From c6db087b3e6c9fd7164a32269612344922349fc3 Mon Sep 17 00:00:00 2001
From: andy-casagrande <163665556+andy-casagrande@users.noreply.github.com>
Date: Sat, 20 Apr 2024 15:02:59 +0100
Subject: [PATCH] Add files via upload

---
 Lab Customer Analysis Round 5.ipynb | 2121 +++++++++++++++++++++++++++
 1 file changed, 2121 insertions(+)
 create mode 100644 Lab Customer Analysis Round 5.ipynb

diff --git a/Lab Customer Analysis Round 5.ipynb b/Lab Customer Analysis Round 5.ipynb
new file mode 100644
index 0000000..628a59c
--- /dev/null
+++ b/Lab Customer Analysis Round 5.ipynb	
@@ -0,0 +1,2121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b8f8476-8689-487a-bcb3-c19e4a0f18d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#EDA (Exploratory Data Analysis) - Complete the following tasks to explore the data:\n",
+    "#Show DataFrame info.\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fc4634cf-94a4-4327-b65e-2c086baafe35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2a756dee-d040-4b6a-86b2-ae151d891872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dd475813-ca96-4f8c-839c-be06ad5fa3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "17a38de2-4b90-4f93-81f5-d76ffc36210e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3c0fd27c-9195-48e4-b5b2-42b6f756d7ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>income</th>\n",
+       "      <th>...</th>\n",
+       "      <th>months_since_policy_inception</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BU79786</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>2763.519279</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/24/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>56274</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>384.811147</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>6979.535903</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/31/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>1131.464935</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>12887.431650</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/19/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>566.472247</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>7645.861827</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/20/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>529.881344</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HB64268</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>2813.692575</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/3/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>43836</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>138.130879</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9129</th>\n",
+       "      <td>LA72316</td>\n",
+       "      <td>California</td>\n",
+       "      <td>23405.987980</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/10/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>71941</td>\n",
+       "      <td>...</td>\n",
+       "      <td>89</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>198.234764</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9130</th>\n",
+       "      <td>PK87824</td>\n",
+       "      <td>California</td>\n",
+       "      <td>3096.511217</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/12/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>21604</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>379.200000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9131</th>\n",
+       "      <td>TD14365</td>\n",
+       "      <td>California</td>\n",
+       "      <td>8163.890428</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/6/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>790.784983</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9132</th>\n",
+       "      <td>UP19263</td>\n",
+       "      <td>California</td>\n",
+       "      <td>7524.442436</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/3/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21941</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>691.200000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Large</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9133</th>\n",
+       "      <td>Y167826</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2611.836866</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/14/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>369.600000</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9134 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer       state  customer_lifetime_value response  coverage  \\\n",
+       "0     BU79786  Washington              2763.519279       No     Basic   \n",
+       "1     QZ44356     Arizona              6979.535903       No  Extended   \n",
+       "2     AI49188      Nevada             12887.431650       No   Premium   \n",
+       "3     WW63253  California              7645.861827       No     Basic   \n",
+       "4     HB64268  Washington              2813.692575       No     Basic   \n",
+       "...       ...         ...                      ...      ...       ...   \n",
+       "9129  LA72316  California             23405.987980       No     Basic   \n",
+       "9130  PK87824  California              3096.511217      Yes  Extended   \n",
+       "9131  TD14365  California              8163.890428       No  Extended   \n",
+       "9132  UP19263  California              7524.442436       No  Extended   \n",
+       "9133  Y167826  California              2611.836866       No  Extended   \n",
+       "\n",
+       "     education effective_to_date employmentstatus gender  income  ...  \\\n",
+       "0     Bachelor           2/24/11         Employed      F   56274  ...   \n",
+       "1     Bachelor           1/31/11       Unemployed      F       0  ...   \n",
+       "2     Bachelor           2/19/11         Employed      F   48767  ...   \n",
+       "3     Bachelor           1/20/11       Unemployed      M       0  ...   \n",
+       "4     Bachelor            2/3/11         Employed      M   43836  ...   \n",
+       "...        ...               ...              ...    ...     ...  ...   \n",
+       "9129  Bachelor           2/10/11         Employed      M   71941  ...   \n",
+       "9130   College           2/12/11         Employed      F   21604  ...   \n",
+       "9131  Bachelor            2/6/11       Unemployed      M       0  ...   \n",
+       "9132   College            2/3/11         Employed      M   21941  ...   \n",
+       "9133   College           2/14/11       Unemployed      M       0  ...   \n",
+       "\n",
+       "     months_since_policy_inception number_of_open_complaints  \\\n",
+       "0                                5                         0   \n",
+       "1                               42                         0   \n",
+       "2                               38                         0   \n",
+       "3                               65                         0   \n",
+       "4                               44                         0   \n",
+       "...                            ...                       ...   \n",
+       "9129                            89                         0   \n",
