"2023-02-01T14:50:25.142000Z","iopub.status.idle":"2023-02-01T14:50:25.355591Z","shell.execute_reply.started":"2023-02-01T14:50:25.141964Z","shell.execute_reply":"2023-02-01T14:50:25.354536Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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majority of passengers may be travelling on their own without a spouse, sibling, children of parents on board. However, passengers with 1 or 2 siblings/spouse appears to have survived; the percentages is in the range of 46% to 54%. Parents or individuals with one, two or three parents were less likely to perished - the percentages ranges between 50% and 60%.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"SibSp\"]).count()[\"PassengerId\"]/titanic_train.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.432856Z","iopub.execute_input":"2023-02-01T14:50:29.433512Z","iopub.status.idle":"2023-02-01T14:50:29.445537Z","shell.execute_reply.started":"2023-02-01T14:50:29.433478Z","shell.execute_reply":"2023-02-01T14:50:29.444361Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"SibSp\n0 0.682379\n1 0.234568\n2 0.031425\n3 0.017957\n4 0.020202\n5 0.005612\n8 0.007856\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"SibSp\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.890502Z","iopub.execute_input":"2023-02-01T14:50:29.890860Z","iopub.status.idle":"2023-02-01T14:50:29.915654Z","shell.execute_reply.started":"2023-02-01T14:50:29.890829Z","shell.execute_reply":"2023-02-01T14:50:29.914470Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSibSp \n0 0.654605 0.345395\n1 0.464115 0.535885\n2 0.535714 0.464286\n3 0.750000 0.250000\n4 0.833333 0.166667\n5 1.000000 NaN\n8 1.000000 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SibSp
00.6546050.345395
10.4641150.535885
20.5357140.464286
30.7500000.250000
40.8333330.166667
51.000000NaN
81.000000NaN
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.SibSp, bins = 8)\ntitanic_train.SibSp.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:30.548135Z","iopub.execute_input":"2023-02-01T14:50:30.548522Z","iopub.status.idle":"2023-02-01T14:50:30.775129Z","shell.execute_reply.started":"2023-02-01T14:50:30.548488Z","shell.execute_reply":"2023-02-01T14:50:30.774363Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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0.760943\n1 0.132435\n2 0.089787\n3 0.005612\n4 0.004489\n5 0.005612\n6 0.001122\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Parch\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.187336Z","iopub.execute_input":"2023-02-01T14:50:31.187728Z","iopub.status.idle":"2023-02-01T14:50:31.209460Z","shell.execute_reply.started":"2023-02-01T14:50:31.187695Z","shell.execute_reply":"2023-02-01T14:50:31.208365Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nParch \n0 0.656342 0.343658\n1 0.449153 0.550847\n2 0.500000 0.500000\n3 0.400000 0.600000\n4 1.000000 NaN\n5 0.800000 0.200000\n6 1.000000 NaN","text/html":"
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Survived01
Parch
00.6563420.343658
10.4491530.550847
20.5000000.500000
30.4000000.600000
41.000000NaN
50.8000000.200000
61.000000NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Parch, bins = 6)\ntitanic_train.Parch.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.433509Z","iopub.execute_input":"2023-02-01T14:50:31.434117Z","iopub.status.idle":"2023-02-01T14:50:31.664941Z","shell.execute_reply.started":"2023-02-01T14:50:31.434071Z","shell.execute_reply":"2023-02-01T14:50:31.664079Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.381594\nstd 0.806057\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 6.000000\nName: Parch, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We decided to add both fields _Parch_ and _SibSp_ together as a familly. The mean and median age appears to be quite close between the passengers who have survived and perished. For smaller families the spread appears to be smaller than for larger families. \n\nThe highest percentages of surviving the accident suggests that passengers in first and second class with no other familly members. These percentages are loweer than 30%.","metadata":{}},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train.SibSp + titanic_train.Parch\ntemp = titanic_train.groupby([\"fam_members\",\"Survived\"]).agg([np.median, np.mean, np.std])[\"Age\"]\ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.929453Z","iopub.execute_input":"2023-02-01T14:50:31.929858Z","iopub.status.idle":"2023-02-01T14:50:31.977029Z","shell.execute_reply.started":"2023-02-01T14:50:31.929823Z","shell.execute_reply":"2023-02-01T14:50:31.975764Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" median mean std \nSurvived 0 1 0 1 0 1\nfam_members \n0 29.0 30.0 32.414234 31.811538 13.334968 11.970452\n1 30.0 29.0 32.126984 30.781842 11.599836 14.916443\n2 30.5 22.0 31.500000 21.911887 13.776141 17.363697\n3 25.0 14.0 22.833333 16.972381 11.196726 15.054360\n4 12.5 21.0 17.000000 31.000000 15.528775 19.974984\n5 9.0 24.0 17.578947 23.666667 18.637822 0.577350\n6 9.0 11.0 14.875000 15.750000 15.169871 16.070159\n7 12.5 NaN 15.666667 NaN 14.361987 NaN\n10 NaN NaN NaN NaN NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medianmeanstd
Survived010101
fam_members
029.030.032.41423431.81153813.33496811.970452
130.029.032.12698430.78184211.59983614.916443
230.522.031.50000021.91188713.77614117.363697
325.014.022.83333316.97238111.19672615.054360
412.521.017.00000031.00000015.52877519.974984
59.024.017.57894723.66666718.6378220.577350
69.011.014.87500015.75000015.16987116.070159
712.5NaN15.666667NaN14.361987NaN
10NaNNaNNaNNaNNaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.fam_members, bins = 10)\ntitanic_train.fam_members.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.210511Z","iopub.execute_input":"2023-02-01T14:50:32.210873Z","iopub.status.idle":"2023-02-01T14:50:32.431170Z","shell.execute_reply.started":"2023-02-01T14:50:32.210842Z","shell.execute_reply":"2023-02-01T14:50:32.430235Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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twlqUP/Bzr6a6xtewKkAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"fam_members\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.505200Z","iopub.execute_input":"2023-02-01T14:50:32.505646Z","iopub.status.idle":"2023-02-01T14:50:32.533253Z","shell.execute_reply.started":"2023-02-01T14:50:32.505607Z","shell.execute_reply":"2023-02-01T14:50:32.532232Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nfam_members Pclass \n0 1 0.236111 0.268519\n 2 0.369565 0.195652\n 3 0.519348 0.140530\n1 1 0.087963 0.236111\n 2 0.086957 0.097826\n 3 0.075356 0.040733\n2 1 0.027778 0.083333\n 2 0.054348 0.114130\n 3 0.054990 0.040733\n3 1 0.009259 0.023148\n 2 0.016304 0.054348\n 3 0.006110 0.012220\n4 1 NaN 0.009259\n 2 NaN 0.005435\n 3 0.024440 NaN\n5 1 0.009259 0.009259\n 2 NaN 0.005435\n 3 0.034623 NaN\n6 3 0.016293 0.008147\n7 3 0.012220 NaN\n10 3 0.014257 NaN","text/html":"
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Survived01
fam_membersPclass
010.2361110.268519
20.3695650.195652
30.5193480.140530
110.0879630.236111
20.0869570.097826
30.0753560.040733
210.0277780.083333
20.0543480.114130
30.0549900.040733
310.0092590.023148
20.0163040.054348
30.0061100.012220
41NaN0.009259
2NaN0.005435
30.024440NaN
510.0092590.009259
2NaN0.005435
30.034623NaN
630.0162930.008147
730.012220NaN
1030.014257NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Futher analysis and discussions\nThe data in their current states suggests that the distribution for the field _Survived_ is likely to be binomial. It has a lowest occurrences of surviving, which is a shocking statistic.\n\nThe passenger class has more occurrences of third classes. However, First and second class female passengers were more likely to survive the accident. First class male passengers had the also the highest survival rate. The Age is skewed to the left; some age may be unknown. It appears (see below) the younger passengers may have been traveling with other members of a family and perhaps reduced their survival rates; the largest familly appears to be travelling in third class. Most occurrences were families made of 0, 1, or 3 family members. \n\nThis analysis suggests that perhaps the passenger class familly, and the gender may have contributed to a higher survival rate. However, the familly size may have contributed to survived too. The classifiers will need to identify other patterns that may have contributed to survive the accident. It is likely to be quite challenging as no linear relationships or grouping may be present in the data.\n\n","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=3).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.247279Z","iopub.execute_input":"2023-02-01T14:50:33.247672Z","iopub.status.idle":"2023-02-01T14:50:33.275585Z","shell.execute_reply.started":"2023-02-01T14:50:33.247640Z","shell.execute_reply":"2023-02-01T14:50:33.274507Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass fam_members Sex \n1 0 female 0.001821 0.096491\n male 0.091075 0.073099\n 1 female NaN 0.114035\n male 0.034608 0.035088\n 2 female NaN 0.038012\n male 0.010929 0.014620\n 3 female 0.003643 0.005848\n male NaN 0.008772\n 4 female NaN 0.005848\n 5 female NaN 0.005848\n male 0.003643 NaN\n2 0 female 0.005464 0.084795\n male 0.118397 0.020468\n 1 female 0.003643 0.049708\n male 0.025501 0.002924\n 2 female 0.001821 0.038012\n male 0.016393 0.023392\n 3 female NaN 0.026316\n male 0.005464 0.002924\n 4 female NaN 0.002924\n 5 female NaN 0.002924\n3 0 female 0.041894 0.108187\n male 0.422587 0.093567\n 1 female 0.025501 0.043860\n male 0.041894 0.014620\n 2 female 0.018215 0.035088\n male 0.030965 0.023392\n 3 female 0.001821 0.014620\n male 0.003643 0.002924\n 4 female 0.016393 NaN\n male 0.005464 NaN\n 5 female 0.009107 NaN\n male 0.021858 NaN\n 6 female 0.009107 0.008772\n male 0.005464 0.002924\n 7 female 0.003643 NaN\n male 0.007286 NaN\n 10 female 0.005464 NaN\n male 0.007286 NaN","text/html":"
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Survived01
Pclassfam_membersSex
10female0.0018210.096491
male0.0910750.073099
1femaleNaN0.114035
male0.0346080.035088
2femaleNaN0.038012
male0.0109290.014620
3female0.0036430.005848
maleNaN0.008772
4femaleNaN0.005848
5femaleNaN0.005848
male0.003643NaN
20female0.0054640.084795
male0.1183970.020468
1female0.0036430.049708
male0.0255010.002924
2female0.0018210.038012
male0.0163930.023392
3femaleNaN0.026316
male0.0054640.002924
4femaleNaN0.002924
5femaleNaN0.002924
30female0.0418940.108187
male0.4225870.093567
1female0.0255010.043860
male0.0418940.014620
2female0.0182150.035088
male0.0309650.023392
3female0.0018210.014620
male0.0036430.002924
4female0.016393NaN
male0.005464NaN
5female0.009107NaN
male0.021858NaN
6female0.0091070.008772
male0.0054640.002924
7female0.003643NaN
male0.007286NaN
10female0.005464NaN
male0.007286NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns = [\"Survived\",\"Pclass\",\"Age\", \"fam_members\"]\ntitanic_train = titanic_train[columns]\npd.plotting.scatter_matrix(titanic_train, diagonal='kde')","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.689687Z","iopub.execute_input":"2023-02-01T14:50:33.690876Z","iopub.status.idle":"2023-02-01T14:50:34.713667Z","shell.execute_reply.started":"2023-02-01T14:50:33.690832Z","shell.execute_reply":"2023-02-01T14:50:34.712910Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"array([[,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ]],\n dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The percentages suggests passenger class and gender may be the factor that may lead to survival. ","metadata":{}},{"cell_type":"markdown","source":"# Data preparation for classification\nThis section prepares the data for classifiers. We transform the data in suitable data types supported by the classifiers, remove null values and imputes some values when required. Some columns are deleted; they may be either character or we surmise not suitable for classification.","metadata":{}},{"cell_type":"markdown","source":"## Integer to float\nWe upload the data for a cleaning and display the columns with their data types to float on both datasets.","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.368853Z","iopub.execute_input":"2023-02-01T14:50:35.370121Z","iopub.status.idle":"2023-02-01T14:50:35.386127Z","shell.execute_reply.started":"2023-02-01T14:50:35.370069Z","shell.execute_reply":"2023-02-01T14:50:35.385078Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.770203Z","iopub.execute_input":"2023-02-01T14:50:35.770631Z","iopub.status.idle":"2023-02-01T14:50:35.784625Z","shell.execute_reply.started":"2023-02-01T14:50:35.770596Z","shell.execute_reply":"2023-02-01T14:50:35.783551Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_train[\"SibSp\"] = titanic_train[\"SibSp\"].astype(float)\ntitanic_train[\"Parch\"] = titanic_train[\"Parch\"].astype(float)\ntitanic_train[\"Survived\"] = titanic_train[\"Survived\"].astype(float)\ntitanic_train[\"Pclass\"] = titanic_train[\"Pclass\"].astype(float)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.166628Z","iopub.execute_input":"2023-02-01T14:50:36.167303Z","iopub.status.idle":"2023-02-01T14:50:36.181459Z","shell.execute_reply.started":"2023-02-01T14:50:36.167252Z","shell.execute_reply":"2023-02-01T14:50:36.178943Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)\ntitanic_test[\"SibSp\"] = titanic_test[\"SibSp\"].astype(float)\ntitanic_test[\"Parch\"] = titanic_test[\"Parch\"].astype(float)\ntitanic_test[\"Pclass\"] = titanic_test[\"Pclass\"].astype(float)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.666190Z","iopub.execute_input":"2023-02-01T14:50:36.667397Z","iopub.status.idle":"2023-02-01T14:50:36.678991Z","shell.execute_reply.started":"2023-02-01T14:50:36.667345Z","shell.execute_reply":"2023-02-01T14:50:36.677862Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Null values \n\nWe remove all the nulls values from some of the columns; i.e., PassengerId, Fare, SibSp, Parch, and Embarked. Some fares were unknown, but all passengers ID was set to a unique number. ","metadata":{}},{"cell_type":"code","source":"titanic_train.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.489505Z","iopub.execute_input":"2023-02-01T14:50:37.489938Z","iopub.status.idle":"2023-02-01T14:50:37.497591Z","shell.execute_reply.started":"2023-02-01T14:50:37.489901Z","shell.execute_reply":"2023-02-01T14:50:37.496243Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.991239Z","iopub.execute_input":"2023-02-01T14:50:37.992524Z","iopub.status.idle":"2023-02-01T14:50:38.000114Z","shell.execute_reply.started":"2023-02-01T14:50:37.992478Z","shell.execute_reply":"2023-02-01T14:50:37.998884Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Fare.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.437569Z","iopub.execute_input":"2023-02-01T14:50:38.437966Z","iopub.status.idle":"2023-02-01T14:50:38.445766Z","shell.execute_reply.started":"2023-02-01T14:50:38.437933Z","shell.execute_reply":"2023-02-01T14:50:38.444961Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.994637Z","iopub.execute_input":"2023-02-01T14:50:38.995337Z","iopub.status.idle":"2023-02-01T14:50:39.002110Z","shell.execute_reply.started":"2023-02-01T14:50:38.995287Z","shell.execute_reply":"2023-02-01T14:50:39.000886Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Parch.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.436990Z","iopub.execute_input":"2023-02-01T14:50:39.437517Z","iopub.status.idle":"2023-02-01T14:50:39.445363Z","shell.execute_reply.started":"2023-02-01T14:50:39.437381Z","shell.execute_reply":"2023-02-01T14:50:39.444366Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.905392Z","iopub.execute_input":"2023-02-01T14:50:39.905832Z","iopub.status.idle":"2023-02-01T14:50:39.913740Z","shell.execute_reply.started":"2023-02-01T14:50:39.905797Z","shell.execute_reply":"2023-02-01T14:50:39.912816Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.307865Z","iopub.execute_input":"2023-02-01T14:50:40.308905Z","iopub.status.idle":"2023-02-01T14:50:40.316347Z","shell.execute_reply.started":"2023-02-01T14:50:40.308849Z","shell.execute_reply":"2023-02-01T14:50:40.315199Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_test[\"Fare\"].isnull(),\"Fare\"] = -1.0\ntitanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.604978Z","iopub.execute_input":"2023-02-01T14:50:40.605706Z","iopub.status.idle":"2023-02-01T14:50:40.614214Z","shell.execute_reply.started":"2023-02-01T14:50:40.605660Z","shell.execute_reply":"2023-02-01T14:50:40.613381Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.071733Z","iopub.execute_input":"2023-02-01T14:50:41.072626Z","iopub.status.idle":"2023-02-01T14:50:41.081041Z","shell.execute_reply.started":"2023-02-01T14:50:41.072577Z","shell.execute_reply":"2023-02-01T14:50:41.079958Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.270757Z","iopub.execute_input":"2023-02-01T14:50:41.271157Z","iopub.status.idle":"2023-02-01T14:50:41.282542Z","shell.execute_reply.started":"2023-02-01T14:50:41.271122Z","shell.execute_reply":"2023-02-01T14:50:41.281267Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.500496Z","iopub.execute_input":"2023-02-01T14:50:41.500902Z","iopub.status.idle":"2023-02-01T14:50:41.515629Z","shell.execute_reply.started":"2023-02-01T14:50:41.500870Z","shell.execute_reply":"2023-02-01T14:50:41.514309Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.775897Z","iopub.execute_input":"2023-02-01T14:50:41.776267Z","iopub.status.idle":"2023-02-01T14:50:41.789089Z","shell.execute_reply.started":"2023-02-01T14:50:41.776237Z","shell.execute_reply":"2023-02-01T14:50:41.787661Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.000121Z","iopub.execute_input":"2023-02-01T14:50:42.001023Z","iopub.status.idle":"2023-02-01T14:50:42.016796Z","shell.execute_reply.started":"2023-02-01T14:50:42.000983Z","shell.execute_reply":"2023-02-01T14:50:42.015509Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.207362Z","iopub.execute_input":"2023-02-01T14:50:42.207763Z","iopub.status.idle":"2023-02-01T14:50:42.218799Z","shell.execute_reply.started":"2023-02-01T14:50:42.207729Z","shell.execute_reply":"2023-02-01T14:50:42.217490Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.428396Z","iopub.execute_input":"2023-02-01T14:50:42.429337Z","iopub.status.idle":"2023-02-01T14:50:42.442620Z","shell.execute_reply.started":"2023-02-01T14:50:42.429286Z","shell.execute_reply":"2023-02-01T14:50:42.441246Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.657623Z","iopub.execute_input":"2023-02-01T14:50:42.658000Z","iopub.status.idle":"2023-02-01T14:50:42.668231Z","shell.execute_reply.started":"2023-02-01T14:50:42.657969Z","shell.execute_reply":"2023-02-01T14:50:42.666865Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.893929Z","iopub.execute_input":"2023-02-01T14:50:42.894325Z","iopub.status.idle":"2023-02-01T14:50:42.904773Z","shell.execute_reply.started":"2023-02-01T14:50:42.894278Z","shell.execute_reply":"2023-02-01T14:50:42.903541Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.119666Z","iopub.execute_input":"2023-02-01T14:50:43.120081Z","iopub.status.idle":"2023-02-01T14:50:43.128473Z","shell.execute_reply.started":"2023-02-01T14:50:43.120043Z","shell.execute_reply":"2023-02-01T14:50:43.127000Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.558402Z","iopub.execute_input":"2023-02-01T14:50:43.558778Z","iopub.status.idle":"2023-02-01T14:50:43.566705Z","shell.execute_reply.started":"2023-02-01T14:50:43.558746Z","shell.execute_reply":"2023-02-01T14:50:43.565387Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.776835Z","iopub.execute_input":"2023-02-01T14:50:43.777203Z","iopub.status.idle":"2023-02-01T14:50:43.786826Z","shell.execute_reply.started":"2023-02-01T14:50:43.777173Z","shell.execute_reply":"2023-02-01T14:50:43.785678Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.999708Z","iopub.execute_input":"2023-02-01T14:50:44.000611Z","iopub.status.idle":"2023-02-01T14:50:44.012435Z","shell.execute_reply.started":"2023-02-01T14:50:44.000573Z","shell.execute_reply":"2023-02-01T14:50:44.011295Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.273326Z","iopub.execute_input":"2023-02-01T14:50:44.273733Z","iopub.status.idle":"2023-02-01T14:50:44.285873Z","shell.execute_reply.started":"2023-02-01T14:50:44.273696Z","shell.execute_reply":"2023-02-01T14:50:44.284650Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.530974Z","iopub.execute_input":"2023-02-01T14:50:44.531405Z","iopub.status.idle":"2023-02-01T14:50:44.543425Z","shell.execute_reply.started":"2023-02-01T14:50:44.531367Z","shell.execute_reply":"2023-02-01T14:50:44.542121Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.768732Z","iopub.execute_input":"2023-02-01T14:50:44.769126Z","iopub.status.idle":"2023-02-01T14:50:44.779844Z","shell.execute_reply.started":"2023-02-01T14:50:44.769091Z","shell.execute_reply":"2023-02-01T14:50:44.779079Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.009603Z","iopub.execute_input":"2023-02-01T14:50:45.009952Z","iopub.status.idle":"2023-02-01T14:50:45.019372Z","shell.execute_reply.started":"2023-02-01T14:50:45.009923Z","shell.execute_reply":"2023-02-01T14:50:45.018131Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.259413Z","iopub.execute_input":"2023-02-01T14:50:45.259813Z","iopub.status.idle":"2023-02-01T14:50:45.270859Z","shell.execute_reply.started":"2023-02-01T14:50:45.259779Z","shell.execute_reply":"2023-02-01T14:50:45.269750Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.492004Z","iopub.execute_input":"2023-02-01T14:50:45.492453Z","iopub.status.idle":"2023-02-01T14:50:45.505989Z","shell.execute_reply.started":"2023-02-01T14:50:45.492416Z","shell.execute_reply":"2023-02-01T14:50:45.504737Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.736753Z","iopub.execute_input":"2023-02-01T14:50:45.737917Z","iopub.status.idle":"2023-02-01T14:50:45.751164Z","shell.execute_reply.started":"2023-02-01T14:50:45.737860Z","shell.execute_reply":"2023-02-01T14:50:45.749612Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.979633Z","iopub.execute_input":"2023-02-01T14:50:45.980021Z","iopub.status.idle":"2023-02-01T14:50:45.987927Z","shell.execute_reply.started":"2023-02-01T14:50:45.979987Z","shell.execute_reply":"2023-02-01T14:50:45.986675Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarkment \nWe remove any NAs from the embarked column. We replace NaNs values with unknown. However, only the training datasets has some unknown values. It could lower accuracy on the prediction on the testing dataset.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.403953Z","iopub.execute_input":"2023-02-01T14:50:46.404807Z","iopub.status.idle":"2023-02-01T14:50:46.413830Z","shell.execute_reply.started":"2023-02-01T14:50:46.404750Z","shell.execute_reply":"2023-02-01T14:50:46.412619Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' nan]\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Embarked'].isna(),'Embarked'] = 'U'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.636113Z","iopub.execute_input":"2023-02-01T14:50:46.637042Z","iopub.status.idle":"2023-02-01T14:50:46.643930Z","shell.execute_reply.started":"2023-02-01T14:50:46.637002Z","shell.execute_reply":"2023-02-01T14:50:46.642148Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"titanic_test.loc[titanic_test['Embarked'].isna(),'Embarked'] = 'U'\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.902953Z","iopub.execute_input":"2023-02-01T14:50:46.904202Z","iopub.status.idle":"2023-02-01T14:50:46.911244Z","shell.execute_reply.started":"2023-02-01T14:50:46.904144Z","shell.execute_reply":"2023-02-01T14:50:46.910042Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.161516Z","iopub.execute_input":"2023-02-01T14:50:47.162591Z","iopub.status.idle":"2023-02-01T14:50:47.169485Z","shell.execute_reply.started":"2023-02-01T14:50:47.162548Z","shell.execute_reply":"2023-02-01T14:50:47.168028Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' 'U']\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Sex.unique())\nprint(\"Testing : \" , titanic_test.Sex.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.378976Z","iopub.execute_input":"2023-02-01T14:50:47.379690Z","iopub.status.idle":"2023-02-01T14:50:47.386404Z","shell.execute_reply.started":"2023-02-01T14:50:47.379649Z","shell.execute_reply":"2023-02-01T14:50:47.385047Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTesting : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Passenger class\nNo unknown values is present in both datasets.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Pclass.unique())\nprint(\"Testing : \" , titanic_test.Pclass.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.799740Z","iopub.execute_input":"2023-02-01T14:50:47.800431Z","iopub.status.idle":"2023-02-01T14:50:47.807300Z","shell.execute_reply.started":"2023-02-01T14:50:47.800393Z","shell.execute_reply":"2023-02-01T14:50:47.806156Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Training : [3. 1. 2.]\nTesting : [3. 2. 1.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PClass and Fare\n\nThe Fare decreases as the passenger class decrease. However the range is can be quite large and the data data imbalanced; there are a lot more third class tickets than other classes. So we scale robustly the data based on non-parametric statistics.","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Fare\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x:100 * x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.235186Z","iopub.execute_input":"2023-02-01T14:50:48.235601Z","iopub.status.idle":"2023-02-01T14:50:48.274182Z","shell.execute_reply.started":"2023-02-01T14:50:48.235564Z","shell.execute_reply":"2023-02-01T14:50:48.272964Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"Fare 0.0000 4.0125 5.0000 6.2375 6.4375 6.4500 6.4958 \\\nPclass \n1.0 2.314815 NaN 0.462963 NaN NaN NaN NaN \n2.0 3.260870 NaN NaN NaN NaN NaN NaN \n3.0 0.814664 0.203666 NaN 0.203666 0.203666 0.203666 0.407332 \n\nFare 6.7500 6.8583 6.9500 ... 153.4625 164.8667 211.3375 \\\nPclass ... \n1.0 NaN NaN NaN ... 1.388889 0.925926 1.388889 \n2.0 NaN NaN NaN ... NaN NaN NaN \n3.0 0.407332 0.203666 0.203666 ... NaN NaN NaN \n\nFare 211.5000 221.7792 227.5250 247.5208 262.3750 263.0000 512.3292 \nPclass \n1.0 0.462963 0.462963 1.851852 0.925926 0.925926 1.851852 1.388889 \n2.0 NaN NaN NaN NaN NaN NaN NaN \n3.0 NaN NaN NaN NaN NaN NaN NaN \n\n[3 rows x 248 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Fare0.00004.01255.00006.23756.43756.45006.49586.75006.85836.9500...153.4625164.8667211.3375211.5000221.7792227.5250247.5208262.3750263.0000512.3292
Pclass
1.02.314815NaN0.462963NaNNaNNaNNaNNaNNaNNaN...1.3888890.9259261.3888890.4629630.4629631.8518520.9259260.9259261.8518521.388889
2.03.260870NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3.00.8146640.203666NaN0.2036660.2036660.2036660.4073320.4073320.2036660.203666...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n

3 rows × 248 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins=512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.451437Z","iopub.execute_input":"2023-02-01T14:50:48.451845Z","iopub.status.idle":"2023-02-01T14:50:49.547249Z","shell.execute_reply.started":"2023-02-01T14:50:48.451810Z","shell.execute_reply":"2023-02-01T14:50:49.546123Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 32.204208\nstd 49.693429\nmin 0.000000\n25% 7.910400\n50% 14.454200\n75% 31.000000\nmax 512.329200\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,8))\nplt.suptitle('')\ntitanic_train.boxplot(column=['Fare'], by='Pclass', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.549636Z","iopub.execute_input":"2023-02-01T14:50:49.550050Z","iopub.status.idle":"2023-02-01T14:50:49.804155Z","shell.execute_reply.started":"2023-02-01T14:50:49.550008Z","shell.execute_reply":"2023-02-01T14:50:49.803012Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.805690Z","iopub.execute_input":"2023-02-01T14:50:49.806699Z","iopub.status.idle":"2023-02-01T14:50:49.864940Z","shell.execute_reply.started":"2023-02-01T14:50:49.806662Z","shell.execute_reply":"2023-02-01T14:50:49.863879Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% max\nPclass \n1.0 216.0 84.154687 78.380373 0.0 30.92395 60.2875 93.5 512.3292\n2.0 184.0 20.662183 13.417399 0.0 13.00000 14.2500 26.0 73.5000\n3.0 491.0 13.675550 11.778142 0.0 7.75000 8.0500 15.5 69.5500","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0216.084.15468778.3803730.030.9239560.287593.5512.3292
2.0184.020.66218313.4173990.013.0000014.250026.073.5000
3.0491.013.67555011.7781420.07.750008.050015.569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_train.Fare.median()\nIQR_fare = titanic_train.Fare.quantile(0.75) - titanic_train.Fare.quantile(0.25)\ntitanic_train.loc[:,\"Fare\"] = (titanic_train.Fare - median_fare)/IQR_fare\nplt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.867034Z","iopub.execute_input":"2023-02-01T14:50:49.867360Z","iopub.status.idle":"2023-02-01T14:50:51.334840Z","shell.execute_reply.started":"2023-02-01T14:50:49.867301Z","shell.execute_reply":"2023-02-01T14:50:51.334033Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:51.336034Z","iopub.execute_input":"2023-02-01T14:50:51.336529Z","iopub.status.idle":"2023-02-01T14:50:52.406610Z","shell.execute_reply.started":"2023-02-01T14:50:51.336498Z","shell.execute_reply":"2023-02-01T14:50:52.405714Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.407853Z","iopub.execute_input":"2023-02-01T14:50:52.408376Z","iopub.status.idle":"2023-02-01T14:50:52.415841Z","shell.execute_reply.started":"2023-02-01T14:50:52.408342Z","shell.execute_reply":"2023-02-01T14:50:52.414785Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We repeat the same process with the test dataset. The distribution is much different and therefore could lower the accuracy of the prediction.","metadata":{}},{"cell_type":"code","source":"titanic_test.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.418261Z","iopub.execute_input":"2023-02-01T14:50:52.418629Z","iopub.status.idle":"2023-02-01T14:50:52.472603Z","shell.execute_reply.started":"2023-02-01T14:50:52.418596Z","shell.execute_reply":"2023-02-01T14:50:52.471219Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nPclass \n1.0 107.0 94.280297 84.435858 0.0000 30.10 60.0000 134.500000 \n2.0 93.0 22.202104 13.991877 9.6875 13.00 15.7500 26.000000 \n3.0 218.0 12.397936 10.817256 -1.0000 7.75 7.8958 14.327075 \n\n max \nPclass \n1.0 512.3292 \n2.0 73.5000 \n3.0 69.5500 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0107.094.28029784.4358580.000030.1060.0000134.500000512.3292
2.093.022.20210413.9918779.687513.0015.750026.00000073.5000
3.0218.012.39793610.817256-1.00007.757.895814.32707569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_test.Fare.median()\nIQR_fare = titanic_test.Fare.quantile(0.75) - titanic_test.Fare.quantile(0.25)\ntitanic_test.loc[:,\"Fare\"] = (titanic_test.Fare - median_fare)/IQR_fare\nplt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.473824Z","iopub.execute_input":"2023-02-01T14:50:52.474155Z","iopub.status.idle":"2023-02-01T14:50:53.560939Z","shell.execute_reply.started":"2023-02-01T14:50:52.474123Z","shell.execute_reply":"2023-02-01T14:50:53.559872Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:53.562396Z","iopub.execute_input":"2023-02-01T14:50:53.562797Z","iopub.status.idle":"2023-02-01T14:50:54.622056Z","shell.execute_reply.started":"2023-02-01T14:50:53.562764Z","shell.execute_reply":"2023-02-01T14:50:54.620862Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.623442Z","iopub.execute_input":"2023-02-01T14:50:54.623854Z","iopub.status.idle":"2023-02-01T14:50:54.631562Z","shell.execute_reply.started":"2023-02-01T14:50:54.623823Z","shell.execute_reply":"2023-02-01T14:50:54.630628Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age\nWe normalise the age to bring more the data towards the median. The previous transformation have brought more data centrally.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.632748Z","iopub.execute_input":"2023-02-01T14:50:54.633113Z","iopub.status.idle":"2023-02-01T14:50:54.995183Z","shell.execute_reply.started":"2023-02-01T14:50:54.633084Z","shell.execute_reply":"2023-02-01T14:50:54.993205Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_train.Age.median()\nIQR_age = titanic_train.Age.quantile(0.75) - titanic_train.Age.quantile(0.25)\ntitanic_train.loc[:,\"Age\"] = (titanic_train.Age - median_age)/IQR_age\ntitanic_train.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.996341Z","iopub.execute_input":"2023-02-01T14:50:54.996637Z","iopub.status.idle":"2023-02-01T14:50:55.012393Z","shell.execute_reply.started":"2023-02-01T14:50:54.996609Z","shell.execute_reply":"2023-02-01T14:50:55.011269Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.014533Z","iopub.execute_input":"2023-02-01T14:50:55.015228Z","iopub.status.idle":"2023-02-01T14:50:55.377136Z","shell.execute_reply.started":"2023-02-01T14:50:55.015184Z","shell.execute_reply":"2023-02-01T14:50:55.376023Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQ40lEQVR4nO3df6xkZX3H8fengNr4I2C5pQTYLhokQaOLvUEaqqGi7YIGxLSUTYugtKsJJJiaWNCk2jYmpIq2xhazCgFTRGiRQipWt5RITETdxS0uvxQohN2s7ApWaDG0C9/+cc+2w/Xu3jtz5t7Z+9z3K5nMOc85Z873ZOd+9plnzpyTqkKS1JZfmHQBkqTxM9wlqUGGuyQ1yHCXpAYZ7pLUoAMnXQDAoYceWqtXr550GZK0rGzevPnHVTU117L9ItxXr17Npk2bJl2GJC0rSR7Z2zKHZSSpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNmjfckxyV5LYk9yS5O8lFXfvLk2xM8sPu+ZCuPUk+neSBJHclef1iH4Qk6fkW8gvV3cAHqurOJC8FNifZCJwH3FpVlya5GLgY+BPgVOCY7vEG4PLuWXqe1Rd/5XnzD1/6tglVIrVn3p57Ve2oqju76aeAe4EjgDOAq7vVrgbe0U2fAXyhZtwBHJzk8HEXLknau6HG3JOsBo4Hvg0cVlU7ukU/Ag7rpo8AHh3YbFvXNvu11ifZlGTTrl27hq1bkrQPCw73JC8BbgDeX1VPDi6rmRuxDnUz1qraUFXTVTU9NTXnRc0kSSNaULgnOYiZYL+mqr7cNT+2Z7ile97ZtW8HjhrY/MiuTZK0RBZytkyAK4B7q+qTA4tuBs7tps8Fbhpof1d31syJwE8Hhm8kSUtgIWfLnAScA3w/yZau7UPApcD1Sc4HHgHO6pbdApwGPAA8Dbx7nAVLkuY3b7hX1TeB7GXxKXOsX8AFPeuSJPXgL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ1ayG32rkyyM8nWgbbrkmzpHg/vuUNTktVJfjaw7LOLWLskaS8Wcpu9q4DPAF/Y01BVv7dnOsllwE8H1n+wqtaMqT5J0ggWcpu925OsnmtZd/Pss4A3j7kuSVIPfcfc3wg8VlU/HGg7Osn3knwjyRv3tmGS9Uk2Jdm0a9eunmVIkgb1Dfd1wLUD8zuAVVV1PPDHwBeTvGyuDatqQ1VNV9X01NRUzzIkSYNGDvckBwLvBK7b01ZVz1TV4930ZuBB4FV9i5QkDadPz/0twH1VtW1PQ5KpJAd0068AjgEe6leiJGlYCzkV8lrgW8CxSbYlOb9bdDbPH5IBeBNwV3dq5D8A76uqJ8ZYryRpARZytsy6vbSfN0fbDcAN/cuSJPXhL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQQu5zd6VSXYm2TrQ9tEk25Ns6R6nDSy7JMkDSe5P8tuLVbgkae8W0nO/Clg7R/unqmpN97gFIMlxzNxb9dXdNn+754bZkqSlM2+4V9XtwEJvcn0G8KWqeqaq/h14ADihR32SpBH0GXO/MMld3bDNIV3bEcCjA+ts69p+TpL1STYl2bRr164eZUiSZhs13C8HXgmsAXYAlw37AlW1oaqmq2p6ampqxDIkSXMZKdyr6rGqeraqngM+x/8PvWwHjhpY9ciuTZK0hEYK9ySHD8yeCew5k+Zm4OwkL0xyNHAM8J1+JUqShnXgfCskuRY4GTg0yTbgI8DJSdYABTwMvBegqu5Ocj1wD7AbuKCqnl2UyiVJezVvuFfVujmar9jH+h8DPtanKElSP/5CVZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoHnDPcmVSXYm2TrQ9vEk9yW5K8mNSQ7u2lcn+VmSLd3js4tYuyRpLxbSc78KWDurbSPwmqp6LfAD4JKBZQ9W1Zru8b7xlClJGsa84V5VtwNPzGr7elXt7mbvAI5chNokSSMax5j7e4CvDswfneR7Sb6R5I1jeH1J0pDmvUH2viT5MLAbuKZr2gGsqqrHk/wa8I9JXl1VT86x7XpgPcCqVav6lCFJmmXknnuS84C3A79fVQVQVc9U1ePd9GbgQeBVc21fVRuqarqqpqempkYtQ5I0h5HCPcla4IPA6VX19ED7VJIDuulXAMcAD42jUEnSws07LJPkWuBk4NAk24CPMHN2zAuBjUkA7ujOjHkT8OdJ/gd4DnhfVT0x5wtLkhbNvOFeVevmaL5iL+veANzQtyhJUj/+QlWSGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIatKBwT3Jlkp1Jtg60vTzJxiQ/7J4P6dqT5NNJHkhyV5LXL1bxkqS5LbTnfhWwdlbbxcCtVXUMcGs3D3AqMzfGPgZYD1zev0xJ0jAWFO5VdTsw+0bXZwBXd9NXA+8YaP9CzbgDODjJ4WOoVZK0QH3G3A+rqh3d9I+Aw7rpI4BHB9bb1rU9T5L1STYl2bRr164eZUiSZhvLF6pVVUANuc2GqpququmpqalxlCFJ6vQJ98f2DLd0zzu79u3AUQPrHdm1SZKWSJ9wvxk4t5s+F7hpoP1d3VkzJwI/HRi+kSQtgQMXslKSa4GTgUOTbAM+AlwKXJ/kfOAR4Kxu9VuA04AHgKeBd4+5ZknSPBYU7lW1bi+LTplj3QIu6FOUJKkff6EqSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDVrQnZjmkuRY4LqBplcAfwocDPwRsKtr/1BV3TLqfiRJwxs53KvqfmANQJIDgO3AjczcM/VTVfWJcRQoSRreuIZlTgEerKpHxvR6kqQexhXuZwPXDsxfmOSuJFcmOWSuDZKsT7IpyaZdu3bNtYokaUS9wz3JC4DTgb/vmi4HXsnMkM0O4LK5tquqDVU1XVXTU1NTfcuQJA0YR8/9VODOqnoMoKoeq6pnq+o54HPACWPYhyRpCOMI93UMDMkkOXxg2ZnA1jHsQ5I0hJHPlgFI8mLgrcB7B5r/MskaoICHZy2TJC2BXuFeVf8F/NKstnN6VSRJ6s1fqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGtTrZh0ASR4GngKeBXZX1XSSlwPXAauZuRvTWVX1k777kiQtzLh67r9ZVWuqarqbvxi4taqOAW7t5iVJS2SxhmXOAK7upq8G3rFI+5EkzWEc4V7A15NsTrK+azusqnZ00z8CDhvDfiRJC9R7zB34jaranuSXgY1J7htcWFWVpGZv1P1HsB5g1apVYyhDkrRH7557VW3vnncCNwInAI8lORyge945x3Ybqmq6qqanpqb6liFJGtAr3JO8OMlL90wDvwVsBW4Gzu1WOxe4qc9+JEnD6TsscxhwY5I9r/XFqvrnJN8Frk9yPvAIcFbP/UiShtAr3KvqIeB1c7Q/DpzS57UlSaPzF6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUHjuM2eGrb64q88b/7hS982oUokDcOeuyQ1yHCXpAaNHO5JjkpyW5J7ktyd5KKu/aNJtifZ0j1OG1+5kqSF6DPmvhv4QFXd2d0ke3OSjd2yT1XVJ/qXp8XmmLrUppHDvap2ADu66aeS3AscMa7CtHAGtKTZxnK2TJLVwPHAt4GTgAuTvAvYxEzv/idzbLMeWA+watWqcZShznxhP3v5pOqQtHh6h3uSlwA3AO+vqieTXA78BVDd82XAe2ZvV1UbgA0A09PT1bcOLQ0DW1oeep0tk+QgZoL9mqr6MkBVPVZVz1bVc8DngBP6lylJGsbIPfckAa4A7q2qTw60H96NxwOcCWztV6JasVTDQXPty08YWmn6DMucBJwDfD/Jlq7tQ8C6JGuYGZZ5GHhvj31IkkbQ52yZbwKZY9Eto5cjSRoHry2j/cbgUMqwwyhLOeQjLQeGu3pZrFB1zFzqx3BfJvr0altg2EvD8cJhktQge+4rwDBDJ8tl7Hq51ClNShPhvtKHLNSPQz5qURPhvtLYa11cw1ybx/8ItL8y3KVF5KcCTYpfqEpSg+y5a8VxWEsrQXPhPuy1zP2YrOXC966G0Vy4S5M07KeCfa1veKsPw70He1IrQwvDOL5XVx7DXStCCwE9m4GtfWk+3JfLH/VyqVNLp88Qj0Gv5sNdWkzjHGOfpH19CvATwvK04sN9mDeuPSnp5/mL3v3TooV7krXAXwMHAJ+vqksXa1/jtL/2rKTlos/f0GJ+Slhpn0AWJdyTHAD8DfBWYBvw3SQ3V9U9i7G//UWLV19Um+Z7/+1r+TjDezGt9L+xxeq5nwA8UFUPAST5EnAG0HS478tKf6NpabXyfptUb7uFTxCpqvG/aPI7wNqq+sNu/hzgDVV14cA664H13eyxwP1jL2TfDgV+vMT7XAwex/6jhWMAj2N/s6/j+NWqmpprwcS+UK2qDcCGSe0/yaaqmp7U/sfF49h/tHAM4HHsb0Y9jsW6KuR24KiB+SO7NknSEliscP8ucEySo5O8ADgbuHmR9iVJmmVRhmWqaneSC4GvMXMq5JVVdfdi7KuHiQ0JjZnHsf9o4RjA49jfjHQci/KFqiRpsrwTkyQ1yHCXpAat6HBP8vEk9yW5K8mNSQ6edE2jSPK7Se5O8lySZXXqV5K1Se5P8kCSiyddzyiSXJlkZ5Ktk66ljyRHJbktyT3d++miSdc0rCQvSvKdJP/WHcOfTbqmPpIckOR7Sf5p2G1XdLgDG4HXVNVrgR8Al0y4nlFtBd4J3D7pQoYxcJmKU4HjgHVJjptsVSO5Clg76SLGYDfwgao6DjgRuGAZ/ns8A7y5ql4HrAHWJjlxsiX1chFw7ygbruhwr6qvV9XubvYOZs7HX3aq6t6qWupf+I7D/12moqr+G9hzmYplpapuB56YdB19VdWOqrqzm36KmVA5YrJVDadm/Gc3e1D3WJZnjSQ5Engb8PlRtl/R4T7Le4CvTrqIFeYI4NGB+W0sszBpVZLVwPHAtydcytC6oYwtwE5gY1Utu2Po/BXwQeC5UTZu/nruSf4F+JU5Fn24qm7q1vkwMx9Jr1nK2oaxkOOQxiHJS4AbgPdX1ZOTrmdYVfUssKb7Du3GJK+pqmX1fUiStwM7q2pzkpNHeY3mw72q3rKv5UnOA94OnFL78Un/8x3HMuVlKvYzSQ5iJtivqaovT7qePqrqP5Lcxsz3Icsq3IGTgNOTnAa8CHhZkr+rqj9Y6Aus6GGZ7oYiHwROr6qnJ13PCuRlKvYjSQJcAdxbVZ+cdD2jSDK156y3JL/IzD0l7ptoUSOoqkuq6siqWs3M38W/DhPssMLDHfgM8FJgY5ItST476YJGkeTMJNuAXwe+kuRrk65pIbovs/dcpuJe4Pr98DIV80pyLfAt4Ngk25KcP+maRnQScA7w5u7vYUvXc1xODgduS3IXM52HjVU19GmELfDyA5LUoJXec5ekJhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUH/C/1X3MxerTJuAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.378688Z","iopub.execute_input":"2023-02-01T14:50:55.379745Z","iopub.status.idle":"2023-02-01T14:50:55.727506Z","shell.execute_reply.started":"2023-02-01T14:50:55.379709Z","shell.execute_reply":"2023-02-01T14:50:55.726302Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6N52X3A08A/AWdOKds7Gb2c/B7wWHd571DydRl/B3i0y/g48Jfd+t8GvgMcYvRS+denfJzfDTwwtGxdlu92lyeO/V0M7BhfBMx3x/gfgTMGlu804D+AN4+tG0y+SS6eoSpJDdqs0zKSpBOw3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatD/AmLJbG6fuoYqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_test.Age.median()\nIQR_age = titanic_test.Age.quantile(0.75) - titanic_test.Age.quantile(0.25)\ntitanic_test.loc[:,\"Age\"] = (titanic_test.Age - median_age)/IQR_age\ntitanic_test.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.731833Z","iopub.execute_input":"2023-02-01T14:50:55.732609Z","iopub.status.idle":"2023-02-01T14:50:55.747180Z","shell.execute_reply.started":"2023-02-01T14:50:55.732557Z","shell.execute_reply":"2023-02-01T14:50:55.746071Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.748583Z","iopub.execute_input":"2023-02-01T14:50:55.748898Z","iopub.status.idle":"2023-02-01T14:50:56.093344Z","shell.execute_reply.started":"2023-02-01T14:50:55.748868Z","shell.execute_reply":"2023-02-01T14:50:56.092150Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Gender \nWe replace the male with 1 and female with the value 2.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \", titanic_train['Sex'].unique())\nprint(\"Test : \", titanic_train['Sex'].unique())\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.094765Z","iopub.execute_input":"2023-02-01T14:50:56.095091Z","iopub.status.idle":"2023-02-01T14:50:56.103516Z","shell.execute_reply.started":"2023-02-01T14:50:56.095062Z","shell.execute_reply":"2023-02-01T14:50:56.102411Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTest : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_train[\"Sex\"] = titanic_train[\"Sex\"].astype(float)\ntitanic_train.groupby(\"Sex\").count()[\"PassengerId\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.104821Z","iopub.execute_input":"2023-02-01T14:50:56.105350Z","iopub.status.idle":"2023-02-01T14:50:56.122953Z","shell.execute_reply.started":"2023-02-01T14:50:56.105306Z","shell.execute_reply":"2023-02-01T14:50:56.122030Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 577\n2.0 314\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_test[\"Sex\"] = titanic_test[\"Sex\"].astype(float)\ntitanic_test.groupby(\"Sex\").count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.124259Z","iopub.execute_input":"2023-02-01T14:50:56.124612Z","iopub.status.idle":"2023-02-01T14:50:56.139408Z","shell.execute_reply.started":"2023-02-01T14:50:56.124581Z","shell.execute_reply":"2023-02-01T14:50:56.138058Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 266\n2.0 152\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sibling and parentage\n\nWe add both sibling, parents, and children into a family variables. ","metadata":{}},{"cell_type":"code","source":"titanic_train[\"SibSp\"].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.141402Z","iopub.execute_input":"2023-02-01T14:50:56.141813Z","iopub.status.idle":"2023-02-01T14:50:56.148230Z","shell.execute_reply.started":"2023-02-01T14:50:56.141777Z","shell.execute_reply":"2023-02-01T14:50:56.147382Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"array([1., 0., 3., 4., 2., 5., 8.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Parch\"].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.149745Z","iopub.execute_input":"2023-02-01T14:50:56.150349Z","iopub.status.idle":"2023-02-01T14:50:56.159952Z","shell.execute_reply.started":"2023-02-01T14:50:56.150294Z","shell.execute_reply":"2023-02-01T14:50:56.158924Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"array([0., 1., 2., 5., 3., 4., 6.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\ntitanic_train[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.161871Z","iopub.execute_input":"2023-02-01T14:50:56.162175Z","iopub.status.idle":"2023-02-01T14:50:56.176837Z","shell.execute_reply.started":"2023-02-01T14:50:56.162147Z","shell.execute_reply":"2023-02-01T14:50:56.175684Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.178360Z","iopub.execute_input":"2023-02-01T14:50:56.178747Z","iopub.status.idle":"2023-02-01T14:50:56.191340Z","shell.execute_reply.started":"2023-02-01T14:50:56.178698Z","shell.execute_reply":"2023-02-01T14:50:56.190355Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.195050Z","iopub.execute_input":"2023-02-01T14:50:56.195448Z","iopub.status.idle":"2023-02-01T14:50:56.209129Z","shell.execute_reply.started":"2023-02-01T14:50:56.195400Z","shell.execute_reply":"2023-02-01T14:50:56.207967Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.210664Z","iopub.execute_input":"2023-02-01T14:50:56.211090Z","iopub.status.idle":"2023-02-01T14:50:56.219640Z","shell.execute_reply.started":"2023-02-01T14:50:56.211049Z","shell.execute_reply":"2023-02-01T14:50:56.218550Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.221452Z","iopub.execute_input":"2023-02-01T14:50:56.222189Z","iopub.status.idle":"2023-02-01T14:50:56.231508Z","shell.execute_reply.started":"2023-02-01T14:50:56.222146Z","shell.execute_reply":"2023-02-01T14:50:56.230398Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.232676Z","iopub.execute_input":"2023-02-01T14:50:56.233089Z","iopub.status.idle":"2023-02-01T14:50:56.242657Z","shell.execute_reply.started":"2023-02-01T14:50:56.233048Z","shell.execute_reply":"2023-02-01T14:50:56.241737Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.244097Z","iopub.execute_input":"2023-02-01T14:50:56.244427Z","iopub.status.idle":"2023-02-01T14:50:56.251459Z","shell.execute_reply.started":"2023-02-01T14:50:56.244398Z","shell.execute_reply":"2023-02-01T14:50:56.250542Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.256041Z","iopub.execute_input":"2023-02-01T14:50:56.256485Z","iopub.status.idle":"2023-02-01T14:50:56.265940Z","shell.execute_reply.started":"2023-02-01T14:50:56.256450Z","shell.execute_reply":"2023-02-01T14:50:56.264711Z"},"trusted":true},"execution_count":113,"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.267253Z","iopub.execute_input":"2023-02-01T14:50:56.267740Z","iopub.status.idle":"2023-02-01T14:50:56.278020Z","shell.execute_reply.started":"2023-02-01T14:50:56.267696Z","shell.execute_reply":"2023-02-01T14:50:56.276748Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"titanic_test[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.315420Z","iopub.execute_input":"2023-02-01T14:50:56.315791Z","iopub.status.idle":"2023-02-01T14:50:56.322971Z","shell.execute_reply.started":"2023-02-01T14:50:56.315760Z","shell.execute_reply":"2023-02-01T14:50:56.322090Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## Columns to drop \nWe drop some columns; they may have too many unknown values. Some of them may be dependent statistical variables. We assume the price of a ticket may be dependent of the fare. ","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Name\", axis = 1, inplace = True)\ntitanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_train.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.722307Z","iopub.execute_input":"2023-02-01T14:50:56.722753Z","iopub.status.idle":"2023-02-01T14:50:56.744122Z","shell.execute_reply.started":"2023-02-01T14:50:56.722718Z","shell.execute_reply":"2023-02-01T14:50:56.743299Z"},"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.drop(\"Name\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_test.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.963356Z","iopub.execute_input":"2023-02-01T14:50:56.963753Z","iopub.status.idle":"2023-02-01T14:50:56.979754Z","shell.execute_reply.started":"2023-02-01T14:50:56.963719Z","shell.execute_reply":"2023-02-01T14:50:56.978543Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We make of both datasets. These copies will be used to analysed the predictions values from all the classifiers.","metadata":{}},{"cell_type":"code","source":"results_test = titanic_test.copy(deep = True)\nresults_train = titanic_train.copy(deep = True) ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:57.429442Z","iopub.execute_input":"2023-02-01T14:50:57.429827Z","iopub.status.idle":"2023-02-01T14:50:57.435755Z","shell.execute_reply.started":"2023-02-01T14:50:57.429796Z","shell.execute_reply":"2023-02-01T14:50:57.434439Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"markdown","source":"# Method : Logistic regression\n\nOur first classifier is a logistic regression. We surmise it may be the most suitable methods as two classes of labels exist; survived or not. The data is imbalanced towards perishing sadly. So we add some class weight to represent this situation in the data. \n\nWe choose the passenger class, sex, familly members. We surmise the passenger class, gender and being part of a familly or not may have influenced surviving the accident. The training dataset is split into training and validation for validating the model fitting. ","metadata":{}},{"cell_type":"markdown","source":"## Preparation Cross validation \nWe show how the transformation have affected both datasets","metadata":{}},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.108812Z","iopub.execute_input":"2023-02-01T14:50:58.109845Z","iopub.status.idle":"2023-02-01T14:50:58.118552Z","shell.execute_reply.started":"2023-02-01T14:50:58.109806Z","shell.execute_reply":"2023-02-01T14:50:58.117356Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.354904Z","iopub.execute_input":"2023-02-01T14:50:58.355573Z","iopub.status.idle":"2023-02-01T14:50:58.362764Z","shell.execute_reply.started":"2023-02-01T14:50:58.355531Z","shell.execute_reply":"2023-02-01T14:50:58.361542Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"(891, 8)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.590264Z","iopub.execute_input":"2023-02-01T14:50:58.591668Z","iopub.status.idle":"2023-02-01T14:50:58.600773Z","shell.execute_reply.started":"2023-02-01T14:50:58.591627Z","shell.execute_reply":"2023-02-01T14:50:58.599216Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.804713Z","iopub.execute_input":"2023-02-01T14:50:58.805085Z","iopub.status.idle":"2023-02-01T14:50:58.812599Z","shell.execute_reply.started":"2023-02-01T14:50:58.805054Z","shell.execute_reply":"2023-02-01T14:50:58.811376Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"(418, 7)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Split data sets for cross validation\n\nWe use a stratified shuffle split to aim at reducing the variation between the training and validation datasets.","metadata":{}},{"cell_type":"code","source":"\n\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n#X = X[x_cols]\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.267374Z","iopub.execute_input":"2023-02-01T14:50:59.267771Z","iopub.status.idle":"2023-02-01T14:50:59.288554Z","shell.execute_reply.started":"2023-02-01T14:50:59.267735Z","shell.execute_reply":"2023-02-01T14:50:59.287476Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We keep the passengers ids for building up the training dataset results. It will be used to compare all the classifier.","metadata":{}},{"cell_type":"code","source":"x_train_pass_id = X_train[\"PassengerId\"]\nx_train_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.704437Z","iopub.execute_input":"2023-02-01T14:50:59.704815Z","iopub.status.idle":"2023-02-01T14:50:59.714204Z","shell.execute_reply.started":"2023-02-01T14:50:59.704783Z","shell.execute_reply":"2023-02-01T14:50:59.713337Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"844 845.0\n316 317.0\n768 769.0\n255 256.0\n130 131.0\n ... \n476 477.0\n58 59.0\n736 737.0\n462 463.0\n747 748.0\nName: PassengerId, Length: 534, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"x_cols =[\"Pclass\",\"Sex\",\"fam_members\"]\nX_train = X_train[x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.933799Z","iopub.execute_input":"2023-02-01T14:50:59.934191Z","iopub.status.idle":"2023-02-01T14:50:59.947540Z","shell.execute_reply.started":"2023-02-01T14:50:59.934158Z","shell.execute_reply":"2023-02-01T14:50:59.946577Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n844 3.0 1.0 0.0\n316 2.0 2.0 1.0\n768 3.0 1.0 1.0\n255 3.0 2.0 2.0\n130 3.0 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
8443.01.00.0
3162.02.01.0
7683.01.01.0
2553.02.02.0
1303.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid[\"PassengerId\"]\nx_valid_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.149697Z","iopub.execute_input":"2023-02-01T14:51:00.150120Z","iopub.status.idle":"2023-02-01T14:51:00.160439Z","shell.execute_reply.started":"2023-02-01T14:51:00.150083Z","shell.execute_reply":"2023-02-01T14:51:00.159106Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"369 370.0\n541 542.0\n196 197.0\n810 811.0\n427 428.0\n ... \n174 175.0\n297 298.0\n244 245.0\n38 39.0\n371 372.0\nName: PassengerId, Length: 357, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.356159Z","iopub.execute_input":"2023-02-01T14:51:00.357062Z","iopub.status.idle":"2023-02-01T14:51:00.370786Z","shell.execute_reply.started":"2023-02-01T14:51:00.357017Z","shell.execute_reply":"2023-02-01T14:51:00.369619Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n369 1.0 2.0 0.0\n541 3.0 2.0 6.0\n196 3.0 1.0 0.0\n810 3.0 1.0 0.0\n427 2.0 2.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
3691.02.00.0
5413.02.06.0
1963.01.00.0
8103.01.00.0
4272.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test.copy(deep = True)\nX_test = X_test[x_cols]\nX_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.599111Z","iopub.execute_input":"2023-02-01T14:51:00.599521Z","iopub.status.idle":"2023-02-01T14:51:00.616521Z","shell.execute_reply.started":"2023-02-01T14:51:00.599483Z","shell.execute_reply":"2023-02-01T14:51:00.615356Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n0 3.0 1.0 0.0\n1 3.0 2.0 1.0\n2 2.0 1.0 0.0\n3 3.0 1.0 0.0\n4 3.0 2.0 2.0\n.. ... ... ...\n413 3.0 1.0 0.0\n414 1.0 2.0 0.0\n415 3.0 1.0 0.0\n416 3.0 1.0 0.0\n417 3.0 1.0 2.0\n\n[418 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
03.01.00.0
13.02.01.0
22.01.00.0
33.01.00.0
43.02.02.0
............
4133.01.00.0
4141.02.00.0
4153.01.00.0
4163.01.00.0
4173.01.02.0
\n

418 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model fitting","metadata":{}},{"cell_type":"markdown","source":"We fit the model using a stochastic average gradient. We achieve approximately 82% accuracy on the validation dataset. There is not sign of over fitting. ","metadata":{}},{"cell_type":"code","source":"classifier = LogisticRegression(random_state = 0, C = 1000, max_iter= 10000, \n solver=\"sag\", penalty=\"l2\",class_weight={0:6.,1:4})\nclassifier.fit(X_train, y_train)\nclassifier.coef_","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.275079Z","iopub.execute_input":"2023-02-01T14:51:01.275483Z","iopub.status.idle":"2023-02-01T14:51:01.291372Z","shell.execute_reply.started":"2023-02-01T14:51:01.275450Z","shell.execute_reply":"2023-02-01T14:51:01.290133Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([[-0.96687438, 2.71046703, -0.09242397]])"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_train = classifier.score(X_train, y_train)\nlog_reg_score_train","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.505908Z","iopub.execute_input":"2023-02-01T14:51:01.507102Z","iopub.status.idle":"2023-02-01T14:51:01.519460Z","shell.execute_reply.started":"2023-02-01T14:51:01.507059Z","shell.execute_reply":"2023-02-01T14:51:01.518123Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"0.7921348314606742"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_valid = classifier.score(X_valid, y_valid)\nlog_reg_score_valid","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.727365Z","iopub.execute_input":"2023-02-01T14:51:01.727743Z","iopub.status.idle":"2023-02-01T14:51:01.737787Z","shell.execute_reply.started":"2023-02-01T14:51:01.727712Z","shell.execute_reply":"2023-02-01T14:51:01.736406Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"0.8207282913165266"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nTwo confusion matrices show an improvement on predicting the validation dataset. We also store the predicted results in the results_train dataframe. We will use this dataframe later on to analyse difference between classifiers. \n\n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = classifier.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.212411Z","iopub.execute_input":"2023-02-01T14:51:02.212812Z","iopub.status.idle":"2023-02-01T14:51:02.223463Z","shell.execute_reply.started":"2023-02-01T14:51:02.212779Z","shell.execute_reply":"2023-02-01T14:51:02.222427Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"array([[297, 32],\n [ 79, 126]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.417280Z","iopub.execute_input":"2023-02-01T14:51:02.417687Z","iopub.status.idle":"2023-02-01T14:51:02.426591Z","shell.execute_reply.started":"2023-02-01T14:51:02.417653Z","shell.execute_reply":"2023-02-01T14:51:02.425177Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"Accuracy : 0.7921348314606742\nMisclassfication : 0.20786516853932585\nSensitivivity : 0.9027355623100304\nSpecificity : 0.6146341463414634\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = classifier.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.661227Z","iopub.execute_input":"2023-02-01T14:51:02.661653Z","iopub.status.idle":"2023-02-01T14:51:02.672901Z","shell.execute_reply.started":"2023-02-01T14:51:02.661618Z","shell.execute_reply":"2023-02-01T14:51:02.671790Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.907929Z","iopub.execute_input":"2023-02-01T14:51:02.908917Z","iopub.status.idle":"2023-02-01T14:51:02.916300Z","shell.execute_reply.started":"2023-02-01T14:51:02.908877Z","shell.execute_reply":"2023-02-01T14:51:02.915176Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"Accuracy : 0.8207282913165266\nMisclassfication : 0.1792717086834734\nSensitivivity : 0.9363636363636364\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.367005Z","iopub.execute_input":"2023-02-01T14:51:03.367441Z","iopub.status.idle":"2023-02-01T14:51:03.372440Z","shell.execute_reply.started":"2023-02-01T14:51:03.367404Z","shell.execute_reply":"2023-02-01T14:51:03.371375Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"y_pred = classifier.predict(X_train)\nlog_reg_pred = X_train.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_train\nlog_reg_pred[\"PassengerId\"] = x_train_pass_id\nlog_reg_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.610590Z","iopub.execute_input":"2023-02-01T14:51:03.610967Z","iopub.status.idle":"2023-02-01T14:51:03.632961Z","shell.execute_reply.started":"2023-02-01T14:51:03.610936Z","shell.execute_reply":"2023-02-01T14:51:03.631856Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n844 3.0 1.0 0.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 0.0 769.0\n255 3.0 2.0 2.0 0.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_memberslr_y_predyPassengerId
8443.01.00.00.00.0845.0
3162.02.01.01.01.0317.0
7683.01.01.00.00.0769.0
2553.02.02.00.01.0256.0
1303.01.00.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(log_reg_pred[[\"PassengerId\",\"y\", \"lr_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.870519Z","iopub.execute_input":"2023-02-01T14:51:03.870935Z","iopub.status.idle":"2023-02-01T14:51:03.899083Z","shell.execute_reply.started":"2023-02-01T14:51:03.870900Z","shell.execute_reply":"2023-02-01T14:51:03.898021Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 NaN NaN \n2 0.0 1.0 1.0 \n3 1.0 NaN NaN \n4 0.0 NaN NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.0NaNNaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.0NaNNaN
45.00.03.01.00.384615-0.2773632.00.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = classifier.predict(X_valid)\nlog_reg_pred = X_valid.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_valid\nlog_reg_pred[\"PassengerId\"] = x_valid_pass_id\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.094193Z","iopub.execute_input":"2023-02-01T14:51:04.094610Z","iopub.status.idle":"2023-02-01T14:51:04.120418Z","shell.execute_reply.started":"2023-02-01T14:51:04.094576Z","shell.execute_reply":"2023-02-01T14:51:04.119350Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n369 1.0 2.0 0.0 1.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 1.0 428.0\n.. ... ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 1.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 0.0 0.0 39.0\n371 3.0 1.0 1.0 0.0 0.0 372.0\n\n[357 rows x 6 columns]","text/html":"
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PclassSexfam_memberslr_y_predyPassengerId
3691.02.00.01.01.0370.0
5413.02.06.00.00.0542.0
1963.01.00.00.00.0197.0
8103.01.00.00.00.0811.0
4272.02.00.01.01.0428.0
.....................
1741.01.00.00.00.0175.0
2971.02.03.01.00.0298.0
2443.01.00.00.00.0245.0
383.02.02.00.00.039.0
3713.01.01.00.00.0372.0
\n

357 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"y\"] = log_reg_pred[\"y\"]\nresults_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"lr_y_pred\"] = log_reg_pred[\"lr_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.330333Z","iopub.execute_input":"2023-02-01T14:51:04.330729Z","iopub.status.idle":"2023-02-01T14:51:04.353404Z","shell.execute_reply.started":"2023-02-01T14:51:04.330694Z","shell.execute_reply":"2023-02-01T14:51:04.352359Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 1.0 1.0 \n2 0.0 1.0 1.0 \n3 1.0 1.0 1.0 \n4 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"We start with the training dataset. It may be quite unconventional, but it can help us understanding better the features of the data.","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.059608Z","iopub.execute_input":"2023-02-01T14:51:05.059995Z","iopub.status.idle":"2023-02-01T14:51:05.077377Z","shell.execute_reply.started":"2023-02-01T14:51:05.059959Z","shell.execute_reply":"2023-02-01T14:51:05.076249Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n255 3.0 2.0 2.0 1.0 0.0\n707 1.0 1.0 0.0 1.0 0.0\n172 3.0 2.0 2.0 1.0 0.0\n78 2.0 1.0 2.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
2553.02.02.01.00.0
7071.01.00.01.00.0
1723.02.02.01.00.0
782.01.02.01.00.0
2333.02.06.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We complete the same activities to the validation dataset. It appears many male first class passengers traveling alone may have survived more than we anticipated. ","metadata":{}},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.569495Z","iopub.execute_input":"2023-02-01T14:51:05.569879Z","iopub.status.idle":"2023-02-01T14:51:05.589621Z","shell.execute_reply.started":"2023-02-01T14:51:05.569846Z","shell.execute_reply":"2023-02-01T14:51:05.588487Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n340 2.0 1.0 2.0 1.0 0.0\n534 3.0 2.0 0.0 0.0 1.0\n279 3.0 2.0 2.0 1.0 0.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3402.01.02.01.00.0
5343.02.00.00.01.0
2793.02.02.01.00.0
6071.01.00.01.00.0
8043.01.00.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.798455Z","iopub.execute_input":"2023-02-01T14:51:05.799489Z","iopub.status.idle":"2023-02-01T14:51:05.813581Z","shell.execute_reply.started":"2023-02-01T14:51:05.799450Z","shell.execute_reply":"2023-02-01T14:51:05.812556Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 0.0 3\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 1.0 4\n 2.0 1.0 0.0 4\n 2.0 0.0 4\n 3.0 2.0 0.0 2\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.295513Z","iopub.execute_input":"2023-02-01T14:51:06.296134Z","iopub.status.idle":"2023-02-01T14:51:06.315914Z","shell.execute_reply.started":"2023-02-01T14:51:06.296088Z","shell.execute_reply":"2023-02-01T14:51:06.314875Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n130 3.0 1.0 0.0 0.0 0.0\n110 1.0 1.0 0.0 0.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
1303.01.00.00.00.0
1101.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.545374Z","iopub.execute_input":"2023-02-01T14:51:06.546123Z","iopub.status.idle":"2023-02-01T14:51:06.565170Z","shell.execute_reply.started":"2023-02-01T14:51:06.546085Z","shell.execute_reply":"2023-02-01T14:51:06.564022Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 2.0 1.0 9\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 0.0 3\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 1.0 4\n 2.0 1.0 0.0 10\n 2.0 0.0 5\n 3.0 1.0 0.0 2\n 2.0 0.0 1\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. It appears \n\nOther elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees classifiers. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.019601Z","iopub.execute_input":"2023-02-01T14:51:07.020764Z","iopub.status.idle":"2023-02-01T14:51:07.038884Z","shell.execute_reply.started":"2023-02-01T14:51:07.020723Z","shell.execute_reply":"2023-02-01T14:51:07.037796Z"},"trusted":true},"execution_count":146,"outputs":[{"execution_count":146,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.380694Z","iopub.execute_input":"2023-02-01T14:51:07.381775Z","iopub.status.idle":"2023-02-01T14:51:07.399161Z","shell.execute_reply.started":"2023-02-01T14:51:07.381734Z","shell.execute_reply":"2023-02-01T14:51:07.397965Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 6\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 1.0 11\n 2.0 1.0 0.0 7\n 2.0 0.0 5\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Predict with testing dataset","metadata":{}},{"cell_type":"code","source":"y_pred = classifier.predict(X_test)\nlog_reg_pred = X_test.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.781717Z","iopub.execute_input":"2023-02-01T14:51:07.782101Z","iopub.status.idle":"2023-02-01T14:51:07.809230Z","shell.execute_reply.started":"2023-02-01T14:51:07.782070Z","shell.execute_reply":"2023-02-01T14:51:07.808079Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 1.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 0.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
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PclassSexfam_memberslr_y_predPassengerId
03.01.00.00.0892.0
13.02.01.01.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.00.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
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418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.028966Z","iopub.execute_input":"2023-02-01T14:51:08.030264Z","iopub.status.idle":"2023-02-01T14:51:08.036547Z","shell.execute_reply.started":"2023-02-01T14:51:08.030211Z","shell.execute_reply":"2023-02-01T14:51:08.035240Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.293378Z","iopub.execute_input":"2023-02-01T14:51:08.294544Z","iopub.status.idle":"2023-02-01T14:51:08.309861Z","shell.execute_reply.started":"2023-02-01T14:51:08.294483Z","shell.execute_reply":"2023-02-01T14:51:08.308466Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":" PassengerId lr_y_pred\n0 892.0 0.0\n1 893.0 1.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 0.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdlr_y_pred
0892.00.0
1893.01.0
2894.00.0
3895.00.0
4896.00.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
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418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.513449Z","iopub.execute_input":"2023-02-01T14:51:08.513843Z","iopub.status.idle":"2023-02-01T14:51:08.535503Z","shell.execute_reply.started":"2023-02-01T14:51:08.513810Z","shell.execute_reply":"2023-02-01T14:51:08.534386Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 0.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_pred
0892.03.01.00.431373-0.2810053.00.00.0
1893.03.02.01.411765-0.3161762.01.01.0
2894.02.01.02.588235-0.2021843.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: K-Nearest-neighbourn","metadata":{}},{"cell_type":"markdown","source":"We explore whether a reduction of statistical variables may be beneficial to the classification. We focus our model fitting on the same statistical variables as the logistic regression. \n\n\nThe K-NN classifier overfits to the training dataset. We have yet to find a better result. So Decision tree may have found its limit. ","metadata":{}},{"cell_type":"markdown","source":"## Model fitting\nWe discover the hyper-parametrisation of approximately 7 neighbors and the algorithm set the brute.","metadata":{}},{"cell_type":"code","source":"neighbors = range(2, 100)\nfor neighbor in neighbors:\n knn = KNeighborsClassifier(n_neighbors = neighbor, algorithm=\"brute\", weights = \"distance\", p=2)\n knn.fit(X_train,y_train)\n train_score = knn.score(X_train, y_train)\n valid_score = knn.score(X_valid, y_valid)\n print(\" - n neighbor : \", neighbor , \" - train score : \", train_score, \" - valid score : \", valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:09.565134Z","iopub.execute_input":"2023-02-01T14:51:09.565542Z","iopub.status.idle":"2023-02-01T14:51:12.977246Z","shell.execute_reply.started":"2023-02-01T14:51:09.565506Z","shell.execute_reply":"2023-02-01T14:51:12.975689Z"},"trusted":true},"execution_count":152,"outputs":[{"name":"stdout","text":" - n neighbor : 2 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 3 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 4 - train score : 0.8089887640449438 - valid score : 0.7591036414565826\n - n neighbor : 5 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 6 - train score : 0.8164794007490637 - valid score : 0.7927170868347339\n - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 8 - train score : 0.8202247191011236 - valid score : 0.7899159663865546\n - n neighbor : 9 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 10 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 11 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 12 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 13 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 14 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 15 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 16 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 17 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 18 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 19 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 20 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 21 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 22 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 23 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 24 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 25 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 26 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 27 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 28 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 29 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 30 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 31 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 32 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 33 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 34 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 35 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 36 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 37 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 38 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 39 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 40 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 41 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 42 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 43 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 44 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 45 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 46 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 47 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 48 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 49 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 50 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 51 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 52 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 53 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 54 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 55 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 56 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 57 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 58 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 59 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 60 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 61 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 62 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 63 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 64 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 65 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 66 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 67 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 68 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 69 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 70 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 71 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 72 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 73 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 74 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 75 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 76 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 77 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 78 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 79 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 80 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 81 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 82 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 83 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 84 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 85 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 86 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 87 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 88 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 89 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 90 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 91 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 92 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 93 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 94 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 95 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 96 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 97 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 98 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 99 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors = 7, algorithm=\"brute\", weights = \"distance\", p=2)\nknn.fit(X_train,y_train)\nknn_train_score = knn.score(X_train, y_train)\nknn_valid_score = knn.score(X_valid, y_valid)\nprint(\" - n neighbor : \", 7 , \" - train score : \", knn_train_score, \" - valid score : \", knn_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:12.986323Z","iopub.execute_input":"2023-02-01T14:51:12.992081Z","iopub.status.idle":"2023-02-01T14:51:13.043083Z","shell.execute_reply.started":"2023-02-01T14:51:12.992006Z","shell.execute_reply":"2023-02-01T14:51:13.041491Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":" - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = knn.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.051296Z","iopub.execute_input":"2023-02-01T14:51:13.052276Z","iopub.status.idle":"2023-02-01T14:51:13.094020Z","shell.execute_reply.started":"2023-02-01T14:51:13.052210Z","shell.execute_reply":"2023-02-01T14:51:13.092537Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"array([[299, 30],\n [ 63, 142]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.104344Z","iopub.execute_input":"2023-02-01T14:51:13.109854Z","iopub.status.idle":"2023-02-01T14:51:13.138605Z","shell.execute_reply.started":"2023-02-01T14:51:13.109782Z","shell.execute_reply":"2023-02-01T14:51:13.137094Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"Accuracy : 0.8258426966292135\nMisclassfication : 0.17415730337078653\nSensitivivity : 0.9088145896656535\nSpecificity : 0.6926829268292682\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.141155Z","iopub.execute_input":"2023-02-01T14:51:13.151686Z","iopub.status.idle":"2023-02-01T14:51:13.183541Z","shell.execute_reply.started":"2023-02-01T14:51:13.151614Z","shell.execute_reply":"2023-02-01T14:51:13.181982Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.190465Z","iopub.execute_input":"2023-02-01T14:51:13.191601Z","iopub.status.idle":"2023-02-01T14:51:13.214243Z","shell.execute_reply.started":"2023-02-01T14:51:13.191536Z","shell.execute_reply":"2023-02-01T14:51:13.212831Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"Accuracy : 0.7871148459383753\nMisclassfication : 0.21288515406162464\nSensitivivity : 0.8818181818181818\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.216513Z","iopub.execute_input":"2023-02-01T14:51:13.217361Z","iopub.status.idle":"2023-02-01T14:51:13.226018Z","shell.execute_reply.started":"2023-02-01T14:51:13.217286Z","shell.execute_reply":"2023-02-01T14:51:13.224351Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(X_train)\nknn_pred = X_train.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_train_pass_id\nknn_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.228136Z","iopub.execute_input":"2023-02-01T14:51:13.229804Z","iopub.status.idle":"2023-02-01T14:51:13.289272Z","shell.execute_reply.started":"2023-02-01T14:51:13.229740Z","shell.execute_reply":"2023-02-01T14:51:13.287745Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n844 3.0 1.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 769.0\n255 3.0 2.0 2.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
8443.01.00.00.0845.0
3162.02.01.01.0317.0
7683.01.01.00.0769.0
2553.02.02.01.0256.0
1303.01.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(knn_pred[[\"PassengerId\", \"knn_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.297719Z","iopub.execute_input":"2023-02-01T14:51:13.302941Z","iopub.status.idle":"2023-02-01T14:51:13.361563Z","shell.execute_reply.started":"2023-02-01T14:51:13.302872Z","shell.execute_reply":"2023-02-01T14:51:13.359941Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = knn.predict(X_valid)\nknn_pred = X_valid.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_valid_pass_id\nknn_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.366098Z","iopub.execute_input":"2023-02-01T14:51:13.367128Z","iopub.status.idle":"2023-02-01T14:51:13.414267Z","shell.execute_reply.started":"2023-02-01T14:51:13.367081Z","shell.execute_reply":"2023-02-01T14:51:13.412764Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n369 1.0 2.0 0.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 428.0\n.. ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 1.0 39.0\n371 3.0 1.0 1.0 0.0 372.0\n\n[357 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
3691.02.00.01.0370.0
5413.02.06.00.0542.0
1963.01.00.00.0197.0
8103.01.00.00.0811.0
4272.02.00.01.0428.0
..................
1741.01.00.00.0175.0
2971.02.03.00.0298.0
2443.01.00.00.0245.0
383.02.02.01.039.0
3713.01.01.00.0372.0
\n

357 rows × 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(knn_pred.PassengerId), \"knn_y_pred\"] = knn_pred[\"knn_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.416656Z","iopub.execute_input":"2023-02-01T14:51:13.417577Z","iopub.status.idle":"2023-02-01T14:51:13.474919Z","shell.execute_reply.started":"2023-02-01T14:51:13.417518Z","shell.execute_reply":"2023-02-01T14:51:13.473392Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.552373Z","iopub.execute_input":"2023-02-01T14:51:13.552777Z","iopub.status.idle":"2023-02-01T14:51:13.575185Z","shell.execute_reply.started":"2023-02-01T14:51:13.552741Z","shell.execute_reply":"2023-02-01T14:51:13.573826Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n707 1.0 1.0 0.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0\n788 3.0 1.0 3.0 1.0 0.0\n183 2.0 1.0 3.0 1.0 0.0\n654 3.0 2.0 0.0 0.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
7071.01.00.01.00.0
2333.02.06.01.00.0
7883.01.03.01.00.0
1832.01.03.01.00.0
6543.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.851998Z","iopub.execute_input":"2023-02-01T14:51:13.852446Z","iopub.status.idle":"2023-02-01T14:51:13.868236Z","shell.execute_reply.started":"2023-02-01T14:51:13.852408Z","shell.execute_reply":"2023-02-01T14:51:13.867490Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 2.0 1.0 1\n 1.0 1.0 0.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 2\n 1.0 1.0 0.0 1\n 2.0 1.0 2\n 2.0 1.0 1.0 3\n 2.0 1.0 1\n 3.0 1.0 0.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 0.0 4\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 3.0 1.0 0.0 1\n 2.0 1.0 1\n 6.0 2.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.057420Z","iopub.execute_input":"2023-02-01T14:51:14.057804Z","iopub.status.idle":"2023-02-01T14:51:14.084011Z","shell.execute_reply.started":"2023-02-01T14:51:14.057773Z","shell.execute_reply":"2023-02-01T14:51:14.082464Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.355738Z","iopub.execute_input":"2023-02-01T14:51:14.356164Z","iopub.status.idle":"2023-02-01T14:51:14.375540Z","shell.execute_reply.started":"2023-02-01T14:51:14.356115Z","shell.execute_reply":"2023-02-01T14:51:14.374287Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n534 3.0 2.0 0.0 0.0 1.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0\n429 3.0 1.0 0.0 1.0 0.0\n501 3.0 2.0 0.0 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
5343.02.00.00.01.0
6071.01.00.01.00.0
8043.01.00.01.00.0
4293.01.00.01.00.0
5013.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.597501Z","iopub.execute_input":"2023-02-01T14:51:14.597895Z","iopub.status.idle":"2023-02-01T14:51:14.613504Z","shell.execute_reply.started":"2023-02-01T14:51:14.597865Z","shell.execute_reply":"2023-02-01T14:51:14.612422Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 1.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 1.0 6\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.104177Z","iopub.execute_input":"2023-02-01T14:51:15.104569Z","iopub.status.idle":"2023-02-01T14:51:15.123111Z","shell.execute_reply.started":"2023-02-01T14:51:15.104537Z","shell.execute_reply":"2023-02-01T14:51:15.121935Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n255 3.0 2.0 2.0 1.0 1.0\n130 3.0 1.0 0.0 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
2553.02.02.01.01.0
1303.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.344115Z","iopub.execute_input":"2023-02-01T14:51:15.344558Z","iopub.status.idle":"2023-02-01T14:51:15.362850Z","shell.execute_reply.started":"2023-02-01T14:51:15.344502Z","shell.execute_reply":"2023-02-01T14:51:15.361620Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 4\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 0.0 10\n 2.0 1.0 0.0 10\n 2.0 1.0 8\n 3.0 1.0 0.0 2\n 2.0 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.857448Z","iopub.execute_input":"2023-02-01T14:51:15.857837Z","iopub.status.idle":"2023-02-01T14:51:15.877163Z","shell.execute_reply.started":"2023-02-01T14:51:15.857806Z","shell.execute_reply":"2023-02-01T14:51:15.875923Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.132579Z","iopub.execute_input":"2023-02-01T14:51:16.132970Z","iopub.status.idle":"2023-02-01T14:51:16.150755Z","shell.execute_reply.started":"2023-02-01T14:51:16.132936Z","shell.execute_reply":"2023-02-01T14:51:16.149943Z"},"trusted":true},"execution_count":171,"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 1.0 1\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 0.0 4\n 2.0 1.0 0.0 7\n 2.0 1.0 4\n 3.0 2.0 1.0 2\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower.","metadata":{}},{"cell_type":"markdown","source":"## Prediction on the test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = knn.predict(X_test)\nknn_pred = X_test.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nknn_pred\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.910077Z","iopub.execute_input":"2023-02-01T14:51:16.910492Z","iopub.status.idle":"2023-02-01T14:51:16.964596Z","shell.execute_reply.started":"2023-02-01T14:51:16.910456Z","shell.execute_reply":"2023-02-01T14:51:16.963157Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 0.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 1.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
03.01.00.00.0892.0
13.02.01.00.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.01.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
\n

418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.178878Z","iopub.execute_input":"2023-02-01T14:51:17.179931Z","iopub.status.idle":"2023-02-01T14:51:17.185405Z","shell.execute_reply.started":"2023-02-01T14:51:17.179876Z","shell.execute_reply":"2023-02-01T14:51:17.184219Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"code","source":"knn_pred[[\"PassengerId\",\"knn_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.372559Z","iopub.execute_input":"2023-02-01T14:51:17.372948Z","iopub.status.idle":"2023-02-01T14:51:17.390909Z","shell.execute_reply.started":"2023-02-01T14:51:17.372914Z","shell.execute_reply":"2023-02-01T14:51:17.389533Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" PassengerId knn_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdknn_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(knn_pred[[\"PassengerId\",\"knn_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.671274Z","iopub.execute_input":"2023-02-01T14:51:17.672432Z","iopub.status.idle":"2023-02-01T14:51:17.693960Z","shell.execute_reply.started":"2023-02-01T14:51:17.672382Z","shell.execute_reply":"2023-02-01T14:51:17.692706Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred \n0 0.0 0.0 \n1 1.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 1.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.0
2894.02.01.02.588235-0.2021843.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method : Decision Trees\n\nWe use a decision tree classifier and some automated search of the hyper-parametrisation to discover suitable hyper-parameters and validate the quality of a model. \n","metadata":{}},{"cell_type":"code","source":"\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.109673Z","iopub.execute_input":"2023-02-01T14:51:18.110073Z","iopub.status.idle":"2023-02-01T14:51:18.128404Z","shell.execute_reply.started":"2023-02-01T14:51:18.110036Z","shell.execute_reply":"2023-02-01T14:51:18.127375Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = [\"Fare\",\"Pclass\",\"Sex\",\"Embarked\",\"fam_members\", \"Age\"]\nx_train_pass_id = X_train.PassengerId\nX_train = X_train [x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.370422Z","iopub.execute_input":"2023-02-01T14:51:18.370800Z","iopub.status.idle":"2023-02-01T14:51:18.388440Z","shell.execute_reply.started":"2023-02-01T14:51:18.370767Z","shell.execute_reply":"2023-02-01T14:51:18.387202Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538\n768 0.419921 3.0 1.0 3.0 1.0 0.000000\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769","text/html":"
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FarePclassSexEmbarkedfam_membersAge
844-0.2508363.01.02.00.0-1.000000
3160.5000432.02.02.01.0-0.461538
7680.4199213.01.03.01.00.000000
2550.0342843.02.04.02.0-0.076923
130-0.2840413.01.04.00.00.230769
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nx_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.609801Z","iopub.execute_input":"2023-02-01T14:51:18.610554Z","iopub.status.idle":"2023-02-01T14:51:18.628148Z","shell.execute_reply.started":"2023-02-01T14:51:18.610505Z","shell.execute_reply":"2023-02-01T14:51:18.626956Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154","text/html":"
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FarePclassSexEmbarkedfam_membersAge
3692.3753461.02.04.00.0-0.461538
5410.7285013.02.02.06.0-1.615385
196-0.2903563.01.03.00.00.000000
810-0.2844013.01.02.00.0-0.307692
4270.5000432.02.02.00.0-0.846154
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nX = titanic_test.copy(deep = True)\nX_test = X[x_cols]\nX_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.826406Z","iopub.execute_input":"2023-02-01T14:51:18.826797Z","iopub.status.idle":"2023-02-01T14:51:18.835436Z","shell.execute_reply.started":"2023-02-01T14:51:18.826766Z","shell.execute_reply":"2023-02-01T14:51:18.834526Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"Index(['Fare', 'Pclass', 'Sex', 'Embarked', 'fam_members', 'Age'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Decision Tree classifier\n\nWe explore the maximum depths hyper parameter using a deterministic and incremental search. Then we applied the most efficient parametrisation. We chose a low maximum depth, as the model may be overfitting.","metadata":{}},{"cell_type":"code","source":"\ndepths = range(3, 200)\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth = depth, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n # Train Decision Tree Classifer\n clf = clf.fit(X_train,y_train)\n train_score = clf.score(X_train,y_train)\n valid_score = clf.score(X_valid,y_valid)\n print(\"- depth : \", depth, \" - train score : \", train_score, \" - valid score : \", valid_score)\n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:19.301726Z","iopub.execute_input":"2023-02-01T14:51:19.302125Z","iopub.status.idle":"2023-02-01T14:51:20.492365Z","shell.execute_reply.started":"2023-02-01T14:51:19.302089Z","shell.execute_reply":"2023-02-01T14:51:20.491051Z"},"trusted":true},"execution_count":180,"outputs":[{"name":"stdout","text":"- depth : 3 - train score : 0.8295880149812734 - valid score : 0.8011204481792717\n- depth : 4 - train score : 0.8295880149812734 - valid score : 0.8151260504201681\n- depth : 5 - train score : 0.8595505617977528 - valid score : 0.8067226890756303\n- depth : 6 - train score : 0.8820224719101124 - valid score : 0.8235294117647058\n- depth : 7 - train score : 0.8895131086142322 - valid score : 0.8179271708683473\n- depth : 8 - train score : 0.9063670411985019 - valid score : 0.7927170868347339\n- depth : 9 - train score : 0.9119850187265918 - valid score : 0.7843137254901961\n- depth : 10 - train score : 0.9250936329588015 - valid score : 0.803921568627451\n- depth : 11 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n- depth : 12 - train score : 0.9550561797752809 - valid score : 0.773109243697479\n- depth : 13 - train score : 0.9625468164794008 - valid score : 0.7955182072829131\n- depth : 14 - train score : 0.9662921348314607 - valid score : 0.7787114845938375\n- depth : 15 - train score : 0.9700374531835206 - valid score : 0.7927170868347339\n- depth : 16 - train score : 0.9737827715355806 - valid score : 0.7787114845938375\n- depth : 17 - train score : 0.9756554307116105 - valid score : 0.7871148459383753\n- depth : 18 - train score : 0.9794007490636704 - valid score : 0.7871148459383753\n- depth : 19 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 20 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 21 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 22 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 23 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 24 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 25 - train score : 0.9812734082397003 - valid score : 0.7899159663865546\n- depth : 26 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 27 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 28 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 29 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 30 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 31 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 32 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 33 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 34 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 35 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 36 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 37 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 38 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 39 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 40 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 41 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 42 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 43 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 44 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 45 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 46 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 47 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 48 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 49 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 50 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 51 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 52 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 53 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 54 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 55 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 56 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 57 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 58 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 59 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 60 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 61 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 62 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 63 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 64 - train score : 0.9812734082397003 - 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valid score : 0.7703081232492998\n- depth : 197 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 198 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 199 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(max_depth = 8, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n\n# Train Decision Tree Classifer\nclf = clf.fit(X_train,y_train)\nclf_train_score = clf.score(X_train,y_train)\nclf_valid_score = clf.score(X_valid,y_valid)\nprint(\"- depth : \", 8, \" - train score : \", clf_train_score, \" - valid score : \", clf_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.494270Z","iopub.execute_input":"2023-02-01T14:51:20.494649Z","iopub.status.idle":"2023-02-01T14:51:20.508968Z","shell.execute_reply.started":"2023-02-01T14:51:20.494617Z","shell.execute_reply":"2023-02-01T14:51:20.507560Z"},"trusted":true},"execution_count":181,"outputs":[{"name":"stdout","text":"- depth : 8 - train score : 0.9082397003745318 - valid score : 0.8151260504201681\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover that the gender, Fare and age could be contribute to the classification. It constrast to our previous assumptions for KNN and logistic regression.","metadata":{}},{"cell_type":"code","source":"importances = clf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.510227Z","iopub.execute_input":"2023-02-01T14:51:20.510578Z","iopub.status.idle":"2023-02-01T14:51:20.523335Z","shell.execute_reply.started":"2023-02-01T14:51:20.510548Z","shell.execute_reply":"2023-02-01T14:51:20.521845Z"},"trusted":true},"execution_count":182,"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":" 0\n0.200193 Fare\n0.125949 Pclass\n0.315820 Sex\n0.025783 Embarked\n0.094918 fam_members\n0.237337 Age","text/html":"
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0
0.200193Fare
0.125949Pclass
0.315820Sex
0.025783Embarked
0.094918fam_members
0.237337Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.525411Z","iopub.execute_input":"2023-02-01T14:51:20.525712Z","iopub.status.idle":"2023-02-01T14:51:20.536265Z","shell.execute_reply.started":"2023-02-01T14:51:20.525685Z","shell.execute_reply":"2023-02-01T14:51:20.535549Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"array([[326, 3],\n [ 46, 159]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.736687Z","iopub.execute_input":"2023-02-01T14:51:20.737047Z","iopub.status.idle":"2023-02-01T14:51:20.744835Z","shell.execute_reply.started":"2023-02-01T14:51:20.737016Z","shell.execute_reply":"2023-02-01T14:51:20.743620Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"Accuracy : 0.9082397003745318\nMisclassfication : 0.09176029962546817\nSensitivivity : 0.9908814589665653\nSpecificity : 0.775609756097561\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.940682Z","iopub.execute_input":"2023-02-01T14:51:20.941080Z","iopub.status.idle":"2023-02-01T14:51:20.950745Z","shell.execute_reply.started":"2023-02-01T14:51:20.941045Z","shell.execute_reply":"2023-02-01T14:51:20.949939Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.156573Z","iopub.execute_input":"2023-02-01T14:51:21.157555Z","iopub.status.idle":"2023-02-01T14:51:21.164777Z","shell.execute_reply.started":"2023-02-01T14:51:21.157504Z","shell.execute_reply":"2023-02-01T14:51:21.163996Z"},"trusted":true},"execution_count":186,"outputs":[{"name":"stdout","text":"Accuracy : 0.8151260504201681\nMisclassfication : 0.18487394957983194\nSensitivivity : 0.9318181818181818\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.602984Z","iopub.execute_input":"2023-02-01T14:51:21.603408Z","iopub.status.idle":"2023-02-01T14:51:21.609433Z","shell.execute_reply.started":"2023-02-01T14:51:21.603369Z","shell.execute_reply":"2023-02-01T14:51:21.608257Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_train)\nclf_pred = X_train.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_train_pass_id\nclf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.801023Z","iopub.execute_input":"2023-02-01T14:51:21.801826Z","iopub.status.idle":"2023-02-01T14:51:21.826292Z","shell.execute_reply.started":"2023-02-01T14:51:21.801783Z","shell.execute_reply":"2023-02-01T14:51:21.825118Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769231.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(clf_pred[[\"PassengerId\", \"clf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.073441Z","iopub.execute_input":"2023-02-01T14:51:22.073853Z","iopub.status.idle":"2023-02-01T14:51:22.100768Z","shell.execute_reply.started":"2023-02-01T14:51:22.073817Z","shell.execute_reply":"2023-02-01T14:51:22.099989Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = clf.predict(X_valid)\nclf_pred = X_valid.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_valid_pass_id\nclf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.313331Z","iopub.execute_input":"2023-02-01T14:51:22.314186Z","iopub.status.idle":"2023-02-01T14:51:22.339255Z","shell.execute_reply.started":"2023-02-01T14:51:22.314149Z","shell.execute_reply":"2023-02-01T14:51:22.338531Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 0.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538460.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
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357 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(clf_pred.PassengerId), \"clf_y_pred\"] = clf_pred[\"clf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.503867Z","iopub.execute_input":"2023-02-01T14:51:22.504541Z","iopub.status.idle":"2023-02-01T14:51:22.530946Z","shell.execute_reply.started":"2023-02-01T14:51:22.504500Z","shell.execute_reply":"2023-02-01T14:51:22.529880Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.197164Z","iopub.execute_input":"2023-02-01T14:51:23.197598Z","iopub.status.idle":"2023-02-01T14:51:23.221279Z","shell.execute_reply.started":"2023-02-01T14:51:23.197559Z","shell.execute_reply":"2023-02-01T14:51:23.220173Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n220 -0.277363 3.0 1.0 2.0 0.0 -1.076923 1.0 0.0\n510 -0.290356 3.0 1.0 3.0 0.0 -0.076923 1.0 0.0\n724 1.673732 1.0 1.0 2.0 1.0 -0.230769 1.0 0.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
220-0.2773633.01.02.00.0-1.0769231.00.0
510-0.2903563.01.03.00.0-0.0769231.00.0
7241.6737321.01.02.01.0-0.2307691.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.536909Z","iopub.execute_input":"2023-02-01T14:51:23.537537Z","iopub.status.idle":"2023-02-01T14:51:23.553252Z","shell.execute_reply.started":"2023-02-01T14:51:23.537491Z","shell.execute_reply":"2023-02-01T14:51:23.552369Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 10\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n3.0 0.0 1.0 0.0 14\n 2.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.819458Z","iopub.execute_input":"2023-02-01T14:51:23.819831Z","iopub.status.idle":"2023-02-01T14:51:23.828371Z","shell.execute_reply.started":"2023-02-01T14:51:23.819802Z","shell.execute_reply":"2023-02-01T14:51:23.827545Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.944899Z","iopub.execute_input":"2023-02-01T14:51:23.945939Z","iopub.status.idle":"2023-02-01T14:51:24.401522Z","shell.execute_reply.started":"2023-02-01T14:51:23.945899Z","shell.execute_reply":"2023-02-01T14:51:24.400330Z"},"trusted":true},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.403612Z","iopub.execute_input":"2023-02-01T14:51:24.404043Z","iopub.status.idle":"2023-02-01T14:51:24.424956Z","shell.execute_reply.started":"2023-02-01T14:51:24.404007Z","shell.execute_reply":"2023-02-01T14:51:24.423814Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0\n501 -0.290356 3.0 2.0 3.0 0.0 -0.692308 0.0 1.0\n17 -0.062981 2.0 1.0 2.0 0.0 0.000000 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
429-0.2773633.01.02.00.00.1538461.00.0
501-0.2903563.02.03.00.0-0.6923080.01.0
17-0.0629812.01.02.00.00.0000001.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.426286Z","iopub.execute_input":"2023-02-01T14:51:24.426719Z","iopub.status.idle":"2023-02-01T14:51:24.444950Z","shell.execute_reply.started":"2023-02-01T14:51:24.426673Z","shell.execute_reply":"2023-02-01T14:51:24.443790Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 3\n 1.0 1\n 1.0 2.0 0.0 1\n 2.0 1.0 1.0 1\n3.0 0.0 1.0 0.0 12\n 1.0 3\n 2.0 0.0 4\n 1.0 2\n 1.0 1.0 0.0 1\n 2.0 0.0 9\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 2\n 2.0 1.0 3\n 4.0 1.0 1.0 1\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.655585Z","iopub.execute_input":"2023-02-01T14:51:24.655981Z","iopub.status.idle":"2023-02-01T14:51:25.270872Z","shell.execute_reply.started":"2023-02-01T14:51:24.655946Z","shell.execute_reply":"2023-02-01T14:51:25.270073Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.272439Z","iopub.execute_input":"2023-02-01T14:51:25.273391Z","iopub.status.idle":"2023-02-01T14:51:25.295346Z","shell.execute_reply.started":"2023-02-01T14:51:25.273342Z","shell.execute_reply":"2023-02-01T14:51:25.294366Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 1.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
2550.0342843.02.04.02.0-0.0769231.01.0
130-0.2840413.01.04.00.00.2307690.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.310893Z","iopub.execute_input":"2023-02-01T14:51:25.311294Z","iopub.status.idle":"2023-02-01T14:51:25.332606Z","shell.execute_reply.started":"2023-02-01T14:51:25.311259Z","shell.execute_reply":"2023-02-01T14:51:25.331521Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 6\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 2\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 0.0 1\n 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 1.0 5\n 2.0 0.0 14\n 1.0 23\n 1.0 1.0 0.0 15\n 1.0 3\n 2.0 0.0 10\n 1.0 4\n 2.0 1.0 0.0 10\n 1.0 2\n 2.0 0.0 5\n 1.0 8\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.617532Z","iopub.execute_input":"2023-02-01T14:51:25.617910Z","iopub.status.idle":"2023-02-01T14:51:27.648580Z","shell.execute_reply.started":"2023-02-01T14:51:25.617879Z","shell.execute_reply":"2023-02-01T14:51:27.647383Z"},"trusted":true},"execution_count":201,"outputs":[{"execution_count":201,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.650898Z","iopub.execute_input":"2023-02-01T14:51:27.651397Z","iopub.status.idle":"2023-02-01T14:51:27.674977Z","shell.execute_reply.started":"2023-02-01T14:51:27.651353Z","shell.execute_reply":"2023-02-01T14:51:27.673660Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.676558Z","iopub.execute_input":"2023-02-01T14:51:27.676918Z","iopub.status.idle":"2023-02-01T14:51:27.695988Z","shell.execute_reply.started":"2023-02-01T14:51:27.676883Z","shell.execute_reply":"2023-02-01T14:51:27.694729Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 1.0 3\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 12\n 1.0 1.0 0.0 4\n 2.0 1.0 8\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 91\n 1.0 1\n 2.0 0.0 6\n 1.0 4\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 1.0 2\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 0.0 2\n 1.0 4\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.698581Z","iopub.execute_input":"2023-02-01T14:51:27.699104Z","iopub.status.idle":"2023-02-01T14:51:28.312451Z","shell.execute_reply.started":"2023-02-01T14:51:27.699061Z","shell.execute_reply":"2023-02-01T14:51:28.311698Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.313663Z","iopub.execute_input":"2023-02-01T14:51:28.314115Z","iopub.status.idle":"2023-02-01T14:51:28.742585Z","shell.execute_reply.started":"2023-02-01T14:51:28.314085Z","shell.execute_reply":"2023-02-01T14:51:28.741404Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.744143Z","iopub.execute_input":"2023-02-01T14:51:28.744835Z","iopub.status.idle":"2023-02-01T14:51:29.161694Z","shell.execute_reply.started":"2023-02-01T14:51:28.744790Z","shell.execute_reply":"2023-02-01T14:51:29.160329Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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1m2ThN9VJL3Jvm+JD8104kAYAc5ZRQAAKApp4wCAAA0JQgBAACamslzCB/xiEeM48ePz2LXAAAArbzlLW953xjjirN9biZBePz48dx8882z2DUAAEArVfWO+/ucU0YBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABo6uisBzhMqmrWI9zHGGPWIwAAAPuUI4Q7aIyxI2+Pe/7rd2xbAAAA90cQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0NXUQVtWDq+qXqurXqurtVfUdOzEYAAAAu+voDmzjriRfOMa4o6qOJdmoqp8ZY7x5B7YNAADALpk6CMcYI8kdkw+PTd7GtNsFAABgd+3Icwir6khVvTXJHyZ54xjjF3diuwAAAOyeHQnCMcbdY4wnJnl0ks+tqvl736eqnl1VN1fVzbfeeutO7BYAAIAp7OhVRscYH0yynuSLz/K5l40xrhxjXHnFFVfs5G4BAAC4CDtxldErqurhk/cfkuSpSX5z2u0CAACwu3biKqOfmuRVVXUkW4H52jHG63dguwAAAOyinbjK6P9M8jk7MAsAAAB7aEefQwgAAMDBIQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGjq6KwHAABmq6pmPcJ9jDFmPQJAC44QAkBzY4wdeXvc81+/Y9sCYG8IQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhHBJra2uZn5/PkSNHMj8/n7W1tVmPBADAPnd01gMA01tbW8vy8nJWV1ezsLCQjY2NLC0tJUlOnDgx4+kAANivHCGEQ2BlZSWrq6tZXFzMsWPHsri4mNXV1aysrMx6NAAA9jFBCIfA5uZmFhYWzrhtYWEhm5ubM5oIAICDQBDCITA3N5eNjY0zbtvY2Mjc3NyMJgIA4CAQhHAILC8vZ2lpKevr6zl16lTW19eztLSU5eXlWY8GAMA+5qIycAicvnDMyZMns7m5mbm5uaysrLigDAAA5yQI4ZA4ceKEAAQA4AFxyigAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShDCIbG2tpb5+fkcOXIk8/PzWVtbm/VIAADsc0dnPQAwvbW1tSwvL2d1dTULCwvZ2NjI0tJSkuTEiRMzng4AgP3KEUI4BFZWVrK6uprFxcUcO3Ysi4uLWV1dzcrKyqxHAwBgHxOEcAhsbm5mYWHhjNsWFhayubk5o4kAADgIBCEcAnNzc9nY2Djjto2NjczNzc1oIgAADgJBCIfA8vJylpaWsr6+nlOnTmV9fT1LS0tZXl6e9WgAAOxjLioDh8DpC8ecPHkym5ubmZuby8rKigvKAABwToIQDokTJ04IQAAAHhCnjAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCIfE2tpa5ufnc+TIkczPz2dtbW3WIwEAsM95HUI4BNbW1rK8vJzV1dUsLCxkY2MjS0tLSeK1CQEAuF+OEMIhsLKyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aAAD7mCCEQ2BzczMLCwtn3LawsJDNzc0ZTQQAwEEgCOEQmJuby8bGxhm3bWxsZG5ubkYTAQBwEAhCOASWl5eztLSU9fX1nDp1Kuvr61laWsry8vKsRwMAYB9zURk4BE5fOObkyZPZ3NzM3NxcVlZWXFAGAIBzEoRwSJw4cUIAAgDwgDhlFAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQ1NRBWFWPqar1qvqNqnp7Vf2jnRgMAACA3XV0B7bx8ST/eIzxK1V1WZK3VNUbxxi/sQPbBgAAYJdMfYRwjPGeMcavTN6/Pclmkk+bdrsAAADsrh19DmFVHU/yOUl+cSe3CwAAwM7bsSCsqocm+fEkzx1jfOgsn392Vd1cVTffeuutO7VbAAAALtKOBGFVHctWDP7bMcZPnO0+Y4yXjTGuHGNcecUVV+zEbgEAAJjCTlxltJKsJtkcY3z/9CMBAACwF3biCOFfTvK1Sb6wqt46eXv6DmwXAACAXbQTVxndGGPUGOOzxxhPnLz9p50YDoC9sba2lvn5+Rw5ciTz8/NZW1ub9UgAwB7YidchBOAAW1tby/LyclZXV7OwsJCNjY0sLS0lSU6cODHj6QCA3bSjLzsBwMGzsrKS1dXVLC4u5tixY1lcXMzq6mpWVlZmPRoAsMsEIUBzm5ubWVhYOOO2hYWFbG5uzmgiAGCvCEKA5ubm5rKxsXHGbRsbG5mbm5vRRADAXhGEAM0tLy9naWkp6+vrOXXqVNbX17O0tJTl5eVZjwYA7DIXlQFo7vSFY06ePJnNzc3Mzc1lZWXFBWUAoAFBCEBOnDghAAGgIaeMAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAOibW1tczPz+fIkSOZn5/P2trarEcCAGCfOzrrAYDpra2tZXl5Oaurq1lYWMjGxkaWlpaSJCdOnJjxdAAA7FeOEMIhsLKyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aAAD7mCCEQ2BzczMLCwtn3LawsJDNzc0ZTQQAwEEgCOEQmJuby8bGxhm3bWxsZG5ubkYTAQBwEAhCOASWl5eztLSU9fX1nDp1Kuvr61laWsry8vKsRwMAYB9zURk4BE5fOObkyZPZ3NzM3NxcVlZWXFAGAIBzEoRwSJw4cUIAAgDwgDhlFAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz3AfvCE77gpt915atZjnOH4tTfOeoR7XP6QY/m1Fz1t1mMcelU16xHuY4wx6xEAANhFgjDJbXeeyi3XXT3rMfat/RSnh9lOxdfxa2+0ngEAuCBOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQhA1tbWMj8/nyNHjmR+fj5ra2uzHgkA2ANHZz0AALO1traW5eXlrK6uZmFhIRsbG1laWkqSnDhxYsbTAQC7yRFCgOZWVlayurqaxcXFHDt2LIuLi1ldXc3KysqsRwMAdpkjhADNbW5uZmFh4YzbFhYWsrm5OaOJgIOkqmY9wn2MMWY9AhwYjhACNDc3N5eNjY0zbtvY2Mjc3NyMJgIOkjHGjrw97vmv37FtARdOEAI0t7y8nKWlpayvr+fUqVNZX1/P0tJSlpeXZz0aALDLnDIK0NzpC8ecPHkym5ubmZuby8rKigvKAEADghCAnDhxQgACQENOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAGRtbS3z8/M5cuRI5ufns7a2NuuRAIA9cHTWAwAwW2tra1leXs7q6moWFhaysbGRpaWlJMmJEydmPB0AsJscIQRobmVlJaurq1lcXMyxY8eyuLiY1dXVrKyszHo0AGCXCUKA5jY3N7OwsHDGbQsLC9nc3JzRRADAXhGEAM3Nzc1lY2PjjNs2NjYyNzc3o4kAgL0iCAGaW15eztLSUtbX13Pq1Kmsr69naWkpy8vLsx4NANhlLioD0NzpC8ecPHkym5ubmZuby8rKigvKAEADghCAnDhxQgACQENOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAFlbW8v8/HyOHDmS+fn5rK2tzXokAGAPHJ31AADM1traWpaXl7O6upqFhYVsbGxkaWkpSXLixIkZTwcA7CZHCAGaW1lZyerqahYXF3Ps2LEsLi5mdXU1Kysrsx4NANhlghCguc3NzSwsLJxx28LCQjY3N2c0EQCwVwQhQHNzc3PZ2Ng447aNjY3Mzc3NaCIAYK8IQoDmlpeXs7S0lPX19Zw6dSrr6+tZWlrK8vLyrEcDAHaZi8oANHf6wjEnT57M5uZm5ubmsrKy4oIyANCAIAQgJ06cEIAA0JBTRgEAAJoShAAAAE05ZRTgEKiqWY9wH2OMWY8AAJyHI4QAh8AYY0feHvf81+/YtgCA/U8QAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoKkdCcKqekVV/WFVvW0ntgcAAMDu26kjhK9M8sU7tC0AAAD2wI4E4RjjTUnevxPbAgAAYG94DiEAAEBTexaEVfXsqrq5qm6+9dZb92q3AAAA3I89C8IxxsvGGFeOMa684oor9mq3AAAA3A+njAIAADS1Uy87sZbkF5J8ZlW9q6qWdmK7AAAA7J6jO7GRMcaJndgOAAAAe8cpowAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJo6OusBAACAnqpq1iPcxxhj1iPsKUcIAQCAmRhj7Mjb457/+h3bVjeCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0NTRWQ8AAFycJ3zHTbntzlOzHuMMx6+9cdYj3OPyhxzLr73oabMeA2BfE4QAcEDdduep3HLd1bMeY9/aT3EKsF85ZRQAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgqaOzHmA/uGzu2jz+VdfOeox967K5JLl61mMAAAA7TBAmuX3zutxyneC5P8evvXHWIwAAALvAKaMAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJrywvQwpSd8x0257c5Tsx7jDMevvXHWI9zj8occy6+96GmzHgMAgLMQhDCl2+48lVuuu3rWY+xb+ylOAQA4k1NGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaOrorAcAAGDvPeE7bsptd56a9RhnOH7tjbMe4R6XP+RYfu1FT5v1GLDrBCEAQEO33Xkqt1x39azH2Lf2U5zCbnLKKAAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhqR4Kwqr64qn6rqn67qq7diW0CAACwu6YOwqo6kuQlSf5aks9KcqKqPmva7QIAALC7duII4ecm+e0xxu+MMT6W5N8l+bId2C4AAAC7aCeC8NOSvHPbx++a3AYAAMA+dnSvdlRVz07y7CR57GMfu1e7vWDHr71x1iPsW5c/5NisR4BD6wnfcVNuu/PUrMc4w376eXj5Q47l1170tFmPsW9dNndtHv8qT92/P5fNJcnVsx4DDqXHv+rxsx7hDJfNZd/9PPz1Z/36rEe4IDsRhL+f5DHbPn705LYzjDFeluRlSXLllVeOHdjvjrnluv31YHH82hv33UzA7rjtzlP+/34O+ylO96PbN6+zfs7B+oHd4+fPuR2knz87ccroLyf5jKr6U1X1oCRfleQ/7sB2AQAA2EVTHyEcY3y8qv5BkjckOZLkFWOMt089GQAAALtqR55DOMb4T0n+005sCwAAgL2xIy9MDwAAwMEjCAEAAJras5edgMPKZd/PzWXfAQD2L0EIU3LZ5XM7SJddBgDoximjAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlKuMAsyQly05Ny9bAgC7SxACzJCXLTk3L1sCALvLKaMAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmjs56AAAA9t5lc9fm8a+6dtZj7FuXzSXJ1bMeA3adIAQAaOj2zetyy3WC5/4cv/bGWY8Ae8IpowAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANHV01gPAYXD82htnPcK+dflDjs16BAAA7ocghCndct3Vsx7hDMevvXHfzQQAwP7klFEAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGjKVUYB4ADzsjf3z8veAJyfIASAA2q/vcSMl70BOHicMgoAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0NTRWQ8A0N3xa2+c9Qj71uUPOTbrEQDgUBOEADN0y3VXz3qEMxy/9sZ9NxMAsHucMgoAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATR2d9QAAAMzG8WtvnPUI+9blDzk26xFgTwhCAICGbrnu6lmPcIbj196472aCDpwyCgAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKamCsKqekZVvb2q/riqrtypoQAAANh90x4hfFuSL0/yph2YBQAAgD10dJovHmNsJklV7cw0AAAA7BnPIQQAAGjqvEcIq+q/JHnkWT61PMb4qQvdUVU9O8mzk+Sxj33sBQ8IAADA7jhvEI4xvmgndjTGeFmSlyXJlVdeOXZimwAAAFw8p4wCAAA0Ne3LTvzNqnpXks9LcmNVvWFnxgIAAGC3TXuV0Z9M8pM7NAsAAAB7yCmjAAAATQlCAACApqY6ZRQAAOjp+LU3znqEfevyhxyb9QgXTBACAAAPyC3XXT3rEc5w/Nob991MB4VTRgEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz3AYVJVO7etF+/MdsYYO7MhAADg0BGEO0h8AQAAB4lTRgEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0dXTWAwBbqmrntvXindnOGGNnNgQAwL4kCGGfEF8AAOw1p4wCAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz0AANOrqp3b1ot3ZjtjjJ3ZEACwawQhwCEgvgCAi+GUUQAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQ1FRBWFXfW1W/WVX/s6p+sqoevkNzAQAAsMumPUL4xiTzY4zPTvK/krxg+pEA2GsnT57Mgx/84FRVHvzgB+fkyZOzHgkA2ANTBeEY46YxxscnH745yaOnHwmAvXTy5MnccMMN+e7v/u58+MMfznd/93fnhhtuEIUA0MBOPofw65P8zA5uD4A98PKXvzwvfvGL87znPS+f8AmfkOc973l58YtfnJe//OWzHg0A2GXnDcKq+i9V9bazvH3ZtvssJ/l4kn97ju08u6purqqbb7311p2ZHoCp3XXXXbnmmmvOuO2aa67JXXfdNaOJAIC9ct4gHGN80Rhj/ixvP5UkVfV1Sb4kydeMMcY5tvOyMcaVY4wrr7jiih37BgCYzqWXXpobbrjhjNtuuOGGXHrppTOaCADYK0en+eKq+uIk/yTJF4wxPrIzIwGwl77xG78xz3/+85NsHRm84YYb8vznP/8+Rw0BgMNnqiBM8kNJLk3yxqpKkjePMfwGAXCAXH/99UmSb/u2b8s//sf/OJdeemmuueaae24HAA6vqYJwjPHpOzUIALNz/fXXC0AAaGgnrzIKAADAASIIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIh8Ta2lrm5+dz5MiRzM/PZ21tbdYjcYBYPwDQ07QvTA/sA2tra1leXs7q6moWFhaysbGRpaWlJMmJEydmPB37nfUDAH05QgiHwMrKSlZXV7O4uJhjx45lcXExq6urWVlZmfVoHADWDwD0JQjhENjc3MzCwsIZty0sLGRzc3NGE3GQWD8A0JcghENgbm4uGxsbZ9y2sbGRubm5GU3EQWL9AEBfghAOgeXl5SwtLWV9fT2nTp3K+vp6lpaWsry8POvROACsHwDoy0Vl4BA4feGPkydPZnNzM3Nzc1lZWXFBEC6I9QMAfQlCOCROnDjhF3gumvUDAD05ZRQAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIRwSa2trmZ+fz5EjRzI/P5+1tbVZj8QBctVVV+WSSy5JVeWSSy7JVVddNeuRAIA9IAjhEFhbW8vy8nKuv/76fPSjH83111+f5eVlUcgFueqqq3LTTTflmmuuyQc/+MFcc801uemmm0QhADRwdNYDANNbWVnJ6upqFhcXkySLi4tZXV3NyZMnc+LEiRlPx373xje+Md/0Td+UH/7hH06Se/73hhtumOVYAMAeqDHGnu/0yiuvHDfffPOe7xcOqyNHjuSjH/1ojh07ds9tp06dyoMf/ODcfffdM5yMg6Cq8sEPfjCXX375PbfddtttefjDH55ZPEaw96pq1iPch7V3cFg/7AfHr70xt1x39azH2Leq6i1jjCvP9jmnjMIhMDc3l42NjTNu29jYyNzc3Iwm4iCpqrzgBS8447YXvOAF+/KXPHbHGGPfvXFwzHqtWD8wHUEIh8Dy8nKWlpayvr6eU6dOZX19PUtLS1leXp71aBwAT33qU/PSl740z3nOc3LbbbflOc95Tl760pfmqU996qxHAwB2mecQwiFw+nmCJ0+ezObmZubm5rKysuL5g1yQN7zhDbnqqqtyww035KUvfWmqKk972tPyhje8YdajAQC7TBDCIXHixAkByEUTfwDQk1NGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEIGtra5mfn8+RI0cyPz+ftbW1WY8EAOwBr0MI0Nza2lqWl5ezurqahYWFbGxsZGlpKUm8tiUAHHKOEAI0t7KyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aALDLBCFAc5ubm1lYWDjjtoWFhWxubs5oIgBgrwhCgObm5uaysbFxxm0bGxuZm5ub0UQAwF4RhADNLS8vZ2lpKevr6zl16lTW19eztLSU5eXlWY8GAOwyF5UBaO70hWNOnjyZzc3NzM3NZWVlxQVlANh1VbVz23rxzmxnjLEzGzogahbf8JVXXjluvvnmPd8vAABAN1X1ljHGlWf7nFNGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaKrGGHu/06pbk7xjz3d8cDwiyftmPQQHlvXDNKwfpmH9MA3rh2lYP+f2uDHGFWf7xEyCkHOrqpvHGFfOeg4OJuuHaVg/TMP6YRrWD9Owfi6eU0YBAACaEoQAAABNCcL96WWzHoADzfphGtYP07B+mIb1wzSsn4vkOYQAAABNOUIIAADQlCA8BKqqZj0DB5f1wzSsH6Zh/TAN64dpWD//l1NGAQAAmnKE8ACrqmdW1Y9W1V+oqk+d9TwcLNYP06iqZ1TVD1TVp1TVw2Y9DweLnz9Mw/phGh6/7ssRwgOsqo4lOZnksiRPSvJdY4xfnu1UHBTWDxerqo4k+ZQkz0tSSY4medkY4+0zHYwDw88fpmH9cLE8fp2dIDyAqurPJLlkjPG/Jx9fnuRvJ/nOJF8xxvjvs5yP/c36YRqTv8Z/bIzxR5OPPzvJFyV5RpLnjDF+dZbzsb/5+cM0rB+m4fHr/gnCA6aqXpvk4UkemuR/JPlnY4yPTD73dUmuTfK3xhhvm9WM7F/WD9Ooqn+Trb+s3pHkbWOMfzq5/WiSa5Jcna0H1d+d3ZTsV37+MA3rh2l4/Do3zyE8QKrqy5M8fIzxtCRfnmQ+yXdW1WOSZIzxyiQ/nOSrqupSV09iO+uHaVTVs7P1YHpVkn+S5Cuq6nuTZIzx8SSvSbKR5CmT+1s/3MPPH6Zh/TANj1/nJwgPlncn+XhVPWKM8QdJvirJo5I8d9t9/nuST05y93D4lzNZP0zjHUl+O1tnlvzvJH8lyUJVfV+SjDHen+Q3knzB5GPrh+38/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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.164872Z","iopub.execute_input":"2023-02-01T14:51:29.165348Z","iopub.status.idle":"2023-02-01T14:51:29.588614Z","shell.execute_reply.started":"2023-02-01T14:51:29.165277Z","shell.execute_reply":"2023-02-01T14:51:29.587528Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.590230Z","iopub.execute_input":"2023-02-01T14:51:29.591244Z","iopub.status.idle":"2023-02-01T14:51:29.999585Z","shell.execute_reply.started":"2023-02-01T14:51:29.591206Z","shell.execute_reply":"2023-02-01T14:51:29.998460Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = clf.predict(X_test)\ndecision_tree_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"clf_y_pred\": y_pred})\ndecision_tree_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.001184Z","iopub.execute_input":"2023-02-01T14:51:30.001710Z","iopub.status.idle":"2023-02-01T14:51:30.018740Z","shell.execute_reply.started":"2023-02-01T14:51:30.001660Z","shell.execute_reply":"2023-02-01T14:51:30.017976Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.019742Z","iopub.execute_input":"2023-02-01T14:51:30.020678Z","iopub.status.idle":"2023-02-01T14:51:30.025527Z","shell.execute_reply.started":"2023-02-01T14:51:30.020645Z","shell.execute_reply":"2023-02-01T14:51:30.024304Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.027690Z","iopub.execute_input":"2023-02-01T14:51:30.028212Z","iopub.status.idle":"2023-02-01T14:51:30.045818Z","shell.execute_reply.started":"2023-02-01T14:51:30.028170Z","shell.execute_reply":"2023-02-01T14:51:30.044552Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.048587Z","iopub.execute_input":"2023-02-01T14:51:30.048979Z","iopub.status.idle":"2023-02-01T14:51:30.075974Z","shell.execute_reply.started":"2023-02-01T14:51:30.048946Z","shell.execute_reply":"2023-02-01T14:51:30.074745Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred \n0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Random Forrest\n\nWe use Random Forrest to classify the titanic passengers as either surviving or not the accident. We use again the same statistical variable as Decisiont Trees.","metadata":{}},{"cell_type":"markdown","source":"## Model fitting and classification\n\nRandom Forrest overfits to the training dataset. ","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nn_estimators = range(1,20)\nmax_depths = range(1,40)\n\nfor est in n_estimators:\n for depth in max_depths:\n rf = RandomForestClassifier(n_estimators = est, max_depth = depth, \n random_state = 42, class_weight={0:6.,1:4},max_features = 6)\n rf.fit(X_train, y_train)\n train_score = rf.score(X_train, y_train)\n test_score = rf.score(X_valid, y_valid)\n print(\" - estimators : \", est, \n \" - max depths : \", depth, \n \" - train score : \", train_score,\n \" - valid score : \", valid_score)\n \n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.172233Z","iopub.execute_input":"2023-02-01T14:51:30.172931Z","iopub.status.idle":"2023-02-01T14:51:52.273980Z","shell.execute_reply.started":"2023-02-01T14:51:30.172890Z","shell.execute_reply":"2023-02-01T14:51:52.272764Z"},"trusted":true},"execution_count":213,"outputs":[{"name":"stdout","text":" - estimators : 1 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 2 - train score : 0.7771535580524345 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 3 - train score : 0.8071161048689138 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 4 - train score : 0.8277153558052435 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 5 - train score : 0.8314606741573034 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 6 - train score : 0.8651685393258427 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 7 - train score : 0.8820224719101124 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 8 - train score : 0.8857677902621723 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 9 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 10 - train score : 0.900749063670412 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 11 - train score : 0.9082397003745318 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 12 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 13 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 14 - train score : 0.9119850187265918 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 15 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 16 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 17 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 18 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 19 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 20 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 21 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 22 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 23 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 24 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 25 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 26 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 27 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 28 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 29 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 30 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 31 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 32 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 33 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 34 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 35 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 36 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 37 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 38 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 39 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 4 - train score : 0.848314606741573 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 5 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 6 - train score : 0.8689138576779026 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 7 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 8 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 10 - train score : 0.9213483146067416 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 11 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 12 - train score : 0.9288389513108615 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 13 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 14 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 15 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 16 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 17 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 18 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 19 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 20 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 21 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 22 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 23 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 24 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 25 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 26 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 27 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 28 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 29 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 30 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 31 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 32 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 33 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 34 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 35 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 36 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 37 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 38 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 39 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 4 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 5 - train score : 0.8707865168539326 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 6 - train score : 0.8838951310861424 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 7 - train score : 0.897003745318352 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 8 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 10 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 11 - train score : 0.9400749063670412 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 12 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 13 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 14 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 15 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 16 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 17 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 18 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 19 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 20 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 21 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 22 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 23 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 24 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 25 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 26 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 27 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 28 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 29 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 30 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 31 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 32 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 33 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 34 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 35 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 36 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 37 - train score : 0.9456928838951311 - 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estimators : 19 - max depths : 26 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 27 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 28 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 29 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 30 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 31 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 32 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 33 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 34 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 35 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 36 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 37 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 38 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 39 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover again the learning overfit on the training dataset. So we choose a maximum depth at around 6 and n estimator of 11. ","metadata":{}},{"cell_type":"code","source":"rf = RandomForestClassifier(n_estimators = 11, max_depth=6, random_state = 42, class_weight={0:6.,1:4}, max_features = 6)\nrf.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.275894Z","iopub.execute_input":"2023-02-01T14:51:52.276195Z","iopub.status.idle":"2023-02-01T14:51:52.312746Z","shell.execute_reply.started":"2023-02-01T14:51:52.276167Z","shell.execute_reply":"2023-02-01T14:51:52.311257Z"},"trusted":true},"execution_count":214,"outputs":[{"execution_count":214,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight={0: 6.0, 1: 4}, max_depth=6, max_features=6,\n n_estimators=11, random_state=42)"},"metadata":{}}]},{"cell_type":"code","source":"rf_train_score = rf.score(X_train, y_train)\nrf_train_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.314414Z","iopub.execute_input":"2023-02-01T14:51:52.314882Z","iopub.status.idle":"2023-02-01T14:51:52.329948Z","shell.execute_reply.started":"2023-02-01T14:51:52.314839Z","shell.execute_reply":"2023-02-01T14:51:52.328684Z"},"trusted":true},"execution_count":215,"outputs":[{"execution_count":215,"output_type":"execute_result","data":{"text/plain":"0.8801498127340824"},"metadata":{}}]},{"cell_type":"code","source":"rf_valid_score = rf.score(X_valid, y_valid)\nrf_valid_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.332102Z","iopub.execute_input":"2023-02-01T14:51:52.333087Z","iopub.status.idle":"2023-02-01T14:51:52.346061Z","shell.execute_reply.started":"2023-02-01T14:51:52.333051Z","shell.execute_reply":"2023-02-01T14:51:52.344862Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"0.8067226890756303"},"metadata":{}}]},{"cell_type":"markdown","source":"The age, the fare and the gender appears to contribute the most to predicting accurately the surviving or not the accident. It is surprising the passenger class influence less random forrest. ","metadata":{}},{"cell_type":"code","source":"importances = rf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.347466Z","iopub.execute_input":"2023-02-01T14:51:52.347785Z","iopub.status.idle":"2023-02-01T14:51:52.360347Z","shell.execute_reply.started":"2023-02-01T14:51:52.347756Z","shell.execute_reply":"2023-02-01T14:51:52.359060Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":" 0\n0.199528 Fare\n0.140924 Pclass\n0.390318 Sex\n0.023663 Embarked\n0.053330 fam_members\n0.192238 Age","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
0.199528Fare
0.140924Pclass
0.390318Sex
0.023663Embarked
0.053330fam_members
0.192238Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We found the classes of importances are Fares, Sex, and Age. ","metadata":{}},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = rf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.362231Z","iopub.execute_input":"2023-02-01T14:51:52.362868Z","iopub.status.idle":"2023-02-01T14:51:52.379545Z","shell.execute_reply.started":"2023-02-01T14:51:52.362825Z","shell.execute_reply":"2023-02-01T14:51:52.378290Z"},"trusted":true},"execution_count":218,"outputs":[{"execution_count":218,"output_type":"execute_result","data":{"text/plain":"array([[319, 10],\n [ 54, 151]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.381097Z","iopub.execute_input":"2023-02-01T14:51:52.381577Z","iopub.status.idle":"2023-02-01T14:51:52.391168Z","shell.execute_reply.started":"2023-02-01T14:51:52.381537Z","shell.execute_reply":"2023-02-01T14:51:52.390198Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"Accuracy : 0.8801498127340824\nMisclassfication : 0.1198501872659176\nSensitivivity : 0.9696048632218845\nSpecificity : 0.7365853658536585\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.392573Z","iopub.execute_input":"2023-02-01T14:51:52.393224Z","iopub.status.idle":"2023-02-01T14:51:52.412047Z","shell.execute_reply.started":"2023-02-01T14:51:52.393191Z","shell.execute_reply":"2023-02-01T14:51:52.410398Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.413222Z","iopub.execute_input":"2023-02-01T14:51:52.413582Z","iopub.status.idle":"2023-02-01T14:51:52.421900Z","shell.execute_reply.started":"2023-02-01T14:51:52.413554Z","shell.execute_reply":"2023-02-01T14:51:52.420658Z"},"trusted":true},"execution_count":221,"outputs":[{"name":"stdout","text":"Accuracy : 0.8067226890756303\nMisclassfication : 0.19327731092436976\nSensitivivity : 0.9227272727272727\nSpecificity : 0.6204379562043796\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.427307Z","iopub.execute_input":"2023-02-01T14:51:52.427779Z","iopub.status.idle":"2023-02-01T14:51:52.433953Z","shell.execute_reply.started":"2023-02-01T14:51:52.427746Z","shell.execute_reply":"2023-02-01T14:51:52.432477Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.435235Z","iopub.execute_input":"2023-02-01T14:51:52.435660Z","iopub.status.idle":"2023-02-01T14:51:52.465440Z","shell.execute_reply.started":"2023-02-01T14:51:52.435608Z","shell.execute_reply":"2023-02-01T14:51:52.464167Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.466837Z","iopub.execute_input":"2023-02-01T14:51:52.467622Z","iopub.status.idle":"2023-02-01T14:51:52.495143Z","shell.execute_reply.started":"2023-02-01T14:51:52.467589Z","shell.execute_reply":"2023-02-01T14:51:52.494000Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.496752Z","iopub.execute_input":"2023-02-01T14:51:52.497420Z","iopub.status.idle":"2023-02-01T14:51:52.520420Z","shell.execute_reply.started":"2023-02-01T14:51:52.497382Z","shell.execute_reply":"2023-02-01T14:51:52.519633Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.521415Z","iopub.execute_input":"2023-02-01T14:51:52.522394Z","iopub.status.idle":"2023-02-01T14:51:52.546457Z","shell.execute_reply.started":"2023-02-01T14:51:52.522351Z","shell.execute_reply":"2023-02-01T14:51:52.545447Z"},"trusted":true},"execution_count":226,"outputs":[{"execution_count":226,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.547614Z","iopub.execute_input":"2023-02-01T14:51:52.547908Z","iopub.status.idle":"2023-02-01T14:51:52.553613Z","shell.execute_reply.started":"2023-02-01T14:51:52.547880Z","shell.execute_reply":"2023-02-01T14:51:52.552611Z"},"trusted":true},"execution_count":227,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.554829Z","iopub.execute_input":"2023-02-01T14:51:52.555101Z","iopub.status.idle":"2023-02-01T14:51:52.580427Z","shell.execute_reply.started":"2023-02-01T14:51:52.555075Z","shell.execute_reply":"2023-02-01T14:51:52.579665Z"},"trusted":true},"execution_count":228,"outputs":[{"execution_count":228,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.581453Z","iopub.execute_input":"2023-02-01T14:51:52.582459Z","iopub.status.idle":"2023-02-01T14:51:52.610464Z","shell.execute_reply.started":"2023-02-01T14:51:52.582401Z","shell.execute_reply":"2023-02-01T14:51:52.609279Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y \n0 0.0 \n1 NaN \n2 0.0 \n3 NaN \n4 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_y
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.611523Z","iopub.execute_input":"2023-02-01T14:51:52.611803Z","iopub.status.idle":"2023-02-01T14:51:52.639513Z","shell.execute_reply.started":"2023-02-01T14:51:52.611776Z","shell.execute_reply":"2023-02-01T14:51:52.638365Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 1.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538461.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
\n

