From 4be5248e533a4b8bdc9109ab750546f253a7e864 Mon Sep 17 00:00:00 2001
From: Maria Bourbon <141440929+mariabourbon@users.noreply.github.com>
Date: Fri, 24 Nov 2023 13:25:01 +0000
Subject: [PATCH] solved lab

---
 lab_imbalance.ipynb | 1606 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1606 insertions(+)
 create mode 100644 lab_imbalance.ipynb

diff --git a/lab_imbalance.ipynb b/lab_imbalance.ipynb
new file mode 100644
index 0000000..2d6c524
--- /dev/null
+++ b/lab_imbalance.ipynb
@@ -0,0 +1,1606 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inbalanced Classes\n",
+    "## In this lab, we are going to explore a case of imbalanced classes. \n",
+    "\n",
+    "\n",
+    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First, download the data from: https://www.kaggle.com/datasets/ealaxi/paysim1. Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
+    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>step</th>\n",
+       "      <th>type</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>C1231006815</td>\n",
+       "      <td>170136.0</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>M1979787155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>C1666544295</td>\n",
+       "      <td>21249.0</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>M2044282225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>TRANSFER</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>C1305486145</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C553264065</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   step      type   amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
+       "0     1   PAYMENT  9839.64  C1231006815       170136.0       160296.36   \n",
+       "1     1   PAYMENT  1864.28  C1666544295        21249.0        19384.72   \n",
+       "2     1  TRANSFER   181.00  C1305486145          181.0            0.00   \n",
+       "\n",
+       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       "0  M1979787155             0.0             0.0        0               0  \n",
+       "1  M2044282225             0.0             0.0        0               0  \n",
+       "2   C553264065             0.0             0.0        1               0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "data= pd.read_csv(\"PS_20174392719_1491204439457_log.csv\")\n",
+    "data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>step</th>\n",
+       "      <th>type</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3737323</th>\n",
+       "      <td>278</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>330218.42</td>\n",
+       "      <td>C632336343</td>\n",
+       "      <td>20866.00</td>\n",
+       "      <td>351084.42</td>\n",
+       "      <td>C834976624</td>\n",
+       "      <td>452419.57</td>\n",
+       "      <td>122201.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264914</th>\n",
+       "      <td>15</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>11647.08</td>\n",
+       "      <td>C1264712553</td>\n",
+       "      <td>30370.00</td>\n",
+       "      <td>18722.92</td>\n",
+       "      <td>M215391829</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85647</th>\n",
+       "      <td>10</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>152264.21</td>\n",
+       "      <td>C1746846248</td>\n",
+       "      <td>106589.00</td>\n",
+       "      <td>258853.21</td>\n",
+       "      <td>C1607284477</td>\n",
+       "      <td>201303.01</td>\n",
+       "      <td>49038.80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5899326</th>\n",
+       "      <td>403</td>\n",
+       "      <td>TRANSFER</td>\n",
+       "      <td>1551760.63</td>\n",
+       "      <td>C333676753</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C1564353608</td>\n",
+       "      <td>3198359.45</td>\n",
+       "      <td>4750120.08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544263</th>\n",
+       "      <td>206</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>78172.30</td>\n",
+       "      <td>C813403091</td>\n",
+       "      <td>2921331.58</td>\n",
+       "      <td>2999503.88</td>\n",
+       "      <td>C1091768874</td>\n",
+       "      <td>415821.90</td>\n",
+       "      <td>337649.60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3113342</th>\n",
+       "      <td>235</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>371042.71</td>\n",
+       "      <td>C1973614102</td>\n",
+       "      <td>1699365.85</td>\n",
+       "      <td>2070408.56</td>\n",
+       "      <td>C1358214518</td>\n",
+       "      <td>4301572.86</td>\n",
+       "      <td>3930530.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5156943</th>\n",
+       "      <td>357</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>19494.57</td>\n",
+       "      <td>C287170039</td>\n",
+       "      <td>1921089.11</td>\n",
+       "      <td>1940583.68</td>\n",
+       "      <td>C159882434</td>\n",
+       "      <td>75635.52</td>\n",
+       "      <td>56140.95</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5189870</th>\n",
+       "      <td>369</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>141519.24</td>\n",
+       "      <td>C387888280</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C999640037</td>\n",
+       "      <td>12853274.23</td>\n",
+       "      <td>12994793.47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1551677</th>\n",
+       "      <td>154</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>635412.68</td>\n",
+       "      <td>C1458432906</td>\n",
+       "      <td>58610.00</td>\n",
+       "      <td>694022.68</td>\n",
+       "      <td>C8292738</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6142173</th>\n",
+       "      <td>545</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>31617.57</td>\n",
+       "      <td>C1314607150</td>\n",
+       "      <td>10626.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1600734161</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