+       "9130                            28                         0   \n",
+       "9131                            37                         3   \n",
+       "9132                             3                         0   \n",
+       "9133                            90                         0   \n",
+       "\n",
+       "      number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                      1  Corporate Auto  Corporate L3            Offer1   \n",
+       "1                      8   Personal Auto   Personal L3            Offer3   \n",
+       "2                      2   Personal Auto   Personal L3            Offer1   \n",
+       "3                      7  Corporate Auto  Corporate L2            Offer1   \n",
+       "4                      1   Personal Auto   Personal L1            Offer1   \n",
+       "...                  ...             ...           ...               ...   \n",
+       "9129                   2   Personal Auto   Personal L1            Offer2   \n",
+       "9130                   1  Corporate Auto  Corporate L3            Offer1   \n",
+       "9131                   2  Corporate Auto  Corporate L2            Offer1   \n",
+       "9132                   3   Personal Auto   Personal L2            Offer3   \n",
+       "9133                   1  Corporate Auto  Corporate L3            Offer4   \n",
+       "\n",
+       "      sales_channel total_claim_amount  vehicle_class vehicle_size  \n",
+       "0             Agent         384.811147   Two-Door Car      Medsize  \n",
+       "1             Agent        1131.464935  Four-Door Car      Medsize  \n",
+       "2             Agent         566.472247   Two-Door Car      Medsize  \n",
+       "3       Call Center         529.881344            SUV      Medsize  \n",
+       "4             Agent         138.130879  Four-Door Car      Medsize  \n",
+       "...             ...                ...            ...          ...  \n",
+       "9129            Web         198.234764  Four-Door Car      Medsize  \n",
+       "9130         Branch         379.200000  Four-Door Car      Medsize  \n",
+       "9131         Branch         790.784983  Four-Door Car      Medsize  \n",
+       "9132         Branch         691.200000  Four-Door Car        Large  \n",
+       "9133    Call Center         369.600000   Two-Door Car      Medsize  \n",
+       "\n",
+       "[9134 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(r\"C:\\Users\\Anderson\\Desktop\\ironhack\\Ironhack labs\\lab-customer-analysis-round-3\\lab-customer-analysis-round-3\\files_for_lab\\csv_files\\marketing_customer_analysis.csv\")\n",
+    "\n",
+    "cols = []\n",
+    "for i in range(len(df.columns)): \n",
+    "    cols.append(df.columns[i].lower().replace(' ', '_'))\n",
+    "df.columns = cols\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d26df6bf-d815-4b97-84ff-dff7990b24de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer                          object\n",
+       "state                             object\n",
+       "customer_lifetime_value          float64\n",
+       "response                          object\n",
+       "coverage                          object\n",
+       "education                         object\n",
+       "effective_to_date                 object\n",
+       "employmentstatus                  object\n",
+       "gender                            object\n",
+       "income                             int64\n",
+       "location_code                     object\n",
+       "marital_status                    object\n",
+       "monthly_premium_auto               int64\n",
+       "months_since_last_claim            int64\n",
+       "months_since_policy_inception      int64\n",
+       "number_of_open_complaints          int64\n",
+       "number_of_policies                 int64\n",
+       "policy_type                       object\n",
+       "policy                            object\n",
+       "renew_offer_type                  object\n",
+       "sales_channel                     object\n",
+       "total_claim_amount               float64\n",
+       "vehicle_class                     object\n",
+       "vehicle_size                      object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Show DataFrame info.\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "85bac783-43be-4879-8879-6b677272cdbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of      customer       state  customer_lifetime_value response  coverage  \\\n",
+       "0     BU79786  Washington              2763.519279       No     Basic   \n",
+       "1     QZ44356     Arizona              6979.535903       No  Extended   \n",
+       "2     AI49188      Nevada             12887.431650       No   Premium   \n",
+       "3     WW63253  California              7645.861827       No     Basic   \n",
+       "4     HB64268  Washington              2813.692575       No     Basic   \n",
+       "...       ...         ...                      ...      ...       ...   \n",
+       "9129  LA72316  California             23405.987980       No     Basic   \n",
+       "9130  PK87824  California              3096.511217      Yes  Extended   \n",
+       "9131  TD14365  California              8163.890428       No  Extended   \n",
+       "9132  UP19263  California              7524.442436       No  Extended   \n",
+       "9133  Y167826  California              2611.836866       No  Extended   \n",
+       "\n",
+       "     education effective_to_date employmentstatus gender  income  ...  \\\n",
+       "0     Bachelor           2/24/11         Employed      F   56274  ...   \n",
+       "1     Bachelor           1/31/11       Unemployed      F       0  ...   \n",
+       "2     Bachelor           2/19/11         Employed      F   48767  ...   \n",
+       "3     Bachelor           1/20/11       Unemployed      M       0  ...   \n",
+       "4     Bachelor            2/3/11         Employed      M   43836  ...   \n",
+       "...        ...               ...              ...    ...     ...  ...   \n",
+       "9129  Bachelor           2/10/11         Employed      M   71941  ...   \n",
+       "9130   College           2/12/11         Employed      F   21604  ...   \n",
+       "9131  Bachelor            2/6/11       Unemployed      M       0  ...   \n",
+       "9132   College            2/3/11         Employed      M   21941  ...   \n",
+       "9133   College           2/14/11       Unemployed      M       0  ...   \n",
+       "\n",
+       "     months_since_policy_inception number_of_open_complaints  \\\n",
+       "0                                5                         0   \n",
+       "1                               42                         0   \n",
+       "2                               38                         0   \n",
+       "3                               65                         0   \n",
+       "4                               44                         0   \n",
+       "...                            ...                       ...   \n",
+       "9129                            89                         0   \n",
+       "9130                            28                         0   \n",
+       "9131                            37                         3   \n",