357 rows × 8 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.641337Z","iopub.execute_input":"2023-02-01T14:51:52.641775Z","iopub.status.idle":"2023-02-01T14:51:52.669655Z","shell.execute_reply.started":"2023-02-01T14:51:52.641731Z","shell.execute_reply":"2023-02-01T14:51:52.668451Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred \n0 0.0 NaN \n1 NaN 1.0 \n2 0.0 NaN \n3 NaN 1.0 \n4 NaN 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.670923Z","iopub.execute_input":"2023-02-01T14:51:52.671224Z","iopub.status.idle":"2023-02-01T14:51:52.693465Z","shell.execute_reply.started":"2023-02-01T14:51:52.671196Z","shell.execute_reply":"2023-02-01T14:51:52.692202Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 0.0\n233 0.733373 3.0 2.0 2.0 6.0 -1.923077 1.0 0.0\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n235 -0.299018 3.0 2.0 2.0 0.0 0.000000 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
2550.0342843.02.04.02.0-0.0769231.00.0
2330.7333733.02.02.06.0-1.9230771.00.0
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
235-0.2990183.02.02.00.00.0000000.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.694762Z","iopub.execute_input":"2023-02-01T14:51:52.695075Z","iopub.status.idle":"2023-02-01T14:51:52.711272Z","shell.execute_reply.started":"2023-02-01T14:51:52.695047Z","shell.execute_reply":"2023-02-01T14:51:52.710037Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 12\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n 2.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 0.0 5\n 1.0 4\n 1.0 1.0 0.0 2\n 2.0 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n 2.0 0.0 2\n 5.0 1.0 1.0 1\n 6.0 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.712948Z","iopub.execute_input":"2023-02-01T14:51:52.713356Z","iopub.status.idle":"2023-02-01T14:51:52.728466Z","shell.execute_reply.started":"2023-02-01T14:51:52.713299Z","shell.execute_reply":"2023-02-01T14:51:52.727135Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.729743Z","iopub.execute_input":"2023-02-01T14:51:52.730867Z","iopub.status.idle":"2023-02-01T14:51:53.319377Z","shell.execute_reply.started":"2023-02-01T14:51:52.730830Z","shell.execute_reply":"2023-02-01T14:51:53.318257Z"},"trusted":true},"execution_count":235,"outputs":[{"execution_count":235,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.320867Z","iopub.execute_input":"2023-02-01T14:51:53.321309Z","iopub.status.idle":"2023-02-01T14:51:53.344082Z","shell.execute_reply.started":"2023-02-01T14:51:53.321267Z","shell.execute_reply":"2023-02-01T14:51:53.342810Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n386 1.405213 3.0 1.0 2.0 7.0 -2.230769 0.0 1.0\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n824 1.092843 3.0 1.0 2.0 5.0 -2.153846 0.0 1.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3861.4052133.01.02.07.0-2.2307690.01.0
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
8241.0928433.01.02.05.0-2.1538460.01.0
429-0.2773633.01.02.00.00.1538461.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.345774Z","iopub.execute_input":"2023-02-01T14:51:53.346816Z","iopub.status.idle":"2023-02-01T14:51:53.369951Z","shell.execute_reply.started":"2023-02-01T14:51:53.346772Z","shell.execute_reply":"2023-02-01T14:51:53.368730Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 7\n 2.0 0.0 1\n 1.0 1.0 0.0 5\n 2.0 1.0 0.0 1\n 1.0 1\n 3.0 2.0 1.0 1\n 5.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n3.0 0.0 1.0 0.0 13\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 1.0 1\n 2.0 1.0 0.0 3\n 1.0 2\n 2.0 0.0 1\n 1.0 2\n 4.0 1.0 1.0 1\n 5.0 1.0 1.0 2\n 6.0 2.0 0.0 1\n 7.0 1.0 1.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.371853Z","iopub.execute_input":"2023-02-01T14:51:53.372272Z","iopub.status.idle":"2023-02-01T14:51:54.052559Z","shell.execute_reply.started":"2023-02-01T14:51:53.372234Z","shell.execute_reply":"2023-02-01T14:51:54.051607Z"},"trusted":true},"execution_count":238,"outputs":[{"execution_count":238,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.053862Z","iopub.execute_input":"2023-02-01T14:51:54.054160Z","iopub.status.idle":"2023-02-01T14:51:54.076180Z","shell.execute_reply.started":"2023-02-01T14:51:54.054133Z","shell.execute_reply":"2023-02-01T14:51:54.075083Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0\n110 1.626091 1.0 1.0 2.0 0.0 1.307692 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
130-0.2840413.01.04.00.00.2307690.00.0
1101.6260911.01.02.00.01.3076920.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.081370Z","iopub.execute_input":"2023-02-01T14:51:54.081697Z","iopub.status.idle":"2023-02-01T14:51:54.104120Z","shell.execute_reply.started":"2023-02-01T14:51:54.081668Z","shell.execute_reply":"2023-02-01T14:51:54.103001Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 4\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 3\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 0.0 11\n 1.0 24\n 1.0 1.0 0.0 15\n 1.0 2\n 2.0 0.0 9\n 1.0 4\n 2.0 1.0 0.0 9\n 1.0 3\n 2.0 0.0 5\n 1.0 6\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 6\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.106496Z","iopub.execute_input":"2023-02-01T14:51:54.106922Z","iopub.status.idle":"2023-02-01T14:51:55.631830Z","shell.execute_reply.started":"2023-02-01T14:51:54.106868Z","shell.execute_reply":"2023-02-01T14:51:55.630790Z"},"trusted":true},"execution_count":241,"outputs":[{"execution_count":241,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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vSPQ8vytUm+ajb0dscZiffx/vENmfa/h0spj83q/bgl1gxwSy5PWAFwpM3Oav16rfWrGjz39yb5xVrr/Uto+6WZzhp8o4B9qMr05zF+c7f9XEr5qkxD4HaSD6y1/tFh1bcIs8DxhiQfOTt7R2OzM/3fV2v9M61rSZJSSj/J35wNBT8WSimvTvLxSX6m1vopresBThahFDgWDjOUzobH/l6mZyDvzfR7cJ9QZ7+LuODnemk6EkpLKWcyPYP60bXWX21bDQBwXBi+C3Dz3j/Tnyf5wyT/PMmXLSOQdkkp5R9m+junXyeQHo5Syr8tpfzhDpe/37o2Dq6U8uwbvL5/WErZz6RkAMeGM6UAAAA040wpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAsANlFJeU0r5/VLKk1vXAgDHlVAKADsopZxJ8meT1CR/vm01AHB8CaUAsLMvSvLaJK9M8oLLC0sp711K+ZFSyh+UUn62lPI1pZTx3P0fXkp5dSnl90opv1RK+cuHXzoAHB23ty4AADrqi5J8fZKfTvLaUsr71Vp/O8k3JvmjJO+f5EySH0vypiQppbx7klcn+eokn57kbJJXl1LeWGv9hUPfAgA4ApwpBYBrlFL6Se5K8n211tcn+e9JPq+UcirJ5yS5v9b6P2dB86G5hz4/yaO11m+vtb6j1vpfkvzrJP/7IW8CABwZQikAXO8FSR6utb51dvu7Z8uenukoozfPrTt//a4kH1dKedvlS5LPz/SsKgCwA8N3AWBOKeWOJH85yalSyv+YLX5ykqcleb8k70jyzCT/dXbfs+Ye/uYkP1lrfe7hVAsAR1+ptbauAQA6o5Synun3Rj8qyf+au+v7kvxspoH0nUn+epJnJ3k4ya/VWvullDuTvDHJVyX5ntnjPirJH9ZaJ4dRPwAcNYbvAsDVXpDk22utv1Zr/R+XL0n+RaZDcf9WktNJ/keS70yymeSPk6TW+liSe5N8bpLfnK3zYKZnWgGAHThTCgAHUEp5MMn711pfsOfKAMB1nCkFgJsw+x3SjyxTH5tkkOQHW9cFAEeViY4A4ObcmemQ3Wck+e0k/yTJDzWtCACOMMN3AQAAaMbwXQAAAJoRSgEAAGimM98pfZ/3eZ965syZ1mUAAAAce69//evfWmt9eus6kg6F0jNnzuR1r3td6zIAAACOvVLKm1rXcJnhuwAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0sNpaWUU6WU/1JKedUynwcAAICjadlnSr88yWTJzwEAAMARtbRQWkp5ZpL7kvzLZT0HAAAAR9syz5T+syR/L8m7brRCKeWFpZTXlVJe95a3vGWJpQAAANBFSwmlpZTnJ/mdWuvrd1uv1vqKWus9tdZ7nv70py+jFAAAADpsWWdKPzHJny+lPJrke5J8cinlu5b0XAAAABxRSwmltdaX1FqfWWs9k+Rzk/xErfULlvFcAAAAHF1+pxQAAIBmbl/2E9RaX5PkNct+HgAAAI4eZ0oBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmjkxoXRzczOrq6s5depUVldXs7m52bokAACAE+/21gUchs3NzQyHw2xsbKTf72c8HmcwGCRJ1tfXG1cHAABwcpVaa+sakiT33HNPfd3rXreUtldXV/Oyl70sa2trV5ZtbW3l3LlzeeMb37iU5wQAAOiqUsrra633tK4jOSGh9NSpU3n729+elZWVK8u2t7fzlKc8Je985zuX8pwAAABd1aVQeiK+U9rr9TIej69aNh6P0+v1GlUEAABAckJC6XA4zGAwyNbWVra3t7O1tZXBYJDhcNi6NAAAgBPtREx0dHkyo3PnzmUymaTX62U0GpnkCAAAoLET8Z1SAAAAnuA7pQAAABChFAAAgIaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIA6LDNzc2srq7m1KlTWV1dzebmZuuSAGChbm9dAACws83NzQyHw2xsbKTf72c8HmcwGCRJ1tfXG1cHAItRaq2ta0iS3HPPPfV1r3td6zIAoDNWV1fzspe9LGtra1eWbW1t5dy5c3njG9/YsDIAjrpSyutrrfe0riMRSgGgs06dOpW3v/3tWVlZubJse3s7T3nKU/LOd76zYWUAHHVdCqW+UwoAHdXr9TIej69aNh6P0+v1GlUEAIsnlAJARw2HwwwGg2xtbWV7eztbW1sZDAYZDoetSwOAhTHREQB01OXJjM6dO5fJZJJer5fRaGSSIwCOFd8pBQAAOGF8pxQAAAAilAIAANCQUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzdzeuoC9lFJ2XF5rPeRKAAAAWLTOnymttabWmrte/Kor1wVSAACA46HzoRQAAIDjSygFgA7b3NzM6upqTp06ldXV1WxubrYuCQAWqvPfKQWAk2pzczPD4TAbGxvp9/sZj8cZDAZJkvX19cbVAcBiOFMKAB01Go2ysbGRtbW1rKysZG1tLRsbGxmNRq1LA4CFEUoBoKMmk0n6/f5Vy/r9fiaTSaOKAGDxhFIA6Kher5fxeHzVsvF4nF6v16giAFg8oRQAOmo4HGYwGGRrayvb29vZ2trKYDDIcDhsXRoALIxQCseYWTvhaFtfX89oNMq5c+fylKc8JefOnctoNDLJEQDHitl34ZgyayccD+vr645ZAI41Z0rhmDJrJwAAR4FQCseUWTsBADgKhFI4pszaCQDAUSCUwjFl1k4AAI4CEx3BMXV5YpRz585lMpmk1+uZtRMAgM4RSuEYM2snAABdZ/guAAAAzQilAAAANCOUAgAA0IxQCgAAQDNLCaWllKeUUn6mlPKGUsrPl1IeWMbzAAAAcLQta/bdP07yybXWPyylrCQZl1L+ba31tUt6PgAAAI6gpYTSWmtN8oezmyuzS13GcwEAAHB0Le07paWUU6WUn0vyO0leXWv96WU9FwAAAEfT0kJprfWdtdaPSvLMJB9bSlm9dp1SygtLKa8rpbzuLW95y7JKAQAAoKOWPvturfVtSbaSfNoO972i1npPrfWepz/96csuBQAAgI5Z1uy7Ty+lPG12/Y4kz03yi8t4LgAAAI6uZc2++wFJHiqlnMo0+H5frfVVS3ouAAAAjqhlzb77/yX56GW0DQAAwPGx9O+UAgAAwI0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKVwjG1ubmZ1dTWnTp3K6upqNjc3W5cEAABXub11AcBybG5uZjgcZmNjI/1+P+PxOIPBIEmyvr7euDoAAJhyphSOqdFolI2NjaytrWVlZSVra2vZ2NjIaDRqXRoAAFwhlMIxNZlM0u/3r1rW7/czmUwaVQQAANcTSuGY6vV6GY/HVy0bj8fp9XqNKgIAgOsJpXBMDYfDDAaDbG1tZXt7O1tbWxkMBhkOh61LAwCAK0x0BMfU5cmMzp07l8lkkl6vl9FoZJIjAAA6RSiFY2x9fV0IBQCg0wzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAWADtvc3Mzq6mpOnTqV1dXVbG5uti4JABbK7LsA0FGbm5sZDofZ2NhIv9/PeDzOYDBIEjNrA3BsOFMKAB01Go2ysbGRtbW1rKysZG1tLRsbGxmNRq1LA4CFcab0CCql7Li81nrIlQCwTJPJJP1+/6pl/X4/k8mkUUUAsHjOlB5BtdYrl7te/Kor1wE4Xnq9Xsbj8VXLxuNxer1eo4oAYPGEUgDoqOFwmMFgkK2trWxvb2drayuDwSDD4bB1aQCwMIbvAkBHXZ7M6Ny5c5lMJun1ehmNRiY5AuBYEUoBoMPW19eFUACONcN3AQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKD1km5ubWV1dzalTp7K6uprNzc3WJQEAADRze+sCTpLNzc0Mh8NsbGyk3+9nPB5nMBgkSdbX1xtXBwAAcPicKT1Eo9EoGxsbWVtby8rKStbW1rKxsZHRaNS6NAAAgCaE0kM0mUzS7/evWtbv9zOZTBpVBAAA0JZQeoh6vV7G4/FVy8bjcXq9XqOKAAAA2hJKD9FwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAAIAmTHR0iC5PZnTu3LlMJpP0er2MRiOTHAEAACeWUHrI1tfXhVAAAIAZw3cBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAaDDNjc3s7q6mlOnTmV1dTWbm5utSwKAhTL7LgB01ObmZobDYTY2NtLv9zMejzMYDJLETO4AHBvOlAJAR41Go2xsbGRtbS0rKytZW1vLxsZGRqNR69IAYGGEUgDoqMlkkn6/f9Wyfr+fyWTSqCIAWDyhFAA6qtfrZTweX7VsPB6n1+s1qggAFk8oBYCOGg6HGQwG2drayvb2dra2tjIYDDIcDluXBgALY6IjAOioy5MZnTt3LpPJJL1eL6PRyCRHABwrQikAdNj6+roQCsCxZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQDQYZubm1ldXc2pU6eyurqazc3N1iUBwELd3roAAGBnm5ubGQ6H2djYSL/fz3g8zmAwSJKsr683rg4AFsOZUgDoqNFolI2NjaytrWVlZSVra2vZ2NjIaDRqXRoALIxQCgAdNZlM0u/3r1rW7/czmUwaVQQAiyeUAkBH9Xq9jMfjq5aNx+P0er1GFQHA4gmlANBRw+Ewg8EgW1tb2d7eztbWVgaDQYbDYevSAGBhTHQEAB11eTKjc+fOZTKZpNfrZTQameQIgGNFKAWADltfXxdCATjWDN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoPWSbm5tZXV3NqVOnsrq6ms3NzdYlAQAANGP23UO0ubmZ4XCYjY2N9Pv9jMfjDAaDJDGzIgAAcCI5U3qIRqNRNjY2sra2lpWVlaytrWVjYyOj0ah1aQAAAE04U3qIJpNJ+v3+Vcv6/X4mk0mTekop1y2rtTaoBAAAOKmcKT1EvV4v4/H4qmXj8Ti9Xq9JPbXW1Fpz14tfdeU6AADAYRJKD9FwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAAIAmDN89RJcnMzp37lwmk0l6vV5Go5FJjgAAgBNLKD1k6+vrQigAAMCM4bsAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUwjG2ubmZ1dXVnDp1Kqurq9nc3GxdEgAAXMVPwsAxtbm5meFwmI2NjfT7/YzH4wwGgyTxs0QAAHSGM6VwTI1Go2xsbGRtbS0rKytZW1vLxsZGRqNR69IAAOAKZ0rhmJpMJun3+1ct6/f7mUwm+3r82YfO7rnOIy945JZqAwCAy4RSOKZ6vV7G43HW1tauLBuPx+n1evt6vMAJAMBhMHwXjqnhcJjBYJCtra1sb29na2srg8Egw+GwdWkAAHCFM6VwTF2ezOjcuXOZTCbp9XoZjUYmOQIAoFOEUjjG1tfXhVAAADrN8F0AAACaEUoBAABoRigFAACgGaEUAACAZpYSSkspzyqlbJVSfqGU8vOllC9fxvMAAABwtC1r9t13JPk7tdb/XEq5M8nrSymvrrX+wpKeDwAAgCNoKWdKa62/VWv9z7PrjyWZJPnAZTwXAAAAR9fSv1NaSjmT5KOT/PQO972wlPK6Usrr3vKWtyy7FAAAADpmqaG0lPLUJP86yVfUWv/g2vtrra+otd5Ta73n6U9/+jJLAQAAoIOWFkpLKSuZBtJ/VWv9gWU9DwAAAEfXsmbfLUk2kkxqrV+/jOcAAADg6FvWmdJPTPKFST65lPJzs8tnLOm5AAAAOKKW8pMwtdZxkrKMtgEAADg+lj77LgAAANyIUAoAAEAzQikAAADNCKXA0m1ubmZ1dTWnTp3K6upqNjc3W5cEAEBHLGWiI4DLNjc3MxwOs7GxkX6/n/F4nMFgkCRZX19vXB0AAK05Uwos1Wg0ysbGRtbW1rKyspK1tbVsbGxkNBq1Lg0AgA4QSoGlmkwm6ff7Vy3r9/uZTCaNKgIAoEuEUmCper1exuPxVcvG43F6vV6jigAA6BKhFFiq4XCYwWCQra2tbG9vZ2trK4PBIMPhsHVpAAB0gImOgKW6PJnRuXPnMplM0uv1MhqNTHIEAEASoRQ4BOvr60IoAAA7MnwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKCZ21sXACxPKeW6ZbXWBpUsxnHbHrrn7ENn97XeIy94ZMmVAMDJIZTCMXY5sJ05fzGPXrivcTUHd9y2h+4RNgHg8Bm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzdzeugAopey4vNZ6yJUAAACHzZlSmqu1ptaau178qivXBVIAADgZhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmjnWPwlz9wMP59Lj20mSNz34/Ovuv+vFr8rpO1byhvvvPezSAAAAyDEPpZce386jF+6b3riw80+MnDl/8RArAgAAYJ7huwAAADQjlAJAhz3vec/LbbfdllJKbrvttjzvec9rXRIALJRQCgAd9bznPS8PP/xwvvRLvzRve9vb8qVf+qV5+OGHBVMAjpVj/Z1SADjKXv3qV+fLvuzL8k3f9E1JcuXfl7/85S3LAoCFcqYUADqq1pqv/dqvvWrZ137t16bWnSfvA4CjSCgFgI4qpeQlL3nJVcte8pKXpJTSqCIAWDyhFAA66rnPfW6++Zu/OS960Yty6dKlvOhFL8o3f/M357nPfW7r0gBgYXynFAA66sd+7MfyvOc9Ly9/+cvzzd/8zSml5N57782P/diPtS4NABZGKAWADhNAATjuDN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJrxO6X7cPahs3uu88gLHll6HXc/8HAuPb593fIz5y9euX76jpW84f57l14LAADAIgil+3AYgXM/Lj2+nUcv3LfrOvMBFQAAoOsM3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmvE7pSfQ3Q88nEuPb1+1bP73TU/fsZI33H/vYZcFwA42NzczGo0ymUzS6/UyHA6zvr7euiwAWBih9AS69Ph2Hr1w3w3vnw+oALSzubmZ4XCYjY2N9Pv9jMfjDAaDJBFMATg2DN8FgI4ajUbZ2NjI2tpaVlZWsra2lo2NjYxGo9alAcDCOFN6yEop1y2rtTaoBICum0wm6ff7Vy3r9/uZTCaNKgKAxXOm9JDVWlNrzV0vftWV6wCwk16vl/F4fNWy8XicXq/XqCIAWDyhFAA6ajgcZjAYZGtrK9vb29na2spgMMhwOGxdGgAsjOG7ANBRlyczOnfu3JXZd0ejkUmOADhWhFIA6LD19XUhFIBjzfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgE4VjY3N7O6uppTp05ldXU1m5ubrUs6kOO2PQBwLT8JA8Cxsbm5meFwmI2NjfT7/YzH4wwGgyQ5kj+rcty2BwB24kwpAMfGaDTKxsZG1tbWsrKykrW1tWxsbGQ0GrUu7ZYct+0BgJ04UwrAsTGZTNLv969a1u/3M5lMGlV0MMdte4C2SinXLau1NqgEruZMKQDHRq/Xy3g8vmrZeDxOr9drVNHBHLftAdqqtabWmrte/Kor16ELhFIAjo3hcJjBYJCtra1sb29na2srg8Egw+GwdWm35LhtDwDsxPBdAI6Ny5P/nDt3LpPJJL1eL6PR6MhOCnTctgcAdiKUAnCsrK+vH6vQdty2BwCuZfguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQB02ObmZlZXV3Pq1Kmsrq5mc3OzdUkAsFBm3wWAjtrc3MxwOMzGxkb6/X7G43EGg0GSmJEXgGPDmVIA6KjRaJSNjY2sra1lZWUla2tr2djYyGg0al0aACxMZ8+U3v3Aw7n0+PZVy86cv3jV7dN3rOQN9997mGUBwKGZTCbp9/tXLev3+5lMJo0qOj5KKdctq7U2qASAzobSS49v59EL9+26zrUhFQCOk16vl/F4nLW1tSvLxuNxer1ew6qOh8sB9Mz5i3t+3gBguQzfBYCOGg6HGQwG2drayvb2dra2tjIYDDIcDluXBgAL09kzpQBw0l2ezOjcuXOZTCbp9XoZjUYmOQLgWBFKAaDD1tfXhVAAjjXDdwEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFgA7b3NzM6upqTp06ldXV1WxubjZpAwCW5fbWBQAAO9vc3MxwOMzGxkb6/X7G43EGg0GSZH19/dDaAIBlcqYUADpqNBplY2Mja2trWVlZydraWjY2NjIajQ61DQBYJmdKaebuBx7Opce3r1p25vzFq26fvmMlb7j/3sMsiyUopey4vNZ6yJWwk51eH69NN0wmk/T7/auW9fv9TCaTQ20DYNH838M8oZRmLj2+nUcv3LfrOteGVI6my//JnDl/cc/XnMPn9emuXq+X8XictbW1K8vG43F6vd6htgGwaP7vYZ7huwDQUcPhMIPBIFtbW9ne3s7W1lYGg0GGw+GhtgEAy+RMKQB01OWJiM6dO5fJZJJer5fRaHRTExQtog0AWCahFAA6bH19/cABchFtAMCyGL4LAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM7e3LgAAADj+7n7g4Vx6fPu65WfOX7xy/fQdK3nD/fceZll0gFAKAAAs3aXHt/Pohft2XWc+oHJyGL4LAABAM0sLpaWUbyul/E4p5Y3Leg4AAACOtmWeKX1lkk9bYvsAAAAccUsLpbXW/5Dk95bVPgAAAEef75QCAADQTNPZd0spL0zywiR59rOf3bIUAOikUsp1y2qtDSoBYBnOPnR2z3UeecEjh1BJO01Daa31FUlekST33HOP/2EB4BqXA+iZ8xf3/CkFAI6e4x4498PwXQAAAJpZ5k/CbCb5qSQfVkr59VLKYFnPBQAAwNG0tOG7tdb1ZbUNAADA8WD4LgAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAcGg2NzezurqaU6dOZXV1NZubm61LAqCxpc2+CwAwb3NzM8PhMBsbG+n3+xmPxxkMpr8Yt75u0n6Ak8qZUgDgUIxGo2xsbGRtbS0rKytZW1vLxsZGRqNR69IAaMiZUoCbVEq5blmttUElcLRMJpP0+/2rlvX7/Uwmk0YVAYfpzt75nH3o/B7rJMl9h1IP3SGUAtykywH0zPmLefSC/zhhv3q9XsbjcdbW1q4sG4/H6fV6DasCDstjkwt7/r955vzFQ6qGLjF8FwA4FMPhMIPBIFtbW9ne3s7W1lYGg0GGw2Hr0gBoyJlSAOBQXJ7M6Ny5c5lMJun1ehmNRiY5AjjhhFIA4NCsr68LoQBcxfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAsrm5mdXV1Zw6dSqrq6vZ3NxsXRJwQph9FwDghNvc3MxwOMzGxkb6/X7G43EGg0GSmC0ZWDpnSgEATrjRaJSNjY2sra1lZWUla2tr2djYyGg0al0acAIIpQAAJ9xkMkm/379qWb/fz2QyaVQRcJIIpQAAJ1yv18t4PL5q2Xg8Tq/Xa1QRcJIIpQAAJ9xwOMxgMMjW1la2t7eztbWVwWCQ4XDYujTgBDDR0SG5+4GHc+nx7auWnTl/8arbp+9YyRvuv/cwy4LO2+nYSa4+fhw7AAdzeTKjc+fOZTKZpNfrZTQameQIOBRC6SG59Ph2Hr1w367rXBtSAccOwGFZX18XQoEmDN8FAACgGWdKAQCAQ7HX6KbTd6wcUiV0iVAKAAAs3U5fxzlz/uKeX9Ph+DN8FwAAgGaEUgAAAJoRSgEAgH3b3NzM6upqTp06ldXV1WxubrYuiSPOd0oBAIB92dzczHA4zMbGRvr9fsbjcQaDQZL4SSFumTOlAADAvoxGo2xsbGRtbS0rKytZW1vLxsZGRqNR69I4wpwpBQBoqJRy3bJaa4NKYG+TyST9fv+qZf1+P5PJpFFFHAfOlAIANFRrTa01d734VVeuQ1f1er2Mx+Orlo3H4/R6vUYVcRwIpQAAwL4Mh8MMBoNsbW1le3s7W1tbGQwGGQ6HrUvjCDN8FwAA2JfLkxmdO3cuk8kkvV4vo9HIJEcciFAKAADs2/r6uhDKQhm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0c6x/p/TO3vmcfej8HuskyX2HUg9X8/osz90PPJxLj29ftezM+YtXrp++YyVvuP/e5nUcZi0AQHeUUp64/uD031rrLbdx2SLauJV2OJhjHUofm1zIoxd2DzTXfkDm8Hh9lufS49u77tvD2q971XGYtQAA3bGI0He5jTPnL+75eWOZbXBwhu8CAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzt7cugP27s3c+Zx86v8c6SXLfodQDAABwUELpEfLY5EIevbB74Dxz/uIhVQMAAHBwhu8CAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADN3N66AI62Usp1y2qtDSoB4Ea8VwPQZc6UciC11tRac9eLX3XlOgDd4r0agC4TSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAODQbG5uZnV1NadOncrq6mo2NzdblwRAY7e3LgAAOBk2NzczHA6zsbGRfr+f8XicwWCQJFlfX29cHQCtOFMKAByK0WiUjY2NrK2tZWVlJWtra9nY2MhoNGpdGgANOVMKC3L2obP7Wu+RFzyy5Erg8JVSdlxeaz3kSuiyyWSSfr9/1bJ+v5/JZNKoonbufuDhXHp8+7rlZ85fvHL99B0recP99x5mWQBNCKWwIMImJ9nl8Hnm/MU8euG+xtXQVb1eL+PxOGtra1eWjcfj9Hq9hlW1cenx7T2PlfmACnCcGb4LAByK4XCYwWCQra2tbG9vZ2trK4PBIMPhsHVpADR07M+U7vVXxtN3rBxSJcBRt9Nwu2vfYwy3gxu7PJnRuXPnMplM0uv1MhqNTHIEcMId61C607AYQ8uAW2W4HRzc+vq6EArAVQzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaOda/UzqvlPLE9Qen/9ZaD+357+ydz9mHzu+xTpL4DVUAAODkODGh9DAD6E4em1zIoxd2D5xnzl88pGoAAAC6wfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgmdtbFwCwmzt753P2ofN7rJMk9x1KPQAALJZQCnTaY5MLefTC7oHzzPmLh1QNAACLZvguAAAAzThTCnBC3f3Aw7n0+PZVy+bPOp++YyVvuP/ewy4LADhhhFKAE+rS49u7Do02LBoAOAyG7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzXT2d0rv7J3P2YfO77FOktz4N/YAAKC1Usp1y2qtDSqBbupsKH1scmHXH3VP/LA7AADddzmAnjl/cc/Pt3ASGb4LAABAM509UwpA9939wMO59Pj2VcuuHcVy+o6VvOH+ew+zLADgCBFKAbhllx7f9lULAOBADN8FAACgGaEUAACAZoRSAAAAmhFKAQAAaMZER8DS3Nk7n7MPnd9jnSTxm20AACeVUAoszWOTC2ZmBQBgV4bvAgAA0IwzpSfQXkMqDaeE5Sul7Li81nrIlQAn3d0PPJxLj2/nTQ8+/7r77nrxq5Ikp+9YyRvuv/ewSwNOCKH0BNprSKXhlLB88+HzzPmLew5zBliWS49vT9+DLtz4j2I+GwDLZPguAAAAzThTCgAdc3k45bXmz1YZTgnAcSGUAkDHXBlOuQvDKQE4LgzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBXCy7fXj76fvWDmkSgA4Ce5+4OFcenz7qmXz/xedvmMlb7j/3sMuC+BEE0pp5tEL9111+8z5i9ctA4BFuvT49q7/1+z1x1IAFs/wXQAAAJoRSgEAAGjG8F2g83z3GADg+BJKgU7b6btfvn8MAHB8GL4LAABAM0IpAAAAzSwtlJZSPq2U8kullF8upZxf1vMAAABwdC3lO6WllFNJvjHJc5P8epKfLaX8cK31F5bxfADA9e5+4OFcenz7qmXzE4edvmMlb7j/3sMuCwCusqyJjj42yS/XWn8lSUop35Pks5IIpQBwSC49vr3rpGB7zWwNAIdhWcN3PzDJm+du//psGQAAAFzR9CdhSikvTPLCJHn2s5993f3H7bcJj9v20F139s7n7EM3/ir3nb0k8ZMq0FV7HcPTdZLDOI4vDwF+04PPv+6+u178qiR7DwM++9DZfT3XIy945NaKJKWU65bVWvf12EX2t53quJladmvnZts4iC722YPsk72G8ieG8x91XeyzN2NZofQ3kjxr7vYzZ8uuUmt9RZJXJMk999xz1VF17XCjo/67hMdte+i2xyYXDNmDI2yvYzg5vOP4yhDgCzf+8LtXLV3anuPqcji5lc8Xi3x95kPSQT7rHGR7FqGLH9wPsk/2Gsp/uV2Ori722ZuxrOG7P5vkfyulfFAp5UlJPjfJDy/puQAAADiilnKmtNb6jlLK30ryY0lOJfm2WuvPL+O5AAAAboYhzd2ytO+U1lp/NMmPLqt9AACAW2FIc7csa/guAAAA7Knp7LsAHG1dmiUWADiahFIAbplZVQGAgzJ8FwAAgGacKQWWaq+zZKfvWDmkSgBYlp1mMk2u/j/ATKbAjQilwNJcO6yz1Y+gA7BcZjIFDsLwXQAAAJpxphQA4IQ7c/5i3vTg869bfteLX5Xk8L5qsdMwYEOA4fgTSgEATrArw24v1LaFZO9hwIYAw/Fk+C4AAADNCKUAAAA0I5QCAADQjFAKAABAMyY6AtinO3vnc/ah83uskyR+ixXgpNtpJuHEbMKwE6EUYJ8em1zw4/AA7MteMwkn/s+AywzfBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZs+8eMXvN0nb6jpVDqgQOXynliesPTv+ttTaqBuDW+Ympne21X07iPoGTQCg9QnaaVvzM+Yt7TjcOx4UAChwXfmJqZ3vtl5O4T+AkMHwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBkTHQGcUGa5BAC6QCgFOKHMcgkAdIHhuwAAADQjlAIAANCMUAoAAEAzQikAAADNmOiIW3L3Aw/n0uPbVy2bnxTl9B0recP99x52WczZbZKa03esHGIl3XD2obN7rvPICx45hEoA2I3/v+DkEUq5JZce3zZrZ4dd+9qcOX9x19frJBA4AbrP/19wMhm+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMiY4ADtFOM1cnZq+mu+7snc/Zh87vsU6S7D4ZzV4T4JlVFXbmFw84CYRSgEO018zVidmr6ZbHJhcO3Gd3erxZVWF//OIBJ4HhuwAAADTjTCkAB2JYJgBwEEIpALfMD90DAAdl+C4AAADNOFMKAJwYe80mvJ+ZhFm+UsoT1x+c/ltrbVQNsGxCKQBwYuw1m7CZTLtBAIWTxfBdAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGZMdAQAwIHsNavxdJ3EzMbAToRSAAAOZK9ZjRMzGwM3ZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM2YfReSnH3o7J7rPPKCRw6hEgDYv7sfeDiXHt++atn8LLen71jJG+6/97DLomP8ZA9dJ5RCBE4AjqZLj2/v+lMsfoaFxE/20H2G7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDO3ty4AABbh7gcezqXHt/OmB59/3X13vfhVOX3HSt5w/71Lr+PsQ2f3td4jL3hkyZUALd3ZO5+zD53fY50kue9Q6jlOLr/fzztz/uKV64f1fs/iCKUAHAuXHt/OoxfuSy7UHe+f/8CyTMImkCSPTS5M35N2cVjvS8fNlff7G7Bfjx7DdwEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoJnbWxcAwNFXSnni+oNPLK+1HloNd/bO5+xD53e5P0nuO7R6DurM+Yu73n/6jpU92zhu+wROIscxJ4FQCsCBHWb4vJHHJhfy6IUbfzDbK+R1yU7bceb8xV23byfHaZ/ASeU45iQwfBcAAIBmnCk9oXb7q9p+hoQBAAAsglB6Al07BORWhoQBAAAsguG7AAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADN3N66gL2UUp64/uATy2utDaoBAG7V/P/pyRP/r/s/HeBk63wo9R8VABwP/k8HYCeG7wIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzXT+d0oBuuTM+Yu73n/6jpVDqgQA4HgQSgH26dEL9111+8z5i9ctAwDg5hi+CwAAQDPOlEKHnH3o7L7We+QFjyy5ksUqpTxx/cEnltdaG1TT1p298zn70Pk91kkSZ2BZjN2GnBtuDkAXCKXQIUctbO7XSQyfN/LY5MKeQ373+t4q7Jch5wAcBYbvAgAA0IxQCgAAQDNCKQAAAM0IpQAAADRjoiMAAOBEMRt+twilAADAiWI2/G4xfBcAAIBmhFIAAACaEUoBAABoRigFAACgGRMdARyyvSZOOH3HyiFVAifTbsfgUTv+9ppB1OyhwFEglAIcop1m+jtz/uKeMwACi3HtsXbUj7+9ZhA1eyhwFBi+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMiY4AAKDDjtOM0bAToRQAADrquM0YDTsxfBcAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZsy+e8hKKU9cf3D6b621UTXdsNM+SewXjrf5fp94P4Cj6Nrj+DLHMcDNcab0kNVar7ucdDvtE/uF406/h6Pv8nF714tf5TgGOAChFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBQAAHCV3P/BwLj2+fdWyM+cvXnX79B0recP99x5mWQBHllAKAHATLj2+nUcv3LfrOteGVABuzPBdAAAAmnGm9AgqpVx9+8Hpv7XWBtXQZfN9RT8B6Ja9zqaevmPlkCoBaEsoPYKECvZLXwHopp2G/545f3HPYcEAx5HhuwAAADQjlAIAANCMUAoAAEAzQikAAADNmOgIANiVWWIBWCahFAC4oWtngzVDLACLZvguAAAAzSw8lJZS/vdSys+XUt5VSrln0e0DAEfX5uZmVldXc+rUqayurmZzc7N1SQA0tozhu29M8heTfMsS2gYAjqjNzc0Mh8NsbGyk3+9nPB5nMBgkSdbX1xtXB0ArCz9TWmud1Fp/adHtAgBH22g0ysbGRtbW1rKyspK1tbVsbGxkNBq1Lg2AhppOdFRKeWGSFybJs5/97JalcJPu7J3P2YfO73J/kpgIA6ALSilPXH9w+m+t9dDrmEwm6ff7Vy3r9/uZTCaHXgvAQZ196Oy+1nvkBY8suZKj75ZCaSnlx5O8/w53DWutP7Tfdmqtr0jyiiS55557Dv9/R27ZY5MLu86+uNfPBwBweFoE0J30er2Mx+Osra1dWTYej9Pr9RpWBXBrhM3FuaVQWmv91EUXAgAcb8PhMIPB4LrvlBq+C3Cy+Z1SAOBQXJ7M6Ny5c5lMJun1ehmNRiY5AjjhFh5KSyl/IcnLkjw9ycVSys/VWp+36OcBAI6e9fV1IRSAqyw8lNZafzDJDy66XQAAAI6fhf8kDAAAAOyXUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzSz8d0oBgMUppTxx/cHpv7XWRtWwDF5j9qMr/WS+jnn6LAfhTCkAdFit9boLx4vXmP3oSj+Zf/67XvwqfZaFEEoBAABoRigFAACgGaEUAACAZoRSAAAAmjH7LgAAsKu7H3g4lx7fvm75mfMXr1w/fcdK3nD/vYdZFseEUAoAAOzq0uPbefTCfbuuMx9Q4WYYvgsAAEAzQikAAADNCKUAAAA0I5QCAADQjImOTrBSyhPXH5z+W2ttVA0AAHASCaUnmAAKAAC0ZvguAAAAzQilAAAANCOUAgAA0IxQCgAAQDMmOgIAThwz0HMYzpy/uOv9p+9YOaRKoNuEUgDgxBFAWbZHL9x33bIz5y/uuBxOOsN3AQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGjm9tYFAADcjFLKdctqrQ0qgZPjzt75nH3o/B7rJMl9h1IPx4tQCgAcKZcD6JnzF/PoBR+A4TA8Nrmw5/F25vzFQ6qG48bwXQAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmTHQEcILtNinF6TtWDrES4FY5joGjTigFOKGunUXRTKZw9DiOgePA8F0AAACacaYUAAA4cfb6XVXD3w+PUAoAAJwohr53i+G7AAAANONMKQDATbizdz5nHzq/xzpJ4qwLwH4IpQAAN+GxyYU9h/nt9V01AJ5g+C4AAADNCKUAAAA0I5QCAADQjFAKAABAMyY64pbtNomDHxsGAAD2QyjllvjBYQAAYBEM3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmbm9dAAAsypnzF2943+k7Vg6xEjh6Njc3MxqNMplM0uv1MhwOs76+3ros4AQQSgE4Fh69cN9Vt8+cv3jdMmBnm5ubGQ6H2djYSL/fz3g8zmAwSBLBFFg6oRQAOBLufuDhXHp8+6pl82fHT9+xkjfcf+9hl3UsjEajbGxsZG1tLUmytraWjY2NnDt3TigFlk4oBQCOhEuPb+969nu34dvsbjKZpN/vX7Ws3+9nMpnsu4299r8h9CzKnb3zOfvQ+V3uTxIjZY4SoRQA4ITr9XoZj8dXzpQmyXg8Tq/X29fjd/pjgSH0LMtjkwv+QHXMmH0XAOCEGw6HGQwG2drayvb2dra2tjIYDDIcDluXBpwAzpQCAJxwl783eu7cuSuz745GI98nPUYMr6bLhFIAALK+vi6EHlNmJ6frDN8FAACgGWdKAQCAPRkCzLIIpQAAwK7MsMwyGb4LAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0c3vrAgAAAG7GmfMXb3jf6TtWDrESFkEoBQAAjoxHL9x31e0z5y9et4yjxfBdAAAAmnGmFAA4Eu7snc/Zh87vcn+SOFsCcNQIpQDAkfDY5MKuQ/R2+44ZAN1l+C4AAADNOFMKcJNKKU9cf3D6b621UTUAAEebUApwkwRQAIDFMXwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaub11AQBA95VSnrj+4PTfWmujauBocfzA7oRSAGBPPkDDrXP8wO4M3wUAAKAZoRQAAIBmhFIAAACaEUoBAABoRigFAACgGaEUAACAZoRSAAAAmll4KC2lfF0p5RdLKf9fKeUHSylPW/RzAAAAcDws40zpq5Os1lo/Msl/TfKSJTwHAAAAx8DCQ2mt9eFa6ztmN1+b5JmLfg4AAACOh2V/p/SvJfm3S34OAAAAjqjbb+VBpZQfT/L+O9w1rLX+0GydYZJ3JPlXu7TzwiQvTJJnP/vZt1IKjZVSnrj+4PTfWmujaoBbcdyO4+O2PQBdMv8em7R9n/V+f3zcUiittX7qbveXUr44yfOTfErdpWfUWl+R5BVJcs899+hBR5ADH46+43YcH7ftAeiSLr3HdqkWDuaWQuluSimfluTvJfmkWuv/XHT7AAAAHB/L+E7pv0hyZ5JXl1J+rpTy8iU8BwAAAMfAws+U1lo/dNFtAgAAcDwte/ZdAAAAuKGFnykFADjuzpy/uOv9p+9YOaRKAI4+oRQA4CY8euG+q26fOX/xumUA7J9QCgAcGbudoXR2EuBoEkoBjrDNzc2MRqNMJpP0er0Mh8Osr6+3LguWwhlKgONJKAU4ojY3NzMcDrOxsZF+v5/xeJzBYJAkgikAcGSYfRfgiBqNRtnY2Mja2lpWVlaytraWjY2NjEaj1qUBAOybM6UAR9RkMkm/379qWb/fz2QyaVQR0Eop5YnrD07/rbU2qoau0k/oKmdKAY6oXq+X8Xh81bLxeJxer9eoIqCVWut1F7iWfkJXCaUAR9RwOMxgMMjW1la2t7eztbWVwWCQ4XDYujQAgH0zfBfgiLo8mdG5c+euzL47Go1McgQAHClCKcARtr6+LoQCAEea4bsAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDN+pxQA4BaUUp64/uATy2utDaoBboXjuBuEUgCAW+BDKxx9juNuMHwXAACAZoRSAAAAmhFKAQAAaEYoBQAAoBmhFAAAgGaEUgAAAJoRSgEAAGhGKAUAAKAZoRQAAIBmhFIAAACaEUoBAABo5vbWBQBwc+5+4OFcenw7SfKmB59/3f13vfhVOX3HSt5w/72HXdotOfvQ2X2t98gLHllyJcBBlVKuvv3g9N9a66HVsJ/3FO8n0C1CKUeaD7OcRJce386jF+6b3riw8we9M+cvHmJFB+P4hOPjMMPnjXhPgaNHKOVI8x8PJ9GdvfM5+9D5PdZJkvsOpR4AgIMQSgGOmJ3+GHPm/MUnzp4CABwhQinAETb//a0W390CADgooRTgCBNAAYCjzk/CAAAA0IwzpQDAkWLYOsDxIpQCAEeKAApwvBi+CwAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANCOUAgAA0IxQCgAAQDNCKQAAAM0IpQAAADQjlAIAANCMUAoAAEAzQikAAADNCKUAAAA0I5QCAADQjFAKAABAM0IpAAAAzQilAAAANFNqra1rSJKUUt6S5E27rPI+Sd66gKdaRDtdaaNLtXSljS7VYnuW00aXarE9y2mjS7V0pY0u1WJ7ltNGl2qxPctpo0u12J7ltNGlWvbTxl211qcf8HkWo9Z6JC5JXteVdrrSRpdq6UobXarF9tgnR62WrrTRpVq60kaXarE99slRq6UrbXSpFttjn3TtYvguAAAAzQilAAAANHOUQukrOtROV9pYVDvHqY1FtdOVNhbVznFqY1HtdKWNRbVznNpYVDvHqY1FtdOVNhbVznFqY1HtdKWNRbVznNpYVDtdaWNR7RynNhbVTlfaODSdmegIAACAk+conSkFAADgmBFKAYCrlFJKl9qBw9Cl/tqVWrwXcFiE0htYxMFTSjnw/l3gm8EiaunS9izi9fEaL6eNLr3GXdknC3mv7dA+6cTxt8BauvQaN399SilPqgv4bs8C2+lEf3McL6eOrtSyqP46a6srx2BX6ujE9iyqjS7V0pX3gkW0KZTuYBEHTynlybXWd7WuY4G1LKKNOxe0PQduZ0FteI2vb6Mz/4F1aJ8cuI1ZO13ps504/mbtHJt+ssBaDrRvSymfkeS7SykfVUp5VgfaWcRr3Il+0qVaulJHV2pZVH+dtXWg7enKseO9oPu1dOW9YK6tTy6lvEcp5bZaa73ZwCyUXqOUcl+SV5VSPqOU8lG32MZnJPnRUsoLSymf0qqOBdayiDael2SzlPIPSil/+VbaWFQ7C2rDa3x9G4vanq4cg51oY9ZOV/psJ46/WTvHpp8ssJZF7NutJD+e5AuTfFUp5bNbtbOg17gT/aRLtXSljo7VspB+v6D/Bztx7CyijkW105X3gi7V0pX3grm2npbkRUm+Msl3llLe/aYDc63V5ZpLkv89yd9JcjHJX7vFNj4pyXqSn0vy5a3qWGAti2jjw5I8J8kvzTrtHa3aWVAbXuPlbU9XjsFOtDFrpyt9thPH33HrJ61fnyQfmeSuudtnkvylJD+d5PNv4vkX0s6CX+NO9JMu1dKVOlrXsuj+epDt6cqx473g6NTSofeCldm/T5ldvjHJf0jyUTfVzq2+IMftkuTjk3zs3O0nJ+kn+Y0k5/bZxnOT3HfNsrNJfiXJ3zmsOhZYyyLa+JwkX5TkSXOd9sOT/ESS4U1sz4HbWVAbXuPlbU9XjsFOtNGxPtuJ4++49ZOuvD5JvjPJOMn3JvnBa+77tCSvTvJJh9jOIl7jTvSTLtXSlTq6Usui+usitqcrx473gu7X0pX3grnHfnWSr8/0Dw4fcM3yH03yQbPbt+3Z1s088XG9JPmeJK/J9K8Er0ryzMs7L8lHJ5kk+Qt7tPH/JPn3SR5O8oNJ1pKcnt33J5P8epLBsutYYC2LauPHkvxQks0kfz3J+8/u+9Akr99P519EOwtsw2u8nO3pyjHYiTY61mc7cfwdt37Sldcn00D7mtn125P88Kytd58te/ckX5LkK5KUZPr75ktsZxGvcSf6SZdq6UodXallUf11EduzqFo6VEcntmdRbXSplnTkvWCurX8+q+WvzF7nf5DkU+fuv5DpmfHb99XeflY6zpdMhzn9xNztlyf5V0nOzi37rEz/CnB6p4Mn079Q/Njc7fNJviXJZ84dhB8/60xnllXHAmtZRBsfmOSH525/3qz2v53k6bNlfzLJf0pyzy6vz4HbWVAbXuPlbc+B2+nQPjlwGx3rs504/o5bP+nY63M2ySuTvOfcsu9O8u/mbv+ZTD9Uvvce23Ogdhb0Gnein3Splq7U0aVaFtFfF7w9B6qlK3V0bHsO3EaXaklH3gvm1ntKkoeS/G+z2x+WZJhpEO3PrfcNSb7sRu3MX0x0ND3d/cellD+ZJLXWL8309PVLSylPmq3z80neM9O/RtQd2vjNJO95+YvGtdYLs8d8dpKnz7Xx3zN9kZdVx6JqWUQbb0tyVynl82ZtfHemQzk+OElvtuwXMh3a8V43aGNR7SyiDa/x8ranK8dgV9pIutNnF1HHItpIjlc/WVQ7b8vB9+0fJXlqko+5vKDW+nlJbiulfNXs9v+b6feDPqmUG07zv4h2FvEad6WfdKmWrtTRpVoW1e8XsT1dOXa8F+ysK7V05b0gs8e+fVbTS8p01vdfynS0znaSj51b9eFMz47vSShN/iDJLyb56FLK6SSptf69TPfNP5/d/uUkr0vyqdcePKWUUmv93UxPh39UKeVDZ4/550nekeT/nt1+LMkvJPlzNzgAD1THompZYBt/lGSU5GNKKZ8we8wPJPn9JH93bvVfyfQv+ddZRDsLbMNrvITtWUQ7Xdkni9qvXemzXTn+5hyLfrLgWg68b2utv5LpMLB/Ukr5pFLKqdldX5/knXOrPpTpX+l3/JCyoHYW8Z7SvJ90sJau1NGZWhbV7w+6PQuspSt1dGZ7FtRGJ2rpynvBfD2zq9+Z5PeSrJdS3m3utf9bpZQPma3zn5K8o5Tybju1dZW6j9Opx+2Sa05HJ/n0TMdW/5U88V2cD0vyj+fWuT2z0+M3aPPjMj2N/uVJPmK27HSSb0/ylLn13n2ZdSywlptuY4c6PjTTLzr/oyTPmVv+A5kbspHkqXtsz4HbWVAbR/o17tL2LGO/tOr3i2hjUX12GbXcSh2LaqMr/WQZbXTh9Ulyau76X03yU0m+NNMJLy4m+ae71b/odm71Ne5SP+lKLV2po2u1LLq/HnR7FllLF+o4SDuL6CeL6mtdquWg/X6RdVxua+7fU3niayNfcbmGTCc4Wp1bb89JjmqtV1Y+kUopp2qt75xd//OZzl44SfLGTH9P6TdrrS/c4XGl7rDjSin9TKdWfvdMT39/apK31lpfsIw6DqGWW2rjmvb+ZJL7kvzZJI8k+VNJfrfW+gX7bWNR7dxMG8ftNe7S9iyinS72+0UfO7N2bqnfd/E4PkgbLfvJovraovvsNW3sa9+WUj4307+IvzrJH9Ra/7iUcnut9R2z+5+X6ZC7j0zyllrruZ1qX1Q7u2zPLb2ndOX9pEu1dKWOlrUsu7/ezPZ05djp8nvBIj5ftP6Msog2uvJeMPf490jyx7XWP57dvr3W+o4yPRP+mUnWMv2Jmt9M8q5a6/N3q2fH5zhJobSU8rcyHTrwuiSTWusfXnPw3JPpEKdPyvTgOT9bfqVjlFKeXWv9tVLKbXWHH4UtpXxQpr/F9JmZdpJ/tEMbB65jgbV8dZI/TvKrSV5da/39UspKrXX7JtpYrbW+cZf9fjrJMzKdLvpttdaX3WB7DtzOgto4bq9xl7anK8fgIvr9gduY3V5En+3EcbzA94Ku9JMDt7HAWg60b0sp/zrTiSnememHhjcl+cZa6x+UUp58+YPG7DHz+/qqmhfYzgfUWn9rfvk127uf17gT/aRLtXSljq7Usqj+Olt2oD7blWOng+8Fi+gnXfqM0rzfL3KfzJZ9a6b/v/xakjfWWr9xtvzK54zZ7ecmeWet9Sdu1Nau6j5Opx6HS6Zfvn1Vkn+R6Y+6viKzoU1JnrTL426bu/4DSd6V2W8rXXPfDU9NX7PegetYYC0bSX4kyf+Z5J9l+mXk97+ZWjIdO/6uXPObSTeouexy34HbWVAbx+017tL2dOUYXES/P3AbC+yznTiOF9FGx/rJgdtYYC0H2rdJ3idzvxWY6e8Efl2Sf5jkzrnlf+6a29cO+1pUO5uZfrj50zts66la97VPOtFPulRLV+roSi2L6q+L6LOLqqVDdSxyew7aT7r0GaV5v1/kPpkte3GmnyuekWQtyS8l+Uc3amM/td7wMTf7gKN4yfQvOZtzt3uZTln8XUnea27552Q2jf7s9vx/7p89e5FfmOkkEs/ZaadnOnb+mcuqY1G1zO7fyGza6EyHAXxNpl9Ift99bs9zZh19kOS/Jnn+Ddb7v5J8yC51HLidBbVxrF7jjm1PJ47BRfT7BbZx4D67iFoWUccCt6UT/WSBfW0RtSzi9XlappNbfM7l/TVr9/+eW/bJSf7ajbZlUe1k+j2zn0hyLsl/zg4frvfxGnein3Splq7U0aVaFtFfF9hnD1xLV+pY4PYsop906TNKV/r9QvbJ3PLPS/KVc7c/INPJ8146t+z/TPKXdusz+7kc6MFH5ZLpB7X/NH9wZHra+x8l+cpMv6j7UUk+d5c23jtPvAF8YZJLlzvL3Drvm+Szl1nHAmt50qyDfsV8h8z0r1wvn3XqZyX587u08R5JPnJ2/XNmnfT516xzW5J799ieA7ezoDaO22vcpe3pyjG4iH5/4DYW2Gc7cRwvoo2O9ZMDt7HAWg60b5MrX9P5i5nO3PiJs9srSf5Oklfutg1LaOf9knzC7PrfTPKGy/tobp1eks/rej/pUi1dqaMrtSyqvy6izy6qlg7Vsah2FtFPuvQZpXm/X+Q+mXvsX8j0TOmT5pY9a/Yca7PbH7Tf42nX51pEI0fhkumY6Z/JbPjT7OD5CzsdPLn+LyhXZpmaW/b5mU6tfM/s9l+85v4b/cXhlutYQi0flen3AD5/dvv2TL+M/m2ZG26xRxvzwwX+YqbfabvcSb8kyZP3amNR7RykjeP2Gndpezp4DC6i3x+4jQX2+0VsT9Pjr0v9ZFF9bcF9dhGvz/tlOmPjv8wTf31/cqazJD7jRq/HItuZ2yfzZwa+LNMP1x8+u/2J++yznXg/6VItXamjS7UctN8vuM925dhp/l6wiH6yyDa6UMuC+/1C9sncOt+e5GeuWfbSXPOVkv20tevzHOTBR+Uy90KvJ/mxzP2FIclrkvyJm2lrrr3PzvTHy38+yTccZh0LqOXU7N9PyfQ3jV4wd9//m+RjbrGOezP94d5fSPIvD7A9t9TOEto4sq9xl7anQ8fggfv9Io+dg/bZRdbSheOvK/1kkW0sqZZbfX3OZBpifybT7wm9Osm338y2LLKdufZekOS/ZDoL5H6+q9SZftKVWrpSR9dqWUZ/vZU+u6xaWtdxkHYW0U8W1de6VMvltm613y+4jvk/iH53pmdHPyLTs7E/nuTLb7Xv7vh8i2ys65ckdyT5rCS/nOSBJFtJHrqFdub/UvUrSb5jp/uWXcciapmt94lJHk3yj5OMW+yTBW9P8zY6+Bp3Ynu6cgzO1ltEvz9wGx3bnuZ9rYP95Fi9t83WvSfJ3801w75vYf8upJ3Z4x5N8p03+Zgu9ZNO1NKVOrpWy6L766322WXU0oU6DtLOIvrJAvtal2rpynvB/JnZr810FNZrbqWtvS6X0/SJUqa/6XZ3pj8m/q2zZaXe5M4opfyNTE+pf8ns9m11h6mbl13Hfmq5UbuX15tNL/3sTIdcbO70mP3UVkr5rEyHlX3FTnUsqp1F1bKXo/QaH0YbCzx2DuUYXGa/v5k29rt9t9rvD/s4PsTjrxPv1YtqYz/tLPB99spv0l2zfKd1b7gti2pnj+35qkz/ev9Ft9JOx/pJJ2rpSh2HXcth9NfZY/fss105do7Ke8Ei+kmXPqN05Ri8mTp2WT7/G6fvkeR0rfXNt1LPrrXewmfjI2GvD297Ldutjbn736PW+ge30sYtvBncUi376Sx71XIzHa6Ucket9fGdHreIdhZVy9w6R/41PmrbcxjH4GH1+/1sy2H1+8M4jhd9/M3u6/R79c20cZB2DrpvSylfk+Q3ktRa68sv15LZgv0+x6LauabNHT/Mzt3/3rXW392t7a70ky7V0pU6WtayjP46e8xN99muHDsdfy9YWj/p0meUo/ZeUEr54CSP1VrfcqM2b/C4WzrJciPHJpSWUr4+0y8D31Zr/eq55fP/6e91cN9SG/MvyiLqWGAt3zRb/M4k/yDJ79Va37mf559r78BtdKmWY/gaH/ntWcI+6URf61ItXWlj1k5X+smB21hgLQfat6WUlyX58EwnHPk7mf5Uwwvm2v/gJG+atbnbh7RFtbPvD7PX7IeFvi+1fo0XXUtX6uhKLYvqr7N1D9Rnu3LsdPC9oFk/6WotXXkvmHvMD2Q6k/+fyPQnfX6m1vr/zd2/Umvd3qudhagLHg/c4pLkwUy/zPvxSX4y09/t+5C5+z88T0wIcluX21hgLV89a+ODkvw/Sb4p08lQVmb3f2iSZy27jS7Vcgxf40600aVautLXulRLV9roWD/pUr8/0L7N9HtD/ybJ3XPLfizJt83d/sdJfjDZdebJRbXzskwnO/krmU5+8h3z6yf54BP4Gh+bNrpSy6L66yL67KJq6VAdi2qneT/pWi1daWNu3U9L8u9m1587a/tr8sTP0tyV5EWZ+zmYZV5uyxFXSrk9yTOT/JNa62trrZ+U5B1JvrKU8j6z1b40yc/e6K85XWljgbXcluT9k3xXrfVXM32D+41Mp4P+mNlqfyXJD5VSnrSsNrpUyzF8jTvRRpdq6Upf61ItXWlj1k5X+kmX+v2B9u3sL+ePZ/qzEB85qylJ7kvy4aWUl89uf02mPyOzeoNtWVQ7d2T6+3V/t9b6vbXWj830ZyM25lZ7UZLvnz3nSXiNj00bXallUf111taB+mxXjp0Ovhc07yddq6UrbVxjJdOZdFNrfXWmv3v+riTPK6U8LdPfwP6iJF+wRzsLcaRD6ezgeUemUxR/7NwL8qIk75nk65KkTieD+E+Z/kWgk20ssp1ZJ3x1kj9bSvnQ2e2vzfRU/5fM1hll+pMRg2W10ZVajttr3JU2ulZLF/pa12rpShtd6Sdd6vez+w+0b2ud/ik705+GGWT6F/LMant+kvcrpXxgkv+Z5PVJfu0GdRy4nUV8mD1ur/FxaqNLtSyq3y+iz3bl2Onge0HzftKlWrrSxrVqrT+S5JdKKS+ctf9Ikh9K8nFJPrpO/1j6N5N8QinljlKmQ8mXph7C6dhlX5I8J8n3Jvn0JO8+W7aS6Wntj57d7id5WtfbWGAtH57pUJC/keSZc8t/Osmnzq4/K8l7LrONLtVyDF/jTrTRpVq60te6VEtX2uhYPzlwG13qs7N1Xjx7zD1J3m227DVJPmx2fbdhdmUR7czu/yuz9Vfnlr1XpkP9PjDT7y59fqazN56U1/jYtNHBWg7UXw/aZ9OxY+egdSx4e7rUTzpRS1famGvrtkx/z/TrMz0bevts+bkkF+ZuP3mvthZxufzXmCOt1vqaUsqZJF+W5EmllJ+rtb6plPKOuXXGy2pj9teFetA6FtXO7P5fLKX820x/dPdJpZSfrrW+LsmlJH88W+fNe9Rxy210sZbj8hovuJ8ct+1p3tcW3GebtrGE9wLv1de3cdP7tuwwa2+t9cFSytuTnE/ytjL9eaDfqrX+0qyNek0bVyavqLXWW21nh+353tk+2Sil/M0kv1Br/b1Syntm+pMEv1FK+e4btTN7Du9t9sl1bSyi399gv9xUn+3SsdPF94K5/dqJY6cLtXSljR32y7tKKT+c6RDeP5XknlLKS5N8RpLX1OmZ2dRa/3g/7R1U2cfx2jnl6t/LmZ+Naj3TH5D/0CS3J7lUa/2cZbRRSrmz1vrY3O35N4abqWNR7dx+ufNcsz2fmulp+L+U5HeSvL3W+lk7PP59a62/c4M69tVG12o5Tq/xAmtZxLHTpe3pSl9bVDsH2p4u7ZPZ+t6rr27jQPu2lPJXM52U4rfKDh9GZ9fPJjmd5ENrra+8dr/Nbn/dbJ3fTXJ/rfV/lVJKpp8Jbqadq2ZlvKaOL0/yZ5O8LdNJnH6n1rp+g33bvJ/M1u3Se5t98sT6C+n3N3j+m+qzXTl2uvZeMFvWiffqrtTSlTZ2c3nflOl3mj8i06HAT03yh7XWv3YzbS1EPYTTsYu6JPm7Se6aXb9tbvn8cIP3S3I2yWfOLbttwW38s0xPnf/zJB9++fE308ai2sn0+0d/Ynb91A22p2T6xvbxN2jjXyT50STfkuQzbqWNLtVyDF/jRbRx4H3Sse3pRF9bYJ9dxPZ0aZ94r17wvk3y3Ul+Pcn3JPnAneqcLXvyNbev3ZZvzXQ44Ecl+akkX3ttG/ts5+uSvGLW7540V//8Np/NdBjZF99gezvRTxb4Gi+iDftkCf1+tuyvJvmAHWrcV59NR46dRe2TBW5Pl96rO1FLV9qYW/ZvMjd78jX33X7N7aft1tYyL4f2RAcudPpbSb+f6fj2D95lx5++5vZtC27jFUm+P9MPDt+V5OtvUO8N21hUO0m+Icn/SvLbSXqzZad2aON9r7k936G/JdOfIXi/JC9N8i9uUMcN2+hSLcfwNV5EGwfeJx3bnk70tQX22UVsT5f2iffq62s50L7NdLbFf53ph9Rhrv4wOv8HiL+Z5J6d2p7d38/0Q+jlD8LPSvLaTL+3Nt8XXrRHOwf+MNuVfrLA17gT79XHaZ8sqt/P1jlQkEtHjp1F7ZMFbk+X3qs7UUtX2phb9mWz1/ZnknzDLq/lX7jmdrnRusu6HOqT3XKR0//IvynJmUx/xHe+w90+t95fT/JpS2zj7tmbwZ2z2++d5JFMhzTMrze4URuLaifTmbbuz/TLzeeSvCVPfBhdmVvvC5N8wQ3a+OAk35fkvWe33z3Jz2Xur/Sz5V9woza6VMsxfI0X0caB90nHtqcTfW2BfXYR29OlfeK9enn79mmZTnRyV5J/mOmH0btm910Or2s3evxcX7ncN56c5D1mtXzINet98i5tHPjDbFf6yQJf4068Vx+3fbLAfn/gIJeOHDsL3CeL2J4uvVd3opautHHNep+UJybK+qkk37jDOh+R5Mv3amvZl6ZPflOFTg+gywfyV2Wa+C8PVbt8EH7kMttIcuryAZvkKbN//2OSP3Vth9qjjkW1M789X5nph9Gz12zPM/Zo4wMz/TD75NntH8hsxse5dZ65WxtdquU4vcZL6ie3eux0dXta9/tF99lb3Z4u7RPv1Uvat3Pr3pVklOShJO+b6Yew95+7/4Z/4c71Q7VeldnMvpn+vt277dZOFvBhtiv9ZFGv8QL7iX2yvH7/tBw8yHXi2FngPjnQ9iyivy2yn3Shlq60sUObd8z+vTPTYPrNc/d92H7bWfaleQG3XHjy1UnGmf6l6f5cPYX2vk4530obuX64wPcm+YjZ9b+X5Fn7qWNR7VzTxvkkv5Xpl57/cZLn3EIb35rZ950y/U2su2+2jS7VctRf4yX1k1s6djq8PZ3oawvss4vYni7tE+/VC963mX5oeVqmQ4H/V5If3O/rce3zZPoX+T+d5F8l+Y59PvbAH2a70k8W9RovqZ/YJwvu97N27sotBrn5+7tw7CxinxxkexbR35Zx7LSspStt3KjvZRrefzLT76r+UJL/61Ze62VcbssRNJt96h8kuZjkTZn+5eONl++vs72+jDbq3Axns0V/lOT/b+/Mw3Yby/7/+e69jW1zyDxsUsZdG5U0i1RK4n2LI2QWJb1Kr0q94UeGsslQbWxJmQslQ4Reb1GKDKEBDSIpRW1snL8/ruvee+3bPT7rep77fO7n/B7HfTxrXeta3/U9z3Ve61nXuqZ1JJ1NCpg/9KKjFE+DI8+gdQxpAP19pC+A1/fDkTf/Dqwh6QxS95bbe+XwqGW83+OScdLgGWnZ8WaPt1iry1PKHi8+aXDFs3pBW/JmLd+a2XNm9jiwNXCemb0n86vbuRU0tCxGGrv0iJnt2guPVWZ3zklPk3xyLrCFmf27H3sGGSf5mKtnW117SnB49EmhuIc0tvQ44P68vaWZPdyHFjdlZ9DPgioG/az2osULRwvOZ5Vm5/87KV4OJM2ye0Qv548FxmWltHGzgNcA37I8BXI/hacARyPfc8BZpCm7d+tXRwkeS+tJNYJyI+ACM9tpBFoAniB9PXnMzD7YL4c3LXlz3N/jUlpKlJ1CWoYq7kvwlLDHm0/iWd0WbX3b/LetGGld4ObKy+Ok6stJNx7LyyUAd5LWo/tYK54uKPIy6yROwMmzDSaeT0rFfS/oVpHzUnbG2bPA07PaixYvHPOQK6aTgI8BF5nZLpnLR33QHDTXtvpB1+4TawCfquy3mnFqLDg+SnqoteUoxdOOu3L8JcDeNTl2Ai7swR4XWobwHo8FR1efOLPHRayV4hkje8bSJ/GsLuRb0lqAi/TK0y5fPzws2A2smUfVvx04jgXOdnCPx9uzLXxi5eK+qqEHLesCX6qjZTTLjrdnQcl4KxFrnrR44agce8HM+y3ybNRvbI3Fr1FoXEDSSsBcM/tr3pf1IFALLiY7phySpprZk80cpXgkTQMws99W8na1qTSHJy1DeI/HlKOdT5zZ4yLWPGnxwpH341n9Qi21fCtpFmlCpAeBu83spHx8iuUuf620t+Drlad5fwGtkpYCnjKzp7tdV9JqlruTDfIet+Pol8f7s3qYfFIq7nOenmO2lV+8lB2Hz4Jx+aweTS1eOCrHdwOmAFea2Z8q6c33suP+oOGmUirpG6QpnZ8Ebjezz+X05oCcbPO7GgySo1shrs0j6QLSAPappMHa/2Nm/2qhuZM9vXIsoKMGz6hqGcJ7PJYcbX3izB4XsVaKZ4ztGQufxLOaEcdsS99K+gSwFbAr8FLSuqaXm9kn8vG1gfea2XGtfDEKPLVfZr3ESZ887p/Vw+STUvGa89aqyHkpOw6fBePxWT2qWrxwVNLOJk3m9Etgc+Bk4IeWx0lLWtbM/tbKH+5gDpprgX2Aa0h97NcF7gaOrRxfFzhhPHAU1LIDcHXefglwJWlg/ioVjnNHm8OTliG8xy44PGnxEmuetHjhcBYnnuK+xP15H3BoZX8F4DfAkXn/5aQF1LcdbR7SzI5XZ1teD/yqySdrAx+fYPd4aDg8aSkY9yVi1kvZ8fQscBEnnrR44ajkXR34fmV/R9LY0z1J65suDnwfeH8vfIP++RjYmr4o/QaQmf2aVJhfJ+n4fPxhYG1J+48DjlI8DwHPSnqxpa8d7yN9fTsYIPOuIOmYUebwpGXY7rEXDk9avMSaJy1eOMBPnHiK+xK+nQNsJWmxfM5fspY3SnojaRblm0kvGJ1Qguf3wLVm9rCZ3UhaeH0HSUfm44sAm0jatgPHsN3jYeLwpKVU3JeIWS9lx9OzwEuceNLihQMAM/s98IykvfP+ReR1sElL4PwbmAkcojQhlmt4qZT+DViW9BUJS/2r3w68U9LOZvYEaV23f0ma7JyjFM8fgAeA6ZIWtTRT3P7AmyUdkvMcADwgadFR5PCkZdjusRcOT1q8xJonLV44wE+ceIr72r41s0tJLyr/qzwLopk9RPqavoSlrmiXkLoGt0UhnhIvs8N2j4eJw42WUnFPgZj1UnacPQtcxIkzLV44kObNvnshsLqkzTPf5cBNwExJi5nZlaR1Wv134TUHzbWWmpyPBK4l9YtuLPC6B7BH3l4OWHY8cBTUcgBpqvAtgaVz2tbAYXl7cdLDZVQ5PGkZwnvsgsOTFi+x5kmLFw5nceIp7kfs28Y18/Y5wI9Jy/lMBX4AfLSbDSV58vmzgFupzMpIeqnZLm+vAqw4we7x0HB40EJlhtC68Vo3ZktqqaljUgkdpXg8xIlHLYPmgAVn6gVWBr4IfB54fSX9QmCp5pjw/Bu8gAX/kZ4AXAFsT5rK+Rrg8PHCUYenGmRN2/8FnEtaU+iVpPFKXxgtjhack+ryFOKo/tMY6T2uzVFQy6QCHAON2WGL+9Llx1HZqc1RMO5dcLTwy0i19B0nwKbA9Ka0hSrbRwJnANcDsztcuwhP0/kjqtg2+WFEfi3B4U2LN45BaiF9sNm8Ka1alvuO1xYcPcXsaGgZoY69gVfU1VGKx0OceNYyUg7yUjuASvmkmQ9YNd/nWcCZwHdJ65D2xOXlN+az70raBvg38FMzeyqnLWRmc/P2/qQm7U2AB8xsH68cBbUsbGbPqDJjmBacNvpdwPqkRYB/b2YfHg2OnO9NwFzgFjN7ZoRaSnC8B/gncJuZPdaCoxe/1uYoqOWTpMWtf1hJ65fDU8wOW9yX0OKl7NTmyPlclMGC5bhEGfwa6aXupjYcHX0r6VJgYWAacD5wo5ldk48tWilLLwKWMbM/Nl+jMM+mwLNmdlslrVqOjwRWytd5wMx2b+GThcxsbpMf+vVrbQ5PWiRtTKrY3l5JG3MOL1oknUmaaGc90lqLn6kcW8TmL53SMV5z2pbAM2Z2SyWt+tzuGLOltBTQ8Tbg26QK7Jlm9pOcLmDhPnSU4jkGuMDMfl5J6zdOanPkfGeRPupdUnkWVX3bixYvHGeQys4edXyS8x5H+shxF/BjM7s13+dJZvac0lJEywHvAuaY2Vfyea6WfekIG8MaMKngXEHqU38CsGfl2KJNeZerbE/yxlFQyzeAG5nfxF79ujWliWPqaHHk/a8D3wPuAI6i0t2sDy0lOM7IHDeSBmhX7VmkiaOdX2tzFNSyBeml+mrgbZV09cHhKWaHLe5LaPFSdmpzeCqDJTgKlsGTSEsq0Pwjvfx19C0wgzxLIqkL32HA8cC7ms5dpem6zV21SvFcSpqV8T7gCOCtlWOLVrZfBKzaxienAWc37G06tlDT9dr5tTaHJy35/MuBPwKfajpnzDi8aCEtRfKdvL0GcB55Zup+4jWnNVqAfg0c0XRskcp2y5gtpaWujry9PPAt4MM57maM0Ce1eUjPtm83n5ePNT/bOj0fa3Hk/VNJ7xcLteDp9f3CC8dJwI9IExdt23ROz+U4p51M+l+4PXAo6f1rm3xscnP+Tlyef2N3ofS1qPGPdGnSDIWnAgc25XsFC3ZBkjeOglo+RhpkPpPUt3ypVgEGvI0FXxKKcuT9Y8kvXKSvezcCu7W4j520lOA4Dbgsby8JXAy8rIXeTn6tzVGYZ0q+P7sBlwFbtfDJeInZYYv7Elq8lJ3aHJ7KYAmOgmVwJqlXQGN/fdKSCkv2cX82IVUAG125ls/xdwywdk7bH9i9WVvTNWrzUKBiS/rocQ9pLNMsWlTkevBrbQ5PWoATgUvz9jqkFo139xlrtTk8aSG9RDcqhZ8lzTp6HXA4sFFO34/ucV+7QllCSwkdeX8R0kfEnYFDsq9PZH5F40PdfFKCh/RR9pbK/mrA6n3e49ocFVvOYX7ZewOpq/WGfWjxwvFVciWd9H/n6Mzb17Otkn4k8Nq8vRDwH8CdwJsreQ6kqRv3ePtNYmyxrqSXW5qd8Huk/tPrKs1KhqQdSTd9buMEy552yFGC5zrSmlUHkYLr25KWssqiv5K2AF5suTvWKHEA3AL8dz72Z1ILxUuqGXJ3lU48JTjOIxU2gINIL08zgaMlbZc53ktnv5bgKMkjUnehp0ld7Q6UdK6k92eOnUj/EMdDzJbg8BT3JXi8lJ0SHOCnDJYqf1C/DN4CLCNpmqR9gFNILUhHSnp75ng96Wt3S99a6vp4HrCrpBXM7FFSC8dawHY52+lmNruF/nkoxPMsME3Samb2J9JMjw8BW0haO9uzP6n1dF4XvyafXE56gZ9J6so/U9JUM3s+dylrdL3euINfS3C40KI0s/JdpBYrzOw3pMrtklWhknagTayV4PCmhfRh6XlJKwIvBd4C7EL6wLljzvPVbnFPatXfIW/vThpTfY6kwyVtlLXsR+eYLaGlto7cffNp4H7SpEgnA68mPe+eyNm6PgsK8dwErCNpOUm7kJ4FV0g6WtIG+TrdnrO1OZRmCF4cWBNYMT+TjyYtrXWwpA/lfDu043HEsTxpqMh78qFHSLMwr5Djb1KFo+O7XyMvabjGfjnPXDO7gPQhbV9JK0laBrjTzH7BeIaNYQ2YtLDwWeQvKKQpkb8AfKZxL8cLR0Etk/PfhUlft37YOA94WY86SnBMZcGvNR8Ezq7sd5xpsRRHJe9i2bcrk/4JHg58sc/7U5ujLk/lPuzL/K9c1wGPAu8YpzE7NHFfgsdL2SlZ/urGvSeOgmVwV9KL3k9zzK+QtRzfh0+3IrVIHgysnNO2IH1AaNt6PRo8pJkaP0V6UYLUun4+cFA/9yfnfWkuO2dV0pbr9fxSHIPWQvoIVC2DH2/Eat6fMhYc3rRU8le5Ns/lutd4bbRwrkiaUGyNHLNnA/9TzTOaWkrqAN5PqvSfTuqO+UlgNpX/O72Uw7o8pGfj88Bt2a5p+fzP9qGhNkfO9xHS0IJLSM/+hUmV7FP70OKCo5oP+HLmWqT5WI8xOpn04aFa/lbOcbdWq2uOx9+YtJQ2vjLmG/J70iKua5nZ37JDp+fWCWvK746jJA+A5RYZSxOSHEZ6oFwj6Qbmf/XuiEIcT1qa3KGh9Z/AP7L+C0lrKI06R4VrDmlq7IfM7J/ABcBqkpbsxa+lOOryNI6TWiG2V5pkYWHSmIBDJb1yvMTsMMZ9CR4vZadk+ct8LspgXY5SZdDMvk5qTdnfzP5maQ3Ci0nrwy3RyYaKlh8AN5Be2k5Uarn+DPC4dW69Hg2eG4FlgF0krWypdX0msLHSZEn9PCPvI3VXe0TSV3PZeW/j+FhxDFqLmT3cVAbnkFpdkXQx8O6x4PCmpcI3t7L7MeAvvcar5VZHM3uE1B31wRyzp5DK4KI2f/KYUdNSWMdvgf8kdbt/FanL6MVmdk83HSV4Ks+2r5CebfuZ2SNm9ltSN+XVlSZ/6uX5OGKOpvSLSRXbLYB18//lG0ktsav2+I4yUI5myvz3ZNI6oWvl8yZ30XEaMEvSVJj3nrIdMCMfw9K6s2uThpPMQ48x4xKDmH13OmlmqG1IY2AOBO6zNjNAeuYoyVPhE/AYcKWZ7TxAjg1JX9sWJs3itdsgOCpc3yT90/joIDlGyiNpVdI/rUVJE62YpDeY2Q19cEzHScwOa9yX4PFSdkqWv8znogyOlKNOGZRaz17Yj5Yqh6S1gHcC04G/m9khna4zGjw533bAa0kvNieRWk7vNrP/6nZuG761SF2drzWz9w2Kw4sWSa8jjWNcnvSuOJJyXJvDixalrojLkSr8j5nZXjl9xLODSjoPeNDMDh2klho6diSN253blN6XjpHydHi2nU+aDbarPSU4ms5bj7T280bA50itwI+Y2QHjjaPCNYU0zOJxM9u7S96jSB/BriD1DvqomT2Zjy2Z0+8ntUbfb2a79KvHLWwAzbOkwb4fJL3MHlVJ76e7kAuOkjz5nC8C51b2+545qxDHpqSuGKeMlKcuB9D4p3EpcMYI73FtjoJa3sz8RZI1Qg43MTuMcV+Cx0PZKcjhogwWLMe1y2DOvzhpwqSOWrrxUpmVstO9KcXTio/05f7DpK6/x/d6zVZ8pO6M548k3kpwONSydYEyWJtjrLV0ip0cb++vacskUsX428CsTtcdTS11dDRfi967ZRfh6cA/FfhOt2fbGHAsQhrv+1ng8yPhccTR6PK9PnBsD/lfDbyR1DX8JNKzeYEZ3UkT7b2hXRyM19/oko/gpnnlGCstwHpjwdGNh9SHftdueceAY2ngnXV80ivHaGlpsd92+u6SsVaKZyLFfQktjspOTxw9HBuzMjha5bh0GSTNfrhNsxZgT9KEFpu109lKdwt9RXh6PZaPd63Y9sAxfSw4PGnp4vPVgQ93y1uCY5BaasRrxxfpLlpaVihHQ8tY6mjBX4SnD3tWBHbsxl2CoxtPr/donHL0Oia11fj2l9S5/55/RbvvSjqYNNbsSTO7Kqc1+ptb3p9kTYshe+QYtJamLlq1OWryzEsbJo5Ba2niG/cxW4LDWdyX0BJx79ieJs5aWpTGqk4Dfkz6yv0NM/tyJd8KlsahduxSV5BnT9I6inPM7Kdt/NYt7nvheIGGpntcm8OTlhp+LcrhRUuf8dqtDLrQMsY6PD8L3MRsCXsGxDHirukVjvVJE+0tC6wHnGNms+pwuoUVqt2SBjTfAHwUeAA4pOn4KpXtdl/oXHB40jIge9p9BRoajgFp8XSPXcfsOPZJxL1ve0blHpNeHH9Ebm0krSX6MGksEKTuvqcBB7ezozDPmdmeY4Cf8MJ1hFfowScuODxp6ZOjXdzX5vCipVS8etLiSMdQPQs8afHC0c+P1CL/KHBeXS7PvzIkaTrs60jr3wGsC9wOfCLvTyGNzTjOO4cnLWFP+GQi2hM+CZ+MQ3uWJo37WbuStj6pgrtz3t8aOILKshOjwUOBl1kvHJ60eOHwpKVEvHrS4kWHM3uGLWZdcPT6gzLj28fLrwxJGnQ7MxeQxiQS6wEPkqbPh9Qv+lSa1tPxxuFJS9gTPpmI9oRPwifjzZ6c5xTge01p72L+unZrkF4gF2vHUYKHMi+zLjg8afHC4VBL7bj3osWLDk/2DFvMeuFowVlkrP14/xVZp9RS/+mHgL2AJXLavcD7gG0lLU1qdr4t/3XL4UlL2BM+mYj2hE/CJ+PJHqWlJbC0TMAUSVdXDt8BrCRpcTN7EDjS0rqrL0ApHjN7HHiKtC5eI+1u0mLwW+akexuX9czhSYsXDi9aSsWrJy1edHixpxSHJy1eOCTtKen1kjbL51vjvlfyzDvXzG7LaV3nFhnXqFurpVK7B2aTpsZeOe8vAnwXWC7vt5yq2guHJy1hT/hkItoTPgmfjAd7mvmozOZLWrbmO6T17b5HZVmEFtcvwlM5Z1Jl+yrg6sr+Wtm+xRv2eebwpMULx6C1lI5XT1oGrcOLPcMWs045xnQ86nj6jXj2XUmTzey5Ftunkb42P0JaR+dxa7MAvRcOT1rCnuH2SfNMbDV01OYZJg5PWrxweNIyTPZIOoLUfetWM5tdSZ9iZs/m7V2BFwErmdnhba5bimde/hb2XAoYcA3wduBhM9uzhU9ccHjS4oXDi5ZS8epJiyMdLuwpxeFJixeOyrnTSB9A32Jmz0jahFS5PcbMTpS0OHACcJ+Zfakdz7Ci70qppO3M7PK8XZ0uunqT3gS8hLSWzpdy2rzC44XDk5awZ0L45DDSP55fmNm3Kvr6tac2zzBxeNLihcOTlmGyR9LpwArALOB84K1m9hO6QC9cJqAUT+2XWS8cnrR44fCipVS8etLiSIcLe0pxeNLihaMKpeEgJwBHmdnvctr6wBXAYWb2TUlbA6/LeZ5q5hhqWB/NqsA3gT8AJ1bSJjXuXYfzJnnj8KQl7JkQPvkqqQvOzsA9wAcqx/qxpzbPMHF40uKFw5OWYbIH2AH4biX9K8BBwDbA8pX0Pah0v2rBV4rndOAS0tf5J4BXt8vbwScuODxp8cLhRUupePWkxZEOF/YMW8x64mhzvMhEWMP46z0jbAr8kLSA75dZ8AW92vd9L+Dlnjk8aQl7JoRPdgCuqOxvTVoMezEqL8PAnl3sqc0zTByetHjh8KRl2OzJxxfJf/cC5gD7kyq6nwEWIn0t37fd+aV4KPAy64XDkxYvHA611I57L1q86PBkz7DFrBeOFpxFxugO86+/zLA6afKH6cDXgBOBhRrOBhYGdhgPHJ60hD3D7RNS148N8/bCwCqkF+IlK3kW6UFHbZ5h4vCkxQuHJy3DYg/wAeC0prTpwCp5+2WkltdXNOXRaPBU9ea/dSoKLjg8afHCMWgtpePVk5ZB6/Biz7DFrFOO4pODDfOve4ZUeL7S7FRgY9I6PUfntF3JNfy8L28cnrSEPRPGJ6fk7eYH01WNvMD2Tcda2VOLZ5g4PGnxwuFJyxDa82Lgc+QKLC26ZgGXATOa05vy1OahwMusFw5PWrxweNJSIl49afGiw5k9wxazLjgqaUeQlo3ZvSm9OpP7rqTK7uc7cU2UXy/rlH4feEjSkkBjcPVz+aYcCSwr6V/AO8zs342TLHvWGYcnLWHPxPDJo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prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.633364Z","iopub.execute_input":"2023-02-01T14:51:55.633706Z","iopub.status.idle":"2023-02-01T14:51:55.655017Z","shell.execute_reply.started":"2023-02-01T14:51:55.633675Z","shell.execute_reply":"2023-02-01T14:51:55.653820Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.656793Z","iopub.execute_input":"2023-02-01T14:51:55.657669Z","iopub.status.idle":"2023-02-01T14:51:55.680263Z","shell.execute_reply.started":"2023-02-01T14:51:55.657616Z","shell.execute_reply":"2023-02-01T14:51:55.679008Z"},"trusted":true},"execution_count":243,"outputs":[{"execution_count":243,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 1.0 2\n 2.0 1.0 10\n 1.0 1.0 0.0 6\n 1.0 1\n 2.0 1.0 19\n 2.0 1.0 0.0 5\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 13\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 5\n 1.0 2\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 93\n 2.0 0.0 5\n 1.0 7\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 2.0 1.0 0.0 5\n 1.0 1\n 2.0 0.0 3\n 1.0 3\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 3\n 2.0 0.0 3\n 6.0 1.0 1.0 1\n 2.0 0.0 3\n 7.0 1.0 0.0 3\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.681765Z","iopub.execute_input":"2023-02-01T14:51:55.682091Z","iopub.status.idle":"2023-02-01T14:51:56.352496Z","shell.execute_reply.started":"2023-02-01T14:51:55.682062Z","shell.execute_reply":"2023-02-01T14:51:56.351351Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.353913Z","iopub.execute_input":"2023-02-01T14:51:56.355590Z","iopub.status.idle":"2023-02-01T14:51:56.788043Z","shell.execute_reply.started":"2023-02-01T14:51:56.355547Z","shell.execute_reply":"2023-02-01T14:51:56.786828Z"},"trusted":true},"execution_count":245,"outputs":[{"execution_count":245,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.789229Z","iopub.execute_input":"2023-02-01T14:51:56.789583Z","iopub.status.idle":"2023-02-01T14:51:57.215295Z","shell.execute_reply.started":"2023-02-01T14:51:56.789553Z","shell.execute_reply":"2023-02-01T14:51:57.214488Z"},"trusted":true},"execution_count":246,"outputs":[{"execution_count":246,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.216805Z","iopub.execute_input":"2023-02-01T14:51:57.217226Z","iopub.status.idle":"2023-02-01T14:51:57.574988Z","shell.execute_reply.started":"2023-02-01T14:51:57.217185Z","shell.execute_reply":"2023-02-01T14:51:57.574210Z"},"trusted":true},"execution_count":247,"outputs":[{"execution_count":247,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.576156Z","iopub.execute_input":"2023-02-01T14:51:57.576637Z","iopub.status.idle":"2023-02-01T14:51:57.924867Z","shell.execute_reply.started":"2023-02-01T14:51:57.576603Z","shell.execute_reply":"2023-02-01T14:51:57.923105Z"},"trusted":true},"execution_count":248,"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. Nonetheless, the various distribution of age and fare may lower the accuracy of the validation and testing datasets.","metadata":{}},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = rf.predict(X_test)\nrandom_forrest_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"rf_y_pred\": y_pred})\nrandom_forrest_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.926719Z","iopub.execute_input":"2023-02-01T14:51:57.927152Z","iopub.status.idle":"2023-02-01T14:51:57.950525Z","shell.execute_reply.started":"2023-02-01T14:51:57.927100Z","shell.execute_reply":"2023-02-01T14:51:57.949359Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.951752Z","iopub.execute_input":"2023-02-01T14:51:57.952061Z","iopub.status.idle":"2023-02-01T14:51:57.958199Z","shell.execute_reply.started":"2023-02-01T14:51:57.952032Z","shell.execute_reply":"2023-02-01T14:51:57.956976Z"},"trusted":true},"execution_count":250,"outputs":[]},{"cell_type":"code","source":"random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.959366Z","iopub.execute_input":"2023-02-01T14:51:57.960119Z","iopub.status.idle":"2023-02-01T14:51:57.977269Z","shell.execute_reply.started":"2023-02-01T14:51:57.960080Z","shell.execute_reply":"2023-02-01T14:51:57.976084Z"},"trusted":true},"execution_count":251,"outputs":[{"execution_count":251,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.978846Z","iopub.execute_input":"2023-02-01T14:51:57.979227Z","iopub.status.idle":"2023-02-01T14:51:58.007917Z","shell.execute_reply.started":"2023-02-01T14:51:57.979179Z","shell.execute_reply":"2023-02-01T14:51:58.006694Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 0.0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n4 0.0 1.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Neural AI \nIn this section we use some neural network to classify the data. We prepare the data so that it is more suitable for neural networks. We apply cross validation. ","metadata":{"execution":{"iopub.status.busy":"2023-01-09T16:59:50.819233Z","iopub.execute_input":"2023-01-09T16:59:50.819762Z","iopub.status.idle":"2023-01-09T16:59:50.825788Z","shell.execute_reply.started":"2023-01-09T16:59:50.819721Z","shell.execute_reply":"2023-01-09T16:59:50.823990Z"}}},{"cell_type":"markdown","source":"## Prepare data for Neural-AI","metadata":{"execution":{"iopub.status.busy":"2022-12-07T15:38:00.160610Z","iopub.execute_input":"2022-12-07T15:38:00.161030Z","iopub.status.idle":"2022-12-07T15:38:00.169322Z","shell.execute_reply.started":"2022-12-07T15:38:00.160998Z","shell.execute_reply":"2022-12-07T15:38:00.167957Z"}}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:58.009483Z","iopub.execute_input":"2023-02-01T14:51:58.009908Z","iopub.status.idle":"2023-02-01T14:51:58.023101Z","shell.execute_reply.started":"2023-02-01T14:51:58.009868Z","shell.execute_reply":"2023-02-01T14:51:58.021915Z"},"trusted":true},"execution_count":253,"outputs":[{"execution_count":253,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.431458Z","iopub.execute_input":"2023-02-01T14:55:47.431870Z","iopub.status.idle":"2023-02-01T14:55:47.444617Z","shell.execute_reply.started":"2023-02-01T14:55:47.431840Z","shell.execute_reply":"2023-02-01T14:55:47.443399Z"},"trusted":true},"execution_count":254,"outputs":[{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.696681Z","iopub.execute_input":"2023-02-01T14:55:47.697091Z","iopub.status.idle":"2023-02-01T14:55:47.706759Z","shell.execute_reply.started":"2023-02-01T14:55:47.697056Z","shell.execute_reply":"2023-02-01T14:55:47.705377Z"},"trusted":true},"execution_count":255,"outputs":[{"execution_count":255,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.965238Z","iopub.execute_input":"2023-02-01T14:55:47.965693Z","iopub.status.idle":"2023-02-01T14:55:47.976964Z","shell.execute_reply.started":"2023-02-01T14:55:47.965657Z","shell.execute_reply":"2023-02-01T14:55:47.975774Z"},"trusted":true},"execution_count":256,"outputs":[{"execution_count":256,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"I propose to keep Pclass,Sex, Age, SibSP,Parch,Ticket, Fare,Cabin, Embarked, Survived","metadata":{}},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked', 'Survived']\ntitanic_train = titanic_train.loc[:,columns_to_keep]\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.596834Z","iopub.execute_input":"2023-02-01T14:59:41.597224Z","iopub.status.idle":"2023-02-01T14:59:41.617029Z","shell.execute_reply.started":"2023-02-01T14:59:41.597192Z","shell.execute_reply":"2023-02-01T14:59:41.615728Z"},"trusted":true},"execution_count":259,"outputs":[{"execution_count":259,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name \\\n0 1 3 Braund, Mr. Owen Harris \n1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n2 3 3 Heikkinen, Miss. Laina \n3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n4 5 3 Allen, Mr. William Henry \n\n Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n1 female 38.0 1 0 PC 17599 71.2833 C85 C \n2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n3 female 35.0 1 0 113803 53.1000 C123 S \n4 male 35.0 0 0 373450 8.0500 NaN S \n\n Survived \n0 0 \n1 1 \n2 1 \n3 1 \n4 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
013Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
233Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
453Allen, Mr. William Henrymale35.0003734508.0500NaNS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked']\ntitanic_test = titanic_test.loc[:,columns_to_keep]\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.783983Z","iopub.execute_input":"2023-02-01T14:59:41.784720Z","iopub.status.idle":"2023-02-01T14:59:41.804682Z","shell.execute_reply.started":"2023-02-01T14:59:41.784681Z","shell.execute_reply":"2023-02-01T14:59:41.803270Z"},"trusted":true},"execution_count":260,"outputs":[{"execution_count":260,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Passengers ID\nTransforms to float","metadata":{}},{"cell_type":"code","source":"\ntitanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.301290Z","iopub.execute_input":"2023-02-01T14:59:42.302052Z","iopub.status.idle":"2023-02-01T14:59:42.309717Z","shell.execute_reply.started":"2023-02-01T14:59:42.302008Z","shell.execute_reply":"2023-02-01T14:59:42.308660Z"},"trusted":true},"execution_count":261,"outputs":[]},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.953004Z","iopub.execute_input":"2023-02-01T14:59:42.953443Z","iopub.status.idle":"2023-02-01T14:59:42.961302Z","shell.execute_reply.started":"2023-02-01T14:59:42.953406Z","shell.execute_reply":"2023-02-01T14:59:42.960093Z"},"trusted":true},"execution_count":262,"outputs":[{"execution_count":262,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.133436Z","iopub.execute_input":"2023-02-01T14:59:43.133821Z","iopub.status.idle":"2023-02-01T14:59:43.144899Z","shell.execute_reply.started":"2023-02-01T14:59:43.133790Z","shell.execute_reply":"2023-02-01T14:59:43.143759Z"},"trusted":true},"execution_count":263,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.317702Z","iopub.execute_input":"2023-02-01T14:59:43.318112Z","iopub.status.idle":"2023-02-01T14:59:43.329982Z","shell.execute_reply.started":"2023-02-01T14:59:43.318070Z","shell.execute_reply":"2023-02-01T14:59:43.328608Z"},"trusted":true},"execution_count":264,"outputs":[{"execution_count":264,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.526826Z","iopub.execute_input":"2023-02-01T14:59:43.527875Z","iopub.status.idle":"2023-02-01T14:59:43.538926Z","shell.execute_reply.started":"2023-02-01T14:59:43.527837Z","shell.execute_reply":"2023-02-01T14:59:43.538137Z"},"trusted":true},"execution_count":265,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.734794Z","iopub.execute_input":"2023-02-01T14:59:43.735200Z","iopub.status.idle":"2023-02-01T14:59:43.749137Z","shell.execute_reply.started":"2023-02-01T14:59:43.735165Z","shell.execute_reply":"2023-02-01T14:59:43.747731Z"},"trusted":true},"execution_count":266,"outputs":[{"execution_count":266,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.969440Z","iopub.execute_input":"2023-02-01T14:59:43.970219Z","iopub.status.idle":"2023-02-01T14:59:43.982089Z","shell.execute_reply.started":"2023-02-01T14:59:43.970157Z","shell.execute_reply":"2023-02-01T14:59:43.980764Z"},"trusted":true},"execution_count":267,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.130067Z","iopub.execute_input":"2023-02-01T14:59:44.130855Z","iopub.status.idle":"2023-02-01T14:59:44.141446Z","shell.execute_reply.started":"2023-02-01T14:59:44.130814Z","shell.execute_reply":"2023-02-01T14:59:44.140366Z"},"trusted":true},"execution_count":268,"outputs":[{"execution_count":268,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.375519Z","iopub.execute_input":"2023-02-01T14:59:44.376720Z","iopub.status.idle":"2023-02-01T14:59:44.387800Z","shell.execute_reply.started":"2023-02-01T14:59:44.376665Z","shell.execute_reply":"2023-02-01T14:59:44.386558Z"},"trusted":true},"execution_count":269,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.523725Z","iopub.execute_input":"2023-02-01T14:59:44.524363Z","iopub.status.idle":"2023-02-01T14:59:44.536192Z","shell.execute_reply.started":"2023-02-01T14:59:44.524322Z","shell.execute_reply":"2023-02-01T14:59:44.535041Z"},"trusted":true},"execution_count":270,"outputs":[{"execution_count":270,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.806439Z","iopub.execute_input":"2023-02-01T14:59:44.806827Z","iopub.status.idle":"2023-02-01T14:59:44.814441Z","shell.execute_reply.started":"2023-02-01T14:59:44.806794Z","shell.execute_reply":"2023-02-01T14:59:44.813111Z"},"trusted":true},"execution_count":271,"outputs":[{"execution_count":271,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.150811Z","iopub.execute_input":"2023-02-01T14:59:45.151188Z","iopub.status.idle":"2023-02-01T14:59:45.159387Z","shell.execute_reply.started":"2023-02-01T14:59:45.151156Z","shell.execute_reply":"2023-02-01T14:59:45.158248Z"},"trusted":true},"execution_count":272,"outputs":[{"execution_count":272,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.400597Z","iopub.execute_input":"2023-02-01T14:59:45.401226Z","iopub.status.idle":"2023-02-01T14:59:45.410601Z","shell.execute_reply.started":"2023-02-01T14:59:45.401186Z","shell.execute_reply":"2023-02-01T14:59:45.409380Z"},"trusted":true},"execution_count":273,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.540502Z","iopub.execute_input":"2023-02-01T14:59:45.541816Z","iopub.status.idle":"2023-02-01T14:59:45.555066Z","shell.execute_reply.started":"2023-02-01T14:59:45.541649Z","shell.execute_reply":"2023-02-01T14:59:45.553893Z"},"trusted":true},"execution_count":274,"outputs":[{"execution_count":274,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.765213Z","iopub.execute_input":"2023-02-01T14:59:45.766189Z","iopub.status.idle":"2023-02-01T14:59:45.777960Z","shell.execute_reply.started":"2023-02-01T14:59:45.766144Z","shell.execute_reply":"2023-02-01T14:59:45.776759Z"},"trusted":true},"execution_count":275,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.007744Z","iopub.execute_input":"2023-02-01T14:59:46.008172Z","iopub.status.idle":"2023-02-01T14:59:46.020782Z","shell.execute_reply.started":"2023-02-01T14:59:46.008134Z","shell.execute_reply":"2023-02-01T14:59:46.019374Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.158566Z","iopub.execute_input":"2023-02-01T14:59:46.158955Z","iopub.status.idle":"2023-02-01T14:59:46.171385Z","shell.execute_reply.started":"2023-02-01T14:59:46.158921Z","shell.execute_reply":"2023-02-01T14:59:46.170377Z"},"trusted":true},"execution_count":277,"outputs":[{"execution_count":277,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.365352Z","iopub.execute_input":"2023-02-01T14:59:46.365774Z","iopub.status.idle":"2023-02-01T14:59:46.377504Z","shell.execute_reply.started":"2023-02-01T14:59:46.365737Z","shell.execute_reply":"2023-02-01T14:59:46.376059Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.591674Z","iopub.execute_input":"2023-02-01T14:59:46.592065Z","iopub.status.idle":"2023-02-01T14:59:46.602473Z","shell.execute_reply.started":"2023-02-01T14:59:46.592030Z","shell.execute_reply":"2023-02-01T14:59:46.601375Z"},"trusted":true},"execution_count":279,"outputs":[{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.828954Z","iopub.execute_input":"2023-02-01T14:59:46.829390Z","iopub.status.idle":"2023-02-01T14:59:46.840546Z","shell.execute_reply.started":"2023-02-01T14:59:46.829349Z","shell.execute_reply":"2023-02-01T14:59:46.839434Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.034899Z","iopub.execute_input":"2023-02-01T14:59:47.036005Z","iopub.status.idle":"2023-02-01T14:59:47.045477Z","shell.execute_reply.started":"2023-02-01T14:59:47.035966Z","shell.execute_reply":"2023-02-01T14:59:47.044685Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.171565Z","iopub.execute_input":"2023-02-01T14:59:47.172636Z","iopub.status.idle":"2023-02-01T14:59:47.179309Z","shell.execute_reply.started":"2023-02-01T14:59:47.172596Z","shell.execute_reply":"2023-02-01T14:59:47.178195Z"},"trusted":true},"execution_count":282,"outputs":[{"execution_count":282,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.581953Z","iopub.execute_input":"2023-02-01T14:59:47.582616Z","iopub.status.idle":"2023-02-01T14:59:47.591105Z","shell.execute_reply.started":"2023-02-01T14:59:47.582574Z","shell.execute_reply":"2023-02-01T14:59:47.589952Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.831877Z","iopub.execute_input":"2023-02-01T14:59:47.832258Z","iopub.status.idle":"2023-02-01T14:59:47.839367Z","shell.execute_reply.started":"2023-02-01T14:59:47.832227Z","shell.execute_reply":"2023-02-01T14:59:47.838210Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.993877Z","iopub.execute_input":"2023-02-01T14:59:47.994253Z","iopub.status.idle":"2023-02-01T14:59:48.002543Z","shell.execute_reply.started":"2023-02-01T14:59:47.994221Z","shell.execute_reply":"2023-02-01T14:59:48.001550Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.201983Z","iopub.execute_input":"2023-02-01T14:59:48.202420Z","iopub.status.idle":"2023-02-01T14:59:48.212760Z","shell.execute_reply.started":"2023-02-01T14:59:48.202382Z","shell.execute_reply":"2023-02-01T14:59:48.211396Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_embarked_cat(data):\n factors = data['Embarked'].unique()\n gender_columns = pd.get_dummies(data['Embarked'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.431294Z","iopub.execute_input":"2023-02-01T14:59:48.432534Z","iopub.status.idle":"2023-02-01T14:59:48.437882Z","shell.execute_reply.started":"2023-02-01T14:59:48.432467Z","shell.execute_reply":"2023-02-01T14:59:48.437019Z"},"trusted":true},"execution_count":287,"outputs":[]},{"cell_type":"code","source":"\ntitanic_train = transform_embarked_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Embarked\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.629204Z","iopub.execute_input":"2023-02-01T14:59:48.629922Z","iopub.status.idle":"2023-02-01T14:59:48.642617Z","shell.execute_reply.started":"2023-02-01T14:59:48.629880Z","shell.execute_reply":"2023-02-01T14:59:48.641807Z"},"trusted":true},"execution_count":288,"outputs":[{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\ntitanic_test = transform_embarked_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Embarked\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.849824Z","iopub.execute_input":"2023-02-01T14:59:48.850216Z","iopub.status.idle":"2023-02-01T14:59:48.866727Z","shell.execute_reply.started":"2023-02-01T14:59:48.850182Z","shell.execute_reply":"2023-02-01T14:59:48.865657Z"},"trusted":true},"execution_count":289,"outputs":[{"execution_count":289,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"indices = range(0, titanic_test.shape[0])\ntitanic_test['U'] = [0 for i in indices]\ntitanic_test['U'] = titanic_test['U'].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.014240Z","iopub.execute_input":"2023-02-01T14:59:49.014659Z","iopub.status.idle":"2023-02-01T14:59:49.022051Z","shell.execute_reply.started":"2023-02-01T14:59:49.014622Z","shell.execute_reply":"2023-02-01T14:59:49.020812Z"},"trusted":true},"execution_count":290,"outputs":[]},{"cell_type":"markdown","source":"### Number of sibling","metadata":{}},{"cell_type":"code","source":"print(titanic_train[\"SibSp\"].describe())\nplt.hist(titanic_train[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.435498Z","iopub.execute_input":"2023-02-01T14:59:49.435873Z","iopub.status.idle":"2023-02-01T14:59:49.609979Z","shell.execute_reply.started":"2023-02-01T14:59:49.435843Z","shell.execute_reply":"2023-02-01T14:59:49.608818Z"},"trusted":true},"execution_count":291,"outputs":[{"name":"stdout","text":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":291,"output_type":"execute_result","data":{"text/plain":"(array([608., 209., 28., 16., 0., 18., 5., 0., 0., 7.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(titanic_test[\"SibSp\"].describe())\nplt.hist(titanic_test[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.640013Z","iopub.execute_input":"2023-02-01T14:59:49.640429Z","iopub.status.idle":"2023-02-01T14:59:50.199638Z","shell.execute_reply.started":"2023-02-01T14:59:49.640389Z","shell.execute_reply":"2023-02-01T14:59:50.198241Z"},"trusted":true},"execution_count":292,"outputs":[{"name":"stdout","text":"count 418.000000\nmean 0.447368\nstd 0.896760\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":292,"output_type":"execute_result","data":{"text/plain":"(array([283., 110., 14., 4., 0., 4., 1., 0., 0., 2.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def categorise_siblings(data):\n cut_labels_9 = ['sib_0','sib_1','sib_2','sib_3', \n 'sib_4','sib_5','sib_6','sib_7', 'sib_8']\n cut_bins = [0,1,2,3,4,5,6,7,8,9]\n data['Sib_cat'] = pd.cut(data['SibSp'], \n bins=cut_bins, \n labels=cut_labels_9)\n \n data['Sib_cat'] = data.Sib_cat.astype(str)\n data.loc[data[\"Sib_cat\"] == 'nan', \"Sib_cat\"] = \"Sib_Unknown\"\n \n return data\n\ndef transform_sibling_cat(data):\n factors = data['Sib_cat'].unique()\n gender_columns = pd.get_dummies(data['Sib_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.201993Z","iopub.execute_input":"2023-02-01T14:59:50.202490Z","iopub.status.idle":"2023-02-01T14:59:50.212938Z","shell.execute_reply.started":"2023-02-01T14:59:50.202445Z","shell.execute_reply":"2023-02-01T14:59:50.211676Z"},"trusted":true},"execution_count":293,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_siblings(titanic_train)\ntitanic_train = transform_sibling_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"SibSp\", axis = 1)\ntitanic_train = titanic_train.drop(\"Sib_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.214386Z","iopub.execute_input":"2023-02-01T14:59:50.214705Z","iopub.status.idle":"2023-02-01T14:59:50.237526Z","shell.execute_reply.started":"2023-02-01T14:59:50.214675Z","shell.execute_reply":"2023-02-01T14:59:50.236793Z"},"trusted":true},"execution_count":294,"outputs":[{"execution_count":294,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.431533Z","iopub.execute_input":"2023-02-01T14:59:50.432231Z","iopub.status.idle":"2023-02-01T14:59:50.438691Z","shell.execute_reply.started":"2023-02-01T14:59:50.432194Z","shell.execute_reply":"2023-02-01T14:59:50.437673Z"},"trusted":true},"execution_count":295,"outputs":[{"execution_count":295,"output_type":"execute_result","data":{"text/plain":"(891, 21)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_siblings(titanic_test)\ntitanic_test = transform_sibling_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"SibSp\", axis = 1)\ntitanic_test = titanic_test.drop(\"Sib_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.596205Z","iopub.execute_input":"2023-02-01T14:59:50.596606Z","iopub.status.idle":"2023-02-01T14:59:50.618154Z","shell.execute_reply.started":"2023-02-01T14:59:50.596574Z","shell.execute_reply":"2023-02-01T14:59:50.617093Z"},"trusted":true},"execution_count":296,"outputs":[{"execution_count":296,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.849255Z","iopub.execute_input":"2023-02-01T14:59:50.850520Z","iopub.status.idle":"2023-02-01T14:59:50.858028Z","shell.execute_reply.started":"2023-02-01T14:59:50.850477Z","shell.execute_reply":"2023-02-01T14:59:50.856953Z"},"trusted":true},"execution_count":297,"outputs":[{"execution_count":297,"output_type":"execute_result","data":{"text/plain":"(418, 20)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Transforming age into categories\nThe categorise the age into 9 categories; unknown and one for each decade. The categories are then transformed in hot_coding format. ","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.269486Z","iopub.execute_input":"2023-02-01T14:59:51.269885Z","iopub.status.idle":"2023-02-01T14:59:51.572232Z","shell.execute_reply.started":"2023-02-01T14:59:51.269851Z","shell.execute_reply":"2023-02-01T14:59:51.571214Z"},"trusted":true},"execution_count":298,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.573955Z","iopub.execute_input":"2023-02-01T14:59:51.574279Z","iopub.status.idle":"2023-02-01T14:59:51.588745Z","shell.execute_reply.started":"2023-02-01T14:59:51.574249Z","shell.execute_reply":"2023-02-01T14:59:51.587351Z"},"trusted":true},"execution_count":299,"outputs":[{"execution_count":299,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.763907Z","iopub.execute_input":"2023-02-01T14:59:51.764334Z","iopub.status.idle":"2023-02-01T14:59:52.129917Z","shell.execute_reply.started":"2023-02-01T14:59:51.764278Z","shell.execute_reply":"2023-02-01T14:59:52.128918Z"},"trusted":true},"execution_count":300,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.131621Z","iopub.execute_input":"2023-02-01T14:59:52.132130Z","iopub.status.idle":"2023-02-01T14:59:52.142285Z","shell.execute_reply.started":"2023-02-01T14:59:52.132091Z","shell.execute_reply":"2023-02-01T14:59:52.141264Z"},"trusted":true},"execution_count":301,"outputs":[{"execution_count":301,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"def transform_age_cat(data):\n factors = data['Age_cat'].unique()\n gender_columns = pd.get_dummies(data['Age_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.143629Z","iopub.execute_input":"2023-02-01T14:59:52.143919Z","iopub.status.idle":"2023-02-01T14:59:52.154584Z","shell.execute_reply.started":"2023-02-01T14:59:52.143891Z","shell.execute_reply":"2023-02-01T14:59:52.153409Z"},"trusted":true},"execution_count":302,"outputs":[]},{"cell_type":"code","source":"def categorise_age(data):\n cut_labels_8 = ['age_0-9','age_10-19','age_20-29','age_30-39', \n 'age_40-49','age_50-59','age_60-69','age_70-79']\n cut_bins = [0,10,20,30,40,50,60,70,80]\n data['Age_cat'] = pd.cut(data['Age'], \n bins=cut_bins, \n labels=cut_labels_8)\n data['Age_cat'] = data.Age_cat.astype(str)\n data.loc[data[\"Age\"].isna(), \"Age_cat\"] = \"Age_Unknown\"\n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.340509Z","iopub.execute_input":"2023-02-01T14:59:52.340896Z","iopub.status.idle":"2023-02-01T14:59:52.347606Z","shell.execute_reply.started":"2023-02-01T14:59:52.340863Z","shell.execute_reply":"2023-02-01T14:59:52.346572Z"},"trusted":true},"execution_count":303,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_age(titanic_train)\ntitanic_train = transform_age_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Age\", axis = 1)\ntitanic_train = titanic_train.drop(\"Age_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.546266Z","iopub.execute_input":"2023-02-01T14:59:52.546677Z","iopub.status.idle":"2023-02-01T14:59:52.572844Z","shell.execute_reply.started":"2023-02-01T14:59:52.546642Z","shell.execute_reply":"2023-02-01T14:59:52.571757Z"},"trusted":true},"execution_count":304,"outputs":[{"execution_count":304,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_age(titanic_test)\ntitanic_test = transform_age_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Age\", axis = 1)\ntitanic_test = titanic_test.drop(\"Age_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.811521Z","iopub.execute_input":"2023-02-01T14:59:52.812681Z","iopub.status.idle":"2023-02-01T14:59:52.836736Z","shell.execute_reply.started":"2023-02-01T14:59:52.812627Z","shell.execute_reply":"2023-02-01T14:59:52.835513Z"},"trusted":true},"execution_count":305,"outputs":[{"execution_count":305,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"### Gender transformation to hot-coding \nWe check the factor values are the same between both datasets. Then, we generate a hot coding of two columns; i.e., male and female. Both columns replace the Sex column.","metadata":{}},{"cell_type":"code","source":"titanic_train['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.188122Z","iopub.execute_input":"2023-02-01T14:59:53.189282Z","iopub.status.idle":"2023-02-01T14:59:53.197504Z","shell.execute_reply.started":"2023-02-01T14:59:53.189231Z","shell.execute_reply":"2023-02-01T14:59:53.196373Z"},"trusted":true},"execution_count":306,"outputs":[{"execution_count":306,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.420038Z","iopub.execute_input":"2023-02-01T14:59:53.420458Z","iopub.status.idle":"2023-02-01T14:59:53.428009Z","shell.execute_reply.started":"2023-02-01T14:59:53.420423Z","shell.execute_reply":"2023-02-01T14:59:53.426859Z"},"trusted":true},"execution_count":307,"outputs":[{"execution_count":307,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_gender(data):\n factors = data['Sex'].unique()\n gender_columns = pd.get_dummies(data['Sex'])\n columns = range(0,len(factors))\n \n for column in columns:\n data[factors[column]] = gender_columns.loc[:,factors[column]].astype(float)\n \n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.614253Z","iopub.execute_input":"2023-02-01T14:59:53.614984Z","iopub.status.idle":"2023-02-01T14:59:53.620854Z","shell.execute_reply.started":"2023-02-01T14:59:53.614945Z","shell.execute_reply":"2023-02-01T14:59:53.619727Z"},"trusted":true},"execution_count":308,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_gender(titanic_train)\ntitanic_train.drop(\"Sex\", axis = 1, inplace = True)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.853720Z","iopub.execute_input":"2023-02-01T14:59:53.854121Z","iopub.status.idle":"2023-02-01T14:59:53.868139Z","shell.execute_reply.started":"2023-02-01T14:59:53.854084Z","shell.execute_reply":"2023-02-01T14:59:53.867117Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_gender(titanic_test)\ntitanic_test.drop(\"Sex\", axis = 1,inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.077511Z","iopub.execute_input":"2023-02-01T14:59:54.078227Z","iopub.status.idle":"2023-02-01T14:59:54.117482Z","shell.execute_reply.started":"2023-02-01T14:59:54.078188Z","shell.execute_reply":"2023-02-01T14:59:54.116493Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Parch \\\n0 892.0 3 Kelly, Mr. James 0 \n1 893.0 3 Wilkes, Mrs. James (Ellen Needs) 0 \n2 894.0 2 Myles, Mr. Thomas Francis 0 \n3 895.0 3 Wirz, Mr. Albert 0 \n4 896.0 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 \n\n Ticket Fare Cabin Q S C ... age_30-39 age_40-49 \\\n0 330911 7.8292 NaN 1.0 0.0 0.0 ... 1.0 0.0 \n1 363272 7.0000 NaN 0.0 1.0 0.0 ... 0.0 1.0 \n2 240276 9.6875 NaN 1.0 0.0 0.0 ... 0.0 0.0 \n3 315154 8.6625 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n4 3101298 12.2875 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n\n age_60-69 age_20-29 age_10-19 age_50-59 age_0-9 age_70-79 male \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n\n female \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 1.0 \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameParchTicketFareCabinQSC...age_30-39age_40-49age_60-69age_20-29age_10-19age_50-59age_0-9age_70-79malefemale
0892.03Kelly, Mr. James03309117.8292NaN1.00.00.0...1.00.00.00.00.00.00.00.01.00.0
1893.03Wilkes, Mrs. James (Ellen Needs)03632727.0000NaN0.01.00.0...0.01.00.00.00.00.00.00.00.01.0
2894.02Myles, Mr. Thomas Francis02402769.6875NaN1.00.00.0...0.00.01.00.00.00.00.00.01.00.0
3895.03Wirz, Mr. Albert03151548.6625NaN0.01.00.0...0.00.00.01.00.00.00.00.01.00.0
4896.03Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.2875NaN0.01.00.0...0.00.00.01.00.00.00.00.00.01.0
\n

5 rows × 28 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Cabin and Pclass\n\nThe passenger class appears to drive whether a cabin is known. So, we propose to drop the cabin as the percentage of not known values is quite high. We apply an hot encoding the Pclass. ","metadata":{}},{"cell_type":"code","source":"titanic_train['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.494349Z","iopub.execute_input":"2023-02-01T14:59:54.494758Z","iopub.status.idle":"2023-02-01T14:59:54.503695Z","shell.execute_reply.started":"2023-02-01T14:59:54.494724Z","shell.execute_reply":"2023-02-01T14:59:54.502385Z"},"trusted":true},"execution_count":311,"outputs":[{"execution_count":311,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n 'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n 'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n 'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n 'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"print(\"percentage of cabin nan values - training \", titanic_train['Cabin'].isna().sum()/titanic_train.shape[0])\nprint(\"percentage of cabin nan values - test \", titanic_test['Cabin'].isna().sum()/titanic_test.shape[0])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.731246Z","iopub.execute_input":"2023-02-01T14:59:54.732185Z","iopub.status.idle":"2023-02-01T14:59:54.740154Z","shell.execute_reply.started":"2023-02-01T14:59:54.732142Z","shell.execute_reply":"2023-02-01T14:59:54.738880Z"},"trusted":true},"execution_count":312,"outputs":[{"name":"stdout","text":"percentage of cabin nan values - training 0.7710437710437711\npercentage of cabin nan values - test 0.7822966507177034\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.963015Z","iopub.execute_input":"2023-02-01T14:59:54.963847Z","iopub.status.idle":"2023-02-01T14:59:54.971020Z","shell.execute_reply.started":"2023-02-01T14:59:54.963804Z","shell.execute_reply":"2023-02-01T14:59:54.969855Z"},"trusted":true},"execution_count":313,"outputs":[{"execution_count":313,"output_type":"execute_result","data":{"text/plain":"array([3, 1, 2])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.182701Z","iopub.execute_input":"2023-02-01T14:59:55.183488Z","iopub.status.idle":"2023-02-01T14:59:55.190703Z","shell.execute_reply.started":"2023-02-01T14:59:55.183443Z","shell.execute_reply":"2023-02-01T14:59:55.189659Z"},"trusted":true},"execution_count":314,"outputs":[{"execution_count":314,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 1])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 1 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.447423Z","iopub.execute_input":"2023-02-01T14:59:55.447835Z","iopub.status.idle":"2023-02-01T14:59:55.464293Z","shell.execute_reply.started":"2023-02-01T14:59:55.447799Z","shell.execute_reply":"2023-02-01T14:59:55.463098Z"},"trusted":true},"execution_count":315,"outputs":[{"execution_count":315,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n1 1 C85\n3 1 C123\n6 1 E46\n11 1 C103\n23 1 A6\n.. ... ...\n871 1 D35\n872 1 B51 B53 B55\n879 1 C50\n887 1 B42\n889 1 C148\n\n[216 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
11C85
31C123
61E46
111C103
231A6
.........
8711D35
8721B51 B53 B55
8791C50
8871B42
8891C148
\n

216 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 2 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.639329Z","iopub.execute_input":"2023-02-01T14:59:55.640055Z","iopub.status.idle":"2023-02-01T14:59:55.656031Z","shell.execute_reply.started":"2023-02-01T14:59:55.640016Z","shell.execute_reply":"2023-02-01T14:59:55.655083Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n9 2 NaN\n15 2 NaN\n17 2 NaN\n20 2 NaN\n21 2 D56\n.. ... ...\n866 2 NaN\n874 2 NaN\n880 2 NaN\n883 2 NaN\n886 2 NaN\n\n[184 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
92NaN
152NaN
172NaN
202NaN
212D56
.........
8662NaN
8742NaN
8802NaN
8832NaN
8862NaN
\n

184 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 3 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.890762Z","iopub.execute_input":"2023-02-01T14:59:55.891773Z","iopub.status.idle":"2023-02-01T14:59:55.905616Z","shell.execute_reply.started":"2023-02-01T14:59:55.891731Z","shell.execute_reply":"2023-02-01T14:59:55.904841Z"},"trusted":true},"execution_count":317,"outputs":[{"execution_count":317,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n0 3 NaN\n2 3 NaN\n4 3 NaN\n5 3 NaN\n7 3 NaN\n.. ... ...\n882 3 NaN\n884 3 NaN\n885 3 NaN\n888 3 NaN\n890 3 NaN\n\n[491 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
03NaN
23NaN
43NaN
53NaN
73NaN
.........
8823NaN
8843NaN
8853NaN
8883NaN
8903NaN
\n

491 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"xs = titanic_train.loc[titanic_train['Fare'] > 0,'Pclass']\nys = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.128782Z","iopub.execute_input":"2023-02-01T14:59:56.129782Z","iopub.status.idle":"2023-02-01T14:59:56.360461Z","shell.execute_reply.started":"2023-02-01T14:59:56.129741Z","shell.execute_reply":"2023-02-01T14:59:56.359413Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"xs = titanic_test.loc[titanic_test['Fare'] > 0,'Pclass']\nys = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.362001Z","iopub.execute_input":"2023-02-01T14:59:56.362324Z","iopub.status.idle":"2023-02-01T14:59:56.593756Z","shell.execute_reply.started":"2023-02-01T14:59:56.362281Z","shell.execute_reply":"2023-02-01T14:59:56.592791Z"},"trusted":true},"execution_count":319,"outputs":[{"execution_count":319,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.scatter(titanic_train[\"Pclass\"],titanic_train[\"Fare\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.595546Z","iopub.execute_input":"2023-02-01T14:59:56.595846Z","iopub.status.idle":"2023-02-01T14:59:56.826882Z","shell.execute_reply.started":"2023-02-01T14:59:56.595817Z","shell.execute_reply":"2023-02-01T14:59:56.825559Z"},"trusted":true},"execution_count":320,"outputs":[{"execution_count":320,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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transform_Pclass(data):\n factors = data['Pclass'].unique()\n Pclass_columns = pd.get_dummies(data['Pclass'])\n columns = range(0,len(factors))\n \n for column in columns:\n col_name = 'Class_' + str(factors[column])\n data[col_name] = Pclass_columns.loc[:,factors[column]].astype(float)\n \n data.drop(\"Pclass\", axis = 1)\n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.829111Z","iopub.execute_input":"2023-02-01T14:59:56.829859Z","iopub.status.idle":"2023-02-01T14:59:56.838658Z","shell.execute_reply.started":"2023-02-01T14:59:56.829811Z","shell.execute_reply":"2023-02-01T14:59:56.837496Z"},"trusted":true},"execution_count":321,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_Pclass(titanic_train)\ntitanic_train.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.037884Z","iopub.execute_input":"2023-02-01T14:59:57.039017Z","iopub.status.idle":"2023-02-01T14:59:57.077228Z","shell.execute_reply.started":"2023-02-01T14:59:57.038961Z","shell.execute_reply":"2023-02-01T14:59:57.076108Z"},"trusted":true},"execution_count":322,"outputs":[{"execution_count":322,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch \\\n0 1.0 Braund, Mr. Owen Harris 0 \n1 2.0 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n2 3.0 Heikkinen, Miss. Laina 0 \n3 4.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n4 5.0 Allen, Mr. William Henry 0 \n\n Ticket Fare Survived S C Q U ... age_0-9 \\\n0 A/5 21171 7.2500 0 1.0 0.0 0.0 0.0 ... 0.0 \n1 PC 17599 71.2833 1 0.0 1.0 0.0 0.0 ... 0.0 \n2 STON/O2. 3101282 7.9250 1 1.0 0.0 0.0 0.0 ... 0.0 \n3 113803 53.1000 1 1.0 0.0 0.0 0.0 ... 0.0 \n4 373450 8.0500 0 1.0 0.0 0.0 0.0 ... 0.0 \n\n age_10-19 age_60-69 age_40-49 age_70-79 male female Class_3 Class_1 \\\n0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n2 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n4 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n\n Class_2 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 30 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareSurvivedSCQU...age_0-9age_10-19age_60-69age_40-49age_70-79malefemaleClass_3Class_1Class_2
01.0Braund, Mr. Owen Harris0A/5 211717.250001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
12.0Cumings, Mrs. John Bradley (Florence Briggs Th...0PC 1759971.283310.01.00.00.0...0.00.00.00.00.00.01.00.01.00.0
23.0Heikkinen, Miss. Laina0STON/O2. 31012827.925011.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
34.0Futrelle, Mrs. Jacques Heath (Lily May Peel)011380353.100011.00.00.00.0...0.00.00.00.00.00.01.00.01.00.0
45.0Allen, Mr. William Henry03734508.050001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
\n