+       "3737323   278   CASH_IN   330218.42   C632336343       20866.00   \n",
+       "264914     15   PAYMENT    11647.08  C1264712553       30370.00   \n",
+       "85647      10   CASH_IN   152264.21  C1746846248      106589.00   \n",
+       "5899326   403  TRANSFER  1551760.63   C333676753           0.00   \n",
+       "2544263   206   CASH_IN    78172.30   C813403091     2921331.58   \n",
+       "...       ...       ...         ...          ...            ...   \n",
+       "3113342   235   CASH_IN   371042.71  C1973614102     1699365.85   \n",
+       "5156943   357   CASH_IN    19494.57   C287170039     1921089.11   \n",
+       "5189870   369  CASH_OUT   141519.24   C387888280           0.00   \n",
+       "1551677   154   CASH_IN   635412.68  C1458432906       58610.00   \n",
+       "6142173   545   PAYMENT    31617.57  C1314607150       10626.00   \n",
+       "\n",
+       "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
+       "3737323       351084.42   C834976624       452419.57       122201.15        0   \n",
+       "264914         18722.92   M215391829            0.00            0.00        0   \n",
+       "85647         258853.21  C1607284477       201303.01        49038.80        0   \n",
+       "5899326            0.00  C1564353608      3198359.45      4750120.08        0   \n",
+       "2544263      2999503.88  C1091768874       415821.90       337649.60        0   \n",
+       "...                 ...          ...             ...             ...      ...   \n",
+       "3113342      2070408.56  C1358214518      4301572.86      3930530.15        0   \n",
+       "5156943      1940583.68   C159882434        75635.52        56140.95        0   \n",
+       "5189870            0.00   C999640037     12853274.23     12994793.47        0   \n",
+       "1551677       694022.68     C8292738            0.00            0.00        0   \n",
+       "6142173            0.00  M1600734161            0.00            0.00        0   \n",
+       "\n",
+       "         isFlaggedFraud  \n",
+       "3737323               0  \n",
+       "264914                0  \n",
+       "85647                 0  \n",
+       "5899326               0  \n",
+       "2544263               0  \n",
+       "...                 ...  \n",
+       "3113342               0  \n",
+       "5156943               0  \n",
+       "5189870               0  \n",
+       "1551677               0  \n",
+       "6142173               0  \n",
+       "\n",
+       "[100000 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sample = data.sample(n=100000, random_state=42)\n",
+    "data_sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step                int64\n",
+       "type               object\n",
+       "amount            float64\n",
+       "nameOrig           object\n",
+       "oldbalanceOrg     float64\n",
+       "newbalanceOrig    float64\n",
+       "nameDest           object\n",
+       "oldbalanceDest    float64\n",
+       "newbalanceDest    float64\n",
+       "isFraud             int64\n",
+       "isFlaggedFraud      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sample.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"type\"].unique() #need to change it to numeric with dummies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>step</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "      <th>type_CASH_IN</th>\n",
+       "      <th>type_CASH_OUT</th>\n",
+       "      <th>type_DEBIT</th>\n",
+       "      <th>type_PAYMENT</th>\n",
+       "      <th>type_TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3737323</th>\n",
+       "      <td>278</td>\n",
+       "      <td>330218.42</td>\n",
+       "      <td>C632336343</td>\n",
+       "      <td>20866.00</td>\n",
+       "      <td>351084.42</td>\n",
+       "      <td>C834976624</td>\n",
+       "      <td>452419.57</td>\n",
+       "      <td>122201.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264914</th>\n",
+       "      <td>15</td>\n",
+       "      <td>11647.08</td>\n",
+       "      <td>C1264712553</td>\n",
+       "      <td>30370.00</td>\n",
+       "      <td>18722.92</td>\n",
+       "      <td>M215391829</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85647</th>\n",
+       "      <td>10</td>\n",
+       "      <td>152264.21</td>\n",
+       "      <td>C1746846248</td>\n",
+       "      <td>106589.00</td>\n",
+       "      <td>258853.21</td>\n",
+       "      <td>C1607284477</td>\n",
+       "      <td>201303.01</td>\n",
+       "      <td>49038.80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5899326</th>\n",
+       "      <td>403</td>\n",
+       "      <td>1551760.63</td>\n",
+       "      <td>C333676753</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C1564353608</td>\n",
+       "      <td>3198359.45</td>\n",
+       "      <td>4750120.08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544263</th>\n",
+       "      <td>206</td>\n",
+       "      <td>78172.30</td>\n",
+       "      <td>C813403091</td>\n",
+       "      <td>2921331.58</td>\n",
+       "      <td>2999503.88</td>\n",
+       "      <td>C1091768874</td>\n",
+       "      <td>415821.90</td>\n",
+       "      <td>337649.60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3113342</th>\n",
+       "      <td>235</td>\n",
+       "      <td>371042.71</td>\n",
+       "      <td>C1973614102</td>\n",
+       "      <td>1699365.85</td>\n",
+       "      <td>2070408.56</td>\n",
+       "      <td>C1358214518</td>\n",
+       "      <td>4301572.86</td>\n",
+       "      <td>3930530.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5156943</th>\n",
+       "      <td>357</td>\n",
+       "      <td>19494.57</td>\n",
+       "      <td>C287170039</td>\n",
+       "      <td>1921089.11</td>\n",
+       "      <td>1940583.68</td>\n",
+       "      <td>C159882434</td>\n",
+       "      <td>75635.52</td>\n",
+       "      <td>56140.95</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5189870</th>\n",
+       "      <td>369</td>\n",
+       "      <td>141519.24</td>\n",