+       "9132                             3                         0   \n",
+       "9133                            90                         0   \n",
+       "\n",
+       "      number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                      1  Corporate Auto  Corporate L3            Offer1   \n",
+       "1                      8   Personal Auto   Personal L3            Offer3   \n",
+       "2                      2   Personal Auto   Personal L3            Offer1   \n",
+       "3                      7  Corporate Auto  Corporate L2            Offer1   \n",
+       "4                      1   Personal Auto   Personal L1            Offer1   \n",
+       "...                  ...             ...           ...               ...   \n",
+       "9129                   2   Personal Auto   Personal L1            Offer2   \n",
+       "9130                   1  Corporate Auto  Corporate L3            Offer1   \n",
+       "9131                   2  Corporate Auto  Corporate L2            Offer1   \n",
+       "9132                   3   Personal Auto   Personal L2            Offer3   \n",
+       "9133                   1  Corporate Auto  Corporate L3            Offer4   \n",
+       "\n",
+       "      sales_channel total_claim_amount  vehicle_class vehicle_size  \n",
+       "0             Agent         384.811147   Two-Door Car      Medsize  \n",
+       "1             Agent        1131.464935  Four-Door Car      Medsize  \n",
+       "2             Agent         566.472247   Two-Door Car      Medsize  \n",
+       "3       Call Center         529.881344            SUV      Medsize  \n",
+       "4             Agent         138.130879  Four-Door Car      Medsize  \n",
+       "...             ...                ...            ...          ...  \n",
+       "9129            Web         198.234764  Four-Door Car      Medsize  \n",
+       "9130         Branch         379.200000  Four-Door Car      Medsize  \n",
+       "9131         Branch         790.784983  Four-Door Car      Medsize  \n",
+       "9132         Branch         691.200000  Four-Door Car        Large  \n",
+       "9133    Call Center         369.600000   Two-Door Car      Medsize  \n",
+       "\n",
+       "[9134 rows x 24 columns]>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Describe DataFrame.\n",
+    "df.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9536f6e3-cd89-4fb1-a5be-250461ac7a39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Show a plot of the total number of responses.\n",
+    "\n",
+    "plt.hist(df['response'])\n",
+    "\n",
+    "plt.xlabel('Response')\n",
+    "plt.ylabel('Total of response')\n",
+    "plt.title('Response plot')\n",
+    "\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "765e2969-50d4-456d-a516-591b12405273",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Show a plot of the response rate by the sales channel.\n",
+    "plot2 = df.groupby('sales_channel')['response'].value_counts(normalize=True).unstack().fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "eb43b70f-39d4-4773-b535-fb455cceeddb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot2.plot(kind='bar', stacked=True)\n",
+    "plt.title('Response rate by Sales channel')\n",
+    "plt.xlabel('sales_channel')\n",
+    "plt.ylabel('response')\n",
+    "plt.legend(title='results')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fcafec10-738a-4bd0-891a-f761638a43b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Show a plot of the response rate by the total claim amount.\n",
+    "plot3 = df.groupby('total_claim_amount')['response'].value_counts(normalize=True).unstack().fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "752d9fb5-fc84-4a8d-bd67-72208e691c05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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26YknnpCkShctAwAA1CZOBc+SJUu0du1ajR492sXjAAAAuJ5T1/D4+vqqW7durp4FAACgRjgVPFOnTtWKFStcPQsAAECNcOqU1qeffqqPP/5Yf/3rX/Xkk09Wumh527ZtLhkOAADAFZwKnkceeUSxsbGungUAAKBGOP3VEgAAAHUFH5UMAACM5/S3pW/ZskXvvfeesrOzVVZW5rDv2LFj1R4MAADAVZw6wvPWW29pzJgxatq0qY4fP67OnTsrODhYFy9eVP/+/V09IwAAQLU4FTwrV67U6tWrlZKSIh8fH82cOVOpqamaMmWKCgoKXD0jAABAtTgVPNnZ2erataskyc/PT0VFRZKkkSNH6p133nHddAAAAC7gVPCEhobq2rVrkqSIiAgdPnxYkpSVlSXLslw3HQAAgAs4FTy9evXS+++/L0mKj4/XtGnT1KdPHw0bNkxDhgxx6YAAAADV5dS7tFavXq2KigpJ0vjx49WoUSMdOHBAgwYN0vjx4106IAAAQHU5FTweHh7y8Pi/g0NxcXGKi4urtG7ChAlasGCBGjdu7PyEAAAA1VSjHzy4ceNGFRYW1uRTAAAA3FONBg8XMAMAgNqAr5YAAADGI3gAAIDxCB4AAGA8ggcAABivRoPn5z//uQIDA2vyKQAAAO7Jqc/hkaRbt27p5MmTysvLs38I4bcGDx4sSVq1alX1pgMAAHABp4Jn586devHFF/XVV19V2mez2VReXl7twQAAAFzFqVNakyZN0tChQ5WTk6OKigqHG7EDAABqG6eCJy8vT4mJiQoJCXH1PAAAAC7nVPD827/9m/bt2+fiUQAAAGqGU9fwpKSkaOjQodq/f7+ioqLk7e3tsH/KlCkuGQ4AAMAVnAqeTZs2adeuXfLz89O+fftks9ns+2w2G8EDAABqFaeC5ze/+Y0WLFigV155RR4efHYhAACo3ZyqlbKyMg0bNozYAQAAdYJTxTJq1Ci9++67rp4FAACgRjh1Squ8vFyLFi3Srl271L59+0oXLS9ZssQlwwEAALiCU8GTmZmpp556SpJ06tQph33/fAEzAABAbeBU8Ozdu9fVcwAAANQYrjoGAADGq/IRntjYWK1fv16BgYGKjY393rXbtm2r9mAAAACuUuXgCQoKsl+fExQUVGMDAQAAuFqVg2fdunV3/BkAAKC24xoeAABgPKfepSVJW7Zs0Xvvvafs7GyVlZU57Dt27Fi1BwMAAHAVp47wvPXWWxozZoyaNm2q48ePq3PnzgoODtbFixfVv39/V88IAABQLU4Fz8qVK7V69WqlpKTIx8dHM2fOVGpqqqZMmaKCggJXzwgAAFAtTgVPdna2unbtKkny8/NTUVGRJGnkyJF65513XDcdAACACzgVPKGhobp27ZokKSIiQocPH5YkZWVlybIs100HAADgAk4FT69evfT+++9LkuLj4zVt2jT16dNHw4YN05AhQ1w6IAAAQHU59S6t1atXq6KiQpI0fvx4NWrUSAcOHNCgQYM0fvx4lw4IAABQXU4Fj4eHhzw8/u/gUFxcnOLi4lw2FAAAgCtVOXhOnjxZ5Qdt3769U8MAAADUhCoHT8eOHWWz2e55UbLNZlN5eXm1BwMAAHCVKgdPVlZWTc4BAABQY6ocPBERETU5BwAAQI1x6m3pycnJWrt2baXta9eu1ZtvvlntoQAAAFzJqeD5/e9/rzZt2lTa/uSTT+rf//3fqz0UAACAKzkVPLm5uXr00UcrbW/SpIlycnKqPRQAAIArORU84eHhOnjwYKXtBw8eVFhYWJUfJzk5WT/60Y8UEBCgpk2b6mc/+5nOnTvnsMayLM2bN09hYWHy8/NTz549dfr0aYc1paWlmjx5sho3bqwGDRpo8ODBunz5sjMvDQAAGMip4HnppZeUkJCgdevW6csvv9SXX36ptWvXatq0aXr55Zer/DhpaWmaOHGiDh8+rNTUVP3jH/9Q3759dfPmTfuaRYsWacmSJUpJSdHRo0cVGhqqPn362L+wVJISEhK0fft2bd68WQcOHFBxcbEGDhzI2+MBAIAkJz9peebMmbp+/bomTJigsrIySVK9evU0a9YszZ49u8qPs3PnTof769atU9OmTZWRkaHu3bvLsiwtW7ZMc+bMUWxsrCRpw4YNCgkJ0aZNmzRu3DgVFBRozZo1+uMf/6iYmBhJ0saNGxUeHq49e/aoX79+zrxEAABgEKeO8NhsNr355pu6evWqDh8+rM8++0zXr1/X3LlzHdZdvnzZ/p1bVVFQUCBJatSokaRvPvsnNzdXffv2ta/x9fVVjx49dOjQIUlSRkaGbt++7bAmLCxMkZGR9jXfVVpaqsLCQocbAAAwl1PB8y1/f3/96Ec/UmRkpHx9fSvtb9eunb744osqPZZlWUpMTNSPf/xjRUZGSvrm4mhJCgkJcVgbEhJi35ebmysfHx81bNjwrmu+Kzk5WUFBQfZbeHh4lWYEAAB1U7WC517u9TUU/2zSpEk6efKk3nnnnUr7bDZbpcf97rY7Pffd1syePVsFBQX226VLl6o8JwAAqHtqNHiqavLkyfrLX/6ivXv3qlmzZvbtoaGhklTpSE1eXp79qE9oaKjKysqUn59/1zXf5evrq8DAQIcbAAAwl1uDx7IsTZo0Sdu2bdPHH3+sFi1aOOxv0aKFQkNDlZqaat9WVlamtLQ0de3aVZIUHR0tb29vhzU5OTk6deqUfQ0AAHi4OfUuLVeZOHGiNm3apD//+c8KCAiwH8kJCgqSn5+fbDabEhISlJSUpFatWqlVq1ZKSkpS/fr19cILL9jXxsfHa/r06QoODlajRo00Y8YMRUVF2d+1BQAAHm41Gjz3us5m1apVkqSePXs6bF+3bp1Gjx4t6Zu3wJeUlGjChAnKz8/XM888o927dysgIMC+funSpfLy8lJcXJxKSkrUu3dvrV+/Xp6eni59PQAAoG6q0eC510XLVbmo2Wazad68eZo3b95d19SrV08rVqzQihUr7ndEAADwEKjR4Dlz5sx9fdUEAABATahy8Hz7ScdVsW3bNkni820AAECtUOXgCQoKqsk5AAAAakyVg2fdunU1OQcAAECNqRUfPAgAAFCTnL5oecuWLXrvvfeUnZ1t/8b0bx07dqzagwEAALiKU0d43nrrLY0ZM0ZNmzbV8ePH1blzZwUHB+vixYvq37+/q2cEAACoFqeCZ+XKlVq9erVSUlLk4+OjmTNnKjU1VVOmTFFBQYGrZwQAAKgWp4InOzvb/j1Vfn5+KioqkiSNHDnyjt92DgAA4E5OBU9oaKiuXbsmSYqIiNDhw4clSVlZWVX69GQAAIAHyang6dWrl95//31JUnx8vKZNm6Y+ffpo2LBhGjJkiEsHBAAAqC6n3qW1evVqVVRUSJLGjx+vRo0a6cCBAxo0aJDGjx/v0gEBAACqy6nguXz5ssPXRsTFxSkuLk6WZenSpUtq3ry5ywYEAACoLqdOabVo0UJXr16ttP369etq0aJFtYcCAABwJaeCx7Is2Wy2StuLi4tVr169ag8FAADgSvd1SisxMVGSZLPZ9Oqrr6p+/fr2feXl5Tpy5Ig6duzo0gEBAACq676C5/jx45K+OcKTmZkpHx8f+z4fHx916NBBM2bMcO2EAAAA1XRfwbN3715J0pgxY7R8+XIFBgbWyFAAAACu5NS7tNatW2f/+fLly7LZbHrsscdcNhQAAIArOXXRckVFhRYsWKCgoCBFRESoefPmeuSRR/T666/bP58HAACgtnDqCM+cOXO0Zs0aLVy4UN26dZNlWTp48KDmzZunW7du6Y033nD1nAAAAE5zKng2bNig//iP/9DgwYPt2zp06KDHHntMEyZMIHgAAECt4tQprevXr6tNmzaVtrdp00bXr1+v9lAAAACu5FTwdOjQQSkpKZW2p6SkqEOHDtUeCgAAwJWcOqW1aNEiPffcc9qzZ4+6dOkim82mQ4cO6dKlS/rb3/7m6hkBAACqxenv0vr88881ZMgQ3bhxQ9evX1dsbKzOnTuniIgIV88IAABQLU4d4WnRooVycnIqXZx87do1hYeHq7y83CXDAQAAuILTXx56J3x5KAAAqI2c/vLQuXPn8uWhAACgTuDLQwEAgPH48lAAAGC8an95KAAAQG3n1EXLAAAAdQnBAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIzn1uD55JNPNGjQIIWFhclms2nHjh0O+y3L0rx58xQWFiY/Pz/17NlTp0+fdlhTWlqqyZMnq3HjxmrQoIEGDx6sy5cvP8BXAQAAaju3Bs/NmzfVoUMHpaSk3HH/okWLtGTJEqWkpOjo0aMKDQ1Vnz59VFRUZF+TkJCg7du3a/PmzTpw4ICKi4s1cOBAlZeXP6iXAQAAajkvdz55//791b9//zvusyxLy5Yt05w5cxQbGytJ2rBhg0JCQrRp0yaNGzdOBQUFWrNmjf74xz8qJiZGkrRx40aFh4drz5496tev3x0fu7S0VKWlpfb7hYWFLn5lAACgNqm11/BkZWUpNzdXffv2tW/z9fVVjx49dOjQIUlSRkaGbt++7bAmLCxMkZGR9jV3kpycrKCgIPstPDy85l4IAABwu1obPLm5uZKkkJAQh+0hISH2fbm5ufLx8VHDhg3vuuZOZs+erYKCAvvt0qVLLp4eAADUJm49pVUVNpvN4b5lWZW2fde91vj6+srX19cl8wEAgNqv1h7hCQ0NlaRKR2ry8vLsR31CQ0NVVlam/Pz8u64BAACotcHTokULhYaGKjU11b6trKxMaWlp6tq1qyQpOjpa3t7eDmtycnJ06tQp+xoAAAC3ntIqLi7W//zP/9jvZ2Vl6cSJE2rUqJGaN2+uhIQEJSUlqVWrVmrVqpWSkpJUv359vfDCC5KkoKAgxcfHa/r06QoODlajRo00Y8YMRUVF2d+1BQAA4NbgSU9P109+8hP7/cTEREnSqFGjtH79es2cOVMlJSWaMGGC8vPz9cwzz2j37t0KCAiw/87SpUvl5eWluLg4lZSUqHfv3lq/fr08PT0f+OsBAAC1k82yLMvdQ7hbYWGhgoKCVFBQoMDAwBp7nuhf/aHGHhuoqzJ+96K7R3CJ7AVR7h4BqHWaz82s0ce/n3+/a+01PAAAAK5C8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjGdM8KxcuVItWrRQvXr1FB0drf3797t7JAAAUEsYETzvvvuuEhISNGfOHB0/flzPPvus+vfvr+zsbHePBgAAagEjgmfJkiWKj4/XSy+9pLZt22rZsmUKDw/XqlWr3D0aAACoBbzcPUB1lZWVKSMjQ6+88orD9r59++rQoUN3/J3S0lKVlpba7xcUFEiSCgsLa25QSeWlJTX6+EBdVNN/7x6Uolvl7h4BqHVq+u/3t49vWdY919b54Pnqq69UXl6ukJAQh+0hISHKzc294+8kJydr/vz5lbaHh4fXyIwA7i5oxXh3jwCgpiQHPZCnKSoqUlDQ9z9XnQ+eb9lsNof7lmVV2vat2bNnKzEx0X6/oqJC169fV3Bw8F1/B+YoLCxUeHi4Ll26pMDAQHePA8CF+Pv9cLEsS0VFRQoLC7vn2jofPI0bN5anp2elozl5eXmVjvp8y9fXV76+vg7bHnnkkZoaEbVUYGAg/0MEDMXf74fHvY7sfKvOX7Ts4+Oj6OhopaamOmxPTU1V165d3TQVAACoTer8ER5JSkxM1MiRI9WpUyd16dJFq1evVnZ2tsaP59oAAABgSPAMGzZM165d04IFC5STk6PIyEj97W9/U0REhLtHQy3k6+ur1157rdJpTQB1H3+/cTc2qyrv5QIAAKjD6vw1PAAAAPdC8AAAAOMRPAAAwHgEDwAAMB7BAyONHj1aNptNCxcudNi+Y8cOPk0bqIMsy1JMTIz69etXad/KlSsVFBSk7OxsN0yGuoLggbHq1aunN998U/n5+e4eBUA12Ww2rVu3TkeOHNHvf/97+/asrCzNmjVLy5cvV/Pmzd04IWo7ggfGiomJUWhoqJKTk++6ZuvWrXryySfl6+urxx9/XIsXL36AEwK4H+Hh4Vq+fLlmzJihrKwsWZal+Ph49e7dW507d9aAAQPk7++vkJAQjRw5Ul999ZX9d7ds2aKoqCj5+fkpODhYMTExunnzphtfDR40ggfG8vT0VFJSklasWKHLly9X2p+RkaG4uDgNHz5cmZmZmjdvnl599VWtX7/+wQ8LoEpGjRql3r17a8yYMUpJSdGpU6e0fPly9ejRQx07dlR6erp27typK1euKC4uTpKUk5Oj559/XmPHjtXZs2e1b98+xcbGio+he7jwwYMw0ujRo3Xjxg3t2LFDXbp0Ubt27bRmzRrt2LFDQ4YMkWVZGjFihK5evardu3fbf2/mzJn64IMPdPr0aTdOD+D75OXlKTIyUteuXdOWLVt0/PhxHTlyRLt27bKvuXz5ssLDw3Xu3DkVFxcrOjpaX3zxBZ/A/xDjCA+M9+abb2rDhg06c+aMw/azZ8+qW7duDtu6deum8+fPq7y8/EGOCOA+NG3aVL/4xS/Utm1bDRkyRBkZGdq7d6/8/f3ttzZt2kiSLly4oA4dOqh3796KiorS0KFD9fbbb3Nt30OI4IHxunfvrn79+unXv/61w3bLsiq9Y4sDnkDd4OXlJS+vb74OsqKiQoMGDdKJEyccbufPn1f37t3l6emp1NRUffjhh2rXrp1WrFihJ554QllZWW5+FXiQjPjyUOBeFi5cqI4dO6p169b2be3atdOBAwcc1h06dEitW7eWp6fngx4RgJOefvppbd26VY8//rg9gr7LZrOpW7du6tatm+bOnauIiAht375diYmJD3hauAtHePBQiIqK0ogRI7RixQr7tunTp+ujjz7S66+/rs8//1wbNmxQSkqKZsyY4cZJAdyviRMn6vr163r++ef16aef6uLFi9q9e7fGjh2r8vJyHTlyRElJSUpPT1d2dra2bdumq1evqm3btu4eHQ8QwYOHxuuvv+5wyurpp5/We++9p82bNysyMlJz587VggULNHr0aPcNCeC+hYWF6eDBgyovL1e/fv0UGRmpqVOnKigoSB4eHgoMDNQnn3yiAQMGqHXr1vrNb36jxYsXq3///u4eHQ8Q79ICAADG4wgPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAHCbsrIyd48A4CFB8AB4YHr27KlJkyYpMTFRjRs3Vp8+fXTmzBkNGDBA/v7+CgkJ0ciRI/XVV1/Zf2fLli2KioqSn5+fgoODFRMTo5s3b0qSRo8erZ/97GeaP3++mjZtqsDAQI0bN84hpEpLSzVlyhQ1bdpU9erV049//GMdPXrUvn/fvn2y2Wz66KOP1KlTJ9WvX19du3bVuXPn7Gs+++wz/eQnP1FAQIACAwMVHR2t9PR0+/5Dhw6pe/fu8vPzU3h4uKZMmWKfEUDtQPAAeKA2bNggLy8vHTx4UAsXLlSPHj3UsWNHpaena+fOnbpy5Yri4uIkSTk5OXr++ec1duxYnT17Vvv27VNsbKzDt95/9NFHOnv2rPbu3at33nlH27dv1/z58+37Z86cqa1bt2rDhg06duyYWrZsqX79+un69esOc82ZM0eLFy9Wenq6vLy8NHbsWPu+ESNGqFmzZjp69KgyMjL0yiuvyNvbW5KUmZmpfv36KTY2VidPntS7776rAwcOaNKkSTX5xwjgflkA8ID06NHD6tixo/3+q6++avXt29dhzaVLlyxJ1rlz56yMjAxLkvXFF1/c8fFGjRplNWrUyLp586Z926pVqyx/f3+rvLzcKi4utry9va3//M//tO8vKyuzwsLCrEWLFlmWZVl79+61JFl79uyxr/nggw8sSVZJSYllWZYVEBBgrV+//o4zjBw50vrFL37hsG3//v2Wh4eH/fcBuB9HeAA8UJ06dbL/nJGRob1798rf399+a9OmjSTpwoUL6tChg3r37q2oqCgNHTpUb7/9tvLz8x0er0OHDqpfv779fpcuXVRcXKxLly7pwoULun37trp162bf7+3trc6dO+vs2bMOj9O+fXv7z48++qgkKS8vT5KUmJiol156STExMVq4cKEuXLjg8BrWr1/v8Br69euniooKZWVlVfePC4CLEDwAHqgGDRrYf66oqNCgQYN04sQJh9v58+fVvXt3eXp6KjU1VR9++KHatWunFStW6IknnqhSSNhsNvupL5vN5rDPsqxK2749RfXP6ysqKiRJ8+bN0+nTp/Xcc8/p448/Vrt27bR9+3b7mnHjxjnM/9lnn+n8+fP64Q9/6MSfEICaQPAAcJunn35ap0+f1uOPP66WLVs63L4NI5vNpm7dumn+/Pk6fvy4fHx87LEhfXNBcUlJif3+4cOH5e/vr2bNmqlly5by8fHRgQMH7Ptv376t9PR0tW3b9r5mbd26taZNm6bdu3crNjZW69atc3gN353/2+cGUDsQPADcZuLEibp+/bqef/55ffrpp7p48aJ2796tsWPHqry8XEeOHFFSUpLS09OVnZ2tbdu26erVqw6xUlZWpvj4eJ05c0YffvihXnvtNU2aNEkeHh5q0KCBfvnLX+pXv/qVdu7cqTNnzujll1/W119/rfj4+CrNWFJSokmTJmnfvn368ssvdfDgQR09etQ+w6xZs/Rf//Vfmjhxov3o1F/+8hdNnjy5Rv7MADjHy90DAHh4hYWF6eDBg5o1a5b69eun0tJSRURE6Kc//ak8PDwUGBioTz75RMuWLVNhYaEiIiK0ePFi9e/f3/4YvXv3VqtWrdS9e3eVlpZq+PDhmjdvnn3/woULVVFRoZEjR6qoqEidOnXSrl271LBhwyrN6OnpqWvXrunFF1/UlStX1LhxY8XGxtrfCda+fXulpaVpzpw5evbZZ2VZln74wx9q2LBhLv2zAlA9Nsv6p/d3AkAdMnr0aN24cUM7duxw9ygAajlOaQEAAOMRPAAAwHic0gIAAMbjCA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeP8PYg+F6wgfydwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df['response'], y=df['total_claim_amount'], data=df, estimator='mean') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "99916099-d608-4eba-858c-88287886b09c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Show a plot of the response rate by income."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "752ba4b3-5cad-4171-99d3-55b047a0f525",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df['response'], y=df['income'], data=df, estimator='mean') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5b95464a-6bd1-46c6-bb70-fb82ea092eec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###Lab Customer Analysis Round 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e2a69424-63fc-4fff-bbf3-3a7260a22b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Check the data types of the columns. \n",
+    "#Get the numeric data into dataframe called numerical and categorical columns in a dataframe called categoricals. \n",
+    "#(You can use np.number and np.object to select the numerical data types and categorical data types respectively)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e6a9a635-f0fe-4f66-9b99-96476739421f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer                          object\n",
+       "state                             object\n",
+       "customer_lifetime_value          float64\n",
+       "response                          object\n",
+       "coverage                          object\n",