5 rows × 30 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_Pclass(titanic_test)\ntitanic_test.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.320738Z","iopub.execute_input":"2023-02-01T14:59:57.321706Z","iopub.status.idle":"2023-02-01T14:59:57.358787Z","shell.execute_reply.started":"2023-02-01T14:59:57.321665Z","shell.execute_reply":"2023-02-01T14:59:57.357627Z"},"trusted":true},"execution_count":323,"outputs":[{"execution_count":323,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch Ticket \\\n0 892.0 Kelly, Mr. James 0 330911 \n1 893.0 Wilkes, Mrs. James (Ellen Needs) 0 363272 \n2 894.0 Myles, Mr. Thomas Francis 0 240276 \n3 895.0 Wirz, Mr. Albert 0 315154 \n4 896.0 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 3101298 \n\n Fare Q S C U Sib_Unknown ... age_20-29 age_10-19 \\\n0 7.8292 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n1 7.0000 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 \n2 9.6875 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n3 8.6625 0.0 1.0 0.0 0.0 1.0 ... 1.0 0.0 \n4 12.2875 0.0 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n\n age_50-59 age_0-9 age_70-79 male female Class_3 Class_2 Class_1 \n0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareQSCUSib_Unknown...age_20-29age_10-19age_50-59age_0-9age_70-79malefemaleClass_3Class_2Class_1
0892.0Kelly, Mr. James03309117.82921.00.00.00.01.0...0.00.00.00.00.01.00.01.00.00.0
1893.0Wilkes, Mrs. James (Ellen Needs)03632727.00000.01.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
2894.0Myles, Mr. Thomas Francis02402769.68751.00.00.00.01.0...0.00.00.00.00.01.00.00.01.00.0
3895.0Wirz, Mr. Albert03151548.66250.01.00.00.01.0...1.00.00.00.00.01.00.01.00.00.0
4896.0Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.28750.01.00.00.00.0...1.00.00.00.00.00.01.01.00.00.0
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Tickets and Fare\nWe remove the tickets, as it brings no additional characteristic for the prediction.\n\nOld version: We reduce the complexity of the Fare by using the log.\nNew version: The price appears to be dependent on the class, so we drop the price.","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.725423Z","iopub.execute_input":"2023-02-01T14:59:57.726055Z","iopub.status.idle":"2023-02-01T14:59:57.734724Z","shell.execute_reply.started":"2023-02-01T14:59:57.725995Z","shell.execute_reply":"2023-02-01T14:59:57.733640Z"},"trusted":true},"execution_count":324,"outputs":[]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\ntitanic_train.loc[titanic_train['Fare'] > 0,'Fare'] = log_10_values\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.977699Z","iopub.execute_input":"2023-02-01T14:59:57.978673Z","iopub.status.idle":"2023-02-01T14:59:57.991610Z","shell.execute_reply.started":"2023-02-01T14:59:57.978633Z","shell.execute_reply":"2023-02-01T14:59:57.990366Z"},"trusted":true},"execution_count":325,"outputs":[{"execution_count":325,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.256781\nstd 0.435553\nmin 0.000000\n25% 0.898198\n50% 1.159994\n75% 1.491362\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\ntitanic_test.loc[titanic_test['Fare'] > 0,'Fare'] = log_10_values\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.219678Z","iopub.execute_input":"2023-02-01T14:59:58.220097Z","iopub.status.idle":"2023-02-01T14:59:58.235301Z","shell.execute_reply.started":"2023-02-01T14:59:58.220059Z","shell.execute_reply":"2023-02-01T14:59:58.234195Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"count 417.000000\nmean 1.279591\nstd 0.437507\nmin 0.000000\n25% 0.897396\n50% 1.159994\n75% 1.498311\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.drop(\"Fare\", axis = 1, inplace = True)\ntitanic_test.drop(\"Fare\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.471730Z","iopub.execute_input":"2023-02-01T14:59:58.472149Z","iopub.status.idle":"2023-02-01T14:59:58.480205Z","shell.execute_reply.started":"2023-02-01T14:59:58.472111Z","shell.execute_reply":"2023-02-01T14:59:58.479227Z"},"trusted":true},"execution_count":327,"outputs":[]},{"cell_type":"markdown","source":"### Outcome of data preparations","metadata":{}},{"cell_type":"code","source":"\nprint(\"training datasets : \" , titanic_train.shape)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.947799Z","iopub.execute_input":"2023-02-01T14:59:58.948756Z","iopub.status.idle":"2023-02-01T14:59:58.957820Z","shell.execute_reply.started":"2023-02-01T14:59:58.948713Z","shell.execute_reply":"2023-02-01T14:59:58.956624Z"},"trusted":true},"execution_count":328,"outputs":[{"name":"stdout","text":"training datasets : (891, 28)\n","output_type":"stream"},{"execution_count":328,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_1 float64\nClass_2 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(\"testing datasets : \" , titanic_test.shape)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.211439Z","iopub.execute_input":"2023-02-01T14:59:59.211825Z","iopub.status.idle":"2023-02-01T14:59:59.222689Z","shell.execute_reply.started":"2023-02-01T14:59:59.211793Z","shell.execute_reply":"2023-02-01T14:59:59.221460Z"},"trusted":true},"execution_count":329,"outputs":[{"name":"stdout","text":"testing datasets : (418, 27)\n","output_type":"stream"},{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train_cols = titanic_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.478786Z","iopub.execute_input":"2023-02-01T14:59:59.479161Z","iopub.status.idle":"2023-02-01T14:59:59.488399Z","shell.execute_reply.started":"2023-02-01T14:59:59.479130Z","shell.execute_reply":"2023-02-01T14:59:59.487137Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"Index(['Survived'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.773416Z","iopub.execute_input":"2023-02-01T14:59:59.773881Z","iopub.status.idle":"2023-02-01T14:59:59.780592Z","shell.execute_reply.started":"2023-02-01T14:59:59.773845Z","shell.execute_reply":"2023-02-01T14:59:59.779730Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Name', 'Parch', 'Q', 'S', 'C', 'U', 'Sib_Unknown',\n 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', 'age_30-39',\n 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross validation preparation\nWe use a stratified sampling for the training into a train and test dataset. ","metadata":{}},{"cell_type":"code","source":"x_cols = [\"PassengerId\",'Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\nX = X[x_cols]\nX = X.apply(pd.to_numeric)\n\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.360627Z","iopub.execute_input":"2023-02-01T15:00:00.361572Z","iopub.status.idle":"2023-02-01T15:00:00.386989Z","shell.execute_reply.started":"2023-02-01T15:00:00.361528Z","shell.execute_reply":"2023-02-01T15:00:00.385873Z"},"trusted":true},"execution_count":332,"outputs":[{"name":"stdout","text":"0 0.616105\n1 0.383895\nName: Survived, dtype: float64 \n\n\n0 0.616246\n1 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = ['Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\nx_train_pass_id = X_train.PassengerId\nX_train = X_train[x_cols]\nX_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.656949Z","iopub.execute_input":"2023-02-01T15:00:00.657623Z","iopub.status.idle":"2023-02-01T15:00:00.667953Z","shell.execute_reply.started":"2023-02-01T15:00:00.657586Z","shell.execute_reply":"2023-02-01T15:00:00.666758Z"},"trusted":true},"execution_count":333,"outputs":[{"execution_count":333,"output_type":"execute_result","data":{"text/plain":"(534, 25)"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\n\nX_valid.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.982077Z","iopub.execute_input":"2023-02-01T15:00:00.982495Z","iopub.status.idle":"2023-02-01T15:00:00.991483Z","shell.execute_reply.started":"2023-02-01T15:00:00.982459Z","shell.execute_reply":"2023-02-01T15:00:00.990369Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"(357, 25)"},"metadata":{}}]},{"cell_type":"code","source":"y_train_encode=pd.get_dummies(y_train)\ny_valid_encode=pd.get_dummies(y_valid)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.303350Z","iopub.execute_input":"2023-02-01T15:00:01.303749Z","iopub.status.idle":"2023-02-01T15:00:01.310531Z","shell.execute_reply.started":"2023-02-01T15:00:01.303715Z","shell.execute_reply":"2023-02-01T15:00:01.309278Z"},"trusted":true},"execution_count":335,"outputs":[]},{"cell_type":"code","source":"train_cols = X_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.517778Z","iopub.execute_input":"2023-02-01T15:00:01.518178Z","iopub.status.idle":"2023-02-01T15:00:01.527798Z","shell.execute_reply.started":"2023-02-01T15:00:01.518142Z","shell.execute_reply":"2023-02-01T15:00:01.526659Z"},"trusted":true},"execution_count":336,"outputs":[{"execution_count":336,"output_type":"execute_result","data":{"text/plain":"Index([], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test[x_cols]\nX_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.807922Z","iopub.execute_input":"2023-02-01T15:00:01.808982Z","iopub.status.idle":"2023-02-01T15:00:01.817925Z","shell.execute_reply.started":"2023-02-01T15:00:01.808940Z","shell.execute_reply":"2023-02-01T15:00:01.816659Z"},"trusted":true},"execution_count":337,"outputs":[{"execution_count":337,"output_type":"execute_result","data":{"text/plain":"Parch int64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\nQ float64\nS float64\nC float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## ANN\n\nWe apply an ANN to predict the survival of passengers. We create a basic architecture made of 5 layers.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential, load_model\n\ntf.compat.v1.get_default_graph()\n\nno_columns = X_train.shape[1]\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Flatten(input_shape=(no_columns,)))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(2, activation=\"softmax\"))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.368188Z","iopub.execute_input":"2023-02-01T15:00:02.368622Z","iopub.status.idle":"2023-02-01T15:00:02.493557Z","shell.execute_reply.started":"2023-02-01T15:00:02.368582Z","shell.execute_reply":"2023-02-01T15:00:02.492272Z"},"trusted":true},"execution_count":338,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 25) 0 \n_________________________________________________________________\ndense (Dense) (None, 32) 832 \n_________________________________________________________________\ndense_1 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_2 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_3 (Dense) (None, 2) 66 \n=================================================================\nTotal params: 3,010\nTrainable params: 3,010\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"},{"name":"stderr","text":"2023-02-01 15:00:02.406449: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"\nrate = 0.00021\nopt = tf.keras.optimizers.Adam(learning_rate = rate)\nmodel.compile(optimizer= opt, \n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"])\ntf.compat.v1.get_default_graph()\nhistory = model.fit(X_train,\n y_train_encode,\n validation_data=(X_valid, y_valid_encode),\n epochs = 300,\n verbose = True)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.642006Z","iopub.execute_input":"2023-02-01T15:00:02.642833Z","iopub.status.idle":"2023-02-01T15:00:28.751910Z","shell.execute_reply.started":"2023-02-01T15:00:02.642783Z","shell.execute_reply":"2023-02-01T15:00:28.750794Z"},"trusted":true},"execution_count":339,"outputs":[{"name":"stderr","text":"2023-02-01 15:00:02.755801: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/300\n17/17 [==============================] - 1s 19ms/step - loss: 0.7885 - accuracy: 0.6161 - val_loss: 0.7708 - val_accuracy: 0.6162\nEpoch 2/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7574 - accuracy: 0.6161 - val_loss: 0.7429 - val_accuracy: 0.6162\nEpoch 3/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7326 - accuracy: 0.6161 - val_loss: 0.7213 - val_accuracy: 0.6162\nEpoch 4/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.7133 - accuracy: 0.6161 - val_loss: 0.7045 - val_accuracy: 0.6162\nEpoch 5/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.6161 - val_loss: 0.6921 - val_accuracy: 0.6162\nEpoch 6/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6878 - accuracy: 0.6161 - val_loss: 0.6832 - val_accuracy: 0.6162\nEpoch 7/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6802 - accuracy: 0.6161 - val_loss: 0.6771 - val_accuracy: 0.6162\nEpoch 8/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6750 - accuracy: 0.6161 - val_loss: 0.6727 - val_accuracy: 0.6162\nEpoch 9/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6717 - accuracy: 0.6161 - val_loss: 0.6698 - val_accuracy: 0.6162\nEpoch 10/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6692 - accuracy: 0.6161 - val_loss: 0.6682 - val_accuracy: 0.6162\nEpoch 11/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.6161 - val_loss: 0.6670 - val_accuracy: 0.6162\nEpoch 12/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6668 - accuracy: 0.6161 - val_loss: 0.6663 - val_accuracy: 0.6162\nEpoch 13/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6662 - accuracy: 0.6161 - val_loss: 0.6658 - val_accuracy: 0.6162\nEpoch 14/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.6161 - val_loss: 0.6655 - val_accuracy: 0.6162\nEpoch 15/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.6161 - val_loss: 0.6653 - val_accuracy: 0.6162\nEpoch 16/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6655 - accuracy: 0.6161 - val_loss: 0.6651 - val_accuracy: 0.6162\nEpoch 17/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6161 - val_loss: 0.6650 - val_accuracy: 0.6162\nEpoch 18/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6651 - accuracy: 0.6161 - val_loss: 0.6649 - val_accuracy: 0.6162\nEpoch 19/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6647 - val_accuracy: 0.6162\nEpoch 20/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6646 - val_accuracy: 0.6162\nEpoch 21/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6648 - accuracy: 0.6161 - val_loss: 0.6645 - val_accuracy: 0.6162\nEpoch 22/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6647 - accuracy: 0.6161 - val_loss: 0.6644 - val_accuracy: 0.6162\nEpoch 23/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6645 - accuracy: 0.6161 - val_loss: 0.6643 - val_accuracy: 0.6162\nEpoch 24/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6644 - accuracy: 0.6161 - val_loss: 0.6641 - val_accuracy: 0.6162\nEpoch 25/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.6161 - val_loss: 0.6640 - val_accuracy: 0.6162\nEpoch 26/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6641 - accuracy: 0.6161 - val_loss: 0.6639 - val_accuracy: 0.6162\nEpoch 27/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6637 - val_accuracy: 0.6162\nEpoch 28/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6636 - val_accuracy: 0.6162\nEpoch 29/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6637 - accuracy: 0.6161 - val_loss: 0.6634 - val_accuracy: 0.6162\nEpoch 30/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.6161 - val_loss: 0.6633 - val_accuracy: 0.6162\nEpoch 31/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6635 - accuracy: 0.6161 - val_loss: 0.6631 - val_accuracy: 0.6162\nEpoch 32/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6633 - accuracy: 0.6161 - val_loss: 0.6629 - val_accuracy: 0.6162\nEpoch 33/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6630 - accuracy: 0.6161 - val_loss: 0.6627 - val_accuracy: 0.6162\nEpoch 34/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6628 - accuracy: 0.6161 - val_loss: 0.6625 - val_accuracy: 0.6162\nEpoch 35/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6623 - val_accuracy: 0.6162\nEpoch 36/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6621 - val_accuracy: 0.6162\nEpoch 37/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6622 - accuracy: 0.6161 - val_loss: 0.6619 - val_accuracy: 0.6162\nEpoch 38/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6619 - accuracy: 0.6161 - val_loss: 0.6616 - val_accuracy: 0.6162\nEpoch 39/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6618 - accuracy: 0.6161 - val_loss: 0.6614 - val_accuracy: 0.6162\nEpoch 40/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6614 - accuracy: 0.6161 - val_loss: 0.6611 - val_accuracy: 0.6162\nEpoch 41/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6612 - accuracy: 0.6161 - val_loss: 0.6608 - val_accuracy: 0.6162\nEpoch 42/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6610 - accuracy: 0.6161 - val_loss: 0.6605 - val_accuracy: 0.6162\nEpoch 43/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.6161 - val_loss: 0.6601 - val_accuracy: 0.6162\nEpoch 44/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6598 - val_accuracy: 0.6162\nEpoch 45/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6594 - val_accuracy: 0.6162\nEpoch 46/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6595 - accuracy: 0.6161 - val_loss: 0.6590 - val_accuracy: 0.6162\nEpoch 47/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6590 - accuracy: 0.6161 - val_loss: 0.6586 - val_accuracy: 0.6162\nEpoch 48/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6161 - val_loss: 0.6581 - val_accuracy: 0.6162\nEpoch 49/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6580 - accuracy: 0.6161 - val_loss: 0.6576 - val_accuracy: 0.6162\nEpoch 50/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.6161 - val_loss: 0.6571 - val_accuracy: 0.6162\nEpoch 51/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6571 - accuracy: 0.6161 - val_loss: 0.6566 - val_accuracy: 0.6162\nEpoch 52/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6565 - accuracy: 0.6161 - val_loss: 0.6560 - val_accuracy: 0.6162\nEpoch 53/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6161 - val_loss: 0.6553 - val_accuracy: 0.6162\nEpoch 54/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6161 - val_loss: 0.6546 - val_accuracy: 0.6162\nEpoch 55/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6545 - accuracy: 0.6161 - val_loss: 0.6539 - val_accuracy: 0.6162\nEpoch 56/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6542 - accuracy: 0.6161 - val_loss: 0.6531 - val_accuracy: 0.6162\nEpoch 57/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6531 - accuracy: 0.6161 - val_loss: 0.6522 - val_accuracy: 0.6162\nEpoch 58/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6161 - val_loss: 0.6513 - val_accuracy: 0.6162\nEpoch 59/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6510 - accuracy: 0.6161 - val_loss: 0.6503 - val_accuracy: 0.6162\nEpoch 60/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6501 - accuracy: 0.6161 - val_loss: 0.6493 - val_accuracy: 0.6162\nEpoch 61/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6490 - accuracy: 0.6161 - val_loss: 0.6482 - val_accuracy: 0.6162\nEpoch 62/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6480 - accuracy: 0.6161 - val_loss: 0.6469 - val_accuracy: 0.6162\nEpoch 63/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6466 - accuracy: 0.6161 - val_loss: 0.6456 - val_accuracy: 0.6162\nEpoch 64/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6453 - accuracy: 0.6161 - val_loss: 0.6443 - val_accuracy: 0.6162\nEpoch 65/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6439 - accuracy: 0.6161 - val_loss: 0.6428 - val_accuracy: 0.6162\nEpoch 66/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6423 - accuracy: 0.6161 - val_loss: 0.6412 - val_accuracy: 0.6162\nEpoch 67/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6411 - accuracy: 0.6161 - val_loss: 0.6395 - val_accuracy: 0.6162\nEpoch 68/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6393 - accuracy: 0.6161 - val_loss: 0.6376 - val_accuracy: 0.6162\nEpoch 69/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6373 - accuracy: 0.6161 - val_loss: 0.6357 - val_accuracy: 0.6162\nEpoch 70/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6351 - accuracy: 0.6161 - val_loss: 0.6336 - val_accuracy: 0.6162\nEpoch 71/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6330 - accuracy: 0.6161 - val_loss: 0.6313 - val_accuracy: 0.6162\nEpoch 72/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6307 - accuracy: 0.6161 - val_loss: 0.6290 - val_accuracy: 0.6162\nEpoch 73/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6285 - accuracy: 0.6161 - val_loss: 0.6264 - val_accuracy: 0.6162\nEpoch 74/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6258 - accuracy: 0.6161 - val_loss: 0.6239 - val_accuracy: 0.6162\nEpoch 75/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6231 - accuracy: 0.6161 - val_loss: 0.6211 - val_accuracy: 0.6162\nEpoch 76/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6203 - accuracy: 0.6161 - val_loss: 0.6180 - val_accuracy: 0.6162\nEpoch 77/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6161 - val_loss: 0.6150 - val_accuracy: 0.6190\nEpoch 78/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6143 - accuracy: 0.6161 - val_loss: 0.6118 - val_accuracy: 0.6162\nEpoch 79/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6109 - accuracy: 0.6199 - val_loss: 0.6083 - val_accuracy: 0.6218\nEpoch 80/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6074 - accuracy: 0.6273 - val_loss: 0.6048 - val_accuracy: 0.6331\nEpoch 81/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6039 - accuracy: 0.6404 - val_loss: 0.6011 - val_accuracy: 0.6443\nEpoch 82/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6003 - accuracy: 0.6610 - val_loss: 0.5970 - val_accuracy: 0.6667\nEpoch 83/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5962 - accuracy: 0.6798 - val_loss: 0.5932 - val_accuracy: 0.6667\nEpoch 84/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5923 - accuracy: 0.6966 - val_loss: 0.5891 - val_accuracy: 0.7003\nEpoch 85/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7116 - val_loss: 0.5849 - val_accuracy: 0.7087\nEpoch 86/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5843 - accuracy: 0.7172 - val_loss: 0.5807 - val_accuracy: 0.7115\nEpoch 87/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5805 - accuracy: 0.7191 - val_loss: 0.5762 - val_accuracy: 0.7395\nEpoch 88/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7303 - val_loss: 0.5719 - val_accuracy: 0.7395\nEpoch 89/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5713 - accuracy: 0.7378 - val_loss: 0.5674 - val_accuracy: 0.7563\nEpoch 90/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5671 - accuracy: 0.7491 - val_loss: 0.5627 - val_accuracy: 0.7563\nEpoch 91/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7509 - val_loss: 0.5582 - val_accuracy: 0.7563\nEpoch 92/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5582 - accuracy: 0.7659 - val_loss: 0.5537 - val_accuracy: 0.7759\nEpoch 93/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.7640 - val_loss: 0.5492 - val_accuracy: 0.7871\nEpoch 94/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.5499 - accuracy: 0.7640 - val_loss: 0.5447 - val_accuracy: 0.7731\nEpoch 95/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5456 - accuracy: 0.7640 - val_loss: 0.5402 - val_accuracy: 0.7871\nEpoch 96/300\n17/17 [==============================] - 0s 13ms/step - loss: 0.5412 - accuracy: 0.7640 - val_loss: 0.5359 - val_accuracy: 0.7843\nEpoch 97/300\n17/17 [==============================] - 0s 12ms/step - loss: 0.5369 - accuracy: 0.7659 - val_loss: 0.5316 - val_accuracy: 0.7843\nEpoch 98/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.7603 - val_loss: 0.5275 - val_accuracy: 0.7955\nEpoch 99/300\n17/17 [==============================] - 0s 9ms/step - loss: 0.5294 - accuracy: 0.7715 - val_loss: 0.5233 - val_accuracy: 0.8039\nEpoch 100/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7753 - val_loss: 0.5196 - val_accuracy: 0.8039\nEpoch 101/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5217 - accuracy: 0.7734 - val_loss: 0.5158 - val_accuracy: 0.8039\nEpoch 102/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5181 - accuracy: 0.7753 - val_loss: 0.5120 - val_accuracy: 0.8039\nEpoch 103/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5145 - accuracy: 0.7753 - val_loss: 0.5085 - val_accuracy: 0.8011\nEpoch 104/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5113 - accuracy: 0.7715 - val_loss: 0.5049 - val_accuracy: 0.8039\nEpoch 105/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7715 - val_loss: 0.5016 - val_accuracy: 0.8039\nEpoch 106/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7715 - val_loss: 0.4983 - val_accuracy: 0.8039\nEpoch 107/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.7828 - val_loss: 0.4951 - val_accuracy: 0.8095\nEpoch 108/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7921 - val_loss: 0.4921 - val_accuracy: 0.8095\nEpoch 109/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7959 - val_loss: 0.4891 - val_accuracy: 0.8067\nEpoch 110/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7921 - val_loss: 0.4864 - val_accuracy: 0.8067\nEpoch 111/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4919 - accuracy: 0.7940 - val_loss: 0.4840 - val_accuracy: 0.8123\nEpoch 112/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7996 - val_loss: 0.4813 - val_accuracy: 0.8067\nEpoch 113/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7996 - val_loss: 0.4789 - val_accuracy: 0.8067\nEpoch 114/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7996 - val_loss: 0.4767 - val_accuracy: 0.8067\nEpoch 115/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4828 - accuracy: 0.7996 - val_loss: 0.4745 - val_accuracy: 0.8095\nEpoch 116/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4808 - accuracy: 0.7996 - val_loss: 0.4724 - val_accuracy: 0.8095\nEpoch 117/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4789 - accuracy: 0.7996 - val_loss: 0.4706 - val_accuracy: 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0.8179\nEpoch 173/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4420 - accuracy: 0.8240 - val_loss: 0.4308 - val_accuracy: 0.8123\nEpoch 174/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8240 - val_loss: 0.4305 - val_accuracy: 0.8123\nEpoch 175/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8179\nEpoch 176/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8123\nEpoch 177/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4297 - val_accuracy: 0.8151\nEpoch 178/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4410 - accuracy: 0.8258 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 179/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4294 - val_accuracy: 0.8151\nEpoch 180/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4402 - accuracy: 0.8240 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 181/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4400 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 182/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4397 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 183/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4395 - accuracy: 0.8240 - val_loss: 0.4286 - val_accuracy: 0.8151\nEpoch 184/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4393 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8123\nEpoch 185/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4392 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 186/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4389 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 187/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4390 - accuracy: 0.8240 - val_loss: 0.4278 - val_accuracy: 0.8123\nEpoch 188/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4279 - val_accuracy: 0.8151\nEpoch 189/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8151\nEpoch 190/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 191/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8240 - val_loss: 0.4274 - val_accuracy: 0.8151\nEpoch 192/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 193/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8221 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 194/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4375 - accuracy: 0.8240 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 195/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8240 - val_loss: 0.4272 - val_accuracy: 0.8151\nEpoch 196/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4370 - accuracy: 0.8240 - val_loss: 0.4271 - val_accuracy: 0.8151\nEpoch 197/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4368 - accuracy: 0.8240 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 198/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4367 - accuracy: 0.8221 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 199/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 0.8240 - val_loss: 0.4269 - val_accuracy: 0.8151\nEpoch 200/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4364 - accuracy: 0.8240 - val_loss: 0.4265 - val_accuracy: 0.8151\nEpoch 201/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4363 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 202/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4362 - accuracy: 0.8202 - val_loss: 0.4262 - val_accuracy: 0.8123\nEpoch 203/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4359 - accuracy: 0.8258 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 204/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4357 - accuracy: 0.8240 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 205/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8221 - val_loss: 0.4261 - val_accuracy: 0.8151\nEpoch 206/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 207/300\n17/17 [==============================] - 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[==============================] - 0s 5ms/step - loss: 0.4347 - accuracy: 0.8240 - val_loss: 0.4258 - val_accuracy: 0.8151\nEpoch 215/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 216/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4344 - accuracy: 0.8221 - val_loss: 0.4251 - val_accuracy: 0.8123\nEpoch 217/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4255 - val_accuracy: 0.8151\nEpoch 218/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 219/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4339 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 220/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 221/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 222/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4252 - val_accuracy: 0.8151\nEpoch 223/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4335 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 224/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4332 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 225/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 226/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4330 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 227/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 228/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 229/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 230/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4250 - val_accuracy: 0.8151\nEpoch 231/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4325 - accuracy: 0.8240 - val_loss: 0.4249 - val_accuracy: 0.8151\nEpoch 232/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 233/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4322 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 234/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 235/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 236/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 237/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 238/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 239/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 240/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 241/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 242/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4313 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 243/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 244/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 245/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8151\nEpoch 246/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8221 - val_loss: 0.4246 - val_accuracy: 0.8179\nEpoch 247/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 248/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 249/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 250/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4307 - accuracy: 0.8221 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 251/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8151\nEpoch 252/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8221 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 253/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 254/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 255/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4302 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 256/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 257/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 258/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 259/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 260/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 261/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 262/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 263/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 264/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 265/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 266/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 267/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 268/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 269/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 270/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 271/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 272/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 273/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 274/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 275/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4289 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 276/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 277/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4288 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 278/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 279/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 280/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 281/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 282/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 283/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 284/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 285/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 286/300\n17/17 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 287/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 288/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4282 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 289/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 290/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 291/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 292/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 293/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 294/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 295/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 296/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 297/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 298/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 299/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 300/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_train_accuracy = model.evaluate(X_train, y_train_encode)\nprint('Accuracy: %.4f' % (ann_train_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.756511Z","iopub.execute_input":"2023-02-01T15:00:28.757274Z","iopub.status.idle":"2023-02-01T15:00:28.874523Z","shell.execute_reply.started":"2023-02-01T15:00:28.757226Z","shell.execute_reply":"2023-02-01T15:00:28.873360Z"},"trusted":true},"execution_count":340,"outputs":[{"name":"stdout","text":"17/17 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221\nAccuracy: 0.8221\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_valid_accuracy = model.evaluate(X_valid, y_valid_encode)\nprint('Accuracy: %.4f' % (ann_valid_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.876387Z","iopub.execute_input":"2023-02-01T15:00:28.877663Z","iopub.status.idle":"2023-02-01T15:00:28.990657Z","shell.execute_reply.started":"2023-02-01T15:00:28.877614Z","shell.execute_reply":"2023-02-01T15:00:28.989441Z"},"trusted":true},"execution_count":341,"outputs":[{"name":"stdout","text":"12/12 [==============================] - 0s 2ms/step - loss: 0.4238 - accuracy: 0.8179\nAccuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ","metadata":{}},{"cell_type":"code","source":"\ny_pred = model.predict(X_train)\nY_pred = np.argmax(model.predict(X_train),axis=1)\ncm = confusion_matrix(y_train, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.993841Z","iopub.execute_input":"2023-02-01T15:00:28.994285Z","iopub.status.idle":"2023-02-01T15:00:29.270957Z","shell.execute_reply.started":"2023-02-01T15:00:28.994240Z","shell.execute_reply":"2023-02-01T15:00:29.269885Z"},"trusted":true},"execution_count":342,"outputs":[{"execution_count":342,"output_type":"execute_result","data":{"text/plain":"array([[304, 25],\n [ 70, 135]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.272190Z","iopub.execute_input":"2023-02-01T15:00:29.273301Z","iopub.status.idle":"2023-02-01T15:00:29.281676Z","shell.execute_reply.started":"2023-02-01T15:00:29.273267Z","shell.execute_reply":"2023-02-01T15:00:29.280517Z"},"trusted":true},"execution_count":343,"outputs":[{"name":"stdout","text":"Accuracy : 0.8220973782771536\nMisclassfication : 0.17790262172284643\nSensitivivity : 0.9240121580547113\nSpecificity : 0.6585365853658537\n","output_type":"stream"}]},{"cell_type":"code","source":"\ny_pred = model.predict(X_valid)\nY_pred = np.argmax(model.predict(X_valid),axis=1)\ncm = confusion_matrix(y_valid, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.283243Z","iopub.execute_input":"2023-02-01T15:00:29.283612Z","iopub.status.idle":"2023-02-01T15:00:29.451759Z","shell.execute_reply.started":"2023-02-01T15:00:29.283566Z","shell.execute_reply":"2023-02-01T15:00:29.450417Z"},"trusted":true},"execution_count":344,"outputs":[{"execution_count":344,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.453236Z","iopub.execute_input":"2023-02-01T15:00:29.453610Z","iopub.status.idle":"2023-02-01T15:00:29.461774Z","shell.execute_reply.started":"2023-02-01T15:00:29.453579Z","shell.execute_reply":"2023-02-01T15:00:29.460520Z"},"trusted":true},"execution_count":345,"outputs":[{"name":"stdout","text":"Accuracy : 0.8179271708683473\nMisclassfication : 0.18207282913165265\nSensitivivity : 0.9363636363636364\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.463445Z","iopub.execute_input":"2023-02-01T15:00:29.463787Z","iopub.status.idle":"2023-02-01T15:00:29.472285Z","shell.execute_reply.started":"2023-02-01T15:00:29.463752Z","shell.execute_reply":"2023-02-01T15:00:29.471294Z"},"trusted":true},"execution_count":346,"outputs":[]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_train),axis=1)\n\nann_pred = X_train.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_train_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.473634Z","iopub.execute_input":"2023-02-01T15:00:29.474711Z","iopub.status.idle":"2023-02-01T15:00:29.593403Z","shell.execute_reply.started":"2023-02-01T15:00:29.474675Z","shell.execute_reply":"2023-02-01T15:00:29.592290Z"},"trusted":true},"execution_count":347,"outputs":[{"execution_count":347,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n844 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n316 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n768 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n255 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n130 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n844 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n316 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n768 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n255 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n130 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n\n ann_y_pred PassengerId \n844 0 845.0 \n316 1 317.0 \n768 0 769.0 \n255 1 256.0 \n130 0 131.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
84401.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00845.0
31600.01.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01317.0
76800.01.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00769.0
25521.00.00.00.00.00.00.00.00.0...1.01.00.00.00.00.01.00.01256.0
13001.00.00.00.00.00.00.01.00.0...0.01.00.00.00.00.01.00.00131.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(ann_pred[[\"PassengerId\", \"ann_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.598604Z","iopub.execute_input":"2023-02-01T15:00:29.599029Z","iopub.status.idle":"2023-02-01T15:00:29.628142Z","shell.execute_reply.started":"2023-02-01T15:00:29.598995Z","shell.execute_reply":"2023-02-01T15:00:29.627332Z"},"trusted":true},"execution_count":348,"outputs":[{"execution_count":348,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 NaN \n2 0.0 NaN 0.0 \n3 NaN 1.0 NaN \n4 NaN 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_valid),axis=1)\nann_pred = X_valid.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_valid_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.629335Z","iopub.execute_input":"2023-02-01T15:00:29.629823Z","iopub.status.idle":"2023-02-01T15:00:29.739371Z","shell.execute_reply.started":"2023-02-01T15:00:29.629791Z","shell.execute_reply":"2023-02-01T15:00:29.738281Z"},"trusted":true},"execution_count":349,"outputs":[{"execution_count":349,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n369 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n541 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n196 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n810 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n427 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n369 0.0 ... 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n541 0.0 ... 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n196 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n810 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n427 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n\n ann_y_pred PassengerId \n369 1 370.0 \n541 1 542.0 \n196 0 197.0 \n810 0 811.0 \n427 1 428.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
36901.00.00.00.00.00.00.00.00.0...1.00.00.01.00.00.01.00.01370.0
54120.00.00.00.01.00.00.00.00.0...1.01.00.00.00.01.00.00.01542.0
19601.00.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00197.0
81001.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00811.0
42701.00.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01428.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(ann_pred.PassengerId), \"ann_y_pred\"] = ann_pred[\"ann_y_pred\"]\nresults_train.drop(\"rf_y_pred_y\", axis = 1)\nresults_train.drop(\"rf_y_pred_x\", axis = 1)\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.740869Z","iopub.execute_input":"2023-02-01T15:00:29.741291Z","iopub.status.idle":"2023-02-01T15:00:29.771294Z","shell.execute_reply.started":"2023-02-01T15:00:29.741249Z","shell.execute_reply":"2023-02-01T15:00:29.770286Z"},"trusted":true},"execution_count":350,"outputs":[{"execution_count":350,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 1.0 \n2 0.0 NaN 0.0 \n3 NaN 1.0 1.0 \n4 NaN 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Overall, the number of survivors misclassified were greater than misclassified passengers who perished. The next step is to identify those passengers to attempt to find the source of the misclassifcation. So far the lowest number of misclassified passengers who perished. ","metadata":{}},{"cell_type":"markdown","source":"## Predict test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\ny_pred = y_pred.argmax(1)\nann_pred = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"],\n \"ann_y_pred\" : y_pred})\nann_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.772619Z","iopub.execute_input":"2023-02-01T15:00:29.772938Z","iopub.status.idle":"2023-02-01T15:00:29.875387Z","shell.execute_reply.started":"2023-02-01T15:00:29.772908Z","shell.execute_reply":"2023-02-01T15:00:29.874334Z"},"trusted":true},"execution_count":351,"outputs":[{"execution_count":351,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.876431Z","iopub.execute_input":"2023-02-01T15:00:29.876729Z","iopub.status.idle":"2023-02-01T15:00:29.882726Z","shell.execute_reply.started":"2023-02-01T15:00:29.876701Z","shell.execute_reply":"2023-02-01T15:00:29.881480Z"},"trusted":true},"execution_count":352,"outputs":[]},{"cell_type":"code","source":"ann_pred[[\"PassengerId\",\"ann_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.884219Z","iopub.execute_input":"2023-02-01T15:00:29.884571Z","iopub.status.idle":"2023-02-01T15:00:29.900340Z","shell.execute_reply.started":"2023-02-01T15:00:29.884540Z","shell.execute_reply":"2023-02-01T15:00:29.899599Z"},"trusted":true},"execution_count":353,"outputs":[{"execution_count":353,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(ann_pred[[\"PassengerId\",\"ann_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.901356Z","iopub.execute_input":"2023-02-01T15:00:29.901844Z","iopub.status.idle":"2023-02-01T15:00:29.931394Z","shell.execute_reply.started":"2023-02-01T15:00:29.901814Z","shell.execute_reply":"2023-02-01T15:00:29.929969Z"},"trusted":true},"execution_count":354,"outputs":[{"execution_count":354,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred \n0 0.0 0.0 0.0 0.0 0 \n1 1.0 0.0 0.0 0.0 0 \n2 0.0 0.0 0.0 0.0 0 \n3 0.0 0.0 0.0 0.0 0 \n4 0.0 1.0 1.0 1.0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_predann_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.00
1893.03.02.01.411765-0.3161762.01.01.00.00.00.00
2894.02.01.02.588235-0.2021843.00.00.00.00.00.00
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.00
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.00
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Findings","metadata":{}},{"cell_type":"markdown","source":"We compile all the results in a basic structure. We discover that the logistic regression has achieved the highest accuracy on the validation datasets. ANN came close. Both methods appear not to overfit to the training dataset.","metadata":{}},{"cell_type":"code","source":"log_reg_results = {\n \"method\": \"Logistic regression\",\n \"training_accurary\": log_reg_score_train,\n \"valid_accuracy\": log_reg_score_valid\n}\n\nknn_results = {\n \"method\": \"KNN\",\n \"training_accurary\": knn_train_score,\n \"valid_accuracy\": knn_valid_score\n}\n\nclf_results = {\n \"method\": \"decision trees\",\n \"training_accurary\": clf_train_score,\n \"valid_accuracy\": clf_valid_score\n}\n\nrf_results = {\n \"method\": \"Random Forrest\",\n \"training_accurary\": rf_train_score,\n \"valid_accuracy\": rf_valid_score\n}\n\nann_results = {\n \"method\": \"ANN\",\n \"training_accurary\": ann_train_accuracy,\n \"valid_accuracy\": ann_valid_accuracy\n}\n\nresults = [log_reg_results, knn_results, clf_results, rf_results, ann_results]\nresults","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.932994Z","iopub.execute_input":"2023-02-01T15:00:29.933497Z","iopub.status.idle":"2023-02-01T15:00:29.947698Z","shell.execute_reply.started":"2023-02-01T15:00:29.933454Z","shell.execute_reply":"2023-02-01T15:00:29.946484Z"},"trusted":true},"execution_count":355,"outputs":[{"execution_count":355,"output_type":"execute_result","data":{"text/plain":"[{'method': 'Logistic regression',\n 'training_accurary': 0.7921348314606742,\n 'valid_accuracy': 0.8207282913165266},\n {'method': 'KNN',\n 'training_accurary': 0.8258426966292135,\n 'valid_accuracy': 0.7871148459383753},\n {'method': 'decision trees',\n 'training_accurary': 0.9082397003745318,\n 'valid_accuracy': 0.8151260504201681},\n {'method': 'Random Forrest',\n 'training_accurary': 0.8801498127340824,\n 'valid_accuracy': 0.8067226890756303},\n {'method': 'ANN',\n 'training_accurary': 0.8220973610877991,\n 'valid_accuracy': 0.8179271817207336}]"},"metadata":{}}]},{"cell_type":"markdown","source":"Less than 10% errors of passengers have been misclassified, when we compare all predictions together. So, it may be possible to identify some rules to increase accuracy. Nonetheless, these rules may also decrease the accuracy. So, a fine balance needs to be found. ","metadata":{}},{"cell_type":"code","source":"results_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.949130Z","iopub.execute_input":"2023-02-01T15:00:29.949749Z","iopub.status.idle":"2023-02-01T15:00:29.958469Z","shell.execute_reply.started":"2023-02-01T15:00:29.949702Z","shell.execute_reply":"2023-02-01T15:00:29.957602Z"},"trusted":true},"execution_count":356,"outputs":[{"execution_count":356,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked',\n 'fam_members', 'y', 'lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred']\nresults_train['merged_pred'] = results_train.loc[:,cols].apply(\n lambda x: ','.join(x.dropna().astype(str)),\n axis=1\n)\n\nresults_train['y_found'] = results_train.apply(lambda x: str(x.y) in x.merged_pred, axis=1)\nresults_train.drop(\"merged_pred\", axis = 1, inplace = True)\nresults_train.groupby(\"y_found\").count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.959997Z","iopub.execute_input":"2023-02-01T15:00:29.960444Z","iopub.status.idle":"2023-02-01T15:00:30.142912Z","shell.execute_reply.started":"2023-02-01T15:00:29.960402Z","shell.execute_reply":"2023-02-01T15:00:30.141887Z"},"trusted":true},"execution_count":357,"outputs":[{"execution_count":357,"output_type":"execute_result","data":{"text/plain":"y_found\nFalse 0.075196\nTrue 0.924804\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The set of passengers misclassified by all methods appear to have a lower expected fares and much more compact spread of fares. The median passenger class of misclassified passenger appear to be higher than those correctly classified. Both observations contractict each other and suggests some of fares being close to each other between passenger classes may be contributing in misclassifying passengers. \n\nThe misclassified passengers appears to be most women and their age appear to be older than the ones correctly classified by one method. The distribution to gender appears to match the overall observations for correctly classified passengers. Nonetheless, it is worth pointing out some of ages were inputed based on the number of siblings, spouse and parents aboard. This simple method of inputation may have impacted the classifiers; more research should be made to validate or improve inputting the missing information. \n\nOther aspects in the data may lead to misclassification.","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == False,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.144511Z","iopub.execute_input":"2023-02-01T15:00:30.145249Z","iopub.status.idle":"2023-02-01T15:00:30.180088Z","shell.execute_reply.started":"2023-02-01T15:00:30.145205Z","shell.execute_reply":"2023-02-01T15:00:30.178959Z"},"trusted":true},"execution_count":358,"outputs":[{"execution_count":358,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 67.000000 67.000000 67.000000 67.000000 67.000000 67.000000\nmean 2.149254 1.104478 0.129736 0.423026 0.343284 2.537313\nstd 0.908774 0.308188 0.721256 1.008879 0.844810 0.840785\nmin 1.000000 1.000000 -1.076923 -0.626005 0.000000 2.000000\n25% 1.000000 1.000000 -0.269231 -0.282777 0.000000 2.000000\n50% 2.000000 1.000000 0.000000 -0.062981 0.000000 2.000000\n75% 3.000000 1.000000 0.230769 0.694936 0.500000 3.000000\nmax 3.000000 2.000000 2.461538 3.318594 6.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count67.00000067.00000067.00000067.00000067.00000067.000000
mean2.1492541.1044780.1297360.4230260.3432842.537313
std0.9087740.3081880.7212561.0088790.8448100.840785
min1.0000001.000000-1.076923-0.6260050.0000002.000000
25%1.0000001.000000-0.269231-0.2827770.0000002.000000
50%2.0000001.0000000.000000-0.0629810.0000002.000000
75%3.0000001.0000000.2307690.6949360.5000003.000000
max3.0000002.0000002.4615383.3185946.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.182172Z","iopub.execute_input":"2023-02-01T15:00:30.183279Z","iopub.status.idle":"2023-02-01T15:00:30.213352Z","shell.execute_reply.started":"2023-02-01T15:00:30.183235Z","shell.execute_reply":"2023-02-01T15:00:30.212605Z"},"trusted":true},"execution_count":359,"outputs":[{"execution_count":359,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 824.000000 824.000000 824.000000 824.000000 824.000000 824.000000\nmean 2.321602 1.372573 -0.030604 0.796855 0.950243 2.455097\nstd 0.829129 0.483783 1.018913 2.217409 1.652334 0.790541\nmin 1.000000 1.000000 -2.275385 -0.626005 0.000000 1.000000\n25% 2.000000 1.000000 -0.615385 -0.284041 0.000000 2.000000\n50% 3.000000 1.000000 0.000000 0.001984 0.000000 2.000000\n75% 3.000000 2.000000 0.384615 0.719569 1.000000 3.000000\nmax 3.000000 2.000000 3.846154 21.562738 10.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count824.000000824.000000824.000000824.000000824.000000824.000000
mean2.3216021.372573-0.0306040.7968550.9502432.455097
std0.8291290.4837831.0189132.2174091.6523340.790541
min1.0000001.000000-2.275385-0.6260050.0000001.000000
25%2.0000001.000000-0.615385-0.2840410.0000002.000000
50%3.0000001.0000000.0000000.0019840.0000002.000000
75%3.0000002.0000000.3846150.7195691.0000003.000000
max3.0000002.0000003.84615421.56273810.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"incorrect = results_train.loc[results_train[\"y_found\"] == False,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == False,:].groupby(\"Sex\").count()[\"PassengerId\"]/incorrect","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.214494Z","iopub.execute_input":"2023-02-01T15:00:30.215455Z","iopub.status.idle":"2023-02-01T15:00:30.229684Z","shell.execute_reply.started":"2023-02-01T15:00:30.215404Z","shell.execute_reply":"2023-02-01T15:00:30.228567Z"},"trusted":true},"execution_count":360,"outputs":[{"execution_count":360,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.895522\n2.0 0.104478\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"correct = results_train.loc[results_train[\"y_found\"] == True,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == True,:].groupby(\"Sex\").count()[\"PassengerId\"]/correct","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.230783Z","iopub.execute_input":"2023-02-01T15:00:30.231538Z","iopub.status.idle":"2023-02-01T15:00:30.246006Z","shell.execute_reply.started":"2023-02-01T15:00:30.231506Z","shell.execute_reply":"2023-02-01T15:00:30.244736Z"},"trusted":true},"execution_count":361,"outputs":[{"execution_count":361,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.627427\n2.0 0.372573\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We analyse differences between each method on the testing and training data set. We add all the predictions to identify the passenger the classifier could not agree with. So, a total of 0 or 5 suggests all the classifiers have either identify passengers as survivor or not. Values in the range [1,4] indicates some disagreements in classification. Some methodologies appears to correclty classify passengers with at least one method.","metadata":{}},{"cell_type":"code","source":"results_train[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.247816Z","iopub.execute_input":"2023-02-01T15:00:30.248276Z","iopub.status.idle":"2023-02-01T15:00:30.473510Z","shell.execute_reply.started":"2023-02-01T15:00:30.248230Z","shell.execute_reply":"2023-02-01T15:00:30.472297Z"},"trusted":true},"execution_count":362,"outputs":[{"execution_count":362,"output_type":"execute_result","data":{"text/plain":"count 67.000000\nmean 0.447761\nstd 1.438471\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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3bXhOeAm5L843hHGr2qOtV9XATuY/my8FC0EHBv2d/kun/Quws4WVWfGvc8GyHJa5Ns7x6/guV/pP/hWIcasar6WFXtrKpdLP85/lpV/cGYxxqpJNu6f5gnyTbgncDQ3l12yQe8qp4Hzt+yfxK4Z8S37I9dki8A3wTekOSZJPvGPdOI3Qh8kOUzske7X+8a91AjtgN4KMn3WT5JebCqJuJtdRNmGvhGku8B3wK+XFVfHdaLX/JvI5Qkre6SPwOXJK3OgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXqfwEOtkCGTWOUBQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.475100Z","iopub.execute_input":"2023-02-01T15:00:30.475447Z","iopub.status.idle":"2023-02-01T15:00:30.691199Z","shell.execute_reply.started":"2023-02-01T15:00:30.475417Z","shell.execute_reply":"2023-02-01T15:00:30.690153Z"},"trusted":true},"execution_count":363,"outputs":[{"execution_count":363,"output_type":"execute_result","data":{"text/plain":"count 824.000000\nmean 1.577670\nstd 2.058981\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We explore how the techniques may predict differently and but accurately surviving the accident. \n\nKNN misclassified the most passengers who perished. Logistic regression and Random Tree classifier has the higest accuracy; both of them could be influencing the most the prediction, when only one classifier suggests a passenger has survived. ","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 1)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.692627Z","iopub.execute_input":"2023-02-01T15:00:30.692946Z","iopub.status.idle":"2023-02-01T15:00:30.712415Z","shell.execute_reply.started":"2023-02-01T15:00:30.692916Z","shell.execute_reply":"2023-02-01T15:00:30.711228Z"},"trusted":true},"execution_count":364,"outputs":[{"execution_count":364,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 3\n 1.0 0.0 0.0 3\n 1.0 0.0 0.0 0.0 10\n 1.0 0.0 0.0 0.0 0.0 3\n1.0 0.0 0.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 5\n 1.0 0.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 0.0 4\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 4)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.714064Z","iopub.execute_input":"2023-02-01T15:00:30.714806Z","iopub.status.idle":"2023-02-01T15:00:30.734553Z","shell.execute_reply.started":"2023-02-01T15:00:30.714762Z","shell.execute_reply":"2023-02-01T15:00:30.733458Z"},"trusted":true},"execution_count":365,"outputs":[{"execution_count":365,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 1.0 1.0 1.0 2\n 1.0 0.0 1.0 1.0 1.0 1\n1.0 0.0 1.0 1.0 1.0 1.0 6\n 1.0 0.0 1.0 1.0 1.0 1\n 1.0 0.0 1.0 1.0 2\n 1.0 0.0 1.0 2\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"A combination of Logistic regression and ANN may identify some survivors, when other methods do not. KNN in combination with another classifier may misclassify passengers who perished.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 2)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.743560Z","iopub.execute_input":"2023-02-01T15:00:30.743975Z","iopub.status.idle":"2023-02-01T15:00:30.762208Z","shell.execute_reply.started":"2023-02-01T15:00:30.743943Z","shell.execute_reply":"2023-02-01T15:00:30.761101Z"},"trusted":true},"execution_count":366,"outputs":[{"execution_count":366,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 1.0 1.0 0.0 4\n 1.0 0.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 0.0 2\n 1.0 1.0 0.0 0.0 0.0 5\n1.0 0.0 0.0 0.0 1.0 1.0 1\n 1.0 1.0 0.0 2\n 1.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 3)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.763835Z","iopub.execute_input":"2023-02-01T15:00:30.764145Z","iopub.status.idle":"2023-02-01T15:00:30.780211Z","shell.execute_reply.started":"2023-02-01T15:00:30.764116Z","shell.execute_reply":"2023-02-01T15:00:30.779475Z"},"trusted":true},"execution_count":367,"outputs":[{"execution_count":367,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 0.0 1.0 1.0 1\n 1.0 0.0 0.0 1.0 1.0 1\n 1.0 0.0 1.0 0.0 1\n1.0 0.0 1.0 1.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 1.0 0.0 1.0 1\n 1.0 0.0 0.0 1.0 2\n 1.0 0.0 3\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 5)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.781542Z","iopub.execute_input":"2023-02-01T15:00:30.781830Z","iopub.status.idle":"2023-02-01T15:00:30.798259Z","shell.execute_reply.started":"2023-02-01T15:00:30.781802Z","shell.execute_reply":"2023-02-01T15:00:30.796868Z"},"trusted":true},"execution_count":368,"outputs":[{"execution_count":368,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n1.0 1.0 1.0 1.0 1.0 1.0 65\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.799833Z","iopub.execute_input":"2023-02-01T15:00:30.800231Z","iopub.status.idle":"2023-02-01T15:00:30.808910Z","shell.execute_reply.started":"2023-02-01T15:00:30.800191Z","shell.execute_reply":"2023-02-01T15:00:30.808105Z"},"trusted":true},"execution_count":369,"outputs":[{"execution_count":369,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.809985Z","iopub.execute_input":"2023-02-01T15:00:30.810331Z","iopub.status.idle":"2023-02-01T15:00:30.821387Z","shell.execute_reply.started":"2023-02-01T15:00:30.810281Z","shell.execute_reply":"2023-02-01T15:00:30.820530Z"},"trusted":true},"execution_count":370,"outputs":[{"execution_count":370,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.822809Z","iopub.execute_input":"2023-02-01T15:00:30.823089Z","iopub.status.idle":"2023-02-01T15:00:30.834693Z","shell.execute_reply.started":"2023-02-01T15:00:30.823062Z","shell.execute_reply":"2023-02-01T15:00:30.833613Z"},"trusted":true},"execution_count":371,"outputs":[{"execution_count":371,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.sum_pred.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:01:14.796405Z","iopub.execute_input":"2023-02-01T15:01:14.796794Z","iopub.status.idle":"2023-02-01T15:01:14.805737Z","shell.execute_reply.started":"2023-02-01T15:01:14.796762Z","shell.execute_reply":"2023-02-01T15:01:14.804627Z"},"trusted":true},"execution_count":377,"outputs":[{"execution_count":377,"output_type":"execute_result","data":{"text/plain":"0.0 0.576880\n5.0 0.205387\n1.0 0.079686\n2.0 0.062851\n3.0 0.040404\n4.0 0.034792\nName: sum_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"}},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:36:25.295643Z","iopub.execute_input":"2023-02-01T15:36:25.296169Z","iopub.status.idle":"2023-02-01T15:36:25.314079Z","shell.execute_reply.started":"2023-02-01T15:36:25.296132Z","shell.execute_reply":"2023-02-01T15:36:25.312932Z"}},"execution_count":412,"outputs":[{"execution_count":412,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Are they any particular features that may have been picked up by each classifier?","metadata":{}},{"cell_type":"markdown","source":"### All classifiers agrees with the survival predictions\n\nWe found out that approximately 70% of the passengers who perished have been correclty classified by all the classifiers in agreement. But only, 20% of survivors have been correctly classified. Approximately 70% of the observations made in the training datasets have been correct and all the classifiers agree.","metadata":{}},{"cell_type":"code","source":"filter_rows = ((results_train[\"sum_pred\"] == 0.0) & (results_train[\"y\"] == 0))\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:06.719133Z","iopub.execute_input":"2023-02-01T15:45:06.719636Z","iopub.status.idle":"2023-02-01T15:45:06.733253Z","shell.execute_reply.started":"2023-02-01T15:45:06.719598Z","shell.execute_reply":"2023-02-01T15:45:06.732170Z"},"trusted":true},"execution_count":413,"outputs":[{"execution_count":413,"output_type":"execute_result","data":{"text/plain":"50.841750841750844"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"sum_pred\"] == 5.0) & (results_train[\"y\"] == 1)\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:07.933943Z","iopub.execute_input":"2023-02-01T15:45:07.935099Z","iopub.status.idle":"2023-02-01T15:45:07.947554Z","shell.execute_reply.started":"2023-02-01T15:45:07.935043Z","shell.execute_reply":"2023-02-01T15:45:07.946375Z"},"trusted":true},"execution_count":414,"outputs":[{"execution_count":414,"output_type":"execute_result","data":{"text/plain":"19.865319865319865"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"},"trusted":true},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:45.372534Z","iopub.execute_input":"2023-02-01T15:45:45.372921Z","iopub.status.idle":"2023-02-01T15:45:45.388445Z","shell.execute_reply.started":"2023-02-01T15:45:45.372891Z","shell.execute_reply":"2023-02-01T15:45:45.387062Z"},"trusted":true},"execution_count":415,"outputs":[{"execution_count":415,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## The classifiers disagree with each others on the survival predictions ?\n\nDecision Tree classifiers appears to have classified correctly the most passengers, when disagreements between classifiers exists. \n\nWe calculate the proportion of correct predictions, when some classifiers disagree. We found out that Decision tree appears to predict the most correct passengers who survive or perish the accident.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nno_correct = results_train.loc[filter_rows, :].shape[0]\nno_incorrect = results_train.loc[filter_rows, :].shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:30.920933Z","iopub.execute_input":"2023-02-01T16:00:30.921353Z","iopub.status.idle":"2023-02-01T16:00:30.932343Z","shell.execute_reply.started":"2023-02-01T16:00:30.921303Z","shell.execute_reply":"2023-02-01T16:00:30.930975Z"},"trusted":true},"execution_count":433,"outputs":[]},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.lr_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.369868Z","iopub.execute_input":"2023-02-01T16:00:32.370576Z","iopub.status.idle":"2023-02-01T16:00:32.381927Z","shell.execute_reply.started":"2023-02-01T16:00:32.370537Z","shell.execute_reply":"2023-02-01T16:00:32.381022Z"},"trusted":true},"execution_count":434,"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"44.329896907216494"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.knn_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.853276Z","iopub.execute_input":"2023-02-01T16:00:32.854476Z","iopub.status.idle":"2023-02-01T16:00:32.868855Z","shell.execute_reply.started":"2023-02-01T16:00:32.854418Z","shell.execute_reply":"2023-02-01T16:00:32.867407Z"},"trusted":true},"execution_count":435,"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"47.42268041237113"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.ann_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:33.395939Z","iopub.execute_input":"2023-02-01T16:00:33.396354Z","iopub.status.idle":"2023-02-01T16:00:33.410583Z","shell.execute_reply.started":"2023-02-01T16:00:33.396294Z","shell.execute_reply":"2023-02-01T16:00:33.409408Z"},"trusted":true},"execution_count":436,"outputs":[{"execution_count":436,"output_type":"execute_result","data":{"text/plain":"52.0618556701031"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.clf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:34.195555Z","iopub.execute_input":"2023-02-01T16:00:34.196776Z","iopub.status.idle":"2023-02-01T16:00:34.208545Z","shell.execute_reply.started":"2023-02-01T16:00:34.196733Z","shell.execute_reply":"2023-02-01T16:00:34.207295Z"},"trusted":true},"execution_count":437,"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"75.25773195876289"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.rf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:35.044699Z","iopub.execute_input":"2023-02-01T16:00:35.045127Z","iopub.status.idle":"2023-02-01T16:00:35.057811Z","shell.execute_reply.started":"2023-02-01T16:00:35.045090Z","shell.execute_reply":"2023-02-01T16:00:35.056488Z"},"trusted":true},"execution_count":438,"outputs":[{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"25.257731958762886"},"metadata":{}}]},{"cell_type":"markdown","source":"We change the predictions, that has been mispredicted by at least one classifier.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_train.loc[filter_rows, \"y\"] = results_train.clf_y_pred\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:38:03.184402Z","iopub.execute_input":"2023-02-01T16:38:03.184812Z","iopub.status.idle":"2023-02-01T16:38:03.191812Z","shell.execute_reply.started":"2023-02-01T16:38:03.184781Z","shell.execute_reply":"2023-02-01T16:38:03.191010Z"},"trusted":true},"execution_count":462,"outputs":[]},{"cell_type":"markdown","source":"The accuracy has been increased by a considerable level of accuracy. ","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train.Survived == results_train.y,:].count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:40:10.552687Z","iopub.execute_input":"2023-02-01T16:40:10.553066Z","iopub.status.idle":"2023-02-01T16:40:10.564469Z","shell.execute_reply.started":"2023-02-01T16:40:10.553036Z","shell.execute_reply":"2023-02-01T16:40:10.563190Z"},"trusted":true},"execution_count":467,"outputs":[{"execution_count":467,"output_type":"execute_result","data":{"text/plain":"0.9461279461279462"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying to results test\n\nThe distribution appears the be very similar as the training dataset.","metadata":{}},{"cell_type":"markdown","source":"__Testing dataset:__","metadata":{}},{"cell_type":"code","source":"results_test[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_test.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_test.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:04.962156Z","iopub.execute_input":"2023-02-01T16:28:04.962921Z","iopub.status.idle":"2023-02-01T16:28:05.177598Z","shell.execute_reply.started":"2023-02-01T16:28:04.962882Z","shell.execute_reply":"2023-02-01T16:28:05.176388Z"},"trusted":true},"execution_count":459,"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 1.590909\nstd 2.078233\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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dataset:__","metadata":{}},{"cell_type":"code","source":"results_train.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:10.931421Z","iopub.execute_input":"2023-02-01T16:28:10.931875Z","iopub.status.idle":"2023-02-01T16:28:11.153259Z","shell.execute_reply.started":"2023-02-01T16:28:10.931840Z","shell.execute_reply":"2023-02-01T16:28:11.152336Z"},"trusted":true},"execution_count":460,"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.492705\nstd 2.040242\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 3.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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z5aVb8YdT3DVlW/q6oLmb3D++Ik3U7DJXkncLyqDo26lhX21qq6iNkn6F7Tpl0HZjWHu485GBNt3vlrwK1V9fVR17OSquoZ4F5g24hLGaZLgXe1Oeh9wGVJ/nG0JQ1fVR1r78eB25mdah6Y1RzuPuZgDLR/XLwZeLSqPjPqelZCknOSbGjLZzJ70cAPR1rUEFXVJ6tqU1VtYfZ3fE9V/cWIyxqqJOvaBQIkWQe8HRjoVXCrNtyr6lngucccPArcNuTHHIxcki8D/wa8LsnRJDtGXdMKuBT4ALNncw+015WjLmrINgL3JnmQ2ZOYu6tqLC4PHCMTwH1JfgB8B7irqr45yA9YtZdCSpJOb9WeuUuSTs9wl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36PzFqarrIVm2TAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_test.loc[filter_rows, \"y\"] = results_test.clf_y_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:41:15.101164Z","iopub.execute_input":"2023-02-01T16:41:15.101563Z","iopub.status.idle":"2023-02-01T16:41:15.110450Z","shell.execute_reply.started":"2023-02-01T16:41:15.101523Z","shell.execute_reply":"2023-02-01T16:41:15.109235Z"},"trusted":true},"execution_count":468,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:46:41.923470Z","iopub.execute_input":"2023-02-01T16:46:41.923885Z","iopub.status.idle":"2023-02-01T16:46:43.051535Z","shell.execute_reply.started":"2023-02-01T16:46:41.923846Z","shell.execute_reply":"2023-02-01T16:46:43.050096Z"},"trusted":true},"execution_count":471,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:48:10.301809Z","iopub.execute_input":"2023-02-01T16:48:10.302423Z","iopub.status.idle":"2023-02-01T16:48:11.417688Z","shell.execute_reply.started":"2023-02-01T16:48:10.302370Z","shell.execute_reply":"2023-02-01T16:48:11.415704Z"},"trusted":true},"execution_count":472,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({\n \"PassengerId\": results_test[\"PassengerId\"].astype(int),\n \"Survived\": results_test[\"y\"]\n })\n\nsubmission = submission.astype({col: 'int32' for col in submission.select_dtypes('int64').columns})\nsubmission.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:09:39.297418Z","iopub.execute_input":"2023-02-01T17:09:39.297834Z","iopub.status.idle":"2023-02-01T17:09:39.311761Z","shell.execute_reply.started":"2023-02-01T17:09:39.297801Z","shell.execute_reply":"2023-02-01T17:09:39.310602Z"},"trusted":true},"execution_count":490,"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"PassengerId int32\nSurvived float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)\n!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:06:56.872660Z","iopub.execute_input":"2023-02-01T17:06:56.873348Z","iopub.status.idle":"2023-02-01T17:06:57.989149Z","shell.execute_reply.started":"2023-02-01T17:06:56.873282Z","shell.execute_reply":"2023-02-01T17:06:57.987753Z"},"trusted":true},"execution_count":488,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Data engineering and science/population.ipynb b/Data engineering and science/population.ipynb new file mode 100644 index 0000000..3dda9c5 --- /dev/null +++ b/Data engineering and science/population.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Population, GDP and Internet adoption\n\nThis notebook expores all those subjects to answer the following questions: \n\n[add questions here]\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"We will simulate a data pipeline based on the following steps:\n\n- extract : ingest data from various data sources.\n- transform : clean and prepare each dataset for tra\n- load : merge the data sets together \n- visualise: create some meaningful graphical visualisation.","metadata":{}},{"cell_type":"markdown","source":"# Extract\n\nWe upload the data and the libraries required for the notebook. ","metadata":{"_uuid":"caf4ec4f-f5cd-44f6-bd87-a42bb5998fbe","_cell_guid":"2e322c9f-5725-4093-ae2d-3e06fef613fe","_kg_hide-input":true,"trusted":true}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nfrom sklearn.cluster import KMeans\nimport seaborn as sns\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"0704d808-a7ec-4a50-aaa8-fe2134a807d4","_cell_guid":"cf1cf904-4fba-43b6-9f19-322dfa1cbf68","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.344153Z","iopub.execute_input":"2023-10-26T20:28:51.344497Z","iopub.status.idle":"2023-10-26T20:28:51.359482Z","shell.execute_reply.started":"2023-10-26T20:28:51.344472Z","shell.execute_reply":"2023-10-26T20:28:51.358458Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n/kaggle/input/population-dataset/World-population-by-countries-dataset.csv\n/kaggle/input/internet-users/Final.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"path = '/kaggle/input/population-dataset/World-population-by-countries-dataset.csv'\ndata_pop = pd.read_csv(path)\ndata_pop.shape","metadata":{"_uuid":"28a62502-7432-44e5-87eb-877d25cb754f","_cell_guid":"6d1ceed1-1929-4c1f-aa9c-cb9bf17b5a17","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.361232Z","iopub.execute_input":"2023-10-26T20:28:51.361709Z","iopub.status.idle":"2023-10-26T20:28:51.380139Z","shell.execute_reply.started":"2023-10-26T20:28:51.361677Z","shell.execute_reply":"2023-10-26T20:28:51.379042Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"markdown","source":"## World population\nWe transform the datasets from a wide to long format, so that we can merge more easily the datasets together. We aim at having a country name and a country code; both uses the ISO standard. We aim at having a year and the population. ","metadata":{"_uuid":"f352bd71-3a53-4d05-b1a6-15a3391dc608","_cell_guid":"12af0be9-5050-4d14-b22c-036f7f2f79f1","trusted":true}},{"cell_type":"code","source":"data_pop.dtypes","metadata":{"_uuid":"5213c597-6442-4ae6-aeed-299cd30630c5","_cell_guid":"1795b98f-3391-4877-8d9c-702da54af9d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.381506Z","iopub.execute_input":"2023-10-26T20:28:51.382276Z","iopub.status.idle":"2023-10-26T20:28:51.391171Z","shell.execute_reply.started":"2023-10-26T20:28:51.382247Z","shell.execute_reply":"2023-10-26T20:28:51.390268Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop.describe()","metadata":{"_uuid":"0daf98d8-5114-4c54-8a4c-e6bbf6311a74","_cell_guid":"c0f42347-4411-4bab-b06d-1b0e75605146","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.393035Z","iopub.execute_input":"2023-10-26T20:28:51.393739Z","iopub.status.idle":"2023-10-26T20:28:51.516296Z","shell.execute_reply.started":"2023-10-26T20:28:51.393706Z","shell.execute_reply":"2023-10-26T20:28:51.515206Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.172174e+08 1.187633e+08 1.208717e+08 1.234910e+08 1.261315e+08 \nstd 3.695745e+08 3.739180e+08 3.804316e+08 3.889142e+08 3.974401e+08 \nmin 2.833000e+03 3.077000e+03 3.367000e+03 3.703000e+03 4.063000e+03 \n25% 5.022802e+05 5.109642e+05 5.206540e+05 5.311622e+05 5.421252e+05 \n50% 3.718330e+06 3.826398e+06 3.929109e+06 4.015834e+06 4.124521e+06 \n75% 2.636053e+07 2.721235e+07 2.808607e+07 2.890669e+07 2.972333e+07 \nmax 3.032156e+09 3.071596e+09 3.124561e+09 3.189656e+09 3.255146e+09 \n\n 1965 1966 1967 1968 1969 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 1.288372e+08 1.316853e+08 1.345256e+08 1.374350e+08 1.404490e+08 \nstd 4.062000e+08 4.155171e+08 4.247722e+08 4.342805e+08 4.441772e+08 \nmin 4.460000e+03 4.675000e+03 4.922000e+03 5.194000e+03 5.461000e+03 \n25% 5.533362e+05 5.647475e+05 5.823645e+05 5.981078e+05 6.100030e+05 \n50% 4.242788e+06 4.326013e+06 4.387887e+06 4.474171e+06 4.550402e+06 \n75% 3.055227e+07 3.134845e+07 3.200449e+07 3.244145e+07 3.277149e+07 \nmax 3.322047e+09 3.392098e+09 3.461620e+09 3.532783e+09 3.606554e+09 \n\n ... 2012 2013 2014 2015 \\\ncount ... 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean ... 2.874902e+08 2.912969e+08 2.951160e+08 2.989277e+08 \nstd ... 9.017511e+08 9.129343e+08 9.241050e+08 9.352101e+08 \nmin ... 1.013600e+04 1.020800e+04 1.028900e+04 1.037400e+04 \n25% ... 1.539939e+06 1.574621e+06 1.609909e+06 1.645868e+06 \n50% ... 9.824808e+06 9.948838e+06 1.001582e+07 1.022085e+07 \n75% ... 6.057984e+07 6.120753e+07 6.174243e+07 6.182699e+07 \nmax ... 7.089255e+09 7.175500e+09 7.261847e+09 7.347679e+09 \n\n 2016 2017 2018 2019 2020 \\\ncount 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 2.640000e+02 \nmean 3.027560e+08 3.065980e+08 3.103591e+08 3.140425e+08 3.176734e+08 \nstd 9.463321e+08 9.575052e+08 9.683483e+08 9.788967e+08 9.891628e+08 \nmin 1.047400e+04 1.057700e+04 1.067800e+04 1.076400e+04 1.083400e+04 \n25% 1.689616e+06 1.716772e+06 1.740174e+06 1.751950e+06 1.767996e+06 \n50% 1.036160e+07 1.040671e+07 1.045548e+07 1.047907e+07 1.052565e+07 \n75% 6.187352e+07 6.191725e+07 6.193141e+07 6.150589e+07 6.157091e+07 \nmax 7.433651e+09 7.519371e+09 7.602716e+09 7.683806e+09 7.763933e+09 \n\n 2021 \ncount 2.640000e+02 \nmean 3.210893e+08 \nstd 9.988295e+08 \nmin 1.087300e+04 \n25% 1.791783e+06 \n50% 1.054019e+07 \n75% 6.295547e+07 \nmax 7.836631e+09 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02...2.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+022.640000e+02
mean1.172174e+081.187633e+081.208717e+081.234910e+081.261315e+081.288372e+081.316853e+081.345256e+081.374350e+081.404490e+08...2.874902e+082.912969e+082.951160e+082.989277e+083.027560e+083.065980e+083.103591e+083.140425e+083.176734e+083.210893e+08
std3.695745e+083.739180e+083.804316e+083.889142e+083.974401e+084.062000e+084.155171e+084.247722e+084.342805e+084.441772e+08...9.017511e+089.129343e+089.241050e+089.352101e+089.463321e+089.575052e+089.683483e+089.788967e+089.891628e+089.988295e+08
min2.833000e+033.077000e+033.367000e+033.703000e+034.063000e+034.460000e+034.675000e+034.922000e+035.194000e+035.461000e+03...1.013600e+041.020800e+041.028900e+041.037400e+041.047400e+041.057700e+041.067800e+041.076400e+041.083400e+041.087300e+04
25%5.022802e+055.109642e+055.206540e+055.311622e+055.421252e+055.533362e+055.647475e+055.823645e+055.981078e+056.100030e+05...1.539939e+061.574621e+061.609909e+061.645868e+061.689616e+061.716772e+061.740174e+061.751950e+061.767996e+061.791783e+06
50%3.718330e+063.826398e+063.929109e+064.015834e+064.124521e+064.242788e+064.326013e+064.387887e+064.474171e+064.550402e+06...9.824808e+069.948838e+061.001582e+071.022085e+071.036160e+071.040671e+071.045548e+071.047907e+071.052565e+071.054019e+07
75%2.636053e+072.721235e+072.808607e+072.890669e+072.972333e+073.055227e+073.134845e+073.200449e+073.244145e+073.277149e+07...6.057984e+076.120753e+076.174243e+076.182699e+076.187352e+076.191725e+076.193141e+076.150589e+076.157091e+076.295547e+07
max3.032156e+093.071596e+093.124561e+093.189656e+093.255146e+093.322047e+093.392098e+093.461620e+093.532783e+093.606554e+09...7.089255e+097.175500e+097.261847e+097.347679e+097.433651e+097.519371e+097.602716e+097.683806e+097.763933e+097.836631e+09
\n

8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(data_pop['Country Code'].unique())","metadata":{"_uuid":"da9b4784-3633-4778-a860-06055fe06ef6","_cell_guid":"dcc7e57d-b1d5-4413-ba23-9097a88df7f6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.517814Z","iopub.execute_input":"2023-10-26T20:28:51.518099Z","iopub.status.idle":"2023-10-26T20:28:51.524362Z","shell.execute_reply.started":"2023-10-26T20:28:51.518075Z","shell.execute_reply":"2023-10-26T20:28:51.523200Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\npop_long = pd.melt(data_pop, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.525929Z","iopub.execute_input":"2023-10-26T20:28:51.526268Z","iopub.status.idle":"2023-10-26T20:28:51.547676Z","shell.execute_reply.started":"2023-10-26T20:28:51.526243Z","shell.execute_reply":"2023-10-26T20:28:51.546791Z"},"trusted":true},"execution_count":93,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"pop_long.columns = ['Country Name', 'Country Code', 'Year', 'population']\nprint(pop_long.shape)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.548744Z","iopub.execute_input":"2023-10-26T20:28:51.549003Z","iopub.status.idle":"2023-10-26T20:28:51.559081Z","shell.execute_reply.started":"2023-10-26T20:28:51.548982Z","shell.execute_reply":"2023-10-26T20:28:51.558029Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## GDP\nWe repeat a similar process for the GDP. Similar standards are used.","metadata":{"_uuid":"d3617312-9971-49fc-a30c-357d2d407eea","_cell_guid":"98e1e4ad-2241-4d7a-b701-a07fe5aa0509","execution":{"iopub.status.busy":"2023-10-19T16:22:33.566429Z","iopub.execute_input":"2023-10-19T16:22:33.566852Z","iopub.status.idle":"2023-10-19T16:22:34.617823Z","shell.execute_reply.started":"2023-10-19T16:22:33.566821Z","shell.execute_reply":"2023-10-19T16:22:34.616873Z"},"trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/countries-gdp-2012-to-2021/GDP.csv'\ngdp = pd.read_csv(path)\ngdp.shape","metadata":{"_uuid":"a8992442-7dac-4c03-9f3b-13cdc8d45e7b","_cell_guid":"be74ad63-eebe-40b5-844b-021624380077","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.560206Z","iopub.execute_input":"2023-10-26T20:28:51.560460Z","iopub.status.idle":"2023-10-26T20:28:51.580073Z","shell.execute_reply.started":"2023-10-26T20:28:51.560438Z","shell.execute_reply":"2023-10-26T20:28:51.578585Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"(266, 64)"},"metadata":{}}]},{"cell_type":"code","source":"gdp.dtypes","metadata":{"_uuid":"1e5fa2f6-ab9d-4115-aab3-47f9d5952d7c","_cell_guid":"c161c56d-19d2-4157-8122-4e4bff8f0fb1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.581561Z","iopub.execute_input":"2023-10-26T20:28:51.581877Z","iopub.status.idle":"2023-10-26T20:28:51.589480Z","shell.execute_reply.started":"2023-10-26T20:28:51.581850Z","shell.execute_reply":"2023-10-26T20:28:51.588251Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2017 float64\n2018 float64\n2019 float64\n2020 float64\n2021 float64\nLength: 64, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp.describe()","metadata":{"_uuid":"cc48f68b-e22c-470d-8d9d-fe7c874e4ff9","_cell_guid":"39bd124c-d5d9-4541-be4b-affa418221ee","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.593301Z","iopub.execute_input":"2023-10-26T20:28:51.593702Z","iopub.status.idle":"2023-10-26T20:28:51.713345Z","shell.execute_reply.started":"2023-10-26T20:28:51.593675Z","shell.execute_reply":"2023-10-26T20:28:51.712285Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 134.000000 136.000000 138.000000 138.000000 138.000000 \nmean 473.490078 486.392600 510.248600 541.649901 587.373909 \nstd 612.439366 635.127847 666.405011 705.754944 772.265425 \nmin 40.119192 26.318449 26.983496 28.434172 20.018579 \n25% 107.452258 110.089913 114.582873 122.509292 123.574875 \n50% 217.932654 197.938953 202.801243 210.677240 232.182537 \n75% 476.295836 485.401860 538.891433 586.773416 639.414205 \nmax 3007.123445 3066.562869 3243.843078 3374.515171 3573.941185 \n\n 1965 1966 1967 1968 1969 ... \\\ncount 149.000000 152.000000 155.000000 160.000000 160.000000 ... \nmean 648.068814 703.235758 718.916647 735.345411 796.539042 ... \nstd 849.994333 921.818962 954.791908 982.957313 1060.025132 ... \nmin 16.577652 12.786964 12.900238 20.395642 20.682296 ... \n25% 140.756742 145.396584 152.410537 149.457032 151.634207 ... \n50% 251.239040 266.219488 252.252422 292.642193 293.802194 ... \n75% 681.131112 768.852316 763.567965 760.566852 826.288906 ... \nmax 4081.915955 4229.254573 4336.426587 4695.923390 5032.144743 ... \n\n 2012 2013 2014 2015 \\\ncount 258.000000 259.000000 260.000000 258.000000 \nmean 16248.249264 16768.974417 17083.306427 15423.701141 \nstd 23882.158473 25383.007646 25945.938982 23375.375304 \nmin 238.205949 241.547671 257.818552 289.359633 \n25% 1986.934959 2110.418190 2173.282618 2097.331179 \n50% 6454.612266 6755.073675 6904.579093 6192.562429 \n75% 19638.711935 19792.134135 20277.795912 18210.359455 \nmax 165505.178100 185066.578100 195780.006900 170337.924400 \n\n 2016 2017 2018 2019 \\\ncount 257.000000 257.000000 257.000000 255.000000 \nmean 15582.736498 16383.403010 17344.572407 17231.399427 \nstd 23586.086580 24397.646814 25978.513510 25791.905913 \nmin 242.065671 243.135809 231.446476 216.972968 \n25% 2079.448266 2088.500117 2269.177012 2186.046581 \n50% 6079.088736 6436.791746 6912.110297 6837.717826 \n75% 18575.232030 19743.954910 20614.898860 19809.323135 \nmax 174610.637000 173612.864600 194280.822100 199377.481800 \n\n 2020 2021 \ncount 252.000000 245.000000 \nmean 15773.923985 16882.053955 \nstd 24065.495555 26113.837043 \nmin 216.826741 221.477676 \n25% 2139.636129 2304.844567 \n50% 6034.203335 6621.574336 \n75% 18652.166725 18751.026510 \nmax 182538.638300 234315.460500 \n\n[8 rows x 62 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1960196119621963196419651966196719681969...2012201320142015201620172018201920202021
count134.000000136.000000138.000000138.000000138.000000149.000000152.000000155.000000160.000000160.000000...258.000000259.000000260.000000258.000000257.000000257.000000257.000000255.000000252.000000245.000000
mean473.490078486.392600510.248600541.649901587.373909648.068814703.235758718.916647735.345411796.539042...16248.24926416768.97441717083.30642715423.70114115582.73649816383.40301017344.57240717231.39942715773.92398516882.053955
std612.439366635.127847666.405011705.754944772.265425849.994333921.818962954.791908982.9573131060.025132...23882.15847325383.00764625945.93898223375.37530423586.08658024397.64681425978.51351025791.90591324065.49555526113.837043
min40.11919226.31844926.98349628.43417220.01857916.57765212.78696412.90023820.39564220.682296...238.205949241.547671257.818552289.359633242.065671243.135809231.446476216.972968216.826741221.477676
25%107.452258110.089913114.582873122.509292123.574875140.756742145.396584152.410537149.457032151.634207...1986.9349592110.4181902173.2826182097.3311792079.4482662088.5001172269.1770122186.0465812139.6361292304.844567
50%217.932654197.938953202.801243210.677240232.182537251.239040266.219488252.252422292.642193293.802194...6454.6122666755.0736756904.5790936192.5624296079.0887366436.7917466912.1102976837.7178266034.2033356621.574336
75%476.295836485.401860538.891433586.773416639.414205681.131112768.852316763.567965760.566852826.288906...19638.71193519792.13413520277.79591218210.35945518575.23203019743.95491020614.89886019809.32313518652.16672518751.026510
max3007.1234453066.5628693243.8430783374.5151713573.9411854081.9159554229.2545734336.4265874695.9233905032.144743...165505.178100185066.578100195780.006900170337.924400174610.637000173612.864600194280.822100199377.481800182538.638300234315.460500
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8 rows × 62 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"len(gdp['Country Code'].unique())","metadata":{"_uuid":"a6200065-a3c5-4d03-a714-a2fbc8ced590","_cell_guid":"8bfa6fe6-9143-4ca0-a2e7-4d741a6989a9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.714469Z","iopub.execute_input":"2023-10-26T20:28:51.714720Z","iopub.status.idle":"2023-10-26T20:28:51.720357Z","shell.execute_reply.started":"2023-10-26T20:28:51.714698Z","shell.execute_reply":"2023-10-26T20:28:51.719732Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"266"},"metadata":{}}]},{"cell_type":"code","source":"cols = [str(i) for i in range(1960,2022)]\ngdp_long = pd.melt(gdp, id_vars=[\"Country Name\", \"Country Code\"], value_vars=cols)\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.721044Z","iopub.execute_input":"2023-10-26T20:28:51.721267Z","iopub.status.idle":"2023-10-26T20:28:51.745227Z","shell.execute_reply.started":"2023-10-26T20:28:51.721248Z","shell.execute_reply":"2023-10-26T20:28:51.743851Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nvariable object\nvalue float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"gdp_long.columns = ['Country Name', 'Country Code', 'Year', 'USD GDP']\nprint(gdp_long.shape)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.746701Z","iopub.execute_input":"2023-10-26T20:28:51.747001Z","iopub.status.idle":"2023-10-26T20:28:51.757774Z","shell.execute_reply.started":"2023-10-26T20:28:51.746978Z","shell.execute_reply":"2023-10-26T20:28:51.756806Z"},"trusted":true},"execution_count":100,"outputs":[{"name":"stdout","text":"(16492, 4)\n","output_type":"stream"},{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Internet\nThis dataset is a bit simpler. Less transformation is required.","metadata":{"_uuid":"e6be91b8-d8c6-4212-ad69-2ce46452ef6f","_cell_guid":"d299a245-fa89-45c1-9623-569d3e7bcf7a","trusted":true}},{"cell_type":"code","source":"path = '/kaggle/input/internet-users/Final.csv'\ninternet = pd.read_csv(path)\ninternet.shape","metadata":{"_uuid":"ad3170c0-5705-4a57-9f17-853844f08e72","_cell_guid":"5a26b58f-e229-48ec-81f7-dd8ab7a51338","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.759207Z","iopub.execute_input":"2023-10-26T20:28:51.759490Z","iopub.status.idle":"2023-10-26T20:28:51.783791Z","shell.execute_reply.started":"2023-10-26T20:28:51.759467Z","shell.execute_reply":"2023-10-26T20:28:51.782480Z"},"trusted":true},"execution_count":101,"outputs":[{"execution_count":101,"output_type":"execute_result","data":{"text/plain":"(8867, 8)"},"metadata":{}}]},{"cell_type":"code","source":"internet.dtypes","metadata":{"_uuid":"bb11a84f-3d44-4ab2-9864-dc1c4a8df688","_cell_guid":"dedee6b7-ac31-4647-8b00-2a8bed20650f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.784729Z","iopub.execute_input":"2023-10-26T20:28:51.785074Z","iopub.status.idle":"2023-10-26T20:28:51.792523Z","shell.execute_reply.started":"2023-10-26T20:28:51.785043Z","shell.execute_reply":"2023-10-26T20:28:51.791485Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nEntity object\nCode object\nYear int64\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"len(internet.Code.unique())","metadata":{"_uuid":"4906007d-31a3-4312-89f1-25d4bbe6bbdf","_cell_guid":"6d9ec685-820d-4cca-a721-afe2a68bddd7","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-10-26T20:28:51.793712Z","iopub.execute_input":"2023-10-26T20:28:51.793961Z","iopub.status.idle":"2023-10-26T20:28:51.806167Z","shell.execute_reply.started":"2023-10-26T20:28:51.793940Z","shell.execute_reply":"2023-10-26T20:28:51.805509Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"216"},"metadata":{}}]},{"cell_type":"code","source":"internet.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:36:30.135197Z","iopub.execute_input":"2023-10-26T20:36:30.135549Z","iopub.status.idle":"2023-10-26T20:36:30.162264Z","shell.execute_reply.started":"2023-10-26T20:36:30.135525Z","shell.execute_reply":"2023-10-26T20:36:30.160983Z"},"trusted":true},"execution_count":136,"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0 Year Cellular Subscription Internet Users(%) \\\ncount 8867.000000 8867.000000 8867.000000 8867.000000 \nmean 4433.000000 2000.151799 39.989614 17.043606 \nstd 2559.826752 11.812151 51.981410 26.883498 \nmin 0.000000 1980.000000 0.000000 0.000000 \n25% 2216.500000 1990.000000 0.000000 0.000000 \n50% 4433.000000 2000.000000 5.501357 0.855662 \n75% 6649.500000 2010.000000 82.231594 25.449939 \nmax 8866.000000 2020.000000 436.103027 100.000000 \n\n No. of Internet Users Broadband Subscription \ncount 8.867000e+03 8867.000000 \nmean 1.089138e+07 4.440695 \nstd 1.248841e+08 9.755705 \nmin 0.000000e+00 0.000000 \n25% 0.000000e+00 0.000000 \n50% 1.004700e+04 0.000000 \n75% 8.664195e+05 2.007603 \nmax 4.699886e+09 78.524361 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0YearCellular SubscriptionInternet Users(%)No. of Internet UsersBroadband Subscription
count8867.0000008867.0000008867.0000008867.0000008.867000e+038867.000000
mean4433.0000002000.15179939.98961417.0436061.089138e+074.440695
std2559.82675211.81215151.98141026.8834981.248841e+089.755705
min0.0000001980.0000000.0000000.0000000.000000e+000.000000
25%2216.5000001990.0000000.0000000.0000000.000000e+000.000000
50%4433.0000002000.0000005.5013570.8556621.004700e+040.000000
75%6649.5000002010.00000082.23159425.4499398.664195e+052.007603
max8866.0000002020.000000436.103027100.0000004.699886e+0978.524361
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"It appears the number of countries to be smaller for the Internet dataset. Therefore, any merging of this datasets with the GDP and polution will reduce the number of countries by 50. This confounding factor will limit the analysis. It is a bit disappointing, but it will be enough a first exploration. ","metadata":{}},{"cell_type":"markdown","source":"# Tranform and load\nWe merge the datasets based on the year and the ISO country code.","metadata":{}},{"cell_type":"code","source":"print(pop_long.dtypes)\nprint(gdp_long.dtypes)","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.807624Z","iopub.execute_input":"2023-10-26T20:28:51.807989Z","iopub.status.idle":"2023-10-26T20:28:51.817447Z","shell.execute_reply.started":"2023-10-26T20:28:51.807963Z","shell.execute_reply":"2023-10-26T20:28:51.816385Z"},"trusted":true},"execution_count":104,"outputs":[{"name":"stdout","text":"Country Name object\nCountry Code object\nYear object\npopulation float64\ndtype: object\nCountry Name object\nCountry Code object\nYear object\nUSD GDP float64\ndtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"data = pd.merge(pop_long, gdp_long, left_on=['Country Code','Year'], right_on = ['Country Code','Year'])\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.819009Z","iopub.execute_input":"2023-10-26T20:28:51.819327Z","iopub.status.idle":"2023-10-26T20:28:51.840904Z","shell.execute_reply.started":"2023-10-26T20:28:51.819299Z","shell.execute_reply":"2023-10-26T20:28:51.839816Z"},"trusted":true},"execution_count":105,"outputs":[{"name":"stdout","text":"(16492, 6)\n","output_type":"stream"},{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nCountry Name_y object\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['Country Name_x', 'Country Code', 'Year', 'population','USD GDP']\ndata = data.loc[:, cols]\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.842213Z","iopub.execute_input":"2023-10-26T20:28:51.842569Z","iopub.status.idle":"2023-10-26T20:28:51.852162Z","shell.execute_reply.started":"2023-10-26T20:28:51.842539Z","shell.execute_reply":"2023-10-26T20:28:51.850921Z"},"trusted":true},"execution_count":106,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"Country Name_x object\nCountry Code object\nYear object\npopulation float64\nUSD GDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.columns = ['Country Name','Country Code', 'Year', 'Population', 'GDP']\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.853458Z","iopub.execute_input":"2023-10-26T20:28:51.853809Z","iopub.status.idle":"2023-10-26T20:28:51.864097Z","shell.execute_reply.started":"2023-10-26T20:28:51.853780Z","shell.execute_reply":"2023-10-26T20:28:51.862937Z"},"trusted":true},"execution_count":107,"outputs":[{"name":"stdout","text":"(16492, 5)\n","output_type":"stream"},{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear object\nPopulation float64\nGDP float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.865117Z","iopub.execute_input":"2023-10-26T20:28:51.865438Z","iopub.status.idle":"2023-10-26T20:28:51.883869Z","shell.execute_reply.started":"2023-10-26T20:28:51.865413Z","shell.execute_reply":"2023-10-26T20:28:51.882903Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":" Country Name Country Code Year Population GDP\n0 Aruba ABW 1960 54208.0 NaN\n1 Africa Eastern and Southern AFE 1960 130836765.0 162.913035\n2 Afghanistan AFG 1960 8996967.0 62.369375\n3 Africa Western and Central AFW 1960 96396419.0 106.976475\n4 Angola AGO 1960 5454938.0 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Country NameCountry CodeYearPopulationGDP
0ArubaABW196054208.0NaN
1Africa Eastern and SouthernAFE1960130836765.0162.913035
2AfghanistanAFG19608996967.062.369375
3Africa Western and CentralAFW196096396419.0106.976475
4AngolaAGO19605454938.0NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We apply the log normalisation on the data. The range is really large. It will allow us visualising in more details the distributions. However, any patterns through the years will appear as linear. It would be incorrect to interpret it as a linear growth, when it may be instead exponential. For that reasons, the non-normalise data may need to be used. \n\nThe distribute appears to be guassian. However, it includes the population for each year. So, it should be only used as a tool to explore the data.","metadata":{}},{"cell_type":"code","source":"data['log_pop'] = np.log10(data.Population)\ndata.log_pop.hist(bins = 100)\ndata.Population.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:28:51.885011Z","iopub.execute_input":"2023-10-26T20:28:51.885246Z","iopub.status.idle":"2023-10-26T20:28:52.385634Z","shell.execute_reply.started":"2023-10-26T20:28:51.885226Z","shell.execute_reply":"2023-10-26T20:28:52.384400Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"count 1.638700e+04\nmean 2.131655e+08\nstd 7.006673e+08\nmin 2.833000e+03\n25% 9.660195e+05\n50% 6.749849e+06\n75% 4.626525e+07\nmax 7.836631e+09\nName: Population, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"data['Year'] = pd.to_numeric(data.Year)\ndata_int = pd.merge(data, internet, left_on ='Year', right_on = 'Year', how = 'inner')\nprint(data_int.shape)\ndata_int.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:53.274864Z","iopub.execute_input":"2023-10-26T20:31:53.275211Z","iopub.status.idle":"2023-10-26T20:31:53.587275Z","shell.execute_reply.started":"2023-10-26T20:31:53.275181Z","shell.execute_reply":"2023-10-26T20:31:53.586389Z"},"trusted":true},"execution_count":120,"outputs":[{"name":"stdout","text":"(2358622, 13)\n","output_type":"stream"},{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\nUnnamed: 0 int64\nEntity object\nCode object\nCellular Subscription float64\nInternet Users(%) float64\nNo. of Internet Users int64\nBroadband Subscription float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"# Analysis\n## Has the population increased since the 1960s?\n\nWe discover the overall population may have increased since the 1960s. A boxplot shows the non-parametric distribution of yearly population across the world tend to increase. We would need to complete some further investigation to explore further this trend.","metadata":{}},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:31:59.444689Z","iopub.execute_input":"2023-10-26T20:31:59.445040Z","iopub.status.idle":"2023-10-26T20:31:59.453385Z","shell.execute_reply.started":"2023-10-26T20:31:59.445015Z","shell.execute_reply":"2023-10-26T20:31:59.452330Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\nYear int64\nPopulation float64\nGDP float64\nlog_pop float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data_pop['1960'].describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:32:02.772369Z","iopub.execute_input":"2023-10-26T20:32:02.772824Z","iopub.status.idle":"2023-10-26T20:32:02.789620Z","shell.execute_reply.started":"2023-10-26T20:32:02.772781Z","shell.execute_reply":"2023-10-26T20:32:02.786617Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"count 2.640000e+02\nmean 1.172174e+08\nstd 3.695745e+08\nmin 2.833000e+03\n25% 5.022802e+05\n50% 3.718330e+06\n75% 2.636053e+07\nmax 3.032156e+09\nName: 1960, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"years = range(1960, 2022,1)\nrows = data.Year.isin(years) \ncols = ['Year','Population']\ndata_graph = data.loc[rows, cols]\ndata_graph['Population'] = np.log10(data_graph.Population)\n\nsns.set(rc={'figure.figsize':(15,15)})\nsns.boxplot(x = data_graph['Year'], y = data_graph['Population'])\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:23.655917Z","iopub.execute_input":"2023-10-26T20:34:23.656234Z","iopub.status.idle":"2023-10-26T20:34:25.959859Z","shell.execute_reply.started":"2023-10-26T20:34:23.656213Z","shell.execute_reply":"2023-10-26T20:34:25.959148Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## To which extend, the GDP and population may have a relationship?\n\nWe produced a scatter plot to explore the possible relationship between both the GDP and poplution. It challenging to interpret a possible relationship. A 3D scatter plot shows some level of complexity in the data. Some advanced regression or some clustering analysis may help identifying some relationships. ","metadata":{}},{"cell_type":"code","source":"data['log_gdp'] = np.log10(data.GDP)\ndata.log_gdp.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:32.865676Z","iopub.execute_input":"2023-10-26T20:34:32.865982Z","iopub.status.idle":"2023-10-26T20:34:32.876696Z","shell.execute_reply.started":"2023-10-26T20:34:32.865958Z","shell.execute_reply":"2023-10-26T20:34:32.875423Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"count 13156.000000\nmean 3.315185\nstd 0.752098\nmin 1.106767\n25% 2.734065\n50% 3.268659\n75% 3.879667\nmax 5.369801\nName: log_gdp, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.scatter(data.log_gdp, data.log_pop)\nplt.xlabel('GDP (USD) log-values')\nplt.ylabel('Population - log values ')","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:36.264746Z","iopub.execute_input":"2023-10-26T20:34:36.265081Z","iopub.status.idle":"2023-10-26T20:34:36.706387Z","shell.execute_reply.started":"2023-10-26T20:34:36.265056Z","shell.execute_reply":"2023-10-26T20:34:36.705531Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population - log values ')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"\n\n\n# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data.Year, data.Population, data.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:34:40.474750Z","iopub.execute_input":"2023-10-26T20:34:40.475177Z","iopub.status.idle":"2023-10-26T20:34:40.996103Z","shell.execute_reply.started":"2023-10-26T20:34:40.475149Z","shell.execute_reply":"2023-10-26T20:34:40.995418Z"},"trusted":true},"execution_count":131,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## How has the Internet usage increased through time?","metadata":{}},{"cell_type":"code","source":"plt.scatter(internet.Year, internet['No. of Internet Users'])","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:46:20.003157Z","iopub.execute_input":"2023-10-26T20:46:20.003504Z","iopub.status.idle":"2023-10-26T20:46:20.415763Z","shell.execute_reply.started":"2023-10-26T20:46:20.003477Z","shell.execute_reply":"2023-10-26T20:46:20.414596Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# Creating figure\nfig = plt.figure(figsize = (15, 15))\nax = plt.axes(projection =\"3d\")\n \n# Creating plot\nax.scatter3D(data_int['No. of Internet Users'], data_int.Population, data_int.GDP, color = \"green\")\nplt.title(\"Population and GDP between 1960 and 2021\")\nplt.xlabel('Year')\nplt.ylabel('Population - log values ')\n\n \n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:45:47.490885Z","iopub.execute_input":"2023-10-26T20:45:47.491210Z","iopub.status.idle":"2023-10-26T20:46:19.212868Z","shell.execute_reply.started":"2023-10-26T20:45:47.491185Z","shell.execute_reply":"2023-10-26T20:46:19.211734Z"},"trusted":true},"execution_count":141,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"# create data\nx = np.log10(data_int.Population)\ny = np.log10(data_int.GDP)\nz = data_int['Internet Users(%)']\n \n# use the scatter function\nplt.scatter(x, y, s=z, alpha=0.5)\n\n# show the graph\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T20:42:51.380366Z","iopub.execute_input":"2023-10-26T20:42:51.380727Z","iopub.status.idle":"2023-10-26T20:43:12.249788Z","shell.execute_reply.started":"2023-10-26T20:42:51.380701Z","shell.execute_reply":"2023-10-26T20:43:12.248684Z"},"trusted":true},"execution_count":140,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\ No newline at end of file diff --git a/Data engineering and science/titanic-random-forrest.ipynb b/Data engineering and science/titanic-random-forrest.ipynb new file mode 100644 index 0000000..8570048 --- /dev/null +++ b/Data engineering and science/titanic-random-forrest.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-27T13:29:00.362275Z","iopub.execute_input":"2023-02-27T13:29:00.362816Z","iopub.status.idle":"2023-02-27T13:29:00.369677Z","shell.execute_reply.started":"2023-02-27T13:29:00.362765Z","shell.execute_reply":"2023-02-27T13:29:00.368868Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"# Introduction\n\nData can be transformed into knowledge and then enhanced intelligence. We use the titanic datasets to explore first its features. The Titanic datasets contains the records of the Titanic passengers during its maiden voyage and tragic demise. \n\nWe apply some data engineering techniques to prepare the data for some various machine learning techniques - decision trees and random forrests for the purpose of predicting survivors. \n\nThe notebook is structured in this manner:\n\n\n- __[Upload libraires](#Libraries)__\n- __[Data engineering](#Data-engineering)__\n- __[Survival characteristics](#Survival-characteristics)__\n- __[Data preparation for classification](#Data-preparation-for-classification)__ \n- __[Method: Decision Trees](#Method-:-Decision-Trees)__ \n- __[Method: Random Forrest](#Method:-Random-Forrest)__\n\n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Libraries\n\nWe upload all the libraries required for all the operations of this notebook.","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport seaborn as sns\nimport os\nimport random as rand\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\nfrom sklearn.model_selection import train_test_split # Import train_test_split function\nfrom sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nimport scipy.stats as stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\n\nimport tensorflow as tf\nif (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed\n print('Please install tensorflow 2.0 to run this notebook')\nprint('Tensorflow version: ',tf.__version__)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:15.729194Z","iopub.execute_input":"2023-02-27T13:28:15.729546Z","iopub.status.idle":"2023-02-27T13:28:29.512628Z","shell.execute_reply.started":"2023-02-27T13:28:15.729512Z","shell.execute_reply":"2023-02-27T13:28:29.510853Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Tensorflow version: 2.11.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data engineering\n\nWe explore the files in the folder, sets the paths and file names. These variables will be used in each section.","metadata":{}},{"cell_type":"code","source":"!ls ../input/titanic/\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.514792Z","iopub.execute_input":"2023-02-27T13:28:29.515534Z","iopub.status.idle":"2023-02-27T13:28:29.801170Z","shell.execute_reply.started":"2023-02-27T13:28:29.515500Z","shell.execute_reply":"2023-02-27T13:28:29.800164Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"ls: cannot access '../input/titanic/': No such file or directory\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data_path = '../input/titanic/train.csv'\ntest_data_path = '../input/titanic/test.csv'","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.802873Z","iopub.execute_input":"2023-02-27T13:28:29.803472Z","iopub.status.idle":"2023-02-27T13:28:29.808818Z","shell.execute_reply.started":"2023-02-27T13:28:29.803437Z","shell.execute_reply":"2023-02-27T13:28:29.807498Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Import and explore the data \nExplore and import the training and test dataset provided by the competition.","metadata":{}},{"cell_type":"markdown","source":"### Training dataset","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:29.812696Z","iopub.execute_input":"2023-02-27T13:28:29.814300Z","iopub.status.idle":"2023-02-27T13:28:30.152138Z","shell.execute_reply.started":"2023-02-27T13:28:29.814236Z","shell.execute_reply":"2023-02-27T13:28:30.150673Z"},"trusted":true},"execution_count":5,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/207441726.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtitanic_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m 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parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m 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ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m 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storage_options)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m )\n\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/titanic/train.csv'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '../input/titanic/train.csv'","output_type":"error"}]},{"cell_type":"code","source":"titanic_train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.153288Z","iopub.status.idle":"2023-02-27T13:28:30.154841Z","shell.execute_reply.started":"2023-02-27T13:28:30.154432Z","shell.execute_reply":"2023-02-27T13:28:30.154486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.156732Z","iopub.status.idle":"2023-02-27T13:28:30.157230Z","shell.execute_reply.started":"2023-02-27T13:28:30.156985Z","shell.execute_reply":"2023-02-27T13:28:30.157009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Test dataset","metadata":{}},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.159338Z","iopub.status.idle":"2023-02-27T13:28:30.159839Z","shell.execute_reply.started":"2023-02-27T13:28:30.159607Z","shell.execute_reply":"2023-02-27T13:28:30.159633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.161502Z","iopub.status.idle":"2023-02-27T13:28:30.161975Z","shell.execute_reply.started":"2023-02-27T13:28:30.161752Z","shell.execute_reply":"2023-02-27T13:28:30.161777Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"titanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:30.163512Z","iopub.status.idle":"2023-02-27T13:28:30.163966Z","shell.execute_reply.started":"2023-02-27T13:28:30.163747Z","shell.execute_reply":"2023-02-27T13:28:30.163770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" ## Meta data \n \n| Column name | Description|\n|---|---|\n|Passenger_id| unique row indentifier |\n|PClass | Categorical data (1 = 1st; 2 = 2nd; 3 = 3rd)|\n| Survival | Categoricial data (0 = No; 1 = Yes) |\n| Name | Characters - Name of passenger |\n| Sex | Categorical data male or female |\n| Age | integer values representing age |\n| SigSp | integer Number of Siblings/Spouses Aboard |\n| Parch | Number of Parents/Children Aboard |\n| Ticket | Ticket number |\n| Fare | Fare in GBP at time of travel|\n| Cabin | Cabin |\n| Embark | Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)|\n\n\nSource - http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf (7/12/2022)","metadata":{}},{"cell_type":"markdown","source":"# Survival characteristics\nWe explore the survival characteristics using several combinations of columns. We hope to understand better some features that may guide the predictions of survivors.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Passenger and survival\nThe training dataset suggests a minority of passengers survived (i.e., 38% approximately), 62% of passengers perished. Some further decomposition suggests first class passengers may have been more likely to survive than lower classes. The percentages of surviving decreases sharply.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Survived\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:48.498154Z","iopub.execute_input":"2023-02-27T13:28:48.498558Z","iopub.status.idle":"2023-02-27T13:28:48.526098Z","shell.execute_reply.started":"2023-02-27T13:28:48.498520Z","shell.execute_reply":"2023-02-27T13:28:48.525014Z"},"trusted":true},"execution_count":9,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/4256478334.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Survived\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"PassengerId\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtitanic_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'titanic_train' is not defined"],"ename":"NameError","evalue":"name 'titanic_train' is not defined","output_type":"error"}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp = temp.unstack()\ntemp","metadata":{"execution":{"iopub.status.busy":"2023-02-27T13:28:42.082092Z","iopub.execute_input":"2023-02-27T13:28:42.082458Z","iopub.status.idle":"2023-02-27T13:28:42.112345Z","shell.execute_reply.started":"2023-02-27T13:28:42.082431Z","shell.execute_reply":"2023-02-27T13:28:42.110307Z"},"trusted":true},"execution_count":8,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_28/2764600673.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m 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0.01$","metadata":{}},{"cell_type":"code","source":"\nsur_pclass = titanic_train.loc[titanic_train.Survived == 1, \"Pclass\"]\nperish_pclass = titanic_train.loc[titanic_train.Survived == 0, \"Pclass\"]\nfvalue, pvalue = stats.f_oneway(sur_pclass, perish_pclass)\nprint(fvalue, pvalue)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:29:37.721129Z","iopub.execute_input":"2023-02-01T14:29:37.721602Z","iopub.status.idle":"2023-02-01T14:29:37.732491Z","shell.execute_reply.started":"2023-02-01T14:29:37.721566Z","shell.execute_reply":"2023-02-01T14:29:37.731155Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"115.03127218827665 2.5370473879805644e-25\n","output_type":"stream"}]},{"cell_type":"code","source":"model = ols('Survived ~ Pclass', data=titanic_train).fit()\nanova_table = sm.stats.anova_lm(model, typ=2)\nanova_table","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:32:55.703937Z","iopub.execute_input":"2023-02-01T14:32:55.704329Z","iopub.status.idle":"2023-02-01T14:32:55.739204Z","shell.execute_reply.started":"2023-02-01T14:32:55.704285Z","shell.execute_reply":"2023-02-01T14:32:55.738138Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" sum_sq df F PR(>F)\nPclass 24.142900 1.0 115.031272 2.537047e-25\nResidual 186.584373 889.0 NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sum_sqdfFPR(>F)
Pclass24.1429001.0115.0312722.537047e-25
Residual186.584373889.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"__Interpretation__\n\nThe p value obtained from ANOVA analysis is significant (p < 0.01), and therefore, we conclude that there are significant differences among the classes who have perished or survived.","metadata":{}},{"cell_type":"markdown","source":"## Embarkment and survival\nThe port of embarkment appears to have less influence on the survival percentages. It appears most passengers embarked at Southampton (72% approximately), 18% of passengers at Cherbourg, and the remaining from Queenstown. Half of the Cherbourg passengers booked first class tickets. Other embarkment ports appears to be much lower. Half of the passengers from Southampton booked third class tickets. We could surmise the latter may have contributed to the lowest percentages of surviving the accident.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Embarked\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.612759Z","iopub.execute_input":"2023-02-01T14:50:09.613134Z","iopub.status.idle":"2023-02-01T14:50:09.626172Z","shell.execute_reply.started":"2023-02-01T14:50:09.613106Z","shell.execute_reply":"2023-02-01T14:50:09.625109Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Embarked\nC 0.188552\nQ 0.086420\nS 0.722783\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.631955Z","iopub.execute_input":"2023-02-01T14:50:09.632520Z","iopub.status.idle":"2023-02-01T14:50:09.652387Z","shell.execute_reply.started":"2023-02-01T14:50:09.632486Z","shell.execute_reply":"2023-02-01T14:50:09.651379Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Pclass 1 2 3\nEmbarked \nC 0.505952 0.101190 0.392857\nQ 0.025974 0.038961 0.935065\nS 0.197205 0.254658 0.548137","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Pclass123
Embarked
C0.5059520.1011900.392857
Q0.0259740.0389610.935065
S0.1972050.2546580.548137
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:10.164258Z","iopub.execute_input":"2023-02-01T14:50:10.164676Z","iopub.status.idle":"2023-02-01T14:50:10.185023Z","shell.execute_reply.started":"2023-02-01T14:50:10.164643Z","shell.execute_reply":"2023-02-01T14:50:10.183924Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked \nC 0.446429 0.553571\nQ 0.610390 0.389610\nS 0.663043 0.336957","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Embarked
C0.4464290.553571
Q0.6103900.389610
S0.6630430.336957
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:16.126671Z","iopub.execute_input":"2023-02-01T14:50:16.127079Z","iopub.status.idle":"2023-02-01T14:50:16.150013Z","shell.execute_reply.started":"2023-02-01T14:50:16.127043Z","shell.execute_reply":"2023-02-01T14:50:16.149263Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked Pclass \nC 1 0.154762 0.351190\n 2 0.047619 0.053571\n 3 0.244048 0.148810\nQ 1 0.012987 0.012987\n 2 0.012987 0.025974\n 3 0.584416 0.350649\nS 1 0.082298 0.114907\n 2 0.136646 0.118012\n 3 0.444099 0.104037","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
EmbarkedPclass
C10.1547620.351190
20.0476190.053571
30.2440480.148810
Q10.0129870.012987
20.0129870.025974
30.5844160.350649
S10.0822980.114907
20.1366460.118012
30.4440990.104037
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Gender and survival \nThe training dataset suggests that nearly two thirds of passengers were male, and a third were female. Women and girls appears to have a higher survival percentagers - three quarters of female passengers survived the accident, but only 19% of male survived.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Sex\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:21.461493Z","iopub.execute_input":"2023-02-01T14:50:21.461874Z","iopub.status.idle":"2023-02-01T14:50:21.474706Z","shell.execute_reply.started":"2023-02-01T14:50:21.461843Z","shell.execute_reply":"2023-02-01T14:50:21.473520Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Sex\nfemale 0.352413\nmale 0.647587\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:22.544412Z","iopub.execute_input":"2023-02-01T14:50:22.544835Z","iopub.status.idle":"2023-02-01T14:50:22.565483Z","shell.execute_reply.started":"2023-02-01T14:50:22.544801Z","shell.execute_reply":"2023-02-01T14:50:22.564390Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex \nfemale 0.257962 0.742038\nmale 0.811092 0.188908","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Sex
female0.2579620.742038
male0.8110920.188908
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:23.099261Z","iopub.execute_input":"2023-02-01T14:50:23.099666Z","iopub.status.idle":"2023-02-01T14:50:23.126110Z","shell.execute_reply.started":"2023-02-01T14:50:23.099635Z","shell.execute_reply":"2023-02-01T14:50:23.125241Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex Pclass \nfemale 1 0.013889 0.421296\n 2 0.032609 0.380435\n 3 0.146640 0.146640\nmale 1 0.356481 0.208333\n 2 0.494565 0.092391\n 3 0.610998 0.095723","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SexPclass
female10.0138890.421296
20.0326090.380435
30.1466400.146640
male10.3564810.208333
20.4945650.092391
30.6109980.095723
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age, siblings and parents\n\nThe age distribution appears to be multi-modal with some two peaks at around 0 and 25. Both training and testing datasets have a similar mean and standard deviation. However, some skewness may affect a normal distributions and any normalisation processes of the data.\n\nThe survivors and other passengers age appears to be of similar age at the point of centrality. We will need to complete some statistical tests to accept or reject the null hypothesis that the age distribution of survivors and non-survivors are the same. We surmise the values may have be unknown, without any data preparation the tests cannot be completed.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.246337Z","iopub.execute_input":"2023-02-01T14:50:24.247338Z","iopub.status.idle":"2023-02-01T14:50:24.633738Z","shell.execute_reply.started":"2023-02-01T14:50:24.247275Z","shell.execute_reply":"2023-02-01T14:50:24.632647Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"count 714.000000\nmean 29.699118\nstd 14.526497\nmin 0.420000\n25% 20.125000\n50% 28.000000\n75% 38.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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4C9VfWJecnVZVtIcmJ3/TX05ub30ivyy2eVrao+VFWnVdUivWPqn6vqd2adK8kJSV6/fJ3enO4eZvxcVtXTwA+TnNXtegfw6KxzrXAlL02fwOyz/QC4IMlru7/R5d/Z+I+xWb7wMMSLAZcA/05v7vSPZ5zldnrzWT+lNyq5it7c6Q7gceCfgJOnnOkiev88fBjY3V0umXWuLtuvAw922fYAf9Lt/xXgm8AT9P7J+6oZPqdvA+6Zh1zd4z/UXR5ZPt7n5LncDOzsnst/BE6ah1xdthOA/wDe0Ldv5tmAG4Fvd8f+3wGvmsQx5lvpJalR8zyFIkk6AgtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNer/AKGGVs0lKoXzAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.694278Z","iopub.execute_input":"2023-02-01T14:50:24.695165Z","iopub.status.idle":"2023-02-01T14:50:25.062419Z","shell.execute_reply.started":"2023-02-01T14:50:24.695120Z","shell.execute_reply":"2023-02-01T14:50:25.061338Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"count 332.000000\nmean 30.272590\nstd 14.181209\nmin 0.170000\n25% 21.000000\n50% 27.000000\n75% 39.000000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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y=\"Age\", data=titanic_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:25.141606Z","iopub.execute_input":"2023-02-01T14:50:25.142000Z","iopub.status.idle":"2023-02-01T14:50:25.355591Z","shell.execute_reply.started":"2023-02-01T14:50:25.141964Z","shell.execute_reply":"2023-02-01T14:50:25.354536Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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majority of passengers may be travelling on their own without a spouse, sibling, children of parents on board. However, passengers with 1 or 2 siblings/spouse appears to have survived; the percentages is in the range of 46% to 54%. Parents or individuals with one, two or three parents were less likely to perished - the percentages ranges between 50% and 60%.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"SibSp\"]).count()[\"PassengerId\"]/titanic_train.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.432856Z","iopub.execute_input":"2023-02-01T14:50:29.433512Z","iopub.status.idle":"2023-02-01T14:50:29.445537Z","shell.execute_reply.started":"2023-02-01T14:50:29.433478Z","shell.execute_reply":"2023-02-01T14:50:29.444361Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"SibSp\n0 0.682379\n1 0.234568\n2 0.031425\n3 0.017957\n4 0.020202\n5 0.005612\n8 0.007856\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"SibSp\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.890502Z","iopub.execute_input":"2023-02-01T14:50:29.890860Z","iopub.status.idle":"2023-02-01T14:50:29.915654Z","shell.execute_reply.started":"2023-02-01T14:50:29.890829Z","shell.execute_reply":"2023-02-01T14:50:29.914470Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSibSp \n0 0.654605 0.345395\n1 0.464115 0.535885\n2 0.535714 0.464286\n3 0.750000 0.250000\n4 0.833333 0.166667\n5 1.000000 NaN\n8 1.000000 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SibSp
00.6546050.345395
10.4641150.535885
20.5357140.464286
30.7500000.250000
40.8333330.166667
51.000000NaN
81.000000NaN
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.SibSp, bins = 8)\ntitanic_train.SibSp.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:30.548135Z","iopub.execute_input":"2023-02-01T14:50:30.548522Z","iopub.status.idle":"2023-02-01T14:50:30.775129Z","shell.execute_reply.started":"2023-02-01T14:50:30.548488Z","shell.execute_reply":"2023-02-01T14:50:30.774363Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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0.760943\n1 0.132435\n2 0.089787\n3 0.005612\n4 0.004489\n5 0.005612\n6 0.001122\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Parch\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.187336Z","iopub.execute_input":"2023-02-01T14:50:31.187728Z","iopub.status.idle":"2023-02-01T14:50:31.209460Z","shell.execute_reply.started":"2023-02-01T14:50:31.187695Z","shell.execute_reply":"2023-02-01T14:50:31.208365Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nParch \n0 0.656342 0.343658\n1 0.449153 0.550847\n2 0.500000 0.500000\n3 0.400000 0.600000\n4 1.000000 NaN\n5 0.800000 0.200000\n6 1.000000 NaN","text/html":"
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Survived01
Parch
00.6563420.343658
10.4491530.550847
20.5000000.500000
30.4000000.600000
41.000000NaN
50.8000000.200000
61.000000NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Parch, bins = 6)\ntitanic_train.Parch.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.433509Z","iopub.execute_input":"2023-02-01T14:50:31.434117Z","iopub.status.idle":"2023-02-01T14:50:31.664941Z","shell.execute_reply.started":"2023-02-01T14:50:31.434071Z","shell.execute_reply":"2023-02-01T14:50:31.664079Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.381594\nstd 0.806057\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 6.000000\nName: Parch, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD5CAYAAADcDXXiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQiElEQVR4nO3db4xddZ3H8fdHKuqia/kz2zRt3bKx0ZhNFDIBDMa4NhoBY/tACWRXuqRJ9wFrMG6i1QdrTPYBPhE12ZA0VLfs+o9FDY0SV1Ixrg9Ap4ioFNeRlLQN0JF/isQ16HcfzK/rUKfcOzN35nZ+vl/J5P7O9/zuPd+Thk9Pf3PuIVWFJKkvLxp3A5Kk0TPcJalDhrskdchwl6QOGe6S1CHDXZI6tGbQhCSvAb40p/RXwD8Dt7T6ZuAwcGVVPZkkwKeAy4Fngb+vqntf6BjnnXdebd68eRHtS9KfroMHD/6iqibm25eF3Oee5AzgGHAxcB3wRFXdkGQ3cHZVfSjJ5cD7mA33i4FPVdXFL/S5k5OTNTU1NXQfkiRIcrCqJufbt9Blma3Az6vqYWAbsK/V9wHb23gbcEvNuhtYm2T9wtuWJC3WQsP9KuALbbyuqh5p40eBdW28ATgy5z1HW02StEKGDvckZwLvAv7z5H01u7azoOcYJNmVZCrJ1MzMzELeKkkaYCFX7pcB91bVY237sRPLLe31eKsfAzbNed/GVnueqtpTVZNVNTkxMe/vAyRJi7SQcL+aPyzJAOwHdrTxDuD2OfVrMusS4Ok5yzeSpBUw8FZIgCRnAW8D/mFO+Qbg1iQ7gYeBK1v9DmbvlJlm9lbIa0fWrSRpKEOFe1X9Gjj3pNrjzN49c/LcYvY2SUnSmPgNVUnqkOEuSR0aalnmdLZ599fH3cLIHL7hinG3IKkTXrlLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHRoq3JOsTXJbkgeTHEryxiTnJLkzyc/a69ltbpJ8Osl0kvuTXLi8pyBJOtmwV+6fAr5RVa8FXg8cAnYDB6pqC3CgbQNcBmxpP7uAm0basSRpoIHhnuSVwJuBvQBV9duqegrYBuxr0/YB29t4G3BLzbobWJtk/Yj7liS9gGGu3M8HZoDPJvlBkpuTnAWsq6pH2pxHgXVtvAE4Muf9R1tNkrRChgn3NcCFwE1VdQHwa/6wBANAVRVQCzlwkl1JppJMzczMLOStkqQBhgn3o8DRqrqnbd/GbNg/dmK5pb0eb/uPAZvmvH9jqz1PVe2pqsmqmpyYmFhs/5KkeQwM96p6FDiS5DWttBV4ANgP7Gi1HcDtbbwfuKbdNXMJ8PSc5RtJ0gpYM+S89wGfS3Im8BBwLbN/MdyaZCfwMHBlm3sHcDkwDTzb5kqSVtBQ4V5V9wGT8+zaOs/cAq5bWluSpKXwG6qS1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHRoq3JMcTvKjJPclmWq1c5LcmeRn7fXsVk+STyeZTnJ/kguX8wQkSX9sIVfuf1NVb6iqyba9GzhQVVuAA20b4DJgS/vZBdw0qmYlScNZyrLMNmBfG+8Dts+p31Kz7gbWJlm/hONIkhZo2HAv4JtJDibZ1WrrquqRNn4UWNfGG4Ajc957tNUkSStkzZDz3lRVx5L8BXBnkgfn7qyqSlILOXD7S2IXwKte9aqFvFWSNMBQV+5Vday9Hge+ClwEPHZiuaW9Hm/TjwGb5rx9Y6ud/Jl7qmqyqiYnJiYWfwaSpD8yMNyTnJXkFSfGwNuBHwP7gR1t2g7g9jbeD1zT7pq5BHh6zvKNJGkFDLMssw74apIT8z9fVd9I8n3g1iQ7gYeBK9v8O4DLgWngWeDakXctSXpBA8O9qh4CXj9P/XFg6zz1Aq4bSXeSpEXxG6qS1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDQ4d7kjOS/CDJ19r2+UnuSTKd5EtJzmz1l7Tt6bZ/8zL1Lkk6hYVcuV8PHJqz/XHgxqp6NfAksLPVdwJPtvqNbZ4kaQUNFe5JNgJXADe37QBvBW5rU/YB29t4W9um7d/a5kuSVsiwV+6fBD4I/L5tnws8VVXPte2jwIY23gAcAWj7n27zJUkrZGC4J3kncLyqDo7ywEl2JZlKMjUzMzPKj5akP3nDXLlfCrwryWHgi8wux3wKWJtkTZuzETjWxseATQBt/yuBx0/+0KraU1WTVTU5MTGxpJOQJD3fwHCvqg9X1caq2gxcBXyrqv4WuAt4d5u2A7i9jfe3bdr+b1VVjbRrSdILWsp97h8CPpBkmtk19b2tvhc4t9U/AOxeWouSpIVaM3jKH1TVt4Fvt/FDwEXzzPkN8J4R9CZJWiS/oSpJHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUoYHhnuSlSb6X5IdJfpLkY61+fpJ7kkwn+VKSM1v9JW17uu3fvMznIEk6yTBX7v8LvLWqXg+8AXhHkkuAjwM3VtWrgSeBnW3+TuDJVr+xzZMkraCB4V6znmmbL24/BbwVuK3V9wHb23hb26bt35oko2pYkjTYUGvuSc5Ich9wHLgT+DnwVFU916YcBTa08QbgCEDb/zRw7gh7liQNMFS4V9XvquoNwEbgIuC1Sz1wkl1JppJMzczMLPXjJElzLOhumap6CrgLeCOwNsmatmsjcKyNjwGbANr+VwKPz/NZe6pqsqomJyYmFte9JGlew9wtM5FkbRu/DHgbcIjZkH93m7YDuL2N97dt2v5vVVWNsGdJ0gBrBk9hPbAvyRnM/mVwa1V9LckDwBeT/AvwA2Bvm78X+Pck08ATwFXL0Lck6QUMDPequh+4YJ76Q8yuv59c/w3wnpF0J0laFL+hKkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOjQw3JNsSnJXkgeS/CTJ9a1+TpI7k/ysvZ7d6kny6STTSe5PcuFyn4Qk6fmGuXJ/DvinqnodcAlwXZLXAbuBA1W1BTjQtgEuA7a0n13ATSPvWpL0ggaGe1U9UlX3tvGvgEPABmAbsK9N2wdsb+NtwC01625gbZL1o25cknRqC1pzT7IZuAC4B1hXVY+0XY8C69p4A3BkztuOtpokaYUMHe5JXg58GXh/Vf1y7r6qKqAWcuAku5JMJZmamZlZyFslSQMMFe5JXsxssH+uqr7Syo+dWG5pr8db/Riwac7bN7ba81TVnqqarKrJiYmJxfYvSZrHMHfLBNgLHKqqT8zZtR/Y0cY7gNvn1K9pd81cAjw9Z/lGkrQC1gwx51LgvcCPktzXah8BbgBuTbITeBi4su27A7gcmAaeBa4dZcOSpMEGhntVfRfIKXZvnWd+AdctsS9J0hL4DVVJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHVo4P8gWytn8+6vj7uFkTl8wxXjbkH6kzbwyj3JZ5IcT/LjObVzktyZ5Gft9exWT5JPJ5lOcn+SC5ezeUnS/IZZlvk34B0n1XYDB6pqC3CgbQNcBmxpP7uAm0bTpiRpIQaGe1V9B3jipPI2YF8b7wO2z6nfUrPuBtYmWT+iXiVJQ1rsL1TXVdUjbfwosK6NNwBH5sw72mqSpBW05LtlqqqAWuj7kuxKMpVkamZmZqltSJLmWGy4P3ZiuaW9Hm/1Y8CmOfM2ttofqao9VTVZVZMTExOLbEOSNJ/Fhvt+YEcb7wBun1O/pt01cwnw9JzlG0nSChl4n3uSLwBvAc5LchT4KHADcGuSncDDwJVt+h3A5cA08Cxw7TL0LEkaYGC4V9XVp9i1dZ65BVy31KYkSUvj4wckqUM+fkDLopdHKfgYBa1WXrlLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDyxLuSd6R5KdJppPsXo5jSJJObeThnuQM4F+By4DXAVcned2ojyNJOrU1y/CZFwHTVfUQQJIvAtuAB5bhWNKy2rz76+NuYWQO33DFuFsYGf9cBluOcN8AHJmzfRS4eBmOI2kBegpEDbYc4T6UJLuAXW3zmSQ/XeRHnQf8YjRdjZ3ncvrp5TzAczkt5eNLOpe/PNWO5Qj3Y8CmOdsbW+15qmoPsGepB0syVVWTS/2c04Hncvrp5TzAczldLde5LMfdMt8HtiQ5P8mZwFXA/mU4jiTpFEZ+5V5VzyX5R+C/gDOAz1TVT0Z9HEnSqS3LmntV3QHcsRyfPY8lL+2cRjyX008v5wGey+lqWc4lVbUcnytJGiMfPyBJHVrV4d7LYw6SfCbJ8SQ/HncvS5FkU5K7kjyQ5CdJrh93T4uV5KVJvpfkh+1cPjbunpYqyRlJfpDka+PuZSmSHE7yoyT3JZkadz+LlWRtktuSPJjkUJI3jvTzV+uyTHvMwf8Ab2P2i1LfB66uqlX3TdgkbwaeAW6pqr8edz+LlWQ9sL6q7k3yCuAgsH2V/pkEOKuqnknyYuC7wPVVdfeYW1u0JB8AJoE/r6p3jrufxUpyGJisqlV9n3uSfcB/V9XN7c7CP6uqp0b1+av5yv3/H3NQVb8FTjzmYNWpqu8AT4y7j6Wqqkeq6t42/hVwiNlvLK86NeuZtvni9rM6r4SAJBuBK4Cbx92LIMkrgTcDewGq6rejDHZY3eE+32MOVmWQ9CjJZuAC4J4xt7JobRnjPuA4cGdVrdpzAT4JfBD4/Zj7GIUCvpnkYPum+2p0PjADfLYtld2c5KxRHmA1h7tOU0leDnwZeH9V/XLc/SxWVf2uqt7A7LesL0qyKpfMkrwTOF5VB8fdy4i8qaouZPbJs9e1Zc3VZg1wIXBTVV0A/BoY6e8NV3O4D/WYA62stj79ZeBzVfWVcfczCu2fy3cB7xhzK4t1KfCutlb9ReCtSf5jvC0tXlUda6/Hga8yu0S72hwFjs751+BtzIb9yKzmcPcxB6eZ9kvIvcChqvrEuPtZiiQTSda28cuY/cX9g2NtapGq6sNVtbGqNjP738m3qurvxtzWoiQ5q/2ynraM8XZg1d1lVlWPAkeSvKaVtjLix6KP7amQS9XTYw6SfAF4C3BekqPAR6tq73i7WpRLgfcCP2pr1QAfad9YXm3WA/vaXVkvAm6tqlV9C2En1gFfnb2OYA3w+ar6xnhbWrT3AZ9rF6cPAdeO8sNX7a2QkqRTW83LMpKkUzDcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nq0P8BlAp2Zm3rspoAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We decided to add both fields _Parch_ and _SibSp_ together as a familly. The mean and median age appears to be quite close between the passengers who have survived and perished. For smaller families the spread appears to be smaller than for larger families. \n\nThe highest percentages of surviving the accident suggests that passengers in first and second class with no other familly members. These percentages are loweer than 30%.","metadata":{}},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train.SibSp + titanic_train.Parch\ntemp = titanic_train.groupby([\"fam_members\",\"Survived\"]).agg([np.median, np.mean, np.std])[\"Age\"]\ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.929453Z","iopub.execute_input":"2023-02-01T14:50:31.929858Z","iopub.status.idle":"2023-02-01T14:50:31.977029Z","shell.execute_reply.started":"2023-02-01T14:50:31.929823Z","shell.execute_reply":"2023-02-01T14:50:31.975764Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" median mean std \nSurvived 0 1 0 1 0 1\nfam_members \n0 29.0 30.0 32.414234 31.811538 13.334968 11.970452\n1 30.0 29.0 32.126984 30.781842 11.599836 14.916443\n2 30.5 22.0 31.500000 21.911887 13.776141 17.363697\n3 25.0 14.0 22.833333 16.972381 11.196726 15.054360\n4 12.5 21.0 17.000000 31.000000 15.528775 19.974984\n5 9.0 24.0 17.578947 23.666667 18.637822 0.577350\n6 9.0 11.0 14.875000 15.750000 15.169871 16.070159\n7 12.5 NaN 15.666667 NaN 14.361987 NaN\n10 NaN NaN NaN NaN NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medianmeanstd
Survived010101
fam_members
029.030.032.41423431.81153813.33496811.970452
130.029.032.12698430.78184211.59983614.916443
230.522.031.50000021.91188713.77614117.363697
325.014.022.83333316.97238111.19672615.054360
412.521.017.00000031.00000015.52877519.974984
59.024.017.57894723.66666718.6378220.577350
69.011.014.87500015.75000015.16987116.070159
712.5NaN15.666667NaN14.361987NaN
10NaNNaNNaNNaNNaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.fam_members, bins = 10)\ntitanic_train.fam_members.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.210511Z","iopub.execute_input":"2023-02-01T14:50:32.210873Z","iopub.status.idle":"2023-02-01T14:50:32.431170Z","shell.execute_reply.started":"2023-02-01T14:50:32.210842Z","shell.execute_reply":"2023-02-01T14:50:32.430235Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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twlqUP/Bzr6a6xtewKkAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"fam_members\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.505200Z","iopub.execute_input":"2023-02-01T14:50:32.505646Z","iopub.status.idle":"2023-02-01T14:50:32.533253Z","shell.execute_reply.started":"2023-02-01T14:50:32.505607Z","shell.execute_reply":"2023-02-01T14:50:32.532232Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nfam_members Pclass \n0 1 0.236111 0.268519\n 2 0.369565 0.195652\n 3 0.519348 0.140530\n1 1 0.087963 0.236111\n 2 0.086957 0.097826\n 3 0.075356 0.040733\n2 1 0.027778 0.083333\n 2 0.054348 0.114130\n 3 0.054990 0.040733\n3 1 0.009259 0.023148\n 2 0.016304 0.054348\n 3 0.006110 0.012220\n4 1 NaN 0.009259\n 2 NaN 0.005435\n 3 0.024440 NaN\n5 1 0.009259 0.009259\n 2 NaN 0.005435\n 3 0.034623 NaN\n6 3 0.016293 0.008147\n7 3 0.012220 NaN\n10 3 0.014257 NaN","text/html":"
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Survived01
fam_membersPclass
010.2361110.268519
20.3695650.195652
30.5193480.140530
110.0879630.236111
20.0869570.097826
30.0753560.040733
210.0277780.083333
20.0543480.114130
30.0549900.040733
310.0092590.023148
20.0163040.054348
30.0061100.012220
41NaN0.009259
2NaN0.005435
30.024440NaN
510.0092590.009259
2NaN0.005435
30.034623NaN
630.0162930.008147
730.012220NaN
1030.014257NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Futher analysis and discussions\nThe data in their current states suggests that the distribution for the field _Survived_ is likely to be binomial. It has a lowest occurrences of surviving, which is a shocking statistic.\n\nThe passenger class has more occurrences of third classes. However, First and second class female passengers were more likely to survive the accident. First class male passengers had the also the highest survival rate. The Age is skewed to the left; some age may be unknown. It appears (see below) the younger passengers may have been traveling with other members of a family and perhaps reduced their survival rates; the largest familly appears to be travelling in third class. Most occurrences were families made of 0, 1, or 3 family members. \n\nThis analysis suggests that perhaps the passenger class familly, and the gender may have contributed to a higher survival rate. However, the familly size may have contributed to survived too. The classifiers will need to identify other patterns that may have contributed to survive the accident. It is likely to be quite challenging as no linear relationships or grouping may be present in the data.\n\n","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=3).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.247279Z","iopub.execute_input":"2023-02-01T14:50:33.247672Z","iopub.status.idle":"2023-02-01T14:50:33.275585Z","shell.execute_reply.started":"2023-02-01T14:50:33.247640Z","shell.execute_reply":"2023-02-01T14:50:33.274507Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass fam_members Sex \n1 0 female 0.001821 0.096491\n male 0.091075 0.073099\n 1 female NaN 0.114035\n male 0.034608 0.035088\n 2 female NaN 0.038012\n male 0.010929 0.014620\n 3 female 0.003643 0.005848\n male NaN 0.008772\n 4 female NaN 0.005848\n 5 female NaN 0.005848\n male 0.003643 NaN\n2 0 female 0.005464 0.084795\n male 0.118397 0.020468\n 1 female 0.003643 0.049708\n male 0.025501 0.002924\n 2 female 0.001821 0.038012\n male 0.016393 0.023392\n 3 female NaN 0.026316\n male 0.005464 0.002924\n 4 female NaN 0.002924\n 5 female NaN 0.002924\n3 0 female 0.041894 0.108187\n male 0.422587 0.093567\n 1 female 0.025501 0.043860\n male 0.041894 0.014620\n 2 female 0.018215 0.035088\n male 0.030965 0.023392\n 3 female 0.001821 0.014620\n male 0.003643 0.002924\n 4 female 0.016393 NaN\n male 0.005464 NaN\n 5 female 0.009107 NaN\n male 0.021858 NaN\n 6 female 0.009107 0.008772\n male 0.005464 0.002924\n 7 female 0.003643 NaN\n male 0.007286 NaN\n 10 female 0.005464 NaN\n male 0.007286 NaN","text/html":"
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Survived01
Pclassfam_membersSex
10female0.0018210.096491
male0.0910750.073099
1femaleNaN0.114035
male0.0346080.035088
2femaleNaN0.038012
male0.0109290.014620
3female0.0036430.005848
maleNaN0.008772
4femaleNaN0.005848
5femaleNaN0.005848
male0.003643NaN
20female0.0054640.084795
male0.1183970.020468
1female0.0036430.049708
male0.0255010.002924
2female0.0018210.038012
male0.0163930.023392
3femaleNaN0.026316
male0.0054640.002924
4femaleNaN0.002924
5femaleNaN0.002924
30female0.0418940.108187
male0.4225870.093567
1female0.0255010.043860
male0.0418940.014620
2female0.0182150.035088
male0.0309650.023392
3female0.0018210.014620
male0.0036430.002924
4female0.016393NaN
male0.005464NaN
5female0.009107NaN
male0.021858NaN
6female0.0091070.008772
male0.0054640.002924
7female0.003643NaN
male0.007286NaN
10female0.005464NaN
male0.007286NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns = [\"Survived\",\"Pclass\",\"Age\", \"fam_members\"]\ntitanic_train = titanic_train[columns]\npd.plotting.scatter_matrix(titanic_train, diagonal='kde')","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.689687Z","iopub.execute_input":"2023-02-01T14:50:33.690876Z","iopub.status.idle":"2023-02-01T14:50:34.713667Z","shell.execute_reply.started":"2023-02-01T14:50:33.690832Z","shell.execute_reply":"2023-02-01T14:50:34.712910Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"array([[,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ]],\n dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The percentages suggests passenger class and gender may be the factor that may lead to survival. ","metadata":{}},{"cell_type":"markdown","source":"# Data preparation for classification\nThis section prepares the data for classifiers. We transform the data in suitable data types supported by the classifiers, remove null values and imputes some values when required. Some columns are deleted; they may be either character or we surmise not suitable for classification.","metadata":{}},{"cell_type":"markdown","source":"## Integer to float\nWe upload the data for a cleaning and display the columns with their data types to float on both datasets.","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.368853Z","iopub.execute_input":"2023-02-01T14:50:35.370121Z","iopub.status.idle":"2023-02-01T14:50:35.386127Z","shell.execute_reply.started":"2023-02-01T14:50:35.370069Z","shell.execute_reply":"2023-02-01T14:50:35.385078Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.770203Z","iopub.execute_input":"2023-02-01T14:50:35.770631Z","iopub.status.idle":"2023-02-01T14:50:35.784625Z","shell.execute_reply.started":"2023-02-01T14:50:35.770596Z","shell.execute_reply":"2023-02-01T14:50:35.783551Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_train[\"SibSp\"] = titanic_train[\"SibSp\"].astype(float)\ntitanic_train[\"Parch\"] = titanic_train[\"Parch\"].astype(float)\ntitanic_train[\"Survived\"] = titanic_train[\"Survived\"].astype(float)\ntitanic_train[\"Pclass\"] = titanic_train[\"Pclass\"].astype(float)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.166628Z","iopub.execute_input":"2023-02-01T14:50:36.167303Z","iopub.status.idle":"2023-02-01T14:50:36.181459Z","shell.execute_reply.started":"2023-02-01T14:50:36.167252Z","shell.execute_reply":"2023-02-01T14:50:36.178943Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)\ntitanic_test[\"SibSp\"] = titanic_test[\"SibSp\"].astype(float)\ntitanic_test[\"Parch\"] = titanic_test[\"Parch\"].astype(float)\ntitanic_test[\"Pclass\"] = titanic_test[\"Pclass\"].astype(float)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.666190Z","iopub.execute_input":"2023-02-01T14:50:36.667397Z","iopub.status.idle":"2023-02-01T14:50:36.678991Z","shell.execute_reply.started":"2023-02-01T14:50:36.667345Z","shell.execute_reply":"2023-02-01T14:50:36.677862Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Null values \n\nWe remove all the nulls values from some of the columns; i.e., PassengerId, Fare, SibSp, Parch, and Embarked. Some fares were unknown, but all passengers ID was set to a unique number. ","metadata":{}},{"cell_type":"code","source":"titanic_train.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.489505Z","iopub.execute_input":"2023-02-01T14:50:37.489938Z","iopub.status.idle":"2023-02-01T14:50:37.497591Z","shell.execute_reply.started":"2023-02-01T14:50:37.489901Z","shell.execute_reply":"2023-02-01T14:50:37.496243Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.991239Z","iopub.execute_input":"2023-02-01T14:50:37.992524Z","iopub.status.idle":"2023-02-01T14:50:38.000114Z","shell.execute_reply.started":"2023-02-01T14:50:37.992478Z","shell.execute_reply":"2023-02-01T14:50:37.998884Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Fare.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.437569Z","iopub.execute_input":"2023-02-01T14:50:38.437966Z","iopub.status.idle":"2023-02-01T14:50:38.445766Z","shell.execute_reply.started":"2023-02-01T14:50:38.437933Z","shell.execute_reply":"2023-02-01T14:50:38.444961Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.994637Z","iopub.execute_input":"2023-02-01T14:50:38.995337Z","iopub.status.idle":"2023-02-01T14:50:39.002110Z","shell.execute_reply.started":"2023-02-01T14:50:38.995287Z","shell.execute_reply":"2023-02-01T14:50:39.000886Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Parch.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.436990Z","iopub.execute_input":"2023-02-01T14:50:39.437517Z","iopub.status.idle":"2023-02-01T14:50:39.445363Z","shell.execute_reply.started":"2023-02-01T14:50:39.437381Z","shell.execute_reply":"2023-02-01T14:50:39.444366Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.905392Z","iopub.execute_input":"2023-02-01T14:50:39.905832Z","iopub.status.idle":"2023-02-01T14:50:39.913740Z","shell.execute_reply.started":"2023-02-01T14:50:39.905797Z","shell.execute_reply":"2023-02-01T14:50:39.912816Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.307865Z","iopub.execute_input":"2023-02-01T14:50:40.308905Z","iopub.status.idle":"2023-02-01T14:50:40.316347Z","shell.execute_reply.started":"2023-02-01T14:50:40.308849Z","shell.execute_reply":"2023-02-01T14:50:40.315199Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_test[\"Fare\"].isnull(),\"Fare\"] = -1.0\ntitanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.604978Z","iopub.execute_input":"2023-02-01T14:50:40.605706Z","iopub.status.idle":"2023-02-01T14:50:40.614214Z","shell.execute_reply.started":"2023-02-01T14:50:40.605660Z","shell.execute_reply":"2023-02-01T14:50:40.613381Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.071733Z","iopub.execute_input":"2023-02-01T14:50:41.072626Z","iopub.status.idle":"2023-02-01T14:50:41.081041Z","shell.execute_reply.started":"2023-02-01T14:50:41.072577Z","shell.execute_reply":"2023-02-01T14:50:41.079958Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.270757Z","iopub.execute_input":"2023-02-01T14:50:41.271157Z","iopub.status.idle":"2023-02-01T14:50:41.282542Z","shell.execute_reply.started":"2023-02-01T14:50:41.271122Z","shell.execute_reply":"2023-02-01T14:50:41.281267Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.500496Z","iopub.execute_input":"2023-02-01T14:50:41.500902Z","iopub.status.idle":"2023-02-01T14:50:41.515629Z","shell.execute_reply.started":"2023-02-01T14:50:41.500870Z","shell.execute_reply":"2023-02-01T14:50:41.514309Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.775897Z","iopub.execute_input":"2023-02-01T14:50:41.776267Z","iopub.status.idle":"2023-02-01T14:50:41.789089Z","shell.execute_reply.started":"2023-02-01T14:50:41.776237Z","shell.execute_reply":"2023-02-01T14:50:41.787661Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.000121Z","iopub.execute_input":"2023-02-01T14:50:42.001023Z","iopub.status.idle":"2023-02-01T14:50:42.016796Z","shell.execute_reply.started":"2023-02-01T14:50:42.000983Z","shell.execute_reply":"2023-02-01T14:50:42.015509Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.207362Z","iopub.execute_input":"2023-02-01T14:50:42.207763Z","iopub.status.idle":"2023-02-01T14:50:42.218799Z","shell.execute_reply.started":"2023-02-01T14:50:42.207729Z","shell.execute_reply":"2023-02-01T14:50:42.217490Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.428396Z","iopub.execute_input":"2023-02-01T14:50:42.429337Z","iopub.status.idle":"2023-02-01T14:50:42.442620Z","shell.execute_reply.started":"2023-02-01T14:50:42.429286Z","shell.execute_reply":"2023-02-01T14:50:42.441246Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.657623Z","iopub.execute_input":"2023-02-01T14:50:42.658000Z","iopub.status.idle":"2023-02-01T14:50:42.668231Z","shell.execute_reply.started":"2023-02-01T14:50:42.657969Z","shell.execute_reply":"2023-02-01T14:50:42.666865Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.893929Z","iopub.execute_input":"2023-02-01T14:50:42.894325Z","iopub.status.idle":"2023-02-01T14:50:42.904773Z","shell.execute_reply.started":"2023-02-01T14:50:42.894278Z","shell.execute_reply":"2023-02-01T14:50:42.903541Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.119666Z","iopub.execute_input":"2023-02-01T14:50:43.120081Z","iopub.status.idle":"2023-02-01T14:50:43.128473Z","shell.execute_reply.started":"2023-02-01T14:50:43.120043Z","shell.execute_reply":"2023-02-01T14:50:43.127000Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.558402Z","iopub.execute_input":"2023-02-01T14:50:43.558778Z","iopub.status.idle":"2023-02-01T14:50:43.566705Z","shell.execute_reply.started":"2023-02-01T14:50:43.558746Z","shell.execute_reply":"2023-02-01T14:50:43.565387Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.776835Z","iopub.execute_input":"2023-02-01T14:50:43.777203Z","iopub.status.idle":"2023-02-01T14:50:43.786826Z","shell.execute_reply.started":"2023-02-01T14:50:43.777173Z","shell.execute_reply":"2023-02-01T14:50:43.785678Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.999708Z","iopub.execute_input":"2023-02-01T14:50:44.000611Z","iopub.status.idle":"2023-02-01T14:50:44.012435Z","shell.execute_reply.started":"2023-02-01T14:50:44.000573Z","shell.execute_reply":"2023-02-01T14:50:44.011295Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.273326Z","iopub.execute_input":"2023-02-01T14:50:44.273733Z","iopub.status.idle":"2023-02-01T14:50:44.285873Z","shell.execute_reply.started":"2023-02-01T14:50:44.273696Z","shell.execute_reply":"2023-02-01T14:50:44.284650Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.530974Z","iopub.execute_input":"2023-02-01T14:50:44.531405Z","iopub.status.idle":"2023-02-01T14:50:44.543425Z","shell.execute_reply.started":"2023-02-01T14:50:44.531367Z","shell.execute_reply":"2023-02-01T14:50:44.542121Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.768732Z","iopub.execute_input":"2023-02-01T14:50:44.769126Z","iopub.status.idle":"2023-02-01T14:50:44.779844Z","shell.execute_reply.started":"2023-02-01T14:50:44.769091Z","shell.execute_reply":"2023-02-01T14:50:44.779079Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.009603Z","iopub.execute_input":"2023-02-01T14:50:45.009952Z","iopub.status.idle":"2023-02-01T14:50:45.019372Z","shell.execute_reply.started":"2023-02-01T14:50:45.009923Z","shell.execute_reply":"2023-02-01T14:50:45.018131Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.259413Z","iopub.execute_input":"2023-02-01T14:50:45.259813Z","iopub.status.idle":"2023-02-01T14:50:45.270859Z","shell.execute_reply.started":"2023-02-01T14:50:45.259779Z","shell.execute_reply":"2023-02-01T14:50:45.269750Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.492004Z","iopub.execute_input":"2023-02-01T14:50:45.492453Z","iopub.status.idle":"2023-02-01T14:50:45.505989Z","shell.execute_reply.started":"2023-02-01T14:50:45.492416Z","shell.execute_reply":"2023-02-01T14:50:45.504737Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.736753Z","iopub.execute_input":"2023-02-01T14:50:45.737917Z","iopub.status.idle":"2023-02-01T14:50:45.751164Z","shell.execute_reply.started":"2023-02-01T14:50:45.737860Z","shell.execute_reply":"2023-02-01T14:50:45.749612Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.979633Z","iopub.execute_input":"2023-02-01T14:50:45.980021Z","iopub.status.idle":"2023-02-01T14:50:45.987927Z","shell.execute_reply.started":"2023-02-01T14:50:45.979987Z","shell.execute_reply":"2023-02-01T14:50:45.986675Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarkment \nWe remove any NAs from the embarked column. We replace NaNs values with unknown. However, only the training datasets has some unknown values. It could lower accuracy on the prediction on the testing dataset.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.403953Z","iopub.execute_input":"2023-02-01T14:50:46.404807Z","iopub.status.idle":"2023-02-01T14:50:46.413830Z","shell.execute_reply.started":"2023-02-01T14:50:46.404750Z","shell.execute_reply":"2023-02-01T14:50:46.412619Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' nan]\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Embarked'].isna(),'Embarked'] = 'U'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.636113Z","iopub.execute_input":"2023-02-01T14:50:46.637042Z","iopub.status.idle":"2023-02-01T14:50:46.643930Z","shell.execute_reply.started":"2023-02-01T14:50:46.637002Z","shell.execute_reply":"2023-02-01T14:50:46.642148Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"titanic_test.loc[titanic_test['Embarked'].isna(),'Embarked'] = 'U'\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.902953Z","iopub.execute_input":"2023-02-01T14:50:46.904202Z","iopub.status.idle":"2023-02-01T14:50:46.911244Z","shell.execute_reply.started":"2023-02-01T14:50:46.904144Z","shell.execute_reply":"2023-02-01T14:50:46.910042Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.161516Z","iopub.execute_input":"2023-02-01T14:50:47.162591Z","iopub.status.idle":"2023-02-01T14:50:47.169485Z","shell.execute_reply.started":"2023-02-01T14:50:47.162548Z","shell.execute_reply":"2023-02-01T14:50:47.168028Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' 'U']\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Sex.unique())\nprint(\"Testing : \" , titanic_test.Sex.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.378976Z","iopub.execute_input":"2023-02-01T14:50:47.379690Z","iopub.status.idle":"2023-02-01T14:50:47.386404Z","shell.execute_reply.started":"2023-02-01T14:50:47.379649Z","shell.execute_reply":"2023-02-01T14:50:47.385047Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTesting : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Passenger class\nNo unknown values is present in both datasets.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Pclass.unique())\nprint(\"Testing : \" , titanic_test.Pclass.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.799740Z","iopub.execute_input":"2023-02-01T14:50:47.800431Z","iopub.status.idle":"2023-02-01T14:50:47.807300Z","shell.execute_reply.started":"2023-02-01T14:50:47.800393Z","shell.execute_reply":"2023-02-01T14:50:47.806156Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Training : [3. 1. 2.]\nTesting : [3. 2. 1.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PClass and Fare\n\nThe Fare decreases as the passenger class decrease. However the range is can be quite large and the data data imbalanced; there are a lot more third class tickets than other classes. So we scale robustly the data based on non-parametric statistics.","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Fare\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x:100 * x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.235186Z","iopub.execute_input":"2023-02-01T14:50:48.235601Z","iopub.status.idle":"2023-02-01T14:50:48.274182Z","shell.execute_reply.started":"2023-02-01T14:50:48.235564Z","shell.execute_reply":"2023-02-01T14:50:48.272964Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"Fare 0.0000 4.0125 5.0000 6.2375 6.4375 6.4500 6.4958 \\\nPclass \n1.0 2.314815 NaN 0.462963 NaN NaN NaN NaN \n2.0 3.260870 NaN NaN NaN NaN NaN NaN \n3.0 0.814664 0.203666 NaN 0.203666 0.203666 0.203666 0.407332 \n\nFare 6.7500 6.8583 6.9500 ... 153.4625 164.8667 211.3375 \\\nPclass ... \n1.0 NaN NaN NaN ... 1.388889 0.925926 1.388889 \n2.0 NaN NaN NaN ... NaN NaN NaN \n3.0 0.407332 0.203666 0.203666 ... NaN NaN NaN \n\nFare 211.5000 221.7792 227.5250 247.5208 262.3750 263.0000 512.3292 \nPclass \n1.0 0.462963 0.462963 1.851852 0.925926 0.925926 1.851852 1.388889 \n2.0 NaN NaN NaN NaN NaN NaN NaN \n3.0 NaN NaN NaN NaN NaN NaN NaN \n\n[3 rows x 248 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Fare0.00004.01255.00006.23756.43756.45006.49586.75006.85836.9500...153.4625164.8667211.3375211.5000221.7792227.5250247.5208262.3750263.0000512.3292
Pclass
1.02.314815NaN0.462963NaNNaNNaNNaNNaNNaNNaN...1.3888890.9259261.3888890.4629630.4629631.8518520.9259260.9259261.8518521.388889
2.03.260870NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3.00.8146640.203666NaN0.2036660.2036660.2036660.4073320.4073320.2036660.203666...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n

3 rows × 248 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins=512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.451437Z","iopub.execute_input":"2023-02-01T14:50:48.451845Z","iopub.status.idle":"2023-02-01T14:50:49.547249Z","shell.execute_reply.started":"2023-02-01T14:50:48.451810Z","shell.execute_reply":"2023-02-01T14:50:49.546123Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 32.204208\nstd 49.693429\nmin 0.000000\n25% 7.910400\n50% 14.454200\n75% 31.000000\nmax 512.329200\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,8))\nplt.suptitle('')\ntitanic_train.boxplot(column=['Fare'], by='Pclass', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.549636Z","iopub.execute_input":"2023-02-01T14:50:49.550050Z","iopub.status.idle":"2023-02-01T14:50:49.804155Z","shell.execute_reply.started":"2023-02-01T14:50:49.550008Z","shell.execute_reply":"2023-02-01T14:50:49.803012Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.805690Z","iopub.execute_input":"2023-02-01T14:50:49.806699Z","iopub.status.idle":"2023-02-01T14:50:49.864940Z","shell.execute_reply.started":"2023-02-01T14:50:49.806662Z","shell.execute_reply":"2023-02-01T14:50:49.863879Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% max\nPclass \n1.0 216.0 84.154687 78.380373 0.0 30.92395 60.2875 93.5 512.3292\n2.0 184.0 20.662183 13.417399 0.0 13.00000 14.2500 26.0 73.5000\n3.0 491.0 13.675550 11.778142 0.0 7.75000 8.0500 15.5 69.5500","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0216.084.15468778.3803730.030.9239560.287593.5512.3292
2.0184.020.66218313.4173990.013.0000014.250026.073.5000
3.0491.013.67555011.7781420.07.750008.050015.569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_train.Fare.median()\nIQR_fare = titanic_train.Fare.quantile(0.75) - titanic_train.Fare.quantile(0.25)\ntitanic_train.loc[:,\"Fare\"] = (titanic_train.Fare - median_fare)/IQR_fare\nplt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.867034Z","iopub.execute_input":"2023-02-01T14:50:49.867360Z","iopub.status.idle":"2023-02-01T14:50:51.334840Z","shell.execute_reply.started":"2023-02-01T14:50:49.867301Z","shell.execute_reply":"2023-02-01T14:50:51.334033Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:51.336034Z","iopub.execute_input":"2023-02-01T14:50:51.336529Z","iopub.status.idle":"2023-02-01T14:50:52.406610Z","shell.execute_reply.started":"2023-02-01T14:50:51.336498Z","shell.execute_reply":"2023-02-01T14:50:52.405714Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQEElEQVR4nO3df6hf9X3H8edrpu0fbYe63IUQ465KWkjHFruLE/oDO7cuymh0DKcMm3ZusRChZYWROphlMCjbrKNss0QUU7CpbqlVqNsqodQVZtcbGzRqndFFTIjJrY4qa+mmvvfHPRe/ud7r/d77/d57cz/3+YAv33Pe55zveefk5MWXT873nFQVkqS2/NxyNyBJGj7DXZIaZLhLUoMMd0lqkOEuSQ1as9wNAKxdu7ZGR0eXuw1JWlEOHDjwo6oamWnZaRHuo6OjjI+PL3cbkrSiJHlutmUOy0hSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCc4Z5kY5JvJ3kiyeNJPt3Vz07yYJKnu/ezunqSfCnJ4SSPJnn/Yv8hJEmn6ueb+6vAZ6tqM3AxsDPJZmAXsL+qNgH7u3mAy4BN3WsHcOvQu5YkvaU5w72qjlfVI930K8CTwAZgG7CnW20PcEU3vQ34Sk16GDgzyfphNy5Jmt28xtyTjAIXAt8D1lXV8W7RC8C6bnoD8HzPZke72vTP2pFkPMn4xMTEfPuWJL2FvsM9ybuAfcBnqurl3mVVVUDNZ8dVtbuqxqpqbGRkZD6bSpLm0Fe4J3kbk8F+V1V9vSufmBpu6d5PdvVjwMaezc/papKkJdLP1TIBbgeerKov9iy6H9jeTW8H7uupf7y7auZi4Mc9wzeSpCWwpo91PgBcCzyW5GBXuxH4AnBPkuuA54CrumUPAJcDh4GfAJ8cZsOSpLnNGe5V9V0gsyy+dIb1C9g5YF+SpAH4C1VJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIa1M+TmO5IcjLJoZ7a3UkOdq8jUw/xSDKa5Kc9y768iL1LkmbRz5OY7gT+DvjKVKGqfn9qOsnNwI971n+mqrYMqT9J0gL08ySmh5KMzrSse77qVcBvDLkvSdIABh1z/xBwoqqe7qmdl+QHSb6T5EMDfr4kaQH6GZZ5K9cAe3vmjwPnVtWLSX4N+EaS91XVy9M3TLID2AFw7rnnDtiGJKnXgr+5J1kD/C5w91Stqn5WVS920weAZ4D3zLR9Ve2uqrGqGhsZGVloG5KkGQwyLPObwA+r6uhUIclIkjO66fOBTcCzg7UoSZqvfi6F3Av8O/DeJEeTXNctuppTh2QAPgw82l0a+U/Ap6rqpSH2K0nqQz9Xy1wzS/0TM9T2AfsGb0uSNAh/oSpJDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJalA/T2K6I8nJJId6ap9PcizJwe51ec+yzyU5nOSpJL+9WI1LkmbXzzf3O4GtM9Rvqaot3esBgCSbmXz83vu6bf5h6pmqkqSlM2e4V9VDQL/PQd0GfK2qflZV/wUcBi4aoD9J0gIMMuZ+Q5JHu2Gbs7raBuD5nnWOdrU3SbIjyXiS8YmJiQHakCRNt9BwvxW4ANgCHAdunu8HVNXuqhqrqrGRkZEFtiFJmsmCwr2qTlTVa1X1OnAbbwy9HAM29qx6TleTJC2hBYV7kvU9s1cCU1fS3A9cneQdSc4DNgH/MViLkqT5WjPXCkn2ApcAa5McBW4CLkmyBSjgCHA9QFU9nuQe4AngVWBnVb22KJ1LkmaVqlruHhgbG6vx8fHlbkOSVpQkB6pqbKZl/kJVkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBs0Z7t0DsE8mOdRT++skP+wekH1vkjO7+miSnyY52L2+vIi9S5Jm0c839zuBrdNqDwK/XFW/Avwn8LmeZc9U1Zbu9anhtClJmo85w72qHgJemlb7VlW92s0+zOSDsCVJp4lhjLn/IfDPPfPnJflBku8k+dBsGyXZkWQ8yfjExMQQ2pAkTRko3JP8GZMPwr6rKx0Hzq2qC4E/Ab6a5Odn2raqdlfVWFWNjYyMDNKGJGmaBYd7kk8AvwP8QXVP2a6qn1XVi930AeAZ4D1D6FOSNA8LCvckW4E/BT5WVT/pqY8kOaObPh/YBDw7jEYlSf1bM9cKSfYClwBrkxwFbmLy6ph3AA8mAXi4uzLmw8BfJPk/4HXgU1X10owfLElaNHOGe1VdM0P59lnW3QfsG7QpSdJg/IWqJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDeor3JPckeRkkkM9tbOTPJjk6e79rK6eJF9KcjjJo0nev1jNS5Jm1u839zuBrdNqu4D9VbUJ2N/NA1zG5OP1NgE7gFsHb1OSNB99hXtVPQRMf1zeNmBPN70HuKKn/pWa9DBwZpL1Q+hVktSnQcbc11XV8W76BWBdN70BeL5nvaNdTZK0RIbyH6pVVUDNZ5skO5KMJxmfmJgYRhuSpM4g4X5iarilez/Z1Y8BG3vWO6ernaKqdlfVWFWNjYyMDNCGJGm6QcL9fmB7N70duK+n/vHuqpmLgR/3DN9IkpbAmn5WSrIXuARYm+QocBPwBeCeJNcBzwFXdas/AFwOHAZ+AnxyyD1LkubQV7hX1TWzLLp0hnUL2DlIU5KkwfgLVUlqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg/p6WMdMkrwXuLundD7w58CZwB8DU0+9vrGqHljofiRJ87fgcK+qp4AtAEnOYPIh2Pcy+Vi9W6rqb4bRoCRp/oY1LHMp8ExVPTekz5MkDWBY4X41sLdn/oYkjya5I8lZM22QZEeS8STjExMTM60iSVqggcM9yduBjwH/2JVuBS5gcsjmOHDzTNtV1e6qGquqsZGRkUHbkCT1GMY398uAR6rqBEBVnaiq16rqdeA24KIh7EOSNA/DCPdr6BmSSbK+Z9mVwKEh7GNeRnd9c6l3KUmnlQVfLQOQ5J3AbwHX95T/KskWoIAj05ZJkpbAQOFeVf8D/MK02rUDdSRJGpi/UJWkBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBA93PHSDJEeAV4DXg1aoaS3I2cDcwyuQDO66qqv8edF+SpP4M65v7R6pqS1WNdfO7gP1VtQnY381LkpbIYg3LbAP2dNN7gCsWaT+SpBkMI9wL+FaSA0l2dLV1VXW8m34BWDd9oyQ7kownGZ+YmBhCG5KkKQOPuQMfrKpjSX4ReDDJD3sXVlUlqekbVdVuYDfA2NjYm5ZLkhZu4G/uVXWsez8J3AtcBJxIsh6gez856H4kSf0bKNyTvDPJu6emgY8Ch4D7ge3datuB+wbZjyRpfgYdllkH3Jtk6rO+WlX/kuT7wD1JrgOeA64acD+SpHkYKNyr6lngV2eovwhcOshnS5IWzl+oSlKDDHdJapDhLkkNMtwlqUGGuyQ1aFWH++iuby53C5K0KFZ1uEtSqwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMWHO5JNib5dpInkjye5NNd/fNJjiU52L0uH167kqR+DPLN/VXgs1W1GbgY2Jlkc7fslqra0r0eGLjLBej31gLegkBSixYc7lV1vKoe6aZfAZ4ENgyrsWEywCWtNkMZc08yClwIfK8r3ZDk0SR3JDlrlm12JBlPMj4xMTGMNiRJnYHDPcm7gH3AZ6rqZeBW4AJgC3AcuHmm7apqd1WNVdXYyMjIoG1IknoMFO5J3sZksN9VVV8HqKoTVfVaVb0O3AZcNHibkqT5GORqmQC3A09W1Rd76ut7VrsSOLTw9iRJC7FmgG0/AFwLPJbkYFe7EbgmyRaggCPA9QPsQ5K0AAsO96r6LpAZFi3LpY+SpDf4C1VJapDhLkkNMtwlqUGGuyQ1aNWFu7cikLQarLpwl6TVwHCXpAYZ7h2HayS1pLlwny2k+wlvA15SK5oL916GtaTVqulwBwNe0urUfLhL0mpkuEtSg1ZtuJ/OwzWnc2+rnX83WilWVbhP/cPs9x/oYlxhM98eJGkhVlW4z2Z60I7u+uaShO8w9zHTZy325y8nL22V3tqihXuSrUmeSnI4ya7F2s9SmOva+dUSIqvlzyktlcX8N7Uo4Z7kDODvgcuAzUw+em/zYuyr1zAO1EI/Y67t3mr5cobmsPpqIfhb+DNIUxbrm/tFwOGqeraq/hf4GrBtkfa1qGYaspmpPtd2/S7TYDy20qRU1fA/NPk9YGtV/VE3fy3w61V1Q886O4Ad3ex7gaeG3sgb1gI/WsTPX4k8JqfyeLyZx+RUp+Px+KWqGplpwYIfkD2oqtoN7F6KfSUZr6qxpdjXSuExOZXH4808JqdaacdjsYZljgEbe+bP6WqSpCWwWOH+fWBTkvOSvB24Grh/kfYlSZpmUYZlqurVJDcA/wqcAdxRVY8vxr76tCTDPyuMx+RUHo8385icakUdj0X5D1VJ0vLyF6qS1CDDXZIa1Hy4t3QbhGFIciTJY0kOJhlf7n6WQ5I7kpxMcqindnaSB5M83b2ftZw9LrVZjsnnkxzrzpWDSS5fzh6XUpKNSb6d5Ikkjyf5dFdfMedJ0+G+XLdBWAE+UlVbVtI1u0N2J7B1Wm0XsL+qNgH7u/nV5E7efEwAbunOlS1V9cAS97ScXgU+W1WbgYuBnV12rJjzpOlwp6HbIGh4quoh4KVp5W3Anm56D3DFUva03GY5JqtWVR2vqke66VeAJ4ENrKDzpPVw3wA83zN/tKutZgV8K8mB7hYQmrSuqo530y8A65azmdPIDUke7YZtTtshiMWUZBS4EPgeK+g8aT3c9WYfrKr3MzlUtTPJh5e7odNNTV4f7DXCcCtwAbAFOA7cvKzdLIMk7wL2AZ+pqpd7l53u50nr4e5tEKapqmPd+0ngXiaHrgQnkqwH6N5PLnM/y66qTlTVa1X1OnAbq+xcSfI2JoP9rqr6eldeMedJ6+HubRB6JHlnkndPTQMfBQ699Varxv3A9m56O3DfMvZyWpgKsc6VrKJzJUmA24Enq+qLPYtWzHnS/C9Uu8u3/pY3boPwl8vb0fJJcj6T39Zh8tYTX12NxyPJXuASJm/hegK4CfgGcA9wLvAccFVVrZr/YJzlmFzC5JBMAUeA63vGm5uW5IPAvwGPAa935RuZHHdfEedJ8+EuSatR68MykrQqGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQf8Po+eCZUrdk2EAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.407853Z","iopub.execute_input":"2023-02-01T14:50:52.408376Z","iopub.status.idle":"2023-02-01T14:50:52.415841Z","shell.execute_reply.started":"2023-02-01T14:50:52.408342Z","shell.execute_reply":"2023-02-01T14:50:52.414785Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We repeat the same process with the test dataset. The distribution is much different and therefore could lower the accuracy of the prediction.","metadata":{}},{"cell_type":"code","source":"titanic_test.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.418261Z","iopub.execute_input":"2023-02-01T14:50:52.418629Z","iopub.status.idle":"2023-02-01T14:50:52.472603Z","shell.execute_reply.started":"2023-02-01T14:50:52.418596Z","shell.execute_reply":"2023-02-01T14:50:52.471219Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nPclass \n1.0 107.0 94.280297 84.435858 0.0000 30.10 60.0000 134.500000 \n2.0 93.0 22.202104 13.991877 9.6875 13.00 15.7500 26.000000 \n3.0 218.0 12.397936 10.817256 -1.0000 7.75 7.8958 14.327075 \n\n max \nPclass \n1.0 512.3292 \n2.0 73.5000 \n3.0 69.5500 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0107.094.28029784.4358580.000030.1060.0000134.500000512.3292
2.093.022.20210413.9918779.687513.0015.750026.00000073.5000
3.0218.012.39793610.817256-1.00007.757.895814.32707569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_test.Fare.median()\nIQR_fare = titanic_test.Fare.quantile(0.75) - titanic_test.Fare.quantile(0.25)\ntitanic_test.loc[:,\"Fare\"] = (titanic_test.Fare - median_fare)/IQR_fare\nplt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.473824Z","iopub.execute_input":"2023-02-01T14:50:52.474155Z","iopub.status.idle":"2023-02-01T14:50:53.560939Z","shell.execute_reply.started":"2023-02-01T14:50:52.474123Z","shell.execute_reply":"2023-02-01T14:50:53.559872Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:53.562396Z","iopub.execute_input":"2023-02-01T14:50:53.562797Z","iopub.status.idle":"2023-02-01T14:50:54.622056Z","shell.execute_reply.started":"2023-02-01T14:50:53.562764Z","shell.execute_reply":"2023-02-01T14:50:54.620862Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.623442Z","iopub.execute_input":"2023-02-01T14:50:54.623854Z","iopub.status.idle":"2023-02-01T14:50:54.631562Z","shell.execute_reply.started":"2023-02-01T14:50:54.623823Z","shell.execute_reply":"2023-02-01T14:50:54.630628Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age\nWe normalise the age to bring more the data towards the median. The previous transformation have brought more data centrally.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.632748Z","iopub.execute_input":"2023-02-01T14:50:54.633113Z","iopub.status.idle":"2023-02-01T14:50:54.995183Z","shell.execute_reply.started":"2023-02-01T14:50:54.633084Z","shell.execute_reply":"2023-02-01T14:50:54.993205Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAASdUlEQVR4nO3dfYwk913n8ffn7BDAyWEbz632bO+NA4tRQGQdRo5RHmRiHtYGxQlCxiuUOOC7TSRbl9xF4uwgEeCE5LvLw4HuzmiDfXZOYeMQx8QKBrL4IiJOxMmsszjrJ7xO1nhX693BCXFEUJS1v/zRNZf2ZNY7M9U93fPb90tqddWvqrq+213z2apfV1elqpAkteVfTLoASdLoGe6S1CDDXZIaZLhLUoMMd0lq0OmTLgDgnHPOqdnZ2UmXIUkbyt69e/++qmaWmzYV4T47O8v8/Pyky5CkDSXJEyeaZreMJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUEnDfck5yf5dJKHkjyY5B1d+9lJ9iR5rHs+q2tPkt9LciDJA0leOe5/hCTp+VbyC9XjwLuq6v4kLwX2JtkDvBW4t6puSnIDcAPwn4DLga3d41XAzd2z9DyzN/zJ88YP3vRzE6pEas9J99yr6khV3d8Nfx14GDgXuBK4vZvtduCN3fCVwIdq4LPAmUk2j7pwSdKJrarPPckscBFwH7Cpqo50k54CNnXD5wJPDi12qGtb+lo7k8wnmV9YWFht3ZKkF7DicE/yEuBO4J1V9czwtBrciHVVN2Otql1VNVdVczMzy17UTJK0RisK9yQvYhDsH66qj3fNRxe7W7rnY137YeD8ocXP69okSetkJWfLBLgFeLiq3j806W7gmm74GuATQ+1v6c6auQT42lD3jSRpHazkbJlXA28GvphkX9f2buAm4KNJrgWeAK7qpt0DXAEcAL4B/MooC5YkndxJw72q/grICSZftsz8BVzXsy5JUg/+QlWSGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KCV3Gbv1iTHkuwfarsjyb7ucXDxDk1JZpP809C03x9j7ZKkE1jJbfZuA/4H8KHFhqr6pcXhJO8DvjY0/+NVtW1E9UmS1mAlt9n7TJLZ5aZ1N8++Cnj9iOuSJPXQt8/9tcDRqnpsqO2CJF9I8pdJXnuiBZPsTDKfZH5hYaFnGZKkYX3DfQewe2j8CLClqi4C/iPwh0n+5XILVtWuqpqrqrmZmZmeZUiShq053JOcDvwCcMdiW1V9s6qe7ob3Ao8DP9S3SEnS6vTZc/8p4JGqOrTYkGQmyWnd8MuArcCX+pUoSVqtlZwKuRv4a+DCJIeSXNtNuprnd8kAvA54oDs18mPA26vqKyOsV5K0Ais5W2bHCdrfukzbncCd/cuSJPXhL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQSu5zd6tSY4l2T/U9ptJDifZ1z2uGJp2Y5IDSR5N8rPjKlySdGIr2XO/Ddi+TPsHqmpb97gHIMnLGdxb9Ue6Zf7X4g2zJUnr56ThXlWfAVZ6k+srgY9U1Ter6svAAeDiHvVJktagT5/79Uke6LptzurazgWeHJrnUNf2HZLsTDKfZH5hYaFHGZKkpdYa7jcDPwBsA44A71vtC1TVrqqaq6q5mZmZNZYhSVrOmsK9qo5W1bNV9RzwQb7d9XIYOH9o1vO6NknSOlpTuCfZPDT6JmDxTJq7gauTvDjJBcBW4HP9SpQkrdbpJ5shyW7gUuCcJIeA9wCXJtkGFHAQeBtAVT2Y5KPAQ8Bx4LqqenYslUuSTuik4V5VO5ZpvuUF5v8d4Hf6FCVJ6sdfqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGnTScE9ya5JjSfYPtf23JI8keSDJXUnO7Npnk/xTkn3d4/fHWLsk6QRWsud+G7B9Sdse4Eer6seAvwVuHJr2eFVt6x5vH02ZkqTVOGm4V9VngK8saftUVR3vRj8LnDeG2iRJazSKPvdfBf50aPyCJF9I8pdJXjuC15ckrdJJb5D9QpL8OnAc+HDXdATYUlVPJ/lx4I+T/EhVPbPMsjuBnQBbtmzpU4YkaYk177kneSvw88AvV1UBVNU3q+rpbngv8DjwQ8stX1W7qmququZmZmbWWoYkaRlrCvck24FfA95QVd8Yap9Jclo3/DJgK/ClURQqSVq5k3bLJNkNXAqck+QQ8B4GZ8e8GNiTBOCz3ZkxrwN+O8m3gOeAt1fVV5Z9YUnS2Jw03KtqxzLNt5xg3juBO/sWJUnqx1+oSlKDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoNWFO5Jbk1yLMn+obazk+xJ8lj3fFbXniS/l+RAkgeSvHJcxUuSlrfSPffbgO1L2m4A7q2qrcC93TjA5QxujL0V2Anc3L9MSdJqrCjcq+ozwNIbXV8J3N4N3w68caj9QzXwWeDMJJtHUKskaYX69Llvqqoj3fBTwKZu+FzgyaH5DnVtz5NkZ5L5JPMLCws9ypAkLTWSL1SrqoBa5TK7qmququZmZmZGUYYkqdMn3I8udrd0z8e69sPA+UPznde1SZLWSZ9wvxu4phu+BvjEUPtburNmLgG+NtR9I0laB6evZKYku4FLgXOSHALeA9wEfDTJtcATwFXd7PcAVwAHgG8AvzLimiVJJ7GicK+qHSeYdNky8xZwXZ+iJEn9+AtVSWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCK7sS0nCQXAncMNb0M+A3gTODfAQtd+7ur6p61rkeStHprDveqehTYBpDkNOAwcBeDe6Z+oKreO4oCJUmrN6pumcuAx6vqiRG9niSph1GF+9XA7qHx65M8kOTWJGctt0CSnUnmk8wvLCwsN4skaY16h3uS7wLeAPxR13Qz8AMMumyOAO9bbrmq2lVVc1U1NzMz07cMSdKQUey5Xw7cX1VHAarqaFU9W1XPAR8ELh7BOiRJqzCKcN/BUJdMks1D094E7B/BOiRJq7Dms2UAkpwB/DTwtqHm/5pkG1DAwSXTJEnroFe4V9U/At+/pO3NvSqSJPXmL1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ3qdbMOgCQHga8DzwLHq2ouydnAHcAsg7sxXVVVX+27LknSyoxqz/0nq2pbVc114zcA91bVVuDeblyStE7G1S1zJXB7N3w78MYxrUeStIxRhHsBn0qyN8nOrm1TVR3php8CNo1gPZKkFerd5w68pqoOJ/lXwJ4kjwxPrKpKUksX6v4j2AmwZcuWEZQhSVrUe8+9qg53z8eAu4CLgaNJNgN0z8eWWW5XVc1V1dzMzEzfMiRJQ3qFe5Izkrx0cRj4GWA/cDdwTTfbNcAn+qxHkrQ6fbtlNgF3JVl8rT+sqj9L8nngo0muBZ4Aruq5HknSKvQK96r6EvCKZdqfBi7r89qSpLXzF6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGjuM2eGjZ7w588b/zgTT83oUokrYZ77pLUIMNdkhq05nBPcn6STyd5KMmDSd7Rtf9mksNJ9nWPK0ZXriRpJfr0uR8H3lVV93c3yd6bZE837QNV9d7+5Wnc7FOX2rTmcK+qI8CRbvjrSR4Gzh1VYVo5A1rSUiM5WybJLHARcB/wauD6JG8B5hns3X91mWV2AjsBtmzZMooy1DlZ2C+dPqk6JI1P73BP8hLgTuCdVfVMkpuB/wxU9/w+4FeXLldVu4BdAHNzc9W3Dq0PA1vaGHqdLZPkRQyC/cNV9XGAqjpaVc9W1XPAB4GL+5cpSVqNNe+5JwlwC/BwVb1/qH1z1x8P8CZgf78S1Yr16g5abl0eYehU06db5tXAm4EvJtnXtb0b2JFkG4NumYPA23qsQ5K0Bn3OlvkrIMtMumft5UiSRsFry2hqDHelrLYbZT27fKSNwHBXL+MKVfvMpX4M9w2iz15tCwx7aXW8cJgkNcg991PAarpONkrf9UapU5qUJsL9VO+yUD92+ahFTYT7qca91vFazbV5/I9A08pwl8bIowJNil+oSlKD3HPXKcduLZ0Kmgv31V7L3MNkbRRuu1qN5sJdmqTVHhW80PyGt/ow3HtwT+rU0EI3jtvqqcdw1ymhhYBeysDWC2k+3DfKH/VGqVPrp08Xj0Gv5sNdGqdR9rFP0gsdBXiEsDGd8uG+mg3XPSnpO/mL3uk0tnBPsh34XeA04A+q6qZxrWuUpnXPStoo+vwNjfMo4VQ7AhlLuCc5DfifwE8Dh4DPJ7m7qh4ax/qmRYtXX1SbTrb9vdD0UYb3OJ3qf2Pj2nO/GDhQVV8CSPIR4Eqg6XB/Iaf6hqb11cr2Nqm97RaOIFJVo3/R5BeB7VX1b7vxNwOvqqrrh+bZCezsRi8EHl3Dqs4B/r5nueNgXas3rbVZ1+pMa10wvbX1qevfVNXMchMm9oVqVe0CdvV5jSTzVTU3opJGxrpWb1prs67Vmda6YHprG1dd47oq5GHg/KHx87o2SdI6GFe4fx7YmuSCJN8FXA3cPaZ1SZKWGEu3TFUdT3I98OcMToW8taoeHMOqenXrjJF1rd601mZdqzOtdcH01jaWusbyhaokabK8E5MkNchwl6QGbchwT7I9yaNJDiS5YcK13JrkWJL9Q21nJ9mT5LHu+awJ1HV+kk8neSjJg0neMQ21JfnuJJ9L8jddXb/VtV+Q5L7uM72j+yJ+3SU5LckXknxyyuo6mOSLSfYlme/apmE7OzPJx5I8kuThJD8x6bqSXNi9T4uPZ5K8c9J1dbX9h267359kd/f3MJZtbMOF+9ClDS4HXg7sSPLyCZZ0G7B9SdsNwL1VtRW4txtfb8eBd1XVy4FLgOu692nStX0TeH1VvQLYBmxPcgnwX4APVNUPAl8Frl3nuha9A3h4aHxa6gL4yaraNnRO9KQ/SxhcP+rPquqHgVcweO8mWldVPdq9T9uAHwe+Adw16bqSnAv8e2Cuqn6UwckmVzOubayqNtQD+Angz4fGbwRunHBNs8D+ofFHgc3d8Gbg0Sl43z7B4Fo/U1Mb8L3A/cCrGPxC7/TlPuN1rOc8Bn/0rwc+CWQa6urWfRA4Z0nbRD9L4PuAL9OdmDEtdS2p5WeA/zcNdQHnAk8CZzM4U/GTwM+OaxvbcHvufPsNWnSoa5smm6rqSDf8FLBpksUkmQUuAu5jCmrruj72AceAPcDjwD9U1fFulkl9pv8d+DXguW78+6ekLoACPpVkb3fpDpj8Z3kBsAD8764r6w+SnDEFdQ27GtjdDU+0rqo6DLwX+DvgCPA1YC9j2sY2YrhvKDX473hi55smeQlwJ/DOqnpmeNqkaquqZ2twyHweg4vM/fB617BUkp8HjlXV3knXcgKvqapXMuiOvC7J64YnTuizPB14JXBzVV0E/CNLujomuf13fddvAP5o6bRJ1NX18V/J4D/Ffw2cwXd26Y7MRgz3jXBpg6NJNgN0z8cmUUSSFzEI9g9X1cenqTaAqvoH4NMMDkXPTLL4o7pJfKavBt6Q5CDwEQZdM787BXUB/3+vj6o6xqD/+GIm/1keAg5V1X3d+McYhP2k61p0OXB/VR3txidd108BX66qhar6FvBxBtvdWLaxjRjuG+HSBncD13TD1zDo715XSQLcAjxcVe+fltqSzCQ5sxv+HgbfAzzMIOR/cVJ1VdWNVXVeVc0y2Kb+b1X98qTrAkhyRpKXLg4z6Efez4Q/y6p6CngyyYVd02UMLus98e2/s4Nvd8nA5Ov6O+CSJN/b/X0uvl/j2cYm9UVHzy8mrgD+lkFf7a9PuJbdDPrPvsVgT+ZaBn219wKPAX8BnD2Bul7D4LDzAWBf97hi0rUBPwZ8oatrP/AbXfvLgM8BBxgcRr94gp/ppcAnp6Wuroa/6R4PLm7zk/4suxq2AfPd5/nHwFlTUtcZwNPA9w21TUNdvwU80m37/wd48bi2MS8/IEkN2ojdMpKkkzDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+GQQx1jIEIXnhAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_train.Age.median()\nIQR_age = titanic_train.Age.quantile(0.75) - titanic_train.Age.quantile(0.25)\ntitanic_train.loc[:,\"Age\"] = (titanic_train.Age - median_age)/IQR_age\ntitanic_train.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.996341Z","iopub.execute_input":"2023-02-01T14:50:54.996637Z","iopub.status.idle":"2023-02-01T14:50:55.012393Z","shell.execute_reply.started":"2023-02-01T14:50:54.996609Z","shell.execute_reply":"2023-02-01T14:50:55.011269Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.014533Z","iopub.execute_input":"2023-02-01T14:50:55.015228Z","iopub.status.idle":"2023-02-01T14:50:55.377136Z","shell.execute_reply.started":"2023-02-01T14:50:55.015184Z","shell.execute_reply":"2023-02-01T14:50:55.376023Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.378688Z","iopub.execute_input":"2023-02-01T14:50:55.379745Z","iopub.status.idle":"2023-02-01T14:50:55.727506Z","shell.execute_reply.started":"2023-02-01T14:50:55.379709Z","shell.execute_reply":"2023-02-01T14:50:55.726302Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6N52X3A08A/AWdOKds7Gb2c/B7wWHd571DydRl/B3i0y/g48Jfd+t8GvgMcYvRS+denfJzfDTwwtGxdlu92lyeO/V0M7BhfBMx3x/gfgTMGlu804D+AN4+tG0y+SS6eoSpJDdqs0zKSpBOw3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatD/AmLJbG6fuoYqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_test.Age.median()\nIQR_age = titanic_test.Age.quantile(0.75) - titanic_test.Age.quantile(0.25)\ntitanic_test.loc[:,\"Age\"] = (titanic_test.Age - median_age)/IQR_age\ntitanic_test.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.731833Z","iopub.execute_input":"2023-02-01T14:50:55.732609Z","iopub.status.idle":"2023-02-01T14:50:55.747180Z","shell.execute_reply.started":"2023-02-01T14:50:55.732557Z","shell.execute_reply":"2023-02-01T14:50:55.746071Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.748583Z","iopub.execute_input":"2023-02-01T14:50:55.748898Z","iopub.status.idle":"2023-02-01T14:50:56.093344Z","shell.execute_reply.started":"2023-02-01T14:50:55.748868Z","shell.execute_reply":"2023-02-01T14:50:56.092150Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Gender \nWe replace the male with 1 and female with the value 2.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \", titanic_train['Sex'].unique())\nprint(\"Test : \", titanic_train['Sex'].unique())\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.094765Z","iopub.execute_input":"2023-02-01T14:50:56.095091Z","iopub.status.idle":"2023-02-01T14:50:56.103516Z","shell.execute_reply.started":"2023-02-01T14:50:56.095062Z","shell.execute_reply":"2023-02-01T14:50:56.102411Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTest : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_train[\"Sex\"] = titanic_train[\"Sex\"].astype(float)\ntitanic_train.groupby(\"Sex\").count()[\"PassengerId\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.104821Z","iopub.execute_input":"2023-02-01T14:50:56.105350Z","iopub.status.idle":"2023-02-01T14:50:56.122953Z","shell.execute_reply.started":"2023-02-01T14:50:56.105306Z","shell.execute_reply":"2023-02-01T14:50:56.122030Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 577\n2.0 314\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_test[\"Sex\"] = titanic_test[\"Sex\"].astype(float)\ntitanic_test.groupby(\"Sex\").count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.124259Z","iopub.execute_input":"2023-02-01T14:50:56.124612Z","iopub.status.idle":"2023-02-01T14:50:56.139408Z","shell.execute_reply.started":"2023-02-01T14:50:56.124581Z","shell.execute_reply":"2023-02-01T14:50:56.138058Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 266\n2.0 152\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sibling and parentage\n\nWe add both sibling, parents, and children into a family variables. ","metadata":{}},{"cell_type":"code","source":"titanic_train[\"SibSp\"].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.141402Z","iopub.execute_input":"2023-02-01T14:50:56.141813Z","iopub.status.idle":"2023-02-01T14:50:56.148230Z","shell.execute_reply.started":"2023-02-01T14:50:56.141777Z","shell.execute_reply":"2023-02-01T14:50:56.147382Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"array([1., 0., 3., 4., 2., 5., 8.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Parch\"].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.149745Z","iopub.execute_input":"2023-02-01T14:50:56.150349Z","iopub.status.idle":"2023-02-01T14:50:56.159952Z","shell.execute_reply.started":"2023-02-01T14:50:56.150294Z","shell.execute_reply":"2023-02-01T14:50:56.158924Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"array([0., 1., 2., 5., 3., 4., 6.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\ntitanic_train[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.161871Z","iopub.execute_input":"2023-02-01T14:50:56.162175Z","iopub.status.idle":"2023-02-01T14:50:56.176837Z","shell.execute_reply.started":"2023-02-01T14:50:56.162147Z","shell.execute_reply":"2023-02-01T14:50:56.175684Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.178360Z","iopub.execute_input":"2023-02-01T14:50:56.178747Z","iopub.status.idle":"2023-02-01T14:50:56.191340Z","shell.execute_reply.started":"2023-02-01T14:50:56.178698Z","shell.execute_reply":"2023-02-01T14:50:56.190355Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.195050Z","iopub.execute_input":"2023-02-01T14:50:56.195448Z","iopub.status.idle":"2023-02-01T14:50:56.209129Z","shell.execute_reply.started":"2023-02-01T14:50:56.195400Z","shell.execute_reply":"2023-02-01T14:50:56.207967Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.210664Z","iopub.execute_input":"2023-02-01T14:50:56.211090Z","iopub.status.idle":"2023-02-01T14:50:56.219640Z","shell.execute_reply.started":"2023-02-01T14:50:56.211049Z","shell.execute_reply":"2023-02-01T14:50:56.218550Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.221452Z","iopub.execute_input":"2023-02-01T14:50:56.222189Z","iopub.status.idle":"2023-02-01T14:50:56.231508Z","shell.execute_reply.started":"2023-02-01T14:50:56.222146Z","shell.execute_reply":"2023-02-01T14:50:56.230398Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.232676Z","iopub.execute_input":"2023-02-01T14:50:56.233089Z","iopub.status.idle":"2023-02-01T14:50:56.242657Z","shell.execute_reply.started":"2023-02-01T14:50:56.233048Z","shell.execute_reply":"2023-02-01T14:50:56.241737Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.244097Z","iopub.execute_input":"2023-02-01T14:50:56.244427Z","iopub.status.idle":"2023-02-01T14:50:56.251459Z","shell.execute_reply.started":"2023-02-01T14:50:56.244398Z","shell.execute_reply":"2023-02-01T14:50:56.250542Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.256041Z","iopub.execute_input":"2023-02-01T14:50:56.256485Z","iopub.status.idle":"2023-02-01T14:50:56.265940Z","shell.execute_reply.started":"2023-02-01T14:50:56.256450Z","shell.execute_reply":"2023-02-01T14:50:56.264711Z"},"trusted":true},"execution_count":113,"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.267253Z","iopub.execute_input":"2023-02-01T14:50:56.267740Z","iopub.status.idle":"2023-02-01T14:50:56.278020Z","shell.execute_reply.started":"2023-02-01T14:50:56.267696Z","shell.execute_reply":"2023-02-01T14:50:56.276748Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"titanic_test[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.315420Z","iopub.execute_input":"2023-02-01T14:50:56.315791Z","iopub.status.idle":"2023-02-01T14:50:56.322971Z","shell.execute_reply.started":"2023-02-01T14:50:56.315760Z","shell.execute_reply":"2023-02-01T14:50:56.322090Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## Columns to drop \nWe drop some columns; they may have too many unknown values. Some of them may be dependent statistical variables. We assume the price of a ticket may be dependent of the fare. ","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Name\", axis = 1, inplace = True)\ntitanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_train.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.722307Z","iopub.execute_input":"2023-02-01T14:50:56.722753Z","iopub.status.idle":"2023-02-01T14:50:56.744122Z","shell.execute_reply.started":"2023-02-01T14:50:56.722718Z","shell.execute_reply":"2023-02-01T14:50:56.743299Z"},"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.drop(\"Name\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_test.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.963356Z","iopub.execute_input":"2023-02-01T14:50:56.963753Z","iopub.status.idle":"2023-02-01T14:50:56.979754Z","shell.execute_reply.started":"2023-02-01T14:50:56.963719Z","shell.execute_reply":"2023-02-01T14:50:56.978543Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We make of both datasets. These copies will be used to analysed the predictions values from all the classifiers.","metadata":{}},{"cell_type":"code","source":"results_test = titanic_test.copy(deep = True)\nresults_train = titanic_train.copy(deep = True) ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:57.429442Z","iopub.execute_input":"2023-02-01T14:50:57.429827Z","iopub.status.idle":"2023-02-01T14:50:57.435755Z","shell.execute_reply.started":"2023-02-01T14:50:57.429796Z","shell.execute_reply":"2023-02-01T14:50:57.434439Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"markdown","source":"# Method : Logistic regression\n\nOur first classifier is a logistic regression. We surmise it may be the most suitable methods as two classes of labels exist; survived or not. The data is imbalanced towards perishing sadly. So we add some class weight to represent this situation in the data. \n\nWe choose the passenger class, sex, familly members. We surmise the passenger class, gender and being part of a familly or not may have influenced surviving the accident. The training dataset is split into training and validation for validating the model fitting. ","metadata":{}},{"cell_type":"markdown","source":"## Preparation Cross validation \nWe show how the transformation have affected both datasets","metadata":{}},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.108812Z","iopub.execute_input":"2023-02-01T14:50:58.109845Z","iopub.status.idle":"2023-02-01T14:50:58.118552Z","shell.execute_reply.started":"2023-02-01T14:50:58.109806Z","shell.execute_reply":"2023-02-01T14:50:58.117356Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.354904Z","iopub.execute_input":"2023-02-01T14:50:58.355573Z","iopub.status.idle":"2023-02-01T14:50:58.362764Z","shell.execute_reply.started":"2023-02-01T14:50:58.355531Z","shell.execute_reply":"2023-02-01T14:50:58.361542Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"(891, 8)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.590264Z","iopub.execute_input":"2023-02-01T14:50:58.591668Z","iopub.status.idle":"2023-02-01T14:50:58.600773Z","shell.execute_reply.started":"2023-02-01T14:50:58.591627Z","shell.execute_reply":"2023-02-01T14:50:58.599216Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.804713Z","iopub.execute_input":"2023-02-01T14:50:58.805085Z","iopub.status.idle":"2023-02-01T14:50:58.812599Z","shell.execute_reply.started":"2023-02-01T14:50:58.805054Z","shell.execute_reply":"2023-02-01T14:50:58.811376Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"(418, 7)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Split data sets for cross validation\n\nWe use a stratified shuffle split to aim at reducing the variation between the training and validation datasets.","metadata":{}},{"cell_type":"code","source":"\n\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n#X = X[x_cols]\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.267374Z","iopub.execute_input":"2023-02-01T14:50:59.267771Z","iopub.status.idle":"2023-02-01T14:50:59.288554Z","shell.execute_reply.started":"2023-02-01T14:50:59.267735Z","shell.execute_reply":"2023-02-01T14:50:59.287476Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We keep the passengers ids for building up the training dataset results. It will be used to compare all the classifier.","metadata":{}},{"cell_type":"code","source":"x_train_pass_id = X_train[\"PassengerId\"]\nx_train_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.704437Z","iopub.execute_input":"2023-02-01T14:50:59.704815Z","iopub.status.idle":"2023-02-01T14:50:59.714204Z","shell.execute_reply.started":"2023-02-01T14:50:59.704783Z","shell.execute_reply":"2023-02-01T14:50:59.713337Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"844 845.0\n316 317.0\n768 769.0\n255 256.0\n130 131.0\n ... \n476 477.0\n58 59.0\n736 737.0\n462 463.0\n747 748.0\nName: PassengerId, Length: 534, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"x_cols =[\"Pclass\",\"Sex\",\"fam_members\"]\nX_train = X_train[x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.933799Z","iopub.execute_input":"2023-02-01T14:50:59.934191Z","iopub.status.idle":"2023-02-01T14:50:59.947540Z","shell.execute_reply.started":"2023-02-01T14:50:59.934158Z","shell.execute_reply":"2023-02-01T14:50:59.946577Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n844 3.0 1.0 0.0\n316 2.0 2.0 1.0\n768 3.0 1.0 1.0\n255 3.0 2.0 2.0\n130 3.0 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
8443.01.00.0
3162.02.01.0
7683.01.01.0
2553.02.02.0
1303.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid[\"PassengerId\"]\nx_valid_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.149697Z","iopub.execute_input":"2023-02-01T14:51:00.150120Z","iopub.status.idle":"2023-02-01T14:51:00.160439Z","shell.execute_reply.started":"2023-02-01T14:51:00.150083Z","shell.execute_reply":"2023-02-01T14:51:00.159106Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"369 370.0\n541 542.0\n196 197.0\n810 811.0\n427 428.0\n ... \n174 175.0\n297 298.0\n244 245.0\n38 39.0\n371 372.0\nName: PassengerId, Length: 357, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.356159Z","iopub.execute_input":"2023-02-01T14:51:00.357062Z","iopub.status.idle":"2023-02-01T14:51:00.370786Z","shell.execute_reply.started":"2023-02-01T14:51:00.357017Z","shell.execute_reply":"2023-02-01T14:51:00.369619Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n369 1.0 2.0 0.0\n541 3.0 2.0 6.0\n196 3.0 1.0 0.0\n810 3.0 1.0 0.0\n427 2.0 2.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
3691.02.00.0
5413.02.06.0
1963.01.00.0
8103.01.00.0
4272.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test.copy(deep = True)\nX_test = X_test[x_cols]\nX_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.599111Z","iopub.execute_input":"2023-02-01T14:51:00.599521Z","iopub.status.idle":"2023-02-01T14:51:00.616521Z","shell.execute_reply.started":"2023-02-01T14:51:00.599483Z","shell.execute_reply":"2023-02-01T14:51:00.615356Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n0 3.0 1.0 0.0\n1 3.0 2.0 1.0\n2 2.0 1.0 0.0\n3 3.0 1.0 0.0\n4 3.0 2.0 2.0\n.. ... ... ...\n413 3.0 1.0 0.0\n414 1.0 2.0 0.0\n415 3.0 1.0 0.0\n416 3.0 1.0 0.0\n417 3.0 1.0 2.0\n\n[418 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
03.01.00.0
13.02.01.0
22.01.00.0
33.01.00.0
43.02.02.0
............
4133.01.00.0
4141.02.00.0
4153.01.00.0
4163.01.00.0
4173.01.02.0
\n

418 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model fitting","metadata":{}},{"cell_type":"markdown","source":"We fit the model using a stochastic average gradient. We achieve approximately 82% accuracy on the validation dataset. There is not sign of over fitting. ","metadata":{}},{"cell_type":"code","source":"classifier = LogisticRegression(random_state = 0, C = 1000, max_iter= 10000, \n solver=\"sag\", penalty=\"l2\",class_weight={0:6.,1:4})\nclassifier.fit(X_train, y_train)\nclassifier.coef_","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.275079Z","iopub.execute_input":"2023-02-01T14:51:01.275483Z","iopub.status.idle":"2023-02-01T14:51:01.291372Z","shell.execute_reply.started":"2023-02-01T14:51:01.275450Z","shell.execute_reply":"2023-02-01T14:51:01.290133Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([[-0.96687438, 2.71046703, -0.09242397]])"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_train = classifier.score(X_train, y_train)\nlog_reg_score_train","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.505908Z","iopub.execute_input":"2023-02-01T14:51:01.507102Z","iopub.status.idle":"2023-02-01T14:51:01.519460Z","shell.execute_reply.started":"2023-02-01T14:51:01.507059Z","shell.execute_reply":"2023-02-01T14:51:01.518123Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"0.7921348314606742"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_valid = classifier.score(X_valid, y_valid)\nlog_reg_score_valid","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.727365Z","iopub.execute_input":"2023-02-01T14:51:01.727743Z","iopub.status.idle":"2023-02-01T14:51:01.737787Z","shell.execute_reply.started":"2023-02-01T14:51:01.727712Z","shell.execute_reply":"2023-02-01T14:51:01.736406Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"0.8207282913165266"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nTwo confusion matrices show an improvement on predicting the validation dataset. We also store the predicted results in the results_train dataframe. We will use this dataframe later on to analyse difference between classifiers. \n\n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = classifier.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.212411Z","iopub.execute_input":"2023-02-01T14:51:02.212812Z","iopub.status.idle":"2023-02-01T14:51:02.223463Z","shell.execute_reply.started":"2023-02-01T14:51:02.212779Z","shell.execute_reply":"2023-02-01T14:51:02.222427Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"array([[297, 32],\n [ 79, 126]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.417280Z","iopub.execute_input":"2023-02-01T14:51:02.417687Z","iopub.status.idle":"2023-02-01T14:51:02.426591Z","shell.execute_reply.started":"2023-02-01T14:51:02.417653Z","shell.execute_reply":"2023-02-01T14:51:02.425177Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"Accuracy : 0.7921348314606742\nMisclassfication : 0.20786516853932585\nSensitivivity : 0.9027355623100304\nSpecificity : 0.6146341463414634\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = classifier.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.661227Z","iopub.execute_input":"2023-02-01T14:51:02.661653Z","iopub.status.idle":"2023-02-01T14:51:02.672901Z","shell.execute_reply.started":"2023-02-01T14:51:02.661618Z","shell.execute_reply":"2023-02-01T14:51:02.671790Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.907929Z","iopub.execute_input":"2023-02-01T14:51:02.908917Z","iopub.status.idle":"2023-02-01T14:51:02.916300Z","shell.execute_reply.started":"2023-02-01T14:51:02.908877Z","shell.execute_reply":"2023-02-01T14:51:02.915176Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"Accuracy : 0.8207282913165266\nMisclassfication : 0.1792717086834734\nSensitivivity : 0.9363636363636364\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.367005Z","iopub.execute_input":"2023-02-01T14:51:03.367441Z","iopub.status.idle":"2023-02-01T14:51:03.372440Z","shell.execute_reply.started":"2023-02-01T14:51:03.367404Z","shell.execute_reply":"2023-02-01T14:51:03.371375Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"y_pred = classifier.predict(X_train)\nlog_reg_pred = X_train.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_train\nlog_reg_pred[\"PassengerId\"] = x_train_pass_id\nlog_reg_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.610590Z","iopub.execute_input":"2023-02-01T14:51:03.610967Z","iopub.status.idle":"2023-02-01T14:51:03.632961Z","shell.execute_reply.started":"2023-02-01T14:51:03.610936Z","shell.execute_reply":"2023-02-01T14:51:03.631856Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n844 3.0 1.0 0.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 0.0 769.0\n255 3.0 2.0 2.0 0.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_memberslr_y_predyPassengerId
8443.01.00.00.00.0845.0
3162.02.01.01.01.0317.0
7683.01.01.00.00.0769.0
2553.02.02.00.01.0256.0
1303.01.00.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(log_reg_pred[[\"PassengerId\",\"y\", \"lr_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.870519Z","iopub.execute_input":"2023-02-01T14:51:03.870935Z","iopub.status.idle":"2023-02-01T14:51:03.899083Z","shell.execute_reply.started":"2023-02-01T14:51:03.870900Z","shell.execute_reply":"2023-02-01T14:51:03.898021Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 NaN NaN \n2 0.0 1.0 1.0 \n3 1.0 NaN NaN \n4 0.0 NaN NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.0NaNNaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.0NaNNaN
45.00.03.01.00.384615-0.2773632.00.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = classifier.predict(X_valid)\nlog_reg_pred = X_valid.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_valid\nlog_reg_pred[\"PassengerId\"] = x_valid_pass_id\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.094193Z","iopub.execute_input":"2023-02-01T14:51:04.094610Z","iopub.status.idle":"2023-02-01T14:51:04.120418Z","shell.execute_reply.started":"2023-02-01T14:51:04.094576Z","shell.execute_reply":"2023-02-01T14:51:04.119350Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n369 1.0 2.0 0.0 1.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 1.0 428.0\n.. ... ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 1.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 0.0 0.0 39.0\n371 3.0 1.0 1.0 0.0 0.0 372.0\n\n[357 rows x 6 columns]","text/html":"
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PclassSexfam_memberslr_y_predyPassengerId
3691.02.00.01.01.0370.0
5413.02.06.00.00.0542.0
1963.01.00.00.00.0197.0
8103.01.00.00.00.0811.0
4272.02.00.01.01.0428.0
.....................
1741.01.00.00.00.0175.0
2971.02.03.01.00.0298.0
2443.01.00.00.00.0245.0
383.02.02.00.00.039.0
3713.01.01.00.00.0372.0
\n

357 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"y\"] = log_reg_pred[\"y\"]\nresults_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"lr_y_pred\"] = log_reg_pred[\"lr_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.330333Z","iopub.execute_input":"2023-02-01T14:51:04.330729Z","iopub.status.idle":"2023-02-01T14:51:04.353404Z","shell.execute_reply.started":"2023-02-01T14:51:04.330694Z","shell.execute_reply":"2023-02-01T14:51:04.352359Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 1.0 1.0 \n2 0.0 1.0 1.0 \n3 1.0 1.0 1.0 \n4 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"We start with the training dataset. It may be quite unconventional, but it can help us understanding better the features of the data.","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.059608Z","iopub.execute_input":"2023-02-01T14:51:05.059995Z","iopub.status.idle":"2023-02-01T14:51:05.077377Z","shell.execute_reply.started":"2023-02-01T14:51:05.059959Z","shell.execute_reply":"2023-02-01T14:51:05.076249Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n255 3.0 2.0 2.0 1.0 0.0\n707 1.0 1.0 0.0 1.0 0.0\n172 3.0 2.0 2.0 1.0 0.0\n78 2.0 1.0 2.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
2553.02.02.01.00.0
7071.01.00.01.00.0
1723.02.02.01.00.0
782.01.02.01.00.0
2333.02.06.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We complete the same activities to the validation dataset. It appears many male first class passengers traveling alone may have survived more than we anticipated. ","metadata":{}},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.569495Z","iopub.execute_input":"2023-02-01T14:51:05.569879Z","iopub.status.idle":"2023-02-01T14:51:05.589621Z","shell.execute_reply.started":"2023-02-01T14:51:05.569846Z","shell.execute_reply":"2023-02-01T14:51:05.588487Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n340 2.0 1.0 2.0 1.0 0.0\n534 3.0 2.0 0.0 0.0 1.0\n279 3.0 2.0 2.0 1.0 0.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3402.01.02.01.00.0
5343.02.00.00.01.0
2793.02.02.01.00.0
6071.01.00.01.00.0
8043.01.00.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.798455Z","iopub.execute_input":"2023-02-01T14:51:05.799489Z","iopub.status.idle":"2023-02-01T14:51:05.813581Z","shell.execute_reply.started":"2023-02-01T14:51:05.799450Z","shell.execute_reply":"2023-02-01T14:51:05.812556Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 0.0 3\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 1.0 4\n 2.0 1.0 0.0 4\n 2.0 0.0 4\n 3.0 2.0 0.0 2\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.295513Z","iopub.execute_input":"2023-02-01T14:51:06.296134Z","iopub.status.idle":"2023-02-01T14:51:06.315914Z","shell.execute_reply.started":"2023-02-01T14:51:06.296088Z","shell.execute_reply":"2023-02-01T14:51:06.314875Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n130 3.0 1.0 0.0 0.0 0.0\n110 1.0 1.0 0.0 0.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
1303.01.00.00.00.0
1101.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.545374Z","iopub.execute_input":"2023-02-01T14:51:06.546123Z","iopub.status.idle":"2023-02-01T14:51:06.565170Z","shell.execute_reply.started":"2023-02-01T14:51:06.546085Z","shell.execute_reply":"2023-02-01T14:51:06.564022Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 2.0 1.0 9\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 0.0 3\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 1.0 4\n 2.0 1.0 0.0 10\n 2.0 0.0 5\n 3.0 1.0 0.0 2\n 2.0 0.0 1\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. It appears \n\nOther elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees classifiers. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.019601Z","iopub.execute_input":"2023-02-01T14:51:07.020764Z","iopub.status.idle":"2023-02-01T14:51:07.038884Z","shell.execute_reply.started":"2023-02-01T14:51:07.020723Z","shell.execute_reply":"2023-02-01T14:51:07.037796Z"},"trusted":true},"execution_count":146,"outputs":[{"execution_count":146,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.380694Z","iopub.execute_input":"2023-02-01T14:51:07.381775Z","iopub.status.idle":"2023-02-01T14:51:07.399161Z","shell.execute_reply.started":"2023-02-01T14:51:07.381734Z","shell.execute_reply":"2023-02-01T14:51:07.397965Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 6\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 1.0 11\n 2.0 1.0 0.0 7\n 2.0 0.0 5\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Predict with testing dataset","metadata":{}},{"cell_type":"code","source":"y_pred = classifier.predict(X_test)\nlog_reg_pred = X_test.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.781717Z","iopub.execute_input":"2023-02-01T14:51:07.782101Z","iopub.status.idle":"2023-02-01T14:51:07.809230Z","shell.execute_reply.started":"2023-02-01T14:51:07.782070Z","shell.execute_reply":"2023-02-01T14:51:07.808079Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 1.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 0.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
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PclassSexfam_memberslr_y_predPassengerId
03.01.00.00.0892.0
13.02.01.01.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.00.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
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418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.028966Z","iopub.execute_input":"2023-02-01T14:51:08.030264Z","iopub.status.idle":"2023-02-01T14:51:08.036547Z","shell.execute_reply.started":"2023-02-01T14:51:08.030211Z","shell.execute_reply":"2023-02-01T14:51:08.035240Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.293378Z","iopub.execute_input":"2023-02-01T14:51:08.294544Z","iopub.status.idle":"2023-02-01T14:51:08.309861Z","shell.execute_reply.started":"2023-02-01T14:51:08.294483Z","shell.execute_reply":"2023-02-01T14:51:08.308466Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":" PassengerId lr_y_pred\n0 892.0 0.0\n1 893.0 1.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 0.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdlr_y_pred
0892.00.0
1893.01.0
2894.00.0
3895.00.0
4896.00.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
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418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.513449Z","iopub.execute_input":"2023-02-01T14:51:08.513843Z","iopub.status.idle":"2023-02-01T14:51:08.535503Z","shell.execute_reply.started":"2023-02-01T14:51:08.513810Z","shell.execute_reply":"2023-02-01T14:51:08.534386Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 0.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_pred
0892.03.01.00.431373-0.2810053.00.00.0
1893.03.02.01.411765-0.3161762.01.01.0
2894.02.01.02.588235-0.2021843.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: K-Nearest-neighbourn","metadata":{}},{"cell_type":"markdown","source":"We explore whether a reduction of statistical variables may be beneficial to the classification. We focus our model fitting on the same statistical variables as the logistic regression. \n\n\nThe K-NN classifier overfits to the training dataset. We have yet to find a better result. So Decision tree may have found its limit. ","metadata":{}},{"cell_type":"markdown","source":"## Model fitting\nWe discover the hyper-parametrisation of approximately 7 neighbors and the algorithm set the brute.","metadata":{}},{"cell_type":"code","source":"neighbors = range(2, 100)\nfor neighbor in neighbors:\n knn = KNeighborsClassifier(n_neighbors = neighbor, algorithm=\"brute\", weights = \"distance\", p=2)\n knn.fit(X_train,y_train)\n train_score = knn.score(X_train, y_train)\n valid_score = knn.score(X_valid, y_valid)\n print(\" - n neighbor : \", neighbor , \" - train score : \", train_score, \" - valid score : \", valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:09.565134Z","iopub.execute_input":"2023-02-01T14:51:09.565542Z","iopub.status.idle":"2023-02-01T14:51:12.977246Z","shell.execute_reply.started":"2023-02-01T14:51:09.565506Z","shell.execute_reply":"2023-02-01T14:51:12.975689Z"},"trusted":true},"execution_count":152,"outputs":[{"name":"stdout","text":" - n neighbor : 2 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 3 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 4 - train score : 0.8089887640449438 - valid score : 0.7591036414565826\n - n neighbor : 5 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 6 - train score : 0.8164794007490637 - valid score : 0.7927170868347339\n - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 8 - train score : 0.8202247191011236 - valid score : 0.7899159663865546\n - n neighbor : 9 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 10 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 11 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 12 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 13 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 14 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 15 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 16 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 17 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 18 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 19 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 20 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 21 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 22 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 23 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 24 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 25 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 26 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 27 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 28 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 29 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 30 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 31 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 32 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 33 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 34 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 35 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 36 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 37 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 38 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 39 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 40 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 41 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 42 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 43 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 44 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 45 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 46 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 47 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 48 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 49 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 50 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 51 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 52 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 53 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 54 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 55 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 56 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 57 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 58 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 59 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 60 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 61 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 62 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 63 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 64 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 65 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 66 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 67 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 68 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 69 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 70 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 71 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 72 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 73 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 74 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 75 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 76 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 77 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 78 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 79 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 80 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 81 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 82 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 83 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 84 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 85 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 86 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 87 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 88 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 89 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 90 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 91 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 92 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 93 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 94 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 95 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 96 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 97 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 98 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 99 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors = 7, algorithm=\"brute\", weights = \"distance\", p=2)\nknn.fit(X_train,y_train)\nknn_train_score = knn.score(X_train, y_train)\nknn_valid_score = knn.score(X_valid, y_valid)\nprint(\" - n neighbor : \", 7 , \" - train score : \", knn_train_score, \" - valid score : \", knn_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:12.986323Z","iopub.execute_input":"2023-02-01T14:51:12.992081Z","iopub.status.idle":"2023-02-01T14:51:13.043083Z","shell.execute_reply.started":"2023-02-01T14:51:12.992006Z","shell.execute_reply":"2023-02-01T14:51:13.041491Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":" - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = knn.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.051296Z","iopub.execute_input":"2023-02-01T14:51:13.052276Z","iopub.status.idle":"2023-02-01T14:51:13.094020Z","shell.execute_reply.started":"2023-02-01T14:51:13.052210Z","shell.execute_reply":"2023-02-01T14:51:13.092537Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"array([[299, 30],\n [ 63, 142]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.104344Z","iopub.execute_input":"2023-02-01T14:51:13.109854Z","iopub.status.idle":"2023-02-01T14:51:13.138605Z","shell.execute_reply.started":"2023-02-01T14:51:13.109782Z","shell.execute_reply":"2023-02-01T14:51:13.137094Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"Accuracy : 0.8258426966292135\nMisclassfication : 0.17415730337078653\nSensitivivity : 0.9088145896656535\nSpecificity : 0.6926829268292682\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.141155Z","iopub.execute_input":"2023-02-01T14:51:13.151686Z","iopub.status.idle":"2023-02-01T14:51:13.183541Z","shell.execute_reply.started":"2023-02-01T14:51:13.151614Z","shell.execute_reply":"2023-02-01T14:51:13.181982Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.190465Z","iopub.execute_input":"2023-02-01T14:51:13.191601Z","iopub.status.idle":"2023-02-01T14:51:13.214243Z","shell.execute_reply.started":"2023-02-01T14:51:13.191536Z","shell.execute_reply":"2023-02-01T14:51:13.212831Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"Accuracy : 0.7871148459383753\nMisclassfication : 0.21288515406162464\nSensitivivity : 0.8818181818181818\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.216513Z","iopub.execute_input":"2023-02-01T14:51:13.217361Z","iopub.status.idle":"2023-02-01T14:51:13.226018Z","shell.execute_reply.started":"2023-02-01T14:51:13.217286Z","shell.execute_reply":"2023-02-01T14:51:13.224351Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(X_train)\nknn_pred = X_train.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_train_pass_id\nknn_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.228136Z","iopub.execute_input":"2023-02-01T14:51:13.229804Z","iopub.status.idle":"2023-02-01T14:51:13.289272Z","shell.execute_reply.started":"2023-02-01T14:51:13.229740Z","shell.execute_reply":"2023-02-01T14:51:13.287745Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n844 3.0 1.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 769.0\n255 3.0 2.0 2.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
8443.01.00.00.0845.0
3162.02.01.01.0317.0
7683.01.01.00.0769.0
2553.02.02.01.0256.0
1303.01.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(knn_pred[[\"PassengerId\", \"knn_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.297719Z","iopub.execute_input":"2023-02-01T14:51:13.302941Z","iopub.status.idle":"2023-02-01T14:51:13.361563Z","shell.execute_reply.started":"2023-02-01T14:51:13.302872Z","shell.execute_reply":"2023-02-01T14:51:13.359941Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = knn.predict(X_valid)\nknn_pred = X_valid.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_valid_pass_id\nknn_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.366098Z","iopub.execute_input":"2023-02-01T14:51:13.367128Z","iopub.status.idle":"2023-02-01T14:51:13.414267Z","shell.execute_reply.started":"2023-02-01T14:51:13.367081Z","shell.execute_reply":"2023-02-01T14:51:13.412764Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n369 1.0 2.0 0.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 428.0\n.. ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 1.0 39.0\n371 3.0 1.0 1.0 0.0 372.0\n\n[357 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
3691.02.00.01.0370.0
5413.02.06.00.0542.0
1963.01.00.00.0197.0
8103.01.00.00.0811.0
4272.02.00.01.0428.0
..................
1741.01.00.00.0175.0
2971.02.03.00.0298.0
2443.01.00.00.0245.0
383.02.02.01.039.0
3713.01.01.00.0372.0
\n

357 rows × 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(knn_pred.PassengerId), \"knn_y_pred\"] = knn_pred[\"knn_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.416656Z","iopub.execute_input":"2023-02-01T14:51:13.417577Z","iopub.status.idle":"2023-02-01T14:51:13.474919Z","shell.execute_reply.started":"2023-02-01T14:51:13.417518Z","shell.execute_reply":"2023-02-01T14:51:13.473392Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.552373Z","iopub.execute_input":"2023-02-01T14:51:13.552777Z","iopub.status.idle":"2023-02-01T14:51:13.575185Z","shell.execute_reply.started":"2023-02-01T14:51:13.552741Z","shell.execute_reply":"2023-02-01T14:51:13.573826Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n707 1.0 1.0 0.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0\n788 3.0 1.0 3.0 1.0 0.0\n183 2.0 1.0 3.0 1.0 0.0\n654 3.0 2.0 0.0 0.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
7071.01.00.01.00.0
2333.02.06.01.00.0
7883.01.03.01.00.0
1832.01.03.01.00.0
6543.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.851998Z","iopub.execute_input":"2023-02-01T14:51:13.852446Z","iopub.status.idle":"2023-02-01T14:51:13.868236Z","shell.execute_reply.started":"2023-02-01T14:51:13.852408Z","shell.execute_reply":"2023-02-01T14:51:13.867490Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 2.0 1.0 1\n 1.0 1.0 0.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 2\n 1.0 1.0 0.0 1\n 2.0 1.0 2\n 2.0 1.0 1.0 3\n 2.0 1.0 1\n 3.0 1.0 0.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 0.0 4\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 3.0 1.0 0.0 1\n 2.0 1.0 1\n 6.0 2.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.057420Z","iopub.execute_input":"2023-02-01T14:51:14.057804Z","iopub.status.idle":"2023-02-01T14:51:14.084011Z","shell.execute_reply.started":"2023-02-01T14:51:14.057773Z","shell.execute_reply":"2023-02-01T14:51:14.082464Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.355738Z","iopub.execute_input":"2023-02-01T14:51:14.356164Z","iopub.status.idle":"2023-02-01T14:51:14.375540Z","shell.execute_reply.started":"2023-02-01T14:51:14.356115Z","shell.execute_reply":"2023-02-01T14:51:14.374287Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n534 3.0 2.0 0.0 0.0 1.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0\n429 3.0 1.0 0.0 1.0 0.0\n501 3.0 2.0 0.0 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
5343.02.00.00.01.0
6071.01.00.01.00.0
8043.01.00.01.00.0
4293.01.00.01.00.0
5013.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.597501Z","iopub.execute_input":"2023-02-01T14:51:14.597895Z","iopub.status.idle":"2023-02-01T14:51:14.613504Z","shell.execute_reply.started":"2023-02-01T14:51:14.597865Z","shell.execute_reply":"2023-02-01T14:51:14.612422Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 1.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 1.0 6\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.104177Z","iopub.execute_input":"2023-02-01T14:51:15.104569Z","iopub.status.idle":"2023-02-01T14:51:15.123111Z","shell.execute_reply.started":"2023-02-01T14:51:15.104537Z","shell.execute_reply":"2023-02-01T14:51:15.121935Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n255 3.0 2.0 2.0 1.0 1.0\n130 3.0 1.0 0.0 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
2553.02.02.01.01.0
1303.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.344115Z","iopub.execute_input":"2023-02-01T14:51:15.344558Z","iopub.status.idle":"2023-02-01T14:51:15.362850Z","shell.execute_reply.started":"2023-02-01T14:51:15.344502Z","shell.execute_reply":"2023-02-01T14:51:15.361620Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 4\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 0.0 10\n 2.0 1.0 0.0 10\n 2.0 1.0 8\n 3.0 1.0 0.0 2\n 2.0 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.857448Z","iopub.execute_input":"2023-02-01T14:51:15.857837Z","iopub.status.idle":"2023-02-01T14:51:15.877163Z","shell.execute_reply.started":"2023-02-01T14:51:15.857806Z","shell.execute_reply":"2023-02-01T14:51:15.875923Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.132579Z","iopub.execute_input":"2023-02-01T14:51:16.132970Z","iopub.status.idle":"2023-02-01T14:51:16.150755Z","shell.execute_reply.started":"2023-02-01T14:51:16.132936Z","shell.execute_reply":"2023-02-01T14:51:16.149943Z"},"trusted":true},"execution_count":171,"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 1.0 1\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 0.0 4\n 2.0 1.0 0.0 7\n 2.0 1.0 4\n 3.0 2.0 1.0 2\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower.","metadata":{}},{"cell_type":"markdown","source":"## Prediction on the test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = knn.predict(X_test)\nknn_pred = X_test.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nknn_pred\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.910077Z","iopub.execute_input":"2023-02-01T14:51:16.910492Z","iopub.status.idle":"2023-02-01T14:51:16.964596Z","shell.execute_reply.started":"2023-02-01T14:51:16.910456Z","shell.execute_reply":"2023-02-01T14:51:16.963157Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 0.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 1.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
03.01.00.00.0892.0
13.02.01.00.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.01.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
\n

418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.178878Z","iopub.execute_input":"2023-02-01T14:51:17.179931Z","iopub.status.idle":"2023-02-01T14:51:17.185405Z","shell.execute_reply.started":"2023-02-01T14:51:17.179876Z","shell.execute_reply":"2023-02-01T14:51:17.184219Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"code","source":"knn_pred[[\"PassengerId\",\"knn_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.372559Z","iopub.execute_input":"2023-02-01T14:51:17.372948Z","iopub.status.idle":"2023-02-01T14:51:17.390909Z","shell.execute_reply.started":"2023-02-01T14:51:17.372914Z","shell.execute_reply":"2023-02-01T14:51:17.389533Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" PassengerId knn_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdknn_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(knn_pred[[\"PassengerId\",\"knn_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.671274Z","iopub.execute_input":"2023-02-01T14:51:17.672432Z","iopub.status.idle":"2023-02-01T14:51:17.693960Z","shell.execute_reply.started":"2023-02-01T14:51:17.672382Z","shell.execute_reply":"2023-02-01T14:51:17.692706Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred \n0 0.0 0.0 \n1 1.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 1.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.0
2894.02.01.02.588235-0.2021843.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method : Decision Trees\n\nWe use a decision tree classifier and some automated search of the hyper-parametrisation to discover suitable hyper-parameters and validate the quality of a model. \n","metadata":{}},{"cell_type":"code","source":"\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.109673Z","iopub.execute_input":"2023-02-01T14:51:18.110073Z","iopub.status.idle":"2023-02-01T14:51:18.128404Z","shell.execute_reply.started":"2023-02-01T14:51:18.110036Z","shell.execute_reply":"2023-02-01T14:51:18.127375Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = [\"Fare\",\"Pclass\",\"Sex\",\"Embarked\",\"fam_members\", \"Age\"]\nx_train_pass_id = X_train.PassengerId\nX_train = X_train [x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.370422Z","iopub.execute_input":"2023-02-01T14:51:18.370800Z","iopub.status.idle":"2023-02-01T14:51:18.388440Z","shell.execute_reply.started":"2023-02-01T14:51:18.370767Z","shell.execute_reply":"2023-02-01T14:51:18.387202Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538\n768 0.419921 3.0 1.0 3.0 1.0 0.000000\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769","text/html":"
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FarePclassSexEmbarkedfam_membersAge
844-0.2508363.01.02.00.0-1.000000
3160.5000432.02.02.01.0-0.461538
7680.4199213.01.03.01.00.000000
2550.0342843.02.04.02.0-0.076923
130-0.2840413.01.04.00.00.230769
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nx_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.609801Z","iopub.execute_input":"2023-02-01T14:51:18.610554Z","iopub.status.idle":"2023-02-01T14:51:18.628148Z","shell.execute_reply.started":"2023-02-01T14:51:18.610505Z","shell.execute_reply":"2023-02-01T14:51:18.626956Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154","text/html":"
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FarePclassSexEmbarkedfam_membersAge
3692.3753461.02.04.00.0-0.461538
5410.7285013.02.02.06.0-1.615385
196-0.2903563.01.03.00.00.000000
810-0.2844013.01.02.00.0-0.307692
4270.5000432.02.02.00.0-0.846154
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nX = titanic_test.copy(deep = True)\nX_test = X[x_cols]\nX_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.826406Z","iopub.execute_input":"2023-02-01T14:51:18.826797Z","iopub.status.idle":"2023-02-01T14:51:18.835436Z","shell.execute_reply.started":"2023-02-01T14:51:18.826766Z","shell.execute_reply":"2023-02-01T14:51:18.834526Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"Index(['Fare', 'Pclass', 'Sex', 'Embarked', 'fam_members', 'Age'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Decision Tree classifier\n\nWe explore the maximum depths hyper parameter using a deterministic and incremental search. Then we applied the most efficient parametrisation. We chose a low maximum depth, as the model may be overfitting.","metadata":{}},{"cell_type":"code","source":"\ndepths = range(3, 200)\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth = depth, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n # Train Decision Tree Classifer\n clf = clf.fit(X_train,y_train)\n train_score = clf.score(X_train,y_train)\n valid_score = clf.score(X_valid,y_valid)\n print(\"- depth : \", depth, \" - train score : \", train_score, \" - valid score : \", valid_score)\n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:19.301726Z","iopub.execute_input":"2023-02-01T14:51:19.302125Z","iopub.status.idle":"2023-02-01T14:51:20.492365Z","shell.execute_reply.started":"2023-02-01T14:51:19.302089Z","shell.execute_reply":"2023-02-01T14:51:20.491051Z"},"trusted":true},"execution_count":180,"outputs":[{"name":"stdout","text":"- depth : 3 - train score : 0.8295880149812734 - valid score : 0.8011204481792717\n- depth : 4 - train score : 0.8295880149812734 - valid score : 0.8151260504201681\n- depth : 5 - train score : 0.8595505617977528 - valid score : 0.8067226890756303\n- depth : 6 - train score : 0.8820224719101124 - valid score : 0.8235294117647058\n- depth : 7 - train score : 0.8895131086142322 - valid score : 0.8179271708683473\n- depth : 8 - train score : 0.9063670411985019 - valid score : 0.7927170868347339\n- depth : 9 - train score : 0.9119850187265918 - valid score : 0.7843137254901961\n- depth : 10 - train score : 0.9250936329588015 - valid score : 0.803921568627451\n- depth : 11 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n- depth : 12 - train score : 0.9550561797752809 - valid score : 0.773109243697479\n- depth : 13 - train score : 0.9625468164794008 - valid score : 0.7955182072829131\n- depth : 14 - train score : 0.9662921348314607 - valid score : 0.7787114845938375\n- depth : 15 - train score : 0.9700374531835206 - valid score : 0.7927170868347339\n- depth : 16 - train score : 0.9737827715355806 - valid score : 0.7787114845938375\n- depth : 17 - train score : 0.9756554307116105 - valid score : 0.7871148459383753\n- depth : 18 - train score : 0.9794007490636704 - valid score : 0.7871148459383753\n- depth : 19 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 20 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 21 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 22 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 23 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 24 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 25 - train score : 0.9812734082397003 - valid score : 0.7899159663865546\n- depth : 26 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 27 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 28 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 29 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 30 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 31 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 32 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 33 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 34 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 35 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 36 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 37 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 38 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 39 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 40 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 41 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 42 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 43 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 44 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 45 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 46 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 47 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 48 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 49 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 50 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 51 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 52 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 53 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 54 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 55 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 56 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 57 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 58 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 59 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 60 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 61 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 62 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 63 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 64 - train score : 0.9812734082397003 - 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valid score : 0.7703081232492998\n- depth : 197 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 198 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 199 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(max_depth = 8, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n\n# Train Decision Tree Classifer\nclf = clf.fit(X_train,y_train)\nclf_train_score = clf.score(X_train,y_train)\nclf_valid_score = clf.score(X_valid,y_valid)\nprint(\"- depth : \", 8, \" - train score : \", clf_train_score, \" - valid score : \", clf_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.494270Z","iopub.execute_input":"2023-02-01T14:51:20.494649Z","iopub.status.idle":"2023-02-01T14:51:20.508968Z","shell.execute_reply.started":"2023-02-01T14:51:20.494617Z","shell.execute_reply":"2023-02-01T14:51:20.507560Z"},"trusted":true},"execution_count":181,"outputs":[{"name":"stdout","text":"- depth : 8 - train score : 0.9082397003745318 - valid score : 0.8151260504201681\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover that the gender, Fare and age could be contribute to the classification. It constrast to our previous assumptions for KNN and logistic regression.","metadata":{}},{"cell_type":"code","source":"importances = clf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.510227Z","iopub.execute_input":"2023-02-01T14:51:20.510578Z","iopub.status.idle":"2023-02-01T14:51:20.523335Z","shell.execute_reply.started":"2023-02-01T14:51:20.510548Z","shell.execute_reply":"2023-02-01T14:51:20.521845Z"},"trusted":true},"execution_count":182,"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":" 0\n0.200193 Fare\n0.125949 Pclass\n0.315820 Sex\n0.025783 Embarked\n0.094918 fam_members\n0.237337 Age","text/html":"
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0
0.200193Fare
0.125949Pclass
0.315820Sex
0.025783Embarked
0.094918fam_members
0.237337Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.525411Z","iopub.execute_input":"2023-02-01T14:51:20.525712Z","iopub.status.idle":"2023-02-01T14:51:20.536265Z","shell.execute_reply.started":"2023-02-01T14:51:20.525685Z","shell.execute_reply":"2023-02-01T14:51:20.535549Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"array([[326, 3],\n [ 46, 159]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.736687Z","iopub.execute_input":"2023-02-01T14:51:20.737047Z","iopub.status.idle":"2023-02-01T14:51:20.744835Z","shell.execute_reply.started":"2023-02-01T14:51:20.737016Z","shell.execute_reply":"2023-02-01T14:51:20.743620Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"Accuracy : 0.9082397003745318\nMisclassfication : 0.09176029962546817\nSensitivivity : 0.9908814589665653\nSpecificity : 0.775609756097561\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.940682Z","iopub.execute_input":"2023-02-01T14:51:20.941080Z","iopub.status.idle":"2023-02-01T14:51:20.950745Z","shell.execute_reply.started":"2023-02-01T14:51:20.941045Z","shell.execute_reply":"2023-02-01T14:51:20.949939Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.156573Z","iopub.execute_input":"2023-02-01T14:51:21.157555Z","iopub.status.idle":"2023-02-01T14:51:21.164777Z","shell.execute_reply.started":"2023-02-01T14:51:21.157504Z","shell.execute_reply":"2023-02-01T14:51:21.163996Z"},"trusted":true},"execution_count":186,"outputs":[{"name":"stdout","text":"Accuracy : 0.8151260504201681\nMisclassfication : 0.18487394957983194\nSensitivivity : 0.9318181818181818\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.602984Z","iopub.execute_input":"2023-02-01T14:51:21.603408Z","iopub.status.idle":"2023-02-01T14:51:21.609433Z","shell.execute_reply.started":"2023-02-01T14:51:21.603369Z","shell.execute_reply":"2023-02-01T14:51:21.608257Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_train)\nclf_pred = X_train.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_train_pass_id\nclf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.801023Z","iopub.execute_input":"2023-02-01T14:51:21.801826Z","iopub.status.idle":"2023-02-01T14:51:21.826292Z","shell.execute_reply.started":"2023-02-01T14:51:21.801783Z","shell.execute_reply":"2023-02-01T14:51:21.825118Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769231.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(clf_pred[[\"PassengerId\", \"clf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.073441Z","iopub.execute_input":"2023-02-01T14:51:22.073853Z","iopub.status.idle":"2023-02-01T14:51:22.100768Z","shell.execute_reply.started":"2023-02-01T14:51:22.073817Z","shell.execute_reply":"2023-02-01T14:51:22.099989Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = clf.predict(X_valid)\nclf_pred = X_valid.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_valid_pass_id\nclf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.313331Z","iopub.execute_input":"2023-02-01T14:51:22.314186Z","iopub.status.idle":"2023-02-01T14:51:22.339255Z","shell.execute_reply.started":"2023-02-01T14:51:22.314149Z","shell.execute_reply":"2023-02-01T14:51:22.338531Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 0.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538460.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
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357 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(clf_pred.PassengerId), \"clf_y_pred\"] = clf_pred[\"clf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.503867Z","iopub.execute_input":"2023-02-01T14:51:22.504541Z","iopub.status.idle":"2023-02-01T14:51:22.530946Z","shell.execute_reply.started":"2023-02-01T14:51:22.504500Z","shell.execute_reply":"2023-02-01T14:51:22.529880Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.197164Z","iopub.execute_input":"2023-02-01T14:51:23.197598Z","iopub.status.idle":"2023-02-01T14:51:23.221279Z","shell.execute_reply.started":"2023-02-01T14:51:23.197559Z","shell.execute_reply":"2023-02-01T14:51:23.220173Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n220 -0.277363 3.0 1.0 2.0 0.0 -1.076923 1.0 0.0\n510 -0.290356 3.0 1.0 3.0 0.0 -0.076923 1.0 0.0\n724 1.673732 1.0 1.0 2.0 1.0 -0.230769 1.0 0.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
220-0.2773633.01.02.00.0-1.0769231.00.0
510-0.2903563.01.03.00.0-0.0769231.00.0
7241.6737321.01.02.01.0-0.2307691.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.536909Z","iopub.execute_input":"2023-02-01T14:51:23.537537Z","iopub.status.idle":"2023-02-01T14:51:23.553252Z","shell.execute_reply.started":"2023-02-01T14:51:23.537491Z","shell.execute_reply":"2023-02-01T14:51:23.552369Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 10\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n3.0 0.0 1.0 0.0 14\n 2.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.819458Z","iopub.execute_input":"2023-02-01T14:51:23.819831Z","iopub.status.idle":"2023-02-01T14:51:23.828371Z","shell.execute_reply.started":"2023-02-01T14:51:23.819802Z","shell.execute_reply":"2023-02-01T14:51:23.827545Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.944899Z","iopub.execute_input":"2023-02-01T14:51:23.945939Z","iopub.status.idle":"2023-02-01T14:51:24.401522Z","shell.execute_reply.started":"2023-02-01T14:51:23.945899Z","shell.execute_reply":"2023-02-01T14:51:24.400330Z"},"trusted":true},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.403612Z","iopub.execute_input":"2023-02-01T14:51:24.404043Z","iopub.status.idle":"2023-02-01T14:51:24.424956Z","shell.execute_reply.started":"2023-02-01T14:51:24.404007Z","shell.execute_reply":"2023-02-01T14:51:24.423814Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0\n501 -0.290356 3.0 2.0 3.0 0.0 -0.692308 0.0 1.0\n17 -0.062981 2.0 1.0 2.0 0.0 0.000000 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
429-0.2773633.01.02.00.00.1538461.00.0
501-0.2903563.02.03.00.0-0.6923080.01.0
17-0.0629812.01.02.00.00.0000001.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.426286Z","iopub.execute_input":"2023-02-01T14:51:24.426719Z","iopub.status.idle":"2023-02-01T14:51:24.444950Z","shell.execute_reply.started":"2023-02-01T14:51:24.426673Z","shell.execute_reply":"2023-02-01T14:51:24.443790Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 3\n 1.0 1\n 1.0 2.0 0.0 1\n 2.0 1.0 1.0 1\n3.0 0.0 1.0 0.0 12\n 1.0 3\n 2.0 0.0 4\n 1.0 2\n 1.0 1.0 0.0 1\n 2.0 0.0 9\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 2\n 2.0 1.0 3\n 4.0 1.0 1.0 1\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.655585Z","iopub.execute_input":"2023-02-01T14:51:24.655981Z","iopub.status.idle":"2023-02-01T14:51:25.270872Z","shell.execute_reply.started":"2023-02-01T14:51:24.655946Z","shell.execute_reply":"2023-02-01T14:51:25.270073Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.272439Z","iopub.execute_input":"2023-02-01T14:51:25.273391Z","iopub.status.idle":"2023-02-01T14:51:25.295346Z","shell.execute_reply.started":"2023-02-01T14:51:25.273342Z","shell.execute_reply":"2023-02-01T14:51:25.294366Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 1.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
2550.0342843.02.04.02.0-0.0769231.01.0
130-0.2840413.01.04.00.00.2307690.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.310893Z","iopub.execute_input":"2023-02-01T14:51:25.311294Z","iopub.status.idle":"2023-02-01T14:51:25.332606Z","shell.execute_reply.started":"2023-02-01T14:51:25.311259Z","shell.execute_reply":"2023-02-01T14:51:25.331521Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 6\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 2\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 0.0 1\n 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 1.0 5\n 2.0 0.0 14\n 1.0 23\n 1.0 1.0 0.0 15\n 1.0 3\n 2.0 0.0 10\n 1.0 4\n 2.0 1.0 0.0 10\n 1.0 2\n 2.0 0.0 5\n 1.0 8\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.617532Z","iopub.execute_input":"2023-02-01T14:51:25.617910Z","iopub.status.idle":"2023-02-01T14:51:27.648580Z","shell.execute_reply.started":"2023-02-01T14:51:25.617879Z","shell.execute_reply":"2023-02-01T14:51:27.647383Z"},"trusted":true},"execution_count":201,"outputs":[{"execution_count":201,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.650898Z","iopub.execute_input":"2023-02-01T14:51:27.651397Z","iopub.status.idle":"2023-02-01T14:51:27.674977Z","shell.execute_reply.started":"2023-02-01T14:51:27.651353Z","shell.execute_reply":"2023-02-01T14:51:27.673660Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.676558Z","iopub.execute_input":"2023-02-01T14:51:27.676918Z","iopub.status.idle":"2023-02-01T14:51:27.695988Z","shell.execute_reply.started":"2023-02-01T14:51:27.676883Z","shell.execute_reply":"2023-02-01T14:51:27.694729Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 1.0 3\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 12\n 1.0 1.0 0.0 4\n 2.0 1.0 8\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 91\n 1.0 1\n 2.0 0.0 6\n 1.0 4\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 1.0 2\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 0.0 2\n 1.0 4\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.698581Z","iopub.execute_input":"2023-02-01T14:51:27.699104Z","iopub.status.idle":"2023-02-01T14:51:28.312451Z","shell.execute_reply.started":"2023-02-01T14:51:27.699061Z","shell.execute_reply":"2023-02-01T14:51:28.311698Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.313663Z","iopub.execute_input":"2023-02-01T14:51:28.314115Z","iopub.status.idle":"2023-02-01T14:51:28.742585Z","shell.execute_reply.started":"2023-02-01T14:51:28.314085Z","shell.execute_reply":"2023-02-01T14:51:28.741404Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.744143Z","iopub.execute_input":"2023-02-01T14:51:28.744835Z","iopub.status.idle":"2023-02-01T14:51:29.161694Z","shell.execute_reply.started":"2023-02-01T14:51:28.744790Z","shell.execute_reply":"2023-02-01T14:51:29.160329Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.164872Z","iopub.execute_input":"2023-02-01T14:51:29.165348Z","iopub.status.idle":"2023-02-01T14:51:29.588614Z","shell.execute_reply.started":"2023-02-01T14:51:29.165277Z","shell.execute_reply":"2023-02-01T14:51:29.587528Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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NAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIQgAANAZIbgKzM7OZtOmTVm3bl02bdqU2dnZUY8EAIdl/wWwdq0f9QC9m52dzeTkZGZmZjI+Pp65ublMTEwkSbZu3Tri6QDgwOy/ANa2aq2t2IOde+657aabblqxx1sLNm3alGuuuSabN2/et23Xrl3Zvn17br755hFOBgAHZ/8FsPpV1Qdaa+ce8HNCcLTWrVuXr3zlK9mwYcO+bXv27MkJJ5yQBx98cISTAcDB2X8BrH6HCkHPERyxsbGxzM3N7bdtbm4uY2NjI5oIAA7P/gtgbROCIzY5OZmJiYns2rUre/bsya5duzIxMZHJyclRjwYAB2X/BbC2uVjMiO19Qv327dszPz+fsbGxTE1NeaI9AKua/RfA2uY5ggAAAMcgzxEEAABgHyEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQGSEIAADQmcOGYFWdUVW7quqvq+qWqvqXg+1PrKobquojgz+fsPzjAgAAMKwjOSL4QJLXtNa+Jck/SPLPqupbklyW5I9ba09L8seDjwEAAFjlDhuCrbXPtNb+cvD+PUnmk3x9kpckedvgZm9L8tJlmhEAAIAl9KieI1hVZyf5+0n+R5Int9Y+M/jUZ5M8eWlHAwAAYDkccQhW1UlJfifJq1trX1r8udZaS9IO8nWvqqqbquqmO+64Y6hhAQAAGN4RhWBVbchCBL6jtfa7g82fq6qnDD7/lCSfP9DXttbe1Fo7t7V27qmnnroUMwMAADCEI7lqaCWZSTLfWvu3iz71riSvGLz/iiR/sPTjAQAAsNTWH8Ftnpvk5Uk+XFUfGmy7Isnrkvx2VU0kuS3JDy7LhAAAACypw4Zga20uSR3k0/9oaccBAABguT2qq4YCAACw9glBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzhw2BKvqLVX1+aq6edG2n6+qT1fVhwZvL1zeMQEAAFgqR3JE8K1JvvsA23+ttfaswdt/WdqxAAAAWC6HDcHW2nuT3LkCswAAALAChnmO4D+vqv81OHX0CUs2EQAAAMvqaENwR5JvTPKsJJ9J8qsHu2FVvaqqbqqqm+64446jfDgAAACWylGFYGvtc621B1trDyW5Nsl3HOK2b2qtndtaO/fUU0892jkBAABYIkcVglX1lEUffm+Smw92WwAAAFaX9Ye7QVXNJjkvyZOq6lNJXpvkvKp6VpKW5NYk/3T5RgQAAGApHTYEW2tbD7B5ZhlmAQAAYAUMc9VQAAAA1iAhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0Jn1ox4AABiNqhr1CI/QWhv1CABdcEQQADrVWluSt7MuffeS3RcAK0MIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIrgKzs7PZtGlT1q1bl02bNmV2dnbUIwEAAMew9aMeoHezs7OZnJzMzMxMxsfHMzc3l4mJiSTJ1q1bRzwdAABwLHJEcMSmpqYyMzOTzZs3Z8OGDdm8eXNmZmYyNTU16tEAAIBj1GFDsKreUlWfr6qbF217YlXdUFUfGfz5hOUd89g1Pz+f8fHx/baNj49nfn5+RBMBAADHuiM5IvjWJN/9sG2XJfnj1trTkvzx4GOOwtjYWObm5vbbNjc3l7GxsRFNBAAAHOsOG4KttfcmufNhm1+S5G2D99+W5KVLO1Y/JicnMzExkV27dmXPnj3ZtWtXJiYmMjk5OerRAACAY9TRXizmya21zwze/2ySJy/RPN3Ze0GY7du3Z35+PmNjY5mamnKhGAAAYNkMfdXQ1lqrqnawz1fVq5K8KknOPPPMYR/umLR161bhBwAArJijvWro56rqKUky+PPzB7tha+1NrbVzW2vnnnrqqUf5cAAAACyVow3BdyV5xeD9VyT5g6UZBwAAgOV2JC8fMZvk/Um+qao+VVUTSV6X5Pyq+kiSfzz4GAAAgDXgsM8RbK0d7Mlr/2iJZwEAAGAFHO2poQAAAKxRQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQnAVmJ2dzaZNm7Ju3bps2rQps7Ozox4JAAA4hq0f9QC9m52dzeTkZGZmZjI+Pp65ublMTEwkSbZu3Tri6QAAgGORI4IjNjU1lZmZmWzevDkbNmzI5s2bMzMzk6mpqVGPBgAAHKOE4IjNz89nfHx8v23j4+OZn58f0UQAAMCxTgiO2NjYWObm5vbbNjc3l7GxsRFNBAAAHOuE4IhNTk5mYmIiu3btyp49e7Jr165MTExkcnJy1KMBAADHKBeLGbG9F4TZvn175ufnMzY2lqmpKReKAQAAlo0QXAW2bt0q/AAAgBXj1FAAAIDOCEEAAIDOCEEAAIDOCMFVYHZ2Nps2bcq6deuyadOmzM7OjnokAADgGOZiMSM2OzubycnJzMzMZHx8PHNzc5mYmEgSF5ABAACWhSOCIzY1NZWZmZls3rw5GzZsyObNmzMzM5OpqalRjwYAAByjhOCIzc/PZ3x8fL9t4+PjmZ+fH9FEAADAsU4IjtjY2Fjm5ub22zY3N5exsbERTQQAABzrhOCITU5OZmJiIrt27cqePXuya9euTExMZHJyctSjAQAAxygXixmxvReE2b59e+bn5zM2NpapqSkXigEAAJaNEFwFtm7dKvwAAIAV49RQAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzgjBVWB2djabNm3KunXrsmnTpszOzo56JAAA4Bi2ftQD9G52djaTk5OZmZnJ+Ph45ubmMjExkSTZunXriKcDAACORY4IjtjU1FRmZmayefPmbNiwIZs3b87MzEympqZGPRoAAHCMEoIjNj8/n/Hx8f22jY+PZ35+fkQTAQAAxzohOGJjY2OZm5vbb9vc3FzGxsZGNBEAAHCsE4IjNjk5mYmJiezatSt79uzJrl27MjExkcnJyVGPBgAAHKNcLGbE9l4QZvv27Zmfn8/Y2FimpqZcKAYAAFg2QnAJVNWS3dctt9ySCy+8MBdeeOFQ99NaW6KJAACAY41TQ5dAa21J3s669N1Ldl8AAAAHIwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6IwQBAAA6s37UAwAAj84zf2Fn7r5/z6jH2M/Zl71n1CPsc8qJG/JXr71g1GMArGpCEADWmLvv35NbX/eiUY+xaq2mKAVYrZwaCgAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0Jn1w3xxVd2a5J4kDyZ5oLV27lIMBQAAwPIZKgQHNrfWvrAE9wMAAMAKcGooAABAZ4YNwZZkZ1V9oKpetRQDAQAAsLyGPTV0vLX26ar62iQ3VNXftNbeu/gGg0B8VZKceeaZQz4cAAAAwxrqiGBr7dODPz+f5PeSfMcBbvOm1tq5rbVzTz311GEeDgAAgCVw1CFYVY+tqpP3vp/kgiQ3L9VgAAAALI9hTg19cpLfq6q99/PO1tofLclUAAAALJujDsHW2seSPHMJZwEAAGAFePkIAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzghBAACAzqwf9QAAwKNz8thl+da3XTbqMVatk8eS5EWjHgNgVROCALDG3DP/utz6OqFzMGdf9p5RjwCw6jk1FAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDNCEAAAoDPrRz3AKD3zF3bm7vv3jHqM/Zx92XtGPcI+p5y4IX/12gtGPQYAALDEug7Bu+/fk1tf96JRj7FqraYoBQAAlo5TQwEAADojBAEAADojBGGN27JlS4477rhUVY477rhs2bJl1CMBALDKCUFYw7Zs2ZKdO3dm27Ztueuuu7Jt27bs3LlTDAIAcEhdXywG1robbrghF110Ud7whjckyb4/p6enRzkWAACrnCOCsIa11nLVVVftt+2qq65Ka21EEwEAsBYIQVjDqiqXX375ftsuv/zyVNWIJgIAYC0QgrCGnX/++dmxY0cuvvji3H333bn44ouzY8eOnH/++aMeDQCAVcxzBGENu/7667Nly5ZMT09nx44dqapccMEFuf7660c9GgAAq5gQhDVO9AEA8Gg5NRSgY9u3b88JJ5yQqsoJJ5yQ7du3j3okAGAFCEGATm3fvj3T09O58sorc9999+XKK6/M9PS0GASADghBgE5de+21ufrqq3PJJZfkMY95TC655JJcffXVufbaa0c9GgCwzIQgQKd2796dbdu27bdt27Zt2b1794gmAgBWihAE6NTGjRszPT2937bp6els3LhxRBMBACvFVUMBOvXKV74yl156aZKFI4HT09O59NJLH3GUEAA49ghBgE5dc801SZIrrrgir3nNa7Jx48Zs27Zt33YA4NglBAE6ds011wg/AOiQ5wgCAAB0RggCALDitm/fnhNOOCFVlRNOOMFrmMIKE4IAAKyo7du3Z3p6OldeeWXuu+++XHnllZmenhaDsIKEIAAAK+raa6/N1VdfnUsuuSSPecxjcskll+Tqq6/OtddeO+rRoBtCEACAFbV79+5HvFTNtm3bsnv37hFNBP0RggAArKiNGzdmenp6v23T09PZuHHjiCaC/nj5CAAAVtQrX/nKXHrppUkWjgROT0/n0ksvfcRRQmD5CEEAAFbU3tcvveKKK/Ka17wmGzduzLZt27yuKawgIQgAwIq75pprhB+MkOcIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAgAAdEYIAnTszDPPTFXtezvzzDNHPRIAsALWj3oAAEbjzDPPzCc/+ck85znPyXXXXZeXvexlufHGG3PmmWfmE5/4xKjH4zDOvuw9ox5h1TrlxA2jHgFg1ROCAJ3aG4Hve9/7kiTve9/78tznPjc33njjiCfjcG593YtGPcJ+zr7sPatuJgAOzamhAB277rrrDvkxAHBsEoIAHXvZy152yI8BgGOTEATo1BlnnJEbb7wxz33uc/OZz3xm32mhZ5xxxqhHAwCWmecIAnTqE5/4RM4888zceOONOe2005IsxKELxQDAsU8IAnRM9AFAn5waCgAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAA0BkhCAAArCmzs7PZtGlT1q1bl02bNmV2dnbUI60560c9AAAAwJGanZ3N5ORkZmZmMj4+nrm5uUxMTCRJtm7dOuLp1g5HBAEAgDVjamoqMzMz2bx5czZs2JDNmzdnZmYmU1NTox5tTRGCAADAmjE/P5/x8fH9to2Pj2d+fn5EE61NQhAAAFgzxsbGMjc3t9+2ubm5jI2NjWiitUkIAgAAa8bk5GQmJiaya9eu7NmzJ7t27crExEQmJydHPdqa4mIxAADAmrH3gjDbt2/P/Px8xsbGMjU15UIxj5IQBAAA1pStW7cKvyE5NRQAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhAAAKAzQhCgY1u2bMlxxx2Xqspxxx2XLVu2jHokYI2oqlX3Bhw5IQjQqS1btmTnzp3Ztm1b7rrrrmzbti07d+4Ug8ARaa0tydtZl757ye4LOHJeUB6gUzfccEMuuuiivOENb0iSfX9OT0+PciwAYAU4IgjQqdZarrrqqv22XXXVVX6rDgAdEIIAnaqqXH755fttu/zyyz3PBgA64NRQGLHV+I9uR4T6cP7552fHjh1JFo4EXn755dmxY0cuuOCCEU8GACy3rkPw5LHL8q1vu2zUY6xaJ48lyYtGPcYxb6mi6+zL3pNbX+e/F0fu+uuvz5YtWzI9PZ0dO3akqnLBBRfk+uuvH/VoAMAy6zoE75l/nX84H8LZl71n1CMAy0z0AUCfPEcQAACgM0IQAACgM0IQoGOzs7PZtGlT1q1bl02bNmV2dnbUIwHAYW3ZsiXHHXdcqirHHXdctmzZMuqR1hwhCNCp2dnZTE5O5pprrslXvvKVXHPNNZmcnBSDAKxqW7Zsyc6dO7Nt27bcdddd2bZtW3bu3CkGH6WuLxYD0LOpqanMzMxk8+bNSZLNmzdnZmYm27dvz9atW0c8HQAc2A033JCLLroob3jDG5Jk35/T09OjHGvNcUQQoFPz8/MZHx/fb9v4+Hjm5+dHNBEAHF5rLVddddV+26666iqvg/woCUGATo2NjWVubm6/bXNzcxkbGxvRRABweFWVyy+/fL9tl19+eapqRBOtTUIQoFOTk5OZmJjIrl27smfPnuzatSsTExOZnJwc9WgAcFDnn39+duzYkYsvvjh33313Lr744uzYsSPnn3/+qEdbU4QgQKe2bt2ak046KS94wQty/PHH5wUveEFOOukkzw8EYFW7/vrr88QnPjE7duzI4x//+OzYsSNPfOITc/311496tDVFCAJ0asuWLfnwhz+ciy66KHfddVcuuuiifPjDH3bVNQBWtS1btuTOO+/cb/9155132n89Sq4aCtApV10DYC2y/1oajggCdMpV1wBYi+y/loYQBOhUVeW5z31uTjjhhFRVTjjhhDz3uc911TUAVjVXDV0aQhCgU6effnpuueWWnHPOObn99ttzzjnn5JZbbsnpp58+6tEA4KBcNXRpeI4gQKc+//nP5+lPf3re//7357TTTktV5elPf3puu+22UY8GAAd1/fXXZ8uWLZmens6OHTtSVbngggtcNfRRckQQoFO7d+/Oeeedl+OPPz5Jcvzxx+e8887L7t27RzwZAMe6qhrqbefOnfueE9hay86dO4e+z94IQYBOrVu3Lm9+85tz5ZVX5r777suVV16ZN7/5zVm3bt2oRwPgGNdaW5K3sy5995LdV2+EIECnDrbT63FnCAC96f45gmdf9p5Rj7BqnXLihlGPACyjhx56KK961atyxRVX5DWveU02btyYn/qpn8qb3vSmUY8GACyzrkPw1te9aNQj7Ofsy96z6mYCjl0bN27Mfffdl6c+9amZn5/PU5/61Nx3333ZuHHjqEcDAJaZU0MBOvX85z8/73jHO/K85z0vd955Z573vOflHe94R57//OePejQAYJl1fUQQhvHMX9iZu+/fM+ox9rOaTnU+5cQN+avXXjDqMTiET3/603npS1+at7zlLdmxY0c2btyYl770pfnIRz4y6tGAZWT/dWj2X/RCCMJRuvv+PU7lPYTVtFPnwObn51NV+14uYvfu3fnoRz+a+fn5EU/GSlnKy6XX1UtzPy5WtPzsvw7N/oteCEGATm3YsCE333xzTjrppNx777056aSTcvPNN3uOYEdEF0C/PEcQoFN7jwQ+9rGPTVXlsY997H7bAYBjlxAE6NgJJ5yQO++8M6213HnnnTnhhBNGPRIAsAKEIEDHWmu5/vrr89WvfjXXX3+9UwUBoBOeIwjQsd27d+clL3lJ7rvvvjz2sY91Wih04OSxy/Ktb7ts1GOsWiePJYmL6XDsE4IAnbvnnnv2+xM4tt0z/zpXDT0EVw2lF04NBejU+vXrH/HyAVWV9ev9jhAAjnX29gCdeuCBBx6xrbV2wO0AwLHFEUGAzj3hCU9IVeUJT3jCqEcBAFaIEATo2MaNG3PKKackSU455RQvJg8AnRCCAB178MEHk2TfcwX3fgwAHNuEIEDHHnjggXzbt31bPve5z+Xbvu3bPD8QADrhYjEAnXvXu96VU089ddRjAAArSAgCdOoZz3hGTjzxxHzgAx9Iay1VlXPOOSf333//qEcDAJaZU0MBOjU5OZnbbrstZ511VqoqZ511Vm677bZMTk6OejQAYJk5IgjQsTvvvDN33HFHkuTWW2/NunXrRjwRALAShCBAp37iJ37iEVcJffDBB/MTP/ET2bp164imAmA1e+Yv7Mzd9+8Z9Rj7Ofuy94x6hH1OOXFD/uq1F4x6jCMyVAhW1Xcn+XdJ1iV5c2vtdUsyFQDLbvfu3UmS7/me78nMzEwmJibyrne9a992AHi4u+/fk1tf96JRj7FqraYoPZyjDsGqWpfk9UnOT/KpJH9RVe9qrf31Ug0HwPLasGHDflcN3bBhQ/bsWV2/6QUAlt4wF4v5jiQfba19rLX21ST/KclLlmYsAFbCnj178oxnPCO33XZbnvGMZ4hAAOjEMKeGfn2STy76+FNJvnO4cQBYaXuvHHrSSSeNehQAYIUs+8tHVNWrquqmqrpp75XpAFg97r333v3+BACOfcOE4KeTnLHo49MH2/bTWntTa+3c1tq5e5+DAgAAwOgMc2roXyR5WlX9vSwE4A8nuXBJpoI14OSxy/Ktb7ts1GOsWiePJYmriq1m69evzwMPPJDnPOc5ue666/Kyl70sN954Y9av98pCcKxbS1c2XGmnnLhh1CPAijjqvX1r7YGq+udJrs/Cy0e8pbV2y5JNBqvcPfNeLeVQ7EhXv4ceeiinn356brzxxpx22mlJktNPPz233377iCcDltNqu/T/2Ze9Z9XNBD0Y6te+rbX/kuS/LNEssKastp2WHSmP1tjYWK655pps3rx537Zdu3Zl+/btI5wKAFgJy36xGABWp8nJyUxMTGTXrl3Zs2dPdu3alYmJiUxOTo56NABgmXkiCMAaVlVD38cLXvCC/T6+8MILc+GFR/+U79basCMBAMtMCAKsYUsVXU4tBh6tpfhF1L77unpp7scvouDICUEAAB410dUnV00/tLV01XQhCDACz/yFnbn7/j2jHmM/q+ly8qecuCF/9doLRj0GAA9zz/zrnEFyCKtpX3o4QhBgBO6+f48d6SGspR0pAKxFrhoKAADQGUcEAUbAcywObS09xwKgN87aOLhTTtww6hGOmBAEGIEPv+LDox5hP64aCsCRWG37CvuvoycEAQCAFeXlR0ZPCAKsYXakAKxF9hWjJwQB1jA7UgDgaLhqKEDHZmdns2nTpqxbty6bNm3K7OzsqEcCAFaAI4IAnZqdnc3k5GRmZmYyPj6eubm5TExMJEm2bt064ukAgOXkiCBAp6ampjIzM5PNmzdnw4YN2bx5c2ZmZjI1NTXq0QCAZSYEATo1Pz+f8fHx/baNj49nfn5+RBMBACtFCAJ0amxsLHNzc/ttm5uby9jY2IgmAgBWihAE6NTk5GQmJiaya9eu7NmzJ7t27crExEQmJydHPRoAsMxcLAagU3svCLN9+/bMz89nbGwsU1NTLhQDAB1wRBCgYzfeeGM++tGP5qGHHspHP/rR3HjjjaMeCQBYAUIQoFPbt2/P9PR0rrzyytx333258sorMz09ne3bt496NABgmQlBgE5de+21ufrqq3PJJZfkMY95TC655JJcffXVufbaa0c9GgCwzKq1tmIPdu6557abbrppxR5vpVTVqEd4hJX878pwrB9Gpapy33335TGPecy+bV/+8pfz2Mc+1hoAgGNAVX2gtXbugT7niOASaK2tujfWjlGvFeunXxs3bsz09PR+26anp7Nx48YRTQQArBRXDQXo1Ctf+cpceumlSZJt27Zleno6l156abZt2zbiyQCA5SYEATp1zTXXJEmuuOKKvOY1r8nGjRuzbdu2fdsBgGOX5wgCAAAcgzxHEAAAgH2EIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeqtbZyD1Z1R5LbVuwB154nJfnCqIdgzbJ+GIb1wzCsH4Zh/TAM6+fQzmqtnXqgT6xoCHJoVXVTa+3cUc/B2mT9MAzrh2FYPwzD+mEY1s/Rc2ooAABAZ4QgAABAZ4Tg6vKmUQ/Ammb9MAzrh2FYPwzD+mEY1s9R8hxBAACAzjgiCAAA0BkhuIZVVY16BtYu64dhWD8Mw/phGNYPw7B+/i+nhgIAAHTGEcE1qKp+rKp+s6q+vaqeMup5WFusH4ZRVT9QVb9eVU+uqseNeh7WFj9/GIb1wzDsvx7JEcE1qKo2JNme5OQkz07yS621vxjtVKwV1g9Hq6rWJXlykkuSVJL1Sd7UWrtlpIOxZvj5wzCsH46W/deBCcE1pKq+MclxrbWPDD4+JckPJfnFJN/fWnvfKOdjdbN+GMbgt+9fba393eDjb0vyj5P8QJKLW2sfHOV8rG5+/jAM64dh2H8dnBBcI6rqt5M8PslJSW5M8q9ba18efO7Hk1yW5GWttZtHNSOrl/XDMKrqP2bhN6n3Jrm5tfb/DravT7ItyYuysDP9+OimZLXy84dhWD8Mw/7r0DxHcA2oqu9L8vjW2gVJvi/JpiS/WFVnJElr7a1J3pDkh6tqo6shsZj1wzCq6lVZ2IluSfKvknx/Vf1KkrTWHkjyziRzSc4b3N76YR8/fxiG9cMw7L8OTwiuDbcneaCqntRa+2ySH05yWpJXL7rN+5J8TZIHm8O87M/6YRi3JfloFs4g+UiS5yUZr6pfTZLW2p1J/jrJ8wcfWz8s5ucPw7B+GIb912EIwbXhk0luTfKsqjqhtXZXkouSbK6qVydJa+0DSXYn+Z4RzcjqZf0wjDuTPDHJNyRJa+0LSV6Y5IVV9SODbb+X5P6q+kcjm5LVys8fhmH9MAz7r8NYP+oBOLzW2qer6pYsnMv8laq6ubV2V1VdluTcRTf9f5N099sMDs36YRittb+oqo8kmR48H+czrbUvDk6vOX7RTa9M8nejmJHVy88fhmH9MAz7r8NzRHAVWnyO8t73W2uvT/L+LPwm7Cer6tlZuATuKXtv21q7p7V27wqPyypWVccl1g9HZ3C57bTWfi7Jh5K8Mck/qaqzkmxNcsaim39q7wUc6Jf9F0vF/oth2H8dGVcNXWWq6vjW2leral1r7cHBtuNaaw8N3v+eJN+S5LuSfKK1tn2E47LKVNXmJHuS/M/W2lcH26wfjkhVfW+SLyX50KLLbC9ePxdl4RSbZya5tbX2qpENy6pj/8Uw7L8Yhv3X0RGCq8jgErdnJnlxa+3uh+1M1w+ucLT3tift/e3X4oVOv6rq7Vl4wvyZSd6V5HWttXsGn7N+OKSqmknydVl4oeYPJrlk0c+fja213Ytu+zUH2tHSL/svhmH/xTDsv46eU0NXiaq6JMnZWVjAv1tVp7TWHlx0aPuBwe2+e/CE6b0/BKv3RUxSVb+c5AmttRcluSDJP8zCpbaTWD8cWlXtSHLqYP38kySnJ3naolP7dg9u9/erasOinaj1g/0XQ7H/Yhj2X8NxsZjV40+SvL+19v6q+ndJfq+qvre1dvfeG1TVc5I8qbX2lb3berzULQf0P5O8PUlaa59Z9NuxfapqPNYPB/afkvyPwfv/Msk5Sf5dkg9W1ftaa39YVd+f5DGttQ/u/SLrhwH7L4Zh/8Uw7L+G4NTQVWTvqTRVdXySX87CecwvaK21qvrm1trfjHhEVqmqOinJ7tbansHHP5HkvNbaKwYfP7m19rlRzsjqV1UnZuHFmSeT3JuF1+p6fGvtksFvT+0wOCD7L46W/RdLwf7r6Dg1dBXZez7z4EnSV2ThNxw3VNWfJXnxKGdjdWut3dta27P3VIgsPGH67iSpqv+chdfNgUNqrd2f5Cdba7e31r6U5LeTnFFVj9u7E120xmAf+y+Olv0XS8H+6+g4IriKDRbs3yX5o9bahaOeh7WjqjYluSwLr5Nz/97frMKjUVXvTPL51tqrRz0La4v9F0fL/oulYP91ZBwRXN1+Ncl/3bsTrcFr6sAROCHJhUnuWHR6jfXDYVXVcVX1NVX1B1n4R9irB9v9JpVHw/6Lo2X/xVGx/3r0HBEcocOds1xV39Ra+9vB+91f4pb9HWr9DM6V/4HW2tsPd1v6dJj18/gk4621dw8+9vOH/dh/MQz7L4Zh/7V0hOAKqqqfTvKJJPe21q4fbNt7edu95y8/YsH6IUgy1PrxQxDrh6HYfzEMP38YhvWzfBxqXyFV9cYkL01yRpI3VtXPJAsLeHBVta8ffPzQww9h24nyKNfPfv9f+yGI9cMw7L8Yhp8/DMP6WV5eR3AFVNVTkjwtyfe31r5QVe9Jct3gNxW/XFXrk/xyVd3eWvtZO04WO4r14wcf+1g/DMP+i2H4+cMwrJ/l54jgyvhckg8neXZVrW+tfSTJDyb5Z1V1UWvtgSS/kOSxVfX3Rjkoq5L1wzCsH4Zh/TAM64dhWD/LTAiugMFvKG5P8lNJTh5s+9skP5zk/xk8sfWOJB8a/An7WD8Mw/phGNYPw7B+GIb1s/yE4DJb9GTWq5N8Oclbquq0waf/Mgv/Dda11r6Y5C2ttXtHMymrkfXDMKwfhmH9MAzrh2FYPyvDVUOXSVWta609eID3d2ThtxqfSzKW5K7mxXZ5GOuHYVg/HK2HX+XT+uHRsH4YhvWz8oTgEquqF7fW/nDw/r7L1j5sMW9O8nVJvq619muDbS6xjfXDUKwfhlFVVyR5fJIPttZmF223fjgs64dhWD+jIQSXUFW9M8k/TPI7rbVXD7Yd1waX1D7YQi2vc0KsH4Zj/TCMqnpTkicn+a0k/zrJVGvtPww+Z/1wSNYPw7B+RsdzBJdIVZ2b5ClJXp5kfVX9erLvdU3W7V3EVfVTVTW2+GstYqwfhmH9MIyq+r4kp7fWXtJae2eSf5Hk4qo6cfE/wqpqwvrh4awfhmH9jJYQXCKttZuSvCLJ+5O8OQuXsv31qtrQWnuwqo6rquOT3Nlamx/psKw61g/DsH4Y0p8k+VdJMlgntwy2b1j0j7CNSb5o/XAA1g/DsH5GSAgOqapeXlVvTJLW2idaa7uz8Jon12ThSa2/OLjpjyZZ31r73cHX1SjmZXWxfhiG9cMwBuvn9a21u5L8TZK01r7aWvt0ki8luWdwu5e21nZbPyxm/TAM62d1EILD+69Jbq+qxyX7zld+MAuL+peSPLGq7kvyotbal/d+kSe2MmD9MAzrh2H81yR3VNXJrbUHasG6wW/l1yc5u6p+O8kLF3+R9cOA9cMwrJ9VQAgO78Ekm5JsTfY9J+e4wW81Pp7k7ye5rrX2Q4nfZPAI1g/DsH4YxoNJnpHkwmTfP7DWJdmTpJJcl+T21tqrRjYhq5n1wzCsn1Vg/agHWOtaa1+sql9K8u6quqe19s69/xhL8vwkH26tTSSubsQjWT8Mw/phGAdZP19Nkqq6J8ln2sOuQDvCcVllrB+GYf2sDl4+YolU1T9O8htJfrm19tYDfN4i5qCsH4Zh/TCMA62fqnpqa+2jg/etHw7K+mEY1s9oCcElVFXjSf5Dkl9L8rHW2rsH273YJYdl/TAM64dhLFo/v55kvrW2c7DdP8I4LOuHYVg/oyMEl1hVPS3J+Um+IQunZb1txCOxhlg/DMP6YRgPWz83H+joMhyM9cMwrJ/REILLqKoe11r70qjnYG2yfhiG9cMwrB+GYf0wDOtn5QhBAACAznj5CAAAgM4IQQAAgM4IQQAAgM4IQQAAgM4IQQAAgM4IQQBWvao6u6rur6oPLdo2WVW3VNX/qqoPVdV3LvFjvrWqPj6477+squ86zHw3L+Fj/0pVfbaqfmap7hMAFls/6gEA4Aj9n9bas5JkEGX/JMmzW2u7q+pJSY5fhsf82dbadVV1QZI3Jvm2ZXiMR2it/WxV3bcSjwVAnxwRBGAtekqSL7TWdidJa+0LrbXbk6SqzqmqP6uqD1TV9VX1lKo6par+tqq+aXCb2ap65aN4vPcmeerga59aVf+tqv5qcKTwGxffcHB08L8PPveXVfWcwfanVNV7B0cYb66qf1hV6wZHHm+uqg9X1U8vwd8NAByWEARgLdqZ5Iyq+t9V9Yaqen6SVNWGJNckeVlr7Zwkb0ky1Vq7O8k/T/LWqvrhJE9orV37KB7vxUk+PHj/HUle31p7ZpLnJPnMw277+STnt9aeneSHkvzGYPuFSa4fHNV8ZpIPJXlWkq9vrW1qrX1rkt98FDMBwFFzaigAa05r7d6qOifJP0yyOclvVdVlSW5KsinJDVWVJOsyCLXW2g1V9QNJXp+FEDsSv1JVP5fkjiQTVXVyFsLt9wb3+ZUkGTzWXhuS/PuqelaSB5M8fbD9L5K8ZRCrv99a+1BVfSzJN1TVNUnek4XABYBlJwQBWJNaaw8m+dMkf1pVH07yiiQfSHJLa+0RF3apquOSjCX5cpInJPnUETzMz7bWrlt0Hycfwdf8dJLPZSE2j0vylcG8762q5yV5URaOTP7b1trbq+qZSbYk2ZbkB5P85BE8BgAMxamhAKw5VfVNVfW0RZueleS2JH+b5NS9V/isqg1V9YzBbX46yXwWTtH8zcGRuVTV26vqO47kcVtr9yT5VFW9dPC1G6vqMQ+72SlJPtNaeyjJy7NwVDJVdVaSzw1OSX1zkmcPLnJzXGvtd5L8XJJnP4q/BgA4ao4IArAWnZTkmqp6fJIHknw0yataa1+tqpcl+Y2qOiUL+7lfr6oHkvxUku9ord1TVe/NQni9NgtXAr39UTz2y5O8sap+McmeJD+Q5KFFn39Dkt+pqh9L8kdJ9l7987wkP1tVe5Lcm+THknx9FqJ07y9mL38UcwDAUavW2qhnAIBDqqqzk7y7tbZpie/3cUlmWms/sJT3uxSq6ueT3Nta+/9GPQsAxx6nhgKwFjyY5JTFLyi/FFprX1qlEfgrSX40//doIgAsKUcEAQAAOuOIIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGeEIAAAQGf+f5W5Px6WjuCwAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.590230Z","iopub.execute_input":"2023-02-01T14:51:29.591244Z","iopub.status.idle":"2023-02-01T14:51:29.999585Z","shell.execute_reply.started":"2023-02-01T14:51:29.591206Z","shell.execute_reply":"2023-02-01T14:51:29.998460Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = clf.predict(X_test)\ndecision_tree_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"clf_y_pred\": y_pred})\ndecision_tree_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.001184Z","iopub.execute_input":"2023-02-01T14:51:30.001710Z","iopub.status.idle":"2023-02-01T14:51:30.018740Z","shell.execute_reply.started":"2023-02-01T14:51:30.001660Z","shell.execute_reply":"2023-02-01T14:51:30.017976Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.019742Z","iopub.execute_input":"2023-02-01T14:51:30.020678Z","iopub.status.idle":"2023-02-01T14:51:30.025527Z","shell.execute_reply.started":"2023-02-01T14:51:30.020645Z","shell.execute_reply":"2023-02-01T14:51:30.024304Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.027690Z","iopub.execute_input":"2023-02-01T14:51:30.028212Z","iopub.status.idle":"2023-02-01T14:51:30.045818Z","shell.execute_reply.started":"2023-02-01T14:51:30.028170Z","shell.execute_reply":"2023-02-01T14:51:30.044552Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.048587Z","iopub.execute_input":"2023-02-01T14:51:30.048979Z","iopub.status.idle":"2023-02-01T14:51:30.075974Z","shell.execute_reply.started":"2023-02-01T14:51:30.048946Z","shell.execute_reply":"2023-02-01T14:51:30.074745Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred \n0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Random Forrest\n\nWe use Random Forrest to classify the titanic passengers as either surviving or not the accident. We use again the same statistical variable as Decisiont Trees.","metadata":{}},{"cell_type":"markdown","source":"## Model fitting and classification\n\nRandom Forrest overfits to the training dataset. ","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nn_estimators = range(1,20)\nmax_depths = range(1,40)\n\nfor est in n_estimators:\n for depth in max_depths:\n rf = RandomForestClassifier(n_estimators = est, max_depth = depth, \n random_state = 42, class_weight={0:6.,1:4},max_features = 6)\n rf.fit(X_train, y_train)\n train_score = rf.score(X_train, y_train)\n test_score = rf.score(X_valid, y_valid)\n print(\" - estimators : \", est, \n \" - max depths : \", depth, \n \" - train score : \", train_score,\n \" - valid score : \", valid_score)\n \n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.172233Z","iopub.execute_input":"2023-02-01T14:51:30.172931Z","iopub.status.idle":"2023-02-01T14:51:52.273980Z","shell.execute_reply.started":"2023-02-01T14:51:30.172890Z","shell.execute_reply":"2023-02-01T14:51:52.272764Z"},"trusted":true},"execution_count":213,"outputs":[{"name":"stdout","text":" - estimators : 1 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 2 - train score : 0.7771535580524345 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 3 - train score : 0.8071161048689138 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 4 - train score : 0.8277153558052435 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 5 - train score : 0.8314606741573034 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 6 - train score : 0.8651685393258427 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 7 - train score : 0.8820224719101124 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 8 - train score : 0.8857677902621723 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 9 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 10 - train score : 0.900749063670412 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 11 - train score : 0.9082397003745318 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 12 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 13 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 14 - train score : 0.9119850187265918 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 15 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 16 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 17 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 18 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 19 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 20 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 21 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 22 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 23 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 24 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 25 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 26 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 27 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 28 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 29 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 30 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 31 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 32 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 33 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 34 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 35 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 36 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 37 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 38 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 39 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 4 - train score : 0.848314606741573 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 5 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 6 - train score : 0.8689138576779026 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 7 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 8 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 10 - train score : 0.9213483146067416 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 11 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 12 - train score : 0.9288389513108615 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 13 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 14 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 15 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 16 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 17 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 18 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 19 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 20 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 21 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 22 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 23 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 24 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 25 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 26 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 27 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 28 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 29 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 30 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 31 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 32 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 33 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 34 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 35 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 36 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 37 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 38 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 39 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 4 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 5 - train score : 0.8707865168539326 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 6 - train score : 0.8838951310861424 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 7 - train score : 0.897003745318352 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 8 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 10 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 11 - train score : 0.9400749063670412 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 12 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 13 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 14 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 15 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 16 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 17 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 18 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 19 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 20 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 21 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 22 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 23 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 24 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 25 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 26 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 27 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 28 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 29 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 30 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 31 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 32 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 33 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 34 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 35 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 36 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 37 - train score : 0.9456928838951311 - 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estimators : 19 - max depths : 26 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 27 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 28 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 29 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 30 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 31 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 32 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 33 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 34 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 35 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 36 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 37 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 38 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 39 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover again the learning overfit on the training dataset. So we choose a maximum depth at around 6 and n estimator of 11. ","metadata":{}},{"cell_type":"code","source":"rf = RandomForestClassifier(n_estimators = 11, max_depth=6, random_state = 42, class_weight={0:6.,1:4}, max_features = 6)\nrf.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.275894Z","iopub.execute_input":"2023-02-01T14:51:52.276195Z","iopub.status.idle":"2023-02-01T14:51:52.312746Z","shell.execute_reply.started":"2023-02-01T14:51:52.276167Z","shell.execute_reply":"2023-02-01T14:51:52.311257Z"},"trusted":true},"execution_count":214,"outputs":[{"execution_count":214,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight={0: 6.0, 1: 4}, max_depth=6, max_features=6,\n n_estimators=11, random_state=42)"},"metadata":{}}]},{"cell_type":"code","source":"rf_train_score = rf.score(X_train, y_train)\nrf_train_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.314414Z","iopub.execute_input":"2023-02-01T14:51:52.314882Z","iopub.status.idle":"2023-02-01T14:51:52.329948Z","shell.execute_reply.started":"2023-02-01T14:51:52.314839Z","shell.execute_reply":"2023-02-01T14:51:52.328684Z"},"trusted":true},"execution_count":215,"outputs":[{"execution_count":215,"output_type":"execute_result","data":{"text/plain":"0.8801498127340824"},"metadata":{}}]},{"cell_type":"code","source":"rf_valid_score = rf.score(X_valid, y_valid)\nrf_valid_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.332102Z","iopub.execute_input":"2023-02-01T14:51:52.333087Z","iopub.status.idle":"2023-02-01T14:51:52.346061Z","shell.execute_reply.started":"2023-02-01T14:51:52.333051Z","shell.execute_reply":"2023-02-01T14:51:52.344862Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"0.8067226890756303"},"metadata":{}}]},{"cell_type":"markdown","source":"The age, the fare and the gender appears to contribute the most to predicting accurately the surviving or not the accident. It is surprising the passenger class influence less random forrest. ","metadata":{}},{"cell_type":"code","source":"importances = rf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.347466Z","iopub.execute_input":"2023-02-01T14:51:52.347785Z","iopub.status.idle":"2023-02-01T14:51:52.360347Z","shell.execute_reply.started":"2023-02-01T14:51:52.347756Z","shell.execute_reply":"2023-02-01T14:51:52.359060Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":" 0\n0.199528 Fare\n0.140924 Pclass\n0.390318 Sex\n0.023663 Embarked\n0.053330 fam_members\n0.192238 Age","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
0.199528Fare
0.140924Pclass
0.390318Sex
0.023663Embarked
0.053330fam_members
0.192238Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We found the classes of importances are Fares, Sex, and Age. ","metadata":{}},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = rf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.362231Z","iopub.execute_input":"2023-02-01T14:51:52.362868Z","iopub.status.idle":"2023-02-01T14:51:52.379545Z","shell.execute_reply.started":"2023-02-01T14:51:52.362825Z","shell.execute_reply":"2023-02-01T14:51:52.378290Z"},"trusted":true},"execution_count":218,"outputs":[{"execution_count":218,"output_type":"execute_result","data":{"text/plain":"array([[319, 10],\n [ 54, 151]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.381097Z","iopub.execute_input":"2023-02-01T14:51:52.381577Z","iopub.status.idle":"2023-02-01T14:51:52.391168Z","shell.execute_reply.started":"2023-02-01T14:51:52.381537Z","shell.execute_reply":"2023-02-01T14:51:52.390198Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"Accuracy : 0.8801498127340824\nMisclassfication : 0.1198501872659176\nSensitivivity : 0.9696048632218845\nSpecificity : 0.7365853658536585\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.392573Z","iopub.execute_input":"2023-02-01T14:51:52.393224Z","iopub.status.idle":"2023-02-01T14:51:52.412047Z","shell.execute_reply.started":"2023-02-01T14:51:52.393191Z","shell.execute_reply":"2023-02-01T14:51:52.410398Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.413222Z","iopub.execute_input":"2023-02-01T14:51:52.413582Z","iopub.status.idle":"2023-02-01T14:51:52.421900Z","shell.execute_reply.started":"2023-02-01T14:51:52.413554Z","shell.execute_reply":"2023-02-01T14:51:52.420658Z"},"trusted":true},"execution_count":221,"outputs":[{"name":"stdout","text":"Accuracy : 0.8067226890756303\nMisclassfication : 0.19327731092436976\nSensitivivity : 0.9227272727272727\nSpecificity : 0.6204379562043796\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.427307Z","iopub.execute_input":"2023-02-01T14:51:52.427779Z","iopub.status.idle":"2023-02-01T14:51:52.433953Z","shell.execute_reply.started":"2023-02-01T14:51:52.427746Z","shell.execute_reply":"2023-02-01T14:51:52.432477Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.435235Z","iopub.execute_input":"2023-02-01T14:51:52.435660Z","iopub.status.idle":"2023-02-01T14:51:52.465440Z","shell.execute_reply.started":"2023-02-01T14:51:52.435608Z","shell.execute_reply":"2023-02-01T14:51:52.464167Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.466837Z","iopub.execute_input":"2023-02-01T14:51:52.467622Z","iopub.status.idle":"2023-02-01T14:51:52.495143Z","shell.execute_reply.started":"2023-02-01T14:51:52.467589Z","shell.execute_reply":"2023-02-01T14:51:52.494000Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.496752Z","iopub.execute_input":"2023-02-01T14:51:52.497420Z","iopub.status.idle":"2023-02-01T14:51:52.520420Z","shell.execute_reply.started":"2023-02-01T14:51:52.497382Z","shell.execute_reply":"2023-02-01T14:51:52.519633Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.521415Z","iopub.execute_input":"2023-02-01T14:51:52.522394Z","iopub.status.idle":"2023-02-01T14:51:52.546457Z","shell.execute_reply.started":"2023-02-01T14:51:52.522351Z","shell.execute_reply":"2023-02-01T14:51:52.545447Z"},"trusted":true},"execution_count":226,"outputs":[{"execution_count":226,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.547614Z","iopub.execute_input":"2023-02-01T14:51:52.547908Z","iopub.status.idle":"2023-02-01T14:51:52.553613Z","shell.execute_reply.started":"2023-02-01T14:51:52.547880Z","shell.execute_reply":"2023-02-01T14:51:52.552611Z"},"trusted":true},"execution_count":227,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.554829Z","iopub.execute_input":"2023-02-01T14:51:52.555101Z","iopub.status.idle":"2023-02-01T14:51:52.580427Z","shell.execute_reply.started":"2023-02-01T14:51:52.555075Z","shell.execute_reply":"2023-02-01T14:51:52.579665Z"},"trusted":true},"execution_count":228,"outputs":[{"execution_count":228,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.581453Z","iopub.execute_input":"2023-02-01T14:51:52.582459Z","iopub.status.idle":"2023-02-01T14:51:52.610464Z","shell.execute_reply.started":"2023-02-01T14:51:52.582401Z","shell.execute_reply":"2023-02-01T14:51:52.609279Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y \n0 0.0 \n1 NaN \n2 0.0 \n3 NaN \n4 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_y
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.611523Z","iopub.execute_input":"2023-02-01T14:51:52.611803Z","iopub.status.idle":"2023-02-01T14:51:52.639513Z","shell.execute_reply.started":"2023-02-01T14:51:52.611776Z","shell.execute_reply":"2023-02-01T14:51:52.638365Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 1.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538461.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
\n