+       "education                         object\n",
+       "effective_to_date                 object\n",
+       "employmentstatus                  object\n",
+       "gender                            object\n",
+       "income                             int64\n",
+       "location_code                     object\n",
+       "marital_status                    object\n",
+       "monthly_premium_auto               int64\n",
+       "months_since_last_claim            int64\n",
+       "months_since_policy_inception      int64\n",
+       "number_of_open_complaints          int64\n",
+       "number_of_policies                 int64\n",
+       "policy_type                       object\n",
+       "policy                            object\n",
+       "renew_offer_type                  object\n",
+       "sales_channel                     object\n",
+       "total_claim_amount               float64\n",
+       "vehicle_class                     object\n",
+       "vehicle_size                      object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6418722e-f06c-4574-84b3-5b61bd8b713a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>months_since_last_claim</th>\n",
+       "      <th>months_since_policy_inception</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2763.519279</td>\n",
+       "      <td>56274</td>\n",
+       "      <td>69</td>\n",
+       "      <td>32</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>384.811147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6979.535903</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>13</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12887.431650</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>108</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7645.861827</td>\n",
+       "      <td>0</td>\n",
+       "      <td>106</td>\n",
+       "      <td>18</td>\n",
+       "      <td>65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2813.692575</td>\n",
+       "      <td>43836</td>\n",
+       "      <td>73</td>\n",
+       "      <td>12</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>138.130879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9129</th>\n",
+       "      <td>23405.987980</td>\n",
+       "      <td>71941</td>\n",
+       "      <td>73</td>\n",
+       "      <td>18</td>\n",
+       "      <td>89</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>198.234764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9130</th>\n",
+       "      <td>3096.511217</td>\n",
+       "      <td>21604</td>\n",
+       "      <td>79</td>\n",
+       "      <td>14</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>379.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9131</th>\n",
+       "      <td>8163.890428</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85</td>\n",
+       "      <td>9</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>790.784983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9132</th>\n",
+       "      <td>7524.442436</td>\n",
+       "      <td>21941</td>\n",
+       "      <td>96</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>691.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9133</th>\n",
+       "      <td>2611.836866</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77</td>\n",
+       "      <td>3</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>369.600000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9134 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      customer_lifetime_value  income  monthly_premium_auto  \\\n",
+       "0                 2763.519279   56274                    69   \n",
+       "1                 6979.535903       0                    94   \n",
+       "2                12887.431650   48767                   108   \n",
+       "3                 7645.861827       0                   106   \n",
+       "4                 2813.692575   43836                    73   \n",
+       "...                       ...     ...                   ...   \n",
+       "9129             23405.987980   71941                    73   \n",
+       "9130              3096.511217   21604                    79   \n",
+       "9131              8163.890428       0                    85   \n",
+       "9132              7524.442436   21941                    96   \n",
+       "9133              2611.836866       0                    77   \n",
+       "\n",
+       "      months_since_last_claim  months_since_policy_inception  \\\n",
+       "0                          32                              5   \n",
+       "1                          13                             42   \n",
+       "2                          18                             38   \n",
+       "3                          18                             65   \n",
+       "4                          12                             44   \n",
+       "...                       ...                            ...   \n",
+       "9129                       18                             89   \n",
+       "9130                       14                             28   \n",
+       "9131                        9                             37   \n",
+       "9132                       34                              3   \n",
+       "9133                        3                             90   \n",
+       "\n",
+       "      number_of_open_complaints  number_of_policies  total_claim_amount  \n",
+       "0                             0                   1          384.811147  \n",
+       "1                             0                   8         1131.464935  \n",
+       "2                             0                   2          566.472247  \n",
+       "3                             0                   7          529.881344  \n",
+       "4                             0                   1          138.130879  \n",
+       "...                         ...                 ...                 ...  \n",
+       "9129                          0                   2          198.234764  \n",
+       "9130                          0                   1          379.200000  \n",
+       "9131                          3                   2          790.784983  \n",
+       "9132                          0                   3          691.200000  \n",
+       "9133                          0                   1          369.600000  \n",
+       "\n",
+       "[9134 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical = df.select_dtypes(include=np.number)\n",
+    "numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b197f37-efb7-4eb4-8f1f-b13c1d27943d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b82cad75-f119-4ca9-9251-10525361f811",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>location_code</th>\n",
+       "      <th>marital_status</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BU79786</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/24/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/31/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Single</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/19/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/20/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HB64268</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/3/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Single</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9129</th>\n",