357 rows × 8 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.641337Z","iopub.execute_input":"2023-02-01T14:51:52.641775Z","iopub.status.idle":"2023-02-01T14:51:52.669655Z","shell.execute_reply.started":"2023-02-01T14:51:52.641731Z","shell.execute_reply":"2023-02-01T14:51:52.668451Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred \n0 0.0 NaN \n1 NaN 1.0 \n2 0.0 NaN \n3 NaN 1.0 \n4 NaN 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.670923Z","iopub.execute_input":"2023-02-01T14:51:52.671224Z","iopub.status.idle":"2023-02-01T14:51:52.693465Z","shell.execute_reply.started":"2023-02-01T14:51:52.671196Z","shell.execute_reply":"2023-02-01T14:51:52.692202Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 0.0\n233 0.733373 3.0 2.0 2.0 6.0 -1.923077 1.0 0.0\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n235 -0.299018 3.0 2.0 2.0 0.0 0.000000 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
2550.0342843.02.04.02.0-0.0769231.00.0
2330.7333733.02.02.06.0-1.9230771.00.0
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
235-0.2990183.02.02.00.00.0000000.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.694762Z","iopub.execute_input":"2023-02-01T14:51:52.695075Z","iopub.status.idle":"2023-02-01T14:51:52.711272Z","shell.execute_reply.started":"2023-02-01T14:51:52.695047Z","shell.execute_reply":"2023-02-01T14:51:52.710037Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 12\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n 2.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 0.0 5\n 1.0 4\n 1.0 1.0 0.0 2\n 2.0 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n 2.0 0.0 2\n 5.0 1.0 1.0 1\n 6.0 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.712948Z","iopub.execute_input":"2023-02-01T14:51:52.713356Z","iopub.status.idle":"2023-02-01T14:51:52.728466Z","shell.execute_reply.started":"2023-02-01T14:51:52.713299Z","shell.execute_reply":"2023-02-01T14:51:52.727135Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.729743Z","iopub.execute_input":"2023-02-01T14:51:52.730867Z","iopub.status.idle":"2023-02-01T14:51:53.319377Z","shell.execute_reply.started":"2023-02-01T14:51:52.730830Z","shell.execute_reply":"2023-02-01T14:51:53.318257Z"},"trusted":true},"execution_count":235,"outputs":[{"execution_count":235,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.320867Z","iopub.execute_input":"2023-02-01T14:51:53.321309Z","iopub.status.idle":"2023-02-01T14:51:53.344082Z","shell.execute_reply.started":"2023-02-01T14:51:53.321267Z","shell.execute_reply":"2023-02-01T14:51:53.342810Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n386 1.405213 3.0 1.0 2.0 7.0 -2.230769 0.0 1.0\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n824 1.092843 3.0 1.0 2.0 5.0 -2.153846 0.0 1.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3861.4052133.01.02.07.0-2.2307690.01.0
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
8241.0928433.01.02.05.0-2.1538460.01.0
429-0.2773633.01.02.00.00.1538461.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.345774Z","iopub.execute_input":"2023-02-01T14:51:53.346816Z","iopub.status.idle":"2023-02-01T14:51:53.369951Z","shell.execute_reply.started":"2023-02-01T14:51:53.346772Z","shell.execute_reply":"2023-02-01T14:51:53.368730Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 7\n 2.0 0.0 1\n 1.0 1.0 0.0 5\n 2.0 1.0 0.0 1\n 1.0 1\n 3.0 2.0 1.0 1\n 5.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n3.0 0.0 1.0 0.0 13\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 1.0 1\n 2.0 1.0 0.0 3\n 1.0 2\n 2.0 0.0 1\n 1.0 2\n 4.0 1.0 1.0 1\n 5.0 1.0 1.0 2\n 6.0 2.0 0.0 1\n 7.0 1.0 1.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.371853Z","iopub.execute_input":"2023-02-01T14:51:53.372272Z","iopub.status.idle":"2023-02-01T14:51:54.052559Z","shell.execute_reply.started":"2023-02-01T14:51:53.372234Z","shell.execute_reply":"2023-02-01T14:51:54.051607Z"},"trusted":true},"execution_count":238,"outputs":[{"execution_count":238,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.053862Z","iopub.execute_input":"2023-02-01T14:51:54.054160Z","iopub.status.idle":"2023-02-01T14:51:54.076180Z","shell.execute_reply.started":"2023-02-01T14:51:54.054133Z","shell.execute_reply":"2023-02-01T14:51:54.075083Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0\n110 1.626091 1.0 1.0 2.0 0.0 1.307692 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
130-0.2840413.01.04.00.00.2307690.00.0
1101.6260911.01.02.00.01.3076920.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.081370Z","iopub.execute_input":"2023-02-01T14:51:54.081697Z","iopub.status.idle":"2023-02-01T14:51:54.104120Z","shell.execute_reply.started":"2023-02-01T14:51:54.081668Z","shell.execute_reply":"2023-02-01T14:51:54.103001Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 4\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 3\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 0.0 11\n 1.0 24\n 1.0 1.0 0.0 15\n 1.0 2\n 2.0 0.0 9\n 1.0 4\n 2.0 1.0 0.0 9\n 1.0 3\n 2.0 0.0 5\n 1.0 6\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 6\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.106496Z","iopub.execute_input":"2023-02-01T14:51:54.106922Z","iopub.status.idle":"2023-02-01T14:51:55.631830Z","shell.execute_reply.started":"2023-02-01T14:51:54.106868Z","shell.execute_reply":"2023-02-01T14:51:55.630790Z"},"trusted":true},"execution_count":241,"outputs":[{"execution_count":241,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.633364Z","iopub.execute_input":"2023-02-01T14:51:55.633706Z","iopub.status.idle":"2023-02-01T14:51:55.655017Z","shell.execute_reply.started":"2023-02-01T14:51:55.633675Z","shell.execute_reply":"2023-02-01T14:51:55.653820Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.656793Z","iopub.execute_input":"2023-02-01T14:51:55.657669Z","iopub.status.idle":"2023-02-01T14:51:55.680263Z","shell.execute_reply.started":"2023-02-01T14:51:55.657616Z","shell.execute_reply":"2023-02-01T14:51:55.679008Z"},"trusted":true},"execution_count":243,"outputs":[{"execution_count":243,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 1.0 2\n 2.0 1.0 10\n 1.0 1.0 0.0 6\n 1.0 1\n 2.0 1.0 19\n 2.0 1.0 0.0 5\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 13\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 5\n 1.0 2\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 93\n 2.0 0.0 5\n 1.0 7\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 2.0 1.0 0.0 5\n 1.0 1\n 2.0 0.0 3\n 1.0 3\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 3\n 2.0 0.0 3\n 6.0 1.0 1.0 1\n 2.0 0.0 3\n 7.0 1.0 0.0 3\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.681765Z","iopub.execute_input":"2023-02-01T14:51:55.682091Z","iopub.status.idle":"2023-02-01T14:51:56.352496Z","shell.execute_reply.started":"2023-02-01T14:51:55.682062Z","shell.execute_reply":"2023-02-01T14:51:56.351351Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.353913Z","iopub.execute_input":"2023-02-01T14:51:56.355590Z","iopub.status.idle":"2023-02-01T14:51:56.788043Z","shell.execute_reply.started":"2023-02-01T14:51:56.355547Z","shell.execute_reply":"2023-02-01T14:51:56.786828Z"},"trusted":true},"execution_count":245,"outputs":[{"execution_count":245,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.789229Z","iopub.execute_input":"2023-02-01T14:51:56.789583Z","iopub.status.idle":"2023-02-01T14:51:57.215295Z","shell.execute_reply.started":"2023-02-01T14:51:56.789553Z","shell.execute_reply":"2023-02-01T14:51:57.214488Z"},"trusted":true},"execution_count":246,"outputs":[{"execution_count":246,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.216805Z","iopub.execute_input":"2023-02-01T14:51:57.217226Z","iopub.status.idle":"2023-02-01T14:51:57.574988Z","shell.execute_reply.started":"2023-02-01T14:51:57.217185Z","shell.execute_reply":"2023-02-01T14:51:57.574210Z"},"trusted":true},"execution_count":247,"outputs":[{"execution_count":247,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.576156Z","iopub.execute_input":"2023-02-01T14:51:57.576637Z","iopub.status.idle":"2023-02-01T14:51:57.924867Z","shell.execute_reply.started":"2023-02-01T14:51:57.576603Z","shell.execute_reply":"2023-02-01T14:51:57.923105Z"},"trusted":true},"execution_count":248,"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. Nonetheless, the various distribution of age and fare may lower the accuracy of the validation and testing datasets.","metadata":{}},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = rf.predict(X_test)\nrandom_forrest_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"rf_y_pred\": y_pred})\nrandom_forrest_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.926719Z","iopub.execute_input":"2023-02-01T14:51:57.927152Z","iopub.status.idle":"2023-02-01T14:51:57.950525Z","shell.execute_reply.started":"2023-02-01T14:51:57.927100Z","shell.execute_reply":"2023-02-01T14:51:57.949359Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.951752Z","iopub.execute_input":"2023-02-01T14:51:57.952061Z","iopub.status.idle":"2023-02-01T14:51:57.958199Z","shell.execute_reply.started":"2023-02-01T14:51:57.952032Z","shell.execute_reply":"2023-02-01T14:51:57.956976Z"},"trusted":true},"execution_count":250,"outputs":[]},{"cell_type":"code","source":"random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.959366Z","iopub.execute_input":"2023-02-01T14:51:57.960119Z","iopub.status.idle":"2023-02-01T14:51:57.977269Z","shell.execute_reply.started":"2023-02-01T14:51:57.960080Z","shell.execute_reply":"2023-02-01T14:51:57.976084Z"},"trusted":true},"execution_count":251,"outputs":[{"execution_count":251,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.978846Z","iopub.execute_input":"2023-02-01T14:51:57.979227Z","iopub.status.idle":"2023-02-01T14:51:58.007917Z","shell.execute_reply.started":"2023-02-01T14:51:57.979179Z","shell.execute_reply":"2023-02-01T14:51:58.006694Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 0.0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n4 0.0 1.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Neural AI \nIn this section we use some neural network to classify the data. We prepare the data so that it is more suitable for neural networks. We apply cross validation. ","metadata":{"execution":{"iopub.status.busy":"2023-01-09T16:59:50.819233Z","iopub.execute_input":"2023-01-09T16:59:50.819762Z","iopub.status.idle":"2023-01-09T16:59:50.825788Z","shell.execute_reply.started":"2023-01-09T16:59:50.819721Z","shell.execute_reply":"2023-01-09T16:59:50.823990Z"}}},{"cell_type":"markdown","source":"## Prepare data for Neural-AI","metadata":{"execution":{"iopub.status.busy":"2022-12-07T15:38:00.160610Z","iopub.execute_input":"2022-12-07T15:38:00.161030Z","iopub.status.idle":"2022-12-07T15:38:00.169322Z","shell.execute_reply.started":"2022-12-07T15:38:00.160998Z","shell.execute_reply":"2022-12-07T15:38:00.167957Z"}}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:58.009483Z","iopub.execute_input":"2023-02-01T14:51:58.009908Z","iopub.status.idle":"2023-02-01T14:51:58.023101Z","shell.execute_reply.started":"2023-02-01T14:51:58.009868Z","shell.execute_reply":"2023-02-01T14:51:58.021915Z"},"trusted":true},"execution_count":253,"outputs":[{"execution_count":253,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.431458Z","iopub.execute_input":"2023-02-01T14:55:47.431870Z","iopub.status.idle":"2023-02-01T14:55:47.444617Z","shell.execute_reply.started":"2023-02-01T14:55:47.431840Z","shell.execute_reply":"2023-02-01T14:55:47.443399Z"},"trusted":true},"execution_count":254,"outputs":[{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.696681Z","iopub.execute_input":"2023-02-01T14:55:47.697091Z","iopub.status.idle":"2023-02-01T14:55:47.706759Z","shell.execute_reply.started":"2023-02-01T14:55:47.697056Z","shell.execute_reply":"2023-02-01T14:55:47.705377Z"},"trusted":true},"execution_count":255,"outputs":[{"execution_count":255,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.965238Z","iopub.execute_input":"2023-02-01T14:55:47.965693Z","iopub.status.idle":"2023-02-01T14:55:47.976964Z","shell.execute_reply.started":"2023-02-01T14:55:47.965657Z","shell.execute_reply":"2023-02-01T14:55:47.975774Z"},"trusted":true},"execution_count":256,"outputs":[{"execution_count":256,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"I propose to keep Pclass,Sex, Age, SibSP,Parch,Ticket, Fare,Cabin, Embarked, Survived","metadata":{}},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked', 'Survived']\ntitanic_train = titanic_train.loc[:,columns_to_keep]\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.596834Z","iopub.execute_input":"2023-02-01T14:59:41.597224Z","iopub.status.idle":"2023-02-01T14:59:41.617029Z","shell.execute_reply.started":"2023-02-01T14:59:41.597192Z","shell.execute_reply":"2023-02-01T14:59:41.615728Z"},"trusted":true},"execution_count":259,"outputs":[{"execution_count":259,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name \\\n0 1 3 Braund, Mr. Owen Harris \n1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n2 3 3 Heikkinen, Miss. Laina \n3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n4 5 3 Allen, Mr. William Henry \n\n Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n1 female 38.0 1 0 PC 17599 71.2833 C85 C \n2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n3 female 35.0 1 0 113803 53.1000 C123 S \n4 male 35.0 0 0 373450 8.0500 NaN S \n\n Survived \n0 0 \n1 1 \n2 1 \n3 1 \n4 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
013Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
233Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
453Allen, Mr. William Henrymale35.0003734508.0500NaNS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked']\ntitanic_test = titanic_test.loc[:,columns_to_keep]\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.783983Z","iopub.execute_input":"2023-02-01T14:59:41.784720Z","iopub.status.idle":"2023-02-01T14:59:41.804682Z","shell.execute_reply.started":"2023-02-01T14:59:41.784681Z","shell.execute_reply":"2023-02-01T14:59:41.803270Z"},"trusted":true},"execution_count":260,"outputs":[{"execution_count":260,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Passengers ID\nTransforms to float","metadata":{}},{"cell_type":"code","source":"\ntitanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.301290Z","iopub.execute_input":"2023-02-01T14:59:42.302052Z","iopub.status.idle":"2023-02-01T14:59:42.309717Z","shell.execute_reply.started":"2023-02-01T14:59:42.302008Z","shell.execute_reply":"2023-02-01T14:59:42.308660Z"},"trusted":true},"execution_count":261,"outputs":[]},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.953004Z","iopub.execute_input":"2023-02-01T14:59:42.953443Z","iopub.status.idle":"2023-02-01T14:59:42.961302Z","shell.execute_reply.started":"2023-02-01T14:59:42.953406Z","shell.execute_reply":"2023-02-01T14:59:42.960093Z"},"trusted":true},"execution_count":262,"outputs":[{"execution_count":262,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.133436Z","iopub.execute_input":"2023-02-01T14:59:43.133821Z","iopub.status.idle":"2023-02-01T14:59:43.144899Z","shell.execute_reply.started":"2023-02-01T14:59:43.133790Z","shell.execute_reply":"2023-02-01T14:59:43.143759Z"},"trusted":true},"execution_count":263,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.317702Z","iopub.execute_input":"2023-02-01T14:59:43.318112Z","iopub.status.idle":"2023-02-01T14:59:43.329982Z","shell.execute_reply.started":"2023-02-01T14:59:43.318070Z","shell.execute_reply":"2023-02-01T14:59:43.328608Z"},"trusted":true},"execution_count":264,"outputs":[{"execution_count":264,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.526826Z","iopub.execute_input":"2023-02-01T14:59:43.527875Z","iopub.status.idle":"2023-02-01T14:59:43.538926Z","shell.execute_reply.started":"2023-02-01T14:59:43.527837Z","shell.execute_reply":"2023-02-01T14:59:43.538137Z"},"trusted":true},"execution_count":265,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.734794Z","iopub.execute_input":"2023-02-01T14:59:43.735200Z","iopub.status.idle":"2023-02-01T14:59:43.749137Z","shell.execute_reply.started":"2023-02-01T14:59:43.735165Z","shell.execute_reply":"2023-02-01T14:59:43.747731Z"},"trusted":true},"execution_count":266,"outputs":[{"execution_count":266,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.969440Z","iopub.execute_input":"2023-02-01T14:59:43.970219Z","iopub.status.idle":"2023-02-01T14:59:43.982089Z","shell.execute_reply.started":"2023-02-01T14:59:43.970157Z","shell.execute_reply":"2023-02-01T14:59:43.980764Z"},"trusted":true},"execution_count":267,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.130067Z","iopub.execute_input":"2023-02-01T14:59:44.130855Z","iopub.status.idle":"2023-02-01T14:59:44.141446Z","shell.execute_reply.started":"2023-02-01T14:59:44.130814Z","shell.execute_reply":"2023-02-01T14:59:44.140366Z"},"trusted":true},"execution_count":268,"outputs":[{"execution_count":268,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.375519Z","iopub.execute_input":"2023-02-01T14:59:44.376720Z","iopub.status.idle":"2023-02-01T14:59:44.387800Z","shell.execute_reply.started":"2023-02-01T14:59:44.376665Z","shell.execute_reply":"2023-02-01T14:59:44.386558Z"},"trusted":true},"execution_count":269,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.523725Z","iopub.execute_input":"2023-02-01T14:59:44.524363Z","iopub.status.idle":"2023-02-01T14:59:44.536192Z","shell.execute_reply.started":"2023-02-01T14:59:44.524322Z","shell.execute_reply":"2023-02-01T14:59:44.535041Z"},"trusted":true},"execution_count":270,"outputs":[{"execution_count":270,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.806439Z","iopub.execute_input":"2023-02-01T14:59:44.806827Z","iopub.status.idle":"2023-02-01T14:59:44.814441Z","shell.execute_reply.started":"2023-02-01T14:59:44.806794Z","shell.execute_reply":"2023-02-01T14:59:44.813111Z"},"trusted":true},"execution_count":271,"outputs":[{"execution_count":271,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.150811Z","iopub.execute_input":"2023-02-01T14:59:45.151188Z","iopub.status.idle":"2023-02-01T14:59:45.159387Z","shell.execute_reply.started":"2023-02-01T14:59:45.151156Z","shell.execute_reply":"2023-02-01T14:59:45.158248Z"},"trusted":true},"execution_count":272,"outputs":[{"execution_count":272,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.400597Z","iopub.execute_input":"2023-02-01T14:59:45.401226Z","iopub.status.idle":"2023-02-01T14:59:45.410601Z","shell.execute_reply.started":"2023-02-01T14:59:45.401186Z","shell.execute_reply":"2023-02-01T14:59:45.409380Z"},"trusted":true},"execution_count":273,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.540502Z","iopub.execute_input":"2023-02-01T14:59:45.541816Z","iopub.status.idle":"2023-02-01T14:59:45.555066Z","shell.execute_reply.started":"2023-02-01T14:59:45.541649Z","shell.execute_reply":"2023-02-01T14:59:45.553893Z"},"trusted":true},"execution_count":274,"outputs":[{"execution_count":274,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.765213Z","iopub.execute_input":"2023-02-01T14:59:45.766189Z","iopub.status.idle":"2023-02-01T14:59:45.777960Z","shell.execute_reply.started":"2023-02-01T14:59:45.766144Z","shell.execute_reply":"2023-02-01T14:59:45.776759Z"},"trusted":true},"execution_count":275,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.007744Z","iopub.execute_input":"2023-02-01T14:59:46.008172Z","iopub.status.idle":"2023-02-01T14:59:46.020782Z","shell.execute_reply.started":"2023-02-01T14:59:46.008134Z","shell.execute_reply":"2023-02-01T14:59:46.019374Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.158566Z","iopub.execute_input":"2023-02-01T14:59:46.158955Z","iopub.status.idle":"2023-02-01T14:59:46.171385Z","shell.execute_reply.started":"2023-02-01T14:59:46.158921Z","shell.execute_reply":"2023-02-01T14:59:46.170377Z"},"trusted":true},"execution_count":277,"outputs":[{"execution_count":277,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.365352Z","iopub.execute_input":"2023-02-01T14:59:46.365774Z","iopub.status.idle":"2023-02-01T14:59:46.377504Z","shell.execute_reply.started":"2023-02-01T14:59:46.365737Z","shell.execute_reply":"2023-02-01T14:59:46.376059Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.591674Z","iopub.execute_input":"2023-02-01T14:59:46.592065Z","iopub.status.idle":"2023-02-01T14:59:46.602473Z","shell.execute_reply.started":"2023-02-01T14:59:46.592030Z","shell.execute_reply":"2023-02-01T14:59:46.601375Z"},"trusted":true},"execution_count":279,"outputs":[{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.828954Z","iopub.execute_input":"2023-02-01T14:59:46.829390Z","iopub.status.idle":"2023-02-01T14:59:46.840546Z","shell.execute_reply.started":"2023-02-01T14:59:46.829349Z","shell.execute_reply":"2023-02-01T14:59:46.839434Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.034899Z","iopub.execute_input":"2023-02-01T14:59:47.036005Z","iopub.status.idle":"2023-02-01T14:59:47.045477Z","shell.execute_reply.started":"2023-02-01T14:59:47.035966Z","shell.execute_reply":"2023-02-01T14:59:47.044685Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.171565Z","iopub.execute_input":"2023-02-01T14:59:47.172636Z","iopub.status.idle":"2023-02-01T14:59:47.179309Z","shell.execute_reply.started":"2023-02-01T14:59:47.172596Z","shell.execute_reply":"2023-02-01T14:59:47.178195Z"},"trusted":true},"execution_count":282,"outputs":[{"execution_count":282,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.581953Z","iopub.execute_input":"2023-02-01T14:59:47.582616Z","iopub.status.idle":"2023-02-01T14:59:47.591105Z","shell.execute_reply.started":"2023-02-01T14:59:47.582574Z","shell.execute_reply":"2023-02-01T14:59:47.589952Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.831877Z","iopub.execute_input":"2023-02-01T14:59:47.832258Z","iopub.status.idle":"2023-02-01T14:59:47.839367Z","shell.execute_reply.started":"2023-02-01T14:59:47.832227Z","shell.execute_reply":"2023-02-01T14:59:47.838210Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.993877Z","iopub.execute_input":"2023-02-01T14:59:47.994253Z","iopub.status.idle":"2023-02-01T14:59:48.002543Z","shell.execute_reply.started":"2023-02-01T14:59:47.994221Z","shell.execute_reply":"2023-02-01T14:59:48.001550Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.201983Z","iopub.execute_input":"2023-02-01T14:59:48.202420Z","iopub.status.idle":"2023-02-01T14:59:48.212760Z","shell.execute_reply.started":"2023-02-01T14:59:48.202382Z","shell.execute_reply":"2023-02-01T14:59:48.211396Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_embarked_cat(data):\n factors = data['Embarked'].unique()\n gender_columns = pd.get_dummies(data['Embarked'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.431294Z","iopub.execute_input":"2023-02-01T14:59:48.432534Z","iopub.status.idle":"2023-02-01T14:59:48.437882Z","shell.execute_reply.started":"2023-02-01T14:59:48.432467Z","shell.execute_reply":"2023-02-01T14:59:48.437019Z"},"trusted":true},"execution_count":287,"outputs":[]},{"cell_type":"code","source":"\ntitanic_train = transform_embarked_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Embarked\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.629204Z","iopub.execute_input":"2023-02-01T14:59:48.629922Z","iopub.status.idle":"2023-02-01T14:59:48.642617Z","shell.execute_reply.started":"2023-02-01T14:59:48.629880Z","shell.execute_reply":"2023-02-01T14:59:48.641807Z"},"trusted":true},"execution_count":288,"outputs":[{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\ntitanic_test = transform_embarked_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Embarked\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.849824Z","iopub.execute_input":"2023-02-01T14:59:48.850216Z","iopub.status.idle":"2023-02-01T14:59:48.866727Z","shell.execute_reply.started":"2023-02-01T14:59:48.850182Z","shell.execute_reply":"2023-02-01T14:59:48.865657Z"},"trusted":true},"execution_count":289,"outputs":[{"execution_count":289,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"indices = range(0, titanic_test.shape[0])\ntitanic_test['U'] = [0 for i in indices]\ntitanic_test['U'] = titanic_test['U'].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.014240Z","iopub.execute_input":"2023-02-01T14:59:49.014659Z","iopub.status.idle":"2023-02-01T14:59:49.022051Z","shell.execute_reply.started":"2023-02-01T14:59:49.014622Z","shell.execute_reply":"2023-02-01T14:59:49.020812Z"},"trusted":true},"execution_count":290,"outputs":[]},{"cell_type":"markdown","source":"### Number of sibling","metadata":{}},{"cell_type":"code","source":"print(titanic_train[\"SibSp\"].describe())\nplt.hist(titanic_train[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.435498Z","iopub.execute_input":"2023-02-01T14:59:49.435873Z","iopub.status.idle":"2023-02-01T14:59:49.609979Z","shell.execute_reply.started":"2023-02-01T14:59:49.435843Z","shell.execute_reply":"2023-02-01T14:59:49.608818Z"},"trusted":true},"execution_count":291,"outputs":[{"name":"stdout","text":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":291,"output_type":"execute_result","data":{"text/plain":"(array([608., 209., 28., 16., 0., 18., 5., 0., 0., 7.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(titanic_test[\"SibSp\"].describe())\nplt.hist(titanic_test[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.640013Z","iopub.execute_input":"2023-02-01T14:59:49.640429Z","iopub.status.idle":"2023-02-01T14:59:50.199638Z","shell.execute_reply.started":"2023-02-01T14:59:49.640389Z","shell.execute_reply":"2023-02-01T14:59:50.198241Z"},"trusted":true},"execution_count":292,"outputs":[{"name":"stdout","text":"count 418.000000\nmean 0.447368\nstd 0.896760\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":292,"output_type":"execute_result","data":{"text/plain":"(array([283., 110., 14., 4., 0., 4., 1., 0., 0., 2.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def categorise_siblings(data):\n cut_labels_9 = ['sib_0','sib_1','sib_2','sib_3', \n 'sib_4','sib_5','sib_6','sib_7', 'sib_8']\n cut_bins = [0,1,2,3,4,5,6,7,8,9]\n data['Sib_cat'] = pd.cut(data['SibSp'], \n bins=cut_bins, \n labels=cut_labels_9)\n \n data['Sib_cat'] = data.Sib_cat.astype(str)\n data.loc[data[\"Sib_cat\"] == 'nan', \"Sib_cat\"] = \"Sib_Unknown\"\n \n return data\n\ndef transform_sibling_cat(data):\n factors = data['Sib_cat'].unique()\n gender_columns = pd.get_dummies(data['Sib_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.201993Z","iopub.execute_input":"2023-02-01T14:59:50.202490Z","iopub.status.idle":"2023-02-01T14:59:50.212938Z","shell.execute_reply.started":"2023-02-01T14:59:50.202445Z","shell.execute_reply":"2023-02-01T14:59:50.211676Z"},"trusted":true},"execution_count":293,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_siblings(titanic_train)\ntitanic_train = transform_sibling_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"SibSp\", axis = 1)\ntitanic_train = titanic_train.drop(\"Sib_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.214386Z","iopub.execute_input":"2023-02-01T14:59:50.214705Z","iopub.status.idle":"2023-02-01T14:59:50.237526Z","shell.execute_reply.started":"2023-02-01T14:59:50.214675Z","shell.execute_reply":"2023-02-01T14:59:50.236793Z"},"trusted":true},"execution_count":294,"outputs":[{"execution_count":294,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.431533Z","iopub.execute_input":"2023-02-01T14:59:50.432231Z","iopub.status.idle":"2023-02-01T14:59:50.438691Z","shell.execute_reply.started":"2023-02-01T14:59:50.432194Z","shell.execute_reply":"2023-02-01T14:59:50.437673Z"},"trusted":true},"execution_count":295,"outputs":[{"execution_count":295,"output_type":"execute_result","data":{"text/plain":"(891, 21)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_siblings(titanic_test)\ntitanic_test = transform_sibling_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"SibSp\", axis = 1)\ntitanic_test = titanic_test.drop(\"Sib_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.596205Z","iopub.execute_input":"2023-02-01T14:59:50.596606Z","iopub.status.idle":"2023-02-01T14:59:50.618154Z","shell.execute_reply.started":"2023-02-01T14:59:50.596574Z","shell.execute_reply":"2023-02-01T14:59:50.617093Z"},"trusted":true},"execution_count":296,"outputs":[{"execution_count":296,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.849255Z","iopub.execute_input":"2023-02-01T14:59:50.850520Z","iopub.status.idle":"2023-02-01T14:59:50.858028Z","shell.execute_reply.started":"2023-02-01T14:59:50.850477Z","shell.execute_reply":"2023-02-01T14:59:50.856953Z"},"trusted":true},"execution_count":297,"outputs":[{"execution_count":297,"output_type":"execute_result","data":{"text/plain":"(418, 20)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Transforming age into categories\nThe categorise the age into 9 categories; unknown and one for each decade. The categories are then transformed in hot_coding format. ","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.269486Z","iopub.execute_input":"2023-02-01T14:59:51.269885Z","iopub.status.idle":"2023-02-01T14:59:51.572232Z","shell.execute_reply.started":"2023-02-01T14:59:51.269851Z","shell.execute_reply":"2023-02-01T14:59:51.571214Z"},"trusted":true},"execution_count":298,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.573955Z","iopub.execute_input":"2023-02-01T14:59:51.574279Z","iopub.status.idle":"2023-02-01T14:59:51.588745Z","shell.execute_reply.started":"2023-02-01T14:59:51.574249Z","shell.execute_reply":"2023-02-01T14:59:51.587351Z"},"trusted":true},"execution_count":299,"outputs":[{"execution_count":299,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.763907Z","iopub.execute_input":"2023-02-01T14:59:51.764334Z","iopub.status.idle":"2023-02-01T14:59:52.129917Z","shell.execute_reply.started":"2023-02-01T14:59:51.764278Z","shell.execute_reply":"2023-02-01T14:59:52.128918Z"},"trusted":true},"execution_count":300,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.131621Z","iopub.execute_input":"2023-02-01T14:59:52.132130Z","iopub.status.idle":"2023-02-01T14:59:52.142285Z","shell.execute_reply.started":"2023-02-01T14:59:52.132091Z","shell.execute_reply":"2023-02-01T14:59:52.141264Z"},"trusted":true},"execution_count":301,"outputs":[{"execution_count":301,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"def transform_age_cat(data):\n factors = data['Age_cat'].unique()\n gender_columns = pd.get_dummies(data['Age_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.143629Z","iopub.execute_input":"2023-02-01T14:59:52.143919Z","iopub.status.idle":"2023-02-01T14:59:52.154584Z","shell.execute_reply.started":"2023-02-01T14:59:52.143891Z","shell.execute_reply":"2023-02-01T14:59:52.153409Z"},"trusted":true},"execution_count":302,"outputs":[]},{"cell_type":"code","source":"def categorise_age(data):\n cut_labels_8 = ['age_0-9','age_10-19','age_20-29','age_30-39', \n 'age_40-49','age_50-59','age_60-69','age_70-79']\n cut_bins = [0,10,20,30,40,50,60,70,80]\n data['Age_cat'] = pd.cut(data['Age'], \n bins=cut_bins, \n labels=cut_labels_8)\n data['Age_cat'] = data.Age_cat.astype(str)\n data.loc[data[\"Age\"].isna(), \"Age_cat\"] = \"Age_Unknown\"\n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.340509Z","iopub.execute_input":"2023-02-01T14:59:52.340896Z","iopub.status.idle":"2023-02-01T14:59:52.347606Z","shell.execute_reply.started":"2023-02-01T14:59:52.340863Z","shell.execute_reply":"2023-02-01T14:59:52.346572Z"},"trusted":true},"execution_count":303,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_age(titanic_train)\ntitanic_train = transform_age_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Age\", axis = 1)\ntitanic_train = titanic_train.drop(\"Age_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.546266Z","iopub.execute_input":"2023-02-01T14:59:52.546677Z","iopub.status.idle":"2023-02-01T14:59:52.572844Z","shell.execute_reply.started":"2023-02-01T14:59:52.546642Z","shell.execute_reply":"2023-02-01T14:59:52.571757Z"},"trusted":true},"execution_count":304,"outputs":[{"execution_count":304,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_age(titanic_test)\ntitanic_test = transform_age_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Age\", axis = 1)\ntitanic_test = titanic_test.drop(\"Age_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.811521Z","iopub.execute_input":"2023-02-01T14:59:52.812681Z","iopub.status.idle":"2023-02-01T14:59:52.836736Z","shell.execute_reply.started":"2023-02-01T14:59:52.812627Z","shell.execute_reply":"2023-02-01T14:59:52.835513Z"},"trusted":true},"execution_count":305,"outputs":[{"execution_count":305,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"### Gender transformation to hot-coding \nWe check the factor values are the same between both datasets. Then, we generate a hot coding of two columns; i.e., male and female. Both columns replace the Sex column.","metadata":{}},{"cell_type":"code","source":"titanic_train['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.188122Z","iopub.execute_input":"2023-02-01T14:59:53.189282Z","iopub.status.idle":"2023-02-01T14:59:53.197504Z","shell.execute_reply.started":"2023-02-01T14:59:53.189231Z","shell.execute_reply":"2023-02-01T14:59:53.196373Z"},"trusted":true},"execution_count":306,"outputs":[{"execution_count":306,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.420038Z","iopub.execute_input":"2023-02-01T14:59:53.420458Z","iopub.status.idle":"2023-02-01T14:59:53.428009Z","shell.execute_reply.started":"2023-02-01T14:59:53.420423Z","shell.execute_reply":"2023-02-01T14:59:53.426859Z"},"trusted":true},"execution_count":307,"outputs":[{"execution_count":307,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_gender(data):\n factors = data['Sex'].unique()\n gender_columns = pd.get_dummies(data['Sex'])\n columns = range(0,len(factors))\n \n for column in columns:\n data[factors[column]] = gender_columns.loc[:,factors[column]].astype(float)\n \n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.614253Z","iopub.execute_input":"2023-02-01T14:59:53.614984Z","iopub.status.idle":"2023-02-01T14:59:53.620854Z","shell.execute_reply.started":"2023-02-01T14:59:53.614945Z","shell.execute_reply":"2023-02-01T14:59:53.619727Z"},"trusted":true},"execution_count":308,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_gender(titanic_train)\ntitanic_train.drop(\"Sex\", axis = 1, inplace = True)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.853720Z","iopub.execute_input":"2023-02-01T14:59:53.854121Z","iopub.status.idle":"2023-02-01T14:59:53.868139Z","shell.execute_reply.started":"2023-02-01T14:59:53.854084Z","shell.execute_reply":"2023-02-01T14:59:53.867117Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_gender(titanic_test)\ntitanic_test.drop(\"Sex\", axis = 1,inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.077511Z","iopub.execute_input":"2023-02-01T14:59:54.078227Z","iopub.status.idle":"2023-02-01T14:59:54.117482Z","shell.execute_reply.started":"2023-02-01T14:59:54.078188Z","shell.execute_reply":"2023-02-01T14:59:54.116493Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Parch \\\n0 892.0 3 Kelly, Mr. James 0 \n1 893.0 3 Wilkes, Mrs. James (Ellen Needs) 0 \n2 894.0 2 Myles, Mr. Thomas Francis 0 \n3 895.0 3 Wirz, Mr. Albert 0 \n4 896.0 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 \n\n Ticket Fare Cabin Q S C ... age_30-39 age_40-49 \\\n0 330911 7.8292 NaN 1.0 0.0 0.0 ... 1.0 0.0 \n1 363272 7.0000 NaN 0.0 1.0 0.0 ... 0.0 1.0 \n2 240276 9.6875 NaN 1.0 0.0 0.0 ... 0.0 0.0 \n3 315154 8.6625 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n4 3101298 12.2875 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n\n age_60-69 age_20-29 age_10-19 age_50-59 age_0-9 age_70-79 male \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n\n female \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 1.0 \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameParchTicketFareCabinQSC...age_30-39age_40-49age_60-69age_20-29age_10-19age_50-59age_0-9age_70-79malefemale
0892.03Kelly, Mr. James03309117.8292NaN1.00.00.0...1.00.00.00.00.00.00.00.01.00.0
1893.03Wilkes, Mrs. James (Ellen Needs)03632727.0000NaN0.01.00.0...0.01.00.00.00.00.00.00.00.01.0
2894.02Myles, Mr. Thomas Francis02402769.6875NaN1.00.00.0...0.00.01.00.00.00.00.00.01.00.0
3895.03Wirz, Mr. Albert03151548.6625NaN0.01.00.0...0.00.00.01.00.00.00.00.01.00.0
4896.03Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.2875NaN0.01.00.0...0.00.00.01.00.00.00.00.00.01.0
\n

5 rows × 28 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Cabin and Pclass\n\nThe passenger class appears to drive whether a cabin is known. So, we propose to drop the cabin as the percentage of not known values is quite high. We apply an hot encoding the Pclass. ","metadata":{}},{"cell_type":"code","source":"titanic_train['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.494349Z","iopub.execute_input":"2023-02-01T14:59:54.494758Z","iopub.status.idle":"2023-02-01T14:59:54.503695Z","shell.execute_reply.started":"2023-02-01T14:59:54.494724Z","shell.execute_reply":"2023-02-01T14:59:54.502385Z"},"trusted":true},"execution_count":311,"outputs":[{"execution_count":311,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n 'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n 'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n 'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n 'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"print(\"percentage of cabin nan values - training \", titanic_train['Cabin'].isna().sum()/titanic_train.shape[0])\nprint(\"percentage of cabin nan values - test \", titanic_test['Cabin'].isna().sum()/titanic_test.shape[0])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.731246Z","iopub.execute_input":"2023-02-01T14:59:54.732185Z","iopub.status.idle":"2023-02-01T14:59:54.740154Z","shell.execute_reply.started":"2023-02-01T14:59:54.732142Z","shell.execute_reply":"2023-02-01T14:59:54.738880Z"},"trusted":true},"execution_count":312,"outputs":[{"name":"stdout","text":"percentage of cabin nan values - training 0.7710437710437711\npercentage of cabin nan values - test 0.7822966507177034\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.963015Z","iopub.execute_input":"2023-02-01T14:59:54.963847Z","iopub.status.idle":"2023-02-01T14:59:54.971020Z","shell.execute_reply.started":"2023-02-01T14:59:54.963804Z","shell.execute_reply":"2023-02-01T14:59:54.969855Z"},"trusted":true},"execution_count":313,"outputs":[{"execution_count":313,"output_type":"execute_result","data":{"text/plain":"array([3, 1, 2])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.182701Z","iopub.execute_input":"2023-02-01T14:59:55.183488Z","iopub.status.idle":"2023-02-01T14:59:55.190703Z","shell.execute_reply.started":"2023-02-01T14:59:55.183443Z","shell.execute_reply":"2023-02-01T14:59:55.189659Z"},"trusted":true},"execution_count":314,"outputs":[{"execution_count":314,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 1])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 1 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.447423Z","iopub.execute_input":"2023-02-01T14:59:55.447835Z","iopub.status.idle":"2023-02-01T14:59:55.464293Z","shell.execute_reply.started":"2023-02-01T14:59:55.447799Z","shell.execute_reply":"2023-02-01T14:59:55.463098Z"},"trusted":true},"execution_count":315,"outputs":[{"execution_count":315,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n1 1 C85\n3 1 C123\n6 1 E46\n11 1 C103\n23 1 A6\n.. ... ...\n871 1 D35\n872 1 B51 B53 B55\n879 1 C50\n887 1 B42\n889 1 C148\n\n[216 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
11C85
31C123
61E46
111C103
231A6
.........
8711D35
8721B51 B53 B55
8791C50
8871B42
8891C148
\n

216 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 2 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.639329Z","iopub.execute_input":"2023-02-01T14:59:55.640055Z","iopub.status.idle":"2023-02-01T14:59:55.656031Z","shell.execute_reply.started":"2023-02-01T14:59:55.640016Z","shell.execute_reply":"2023-02-01T14:59:55.655083Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n9 2 NaN\n15 2 NaN\n17 2 NaN\n20 2 NaN\n21 2 D56\n.. ... ...\n866 2 NaN\n874 2 NaN\n880 2 NaN\n883 2 NaN\n886 2 NaN\n\n[184 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
92NaN
152NaN
172NaN
202NaN
212D56
.........
8662NaN
8742NaN
8802NaN
8832NaN
8862NaN
\n

184 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 3 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.890762Z","iopub.execute_input":"2023-02-01T14:59:55.891773Z","iopub.status.idle":"2023-02-01T14:59:55.905616Z","shell.execute_reply.started":"2023-02-01T14:59:55.891731Z","shell.execute_reply":"2023-02-01T14:59:55.904841Z"},"trusted":true},"execution_count":317,"outputs":[{"execution_count":317,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n0 3 NaN\n2 3 NaN\n4 3 NaN\n5 3 NaN\n7 3 NaN\n.. ... ...\n882 3 NaN\n884 3 NaN\n885 3 NaN\n888 3 NaN\n890 3 NaN\n\n[491 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
03NaN
23NaN
43NaN
53NaN
73NaN
.........
8823NaN
8843NaN
8853NaN
8883NaN
8903NaN
\n

491 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"xs = titanic_train.loc[titanic_train['Fare'] > 0,'Pclass']\nys = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.128782Z","iopub.execute_input":"2023-02-01T14:59:56.129782Z","iopub.status.idle":"2023-02-01T14:59:56.360461Z","shell.execute_reply.started":"2023-02-01T14:59:56.129741Z","shell.execute_reply":"2023-02-01T14:59:56.359413Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAARlElEQVR4nO3df4wc5X3H8c8nxwEuGIzr40eMwRRZQaGQgk4Yx1HltEI4zg/clj+waH6pxVVK1ESJkEpAUFJQKiGhKkkbBAlKaFwnUSAWjQzEUqlCATs5O8YGA8UQiu2g+IIL5ocD8enbP3Zs1uvd25nz7OzOc++XtLqZZ+Z2vh6e+zA788ysI0IAgPp7V78LAACUg0AHgEQQ6ACQCAIdABJBoANAIo7q14bnzJkT8+fP79fmAaCWNm7c+JuIGGm3rG+BPn/+fI2NjfVr8wBQS7b/t9MyTrkAQCIIdABIBIEOAIkg0AEgEQQ6ACSib6NcpuLKOx/TI8/tOTi/+OzZWnXVoj5WBACDozZH6K1hLkmPPLdHV975WJ8qAoDBUptAbw3zbu0AMN3UJtABAJMj0AEgEbUJ9MVnzy7UDgDTTW0CfdVViw4Lb0a5AMA7ajVskfAGgM5qc4QOAJgcgQ4AiSDQASARBDoAJIJAB4BEEOgAkAgCHQASQaADQCIIdABIBIEOAIkg0AEgEQQ6ACSia6Dbnmf7IdvbbD9p+3Nt1lli+1Xbm7PXDb0pFwDQSZ6nLe6X9MWI2GR7pqSNttdFxLaW9R6OiI+UXyIAII+uR+gR8VJEbMqmX5P0lKS5vS4MAFBMoXPotudLukDShjaLF9l+3Pb9ts/t8PsrbY/ZHhsfHy9eLQCgo9yBbvt4SfdI+nxE7G1ZvEnSmRHxPklfk7Sm3XtExB0RMRoRoyMjI1MsGQDQTq5Atz2sRpivioh7W5dHxN6IeD2bXitp2PacUisFAEwqzygXS/qWpKci4rYO65yarSfbF2Xv+3KZhQIAJpdnlMtiSR+XtNX25qztS5LOkKSIuF3S5ZI+Y3u/pH2SroiIKLvYS277Lz27+42D8wtOPk7rvrCk7M0AQC25B7mby+joaIyNjeVevzXMDyDUAUwntjdGxGi7ZbW5U7RdmE/WDgDTTW0CHQAwOQIdABJBoANAIgh0AEgEgQ4AiSDQASARBDoAJIJAB4BEEOgAkAgCHQASQaADQCJqE+gLTj6uUDsATDe1CfR1X1hyWHjzpEUAeEee56EPDMIbADqrzRE6AGByBDoAJKJWp1yuX7NVqzfs0ESEhmytWDhPNy8/r99lAcBAqE2gX79mq767/sWD8xMRB+cJdQCo0SmX1Rt2FGoHgOmmNoE+0eHLrDu1A8B0U5tABwBMjkAHgEQQ6ACQCAIdABJBoANAIgh0AEgEgQ4AiSDQASARBDoAJIJAB4BE1CbQj3KxdgCYbmoT6Ps7PLKlUzsATDe1CXQAwOQIdABIBIEOAIkg0AEgEQQ6ACSia6Dbnmf7IdvbbD9p+3Nt1rHtr9rebnuL7Qt7Uy4AoJM8XxK9X9IXI2KT7ZmSNtpeFxHbmtb5kKQF2WuhpG9kPwEAFel6hB4RL0XEpmz6NUlPSZrbstplku6OhvWSZtk+rcxC//LiMwq1A8B0U+gcuu35ki6QtKFl0VxJO5rmd+rw0JftlbbHbI+Nj48XKvTm5edpwcnHHdK24OTjdPPy8wq9DwCkKneg2z5e0j2SPh8Re6eysYi4IyJGI2J0ZGSk0O9ev2arnt39xiFtz+5+Q9ev2TqVUgAgObkC3fawGmG+KiLubbPKLknzmuZPz9pK8931LxZqB4DpJs8oF0v6lqSnIuK2DqvdJ+kT2WiXiyW9GhEvlVgnAKCLPKNcFkv6uKSttjdnbV+SdIYkRcTtktZKWiZpu6Q3JX269EoBAJPqGugR8d+SJn1IbUSEpKvLKgoAUBx3igJAIgh0AEgEgQ4AichzURSYNhbesk6/fu3tg/OnzDxaG667pI8VAflxhA5kWsNckn792ttaeMu6PlUEFEOgA5nWMO/WDgwaTrkAQEXOuW6tfjvxzjfbHztkPX3LstLenyN0AKhAa5hL0m8nQudct7a0bRDoQOaUmUcXageKaA3zbu1TQaADmUvOPbVQOzBoCHQgs3rDjkLtwKAh0IHMRLT/6NupHSji2KH2j8Tq1D4VBDqQGXL7P6xO7UART9+y7LDwLnuUC8MWgcyKhfPafmHKioXz2qwNFFdmeLdDoAOZA99Pu3rDDk1EaMjWioXz+N5alKbX49AJdKDJzcvPI8DRE5ONQy8r1DmHDgAVYBw6ACA3Ah0AEkGgA0AFGIcOAIlgHDoAJIRx6ACQiPNvfEB735o4OH/CMUPactPS0t6fUy4AUIHWMJekvW9N6PwbHyhtGwQ6AFSgNcy7tU8FgQ4AiahNoM+dNaNQOwBMN7W5KPrBc0baPgnvg+eM9KEapOrKOx/TI8/tOTi/+OzZWnXVoj5WhFSccMxQ29MrJxwzVNo2anOE/tDT44XagaJaw1ySHnluj66887E+VYSUbLlp6WHhPW1Huex6ZV+hdqCo1jDv1g4MmtoEOgDUGcMWASARDFsEAORGoANAIgh0AKhAp+GJ03LYIgDU2SknHluofSoIdACowLO73yjUPhUEOpA5ZebRhdqBQdM10G3fZXu37Sc6LF9i+1Xbm7PXDeWXCfTer197u1A7MGjyPMvl25K+LunuSdZ5OCI+UkpFAIAp6XqEHhE/lcS9zwAw4Mo6h77I9uO277d9bqeVbK+0PWZ7bHych2oBQJnKCPRNks6MiPdJ+pqkNZ1WjIg7ImI0IkZHRnjsLQCU6YgDPSL2RsTr2fRaScO25xxxZQCAQo440G2fatvZ9EXZe758pO8LACim6ygX26slLZE0x/ZOSTdKGpakiLhd0uWSPmN7v6R9kq6IiOhZxQCAtroGekSs6LL862oMawQA9BF3igJAIgh0AEgEgQ4AiSDQASARtQn0Kh4ODwB1VptA33LT0sPC+4RjhrTlpqV9qggABkuepy0ODMIbADqrzRE6AGByBDoAJIJAB4BEEOgAkAgCHQASQaADQCIIdABIBIEOAIkg0AEgEQQ6ACSCQAcyi8+eXagdGDQEOpBZddWiw8J78dmzteqqRX2qCCiGQAeanDVyvIZsSdKQrbNGju9zRUB+tXraItBL16/Zqu+uf/Hg/ETEwfmbl5/Xr7KA3DhCBzKrN+wo1A4MGgIdyExEFGoHBg2BDgCJINABIBEEOgAkgkAHgEQQ6ACQCAIdABJBoANAIgh0AKiAC7ZPBYEOABXodHtambetEegAkAgCHQASQaADQCIIdABIBIEOAIkg0AEgEV0D3fZdtnfbfqLDctv+qu3ttrfYvrD8MgEA3eQ5Qv+2pKWTLP+QpAXZa6Wkbxx5WQCAoroGekT8VNKeSVa5TNLd0bBe0izbp5VVIAAgnzLOoc+V1PylizuztsPYXml7zPbY+Ph4CZsGABxQ6UXRiLgjIkYjYnRkZKTKTQNdvfBPHy7UDgyao0p4j12S5jXNn561AbVDeKPOyjhCv0/SJ7LRLhdLejUiXirhfQEABXQ9Qre9WtISSXNs75R0o6RhSYqI2yWtlbRM0nZJb0r6dK+KBQB01jXQI2JFl+Uh6erSKgIATAl3igJAIgh0AEgEgQ4AiSDQASARBDoAJIJAB4BEEOgAkAgCHQASQaADQCIIdABIBIEOAIkg0AEgEQQ6ACSCQAeARBDoAFCBGcPt47ZT+1QQ6ABQga/8+fmHBe67svaylPGdogCALpZfMFeSdOuDz+hXr+zTu2fN0DWXvudgexkIdACoyPIL5pYa4K045QIAiajVEfqaX+zq6ccVAKiz2gT6ml/s0rX3btW+301Ikna9sk/X3rtVkgh1AFCNTrnc+uAzB8P8gH2/m9CtDz7Tp4oAYLDUJtB/9cq+Qu0AMN3UJtDfPWtGoXYAmG5qE+jXXPoezRgeOqRtxvCQrrn0PX2qCAAGS20uilYxKB8A6qw2gS71flA+ANRZbU65AAAmV6sjdACos17fHEmgA0AFqrg5klMuAFCBKm6OJNABoAJV3BxJoANABaq4OZJAB4AKVHFzJBdFAaACfGMRACSEbywCAOSSK9BtL7X9jO3ttv++zfJP2R63vTl7/XX5pQIAJtP1lIvtIUn/IukSSTsl/dz2fRGxrWXV70fEZ3tQIwAghzxH6BdJ2h4Rz0fE25K+J+my3pYFACgqT6DPlbSjaX5n1tbqL2xvsf1D2/NKqQ4AkFtZo1z+Q9LqiHjL9t9I+o6kP2ldyfZKSSuz2ddtT/We1zmSfjPF3+2lQa1LGtzaqKsY6iomxbrO7LTAETHpb9peJOkfIuLSbP5aSYqIr3RYf0jSnog4cYrFdmV7LCJGe/X+UzWodUmDWxt1FUNdxUy3uvKccvm5pAW2z7J9tKQrJN3XUtxpTbMfk/RUeSUCAPLoesolIvbb/qykByUNSborIp60/WVJYxFxn6S/s/0xSfsl7ZH0qR7WDABoI9c59IhYK2ltS9sNTdPXSrq23NImdUeF2ypiUOuSBrc26iqGuoqZVnV1PYcOAKgHbv0HgEQQ6ACQiIEKdNt32d5t+4kOy237q9kzZbbYvrBp2SdtP5u9PllxXVdm9Wy1/ajt9zUteyFr32x7rMy6cta2xParTc/ZuaFp2aTP6OlhTdc01fOE7Qnbs7NlPdtftufZfsj2NttP2v5cm3Uq72M566q8j+Wsqx/9K09d/epjx9r+me3Hs9puarPOMba/n+2XDbbnNy27Nmt/xvalhQuIiIF5SfpjSRdKeqLD8mWS7pdkSRdL2pC1z5b0fPbzpGz6pArrev+B7Un60IG6svkXJM3p4z5bIunHbdqHJD0n6Q8kHS3pcUnvraKmlnU/Kuk/q9hfkk6TdGE2PVPS/7T+m/vRx3LWVXkfy1lXP/pX17r62Mcs6fhseljSBkkXt6zzt5Juz6avUOM5WJL03mw/HSPprGz/DRXZ/kAdoUfET9UY9tjJZZLujob1kma5MQb+UknrImJPRPyfpHWSllZVV0Q8mm1XktZLOr2sbXeTY5910rNn9BSsaYWk1WVst5uIeCkiNmXTr6lxv0TrYywq72N56upHH8u5vzrpZf8qWleVfSwi4vVsdjh7tY48uUyNu+kl6YeS/tS2s/bvRcRbEfFLSdvV2I+5DVSg59DpuTJ5nzdThb9S4wjvgJD0E9sb3Xj0QT8syj4C3m/73Kyt7/vM9u+pEYr3NDVXsr+yj7kXqHEE1ayvfWySuppV3se61NW3/tVtf/Wjj9kesr1Z0m41DgI69rGI2C/pVUm/rxL2Gd9YVCLbH1Tjj+0DTc0fiIhdtk+WtM7209kRbFU2STozIl63vUzSGkkLKtz+ZD4q6ZGIaD6a7/n+sn28Gn/gn4+IvWW+95HIU1c/+liXuvrWv3L+d6y8j0XEhKQ/sj1L0o9s/2FEtL2eVLa6HaHvktT8JMfTs7ZO7ZWxfb6kb0q6LCJePtAeEbuyn7sl/UgFP0IdqYjYe+AjYDRuEBu2PUcDsM/UOH94yEfhXu8v28NqhMCqiLi3zSp96WM56upLH+tWV7/6V579lam8jzVt5xVJD+nwU3MH943toySdKOlllbHPenFh4Ehekuar8wW+D+vQC1Y/y9pnS/qlGherTsqmZ1dY1xlqnO96f0v7cZJmNk0/KmlpxfvsVL1zA9lFkl7M9t9RalzYO0vvXLQ6t4qasuUnqnGe/biq9lf2775b0j9Psk7lfSxnXZX3sZx1Vd6/8tTVxz42ImlWNj1D0sOSPtKyztU69KLoD7Lpc3XoRdHnVfCi6ECdcrG9Wo2r5nNs75R0oxoXFRQRt6vx+IFlanTsNyV9Olu2x/Y/qvEgMUn6chz6EavXdd2gxjmwf21c29D+aDxJ7RQ1PnJJjQ7+7xHxQFl15aztckmfsb1f0j5JV0Sj97R9Rk9FNUnSn0n6SUS80fSrvd5fiyV9XNLW7BynJH1JjbDsZx/LU1c/+lieuirvXznrkvrTx06T9B03njr7LjXC+sc+9NlX35L0b7a3q/E/nCuyup+0/QNJ29R4LtbV0Th9kxu3/gNAIup2Dh0A0AGBDgCJINABIBEEOgAkgkAHgEQQ6ACQCAIdABLx/yyGuKIeczEIAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"xs = titanic_test.loc[titanic_test['Fare'] > 0,'Pclass']\nys = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.362001Z","iopub.execute_input":"2023-02-01T14:59:56.362324Z","iopub.status.idle":"2023-02-01T14:59:56.593756Z","shell.execute_reply.started":"2023-02-01T14:59:56.362281Z","shell.execute_reply":"2023-02-01T14:59:56.592791Z"},"trusted":true},"execution_count":319,"outputs":[{"execution_count":319,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.scatter(titanic_train[\"Pclass\"],titanic_train[\"Fare\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.595546Z","iopub.execute_input":"2023-02-01T14:59:56.595846Z","iopub.status.idle":"2023-02-01T14:59:56.826882Z","shell.execute_reply.started":"2023-02-01T14:59:56.595817Z","shell.execute_reply":"2023-02-01T14:59:56.825559Z"},"trusted":true},"execution_count":320,"outputs":[{"execution_count":320,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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transform_Pclass(data):\n factors = data['Pclass'].unique()\n Pclass_columns = pd.get_dummies(data['Pclass'])\n columns = range(0,len(factors))\n \n for column in columns:\n col_name = 'Class_' + str(factors[column])\n data[col_name] = Pclass_columns.loc[:,factors[column]].astype(float)\n \n data.drop(\"Pclass\", axis = 1)\n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.829111Z","iopub.execute_input":"2023-02-01T14:59:56.829859Z","iopub.status.idle":"2023-02-01T14:59:56.838658Z","shell.execute_reply.started":"2023-02-01T14:59:56.829811Z","shell.execute_reply":"2023-02-01T14:59:56.837496Z"},"trusted":true},"execution_count":321,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_Pclass(titanic_train)\ntitanic_train.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.037884Z","iopub.execute_input":"2023-02-01T14:59:57.039017Z","iopub.status.idle":"2023-02-01T14:59:57.077228Z","shell.execute_reply.started":"2023-02-01T14:59:57.038961Z","shell.execute_reply":"2023-02-01T14:59:57.076108Z"},"trusted":true},"execution_count":322,"outputs":[{"execution_count":322,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch \\\n0 1.0 Braund, Mr. Owen Harris 0 \n1 2.0 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n2 3.0 Heikkinen, Miss. Laina 0 \n3 4.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n4 5.0 Allen, Mr. William Henry 0 \n\n Ticket Fare Survived S C Q U ... age_0-9 \\\n0 A/5 21171 7.2500 0 1.0 0.0 0.0 0.0 ... 0.0 \n1 PC 17599 71.2833 1 0.0 1.0 0.0 0.0 ... 0.0 \n2 STON/O2. 3101282 7.9250 1 1.0 0.0 0.0 0.0 ... 0.0 \n3 113803 53.1000 1 1.0 0.0 0.0 0.0 ... 0.0 \n4 373450 8.0500 0 1.0 0.0 0.0 0.0 ... 0.0 \n\n age_10-19 age_60-69 age_40-49 age_70-79 male female Class_3 Class_1 \\\n0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n2 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n4 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n\n Class_2 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 30 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareSurvivedSCQU...age_0-9age_10-19age_60-69age_40-49age_70-79malefemaleClass_3Class_1Class_2
01.0Braund, Mr. Owen Harris0A/5 211717.250001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
12.0Cumings, Mrs. John Bradley (Florence Briggs Th...0PC 1759971.283310.01.00.00.0...0.00.00.00.00.00.01.00.01.00.0
23.0Heikkinen, Miss. Laina0STON/O2. 31012827.925011.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
34.0Futrelle, Mrs. Jacques Heath (Lily May Peel)011380353.100011.00.00.00.0...0.00.00.00.00.00.01.00.01.00.0
45.0Allen, Mr. William Henry03734508.050001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
\n

5 rows × 30 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_Pclass(titanic_test)\ntitanic_test.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.320738Z","iopub.execute_input":"2023-02-01T14:59:57.321706Z","iopub.status.idle":"2023-02-01T14:59:57.358787Z","shell.execute_reply.started":"2023-02-01T14:59:57.321665Z","shell.execute_reply":"2023-02-01T14:59:57.357627Z"},"trusted":true},"execution_count":323,"outputs":[{"execution_count":323,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch Ticket \\\n0 892.0 Kelly, Mr. James 0 330911 \n1 893.0 Wilkes, Mrs. James (Ellen Needs) 0 363272 \n2 894.0 Myles, Mr. Thomas Francis 0 240276 \n3 895.0 Wirz, Mr. Albert 0 315154 \n4 896.0 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 3101298 \n\n Fare Q S C U Sib_Unknown ... age_20-29 age_10-19 \\\n0 7.8292 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n1 7.0000 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 \n2 9.6875 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n3 8.6625 0.0 1.0 0.0 0.0 1.0 ... 1.0 0.0 \n4 12.2875 0.0 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n\n age_50-59 age_0-9 age_70-79 male female Class_3 Class_2 Class_1 \n0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareQSCUSib_Unknown...age_20-29age_10-19age_50-59age_0-9age_70-79malefemaleClass_3Class_2Class_1
0892.0Kelly, Mr. James03309117.82921.00.00.00.01.0...0.00.00.00.00.01.00.01.00.00.0
1893.0Wilkes, Mrs. James (Ellen Needs)03632727.00000.01.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
2894.0Myles, Mr. Thomas Francis02402769.68751.00.00.00.01.0...0.00.00.00.00.01.00.00.01.00.0
3895.0Wirz, Mr. Albert03151548.66250.01.00.00.01.0...1.00.00.00.00.01.00.01.00.00.0
4896.0Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.28750.01.00.00.00.0...1.00.00.00.00.00.01.01.00.00.0
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Tickets and Fare\nWe remove the tickets, as it brings no additional characteristic for the prediction.\n\nOld version: We reduce the complexity of the Fare by using the log.\nNew version: The price appears to be dependent on the class, so we drop the price.","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.725423Z","iopub.execute_input":"2023-02-01T14:59:57.726055Z","iopub.status.idle":"2023-02-01T14:59:57.734724Z","shell.execute_reply.started":"2023-02-01T14:59:57.725995Z","shell.execute_reply":"2023-02-01T14:59:57.733640Z"},"trusted":true},"execution_count":324,"outputs":[]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\ntitanic_train.loc[titanic_train['Fare'] > 0,'Fare'] = log_10_values\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.977699Z","iopub.execute_input":"2023-02-01T14:59:57.978673Z","iopub.status.idle":"2023-02-01T14:59:57.991610Z","shell.execute_reply.started":"2023-02-01T14:59:57.978633Z","shell.execute_reply":"2023-02-01T14:59:57.990366Z"},"trusted":true},"execution_count":325,"outputs":[{"execution_count":325,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.256781\nstd 0.435553\nmin 0.000000\n25% 0.898198\n50% 1.159994\n75% 1.491362\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\ntitanic_test.loc[titanic_test['Fare'] > 0,'Fare'] = log_10_values\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.219678Z","iopub.execute_input":"2023-02-01T14:59:58.220097Z","iopub.status.idle":"2023-02-01T14:59:58.235301Z","shell.execute_reply.started":"2023-02-01T14:59:58.220059Z","shell.execute_reply":"2023-02-01T14:59:58.234195Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"count 417.000000\nmean 1.279591\nstd 0.437507\nmin 0.000000\n25% 0.897396\n50% 1.159994\n75% 1.498311\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.drop(\"Fare\", axis = 1, inplace = True)\ntitanic_test.drop(\"Fare\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.471730Z","iopub.execute_input":"2023-02-01T14:59:58.472149Z","iopub.status.idle":"2023-02-01T14:59:58.480205Z","shell.execute_reply.started":"2023-02-01T14:59:58.472111Z","shell.execute_reply":"2023-02-01T14:59:58.479227Z"},"trusted":true},"execution_count":327,"outputs":[]},{"cell_type":"markdown","source":"### Outcome of data preparations","metadata":{}},{"cell_type":"code","source":"\nprint(\"training datasets : \" , titanic_train.shape)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.947799Z","iopub.execute_input":"2023-02-01T14:59:58.948756Z","iopub.status.idle":"2023-02-01T14:59:58.957820Z","shell.execute_reply.started":"2023-02-01T14:59:58.948713Z","shell.execute_reply":"2023-02-01T14:59:58.956624Z"},"trusted":true},"execution_count":328,"outputs":[{"name":"stdout","text":"training datasets : (891, 28)\n","output_type":"stream"},{"execution_count":328,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_1 float64\nClass_2 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(\"testing datasets : \" , titanic_test.shape)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.211439Z","iopub.execute_input":"2023-02-01T14:59:59.211825Z","iopub.status.idle":"2023-02-01T14:59:59.222689Z","shell.execute_reply.started":"2023-02-01T14:59:59.211793Z","shell.execute_reply":"2023-02-01T14:59:59.221460Z"},"trusted":true},"execution_count":329,"outputs":[{"name":"stdout","text":"testing datasets : (418, 27)\n","output_type":"stream"},{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train_cols = titanic_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.478786Z","iopub.execute_input":"2023-02-01T14:59:59.479161Z","iopub.status.idle":"2023-02-01T14:59:59.488399Z","shell.execute_reply.started":"2023-02-01T14:59:59.479130Z","shell.execute_reply":"2023-02-01T14:59:59.487137Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"Index(['Survived'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.773416Z","iopub.execute_input":"2023-02-01T14:59:59.773881Z","iopub.status.idle":"2023-02-01T14:59:59.780592Z","shell.execute_reply.started":"2023-02-01T14:59:59.773845Z","shell.execute_reply":"2023-02-01T14:59:59.779730Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Name', 'Parch', 'Q', 'S', 'C', 'U', 'Sib_Unknown',\n 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', 'age_30-39',\n 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross validation preparation\nWe use a stratified sampling for the training into a train and test dataset. ","metadata":{}},{"cell_type":"code","source":"x_cols = [\"PassengerId\",'Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\nX = X[x_cols]\nX = X.apply(pd.to_numeric)\n\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.360627Z","iopub.execute_input":"2023-02-01T15:00:00.361572Z","iopub.status.idle":"2023-02-01T15:00:00.386989Z","shell.execute_reply.started":"2023-02-01T15:00:00.361528Z","shell.execute_reply":"2023-02-01T15:00:00.385873Z"},"trusted":true},"execution_count":332,"outputs":[{"name":"stdout","text":"0 0.616105\n1 0.383895\nName: Survived, dtype: float64 \n\n\n0 0.616246\n1 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = ['Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\nx_train_pass_id = X_train.PassengerId\nX_train = X_train[x_cols]\nX_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.656949Z","iopub.execute_input":"2023-02-01T15:00:00.657623Z","iopub.status.idle":"2023-02-01T15:00:00.667953Z","shell.execute_reply.started":"2023-02-01T15:00:00.657586Z","shell.execute_reply":"2023-02-01T15:00:00.666758Z"},"trusted":true},"execution_count":333,"outputs":[{"execution_count":333,"output_type":"execute_result","data":{"text/plain":"(534, 25)"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\n\nX_valid.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.982077Z","iopub.execute_input":"2023-02-01T15:00:00.982495Z","iopub.status.idle":"2023-02-01T15:00:00.991483Z","shell.execute_reply.started":"2023-02-01T15:00:00.982459Z","shell.execute_reply":"2023-02-01T15:00:00.990369Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"(357, 25)"},"metadata":{}}]},{"cell_type":"code","source":"y_train_encode=pd.get_dummies(y_train)\ny_valid_encode=pd.get_dummies(y_valid)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.303350Z","iopub.execute_input":"2023-02-01T15:00:01.303749Z","iopub.status.idle":"2023-02-01T15:00:01.310531Z","shell.execute_reply.started":"2023-02-01T15:00:01.303715Z","shell.execute_reply":"2023-02-01T15:00:01.309278Z"},"trusted":true},"execution_count":335,"outputs":[]},{"cell_type":"code","source":"train_cols = X_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.517778Z","iopub.execute_input":"2023-02-01T15:00:01.518178Z","iopub.status.idle":"2023-02-01T15:00:01.527798Z","shell.execute_reply.started":"2023-02-01T15:00:01.518142Z","shell.execute_reply":"2023-02-01T15:00:01.526659Z"},"trusted":true},"execution_count":336,"outputs":[{"execution_count":336,"output_type":"execute_result","data":{"text/plain":"Index([], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test[x_cols]\nX_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.807922Z","iopub.execute_input":"2023-02-01T15:00:01.808982Z","iopub.status.idle":"2023-02-01T15:00:01.817925Z","shell.execute_reply.started":"2023-02-01T15:00:01.808940Z","shell.execute_reply":"2023-02-01T15:00:01.816659Z"},"trusted":true},"execution_count":337,"outputs":[{"execution_count":337,"output_type":"execute_result","data":{"text/plain":"Parch int64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\nQ float64\nS float64\nC float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## ANN\n\nWe apply an ANN to predict the survival of passengers. We create a basic architecture made of 5 layers.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential, load_model\n\ntf.compat.v1.get_default_graph()\n\nno_columns = X_train.shape[1]\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Flatten(input_shape=(no_columns,)))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(2, activation=\"softmax\"))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.368188Z","iopub.execute_input":"2023-02-01T15:00:02.368622Z","iopub.status.idle":"2023-02-01T15:00:02.493557Z","shell.execute_reply.started":"2023-02-01T15:00:02.368582Z","shell.execute_reply":"2023-02-01T15:00:02.492272Z"},"trusted":true},"execution_count":338,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 25) 0 \n_________________________________________________________________\ndense (Dense) (None, 32) 832 \n_________________________________________________________________\ndense_1 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_2 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_3 (Dense) (None, 2) 66 \n=================================================================\nTotal params: 3,010\nTrainable params: 3,010\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"},{"name":"stderr","text":"2023-02-01 15:00:02.406449: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"\nrate = 0.00021\nopt = tf.keras.optimizers.Adam(learning_rate = rate)\nmodel.compile(optimizer= opt, \n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"])\ntf.compat.v1.get_default_graph()\nhistory = model.fit(X_train,\n y_train_encode,\n validation_data=(X_valid, y_valid_encode),\n epochs = 300,\n verbose = True)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.642006Z","iopub.execute_input":"2023-02-01T15:00:02.642833Z","iopub.status.idle":"2023-02-01T15:00:28.751910Z","shell.execute_reply.started":"2023-02-01T15:00:02.642783Z","shell.execute_reply":"2023-02-01T15:00:28.750794Z"},"trusted":true},"execution_count":339,"outputs":[{"name":"stderr","text":"2023-02-01 15:00:02.755801: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/300\n17/17 [==============================] - 1s 19ms/step - loss: 0.7885 - accuracy: 0.6161 - val_loss: 0.7708 - val_accuracy: 0.6162\nEpoch 2/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7574 - accuracy: 0.6161 - val_loss: 0.7429 - val_accuracy: 0.6162\nEpoch 3/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7326 - accuracy: 0.6161 - val_loss: 0.7213 - val_accuracy: 0.6162\nEpoch 4/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.7133 - accuracy: 0.6161 - val_loss: 0.7045 - val_accuracy: 0.6162\nEpoch 5/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.6161 - val_loss: 0.6921 - val_accuracy: 0.6162\nEpoch 6/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6878 - accuracy: 0.6161 - val_loss: 0.6832 - val_accuracy: 0.6162\nEpoch 7/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6802 - accuracy: 0.6161 - val_loss: 0.6771 - val_accuracy: 0.6162\nEpoch 8/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6750 - accuracy: 0.6161 - val_loss: 0.6727 - val_accuracy: 0.6162\nEpoch 9/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6717 - accuracy: 0.6161 - val_loss: 0.6698 - val_accuracy: 0.6162\nEpoch 10/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6692 - accuracy: 0.6161 - val_loss: 0.6682 - val_accuracy: 0.6162\nEpoch 11/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.6161 - val_loss: 0.6670 - val_accuracy: 0.6162\nEpoch 12/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6668 - accuracy: 0.6161 - val_loss: 0.6663 - val_accuracy: 0.6162\nEpoch 13/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6662 - accuracy: 0.6161 - val_loss: 0.6658 - val_accuracy: 0.6162\nEpoch 14/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.6161 - val_loss: 0.6655 - val_accuracy: 0.6162\nEpoch 15/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.6161 - val_loss: 0.6653 - val_accuracy: 0.6162\nEpoch 16/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6655 - accuracy: 0.6161 - val_loss: 0.6651 - val_accuracy: 0.6162\nEpoch 17/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6161 - val_loss: 0.6650 - val_accuracy: 0.6162\nEpoch 18/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6651 - accuracy: 0.6161 - val_loss: 0.6649 - val_accuracy: 0.6162\nEpoch 19/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6647 - val_accuracy: 0.6162\nEpoch 20/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6646 - val_accuracy: 0.6162\nEpoch 21/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6648 - accuracy: 0.6161 - val_loss: 0.6645 - val_accuracy: 0.6162\nEpoch 22/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6647 - accuracy: 0.6161 - val_loss: 0.6644 - val_accuracy: 0.6162\nEpoch 23/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6645 - accuracy: 0.6161 - val_loss: 0.6643 - val_accuracy: 0.6162\nEpoch 24/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6644 - accuracy: 0.6161 - val_loss: 0.6641 - val_accuracy: 0.6162\nEpoch 25/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.6161 - val_loss: 0.6640 - val_accuracy: 0.6162\nEpoch 26/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6641 - accuracy: 0.6161 - val_loss: 0.6639 - val_accuracy: 0.6162\nEpoch 27/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6637 - val_accuracy: 0.6162\nEpoch 28/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6636 - val_accuracy: 0.6162\nEpoch 29/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6637 - accuracy: 0.6161 - val_loss: 0.6634 - val_accuracy: 0.6162\nEpoch 30/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.6161 - val_loss: 0.6633 - val_accuracy: 0.6162\nEpoch 31/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6635 - accuracy: 0.6161 - val_loss: 0.6631 - val_accuracy: 0.6162\nEpoch 32/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6633 - accuracy: 0.6161 - val_loss: 0.6629 - val_accuracy: 0.6162\nEpoch 33/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6630 - accuracy: 0.6161 - val_loss: 0.6627 - val_accuracy: 0.6162\nEpoch 34/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6628 - accuracy: 0.6161 - val_loss: 0.6625 - val_accuracy: 0.6162\nEpoch 35/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6623 - val_accuracy: 0.6162\nEpoch 36/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6621 - val_accuracy: 0.6162\nEpoch 37/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6622 - accuracy: 0.6161 - val_loss: 0.6619 - val_accuracy: 0.6162\nEpoch 38/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6619 - accuracy: 0.6161 - val_loss: 0.6616 - val_accuracy: 0.6162\nEpoch 39/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6618 - accuracy: 0.6161 - val_loss: 0.6614 - val_accuracy: 0.6162\nEpoch 40/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6614 - accuracy: 0.6161 - val_loss: 0.6611 - val_accuracy: 0.6162\nEpoch 41/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6612 - accuracy: 0.6161 - val_loss: 0.6608 - val_accuracy: 0.6162\nEpoch 42/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6610 - accuracy: 0.6161 - val_loss: 0.6605 - val_accuracy: 0.6162\nEpoch 43/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.6161 - val_loss: 0.6601 - val_accuracy: 0.6162\nEpoch 44/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6598 - val_accuracy: 0.6162\nEpoch 45/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6594 - val_accuracy: 0.6162\nEpoch 46/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6595 - accuracy: 0.6161 - val_loss: 0.6590 - val_accuracy: 0.6162\nEpoch 47/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6590 - accuracy: 0.6161 - val_loss: 0.6586 - val_accuracy: 0.6162\nEpoch 48/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6161 - val_loss: 0.6581 - val_accuracy: 0.6162\nEpoch 49/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6580 - accuracy: 0.6161 - val_loss: 0.6576 - val_accuracy: 0.6162\nEpoch 50/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.6161 - val_loss: 0.6571 - val_accuracy: 0.6162\nEpoch 51/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6571 - accuracy: 0.6161 - val_loss: 0.6566 - val_accuracy: 0.6162\nEpoch 52/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6565 - accuracy: 0.6161 - val_loss: 0.6560 - val_accuracy: 0.6162\nEpoch 53/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6161 - val_loss: 0.6553 - val_accuracy: 0.6162\nEpoch 54/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6161 - val_loss: 0.6546 - val_accuracy: 0.6162\nEpoch 55/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6545 - accuracy: 0.6161 - val_loss: 0.6539 - val_accuracy: 0.6162\nEpoch 56/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6542 - accuracy: 0.6161 - val_loss: 0.6531 - val_accuracy: 0.6162\nEpoch 57/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6531 - accuracy: 0.6161 - val_loss: 0.6522 - val_accuracy: 0.6162\nEpoch 58/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6161 - val_loss: 0.6513 - val_accuracy: 0.6162\nEpoch 59/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6510 - accuracy: 0.6161 - val_loss: 0.6503 - val_accuracy: 0.6162\nEpoch 60/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6501 - accuracy: 0.6161 - val_loss: 0.6493 - val_accuracy: 0.6162\nEpoch 61/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6490 - accuracy: 0.6161 - val_loss: 0.6482 - val_accuracy: 0.6162\nEpoch 62/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6480 - accuracy: 0.6161 - val_loss: 0.6469 - val_accuracy: 0.6162\nEpoch 63/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6466 - accuracy: 0.6161 - val_loss: 0.6456 - val_accuracy: 0.6162\nEpoch 64/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6453 - accuracy: 0.6161 - val_loss: 0.6443 - val_accuracy: 0.6162\nEpoch 65/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6439 - accuracy: 0.6161 - val_loss: 0.6428 - val_accuracy: 0.6162\nEpoch 66/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6423 - accuracy: 0.6161 - val_loss: 0.6412 - val_accuracy: 0.6162\nEpoch 67/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6411 - accuracy: 0.6161 - val_loss: 0.6395 - val_accuracy: 0.6162\nEpoch 68/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6393 - accuracy: 0.6161 - val_loss: 0.6376 - val_accuracy: 0.6162\nEpoch 69/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6373 - accuracy: 0.6161 - val_loss: 0.6357 - val_accuracy: 0.6162\nEpoch 70/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6351 - accuracy: 0.6161 - val_loss: 0.6336 - val_accuracy: 0.6162\nEpoch 71/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6330 - accuracy: 0.6161 - val_loss: 0.6313 - val_accuracy: 0.6162\nEpoch 72/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6307 - accuracy: 0.6161 - val_loss: 0.6290 - val_accuracy: 0.6162\nEpoch 73/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6285 - accuracy: 0.6161 - val_loss: 0.6264 - val_accuracy: 0.6162\nEpoch 74/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6258 - accuracy: 0.6161 - val_loss: 0.6239 - val_accuracy: 0.6162\nEpoch 75/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6231 - accuracy: 0.6161 - val_loss: 0.6211 - val_accuracy: 0.6162\nEpoch 76/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6203 - accuracy: 0.6161 - val_loss: 0.6180 - val_accuracy: 0.6162\nEpoch 77/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6161 - val_loss: 0.6150 - val_accuracy: 0.6190\nEpoch 78/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6143 - accuracy: 0.6161 - val_loss: 0.6118 - val_accuracy: 0.6162\nEpoch 79/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6109 - accuracy: 0.6199 - val_loss: 0.6083 - val_accuracy: 0.6218\nEpoch 80/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6074 - accuracy: 0.6273 - val_loss: 0.6048 - val_accuracy: 0.6331\nEpoch 81/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6039 - accuracy: 0.6404 - val_loss: 0.6011 - val_accuracy: 0.6443\nEpoch 82/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6003 - accuracy: 0.6610 - val_loss: 0.5970 - val_accuracy: 0.6667\nEpoch 83/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5962 - accuracy: 0.6798 - val_loss: 0.5932 - val_accuracy: 0.6667\nEpoch 84/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5923 - accuracy: 0.6966 - val_loss: 0.5891 - val_accuracy: 0.7003\nEpoch 85/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7116 - val_loss: 0.5849 - val_accuracy: 0.7087\nEpoch 86/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5843 - accuracy: 0.7172 - val_loss: 0.5807 - val_accuracy: 0.7115\nEpoch 87/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5805 - accuracy: 0.7191 - val_loss: 0.5762 - val_accuracy: 0.7395\nEpoch 88/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7303 - val_loss: 0.5719 - val_accuracy: 0.7395\nEpoch 89/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5713 - accuracy: 0.7378 - val_loss: 0.5674 - val_accuracy: 0.7563\nEpoch 90/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5671 - accuracy: 0.7491 - val_loss: 0.5627 - val_accuracy: 0.7563\nEpoch 91/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7509 - val_loss: 0.5582 - val_accuracy: 0.7563\nEpoch 92/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5582 - accuracy: 0.7659 - val_loss: 0.5537 - val_accuracy: 0.7759\nEpoch 93/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.7640 - val_loss: 0.5492 - val_accuracy: 0.7871\nEpoch 94/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.5499 - accuracy: 0.7640 - val_loss: 0.5447 - val_accuracy: 0.7731\nEpoch 95/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5456 - accuracy: 0.7640 - val_loss: 0.5402 - val_accuracy: 0.7871\nEpoch 96/300\n17/17 [==============================] - 0s 13ms/step - loss: 0.5412 - accuracy: 0.7640 - val_loss: 0.5359 - val_accuracy: 0.7843\nEpoch 97/300\n17/17 [==============================] - 0s 12ms/step - loss: 0.5369 - accuracy: 0.7659 - val_loss: 0.5316 - val_accuracy: 0.7843\nEpoch 98/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.7603 - val_loss: 0.5275 - val_accuracy: 0.7955\nEpoch 99/300\n17/17 [==============================] - 0s 9ms/step - loss: 0.5294 - accuracy: 0.7715 - val_loss: 0.5233 - val_accuracy: 0.8039\nEpoch 100/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7753 - val_loss: 0.5196 - val_accuracy: 0.8039\nEpoch 101/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5217 - accuracy: 0.7734 - val_loss: 0.5158 - val_accuracy: 0.8039\nEpoch 102/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5181 - accuracy: 0.7753 - val_loss: 0.5120 - val_accuracy: 0.8039\nEpoch 103/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5145 - accuracy: 0.7753 - val_loss: 0.5085 - val_accuracy: 0.8011\nEpoch 104/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5113 - accuracy: 0.7715 - val_loss: 0.5049 - val_accuracy: 0.8039\nEpoch 105/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7715 - val_loss: 0.5016 - val_accuracy: 0.8039\nEpoch 106/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7715 - val_loss: 0.4983 - val_accuracy: 0.8039\nEpoch 107/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.7828 - val_loss: 0.4951 - val_accuracy: 0.8095\nEpoch 108/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7921 - val_loss: 0.4921 - val_accuracy: 0.8095\nEpoch 109/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7959 - val_loss: 0.4891 - val_accuracy: 0.8067\nEpoch 110/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7921 - val_loss: 0.4864 - val_accuracy: 0.8067\nEpoch 111/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4919 - accuracy: 0.7940 - val_loss: 0.4840 - val_accuracy: 0.8123\nEpoch 112/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7996 - val_loss: 0.4813 - val_accuracy: 0.8067\nEpoch 113/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7996 - val_loss: 0.4789 - val_accuracy: 0.8067\nEpoch 114/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7996 - val_loss: 0.4767 - val_accuracy: 0.8067\nEpoch 115/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4828 - accuracy: 0.7996 - val_loss: 0.4745 - val_accuracy: 0.8095\nEpoch 116/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4808 - accuracy: 0.7996 - val_loss: 0.4724 - val_accuracy: 0.8095\nEpoch 117/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4789 - accuracy: 0.7996 - val_loss: 0.4706 - val_accuracy: 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0.8179\nEpoch 173/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4420 - accuracy: 0.8240 - val_loss: 0.4308 - val_accuracy: 0.8123\nEpoch 174/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8240 - val_loss: 0.4305 - val_accuracy: 0.8123\nEpoch 175/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8179\nEpoch 176/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8123\nEpoch 177/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4297 - val_accuracy: 0.8151\nEpoch 178/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4410 - accuracy: 0.8258 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 179/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4294 - val_accuracy: 0.8151\nEpoch 180/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4402 - accuracy: 0.8240 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 181/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4400 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 182/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4397 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 183/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4395 - accuracy: 0.8240 - val_loss: 0.4286 - val_accuracy: 0.8151\nEpoch 184/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4393 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8123\nEpoch 185/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4392 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 186/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4389 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 187/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4390 - accuracy: 0.8240 - val_loss: 0.4278 - val_accuracy: 0.8123\nEpoch 188/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4279 - val_accuracy: 0.8151\nEpoch 189/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8151\nEpoch 190/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 191/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8240 - val_loss: 0.4274 - val_accuracy: 0.8151\nEpoch 192/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 193/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8221 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 194/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4375 - accuracy: 0.8240 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 195/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8240 - val_loss: 0.4272 - val_accuracy: 0.8151\nEpoch 196/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4370 - accuracy: 0.8240 - val_loss: 0.4271 - val_accuracy: 0.8151\nEpoch 197/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4368 - accuracy: 0.8240 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 198/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4367 - accuracy: 0.8221 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 199/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 0.8240 - val_loss: 0.4269 - val_accuracy: 0.8151\nEpoch 200/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4364 - accuracy: 0.8240 - val_loss: 0.4265 - val_accuracy: 0.8151\nEpoch 201/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4363 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 202/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4362 - accuracy: 0.8202 - val_loss: 0.4262 - val_accuracy: 0.8123\nEpoch 203/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4359 - accuracy: 0.8258 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 204/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4357 - accuracy: 0.8240 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 205/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8221 - val_loss: 0.4261 - val_accuracy: 0.8151\nEpoch 206/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 207/300\n17/17 [==============================] - 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[==============================] - 0s 5ms/step - loss: 0.4347 - accuracy: 0.8240 - val_loss: 0.4258 - val_accuracy: 0.8151\nEpoch 215/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 216/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4344 - accuracy: 0.8221 - val_loss: 0.4251 - val_accuracy: 0.8123\nEpoch 217/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4255 - val_accuracy: 0.8151\nEpoch 218/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 219/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4339 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 220/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 221/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 222/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4252 - val_accuracy: 0.8151\nEpoch 223/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4335 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 224/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4332 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 225/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 226/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4330 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 227/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 228/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 229/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 230/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4250 - val_accuracy: 0.8151\nEpoch 231/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4325 - accuracy: 0.8240 - val_loss: 0.4249 - val_accuracy: 0.8151\nEpoch 232/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 233/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4322 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 234/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 235/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 236/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 237/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 238/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 239/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 240/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 241/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 242/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4313 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 243/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 244/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 245/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8151\nEpoch 246/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8221 - val_loss: 0.4246 - val_accuracy: 0.8179\nEpoch 247/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 248/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 249/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 250/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4307 - accuracy: 0.8221 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 251/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8151\nEpoch 252/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8221 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 253/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 254/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 255/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4302 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 256/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 257/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 258/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 259/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 260/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 261/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 262/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 263/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 264/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 265/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 266/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 267/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 268/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 269/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 270/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 271/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 272/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 273/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 274/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 275/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4289 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 276/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 277/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4288 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 278/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 279/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 280/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 281/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 282/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 283/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 284/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 285/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 286/300\n17/17 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 287/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 288/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4282 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 289/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 290/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 291/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 292/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 293/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 294/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 295/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 296/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 297/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 298/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 299/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 300/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_train_accuracy = model.evaluate(X_train, y_train_encode)\nprint('Accuracy: %.4f' % (ann_train_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.756511Z","iopub.execute_input":"2023-02-01T15:00:28.757274Z","iopub.status.idle":"2023-02-01T15:00:28.874523Z","shell.execute_reply.started":"2023-02-01T15:00:28.757226Z","shell.execute_reply":"2023-02-01T15:00:28.873360Z"},"trusted":true},"execution_count":340,"outputs":[{"name":"stdout","text":"17/17 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221\nAccuracy: 0.8221\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_valid_accuracy = model.evaluate(X_valid, y_valid_encode)\nprint('Accuracy: %.4f' % (ann_valid_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.876387Z","iopub.execute_input":"2023-02-01T15:00:28.877663Z","iopub.status.idle":"2023-02-01T15:00:28.990657Z","shell.execute_reply.started":"2023-02-01T15:00:28.877614Z","shell.execute_reply":"2023-02-01T15:00:28.989441Z"},"trusted":true},"execution_count":341,"outputs":[{"name":"stdout","text":"12/12 [==============================] - 0s 2ms/step - loss: 0.4238 - accuracy: 0.8179\nAccuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ","metadata":{}},{"cell_type":"code","source":"\ny_pred = model.predict(X_train)\nY_pred = np.argmax(model.predict(X_train),axis=1)\ncm = confusion_matrix(y_train, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.993841Z","iopub.execute_input":"2023-02-01T15:00:28.994285Z","iopub.status.idle":"2023-02-01T15:00:29.270957Z","shell.execute_reply.started":"2023-02-01T15:00:28.994240Z","shell.execute_reply":"2023-02-01T15:00:29.269885Z"},"trusted":true},"execution_count":342,"outputs":[{"execution_count":342,"output_type":"execute_result","data":{"text/plain":"array([[304, 25],\n [ 70, 135]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.272190Z","iopub.execute_input":"2023-02-01T15:00:29.273301Z","iopub.status.idle":"2023-02-01T15:00:29.281676Z","shell.execute_reply.started":"2023-02-01T15:00:29.273267Z","shell.execute_reply":"2023-02-01T15:00:29.280517Z"},"trusted":true},"execution_count":343,"outputs":[{"name":"stdout","text":"Accuracy : 0.8220973782771536\nMisclassfication : 0.17790262172284643\nSensitivivity : 0.9240121580547113\nSpecificity : 0.6585365853658537\n","output_type":"stream"}]},{"cell_type":"code","source":"\ny_pred = model.predict(X_valid)\nY_pred = np.argmax(model.predict(X_valid),axis=1)\ncm = confusion_matrix(y_valid, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.283243Z","iopub.execute_input":"2023-02-01T15:00:29.283612Z","iopub.status.idle":"2023-02-01T15:00:29.451759Z","shell.execute_reply.started":"2023-02-01T15:00:29.283566Z","shell.execute_reply":"2023-02-01T15:00:29.450417Z"},"trusted":true},"execution_count":344,"outputs":[{"execution_count":344,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.453236Z","iopub.execute_input":"2023-02-01T15:00:29.453610Z","iopub.status.idle":"2023-02-01T15:00:29.461774Z","shell.execute_reply.started":"2023-02-01T15:00:29.453579Z","shell.execute_reply":"2023-02-01T15:00:29.460520Z"},"trusted":true},"execution_count":345,"outputs":[{"name":"stdout","text":"Accuracy : 0.8179271708683473\nMisclassfication : 0.18207282913165265\nSensitivivity : 0.9363636363636364\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.463445Z","iopub.execute_input":"2023-02-01T15:00:29.463787Z","iopub.status.idle":"2023-02-01T15:00:29.472285Z","shell.execute_reply.started":"2023-02-01T15:00:29.463752Z","shell.execute_reply":"2023-02-01T15:00:29.471294Z"},"trusted":true},"execution_count":346,"outputs":[]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_train),axis=1)\n\nann_pred = X_train.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_train_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.473634Z","iopub.execute_input":"2023-02-01T15:00:29.474711Z","iopub.status.idle":"2023-02-01T15:00:29.593403Z","shell.execute_reply.started":"2023-02-01T15:00:29.474675Z","shell.execute_reply":"2023-02-01T15:00:29.592290Z"},"trusted":true},"execution_count":347,"outputs":[{"execution_count":347,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n844 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n316 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n768 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n255 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n130 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n844 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n316 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n768 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n255 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n130 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n\n ann_y_pred PassengerId \n844 0 845.0 \n316 1 317.0 \n768 0 769.0 \n255 1 256.0 \n130 0 131.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
84401.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00845.0
31600.01.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01317.0
76800.01.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00769.0
25521.00.00.00.00.00.00.00.00.0...1.01.00.00.00.00.01.00.01256.0
13001.00.00.00.00.00.00.01.00.0...0.01.00.00.00.00.01.00.00131.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(ann_pred[[\"PassengerId\", \"ann_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.598604Z","iopub.execute_input":"2023-02-01T15:00:29.599029Z","iopub.status.idle":"2023-02-01T15:00:29.628142Z","shell.execute_reply.started":"2023-02-01T15:00:29.598995Z","shell.execute_reply":"2023-02-01T15:00:29.627332Z"},"trusted":true},"execution_count":348,"outputs":[{"execution_count":348,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 NaN \n2 0.0 NaN 0.0 \n3 NaN 1.0 NaN \n4 NaN 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_valid),axis=1)\nann_pred = X_valid.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_valid_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.629335Z","iopub.execute_input":"2023-02-01T15:00:29.629823Z","iopub.status.idle":"2023-02-01T15:00:29.739371Z","shell.execute_reply.started":"2023-02-01T15:00:29.629791Z","shell.execute_reply":"2023-02-01T15:00:29.738281Z"},"trusted":true},"execution_count":349,"outputs":[{"execution_count":349,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n369 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n541 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n196 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n810 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n427 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n369 0.0 ... 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n541 0.0 ... 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n196 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n810 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n427 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n\n ann_y_pred PassengerId \n369 1 370.0 \n541 1 542.0 \n196 0 197.0 \n810 0 811.0 \n427 1 428.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
36901.00.00.00.00.00.00.00.00.0...1.00.00.01.00.00.01.00.01370.0
54120.00.00.00.01.00.00.00.00.0...1.01.00.00.00.01.00.00.01542.0
19601.00.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00197.0
81001.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00811.0
42701.00.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01428.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(ann_pred.PassengerId), \"ann_y_pred\"] = ann_pred[\"ann_y_pred\"]\nresults_train.drop(\"rf_y_pred_y\", axis = 1)\nresults_train.drop(\"rf_y_pred_x\", axis = 1)\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.740869Z","iopub.execute_input":"2023-02-01T15:00:29.741291Z","iopub.status.idle":"2023-02-01T15:00:29.771294Z","shell.execute_reply.started":"2023-02-01T15:00:29.741249Z","shell.execute_reply":"2023-02-01T15:00:29.770286Z"},"trusted":true},"execution_count":350,"outputs":[{"execution_count":350,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 1.0 \n2 0.0 NaN 0.0 \n3 NaN 1.0 1.0 \n4 NaN 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Overall, the number of survivors misclassified were greater than misclassified passengers who perished. The next step is to identify those passengers to attempt to find the source of the misclassifcation. So far the lowest number of misclassified passengers who perished. ","metadata":{}},{"cell_type":"markdown","source":"## Predict test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\ny_pred = y_pred.argmax(1)\nann_pred = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"],\n \"ann_y_pred\" : y_pred})\nann_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.772619Z","iopub.execute_input":"2023-02-01T15:00:29.772938Z","iopub.status.idle":"2023-02-01T15:00:29.875387Z","shell.execute_reply.started":"2023-02-01T15:00:29.772908Z","shell.execute_reply":"2023-02-01T15:00:29.874334Z"},"trusted":true},"execution_count":351,"outputs":[{"execution_count":351,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.876431Z","iopub.execute_input":"2023-02-01T15:00:29.876729Z","iopub.status.idle":"2023-02-01T15:00:29.882726Z","shell.execute_reply.started":"2023-02-01T15:00:29.876701Z","shell.execute_reply":"2023-02-01T15:00:29.881480Z"},"trusted":true},"execution_count":352,"outputs":[]},{"cell_type":"code","source":"ann_pred[[\"PassengerId\",\"ann_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.884219Z","iopub.execute_input":"2023-02-01T15:00:29.884571Z","iopub.status.idle":"2023-02-01T15:00:29.900340Z","shell.execute_reply.started":"2023-02-01T15:00:29.884540Z","shell.execute_reply":"2023-02-01T15:00:29.899599Z"},"trusted":true},"execution_count":353,"outputs":[{"execution_count":353,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(ann_pred[[\"PassengerId\",\"ann_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.901356Z","iopub.execute_input":"2023-02-01T15:00:29.901844Z","iopub.status.idle":"2023-02-01T15:00:29.931394Z","shell.execute_reply.started":"2023-02-01T15:00:29.901814Z","shell.execute_reply":"2023-02-01T15:00:29.929969Z"},"trusted":true},"execution_count":354,"outputs":[{"execution_count":354,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred \n0 0.0 0.0 0.0 0.0 0 \n1 1.0 0.0 0.0 0.0 0 \n2 0.0 0.0 0.0 0.0 0 \n3 0.0 0.0 0.0 0.0 0 \n4 0.0 1.0 1.0 1.0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_predann_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.00
1893.03.02.01.411765-0.3161762.01.01.00.00.00.00
2894.02.01.02.588235-0.2021843.00.00.00.00.00.00
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.00
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.00
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Findings","metadata":{}},{"cell_type":"markdown","source":"We compile all the results in a basic structure. We discover that the logistic regression has achieved the highest accuracy on the validation datasets. ANN came close. Both methods appear not to overfit to the training dataset.","metadata":{}},{"cell_type":"code","source":"log_reg_results = {\n \"method\": \"Logistic regression\",\n \"training_accurary\": log_reg_score_train,\n \"valid_accuracy\": log_reg_score_valid\n}\n\nknn_results = {\n \"method\": \"KNN\",\n \"training_accurary\": knn_train_score,\n \"valid_accuracy\": knn_valid_score\n}\n\nclf_results = {\n \"method\": \"decision trees\",\n \"training_accurary\": clf_train_score,\n \"valid_accuracy\": clf_valid_score\n}\n\nrf_results = {\n \"method\": \"Random Forrest\",\n \"training_accurary\": rf_train_score,\n \"valid_accuracy\": rf_valid_score\n}\n\nann_results = {\n \"method\": \"ANN\",\n \"training_accurary\": ann_train_accuracy,\n \"valid_accuracy\": ann_valid_accuracy\n}\n\nresults = [log_reg_results, knn_results, clf_results, rf_results, ann_results]\nresults","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.932994Z","iopub.execute_input":"2023-02-01T15:00:29.933497Z","iopub.status.idle":"2023-02-01T15:00:29.947698Z","shell.execute_reply.started":"2023-02-01T15:00:29.933454Z","shell.execute_reply":"2023-02-01T15:00:29.946484Z"},"trusted":true},"execution_count":355,"outputs":[{"execution_count":355,"output_type":"execute_result","data":{"text/plain":"[{'method': 'Logistic regression',\n 'training_accurary': 0.7921348314606742,\n 'valid_accuracy': 0.8207282913165266},\n {'method': 'KNN',\n 'training_accurary': 0.8258426966292135,\n 'valid_accuracy': 0.7871148459383753},\n {'method': 'decision trees',\n 'training_accurary': 0.9082397003745318,\n 'valid_accuracy': 0.8151260504201681},\n {'method': 'Random Forrest',\n 'training_accurary': 0.8801498127340824,\n 'valid_accuracy': 0.8067226890756303},\n {'method': 'ANN',\n 'training_accurary': 0.8220973610877991,\n 'valid_accuracy': 0.8179271817207336}]"},"metadata":{}}]},{"cell_type":"markdown","source":"Less than 10% errors of passengers have been misclassified, when we compare all predictions together. So, it may be possible to identify some rules to increase accuracy. Nonetheless, these rules may also decrease the accuracy. So, a fine balance needs to be found. ","metadata":{}},{"cell_type":"code","source":"results_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.949130Z","iopub.execute_input":"2023-02-01T15:00:29.949749Z","iopub.status.idle":"2023-02-01T15:00:29.958469Z","shell.execute_reply.started":"2023-02-01T15:00:29.949702Z","shell.execute_reply":"2023-02-01T15:00:29.957602Z"},"trusted":true},"execution_count":356,"outputs":[{"execution_count":356,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked',\n 'fam_members', 'y', 'lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred']\nresults_train['merged_pred'] = results_train.loc[:,cols].apply(\n lambda x: ','.join(x.dropna().astype(str)),\n axis=1\n)\n\nresults_train['y_found'] = results_train.apply(lambda x: str(x.y) in x.merged_pred, axis=1)\nresults_train.drop(\"merged_pred\", axis = 1, inplace = True)\nresults_train.groupby(\"y_found\").count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.959997Z","iopub.execute_input":"2023-02-01T15:00:29.960444Z","iopub.status.idle":"2023-02-01T15:00:30.142912Z","shell.execute_reply.started":"2023-02-01T15:00:29.960402Z","shell.execute_reply":"2023-02-01T15:00:30.141887Z"},"trusted":true},"execution_count":357,"outputs":[{"execution_count":357,"output_type":"execute_result","data":{"text/plain":"y_found\nFalse 0.075196\nTrue 0.924804\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The set of passengers misclassified by all methods appear to have a lower expected fares and much more compact spread of fares. The median passenger class of misclassified passenger appear to be higher than those correctly classified. Both observations contractict each other and suggests some of fares being close to each other between passenger classes may be contributing in misclassifying passengers. \n\nThe misclassified passengers appears to be most women and their age appear to be older than the ones correctly classified by one method. The distribution to gender appears to match the overall observations for correctly classified passengers. Nonetheless, it is worth pointing out some of ages were inputed based on the number of siblings, spouse and parents aboard. This simple method of inputation may have impacted the classifiers; more research should be made to validate or improve inputting the missing information. \n\nOther aspects in the data may lead to misclassification.","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == False,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.144511Z","iopub.execute_input":"2023-02-01T15:00:30.145249Z","iopub.status.idle":"2023-02-01T15:00:30.180088Z","shell.execute_reply.started":"2023-02-01T15:00:30.145205Z","shell.execute_reply":"2023-02-01T15:00:30.178959Z"},"trusted":true},"execution_count":358,"outputs":[{"execution_count":358,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 67.000000 67.000000 67.000000 67.000000 67.000000 67.000000\nmean 2.149254 1.104478 0.129736 0.423026 0.343284 2.537313\nstd 0.908774 0.308188 0.721256 1.008879 0.844810 0.840785\nmin 1.000000 1.000000 -1.076923 -0.626005 0.000000 2.000000\n25% 1.000000 1.000000 -0.269231 -0.282777 0.000000 2.000000\n50% 2.000000 1.000000 0.000000 -0.062981 0.000000 2.000000\n75% 3.000000 1.000000 0.230769 0.694936 0.500000 3.000000\nmax 3.000000 2.000000 2.461538 3.318594 6.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count67.00000067.00000067.00000067.00000067.00000067.000000
mean2.1492541.1044780.1297360.4230260.3432842.537313
std0.9087740.3081880.7212561.0088790.8448100.840785
min1.0000001.000000-1.076923-0.6260050.0000002.000000
25%1.0000001.000000-0.269231-0.2827770.0000002.000000
50%2.0000001.0000000.000000-0.0629810.0000002.000000
75%3.0000001.0000000.2307690.6949360.5000003.000000
max3.0000002.0000002.4615383.3185946.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.182172Z","iopub.execute_input":"2023-02-01T15:00:30.183279Z","iopub.status.idle":"2023-02-01T15:00:30.213352Z","shell.execute_reply.started":"2023-02-01T15:00:30.183235Z","shell.execute_reply":"2023-02-01T15:00:30.212605Z"},"trusted":true},"execution_count":359,"outputs":[{"execution_count":359,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 824.000000 824.000000 824.000000 824.000000 824.000000 824.000000\nmean 2.321602 1.372573 -0.030604 0.796855 0.950243 2.455097\nstd 0.829129 0.483783 1.018913 2.217409 1.652334 0.790541\nmin 1.000000 1.000000 -2.275385 -0.626005 0.000000 1.000000\n25% 2.000000 1.000000 -0.615385 -0.284041 0.000000 2.000000\n50% 3.000000 1.000000 0.000000 0.001984 0.000000 2.000000\n75% 3.000000 2.000000 0.384615 0.719569 1.000000 3.000000\nmax 3.000000 2.000000 3.846154 21.562738 10.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count824.000000824.000000824.000000824.000000824.000000824.000000
mean2.3216021.372573-0.0306040.7968550.9502432.455097
std0.8291290.4837831.0189132.2174091.6523340.790541
min1.0000001.000000-2.275385-0.6260050.0000001.000000
25%2.0000001.000000-0.615385-0.2840410.0000002.000000
50%3.0000001.0000000.0000000.0019840.0000002.000000
75%3.0000002.0000000.3846150.7195691.0000003.000000
max3.0000002.0000003.84615421.56273810.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"incorrect = results_train.loc[results_train[\"y_found\"] == False,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == False,:].groupby(\"Sex\").count()[\"PassengerId\"]/incorrect","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.214494Z","iopub.execute_input":"2023-02-01T15:00:30.215455Z","iopub.status.idle":"2023-02-01T15:00:30.229684Z","shell.execute_reply.started":"2023-02-01T15:00:30.215404Z","shell.execute_reply":"2023-02-01T15:00:30.228567Z"},"trusted":true},"execution_count":360,"outputs":[{"execution_count":360,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.895522\n2.0 0.104478\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"correct = results_train.loc[results_train[\"y_found\"] == True,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == True,:].groupby(\"Sex\").count()[\"PassengerId\"]/correct","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.230783Z","iopub.execute_input":"2023-02-01T15:00:30.231538Z","iopub.status.idle":"2023-02-01T15:00:30.246006Z","shell.execute_reply.started":"2023-02-01T15:00:30.231506Z","shell.execute_reply":"2023-02-01T15:00:30.244736Z"},"trusted":true},"execution_count":361,"outputs":[{"execution_count":361,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.627427\n2.0 0.372573\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We analyse differences between each method on the testing and training data set. We add all the predictions to identify the passenger the classifier could not agree with. So, a total of 0 or 5 suggests all the classifiers have either identify passengers as survivor or not. Values in the range [1,4] indicates some disagreements in classification. Some methodologies appears to correclty classify passengers with at least one method.","metadata":{}},{"cell_type":"code","source":"results_train[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.247816Z","iopub.execute_input":"2023-02-01T15:00:30.248276Z","iopub.status.idle":"2023-02-01T15:00:30.473510Z","shell.execute_reply.started":"2023-02-01T15:00:30.248230Z","shell.execute_reply":"2023-02-01T15:00:30.472297Z"},"trusted":true},"execution_count":362,"outputs":[{"execution_count":362,"output_type":"execute_result","data":{"text/plain":"count 67.000000\nmean 0.447761\nstd 1.438471\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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3bXhOeAm5L843hHGr2qOtV9XATuY/my8FC0EHBv2d/kun/Quws4WVWfGvc8GyHJa5Ns7x6/guV/pP/hWIcasar6WFXtrKpdLP85/lpV/cGYxxqpJNu6f5gnyTbgncDQ3l12yQe8qp4Hzt+yfxK4Z8S37I9dki8A3wTekOSZJPvGPdOI3Qh8kOUzske7X+8a91AjtgN4KMn3WT5JebCqJuJtdRNmGvhGku8B3wK+XFVfHdaLX/JvI5Qkre6SPwOXJK3OgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXqfwEOtkCGTWOUBQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.475100Z","iopub.execute_input":"2023-02-01T15:00:30.475447Z","iopub.status.idle":"2023-02-01T15:00:30.691199Z","shell.execute_reply.started":"2023-02-01T15:00:30.475417Z","shell.execute_reply":"2023-02-01T15:00:30.690153Z"},"trusted":true},"execution_count":363,"outputs":[{"execution_count":363,"output_type":"execute_result","data":{"text/plain":"count 824.000000\nmean 1.577670\nstd 2.058981\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We explore how the techniques may predict differently and but accurately surviving the accident. \n\nKNN misclassified the most passengers who perished. Logistic regression and Random Tree classifier has the higest accuracy; both of them could be influencing the most the prediction, when only one classifier suggests a passenger has survived. ","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 1)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.692627Z","iopub.execute_input":"2023-02-01T15:00:30.692946Z","iopub.status.idle":"2023-02-01T15:00:30.712415Z","shell.execute_reply.started":"2023-02-01T15:00:30.692916Z","shell.execute_reply":"2023-02-01T15:00:30.711228Z"},"trusted":true},"execution_count":364,"outputs":[{"execution_count":364,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 3\n 1.0 0.0 0.0 3\n 1.0 0.0 0.0 0.0 10\n 1.0 0.0 0.0 0.0 0.0 3\n1.0 0.0 0.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 5\n 1.0 0.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 0.0 4\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 4)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.714064Z","iopub.execute_input":"2023-02-01T15:00:30.714806Z","iopub.status.idle":"2023-02-01T15:00:30.734553Z","shell.execute_reply.started":"2023-02-01T15:00:30.714762Z","shell.execute_reply":"2023-02-01T15:00:30.733458Z"},"trusted":true},"execution_count":365,"outputs":[{"execution_count":365,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 1.0 1.0 1.0 2\n 1.0 0.0 1.0 1.0 1.0 1\n1.0 0.0 1.0 1.0 1.0 1.0 6\n 1.0 0.0 1.0 1.0 1.0 1\n 1.0 0.0 1.0 1.0 2\n 1.0 0.0 1.0 2\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"A combination of Logistic regression and ANN may identify some survivors, when other methods do not. KNN in combination with another classifier may misclassify passengers who perished.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 2)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.743560Z","iopub.execute_input":"2023-02-01T15:00:30.743975Z","iopub.status.idle":"2023-02-01T15:00:30.762208Z","shell.execute_reply.started":"2023-02-01T15:00:30.743943Z","shell.execute_reply":"2023-02-01T15:00:30.761101Z"},"trusted":true},"execution_count":366,"outputs":[{"execution_count":366,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 1.0 1.0 0.0 4\n 1.0 0.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 0.0 2\n 1.0 1.0 0.0 0.0 0.0 5\n1.0 0.0 0.0 0.0 1.0 1.0 1\n 1.0 1.0 0.0 2\n 1.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 3)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.763835Z","iopub.execute_input":"2023-02-01T15:00:30.764145Z","iopub.status.idle":"2023-02-01T15:00:30.780211Z","shell.execute_reply.started":"2023-02-01T15:00:30.764116Z","shell.execute_reply":"2023-02-01T15:00:30.779475Z"},"trusted":true},"execution_count":367,"outputs":[{"execution_count":367,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 0.0 1.0 1.0 1\n 1.0 0.0 0.0 1.0 1.0 1\n 1.0 0.0 1.0 0.0 1\n1.0 0.0 1.0 1.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 1.0 0.0 1.0 1\n 1.0 0.0 0.0 1.0 2\n 1.0 0.0 3\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 5)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.781542Z","iopub.execute_input":"2023-02-01T15:00:30.781830Z","iopub.status.idle":"2023-02-01T15:00:30.798259Z","shell.execute_reply.started":"2023-02-01T15:00:30.781802Z","shell.execute_reply":"2023-02-01T15:00:30.796868Z"},"trusted":true},"execution_count":368,"outputs":[{"execution_count":368,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n1.0 1.0 1.0 1.0 1.0 1.0 65\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.799833Z","iopub.execute_input":"2023-02-01T15:00:30.800231Z","iopub.status.idle":"2023-02-01T15:00:30.808910Z","shell.execute_reply.started":"2023-02-01T15:00:30.800191Z","shell.execute_reply":"2023-02-01T15:00:30.808105Z"},"trusted":true},"execution_count":369,"outputs":[{"execution_count":369,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.809985Z","iopub.execute_input":"2023-02-01T15:00:30.810331Z","iopub.status.idle":"2023-02-01T15:00:30.821387Z","shell.execute_reply.started":"2023-02-01T15:00:30.810281Z","shell.execute_reply":"2023-02-01T15:00:30.820530Z"},"trusted":true},"execution_count":370,"outputs":[{"execution_count":370,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.822809Z","iopub.execute_input":"2023-02-01T15:00:30.823089Z","iopub.status.idle":"2023-02-01T15:00:30.834693Z","shell.execute_reply.started":"2023-02-01T15:00:30.823062Z","shell.execute_reply":"2023-02-01T15:00:30.833613Z"},"trusted":true},"execution_count":371,"outputs":[{"execution_count":371,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.sum_pred.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:01:14.796405Z","iopub.execute_input":"2023-02-01T15:01:14.796794Z","iopub.status.idle":"2023-02-01T15:01:14.805737Z","shell.execute_reply.started":"2023-02-01T15:01:14.796762Z","shell.execute_reply":"2023-02-01T15:01:14.804627Z"},"trusted":true},"execution_count":377,"outputs":[{"execution_count":377,"output_type":"execute_result","data":{"text/plain":"0.0 0.576880\n5.0 0.205387\n1.0 0.079686\n2.0 0.062851\n3.0 0.040404\n4.0 0.034792\nName: sum_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"}},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:36:25.295643Z","iopub.execute_input":"2023-02-01T15:36:25.296169Z","iopub.status.idle":"2023-02-01T15:36:25.314079Z","shell.execute_reply.started":"2023-02-01T15:36:25.296132Z","shell.execute_reply":"2023-02-01T15:36:25.312932Z"}},"execution_count":412,"outputs":[{"execution_count":412,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Are they any particular features that may have been picked up by each classifier?","metadata":{}},{"cell_type":"markdown","source":"### All classifiers agrees with the survival predictions\n\nWe found out that approximately 70% of the passengers who perished have been correclty classified by all the classifiers in agreement. But only, 20% of survivors have been correctly classified. Approximately 70% of the observations made in the training datasets have been correct and all the classifiers agree.","metadata":{}},{"cell_type":"code","source":"filter_rows = ((results_train[\"sum_pred\"] == 0.0) & (results_train[\"y\"] == 0))\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:06.719133Z","iopub.execute_input":"2023-02-01T15:45:06.719636Z","iopub.status.idle":"2023-02-01T15:45:06.733253Z","shell.execute_reply.started":"2023-02-01T15:45:06.719598Z","shell.execute_reply":"2023-02-01T15:45:06.732170Z"},"trusted":true},"execution_count":413,"outputs":[{"execution_count":413,"output_type":"execute_result","data":{"text/plain":"50.841750841750844"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"sum_pred\"] == 5.0) & (results_train[\"y\"] == 1)\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:07.933943Z","iopub.execute_input":"2023-02-01T15:45:07.935099Z","iopub.status.idle":"2023-02-01T15:45:07.947554Z","shell.execute_reply.started":"2023-02-01T15:45:07.935043Z","shell.execute_reply":"2023-02-01T15:45:07.946375Z"},"trusted":true},"execution_count":414,"outputs":[{"execution_count":414,"output_type":"execute_result","data":{"text/plain":"19.865319865319865"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"},"trusted":true},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:45.372534Z","iopub.execute_input":"2023-02-01T15:45:45.372921Z","iopub.status.idle":"2023-02-01T15:45:45.388445Z","shell.execute_reply.started":"2023-02-01T15:45:45.372891Z","shell.execute_reply":"2023-02-01T15:45:45.387062Z"},"trusted":true},"execution_count":415,"outputs":[{"execution_count":415,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## The classifiers disagree with each others on the survival predictions ?\n\nDecision Tree classifiers appears to have classified correctly the most passengers, when disagreements between classifiers exists. \n\nWe calculate the proportion of correct predictions, when some classifiers disagree. We found out that Decision tree appears to predict the most correct passengers who survive or perish the accident.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nno_correct = results_train.loc[filter_rows, :].shape[0]\nno_incorrect = results_train.loc[filter_rows, :].shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:30.920933Z","iopub.execute_input":"2023-02-01T16:00:30.921353Z","iopub.status.idle":"2023-02-01T16:00:30.932343Z","shell.execute_reply.started":"2023-02-01T16:00:30.921303Z","shell.execute_reply":"2023-02-01T16:00:30.930975Z"},"trusted":true},"execution_count":433,"outputs":[]},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.lr_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.369868Z","iopub.execute_input":"2023-02-01T16:00:32.370576Z","iopub.status.idle":"2023-02-01T16:00:32.381927Z","shell.execute_reply.started":"2023-02-01T16:00:32.370537Z","shell.execute_reply":"2023-02-01T16:00:32.381022Z"},"trusted":true},"execution_count":434,"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"44.329896907216494"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.knn_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.853276Z","iopub.execute_input":"2023-02-01T16:00:32.854476Z","iopub.status.idle":"2023-02-01T16:00:32.868855Z","shell.execute_reply.started":"2023-02-01T16:00:32.854418Z","shell.execute_reply":"2023-02-01T16:00:32.867407Z"},"trusted":true},"execution_count":435,"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"47.42268041237113"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.ann_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:33.395939Z","iopub.execute_input":"2023-02-01T16:00:33.396354Z","iopub.status.idle":"2023-02-01T16:00:33.410583Z","shell.execute_reply.started":"2023-02-01T16:00:33.396294Z","shell.execute_reply":"2023-02-01T16:00:33.409408Z"},"trusted":true},"execution_count":436,"outputs":[{"execution_count":436,"output_type":"execute_result","data":{"text/plain":"52.0618556701031"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.clf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:34.195555Z","iopub.execute_input":"2023-02-01T16:00:34.196776Z","iopub.status.idle":"2023-02-01T16:00:34.208545Z","shell.execute_reply.started":"2023-02-01T16:00:34.196733Z","shell.execute_reply":"2023-02-01T16:00:34.207295Z"},"trusted":true},"execution_count":437,"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"75.25773195876289"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.rf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:35.044699Z","iopub.execute_input":"2023-02-01T16:00:35.045127Z","iopub.status.idle":"2023-02-01T16:00:35.057811Z","shell.execute_reply.started":"2023-02-01T16:00:35.045090Z","shell.execute_reply":"2023-02-01T16:00:35.056488Z"},"trusted":true},"execution_count":438,"outputs":[{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"25.257731958762886"},"metadata":{}}]},{"cell_type":"markdown","source":"We change the predictions, that has been mispredicted by at least one classifier.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_train.loc[filter_rows, \"y\"] = results_train.clf_y_pred\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:38:03.184402Z","iopub.execute_input":"2023-02-01T16:38:03.184812Z","iopub.status.idle":"2023-02-01T16:38:03.191812Z","shell.execute_reply.started":"2023-02-01T16:38:03.184781Z","shell.execute_reply":"2023-02-01T16:38:03.191010Z"},"trusted":true},"execution_count":462,"outputs":[]},{"cell_type":"markdown","source":"The accuracy has been increased by a considerable level of accuracy. ","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train.Survived == results_train.y,:].count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:40:10.552687Z","iopub.execute_input":"2023-02-01T16:40:10.553066Z","iopub.status.idle":"2023-02-01T16:40:10.564469Z","shell.execute_reply.started":"2023-02-01T16:40:10.553036Z","shell.execute_reply":"2023-02-01T16:40:10.563190Z"},"trusted":true},"execution_count":467,"outputs":[{"execution_count":467,"output_type":"execute_result","data":{"text/plain":"0.9461279461279462"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying to results test\n\nThe distribution appears the be very similar as the training dataset.","metadata":{}},{"cell_type":"markdown","source":"__Testing dataset:__","metadata":{}},{"cell_type":"code","source":"results_test[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_test.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_test.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:04.962156Z","iopub.execute_input":"2023-02-01T16:28:04.962921Z","iopub.status.idle":"2023-02-01T16:28:05.177598Z","shell.execute_reply.started":"2023-02-01T16:28:04.962882Z","shell.execute_reply":"2023-02-01T16:28:05.176388Z"},"trusted":true},"execution_count":459,"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 1.590909\nstd 2.078233\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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dataset:__","metadata":{}},{"cell_type":"code","source":"results_train.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:10.931421Z","iopub.execute_input":"2023-02-01T16:28:10.931875Z","iopub.status.idle":"2023-02-01T16:28:11.153259Z","shell.execute_reply.started":"2023-02-01T16:28:10.931840Z","shell.execute_reply":"2023-02-01T16:28:11.152336Z"},"trusted":true},"execution_count":460,"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.492705\nstd 2.040242\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 3.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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z5aVb8YdT3DVlW/q6oLmb3D++Ik3U7DJXkncLyqDo26lhX21qq6iNkn6F7Tpl0HZjWHu485GBNt3vlrwK1V9fVR17OSquoZ4F5g24hLGaZLgXe1Oeh9wGVJ/nG0JQ1fVR1r78eB25mdah6Y1RzuPuZgDLR/XLwZeLSqPjPqelZCknOSbGjLZzJ70cAPR1rUEFXVJ6tqU1VtYfZ3fE9V/cWIyxqqJOvaBQIkWQe8HRjoVXCrNtyr6lngucccPArcNuTHHIxcki8D/wa8LsnRJDtGXdMKuBT4ALNncw+015WjLmrINgL3JnmQ2ZOYu6tqLC4PHCMTwH1JfgB8B7irqr45yA9YtZdCSpJOb9WeuUuSTs9wl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36PzFqarrIVm2TAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_test.loc[filter_rows, \"y\"] = results_test.clf_y_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:41:15.101164Z","iopub.execute_input":"2023-02-01T16:41:15.101563Z","iopub.status.idle":"2023-02-01T16:41:15.110450Z","shell.execute_reply.started":"2023-02-01T16:41:15.101523Z","shell.execute_reply":"2023-02-01T16:41:15.109235Z"},"trusted":true},"execution_count":468,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:46:41.923470Z","iopub.execute_input":"2023-02-01T16:46:41.923885Z","iopub.status.idle":"2023-02-01T16:46:43.051535Z","shell.execute_reply.started":"2023-02-01T16:46:41.923846Z","shell.execute_reply":"2023-02-01T16:46:43.050096Z"},"trusted":true},"execution_count":471,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:48:10.301809Z","iopub.execute_input":"2023-02-01T16:48:10.302423Z","iopub.status.idle":"2023-02-01T16:48:11.417688Z","shell.execute_reply.started":"2023-02-01T16:48:10.302370Z","shell.execute_reply":"2023-02-01T16:48:11.415704Z"},"trusted":true},"execution_count":472,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({\n \"PassengerId\": results_test[\"PassengerId\"].astype(int),\n \"Survived\": results_test[\"y\"]\n })\n\nsubmission = submission.astype({col: 'int32' for col in submission.select_dtypes('int64').columns})\nsubmission.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:09:39.297418Z","iopub.execute_input":"2023-02-01T17:09:39.297834Z","iopub.status.idle":"2023-02-01T17:09:39.311761Z","shell.execute_reply.started":"2023-02-01T17:09:39.297801Z","shell.execute_reply":"2023-02-01T17:09:39.310602Z"},"trusted":true},"execution_count":490,"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"PassengerId int32\nSurvived float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)\n!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:06:56.872660Z","iopub.execute_input":"2023-02-01T17:06:56.873348Z","iopub.status.idle":"2023-02-01T17:06:57.989149Z","shell.execute_reply.started":"2023-02-01T17:06:56.873282Z","shell.execute_reply":"2023-02-01T17:06:57.987753Z"},"trusted":true},"execution_count":488,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Demonstration of math concepts/.DS_Store b/Demonstration of math concepts/.DS_Store index 5008ddf..e7a3c93 100644 Binary files a/Demonstration of math concepts/.DS_Store and b/Demonstration of math concepts/.DS_Store differ diff --git a/Demonstration of math concepts/Turing machines and complexity classes/Church-Turing thesis.pdf b/Demonstration of math concepts/Turing machines and complexity classes/Church-Turing thesis.pdf new file mode 100644 index 0000000..80ae59f Binary files /dev/null and b/Demonstration of math concepts/Turing machines and complexity classes/Church-Turing thesis.pdf differ diff --git a/Demonstration of math concepts/Turing machines and complexity classes/Turing machine.pdf b/Demonstration of math concepts/Turing machines and complexity classes/Turing machine.pdf new file mode 100644 index 0000000..4ed0f7e Binary files /dev/null and b/Demonstration of math concepts/Turing machines and complexity classes/Turing machine.pdf differ diff --git a/Demonstration of math concepts/Turing machines and complexity classes/review_complexity_classes.ipynb b/Demonstration of math concepts/Turing machines and complexity classes/review_complexity_classes.ipynb new file mode 100644 index 0000000..0f24b32 --- /dev/null +++ b/Demonstration of math concepts/Turing machines and complexity classes/review_complexity_classes.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Review of complexity classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some additional resources to read: \n", + "\n", + "- [wikipedia](https://en.wikipedia.org/wiki/Complexity_class) is informative and exhaustive - readers may required good set theories and other mathematical concepts\n", + "\n", + "- [Complexity classes P and NP](http://mercury.webster.edu/aleshunas/Support%20Materials/Presentations/Complexity%20Classes%20P%20and%20NP%20(13%20sep%2006).pdf) - an approachable presentation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's start with a selection sort ...." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[wikipedia](https://en.wikipedia.org/wiki/Selection_sort)\n", + "\n", + "[graphical representation](https://www.programiz.com/dsa/selection-sort)\n", + "\n", + "This procedure sort an array of values. The references to the array manipulates the array itself. We pass the array as an argument. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def selectionSort(array, size):\n", + " outer_steps = range(0,size)\n", + " for outer_step in outer_steps:\n", + " min_idx = outer_step\n", + " \n", + " internal_steps = range(outer_step + 1, size)\n", + " for index in internal_steps:\n", + " # to sort in descending order, change > to < in this line\n", + " # select the minimum element in each loop\n", + " if array[index] < array[min_idx]:\n", + " min_idx = index\n", + " \n", + " # put min at the correct position\n", + " (array[outer_step], array[min_idx]) = (array[min_idx], array[outer_step])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__The best case:__\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_list = [10,20,30,40,50]\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "size = len(a_list)\n", + "selectionSort(a_list, size)\n", + "a_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__The worst case:__\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[50, 40, 30, 20, 10]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_list = [50,40,30,20,10]\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20, 30, 40, 50]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "size = len(a_list)\n", + "selectionSort(a_list, size)\n", + "a_list" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7 µs, sys: 2 µs, total: 9 µs\n", + "Wall time: 10.3 µs\n" + ] + } + ], + "source": [ + "max_value = int(1e1)\n", + "a_list = [*range(max_value,0, -1)]\n", + "\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 250 µs, sys: 43 µs, total: 293 µs\n", + "Wall time: 294 µs\n" + ] + } + ], + "source": [ + "max_value = int(1e2)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 26 ms, sys: 0 ns, total: 26 ms\n", + "Wall time: 26 ms\n" + ] + } + ], + "source": [ + "max_value = int(1e3)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.46 s, sys: 0 ns, total: 2.46 s\n", + "Wall time: 2.47 s\n" + ] + } + ], + "source": [ + "max_value = int(1e4)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4min 13s, sys: 69.5 ms, total: 4min 13s\n", + "Wall time: 4min 14s\n" + ] + } + ], + "source": [ + "max_value = int(1e5)\n", + "a_list = [*range(max_value,0, -1)]\n", + "size = len(a_list)\n", + "%time selectionSort(a_list, size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time complexity\n", + "\n", + "Best and worst case complete $\\theta(n^2)$ comparisons, whether all the values or in ascending order or not. \n", + "\n", + "So, it is considered to be $\\theta(n^2)$, which is quadratic. Quadratic is a form a polynomial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is a polynomial?\n", + "\n", + "- [A simple explanation](https://www.mathsisfun.com/algebra/polynomials.html)\n", + "\n", + "- [A mathematical explanation](https://mathworld.wolfram.com/Polynomial.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "xs = range(-100, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + " \n", + "ys = [x for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('Linear - n^1')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,2) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('quadratic - n^2')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,3) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('cubic - n^3')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "\n", + "ys = [pow(x,4) for x in xs ]\n", + " \n", + "plt.plot(xs, ys)\n", + "plt.title('biquadratic - n^4')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__What is a polynomial time?__\n", + "\n", + "It is a time complexity expressed as $O(n^k)$, where $k \\geq 1$ or $\\theta(n^k)$, where $k \\geq 1$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the complexity of a problem?\n", + "The complexity of the most efficient algorithm that\n", + "solves it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is a tractable algorithm?\n", + "\n", + "A problem is tractable if it has polynomial complexity, i.e. if the most efficient algorithm that\n", + "solves the problem has complexity $\\theta(n^c)$ or better, for input size 𝑛 and some constant 𝑐." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## P vs NP\n", + "\n", + "Class P is the set of tractable decision problems: those that can be solved in polynomial time.\n", + "\n", + "[P class](https://en.wikipedia.org/wiki/P_(complexity))\n", + "\n", + "[NP hard](https://en.wikipedia.org/wiki/NP_(complexity))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Turing machine\n", + "\n", + "[Turing machine](https://en.wikipedia.org/wiki/Turing_machine)\n", + "\n", + "[An implementation in Python](https://medium.com/practical-coding/turing-machines-in-python-8314fd6077d7)\n", + "\n", + "[Church-Turing thesis](https://en.wikipedia.org/wiki/History_of_the_Church–Turing_thesis)\n", + "\n", + "[Church-Turing thesis - simpler explanation](https://mathworld.wolfram.com/Church-TuringThesis.html)\n", + "\n", + "[History of computing](https://history-computer.com/the-church-turing-thesis-explained-what-it-is-when-it-was-formed/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Introcution to machine learning/.DS_Store b/Introcution to machine learning/.DS_Store new file mode 100644 index 0000000..62b55b1 Binary files /dev/null and b/Introcution to machine learning/.DS_Store differ diff --git a/Introcution to machine learning/IntroToML.pdf b/Introcution to machine learning/IntroToML.pdf new file mode 100644 index 0000000..388d7b3 Binary files /dev/null and b/Introcution to machine learning/IntroToML.pdf differ diff --git a/Introcution to machine learning/PAC learning/introduction-to-pac-learning.ipynb b/Introcution to machine learning/PAC learning/introduction-to-pac-learning.ipynb new file mode 100644 index 0000000..500e7d6 --- /dev/null +++ b/Introcution to machine learning/PAC learning/introduction-to-pac-learning.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# A simple linear regression ","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\nimport random\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-07T16:09:32.572532Z","iopub.execute_input":"2023-10-07T16:09:32.573682Z","iopub.status.idle":"2023-10-07T16:09:32.596272Z","shell.execute_reply.started":"2023-10-07T16:09:32.573637Z","shell.execute_reply":"2023-10-07T16:09:32.595097Z"},"trusted":true},"execution_count":139,"outputs":[{"name":"stdout","text":"/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"source = \"/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv\"\ndata = pd.read_csv(source)\nprint(data.shape)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:09:32.598314Z","iopub.execute_input":"2023-10-07T16:09:32.598841Z","iopub.status.idle":"2023-10-07T16:09:32.613041Z","shell.execute_reply.started":"2023-10-07T16:09:32.598799Z","shell.execute_reply":"2023-10-07T16:09:32.611583Z"},"trusted":true},"execution_count":140,"outputs":[{"name":"stdout","text":"(30, 3)\n","output_type":"stream"},{"execution_count":140,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nYearsExperience float64\nSalary float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"The salary distribution appears to be skewed to the left. The latter will impact on some parametric measure of centrality - arithmetical mean.","metadata":{}},{"cell_type":"code","source":"data.Salary.hist(bins=20)\ndata.Salary.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:09:32.614646Z","iopub.execute_input":"2023-10-07T16:09:32.615472Z","iopub.status.idle":"2023-10-07T16:09:32.876015Z","shell.execute_reply.started":"2023-10-07T16:09:32.615411Z","shell.execute_reply":"2023-10-07T16:09:32.874680Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"count 30.000000\nmean 76004.000000\nstd 27414.429785\nmin 37732.000000\n25% 56721.750000\n50% 65238.000000\n75% 100545.750000\nmax 122392.000000\nName: Salary, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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W/fvjj33HPjlltuqcV+AIA61ljtOyxcuDAWLlxYi70AAHWu6rCoVrFYjGKxWL7c09MTERGlUilKpVLW+yqMTgNfOypV/PdIcu+1nlVznrPcX5WzGmo+N37k4Hmo1Zyc53wOnsuhPqdD/b0jh1qeoyOdj8F+76vVngd6uw0ppUFPuqGhIe688854//vff9g1HR0dsXLlyj7HOzs7o7m5ebB3DQAMof3798eSJUuiu7s7WlpaDruu5mFxqEcs2traYufOnf1ubDBmdawd8NrCqBSfntMb128YFcXehqz7IK/jeVZbOhYc6y0MWKlUiq6urprNqZbnoprvHSPB8fw1VU8GO6dafa309PREa2vrEcOi5j8KKRQKUSgU+hxvamqKpqamrPdVPFD9F0ixt2FQ78fQOx5nlftrZCjUak61PBfH2+fVQcfj11Q9qnZOtfpaGejt+jsWAEA2VT9isXfv3nj22WfLl59//vnYtGlTnHTSSTF9+vSsmwMA6kvVYbFhw4b4xV/8xfLlFStWRETEsmXL4rbbbsu2MQCg/lQdFpdcckkcxe97AgAjmN+xAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyGZQYXHLLbfEO97xjhg7dmxcdNFF8eijj+beFwBQh6oOi9tvvz1WrFgRN9xwQ3z3u9+Nc889NxYsWBA7duyoxf4AgDpSdVjcfPPN8bGPfSyuvPLKOPvss+OLX/xiNDc3x5e//OVa7A8AqCON1Sx+4403YuPGjXHdddeVj40aNSrmzZsXDz/88CHfp1gsRrFYLF/u7u6OiIhdu3ZFqVQazJ4Pq/HNfQNf25ti//7eaCyNigO9DVn3QV7H86xeffXVY72FASuVSrF///6azamW56Ka7x0jwfH8NVVPBjunWn2t7NmzJyIiUkr9L0xVePHFF1NEpO985zsVx6+55po0d+7cQ77PDTfckCLCmzdv3rx58zYC3rZt29ZvK1T1iMVgXHfddbFixYry5d7e3ti1a1dMmjQpGhqOXSn39PREW1tbbNu2LVpaWo7ZPjgys6oP5lQ/zKo+DLc5pZRiz549MW3atH7XVRUWra2tMXr06HjllVcqjr/yyisxderUQ75PoVCIQqFQcWzixInV3G1NtbS0DIuBcWRmVR/MqX6YVX0YTnOaMGHCEddU9cubY8aMiQsvvDDuu+++8rHe3t647777or29vfodAgAjStU/ClmxYkUsW7Ys5syZE3Pnzo3PfvazsW/fvrjyyitrsT8AoI5UHRYf+tCH4n//93/jU5/6VGzfvj3OO++8uPfee2PKlCm12F/NFAqFuOGGG/r8mIbhx6zqgznVD7OqD/U6p4Z0xOeNAAAMjNcKAQCyERYAQDbCAgDIRlgAANnUbVisWrUqGhoa4uqrry4fe/3112P58uUxadKkOOGEE+JXf/VX+/wxrxdeeCEWLVoUzc3NMXny5LjmmmvizTffrFjzwAMPxAUXXBCFQiHOOOOMuO222/rcv5eO79+LL74Yv/EbvxGTJk2KcePGxezZs2PDhg3l61NK8alPfSpOOeWUGDduXMybNy+eeeaZitvYtWtXLF26NFpaWmLixInx0Y9+NPbu3Vux5nvf+178/M//fIwdOzba2triM5/5TJ+93HHHHTFjxowYO3ZszJ49O+6+++7afNB15sCBA3H99dfH6aefHuPGjYuf+qmfik9/+tMVrwNgTsfGunXr4vLLL49p06ZFQ0ND3HXXXRXXD6e5DGQvI1l/syqVSnHttdfG7Nmz421ve1tMmzYtPvKRj8RLL71UcRsjblbVvFbIcPHoo4+md7zjHemcc85JV111Vfn47/7u76a2trZ03333pQ0bNqSf/dmfTe9+97vL17/55ptp1qxZad68eenxxx9Pd999d2ptbU3XXXddec1zzz2Xmpub04oVK9JTTz2VvvCFL6TRo0ene++9t7xmzZo1acyYMenLX/5yevLJJ9PHPvaxNHHixPTKK68Mycc/3O3atSuddtpp6Td/8zfTI488kp577rm0du3a9Oyzz5bXrFq1Kk2YMCHddddd6YknnkhXXHFFOv3009Nrr71WXvNLv/RL6dxzz03r169P//Ef/5HOOOOMtHjx4vL13d3dacqUKWnp0qVpy5Yt6atf/WoaN25cuvXWW8tr/vM//zONHj06feYzn0lPPfVU+rM/+7PU1NSUNm/ePDQnYxi78cYb06RJk9I3v/nN9Pzzz6c77rgjnXDCCelzn/tceY05HRt33313+uQnP5m+/vWvp4hId955Z8X1w2kuA9nLSNbfrHbv3p3mzZuXbr/99vRf//Vf6eGHH05z585NF154YcVtjLRZ1V1Y7NmzJ5155pmpq6sr/cIv/EI5LHbv3p2amprSHXfcUV779NNPp4hIDz/8cErpR58Ao0aNStu3by+vWb16dWppaUnFYjGllNInPvGJNHPmzIr7/NCHPpQWLFhQvjx37ty0fPny8uUDBw6kadOmpZtuuin7x1uPrr322nTxxRcf9vre3t40derU9Jd/+ZflY7t3706FQiF99atfTSml9NRTT6WISI899lh5zT333JMaGhrSiy++mFJK6W/+5m/SiSeeWJ7dwfs+66yzypc/+MEPpkWLFlXc/0UXXZR+53d+5+g+yBFg0aJF6bd+67cqjv3Kr/xKWrp0aUrJnIaLn/zHajjNZSB7OZ4cKgJ/0qOPPpoiIv3whz9MKY3MWdXdj0KWL18eixYtinnz5lUc37hxY5RKpYrjM2bMiOnTp5df0v3hhx+O2bNnV/wxrwULFkRPT088+eST5TU/edsLFiwo38bBl45/65ojvXT88eZf/uVfYs6cOfFrv/ZrMXny5Dj//PPjS1/6Uvn6559/PrZv315xDidMmBAXXXRRxawmTpwYc+bMKa+ZN29ejBo1Kh555JHymve85z0xZsyY8poFCxbE1q1b4//+7//Ka/qb5/Hs3e9+d9x3333x/e9/PyIinnjiiXjooYdi4cKFEWFOw9VwmstA9kKl7u7uaGhoKL9m1kicVV2FxZo1a+K73/1u3HTTTX2u2759e4wZM6bPC5xNmTIltm/fXl7zk38h9ODlI63p6emJ1157LXbu3BkHDhw45JqDt3G8e+6552L16tVx5plnxtq1a+PjH/94/OEf/mH84z/+Y0T8+Fz3dw63b98ekydPrri+sbExTjrppCzzNKuIP/3TP41f//VfjxkzZkRTU1Ocf/75cfXVV8fSpUsjwpyGq+E0l4HshR97/fXX49prr43FixeXX1RsJM6q5i+bnsu2bdviqquuiq6urhg7duyx3g796O3tjTlz5sRf/MVfRETE+eefH1u2bIkvfvGLsWzZsmO8Ow762te+Fl/5yleis7MzZs6cGZs2bYqrr746pk2bZk6QWalUig9+8IORUorVq1cf6+3UVN08YrFx48bYsWNHXHDBBdHY2BiNjY3x4IMPxuc///lobGyMKVOmxBtvvBG7d++ueL+3vqT71KlTD/mS7wev629NS0tLjBs3blAvHX+8OeWUU+Lss8+uOPbTP/3T8cILL0TEj891f+dw6tSpsWPHjorr33zzzdi1a1eWeZpVxDXXXFN+1GL27Nnx4Q9/OP7oj/6o/IigOQ1Pw2kuA9kLP46KH/7wh9HV1VXxEugjcVZ1ExaXXnppbN68OTZt2lR+mzNnTixdurT8/01NTRUv6b5169Z44YUXyi/p3t7eHps3b64Y4sEhH/yHsL29veI2Dq45eBteOv7Ifu7nfi62bt1acez73/9+nHbaaRERcfrpp8fUqVMrzmFPT0888sgjFbPavXt3bNy4sbzm29/+dvT29sZFF11UXrNu3boolUrlNV1dXXHWWWfFiSeeWF7T3zyPZ/v3749Royq/BYwePTp6e3sjwpyGq+E0l4Hs5Xh3MCqeeeaZ+Na3vhWTJk2quH5Ezirrr4IOsbc+KySlHz3ddPr06enb3/522rBhQ2pvb0/t7e3l6w8+3fSyyy5LmzZtSvfee286+eSTD/l002uuuSY9/fTT6ZZbbjnk000LhUK67bbb0lNPPZV++7d/O02cOLHi2SbHs0cffTQ1NjamG2+8MT3zzDPpK1/5Smpubk7/9E//VF6zatWqNHHixPSNb3wjfe9730vve9/7Dvl0ufPPPz898sgj6aGHHkpnnnlmxVOwdu/enaZMmZI+/OEPpy1btqQ1a9ak5ubmPk/BamxsTH/1V3+Vnn766XTDDTcc109jfKtly5alt7/97eWnm379619Pra2t6ROf+ER5jTkdG3v27EmPP/54evzxx1NEpJtvvjk9/vjj5WcSDKe5DGQvI1l/s3rjjTfSFVdckU499dS0adOm9PLLL5ff3voMj5E2qxEVFq+99lr6vd/7vXTiiSem5ubm9IEPfCC9/PLLFe/zgx/8IC1cuDCNGzcutba2pj/+4z9OpVKpYs3999+fzjvvvDRmzJj0zne+M/3DP/xDn/v+whe+kKZPn57GjBmT5s6dm9avX1+LD7Fu/eu//muaNWtWKhQKacaMGelv//ZvK67v7e1N119/fZoyZUoqFArp0ksvTVu3bq1Y8+qrr6bFixenE044IbW0tKQrr7wy7dmzp2LNE088kS6++OJUKBTS29/+9rRq1ao+e/na176W3vWud6UxY8akmTNnpn/7t3/L/wHXoZ6ennTVVVel6dOnp7Fjx6Z3vvOd6ZOf/GTFNzxzOjbuv//+FBF93pYtW5ZSGl5zGcheRrL+ZvX8888f8rqISPfff3/5NkbarLxsOgCQTd38jgUAMPwJCwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGz+H/IHoKFwB6coAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"The distribution appears to be multi-modal. Many observations made appears to be at earlier years of a career, bringing skewing the data to the left. Any parametric statistical methodologies will be affected.","metadata":{}},{"cell_type":"code","source":"data.YearsExperience.hist(bins=20)\ndata.YearsExperience.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:09:32.879090Z","iopub.execute_input":"2023-10-07T16:09:32.881616Z","iopub.status.idle":"2023-10-07T16:09:33.150056Z","shell.execute_reply.started":"2023-10-07T16:09:32.881576Z","shell.execute_reply":"2023-10-07T16:09:33.148618Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":"count 30.000000\nmean 5.413333\nstd 2.837888\nmin 1.200000\n25% 3.300000\n50% 4.800000\n75% 7.800000\nmax 10.600000\nName: YearsExperience, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"Xs = data.YearsExperience.values.reshape(-1, 1) \nys = data.Salary.values.reshape(-1, 1) \nplt.scatter(Xs, ys)\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:09:33.151771Z","iopub.execute_input":"2023-10-07T16:09:33.152063Z","iopub.status.idle":"2023-10-07T16:09:33.404055Z","shell.execute_reply.started":"2023-10-07T16:09:33.152038Z","shell.execute_reply":"2023-10-07T16:09:33.402906Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Salary')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"We are assuming these 30 observations represents a sample of population. We have only 30 observations, consequently, it may not be statistical significant. But it illustrates well the process of model fitting.","metadata":{}},{"cell_type":"code","source":" \nmodel = LinearRegression().fit(Xs, ys) \nprint(\"Intercept : \", model.intercept_)\nprint(\"Coeficient : \", model.coef_)\nprint(model.coef_ , \" x + \", model.intercept_ )","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:12:22.147933Z","iopub.execute_input":"2023-10-07T16:12:22.148312Z","iopub.status.idle":"2023-10-07T16:12:22.159445Z","shell.execute_reply.started":"2023-10-07T16:12:22.148286Z","shell.execute_reply":"2023-10-07T16:12:22.158168Z"},"trusted":true},"execution_count":149,"outputs":[{"name":"stdout","text":"Intercept : [24848.20396652]\nCoeficient : [[9449.96232146]]\n[[9449.96232146]] x + [24848.20396652]\n","output_type":"stream"}]},{"cell_type":"code","source":"Xs = data.YearsExperience.values.reshape(-1, 1) \nys = data.Salary.values.reshape(-1, 1) \nY_pred = model.predict(Xs) \nplt.scatter(Xs, ys)\nplt.plot(Xs, Y_pred, color = 'black')\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:12:43.250779Z","iopub.execute_input":"2023-10-07T16:12:43.251228Z","iopub.status.idle":"2023-10-07T16:12:43.514746Z","shell.execute_reply.started":"2023-10-07T16:12:43.251194Z","shell.execute_reply":"2023-10-07T16:12:43.513497Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Salary')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"We use the [mean absolute average error](https://stephenallwright.com/interpret-mape/) to evaluate the model. The model appears to be quite accurate. The error is less than 10% and, consequently, the accuracy is 90% or greater.","metadata":{}},{"cell_type":"code","source":"mape_sum = sum(abs(ys - Y_pred)/ys)\nmape = (1/len(Xs)) * mape_sum\nmape\n","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:13:00.412001Z","iopub.execute_input":"2023-10-07T16:13:00.412341Z","iopub.status.idle":"2023-10-07T16:13:00.420877Z","shell.execute_reply.started":"2023-10-07T16:13:00.412317Z","shell.execute_reply":"2023-10-07T16:13:00.419441Z"},"trusted":true},"execution_count":153,"outputs":[{"execution_count":153,"output_type":"execute_result","data":{"text/plain":"array([0.07047916])"},"metadata":{}}]},{"cell_type":"markdown","source":"# Some additional observations \n\nSome new observations are made and the model is applied to check the accuracy. We add more observations to our samples, using a covariance matrix. [Covariance matrix](https://en.wikipedia.org/wiki/Covariance_matrix)","metadata":{}},{"cell_type":"code","source":"years = data.YearsExperience\nsalary = data.Salary\n\nsample = np.array([years, salary])\n\ncov_matrix = np.cov(sample, bias=True)\nprint(cov_matrix)\n","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:13:04.743076Z","iopub.execute_input":"2023-10-07T16:13:04.743575Z","iopub.status.idle":"2023-10-07T16:13:04.751908Z","shell.execute_reply.started":"2023-10-07T16:13:04.743540Z","shell.execute_reply":"2023-10-07T16:13:04.750774Z"},"trusted":true},"execution_count":154,"outputs":[{"name":"stdout","text":"[[7.78515556e+00 7.35694267e+04]\n [7.35694267e+04 7.26499262e+08]]\n","output_type":"stream"}]},{"cell_type":"code","source":"mean = [np.mean(years), np.mean(salary)]\nnew_obs = abs(np.random.multivariate_normal(mean, cov_matrix, size=100))\nnew_data = pd.DataFrame({'YearsExperience': new_obs[:, 0], 'Salary': new_obs[:, 1]})\nnew_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:13:09.851905Z","iopub.execute_input":"2023-10-07T16:13:09.852267Z","iopub.status.idle":"2023-10-07T16:13:09.861793Z","shell.execute_reply.started":"2023-10-07T16:13:09.852241Z","shell.execute_reply":"2023-10-07T16:13:09.860755Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"(100, 2)"},"metadata":{}}]},{"cell_type":"code","source":"Xs = data.YearsExperience.values.reshape(-1,1)\nys = data.Salary.values.reshape(-1,1)\nnew_Xs = new_data.YearsExperience.values.reshape(-1,1)\nnew_ys = new_data.Salary.values.reshape(-1,1)\nnew_Y_pred = model.predict(new_Xs) \nplt.scatter(Xs, ys, label=\"original observations\" )\nplt.scatter(new_data.YearsExperience, new_data.Salary, alpha = 0.3, color = \"green\", label=\"new observations\")\nplt.plot(new_Xs, new_Y_pred, color = \"black\", label= \"predicted values\")\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")\nplt.legend()","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:13:33.523371Z","iopub.execute_input":"2023-10-07T16:13:33.523790Z","iopub.status.idle":"2023-10-07T16:13:33.897340Z","shell.execute_reply.started":"2023-10-07T16:13:33.523762Z","shell.execute_reply":"2023-10-07T16:13:33.896532Z"},"trusted":true},"execution_count":158,"outputs":[{"execution_count":158,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"The model may not fit well to the additional observations. More than 20% predictions were incorrect, lowering the accuracy below 80%.","metadata":{}},{"cell_type":"code","source":"new_Xs = new_data.YearsExperience.values.reshape(-1,1)\nnew_ys = new_data.Salary.values.reshape(-1,1)\nnew_Y_pred = model.predict(new_Xs) \nmape_sum = sum(abs(new_ys - new_Y_pred)/new_ys)\nmape = (1/new_data.shape[0]) * mape_sum\nmape","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:13:46.573531Z","iopub.execute_input":"2023-10-07T16:13:46.573911Z","iopub.status.idle":"2023-10-07T16:13:46.583275Z","shell.execute_reply.started":"2023-10-07T16:13:46.573883Z","shell.execute_reply":"2023-10-07T16:13:46.581789Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":"array([0.21114622])"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"# Latest obersvations\nWe apply our model to some new salary data. We simulate them by generated it more randomly.","metadata":{}},{"cell_type":"code","source":"number = random.randrange(10000,30000)\nrng = np.random.default_rng(number)\nyears = rng.integers(low=1, high=20, size=200)\nsalary = rng.integers(low = 37712.4, high= 150000, size = 200)\nlatest_data = pd.DataFrame({\"YearsExperience\": years, \"Salary\": salary})\nprint(latest_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:16:06.392499Z","iopub.execute_input":"2023-10-07T16:16:06.392927Z","iopub.status.idle":"2023-10-07T16:16:06.402199Z","shell.execute_reply.started":"2023-10-07T16:16:06.392899Z","shell.execute_reply":"2023-10-07T16:16:06.400700Z"},"trusted":true},"execution_count":160,"outputs":[{"name":"stdout","text":"(200, 2)\n","output_type":"stream"}]},{"cell_type":"code","source":"Xs = data.YearsExperience.values.reshape(-1,1)\nys = data.Salary.values.reshape(-1,1)\nlatest_Xs = latest_data.YearsExperience.values.reshape(-1,1)\nlatest_ys = latest_data.Salary.values.reshape(-1,1)\nlatest_Y_pred = model.predict(latest_Xs) \nplt.scatter(Xs, ys, label=\"original observations\" )\nplt.scatter(latest_data.YearsExperience, latest_data.Salary, alpha = 0.3, color = \"red\", label=\"latest observations\")\nplt.plot(latest_Xs, latest_Y_pred, color = \"black\", label= \"predicted salary\")\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")\nplt.legend()","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:16:09.506084Z","iopub.execute_input":"2023-10-07T16:16:09.506547Z","iopub.status.idle":"2023-10-07T16:16:09.869940Z","shell.execute_reply.started":"2023-10-07T16:16:09.506517Z","shell.execute_reply":"2023-10-07T16:16:09.868817Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"The latest observations suggest the model may not fit well to the new data. The proportion of mis-predicted salary rises to above 60% and lowering the accuracy below 40%.","metadata":{}},{"cell_type":"code","source":"latest_Xs = latest_data.YearsExperience.values.reshape(-1,1)\nlatest_ys = latest_data.Salary.values.reshape(-1,1)\nlatest_Y_pred = model.predict(latest_Xs) \nmape_sum = sum(abs(latest_ys - latest_Y_pred)/latest_ys)\nmape = (1/latest_data.shape[0]) * mape_sum\nmape","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:16:15.933112Z","iopub.execute_input":"2023-10-07T16:16:15.933555Z","iopub.status.idle":"2023-10-07T16:16:15.944257Z","shell.execute_reply.started":"2023-10-07T16:16:15.933525Z","shell.execute_reply":"2023-10-07T16:16:15.942900Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":"array([0.68805733])"},"metadata":{}}]},{"cell_type":"markdown","source":"# Comparison of distritbutions\n","metadata":{}},{"cell_type":"markdown","source":"## Years","metadata":{}},{"cell_type":"markdown","source":"### Original observations","metadata":{}},{"cell_type":"code","source":"data.YearsExperience.hist(bins = 20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:21:51.148186Z","iopub.execute_input":"2023-10-07T16:21:51.148632Z","iopub.status.idle":"2023-10-07T16:21:51.426589Z","shell.execute_reply.started":"2023-10-07T16:21:51.148602Z","shell.execute_reply":"2023-10-07T16:21:51.425602Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6/X2LFjtX79+qDWHT9+XKdPn266JSUlBXvXAAAgAvUOdsHMmTM1c+bMoO8oKSlJl112WdDrAABAZAs6jHTUuHHj5PF4NGrUKBUUFGjKlCkB53o8Hnk8nqZtt9stSfJ6vfJ6vSGty9XLH9L9XagztZ5fG+rjtRLOPoeDK8bf7N9QC+fvNVy97sqaQ9V/+tzx+7eu47zu+hwdLm31v6edd+3dr8Pv93f4yBwOh7Zu3ars7OyAc44fP67y8nJNmjRJHo9HL7zwgl555RUdOHBAEyZMaHVNQUGBCgsLW4yXlJQoPj6+o+UCAIAu1NDQoHnz5qmurk6JiYkB54U9jLRm6tSpGjx4sF555ZVWf97alZG0tDSdOXPmogfTEaMKdoV0fxc6WpDV4bVer1elpaWaMWOGnE5nCKuyEc4+h4Mrxq+Vk3xaXhkjj88R8v135txoS7h63ZU1h6r/9Dl43e25p7s+R4dLW/3vaeed2+1W//792wwjXfYyzYWuu+467d27N+DPXS6XXC5Xi3Gn0xnyB4enMfT/0ZwXilrDccwWwtnncPL4HGGpPZy/03D12qLmzvafPndcd3nu6e7P0eESqP897bxr735N/s5IVVWVBgwYYHHXAACgmwn6ysi5c+d04sSJpu2TJ0+qqqpK/fr10+DBg5Wfn69PPvlEf/rTnyRJa9asUXp6ukaOHKmvv/5aL7zwgt5++2299dZboTsKAADQYwUdRiorK3XTTTc1befl5UmSFixYoOLiYp0+fVrV1dVNP//mm29033336ZNPPlF8fLzGjBmjf/zjH832AQAAolfQYWTatGm62Htei4uLm20vW7ZMy5YtC7owAAAQHfhuGgAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFNBh5E9e/Zo9uzZSk1NlcPh0LZt29pcU15ergkTJsjlcmnIkCEqLi7uQKkAACASBR1G6uvrNXbsWK1fv75d80+ePKlZs2bppptuUlVVlXJzc3X33Xdr165dQRcLAAAiT+9gF8ycOVMzZ85s9/yNGzcqPT1dq1atkiQNHz5ce/fu1erVq5WVlRXs3QMAgAgTdBgJVkVFhaZPn95sLCsrS7m5uQHXeDweeTyepm232y1J8nq98nq9Ia3P1csf0v1dqDO1nl8b6uO1Es4+h4Mrxt/s31AL5+81XL3uyppD1X/63PH7t67jvO76HB0ubfW/p5137d2vw+/3d/jIHA6Htm7dquzs7IBzrr76ai1cuFD5+flNY9u3b9esWbPU0NCguLi4FmsKCgpUWFjYYrykpETx8fEdLRcAAHShhoYGzZs3T3V1dUpMTAw4L+xXRjoiPz9feXl5Tdtut1tpaWnKzMy86MF0xKiC7vneFVeMXysn+bS8MkYen8O6nKhD/1s6WhC+l1W//zgMVf+7suZQsa6Zc9+WVf/Ddd6df2WjLWEPIykpKaqtrW02Vltbq8TExFavikiSy+WSy+VqMe50OuV0OkNan6exez/YPD5Ht68xktH/74T6sXehQD3ubP8tau6s7lIz576tru5/uM679u437H9nJCMjQ2VlZc3GSktLlZGREe67BgAAPUDQYeTcuXOqqqpSVVWVpG8/ultVVaXq6mpJ377EMn/+/Kb5v/71r/XRRx9p2bJlev/99/Xcc8/ptdde07333huaIwAAAD1a0GGksrJS48eP1/jx4yVJeXl5Gj9+vFasWCFJOn36dFMwkaT09HT9/e9/V2lpqcaOHatVq1bphRde4GO9AABAUgfeMzJt2jRd7AM4rf111WnTpunw4cPB3hUAAIgCfDcNAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATHUojKxfv15XXXWVYmNjdf311+vdd98NOLe4uFgOh6PZLTY2tsMFAwCAyBJ0GHn11VeVl5enhx56SO+9957Gjh2rrKwsffbZZwHXJCYm6vTp0023U6dOdapoAAAQOYIOI88884wWLVqkhQsXasSIEdq4caPi4+P10ksvBVzjcDiUkpLSdEtOTu5U0QAAIHIEFUa++eYbHTp0SNOnT/9uBzExmj59uioqKgKuO3funK688kqlpaXptttu07FjxzpeMQAAiCi9g5l85swZNTY2triykZycrPfff7/VNddcc41eeukljRkzRnV1dXr66ac1efJkHTt2TIMGDWp1jcfjkcfjadp2u92SJK/XK6/XG0zJbXL18od0f6HiivE3+xddi/63FOrH3oW+/zgMVf+7suZQsa6Zc9+WVf/Ddd61d78Ov9/f7iP+9NNPNXDgQO3bt08ZGRlN48uWLdPu3bt14MCBdhU2fPhwzZ07VytXrmx1TkFBgQoLC1uMl5SUKD4+vr3lAgAAQw0NDZo3b57q6uqUmJgYcF5QV0b69++vXr16qba2ttl4bW2tUlJS2rUPp9Op8ePH68SJEwHn5OfnKy8vr2nb7XYrLS1NmZmZFz2YjhhVsCuk+wsVV4xfKyf5tLwyRh6fw7qcqEP/WzpakBW2fX//cRiq/ndlzaFiXTPnvi2r/ofrvDv/ykZbggojffr00cSJE1VWVqbs7GxJks/nU1lZmRYvXtyufTQ2NurIkSO69dZbA85xuVxyuVwtxp1Op5xOZzAlt8nT2L0fbB6fo9vXGMno/3dC/di7UKAed7b/FjV3VnepmXPfVlf3P1znXXv3G1QYkaS8vDwtWLBAkyZN0nXXXac1a9aovr5eCxculCTNnz9fAwcOVFFRkSTp4Ycf1g033KAhQ4boq6++0lNPPaVTp07p7rvvDvauAQBABAo6jNx+++36/PPPtWLFCtXU1GjcuHHauXNn05taq6urFRPz3Yd0vvzySy1atEg1NTW6/PLLNXHiRO3bt08jRowI3VEAAIAeK+gwIkmLFy8O+LJMeXl5s+3Vq1dr9erVHbkbAAAQBfhuGgAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFMdCiPr16/XVVddpdjYWF1//fV69913Lzp/y5YtGjZsmGJjYzV69Ght3769Q8UCAIDIE3QYefXVV5WXl6eHHnpI7733nsaOHausrCx99tlnrc7ft2+f5s6dq7vuukuHDx9Wdna2srOzdfTo0U4XDwAAer6gw8gzzzyjRYsWaeHChRoxYoQ2btyo+Ph4vfTSS63OX7t2rW655RYtXbpUw4cP18qVKzVhwgStW7eu08UDAICer3cwk7/55hsdOnRI+fn5TWMxMTGaPn26KioqWl1TUVGhvLy8ZmNZWVnatm1bwPvxeDzyeDxN23V1dZKkL774Ql6vN5iS29T7/9WHdH+h0tvnV0ODT729MWr0OazLiTr0v6X//e9/Ydv39x+Hoep/V9YcKtY1c+7bsup/uM67s2fPSpL8fv9F5wUVRs6cOaPGxkYlJyc3G09OTtb777/f6pqamppW59fU1AS8n6KiIhUWFrYYT09PD6bcHm+edQFRjv43139V195fKPrf1TWHQneomXPflkX/w33enT17Vn379g3486DCSFfJz89vdjXF5/Ppiy++0BVXXCGHIzqSutvtVlpamj7++GMlJiZalxN16L8t+m+H3tuKtP77/X6dPXtWqampF50XVBjp37+/evXqpdra2mbjtbW1SklJaXVNSkpKUPMlyeVyyeVyNRu77LLLgik1YiQmJkbECdlT0X9b9N8OvbcVSf2/2BWR84J6A2ufPn00ceJElZWVNY35fD6VlZUpIyOj1TUZGRnN5ktSaWlpwPkAACC6BP0yTV5enhYsWKBJkybpuuuu05o1a1RfX6+FCxdKkubPn6+BAweqqKhIkrRkyRJNnTpVq1at0qxZs7R582ZVVlZq06ZNoT0SAADQIwUdRm6//XZ9/vnnWrFihWpqajRu3Djt3Lmz6U2q1dXVion57oLL5MmTVVJSogcffFAPPPCAhg4dqm3btmnUqFGhO4oI5HK59NBDD7V4uQpdg/7bov926L2taO2/w9/W520AAADCiO+mAQAApggjAADAFGEEAACYIowAAABThJFupKioSNdee60SEhKUlJSk7OxsHT9+3LqsqPX444/L4XAoNzfXupSo8cknn+jnP/+5rrjiCsXFxWn06NGqrKy0LisqNDY2avny5UpPT1dcXJx++MMfauXKlW1+pwg6Zs+ePZo9e7ZSU1PlcDhafF+b3+/XihUrNGDAAMXFxWn69On68MMPbYrtAoSRbmT37t3KycnR/v37VVpaKq/Xq8zMTNXXd88v84tkBw8e1PPPP68xY8ZYlxI1vvzyS02ZMkVOp1M7duzQf/7zH61atUqXX365dWlR4YknntCGDRu0bt06/fe//9UTTzyhJ598Ur///e+tS4tI9fX1Gjt2rNavX9/qz5988kk9++yz2rhxow4cOKBLLrlEWVlZ+vrrr7u40q7BR3u7sc8//1xJSUnavXu3brzxRutyosa5c+c0YcIEPffcc3rkkUc0btw4rVmzxrqsiHf//ffrX//6l/75z39alxKVfvzjHys5OVkvvvhi09hPf/pTxcXF6c9//rNhZZHP4XBo69atys7OlvTtVZHU1FTdd999+t3vfifp22+vT05OVnFxse644w7DasODKyPdWF1dnSSpX79+xpVEl5ycHM2aNUvTp0+3LiWq/PWvf9WkSZM0Z84cJSUlafz48frDH/5gXVbUmDx5ssrKyvTBBx9Ikv79739r7969mjlzpnFl0efkyZOqqalp9hzUt29fXX/99aqoqDCsLHy65bf24tvv/MnNzdWUKVP4a7VdaPPmzXrvvfd08OBB61KizkcffaQNGzYoLy9PDzzwgA4ePKjf/va36tOnjxYsWGBdXsS7//775Xa7NWzYMPXq1UuNjY169NFHdeedd1qXFnVqamokqekvm5+XnJzc9LNIQxjppnJycnT06FHt3bvXupSo8fHHH2vJkiUqLS1VbGysdTlRx+fzadKkSXrsscckSePHj9fRo0e1ceNGwkgXeO211/SXv/xFJSUlGjlypKqqqpSbm6vU1FT6j7DjZZpuaPHixXrzzTf1zjvvaNCgQdblRI1Dhw7ps88+04QJE9S7d2/17t1bu3fv1rPPPqvevXursbHRusSINmDAAI0YMaLZ2PDhw1VdXW1UUXRZunSp7r//ft1xxx0aPXq0fvGLX+jee+9t+tJTdJ2UlBRJUm1tbbPx2trapp9FGsJIN+L3+7V48WJt3bpVb7/9ttLT061Liio333yzjhw5oqqqqqbbpEmTdOedd6qqqkq9evWyLjGiTZkypcVH2T/44ANdeeWVRhVFl4aGhmZfcipJvXr1ks/nM6ooeqWnpyslJUVlZWVNY263WwcOHFBGRoZhZeHDyzTdSE5OjkpKSvTGG28oISGh6bXBvn37Ki4uzri6yJeQkNDi/TmXXHKJrrjiCt630wXuvfdeTZ48WY899ph+9rOf6d1339WmTZu0adMm69KiwuzZs/Xoo49q8ODBGjlypA4fPqxnnnlGv/rVr6xLi0jnzp3TiRMnmrZPnjypqqoq9evXT4MHD1Zubq4eeeQRDR06VOnp6Vq+fLlSU1ObPnETcfzoNiS1env55ZetS4taU6dO9S9ZssS6jKjxt7/9zT9q1Ci/y+XyDxs2zL9p0ybrkqKG2+32L1myxD948GB/bGys/wc/+IH///7v//wej8e6tIj0zjvvtPp8v2DBAr/f7/f7fD7/8uXL/cnJyX6Xy+W/+eab/cePH7ctOoz4OyMAAMAU7xkBAACmCCMAAMAUYQQAAJgijAAAAFOEEQAAYIowAgAATBFGAACAKcIIAAAwRRgBAACmCCMAAMAUYQQAAJgijAAAAFP/H5fUdD0wJYl9AAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"### Some new observations","metadata":{}},{"cell_type":"code","source":"new_data.YearsExperience.hist(bins = 20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:22:36.828103Z","iopub.execute_input":"2023-10-07T16:22:36.828490Z","iopub.status.idle":"2023-10-07T16:22:37.139193Z","shell.execute_reply.started":"2023-10-07T16:22:36.828447Z","shell.execute_reply":"2023-10-07T16:22:37.137865Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Latest observation","metadata":{}},{"cell_type":"code","source":"latest_data.YearsExperience.hist(bins = 20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:23:32.518914Z","iopub.execute_input":"2023-10-07T16:23:32.519405Z","iopub.status.idle":"2023-10-07T16:23:32.829220Z","shell.execute_reply.started":"2023-10-07T16:23:32.519372Z","shell.execute_reply":"2023-10-07T16:23:32.827774Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Salary\n### Original oberservations","metadata":{}},{"cell_type":"code","source":"data.Salary.hist(bins =20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:25:18.155726Z","iopub.execute_input":"2023-10-07T16:25:18.156166Z","iopub.status.idle":"2023-10-07T16:25:18.438863Z","shell.execute_reply.started":"2023-10-07T16:25:18.156136Z","shell.execute_reply":"2023-10-07T16:25:18.437280Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Some additional observations ","metadata":{}},{"cell_type":"code","source":"new_data.Salary.hist(bins = 20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:26:33.302110Z","iopub.execute_input":"2023-10-07T16:26:33.302624Z","iopub.status.idle":"2023-10-07T16:26:33.608524Z","shell.execute_reply.started":"2023-10-07T16:26:33.302587Z","shell.execute_reply":"2023-10-07T16:26:33.607163Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Latest observations","metadata":{}},{"cell_type":"code","source":"latest_data.Salary.hist(bins = 20)","metadata":{"execution":{"iopub.status.busy":"2023-10-07T16:27:34.770412Z","iopub.execute_input":"2023-10-07T16:27:34.770885Z","iopub.status.idle":"2023-10-07T16:27:35.082942Z","shell.execute_reply.started":"2023-10-07T16:27:34.770854Z","shell.execute_reply":"2023-10-07T16:27:35.081713Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}}]} \ No newline at end of file diff --git a/Introcution to machine learning/data-preparation-for-ml-techniques (1).ipynb b/Introcution to machine learning/data-preparation-for-ml-techniques (1).ipynb new file mode 100644 index 0000000..e4d57be --- /dev/null +++ b/Introcution to machine learning/data-preparation-for-ml-techniques (1).ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-01T14:12:24.884916Z","iopub.execute_input":"2023-02-01T14:12:24.885428Z","iopub.status.idle":"2023-02-01T14:12:24.912232Z","shell.execute_reply.started":"2023-02-01T14:12:24.885325Z","shell.execute_reply":"2023-02-01T14:12:24.911114Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/titanic/train.csv\n/kaggle/input/titanic/test.csv\n/kaggle/input/titanic/gender_submission.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Introduction\n\nData can be transformed into knowledge and then enhanced intelligence. We use the titanic datasets to explore first its features. The Titanic datasets contains the records of the Titanic passengers during its maiden voyage and tragic demise. \n\nWe apply some data engineering techniques to prepare the data for some various machine learning techniques - including _registic regression, decision trees, random forrests, KNN and artificial neural network_ - for the purpose of predicting survivors. \n\nWe also analyse and compare the predictions from all the classifier methods to explore further how the data and our data preparation may have affected the model fitting as well as the prediction on unseen data.\n\n\nThe notebook is structured in this manner:\n\n\n- __[Upload libraires](#Libraries)__\n- __[Data engineering](#Data-engineering)__\n- __[Survival characteristics](#Survival-characteristics)__\n- __[Data preparation for classification](#Data-preparation-for-classification)__ \n- __[Method: Logistic regression](#Method-:-Logistic-regression)__\n- __[Method: K-Nearest neighorn](#Method:-K-Nearest-neighbourn)__\n- __[Method: Decision Trees](#Method-:-Decision-Trees)__ \n- __[Method: Random Forrest](#Method:-Random-Forrest)__\n- __[Method: Neural AI](#Method:-Neural-AI)__ \n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Libraries\n\nWe upload all the libraries required for all the operations of this notebook.","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport seaborn as sns\nimport os\nimport random as rand\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import Sequential\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\nfrom sklearn.model_selection import train_test_split # Import train_test_split function\nfrom sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nimport scipy.stats as stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\n\nimport tensorflow as tf\nif (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed\n print('Please install tensorflow 2.0 to run this notebook')\nprint('Tensorflow version: ',tf.__version__)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:30:21.088212Z","iopub.execute_input":"2023-02-01T14:30:21.088884Z","iopub.status.idle":"2023-02-01T14:30:21.739453Z","shell.execute_reply.started":"2023-02-01T14:30:21.088844Z","shell.execute_reply":"2023-02-01T14:30:21.738425Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Tensorflow version: 2.6.4\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Data engineering\n\nWe explore the files in the folder, sets the paths and file names. These variables will be used in each section.","metadata":{}},{"cell_type":"code","source":"!ls ../input/titanic/\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:36.483134Z","iopub.execute_input":"2023-02-01T14:12:36.484288Z","iopub.status.idle":"2023-02-01T14:12:37.583058Z","shell.execute_reply.started":"2023-02-01T14:12:36.484239Z","shell.execute_reply":"2023-02-01T14:12:37.581624Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"gender_submission.csv test.csv train.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"train_data_path = '../input/titanic/train.csv'\ntest_data_path = '../input/titanic/test.csv'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.584978Z","iopub.execute_input":"2023-02-01T14:12:37.586181Z","iopub.status.idle":"2023-02-01T14:12:37.591422Z","shell.execute_reply.started":"2023-02-01T14:12:37.586143Z","shell.execute_reply":"2023-02-01T14:12:37.590256Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Import and explore the data \nExplore and import the training and test dataset provided by the competition.","metadata":{}},{"cell_type":"markdown","source":"### Training dataset","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:37.825672Z","iopub.execute_input":"2023-02-01T14:12:37.827141Z","iopub.status.idle":"2023-02-01T14:12:37.862872Z","shell.execute_reply.started":"2023-02-01T14:12:37.827090Z","shell.execute_reply":"2023-02-01T14:12:37.861760Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.735534Z","iopub.execute_input":"2023-02-01T14:12:40.736625Z","iopub.status.idle":"2023-02-01T14:12:40.784900Z","shell.execute_reply.started":"2023-02-01T14:12:40.736575Z","shell.execute_reply":"2023-02-01T14:12:40.783854Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 38.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:40.786409Z","iopub.execute_input":"2023-02-01T14:12:40.786701Z","iopub.status.idle":"2023-02-01T14:12:40.803610Z","shell.execute_reply.started":"2023-02-01T14:12:40.786675Z","shell.execute_reply":"2023-02-01T14:12:40.802382Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Test dataset","metadata":{}},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:45.642106Z","iopub.execute_input":"2023-02-01T14:12:45.642583Z","iopub.status.idle":"2023-02-01T14:12:45.662691Z","shell.execute_reply.started":"2023-02-01T14:12:45.642542Z","shell.execute_reply":"2023-02-01T14:12:45.661472Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.221543Z","iopub.execute_input":"2023-02-01T14:12:46.222107Z","iopub.status.idle":"2023-02-01T14:12:46.261833Z","shell.execute_reply.started":"2023-02-01T14:12:46.222059Z","shell.execute_reply":"2023-02-01T14:12:46.260714Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Age SibSp Parch Fare\ncount 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\nmean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\nstd 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\nmin 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\nmax 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:46.919995Z","iopub.execute_input":"2023-02-01T14:12:46.920463Z","iopub.status.idle":"2023-02-01T14:12:46.940798Z","shell.execute_reply.started":"2023-02-01T14:12:46.920405Z","shell.execute_reply":"2023-02-01T14:12:46.939404Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":" ## Meta data \n \n| Column name | Description|\n|---|---|\n|Passenger_id| unique row indentifier |\n|PClass | Categorical data (1 = 1st; 2 = 2nd; 3 = 3rd)|\n| Survival | Categoricial data (0 = No; 1 = Yes) |\n| Name | Characters - Name of passenger |\n| Sex | Categorical data male or female |\n| Age | integer values representing age |\n| SigSp | integer Number of Siblings/Spouses Aboard |\n| Parch | Number of Parents/Children Aboard |\n| Ticket | Ticket number |\n| Fare | Fare in GBP at time of travel|\n| Cabin | Cabin |\n| Embark | Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)|\n\n\nSource - http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf (7/12/2022)","metadata":{}},{"cell_type":"markdown","source":"# Survival characteristics\nWe explore the survival characteristics using several combinations of columns. We hope to understand better some features that may guide the predictions of survivors.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Passenger and survival\nThe training dataset suggests a minority of passengers survived (i.e., 38% approximately), 62% of passengers perished. Some further decomposition suggests first class passengers may have been more likely to survive than lower classes. The percentages of surviving decreases sharply.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Survived\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:12:50.918803Z","iopub.execute_input":"2023-02-01T14:12:50.919222Z","iopub.status.idle":"2023-02-01T14:12:50.940405Z","shell.execute_reply.started":"2023-02-01T14:12:50.919186Z","shell.execute_reply":"2023-02-01T14:12:50.939284Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Survived\n0 0.616162\n1 0.383838\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp = temp.unstack()\ntemp","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:18:15.229152Z","iopub.execute_input":"2023-02-01T14:18:15.229539Z","iopub.status.idle":"2023-02-01T14:18:15.255184Z","shell.execute_reply.started":"2023-02-01T14:18:15.229507Z","shell.execute_reply":"2023-02-01T14:18:15.254098Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass \n1 0.370370 0.629630\n2 0.527174 0.472826\n3 0.757637 0.242363","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Pclass
10.3703700.629630
20.5271740.472826
30.7576370.242363
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Null hypothesis: Pclass means are equal (no variation in means of groups)\nH0:$μ_0=μ-1$\n\nAlternative hypothesis: At least, one group mean is different from other groups\nH1: All μ are not equal\n\n$p_{value} = 0.01$","metadata":{}},{"cell_type":"code","source":"\nsur_pclass = titanic_train.loc[titanic_train.Survived == 1, \"Pclass\"]\nperish_pclass = titanic_train.loc[titanic_train.Survived == 0, \"Pclass\"]\nfvalue, pvalue = stats.f_oneway(sur_pclass, perish_pclass)\nprint(fvalue, pvalue)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:29:37.721129Z","iopub.execute_input":"2023-02-01T14:29:37.721602Z","iopub.status.idle":"2023-02-01T14:29:37.732491Z","shell.execute_reply.started":"2023-02-01T14:29:37.721566Z","shell.execute_reply":"2023-02-01T14:29:37.731155Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"115.03127218827665 2.5370473879805644e-25\n","output_type":"stream"}]},{"cell_type":"code","source":"model = ols('Survived ~ Pclass', data=titanic_train).fit()\nanova_table = sm.stats.anova_lm(model, typ=2)\nanova_table","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:32:55.703937Z","iopub.execute_input":"2023-02-01T14:32:55.704329Z","iopub.status.idle":"2023-02-01T14:32:55.739204Z","shell.execute_reply.started":"2023-02-01T14:32:55.704285Z","shell.execute_reply":"2023-02-01T14:32:55.738138Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" sum_sq df F PR(>F)\nPclass 24.142900 1.0 115.031272 2.537047e-25\nResidual 186.584373 889.0 NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sum_sqdfFPR(>F)
Pclass24.1429001.0115.0312722.537047e-25
Residual186.584373889.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"__Interpretation__\n\nThe p value obtained from ANOVA analysis is significant (p < 0.01), and therefore, we conclude that there are significant differences among the classes who have perished or survived.","metadata":{}},{"cell_type":"markdown","source":"## Embarkment and survival\nThe port of embarkment appears to have less influence on the survival percentages. It appears most passengers embarked at Southampton (72% approximately), 18% of passengers at Cherbourg, and the remaining from Queenstown. Half of the Cherbourg passengers booked first class tickets. Other embarkment ports appears to be much lower. Half of the passengers from Southampton booked third class tickets. We could surmise the latter may have contributed to the lowest percentages of surviving the accident.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Embarked\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.612759Z","iopub.execute_input":"2023-02-01T14:50:09.613134Z","iopub.status.idle":"2023-02-01T14:50:09.626172Z","shell.execute_reply.started":"2023-02-01T14:50:09.613106Z","shell.execute_reply":"2023-02-01T14:50:09.625109Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Embarked\nC 0.188552\nQ 0.086420\nS 0.722783\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:09.631955Z","iopub.execute_input":"2023-02-01T14:50:09.632520Z","iopub.status.idle":"2023-02-01T14:50:09.652387Z","shell.execute_reply.started":"2023-02-01T14:50:09.632486Z","shell.execute_reply":"2023-02-01T14:50:09.651379Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Pclass 1 2 3\nEmbarked \nC 0.505952 0.101190 0.392857\nQ 0.025974 0.038961 0.935065\nS 0.197205 0.254658 0.548137","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Pclass123
Embarked
C0.5059520.1011900.392857
Q0.0259740.0389610.935065
S0.1972050.2546580.548137
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:10.164258Z","iopub.execute_input":"2023-02-01T14:50:10.164676Z","iopub.status.idle":"2023-02-01T14:50:10.185023Z","shell.execute_reply.started":"2023-02-01T14:50:10.164643Z","shell.execute_reply":"2023-02-01T14:50:10.183924Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked \nC 0.446429 0.553571\nQ 0.610390 0.389610\nS 0.663043 0.336957","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Embarked
C0.4464290.553571
Q0.6103900.389610
S0.6630430.336957
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Embarked\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:16.126671Z","iopub.execute_input":"2023-02-01T14:50:16.127079Z","iopub.status.idle":"2023-02-01T14:50:16.150013Z","shell.execute_reply.started":"2023-02-01T14:50:16.127043Z","shell.execute_reply":"2023-02-01T14:50:16.149263Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nEmbarked Pclass \nC 1 0.154762 0.351190\n 2 0.047619 0.053571\n 3 0.244048 0.148810\nQ 1 0.012987 0.012987\n 2 0.012987 0.025974\n 3 0.584416 0.350649\nS 1 0.082298 0.114907\n 2 0.136646 0.118012\n 3 0.444099 0.104037","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
EmbarkedPclass
C10.1547620.351190
20.0476190.053571
30.2440480.148810
Q10.0129870.012987
20.0129870.025974
30.5844160.350649
S10.0822980.114907
20.1366460.118012
30.4440990.104037
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Gender and survival \nThe training dataset suggests that nearly two thirds of passengers were male, and a third were female. Women and girls appears to have a higher survival percentagers - three quarters of female passengers survived the accident, but only 19% of male survived.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"Sex\"]).count()[\"PassengerId\"]/titanic_train.shape[0] ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:21.461493Z","iopub.execute_input":"2023-02-01T14:50:21.461874Z","iopub.status.idle":"2023-02-01T14:50:21.474706Z","shell.execute_reply.started":"2023-02-01T14:50:21.461843Z","shell.execute_reply":"2023-02-01T14:50:21.473520Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Sex\nfemale 0.352413\nmale 0.647587\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:22.544412Z","iopub.execute_input":"2023-02-01T14:50:22.544835Z","iopub.status.idle":"2023-02-01T14:50:22.565483Z","shell.execute_reply.started":"2023-02-01T14:50:22.544801Z","shell.execute_reply":"2023-02-01T14:50:22.564390Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex \nfemale 0.257962 0.742038\nmale 0.811092 0.188908","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
Sex
female0.2579620.742038
male0.8110920.188908
\n
"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Sex\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:23.099261Z","iopub.execute_input":"2023-02-01T14:50:23.099666Z","iopub.status.idle":"2023-02-01T14:50:23.126110Z","shell.execute_reply.started":"2023-02-01T14:50:23.099635Z","shell.execute_reply":"2023-02-01T14:50:23.125241Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSex Pclass \nfemale 1 0.013889 0.421296\n 2 0.032609 0.380435\n 3 0.146640 0.146640\nmale 1 0.356481 0.208333\n 2 0.494565 0.092391\n 3 0.610998 0.095723","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SexPclass
female10.0138890.421296
20.0326090.380435
30.1466400.146640
male10.3564810.208333
20.4945650.092391
30.6109980.095723
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age, siblings and parents\n\nThe age distribution appears to be multi-modal with some two peaks at around 0 and 25. Both training and testing datasets have a similar mean and standard deviation. However, some skewness may affect a normal distributions and any normalisation processes of the data.\n\nThe survivors and other passengers age appears to be of similar age at the point of centrality. We will need to complete some statistical tests to accept or reject the null hypothesis that the age distribution of survivors and non-survivors are the same. We surmise the values may have be unknown, without any data preparation the tests cannot be completed.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.246337Z","iopub.execute_input":"2023-02-01T14:50:24.247338Z","iopub.status.idle":"2023-02-01T14:50:24.633738Z","shell.execute_reply.started":"2023-02-01T14:50:24.247275Z","shell.execute_reply":"2023-02-01T14:50:24.632647Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"count 714.000000\nmean 29.699118\nstd 14.526497\nmin 0.420000\n25% 20.125000\n50% 28.000000\n75% 38.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:24.694278Z","iopub.execute_input":"2023-02-01T14:50:24.695165Z","iopub.status.idle":"2023-02-01T14:50:25.062419Z","shell.execute_reply.started":"2023-02-01T14:50:24.695120Z","shell.execute_reply":"2023-02-01T14:50:25.061338Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"count 332.000000\nmean 30.272590\nstd 14.181209\nmin 0.170000\n25% 21.000000\n50% 27.000000\n75% 39.000000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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y=\"Age\", data=titanic_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:25.141606Z","iopub.execute_input":"2023-02-01T14:50:25.142000Z","iopub.status.idle":"2023-02-01T14:50:25.355591Z","shell.execute_reply.started":"2023-02-01T14:50:25.141964Z","shell.execute_reply":"2023-02-01T14:50:25.354536Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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majority of passengers may be travelling on their own without a spouse, sibling, children of parents on board. However, passengers with 1 or 2 siblings/spouse appears to have survived; the percentages is in the range of 46% to 54%. Parents or individuals with one, two or three parents were less likely to perished - the percentages ranges between 50% and 60%.","metadata":{}},{"cell_type":"code","source":"titanic_train.groupby([\"SibSp\"]).count()[\"PassengerId\"]/titanic_train.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.432856Z","iopub.execute_input":"2023-02-01T14:50:29.433512Z","iopub.status.idle":"2023-02-01T14:50:29.445537Z","shell.execute_reply.started":"2023-02-01T14:50:29.433478Z","shell.execute_reply":"2023-02-01T14:50:29.444361Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"SibSp\n0 0.682379\n1 0.234568\n2 0.031425\n3 0.017957\n4 0.020202\n5 0.005612\n8 0.007856\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"SibSp\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:29.890502Z","iopub.execute_input":"2023-02-01T14:50:29.890860Z","iopub.status.idle":"2023-02-01T14:50:29.915654Z","shell.execute_reply.started":"2023-02-01T14:50:29.890829Z","shell.execute_reply":"2023-02-01T14:50:29.914470Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nSibSp \n0 0.654605 0.345395\n1 0.464115 0.535885\n2 0.535714 0.464286\n3 0.750000 0.250000\n4 0.833333 0.166667\n5 1.000000 NaN\n8 1.000000 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Survived01
SibSp
00.6546050.345395
10.4641150.535885
20.5357140.464286
30.7500000.250000
40.8333330.166667
51.000000NaN
81.000000NaN
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.SibSp, bins = 8)\ntitanic_train.SibSp.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:30.548135Z","iopub.execute_input":"2023-02-01T14:50:30.548522Z","iopub.status.idle":"2023-02-01T14:50:30.775129Z","shell.execute_reply.started":"2023-02-01T14:50:30.548488Z","shell.execute_reply":"2023-02-01T14:50:30.774363Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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0.760943\n1 0.132435\n2 0.089787\n3 0.005612\n4 0.004489\n5 0.005612\n6 0.001122\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Parch\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x: x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.187336Z","iopub.execute_input":"2023-02-01T14:50:31.187728Z","iopub.status.idle":"2023-02-01T14:50:31.209460Z","shell.execute_reply.started":"2023-02-01T14:50:31.187695Z","shell.execute_reply":"2023-02-01T14:50:31.208365Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nParch \n0 0.656342 0.343658\n1 0.449153 0.550847\n2 0.500000 0.500000\n3 0.400000 0.600000\n4 1.000000 NaN\n5 0.800000 0.200000\n6 1.000000 NaN","text/html":"
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Survived01
Parch
00.6563420.343658
10.4491530.550847
20.5000000.500000
30.4000000.600000
41.000000NaN
50.8000000.200000
61.000000NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Parch, bins = 6)\ntitanic_train.Parch.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.433509Z","iopub.execute_input":"2023-02-01T14:50:31.434117Z","iopub.status.idle":"2023-02-01T14:50:31.664941Z","shell.execute_reply.started":"2023-02-01T14:50:31.434071Z","shell.execute_reply":"2023-02-01T14:50:31.664079Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.381594\nstd 0.806057\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 6.000000\nName: Parch, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We decided to add both fields _Parch_ and _SibSp_ together as a familly. The mean and median age appears to be quite close between the passengers who have survived and perished. For smaller families the spread appears to be smaller than for larger families. \n\nThe highest percentages of surviving the accident suggests that passengers in first and second class with no other familly members. These percentages are loweer than 30%.","metadata":{}},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train.SibSp + titanic_train.Parch\ntemp = titanic_train.groupby([\"fam_members\",\"Survived\"]).agg([np.median, np.mean, np.std])[\"Age\"]\ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:31.929453Z","iopub.execute_input":"2023-02-01T14:50:31.929858Z","iopub.status.idle":"2023-02-01T14:50:31.977029Z","shell.execute_reply.started":"2023-02-01T14:50:31.929823Z","shell.execute_reply":"2023-02-01T14:50:31.975764Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" median mean std \nSurvived 0 1 0 1 0 1\nfam_members \n0 29.0 30.0 32.414234 31.811538 13.334968 11.970452\n1 30.0 29.0 32.126984 30.781842 11.599836 14.916443\n2 30.5 22.0 31.500000 21.911887 13.776141 17.363697\n3 25.0 14.0 22.833333 16.972381 11.196726 15.054360\n4 12.5 21.0 17.000000 31.000000 15.528775 19.974984\n5 9.0 24.0 17.578947 23.666667 18.637822 0.577350\n6 9.0 11.0 14.875000 15.750000 15.169871 16.070159\n7 12.5 NaN 15.666667 NaN 14.361987 NaN\n10 NaN NaN NaN NaN NaN NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medianmeanstd
Survived010101
fam_members
029.030.032.41423431.81153813.33496811.970452
130.029.032.12698430.78184211.59983614.916443
230.522.031.50000021.91188713.77614117.363697
325.014.022.83333316.97238111.19672615.054360
412.521.017.00000031.00000015.52877519.974984
59.024.017.57894723.66666718.6378220.577350
69.011.014.87500015.75000015.16987116.070159
712.5NaN15.666667NaN14.361987NaN
10NaNNaNNaNNaNNaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.fam_members, bins = 10)\ntitanic_train.fam_members.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.210511Z","iopub.execute_input":"2023-02-01T14:50:32.210873Z","iopub.status.idle":"2023-02-01T14:50:32.431170Z","shell.execute_reply.started":"2023-02-01T14:50:32.210842Z","shell.execute_reply":"2023-02-01T14:50:32.430235Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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twlqUP/Bzr6a6xtewKkAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"temp = titanic_train.groupby([\"fam_members\",\"Pclass\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=1).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:32.505200Z","iopub.execute_input":"2023-02-01T14:50:32.505646Z","iopub.status.idle":"2023-02-01T14:50:32.533253Z","shell.execute_reply.started":"2023-02-01T14:50:32.505607Z","shell.execute_reply":"2023-02-01T14:50:32.532232Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nfam_members Pclass \n0 1 0.236111 0.268519\n 2 0.369565 0.195652\n 3 0.519348 0.140530\n1 1 0.087963 0.236111\n 2 0.086957 0.097826\n 3 0.075356 0.040733\n2 1 0.027778 0.083333\n 2 0.054348 0.114130\n 3 0.054990 0.040733\n3 1 0.009259 0.023148\n 2 0.016304 0.054348\n 3 0.006110 0.012220\n4 1 NaN 0.009259\n 2 NaN 0.005435\n 3 0.024440 NaN\n5 1 0.009259 0.009259\n 2 NaN 0.005435\n 3 0.034623 NaN\n6 3 0.016293 0.008147\n7 3 0.012220 NaN\n10 3 0.014257 NaN","text/html":"
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Survived01
fam_membersPclass
010.2361110.268519
20.3695650.195652
30.5193480.140530
110.0879630.236111
20.0869570.097826
30.0753560.040733
210.0277780.083333
20.0543480.114130
30.0549900.040733
310.0092590.023148
20.0163040.054348
30.0061100.012220
41NaN0.009259
2NaN0.005435
30.024440NaN
510.0092590.009259
2NaN0.005435
30.034623NaN
630.0162930.008147
730.012220NaN
1030.014257NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Futher analysis and discussions\nThe data in their current states suggests that the distribution for the field _Survived_ is likely to be binomial. It has a lowest occurrences of surviving, which is a shocking statistic.\n\nThe passenger class has more occurrences of third classes. However, First and second class female passengers were more likely to survive the accident. First class male passengers had the also the highest survival rate. The Age is skewed to the left; some age may be unknown. It appears (see below) the younger passengers may have been traveling with other members of a family and perhaps reduced their survival rates; the largest familly appears to be travelling in third class. Most occurrences were families made of 0, 1, or 3 family members. \n\nThis analysis suggests that perhaps the passenger class familly, and the gender may have contributed to a higher survival rate. However, the familly size may have contributed to survived too. The classifiers will need to identify other patterns that may have contributed to survive the accident. It is likely to be quite challenging as no linear relationships or grouping may be present in the data.\n\n","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Survived\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=3).apply(lambda x: x / float(x.sum())) \ntemp.unstack() ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.247279Z","iopub.execute_input":"2023-02-01T14:50:33.247672Z","iopub.status.idle":"2023-02-01T14:50:33.275585Z","shell.execute_reply.started":"2023-02-01T14:50:33.247640Z","shell.execute_reply":"2023-02-01T14:50:33.274507Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Survived 0 1\nPclass fam_members Sex \n1 0 female 0.001821 0.096491\n male 0.091075 0.073099\n 1 female NaN 0.114035\n male 0.034608 0.035088\n 2 female NaN 0.038012\n male 0.010929 0.014620\n 3 female 0.003643 0.005848\n male NaN 0.008772\n 4 female NaN 0.005848\n 5 female NaN 0.005848\n male 0.003643 NaN\n2 0 female 0.005464 0.084795\n male 0.118397 0.020468\n 1 female 0.003643 0.049708\n male 0.025501 0.002924\n 2 female 0.001821 0.038012\n male 0.016393 0.023392\n 3 female NaN 0.026316\n male 0.005464 0.002924\n 4 female NaN 0.002924\n 5 female NaN 0.002924\n3 0 female 0.041894 0.108187\n male 0.422587 0.093567\n 1 female 0.025501 0.043860\n male 0.041894 0.014620\n 2 female 0.018215 0.035088\n male 0.030965 0.023392\n 3 female 0.001821 0.014620\n male 0.003643 0.002924\n 4 female 0.016393 NaN\n male 0.005464 NaN\n 5 female 0.009107 NaN\n male 0.021858 NaN\n 6 female 0.009107 0.008772\n male 0.005464 0.002924\n 7 female 0.003643 NaN\n male 0.007286 NaN\n 10 female 0.005464 NaN\n male 0.007286 NaN","text/html":"
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Survived01
Pclassfam_membersSex
10female0.0018210.096491
male0.0910750.073099
1femaleNaN0.114035
male0.0346080.035088
2femaleNaN0.038012
male0.0109290.014620
3female0.0036430.005848
maleNaN0.008772
4femaleNaN0.005848
5femaleNaN0.005848
male0.003643NaN
20female0.0054640.084795
male0.1183970.020468
1female0.0036430.049708
male0.0255010.002924
2female0.0018210.038012
male0.0163930.023392
3femaleNaN0.026316
male0.0054640.002924
4femaleNaN0.002924
5femaleNaN0.002924
30female0.0418940.108187
male0.4225870.093567
1female0.0255010.043860
male0.0418940.014620
2female0.0182150.035088
male0.0309650.023392
3female0.0018210.014620
male0.0036430.002924
4female0.016393NaN
male0.005464NaN
5female0.009107NaN
male0.021858NaN
6female0.0091070.008772
male0.0054640.002924
7female0.003643NaN
male0.007286NaN
10female0.005464NaN
male0.007286NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns = [\"Survived\",\"Pclass\",\"Age\", \"fam_members\"]\ntitanic_train = titanic_train[columns]\npd.plotting.scatter_matrix(titanic_train, diagonal='kde')","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:33.689687Z","iopub.execute_input":"2023-02-01T14:50:33.690876Z","iopub.status.idle":"2023-02-01T14:50:34.713667Z","shell.execute_reply.started":"2023-02-01T14:50:33.690832Z","shell.execute_reply":"2023-02-01T14:50:34.712910Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"array([[,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ],\n [,\n ,\n ,\n ]],\n dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The percentages suggests passenger class and gender may be the factor that may lead to survival. ","metadata":{}},{"cell_type":"markdown","source":"# Data preparation for classification\nThis section prepares the data for classifiers. We transform the data in suitable data types supported by the classifiers, remove null values and imputes some values when required. Some columns are deleted; they may be either character or we surmise not suitable for classification.","metadata":{}},{"cell_type":"markdown","source":"## Integer to float\nWe upload the data for a cleaning and display the columns with their data types to float on both datasets.","metadata":{}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.368853Z","iopub.execute_input":"2023-02-01T14:50:35.370121Z","iopub.status.idle":"2023-02-01T14:50:35.386127Z","shell.execute_reply.started":"2023-02-01T14:50:35.370069Z","shell.execute_reply":"2023-02-01T14:50:35.385078Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.dtypes\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:35.770203Z","iopub.execute_input":"2023-02-01T14:50:35.770631Z","iopub.status.idle":"2023-02-01T14:50:35.784625Z","shell.execute_reply.started":"2023-02-01T14:50:35.770596Z","shell.execute_reply":"2023-02-01T14:50:35.783551Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_train[\"SibSp\"] = titanic_train[\"SibSp\"].astype(float)\ntitanic_train[\"Parch\"] = titanic_train[\"Parch\"].astype(float)\ntitanic_train[\"Survived\"] = titanic_train[\"Survived\"].astype(float)\ntitanic_train[\"Pclass\"] = titanic_train[\"Pclass\"].astype(float)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.166628Z","iopub.execute_input":"2023-02-01T14:50:36.167303Z","iopub.status.idle":"2023-02-01T14:50:36.181459Z","shell.execute_reply.started":"2023-02-01T14:50:36.167252Z","shell.execute_reply":"2023-02-01T14:50:36.178943Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)\ntitanic_test[\"SibSp\"] = titanic_test[\"SibSp\"].astype(float)\ntitanic_test[\"Parch\"] = titanic_test[\"Parch\"].astype(float)\ntitanic_test[\"Pclass\"] = titanic_test[\"Pclass\"].astype(float)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:36.666190Z","iopub.execute_input":"2023-02-01T14:50:36.667397Z","iopub.status.idle":"2023-02-01T14:50:36.678991Z","shell.execute_reply.started":"2023-02-01T14:50:36.667345Z","shell.execute_reply":"2023-02-01T14:50:36.677862Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Null values \n\nWe remove all the nulls values from some of the columns; i.e., PassengerId, Fare, SibSp, Parch, and Embarked. Some fares were unknown, but all passengers ID was set to a unique number. ","metadata":{}},{"cell_type":"code","source":"titanic_train.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.489505Z","iopub.execute_input":"2023-02-01T14:50:37.489938Z","iopub.status.idle":"2023-02-01T14:50:37.497591Z","shell.execute_reply.started":"2023-02-01T14:50:37.489901Z","shell.execute_reply":"2023-02-01T14:50:37.496243Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.PassengerId.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:37.991239Z","iopub.execute_input":"2023-02-01T14:50:37.992524Z","iopub.status.idle":"2023-02-01T14:50:38.000114Z","shell.execute_reply.started":"2023-02-01T14:50:37.992478Z","shell.execute_reply":"2023-02-01T14:50:37.998884Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Fare.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.437569Z","iopub.execute_input":"2023-02-01T14:50:38.437966Z","iopub.status.idle":"2023-02-01T14:50:38.445766Z","shell.execute_reply.started":"2023-02-01T14:50:38.437933Z","shell.execute_reply":"2023-02-01T14:50:38.444961Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:38.994637Z","iopub.execute_input":"2023-02-01T14:50:38.995337Z","iopub.status.idle":"2023-02-01T14:50:39.002110Z","shell.execute_reply.started":"2023-02-01T14:50:38.995287Z","shell.execute_reply":"2023-02-01T14:50:39.000886Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Parch.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.436990Z","iopub.execute_input":"2023-02-01T14:50:39.437517Z","iopub.status.idle":"2023-02-01T14:50:39.445363Z","shell.execute_reply.started":"2023-02-01T14:50:39.437381Z","shell.execute_reply":"2023-02-01T14:50:39.444366Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:39.905392Z","iopub.execute_input":"2023-02-01T14:50:39.905832Z","iopub.status.idle":"2023-02-01T14:50:39.913740Z","shell.execute_reply.started":"2023-02-01T14:50:39.905797Z","shell.execute_reply":"2023-02-01T14:50:39.912816Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.SibSp.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.307865Z","iopub.execute_input":"2023-02-01T14:50:40.308905Z","iopub.status.idle":"2023-02-01T14:50:40.316347Z","shell.execute_reply.started":"2023-02-01T14:50:40.308849Z","shell.execute_reply":"2023-02-01T14:50:40.315199Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_test[\"Fare\"].isnull(),\"Fare\"] = -1.0\ntitanic_test.Fare.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:40.604978Z","iopub.execute_input":"2023-02-01T14:50:40.605706Z","iopub.status.idle":"2023-02-01T14:50:40.614214Z","shell.execute_reply.started":"2023-02-01T14:50:40.605660Z","shell.execute_reply":"2023-02-01T14:50:40.613381Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.071733Z","iopub.execute_input":"2023-02-01T14:50:41.072626Z","iopub.status.idle":"2023-02-01T14:50:41.081041Z","shell.execute_reply.started":"2023-02-01T14:50:41.072577Z","shell.execute_reply":"2023-02-01T14:50:41.079958Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.270757Z","iopub.execute_input":"2023-02-01T14:50:41.271157Z","iopub.status.idle":"2023-02-01T14:50:41.282542Z","shell.execute_reply.started":"2023-02-01T14:50:41.271122Z","shell.execute_reply":"2023-02-01T14:50:41.281267Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.500496Z","iopub.execute_input":"2023-02-01T14:50:41.500902Z","iopub.status.idle":"2023-02-01T14:50:41.515629Z","shell.execute_reply.started":"2023-02-01T14:50:41.500870Z","shell.execute_reply":"2023-02-01T14:50:41.514309Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:41.775897Z","iopub.execute_input":"2023-02-01T14:50:41.776267Z","iopub.status.idle":"2023-02-01T14:50:41.789089Z","shell.execute_reply.started":"2023-02-01T14:50:41.776237Z","shell.execute_reply":"2023-02-01T14:50:41.787661Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.000121Z","iopub.execute_input":"2023-02-01T14:50:42.001023Z","iopub.status.idle":"2023-02-01T14:50:42.016796Z","shell.execute_reply.started":"2023-02-01T14:50:42.000983Z","shell.execute_reply":"2023-02-01T14:50:42.015509Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.207362Z","iopub.execute_input":"2023-02-01T14:50:42.207763Z","iopub.status.idle":"2023-02-01T14:50:42.218799Z","shell.execute_reply.started":"2023-02-01T14:50:42.207729Z","shell.execute_reply":"2023-02-01T14:50:42.217490Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.428396Z","iopub.execute_input":"2023-02-01T14:50:42.429337Z","iopub.status.idle":"2023-02-01T14:50:42.442620Z","shell.execute_reply.started":"2023-02-01T14:50:42.429286Z","shell.execute_reply":"2023-02-01T14:50:42.441246Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.657623Z","iopub.execute_input":"2023-02-01T14:50:42.658000Z","iopub.status.idle":"2023-02-01T14:50:42.668231Z","shell.execute_reply.started":"2023-02-01T14:50:42.657969Z","shell.execute_reply":"2023-02-01T14:50:42.666865Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:42.893929Z","iopub.execute_input":"2023-02-01T14:50:42.894325Z","iopub.status.idle":"2023-02-01T14:50:42.904773Z","shell.execute_reply.started":"2023-02-01T14:50:42.894278Z","shell.execute_reply":"2023-02-01T14:50:42.903541Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.119666Z","iopub.execute_input":"2023-02-01T14:50:43.120081Z","iopub.status.idle":"2023-02-01T14:50:43.128473Z","shell.execute_reply.started":"2023-02-01T14:50:43.120043Z","shell.execute_reply":"2023-02-01T14:50:43.127000Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.558402Z","iopub.execute_input":"2023-02-01T14:50:43.558778Z","iopub.status.idle":"2023-02-01T14:50:43.566705Z","shell.execute_reply.started":"2023-02-01T14:50:43.558746Z","shell.execute_reply":"2023-02-01T14:50:43.565387Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.776835Z","iopub.execute_input":"2023-02-01T14:50:43.777203Z","iopub.status.idle":"2023-02-01T14:50:43.786826Z","shell.execute_reply.started":"2023-02-01T14:50:43.777173Z","shell.execute_reply":"2023-02-01T14:50:43.785678Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:43.999708Z","iopub.execute_input":"2023-02-01T14:50:44.000611Z","iopub.status.idle":"2023-02-01T14:50:44.012435Z","shell.execute_reply.started":"2023-02-01T14:50:44.000573Z","shell.execute_reply":"2023-02-01T14:50:44.011295Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.273326Z","iopub.execute_input":"2023-02-01T14:50:44.273733Z","iopub.status.idle":"2023-02-01T14:50:44.285873Z","shell.execute_reply.started":"2023-02-01T14:50:44.273696Z","shell.execute_reply":"2023-02-01T14:50:44.284650Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.530974Z","iopub.execute_input":"2023-02-01T14:50:44.531405Z","iopub.status.idle":"2023-02-01T14:50:44.543425Z","shell.execute_reply.started":"2023-02-01T14:50:44.531367Z","shell.execute_reply":"2023-02-01T14:50:44.542121Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:44.768732Z","iopub.execute_input":"2023-02-01T14:50:44.769126Z","iopub.status.idle":"2023-02-01T14:50:44.779844Z","shell.execute_reply.started":"2023-02-01T14:50:44.769091Z","shell.execute_reply":"2023-02-01T14:50:44.779079Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.009603Z","iopub.execute_input":"2023-02-01T14:50:45.009952Z","iopub.status.idle":"2023-02-01T14:50:45.019372Z","shell.execute_reply.started":"2023-02-01T14:50:45.009923Z","shell.execute_reply":"2023-02-01T14:50:45.018131Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.259413Z","iopub.execute_input":"2023-02-01T14:50:45.259813Z","iopub.status.idle":"2023-02-01T14:50:45.270859Z","shell.execute_reply.started":"2023-02-01T14:50:45.259779Z","shell.execute_reply":"2023-02-01T14:50:45.269750Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.492004Z","iopub.execute_input":"2023-02-01T14:50:45.492453Z","iopub.status.idle":"2023-02-01T14:50:45.505989Z","shell.execute_reply.started":"2023-02-01T14:50:45.492416Z","shell.execute_reply":"2023-02-01T14:50:45.504737Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.736753Z","iopub.execute_input":"2023-02-01T14:50:45.737917Z","iopub.status.idle":"2023-02-01T14:50:45.751164Z","shell.execute_reply.started":"2023-02-01T14:50:45.737860Z","shell.execute_reply":"2023-02-01T14:50:45.749612Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:45.979633Z","iopub.execute_input":"2023-02-01T14:50:45.980021Z","iopub.status.idle":"2023-02-01T14:50:45.987927Z","shell.execute_reply.started":"2023-02-01T14:50:45.979987Z","shell.execute_reply":"2023-02-01T14:50:45.986675Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarkment \nWe remove any NAs from the embarked column. We replace NaNs values with unknown. However, only the training datasets has some unknown values. It could lower accuracy on the prediction on the testing dataset.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.403953Z","iopub.execute_input":"2023-02-01T14:50:46.404807Z","iopub.status.idle":"2023-02-01T14:50:46.413830Z","shell.execute_reply.started":"2023-02-01T14:50:46.404750Z","shell.execute_reply":"2023-02-01T14:50:46.412619Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' nan]\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Embarked'].isna(),'Embarked'] = 'U'","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.636113Z","iopub.execute_input":"2023-02-01T14:50:46.637042Z","iopub.status.idle":"2023-02-01T14:50:46.643930Z","shell.execute_reply.started":"2023-02-01T14:50:46.637002Z","shell.execute_reply":"2023-02-01T14:50:46.642148Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"titanic_test.loc[titanic_test['Embarked'].isna(),'Embarked'] = 'U'\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:46.902953Z","iopub.execute_input":"2023-02-01T14:50:46.904202Z","iopub.status.idle":"2023-02-01T14:50:46.911244Z","shell.execute_reply.started":"2023-02-01T14:50:46.904144Z","shell.execute_reply":"2023-02-01T14:50:46.910042Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Embarked.unique())\nprint(\"Testing : \" , titanic_test.Embarked.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.161516Z","iopub.execute_input":"2023-02-01T14:50:47.162591Z","iopub.status.idle":"2023-02-01T14:50:47.169485Z","shell.execute_reply.started":"2023-02-01T14:50:47.162548Z","shell.execute_reply":"2023-02-01T14:50:47.168028Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Training : ['S' 'C' 'Q' 'U']\nTesting : ['Q' 'S' 'C']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Sex.unique())\nprint(\"Testing : \" , titanic_test.Sex.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.378976Z","iopub.execute_input":"2023-02-01T14:50:47.379690Z","iopub.status.idle":"2023-02-01T14:50:47.386404Z","shell.execute_reply.started":"2023-02-01T14:50:47.379649Z","shell.execute_reply":"2023-02-01T14:50:47.385047Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTesting : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Passenger class\nNo unknown values is present in both datasets.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \" , titanic_train.Pclass.unique())\nprint(\"Testing : \" , titanic_test.Pclass.unique())","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:47.799740Z","iopub.execute_input":"2023-02-01T14:50:47.800431Z","iopub.status.idle":"2023-02-01T14:50:47.807300Z","shell.execute_reply.started":"2023-02-01T14:50:47.800393Z","shell.execute_reply":"2023-02-01T14:50:47.806156Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Training : [3. 1. 2.]\nTesting : [3. 2. 1.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## PClass and Fare\n\nThe Fare decreases as the passenger class decrease. However the range is can be quite large and the data data imbalanced; there are a lot more third class tickets than other classes. So we scale robustly the data based on non-parametric statistics.","metadata":{}},{"cell_type":"code","source":"temp = titanic_train.groupby([\"Pclass\",\"Fare\"]).count()[\"PassengerId\"]\ntemp = temp.groupby(level=0).apply(lambda x:100 * x / float(x.sum())) \ntemp.unstack()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.235186Z","iopub.execute_input":"2023-02-01T14:50:48.235601Z","iopub.status.idle":"2023-02-01T14:50:48.274182Z","shell.execute_reply.started":"2023-02-01T14:50:48.235564Z","shell.execute_reply":"2023-02-01T14:50:48.272964Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"Fare 0.0000 4.0125 5.0000 6.2375 6.4375 6.4500 6.4958 \\\nPclass \n1.0 2.314815 NaN 0.462963 NaN NaN NaN NaN \n2.0 3.260870 NaN NaN NaN NaN NaN NaN \n3.0 0.814664 0.203666 NaN 0.203666 0.203666 0.203666 0.407332 \n\nFare 6.7500 6.8583 6.9500 ... 153.4625 164.8667 211.3375 \\\nPclass ... \n1.0 NaN NaN NaN ... 1.388889 0.925926 1.388889 \n2.0 NaN NaN NaN ... NaN NaN NaN \n3.0 0.407332 0.203666 0.203666 ... NaN NaN NaN \n\nFare 211.5000 221.7792 227.5250 247.5208 262.3750 263.0000 512.3292 \nPclass \n1.0 0.462963 0.462963 1.851852 0.925926 0.925926 1.851852 1.388889 \n2.0 NaN NaN NaN NaN NaN NaN NaN \n3.0 NaN NaN NaN NaN NaN NaN NaN \n\n[3 rows x 248 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Fare0.00004.01255.00006.23756.43756.45006.49586.75006.85836.9500...153.4625164.8667211.3375211.5000221.7792227.5250247.5208262.3750263.0000512.3292
Pclass
1.02.314815NaN0.462963NaNNaNNaNNaNNaNNaNNaN...1.3888890.9259261.3888890.4629630.4629631.8518520.9259260.9259261.8518521.388889
2.03.260870NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3.00.8146640.203666NaN0.2036660.2036660.2036660.4073320.4073320.2036660.203666...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n