+       "      <td>LA72316</td>\n",
+       "      <td>California</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/10/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9130</th>\n",
+       "      <td>PK87824</td>\n",
+       "      <td>California</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/12/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9131</th>\n",
+       "      <td>TD14365</td>\n",
+       "      <td>California</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/6/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Single</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9132</th>\n",
+       "      <td>UP19263</td>\n",
+       "      <td>California</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/3/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Large</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9133</th>\n",
+       "      <td>Y167826</td>\n",
+       "      <td>California</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/14/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Suburban</td>\n",
+       "      <td>Single</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9134 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer       state response  coverage education effective_to_date  \\\n",
+       "0     BU79786  Washington       No     Basic  Bachelor           2/24/11   \n",
+       "1     QZ44356     Arizona       No  Extended  Bachelor           1/31/11   \n",
+       "2     AI49188      Nevada       No   Premium  Bachelor           2/19/11   \n",
+       "3     WW63253  California       No     Basic  Bachelor           1/20/11   \n",
+       "4     HB64268  Washington       No     Basic  Bachelor            2/3/11   \n",
+       "...       ...         ...      ...       ...       ...               ...   \n",
+       "9129  LA72316  California       No     Basic  Bachelor           2/10/11   \n",
+       "9130  PK87824  California      Yes  Extended   College           2/12/11   \n",
+       "9131  TD14365  California       No  Extended  Bachelor            2/6/11   \n",
+       "9132  UP19263  California       No  Extended   College            2/3/11   \n",
+       "9133  Y167826  California       No  Extended   College           2/14/11   \n",
+       "\n",
+       "     employmentstatus gender location_code marital_status     policy_type  \\\n",
+       "0            Employed      F      Suburban        Married  Corporate Auto   \n",
+       "1          Unemployed      F      Suburban         Single   Personal Auto   \n",
+       "2            Employed      F      Suburban        Married   Personal Auto   \n",
+       "3          Unemployed      M      Suburban        Married  Corporate Auto   \n",
+       "4            Employed      M         Rural         Single   Personal Auto   \n",
+       "...               ...    ...           ...            ...             ...   \n",
+       "9129         Employed      M         Urban        Married   Personal Auto   \n",
+       "9130         Employed      F      Suburban       Divorced  Corporate Auto   \n",
+       "9131       Unemployed      M      Suburban         Single  Corporate Auto   \n",
+       "9132         Employed      M      Suburban        Married   Personal Auto   \n",
+       "9133       Unemployed      M      Suburban         Single  Corporate Auto   \n",
+       "\n",
+       "            policy renew_offer_type sales_channel  vehicle_class vehicle_size  \n",
+       "0     Corporate L3           Offer1         Agent   Two-Door Car      Medsize  \n",
+       "1      Personal L3           Offer3         Agent  Four-Door Car      Medsize  \n",
+       "2      Personal L3           Offer1         Agent   Two-Door Car      Medsize  \n",
+       "3     Corporate L2           Offer1   Call Center            SUV      Medsize  \n",
+       "4      Personal L1           Offer1         Agent  Four-Door Car      Medsize  \n",
+       "...            ...              ...           ...            ...          ...  \n",
+       "9129   Personal L1           Offer2           Web  Four-Door Car      Medsize  \n",
+       "9130  Corporate L3           Offer1        Branch  Four-Door Car      Medsize  \n",
+       "9131  Corporate L2           Offer1        Branch  Four-Door Car      Medsize  \n",
+       "9132   Personal L2           Offer3        Branch  Four-Door Car        Large  \n",
+       "9133  Corporate L3           Offer4   Call Center   Two-Door Car      Medsize  \n",
+       "\n",
+       "[9134 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categoricals = df.select_dtypes(include='object')\n",
+    "categoricals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "77c9badd-877e-46b7-a97a-b14f8671663f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Now we will try to check the normality of the numerical variables visually\n",
+    "#Use seaborn library to construct distribution plots for the numerical variables\n",
+    "#Use Matplotlib to construct histograms\n",
+    "#Do the distributions for different numerical variables look like a normal distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "94d0cbe5-e31b-4d3a-abe6-e8b6656fa810",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: seaborn in c:\\users\\anderson\\anaconda3\\lib\\site-packages (0.12.2)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\anderson\\anaconda3\\lib\\site-packages (3.8.0)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from seaborn) (1.26.4)\n",
+      "Requirement already satisfied: pandas>=0.25 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from seaborn) (2.1.4)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from pandas>=0.25->seaborn) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\anderson\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "acc11e5a-76a6-41df-bad9-9d3f6e9ce72e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "for column in numerical.columns:\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    sns.distplot(numerical[column]).set_title(column)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6cc1a7db-8f6f-452a-925a-6c6b8b743920",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for column in numerical.columns:\n",
+    "    plt.figure(figsize=(10, 6)) \n",
+    "    plt.hist(numerical[column], bins=20)\n",
+    "    plt.title(column)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a91cc9e9-79cd-4684-8940-b8d4560b574d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistics=0.9216634035110474, p-value=0.0\n",
+      "Sample does not look Gaussian (reject H0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import shapiro\n",
+    "\n",
+    "column_data = numerical['income']\n",
+    "stat, p = shapiro(column_data)\n",
+    "print(f'Statistics={stat}, p-value={p}')\n",
+    "\n",
+    "alpha = 0.05\n",
+    "if p > alpha:\n",
+    "    print('Sample looks Gaussian (fail to reject H0)')\n",
+    "else:\n",