3 rows × 248 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins=512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:48.451437Z","iopub.execute_input":"2023-02-01T14:50:48.451845Z","iopub.status.idle":"2023-02-01T14:50:49.547249Z","shell.execute_reply.started":"2023-02-01T14:50:48.451810Z","shell.execute_reply":"2023-02-01T14:50:49.546123Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 32.204208\nstd 49.693429\nmin 0.000000\n25% 7.910400\n50% 14.454200\n75% 31.000000\nmax 512.329200\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(10,8))\nplt.suptitle('')\ntitanic_train.boxplot(column=['Fare'], by='Pclass', ax=ax)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.549636Z","iopub.execute_input":"2023-02-01T14:50:49.550050Z","iopub.status.idle":"2023-02-01T14:50:49.804155Z","shell.execute_reply.started":"2023-02-01T14:50:49.550008Z","shell.execute_reply":"2023-02-01T14:50:49.803012Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.805690Z","iopub.execute_input":"2023-02-01T14:50:49.806699Z","iopub.status.idle":"2023-02-01T14:50:49.864940Z","shell.execute_reply.started":"2023-02-01T14:50:49.806662Z","shell.execute_reply":"2023-02-01T14:50:49.863879Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% max\nPclass \n1.0 216.0 84.154687 78.380373 0.0 30.92395 60.2875 93.5 512.3292\n2.0 184.0 20.662183 13.417399 0.0 13.00000 14.2500 26.0 73.5000\n3.0 491.0 13.675550 11.778142 0.0 7.75000 8.0500 15.5 69.5500","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0216.084.15468778.3803730.030.9239560.287593.5512.3292
2.0184.020.66218313.4173990.013.0000014.250026.073.5000
3.0491.013.67555011.7781420.07.750008.050015.569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_train.Fare.median()\nIQR_fare = titanic_train.Fare.quantile(0.75) - titanic_train.Fare.quantile(0.25)\ntitanic_train.loc[:,\"Fare\"] = (titanic_train.Fare - median_fare)/IQR_fare\nplt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:49.867034Z","iopub.execute_input":"2023-02-01T14:50:49.867360Z","iopub.status.idle":"2023-02-01T14:50:51.334840Z","shell.execute_reply.started":"2023-02-01T14:50:49.867301Z","shell.execute_reply":"2023-02-01T14:50:51.334033Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Fare, bins = 512)\ntitanic_train.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:51.336034Z","iopub.execute_input":"2023-02-01T14:50:51.336529Z","iopub.status.idle":"2023-02-01T14:50:52.406610Z","shell.execute_reply.started":"2023-02-01T14:50:51.336498Z","shell.execute_reply":"2023-02-01T14:50:52.405714Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.768745\nstd 2.152200\nmin -0.626005\n25% -0.283409\n50% 0.000000\n75% 0.716591\nmax 21.562738\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.407853Z","iopub.execute_input":"2023-02-01T14:50:52.408376Z","iopub.status.idle":"2023-02-01T14:50:52.415841Z","shell.execute_reply.started":"2023-02-01T14:50:52.408342Z","shell.execute_reply":"2023-02-01T14:50:52.414785Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We repeat the same process with the test dataset. The distribution is much different and therefore could lower the accuracy of the prediction.","metadata":{}},{"cell_type":"code","source":"titanic_test.groupby(\"Pclass\").describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.418261Z","iopub.execute_input":"2023-02-01T14:50:52.418629Z","iopub.status.idle":"2023-02-01T14:50:52.472603Z","shell.execute_reply.started":"2023-02-01T14:50:52.418596Z","shell.execute_reply":"2023-02-01T14:50:52.471219Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nPclass \n1.0 107.0 94.280297 84.435858 0.0000 30.10 60.0000 134.500000 \n2.0 93.0 22.202104 13.991877 9.6875 13.00 15.7500 26.000000 \n3.0 218.0 12.397936 10.817256 -1.0000 7.75 7.8958 14.327075 \n\n max \nPclass \n1.0 512.3292 \n2.0 73.5000 \n3.0 69.5500 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
Pclass
1.0107.094.28029784.4358580.000030.1060.0000134.500000512.3292
2.093.022.20210413.9918779.687513.0015.750026.00000073.5000
3.0218.012.39793610.817256-1.00007.757.895814.32707569.5500
\n
"},"metadata":{}}]},{"cell_type":"code","source":"median_fare = titanic_test.Fare.median()\nIQR_fare = titanic_test.Fare.quantile(0.75) - titanic_test.Fare.quantile(0.25)\ntitanic_test.loc[:,\"Fare\"] = (titanic_test.Fare - median_fare)/IQR_fare\nplt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:52.473824Z","iopub.execute_input":"2023-02-01T14:50:52.474155Z","iopub.status.idle":"2023-02-01T14:50:53.560939Z","shell.execute_reply.started":"2023-02-01T14:50:52.474123Z","shell.execute_reply":"2023-02-01T14:50:53.559872Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Fare, bins = 512)\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:53.562396Z","iopub.execute_input":"2023-02-01T14:50:53.562797Z","iopub.status.idle":"2023-02-01T14:50:54.622056Z","shell.execute_reply.started":"2023-02-01T14:50:53.562764Z","shell.execute_reply":"2023-02-01T14:50:54.620862Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.894354\nstd 2.369743\nmin -0.655504\n25% -0.278180\n50% 0.000000\n75% 0.721820\nmax 21.117807\nName: Fare, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.623442Z","iopub.execute_input":"2023-02-01T14:50:54.623854Z","iopub.status.idle":"2023-02-01T14:50:54.631562Z","shell.execute_reply.started":"2023-02-01T14:50:54.623823Z","shell.execute_reply":"2023-02-01T14:50:54.630628Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex object\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Age\nWe normalise the age to bring more the data towards the median. The previous transformation have brought more data centrally.","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.632748Z","iopub.execute_input":"2023-02-01T14:50:54.633113Z","iopub.status.idle":"2023-02-01T14:50:54.995183Z","shell.execute_reply.started":"2023-02-01T14:50:54.633084Z","shell.execute_reply":"2023-02-01T14:50:54.993205Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_train.Age.median()\nIQR_age = titanic_train.Age.quantile(0.75) - titanic_train.Age.quantile(0.25)\ntitanic_train.loc[:,\"Age\"] = (titanic_train.Age - median_age)/IQR_age\ntitanic_train.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:54.996341Z","iopub.execute_input":"2023-02-01T14:50:54.996637Z","iopub.status.idle":"2023-02-01T14:50:55.012393Z","shell.execute_reply.started":"2023-02-01T14:50:54.996609Z","shell.execute_reply":"2023-02-01T14:50:55.011269Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_train.Age, bins = 80)\ntitanic_train.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.014533Z","iopub.execute_input":"2023-02-01T14:50:55.015228Z","iopub.status.idle":"2023-02-01T14:50:55.377136Z","shell.execute_reply.started":"2023-02-01T14:50:55.015184Z","shell.execute_reply":"2023-02-01T14:50:55.376023Z"},"trusted":true},"execution_count":97,"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean -0.018547\nstd 1.000198\nmin -2.275385\n25% -0.615385\n50% 0.000000\n75% 0.384615\nmax 3.846154\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test[\"Age\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.378688Z","iopub.execute_input":"2023-02-01T14:50:55.379745Z","iopub.status.idle":"2023-02-01T14:50:55.727506Z","shell.execute_reply.started":"2023-02-01T14:50:55.379709Z","shell.execute_reply":"2023-02-01T14:50:55.726302Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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6N52X3A08A/AWdOKds7Gb2c/B7wWHd571DydRl/B3i0y/g48Jfd+t8GvgMcYvRS+denfJzfDTwwtGxdlu92lyeO/V0M7BhfBMx3x/gfgTMGlu804D+AN4+tG0y+SS6eoSpJDdqs0zKSpBOw3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatD/AmLJbG6fuoYqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"median_age = titanic_test.Age.median()\nIQR_age = titanic_test.Age.quantile(0.75) - titanic_test.Age.quantile(0.25)\ntitanic_test.loc[:,\"Age\"] = (titanic_test.Age - median_age)/IQR_age\ntitanic_test.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.731833Z","iopub.execute_input":"2023-02-01T14:50:55.732609Z","iopub.status.idle":"2023-02-01T14:50:55.747180Z","shell.execute_reply.started":"2023-02-01T14:50:55.732557Z","shell.execute_reply":"2023-02-01T14:50:55.746071Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test.Age, bins = 80)\ntitanic_test.Age.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:55.748583Z","iopub.execute_input":"2023-02-01T14:50:55.748898Z","iopub.status.idle":"2023-02-01T14:50:56.093344Z","shell.execute_reply.started":"2023-02-01T14:50:55.748868Z","shell.execute_reply":"2023-02-01T14:50:56.092150Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.079276\nstd 0.991767\nmin -2.261176\n25% -0.470588\n50% 0.000000\n75% 0.529412\nmax 3.686275\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Gender \nWe replace the male with 1 and female with the value 2.","metadata":{}},{"cell_type":"code","source":"print(\"Training : \", titanic_train['Sex'].unique())\nprint(\"Test : \", titanic_train['Sex'].unique())\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.094765Z","iopub.execute_input":"2023-02-01T14:50:56.095091Z","iopub.status.idle":"2023-02-01T14:50:56.103516Z","shell.execute_reply.started":"2023-02-01T14:50:56.095062Z","shell.execute_reply":"2023-02-01T14:50:56.102411Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Training : ['male' 'female']\nTest : ['male' 'female']\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_train[\"Sex\"] = titanic_train[\"Sex\"].astype(float)\ntitanic_train.groupby(\"Sex\").count()[\"PassengerId\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.104821Z","iopub.execute_input":"2023-02-01T14:50:56.105350Z","iopub.status.idle":"2023-02-01T14:50:56.122953Z","shell.execute_reply.started":"2023-02-01T14:50:56.105306Z","shell.execute_reply":"2023-02-01T14:50:56.122030Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 577\n2.0 314\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"Sex\"].replace({\"male\":1.0, \"female\":2.0}, inplace = True)\ntitanic_test[\"Sex\"] = titanic_test[\"Sex\"].astype(float)\ntitanic_test.groupby(\"Sex\").count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.124259Z","iopub.execute_input":"2023-02-01T14:50:56.124612Z","iopub.status.idle":"2023-02-01T14:50:56.139408Z","shell.execute_reply.started":"2023-02-01T14:50:56.124581Z","shell.execute_reply":"2023-02-01T14:50:56.138058Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 266\n2.0 152\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sibling and parentage\n\nWe add both sibling, parents, and children into a family variables. ","metadata":{}},{"cell_type":"code","source":"titanic_train[\"SibSp\"].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.141402Z","iopub.execute_input":"2023-02-01T14:50:56.141813Z","iopub.status.idle":"2023-02-01T14:50:56.148230Z","shell.execute_reply.started":"2023-02-01T14:50:56.141777Z","shell.execute_reply":"2023-02-01T14:50:56.147382Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"array([1., 0., 3., 4., 2., 5., 8.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Parch\"].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.149745Z","iopub.execute_input":"2023-02-01T14:50:56.150349Z","iopub.status.idle":"2023-02-01T14:50:56.159952Z","shell.execute_reply.started":"2023-02-01T14:50:56.150294Z","shell.execute_reply":"2023-02-01T14:50:56.158924Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"array([0., 1., 2., 5., 3., 4., 6.])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"fam_members\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\ntitanic_train[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.161871Z","iopub.execute_input":"2023-02-01T14:50:56.162175Z","iopub.status.idle":"2023-02-01T14:50:56.176837Z","shell.execute_reply.started":"2023-02-01T14:50:56.162147Z","shell.execute_reply":"2023-02-01T14:50:56.175684Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 0.904602\nstd 1.613459\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.178360Z","iopub.execute_input":"2023-02-01T14:50:56.178747Z","iopub.status.idle":"2023-02-01T14:50:56.191340Z","shell.execute_reply.started":"2023-02-01T14:50:56.178698Z","shell.execute_reply":"2023-02-01T14:50:56.190355Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test[\"fam_members\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\ntitanic_test[\"fam_members\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.195050Z","iopub.execute_input":"2023-02-01T14:50:56.195448Z","iopub.status.idle":"2023-02-01T14:50:56.209129Z","shell.execute_reply.started":"2023-02-01T14:50:56.195400Z","shell.execute_reply":"2023-02-01T14:50:56.207967Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 0.839713\nstd 1.519072\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 10.000000\nName: fam_members, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.210664Z","iopub.execute_input":"2023-02-01T14:50:56.211090Z","iopub.status.idle":"2023-02-01T14:50:56.219640Z","shell.execute_reply.started":"2023-02-01T14:50:56.211049Z","shell.execute_reply":"2023-02-01T14:50:56.218550Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.221452Z","iopub.execute_input":"2023-02-01T14:50:56.222189Z","iopub.status.idle":"2023-02-01T14:50:56.231508Z","shell.execute_reply.started":"2023-02-01T14:50:56.222146Z","shell.execute_reply":"2023-02-01T14:50:56.230398Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nName object\nSex float64\nAge float64\nSibSp float64\nParch float64\nTicket object\nFare float64\nCabin object\nEmbarked object\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.232676Z","iopub.execute_input":"2023-02-01T14:50:56.233089Z","iopub.status.idle":"2023-02-01T14:50:56.242657Z","shell.execute_reply.started":"2023-02-01T14:50:56.233048Z","shell.execute_reply":"2023-02-01T14:50:56.241737Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.244097Z","iopub.execute_input":"2023-02-01T14:50:56.244427Z","iopub.status.idle":"2023-02-01T14:50:56.251459Z","shell.execute_reply.started":"2023-02-01T14:50:56.244398Z","shell.execute_reply":"2023-02-01T14:50:56.250542Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.256041Z","iopub.execute_input":"2023-02-01T14:50:56.256485Z","iopub.status.idle":"2023-02-01T14:50:56.265940Z","shell.execute_reply.started":"2023-02-01T14:50:56.256450Z","shell.execute_reply":"2023-02-01T14:50:56.264711Z"},"trusted":true},"execution_count":113,"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.267253Z","iopub.execute_input":"2023-02-01T14:50:56.267740Z","iopub.status.idle":"2023-02-01T14:50:56.278020Z","shell.execute_reply.started":"2023-02-01T14:50:56.267696Z","shell.execute_reply":"2023-02-01T14:50:56.276748Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"titanic_test[\"Embarked\"].replace({\"U\":1.0, \"S\":2.0, \"Q\": 3.0, \"C\":4.0}, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.315420Z","iopub.execute_input":"2023-02-01T14:50:56.315791Z","iopub.status.idle":"2023-02-01T14:50:56.322971Z","shell.execute_reply.started":"2023-02-01T14:50:56.315760Z","shell.execute_reply":"2023-02-01T14:50:56.322090Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## Columns to drop \nWe drop some columns; they may have too many unknown values. Some of them may be dependent statistical variables. We assume the price of a ticket may be dependent of the fare. ","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Name\", axis = 1, inplace = True)\ntitanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_train.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.722307Z","iopub.execute_input":"2023-02-01T14:50:56.722753Z","iopub.status.idle":"2023-02-01T14:50:56.744122Z","shell.execute_reply.started":"2023-02-01T14:50:56.722718Z","shell.execute_reply":"2023-02-01T14:50:56.743299Z"},"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.drop(\"Name\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.drop(\"SibSp\", axis = 1, inplace = True)\ntitanic_test.drop(\"Parch\", axis = 1, inplace = True)\n\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:56.963356Z","iopub.execute_input":"2023-02-01T14:50:56.963753Z","iopub.status.idle":"2023-02-01T14:50:56.979754Z","shell.execute_reply.started":"2023-02-01T14:50:56.963719Z","shell.execute_reply":"2023-02-01T14:50:56.978543Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We make of both datasets. These copies will be used to analysed the predictions values from all the classifiers.","metadata":{}},{"cell_type":"code","source":"results_test = titanic_test.copy(deep = True)\nresults_train = titanic_train.copy(deep = True) ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:57.429442Z","iopub.execute_input":"2023-02-01T14:50:57.429827Z","iopub.status.idle":"2023-02-01T14:50:57.435755Z","shell.execute_reply.started":"2023-02-01T14:50:57.429796Z","shell.execute_reply":"2023-02-01T14:50:57.434439Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"markdown","source":"# Method : Logistic regression\n\nOur first classifier is a logistic regression. We surmise it may be the most suitable methods as two classes of labels exist; survived or not. The data is imbalanced towards perishing sadly. So we add some class weight to represent this situation in the data. \n\nWe choose the passenger class, sex, familly members. We surmise the passenger class, gender and being part of a familly or not may have influenced surviving the accident. The training dataset is split into training and validation for validating the model fitting. ","metadata":{}},{"cell_type":"markdown","source":"## Preparation Cross validation \nWe show how the transformation have affected both datasets","metadata":{}},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.108812Z","iopub.execute_input":"2023-02-01T14:50:58.109845Z","iopub.status.idle":"2023-02-01T14:50:58.118552Z","shell.execute_reply.started":"2023-02-01T14:50:58.109806Z","shell.execute_reply":"2023-02-01T14:50:58.117356Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.354904Z","iopub.execute_input":"2023-02-01T14:50:58.355573Z","iopub.status.idle":"2023-02-01T14:50:58.362764Z","shell.execute_reply.started":"2023-02-01T14:50:58.355531Z","shell.execute_reply":"2023-02-01T14:50:58.361542Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"(891, 8)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.590264Z","iopub.execute_input":"2023-02-01T14:50:58.591668Z","iopub.status.idle":"2023-02-01T14:50:58.600773Z","shell.execute_reply.started":"2023-02-01T14:50:58.591627Z","shell.execute_reply":"2023-02-01T14:50:58.599216Z"},"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:58.804713Z","iopub.execute_input":"2023-02-01T14:50:58.805085Z","iopub.status.idle":"2023-02-01T14:50:58.812599Z","shell.execute_reply.started":"2023-02-01T14:50:58.805054Z","shell.execute_reply":"2023-02-01T14:50:58.811376Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"(418, 7)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Split data sets for cross validation\n\nWe use a stratified shuffle split to aim at reducing the variation between the training and validation datasets.","metadata":{}},{"cell_type":"code","source":"\n\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n#X = X[x_cols]\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.267374Z","iopub.execute_input":"2023-02-01T14:50:59.267771Z","iopub.status.idle":"2023-02-01T14:50:59.288554Z","shell.execute_reply.started":"2023-02-01T14:50:59.267735Z","shell.execute_reply":"2023-02-01T14:50:59.287476Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We keep the passengers ids for building up the training dataset results. It will be used to compare all the classifier.","metadata":{}},{"cell_type":"code","source":"x_train_pass_id = X_train[\"PassengerId\"]\nx_train_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.704437Z","iopub.execute_input":"2023-02-01T14:50:59.704815Z","iopub.status.idle":"2023-02-01T14:50:59.714204Z","shell.execute_reply.started":"2023-02-01T14:50:59.704783Z","shell.execute_reply":"2023-02-01T14:50:59.713337Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"844 845.0\n316 317.0\n768 769.0\n255 256.0\n130 131.0\n ... \n476 477.0\n58 59.0\n736 737.0\n462 463.0\n747 748.0\nName: PassengerId, Length: 534, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"x_cols =[\"Pclass\",\"Sex\",\"fam_members\"]\nX_train = X_train[x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:50:59.933799Z","iopub.execute_input":"2023-02-01T14:50:59.934191Z","iopub.status.idle":"2023-02-01T14:50:59.947540Z","shell.execute_reply.started":"2023-02-01T14:50:59.934158Z","shell.execute_reply":"2023-02-01T14:50:59.946577Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n844 3.0 1.0 0.0\n316 2.0 2.0 1.0\n768 3.0 1.0 1.0\n255 3.0 2.0 2.0\n130 3.0 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
8443.01.00.0
3162.02.01.0
7683.01.01.0
2553.02.02.0
1303.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid[\"PassengerId\"]\nx_valid_pass_id\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.149697Z","iopub.execute_input":"2023-02-01T14:51:00.150120Z","iopub.status.idle":"2023-02-01T14:51:00.160439Z","shell.execute_reply.started":"2023-02-01T14:51:00.150083Z","shell.execute_reply":"2023-02-01T14:51:00.159106Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"369 370.0\n541 542.0\n196 197.0\n810 811.0\n427 428.0\n ... \n174 175.0\n297 298.0\n244 245.0\n38 39.0\n371 372.0\nName: PassengerId, Length: 357, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.356159Z","iopub.execute_input":"2023-02-01T14:51:00.357062Z","iopub.status.idle":"2023-02-01T14:51:00.370786Z","shell.execute_reply.started":"2023-02-01T14:51:00.357017Z","shell.execute_reply":"2023-02-01T14:51:00.369619Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n369 1.0 2.0 0.0\n541 3.0 2.0 6.0\n196 3.0 1.0 0.0\n810 3.0 1.0 0.0\n427 2.0 2.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
3691.02.00.0
5413.02.06.0
1963.01.00.0
8103.01.00.0
4272.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test.copy(deep = True)\nX_test = X_test[x_cols]\nX_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:00.599111Z","iopub.execute_input":"2023-02-01T14:51:00.599521Z","iopub.status.idle":"2023-02-01T14:51:00.616521Z","shell.execute_reply.started":"2023-02-01T14:51:00.599483Z","shell.execute_reply":"2023-02-01T14:51:00.615356Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members\n0 3.0 1.0 0.0\n1 3.0 2.0 1.0\n2 2.0 1.0 0.0\n3 3.0 1.0 0.0\n4 3.0 2.0 2.0\n.. ... ... ...\n413 3.0 1.0 0.0\n414 1.0 2.0 0.0\n415 3.0 1.0 0.0\n416 3.0 1.0 0.0\n417 3.0 1.0 2.0\n\n[418 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_members
03.01.00.0
13.02.01.0
22.01.00.0
33.01.00.0
43.02.02.0
............
4133.01.00.0
4141.02.00.0
4153.01.00.0
4163.01.00.0
4173.01.02.0
\n

418 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Model fitting","metadata":{}},{"cell_type":"markdown","source":"We fit the model using a stochastic average gradient. We achieve approximately 82% accuracy on the validation dataset. There is not sign of over fitting. ","metadata":{}},{"cell_type":"code","source":"classifier = LogisticRegression(random_state = 0, C = 1000, max_iter= 10000, \n solver=\"sag\", penalty=\"l2\",class_weight={0:6.,1:4})\nclassifier.fit(X_train, y_train)\nclassifier.coef_","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.275079Z","iopub.execute_input":"2023-02-01T14:51:01.275483Z","iopub.status.idle":"2023-02-01T14:51:01.291372Z","shell.execute_reply.started":"2023-02-01T14:51:01.275450Z","shell.execute_reply":"2023-02-01T14:51:01.290133Z"},"trusted":true},"execution_count":129,"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"array([[-0.96687438, 2.71046703, -0.09242397]])"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_train = classifier.score(X_train, y_train)\nlog_reg_score_train","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.505908Z","iopub.execute_input":"2023-02-01T14:51:01.507102Z","iopub.status.idle":"2023-02-01T14:51:01.519460Z","shell.execute_reply.started":"2023-02-01T14:51:01.507059Z","shell.execute_reply":"2023-02-01T14:51:01.518123Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"0.7921348314606742"},"metadata":{}}]},{"cell_type":"code","source":"log_reg_score_valid = classifier.score(X_valid, y_valid)\nlog_reg_score_valid","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:01.727365Z","iopub.execute_input":"2023-02-01T14:51:01.727743Z","iopub.status.idle":"2023-02-01T14:51:01.737787Z","shell.execute_reply.started":"2023-02-01T14:51:01.727712Z","shell.execute_reply":"2023-02-01T14:51:01.736406Z"},"trusted":true},"execution_count":131,"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":"0.8207282913165266"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nTwo confusion matrices show an improvement on predicting the validation dataset. We also store the predicted results in the results_train dataframe. We will use this dataframe later on to analyse difference between classifiers. \n\n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = classifier.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.212411Z","iopub.execute_input":"2023-02-01T14:51:02.212812Z","iopub.status.idle":"2023-02-01T14:51:02.223463Z","shell.execute_reply.started":"2023-02-01T14:51:02.212779Z","shell.execute_reply":"2023-02-01T14:51:02.222427Z"},"trusted":true},"execution_count":132,"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":"array([[297, 32],\n [ 79, 126]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.417280Z","iopub.execute_input":"2023-02-01T14:51:02.417687Z","iopub.status.idle":"2023-02-01T14:51:02.426591Z","shell.execute_reply.started":"2023-02-01T14:51:02.417653Z","shell.execute_reply":"2023-02-01T14:51:02.425177Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"Accuracy : 0.7921348314606742\nMisclassfication : 0.20786516853932585\nSensitivivity : 0.9027355623100304\nSpecificity : 0.6146341463414634\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = classifier.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.661227Z","iopub.execute_input":"2023-02-01T14:51:02.661653Z","iopub.status.idle":"2023-02-01T14:51:02.672901Z","shell.execute_reply.started":"2023-02-01T14:51:02.661618Z","shell.execute_reply":"2023-02-01T14:51:02.671790Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:02.907929Z","iopub.execute_input":"2023-02-01T14:51:02.908917Z","iopub.status.idle":"2023-02-01T14:51:02.916300Z","shell.execute_reply.started":"2023-02-01T14:51:02.908877Z","shell.execute_reply":"2023-02-01T14:51:02.915176Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"Accuracy : 0.8207282913165266\nMisclassfication : 0.1792717086834734\nSensitivivity : 0.9363636363636364\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.367005Z","iopub.execute_input":"2023-02-01T14:51:03.367441Z","iopub.status.idle":"2023-02-01T14:51:03.372440Z","shell.execute_reply.started":"2023-02-01T14:51:03.367404Z","shell.execute_reply":"2023-02-01T14:51:03.371375Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"y_pred = classifier.predict(X_train)\nlog_reg_pred = X_train.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_train\nlog_reg_pred[\"PassengerId\"] = x_train_pass_id\nlog_reg_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.610590Z","iopub.execute_input":"2023-02-01T14:51:03.610967Z","iopub.status.idle":"2023-02-01T14:51:03.632961Z","shell.execute_reply.started":"2023-02-01T14:51:03.610936Z","shell.execute_reply":"2023-02-01T14:51:03.631856Z"},"trusted":true},"execution_count":137,"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n844 3.0 1.0 0.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 0.0 769.0\n255 3.0 2.0 2.0 0.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_memberslr_y_predyPassengerId
8443.01.00.00.00.0845.0
3162.02.01.01.01.0317.0
7683.01.01.00.00.0769.0
2553.02.02.00.01.0256.0
1303.01.00.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(log_reg_pred[[\"PassengerId\",\"y\", \"lr_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:03.870519Z","iopub.execute_input":"2023-02-01T14:51:03.870935Z","iopub.status.idle":"2023-02-01T14:51:03.899083Z","shell.execute_reply.started":"2023-02-01T14:51:03.870900Z","shell.execute_reply":"2023-02-01T14:51:03.898021Z"},"trusted":true},"execution_count":138,"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 NaN NaN \n2 0.0 1.0 1.0 \n3 1.0 NaN NaN \n4 0.0 NaN NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.0NaNNaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.0NaNNaN
45.00.03.01.00.384615-0.2773632.00.0NaNNaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = classifier.predict(X_valid)\nlog_reg_pred = X_valid.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"y\"] = y_valid\nlog_reg_pred[\"PassengerId\"] = x_valid_pass_id\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.094193Z","iopub.execute_input":"2023-02-01T14:51:04.094610Z","iopub.status.idle":"2023-02-01T14:51:04.120418Z","shell.execute_reply.started":"2023-02-01T14:51:04.094576Z","shell.execute_reply":"2023-02-01T14:51:04.119350Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred y PassengerId\n369 1.0 2.0 0.0 1.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 1.0 428.0\n.. ... ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 1.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 0.0 0.0 39.0\n371 3.0 1.0 1.0 0.0 0.0 372.0\n\n[357 rows x 6 columns]","text/html":"
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PclassSexfam_memberslr_y_predyPassengerId
3691.02.00.01.01.0370.0
5413.02.06.00.00.0542.0
1963.01.00.00.00.0197.0
8103.01.00.00.00.0811.0
4272.02.00.01.01.0428.0
.....................
1741.01.00.00.00.0175.0
2971.02.03.01.00.0298.0
2443.01.00.00.00.0245.0
383.02.02.00.00.039.0
3713.01.01.00.00.0372.0
\n

357 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"y\"] = log_reg_pred[\"y\"]\nresults_train.loc[results_train.PassengerId.isin(log_reg_pred.PassengerId), \"lr_y_pred\"] = log_reg_pred[\"lr_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:04.330333Z","iopub.execute_input":"2023-02-01T14:51:04.330729Z","iopub.status.idle":"2023-02-01T14:51:04.353404Z","shell.execute_reply.started":"2023-02-01T14:51:04.330694Z","shell.execute_reply":"2023-02-01T14:51:04.352359Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred \n0 1.0 0.0 0.0 \n1 1.0 1.0 1.0 \n2 0.0 1.0 1.0 \n3 1.0 1.0 1.0 \n4 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"We start with the training dataset. It may be quite unconventional, but it can help us understanding better the features of the data.","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.059608Z","iopub.execute_input":"2023-02-01T14:51:05.059995Z","iopub.status.idle":"2023-02-01T14:51:05.077377Z","shell.execute_reply.started":"2023-02-01T14:51:05.059959Z","shell.execute_reply":"2023-02-01T14:51:05.076249Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n255 3.0 2.0 2.0 1.0 0.0\n707 1.0 1.0 0.0 1.0 0.0\n172 3.0 2.0 2.0 1.0 0.0\n78 2.0 1.0 2.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
2553.02.02.01.00.0
7071.01.00.01.00.0
1723.02.02.01.00.0
782.01.02.01.00.0
2333.02.06.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We complete the same activities to the validation dataset. It appears many male first class passengers traveling alone may have survived more than we anticipated. ","metadata":{}},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.569495Z","iopub.execute_input":"2023-02-01T14:51:05.569879Z","iopub.status.idle":"2023-02-01T14:51:05.589621Z","shell.execute_reply.started":"2023-02-01T14:51:05.569846Z","shell.execute_reply":"2023-02-01T14:51:05.588487Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n340 2.0 1.0 2.0 1.0 0.0\n534 3.0 2.0 0.0 0.0 1.0\n279 3.0 2.0 2.0 1.0 0.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3402.01.02.01.00.0
5343.02.00.00.01.0
2793.02.02.01.00.0
6071.01.00.01.00.0
8043.01.00.01.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:05.798455Z","iopub.execute_input":"2023-02-01T14:51:05.799489Z","iopub.status.idle":"2023-02-01T14:51:05.813581Z","shell.execute_reply.started":"2023-02-01T14:51:05.799450Z","shell.execute_reply":"2023-02-01T14:51:05.812556Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 0.0 3\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 1.0 4\n 2.0 1.0 0.0 4\n 2.0 0.0 4\n 3.0 2.0 0.0 2\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.295513Z","iopub.execute_input":"2023-02-01T14:51:06.296134Z","iopub.status.idle":"2023-02-01T14:51:06.315914Z","shell.execute_reply.started":"2023-02-01T14:51:06.296088Z","shell.execute_reply":"2023-02-01T14:51:06.314875Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n130 3.0 1.0 0.0 0.0 0.0\n110 1.0 1.0 0.0 0.0 0.0","text/html":"
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PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
1303.01.00.00.00.0
1101.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:06.545374Z","iopub.execute_input":"2023-02-01T14:51:06.546123Z","iopub.status.idle":"2023-02-01T14:51:06.565170Z","shell.execute_reply.started":"2023-02-01T14:51:06.546085Z","shell.execute_reply":"2023-02-01T14:51:06.564022Z"},"trusted":true},"execution_count":145,"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 2.0 1.0 9\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 0.0 3\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 1.0 4\n 2.0 1.0 0.0 10\n 2.0 0.0 5\n 3.0 1.0 0.0 2\n 2.0 0.0 1\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. It appears \n\nOther elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees classifiers. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.019601Z","iopub.execute_input":"2023-02-01T14:51:07.020764Z","iopub.status.idle":"2023-02-01T14:51:07.038884Z","shell.execute_reply.started":"2023-02-01T14:51:07.020723Z","shell.execute_reply":"2023-02-01T14:51:07.037796Z"},"trusted":true},"execution_count":146,"outputs":[{"execution_count":146,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.380694Z","iopub.execute_input":"2023-02-01T14:51:07.381775Z","iopub.status.idle":"2023-02-01T14:51:07.399161Z","shell.execute_reply.started":"2023-02-01T14:51:07.381734Z","shell.execute_reply":"2023-02-01T14:51:07.397965Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 6\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 1.0 11\n 2.0 1.0 0.0 7\n 2.0 0.0 5\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Predict with testing dataset","metadata":{}},{"cell_type":"code","source":"y_pred = classifier.predict(X_test)\nlog_reg_pred = X_test.copy()\nlog_reg_pred[\"lr_y_pred\"] = y_pred\nlog_reg_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nlog_reg_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:07.781717Z","iopub.execute_input":"2023-02-01T14:51:07.782101Z","iopub.status.idle":"2023-02-01T14:51:07.809230Z","shell.execute_reply.started":"2023-02-01T14:51:07.782070Z","shell.execute_reply":"2023-02-01T14:51:07.808079Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members lr_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 1.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 0.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
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PclassSexfam_memberslr_y_predPassengerId
03.01.00.00.0892.0
13.02.01.01.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.00.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
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418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.028966Z","iopub.execute_input":"2023-02-01T14:51:08.030264Z","iopub.status.idle":"2023-02-01T14:51:08.036547Z","shell.execute_reply.started":"2023-02-01T14:51:08.030211Z","shell.execute_reply":"2023-02-01T14:51:08.035240Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.293378Z","iopub.execute_input":"2023-02-01T14:51:08.294544Z","iopub.status.idle":"2023-02-01T14:51:08.309861Z","shell.execute_reply.started":"2023-02-01T14:51:08.294483Z","shell.execute_reply":"2023-02-01T14:51:08.308466Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":" PassengerId lr_y_pred\n0 892.0 0.0\n1 893.0 1.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 0.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdlr_y_pred
0892.00.0
1893.01.0
2894.00.0
3895.00.0
4896.00.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
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418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(log_reg_pred[[\"PassengerId\",\"lr_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:08.513449Z","iopub.execute_input":"2023-02-01T14:51:08.513843Z","iopub.status.idle":"2023-02-01T14:51:08.535503Z","shell.execute_reply.started":"2023-02-01T14:51:08.513810Z","shell.execute_reply":"2023-02-01T14:51:08.534386Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 0.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_pred
0892.03.01.00.431373-0.2810053.00.00.0
1893.03.02.01.411765-0.3161762.01.01.0
2894.02.01.02.588235-0.2021843.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: K-Nearest-neighbourn","metadata":{}},{"cell_type":"markdown","source":"We explore whether a reduction of statistical variables may be beneficial to the classification. We focus our model fitting on the same statistical variables as the logistic regression. \n\n\nThe K-NN classifier overfits to the training dataset. We have yet to find a better result. So Decision tree may have found its limit. ","metadata":{}},{"cell_type":"markdown","source":"## Model fitting\nWe discover the hyper-parametrisation of approximately 7 neighbors and the algorithm set the brute.","metadata":{}},{"cell_type":"code","source":"neighbors = range(2, 100)\nfor neighbor in neighbors:\n knn = KNeighborsClassifier(n_neighbors = neighbor, algorithm=\"brute\", weights = \"distance\", p=2)\n knn.fit(X_train,y_train)\n train_score = knn.score(X_train, y_train)\n valid_score = knn.score(X_valid, y_valid)\n print(\" - n neighbor : \", neighbor , \" - train score : \", train_score, \" - valid score : \", valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:09.565134Z","iopub.execute_input":"2023-02-01T14:51:09.565542Z","iopub.status.idle":"2023-02-01T14:51:12.977246Z","shell.execute_reply.started":"2023-02-01T14:51:09.565506Z","shell.execute_reply":"2023-02-01T14:51:12.975689Z"},"trusted":true},"execution_count":152,"outputs":[{"name":"stdout","text":" - n neighbor : 2 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 3 - train score : 0.7771535580524345 - valid score : 0.7478991596638656\n - n neighbor : 4 - train score : 0.8089887640449438 - valid score : 0.7591036414565826\n - n neighbor : 5 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 6 - train score : 0.8164794007490637 - valid score : 0.7927170868347339\n - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 8 - train score : 0.8202247191011236 - valid score : 0.7899159663865546\n - n neighbor : 9 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 10 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 11 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 12 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 13 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 14 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 15 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 16 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 17 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 18 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 19 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 20 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 21 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 22 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 23 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 24 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 25 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 26 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 27 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 28 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 29 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 30 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 31 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 32 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 33 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 34 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 35 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 36 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 37 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 38 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 39 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 40 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 41 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 42 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 43 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 44 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 45 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 46 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 47 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 48 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 49 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 50 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 51 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 52 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 53 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 54 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 55 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 56 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 57 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 58 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 59 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 60 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 61 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 62 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 63 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 64 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 65 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 66 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 67 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 68 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 69 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 70 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 71 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 72 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n - n neighbor : 73 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 74 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 75 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 76 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 77 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 78 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 79 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 80 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 81 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 82 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 83 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 84 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 85 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 86 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 87 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 88 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 89 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 90 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 91 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 92 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 93 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 94 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 95 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 96 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 97 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 98 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - n neighbor : 99 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors = 7, algorithm=\"brute\", weights = \"distance\", p=2)\nknn.fit(X_train,y_train)\nknn_train_score = knn.score(X_train, y_train)\nknn_valid_score = knn.score(X_valid, y_valid)\nprint(\" - n neighbor : \", 7 , \" - train score : \", knn_train_score, \" - valid score : \", knn_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:12.986323Z","iopub.execute_input":"2023-02-01T14:51:12.992081Z","iopub.status.idle":"2023-02-01T14:51:13.043083Z","shell.execute_reply.started":"2023-02-01T14:51:12.992006Z","shell.execute_reply":"2023-02-01T14:51:13.041491Z"},"trusted":true},"execution_count":153,"outputs":[{"name":"stdout","text":" - n neighbor : 7 - train score : 0.8258426966292135 - valid score : 0.7871148459383753\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = knn.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.051296Z","iopub.execute_input":"2023-02-01T14:51:13.052276Z","iopub.status.idle":"2023-02-01T14:51:13.094020Z","shell.execute_reply.started":"2023-02-01T14:51:13.052210Z","shell.execute_reply":"2023-02-01T14:51:13.092537Z"},"trusted":true},"execution_count":154,"outputs":[{"execution_count":154,"output_type":"execute_result","data":{"text/plain":"array([[299, 30],\n [ 63, 142]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.104344Z","iopub.execute_input":"2023-02-01T14:51:13.109854Z","iopub.status.idle":"2023-02-01T14:51:13.138605Z","shell.execute_reply.started":"2023-02-01T14:51:13.109782Z","shell.execute_reply":"2023-02-01T14:51:13.137094Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"Accuracy : 0.8258426966292135\nMisclassfication : 0.17415730337078653\nSensitivivity : 0.9088145896656535\nSpecificity : 0.6926829268292682\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.141155Z","iopub.execute_input":"2023-02-01T14:51:13.151686Z","iopub.status.idle":"2023-02-01T14:51:13.183541Z","shell.execute_reply.started":"2023-02-01T14:51:13.151614Z","shell.execute_reply":"2023-02-01T14:51:13.181982Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.190465Z","iopub.execute_input":"2023-02-01T14:51:13.191601Z","iopub.status.idle":"2023-02-01T14:51:13.214243Z","shell.execute_reply.started":"2023-02-01T14:51:13.191536Z","shell.execute_reply":"2023-02-01T14:51:13.212831Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stdout","text":"Accuracy : 0.7871148459383753\nMisclassfication : 0.21288515406162464\nSensitivivity : 0.8818181818181818\nSpecificity : 0.635036496350365\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.216513Z","iopub.execute_input":"2023-02-01T14:51:13.217361Z","iopub.status.idle":"2023-02-01T14:51:13.226018Z","shell.execute_reply.started":"2023-02-01T14:51:13.217286Z","shell.execute_reply":"2023-02-01T14:51:13.224351Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(X_train)\nknn_pred = X_train.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_train_pass_id\nknn_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.228136Z","iopub.execute_input":"2023-02-01T14:51:13.229804Z","iopub.status.idle":"2023-02-01T14:51:13.289272Z","shell.execute_reply.started":"2023-02-01T14:51:13.229740Z","shell.execute_reply":"2023-02-01T14:51:13.287745Z"},"trusted":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n844 3.0 1.0 0.0 0.0 845.0\n316 2.0 2.0 1.0 1.0 317.0\n768 3.0 1.0 1.0 0.0 769.0\n255 3.0 2.0 2.0 1.0 256.0\n130 3.0 1.0 0.0 0.0 131.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
8443.01.00.00.0845.0
3162.02.01.01.0317.0
7683.01.01.00.0769.0
2553.02.02.01.0256.0
1303.01.00.00.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(knn_pred[[\"PassengerId\", \"knn_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.297719Z","iopub.execute_input":"2023-02-01T14:51:13.302941Z","iopub.status.idle":"2023-02-01T14:51:13.361563Z","shell.execute_reply.started":"2023-02-01T14:51:13.302872Z","shell.execute_reply":"2023-02-01T14:51:13.359941Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = knn.predict(X_valid)\nknn_pred = X_valid.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = x_valid_pass_id\nknn_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.366098Z","iopub.execute_input":"2023-02-01T14:51:13.367128Z","iopub.status.idle":"2023-02-01T14:51:13.414267Z","shell.execute_reply.started":"2023-02-01T14:51:13.367081Z","shell.execute_reply":"2023-02-01T14:51:13.412764Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n369 1.0 2.0 0.0 1.0 370.0\n541 3.0 2.0 6.0 0.0 542.0\n196 3.0 1.0 0.0 0.0 197.0\n810 3.0 1.0 0.0 0.0 811.0\n427 2.0 2.0 0.0 1.0 428.0\n.. ... ... ... ... ...\n174 1.0 1.0 0.0 0.0 175.0\n297 1.0 2.0 3.0 0.0 298.0\n244 3.0 1.0 0.0 0.0 245.0\n38 3.0 2.0 2.0 1.0 39.0\n371 3.0 1.0 1.0 0.0 372.0\n\n[357 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
3691.02.00.01.0370.0
5413.02.06.00.0542.0
1963.01.00.00.0197.0
8103.01.00.00.0811.0
4272.02.00.01.0428.0
..................
1741.01.00.00.0175.0
2971.02.03.00.0298.0
2443.01.00.00.0245.0
383.02.02.01.039.0
3713.01.01.00.0372.0
\n

357 rows × 5 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(knn_pred.PassengerId), \"knn_y_pred\"] = knn_pred[\"knn_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.416656Z","iopub.execute_input":"2023-02-01T14:51:13.417577Z","iopub.status.idle":"2023-02-01T14:51:13.474919Z","shell.execute_reply.started":"2023-02-01T14:51:13.417518Z","shell.execute_reply":"2023-02-01T14:51:13.473392Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred \n0 1.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 \n3 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.552373Z","iopub.execute_input":"2023-02-01T14:51:13.552777Z","iopub.status.idle":"2023-02-01T14:51:13.575185Z","shell.execute_reply.started":"2023-02-01T14:51:13.552741Z","shell.execute_reply":"2023-02-01T14:51:13.573826Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n707 1.0 1.0 0.0 1.0 0.0\n233 3.0 2.0 6.0 1.0 0.0\n788 3.0 1.0 3.0 1.0 0.0\n183 2.0 1.0 3.0 1.0 0.0\n654 3.0 2.0 0.0 0.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
7071.01.00.01.00.0
2333.02.06.01.00.0
7883.01.03.01.00.0
1832.01.03.01.00.0
6543.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:13.851998Z","iopub.execute_input":"2023-02-01T14:51:13.852446Z","iopub.status.idle":"2023-02-01T14:51:13.868236Z","shell.execute_reply.started":"2023-02-01T14:51:13.852408Z","shell.execute_reply":"2023-02-01T14:51:13.867490Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 2.0 1.0 1\n 1.0 1.0 0.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 2\n 1.0 1.0 0.0 1\n 2.0 1.0 2\n 2.0 1.0 1.0 3\n 2.0 1.0 1\n 3.0 1.0 0.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 0.0 4\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 3.0 1.0 0.0 1\n 2.0 1.0 1\n 6.0 2.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = knn.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.057420Z","iopub.execute_input":"2023-02-01T14:51:14.057804Z","iopub.status.idle":"2023-02-01T14:51:14.084011Z","shell.execute_reply.started":"2023-02-01T14:51:14.057773Z","shell.execute_reply":"2023-02-01T14:51:14.082464Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"array([[194, 26],\n [ 50, 87]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.355738Z","iopub.execute_input":"2023-02-01T14:51:14.356164Z","iopub.status.idle":"2023-02-01T14:51:14.375540Z","shell.execute_reply.started":"2023-02-01T14:51:14.356115Z","shell.execute_reply":"2023-02-01T14:51:14.374287Z"},"trusted":true},"execution_count":166,"outputs":[{"execution_count":166,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n534 3.0 2.0 0.0 0.0 1.0\n607 1.0 1.0 0.0 1.0 0.0\n804 3.0 1.0 0.0 1.0 0.0\n429 3.0 1.0 0.0 1.0 0.0\n501 3.0 2.0 0.0 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
5343.02.00.00.01.0
6071.01.00.01.00.0
8043.01.00.01.00.0
4293.01.00.01.00.0
5013.02.00.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:14.597501Z","iopub.execute_input":"2023-02-01T14:51:14.597895Z","iopub.status.idle":"2023-02-01T14:51:14.613504Z","shell.execute_reply.started":"2023-02-01T14:51:14.597865Z","shell.execute_reply":"2023-02-01T14:51:14.612422Z"},"trusted":true},"execution_count":167,"outputs":[{"execution_count":167,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 9\n 1.0 1.0 0.0 6\n 2.0 1.0 1.0 6\n 3.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 1.0 1\n 2.0 1.0 1.0 6\n3.0 0.0 1.0 0.0 13\n 2.0 1.0 8\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 2.0 1.0 0.0 4\n 2.0 1.0 5\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well across the passenger class, family and gender. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.104177Z","iopub.execute_input":"2023-02-01T14:51:15.104569Z","iopub.status.idle":"2023-02-01T14:51:15.123111Z","shell.execute_reply.started":"2023-02-01T14:51:15.104537Z","shell.execute_reply":"2023-02-01T14:51:15.121935Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n844 3.0 1.0 0.0 0.0 0.0\n316 2.0 2.0 1.0 1.0 1.0\n768 3.0 1.0 1.0 0.0 0.0\n255 3.0 2.0 2.0 1.0 1.0\n130 3.0 1.0 0.0 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersY_trueY_pred
8443.01.00.00.00.0
3162.02.01.01.01.0
7683.01.01.00.00.0
2553.02.02.01.01.0
1303.01.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.344115Z","iopub.execute_input":"2023-02-01T14:51:15.344558Z","iopub.status.idle":"2023-02-01T14:51:15.362850Z","shell.execute_reply.started":"2023-02-01T14:51:15.344502Z","shell.execute_reply":"2023-02-01T14:51:15.361620Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 2.0 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 4\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 1.0 14\n 1.0 1.0 0.0 10\n 2.0 1.0 8\n 2.0 1.0 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 1.0 29\n 1.0 1.0 0.0 15\n 2.0 0.0 10\n 2.0 1.0 0.0 10\n 2.0 1.0 8\n 3.0 1.0 0.0 2\n 2.0 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:15.857448Z","iopub.execute_input":"2023-02-01T14:51:15.857837Z","iopub.status.idle":"2023-02-01T14:51:15.877163Z","shell.execute_reply.started":"2023-02-01T14:51:15.857806Z","shell.execute_reply":"2023-02-01T14:51:15.875923Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members Y_true Y_pred\n369 1.0 2.0 0.0 1.0 1.0\n541 3.0 2.0 6.0 0.0 0.0\n196 3.0 1.0 0.0 0.0 0.0\n810 3.0 1.0 0.0 0.0 0.0\n427 2.0 2.0 0.0 1.0 1.0","text/html":"
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PclassSexfam_membersY_trueY_pred
3691.02.00.01.01.0
5413.02.06.00.00.0
1963.01.00.00.00.0
8103.01.00.00.00.0
4272.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.132579Z","iopub.execute_input":"2023-02-01T14:51:16.132970Z","iopub.status.idle":"2023-02-01T14:51:16.150755Z","shell.execute_reply.started":"2023-02-01T14:51:16.132936Z","shell.execute_reply":"2023-02-01T14:51:16.149943Z"},"trusted":true},"execution_count":171,"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 1.0 1\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 0.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 15\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 94\n 2.0 1.0 8\n 1.0 1.0 0.0 8\n 2.0 0.0 4\n 2.0 1.0 0.0 7\n 2.0 1.0 4\n 3.0 2.0 1.0 2\n 4.0 1.0 0.0 1\n 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower.","metadata":{}},{"cell_type":"markdown","source":"## Prediction on the test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = knn.predict(X_test)\nknn_pred = X_test.copy()\nknn_pred[\"knn_y_pred\"] = y_pred\nknn_pred[\"PassengerId\"] = titanic_test.PassengerId\n#log_reg_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n# \"Survived\": y_pred})\n\n#log_reg_pred.to_csv('../output/log_reg_pred.csv', index=False)\nknn_pred\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:16.910077Z","iopub.execute_input":"2023-02-01T14:51:16.910492Z","iopub.status.idle":"2023-02-01T14:51:16.964596Z","shell.execute_reply.started":"2023-02-01T14:51:16.910456Z","shell.execute_reply":"2023-02-01T14:51:16.963157Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" Pclass Sex fam_members knn_y_pred PassengerId\n0 3.0 1.0 0.0 0.0 892.0\n1 3.0 2.0 1.0 0.0 893.0\n2 2.0 1.0 0.0 0.0 894.0\n3 3.0 1.0 0.0 0.0 895.0\n4 3.0 2.0 2.0 1.0 896.0\n.. ... ... ... ... ...\n413 3.0 1.0 0.0 0.0 1305.0\n414 1.0 2.0 0.0 1.0 1306.0\n415 3.0 1.0 0.0 0.0 1307.0\n416 3.0 1.0 0.0 0.0 1308.0\n417 3.0 1.0 2.0 0.0 1309.0\n\n[418 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassSexfam_membersknn_y_predPassengerId
03.01.00.00.0892.0
13.02.01.00.0893.0
22.01.00.00.0894.0
33.01.00.00.0895.0
43.02.02.01.0896.0
..................
4133.01.00.00.01305.0
4141.02.00.01.01306.0
4153.01.00.00.01307.0
4163.01.00.00.01308.0
4173.01.02.00.01309.0
\n

418 rows × 5 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.178878Z","iopub.execute_input":"2023-02-01T14:51:17.179931Z","iopub.status.idle":"2023-02-01T14:51:17.185405Z","shell.execute_reply.started":"2023-02-01T14:51:17.179876Z","shell.execute_reply":"2023-02-01T14:51:17.184219Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"code","source":"knn_pred[[\"PassengerId\",\"knn_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.372559Z","iopub.execute_input":"2023-02-01T14:51:17.372948Z","iopub.status.idle":"2023-02-01T14:51:17.390909Z","shell.execute_reply.started":"2023-02-01T14:51:17.372914Z","shell.execute_reply":"2023-02-01T14:51:17.389533Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" PassengerId knn_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdknn_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(knn_pred[[\"PassengerId\",\"knn_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:17.671274Z","iopub.execute_input":"2023-02-01T14:51:17.672432Z","iopub.status.idle":"2023-02-01T14:51:17.693960Z","shell.execute_reply.started":"2023-02-01T14:51:17.672382Z","shell.execute_reply":"2023-02-01T14:51:17.692706Z"},"trusted":true},"execution_count":175,"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred \n0 0.0 0.0 \n1 1.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 1.0 ","text/html":"
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PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.0
2894.02.01.02.588235-0.2021843.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method : Decision Trees\n\nWe use a decision tree classifier and some automated search of the hyper-parametrisation to discover suitable hyper-parameters and validate the quality of a model. \n","metadata":{}},{"cell_type":"code","source":"\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\n\n#X = X.apply(pd.to_numeric)\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.109673Z","iopub.execute_input":"2023-02-01T14:51:18.110073Z","iopub.status.idle":"2023-02-01T14:51:18.128404Z","shell.execute_reply.started":"2023-02-01T14:51:18.110036Z","shell.execute_reply":"2023-02-01T14:51:18.127375Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"0.0 0.616105\n1.0 0.383895\nName: Survived, dtype: float64 \n\n\n0.0 0.616246\n1.0 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = [\"Fare\",\"Pclass\",\"Sex\",\"Embarked\",\"fam_members\", \"Age\"]\nx_train_pass_id = X_train.PassengerId\nX_train = X_train [x_cols]\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.370422Z","iopub.execute_input":"2023-02-01T14:51:18.370800Z","iopub.status.idle":"2023-02-01T14:51:18.388440Z","shell.execute_reply.started":"2023-02-01T14:51:18.370767Z","shell.execute_reply":"2023-02-01T14:51:18.387202Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538\n768 0.419921 3.0 1.0 3.0 1.0 0.000000\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769","text/html":"
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FarePclassSexEmbarkedfam_membersAge
844-0.2508363.01.02.00.0-1.000000
3160.5000432.02.02.01.0-0.461538
7680.4199213.01.03.01.00.000000
2550.0342843.02.04.02.0-0.076923
130-0.2840413.01.04.00.00.230769
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nx_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\nX_valid.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.609801Z","iopub.execute_input":"2023-02-01T14:51:18.610554Z","iopub.status.idle":"2023-02-01T14:51:18.628148Z","shell.execute_reply.started":"2023-02-01T14:51:18.610505Z","shell.execute_reply":"2023-02-01T14:51:18.626956Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154","text/html":"
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FarePclassSexEmbarkedfam_membersAge
3692.3753461.02.04.00.0-0.461538
5410.7285013.02.02.06.0-1.615385
196-0.2903563.01.03.00.00.000000
810-0.2844013.01.02.00.0-0.307692
4270.5000432.02.02.00.0-0.846154
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nX = titanic_test.copy(deep = True)\nX_test = X[x_cols]\nX_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:18.826406Z","iopub.execute_input":"2023-02-01T14:51:18.826797Z","iopub.status.idle":"2023-02-01T14:51:18.835436Z","shell.execute_reply.started":"2023-02-01T14:51:18.826766Z","shell.execute_reply":"2023-02-01T14:51:18.834526Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"Index(['Fare', 'Pclass', 'Sex', 'Embarked', 'fam_members', 'Age'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Decision Tree classifier\n\nWe explore the maximum depths hyper parameter using a deterministic and incremental search. Then we applied the most efficient parametrisation. We chose a low maximum depth, as the model may be overfitting.","metadata":{}},{"cell_type":"code","source":"\ndepths = range(3, 200)\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth = depth, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n # Train Decision Tree Classifer\n clf = clf.fit(X_train,y_train)\n train_score = clf.score(X_train,y_train)\n valid_score = clf.score(X_valid,y_valid)\n print(\"- depth : \", depth, \" - train score : \", train_score, \" - valid score : \", valid_score)\n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:19.301726Z","iopub.execute_input":"2023-02-01T14:51:19.302125Z","iopub.status.idle":"2023-02-01T14:51:20.492365Z","shell.execute_reply.started":"2023-02-01T14:51:19.302089Z","shell.execute_reply":"2023-02-01T14:51:20.491051Z"},"trusted":true},"execution_count":180,"outputs":[{"name":"stdout","text":"- depth : 3 - train score : 0.8295880149812734 - valid score : 0.8011204481792717\n- depth : 4 - train score : 0.8295880149812734 - valid score : 0.8151260504201681\n- depth : 5 - train score : 0.8595505617977528 - valid score : 0.8067226890756303\n- depth : 6 - train score : 0.8820224719101124 - valid score : 0.8235294117647058\n- depth : 7 - train score : 0.8895131086142322 - valid score : 0.8179271708683473\n- depth : 8 - train score : 0.9063670411985019 - valid score : 0.7927170868347339\n- depth : 9 - train score : 0.9119850187265918 - valid score : 0.7843137254901961\n- depth : 10 - train score : 0.9250936329588015 - valid score : 0.803921568627451\n- depth : 11 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n- depth : 12 - train score : 0.9550561797752809 - valid score : 0.773109243697479\n- depth : 13 - train score : 0.9625468164794008 - valid score : 0.7955182072829131\n- depth : 14 - train score : 0.9662921348314607 - valid score : 0.7787114845938375\n- depth : 15 - train score : 0.9700374531835206 - valid score : 0.7927170868347339\n- depth : 16 - train score : 0.9737827715355806 - valid score : 0.7787114845938375\n- depth : 17 - train score : 0.9756554307116105 - valid score : 0.7871148459383753\n- depth : 18 - train score : 0.9794007490636704 - valid score : 0.7871148459383753\n- depth : 19 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 20 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 21 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 22 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 23 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 24 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 25 - train score : 0.9812734082397003 - valid score : 0.7899159663865546\n- depth : 26 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 27 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 28 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 29 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 30 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 31 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 32 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 33 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 34 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 35 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 36 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 37 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 38 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 39 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 40 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 41 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 42 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 43 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 44 - train score : 0.9812734082397003 - valid score : 0.7703081232492998\n- depth : 45 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 46 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 47 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 48 - train score : 0.9812734082397003 - valid score : 0.7675070028011205\n- depth : 49 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 50 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 51 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 52 - train score : 0.9812734082397003 - valid score : 0.7619047619047619\n- depth : 53 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 54 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 55 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n- depth : 56 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 57 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 58 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 59 - train score : 0.9812734082397003 - valid score : 0.773109243697479\n- depth : 60 - train score : 0.9812734082397003 - valid score : 0.7843137254901961\n- depth : 61 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 62 - train score : 0.9812734082397003 - valid score : 0.7871148459383753\n- depth : 63 - train score : 0.9812734082397003 - valid score : 0.7759103641456583\n- depth : 64 - train score : 0.9812734082397003 - 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valid score : 0.7703081232492998\n- depth : 197 - train score : 0.9812734082397003 - valid score : 0.7787114845938375\n- depth : 198 - train score : 0.9812734082397003 - valid score : 0.7647058823529411\n- depth : 199 - train score : 0.9812734082397003 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(max_depth = 8, criterion =\"entropy\",class_weight={0:6.,1:4}, max_features = 6)\n\n\n# Train Decision Tree Classifer\nclf = clf.fit(X_train,y_train)\nclf_train_score = clf.score(X_train,y_train)\nclf_valid_score = clf.score(X_valid,y_valid)\nprint(\"- depth : \", 8, \" - train score : \", clf_train_score, \" - valid score : \", clf_valid_score)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.494270Z","iopub.execute_input":"2023-02-01T14:51:20.494649Z","iopub.status.idle":"2023-02-01T14:51:20.508968Z","shell.execute_reply.started":"2023-02-01T14:51:20.494617Z","shell.execute_reply":"2023-02-01T14:51:20.507560Z"},"trusted":true},"execution_count":181,"outputs":[{"name":"stdout","text":"- depth : 8 - train score : 0.9082397003745318 - valid score : 0.8151260504201681\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover that the gender, Fare and age could be contribute to the classification. It constrast to our previous assumptions for KNN and logistic regression.","metadata":{}},{"cell_type":"code","source":"importances = clf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.510227Z","iopub.execute_input":"2023-02-01T14:51:20.510578Z","iopub.status.idle":"2023-02-01T14:51:20.523335Z","shell.execute_reply.started":"2023-02-01T14:51:20.510548Z","shell.execute_reply":"2023-02-01T14:51:20.521845Z"},"trusted":true},"execution_count":182,"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":" 0\n0.200193 Fare\n0.125949 Pclass\n0.315820 Sex\n0.025783 Embarked\n0.094918 fam_members\n0.237337 Age","text/html":"
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0
0.200193Fare
0.125949Pclass
0.315820Sex
0.025783Embarked
0.094918fam_members
0.237337Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.525411Z","iopub.execute_input":"2023-02-01T14:51:20.525712Z","iopub.status.idle":"2023-02-01T14:51:20.536265Z","shell.execute_reply.started":"2023-02-01T14:51:20.525685Z","shell.execute_reply":"2023-02-01T14:51:20.535549Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"array([[326, 3],\n [ 46, 159]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.736687Z","iopub.execute_input":"2023-02-01T14:51:20.737047Z","iopub.status.idle":"2023-02-01T14:51:20.744835Z","shell.execute_reply.started":"2023-02-01T14:51:20.737016Z","shell.execute_reply":"2023-02-01T14:51:20.743620Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"Accuracy : 0.9082397003745318\nMisclassfication : 0.09176029962546817\nSensitivivity : 0.9908814589665653\nSpecificity : 0.775609756097561\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:20.940682Z","iopub.execute_input":"2023-02-01T14:51:20.941080Z","iopub.status.idle":"2023-02-01T14:51:20.950745Z","shell.execute_reply.started":"2023-02-01T14:51:20.941045Z","shell.execute_reply":"2023-02-01T14:51:20.949939Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.156573Z","iopub.execute_input":"2023-02-01T14:51:21.157555Z","iopub.status.idle":"2023-02-01T14:51:21.164777Z","shell.execute_reply.started":"2023-02-01T14:51:21.157504Z","shell.execute_reply":"2023-02-01T14:51:21.163996Z"},"trusted":true},"execution_count":186,"outputs":[{"name":"stdout","text":"Accuracy : 0.8151260504201681\nMisclassfication : 0.18487394957983194\nSensitivivity : 0.9318181818181818\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.602984Z","iopub.execute_input":"2023-02-01T14:51:21.603408Z","iopub.status.idle":"2023-02-01T14:51:21.609433Z","shell.execute_reply.started":"2023-02-01T14:51:21.603369Z","shell.execute_reply":"2023-02-01T14:51:21.608257Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"y_pred = clf.predict(X_train)\nclf_pred = X_train.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_train_pass_id\nclf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:21.801023Z","iopub.execute_input":"2023-02-01T14:51:21.801826Z","iopub.status.idle":"2023-02-01T14:51:21.826292Z","shell.execute_reply.started":"2023-02-01T14:51:21.801783Z","shell.execute_reply":"2023-02-01T14:51:21.825118Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769231.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(clf_pred[[\"PassengerId\", \"clf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.073441Z","iopub.execute_input":"2023-02-01T14:51:22.073853Z","iopub.status.idle":"2023-02-01T14:51:22.100768Z","shell.execute_reply.started":"2023-02-01T14:51:22.073817Z","shell.execute_reply":"2023-02-01T14:51:22.099989Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = clf.predict(X_valid)\nclf_pred = X_valid.copy()\nclf_pred[\"clf_y_pred\"] = y_pred\nclf_pred[\"PassengerId\"] = x_valid_pass_id\nclf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.313331Z","iopub.execute_input":"2023-02-01T14:51:22.314186Z","iopub.status.idle":"2023-02-01T14:51:22.339255Z","shell.execute_reply.started":"2023-02-01T14:51:22.314149Z","shell.execute_reply":"2023-02-01T14:51:22.338531Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age clf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 0.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
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FarePclassSexEmbarkedfam_membersAgeclf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538460.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
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357 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(clf_pred.PassengerId), \"clf_y_pred\"] = clf_pred[\"clf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:22.503867Z","iopub.execute_input":"2023-02-01T14:51:22.504541Z","iopub.status.idle":"2023-02-01T14:51:22.530946Z","shell.execute_reply.started":"2023-02-01T14:51:22.504500Z","shell.execute_reply":"2023-02-01T14:51:22.529880Z"},"trusted":true},"execution_count":191,"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassification\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n","metadata":{}},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.197164Z","iopub.execute_input":"2023-02-01T14:51:23.197598Z","iopub.status.idle":"2023-02-01T14:51:23.221279Z","shell.execute_reply.started":"2023-02-01T14:51:23.197559Z","shell.execute_reply":"2023-02-01T14:51:23.220173Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n220 -0.277363 3.0 1.0 2.0 0.0 -1.076923 1.0 0.0\n510 -0.290356 3.0 1.0 3.0 0.0 -0.076923 1.0 0.0\n724 1.673732 1.0 1.0 2.0 1.0 -0.230769 1.0 0.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
220-0.2773633.01.02.00.0-1.0769231.00.0
510-0.2903563.01.03.00.0-0.0769231.00.0
7241.6737321.01.02.01.0-0.2307691.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.536909Z","iopub.execute_input":"2023-02-01T14:51:23.537537Z","iopub.status.idle":"2023-02-01T14:51:23.553252Z","shell.execute_reply.started":"2023-02-01T14:51:23.537491Z","shell.execute_reply":"2023-02-01T14:51:23.552369Z"},"trusted":true},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 10\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n3.0 0.0 1.0 0.0 14\n 2.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.819458Z","iopub.execute_input":"2023-02-01T14:51:23.819831Z","iopub.status.idle":"2023-02-01T14:51:23.828371Z","shell.execute_reply.started":"2023-02-01T14:51:23.819802Z","shell.execute_reply":"2023-02-01T14:51:23.827545Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"array([[205, 15],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:23.944899Z","iopub.execute_input":"2023-02-01T14:51:23.945939Z","iopub.status.idle":"2023-02-01T14:51:24.401522Z","shell.execute_reply.started":"2023-02-01T14:51:23.945899Z","shell.execute_reply":"2023-02-01T14:51:24.400330Z"},"trusted":true},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.403612Z","iopub.execute_input":"2023-02-01T14:51:24.404043Z","iopub.status.idle":"2023-02-01T14:51:24.424956Z","shell.execute_reply.started":"2023-02-01T14:51:24.404007Z","shell.execute_reply":"2023-02-01T14:51:24.423814Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0\n501 -0.290356 3.0 2.0 3.0 0.0 -0.692308 0.0 1.0\n17 -0.062981 2.0 1.0 2.0 0.0 0.000000 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
429-0.2773633.01.02.00.00.1538461.00.0
501-0.2903563.02.03.00.0-0.6923080.01.0
17-0.0629812.01.02.00.00.0000001.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.426286Z","iopub.execute_input":"2023-02-01T14:51:24.426719Z","iopub.status.idle":"2023-02-01T14:51:24.444950Z","shell.execute_reply.started":"2023-02-01T14:51:24.426673Z","shell.execute_reply":"2023-02-01T14:51:24.443790Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 6\n 1.0 1\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n 3.0 1.0 0.0 2\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 3\n 1.0 1\n 1.0 2.0 0.0 1\n 2.0 1.0 1.0 1\n3.0 0.0 1.0 0.0 12\n 1.0 3\n 2.0 0.0 4\n 1.0 2\n 1.0 1.0 0.0 1\n 2.0 0.0 9\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 2\n 2.0 1.0 3\n 4.0 1.0 1.0 1\n 6.0 1.0 0.0 1\n 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:24.655585Z","iopub.execute_input":"2023-02-01T14:51:24.655981Z","iopub.status.idle":"2023-02-01T14:51:25.270872Z","shell.execute_reply.started":"2023-02-01T14:51:24.655946Z","shell.execute_reply":"2023-02-01T14:51:25.270073Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.272439Z","iopub.execute_input":"2023-02-01T14:51:25.273391Z","iopub.status.idle":"2023-02-01T14:51:25.295346Z","shell.execute_reply.started":"2023-02-01T14:51:25.273342Z","shell.execute_reply":"2023-02-01T14:51:25.294366Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 1.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
2550.0342843.02.04.02.0-0.0769231.01.0
130-0.2840413.01.04.00.00.2307690.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.310893Z","iopub.execute_input":"2023-02-01T14:51:25.311294Z","iopub.status.idle":"2023-02-01T14:51:25.332606Z","shell.execute_reply.started":"2023-02-01T14:51:25.311259Z","shell.execute_reply":"2023-02-01T14:51:25.331521Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 6\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 2\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 0.0 1\n 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 1.0 5\n 2.0 0.0 14\n 1.0 23\n 1.0 1.0 0.0 15\n 1.0 3\n 2.0 0.0 10\n 1.0 4\n 2.0 1.0 0.0 10\n 1.0 2\n 2.0 0.0 5\n 1.0 8\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 7\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 2\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:25.617532Z","iopub.execute_input":"2023-02-01T14:51:25.617910Z","iopub.status.idle":"2023-02-01T14:51:27.648580Z","shell.execute_reply.started":"2023-02-01T14:51:25.617879Z","shell.execute_reply":"2023-02-01T14:51:27.647383Z"},"trusted":true},"execution_count":201,"outputs":[{"execution_count":201,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.650898Z","iopub.execute_input":"2023-02-01T14:51:27.651397Z","iopub.status.idle":"2023-02-01T14:51:27.674977Z","shell.execute_reply.started":"2023-02-01T14:51:27.651353Z","shell.execute_reply":"2023-02-01T14:51:27.673660Z"},"trusted":true},"execution_count":202,"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
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FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.676558Z","iopub.execute_input":"2023-02-01T14:51:27.676918Z","iopub.status.idle":"2023-02-01T14:51:27.695988Z","shell.execute_reply.started":"2023-02-01T14:51:27.676883Z","shell.execute_reply":"2023-02-01T14:51:27.694729Z"},"trusted":true},"execution_count":203,"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 16\n 1.0 3\n 2.0 1.0 11\n 1.0 1.0 0.0 6\n 2.0 1.0 19\n 2.0 1.0 0.0 6\n 2.0 1.0 4\n 3.0 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 2\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 12\n 1.0 1.0 0.0 4\n 2.0 1.0 8\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 91\n 1.0 1\n 2.0 0.0 6\n 1.0 4\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 1.0 2\n 2.0 1.0 0.0 5\n 1.0 3\n 2.0 0.0 2\n 1.0 4\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 5\n 2.0 0.0 3\n 6.0 2.0 0.0 3\n 7.0 1.0 0.0 4\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:27.698581Z","iopub.execute_input":"2023-02-01T14:51:27.699104Z","iopub.status.idle":"2023-02-01T14:51:28.312451Z","shell.execute_reply.started":"2023-02-01T14:51:27.699061Z","shell.execute_reply":"2023-02-01T14:51:28.311698Z"},"trusted":true},"execution_count":204,"outputs":[{"execution_count":204,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.313663Z","iopub.execute_input":"2023-02-01T14:51:28.314115Z","iopub.status.idle":"2023-02-01T14:51:28.742585Z","shell.execute_reply.started":"2023-02-01T14:51:28.314085Z","shell.execute_reply":"2023-02-01T14:51:28.741404Z"},"trusted":true},"execution_count":205,"outputs":[{"execution_count":205,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:28.744143Z","iopub.execute_input":"2023-02-01T14:51:28.744835Z","iopub.status.idle":"2023-02-01T14:51:29.161694Z","shell.execute_reply.started":"2023-02-01T14:51:28.744790Z","shell.execute_reply":"2023-02-01T14:51:29.160329Z"},"trusted":true},"execution_count":206,"outputs":[{"execution_count":206,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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1m2ThN9VJL3Jvm+JD8104kAYAc5ZRQAAKApp4wCAAA0JQgBAACamslzCB/xiEeM48ePz2LXAAAArbzlLW953xjjirN9biZBePz48dx8882z2DUAAEArVfWO+/ucU0YBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABo6uisBzhMqmrWI9zHGGPWIwAAAPuUI4Q7aIyxI2+Pe/7rd2xbAAAA90cQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0NXUQVtWDq+qXqurXqurtVfUdOzEYAAAAu+voDmzjriRfOMa4o6qOJdmoqp8ZY7x5B7YNAADALpk6CMcYI8kdkw+PTd7GtNsFAABgd+3Icwir6khVvTXJHyZ54xjjF3diuwAAAOyeHQnCMcbdY4wnJnl0ks+tqvl736eqnl1VN1fVzbfeeutO7BYAAIAp7OhVRscYH0yynuSLz/K5l40xrhxjXHnFFVfs5G4BAAC4CDtxldErqurhk/cfkuSpSX5z2u0CAACwu3biKqOfmuRVVXUkW4H52jHG63dguwAAAOyinbjK6P9M8jk7MAsAAAB7aEefQwgAAMDBIQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGjq6KwHAABmq6pmPcJ9jDFmPQJAC44QAkBzY4wdeXvc81+/Y9sCYG8IQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhHBJra2uZn5/PkSNHMj8/n7W1tVmPBADAPnd01gMA01tbW8vy8nJWV1ezsLCQjY2NLC0tJUlOnDgx4+kAANivHCGEQ2BlZSWrq6tZXFzMsWPHsri4mNXV1aysrMx6NAAA9jFBCIfA5uZmFhYWzrhtYWEhm5ubM5oIAICDQBDCITA3N5eNjY0zbtvY2Mjc3NyMJgIA4CAQhHAILC8vZ2lpKevr6zl16lTW19eztLSU5eXlWY8GAMA+5qIycAicvnDMyZMns7m5mbm5uaysrLigDAAA5yQI4ZA4ceKEAAQA4AFxyigAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShDCIbG2tpb5+fkcOXIk8/PzWVtbm/VIAADsc0dnPQAwvbW1tSwvL2d1dTULCwvZ2NjI0tJSkuTEiRMzng4AgP3KEUI4BFZWVrK6uprFxcUcO3Ysi4uLWV1dzcrKyqxHAwBgHxOEcAhsbm5mYWHhjNsWFhayubk5o4kAADgIBCEcAnNzc9nY2Djjto2NjczNzc1oIgAADgJBCIfA8vJylpaWsr6+nlOnTmV9fT1LS0tZXl6e9WgAAOxjLioDh8DpC8ecPHkym5ubmZuby8rKigvKAABwToIQDokTJ04IQAAAHhCnjAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCIfE2tpa5ufnc+TIkczPz2dtbW3WIwEAsM95HUI4BNbW1rK8vJzV1dUsLCxkY2MjS0tLSeK1CQEAuF+OEMIhsLKyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aAAD7mCCEQ2BzczMLCwtn3LawsJDNzc0ZTQQAwEEgCOEQmJuby8bGxhm3bWxsZG5ubkYTAQBwEAhCOASWl5eztLSU9fX1nDp1Kuvr61laWsry8vKsRwMAYB9zURk4BE5fOObkyZPZ3NzM3NxcVlZWXFAGAIBzEoRwSJw4cUIAAgDwgDhlFAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQ1NRBWFWPqar1qvqNqnp7Vf2jnRgMAACA3XV0B7bx8ST/eIzxK1V1WZK3VNUbxxi/sQPbBgAAYJdMfYRwjPGeMcavTN6/Pclmkk+bdrsAAADsrh19DmFVHU/yOUl+cSe3CwAAwM7bsSCsqocm+fEkzx1jfOgsn392Vd1cVTffeuutO7VbAAAALtKOBGFVHctWDP7bMcZPnO0+Y4yXjTGuHGNcecUVV+zEbgEAAJjCTlxltJKsJtkcY3z/9CMBAACwF3biCOFfTvK1Sb6wqt46eXv6DmwXAACAXbQTVxndGGPUGOOzxxhPnLz9p50YDoC9sba2lvn5+Rw5ciTz8/NZW1ub9UgAwB7YidchBOAAW1tby/LyclZXV7OwsJCNjY0sLS0lSU6cODHj6QCA3bSjLzsBwMGzsrKS1dXVLC4u5tixY1lcXMzq6mpWVlZmPRoAsMsEIUBzm5ubWVhYOOO2hYWFbG5uzmgiAGCvCEKA5ubm5rKxsXHGbRsbG5mbm5vRRADAXhGEAM0tLy9naWkp6+vrOXXqVNbX17O0tJTl5eVZjwYA7DIXlQFo7vSFY06ePJnNzc3Mzc1lZWXFBWUAoAFBCEBOnDghAAGgIaeMAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAOibW1tczPz+fIkSOZn5/P2trarEcCAGCfOzrrAYDpra2tZXl5Oaurq1lYWMjGxkaWlpaSJCdOnJjxdAAA7FeOEMIhsLKyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aAAD7mCCEQ2BzczMLCwtn3LawsJDNzc0ZTQQAwEEgCOEQmJuby8bGxhm3bWxsZG5ubkYTAQBwEAhCOASWl5eztLSU9fX1nDp1Kuvr61laWsry8vKsRwMAYB9zURk4BE5fOObkyZPZ3NzM3NxcVlZWXFAGAIBzEoRwSJw4cUIAAgDwgDhlFAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz3AfvCE77gpt915atZjnOH4tTfOeoR7XP6QY/m1Fz1t1mMcelU16xHuY4wx6xEAANhFgjDJbXeeyi3XXT3rMfat/RSnh9lOxdfxa2+0ngEAuCBOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQhA1tbWMj8/nyNHjmR+fj5ra2uzHgkA2ANHZz0AALO1traW5eXlrK6uZmFhIRsbG1laWkqSnDhxYsbTAQC7yRFCgOZWVlayurqaxcXFHDt2LIuLi1ldXc3KysqsRwMAdpkjhADNbW5uZmFh4YzbFhYWsrm5OaOJgIOkqmY9wn2MMWY9AhwYjhACNDc3N5eNjY0zbtvY2Mjc3NyMJgIOkjHGjrw97vmv37FtARdOEAI0t7y8nKWlpayvr+fUqVNZX1/P0tJSlpeXZz0aALDLnDIK0NzpC8ecPHkym5ubmZuby8rKigvKAEADghCAnDhxQgACQENOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAGRtbS3z8/M5cuRI5ufns7a2NuuRAIA9cHTWAwAwW2tra1leXs7q6moWFhaysbGRpaWlJMmJEydmPB0AsJscIQRobmVlJaurq1lcXMyxY8eyuLiY1dXVrKyszHo0AGCXCUKA5jY3N7OwsHDGbQsLC9nc3JzRRADAXhGEAM3Nzc1lY2PjjNs2NjYyNzc3o4kAgL0iCAGaW15eztLSUtbX13Pq1Kmsr69naWkpy8vLsx4NANhlLioD0NzpC8ecPHkym5ubmZuby8rKigvKAEADghCAnDhxQgACQENOGQUAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAFlbW8v8/HyOHDmS+fn5rK2tzXokAGAPHJ31AADM1traWpaXl7O6upqFhYVsbGxkaWkpSXLixIkZTwcA7CZHCAGaW1lZyerqahYXF3Ps2LEsLi5mdXU1Kysrsx4NANhlghCguc3NzSwsLJxx28LCQjY3N2c0EQCwVwQhQHNzc3PZ2Ng447aNjY3Mzc3NaCIAYK8IQoDmlpeXs7S0lPX19Zw6dSrr6+tZWlrK8vLyrEcDAHaZi8oANHf6wjEnT57M5uZm5ubmsrKy4oIyANCAIAQgJ06cEIAA0JBTRgEAAJoShAAAAE05ZRTgEKiqWY9wH2OMWY8AAJyHI4QAh8AYY0feHvf81+/YtgCA/U8QAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoKkdCcKqekVV/WFVvW0ntgcAAMDu26kjhK9M8sU7tC0AAAD2wI4E4RjjTUnevxPbAgAAYG94DiEAAEBTexaEVfXsqrq5qm6+9dZb92q3AAAA3I89C8IxxsvGGFeOMa684oor9mq3AAAA3A+njAIAADS1Uy87sZbkF5J8ZlW9q6qWdmK7AAAA7J6jO7GRMcaJndgOAAAAe8cpowAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJo6OusBAACAnqpq1iPcxxhj1iPsKUcIAQCAmRhj7Mjb457/+h3bVjeCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0NTRWQ8AAFycJ3zHTbntzlOzHuMMx6+9cdYj3OPyhxzLr73oabMeA2BfE4QAcEDdduep3HLd1bMeY9/aT3EKsF85ZRQAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgqaOzHmA/uGzu2jz+VdfOeox967K5JLl61mMAAAA7TBAmuX3zutxyneC5P8evvXHWIwAAALvAKaMAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJrywvQwpSd8x0257c5Tsx7jDMevvXHWI9zj8occy6+96GmzHgMAgLMQhDCl2+48lVuuu3rWY+xb+ylOAQA4k1NGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaOrorAcAAGDvPeE7bsptd56a9RhnOH7tjbMe4R6XP+RYfu1FT5v1GLDrBCEAQEO33Xkqt1x39azH2Lf2U5zCbnLKKAAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhqR4Kwqr64qn6rqn67qq7diW0CAACwu6YOwqo6kuQlSf5aks9KcqKqPmva7QIAALC7duII4ecm+e0xxu+MMT6W5N8l+bId2C4AAAC7aCeC8NOSvHPbx++a3AYAAMA+dnSvdlRVz07y7CR57GMfu1e7vWDHr71x1iPsW5c/5NisR4BD6wnfcVNuu/PUrMc4w376eXj5Q47l1170tFmPsW9dNndtHv8qT92/P5fNJcnVsx4DDqXHv+rxsx7hDJfNZd/9PPz1Z/36rEe4IDsRhL+f5DHbPn705LYzjDFeluRlSXLllVeOHdjvjrnluv31YHH82hv33UzA7rjtzlP+/34O+ylO96PbN6+zfs7B+oHd4+fPuR2knz87ccroLyf5jKr6U1X1oCRfleQ/7sB2AQAA2EVTHyEcY3y8qv5BkjckOZLkFWOMt089GQAAALtqR55DOMb4T0n+005sCwAAgL2xIy9MDwAAwMEjCAEAAJras5edgMPKZd/PzWXfAQD2L0EIU3LZ5XM7SJddBgDoximjAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlKuMAsyQly05Ny9bAgC7SxACzJCXLTk3L1sCALvLKaMAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmjs56AAAA9t5lc9fm8a+6dtZj7FuXzSXJ1bMeA3adIAQAaOj2zetyy3WC5/4cv/bGWY8Ae8IpowAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANHV01gPAYXD82htnPcK+dflDjs16BAAA7ocghCndct3Vsx7hDMevvXHfzQQAwP7klFEAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGjKVUYB4ADzsjf3z8veAJyfIASAA2q/vcSMl70BOHicMgoAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0NTRWQ8A0N3xa2+c9Qj71uUPOTbrEQDgUBOEADN0y3VXz3qEMxy/9sZ9NxMAsHucMgoAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATR2d9QAAAMzG8WtvnPUI+9blDzk26xFgTwhCAICGbrnu6lmPcIbj196472aCDpwyCgAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKamCsKqekZVvb2q/riqrtypoQAAANh90x4hfFuSL0/yph2YBQAAgD10dJovHmNsJklV7cw0AAAA7BnPIQQAAGjqvEcIq+q/JHnkWT61PMb4qQvdUVU9O8mzk+Sxj33sBQ8IAADA7jhvEI4xvmgndjTGeFmSlyXJlVdeOXZimwAAAFw8p4wCAAA0Ne3LTvzNqnpXks9LcmNVvWFnxgIAAGC3TXuV0Z9M8pM7NAsAAAB7yCmjAAAATQlCAACApqY6ZRQAAOjp+LU3znqEfevyhxyb9QgXTBACAAAPyC3XXT3rEc5w/Nob991MB4VTRgEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz3AYVJVO7etF+/MdsYYO7MhAADg0BGEO0h8AQAAB4lTRgEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0dXTWAwBbqmrntvXindnOGGNnNgQAwL4kCGGfEF8AAOw1p4wCAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFNHZz0AANOrqp3b1ot3ZjtjjJ3ZEACwawQhwCEgvgCAi+GUUQAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQ1FRBWFXfW1W/WVX/s6p+sqoevkNzAQAAsMumPUL4xiTzY4zPTvK/krxg+pEA2GsnT57Mgx/84FRVHvzgB+fkyZOzHgkA2ANTBeEY46YxxscnH745yaOnHwmAvXTy5MnccMMN+e7v/u58+MMfznd/93fnhhtuEIUA0MBOPofw65P8zA5uD4A98PKXvzwvfvGL87znPS+f8AmfkOc973l58YtfnJe//OWzHg0A2GXnDcKq+i9V9bazvH3ZtvssJ/l4kn97ju08u6purqqbb7311p2ZHoCp3XXXXbnmmmvOuO2aa67JXXfdNaOJAIC9ct4gHGN80Rhj/ixvP5UkVfV1Sb4kydeMMcY5tvOyMcaVY4wrr7jiih37BgCYzqWXXpobbrjhjNtuuOGGXHrppTOaCADYK0en+eKq+uIk/yTJF4wxPrIzIwGwl77xG78xz3/+85NsHRm84YYb8vznP/8+Rw0BgMNnqiBM8kNJLk3yxqpKkjePMfwGAXCAXH/99UmSb/u2b8s//sf/OJdeemmuueaae24HAA6vqYJwjPHpOzUIALNz/fXXC0AAaGgnrzIKAADAASIIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIh8Ta2lrm5+dz5MiRzM/PZ21tbdYjcYBYPwDQ07QvTA/sA2tra1leXs7q6moWFhaysbGRpaWlJMmJEydmPB37nfUDAH05QgiHwMrKSlZXV7O4uJhjx45lcXExq6urWVlZmfVoHADWDwD0JQjhENjc3MzCwsIZty0sLGRzc3NGE3GQWD8A0JcghENgbm4uGxsbZ9y2sbGRubm5GU3EQWL9AEBfghAOgeXl5SwtLWV9fT2nTp3K+vp6lpaWsry8POvROACsHwDoy0Vl4BA4feGPkydPZnNzM3Nzc1lZWXFBEC6I9QMAfQlCOCROnDjhF3gumvUDAD05ZRQAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIRwSa2trmZ+fz5EjRzI/P5+1tbVZj8QBctVVV+WSSy5JVeWSSy7JVVddNeuRAIA9IAjhEFhbW8vy8nKuv/76fPSjH83111+f5eVlUcgFueqqq3LTTTflmmuuyQc/+MFcc801uemmm0QhADRwdNYDANNbWVnJ6upqFhcXkySLi4tZXV3NyZMnc+LEiRlPx373xje+Md/0Td+UH/7hH06Se/73hhtumOVYAMAeqDHGnu/0yiuvHDfffPOe7xcOqyNHjuSjH/1ojh07ds9tp06dyoMf/ODcfffdM5yMg6Cq8sEPfjCXX375PbfddtttefjDH55ZPEaw96pq1iPch7V3cFg/7AfHr70xt1x39azH2Leq6i1jjCvP9jmnjMIhMDc3l42NjTNu29jYyNzc3Iwm4iCpqrzgBS8447YXvOAF+/KXPHbHGGPfvXFwzHqtWD8wHUEIh8Dy8nKWlpayvr6eU6dOZX19PUtLS1leXp71aBwAT33qU/PSl740z3nOc3LbbbflOc95Tl760pfmqU996qxHAwB2mecQwiFw+nmCJ0+ezObmZubm5rKysuL5g1yQN7zhDbnqqqtyww035KUvfWmqKk972tPyhje8YdajAQC7TBDCIXHixAkByEUTfwDQk1NGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEIGtra5mfn8+RI0cyPz+ftbW1WY8EAOwBr0MI0Nza2lqWl5ezurqahYWFbGxsZGlpKUm8tiUAHHKOEAI0t7KyktXV1SwuLubYsWNZXFzM6upqVlZWZj0aALDLBCFAc5ubm1lYWDjjtoWFhWxubs5oIgBgrwhCgObm5uaysbFxxm0bGxuZm5ub0UQAwF4RhADNLS8vZ2lpKevr6zl16lTW19eztLSU5eXlWY8GAOwyF5UBaO70hWNOnjyZzc3NzM3NZWVlxQVlANh1VbVz23rxzmxnjLEzGzogahbf8JVXXjluvvnmPd8vAABAN1X1ljHGlWf7nFNGAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaEoQAgAANCUIAQAAmhKEAAAATQlCAACApgQhAABAU4IQAACgKUEIAADQlCAEAABoShACAAA0JQgBAACaEoQAAABNCUIAAICmBCEAAEBTghAAAKApQQgAANCUIAQAAGhKEAIAADQlCAEAAJoShAAAAE0JQgAAgKYEIQAAQFOCEAAAoClBCAAA0JQgBAAAaKrGGHu/06pbk7xjz3d8cDwiyftmPQQHlvXDNKwfpmH9MA3rh2lYP+f2uDHGFWf7xEyCkHOrqpvHGFfOeg4OJuuHaVg/TMP6YRrWD9Owfi6eU0YBAACaEoQAAABNCcL96WWzHoADzfphGtYP07B+mIb1wzSsn4vkOYQAAABNOUIIAADQlCA8BKqqZj0DB5f1wzSsH6Zh/TAN64dpWD//l1NGAQAAmnKE8ACrqmdW1Y9W1V+oqk+d9TwcLNYP06iqZ1TVD1TVp1TVw2Y9DweLnz9Mw/phGh6/7ssRwgOsqo4lOZnksiRPSvJdY4xfnu1UHBTWDxerqo4k+ZQkz0tSSY4medkY4+0zHYwDw88fpmH9cLE8fp2dIDyAqurPJLlkjPG/Jx9fnuRvJ/nOJF8xxvjvs5yP/c36YRqTv8Z/bIzxR5OPPzvJFyV5RpLnjDF+dZbzsb/5+cM0rB+m4fHr/gnCA6aqXpvk4UkemuR/JPlnY4yPTD73dUmuTfK3xhhvm9WM7F/WD9Ooqn+Trb+s3pHkbWOMfzq5/WiSa5Jcna0H1d+d3ZTsV37+MA3rh2l4/Do3zyE8QKrqy5M8fIzxtCRfnmQ+yXdW1WOSZIzxyiQ/nOSrqupSV09iO+uHaVTVs7P1YHpVkn+S5Cuq6nuTZIzx8SSvSbKR5CmT+1s/3MPPH6Zh/TANj1/nJwgPlncn+XhVPWKM8QdJvirJo5I8d9t9/nuST05y93D4lzNZP0zjHUl+O1tnlvzvJH8lyUJVfV+SjDHen+Q3knzB5GPrh+38/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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.164872Z","iopub.execute_input":"2023-02-01T14:51:29.165348Z","iopub.status.idle":"2023-02-01T14:51:29.588614Z","shell.execute_reply.started":"2023-02-01T14:51:29.165277Z","shell.execute_reply":"2023-02-01T14:51:29.587528Z"},"trusted":true},"execution_count":207,"outputs":[{"execution_count":207,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:29.590230Z","iopub.execute_input":"2023-02-01T14:51:29.591244Z","iopub.status.idle":"2023-02-01T14:51:29.999585Z","shell.execute_reply.started":"2023-02-01T14:51:29.591206Z","shell.execute_reply":"2023-02-01T14:51:29.998460Z"},"trusted":true},"execution_count":208,"outputs":[{"execution_count":208,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = clf.predict(X_test)\ndecision_tree_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"clf_y_pred\": y_pred})\ndecision_tree_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.001184Z","iopub.execute_input":"2023-02-01T14:51:30.001710Z","iopub.status.idle":"2023-02-01T14:51:30.018740Z","shell.execute_reply.started":"2023-02-01T14:51:30.001660Z","shell.execute_reply":"2023-02-01T14:51:30.017976Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.019742Z","iopub.execute_input":"2023-02-01T14:51:30.020678Z","iopub.status.idle":"2023-02-01T14:51:30.025527Z","shell.execute_reply.started":"2023-02-01T14:51:30.020645Z","shell.execute_reply":"2023-02-01T14:51:30.024304Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.027690Z","iopub.execute_input":"2023-02-01T14:51:30.028212Z","iopub.status.idle":"2023-02-01T14:51:30.045818Z","shell.execute_reply.started":"2023-02-01T14:51:30.028170Z","shell.execute_reply":"2023-02-01T14:51:30.044552Z"},"trusted":true},"execution_count":211,"outputs":[{"execution_count":211,"output_type":"execute_result","data":{"text/plain":" PassengerId clf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 0.0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdclf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.00.0
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(decision_tree_pred[[\"PassengerId\",\"clf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.048587Z","iopub.execute_input":"2023-02-01T14:51:30.048979Z","iopub.status.idle":"2023-02-01T14:51:30.075974Z","shell.execute_reply.started":"2023-02-01T14:51:30.048946Z","shell.execute_reply":"2023-02-01T14:51:30.074745Z"},"trusted":true},"execution_count":212,"outputs":[{"execution_count":212,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred \n0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Random Forrest\n\nWe use Random Forrest to classify the titanic passengers as either surviving or not the accident. We use again the same statistical variable as Decisiont Trees.","metadata":{}},{"cell_type":"markdown","source":"## Model fitting and classification\n\nRandom Forrest overfits to the training dataset. ","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nn_estimators = range(1,20)\nmax_depths = range(1,40)\n\nfor est in n_estimators:\n for depth in max_depths:\n rf = RandomForestClassifier(n_estimators = est, max_depth = depth, \n random_state = 42, class_weight={0:6.,1:4},max_features = 6)\n rf.fit(X_train, y_train)\n train_score = rf.score(X_train, y_train)\n test_score = rf.score(X_valid, y_valid)\n print(\" - estimators : \", est, \n \" - max depths : \", depth, \n \" - train score : \", train_score,\n \" - valid score : \", valid_score)\n \n \n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:30.172233Z","iopub.execute_input":"2023-02-01T14:51:30.172931Z","iopub.status.idle":"2023-02-01T14:51:52.273980Z","shell.execute_reply.started":"2023-02-01T14:51:30.172890Z","shell.execute_reply":"2023-02-01T14:51:52.272764Z"},"trusted":true},"execution_count":213,"outputs":[{"name":"stdout","text":" - estimators : 1 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 2 - train score : 0.7771535580524345 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 3 - train score : 0.8071161048689138 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 4 - train score : 0.8277153558052435 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 5 - train score : 0.8314606741573034 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 6 - train score : 0.8651685393258427 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 7 - train score : 0.8820224719101124 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 8 - train score : 0.8857677902621723 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 9 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 10 - train score : 0.900749063670412 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 11 - train score : 0.9082397003745318 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 12 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 13 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 14 - train score : 0.9119850187265918 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 15 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 16 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 17 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 18 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 19 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 20 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 21 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 22 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 23 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 24 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 25 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 26 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 27 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 28 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 29 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 30 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 31 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 32 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 33 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 34 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 35 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 36 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 37 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 38 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 1 - max depths : 39 - train score : 0.9138576779026217 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 4 - train score : 0.848314606741573 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 5 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 6 - train score : 0.8689138576779026 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 7 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 8 - train score : 0.8895131086142322 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 10 - train score : 0.9213483146067416 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 11 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 12 - train score : 0.9288389513108615 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 13 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 14 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 15 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 16 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 17 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 18 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 19 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 20 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 21 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 22 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 23 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 24 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 25 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 26 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 27 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 28 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 29 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 30 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 31 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 32 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 33 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 34 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 35 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 36 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 37 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 38 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 2 - max depths : 39 - train score : 0.9269662921348315 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 1 - train score : 0.7808988764044944 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 2 - train score : 0.7940074906367042 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 3 - train score : 0.8258426966292135 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 4 - train score : 0.8539325842696629 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 5 - train score : 0.8707865168539326 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 6 - train score : 0.8838951310861424 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 7 - train score : 0.897003745318352 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 8 - train score : 0.9101123595505618 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 9 - train score : 0.9194756554307116 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 10 - train score : 0.9250936329588015 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 11 - train score : 0.9400749063670412 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 12 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 13 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 14 - train score : 0.9438202247191011 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 15 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 16 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 17 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 18 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 19 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 20 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 21 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 22 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 23 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 24 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 25 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 26 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 27 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 28 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 29 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 30 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 31 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 32 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 33 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 34 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 35 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 36 - train score : 0.9456928838951311 - valid score : 0.7815126050420168\n - estimators : 3 - max depths : 37 - train score : 0.9456928838951311 - 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estimators : 19 - max depths : 26 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 27 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 28 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 29 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 30 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 31 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 32 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 33 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 34 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 35 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 36 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 37 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 38 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n - estimators : 19 - max depths : 39 - train score : 0.9756554307116105 - valid score : 0.7815126050420168\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We discover again the learning overfit on the training dataset. So we choose a maximum depth at around 6 and n estimator of 11. ","metadata":{}},{"cell_type":"code","source":"rf = RandomForestClassifier(n_estimators = 11, max_depth=6, random_state = 42, class_weight={0:6.,1:4}, max_features = 6)\nrf.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.275894Z","iopub.execute_input":"2023-02-01T14:51:52.276195Z","iopub.status.idle":"2023-02-01T14:51:52.312746Z","shell.execute_reply.started":"2023-02-01T14:51:52.276167Z","shell.execute_reply":"2023-02-01T14:51:52.311257Z"},"trusted":true},"execution_count":214,"outputs":[{"execution_count":214,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight={0: 6.0, 1: 4}, max_depth=6, max_features=6,\n n_estimators=11, random_state=42)"},"metadata":{}}]},{"cell_type":"code","source":"rf_train_score = rf.score(X_train, y_train)\nrf_train_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.314414Z","iopub.execute_input":"2023-02-01T14:51:52.314882Z","iopub.status.idle":"2023-02-01T14:51:52.329948Z","shell.execute_reply.started":"2023-02-01T14:51:52.314839Z","shell.execute_reply":"2023-02-01T14:51:52.328684Z"},"trusted":true},"execution_count":215,"outputs":[{"execution_count":215,"output_type":"execute_result","data":{"text/plain":"0.8801498127340824"},"metadata":{}}]},{"cell_type":"code","source":"rf_valid_score = rf.score(X_valid, y_valid)\nrf_valid_score","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.332102Z","iopub.execute_input":"2023-02-01T14:51:52.333087Z","iopub.status.idle":"2023-02-01T14:51:52.346061Z","shell.execute_reply.started":"2023-02-01T14:51:52.333051Z","shell.execute_reply":"2023-02-01T14:51:52.344862Z"},"trusted":true},"execution_count":216,"outputs":[{"execution_count":216,"output_type":"execute_result","data":{"text/plain":"0.8067226890756303"},"metadata":{}}]},{"cell_type":"markdown","source":"The age, the fare and the gender appears to contribute the most to predicting accurately the surviving or not the accident. It is surprising the passenger class influence less random forrest. ","metadata":{}},{"cell_type":"code","source":"importances = rf.feature_importances_\nimportances = pd.DataFrame(x_cols, importances)\nimportances\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.347466Z","iopub.execute_input":"2023-02-01T14:51:52.347785Z","iopub.status.idle":"2023-02-01T14:51:52.360347Z","shell.execute_reply.started":"2023-02-01T14:51:52.347756Z","shell.execute_reply":"2023-02-01T14:51:52.359060Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":" 0\n0.199528 Fare\n0.140924 Pclass\n0.390318 Sex\n0.023663 Embarked\n0.053330 fam_members\n0.192238 Age","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
0.199528Fare
0.140924Pclass
0.390318Sex
0.023663Embarked
0.053330fam_members
0.192238Age
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"We found the classes of importances are Fares, Sex, and Age. ","metadata":{}},{"cell_type":"markdown","source":"### Which passengers were misclassified ?\n\nWe explore further the statistical variables and their values that may have led to misclassification for the training and validation dataset. The model predicted that more passengers appeared perished during the accident, than the labels suggest. We notice that most mispredictions involves single passengers - both genders. Male singles passengers appears to have been misclassified the most as perishing, when they have survived and single women the reverse. \n\n","metadata":{}},{"cell_type":"code","source":"y_pred_train = rf.predict(X_train)\ncm = confusion_matrix(y_train, y_pred_train)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.362231Z","iopub.execute_input":"2023-02-01T14:51:52.362868Z","iopub.status.idle":"2023-02-01T14:51:52.379545Z","shell.execute_reply.started":"2023-02-01T14:51:52.362825Z","shell.execute_reply":"2023-02-01T14:51:52.378290Z"},"trusted":true},"execution_count":218,"outputs":[{"execution_count":218,"output_type":"execute_result","data":{"text/plain":"array([[319, 10],\n [ 54, 151]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_train)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_train)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.381097Z","iopub.execute_input":"2023-02-01T14:51:52.381577Z","iopub.status.idle":"2023-02-01T14:51:52.391168Z","shell.execute_reply.started":"2023-02-01T14:51:52.381537Z","shell.execute_reply":"2023-02-01T14:51:52.390198Z"},"trusted":true},"execution_count":219,"outputs":[{"name":"stdout","text":"Accuracy : 0.8801498127340824\nMisclassfication : 0.1198501872659176\nSensitivivity : 0.9696048632218845\nSpecificity : 0.7365853658536585\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\ncm = confusion_matrix(y_valid, y_pred_valid)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.392573Z","iopub.execute_input":"2023-02-01T14:51:52.393224Z","iopub.status.idle":"2023-02-01T14:51:52.412047Z","shell.execute_reply.started":"2023-02-01T14:51:52.393191Z","shell.execute_reply":"2023-02-01T14:51:52.410398Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred_valid)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred_valid)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.413222Z","iopub.execute_input":"2023-02-01T14:51:52.413582Z","iopub.status.idle":"2023-02-01T14:51:52.421900Z","shell.execute_reply.started":"2023-02-01T14:51:52.413554Z","shell.execute_reply":"2023-02-01T14:51:52.420658Z"},"trusted":true},"execution_count":221,"outputs":[{"name":"stdout","text":"Accuracy : 0.8067226890756303\nMisclassfication : 0.19327731092436976\nSensitivivity : 0.9227272727272727\nSpecificity : 0.6204379562043796\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.427307Z","iopub.execute_input":"2023-02-01T14:51:52.427779Z","iopub.status.idle":"2023-02-01T14:51:52.433953Z","shell.execute_reply.started":"2023-02-01T14:51:52.427746Z","shell.execute_reply":"2023-02-01T14:51:52.432477Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.435235Z","iopub.execute_input":"2023-02-01T14:51:52.435660Z","iopub.status.idle":"2023-02-01T14:51:52.465440Z","shell.execute_reply.started":"2023-02-01T14:51:52.435608Z","shell.execute_reply":"2023-02-01T14:51:52.464167Z"},"trusted":true},"execution_count":223,"outputs":[{"execution_count":223,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.466837Z","iopub.execute_input":"2023-02-01T14:51:52.467622Z","iopub.status.idle":"2023-02-01T14:51:52.495143Z","shell.execute_reply.started":"2023-02-01T14:51:52.467589Z","shell.execute_reply":"2023-02-01T14:51:52.494000Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 NaN \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 NaN \n4 0.0 0.0 0.0 0.0 0.0 NaN ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.496752Z","iopub.execute_input":"2023-02-01T14:51:52.497420Z","iopub.status.idle":"2023-02-01T14:51:52.520420Z","shell.execute_reply.started":"2023-02-01T14:51:52.497382Z","shell.execute_reply":"2023-02-01T14:51:52.519633Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 ","text/html":"
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FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.521415Z","iopub.execute_input":"2023-02-01T14:51:52.522394Z","iopub.status.idle":"2023-02-01T14:51:52.546457Z","shell.execute_reply.started":"2023-02-01T14:51:52.522351Z","shell.execute_reply":"2023-02-01T14:51:52.545447Z"},"trusted":true},"execution_count":226,"outputs":[{"execution_count":226,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 ","text/html":"
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PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.547614Z","iopub.execute_input":"2023-02-01T14:51:52.547908Z","iopub.status.idle":"2023-02-01T14:51:52.553613Z","shell.execute_reply.started":"2023-02-01T14:51:52.547880Z","shell.execute_reply":"2023-02-01T14:51:52.552611Z"},"trusted":true},"execution_count":227,"outputs":[]},{"cell_type":"code","source":"y_pred = rf.predict(X_train)\nrf_pred = X_train.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_train_pass_id\nrf_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.554829Z","iopub.execute_input":"2023-02-01T14:51:52.555101Z","iopub.status.idle":"2023-02-01T14:51:52.580427Z","shell.execute_reply.started":"2023-02-01T14:51:52.555075Z","shell.execute_reply":"2023-02-01T14:51:52.579665Z"},"trusted":true},"execution_count":228,"outputs":[{"execution_count":228,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 \n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 \n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 \n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 0.0 \n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 \n\n PassengerId \n844 845.0 \n316 317.0 \n768 769.0 \n255 256.0 \n130 131.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
844-0.2508363.01.02.00.0-1.0000000.0845.0
3160.5000432.02.02.01.0-0.4615381.0317.0
7680.4199213.01.03.01.00.0000000.0769.0
2550.0342843.02.04.02.0-0.0769230.0256.0
130-0.2840413.01.04.00.00.2307690.0131.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(rf_pred[[\"PassengerId\", \"rf_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.581453Z","iopub.execute_input":"2023-02-01T14:51:52.582459Z","iopub.status.idle":"2023-02-01T14:51:52.610464Z","shell.execute_reply.started":"2023-02-01T14:51:52.582401Z","shell.execute_reply":"2023-02-01T14:51:52.609279Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y \n0 0.0 \n1 NaN \n2 0.0 \n3 NaN \n4 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_y
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = rf.predict(X_valid)\nrf_pred = X_valid.copy()\nrf_pred[\"rf_y_pred\"] = y_pred\nrf_pred[\"PassengerId\"] = x_valid_pass_id\nrf_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.611523Z","iopub.execute_input":"2023-02-01T14:51:52.611803Z","iopub.status.idle":"2023-02-01T14:51:52.639513Z","shell.execute_reply.started":"2023-02-01T14:51:52.611776Z","shell.execute_reply":"2023-02-01T14:51:52.638365Z"},"trusted":true},"execution_count":230,"outputs":[{"execution_count":230,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age rf_y_pred \\\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 \n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 \n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 \n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 \n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 \n.. ... ... ... ... ... ... ... \n174 0.703416 1.0 1.0 4.0 0.0 2.000000 0.0 \n297 5.937556 1.0 2.0 2.0 3.0 -2.153846 1.0 \n244 -0.313093 3.0 1.0 4.0 0.0 0.000000 0.0 \n38 0.153567 3.0 2.0 2.0 2.0 -0.923077 1.0 \n371 -0.344675 3.0 1.0 2.0 1.0 -0.923077 0.0 \n\n PassengerId \n369 370.0 \n541 542.0 \n196 197.0 \n810 811.0 \n427 428.0 \n.. ... \n174 175.0 \n297 298.0 \n244 245.0 \n38 39.0 \n371 372.0 \n\n[357 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgerf_y_predPassengerId
3692.3753461.02.04.00.0-0.4615381.0370.0
5410.7285013.02.02.06.0-1.6153850.0542.0
196-0.2903563.01.03.00.00.0000000.0197.0
810-0.2844013.01.02.00.0-0.3076920.0811.0
4270.5000432.02.02.00.0-0.8461541.0428.0
...........................
1740.7034161.01.04.00.02.0000000.0175.0
2975.9375561.02.02.03.0-2.1538461.0298.0
244-0.3130933.01.04.00.00.0000000.0245.0
380.1535673.02.02.02.0-0.9230771.039.0
371-0.3446753.01.02.01.0-0.9230770.0372.0
\n

357 rows × 8 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(rf_pred.PassengerId), \"rf_y_pred\"] = rf_pred[\"rf_y_pred\"]\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.641337Z","iopub.execute_input":"2023-02-01T14:51:52.641775Z","iopub.status.idle":"2023-02-01T14:51:52.669655Z","shell.execute_reply.started":"2023-02-01T14:51:52.641731Z","shell.execute_reply":"2023-02-01T14:51:52.668451Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred \n0 0.0 NaN \n1 NaN 1.0 \n2 0.0 NaN \n3 NaN 1.0 \n4 NaN 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Misclassified entries","metadata":{}},{"cell_type":"code","source":"errors_pd = X_train.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_train\nerrors_pd[\"Y_pred\"] = y_pred_train\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.670923Z","iopub.execute_input":"2023-02-01T14:51:52.671224Z","iopub.status.idle":"2023-02-01T14:51:52.693465Z","shell.execute_reply.started":"2023-02-01T14:51:52.671196Z","shell.execute_reply":"2023-02-01T14:51:52.692202Z"},"trusted":true},"execution_count":232,"outputs":[{"execution_count":232,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n255 0.034284 3.0 2.0 4.0 2.0 -0.076923 1.0 0.0\n233 0.733373 3.0 2.0 2.0 6.0 -1.923077 1.0 0.0\n821 -0.250836 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n673 -0.062981 2.0 1.0 2.0 0.0 0.076923 1.0 0.0\n235 -0.299018 3.0 2.0 2.0 0.0 0.000000 0.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
2550.0342843.02.04.02.0-0.0769231.00.0
2330.7333733.02.02.06.0-1.9230771.00.0
821-0.2508363.01.02.00.0-0.2307691.00.0
673-0.0629812.01.02.00.00.0769231.00.0
235-0.2990183.02.02.00.00.0000000.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.694762Z","iopub.execute_input":"2023-02-01T14:51:52.695075Z","iopub.status.idle":"2023-02-01T14:51:52.711272Z","shell.execute_reply.started":"2023-02-01T14:51:52.695047Z","shell.execute_reply":"2023-02-01T14:51:52.710037Z"},"trusted":true},"execution_count":233,"outputs":[{"execution_count":233,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 12\n 1.0 1.0 0.0 6\n 2.0 1.0 0.0 1\n2.0 0.0 1.0 0.0 4\n 2.0 1.0 1\n 1.0 1.0 0.0 1\n 2.0 1.0 1\n 2.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 19\n 2.0 0.0 5\n 1.0 4\n 1.0 1.0 0.0 2\n 2.0 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n 2.0 0.0 2\n 5.0 1.0 1.0 1\n 6.0 2.0 0.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_valid = rf.predict(X_valid)\nconfusion_matrix(y_valid, y_pred_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.712948Z","iopub.execute_input":"2023-02-01T14:51:52.713356Z","iopub.status.idle":"2023-02-01T14:51:52.728466Z","shell.execute_reply.started":"2023-02-01T14:51:52.713299Z","shell.execute_reply":"2023-02-01T14:51:52.727135Z"},"trusted":true},"execution_count":234,"outputs":[{"execution_count":234,"output_type":"execute_result","data":{"text/plain":"array([[203, 17],\n [ 52, 85]])"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:52.729743Z","iopub.execute_input":"2023-02-01T14:51:52.730867Z","iopub.status.idle":"2023-02-01T14:51:53.319377Z","shell.execute_reply.started":"2023-02-01T14:51:52.730830Z","shell.execute_reply":"2023-02-01T14:51:53.318257Z"},"trusted":true},"execution_count":235,"outputs":[{"execution_count":235,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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1UAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6lEcAAAC6Nk0dAGCZHX/28ev+GOedet66PwYAwMFSHgH2QbEDABjYbRUAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAICuNSmPVfW8qvpUVb1jLZYHAADAclmrLY/PT3KvNVoWAAAAS2ZNymNr7Y1JPrcWywIAAGD5+M4jAAAAXYesPFbVI6rqLVX1lk9/+tOH6mEBAABYA4esPLbWnttaO6G1dsL1rne9Q/WwAAAArAG7rQIAANC1Vqfq2JnkX5Lcpqo+UlXb12K5AAAALIdNa7GQ1topa7EcAAAAlpPdVgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOjaNHUAmEpVHfB9WmvrkAQAAJafLY8ctlpre73c/PGvXfU2AAA4XCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdG2aOsDh4Oitp+f4s09fx+UnycnrtnwAAADl8RD40u4zc/6Z61futpx+zrotGwAAILHbKgAAAPtBeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQRg9nbu3JnjjjsuRxxxRI477rjs3Llz6kgAsOFsmjoAAByMnTt3ZseOHTnrrLOybdu27Nq1K9u3b0+SnHLKKROnA4CNw5ZHAGbtjDPOyFlnnZWTTjopmzdvzkknnZSzzjorZ5xxxtTRAGBDUR4BmLXdu3dn27Ztl5m2bdu27N69e6JEALAxKY8AzNrWrVuza9euy0zbtWtXtm7dOlEiANiYlEcAZm3Hjh3Zvn17zj333Fx00UU599xzs3379uzYsWPqaACwoThgDgCztnJQnNNOOy27d+/O1q1bc8YZZzhYDgCsMeURgNk75ZRTlEUAWGd2WwUAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBr09QBgH07/uzj1/0xzjv1vHV/DAAA5k15hCWn2AEAsAzstgoAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAEDXpqkDwHq7w5NflwsuvOiA7rPl9HP2e95jjtqctz/xHgcaCwAAZkV5ZMO74MKLcv6ZJ6/b8g+kaAIAwFzZbRUAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAICuTVMH2B9Hbz09x599+jouP0lOXrflAwAAzN0syuOXdp+Z889cv3K35fRz1m3ZAAAAG4HdVgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOjaNHUAWG9Hbz09x599+jouP0lOXrflAwDAMlAe2fC+tPvMnH/m+pW7Laefs27LBgCAZWG3VQAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqcqgNYV8efffy6P8Z5p5637o/BcqmqA75Pa20dkgDA4UN5BNaVYsd6WK0Ibjn9nHU9rysAHM7stgoAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAEDXpqkDAMBq7vDk1+WCCy86oPtsOf2c/Z73mKM25+1PvMeBxjpsrLb+P/jU+xzwsm7++Ndebpr1DzAvyiMAS+uCCy/K+WeevG7LP5CieThadf2f2dZk+dY/wLzYbRUAAIAu5REAAIAuu61yWFjPXaOOOWrzui0bAACWhfLIhneg35facvo56/odKwAAmCO7rQIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANC1JuWxqu5VVe+pqv+uqtPXYpkAAAAsj4Muj1V1RJI/THLvJN+R5JSq+o6DXS4AAADLYy22PH5Pkv9urb2/tfaNJC9Jcr81WC4AAABLYi3K402SfHjh+kfGaQAAAGwQmw7VA1XVI5I8IkmOPfbYA77/ltPPWetIlzrmqM3rtmyWV1WtfttT9z69tbZOaYC9OXrr6Tn+7PX7Kv3RW5Pk5HVb/h2e/LpccOFFl5v+wafe54CXdfPHv/Zy0445anPe/sR7XKFs+2Pu659pHX/28ev+GOedet66LXvu+Vezr/c/q/H+hxVrUR4/muRmC9dvOk67jNbac5M8N0lOOOGEAxqB5595YH9Ytpx+zgHfh8OPF0JYfl/afea6vp6v5weTSXLBhRftPf+Za/P6s975577+mdYUxWgtzT3/alZ7/+P9M/tjLXZbfXOSW1fVt1XVlZM8KMlfrsFyAQAAWBIHveWxtXZxVf1ikr9NckSS57XW3nnQyQAAAFgaa/Kdx9baXyX5q7VY1kblO5sAABwqq33nel8O5P3qen/nmuV0yA6YczjznU0AAA6lVb9zvUZ8Z/nwtBbfeQQAAGCDUx4BAADostsqAABsMM7TynpQHgEAYINxnlbWg91WAQAA6FIeAQAA6LLbKgAAbEDOM85aUx4BAGCDcZ5x1oPdVgEAAOhSHgEAAOiy2yoAABwmqmr125669+mttXVKw9wojwAAcJhQBDkYdlsFAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACga9PUAQ5GVa1+21P3Pr21tk5pAJbP8Wcfv+6Pcd6p563r8recfs66LfuYozav27I3ir2t/w8+9T4HvJybP/61l5tm/QPMy6zLoyIIsG/rXezW2/lnnnxA8285/ZwDvg+rW3VdnunvL8DhyG6rAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdG2aOgAAwKFUVQd8n9baOiTZt+PPPn7dH+O8U89b98cANg7lEQA4rKxWBLecfk7OP/PkQ5xmdYodsGzstgoAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAEDXpqkDAMBGdfTW03P82aev4/KT5OR1Wz4ALFIeAWCdfGn3mTn/zPUrd1tOP2fdlg0Ae7LbKgAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF2bpg5wOKuq1W976t6nt9bWKQ0AAMDqlMcJKYIAAMBc2G0VAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACArk1TBwAAWA93ePLrcsGFFx3Qfbacfs5+z3vMUZvz9ife40BjAcyW8ggAbEgXXHhRzj/z5HVb/oEUTYCNwG6rAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdG2aOgAAHKiqWv22p+59emttndIAwOFBeQRgdhRBADj07LYKAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAByUnTt35rjjjssRRxyR4447Ljt37pw6EgDrYNPUAQCA+dq5c2d27NiRs846K9u2bcuuXbuyffv2JMkpp5wycToA1pItjwDAFXbGGWfkrLPOykknnZTNmzfnpJNOyllnnZUzzjhj6mgArDHlEQC4wnbv3p1t27ZdZtq2bduye/fuiRIBsF6URwDgCtu6dWt27dp1mWm7du3K1q1bJ0oEwHpRHgGAK2zHjh3Zvn17zj333Fx00UU599xzs3379uzYsWPqaACsMQfMAQCusJWD4px22mnZvXt3tm7dmjPOOMPBcgA2IOURADgop5xyirIIcBiw2yoAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdB1Ueq+onquqdVfXNqjphrUIBAACwXA52y+M7kjwgyRvXIAsAAABLatPB3Lm1tjtJqmpt0gAAALCUfOcRAACAru6Wx6r6uyQ33MtNO1prr97fB6qqRyR5RJIce+yx+x0QAOZsy+nnrNuyjzlq87otGwD21C2PrbW7r8UDtdaem+S5SXLCCSe0tVgmACyz8888+YDm33L6OQd8HwA4VOy2CgAAQNfBnqrjx6rqI0nunOScqvrbtYkFAADAMjnYo62+Ksmr1igLAAAAS8puqwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwDM3s6dO3PcccfliCOOyHHHHZedO3dOHQkANpxNUwcAgIOxc+fO7NixI2eddVa2bduWXbt2Zfv27UmSU045ZeJ0ALBx2PIIwKydccYZOeuss3LSSSdl8+bNOemkk3LWWWfljDPOmDoaAGwoyiMAs7Z79+5s27btMtO2bduW3bt3T5QIADYm5RGAWdu6dWt27dp1mWm7du3K1q1bJ0oEABuT8gjArO3YsSPbt2/Pueeem4suuijnnntutm/fnh07dkwdDQA2FAfMAWDWVg6Kc9ppp2X37t3ZunVrzjjjDAfLAYA1pjwCMHunnHKKsggA68xuqwAAAHQpjwAAAHTZbRUA2JCO3np6jj/79HVcfpKcvG7LB1g2yiMAsCF9afeZOf/M9St3W04/Z92WDbCM7LYKAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABA16apAwDA4aaqVr/tqXuf3lpbpzQAsH+URwA4xBRBAObIbqsAAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0bZo6AADAetly+jnrtuxjjtq8bssGWEbKIwCwIZ1/5skHNP+W08854PsAHE7stgoAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAEDXpqkDAAAcSlW1+m1P3fv01to6pQGYD+URADisKIIAV4zdVgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOjaNHUAAACAjeT4s49f98c479Tz1v0x9qQ8AgAArKEpit2hYLdVAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAug6qPFbV06rq3VX1n1X1qqq65hrlAgAAYIkc7JbH1yc5rrV2+yTvTfJrBx8JAACAZXNQ5bG19rrW2sXj1TcluenBRwIAAGDZrOV3Hn82yV+v4fIAAABYEpt6M1TV3yW54V5u2tFae/U4z44kFyd50T6W84gkj0iSY4899gqFBQAAYBrd8thau/u+bq+qhye5T5Ifaq21fSznuUmemyQnnHDCqvMBAACwfLrlcV+q6l5JHpfkrq21r65NJAAAAJbNwX7n8Q+SHJ3k9VX1tqp69hpkAgAAYMkc1JbH1tqt1ioIAAAAy2stj7YKAADABqU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0LVp6gAAAACLjj/7+HV/jPNOPW/dH2OjUR4BAIClotgtJ7utAgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0FWttUP/oFWfTvLBdXyI6yb5zDouf73JPy35pyX/tOSflvzTkn9a8k9L/mmtd/6bt9aud7ALmaQ8rreqektr7YSpc1xR8k9L/mnJPy35pyX/tOSflvzTkn9ac8lvt1UAAAC6lEcAAAC6Nmp5fO7UAQ6S/NOSf1ryT0v+ack/LfmnJf+05J/WLPJvyO88AgAAsLY26pZHAAAA1pDyCACHoaqqqTMAMC/K40Gqqlmvw7m/edgA+Y2fCW2A/HMfP3PPP9vxU1VXbjP/3sqc139i/E9tA+Sf+/iZe/7DdvzM+j9ualV1ZGvtm1PnuKKq6ug5v3nYAPnnPn5m/ebT+JnWBsg/2/FfVT+S5MVVdcequtnUea6IOa//xPif2gbIP/fxM/f8h/X4UR6voPGP719V1SOq6oemznOgquqeSXZW1W9W1U9OnedAbYD8cx8/Jyd5bVX9SFXdceo8B8r4mdYGyD/r8Z/k3CR/l+ShSZ5QVfefNs6Bmfv6N/6ntQHyz338zD3/YT9+HG31IFTVXZPcOMnjk/xpa+2ZE0c6IFV1myQ3SvKcJH+S5A9aaxdOm2r/bYD8cx8/P5Hk2CR3S/KK1trzJo50QIyfaW2A/LMb/1V1+yQXtNY+OF7fkuSEJL+a5Pdbay+aMN4BmeP6X2T8T2sD5J/7+Jl7/sN7/LTWXA7gkuSHk5y8x7Tjk7w/yf+aOt9+5H9gkocluXKSzeO02yb5hyQ7ps53GOSf+/j5viTfs3D9yCTbknw0yWlT5zN+ps+4wfPPdvwn+bMku5K8NMmr9rjtXklen+SuU+fcqOt/zGv8y384j5+55zd+xostjwegqv48ybWTXJLkK0l+P8m/t9YuqKrvSPK6JE9srZ01YcxVjfmvkeRrSb6a5O+TvLa19omqulWGNxXPb609a8KYq9og+ec8fl6S5IYZsrckj0zysdbaN6vqO5O8OMmvt9ZeNWHMVRk/09oA+Wc7/qvqgRne3JxYVZuSvDLD7/Dg1tpXqupqSX46yVFJnpkkbcneHMx5/SfG/9Q2QP65j5+55zd+FvjO436qquOTXKO19kOttXsk+dckD0ryg1V1tdbau5L8eJJ7jbsCLZWqukmSI1tr92yt3S/Ja5J8R5KfrKrrtdb+O8P3Xx5UVSdMmXVvNkD+uY+fE5Ncv7V2Ymvt5CQfSfLUJLdLktbafyQ5PcldquqYquU6CpnxM60NkP/EzHj8J3lvkvOr6lqttYtbaz+a4Q3EK5KktfaVJOclOSnJtZewOJ6YGa9/439aGyD/3MfP3POfGOPnMpTH/fexJNda+XJpa+3MJO9Mcv8k1xvneWeS9yU5ZoqAHV9IcvOqenCStNZenGEXplsk2TpOe1eGrS/Xnijjvnwh884/9/Hz/iRfHz+hSmvtkRl21XhSVV15nOedSa6V5Jhle/MZ42dqc88/9/H/lSRXT3KnlQmttQcnuVJVPWG8/v+SvDHJXZftzU/mv/6N/2nNPf/cx8/c8xs/e1Ae90NVVWvts0lekuSO4y5uaa39fpKLk/zOeP1LSd6Voc0vzR/fMf9XkpyR5E5Vdeckaa29Msnnkzx2Yfb3Z9giszQ2SP7Zjp/RF5O8O8l3VtUxSdJae1yG15DfH6//d5K3JLn7MuU3fqY19/yj2Y7/JGmtvT/Jq5I8varuWlVHjDf9bobdmFacneRvl/DNz2zXv/G/FGabf+7jZ+75R8bPntoSfIlzLpck35vhyIyPSXK7cdoxSf40yVUW5rva1FlXyX+rJL+R5H8nOXFh+iuTXGfh+tWnzrpB889q/GQ8GvPC9XsnOSfJTyW54TjtNkn+z8I8m5Yl/9zHz17W/9zHz6zy7+X3meX4T3LEws8/k+RfMnxfZ9v4+/ze1Bk38vpfyDar8T/31/+555/7+Jl7fuNn3xcHzFnF2NYvt3KqaluSn0hytQybee+e5DOttVMPccQrZNzsfnKSu2T4jst3Jflsa+2nJw22n+aSfyONn6o6orV2yfjzj2Y4WunuJO/I8D3Bj7XWHjFhxP02l/GzaI/1P/fxM7v8i+Yw/qvqQRk+EX99ki+21r5eVZtaaxePt98zw+6rt0/y6dbaaeP0vb5mLZM5rP/E6/8ymWP+uY+fuedfZPys8hhL/rdiElV1bGvtQ1V1pdbaN/dy+7cl2ZLkvhlW/P8epy/FH9+qOq619o593H5MhvO7/HiSL7Tx6JLyr40NMH5+McOubG9Jsru19uU93nyekGHXzrtmePN5+jh9WfLPffz8RpKvJ/lAkte31j5fVZtbaxeNty/7+Jl7/hu11j6++PxdzLbM47+qXpHkKhmevx9L8sEkf9ha+2JVHdla+/rCvIvP6b2+Vk1hzut/zOH1f0IbIP/cx8/c8xs/+/M4S/C7LpWqemWGL5Ge1Fr7xz3+gK36B3ZZ/vhW1auS3C/JfVtr5+xx22Uy7vEHWf41sAHGz84kRyc5P0kl2Zzk11prn62qK7fWvrHK/ZYl/9zHz1lJrp/k3AwnIP6OJA9rw+lE5rD+555/Z5IfSPLA1tqb93j+XvoJ9F7uN3n+qrpukj9urf3YeP1eSX4ow6lpfqcN32lJVf1gkv9YuL4Ub3qSea//MYfX/wltgPxzHz9zz2/87CcHzFlQVffP8In5I5P8RVWd2IZzuFwpSRb+Ex5ZVTddvO+SDJwTk3w6yf9I8ntVdZ/F2xfy/39VdcvFNwzyH7wNMH6uMma5T2vtF5P8QZLPJXlmVV175YWzqh5YVddbuF8tSf4TM+Pxs+C01trvJtmR5N+SvKKqrr+w/pdy/CyYZf6q+pkkN0jytCTPqarv3uP5u7Lr0lKO/wwHP9hawzkdk+RvM3xH58gk90iSqrpbklutFMdkec7nOPf17/V/Whsg//0z7/Fz/8w7v/FzINoSfJFzWS5JrpPku8efH5rkgiwcWGOcfv0k95866yr5r5Hk9uPPD8xw5Mj77DHPlZLcY+qsGzT/3MfP1ZL8c5KfXZi2JcMBZn41yRFJ7pjkQVNn3aDj58pJXpjklxamVZLfSvLsDLsj3izJj06ddYPmv0GSO48/PzrJ21eezwvzbE3y4Kmz7iX7yl5ED8hwVL0fGK9vTvK/kjx/6owbef2P2bz+y384j5+55zd+DuTxpv6Fl+Wy8Md38ch0D8lwiN4TxusP2OP2OlT5DuD3uNLCzw/I8L2jk8brP5/hROnyGz+r/R53zbC16OTx+uYkP5a9vPlc0vyzHD8Lme6Y4btqDxmvb8pwYJPnJTl6But/lvkXnr+1MO0XMhSY247Xf2BZ8y9kukGGo+n9ycobhwxbHv8qyY2nzrdR17/X/+W4zDX/3MfP3PMbP1fgMaf+pZfxkuHT8pX/jPtnOMH4O5M8c+psVyD/PTKc+PNdSf5k6myHYf7ZjJ+FzKdk2OXt/gu3vSHJt0+dcSOPn5UX9gzfU3tXklMXbvt/Se40dcaNnH+V3+nUJP+R4ch6/3vqPPuZeUuGD0r+LcnjMxx19U+nznUYrX+v//IfduNn7vmNnwO7OGDOKhYPIlBV70+yq7X2sD1vW1byT2vO+avqqAyl6+lJXpTkB5N8qC3x4bT3NOf1nyRV9QMZ1v3Lk3xfkvfNbP3POv+iqjo/yT+11h46dZYDUcNRAU9McnFr7RnjtKUf+3ua4/qf8+vP3F//554/mff4Sead3/jZz8dY4v/DpVBV/yPDZt+fH68vxVGVkv0bBFV1vwy77f3SeF3+Q2iZx09PDedEvEOSq7fW/nictjQv/HMfP6vlX8lYwyG1j82wu+HOfd1nCnPPvz+q6gkZPnFe+cO7NOMnSWqVI5DuLeeyZd8fy77+e7z+T2fu+ZN5j59k3vmNn86yZ7Qe1kVvMFTVNVprXxx/XoqBfyA5quqo1tqFB3q/9TT3/IvmOH4W9QpAb9oU5j5+9ifH3Nf/MudftFr5Wrj9Oq21z44/T56/qp6S5KMZDpL67HFaJd86auoy5Nxfc1v/e/L6P62Nmn/h9lmOn4XbZ5nf+NmPxz7cymNV/W6GL5FeqbX2GwvT93k+qWX5xKGq/u/44yVJfjPJ51prl/T+CC+LDZB/7uPnCuVfFhtg/Mg/oQMpX4vP2WV4/lbVs5LcNsPBcP5Xkndn+F7pSsZbJPng+P+xdG90knmv/zGH1/8JHa755z5+5p5/WSzT+j+szvNYVU9Ncrskf5PkrlV1VlXdMhnOc1JVt11Z8TWeG2XFkgz830hyywznwbphkicnObGqNo+Zb1VVNxvnXbr/2w2Qf+7j5wrnXwYbYPzIP6GxfH1vhnN3/WxVvWDlj2prrVXVLcbx/83xj/Glz9mpn781fA/nZkke21p7aWvtezIcWfWshdkeleTl4++0jMVxtus/8fo/tcM5/9zHz9zzL4NlW/9Lt4LWS1VtSnLTJE9vrb2ptXbXDCdV/tWquu442yOTvLmW8FPbcTDcMMkLW2sfSPJTGT7B/bEkdxpn+6kkr66qK8u/tjbA+Jl7/rmPH/knNOfyNea5MMNpK24/PpeT5OQkt62qZ4/Xn5Lh1DTHTRBzn+a8/pMN8fop/4Tkn5b8a++wKI/jH6OLM5wA9HsWVvajklwrwyfpacNBNf45yb2myLkv42B4fZK7VNWtxuu/nWET9s+P85yR4ZD42ycLuoo555/7+Jl7/mTe4yeRf0pzL18Lnxq/K8O6ve04/eIk90lyg6q6SZKvJnlrkg9NkXM1c1//c3/9lH9a8k9L/vVxWJTHPf74Hp/ku6vqam3YL/ink9yiqr5znOelGd4ALaPdSb6e5KSqumlr7ZuttV9P8h1VdfdxnqcmeclkCfdtlvnnPn7mnn/BLMfPAvknsBHKV5K01l6a5K+TnFVVJ1TVVVtrn8vwBuLqrbVvJHlxa+2CCeNeztzX/9xfP+WflvzTkn99bOrPsnG01t5QVVuS/EKSK1fV21prH6yqixfm2TVZwI7W2rur6q8znPjzylX1r621tyS5IMOburTWPjxhxH3aAPlnOX7GT67aXPOvmOv4WVj/8k+otfbScfyfVVWPTvKu1trnqmqlfH20ql688Md6MjV8j/SiZHjzsLIrUmvtqVX1tSSnJ/lCDadD+Xhr7T0r804Ye68Wxs9s1v/ezPX1cwO9/ss/AeNnWsu6/jf00VZr4ahDK/8B48+nJPmBJLfKUKAvaK09cLqke1dVm8ZPZ/fMf/cMBx748SSfSvK11tr9pku6dxsg/2zHT1Ud3Vr70sL1xaNxLX3+ZN7jp6qu31r71ML1xfUv/yGwR+bFnx+T5C5JvpDk25J8qrV2ymRB91BVT0tyTJLPJnlia+0b49bHWvgdjh/nuVVr7fnjtKU4omFy2fI7Xp/N+l/h9X9ac17/ybzzGz/TmsP635Dlsaoem+TPx1a+uNIXB9ANklw/yZbW2mvGaUvxRdmq+u0kf9pae+8+ngCVZEuSG7TW3jROk38NbIDx84wkN0ryyST/tw1bjPY8HP4y55/7+PmDJLdI8uEkr26t/dU4Xf5DoKp+JsnftNY+vo8CuZTlq6r+OMl1MxzJ9o+SvKG19mt7me/I1trXF64vxbpP5l9+vf5PawOs/7nnf0aMn8nMZv231jbUJcM5sD6f5F+T3GKcdqW9zHfMHtcvN89E+Z+Z5BvjwNk6TjtiL/Ndf4/rNXX2DZJ/7uPnuUlenuET/Rcm+d1V5lvW/HMfP89J8ucZjiT5pCR/sMp88q9P/hcn+UiG713eZJy2t+fvkXtcn3z8J9mW5FVJrjxev1mSNyW59uL6zXCghBOmzrvK7/DH4+9wxyT/kuS3V5lv6db/mMPrv/V/OOc3fqz//bpsqAPmjG38G0m+M8nLkuysqlu04Rwomxbm+7kkd168b1uOTxyuleEcWFfLcOS5N1bV1jact2XzwnwPTXKPxfu2cQRNaQPkn/v4uUOS6yT5mTacTuExSX64qm61x3zbs5z55z5+bpHh4CWPbK19MsNR0LZV1fftMd9PR/41V1U3TXJkkgclOS/J06vqJuPz94iF+R6d4cADl1qG8Z/knUl+vQ1b6o7M8F3SqyS51h7r991t+K7pUqmqbRm2mv5Ua+1tSX4yyUlVde2VT87H+R6VJVz/Xv+ntQHW/9zzGz8Tmt36n7ppr/Ulw5uflU9un5Dk35J8+3h9ZTfd20+dcz/z/2qSTyc5fo/8N54652GSf1bjJ8kRSW45/nyV8d9/SvJde8x3h6mzbuDxc5MkmzNuWUnyyiR332Oem06dcwPnv2aSKye5eZLfyrAF8uZ7jJ+Tps65j/yb9rj+2gzlMRnO43XVhduWYmvvQp5r5Vt7CxyZ5BpJ3rbymrQw392mztr5Hbz+W/+HXX7jZ/Lss1r/G2rLY5K01j7fhkOWp7X2lAx/fJ9XVTdL8htVdVxr7T+TS7+3s1T2yP+0JE9P8rrx04enVdWJrbWPJfKvhzmPnzZ8N/AD489fGyd/LOORMKvqcVV1s9ba28frS5U/2RDj56OttYvat76P9tkMZSZV9ZSqukNr7SPjdfnXWGvtC621b7TWPphhF6b3JfnNqrp+kl+pqhu21s5Nljb/pQeIGid9PcmtqupFSb6/tfbVhXkn39q7qLX2+ST/Nf789dbaFzPsQvy5JKmqR9ZwepF/GK8v4/r3+j+hOa//ZN75jZ9pzW39b7jyuKiGL5D+ZpJzknwwwycO71i5fdn++C6qqitVVbXWzsyw+9h7M3yC/oaVeeRfX3McP23h4CDjpK9kePN5dpLbtYVTKSxj/hVzHz8L6//zSW5eVWdl+A7e21fmkX/dfSTD2PnA+PO21tonVm5c8vwr6/+oDN+B+WRr7WHJ9G8a9mXO5XdPXv+nNcf1v2iO+Y2fac1p/W/o8ti+tR/wnZPsbOPhbJf5j++KNpzTa2VwHJ/kZa21n0jkP1TmPH6SrGS8JMmfZjgc/qnJPPJvhPEz+lKS30/y2dbazyTyHyqttUtaa1/I8P3Ml7TWfixZjvwrGVbLMn4KnSTvyHDE1V8Z57/S1G8a9tMsy+8ir//Tmvn6n3t+42daS7/+Z18eeyuyqm6e5F9baw8Zry/VH9+FTxhWu/2GSf5fa+1BK/PLv3Y2wPjpvfl8Z5LXtdZ+dZx/2fLPffzsNf/CH653J/mL1trjVuaX/+D1ytfCfLfO8PxdKS6T56+qYzLuCtxaa53nwLMW3jQsxaHkk41Tfr3+T2ujrv+F22eZ3/g5NOa8/md5nsequlGSi1prnxmv79f5oZblj29V3TJJWmvvW5jW/R3kXxsbYPzsd/6qunpr7cvjz8uSf+7jR/4JjeXra238Xub+5lqG/FX1JxkOSvTBJO9qrf3+OH1TG3f5XOX6UpwDMTmw9V/Dd3Q+3JvvUPL6P63Daf3vcb/Z5Td+1t7c1/+K2ZXHqnphhnOQfTnJ21trTxqnX2bF1sLJxZdJVb0swxEBr55kV5Int9a+spf55F8HG2D87G/+pXzzuQHGz/7mv8z6XxYbIP/+lq+l+kObJFX1uCR3T/KwJN+e4Zyar2nf2qp7iyQPbMOBopbS3Muv1/9pHUbrf+75jZ91MPf1fxltCQ75ur+XJI9I8voMu9veOsm7kvzOwu23TvL0qXPuI/8DMmyCTpIbJvmbDAd0WDmZ9a2TvGjqnBs4/9zHz9zzz338yD9t/scled2Y/QeT7N5j/N8iya9OnXMf+R+U5PEL16+f5L+TPGW8vjXDiaHvPXXWDbr+5/76Kb/88su/FJe5fefxgxn+2FZr7b8y/AG7S1X9n/H2TyS5RVX9wlQBOz6W5OKqum4bjvr3oAyf4v5ykoy/0/Wr6swJM+7L3PPPffzMPf/cx4/80/pQkr9vrX2itfbGJHdN8oCqesp4+5FJ7lBV954s4b5dmOTuVXVUkrTWPpXhOXxiVZ2Y4YjC/5rkqlMF7Jj7+p/766f805J/WvIvkbmVx88luXaGTzjThn2GfyTJfarqwa21LyX54yRfqaojpou5qg8nOT/JHavqKm04EuAvJLlbVT12nOfRSc6vqqtME3Gf5p5/7uNn7vnnPn7kn9asy1dr7dUZ3kDsqvEgOW04Z+nrkxzdht2sXplhd+JlNOv1n/m/fso/LfmnJf8SmVV5bK29OcNJiJ9dVTcb9wv+fJLfSbLyZudfk7y2LeH+zq21j2Y4etIjk5xQVddsrV2Q5PSMR9/LcD6yF7VvnSR0aWyA/HMfP3PPP/fxI/+E5ly+qmpTkrTWfi7D7kr/XFXHV9XVk9wlycpBjD7aWvvkdElXN+f1n2yI10/5JyT/tORfLrMpjwt/fJ+Q5G0ZDjZwnxoOxXtKkpuOt3+2tfa5qXIuqvrWYXhXfm6t/WGSf8nwif/PVtV3JfmVJMeMt391/ARiqSy8WZhr/tmNn0VzzD/38T/3/Is20PN3FuWrqk6oqjuuXG+tXVxVm8efH5rk75P8UpLXJvlIa+0ZE8Tcb3Nb/8nlnr8r43+ur5+zy79I/mnJP62559+bpT3aalXdM8lXk7x55VPwqtrcWrto/PkXMmz+vUOS81trj5gs7F5U1ZVba9+ohaM+1cIRlarqR5N8R4YTmH6otXbahHEvp6pOSnJRkn9rrX1jnDan/HMfP3PPP/fxP/f8c3/+npDk4tba2xamLY7/pyS5UYbScn5r7eFT5Nybqnp1hi25t0zy0iRvbK29frztKgvP56sluVZr7SPj9aU5Quyc13/yrax7jPnFn5f99XPu+W+f4f3l2xemyX+IbID8ZyZ5WWvt3xemzSn/n2Y4IN0rF14zF99LLHX+/bGU5bGqXpXhy/fXybALzLtaa2eNt136x3e8fp3W2mfHn5fij28Nh+M9Nsl9W2sX7DFo9jwE79Kdx6WqXpBh3R+b5C+TnLmyNWIm+ec+fuaef+7jf+755/78nW35qqo7ZTh66r2r6iZJTs3wPZc3ttb+cmG+myT5+MKboaU5FPuc1/+Y448yfO/y0a21L+/xpvPSAjxeX8bXz7nnPzvDmP/OJH/UWjtj4Tb519kGyP/7SW7WWvuxvdx25ZUPQ8fry5j//ybZkuR+i+t6vG3p37/tr6XbbbWGE1hfpbX2I0numeTNSe5UVb+YJAt/uL5zfCKsrPhahhVfVb+SYeD8R5JXVtUxrbVLavwC7Mobt6q61ziQVt64LUv+38nwhuDkJPfIsEvSA1Zun0H+uY+fueef+/ife/65P3/vlOTKrbV7Jzkpw0Fa7lnDltLF8X+TJBcuFJelyJ/k4iS3rKqbteE7pn+c4Si331/DeRxXPnX+4cW8S1QcZ73+q+qMDLk/m+QZ44cj36xv7Ta2shVgWV8/557/GUmu2Vq7b5ITkzy4qu63crv862sD5H9hku9bKY41fDfw2JXb27f2olnW/EcmOTrJT7Zhz4G7VtW2qjouWf73bwdi6crj6NZVtbUNRwM8J8MX8m9dwxHdUlU/nuS4xVa/LH98k/xDhnNdPSbJO5K8auUN6MoMVfX9Sa67+AnEEuX/tyS/liSttY8nOSvDeb0uVVXbsrz5k3mPn2Te+ec+/ueef+7P31mXrzbsJvaSJA+rquu31j6dZGeSb0ty33G2Z7fWnj9RxJ5Zr/8kr8lwQKhnZtjt/5kLBaySpKp+LMntl/T1c7b5azhC8zuTnJYkrbX/TvInSa6xx3wPSHK8/Gtr7vlH/5zkVlV1nap6SIbXn7+qqt+uqtslSVU9MEv4/mf8gOeqGT58vkFV/USS385wKqxfrqpHjfM9IEuY/0At626rj8twwuQnttY+VFXXTvL4JF9urf3W2NKXL/ioxt3cqurKGY6kdIckd2uttaq6bWvt3RNHXFUNB0D4+sInVD+T5MTW2qnj9Ru0JTkgwmo2wPiZe/7Zjv9k3vk3yPP3N5N8Pckft9Y+VVU3SvKMJP+vtfbMGYz/uye5V5KPJnlpa+1j4wcO2zPsirjy6fNS/h5zX/8rqurbkzwqyTGttZ8Zp126m9iym2P+qrphks8uvP78apIbtdZ+Zbx+md3ml43806uqn0/yR0n+M8PeV1dP8v8l+UBr7cnL/vpTVf8zyQ8luSTJQ8Z/75/h7/Cjlj3//lqqLY8rn6xlONz3h5I8tqq+rQ1HHzo7w/nJjllZ8QvzL5WVrRRt2MT+6xkOv/v6qvrHfOvT56XUWvtyGza3r6zbLya5IEmq6s8znJdmKc19/Mw9/4o5j/9k3vnn/Pxd8MYk10rykKq6cRu2oD4zye1r2NV22cf/3yX5xyQ3yLDr4bYMb36+0JZza++eZr3+V7TW3pvkuUk+WVXPHZ+/D1y5Xf6111r7xB6vPxdm2IKaqnpFkkt3oZR/7c05/0qe1tpzkvx4kke21j7ZWntfhqOTHltVRy7r689CnldkOKLq9ye59fg+4o0ZtqjedFnzH6il3PKYJDUc5vxHM3zycGaSX0zy3rZkRwXcH+Mg+WySv2mtPXjqPAeihn21V84Dd+HKFoxlN/fxM/f8i+Y8/pN555/x8/e+SX4gwxHpfj/JjgwHjvpfkwbrWPxUuaq+Lcl9ktwxyedba4/dc55lNdf1vzfj/8O/Jfn71tqDps5zoOacv6rukmGry/UyfF4yi9efFfIfWqu9NlbVSzMclfTxE8Q6YFV1mySPTnJ8kidl2JX4k621R0+Zay0tbXlMLv3y6YOT3CbJJa21HeP0pf/ju6iqfjfJDVprDxmvz+aoSjUcsv3fMhy169HjtFnkn/v4mXv+FXMe/8m888/t+TvH8tXLUwtHCFzmdZ/Mc/2vZiVnVb0oyabW2k+N05f6/2DF3PMnSVXdI8MpC2bx+rMn+adVw9cwXphhV9zt47S5vP4cmWEL6q2SHNFa+41x+izy9yxFeTyQlbmMA38/3jzcprX2nvHnWeWvqqOS/ERr7QW9eaey0cfPHvPOLv+cx/94+2zzb4Tn77KVr6ranuS/MmzJffPecu0t5zKu+2R+639P+5H/jm08X6X8a6/z+nNshlMWPKs371Tkn1Yn/w2S3KW19vLx+qzG/17mXbr8V9Qk5bGqfjnDd7q+3Fr723Hayv7OK596Lu1KvqL5l+WJexD5l+L/5HAdP8viMB7/c8+/FGNqzuWrqp6X4fyH/5LhUPgvbK39wcLt12+tfWr8efK8ezPn9T/m2J/8l8s6s/E/9/wb8fVH/jVwuObfaA75AXOq6jkZ9sG+WZLnVNVjk+FNT2ut1XD+qLSFQ1Mvk4PJvyR/eA8k/2XGxzI8GQ7n8bMMDrPxP/f8y/j8fV6Sh2U4cM+z6lvnL105Cfr1V64v2/qv4Ryst85wmorTk/x8kidU1S+Nt181yZPHcj953r2Z8/pPDih/20v+OY3/ueffCK8/8q+xg3z9mXX+Dae1dsguSW6U4Txq1x2v3zrJ25M8bry+KcmLkjztUOaSX3755Zd/3fPfMsk/ZTgJfTKcAuUTSX5pvH7VDIdo/+Wps66S/5oZzpt5i4Vp35Hk/CQPHq/fI8lvJbnK1Hk34PqXX3755Zd/CS6HesvjJ5Ocl+S7ajjfzH8l+ckkj66qX2jD+WeenORqNXxZf9nIPy35pyX/tOae/7NJ3pvkpknSWnt7krsl+aWqenBr7atJXpXk2jWc8HqptNa+kORrSZ61MO1dSf5nkm3jpPeM/y7jp86zXv+Rf2ryT0v+ac09/5o6pOWxDZt2P5bk55IcPU57T5IHJbl3VV0zyacznCPl04cy2/6Qf1ryT0v+aW2A/F/ITMvXyi5gbThi4aaqet3CzecluVFVXbW19sEkT2mtXThFzn2Z8/pP5J+a/NOSf1pzz7/mDtUmzowH5xl/fn6Ghn7j8fqRSV6b5Drj9U2HKpf88ssvv/zrnv9KCz//bZLXLVz/tvH3uerK7zN13r2txwyHW1/5+dVJ/iLDubzOSXLW1Hk32vqXf3ku8ssv/3zzr8dl3Y+2WlVHtNYu2cvPf5Th0/NPJtma5AttCU/ALf90qi57xDn5Dy35p7UB8m9qw660K9cX8786SUvy+gwHH/hEG8/jtQyq6rcyfMfxra215y9Mv/R3qqqHJblakhu1JTyH15zXfyL/1OSflvzTmnv+9bZu5bGq7ttae83486WHrd3jP+CkJDdMcsPW2u+N05bij6/806qqX8/w5u0/Wms7F6bLfwjIP60NkH+25auqnp3k+kn+JMlLMxxd9U37cb+lOTz7nNf/mEP+Cck/LfmnNff8h0Rbn028L07y4STPWJh2pfHf2sf9rrQeeeSfXf7nZtgt7MFJ3p3koQu3yS+//Mud/9lJXpnhE9kvJfm+/bzf5PmTPCDJaxeuPyfJY5LcM8n1Fqb/bJLrT513o61/+eWXX375l/+y5gfMqaoTMhxS/qEZDizwjOTS854c0ca1XFU/V1VbF+/bluBTW/mnVVUPSHLT1tr9WmsvzvBl5EdV1VGLn+pU1Xb5157809pA+R/QWvurDB9kfW9V3bOqrrcw38/WeE6sFcuQv7X2yiQPTIbXyAzn9PpGkkcleWRVba6qqyXZ3Fr71HRJ927u61/+ack/LfmnNff8h9Kal8fW2luSnJrkXzLs9nO1qnpGVW1urV1SVVeqqisn+VxrbfdaP/7Bkn9y/5DkcUky5nznOH3zwhvnI5N8Xv51If+0Zp1/ruWrqh5aw/dI01r7+jj5LUlu1Vr7oySPT/KQJMe11r7SWnvOeL+lOqreXNf/CvmnJf+05J/W3PMfUqttkjzQS4YtXc/ZY9oRSW6f4cTKvz1Oe1jGoxKtvBdaqwzyzz7/H44/73mUw79dyZnk/nvcJr/88i9H/j/aY9odk9xk/Pm2GXbB/c4lzX/dJE9Kco3x+uV2QUryl0nuNHXWDbr+5Zdffvnln8llLbc8/nWSj1XVNZJLDx5wybjCn5LhxJlfSXJyG06mmSRp4//AEpB/Wn+d5NNVdXRr7eIaHDFufdmUZEtVvSzDfuiXkn/NyD+tjZD/k3u8/ryttfbRJGmtvTvDCZYv8zdnifJfkuR2SU5JLrsL0vh/8dIMW3vfOlG+nrmvf/mnJf+05J/W3PMfcmtZHi9JclwW/viO/wHfaK19IMl3Jnl5a+2nkuXb3SfyT23lzduDk0uflEckuShJJXl5ko+11h4xWcJ9k39a8k9r1uWrtfb5DB+yPaGqLj3lSVXdIMMuqxe21k4dpy3ba2cy8/Uf+acm/7Tkn9bc8x96bW03/d4hw1E+H7ww7UpJTsrCSZSzpEclkn/58o/TX53kRfLLL/+88ie5QZLTkzx/YdrS7uqT5O5J3pXk4QvTrrPs634jrH/55ZdffvnncVmP/4DL/fHd4/al/eMr//SXVd683Up++eWfbf5ZlK+FjNuSfCDDKTrutTB96d80zH39yy+//PLLv/yXlYMwrKmq2pbkz5L8XpL3t9ZeO06fxQk05Z/WQv5nJNndWnvdOH1pTsK9L/JPS/5p7ZH/Pa21vxmnz+L1J0mq6tZJfjjJLZK8oy2cKHrZzX39yz8t+acl/7Tmnv9QWZfymFzuj+95rbWz1+WB1on805rzm7dE/qnJP625519UVddorX1x6hwHYu7rX/5pyT8t+ac19/yHwrqVx8s8yAz/+C6Sf1ryT0v+acnPwZj7+pd/WvJPS/5pzT3/ejkk5REAAIB5W8tTdQAAALBBKY8AAAB0KY8AAAB0KY8AAAB0KY8AG0RVbamqC6vqbQvTLqmqt1XVO6rqz6vqqvu4/5Oq6rGHIOddquqdY66j1vvx1sO4rt9xiB9zx7je/nNcd9+7hsv+H1X10oXr16iq91XVLVaZ/+FVdeM1fPxbjr/Tl9dqmQCsPeURYGN5X2vtjgvXL2yt3bG1dlySbyR55DSxLuMhSX57zHXh1GGmUFWbDnD+Oye5T5Lvaq3dPsndk3x4DSP9SZKbVdXdx+u/meR5rbX3rzL/w5PstTxW1REH+uCttT3HLQBLSHkEOHz8U5JbJUlVPWzcgvX2qvqzPWcct0S9ebz9FStbLKvqJ8atmG+vqjeO025XVf82bjn6z/Eky3tVVT+X5CeT/FZVvaiqrl5Vf19V/15V51XV/cb5tlTVu6vq+VX13nHeu1fVP1fVf1XV9+zjMZ5UVWdX1T9V1Qer6gFV9Tvj8v+mqjaP892pqv6xqt5aVX9bVTcap7+hqn6vqt5SVbur6rur6pXj4z5l4aE2jbl2V9XLF9bRvpb7jKp6S5LH7G1d7sONknymtfb1JGmtfaa19rHVHq+qjqmq91TVbcZ5dlbV/1ht4W04b9cjkzyjqk5I8kNJnrbK+v3xJCckedHK1uOqOr+qnlpV/57kJ8bf9YRx/utW1fnjz0dU1dPGsfWfVfXznd8bgCWiPAIcBsYtXfdOcl5V3S7JE5LcrbV2hySP2ctdXtla++7x9t1Jto/TfyPJPcfpPzpOe2SSZ45bjk5I8pHVcrTW/iTJXyb51dbaQ5J8LcmPtda+K8lJSZ5eVTXOfqskT09y2/Hy4CTbkjw2ya93fuVbJrnbmPGFSc5trR2f/P/t3VuIVWUYxvH/oxRGmppIF2ZZUiKISaNBRKebILzxQiGSyE5aaUHdFHS6spsKStGE8kBUpB0gO5qMGGUxoIXJRFaoFWRkRqYmeZini7WmWU17z97jkNPo84OBvb/9Hd61GNi8fO+3NoeA6WUCuRiYabsFWAEsrIw/bHsqsAx4E5gPTALmSBpV9pkALLU9EfgduLuJeU+3PdX2U3XuZT0fUOwMfi1pqaSrAeqtZ3sfsABYJekGYKTt53pawPYXwDqgFbjH9uE6/V4DNgOzu+0e77V9qe1XeljmNmCf7WnANOAOSRc0uPaIiPif6FXZTEREDDhnqOsM5EfAcmAe8KrtXwBs/1pj3KRyl20EMJQiqQDYRJGQrAHeKNs+BR6SdC5F0vlNL+IT8Likq4AOYAxwTvnZTtvbACS1A622LWkbMK7BvO/ZPlL2HQy8X7Z3jp1AkQyuL3PVwcDuyvi1lf7ttneXcewAxgK/AT/Y3lT2exG4t1ynp3lXV17Xupc12T4gqQW4kiLJXi3pQYokruZ6ttdLmgUsAS7paf6KJcD1tjc22b9qdeMuXAdMLncvAYYDFwE7j2O9iIg4wZI8RkSc3A51P0vWtbHXo1XADNtbJc0BrgGwfaeKB7VMB7ZIarH9sqS2su1dSfNsb2gyvtnAaKClTPZ2AUPKz/6s9OuovO+g8fdXZ3lnh6QjZVlmdawoksLLexrfbd3ua5t/chPzHvy7c+17ubfeBdk+BmwENpZJ8c3AlnrrSRoETAT+AEbSw45wRUf5dzwOVl4fpau6aUilXRS7muuIiIgBJ2WrERGnng0U59JGAUg6u0afYcDusixydmejpPG222w/CuyhKKW8ENhhexFFiefksm+rpDENYhkO/FwmjtcC5/f14pq0HRit4kE0SDqtLOftjfM6x1OU1H7cm3nr3Msxklpr9J2gf54lnQJ812C9+yhKjm8EVlbOer6gHs6MNmk/xf9IPbuAlvL1zEr7OuCuSiwXSzqzj7FERMQJkp3HiIhTjO12SQuBDyUdAz6neHpm1SNAG0VS00ZXovBEmcSI4mzcVuAB4CZJR4CfKMpQB1GcWaxVElv1EvBWuZO2Gfiqj5fXFNuHy9LJRZKGU3wfPg2092Ka7cB8SSuAL4FnezlvrXvZQrFr191QYLGkEeXn3wJz660n6ShwO3CZ7f3lA3keBh6jSO5/7MV11rIKWCbpEFBrl/VJYI2kucA7lfbnKcqGPyvPtu4BZvQxloiIOEHUVckTEREDmaRxwNvlz3L0dyyTgFtt39/fsQwkkhYA39te27Dz8c1/FrDc9qz/Yv6+knTA9tD+jiMiImpL8hgRcZKQNBb4hOKpl1P6OZyIpkkaD7wODLM9vr/jiYiI2pI8RkTEgCTpFv79MyObbM/vj3hORpKWAFd0a37G9sr+iCciIvpXkseIiIiIiIhoKE9bjYiIiIiIiIaSPEZERERERERDSR4jIiIiIiKioSSPERERERER0VCSx4iIiIiIiGjoL0MoeTTDXa+6AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"errors_pd = X_valid.copy(deep=True)\nerrors_pd[\"Y_true\"] = y_valid\nerrors_pd[\"Y_pred\"] = y_pred_valid\nerrors_pd = errors_pd.loc[errors_pd[\"Y_true\"] != errors_pd[\"Y_pred\"], :]\nerrors_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.320867Z","iopub.execute_input":"2023-02-01T14:51:53.321309Z","iopub.status.idle":"2023-02-01T14:51:53.344082Z","shell.execute_reply.started":"2023-02-01T14:51:53.321267Z","shell.execute_reply":"2023-02-01T14:51:53.342810Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n386 1.405213 3.0 1.0 2.0 7.0 -2.230769 0.0 1.0\n607 0.694936 1.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n804 -0.323921 3.0 1.0 2.0 0.0 -0.230769 1.0 0.0\n824 1.092843 3.0 1.0 2.0 5.0 -2.153846 0.0 1.0\n429 -0.277363 3.0 1.0 2.0 0.0 0.153846 1.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3861.4052133.01.02.07.0-2.2307690.01.0
6070.6949361.01.02.00.0-0.2307691.00.0
804-0.3239213.01.02.00.0-0.2307691.00.0
8241.0928433.01.02.05.0-2.1538460.01.0
429-0.2773633.01.02.00.00.1538461.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.345774Z","iopub.execute_input":"2023-02-01T14:51:53.346816Z","iopub.status.idle":"2023-02-01T14:51:53.369951Z","shell.execute_reply.started":"2023-02-01T14:51:53.346772Z","shell.execute_reply":"2023-02-01T14:51:53.368730Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 7\n 2.0 0.0 1\n 1.0 1.0 0.0 5\n 2.0 1.0 0.0 1\n 1.0 1\n 3.0 2.0 1.0 1\n 5.0 2.0 0.0 1\n2.0 0.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 2.0 1.0 0.0 1\n 1.0 1\n3.0 0.0 1.0 0.0 13\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 1.0 1.0 0.0 1\n 2.0 0.0 11\n 1.0 1\n 2.0 1.0 0.0 3\n 1.0 2\n 2.0 0.0 1\n 1.0 2\n 4.0 1.0 1.0 1\n 5.0 1.0 1.0 2\n 6.0 2.0 0.0 1\n 7.0 1.0 1.0 1\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:53.371853Z","iopub.execute_input":"2023-02-01T14:51:53.372272Z","iopub.status.idle":"2023-02-01T14:51:54.052559Z","shell.execute_reply.started":"2023-02-01T14:51:53.372234Z","shell.execute_reply":"2023-02-01T14:51:54.051607Z"},"trusted":true},"execution_count":238,"outputs":[{"execution_count":238,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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Correctly classified\nWe repeat the same analysis to explore the correct classification. The training dataset has let classified well the dataset. However it tends to overfit. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_train.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_train\ncorrect_pd[\"Y_pred\"] = y_pred_train\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.053862Z","iopub.execute_input":"2023-02-01T14:51:54.054160Z","iopub.status.idle":"2023-02-01T14:51:54.076180Z","shell.execute_reply.started":"2023-02-01T14:51:54.054133Z","shell.execute_reply":"2023-02-01T14:51:54.075083Z"},"trusted":true},"execution_count":239,"outputs":[{"execution_count":239,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n844 -0.250836 3.0 1.0 2.0 0.0 -1.000000 0.0 0.0\n316 0.500043 2.0 2.0 2.0 1.0 -0.461538 1.0 1.0\n768 0.419921 3.0 1.0 3.0 1.0 0.000000 0.0 0.0\n130 -0.284041 3.0 1.0 4.0 0.0 0.230769 0.0 0.0\n110 1.626091 1.0 1.0 2.0 0.0 1.307692 0.0 0.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
844-0.2508363.01.02.00.0-1.0000000.00.0
3160.5000432.02.02.01.0-0.4615381.01.0
7680.4199213.01.03.01.00.0000000.00.0
130-0.2840413.01.04.00.00.2307690.00.0
1101.6260911.01.02.00.01.3076920.00.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.081370Z","iopub.execute_input":"2023-02-01T14:51:54.081697Z","iopub.status.idle":"2023-02-01T14:51:54.104120Z","shell.execute_reply.started":"2023-02-01T14:51:54.081668Z","shell.execute_reply":"2023-02-01T14:51:54.103001Z"},"trusted":true},"execution_count":240,"outputs":[{"execution_count":240,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 33\n 1.0 4\n 2.0 0.0 1\n 1.0 22\n 1.0 1.0 0.0 13\n 2.0 1.0 20\n 2.0 1.0 1.0 3\n 2.0 1.0 9\n 3.0 1.0 1.0 1\n 2.0 0.0 1\n 1.0 1\n 4.0 2.0 1.0 1\n2.0 0.0 1.0 0.0 38\n 2.0 0.0 1\n 1.0 14\n 1.0 1.0 0.0 10\n 2.0 0.0 1\n 1.0 8\n 2.0 1.0 0.0 3\n 1.0 5\n 2.0 1.0 10\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 1.0 6\n 4.0 2.0 1.0 1\n 5.0 2.0 1.0 1\n3.0 0.0 1.0 0.0 138\n 2.0 0.0 11\n 1.0 24\n 1.0 1.0 0.0 15\n 1.0 2\n 2.0 0.0 9\n 1.0 4\n 2.0 1.0 0.0 9\n 1.0 3\n 2.0 0.0 5\n 1.0 6\n 3.0 1.0 0.0 2\n 1.0 1\n 2.0 0.0 1\n 1.0 3\n 4.0 1.0 0.0 2\n 2.0 0.0 8\n 5.0 1.0 0.0 6\n 2.0 0.0 2\n 6.0 1.0 0.0 3\n 2.0 0.0 2\n 1.0 1\n 7.0 2.0 0.0 1\n 10.0 1.0 0.0 2\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:54.106496Z","iopub.execute_input":"2023-02-01T14:51:54.106922Z","iopub.status.idle":"2023-02-01T14:51:55.631830Z","shell.execute_reply.started":"2023-02-01T14:51:54.106868Z","shell.execute_reply":"2023-02-01T14:51:55.630790Z"},"trusted":true},"execution_count":241,"outputs":[{"execution_count":241,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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prediction on the validation dataset has been correct across the classes the family and the genders. Other elements may be affecting the misclassification. We will add more statistical variable for random forrest and decision trees. ","metadata":{}},{"cell_type":"code","source":"correct_pd = X_valid.copy(deep=True)\ncorrect_pd[\"Y_true\"] = y_valid\ncorrect_pd[\"Y_pred\"] = y_pred_valid\ncorrect_pd = correct_pd.loc[correct_pd[\"Y_true\"] == correct_pd[\"Y_pred\"], :]\ncorrect_pd.head()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.633364Z","iopub.execute_input":"2023-02-01T14:51:55.633706Z","iopub.status.idle":"2023-02-01T14:51:55.655017Z","shell.execute_reply.started":"2023-02-01T14:51:55.633675Z","shell.execute_reply":"2023-02-01T14:51:55.653820Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":" Fare Pclass Sex Embarked fam_members Age Y_true Y_pred\n369 2.375346 1.0 2.0 4.0 0.0 -0.461538 1.0 1.0\n541 0.728501 3.0 2.0 2.0 6.0 -1.615385 0.0 0.0\n196 -0.290356 3.0 1.0 3.0 0.0 0.000000 0.0 0.0\n810 -0.284401 3.0 1.0 2.0 0.0 -0.307692 0.0 0.0\n427 0.500043 2.0 2.0 2.0 0.0 -0.846154 1.0 1.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FarePclassSexEmbarkedfam_membersAgeY_trueY_pred
3692.3753461.02.04.00.0-0.4615381.01.0
5410.7285013.02.02.06.0-1.6153850.00.0
196-0.2903563.01.03.00.00.0000000.00.0
810-0.2844013.01.02.00.0-0.3076920.00.0
4270.5000432.02.02.00.0-0.8461541.01.0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"correct_pd.groupby([\"Pclass\",\"fam_members\",\"Sex\",\"Y_pred\"]).count()[\"Y_true\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.656793Z","iopub.execute_input":"2023-02-01T14:51:55.657669Z","iopub.status.idle":"2023-02-01T14:51:55.680263Z","shell.execute_reply.started":"2023-02-01T14:51:55.657616Z","shell.execute_reply":"2023-02-01T14:51:55.679008Z"},"trusted":true},"execution_count":243,"outputs":[{"execution_count":243,"output_type":"execute_result","data":{"text/plain":"Pclass fam_members Sex Y_pred\n1.0 0.0 1.0 0.0 17\n 1.0 2\n 2.0 1.0 10\n 1.0 1.0 0.0 6\n 1.0 1\n 2.0 1.0 19\n 2.0 1.0 0.0 5\n 2.0 1.0 4\n 3.0 1.0 1.0 2\n 2.0 1.0 1\n 4.0 2.0 1.0 1\n 5.0 1.0 0.0 2\n 2.0 1.0 1\n2.0 0.0 1.0 0.0 27\n 2.0 1.0 13\n 1.0 1.0 0.0 4\n 2.0 1.0 9\n 2.0 1.0 0.0 5\n 1.0 2\n 2.0 1.0 3\n 3.0 1.0 0.0 1\n 2.0 1.0 3\n3.0 0.0 1.0 0.0 93\n 2.0 0.0 5\n 1.0 7\n 1.0 1.0 0.0 8\n 2.0 0.0 3\n 2.0 1.0 0.0 5\n 1.0 1\n 2.0 0.0 3\n 1.0 3\n 3.0 2.0 1.0 2\n 4.0 2.0 0.0 1\n 5.0 1.0 0.0 3\n 2.0 0.0 3\n 6.0 1.0 1.0 1\n 2.0 0.0 3\n 7.0 1.0 0.0 3\n 2.0 0.0 1\n 10.0 1.0 0.0 2\n 2.0 0.0 3\nName: Y_true, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"errors_pd.boxplot(column='Age', \n by=['Pclass',\"fam_members\", \"Sex\", \"Y_true\"], \n figsize= (15,20), \n grid=False,\n rot = 45)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:55.681765Z","iopub.execute_input":"2023-02-01T14:51:55.682091Z","iopub.status.idle":"2023-02-01T14:51:56.352496Z","shell.execute_reply.started":"2023-02-01T14:51:55.682062Z","shell.execute_reply":"2023-02-01T14:51:56.351351Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. The importance suggests the Age, Sex and Fare may play an important to identify a survivor to someone who perished. The fare may indicate the passenger class. \n\nThe distribution of age grouped by gender and passenger class appears to to varies between the validation and training datasets. Many of the grouping appears to be skewed too. Therefore, it could lower the accuracy of the decision tree predictions on validation and prediction datasets. ","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.353913Z","iopub.execute_input":"2023-02-01T14:51:56.355590Z","iopub.status.idle":"2023-02-01T14:51:56.788043Z","shell.execute_reply.started":"2023-02-01T14:51:56.355547Z","shell.execute_reply":"2023-02-01T14:51:56.786828Z"},"trusted":true},"execution_count":245,"outputs":[{"execution_count":245,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 0.670622 1.126949 -2.236923 0.000000 0.538462 1.384615 \n 2.0 64.0 0.079123 1.120334 -2.256154 -0.403846 0.000000 0.480769 \n 3.0 207.0 -0.128421 0.815541 -2.275385 -0.615385 0.000000 0.000000 \n2.0 1.0 55.0 0.425175 0.919849 -1.153846 -0.192308 0.384615 1.115385 \n 2.0 45.0 -0.009402 1.034607 -2.000000 -0.461538 0.000000 0.769231 \n 3.0 90.0 -0.414530 0.893464 -2.230769 -0.923077 -0.269231 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.846154 \n 2.0 3.076923 \n 3.0 3.384615 \n2.0 1.0 2.461538 \n 2.0 2.076923 \n 3.0 2.538462 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.00.6706221.126949-2.2369230.0000000.5384621.3846153.846154
2.064.00.0791231.120334-2.256154-0.4038460.0000000.4807693.076923
3.0207.0-0.1284210.815541-2.275385-0.6153850.0000000.0000003.384615
2.01.055.00.4251750.919849-1.153846-0.1923080.3846151.1153852.461538
2.045.0-0.0094021.034607-2.000000-0.4615380.0000000.7692312.076923
3.090.0-0.4145300.893464-2.230769-0.923077-0.2692310.0000002.538462
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Age', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Age\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:56.789229Z","iopub.execute_input":"2023-02-01T14:51:56.789583Z","iopub.status.idle":"2023-02-01T14:51:57.215295Z","shell.execute_reply.started":"2023-02-01T14:51:56.789553Z","shell.execute_reply":"2023-02-01T14:51:57.214488Z"},"trusted":true},"execution_count":246,"outputs":[{"execution_count":246,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 0.789639 1.088398 -1.461538 0.000000 0.461538 1.692308 \n 2.0 44.0 0.013112 1.053801 -2.153846 -0.538462 -0.038462 0.403846 \n 3.0 140.0 -0.295604 0.786890 -2.230769 -0.769231 -0.076923 0.000000 \n2.0 1.0 39.0 0.173570 1.100058 -2.153846 -0.576923 0.000000 0.692308 \n 2.0 31.0 -0.220844 0.888396 -2.153846 -0.846154 -0.230769 0.269231 \n 3.0 54.0 -0.507835 0.841030 -2.250000 -1.038462 0.000000 0.000000 \n\n max \nSex Pclass \n1.0 1.0 3.076923 \n 2.0 2.461538 \n 3.0 1.961538 \n2.0 1.0 2.538462 \n 2.0 1.538462 \n 3.0 0.846154 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
SexPclass
1.01.049.00.7896391.088398-1.4615380.0000000.4615381.6923083.076923
2.044.00.0131121.053801-2.153846-0.538462-0.0384620.4038462.461538
3.0140.0-0.2956040.786890-2.230769-0.769231-0.0769230.0000001.961538
2.01.039.00.1735701.100058-2.153846-0.5769230.0000000.6923082.538462
2.031.0-0.2208440.888396-2.153846-0.846154-0.2307690.2692311.538462
3.054.0-0.5078350.841030-2.250000-1.0384620.0000000.0000000.846154
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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distibution per gender and passenger class of fare also varies between the training and validation dataset. For that reason, we surmise it could lead to overfitting to towards the training datasets and affect the predictions on the validation and testing datasets.","metadata":{}},{"cell_type":"code","source":"X_train.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_train.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.216805Z","iopub.execute_input":"2023-02-01T14:51:57.217226Z","iopub.status.idle":"2023-02-01T14:51:57.574988Z","shell.execute_reply.started":"2023-02-01T14:51:57.217185Z","shell.execute_reply":"2023-02-01T14:51:57.574210Z"},"trusted":true},"execution_count":247,"outputs":[{"execution_count":247,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 73.0 2.129921 3.147516 -0.626005 0.574570 1.041413 2.721281 \n 2.0 64.0 0.225744 0.636260 -0.626005 -0.068124 -0.052153 0.500043 \n 3.0 207.0 -0.081902 0.490356 -0.626005 -0.290356 -0.282777 -0.214564 \n2.0 1.0 55.0 4.189039 3.469143 0.496977 1.862310 3.233057 5.483978 \n 2.0 45.0 0.381259 0.532352 -0.171255 -0.062981 0.370115 0.510871 \n 3.0 90.0 -0.019152 0.362029 -0.333665 -0.288686 -0.199856 0.097265 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 21.562738 \n 2.0 2.189115 \n 3.0 1.405213 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.073.02.1299213.147516-0.6260050.5745701.0414132.72128121.562738
2.064.00.2257440.636260-0.626005-0.068124-0.0521530.5000432.557247
3.0207.0-0.0819020.490356-0.626005-0.290356-0.282777-0.2145642.386174
2.01.055.04.1890393.4691430.4969771.8623103.2330575.48397821.562738
2.045.00.3812590.532352-0.171255-0.0629810.3701150.5108712.189115
3.090.0-0.0191520.362029-0.333665-0.288686-0.1998560.0972651.405213
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"X_valid.boxplot(column='Fare', \n by=[\"Sex\", \"Pclass\"], \n figsize= (15,20), \n grid=False,\n rot = 45)\nX_valid.groupby([\"Sex\", \"Pclass\"]).describe()[\"Fare\"]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.576156Z","iopub.execute_input":"2023-02-01T14:51:57.576637Z","iopub.status.idle":"2023-02-01T14:51:57.924867Z","shell.execute_reply.started":"2023-02-01T14:51:57.576603Z","shell.execute_reply":"2023-02-01T14:51:57.923105Z"},"trusted":true},"execution_count":248,"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% 75% \\\nSex Pclass \n1.0 1.0 49.0 2.517351 3.671920 -0.626005 0.694936 1.626091 2.804111 \n 2.0 44.0 0.233743 0.667956 -0.626005 -0.127945 -0.062981 0.500043 \n 3.0 140.0 -0.071327 0.529842 -0.626005 -0.290356 -0.282777 -0.175091 \n2.0 1.0 39.0 3.661714 2.836481 0.523864 1.863843 2.788953 3.347646 \n 2.0 31.0 0.244587 0.359467 -0.171255 -0.062981 0.283496 0.500043 \n 3.0 54.0 0.224169 0.658672 -0.312011 -0.277363 0.050527 0.419921 \n\n max \nSex Pclass \n1.0 1.0 21.562738 \n 2.0 2.557247 \n 3.0 2.386174 \n2.0 1.0 10.764405 \n 2.0 1.174771 \n 3.0 2.386174 ","text/html":"
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countmeanstdmin25%50%75%max
SexPclass
1.01.049.02.5173513.671920-0.6260050.6949361.6260912.80411121.562738
2.044.00.2337430.667956-0.626005-0.127945-0.0629810.5000432.557247
3.0140.0-0.0713270.529842-0.626005-0.290356-0.282777-0.1750912.386174
2.01.039.03.6617142.8364810.5238641.8638432.7889533.34764610.764405
2.031.00.2445870.359467-0.171255-0.0629810.2834960.5000431.174771
3.054.00.2241690.658672-0.312011-0.2773630.0505270.4199212.386174
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The passenger class and being male can lead to errors. However, the number of female passengers increases as the class lower. Nonetheless, the various distribution of age and fare may lower the accuracy of the validation and testing datasets.","metadata":{}},{"cell_type":"markdown","source":"## Classification using test datasets","metadata":{}},{"cell_type":"code","source":"y_pred = rf.predict(X_test)\nrandom_forrest_pred = pd.DataFrame({\"PassengerId\": titanic_test.PassengerId,\n \"rf_y_pred\": y_pred})\nrandom_forrest_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.926719Z","iopub.execute_input":"2023-02-01T14:51:57.927152Z","iopub.status.idle":"2023-02-01T14:51:57.950525Z","shell.execute_reply.started":"2023-02-01T14:51:57.927100Z","shell.execute_reply":"2023-02-01T14:51:57.949359Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.951752Z","iopub.execute_input":"2023-02-01T14:51:57.952061Z","iopub.status.idle":"2023-02-01T14:51:57.958199Z","shell.execute_reply.started":"2023-02-01T14:51:57.952032Z","shell.execute_reply":"2023-02-01T14:51:57.956976Z"},"trusted":true},"execution_count":250,"outputs":[]},{"cell_type":"code","source":"random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.959366Z","iopub.execute_input":"2023-02-01T14:51:57.960119Z","iopub.status.idle":"2023-02-01T14:51:57.977269Z","shell.execute_reply.started":"2023-02-01T14:51:57.960080Z","shell.execute_reply":"2023-02-01T14:51:57.976084Z"},"trusted":true},"execution_count":251,"outputs":[{"execution_count":251,"output_type":"execute_result","data":{"text/plain":" PassengerId rf_y_pred\n0 892.0 0.0\n1 893.0 0.0\n2 894.0 0.0\n3 895.0 0.0\n4 896.0 1.0\n.. ... ...\n413 1305.0 0.0\n414 1306.0 1.0\n415 1307.0 0.0\n416 1308.0 0.0\n417 1309.0 1.0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdrf_y_pred
0892.00.0
1893.00.0
2894.00.0
3895.00.0
4896.01.0
.........
4131305.00.0
4141306.01.0
4151307.00.0
4161308.00.0
4171309.01.0
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(random_forrest_pred[[\"PassengerId\",\"rf_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:57.978846Z","iopub.execute_input":"2023-02-01T14:51:57.979227Z","iopub.status.idle":"2023-02-01T14:51:58.007917Z","shell.execute_reply.started":"2023-02-01T14:51:57.979179Z","shell.execute_reply":"2023-02-01T14:51:58.006694Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred \n0 0.0 0.0 0.0 0.0 \n1 1.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n4 0.0 1.0 1.0 1.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.0
1893.03.02.01.411765-0.3161762.01.01.00.00.00.0
2894.02.01.02.588235-0.2021843.00.00.00.00.00.0
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.0
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Method: Neural AI \nIn this section we use some neural network to classify the data. We prepare the data so that it is more suitable for neural networks. We apply cross validation. ","metadata":{"execution":{"iopub.status.busy":"2023-01-09T16:59:50.819233Z","iopub.execute_input":"2023-01-09T16:59:50.819762Z","iopub.status.idle":"2023-01-09T16:59:50.825788Z","shell.execute_reply.started":"2023-01-09T16:59:50.819721Z","shell.execute_reply":"2023-01-09T16:59:50.823990Z"}}},{"cell_type":"markdown","source":"## Prepare data for Neural-AI","metadata":{"execution":{"iopub.status.busy":"2022-12-07T15:38:00.160610Z","iopub.execute_input":"2022-12-07T15:38:00.161030Z","iopub.status.idle":"2022-12-07T15:38:00.169322Z","shell.execute_reply.started":"2022-12-07T15:38:00.160998Z","shell.execute_reply":"2022-12-07T15:38:00.167957Z"}}},{"cell_type":"code","source":"titanic_train = pd.read_csv(train_data_path)\ntitanic_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:51:58.009483Z","iopub.execute_input":"2023-02-01T14:51:58.009908Z","iopub.status.idle":"2023-02-01T14:51:58.023101Z","shell.execute_reply.started":"2023-02-01T14:51:58.009868Z","shell.execute_reply":"2023-02-01T14:51:58.021915Z"},"trusted":true},"execution_count":253,"outputs":[{"execution_count":253,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = pd.read_csv(test_data_path)\ntitanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.431458Z","iopub.execute_input":"2023-02-01T14:55:47.431870Z","iopub.status.idle":"2023-02-01T14:55:47.444617Z","shell.execute_reply.started":"2023-02-01T14:55:47.431840Z","shell.execute_reply":"2023-02-01T14:55:47.443399Z"},"trusted":true},"execution_count":254,"outputs":[{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.696681Z","iopub.execute_input":"2023-02-01T14:55:47.697091Z","iopub.status.idle":"2023-02-01T14:55:47.706759Z","shell.execute_reply.started":"2023-02-01T14:55:47.697056Z","shell.execute_reply":"2023-02-01T14:55:47.705377Z"},"trusted":true},"execution_count":255,"outputs":[{"execution_count":255,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nSurvived int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:55:47.965238Z","iopub.execute_input":"2023-02-01T14:55:47.965693Z","iopub.status.idle":"2023-02-01T14:55:47.976964Z","shell.execute_reply.started":"2023-02-01T14:55:47.965657Z","shell.execute_reply":"2023-02-01T14:55:47.975774Z"},"trusted":true},"execution_count":256,"outputs":[{"execution_count":256,"output_type":"execute_result","data":{"text/plain":"PassengerId int64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nEmbarked object\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"I propose to keep Pclass,Sex, Age, SibSP,Parch,Ticket, Fare,Cabin, Embarked, Survived","metadata":{}},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked', 'Survived']\ntitanic_train = titanic_train.loc[:,columns_to_keep]\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.596834Z","iopub.execute_input":"2023-02-01T14:59:41.597224Z","iopub.status.idle":"2023-02-01T14:59:41.617029Z","shell.execute_reply.started":"2023-02-01T14:59:41.597192Z","shell.execute_reply":"2023-02-01T14:59:41.615728Z"},"trusted":true},"execution_count":259,"outputs":[{"execution_count":259,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name \\\n0 1 3 Braund, Mr. Owen Harris \n1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n2 3 3 Heikkinen, Miss. Laina \n3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n4 5 3 Allen, Mr. William Henry \n\n Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n1 female 38.0 1 0 PC 17599 71.2833 C85 C \n2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n3 female 35.0 1 0 113803 53.1000 C123 S \n4 male 35.0 0 0 373450 8.0500 NaN S \n\n Survived \n0 0 \n1 1 \n2 1 \n3 1 \n4 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
013Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
233Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
453Allen, Mr. William Henrymale35.0003734508.0500NaNS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_keep = ['PassengerId','Pclass', \"Name\", 'Sex', 'Age', 'SibSp', 'Parch','Ticket', 'Fare','Cabin', 'Embarked']\ntitanic_test = titanic_test.loc[:,columns_to_keep]\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:41.783983Z","iopub.execute_input":"2023-02-01T14:59:41.784720Z","iopub.status.idle":"2023-02-01T14:59:41.804682Z","shell.execute_reply.started":"2023-02-01T14:59:41.784681Z","shell.execute_reply":"2023-02-01T14:59:41.803270Z"},"trusted":true},"execution_count":260,"outputs":[{"execution_count":260,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Passengers ID\nTransforms to float","metadata":{}},{"cell_type":"code","source":"\ntitanic_train[\"PassengerId\"] = titanic_train[\"PassengerId\"].astype(float)\ntitanic_test[\"PassengerId\"] = titanic_test[\"PassengerId\"].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.301290Z","iopub.execute_input":"2023-02-01T14:59:42.302052Z","iopub.status.idle":"2023-02-01T14:59:42.309717Z","shell.execute_reply.started":"2023-02-01T14:59:42.302008Z","shell.execute_reply":"2023-02-01T14:59:42.308660Z"},"trusted":true},"execution_count":261,"outputs":[]},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"markdown","source":"Quite a few passengers' age is unknown. We will try to input a value based using the sibling/spouse and parents/children statistical values to infer some replacement values. \n\nWe discover that the median age for parents, single, children, and couples. We rely on age and perception at the time the Titanic sunk. We input the median age for the age that is unknown. We hope to reduce the noise with more meaningful inputations. \n\n__Conditions applied:__\n\n|Category| Age condition | Sibling/spouse | Parents/children|\n|---|---|---|---|\n|Parents| >= 14| >= 0 | > 0| \n|Children | < 14 | none | > 0|\n|Singles| >= 14| == 0 | == 0|\n|Couples| >= 14 | == 1 | == 0|","metadata":{}},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:42.953004Z","iopub.execute_input":"2023-02-01T14:59:42.953443Z","iopub.status.idle":"2023-02-01T14:59:42.961302Z","shell.execute_reply.started":"2023-02-01T14:59:42.953406Z","shell.execute_reply":"2023-02-01T14:59:42.960093Z"},"trusted":true},"execution_count":262,"outputs":[{"execution_count":262,"output_type":"execute_result","data":{"text/plain":"177"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14.0) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.133436Z","iopub.execute_input":"2023-02-01T14:59:43.133821Z","iopub.status.idle":"2023-02-01T14:59:43.144899Z","shell.execute_reply.started":"2023-02-01T14:59:43.133790Z","shell.execute_reply":"2023-02-01T14:59:43.143759Z"},"trusted":true},"execution_count":263,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 32.426127527216174\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] >= 0.0) & (titanic_train[\"SibSp\"] >= 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_parents\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.317702Z","iopub.execute_input":"2023-02-01T14:59:43.318112Z","iopub.status.idle":"2023-02-01T14:59:43.329982Z","shell.execute_reply.started":"2023-02-01T14:59:43.318070Z","shell.execute_reply":"2023-02-01T14:59:43.328608Z"},"trusted":true},"execution_count":264,"outputs":[{"execution_count":264,"output_type":"execute_result","data":{"text/plain":"5 30.0\n17 30.0\n19 30.0\n26 30.0\n28 30.0\n ... \n859 30.0\n863 30.0\n868 30.0\n878 30.0\n888 30.0\nName: Age, Length: 177, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.526826Z","iopub.execute_input":"2023-02-01T14:59:43.527875Z","iopub.status.idle":"2023-02-01T14:59:43.538926Z","shell.execute_reply.started":"2023-02-01T14:59:43.527837Z","shell.execute_reply":"2023-02-01T14:59:43.538137Z"},"trusted":true},"execution_count":265,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\ntitanic_train.loc[filter_rows, \"Age\"] = median_children\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.734794Z","iopub.execute_input":"2023-02-01T14:59:43.735200Z","iopub.status.idle":"2023-02-01T14:59:43.749137Z","shell.execute_reply.started":"2023-02-01T14:59:43.735165Z","shell.execute_reply":"2023-02-01T14:59:43.747731Z"},"trusted":true},"execution_count":266,"outputs":[{"execution_count":266,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\nmedian_single = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:43.969440Z","iopub.execute_input":"2023-02-01T14:59:43.970219Z","iopub.status.idle":"2023-02-01T14:59:43.982089Z","shell.execute_reply.started":"2023-02-01T14:59:43.970157Z","shell.execute_reply":"2023-02-01T14:59:43.980764Z"},"trusted":true},"execution_count":267,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.794007490636705\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"Parch\"] < 1.0) & (titanic_train[\"SibSp\"] < 1.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_single\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.130067Z","iopub.execute_input":"2023-02-01T14:59:44.130855Z","iopub.status.idle":"2023-02-01T14:59:44.141446Z","shell.execute_reply.started":"2023-02-01T14:59:44.130814Z","shell.execute_reply":"2023-02-01T14:59:44.140366Z"},"trusted":true},"execution_count":268,"outputs":[{"execution_count":268,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] >= 14) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.375519Z","iopub.execute_input":"2023-02-01T14:59:44.376720Z","iopub.status.idle":"2023-02-01T14:59:44.387800Z","shell.execute_reply.started":"2023-02-01T14:59:44.376665Z","shell.execute_reply":"2023-02-01T14:59:44.386558Z"},"trusted":true},"execution_count":269,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 31.87704918032787\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"].isnull()) & (titanic_train[\"SibSp\"] == 1.0) & (titanic_train[\"Parch\"] == 0.0)\ntitanic_train.loc[filter_rows, \"Age\"] = median_couple\ntitanic_train.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.523725Z","iopub.execute_input":"2023-02-01T14:59:44.524363Z","iopub.status.idle":"2023-02-01T14:59:44.536192Z","shell.execute_reply.started":"2023-02-01T14:59:44.524322Z","shell.execute_reply":"2023-02-01T14:59:44.535041Z"},"trusted":true},"execution_count":270,"outputs":[{"execution_count":270,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:44.806439Z","iopub.execute_input":"2023-02-01T14:59:44.806827Z","iopub.status.idle":"2023-02-01T14:59:44.814441Z","shell.execute_reply.started":"2023-02-01T14:59:44.806794Z","shell.execute_reply":"2023-02-01T14:59:44.813111Z"},"trusted":true},"execution_count":271,"outputs":[{"execution_count":271,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"The testing dataset has all ages known.","metadata":{}},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.150811Z","iopub.execute_input":"2023-02-01T14:59:45.151188Z","iopub.status.idle":"2023-02-01T14:59:45.159387Z","shell.execute_reply.started":"2023-02-01T14:59:45.151156Z","shell.execute_reply":"2023-02-01T14:59:45.158248Z"},"trusted":true},"execution_count":272,"outputs":[{"execution_count":272,"output_type":"execute_result","data":{"text/plain":"86"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14.0) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\nmedian_parents = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_parents = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_parents, \" mean age \", mean_parents)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.400597Z","iopub.execute_input":"2023-02-01T14:59:45.401226Z","iopub.status.idle":"2023-02-01T14:59:45.410601Z","shell.execute_reply.started":"2023-02-01T14:59:45.401186Z","shell.execute_reply":"2023-02-01T14:59:45.409380Z"},"trusted":true},"execution_count":273,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 32.49671052631579\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] >= 0.0) & (titanic_test[\"SibSp\"] >= 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_parents\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.540502Z","iopub.execute_input":"2023-02-01T14:59:45.541816Z","iopub.status.idle":"2023-02-01T14:59:45.555066Z","shell.execute_reply.started":"2023-02-01T14:59:45.541649Z","shell.execute_reply":"2023-02-01T14:59:45.553893Z"},"trusted":true},"execution_count":274,"outputs":[{"execution_count":274,"output_type":"execute_result","data":{"text/plain":"10 29.0\n22 29.0\n29 29.0\n33 29.0\n36 29.0\n ... \n408 29.0\n410 29.0\n413 29.0\n416 29.0\n417 29.0\nName: Age, Length: 86, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_train[\"Age\"] < 14 ) & (titanic_train[\"Parch\"] > 0.0) & ((titanic_train[\"Name\"].str.contains(\"Master\")) | (titanic_train[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_train.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_train.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:45.765213Z","iopub.execute_input":"2023-02-01T14:59:45.766189Z","iopub.status.idle":"2023-02-01T14:59:45.777960Z","shell.execute_reply.started":"2023-02-01T14:59:45.766144Z","shell.execute_reply":"2023-02-01T14:59:45.776759Z"},"trusted":true},"execution_count":275,"outputs":[{"name":"stdout","text":"median age 4.0 mean age 4.689104477611941\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] < 14 ) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\n \nmedian_children = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_children = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_children, \" mean age \", mean_children)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.007744Z","iopub.execute_input":"2023-02-01T14:59:46.008172Z","iopub.status.idle":"2023-02-01T14:59:46.020782Z","shell.execute_reply.started":"2023-02-01T14:59:46.008134Z","shell.execute_reply":"2023-02-01T14:59:46.019374Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"median age 6.0 mean age 5.907407407407407\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] > 0.0) & ((titanic_test[\"Name\"].str.contains(\"Master\")) | (titanic_test[\"Name\"].str.contains(\"Miss\")))\ntitanic_test.loc[filter_rows, \"Age\"] = median_children\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.158566Z","iopub.execute_input":"2023-02-01T14:59:46.158955Z","iopub.status.idle":"2023-02-01T14:59:46.171385Z","shell.execute_reply.started":"2023-02-01T14:59:46.158921Z","shell.execute_reply":"2023-02-01T14:59:46.170377Z"},"trusted":true},"execution_count":277,"outputs":[{"execution_count":277,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\nmedian_single = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_single = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_single, \" mean age \", mean_single)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.365352Z","iopub.execute_input":"2023-02-01T14:59:46.365774Z","iopub.status.idle":"2023-02-01T14:59:46.377504Z","shell.execute_reply.started":"2023-02-01T14:59:46.365737Z","shell.execute_reply":"2023-02-01T14:59:46.376059Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"median age 29.0 mean age 29.785714285714285\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"Parch\"] < 1.0) & (titanic_test[\"SibSp\"] < 1.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_single\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.591674Z","iopub.execute_input":"2023-02-01T14:59:46.592065Z","iopub.status.idle":"2023-02-01T14:59:46.602473Z","shell.execute_reply.started":"2023-02-01T14:59:46.592030Z","shell.execute_reply":"2023-02-01T14:59:46.601375Z"},"trusted":true},"execution_count":279,"outputs":[{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"] >= 14) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\n\nmedian_couple = titanic_test.loc[filter_rows, \"Age\"].median()\nmean_couple = titanic_test.loc[filter_rows, \"Age\"].mean()\nprint(\"median age \", median_couple, \" mean age \", mean_couple)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:46.828954Z","iopub.execute_input":"2023-02-01T14:59:46.829390Z","iopub.status.idle":"2023-02-01T14:59:46.840546Z","shell.execute_reply.started":"2023-02-01T14:59:46.829349Z","shell.execute_reply":"2023-02-01T14:59:46.839434Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"median age 30.0 mean age 36.075\n","output_type":"stream"}]},{"cell_type":"code","source":"filter_rows = (titanic_test[\"Age\"].isnull()) & (titanic_test[\"SibSp\"] == 1.0) & (titanic_test[\"Parch\"] == 0.0)\ntitanic_test.loc[filter_rows, \"Age\"] = median_couple\ntitanic_test.loc[filter_rows, \"Age\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.034899Z","iopub.execute_input":"2023-02-01T14:59:47.036005Z","iopub.status.idle":"2023-02-01T14:59:47.045477Z","shell.execute_reply.started":"2023-02-01T14:59:47.035966Z","shell.execute_reply":"2023-02-01T14:59:47.044685Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"Series([], Name: Age, dtype: float64)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Age.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.171565Z","iopub.execute_input":"2023-02-01T14:59:47.172636Z","iopub.status.idle":"2023-02-01T14:59:47.179309Z","shell.execute_reply.started":"2023-02-01T14:59:47.172596Z","shell.execute_reply":"2023-02-01T14:59:47.178195Z"},"trusted":true},"execution_count":282,"outputs":[{"execution_count":282,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embarked \nWe transform the port of embarkment as unknown for Nan Values and transform those values into numerical ones. \n\n","metadata":{}},{"cell_type":"code","source":"titanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.581953Z","iopub.execute_input":"2023-02-01T14:59:47.582616Z","iopub.status.idle":"2023-02-01T14:59:47.591105Z","shell.execute_reply.started":"2023-02-01T14:59:47.582574Z","shell.execute_reply":"2023-02-01T14:59:47.589952Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.831877Z","iopub.execute_input":"2023-02-01T14:59:47.832258Z","iopub.status.idle":"2023-02-01T14:59:47.839367Z","shell.execute_reply.started":"2023-02-01T14:59:47.832227Z","shell.execute_reply":"2023-02-01T14:59:47.838210Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_train.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:47.993877Z","iopub.execute_input":"2023-02-01T14:59:47.994253Z","iopub.status.idle":"2023-02-01T14:59:48.002543Z","shell.execute_reply.started":"2023-02-01T14:59:47.994221Z","shell.execute_reply":"2023-02-01T14:59:48.001550Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', 'U'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.loc[titanic_train[\"Embarked\"].isnull(), \"Embarked\"] = \"U\"\ntitanic_test.Embarked.unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.201983Z","iopub.execute_input":"2023-02-01T14:59:48.202420Z","iopub.status.idle":"2023-02-01T14:59:48.212760Z","shell.execute_reply.started":"2023-02-01T14:59:48.202382Z","shell.execute_reply":"2023-02-01T14:59:48.211396Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"array(['Q', 'S', 'C'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_embarked_cat(data):\n factors = data['Embarked'].unique()\n gender_columns = pd.get_dummies(data['Embarked'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.431294Z","iopub.execute_input":"2023-02-01T14:59:48.432534Z","iopub.status.idle":"2023-02-01T14:59:48.437882Z","shell.execute_reply.started":"2023-02-01T14:59:48.432467Z","shell.execute_reply":"2023-02-01T14:59:48.437019Z"},"trusted":true},"execution_count":287,"outputs":[]},{"cell_type":"code","source":"\ntitanic_train = transform_embarked_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Embarked\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.629204Z","iopub.execute_input":"2023-02-01T14:59:48.629922Z","iopub.status.idle":"2023-02-01T14:59:48.642617Z","shell.execute_reply.started":"2023-02-01T14:59:48.629880Z","shell.execute_reply":"2023-02-01T14:59:48.641807Z"},"trusted":true},"execution_count":288,"outputs":[{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\ntitanic_test = transform_embarked_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Embarked\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:48.849824Z","iopub.execute_input":"2023-02-01T14:59:48.850216Z","iopub.status.idle":"2023-02-01T14:59:48.866727Z","shell.execute_reply.started":"2023-02-01T14:59:48.850182Z","shell.execute_reply":"2023-02-01T14:59:48.865657Z"},"trusted":true},"execution_count":289,"outputs":[{"execution_count":289,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nSibSp int64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"indices = range(0, titanic_test.shape[0])\ntitanic_test['U'] = [0 for i in indices]\ntitanic_test['U'] = titanic_test['U'].astype(float)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.014240Z","iopub.execute_input":"2023-02-01T14:59:49.014659Z","iopub.status.idle":"2023-02-01T14:59:49.022051Z","shell.execute_reply.started":"2023-02-01T14:59:49.014622Z","shell.execute_reply":"2023-02-01T14:59:49.020812Z"},"trusted":true},"execution_count":290,"outputs":[]},{"cell_type":"markdown","source":"### Number of sibling","metadata":{}},{"cell_type":"code","source":"print(titanic_train[\"SibSp\"].describe())\nplt.hist(titanic_train[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.435498Z","iopub.execute_input":"2023-02-01T14:59:49.435873Z","iopub.status.idle":"2023-02-01T14:59:49.609979Z","shell.execute_reply.started":"2023-02-01T14:59:49.435843Z","shell.execute_reply":"2023-02-01T14:59:49.608818Z"},"trusted":true},"execution_count":291,"outputs":[{"name":"stdout","text":"count 891.000000\nmean 0.523008\nstd 1.102743\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":291,"output_type":"execute_result","data":{"text/plain":"(array([608., 209., 28., 16., 0., 18., 5., 0., 0., 7.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQP0lEQVR4nO3df4ydVZ3H8fdHCiroUpDZpts2OyQ2bswmApkgrsa4dDX8MJY/lGB2pUtIun+wRtdNtPqPMdk/INmImGxIGqqWXQRZlNAocSWAcf0DdAoISHGtLNh2gY4KKLKui373j3vKXuq0M9O50zs9vl/JzT3POefe5zuT6Weenvs8z6SqkCT15RXjLkCSNHqGuyR1yHCXpA4Z7pLUIcNdkjq0YtwFAJx22mk1OTk57jIk6Ziyc+fOn1TVxGxjyyLcJycnmZ6eHncZknRMSfLEocZclpGkDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1aF7hnmRlkluSPJpkV5K3JDk1yR1JftieT2lzk+SzSXYneTDJWUv7JUiSDjbfK1SvAb5eVe9NcgJwIvAJ4M6qujLJFmAL8DHgfGB9e7wZuLY9L4nJLV9bqree0+NXXji2fUvS4cx55J7kZODtwDaAqvp1VT0LbAS2t2nbgYtaeyNwfQ3cA6xMsnrEdUuSDmM+yzKnAzPA55Pcn+S6JCcBq6rqyTbnKWBVa68B9gy9fm/re5kkm5NMJ5memZk58q9AkvQ75hPuK4CzgGur6kzglwyWYF5Sgz/EuqA/xlpVW6tqqqqmJiZmvamZJOkIzSfc9wJ7q+retn0Lg7B/+sByS3ve38b3AeuGXr+29UmSjpI5w72qngL2JHlD69oAPALsADa1vk3Aba29A7i0nTVzDvDc0PKNJOkomO/ZMh8EbmhnyjwGXMbgF8PNSS4HngAubnNvBy4AdgMvtLmSpKNoXuFeVQ8AU7MMbZhlbgFXLK4sSdJieIWqJHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHVoXuGe5PEkDyV5IMl06zs1yR1JftieT2n9SfLZJLuTPJjkrKX8AiRJv2shR+5/XlVnVNVU294C3FlV64E72zbA+cD69tgMXDuqYiVJ87OYZZmNwPbW3g5cNNR/fQ3cA6xMsnoR+5EkLdB8w72AbyTZmWRz61tVVU+29lPAqtZeA+wZeu3e1vcySTYnmU4yPTMzcwSlS5IOZcU8572tqvYl+UPgjiSPDg9WVSWphey4qrYCWwGmpqYW9FpJ0uHN68i9qva15/3ArcDZwNMHllva8/42fR+wbujla1ufJOkomTPck5yU5LUH2sC7gIeBHcCmNm0TcFtr7wAubWfNnAM8N7R8I0k6CuazLLMKuDXJgflfrKqvJ/kucHOSy4EngIvb/NuBC4DdwAvAZSOvWpJ0WHOGe1U9Brxplv6fAhtm6S/gipFUJ0k6Il6hKkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdmne4Jzkuyf1Jvtq2T09yb5LdSb6U5ITW/8q2vbuNTy5R7ZKkQ1jIkfuHgF1D21cBV1fV64FngMtb/+XAM63/6jZPknQUzSvck6wFLgSua9sBzgVuaVO2Axe19sa2TRvf0OZLko6S+R65fwb4KPDbtv064NmqerFt7wXWtPYaYA9AG3+uzZckHSVzhnuSdwP7q2rnKHecZHOS6STTMzMzo3xrSfq9N58j97cC70nyOHATg+WYa4CVSVa0OWuBfa29D1gH0MZPBn568JtW1daqmqqqqYmJiUV9EZKkl5sz3Kvq41W1tqomgUuAu6rqL4G7gfe2aZuA21p7R9umjd9VVTXSqiVJh7WY89w/BnwkyW4Ga+rbWv824HWt/yPAlsWVKElaqBVzT/l/VfVN4Jut/Rhw9ixzfgW8bwS1SZKOkFeoSlKHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KH5gz3JK9K8p0k30vy/SSfav2nJ7k3ye4kX0pyQut/Zdve3cYnl/hrkCQdZD5H7v8DnFtVbwLOAM5Lcg5wFXB1Vb0eeAa4vM2/HHim9V/d5kmSjqI5w70Gnm+bx7dHAecCt7T+7cBFrb2xbdPGNyTJqAqWJM1tXmvuSY5L8gCwH7gD+BHwbFW92KbsBda09hpgD0Abfw543QhrliTNYV7hXlW/qaozgLXA2cCfLHbHSTYnmU4yPTMzs9i3kyQNWdDZMlX1LHA38BZgZZIVbWgtsK+19wHrANr4ycBPZ3mvrVU1VVVTExMTR1a9JGlW8zlbZiLJytZ+NfBOYBeDkH9vm7YJuK21d7Rt2vhdVVUjrFmSNIcVc09hNbA9yXEMfhncXFVfTfIIcFOSfwDuB7a1+duAf06yG/gZcMkS1C1JOow5w72qHgTOnKX/MQbr7wf3/wp430iqkyQdEa9QlaQOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1KEVc01Isg64HlgFFLC1qq5JcirwJWASeBy4uKqeSRLgGuAC4AXgr6vqvqUpf7wmt3xtLPt9/MoLx7JfSceO+Ry5vwj8fVW9ETgHuCLJG4EtwJ1VtR64s20DnA+sb4/NwLUjr1qSdFhzhntVPXngyLuqfgHsAtYAG4Htbdp24KLW3ghcXwP3ACuTrB514ZKkQ1vQmnuSSeBM4F5gVVU92YaeYrBsA4Pg3zP0sr2t7+D32pxkOsn0zMzMQuuWJB3GvMM9yWuALwMfrqqfD49VVTFYj5+3qtpaVVNVNTUxMbGQl0qS5jCvcE9yPINgv6GqvtK6nz6w3NKe97f+fcC6oZevbX2SpKNkznBvZ79sA3ZV1aeHhnYAm1p7E3DbUP+lGTgHeG5o+UaSdBTMeSok8FbgA8BDSR5ofZ8ArgRuTnI58ARwcRu7ncFpkLsZnAp52SgLliTNbc5wr6pvAznE8IZZ5hdwxSLrkiQtgleoSlKHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KH5gz3JJ9Lsj/Jw0N9pya5I8kP2/MprT9JPptkd5IHk5y1lMVLkmY3nyP3LwDnHdS3BbizqtYDd7ZtgPOB9e2xGbh2NGVKkhZiznCvqm8BPzuoeyOwvbW3AxcN9V9fA/cAK5OsHlGtkqR5OtI191VV9WRrPwWsau01wJ6heXtb3+9IsjnJdJLpmZmZIyxDkjSbRX+gWlUF1BG8bmtVTVXV1MTExGLLkCQNOdJwf/rAckt73t/69wHrhuatbX2SpKPoSMN9B7CptTcBtw31X9rOmjkHeG5o+UaSdJSsmGtCkhuBdwCnJdkLfBK4Erg5yeXAE8DFbfrtwAXAbuAF4LIlqFmSNIc5w72q3n+IoQ2zzC3gisUWJUlaHK9QlaQOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUoTn/zJ6Wn8ktXxvbvh+/8sKx7VvS/HnkLkkdMtwlqUOGuyR1yDV3Lci41vvHtdbv5xs6VnnkLkkdMtwlqUNLsiyT5DzgGuA44LqqunIp9iNJo9Dj8tvIwz3JccA/Ae8E9gLfTbKjqh4Z9b6knv2+fb6h0VqKZZmzgd1V9VhV/Rq4Cdi4BPuRJB3CUizLrAH2DG3vBd588KQkm4HNbfP5JD84wv2dBvzkCF+7lKxrYQ5bV646ipW83HL9fsES1TaC7/Vy/Z4ty7py1aLq+uNDDYztVMiq2gpsXez7JJmuqqkRlDRS1rUw1rVwy7U261qYpaprKZZl9gHrhrbXtj5J0lGyFOH+XWB9ktOTnABcAuxYgv1Ikg5h5MsyVfVikr8F/o3BqZCfq6rvj3o/Qxa9tLNErGthrGvhlmtt1rUwS1JXqmop3leSNEZeoSpJHTLcJalDx3S4JzkvyQ+S7E6yZdz1ACT5XJL9SR4edy3DkqxLcneSR5J8P8mHxl0TQJJXJflOku+1uj417pqGJTkuyf1JvjruWg5I8niSh5I8kGR63PUckGRlkluSPJpkV5K3LIOa3tC+TwceP0/y4XHXBZDk79rP/MNJbkzyqpG+/7G65t5uc/AfDN3mAHj/uG9zkOTtwPPA9VX1p+OsZViS1cDqqrovyWuBncBFy+D7FeCkqno+yfHAt4EPVdU946zrgCQfAaaAP6iqd4+7HhiEOzBVVcvqgpwk24F/r6rr2plyJ1bVs2Mu6yUtM/YBb66qJ8ZcyxoGP+tvrKr/TnIzcHtVfWFU+ziWj9yX5W0OqupbwM/GXcfBqurJqrqvtX8B7GJwNfFY1cDzbfP49lgWRxxJ1gIXAteNu5blLsnJwNuBbQBV9evlFOzNBuBH4w72ISuAVydZAZwI/Nco3/xYDvfZbnMw9rA6FiSZBM4E7h1zKcBLSx8PAPuBO6pqWdQFfAb4KPDbMddxsAK+kWRnu43HcnA6MAN8vi1jXZfkpHEXdZBLgBvHXQRAVe0D/hH4MfAk8FxVfWOU+ziWw11HIMlrgC8DH66qn4+7HoCq+k1VncHgauazk4x9OSvJu4H9VbVz3LXM4m1VdRZwPnBFWwoctxXAWcC1VXUm8EtgWXwOBtCWid4D/Ou4awFIcgqDlYbTgT8CTkryV6Pcx7Ec7t7mYIHamvaXgRuq6ivjrudg7b/xdwPnjbkUgLcC72nr2zcB5yb5l/GWNNCO+qiq/cCtDJYox20vsHfof123MAj75eJ84L6qenrchTR/AfxnVc1U1f8CXwH+bJQ7OJbD3dscLED74HIbsKuqPj3ueg5IMpFkZWu/msEH5I+OtSigqj5eVWurapLBz9ZdVTXSI6sjkeSk9oE4bdnjXcDYz8yqqqeAPUne0Lo2AMvpbzi8n2WyJNP8GDgnyYnt3+YGBp+Djcwx+weyx3Cbg3lJciPwDuC0JHuBT1bVtvFWBQyORD8APNTWtwE+UVW3j68kAFYD29uZDK8Abq6qZXPa4TK0Crh1kAesAL5YVV8fb0kv+SBwQzvYegy4bMz1AC/9Enwn8DfjruWAqro3yS3AfcCLwP2M+DYEx+ypkJKkQzuWl2UkSYdguEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QO/R/6fmTLJacPIQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(titanic_test[\"SibSp\"].describe())\nplt.hist(titanic_test[\"SibSp\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:49.640013Z","iopub.execute_input":"2023-02-01T14:59:49.640429Z","iopub.status.idle":"2023-02-01T14:59:50.199638Z","shell.execute_reply.started":"2023-02-01T14:59:49.640389Z","shell.execute_reply":"2023-02-01T14:59:50.198241Z"},"trusted":true},"execution_count":292,"outputs":[{"name":"stdout","text":"count 418.000000\nmean 0.447368\nstd 0.896760\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 1.000000\nmax 8.000000\nName: SibSp, dtype: float64\n","output_type":"stream"},{"execution_count":292,"output_type":"execute_result","data":{"text/plain":"(array([283., 110., 14., 4., 0., 4., 1., 0., 0., 2.]),\n array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n )"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def categorise_siblings(data):\n cut_labels_9 = ['sib_0','sib_1','sib_2','sib_3', \n 'sib_4','sib_5','sib_6','sib_7', 'sib_8']\n cut_bins = [0,1,2,3,4,5,6,7,8,9]\n data['Sib_cat'] = pd.cut(data['SibSp'], \n bins=cut_bins, \n labels=cut_labels_9)\n \n data['Sib_cat'] = data.Sib_cat.astype(str)\n data.loc[data[\"Sib_cat\"] == 'nan', \"Sib_cat\"] = \"Sib_Unknown\"\n \n return data\n\ndef transform_sibling_cat(data):\n factors = data['Sib_cat'].unique()\n gender_columns = pd.get_dummies(data['Sib_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.201993Z","iopub.execute_input":"2023-02-01T14:59:50.202490Z","iopub.status.idle":"2023-02-01T14:59:50.212938Z","shell.execute_reply.started":"2023-02-01T14:59:50.202445Z","shell.execute_reply":"2023-02-01T14:59:50.211676Z"},"trusted":true},"execution_count":293,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_siblings(titanic_train)\ntitanic_train = transform_sibling_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"SibSp\", axis = 1)\ntitanic_train = titanic_train.drop(\"Sib_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.214386Z","iopub.execute_input":"2023-02-01T14:59:50.214705Z","iopub.status.idle":"2023-02-01T14:59:50.237526Z","shell.execute_reply.started":"2023-02-01T14:59:50.214675Z","shell.execute_reply":"2023-02-01T14:59:50.236793Z"},"trusted":true},"execution_count":294,"outputs":[{"execution_count":294,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.431533Z","iopub.execute_input":"2023-02-01T14:59:50.432231Z","iopub.status.idle":"2023-02-01T14:59:50.438691Z","shell.execute_reply.started":"2023-02-01T14:59:50.432194Z","shell.execute_reply":"2023-02-01T14:59:50.437673Z"},"trusted":true},"execution_count":295,"outputs":[{"execution_count":295,"output_type":"execute_result","data":{"text/plain":"(891, 21)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_siblings(titanic_test)\ntitanic_test = transform_sibling_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"SibSp\", axis = 1)\ntitanic_test = titanic_test.drop(\"Sib_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.596205Z","iopub.execute_input":"2023-02-01T14:59:50.596606Z","iopub.status.idle":"2023-02-01T14:59:50.618154Z","shell.execute_reply.started":"2023-02-01T14:59:50.596574Z","shell.execute_reply":"2023-02-01T14:59:50.617093Z"},"trusted":true},"execution_count":296,"outputs":[{"execution_count":296,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nAge float64\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:50.849255Z","iopub.execute_input":"2023-02-01T14:59:50.850520Z","iopub.status.idle":"2023-02-01T14:59:50.858028Z","shell.execute_reply.started":"2023-02-01T14:59:50.850477Z","shell.execute_reply":"2023-02-01T14:59:50.856953Z"},"trusted":true},"execution_count":297,"outputs":[{"execution_count":297,"output_type":"execute_result","data":{"text/plain":"(418, 20)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Transforming age into categories\nThe categorise the age into 9 categories; unknown and one for each decade. The categories are then transformed in hot_coding format. ","metadata":{}},{"cell_type":"code","source":"plt.hist(titanic_train['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.269486Z","iopub.execute_input":"2023-02-01T14:59:51.269885Z","iopub.status.idle":"2023-02-01T14:59:51.572232Z","shell.execute_reply.started":"2023-02-01T14:59:51.269851Z","shell.execute_reply":"2023-02-01T14:59:51.571214Z"},"trusted":true},"execution_count":298,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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6yqe0ewqIG6dUbIulZuUmuzrpWZ1LpgcmsbSV0j+UJVkjReXolJkhpkuEtSg9ZluCfZluTBJPuTXDPmWm5McjTJvr62U5PcmeSh7v6UMdR1ZpKPJrkvyb1JXj0JtSX5oSSfSvJfXV1/0rWfneSubp3e0n0Rv+aSnJDkM0k+NGF1HUjy2SR7k8x2bZOwnZ2c5P1JHkhyf5JfHHddSc7pXqf529eSvGbcdXW1/UG33e9Lsqv7fxjJNrbuwr3v1AYXA+cC25OcO8aSbgK2LWi7BthdVVuA3d34WjsGvK6qzgUuAK7qXqdx1/Yt4AVV9SxgK7AtyQXAnwNvraqfBL4CXLnGdc17NXB/3/ik1AXwS1W1te+Y6HGvS+idP+pfquqngWfRe+3GWldVPdi9TluBnwe+Adw27rqSnA78PjBTVT9L72CTyxnVNlZV6+oG/CLwr33j1wLXjrmmaWBf3/iDwOZueDPw4AS8bh+kd66fiakNeApwN/AL9H6hd+Ji63gN6zmD3j/9C4APAZmEurplHwBOW9A21nUJ/CjweboDMyalrgW1vAj4j0moCzgdeBQ4ld6Rih8CfmVU29i623Pnuy/QvINd2yTZVFWHu+HHgE3jLCbJNHAecBcTUFvX9bEXOArcCTwM/E9VHetmGdc6/SvgD4EnuvGnT0hdAAV8JMme7tQdMP51eTYwB/x915X1ziQnTUBd/S4HdnXDY62rqg4BbwK+ABwGvgrsYUTb2HoM93Wlem/HYzveNMlTgVuB11TV1/qnjau2qnq8eh+Zz6B3krmfXusaFkrya8DRqtoz7lqW8Nyqeja97sirkjy/f+KY1uWJwLOB66vqPOB/WdDVMc7tv+u7fjHwjwunjaOuro//Unpvij8OnMT/79IdmvUY7uvh1AZHkmwG6O6PjqOIJE+iF+zvrqoPTFJtAFX1P8BH6X0UPTnJ/I/qxrFOnwO8OMkB4L30umbeNgF1Ad/Z66OqjtLrPz6f8a/Lg8DBqrqrG38/vbAfd13zLgburqoj3fi463oh8PmqmquqbwMfoLfdjWQbW4/hvh5ObXA7cEU3fAW9/u41lSTADcD9VfWWSaktyVSSk7vhH6b3PcD99EL+N8ZVV1VdW1VnVNU0vW3q36vqZeOuCyDJSUl+ZH6YXj/yPsa8LqvqMeDRJOd0TRfRO6332Lf/zna+2yUD46/rC8AFSZ7S/X/Ov16j2cbG9UXHgF9MXAL8N72+2jeMuZZd9PrPvk1vT+ZKen21u4GHgH8DTh1DXc+l97HzHmBvd7tk3LUBPwd8pqtrH/BHXfszgE8B++l9jH7yGNfphcCHJqWurob/6m73zm/z416XXQ1bgdluff4TcMqE1HUS8CXgR/vaJqGuPwEe6Lb9fwCePKptzNMPSFKD1mO3jCTpOAx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/Ay2e5XnzEthuAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_train['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.573955Z","iopub.execute_input":"2023-02-01T14:59:51.574279Z","iopub.status.idle":"2023-02-01T14:59:51.588745Z","shell.execute_reply.started":"2023-02-01T14:59:51.574249Z","shell.execute_reply":"2023-02-01T14:59:51.587351Z"},"trusted":true},"execution_count":299,"outputs":[{"execution_count":299,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 29.758889\nstd 13.002570\nmin 0.420000\n25% 22.000000\n50% 30.000000\n75% 35.000000\nmax 80.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(titanic_test['Age'], bins=100)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:51.763907Z","iopub.execute_input":"2023-02-01T14:59:51.764334Z","iopub.status.idle":"2023-02-01T14:59:52.129917Z","shell.execute_reply.started":"2023-02-01T14:59:51.764278Z","shell.execute_reply":"2023-02-01T14:59:52.128918Z"},"trusted":true},"execution_count":300,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"titanic_test['Age'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.131621Z","iopub.execute_input":"2023-02-01T14:59:52.132130Z","iopub.status.idle":"2023-02-01T14:59:52.142285Z","shell.execute_reply.started":"2023-02-01T14:59:52.132091Z","shell.execute_reply":"2023-02-01T14:59:52.141264Z"},"trusted":true},"execution_count":301,"outputs":[{"execution_count":301,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 30.010766\nstd 12.645028\nmin 0.170000\n25% 23.000000\n50% 29.000000\n75% 35.750000\nmax 76.000000\nName: Age, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"def transform_age_cat(data):\n factors = data['Age_cat'].unique()\n gender_columns = pd.get_dummies(data['Age_cat'])\n columns = range(0,len(factors))\n for column in columns:\n data[factors[column]] = gender_columns.loc[:, factors[column]].astype(float)\n \n return data\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.143629Z","iopub.execute_input":"2023-02-01T14:59:52.143919Z","iopub.status.idle":"2023-02-01T14:59:52.154584Z","shell.execute_reply.started":"2023-02-01T14:59:52.143891Z","shell.execute_reply":"2023-02-01T14:59:52.153409Z"},"trusted":true},"execution_count":302,"outputs":[]},{"cell_type":"code","source":"def categorise_age(data):\n cut_labels_8 = ['age_0-9','age_10-19','age_20-29','age_30-39', \n 'age_40-49','age_50-59','age_60-69','age_70-79']\n cut_bins = [0,10,20,30,40,50,60,70,80]\n data['Age_cat'] = pd.cut(data['Age'], \n bins=cut_bins, \n labels=cut_labels_8)\n data['Age_cat'] = data.Age_cat.astype(str)\n data.loc[data[\"Age\"].isna(), \"Age_cat\"] = \"Age_Unknown\"\n return data","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.340509Z","iopub.execute_input":"2023-02-01T14:59:52.340896Z","iopub.status.idle":"2023-02-01T14:59:52.347606Z","shell.execute_reply.started":"2023-02-01T14:59:52.340863Z","shell.execute_reply":"2023-02-01T14:59:52.346572Z"},"trusted":true},"execution_count":303,"outputs":[]},{"cell_type":"code","source":"titanic_train = categorise_age(titanic_train)\ntitanic_train = transform_age_cat(titanic_train)\ntitanic_train = titanic_train.drop(\"Age\", axis = 1)\ntitanic_train = titanic_train.drop(\"Age_cat\", axis = 1)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.546266Z","iopub.execute_input":"2023-02-01T14:59:52.546677Z","iopub.status.idle":"2023-02-01T14:59:52.572844Z","shell.execute_reply.started":"2023-02-01T14:59:52.546642Z","shell.execute_reply":"2023-02-01T14:59:52.571757Z"},"trusted":true},"execution_count":304,"outputs":[{"execution_count":304,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = categorise_age(titanic_test)\ntitanic_test = transform_age_cat(titanic_test)\ntitanic_test = titanic_test.drop(\"Age\", axis = 1)\ntitanic_test = titanic_test.drop(\"Age_cat\", axis = 1)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:52.811521Z","iopub.execute_input":"2023-02-01T14:59:52.812681Z","iopub.status.idle":"2023-02-01T14:59:52.836736Z","shell.execute_reply.started":"2023-02-01T14:59:52.812627Z","shell.execute_reply":"2023-02-01T14:59:52.835513Z"},"trusted":true},"execution_count":305,"outputs":[{"execution_count":305,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nSex object\nParch int64\nTicket object\nFare float64\nCabin object\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"### Gender transformation to hot-coding \nWe check the factor values are the same between both datasets. Then, we generate a hot coding of two columns; i.e., male and female. Both columns replace the Sex column.","metadata":{}},{"cell_type":"code","source":"titanic_train['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.188122Z","iopub.execute_input":"2023-02-01T14:59:53.189282Z","iopub.status.idle":"2023-02-01T14:59:53.197504Z","shell.execute_reply.started":"2023-02-01T14:59:53.189231Z","shell.execute_reply":"2023-02-01T14:59:53.196373Z"},"trusted":true},"execution_count":306,"outputs":[{"execution_count":306,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Sex'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.420038Z","iopub.execute_input":"2023-02-01T14:59:53.420458Z","iopub.status.idle":"2023-02-01T14:59:53.428009Z","shell.execute_reply.started":"2023-02-01T14:59:53.420423Z","shell.execute_reply":"2023-02-01T14:59:53.426859Z"},"trusted":true},"execution_count":307,"outputs":[{"execution_count":307,"output_type":"execute_result","data":{"text/plain":"array(['male', 'female'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def transform_gender(data):\n factors = data['Sex'].unique()\n gender_columns = pd.get_dummies(data['Sex'])\n columns = range(0,len(factors))\n \n for column in columns:\n data[factors[column]] = gender_columns.loc[:,factors[column]].astype(float)\n \n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.614253Z","iopub.execute_input":"2023-02-01T14:59:53.614984Z","iopub.status.idle":"2023-02-01T14:59:53.620854Z","shell.execute_reply.started":"2023-02-01T14:59:53.614945Z","shell.execute_reply":"2023-02-01T14:59:53.619727Z"},"trusted":true},"execution_count":308,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_gender(titanic_train)\ntitanic_train.drop(\"Sex\", axis = 1, inplace = True)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:53.853720Z","iopub.execute_input":"2023-02-01T14:59:53.854121Z","iopub.status.idle":"2023-02-01T14:59:53.868139Z","shell.execute_reply.started":"2023-02-01T14:59:53.854084Z","shell.execute_reply":"2023-02-01T14:59:53.867117Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nPclass int64\nName object\nParch int64\nTicket object\nFare float64\nCabin object\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_gender(titanic_test)\ntitanic_test.drop(\"Sex\", axis = 1,inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.077511Z","iopub.execute_input":"2023-02-01T14:59:54.078227Z","iopub.status.idle":"2023-02-01T14:59:54.117482Z","shell.execute_reply.started":"2023-02-01T14:59:54.078188Z","shell.execute_reply":"2023-02-01T14:59:54.116493Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Name Parch \\\n0 892.0 3 Kelly, Mr. James 0 \n1 893.0 3 Wilkes, Mrs. James (Ellen Needs) 0 \n2 894.0 2 Myles, Mr. Thomas Francis 0 \n3 895.0 3 Wirz, Mr. Albert 0 \n4 896.0 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 \n\n Ticket Fare Cabin Q S C ... age_30-39 age_40-49 \\\n0 330911 7.8292 NaN 1.0 0.0 0.0 ... 1.0 0.0 \n1 363272 7.0000 NaN 0.0 1.0 0.0 ... 0.0 1.0 \n2 240276 9.6875 NaN 1.0 0.0 0.0 ... 0.0 0.0 \n3 315154 8.6625 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n4 3101298 12.2875 NaN 0.0 1.0 0.0 ... 0.0 0.0 \n\n age_60-69 age_20-29 age_10-19 age_50-59 age_0-9 age_70-79 male \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n\n female \n0 0.0 \n1 1.0 \n2 0.0 \n3 0.0 \n4 1.0 \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassNameParchTicketFareCabinQSC...age_30-39age_40-49age_60-69age_20-29age_10-19age_50-59age_0-9age_70-79malefemale
0892.03Kelly, Mr. James03309117.8292NaN1.00.00.0...1.00.00.00.00.00.00.00.01.00.0
1893.03Wilkes, Mrs. James (Ellen Needs)03632727.0000NaN0.01.00.0...0.01.00.00.00.00.00.00.00.01.0
2894.02Myles, Mr. Thomas Francis02402769.6875NaN1.00.00.0...0.00.01.00.00.00.00.00.01.00.0
3895.03Wirz, Mr. Albert03151548.6625NaN0.01.00.0...0.00.00.01.00.00.00.00.01.00.0
4896.03Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.2875NaN0.01.00.0...0.00.00.01.00.00.00.00.00.01.0
\n