+    "    print('Sample does not look Gaussian (reject H0)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "20576a09-31b8-4ca2-a1ed-7328e004c82f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>months_since_last_claim</th>\n",
+       "      <th>months_since_policy_inception</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.024366</td>\n",
+       "      <td>0.396262</td>\n",
+       "      <td>0.011517</td>\n",
+       "      <td>0.009418</td>\n",
+       "      <td>-0.036343</td>\n",
+       "      <td>0.021955</td>\n",
+       "      <td>0.226451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>income</th>\n",
+       "      <td>0.024366</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.016665</td>\n",
+       "      <td>-0.026715</td>\n",
+       "      <td>-0.000875</td>\n",
+       "      <td>0.006408</td>\n",
+       "      <td>-0.008656</td>\n",
+       "      <td>-0.355254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <td>0.396262</td>\n",
+       "      <td>-0.016665</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.005026</td>\n",
+       "      <td>0.020257</td>\n",
+       "      <td>-0.013122</td>\n",
+       "      <td>-0.011233</td>\n",
+       "      <td>0.632017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>months_since_last_claim</th>\n",
+       "      <td>0.011517</td>\n",
+       "      <td>-0.026715</td>\n",
+       "      <td>0.005026</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.042959</td>\n",
+       "      <td>0.005354</td>\n",
+       "      <td>0.009136</td>\n",
+       "      <td>0.007563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>months_since_policy_inception</th>\n",
+       "      <td>0.009418</td>\n",
+       "      <td>-0.000875</td>\n",
+       "      <td>0.020257</td>\n",
+       "      <td>-0.042959</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.001158</td>\n",
+       "      <td>-0.013333</td>\n",
+       "      <td>0.003335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <td>-0.036343</td>\n",
+       "      <td>0.006408</td>\n",
+       "      <td>-0.013122</td>\n",
+       "      <td>0.005354</td>\n",
+       "      <td>-0.001158</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.001498</td>\n",
+       "      <td>-0.014241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <td>0.021955</td>\n",
+       "      <td>-0.008656</td>\n",
+       "      <td>-0.011233</td>\n",
+       "      <td>0.009136</td>\n",
+       "      <td>-0.013333</td>\n",
+       "      <td>0.001498</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.002354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <td>0.226451</td>\n",
+       "      <td>-0.355254</td>\n",
+       "      <td>0.632017</td>\n",
+       "      <td>0.007563</td>\n",
+       "      <td>0.003335</td>\n",
+       "      <td>-0.014241</td>\n",
+       "      <td>-0.002354</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               customer_lifetime_value    income  \\\n",
+       "customer_lifetime_value                       1.000000  0.024366   \n",
+       "income                                        0.024366  1.000000   \n",
+       "monthly_premium_auto                          0.396262 -0.016665   \n",
+       "months_since_last_claim                       0.011517 -0.026715   \n",
+       "months_since_policy_inception                 0.009418 -0.000875   \n",
+       "number_of_open_complaints                    -0.036343  0.006408   \n",
+       "number_of_policies                            0.021955 -0.008656   \n",
+       "total_claim_amount                            0.226451 -0.355254   \n",
+       "\n",
+       "                               monthly_premium_auto  months_since_last_claim  \\\n",
+       "customer_lifetime_value                    0.396262                 0.011517   \n",
+       "income                                    -0.016665                -0.026715   \n",
+       "monthly_premium_auto                       1.000000                 0.005026   \n",
+       "months_since_last_claim                    0.005026                 1.000000   \n",
+       "months_since_policy_inception              0.020257                -0.042959   \n",
+       "number_of_open_complaints                 -0.013122                 0.005354   \n",
+       "number_of_policies                        -0.011233                 0.009136   \n",
+       "total_claim_amount                         0.632017                 0.007563   \n",
+       "\n",
+       "                               months_since_policy_inception  \\\n",
+       "customer_lifetime_value                             0.009418   \n",
+       "income                                             -0.000875   \n",
+       "monthly_premium_auto                                0.020257   \n",
+       "months_since_last_claim                            -0.042959   \n",
+       "months_since_policy_inception                       1.000000   \n",
+       "number_of_open_complaints                          -0.001158   \n",
+       "number_of_policies                                 -0.013333   \n",
+       "total_claim_amount                                  0.003335   \n",
+       "\n",
+       "                               number_of_open_complaints  number_of_policies  \\\n",
+       "customer_lifetime_value                        -0.036343            0.021955   \n",
+       "income                                          0.006408           -0.008656   \n",
+       "monthly_premium_auto                           -0.013122           -0.011233   \n",
+       "months_since_last_claim                         0.005354            0.009136   \n",
+       "months_since_policy_inception                  -0.001158           -0.013333   \n",
+       "number_of_open_complaints                       1.000000            0.001498   \n",
+       "number_of_policies                              0.001498            1.000000   \n",
+       "total_claim_amount                             -0.014241           -0.002354   \n",
+       "\n",
+       "                               total_claim_amount  \n",
+       "customer_lifetime_value                  0.226451  \n",
+       "income                                  -0.355254  \n",
+       "monthly_premium_auto                     0.632017  \n",
+       "months_since_last_claim                  0.007563  \n",
+       "months_since_policy_inception            0.003335  \n",
+       "number_of_open_complaints               -0.014241  \n",
+       "number_of_policies                      -0.002354  \n",
+       "total_claim_amount                       1.000000  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlations = numerical.corr()\n",
+    "correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6b771f55-0574-4e80-9424-52d9f8619964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#no collumns were dropped, no correlation higher then 0.9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "066adf30-1e8c-40ec-a3c1-def4b11adb92",
+   "metadata": {},
+   "source": [
+    "# Lab Customer Analysis Round 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0c985109-854a-4aca-b72b-052d4e12e633",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#normalize(numerical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3a31e209-d47c-405e-b12b-72ba01617a11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_dataframe(customer, columns_to_normalize=None):\n",
+    "    if columns_to_normalize is None:\n",
+    "        \n",
+    "        numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns\n",
+    "        columns_to_normalize = list(numerical_columns)\n",
+    "    \n",
+    "    customer_normalized = customer.copy()\n",
+    "    \n",
+    "    scaler = MinMaxScaler()\n",
+    "    \n",
+    "    customer_normalized[columns_to_normalize] = scaler.fit_transform(customer_normalized[columns_to_normalize])\n",
+    "    \n",
+    "    return customer_normalized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "de1ebd5e-0d87-4458-9ca9-8da08ac937e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>    <td>total_claim_amount</td> <th>  R-squared:         </th>  <td>   1.000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th>  <td>   1.000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th>  <td>2.287e+34</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>              <td>Sat, 20 Apr 2024</td>  <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                  <td>14:32:27</td>      <th>  Log-Likelihood:    </th> <td>2.5491e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>       <td>  9134</td>       <th>  AIC:               </th> <td>-5.098e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>           <td>  9132</td>       <th>  BIC:               </th> <td>-5.098e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>               <td>     1</td>       <th>                     </th>      <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>      <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>              <td> 3.213e-13</td> <td> 3.45e-15</td> <td>   93.025</td> <td> 0.000</td> <td> 3.15e-13</td> <td> 3.28e-13</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>total_claim_amount</th> <td>    1.0000</td> <td> 6.61e-18</td> <td> 1.51e+17</td> <td> 0.000</td> <td>    1.000</td> <td>    1.000</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>3679.241</td> <th>  Durbin-Watson:     </th> <td>   1.002</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>22867.424</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 1.818</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td> 9.846</td>  <th>  Cond. No.          </th> <td>    939.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}       & total\\_claim\\_amount & \\textbf{  R-squared:         } &     1.000   \\\\\n",
+       "\\textbf{Model:}               &         OLS          & \\textbf{  Adj. R-squared:    } &     1.000   \\\\\n",
+       "\\textbf{Method:}              &    Least Squares     & \\textbf{  F-statistic:       } & 2.287e+34   \\\\\n",
+       "\\textbf{Date:}                &   Sat, 20 Apr 2024   & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}                &       14:32:27       & \\textbf{  Log-Likelihood:    } & 2.5491e+05  \\\\\n",
+       "\\textbf{No. Observations:}    &          9134        & \\textbf{  AIC:               } & -5.098e+05  \\\\\n",
+       "\\textbf{Df Residuals:}        &          9132        & \\textbf{  BIC:               } & -5.098e+05  \\\\\n",
+       "\\textbf{Df Model:}            &             1        & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}     &      nonrobust       & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                              & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}                &    3.213e-13  &     3.45e-15     &    93.025  &         0.000        &     3.15e-13    &     3.28e-13     \\\\\n",
+       "\\textbf{total\\_claim\\_amount} &       1.0000  &     6.61e-18     &  1.51e+17  &         0.000        &        1.000    &        1.000     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 3679.241 & \\textbf{  Durbin-Watson:     } &     1.002  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 22867.424  \\\\\n",
+       "\\textbf{Skew:}          &   1.818  & \\textbf{  Prob(JB):          } &      0.00  \\\\\n",
+       "\\textbf{Kurtosis:}      &   9.846  & \\textbf{  Cond. No.          } &      939.  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:     total_claim_amount   R-squared:                       1.000\n",
+       "Model:                            OLS   Adj. R-squared:                  1.000\n",
+       "Method:                 Least Squares   F-statistic:                 2.287e+34\n",
+       "Date:                Sat, 20 Apr 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        14:32:27   Log-Likelihood:             2.5491e+05\n",
+       "No. Observations:                9134   AIC:                        -5.098e+05\n",
+       "Df Residuals:                    9132   BIC:                        -5.098e+05\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "======================================================================================\n",
+       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "--------------------------------------------------------------------------------------\n",
+       "const               3.213e-13   3.45e-15     93.025      0.000    3.15e-13    3.28e-13\n",
+       "total_claim_amount     1.0000   6.61e-18   1.51e+17      0.000       1.000       1.000\n",
+       "==============================================================================\n",
+       "Omnibus:                     3679.241   Durbin-Watson:                   1.002\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            22867.424\n",
+       "Skew:                           1.818   Prob(JB):                         0.00\n",
+       "Kurtosis:                       9.846   Cond. No.                         939.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  X-y split.\n",
+    "\n",
+    "Y = df['total_claim_amount']\n",
+    "X = df['total_claim_amount']\n",
+    "\n",
+    "X = sm.add_constant(X)\n",
+    "\n",
+    "\n",
+    "model = sm.OLS(Y,X).fit()\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d24ca6b-dc81-47de-b882-54f33166658c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f9b6d1-6634-4839-85e9-0b700f54c8fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d35ea8ba-8100-4983-bb80-31a79866d28a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d80c040-5113-4794-abf4-c3d1b9a01d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2801f5a-7118-448b-b654-9910b5c6e43e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "537a7e56-973e-4963-ac13-969d4e7232dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00cf3735-7eb9-42ec-ae6c-d17fda680c70",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02e1419a-7ac6-4d36-b6ba-bc381e838dcf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "627027c9-e5fd-4d2d-bbbe-a5aa69fe8289",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "110c28cd-72a0-4966-b74d-02246ccb93dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56857c44-9415-4425-b575-38fa7203ed36",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cf6250e-2cb8-433f-96bd-357b860b649f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be76e779-5629-4ef6-acb3-e47858f8a3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+}