5 rows × 28 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Cabin and Pclass\n\nThe passenger class appears to drive whether a cabin is known. So, we propose to drop the cabin as the percentage of not known values is quite high. We apply an hot encoding the Pclass. ","metadata":{}},{"cell_type":"code","source":"titanic_train['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.494349Z","iopub.execute_input":"2023-02-01T14:59:54.494758Z","iopub.status.idle":"2023-02-01T14:59:54.503695Z","shell.execute_reply.started":"2023-02-01T14:59:54.494724Z","shell.execute_reply":"2023-02-01T14:59:54.502385Z"},"trusted":true},"execution_count":311,"outputs":[{"execution_count":311,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n 'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n 'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n 'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n 'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"print(\"percentage of cabin nan values - training \", titanic_train['Cabin'].isna().sum()/titanic_train.shape[0])\nprint(\"percentage of cabin nan values - test \", titanic_test['Cabin'].isna().sum()/titanic_test.shape[0])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.731246Z","iopub.execute_input":"2023-02-01T14:59:54.732185Z","iopub.status.idle":"2023-02-01T14:59:54.740154Z","shell.execute_reply.started":"2023-02-01T14:59:54.732142Z","shell.execute_reply":"2023-02-01T14:59:54.738880Z"},"trusted":true},"execution_count":312,"outputs":[{"name":"stdout","text":"percentage of cabin nan values - training 0.7710437710437711\npercentage of cabin nan values - test 0.7822966507177034\n","output_type":"stream"}]},{"cell_type":"code","source":"titanic_train['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:54.963015Z","iopub.execute_input":"2023-02-01T14:59:54.963847Z","iopub.status.idle":"2023-02-01T14:59:54.971020Z","shell.execute_reply.started":"2023-02-01T14:59:54.963804Z","shell.execute_reply":"2023-02-01T14:59:54.969855Z"},"trusted":true},"execution_count":313,"outputs":[{"execution_count":313,"output_type":"execute_result","data":{"text/plain":"array([3, 1, 2])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test['Pclass'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.182701Z","iopub.execute_input":"2023-02-01T14:59:55.183488Z","iopub.status.idle":"2023-02-01T14:59:55.190703Z","shell.execute_reply.started":"2023-02-01T14:59:55.183443Z","shell.execute_reply":"2023-02-01T14:59:55.189659Z"},"trusted":true},"execution_count":314,"outputs":[{"execution_count":314,"output_type":"execute_result","data":{"text/plain":"array([3, 2, 1])"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 1 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.447423Z","iopub.execute_input":"2023-02-01T14:59:55.447835Z","iopub.status.idle":"2023-02-01T14:59:55.464293Z","shell.execute_reply.started":"2023-02-01T14:59:55.447799Z","shell.execute_reply":"2023-02-01T14:59:55.463098Z"},"trusted":true},"execution_count":315,"outputs":[{"execution_count":315,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n1 1 C85\n3 1 C123\n6 1 E46\n11 1 C103\n23 1 A6\n.. ... ...\n871 1 D35\n872 1 B51 B53 B55\n879 1 C50\n887 1 B42\n889 1 C148\n\n[216 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
11C85
31C123
61E46
111C103
231A6
.........
8711D35
8721B51 B53 B55
8791C50
8871B42
8891C148
\n

216 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 2 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.639329Z","iopub.execute_input":"2023-02-01T14:59:55.640055Z","iopub.status.idle":"2023-02-01T14:59:55.656031Z","shell.execute_reply.started":"2023-02-01T14:59:55.640016Z","shell.execute_reply":"2023-02-01T14:59:55.655083Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n9 2 NaN\n15 2 NaN\n17 2 NaN\n20 2 NaN\n21 2 D56\n.. ... ...\n866 2 NaN\n874 2 NaN\n880 2 NaN\n883 2 NaN\n886 2 NaN\n\n[184 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
92NaN
152NaN
172NaN
202NaN
212D56
.........
8662NaN
8742NaN
8802NaN
8832NaN
8862NaN
\n

184 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.loc[titanic_train['Pclass'] == 3 ,['Pclass','Cabin']]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:55.890762Z","iopub.execute_input":"2023-02-01T14:59:55.891773Z","iopub.status.idle":"2023-02-01T14:59:55.905616Z","shell.execute_reply.started":"2023-02-01T14:59:55.891731Z","shell.execute_reply":"2023-02-01T14:59:55.904841Z"},"trusted":true},"execution_count":317,"outputs":[{"execution_count":317,"output_type":"execute_result","data":{"text/plain":" Pclass Cabin\n0 3 NaN\n2 3 NaN\n4 3 NaN\n5 3 NaN\n7 3 NaN\n.. ... ...\n882 3 NaN\n884 3 NaN\n885 3 NaN\n888 3 NaN\n890 3 NaN\n\n[491 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PclassCabin
03NaN
23NaN
43NaN
53NaN
73NaN
.........
8823NaN
8843NaN
8853NaN
8883NaN
8903NaN
\n

491 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"xs = titanic_train.loc[titanic_train['Fare'] > 0,'Pclass']\nys = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.128782Z","iopub.execute_input":"2023-02-01T14:59:56.129782Z","iopub.status.idle":"2023-02-01T14:59:56.360461Z","shell.execute_reply.started":"2023-02-01T14:59:56.129741Z","shell.execute_reply":"2023-02-01T14:59:56.359413Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"xs = titanic_test.loc[titanic_test['Fare'] > 0,'Pclass']\nys = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\nplt.scatter(xs,ys)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.362001Z","iopub.execute_input":"2023-02-01T14:59:56.362324Z","iopub.status.idle":"2023-02-01T14:59:56.593756Z","shell.execute_reply.started":"2023-02-01T14:59:56.362281Z","shell.execute_reply":"2023-02-01T14:59:56.592791Z"},"trusted":true},"execution_count":319,"outputs":[{"execution_count":319,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.scatter(titanic_train[\"Pclass\"],titanic_train[\"Fare\"])","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.595546Z","iopub.execute_input":"2023-02-01T14:59:56.595846Z","iopub.status.idle":"2023-02-01T14:59:56.826882Z","shell.execute_reply.started":"2023-02-01T14:59:56.595817Z","shell.execute_reply":"2023-02-01T14:59:56.825559Z"},"trusted":true},"execution_count":320,"outputs":[{"execution_count":320,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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transform_Pclass(data):\n factors = data['Pclass'].unique()\n Pclass_columns = pd.get_dummies(data['Pclass'])\n columns = range(0,len(factors))\n \n for column in columns:\n col_name = 'Class_' + str(factors[column])\n data[col_name] = Pclass_columns.loc[:,factors[column]].astype(float)\n \n data.drop(\"Pclass\", axis = 1)\n return data\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:56.829111Z","iopub.execute_input":"2023-02-01T14:59:56.829859Z","iopub.status.idle":"2023-02-01T14:59:56.838658Z","shell.execute_reply.started":"2023-02-01T14:59:56.829811Z","shell.execute_reply":"2023-02-01T14:59:56.837496Z"},"trusted":true},"execution_count":321,"outputs":[]},{"cell_type":"code","source":"titanic_train = transform_Pclass(titanic_train)\ntitanic_train.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_train.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.037884Z","iopub.execute_input":"2023-02-01T14:59:57.039017Z","iopub.status.idle":"2023-02-01T14:59:57.077228Z","shell.execute_reply.started":"2023-02-01T14:59:57.038961Z","shell.execute_reply":"2023-02-01T14:59:57.076108Z"},"trusted":true},"execution_count":322,"outputs":[{"execution_count":322,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch \\\n0 1.0 Braund, Mr. Owen Harris 0 \n1 2.0 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n2 3.0 Heikkinen, Miss. Laina 0 \n3 4.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n4 5.0 Allen, Mr. William Henry 0 \n\n Ticket Fare Survived S C Q U ... age_0-9 \\\n0 A/5 21171 7.2500 0 1.0 0.0 0.0 0.0 ... 0.0 \n1 PC 17599 71.2833 1 0.0 1.0 0.0 0.0 ... 0.0 \n2 STON/O2. 3101282 7.9250 1 1.0 0.0 0.0 0.0 ... 0.0 \n3 113803 53.1000 1 1.0 0.0 0.0 0.0 ... 0.0 \n4 373450 8.0500 0 1.0 0.0 0.0 0.0 ... 0.0 \n\n age_10-19 age_60-69 age_40-49 age_70-79 male female Class_3 Class_1 \\\n0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n2 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 \n4 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n\n Class_2 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 30 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareSurvivedSCQU...age_0-9age_10-19age_60-69age_40-49age_70-79malefemaleClass_3Class_1Class_2
01.0Braund, Mr. Owen Harris0A/5 211717.250001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
12.0Cumings, Mrs. John Bradley (Florence Briggs Th...0PC 1759971.283310.01.00.00.0...0.00.00.00.00.00.01.00.01.00.0
23.0Heikkinen, Miss. Laina0STON/O2. 31012827.925011.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
34.0Futrelle, Mrs. Jacques Heath (Lily May Peel)011380353.100011.00.00.00.0...0.00.00.00.00.00.01.00.01.00.0
45.0Allen, Mr. William Henry03734508.050001.00.00.00.0...0.00.00.00.00.01.00.01.00.00.0
\n

5 rows × 30 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test = transform_Pclass(titanic_test)\ntitanic_test.drop(\"Pclass\", axis = 1, inplace = True)\ntitanic_test.drop(\"Cabin\", axis = 1, inplace = True)\ntitanic_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.320738Z","iopub.execute_input":"2023-02-01T14:59:57.321706Z","iopub.status.idle":"2023-02-01T14:59:57.358787Z","shell.execute_reply.started":"2023-02-01T14:59:57.321665Z","shell.execute_reply":"2023-02-01T14:59:57.357627Z"},"trusted":true},"execution_count":323,"outputs":[{"execution_count":323,"output_type":"execute_result","data":{"text/plain":" PassengerId Name Parch Ticket \\\n0 892.0 Kelly, Mr. James 0 330911 \n1 893.0 Wilkes, Mrs. James (Ellen Needs) 0 363272 \n2 894.0 Myles, Mr. Thomas Francis 0 240276 \n3 895.0 Wirz, Mr. Albert 0 315154 \n4 896.0 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 1 3101298 \n\n Fare Q S C U Sib_Unknown ... age_20-29 age_10-19 \\\n0 7.8292 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n1 7.0000 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 \n2 9.6875 1.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n3 8.6625 0.0 1.0 0.0 0.0 1.0 ... 1.0 0.0 \n4 12.2875 0.0 1.0 0.0 0.0 0.0 ... 1.0 0.0 \n\n age_50-59 age_0-9 age_70-79 male female Class_3 Class_2 Class_1 \n0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdNameParchTicketFareQSCUSib_Unknown...age_20-29age_10-19age_50-59age_0-9age_70-79malefemaleClass_3Class_2Class_1
0892.0Kelly, Mr. James03309117.82921.00.00.00.01.0...0.00.00.00.00.01.00.01.00.00.0
1893.0Wilkes, Mrs. James (Ellen Needs)03632727.00000.01.00.00.00.0...0.00.00.00.00.00.01.01.00.00.0
2894.0Myles, Mr. Thomas Francis02402769.68751.00.00.00.01.0...0.00.00.00.00.01.00.00.01.00.0
3895.0Wirz, Mr. Albert03151548.66250.01.00.00.01.0...1.00.00.00.00.01.00.01.00.00.0
4896.0Hirvonen, Mrs. Alexander (Helga E Lindqvist)1310129812.28750.01.00.00.00.0...1.00.00.00.00.00.01.01.00.00.0
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Tickets and Fare\nWe remove the tickets, as it brings no additional characteristic for the prediction.\n\nOld version: We reduce the complexity of the Fare by using the log.\nNew version: The price appears to be dependent on the class, so we drop the price.","metadata":{}},{"cell_type":"code","source":"titanic_train.drop(\"Ticket\", axis = 1, inplace = True)\ntitanic_test.drop(\"Ticket\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.725423Z","iopub.execute_input":"2023-02-01T14:59:57.726055Z","iopub.status.idle":"2023-02-01T14:59:57.734724Z","shell.execute_reply.started":"2023-02-01T14:59:57.725995Z","shell.execute_reply":"2023-02-01T14:59:57.733640Z"},"trusted":true},"execution_count":324,"outputs":[]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_train.loc[titanic_train['Fare'] > 0,'Fare'])\ntitanic_train.loc[titanic_train['Fare'] > 0,'Fare'] = log_10_values\ntitanic_train.Fare.describe()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:57.977699Z","iopub.execute_input":"2023-02-01T14:59:57.978673Z","iopub.status.idle":"2023-02-01T14:59:57.991610Z","shell.execute_reply.started":"2023-02-01T14:59:57.978633Z","shell.execute_reply":"2023-02-01T14:59:57.990366Z"},"trusted":true},"execution_count":325,"outputs":[{"execution_count":325,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.256781\nstd 0.435553\nmin 0.000000\n25% 0.898198\n50% 1.159994\n75% 1.491362\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"log_10_values = np.log10(titanic_test.loc[titanic_test['Fare'] > 0,'Fare'])\ntitanic_test.loc[titanic_test['Fare'] > 0,'Fare'] = log_10_values\ntitanic_test.Fare.describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.219678Z","iopub.execute_input":"2023-02-01T14:59:58.220097Z","iopub.status.idle":"2023-02-01T14:59:58.235301Z","shell.execute_reply.started":"2023-02-01T14:59:58.220059Z","shell.execute_reply":"2023-02-01T14:59:58.234195Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"count 417.000000\nmean 1.279591\nstd 0.437507\nmin 0.000000\n25% 0.897396\n50% 1.159994\n75% 1.498311\nmax 2.709549\nName: Fare, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"titanic_train.drop(\"Fare\", axis = 1, inplace = True)\ntitanic_test.drop(\"Fare\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.471730Z","iopub.execute_input":"2023-02-01T14:59:58.472149Z","iopub.status.idle":"2023-02-01T14:59:58.480205Z","shell.execute_reply.started":"2023-02-01T14:59:58.472111Z","shell.execute_reply":"2023-02-01T14:59:58.479227Z"},"trusted":true},"execution_count":327,"outputs":[]},{"cell_type":"markdown","source":"### Outcome of data preparations","metadata":{}},{"cell_type":"code","source":"\nprint(\"training datasets : \" , titanic_train.shape)\ntitanic_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:58.947799Z","iopub.execute_input":"2023-02-01T14:59:58.948756Z","iopub.status.idle":"2023-02-01T14:59:58.957820Z","shell.execute_reply.started":"2023-02-01T14:59:58.948713Z","shell.execute_reply":"2023-02-01T14:59:58.956624Z"},"trusted":true},"execution_count":328,"outputs":[{"name":"stdout","text":"training datasets : (891, 28)\n","output_type":"stream"},{"execution_count":328,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nSurvived int64\nS float64\nC float64\nQ float64\nU float64\nsib_0 float64\nSib_Unknown float64\nsib_2 float64\nsib_3 float64\nsib_1 float64\nsib_4 float64\nsib_7 float64\nage_20-29 float64\nage_30-39 float64\nage_50-59 float64\nage_0-9 float64\nage_10-19 float64\nage_60-69 float64\nage_40-49 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_1 float64\nClass_2 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(\"testing datasets : \" , titanic_test.shape)\ntitanic_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.211439Z","iopub.execute_input":"2023-02-01T14:59:59.211825Z","iopub.status.idle":"2023-02-01T14:59:59.222689Z","shell.execute_reply.started":"2023-02-01T14:59:59.211793Z","shell.execute_reply":"2023-02-01T14:59:59.221460Z"},"trusted":true},"execution_count":329,"outputs":[{"name":"stdout","text":"testing datasets : (418, 27)\n","output_type":"stream"},{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nName object\nParch int64\nQ float64\nS float64\nC float64\nU float64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"train_cols = titanic_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.478786Z","iopub.execute_input":"2023-02-01T14:59:59.479161Z","iopub.status.idle":"2023-02-01T14:59:59.488399Z","shell.execute_reply.started":"2023-02-01T14:59:59.479130Z","shell.execute_reply":"2023-02-01T14:59:59.487137Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"Index(['Survived'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"titanic_test.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T14:59:59.773416Z","iopub.execute_input":"2023-02-01T14:59:59.773881Z","iopub.status.idle":"2023-02-01T14:59:59.780592Z","shell.execute_reply.started":"2023-02-01T14:59:59.773845Z","shell.execute_reply":"2023-02-01T14:59:59.779730Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Name', 'Parch', 'Q', 'S', 'C', 'U', 'Sib_Unknown',\n 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', 'age_30-39',\n 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross validation preparation\nWe use a stratified sampling for the training into a train and test dataset. ","metadata":{}},{"cell_type":"code","source":"x_cols = [\"PassengerId\",'Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\ny_col = 'Survived'\nX = titanic_train.copy(deep = True)\nX = X[x_cols]\nX = X.apply(pd.to_numeric)\n\ny = titanic_train[y_col].apply(pd.to_numeric)\n\nsplit = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\nfor train_index, test_valid_index in split.split(X, y):\n X_train = X.iloc[train_index]\n y_train = y.iloc[train_index]\n X_valid = X.iloc[test_valid_index]\n y_valid = y.iloc[test_valid_index]\n\n# we see our training set follows the same distribution\nprint(y_train.value_counts(normalize=True), '\\n\\n')\n\n# we see our test set follows the same distribution\nprint(y_valid.value_counts(normalize=True))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.360627Z","iopub.execute_input":"2023-02-01T15:00:00.361572Z","iopub.status.idle":"2023-02-01T15:00:00.386989Z","shell.execute_reply.started":"2023-02-01T15:00:00.361528Z","shell.execute_reply":"2023-02-01T15:00:00.385873Z"},"trusted":true},"execution_count":332,"outputs":[{"name":"stdout","text":"0 0.616105\n1 0.383895\nName: Survived, dtype: float64 \n\n\n0 0.616246\n1 0.383754\nName: Survived, dtype: float64\n","output_type":"stream"}]},{"cell_type":"code","source":"x_cols = ['Parch', 'Sib_Unknown', 'sib_0', 'sib_1', 'sib_2', 'sib_3', 'sib_4', 'sib_7', \n 'age_30-39', 'age_40-49', 'age_60-69', 'age_20-29', 'age_10-19', 'age_50-59',\n 'age_0-9', 'age_70-79', 'male', 'female', 'Class_3', 'Class_2',\n 'Class_1', 'Q', 'S', 'C', 'U']\nx_train_pass_id = X_train.PassengerId\nX_train = X_train[x_cols]\nX_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.656949Z","iopub.execute_input":"2023-02-01T15:00:00.657623Z","iopub.status.idle":"2023-02-01T15:00:00.667953Z","shell.execute_reply.started":"2023-02-01T15:00:00.657586Z","shell.execute_reply":"2023-02-01T15:00:00.666758Z"},"trusted":true},"execution_count":333,"outputs":[{"execution_count":333,"output_type":"execute_result","data":{"text/plain":"(534, 25)"},"metadata":{}}]},{"cell_type":"code","source":"x_valid_pass_id = X_valid.PassengerId\nX_valid = X_valid[x_cols]\n\nX_valid.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:00.982077Z","iopub.execute_input":"2023-02-01T15:00:00.982495Z","iopub.status.idle":"2023-02-01T15:00:00.991483Z","shell.execute_reply.started":"2023-02-01T15:00:00.982459Z","shell.execute_reply":"2023-02-01T15:00:00.990369Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"(357, 25)"},"metadata":{}}]},{"cell_type":"code","source":"y_train_encode=pd.get_dummies(y_train)\ny_valid_encode=pd.get_dummies(y_valid)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.303350Z","iopub.execute_input":"2023-02-01T15:00:01.303749Z","iopub.status.idle":"2023-02-01T15:00:01.310531Z","shell.execute_reply.started":"2023-02-01T15:00:01.303715Z","shell.execute_reply":"2023-02-01T15:00:01.309278Z"},"trusted":true},"execution_count":335,"outputs":[]},{"cell_type":"code","source":"train_cols = X_train.columns\ntest_cols = titanic_test.columns\n\ncommon_cols = train_cols.intersection(test_cols)\ntrain_not_test = train_cols.difference(test_cols)\ntrain_not_test","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.517778Z","iopub.execute_input":"2023-02-01T15:00:01.518178Z","iopub.status.idle":"2023-02-01T15:00:01.527798Z","shell.execute_reply.started":"2023-02-01T15:00:01.518142Z","shell.execute_reply":"2023-02-01T15:00:01.526659Z"},"trusted":true},"execution_count":336,"outputs":[{"execution_count":336,"output_type":"execute_result","data":{"text/plain":"Index([], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"X_test = titanic_test[x_cols]\nX_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:01.807922Z","iopub.execute_input":"2023-02-01T15:00:01.808982Z","iopub.status.idle":"2023-02-01T15:00:01.817925Z","shell.execute_reply.started":"2023-02-01T15:00:01.808940Z","shell.execute_reply":"2023-02-01T15:00:01.816659Z"},"trusted":true},"execution_count":337,"outputs":[{"execution_count":337,"output_type":"execute_result","data":{"text/plain":"Parch int64\nSib_Unknown float64\nsib_0 float64\nsib_1 float64\nsib_2 float64\nsib_3 float64\nsib_4 float64\nsib_7 float64\nage_30-39 float64\nage_40-49 float64\nage_60-69 float64\nage_20-29 float64\nage_10-19 float64\nage_50-59 float64\nage_0-9 float64\nage_70-79 float64\nmale float64\nfemale float64\nClass_3 float64\nClass_2 float64\nClass_1 float64\nQ float64\nS float64\nC float64\nU float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## ANN\n\nWe apply an ANN to predict the survival of passengers. We create a basic architecture made of 5 layers.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential, load_model\n\ntf.compat.v1.get_default_graph()\n\nno_columns = X_train.shape[1]\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Flatten(input_shape=(no_columns,)))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(32, activation=\"sigmoid\"))\nmodel.add(tf.keras.layers.Dense(2, activation=\"softmax\"))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.368188Z","iopub.execute_input":"2023-02-01T15:00:02.368622Z","iopub.status.idle":"2023-02-01T15:00:02.493557Z","shell.execute_reply.started":"2023-02-01T15:00:02.368582Z","shell.execute_reply":"2023-02-01T15:00:02.492272Z"},"trusted":true},"execution_count":338,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 25) 0 \n_________________________________________________________________\ndense (Dense) (None, 32) 832 \n_________________________________________________________________\ndense_1 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_2 (Dense) (None, 32) 1056 \n_________________________________________________________________\ndense_3 (Dense) (None, 2) 66 \n=================================================================\nTotal params: 3,010\nTrainable params: 3,010\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"},{"name":"stderr","text":"2023-02-01 15:00:02.406449: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"\nrate = 0.00021\nopt = tf.keras.optimizers.Adam(learning_rate = rate)\nmodel.compile(optimizer= opt, \n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"])\ntf.compat.v1.get_default_graph()\nhistory = model.fit(X_train,\n y_train_encode,\n validation_data=(X_valid, y_valid_encode),\n epochs = 300,\n verbose = True)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:02.642006Z","iopub.execute_input":"2023-02-01T15:00:02.642833Z","iopub.status.idle":"2023-02-01T15:00:28.751910Z","shell.execute_reply.started":"2023-02-01T15:00:02.642783Z","shell.execute_reply":"2023-02-01T15:00:28.750794Z"},"trusted":true},"execution_count":339,"outputs":[{"name":"stderr","text":"2023-02-01 15:00:02.755801: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/300\n17/17 [==============================] - 1s 19ms/step - loss: 0.7885 - accuracy: 0.6161 - val_loss: 0.7708 - val_accuracy: 0.6162\nEpoch 2/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7574 - accuracy: 0.6161 - val_loss: 0.7429 - val_accuracy: 0.6162\nEpoch 3/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.7326 - accuracy: 0.6161 - val_loss: 0.7213 - val_accuracy: 0.6162\nEpoch 4/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.7133 - accuracy: 0.6161 - val_loss: 0.7045 - val_accuracy: 0.6162\nEpoch 5/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.6161 - val_loss: 0.6921 - val_accuracy: 0.6162\nEpoch 6/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6878 - accuracy: 0.6161 - val_loss: 0.6832 - val_accuracy: 0.6162\nEpoch 7/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6802 - accuracy: 0.6161 - val_loss: 0.6771 - val_accuracy: 0.6162\nEpoch 8/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6750 - accuracy: 0.6161 - val_loss: 0.6727 - val_accuracy: 0.6162\nEpoch 9/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6717 - accuracy: 0.6161 - val_loss: 0.6698 - val_accuracy: 0.6162\nEpoch 10/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6692 - accuracy: 0.6161 - val_loss: 0.6682 - val_accuracy: 0.6162\nEpoch 11/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.6161 - val_loss: 0.6670 - val_accuracy: 0.6162\nEpoch 12/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6668 - accuracy: 0.6161 - val_loss: 0.6663 - val_accuracy: 0.6162\nEpoch 13/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6662 - accuracy: 0.6161 - val_loss: 0.6658 - val_accuracy: 0.6162\nEpoch 14/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.6161 - val_loss: 0.6655 - val_accuracy: 0.6162\nEpoch 15/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6656 - accuracy: 0.6161 - val_loss: 0.6653 - val_accuracy: 0.6162\nEpoch 16/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6655 - accuracy: 0.6161 - val_loss: 0.6651 - val_accuracy: 0.6162\nEpoch 17/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6161 - val_loss: 0.6650 - val_accuracy: 0.6162\nEpoch 18/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6651 - accuracy: 0.6161 - val_loss: 0.6649 - val_accuracy: 0.6162\nEpoch 19/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6647 - val_accuracy: 0.6162\nEpoch 20/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6649 - accuracy: 0.6161 - val_loss: 0.6646 - val_accuracy: 0.6162\nEpoch 21/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6648 - accuracy: 0.6161 - val_loss: 0.6645 - val_accuracy: 0.6162\nEpoch 22/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6647 - accuracy: 0.6161 - val_loss: 0.6644 - val_accuracy: 0.6162\nEpoch 23/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6645 - accuracy: 0.6161 - val_loss: 0.6643 - val_accuracy: 0.6162\nEpoch 24/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6644 - accuracy: 0.6161 - val_loss: 0.6641 - val_accuracy: 0.6162\nEpoch 25/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.6161 - val_loss: 0.6640 - val_accuracy: 0.6162\nEpoch 26/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6641 - accuracy: 0.6161 - val_loss: 0.6639 - val_accuracy: 0.6162\nEpoch 27/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6637 - val_accuracy: 0.6162\nEpoch 28/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.6161 - val_loss: 0.6636 - val_accuracy: 0.6162\nEpoch 29/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6637 - accuracy: 0.6161 - val_loss: 0.6634 - val_accuracy: 0.6162\nEpoch 30/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.6161 - val_loss: 0.6633 - val_accuracy: 0.6162\nEpoch 31/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6635 - accuracy: 0.6161 - val_loss: 0.6631 - val_accuracy: 0.6162\nEpoch 32/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6633 - accuracy: 0.6161 - val_loss: 0.6629 - val_accuracy: 0.6162\nEpoch 33/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6630 - accuracy: 0.6161 - val_loss: 0.6627 - val_accuracy: 0.6162\nEpoch 34/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6628 - accuracy: 0.6161 - val_loss: 0.6625 - val_accuracy: 0.6162\nEpoch 35/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6623 - val_accuracy: 0.6162\nEpoch 36/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6626 - accuracy: 0.6161 - val_loss: 0.6621 - val_accuracy: 0.6162\nEpoch 37/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6622 - accuracy: 0.6161 - val_loss: 0.6619 - val_accuracy: 0.6162\nEpoch 38/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6619 - accuracy: 0.6161 - val_loss: 0.6616 - val_accuracy: 0.6162\nEpoch 39/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6618 - accuracy: 0.6161 - val_loss: 0.6614 - val_accuracy: 0.6162\nEpoch 40/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6614 - accuracy: 0.6161 - val_loss: 0.6611 - val_accuracy: 0.6162\nEpoch 41/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6612 - accuracy: 0.6161 - val_loss: 0.6608 - val_accuracy: 0.6162\nEpoch 42/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6610 - accuracy: 0.6161 - val_loss: 0.6605 - val_accuracy: 0.6162\nEpoch 43/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.6161 - val_loss: 0.6601 - val_accuracy: 0.6162\nEpoch 44/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6598 - val_accuracy: 0.6162\nEpoch 45/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6601 - accuracy: 0.6161 - val_loss: 0.6594 - val_accuracy: 0.6162\nEpoch 46/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6595 - accuracy: 0.6161 - val_loss: 0.6590 - val_accuracy: 0.6162\nEpoch 47/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6590 - accuracy: 0.6161 - val_loss: 0.6586 - val_accuracy: 0.6162\nEpoch 48/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6161 - val_loss: 0.6581 - val_accuracy: 0.6162\nEpoch 49/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6580 - accuracy: 0.6161 - val_loss: 0.6576 - val_accuracy: 0.6162\nEpoch 50/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.6161 - val_loss: 0.6571 - val_accuracy: 0.6162\nEpoch 51/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6571 - accuracy: 0.6161 - val_loss: 0.6566 - val_accuracy: 0.6162\nEpoch 52/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6565 - accuracy: 0.6161 - val_loss: 0.6560 - val_accuracy: 0.6162\nEpoch 53/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6161 - val_loss: 0.6553 - val_accuracy: 0.6162\nEpoch 54/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6161 - val_loss: 0.6546 - val_accuracy: 0.6162\nEpoch 55/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6545 - accuracy: 0.6161 - val_loss: 0.6539 - val_accuracy: 0.6162\nEpoch 56/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6542 - accuracy: 0.6161 - val_loss: 0.6531 - val_accuracy: 0.6162\nEpoch 57/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6531 - accuracy: 0.6161 - val_loss: 0.6522 - val_accuracy: 0.6162\nEpoch 58/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6161 - val_loss: 0.6513 - val_accuracy: 0.6162\nEpoch 59/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6510 - accuracy: 0.6161 - val_loss: 0.6503 - val_accuracy: 0.6162\nEpoch 60/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6501 - accuracy: 0.6161 - val_loss: 0.6493 - val_accuracy: 0.6162\nEpoch 61/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6490 - accuracy: 0.6161 - val_loss: 0.6482 - val_accuracy: 0.6162\nEpoch 62/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6480 - accuracy: 0.6161 - val_loss: 0.6469 - val_accuracy: 0.6162\nEpoch 63/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6466 - accuracy: 0.6161 - val_loss: 0.6456 - val_accuracy: 0.6162\nEpoch 64/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6453 - accuracy: 0.6161 - val_loss: 0.6443 - val_accuracy: 0.6162\nEpoch 65/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6439 - accuracy: 0.6161 - val_loss: 0.6428 - val_accuracy: 0.6162\nEpoch 66/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6423 - accuracy: 0.6161 - val_loss: 0.6412 - val_accuracy: 0.6162\nEpoch 67/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6411 - accuracy: 0.6161 - val_loss: 0.6395 - val_accuracy: 0.6162\nEpoch 68/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6393 - accuracy: 0.6161 - val_loss: 0.6376 - val_accuracy: 0.6162\nEpoch 69/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6373 - accuracy: 0.6161 - val_loss: 0.6357 - val_accuracy: 0.6162\nEpoch 70/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6351 - accuracy: 0.6161 - val_loss: 0.6336 - val_accuracy: 0.6162\nEpoch 71/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6330 - accuracy: 0.6161 - val_loss: 0.6313 - val_accuracy: 0.6162\nEpoch 72/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6307 - accuracy: 0.6161 - val_loss: 0.6290 - val_accuracy: 0.6162\nEpoch 73/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6285 - accuracy: 0.6161 - val_loss: 0.6264 - val_accuracy: 0.6162\nEpoch 74/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6258 - accuracy: 0.6161 - val_loss: 0.6239 - val_accuracy: 0.6162\nEpoch 75/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6231 - accuracy: 0.6161 - val_loss: 0.6211 - val_accuracy: 0.6162\nEpoch 76/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6203 - accuracy: 0.6161 - val_loss: 0.6180 - val_accuracy: 0.6162\nEpoch 77/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6161 - val_loss: 0.6150 - val_accuracy: 0.6190\nEpoch 78/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6143 - accuracy: 0.6161 - val_loss: 0.6118 - val_accuracy: 0.6162\nEpoch 79/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.6109 - accuracy: 0.6199 - val_loss: 0.6083 - val_accuracy: 0.6218\nEpoch 80/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6074 - accuracy: 0.6273 - val_loss: 0.6048 - val_accuracy: 0.6331\nEpoch 81/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6039 - accuracy: 0.6404 - val_loss: 0.6011 - val_accuracy: 0.6443\nEpoch 82/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.6003 - accuracy: 0.6610 - val_loss: 0.5970 - val_accuracy: 0.6667\nEpoch 83/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5962 - accuracy: 0.6798 - val_loss: 0.5932 - val_accuracy: 0.6667\nEpoch 84/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5923 - accuracy: 0.6966 - val_loss: 0.5891 - val_accuracy: 0.7003\nEpoch 85/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7116 - val_loss: 0.5849 - val_accuracy: 0.7087\nEpoch 86/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5843 - accuracy: 0.7172 - val_loss: 0.5807 - val_accuracy: 0.7115\nEpoch 87/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5805 - accuracy: 0.7191 - val_loss: 0.5762 - val_accuracy: 0.7395\nEpoch 88/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7303 - val_loss: 0.5719 - val_accuracy: 0.7395\nEpoch 89/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5713 - accuracy: 0.7378 - val_loss: 0.5674 - val_accuracy: 0.7563\nEpoch 90/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5671 - accuracy: 0.7491 - val_loss: 0.5627 - val_accuracy: 0.7563\nEpoch 91/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7509 - val_loss: 0.5582 - val_accuracy: 0.7563\nEpoch 92/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5582 - accuracy: 0.7659 - val_loss: 0.5537 - val_accuracy: 0.7759\nEpoch 93/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.7640 - val_loss: 0.5492 - val_accuracy: 0.7871\nEpoch 94/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.5499 - accuracy: 0.7640 - val_loss: 0.5447 - val_accuracy: 0.7731\nEpoch 95/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5456 - accuracy: 0.7640 - val_loss: 0.5402 - val_accuracy: 0.7871\nEpoch 96/300\n17/17 [==============================] - 0s 13ms/step - loss: 0.5412 - accuracy: 0.7640 - val_loss: 0.5359 - val_accuracy: 0.7843\nEpoch 97/300\n17/17 [==============================] - 0s 12ms/step - loss: 0.5369 - accuracy: 0.7659 - val_loss: 0.5316 - val_accuracy: 0.7843\nEpoch 98/300\n17/17 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.7603 - val_loss: 0.5275 - val_accuracy: 0.7955\nEpoch 99/300\n17/17 [==============================] - 0s 9ms/step - loss: 0.5294 - accuracy: 0.7715 - val_loss: 0.5233 - val_accuracy: 0.8039\nEpoch 100/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7753 - val_loss: 0.5196 - val_accuracy: 0.8039\nEpoch 101/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5217 - accuracy: 0.7734 - val_loss: 0.5158 - val_accuracy: 0.8039\nEpoch 102/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5181 - accuracy: 0.7753 - val_loss: 0.5120 - val_accuracy: 0.8039\nEpoch 103/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5145 - accuracy: 0.7753 - val_loss: 0.5085 - val_accuracy: 0.8011\nEpoch 104/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5113 - accuracy: 0.7715 - val_loss: 0.5049 - val_accuracy: 0.8039\nEpoch 105/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7715 - val_loss: 0.5016 - val_accuracy: 0.8039\nEpoch 106/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7715 - val_loss: 0.4983 - val_accuracy: 0.8039\nEpoch 107/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.7828 - val_loss: 0.4951 - val_accuracy: 0.8095\nEpoch 108/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7921 - val_loss: 0.4921 - val_accuracy: 0.8095\nEpoch 109/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7959 - val_loss: 0.4891 - val_accuracy: 0.8067\nEpoch 110/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7921 - val_loss: 0.4864 - val_accuracy: 0.8067\nEpoch 111/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4919 - accuracy: 0.7940 - val_loss: 0.4840 - val_accuracy: 0.8123\nEpoch 112/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7996 - val_loss: 0.4813 - val_accuracy: 0.8067\nEpoch 113/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7996 - val_loss: 0.4789 - val_accuracy: 0.8067\nEpoch 114/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7996 - val_loss: 0.4767 - val_accuracy: 0.8067\nEpoch 115/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4828 - accuracy: 0.7996 - val_loss: 0.4745 - val_accuracy: 0.8095\nEpoch 116/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4808 - accuracy: 0.7996 - val_loss: 0.4724 - val_accuracy: 0.8095\nEpoch 117/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4789 - accuracy: 0.7996 - val_loss: 0.4706 - val_accuracy: 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0.8179\nEpoch 173/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4420 - accuracy: 0.8240 - val_loss: 0.4308 - val_accuracy: 0.8123\nEpoch 174/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8240 - val_loss: 0.4305 - val_accuracy: 0.8123\nEpoch 175/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8179\nEpoch 176/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.8240 - val_loss: 0.4300 - val_accuracy: 0.8123\nEpoch 177/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8240 - val_loss: 0.4297 - val_accuracy: 0.8151\nEpoch 178/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4410 - accuracy: 0.8258 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 179/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4294 - val_accuracy: 0.8151\nEpoch 180/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4402 - accuracy: 0.8240 - val_loss: 0.4293 - val_accuracy: 0.8151\nEpoch 181/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4400 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 182/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4397 - accuracy: 0.8240 - val_loss: 0.4290 - val_accuracy: 0.8151\nEpoch 183/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4395 - accuracy: 0.8240 - val_loss: 0.4286 - val_accuracy: 0.8151\nEpoch 184/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4393 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8123\nEpoch 185/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4392 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 186/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4389 - accuracy: 0.8240 - val_loss: 0.4284 - val_accuracy: 0.8151\nEpoch 187/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4390 - accuracy: 0.8240 - val_loss: 0.4278 - val_accuracy: 0.8123\nEpoch 188/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4279 - val_accuracy: 0.8151\nEpoch 189/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4385 - accuracy: 0.8240 - val_loss: 0.4283 - val_accuracy: 0.8151\nEpoch 190/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 191/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8240 - val_loss: 0.4274 - val_accuracy: 0.8151\nEpoch 192/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.8240 - val_loss: 0.4275 - val_accuracy: 0.8151\nEpoch 193/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4379 - accuracy: 0.8221 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 194/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4375 - accuracy: 0.8240 - val_loss: 0.4270 - val_accuracy: 0.8123\nEpoch 195/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4373 - accuracy: 0.8240 - val_loss: 0.4272 - val_accuracy: 0.8151\nEpoch 196/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4370 - accuracy: 0.8240 - val_loss: 0.4271 - val_accuracy: 0.8151\nEpoch 197/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4368 - accuracy: 0.8240 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 198/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4367 - accuracy: 0.8221 - val_loss: 0.4267 - val_accuracy: 0.8151\nEpoch 199/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4366 - accuracy: 0.8240 - val_loss: 0.4269 - val_accuracy: 0.8151\nEpoch 200/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4364 - accuracy: 0.8240 - val_loss: 0.4265 - val_accuracy: 0.8151\nEpoch 201/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4363 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 202/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4362 - accuracy: 0.8202 - val_loss: 0.4262 - val_accuracy: 0.8123\nEpoch 203/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4359 - accuracy: 0.8258 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 204/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4357 - accuracy: 0.8240 - val_loss: 0.4262 - val_accuracy: 0.8151\nEpoch 205/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8221 - val_loss: 0.4261 - val_accuracy: 0.8151\nEpoch 206/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8221 - val_loss: 0.4264 - val_accuracy: 0.8151\nEpoch 207/300\n17/17 [==============================] - 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[==============================] - 0s 5ms/step - loss: 0.4347 - accuracy: 0.8240 - val_loss: 0.4258 - val_accuracy: 0.8151\nEpoch 215/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 216/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4344 - accuracy: 0.8221 - val_loss: 0.4251 - val_accuracy: 0.8123\nEpoch 217/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4342 - accuracy: 0.8240 - val_loss: 0.4255 - val_accuracy: 0.8151\nEpoch 218/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 219/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4339 - accuracy: 0.8240 - val_loss: 0.4253 - val_accuracy: 0.8151\nEpoch 220/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 221/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4337 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 222/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4336 - accuracy: 0.8258 - val_loss: 0.4252 - val_accuracy: 0.8151\nEpoch 223/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4335 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 224/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4332 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 225/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 226/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4330 - accuracy: 0.8240 - val_loss: 0.4247 - val_accuracy: 0.8151\nEpoch 227/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4251 - val_accuracy: 0.8151\nEpoch 228/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4331 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 229/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4248 - val_accuracy: 0.8151\nEpoch 230/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4327 - accuracy: 0.8240 - val_loss: 0.4250 - val_accuracy: 0.8151\nEpoch 231/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4325 - accuracy: 0.8240 - val_loss: 0.4249 - val_accuracy: 0.8151\nEpoch 232/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 233/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4322 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 234/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4323 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 235/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 236/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4321 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 237/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4243 - val_accuracy: 0.8151\nEpoch 238/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8240 - val_loss: 0.4246 - val_accuracy: 0.8151\nEpoch 239/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 240/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4317 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 241/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 242/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4313 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 243/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 244/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8221 - val_loss: 0.4245 - val_accuracy: 0.8151\nEpoch 245/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8151\nEpoch 246/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8221 - val_loss: 0.4246 - val_accuracy: 0.8179\nEpoch 247/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 248/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8151\nEpoch 249/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8151\nEpoch 250/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4307 - accuracy: 0.8221 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 251/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8151\nEpoch 252/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8221 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 253/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4306 - accuracy: 0.8240 - val_loss: 0.4245 - val_accuracy: 0.8179\nEpoch 254/300\n17/17 [==============================] - 0s 8ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 255/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4302 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 256/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 257/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8240 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 258/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 259/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8240 - val_loss: 0.4244 - val_accuracy: 0.8179\nEpoch 260/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 261/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4299 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 262/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 263/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 264/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 265/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 266/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 267/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8240 - val_loss: 0.4241 - val_accuracy: 0.8179\nEpoch 268/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4295 - accuracy: 0.8240 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 269/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 270/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 271/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 272/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4293 - accuracy: 0.8240 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 273/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 274/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 275/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4289 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 276/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 277/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4288 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 278/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8240 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 279/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4287 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 280/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.8240 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 281/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 282/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 283/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4286 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 284/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 285/300\n17/17 [==============================] - 0s 4ms/step - loss: 0.4285 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 286/300\n17/17 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 287/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 288/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4282 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 289/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 290/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4235 - val_accuracy: 0.8179\nEpoch 291/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 292/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 293/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4242 - val_accuracy: 0.8179\nEpoch 294/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4240 - val_accuracy: 0.8179\nEpoch 295/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 296/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4279 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\nEpoch 297/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4277 - accuracy: 0.8221 - val_loss: 0.4237 - val_accuracy: 0.8179\nEpoch 298/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8221 - val_loss: 0.4239 - val_accuracy: 0.8179\nEpoch 299/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4236 - val_accuracy: 0.8179\nEpoch 300/300\n17/17 [==============================] - 0s 5ms/step - loss: 0.4276 - accuracy: 0.8221 - val_loss: 0.4238 - val_accuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_train_accuracy = model.evaluate(X_train, y_train_encode)\nprint('Accuracy: %.4f' % (ann_train_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.756511Z","iopub.execute_input":"2023-02-01T15:00:28.757274Z","iopub.status.idle":"2023-02-01T15:00:28.874523Z","shell.execute_reply.started":"2023-02-01T15:00:28.757226Z","shell.execute_reply":"2023-02-01T15:00:28.873360Z"},"trusted":true},"execution_count":340,"outputs":[{"name":"stdout","text":"17/17 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221\nAccuracy: 0.8221\n","output_type":"stream"}]},{"cell_type":"code","source":"_, ann_valid_accuracy = model.evaluate(X_valid, y_valid_encode)\nprint('Accuracy: %.4f' % (ann_valid_accuracy))","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.876387Z","iopub.execute_input":"2023-02-01T15:00:28.877663Z","iopub.status.idle":"2023-02-01T15:00:28.990657Z","shell.execute_reply.started":"2023-02-01T15:00:28.877614Z","shell.execute_reply":"2023-02-01T15:00:28.989441Z"},"trusted":true},"execution_count":341,"outputs":[{"name":"stdout","text":"12/12 [==============================] - 0s 2ms/step - loss: 0.4238 - accuracy: 0.8179\nAccuracy: 0.8179\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Which passengers were misclassified ","metadata":{}},{"cell_type":"code","source":"\ny_pred = model.predict(X_train)\nY_pred = np.argmax(model.predict(X_train),axis=1)\ncm = confusion_matrix(y_train, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:28.993841Z","iopub.execute_input":"2023-02-01T15:00:28.994285Z","iopub.status.idle":"2023-02-01T15:00:29.270957Z","shell.execute_reply.started":"2023-02-01T15:00:28.994240Z","shell.execute_reply":"2023-02-01T15:00:29.269885Z"},"trusted":true},"execution_count":342,"outputs":[{"execution_count":342,"output_type":"execute_result","data":{"text/plain":"array([[304, 25],\n [ 70, 135]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.272190Z","iopub.execute_input":"2023-02-01T15:00:29.273301Z","iopub.status.idle":"2023-02-01T15:00:29.281676Z","shell.execute_reply.started":"2023-02-01T15:00:29.273267Z","shell.execute_reply":"2023-02-01T15:00:29.280517Z"},"trusted":true},"execution_count":343,"outputs":[{"name":"stdout","text":"Accuracy : 0.8220973782771536\nMisclassfication : 0.17790262172284643\nSensitivivity : 0.9240121580547113\nSpecificity : 0.6585365853658537\n","output_type":"stream"}]},{"cell_type":"code","source":"\ny_pred = model.predict(X_valid)\nY_pred = np.argmax(model.predict(X_valid),axis=1)\ncm = confusion_matrix(y_valid, Y_pred)\ncm","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.283243Z","iopub.execute_input":"2023-02-01T15:00:29.283612Z","iopub.status.idle":"2023-02-01T15:00:29.451759Z","shell.execute_reply.started":"2023-02-01T15:00:29.283566Z","shell.execute_reply":"2023-02-01T15:00:29.450417Z"},"trusted":true},"execution_count":344,"outputs":[{"execution_count":344,"output_type":"execute_result","data":{"text/plain":"array([[206, 14],\n [ 51, 86]])"},"metadata":{}}]},{"cell_type":"code","source":"accuracy = (cm[0][0] + cm[1][1])/len(y_pred)\nmisclassification = (cm[0][1] + cm[1][0])/len(y_pred)\nsensitivity = (cm[0][0])/(cm[0][0] + cm[0][1])\nspecificity = (cm[1][1])/(cm[1][0] + cm[1][1])\nprint(\"Accuracy : \", accuracy)\nprint(\"Misclassfication : \", misclassification)\nprint(\"Sensitivivity : \", sensitivity)\nprint(\"Specificity : \", specificity)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.453236Z","iopub.execute_input":"2023-02-01T15:00:29.453610Z","iopub.status.idle":"2023-02-01T15:00:29.461774Z","shell.execute_reply.started":"2023-02-01T15:00:29.453579Z","shell.execute_reply":"2023-02-01T15:00:29.460520Z"},"trusted":true},"execution_count":345,"outputs":[{"name":"stdout","text":"Accuracy : 0.8179271708683473\nMisclassfication : 0.18207282913165265\nSensitivivity : 0.9363636363636364\nSpecificity : 0.6277372262773723\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Capture classification for analysis","metadata":{}},{"cell_type":"code","source":"results_train_copy = results_train.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.463445Z","iopub.execute_input":"2023-02-01T15:00:29.463787Z","iopub.status.idle":"2023-02-01T15:00:29.472285Z","shell.execute_reply.started":"2023-02-01T15:00:29.463752Z","shell.execute_reply":"2023-02-01T15:00:29.471294Z"},"trusted":true},"execution_count":346,"outputs":[]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_train),axis=1)\n\nann_pred = X_train.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_train_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.473634Z","iopub.execute_input":"2023-02-01T15:00:29.474711Z","iopub.status.idle":"2023-02-01T15:00:29.593403Z","shell.execute_reply.started":"2023-02-01T15:00:29.474675Z","shell.execute_reply":"2023-02-01T15:00:29.592290Z"},"trusted":true},"execution_count":347,"outputs":[{"execution_count":347,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n844 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n316 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n768 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n255 2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n130 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n844 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n316 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n768 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n255 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n130 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n\n ann_y_pred PassengerId \n844 0 845.0 \n316 1 317.0 \n768 0 769.0 \n255 1 256.0 \n130 0 131.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
84401.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00845.0
31600.01.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01317.0
76800.01.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00769.0
25521.00.00.00.00.00.00.00.00.0...1.01.00.00.00.00.01.00.01256.0
13001.00.00.00.00.00.00.01.00.0...0.01.00.00.00.00.01.00.00131.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train = results_train.merge(ann_pred[[\"PassengerId\", \"ann_y_pred\"]], \n on = \"PassengerId\", how=\"outer\")\n\nresults_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.598604Z","iopub.execute_input":"2023-02-01T15:00:29.599029Z","iopub.status.idle":"2023-02-01T15:00:29.628142Z","shell.execute_reply.started":"2023-02-01T15:00:29.598995Z","shell.execute_reply":"2023-02-01T15:00:29.627332Z"},"trusted":true},"execution_count":348,"outputs":[{"execution_count":348,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 NaN \n2 0.0 NaN 0.0 \n3 NaN 1.0 NaN \n4 NaN 0.0 NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.0NaN
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.0NaN
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.0NaN
\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ny_pred = np.argmax(model.predict(X_valid),axis=1)\nann_pred = X_valid.copy()\nann_pred[\"ann_y_pred\"] = y_pred\nann_pred[\"PassengerId\"] = x_valid_pass_id\nann_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.629335Z","iopub.execute_input":"2023-02-01T15:00:29.629823Z","iopub.status.idle":"2023-02-01T15:00:29.739371Z","shell.execute_reply.started":"2023-02-01T15:00:29.629791Z","shell.execute_reply":"2023-02-01T15:00:29.738281Z"},"trusted":true},"execution_count":349,"outputs":[{"execution_count":349,"output_type":"execute_result","data":{"text/plain":" Parch Sib_Unknown sib_0 sib_1 sib_2 sib_3 sib_4 sib_7 age_30-39 \\\n369 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n541 2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n196 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n810 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n427 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n age_40-49 ... female Class_3 Class_2 Class_1 Q S C U \\\n369 0.0 ... 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 \n541 0.0 ... 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n196 0.0 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n810 0.0 ... 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n427 0.0 ... 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n\n ann_y_pred PassengerId \n369 1 370.0 \n541 1 542.0 \n196 0 197.0 \n810 0 811.0 \n427 1 428.0 \n\n[5 rows x 27 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ParchSib_Unknownsib_0sib_1sib_2sib_3sib_4sib_7age_30-39age_40-49...femaleClass_3Class_2Class_1QSCUann_y_predPassengerId
36901.00.00.00.00.00.00.00.00.0...1.00.00.01.00.00.01.00.01370.0
54120.00.00.00.01.00.00.00.00.0...1.01.00.00.00.01.00.00.01542.0
19601.00.00.00.00.00.00.00.00.0...0.01.00.00.01.00.00.00.00197.0
81001.00.00.00.00.00.00.00.00.0...0.01.00.00.00.01.00.00.00811.0
42701.00.00.00.00.00.00.00.00.0...1.00.01.00.00.01.00.00.01428.0
\n

5 rows × 27 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\nresults_train.loc[results_train.PassengerId.isin(ann_pred.PassengerId), \"ann_y_pred\"] = ann_pred[\"ann_y_pred\"]\nresults_train.drop(\"rf_y_pred_y\", axis = 1)\nresults_train.drop(\"rf_y_pred_x\", axis = 1)\nresults_train.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.740869Z","iopub.execute_input":"2023-02-01T15:00:29.741291Z","iopub.status.idle":"2023-02-01T15:00:29.771294Z","shell.execute_reply.started":"2023-02-01T15:00:29.741249Z","shell.execute_reply":"2023-02-01T15:00:29.770286Z"},"trusted":true},"execution_count":350,"outputs":[{"execution_count":350,"output_type":"execute_result","data":{"text/plain":" PassengerId Survived Pclass Sex Age Fare Embarked \\\n0 1.0 0.0 3.0 1.0 -0.615385 -0.312011 2.0 \n1 2.0 1.0 1.0 2.0 0.615385 2.461242 4.0 \n2 3.0 1.0 3.0 2.0 -0.307692 -0.282777 2.0 \n3 4.0 1.0 1.0 2.0 0.384615 1.673732 2.0 \n4 5.0 0.0 3.0 1.0 0.384615 -0.277363 2.0 \n\n fam_members y lr_y_pred knn_y_pred clf_y_pred rf_y_pred_x \\\n0 1.0 0.0 0.0 0.0 0.0 0.0 \n1 1.0 1.0 1.0 1.0 1.0 1.0 \n2 0.0 1.0 1.0 1.0 0.0 0.0 \n3 1.0 1.0 1.0 1.0 1.0 1.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 \n\n rf_y_pred_y rf_y_pred ann_y_pred \n0 0.0 NaN 0.0 \n1 NaN 1.0 1.0 \n2 0.0 NaN 0.0 \n3 NaN 1.0 1.0 \n4 NaN 0.0 0.0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdSurvivedPclassSexAgeFareEmbarkedfam_membersylr_y_predknn_y_predclf_y_predrf_y_pred_xrf_y_pred_yrf_y_predann_y_pred
01.00.03.01.0-0.615385-0.3120112.01.00.00.00.00.00.00.0NaN0.0
12.01.01.02.00.6153852.4612424.01.01.01.01.01.01.0NaN1.01.0
23.01.03.02.0-0.307692-0.2827772.00.01.01.01.00.00.00.0NaN0.0
34.01.01.02.00.3846151.6737322.01.01.01.01.01.01.0NaN1.01.0
45.00.03.01.00.384615-0.2773632.00.00.00.00.00.00.0NaN0.00.0
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Overall, the number of survivors misclassified were greater than misclassified passengers who perished. The next step is to identify those passengers to attempt to find the source of the misclassifcation. So far the lowest number of misclassified passengers who perished. ","metadata":{}},{"cell_type":"markdown","source":"## Predict test dataset","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\ny_pred = y_pred.argmax(1)\nann_pred = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"],\n \"ann_y_pred\" : y_pred})\nann_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.772619Z","iopub.execute_input":"2023-02-01T15:00:29.772938Z","iopub.status.idle":"2023-02-01T15:00:29.875387Z","shell.execute_reply.started":"2023-02-01T15:00:29.772908Z","shell.execute_reply":"2023-02-01T15:00:29.874334Z"},"trusted":true},"execution_count":351,"outputs":[{"execution_count":351,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
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PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"results_test_copy = results_test.copy(deep=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.876431Z","iopub.execute_input":"2023-02-01T15:00:29.876729Z","iopub.status.idle":"2023-02-01T15:00:29.882726Z","shell.execute_reply.started":"2023-02-01T15:00:29.876701Z","shell.execute_reply":"2023-02-01T15:00:29.881480Z"},"trusted":true},"execution_count":352,"outputs":[]},{"cell_type":"code","source":"ann_pred[[\"PassengerId\",\"ann_y_pred\"]]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.884219Z","iopub.execute_input":"2023-02-01T15:00:29.884571Z","iopub.status.idle":"2023-02-01T15:00:29.900340Z","shell.execute_reply.started":"2023-02-01T15:00:29.884540Z","shell.execute_reply":"2023-02-01T15:00:29.899599Z"},"trusted":true},"execution_count":353,"outputs":[{"execution_count":353,"output_type":"execute_result","data":{"text/plain":" PassengerId ann_y_pred\n0 892.0 0\n1 893.0 0\n2 894.0 0\n3 895.0 0\n4 896.0 0\n.. ... ...\n413 1305.0 0\n414 1306.0 1\n415 1307.0 0\n416 1308.0 0\n417 1309.0 0\n\n[418 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdann_y_pred
0892.00
1893.00
2894.00
3895.00
4896.00
.........
4131305.00
4141306.01
4151307.00
4161308.00
4171309.00
\n

418 rows × 2 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_test = results_test.merge(ann_pred[[\"PassengerId\",\"ann_y_pred\"]], on = 'PassengerId', how = \"outer\")\nresults_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.901356Z","iopub.execute_input":"2023-02-01T15:00:29.901844Z","iopub.status.idle":"2023-02-01T15:00:29.931394Z","shell.execute_reply.started":"2023-02-01T15:00:29.901814Z","shell.execute_reply":"2023-02-01T15:00:29.929969Z"},"trusted":true},"execution_count":354,"outputs":[{"execution_count":354,"output_type":"execute_result","data":{"text/plain":" PassengerId Pclass Sex Age Fare Embarked fam_members \\\n0 892.0 3.0 1.0 0.431373 -0.281005 3.0 0.0 \n1 893.0 3.0 2.0 1.411765 -0.316176 2.0 1.0 \n2 894.0 2.0 1.0 2.588235 -0.202184 3.0 0.0 \n3 895.0 3.0 1.0 -0.156863 -0.245660 2.0 0.0 \n4 896.0 3.0 2.0 -0.549020 -0.091902 2.0 2.0 \n\n lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred \n0 0.0 0.0 0.0 0.0 0 \n1 1.0 0.0 0.0 0.0 0 \n2 0.0 0.0 0.0 0.0 0 \n3 0.0 0.0 0.0 0.0 0 \n4 0.0 1.0 1.0 1.0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PassengerIdPclassSexAgeFareEmbarkedfam_memberslr_y_predknn_y_predclf_y_predrf_y_predann_y_pred
0892.03.01.00.431373-0.2810053.00.00.00.00.00.00
1893.03.02.01.411765-0.3161762.01.01.00.00.00.00
2894.02.01.02.588235-0.2021843.00.00.00.00.00.00
3895.03.01.0-0.156863-0.2456602.00.00.00.00.00.00
4896.03.02.0-0.549020-0.0919022.02.00.01.01.01.00
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Findings","metadata":{}},{"cell_type":"markdown","source":"We compile all the results in a basic structure. We discover that the logistic regression has achieved the highest accuracy on the validation datasets. ANN came close. Both methods appear not to overfit to the training dataset.","metadata":{}},{"cell_type":"code","source":"log_reg_results = {\n \"method\": \"Logistic regression\",\n \"training_accurary\": log_reg_score_train,\n \"valid_accuracy\": log_reg_score_valid\n}\n\nknn_results = {\n \"method\": \"KNN\",\n \"training_accurary\": knn_train_score,\n \"valid_accuracy\": knn_valid_score\n}\n\nclf_results = {\n \"method\": \"decision trees\",\n \"training_accurary\": clf_train_score,\n \"valid_accuracy\": clf_valid_score\n}\n\nrf_results = {\n \"method\": \"Random Forrest\",\n \"training_accurary\": rf_train_score,\n \"valid_accuracy\": rf_valid_score\n}\n\nann_results = {\n \"method\": \"ANN\",\n \"training_accurary\": ann_train_accuracy,\n \"valid_accuracy\": ann_valid_accuracy\n}\n\nresults = [log_reg_results, knn_results, clf_results, rf_results, ann_results]\nresults","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.932994Z","iopub.execute_input":"2023-02-01T15:00:29.933497Z","iopub.status.idle":"2023-02-01T15:00:29.947698Z","shell.execute_reply.started":"2023-02-01T15:00:29.933454Z","shell.execute_reply":"2023-02-01T15:00:29.946484Z"},"trusted":true},"execution_count":355,"outputs":[{"execution_count":355,"output_type":"execute_result","data":{"text/plain":"[{'method': 'Logistic regression',\n 'training_accurary': 0.7921348314606742,\n 'valid_accuracy': 0.8207282913165266},\n {'method': 'KNN',\n 'training_accurary': 0.8258426966292135,\n 'valid_accuracy': 0.7871148459383753},\n {'method': 'decision trees',\n 'training_accurary': 0.9082397003745318,\n 'valid_accuracy': 0.8151260504201681},\n {'method': 'Random Forrest',\n 'training_accurary': 0.8801498127340824,\n 'valid_accuracy': 0.8067226890756303},\n {'method': 'ANN',\n 'training_accurary': 0.8220973610877991,\n 'valid_accuracy': 0.8179271817207336}]"},"metadata":{}}]},{"cell_type":"markdown","source":"Less than 10% errors of passengers have been misclassified, when we compare all predictions together. So, it may be possible to identify some rules to increase accuracy. Nonetheless, these rules may also decrease the accuracy. So, a fine balance needs to be found. ","metadata":{}},{"cell_type":"code","source":"results_train.columns","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.949130Z","iopub.execute_input":"2023-02-01T15:00:29.949749Z","iopub.status.idle":"2023-02-01T15:00:29.958469Z","shell.execute_reply.started":"2023-02-01T15:00:29.949702Z","shell.execute_reply":"2023-02-01T15:00:29.957602Z"},"trusted":true},"execution_count":356,"outputs":[{"execution_count":356,"output_type":"execute_result","data":{"text/plain":"Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked',\n 'fam_members', 'y', 'lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"cols = ['lr_y_pred', 'knn_y_pred', 'clf_y_pred',\n 'rf_y_pred_x', 'rf_y_pred_y', 'rf_y_pred', 'ann_y_pred']\nresults_train['merged_pred'] = results_train.loc[:,cols].apply(\n lambda x: ','.join(x.dropna().astype(str)),\n axis=1\n)\n\nresults_train['y_found'] = results_train.apply(lambda x: str(x.y) in x.merged_pred, axis=1)\nresults_train.drop(\"merged_pred\", axis = 1, inplace = True)\nresults_train.groupby(\"y_found\").count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:29.959997Z","iopub.execute_input":"2023-02-01T15:00:29.960444Z","iopub.status.idle":"2023-02-01T15:00:30.142912Z","shell.execute_reply.started":"2023-02-01T15:00:29.960402Z","shell.execute_reply":"2023-02-01T15:00:30.141887Z"},"trusted":true},"execution_count":357,"outputs":[{"execution_count":357,"output_type":"execute_result","data":{"text/plain":"y_found\nFalse 0.075196\nTrue 0.924804\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The set of passengers misclassified by all methods appear to have a lower expected fares and much more compact spread of fares. The median passenger class of misclassified passenger appear to be higher than those correctly classified. Both observations contractict each other and suggests some of fares being close to each other between passenger classes may be contributing in misclassifying passengers. \n\nThe misclassified passengers appears to be most women and their age appear to be older than the ones correctly classified by one method. The distribution to gender appears to match the overall observations for correctly classified passengers. Nonetheless, it is worth pointing out some of ages were inputed based on the number of siblings, spouse and parents aboard. This simple method of inputation may have impacted the classifiers; more research should be made to validate or improve inputting the missing information. \n\nOther aspects in the data may lead to misclassification.","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == False,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.144511Z","iopub.execute_input":"2023-02-01T15:00:30.145249Z","iopub.status.idle":"2023-02-01T15:00:30.180088Z","shell.execute_reply.started":"2023-02-01T15:00:30.145205Z","shell.execute_reply":"2023-02-01T15:00:30.178959Z"},"trusted":true},"execution_count":358,"outputs":[{"execution_count":358,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 67.000000 67.000000 67.000000 67.000000 67.000000 67.000000\nmean 2.149254 1.104478 0.129736 0.423026 0.343284 2.537313\nstd 0.908774 0.308188 0.721256 1.008879 0.844810 0.840785\nmin 1.000000 1.000000 -1.076923 -0.626005 0.000000 2.000000\n25% 1.000000 1.000000 -0.269231 -0.282777 0.000000 2.000000\n50% 2.000000 1.000000 0.000000 -0.062981 0.000000 2.000000\n75% 3.000000 1.000000 0.230769 0.694936 0.500000 3.000000\nmax 3.000000 2.000000 2.461538 3.318594 6.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count67.00000067.00000067.00000067.00000067.00000067.000000
mean2.1492541.1044780.1297360.4230260.3432842.537313
std0.9087740.3081880.7212561.0088790.8448100.840785
min1.0000001.000000-1.076923-0.6260050.0000002.000000
25%1.0000001.000000-0.269231-0.2827770.0000002.000000
50%2.0000001.0000000.000000-0.0629810.0000002.000000
75%3.0000001.0000000.2307690.6949360.5000003.000000
max3.0000002.0000002.4615383.3185946.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,[\"Pclass\", \"Sex\", \"Age\", \"Fare\",\"fam_members\", \"Embarked\"]].describe()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.182172Z","iopub.execute_input":"2023-02-01T15:00:30.183279Z","iopub.status.idle":"2023-02-01T15:00:30.213352Z","shell.execute_reply.started":"2023-02-01T15:00:30.183235Z","shell.execute_reply":"2023-02-01T15:00:30.212605Z"},"trusted":true},"execution_count":359,"outputs":[{"execution_count":359,"output_type":"execute_result","data":{"text/plain":" Pclass Sex Age Fare fam_members Embarked\ncount 824.000000 824.000000 824.000000 824.000000 824.000000 824.000000\nmean 2.321602 1.372573 -0.030604 0.796855 0.950243 2.455097\nstd 0.829129 0.483783 1.018913 2.217409 1.652334 0.790541\nmin 1.000000 1.000000 -2.275385 -0.626005 0.000000 1.000000\n25% 2.000000 1.000000 -0.615385 -0.284041 0.000000 2.000000\n50% 3.000000 1.000000 0.000000 0.001984 0.000000 2.000000\n75% 3.000000 2.000000 0.384615 0.719569 1.000000 3.000000\nmax 3.000000 2.000000 3.846154 21.562738 10.000000 4.000000","text/html":"
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PclassSexAgeFarefam_membersEmbarked
count824.000000824.000000824.000000824.000000824.000000824.000000
mean2.3216021.372573-0.0306040.7968550.9502432.455097
std0.8291290.4837831.0189132.2174091.6523340.790541
min1.0000001.000000-2.275385-0.6260050.0000001.000000
25%2.0000001.000000-0.615385-0.2840410.0000002.000000
50%3.0000001.0000000.0000000.0019840.0000002.000000
75%3.0000002.0000000.3846150.7195691.0000003.000000
max3.0000002.0000003.84615421.56273810.0000004.000000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"incorrect = results_train.loc[results_train[\"y_found\"] == False,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == False,:].groupby(\"Sex\").count()[\"PassengerId\"]/incorrect","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.214494Z","iopub.execute_input":"2023-02-01T15:00:30.215455Z","iopub.status.idle":"2023-02-01T15:00:30.229684Z","shell.execute_reply.started":"2023-02-01T15:00:30.215404Z","shell.execute_reply":"2023-02-01T15:00:30.228567Z"},"trusted":true},"execution_count":360,"outputs":[{"execution_count":360,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.895522\n2.0 0.104478\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"correct = results_train.loc[results_train[\"y_found\"] == True,:].count()[\"PassengerId\"]\nresults_train.loc[results_train[\"y_found\"] == True,:].groupby(\"Sex\").count()[\"PassengerId\"]/correct","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.230783Z","iopub.execute_input":"2023-02-01T15:00:30.231538Z","iopub.status.idle":"2023-02-01T15:00:30.246006Z","shell.execute_reply.started":"2023-02-01T15:00:30.231506Z","shell.execute_reply":"2023-02-01T15:00:30.244736Z"},"trusted":true},"execution_count":361,"outputs":[{"execution_count":361,"output_type":"execute_result","data":{"text/plain":"Sex\n1.0 0.627427\n2.0 0.372573\nName: PassengerId, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We analyse differences between each method on the testing and training data set. We add all the predictions to identify the passenger the classifier could not agree with. So, a total of 0 or 5 suggests all the classifiers have either identify passengers as survivor or not. Values in the range [1,4] indicates some disagreements in classification. Some methodologies appears to correclty classify passengers with at least one method.","metadata":{}},{"cell_type":"code","source":"results_train[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == False,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.247816Z","iopub.execute_input":"2023-02-01T15:00:30.248276Z","iopub.status.idle":"2023-02-01T15:00:30.473510Z","shell.execute_reply.started":"2023-02-01T15:00:30.248230Z","shell.execute_reply":"2023-02-01T15:00:30.472297Z"},"trusted":true},"execution_count":362,"outputs":[{"execution_count":362,"output_type":"execute_result","data":{"text/plain":"count 67.000000\nmean 0.447761\nstd 1.438471\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 0.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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3bXhOeAm5L843hHGr2qOtV9XATuY/my8FC0EHBv2d/kun/Quws4WVWfGvc8GyHJa5Ns7x6/guV/pP/hWIcasar6WFXtrKpdLP85/lpV/cGYxxqpJNu6f5gnyTbgncDQ3l12yQe8qp4Hzt+yfxK4Z8S37I9dki8A3wTekOSZJPvGPdOI3Qh8kOUzske7X+8a91AjtgN4KMn3WT5JebCqJuJtdRNmGvhGku8B3wK+XFVfHdaLX/JvI5Qkre6SPwOXJK3OgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXqfwEOtkCGTWOUBQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[results_train[\"y_found\"] == True,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.475100Z","iopub.execute_input":"2023-02-01T15:00:30.475447Z","iopub.status.idle":"2023-02-01T15:00:30.691199Z","shell.execute_reply.started":"2023-02-01T15:00:30.475417Z","shell.execute_reply":"2023-02-01T15:00:30.690153Z"},"trusted":true},"execution_count":363,"outputs":[{"execution_count":363,"output_type":"execute_result","data":{"text/plain":"count 824.000000\nmean 1.577670\nstd 2.058981\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We explore how the techniques may predict differently and but accurately surviving the accident. \n\nKNN misclassified the most passengers who perished. Logistic regression and Random Tree classifier has the higest accuracy; both of them could be influencing the most the prediction, when only one classifier suggests a passenger has survived. ","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 1)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.692627Z","iopub.execute_input":"2023-02-01T15:00:30.692946Z","iopub.status.idle":"2023-02-01T15:00:30.712415Z","shell.execute_reply.started":"2023-02-01T15:00:30.692916Z","shell.execute_reply":"2023-02-01T15:00:30.711228Z"},"trusted":true},"execution_count":364,"outputs":[{"execution_count":364,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 3\n 1.0 0.0 0.0 3\n 1.0 0.0 0.0 0.0 10\n 1.0 0.0 0.0 0.0 0.0 3\n1.0 0.0 0.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 5\n 1.0 0.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 0.0 4\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 4)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.714064Z","iopub.execute_input":"2023-02-01T15:00:30.714806Z","iopub.status.idle":"2023-02-01T15:00:30.734553Z","shell.execute_reply.started":"2023-02-01T15:00:30.714762Z","shell.execute_reply":"2023-02-01T15:00:30.733458Z"},"trusted":true},"execution_count":365,"outputs":[{"execution_count":365,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 1.0 1.0 1.0 2\n 1.0 0.0 1.0 1.0 1.0 1\n1.0 0.0 1.0 1.0 1.0 1.0 6\n 1.0 0.0 1.0 1.0 1.0 1\n 1.0 0.0 1.0 1.0 2\n 1.0 0.0 1.0 2\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"A combination of Logistic regression and ANN may identify some survivors, when other methods do not. KNN in combination with another classifier may misclassify passengers who perished.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 2)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.743560Z","iopub.execute_input":"2023-02-01T15:00:30.743975Z","iopub.status.idle":"2023-02-01T15:00:30.762208Z","shell.execute_reply.started":"2023-02-01T15:00:30.743943Z","shell.execute_reply":"2023-02-01T15:00:30.761101Z"},"trusted":true},"execution_count":366,"outputs":[{"execution_count":366,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 0.0 1.0 1.0 0.0 4\n 1.0 0.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 0.0 2\n 1.0 1.0 0.0 0.0 0.0 5\n1.0 0.0 0.0 0.0 1.0 1.0 1\n 1.0 1.0 0.0 2\n 1.0 0.0 1.0 0.0 2\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1.0 5\n 1.0 0.0 0.0 1\n 1.0 0.0 0.0 0.0 1\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 3)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.763835Z","iopub.execute_input":"2023-02-01T15:00:30.764145Z","iopub.status.idle":"2023-02-01T15:00:30.780211Z","shell.execute_reply.started":"2023-02-01T15:00:30.764116Z","shell.execute_reply":"2023-02-01T15:00:30.779475Z"},"trusted":true},"execution_count":367,"outputs":[{"execution_count":367,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n0.0 0.0 1.0 0.0 1.0 1.0 1\n 1.0 0.0 0.0 1.0 1.0 1\n 1.0 0.0 1.0 0.0 1\n1.0 0.0 1.0 1.0 0.0 1.0 1\n 1.0 0.0 1\n 1.0 0.0 1.0 0.0 1.0 1\n 1.0 0.0 0.0 1.0 2\n 1.0 0.0 3\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"y_found\"] == True) & (results_train[\"sum_pred\"] == 5)\ncols = [\"PassengerId\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred',\"Survived\"]\nresults_train.loc[filter_rows, cols].groupby([\"Survived\",'lr_y_pred', 'knn_y_pred', 'clf_y_pred', 'rf_y_pred', 'ann_y_pred']).count()[\"PassengerId\"]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.781542Z","iopub.execute_input":"2023-02-01T15:00:30.781830Z","iopub.status.idle":"2023-02-01T15:00:30.798259Z","shell.execute_reply.started":"2023-02-01T15:00:30.781802Z","shell.execute_reply":"2023-02-01T15:00:30.796868Z"},"trusted":true},"execution_count":368,"outputs":[{"execution_count":368,"output_type":"execute_result","data":{"text/plain":"Survived lr_y_pred knn_y_pred clf_y_pred rf_y_pred ann_y_pred\n1.0 1.0 1.0 1.0 1.0 1.0 65\nName: PassengerId, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.799833Z","iopub.execute_input":"2023-02-01T15:00:30.800231Z","iopub.status.idle":"2023-02-01T15:00:30.808910Z","shell.execute_reply.started":"2023-02-01T15:00:30.800191Z","shell.execute_reply":"2023-02-01T15:00:30.808105Z"},"trusted":true},"execution_count":369,"outputs":[{"execution_count":369,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.809985Z","iopub.execute_input":"2023-02-01T15:00:30.810331Z","iopub.status.idle":"2023-02-01T15:00:30.821387Z","shell.execute_reply.started":"2023-02-01T15:00:30.810281Z","shell.execute_reply":"2023-02-01T15:00:30.820530Z"},"trusted":true},"execution_count":370,"outputs":[{"execution_count":370,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:00:30.822809Z","iopub.execute_input":"2023-02-01T15:00:30.823089Z","iopub.status.idle":"2023-02-01T15:00:30.834693Z","shell.execute_reply.started":"2023-02-01T15:00:30.823062Z","shell.execute_reply":"2023-02-01T15:00:30.833613Z"},"trusted":true},"execution_count":371,"outputs":[{"execution_count":371,"output_type":"execute_result","data":{"text/plain":"PassengerId float64\nSurvived float64\nPclass float64\nSex float64\nAge float64\nFare float64\nEmbarked float64\nfam_members float64\ny float64\nlr_y_pred float64\nknn_y_pred float64\nclf_y_pred float64\nrf_y_pred_x float64\nrf_y_pred_y float64\nrf_y_pred float64\nann_y_pred float64\ny_found bool\nsum_pred float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"results_train.sum_pred.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:01:14.796405Z","iopub.execute_input":"2023-02-01T15:01:14.796794Z","iopub.status.idle":"2023-02-01T15:01:14.805737Z","shell.execute_reply.started":"2023-02-01T15:01:14.796762Z","shell.execute_reply":"2023-02-01T15:01:14.804627Z"},"trusted":true},"execution_count":377,"outputs":[{"execution_count":377,"output_type":"execute_result","data":{"text/plain":"0.0 0.576880\n5.0 0.205387\n1.0 0.079686\n2.0 0.062851\n3.0 0.040404\n4.0 0.034792\nName: sum_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"}},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:36:25.295643Z","iopub.execute_input":"2023-02-01T15:36:25.296169Z","iopub.status.idle":"2023-02-01T15:36:25.314079Z","shell.execute_reply.started":"2023-02-01T15:36:25.296132Z","shell.execute_reply":"2023-02-01T15:36:25.312932Z"}},"execution_count":412,"outputs":[{"execution_count":412,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Are they any particular features that may have been picked up by each classifier?","metadata":{}},{"cell_type":"markdown","source":"### All classifiers agrees with the survival predictions\n\nWe found out that approximately 70% of the passengers who perished have been correclty classified by all the classifiers in agreement. But only, 20% of survivors have been correctly classified. Approximately 70% of the observations made in the training datasets have been correct and all the classifiers agree.","metadata":{}},{"cell_type":"code","source":"filter_rows = ((results_train[\"sum_pred\"] == 0.0) & (results_train[\"y\"] == 0))\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:06.719133Z","iopub.execute_input":"2023-02-01T15:45:06.719636Z","iopub.status.idle":"2023-02-01T15:45:06.733253Z","shell.execute_reply.started":"2023-02-01T15:45:06.719598Z","shell.execute_reply":"2023-02-01T15:45:06.732170Z"},"trusted":true},"execution_count":413,"outputs":[{"execution_count":413,"output_type":"execute_result","data":{"text/plain":"50.841750841750844"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train[\"sum_pred\"] == 5.0) & (results_train[\"y\"] == 1)\n(results_train.loc[filter_rows, :].count()[\"PassengerId\"]/results_train.shape[0])*100","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:07.933943Z","iopub.execute_input":"2023-02-01T15:45:07.935099Z","iopub.status.idle":"2023-02-01T15:45:07.947554Z","shell.execute_reply.started":"2023-02-01T15:45:07.935043Z","shell.execute_reply":"2023-02-01T15:45:07.946375Z"},"trusted":true},"execution_count":414,"outputs":[{"execution_count":414,"output_type":"execute_result","data":{"text/plain":"19.865319865319865"},"metadata":{}}]},{"cell_type":"markdown","source":"The accuracy classification of passengers may vary between classifiers. However, the majority of accurate classifion appears to agree with a correct prediction, which is good outcome. A minority disagreement occurs across the classifiers. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == True\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"] /temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:35:57.061742Z","iopub.execute_input":"2023-02-01T15:35:57.062181Z","iopub.status.idle":"2023-02-01T15:35:57.081158Z","shell.execute_reply.started":"2023-02-01T15:35:57.062145Z","shell.execute_reply":"2023-02-01T15:35:57.079692Z"},"trusted":true},"execution_count":411,"outputs":[{"execution_count":411,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 0.0 0.549757\n 1.0 0.054612\n 2.0 0.033981\n 3.0 0.015777\n 4.0 0.004854\n1.0 1.0 0.031553\n 2.0 0.033981\n 3.0 0.027913\n 4.0 0.032767\n 5.0 0.214806\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"When passengers where misclassified across each classifier, all the predictions agreed on an incorrect outcome. It may suggest some noise in the data and rules could not be learnt by any technique. ","metadata":{}},{"cell_type":"code","source":"filter_rows = results_train[\"y_found\"] == False\nfilter_columns = [\"y\",\"sum_pred\",\"lr_y_pred\",\"knn_y_pred\", \"clf_y_pred\", \"rf_y_pred_x\",\"ann_y_pred\"]\ntemp = results_train.loc[filter_rows, filter_columns]\ntemp.groupby([\"y\",\"sum_pred\"]).count()[\"lr_y_pred\"]/temp.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T15:45:45.372534Z","iopub.execute_input":"2023-02-01T15:45:45.372921Z","iopub.status.idle":"2023-02-01T15:45:45.388445Z","shell.execute_reply.started":"2023-02-01T15:45:45.372891Z","shell.execute_reply":"2023-02-01T15:45:45.387062Z"},"trusted":true},"execution_count":415,"outputs":[{"execution_count":415,"output_type":"execute_result","data":{"text/plain":"y sum_pred\n0.0 5.0 0.089552\n1.0 0.0 0.910448\nName: lr_y_pred, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"## The classifiers disagree with each others on the survival predictions ?\n\nDecision Tree classifiers appears to have classified correctly the most passengers, when disagreements between classifiers exists. \n\nWe calculate the proportion of correct predictions, when some classifiers disagree. We found out that Decision tree appears to predict the most correct passengers who survive or perish the accident.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nno_correct = results_train.loc[filter_rows, :].shape[0]\nno_incorrect = results_train.loc[filter_rows, :].shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:30.920933Z","iopub.execute_input":"2023-02-01T16:00:30.921353Z","iopub.status.idle":"2023-02-01T16:00:30.932343Z","shell.execute_reply.started":"2023-02-01T16:00:30.921303Z","shell.execute_reply":"2023-02-01T16:00:30.930975Z"},"trusted":true},"execution_count":433,"outputs":[]},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.lr_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.369868Z","iopub.execute_input":"2023-02-01T16:00:32.370576Z","iopub.status.idle":"2023-02-01T16:00:32.381927Z","shell.execute_reply.started":"2023-02-01T16:00:32.370537Z","shell.execute_reply":"2023-02-01T16:00:32.381022Z"},"trusted":true},"execution_count":434,"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"44.329896907216494"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.knn_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:32.853276Z","iopub.execute_input":"2023-02-01T16:00:32.854476Z","iopub.status.idle":"2023-02-01T16:00:32.868855Z","shell.execute_reply.started":"2023-02-01T16:00:32.854418Z","shell.execute_reply":"2023-02-01T16:00:32.867407Z"},"trusted":true},"execution_count":435,"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"47.42268041237113"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.ann_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:33.395939Z","iopub.execute_input":"2023-02-01T16:00:33.396354Z","iopub.status.idle":"2023-02-01T16:00:33.410583Z","shell.execute_reply.started":"2023-02-01T16:00:33.396294Z","shell.execute_reply":"2023-02-01T16:00:33.409408Z"},"trusted":true},"execution_count":436,"outputs":[{"execution_count":436,"output_type":"execute_result","data":{"text/plain":"52.0618556701031"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.clf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:34.195555Z","iopub.execute_input":"2023-02-01T16:00:34.196776Z","iopub.status.idle":"2023-02-01T16:00:34.208545Z","shell.execute_reply.started":"2023-02-01T16:00:34.196733Z","shell.execute_reply":"2023-02-01T16:00:34.207295Z"},"trusted":true},"execution_count":437,"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"75.25773195876289"},"metadata":{}}]},{"cell_type":"code","source":"filter_rows = (results_train.rf_y_pred == results_train[\"y\"]) & (results_train.sum_pred.isin([1,2,3,4]))\ntemp = results_train.loc[filter_rows, :]\n(temp.count()[\"PassengerId\"]/no_correct)*100\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:00:35.044699Z","iopub.execute_input":"2023-02-01T16:00:35.045127Z","iopub.status.idle":"2023-02-01T16:00:35.057811Z","shell.execute_reply.started":"2023-02-01T16:00:35.045090Z","shell.execute_reply":"2023-02-01T16:00:35.056488Z"},"trusted":true},"execution_count":438,"outputs":[{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"25.257731958762886"},"metadata":{}}]},{"cell_type":"markdown","source":"We change the predictions, that has been mispredicted by at least one classifier.","metadata":{}},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_train.loc[filter_rows, \"y\"] = results_train.clf_y_pred\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:38:03.184402Z","iopub.execute_input":"2023-02-01T16:38:03.184812Z","iopub.status.idle":"2023-02-01T16:38:03.191812Z","shell.execute_reply.started":"2023-02-01T16:38:03.184781Z","shell.execute_reply":"2023-02-01T16:38:03.191010Z"},"trusted":true},"execution_count":462,"outputs":[]},{"cell_type":"markdown","source":"The accuracy has been increased by a considerable level of accuracy. ","metadata":{}},{"cell_type":"code","source":"results_train.loc[results_train.Survived == results_train.y,:].count()[\"PassengerId\"]/results_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:40:10.552687Z","iopub.execute_input":"2023-02-01T16:40:10.553066Z","iopub.status.idle":"2023-02-01T16:40:10.564469Z","shell.execute_reply.started":"2023-02-01T16:40:10.553036Z","shell.execute_reply":"2023-02-01T16:40:10.563190Z"},"trusted":true},"execution_count":467,"outputs":[{"execution_count":467,"output_type":"execute_result","data":{"text/plain":"0.9461279461279462"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying to results test\n\nThe distribution appears the be very similar as the training dataset.","metadata":{}},{"cell_type":"markdown","source":"__Testing dataset:__","metadata":{}},{"cell_type":"code","source":"results_test[\"sum_pred\"] = results_train[\"lr_y_pred\"] + results_train[\"ann_y_pred\"] + results_train[\"knn_y_pred\"] + results_train[\"rf_y_pred_x\"] + results_train[\"clf_y_pred\"] \nresults_test.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_test.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:04.962156Z","iopub.execute_input":"2023-02-01T16:28:04.962921Z","iopub.status.idle":"2023-02-01T16:28:05.177598Z","shell.execute_reply.started":"2023-02-01T16:28:04.962882Z","shell.execute_reply":"2023-02-01T16:28:05.176388Z"},"trusted":true},"execution_count":459,"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"count 418.000000\nmean 1.590909\nstd 2.078233\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 4.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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dataset:__","metadata":{}},{"cell_type":"code","source":"results_train.loc[:,\"sum_pred\"].hist(bins = 5)\nresults_train.loc[:,\"sum_pred\"].describe()","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:28:10.931421Z","iopub.execute_input":"2023-02-01T16:28:10.931875Z","iopub.status.idle":"2023-02-01T16:28:11.153259Z","shell.execute_reply.started":"2023-02-01T16:28:10.931840Z","shell.execute_reply":"2023-02-01T16:28:11.152336Z"},"trusted":true},"execution_count":460,"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"count 891.000000\nmean 1.492705\nstd 2.040242\nmin 0.000000\n25% 0.000000\n50% 0.000000\n75% 3.000000\nmax 5.000000\nName: sum_pred, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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z5aVb8YdT3DVlW/q6oLmb3D++Ik3U7DJXkncLyqDo26lhX21qq6iNkn6F7Tpl0HZjWHu485GBNt3vlrwK1V9fVR17OSquoZ4F5g24hLGaZLgXe1Oeh9wGVJ/nG0JQ1fVR1r78eB25mdah6Y1RzuPuZgDLR/XLwZeLSqPjPqelZCknOSbGjLZzJ70cAPR1rUEFXVJ6tqU1VtYfZ3fE9V/cWIyxqqJOvaBQIkWQe8HRjoVXCrNtyr6lngucccPArcNuTHHIxcki8D/wa8LsnRJDtGXdMKuBT4ALNncw+015WjLmrINgL3JnmQ2ZOYu6tqLC4PHCMTwH1JfgB8B7irqr45yA9YtZdCSpJOb9WeuUuSTs9wl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36PzFqarrIVm2TAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"filter_rows = (results_train.sum_pred.isin([1,2,3,4]))\nresults_test.loc[filter_rows, \"y\"] = results_test.clf_y_pred","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:41:15.101164Z","iopub.execute_input":"2023-02-01T16:41:15.101563Z","iopub.status.idle":"2023-02-01T16:41:15.110450Z","shell.execute_reply.started":"2023-02-01T16:41:15.101523Z","shell.execute_reply":"2023-02-01T16:41:15.109235Z"},"trusted":true},"execution_count":468,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:46:41.923470Z","iopub.execute_input":"2023-02-01T16:46:41.923885Z","iopub.status.idle":"2023-02-01T16:46:43.051535Z","shell.execute_reply.started":"2023-02-01T16:46:41.923846Z","shell.execute_reply":"2023-02-01T16:46:43.050096Z"},"trusted":true},"execution_count":471,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T16:48:10.301809Z","iopub.execute_input":"2023-02-01T16:48:10.302423Z","iopub.status.idle":"2023-02-01T16:48:11.417688Z","shell.execute_reply.started":"2023-02-01T16:48:10.302370Z","shell.execute_reply":"2023-02-01T16:48:11.415704Z"},"trusted":true},"execution_count":472,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"code","source":"submission = pd.DataFrame({\n \"PassengerId\": results_test[\"PassengerId\"].astype(int),\n \"Survived\": results_test[\"y\"]\n })\n\nsubmission = submission.astype({col: 'int32' for col in submission.select_dtypes('int64').columns})\nsubmission.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:09:39.297418Z","iopub.execute_input":"2023-02-01T17:09:39.297834Z","iopub.status.idle":"2023-02-01T17:09:39.311761Z","shell.execute_reply.started":"2023-02-01T17:09:39.297801Z","shell.execute_reply":"2023-02-01T17:09:39.310602Z"},"trusted":true},"execution_count":490,"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"PassengerId int32\nSurvived float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)\n!ls /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2023-02-01T17:06:56.872660Z","iopub.execute_input":"2023-02-01T17:06:56.873348Z","iopub.status.idle":"2023-02-01T17:06:57.989149Z","shell.execute_reply.started":"2023-02-01T17:06:56.873282Z","shell.execute_reply":"2023-02-01T17:06:57.987753Z"},"trusted":true},"execution_count":488,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Introcution to machine learning/human learning of machine learning.pdf b/Introcution to machine learning/human learning of machine learning.pdf new file mode 100644 index 0000000..ddec3bf Binary files /dev/null and b/Introcution to machine learning/human learning of machine learning.pdf differ diff --git a/Introcution to machine learning/inspection-outcome.ipynb b/Introcution to machine learning/inspection-outcome.ipynb new file mode 100644 index 0000000..4175c11 --- /dev/null +++ b/Introcution to machine learning/inspection-outcome.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Is it possible to predict some catering provider may pass of fail an inspection?\n\nThis notebook aims to introduce some concepts of Artificial Intelligence - i.e., machine learning. We are going to use a dataset captured by Chicago food inspections. More details can be found below.\n\nhttps://www.kaggle.com/datasets/tjkyner/chicago-food-inspections\n\nThis notebook demonstrates the extend how a decision tree may predict accurately the outcome of an inspection. ","metadata":{}},{"cell_type":"markdown","source":"# Step 1 - Let's explore the data\n\nWe import the relevant libraries. We discover the data is stored in one reasonably-sized file. The dataset has 17 columns and more than 220,000 rows. The datasets appears to be quite complete, with very few missing observations. ","metadata":{}},{"cell_type":"code","source":"\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport seaborn as sns\n%matplotlib inline\nfrom sklearn.datasets import load_iris\nfrom sklearn import tree\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\n\n\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-09-11T18:49:34.743347Z","iopub.execute_input":"2023-09-11T18:49:34.743801Z","iopub.status.idle":"2023-09-11T18:49:37.926388Z","shell.execute_reply.started":"2023-09-11T18:49:34.743768Z","shell.execute_reply":"2023-09-11T18:49:37.925005Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/chicago-food-inspections/Food_Inspections.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"source = \"/kaggle/input/chicago-food-inspections/Food_Inspections.csv\"\ndata = pd.read_csv(source) \ndata.shape","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:37.929297Z","iopub.execute_input":"2023-09-11T18:49:37.929940Z","iopub.status.idle":"2023-09-11T18:49:44.525510Z","shell.execute_reply.started":"2023-09-11T18:49:37.929884Z","shell.execute_reply":"2023-09-11T18:49:44.523925Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"(221468, 17)"},"metadata":{}}]},{"cell_type":"code","source":"data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.527264Z","iopub.execute_input":"2023-09-11T18:49:44.527729Z","iopub.status.idle":"2023-09-11T18:49:44.544468Z","shell.execute_reply.started":"2023-09-11T18:49:44.527685Z","shell.execute_reply":"2023-09-11T18:49:44.543159Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"Inspection ID int64\nDBA Name object\nAKA Name object\nLicense # float64\nFacility Type object\nRisk object\nAddress object\nCity object\nState object\nZip float64\nInspection Date object\nInspection Type object\nResults object\nViolations object\nLatitude float64\nLongitude float64\nLocation object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"data.isnull().sum()/data.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.546098Z","iopub.execute_input":"2023-09-11T18:49:44.546509Z","iopub.status.idle":"2023-09-11T18:49:44.849068Z","shell.execute_reply.started":"2023-09-11T18:49:44.546477Z","shell.execute_reply":"2023-09-11T18:49:44.848139Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"Inspection ID 0.000000\nDBA Name 0.000009\nAKA Name 0.011203\nLicense # 0.000077\nFacility Type 0.022184\nRisk 0.000321\nAddress 0.000000\nCity 0.000754\nState 0.000248\nZip 0.000235\nInspection Date 0.000000\nInspection Type 0.000005\nResults 0.000000\nViolations 0.267538\nLatitude 0.003414\nLongitude 0.003414\nLocation 0.003414\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"rows =data.isnull","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.852538Z","iopub.execute_input":"2023-09-11T18:49:44.852908Z","iopub.status.idle":"2023-09-11T18:49:44.858293Z","shell.execute_reply.started":"2023-09-11T18:49:44.852860Z","shell.execute_reply":"2023-09-11T18:49:44.857151Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"# Step 2 - Let's clean the data\n","metadata":{}},{"cell_type":"markdown","source":"The list of columns appears to store company names and other information that can lead to an identification. For that reason we remove some of the column to protect those businesses. We keep the outcome of the inspections, the facility type, risk, city, zip code, inspection type, violations, latitude and longitude. The latter could lead to identification. We assume due to the concentration of businesses in Chicago area, we can limit identification. ","metadata":{}},{"cell_type":"code","source":"cols = ['Results','Facility Type', 'Risk','City','Zip','Inspection Type','Violations','Latitude','Longitude']\ndata = data.loc[:,cols]\ndata\n","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.860034Z","iopub.execute_input":"2023-09-11T18:49:44.861182Z","iopub.status.idle":"2023-09-11T18:49:44.916196Z","shell.execute_reply.started":"2023-09-11T18:49:44.861134Z","shell.execute_reply":"2023-09-11T18:49:44.915118Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" Results Facility Type Risk City \\\n0 Pass School Risk 1 (High) CHICAGO \n1 No Entry Restaurant Risk 1 (High) CHICAGO \n2 Not Ready Restaurant Risk 1 (High) CHICAGO \n3 Out of Business Restaurant Risk 1 (High) CHICAGO \n4 No Entry Restaurant Risk 1 (High) CHICAGO \n... ... ... ... ... \n221463 Pass Long-Term Care Facility Risk 1 (High) CHICAGO \n221464 Fail Daycare (2 - 6 Years) Risk 1 (High) CHICAGO \n221465 Pass Restaurant Risk 2 (Medium) CHICAGO \n221466 Pass Restaurant Risk 2 (Medium) CHICAGO \n221467 Pass Grocery Store Risk 3 (Low) CHICAGO \n\n Zip Inspection Type \\\n0 60615.0 Canvass \n1 60611.0 Non-Inspection \n2 60607.0 License \n3 60659.0 Canvass \n4 60625.0 Non-Inspection \n... ... ... \n221463 60611.0 Canvass \n221464 60827.0 License \n221465 60602.0 Short Form Complaint \n221466 60656.0 Suspected Food Poisoning \n221467 60609.0 License Re-Inspection \n\n Violations Latitude \\\n0 NaN 41.798029 \n1 NaN 41.891652 \n2 NaN 41.867330 \n3 NaN 41.985362 \n4 NaN 41.975927 \n... ... ... \n221463 34. FLOORS: CONSTRUCTED PER CODE, CLEANED, GOO... 41.897438 \n221464 18. NO EVIDENCE OF RODENT OR INSECT OUTER OPEN... 41.655907 \n221465 38. VENTILATION: ROOMS AND EQUIPMENT VENTED AS... 41.883115 \n221466 32. FOOD AND NON-FOOD CONTACT SURFACES PROPERL... 41.962768 \n221467 NaN 41.808509 \n\n Longitude \n0 -87.602463 \n1 -87.622604 \n2 -87.642117 \n3 -87.689652 \n4 -87.699046 \n... ... \n221463 -87.626020 \n221464 -87.599022 \n221465 -87.625173 \n221466 -87.836840 \n221467 -87.663877 \n\n[221468 rows x 9 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ResultsFacility TypeRiskCityZipInspection TypeViolationsLatitudeLongitude
0PassSchoolRisk 1 (High)CHICAGO60615.0CanvassNaN41.798029-87.602463
1No EntryRestaurantRisk 1 (High)CHICAGO60611.0Non-InspectionNaN41.891652-87.622604
2Not ReadyRestaurantRisk 1 (High)CHICAGO60607.0LicenseNaN41.867330-87.642117
3Out of BusinessRestaurantRisk 1 (High)CHICAGO60659.0CanvassNaN41.985362-87.689652
4No EntryRestaurantRisk 1 (High)CHICAGO60625.0Non-InspectionNaN41.975927-87.699046
..............................
221463PassLong-Term Care FacilityRisk 1 (High)CHICAGO60611.0Canvass34. FLOORS: CONSTRUCTED PER CODE, CLEANED, GOO...41.897438-87.626020
221464FailDaycare (2 - 6 Years)Risk 1 (High)CHICAGO60827.0License18. NO EVIDENCE OF RODENT OR INSECT OUTER OPEN...41.655907-87.599022
221465PassRestaurantRisk 2 (Medium)CHICAGO60602.0Short Form Complaint38. VENTILATION: ROOMS AND EQUIPMENT VENTED AS...41.883115-87.625173
221466PassRestaurantRisk 2 (Medium)CHICAGO60656.0Suspected Food Poisoning32. FOOD AND NON-FOOD CONTACT SURFACES PROPERL...41.962768-87.836840
221467PassGrocery StoreRisk 3 (Low)CHICAGO60609.0License Re-InspectionNaN41.808509-87.663877
\n

221468 rows × 9 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Some unique and character values appears to be repeated. Therefore, we transform those columns with some numerical categorical values. It will support learning some patterns using a learning algorithm.","metadata":{}},{"cell_type":"markdown","source":"## Results","metadata":{}},{"cell_type":"code","source":"len(data.Results.unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.917645Z","iopub.execute_input":"2023-09-11T18:49:44.918484Z","iopub.status.idle":"2023-09-11T18:49:44.951108Z","shell.execute_reply.started":"2023-09-11T18:49:44.918451Z","shell.execute_reply":"2023-09-11T18:49:44.949533Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"7"},"metadata":{}}]},{"cell_type":"code","source":"data[\"Results\"]= pd.Categorical(data.Results)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.952509Z","iopub.execute_input":"2023-09-11T18:49:44.952862Z","iopub.status.idle":"2023-09-11T18:49:44.985941Z","shell.execute_reply.started":"2023-09-11T18:49:44.952832Z","shell.execute_reply":"2023-09-11T18:49:44.984749Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type object\nRisk object\nCity object\nZip float64\nInspection Type object\nViolations object\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results = data.Results.cat.codes\nresults","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.987371Z","iopub.execute_input":"2023-09-11T18:49:44.987706Z","iopub.status.idle":"2023-09-11T18:49:44.998718Z","shell.execute_reply.started":"2023-09-11T18:49:44.987678Z","shell.execute_reply":"2023-09-11T18:49:44.997276Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"0 5\n1 2\n2 3\n3 4\n4 2\n ..\n221463 5\n221464 1\n221465 5\n221466 5\n221467 5\nLength: 221468, dtype: int8"},"metadata":{}}]},{"cell_type":"markdown","source":"## Facility type","metadata":{}},{"cell_type":"code","source":"len(data['Facility Type'].unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:44.999787Z","iopub.execute_input":"2023-09-11T18:49:45.000162Z","iopub.status.idle":"2023-09-11T18:49:45.043554Z","shell.execute_reply.started":"2023-09-11T18:49:45.000133Z","shell.execute_reply":"2023-09-11T18:49:45.042449Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"503"},"metadata":{}}]},{"cell_type":"code","source":"data[\"Facility Type\"]= pd.Categorical(data[\"Facility Type\"])\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.045182Z","iopub.execute_input":"2023-09-11T18:49:45.045566Z","iopub.status.idle":"2023-09-11T18:49:45.106958Z","shell.execute_reply.started":"2023-09-11T18:49:45.045534Z","shell.execute_reply":"2023-09-11T18:49:45.105603Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type category\nRisk object\nCity object\nZip float64\nInspection Type object\nViolations object\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"facility_type = data['Facility Type'].cat.codes\nfacility_type","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.111507Z","iopub.execute_input":"2023-09-11T18:49:45.111940Z","iopub.status.idle":"2023-09-11T18:49:45.122005Z","shell.execute_reply.started":"2023-09-11T18:49:45.111878Z","shell.execute_reply":"2023-09-11T18:49:45.120689Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"0 405\n1 385\n2 385\n3 385\n4 385\n ... \n221463 276\n221464 142\n221465 385\n221466 385\n221467 216\nLength: 221468, dtype: int16"},"metadata":{}}]},{"cell_type":"markdown","source":"## Inspection Type","metadata":{}},{"cell_type":"code","source":"len(data['Inspection Type'].unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.124286Z","iopub.execute_input":"2023-09-11T18:49:45.124646Z","iopub.status.idle":"2023-09-11T18:49:45.157541Z","shell.execute_reply.started":"2023-09-11T18:49:45.124614Z","shell.execute_reply":"2023-09-11T18:49:45.156503Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"111"},"metadata":{}}]},{"cell_type":"code","source":"data[\"Inspection Type\"]= pd.Categorical(data['Inspection Type'])\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.163801Z","iopub.execute_input":"2023-09-11T18:49:45.164575Z","iopub.status.idle":"2023-09-11T18:49:45.221408Z","shell.execute_reply.started":"2023-09-11T18:49:45.164525Z","shell.execute_reply":"2023-09-11T18:49:45.220260Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type category\nRisk object\nCity object\nZip float64\nInspection Type category\nViolations object\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"inspect_types = data[\"Inspection Type\"].cat.codes\ninspect_types","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.222833Z","iopub.execute_input":"2023-09-11T18:49:45.223215Z","iopub.status.idle":"2023-09-11T18:49:45.234024Z","shell.execute_reply.started":"2023-09-11T18:49:45.223185Z","shell.execute_reply":"2023-09-11T18:49:45.232804Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"0 15\n1 52\n2 44\n3 15\n4 52\n ..\n221463 15\n221464 44\n221465 75\n221466 80\n221467 45\nLength: 221468, dtype: int8"},"metadata":{}}]},{"cell_type":"markdown","source":"## Violations","metadata":{}},{"cell_type":"code","source":"len(data['Violations'].unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.235973Z","iopub.execute_input":"2023-09-11T18:49:45.236357Z","iopub.status.idle":"2023-09-11T18:49:45.430005Z","shell.execute_reply.started":"2023-09-11T18:49:45.236326Z","shell.execute_reply":"2023-09-11T18:49:45.428793Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"161238"},"metadata":{}}]},{"cell_type":"code","source":"data['Violations'] = pd.Categorical(data.Violations)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:45.431638Z","iopub.execute_input":"2023-09-11T18:49:45.432413Z","iopub.status.idle":"2023-09-11T18:49:46.056493Z","shell.execute_reply.started":"2023-09-11T18:49:45.432380Z","shell.execute_reply":"2023-09-11T18:49:46.055360Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type category\nRisk object\nCity object\nZip float64\nInspection Type category\nViolations category\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"violations = data.Violations.cat.codes\nviolations","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.057941Z","iopub.execute_input":"2023-09-11T18:49:46.058855Z","iopub.status.idle":"2023-09-11T18:49:46.068340Z","shell.execute_reply.started":"2023-09-11T18:49:46.058823Z","shell.execute_reply":"2023-09-11T18:49:46.067118Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"0 -1\n1 -1\n2 -1\n3 -1\n4 -1\n ... \n221463 128018\n221464 17634\n221465 141150\n221466 79227\n221467 -1\nLength: 221468, dtype: int32"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cities","metadata":{}},{"cell_type":"code","source":"len(data['City'].unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.069788Z","iopub.execute_input":"2023-09-11T18:49:46.070193Z","iopub.status.idle":"2023-09-11T18:49:46.107346Z","shell.execute_reply.started":"2023-09-11T18:49:46.070163Z","shell.execute_reply":"2023-09-11T18:49:46.106101Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"74"},"metadata":{}}]},{"cell_type":"code","source":"data[\"City\"]= pd.Categorical(data.City)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.109338Z","iopub.execute_input":"2023-09-11T18:49:46.109743Z","iopub.status.idle":"2023-09-11T18:49:46.168583Z","shell.execute_reply.started":"2023-09-11T18:49:46.109713Z","shell.execute_reply":"2023-09-11T18:49:46.167002Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type category\nRisk object\nCity category\nZip float64\nInspection Type category\nViolations category\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"cities = data['City'].cat.codes\ncities","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.170818Z","iopub.execute_input":"2023-09-11T18:49:46.171325Z","iopub.status.idle":"2023-09-11T18:49:46.183915Z","shell.execute_reply.started":"2023-09-11T18:49:46.171285Z","shell.execute_reply":"2023-09-11T18:49:46.182242Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"0 16\n1 16\n2 16\n3 16\n4 16\n ..\n221463 16\n221464 16\n221465 16\n221466 16\n221467 16\nLength: 221468, dtype: int8"},"metadata":{}}]},{"cell_type":"markdown","source":"## Risks","metadata":{}},{"cell_type":"code","source":"len(data.Risk.unique())","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.186000Z","iopub.execute_input":"2023-09-11T18:49:46.186370Z","iopub.status.idle":"2023-09-11T18:49:46.222912Z","shell.execute_reply.started":"2023-09-11T18:49:46.186341Z","shell.execute_reply":"2023-09-11T18:49:46.221386Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"5"},"metadata":{}}]},{"cell_type":"code","source":"data[\"Risk\"] = pd.Categorical(data.Risk)\ndata.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.224662Z","iopub.execute_input":"2023-09-11T18:49:46.225142Z","iopub.status.idle":"2023-09-11T18:49:46.293339Z","shell.execute_reply.started":"2023-09-11T18:49:46.225101Z","shell.execute_reply":"2023-09-11T18:49:46.291489Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"Results category\nFacility Type category\nRisk category\nCity category\nZip float64\nInspection Type category\nViolations category\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"risks = data.Risk.cat.codes\nrisks","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:49:46.295307Z","iopub.execute_input":"2023-09-11T18:49:46.295691Z","iopub.status.idle":"2023-09-11T18:49:46.307663Z","shell.execute_reply.started":"2023-09-11T18:49:46.295662Z","shell.execute_reply":"2023-09-11T18:49:46.305775Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"0 1\n1 1\n2 1\n3 1\n4 1\n ..\n221463 1\n221464 1\n221465 2\n221466 2\n221467 3\nLength: 221468, dtype: int8"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_data : dict = {'results': results,\n 'facility_type' : facility_type,\n 'inspect_type' : inspect_types,\n 'violations': violations,\n 'cities' : cities,\n 'risk' : risks,\n 'zip' : data.Zip,\n 'Lat': data.Latitude,\n 'Long': data.Longitude}\ncleaned_data : pd.DataFrame = pd.DataFrame(cleaned_data)\ncleaned_data.shape\n","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:38.403336Z","iopub.execute_input":"2023-09-11T18:50:38.403794Z","iopub.status.idle":"2023-09-11T18:50:38.418415Z","shell.execute_reply.started":"2023-09-11T18:50:38.403762Z","shell.execute_reply":"2023-09-11T18:50:38.417454Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"(221468, 9)"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_data.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:43.382435Z","iopub.execute_input":"2023-09-11T18:50:43.383000Z","iopub.status.idle":"2023-09-11T18:50:43.394019Z","shell.execute_reply.started":"2023-09-11T18:50:43.382961Z","shell.execute_reply":"2023-09-11T18:50:43.392947Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"results int8\nfacility_type int16\ninspect_type int8\nviolations int32\ncities int8\nrisk int8\nzip float64\nLat float64\nLong float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:49.264499Z","iopub.execute_input":"2023-09-11T18:50:49.264922Z","iopub.status.idle":"2023-09-11T18:50:49.281979Z","shell.execute_reply.started":"2023-09-11T18:50:49.264863Z","shell.execute_reply":"2023-09-11T18:50:49.280486Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"results 0\nfacility_type 0\ninspect_type 0\nviolations 0\ncities 0\nrisk 0\nzip 52\nLat 756\nLong 756\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_data.zip.fillna(-1, inplace = True)\ncleaned_data.Lat.fillna(-1, inplace = True)\ncleaned_data.Long.fillna(-1, inplace = True)\ncleaned_data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:52.649830Z","iopub.execute_input":"2023-09-11T18:50:52.650347Z","iopub.status.idle":"2023-09-11T18:50:52.669561Z","shell.execute_reply.started":"2023-09-11T18:50:52.650310Z","shell.execute_reply":"2023-09-11T18:50:52.668110Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"results 0\nfacility_type 0\ninspect_type 0\nviolations 0\ncities 0\nrisk 0\nzip 0\nLat 0\nLong 0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"# Step 3 - Let's prepare the data for learning","metadata":{}},{"cell_type":"code","source":"cleaned_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:55.971569Z","iopub.execute_input":"2023-09-11T18:50:55.972316Z","iopub.status.idle":"2023-09-11T18:50:55.980672Z","shell.execute_reply.started":"2023-09-11T18:50:55.972270Z","shell.execute_reply":"2023-09-11T18:50:55.979313Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"(221468, 9)"},"metadata":{}}]},{"cell_type":"code","source":"X = cleaned_data.iloc[:, 1:]\nX ","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:50:59.566319Z","iopub.execute_input":"2023-09-11T18:50:59.566979Z","iopub.status.idle":"2023-09-11T18:50:59.597557Z","shell.execute_reply.started":"2023-09-11T18:50:59.566946Z","shell.execute_reply":"2023-09-11T18:50:59.596092Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":" facility_type inspect_type violations cities risk zip \\\n0 405 15 -1 16 1 60615.0 \n1 385 52 -1 16 1 60611.0 \n2 385 44 -1 16 1 60607.0 \n3 385 15 -1 16 1 60659.0 \n4 385 52 -1 16 1 60625.0 \n... ... ... ... ... ... ... \n221463 276 15 128018 16 1 60611.0 \n221464 142 44 17634 16 1 60827.0 \n221465 385 75 141150 16 2 60602.0 \n221466 385 80 79227 16 2 60656.0 \n221467 216 45 -1 16 3 60609.0 \n\n Lat Long \n0 41.798029 -87.602463 \n1 41.891652 -87.622604 \n2 41.867330 -87.642117 \n3 41.985362 -87.689652 \n4 41.975927 -87.699046 \n... ... ... \n221463 41.897438 -87.626020 \n221464 41.655907 -87.599022 \n221465 41.883115 -87.625173 \n221466 41.962768 -87.836840 \n221467 41.808509 -87.663877 \n\n[221468 rows x 8 columns]","text/html":"
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facility_typeinspect_typeviolationscitiesriskzipLatLong
040515-116160615.041.798029-87.602463
138552-116160611.041.891652-87.622604
238544-116160607.041.867330-87.642117
338515-116160659.041.985362-87.689652
438552-116160625.041.975927-87.699046
...........................
2214632761512801816160611.041.897438-87.626020
221464142441763416160827.041.655907-87.599022
2214653857514115016260602.041.883115-87.625173
221466385807922716260656.041.962768-87.836840
22146721645-116360609.041.808509-87.663877
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221468 rows × 8 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"y = cleaned_data.iloc[:, 0]\ny","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:51:04.830683Z","iopub.execute_input":"2023-09-11T18:51:04.831168Z","iopub.status.idle":"2023-09-11T18:51:04.843303Z","shell.execute_reply.started":"2023-09-11T18:51:04.831133Z","shell.execute_reply":"2023-09-11T18:51:04.841770Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"0 5\n1 2\n2 3\n3 4\n4 2\n ..\n221463 5\n221464 1\n221465 5\n221466 5\n221467 5\nName: results, Length: 221468, dtype: int8"},"metadata":{}}]},{"cell_type":"code","source":"split = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\n\n# Split the data into training and test sets\nfor train_index, test_index in split.split(X, y):\n X_train = X.loc[train_index]\n X_test = X.loc[test_index]\n y_train = y.loc[train_index]\n y_test = y.loc[test_index]\n\nprint(\"X_train\", X_train.shape)\nprint(\"X_test\", X_test.shape)","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:51:09.950487Z","iopub.execute_input":"2023-09-11T18:51:09.951018Z","iopub.status.idle":"2023-09-11T18:51:10.121282Z","shell.execute_reply.started":"2023-09-11T18:51:09.950979Z","shell.execute_reply":"2023-09-11T18:51:10.120149Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"X_train (132880, 8)\nX_test (88588, 8)\n","output_type":"stream"}]},{"cell_type":"code","source":"split = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=42)\n\n# Split the data into training and test sets\nfor test_index, valid_index in split.split(X_test, y_test):\n X_test = X.loc[test_index]\n X_valid = X.loc[valid_index]\n y_test = y.loc[test_index]\n y_valid = y.loc[valid_index]\n\nprint(\"X_test\", X_test.shape)\nprint(\"X_valid\", X_valid.shape)","metadata":{"execution":{"iopub.status.busy":"2023-09-11T18:51:30.006310Z","iopub.execute_input":"2023-09-11T18:51:30.007436Z","iopub.status.idle":"2023-09-11T18:51:30.039531Z","shell.execute_reply.started":"2023-09-11T18:51:30.007389Z","shell.execute_reply":"2023-09-11T18:51:30.037870Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"X_test (11073, 8)\nX_valid (11074, 8)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Step 4 - Learning \nThis phase is attempting to fit a model between the known outcome _y_ and some values against those outcomes. 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...]"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_train = clf.predict(X_train)\nconfusion_matrix(y_train,y_pred_train)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_test = clf.predict(X_test)\nconfusion_matrix(y_test,y_pred_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_valid = clf.predict(X_valid)\nconfusion_matrix(y_valid,y_pred_valid)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## What are the distribution of the data?\n","metadata":{}},{"cell_type":"markdown","source":"Many of the statistical variables appears to have some categories appears more than other. It is likely that some statistical observations may be dependent. One example are the geographical columns - cities, zip, latitude and longitude. It may support learning pattern.","metadata":{}},{"cell_type":"code","source":"plt.hist(cleaned_data[\"results\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.Results.unique()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"facility_type\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"inspect_type\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"violations\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"cities\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"zip\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(cleaned_data[\"risk\"])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cleaned_data.groupby(['inspect_type','cities','results']).count()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Some correlations may exists between the results and the type of inspection. Both variables may be also independent. ","metadata":{}},{"cell_type":"code","source":"sns.heatmap(cleaned_data.corr())","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file