+       "      <td>C387888280</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C999640037</td>\n",
+       "      <td>12853274.23</td>\n",
+       "      <td>12994793.47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1551677</th>\n",
+       "      <td>154</td>\n",
+       "      <td>635412.68</td>\n",
+       "      <td>C1458432906</td>\n",
+       "      <td>58610.00</td>\n",
+       "      <td>694022.68</td>\n",
+       "      <td>C8292738</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6142173</th>\n",
+       "      <td>545</td>\n",
+       "      <td>31617.57</td>\n",
+       "      <td>C1314607150</td>\n",
+       "      <td>10626.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1600734161</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         step      amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
+       "3737323   278   330218.42   C632336343       20866.00       351084.42   \n",
+       "264914     15    11647.08  C1264712553       30370.00        18722.92   \n",
+       "85647      10   152264.21  C1746846248      106589.00       258853.21   \n",
+       "5899326   403  1551760.63   C333676753           0.00            0.00   \n",
+       "2544263   206    78172.30   C813403091     2921331.58      2999503.88   \n",
+       "...       ...         ...          ...            ...             ...   \n",
+       "3113342   235   371042.71  C1973614102     1699365.85      2070408.56   \n",
+       "5156943   357    19494.57   C287170039     1921089.11      1940583.68   \n",
+       "5189870   369   141519.24   C387888280           0.00            0.00   \n",
+       "1551677   154   635412.68  C1458432906       58610.00       694022.68   \n",
+       "6142173   545    31617.57  C1314607150       10626.00            0.00   \n",
+       "\n",
+       "            nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \\\n",
+       "3737323   C834976624       452419.57       122201.15        0               0   \n",
+       "264914    M215391829            0.00            0.00        0               0   \n",
+       "85647    C1607284477       201303.01        49038.80        0               0   \n",
+       "5899326  C1564353608      3198359.45      4750120.08        0               0   \n",
+       "2544263  C1091768874       415821.90       337649.60        0               0   \n",
+       "...              ...             ...             ...      ...             ...   \n",
+       "3113342  C1358214518      4301572.86      3930530.15        0               0   \n",
+       "5156943   C159882434        75635.52        56140.95        0               0   \n",
+       "5189870   C999640037     12853274.23     12994793.47        0               0   \n",
+       "1551677     C8292738            0.00            0.00        0               0   \n",
+       "6142173  M1600734161            0.00            0.00        0               0   \n",
+       "\n",
+       "         type_CASH_IN  type_CASH_OUT  type_DEBIT  type_PAYMENT  type_TRANSFER  \n",
+       "3737323             1              0           0             0              0  \n",
+       "264914              0              0           0             1              0  \n",
+       "85647               1              0           0             0              0  \n",
+       "5899326             0              0           0             0              1  \n",
+       "2544263             1              0           0             0              0  \n",
+       "...               ...            ...         ...           ...            ...  \n",
+       "3113342             1              0           0             0              0  \n",
+       "5156943             1              0           0             0              0  \n",
+       "5189870             0              1           0             0              0  \n",
+       "1551677             1              0           0             0              0  \n",
+       "6142173             0              0           0             1              0  \n",
+       "\n",
+       "[100000 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dummies = pd.get_dummies(data_sample, columns=['type'], prefix='type',dtype=int)\n",
+    "data_dummies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>step</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "      <th>type_CASH_IN</th>\n",
+       "      <th>type_CASH_OUT</th>\n",
+       "      <th>type_DEBIT</th>\n",
+       "      <th>type_PAYMENT</th>\n",
+       "      <th>type_TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3737323</th>\n",
+       "      <td>278</td>\n",
+       "      <td>330218.42</td>\n",
+       "      <td>20866.00</td>\n",
+       "      <td>351084.42</td>\n",
+       "      <td>452419.57</td>\n",
+       "      <td>122201.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264914</th>\n",
+       "      <td>15</td>\n",
+       "      <td>11647.08</td>\n",
+       "      <td>30370.00</td>\n",
+       "      <td>18722.92</td>\n",
+       "      <td>0.00</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85647</th>\n",
+       "      <td>10</td>\n",
+       "      <td>152264.21</td>\n",
+       "      <td>106589.00</td>\n",
+       "      <td>258853.21</td>\n",
+       "      <td>201303.01</td>\n",
+       "      <td>49038.80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5899326</th>\n",
+       "      <td>403</td>\n",
+       "      <td>1551760.63</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3198359.45</td>\n",
+       "      <td>4750120.08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544263</th>\n",
+       "      <td>206</td>\n",
+       "      <td>78172.30</td>\n",
+       "      <td>2921331.58</td>\n",
+       "      <td>2999503.88</td>\n",
+       "      <td>415821.90</td>\n",
+       "      <td>337649.60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3113342</th>\n",
+       "      <td>235</td>\n",
+       "      <td>371042.71</td>\n",
+       "      <td>1699365.85</td>\n",
+       "      <td>2070408.56</td>\n",
+       "      <td>4301572.86</td>\n",
+       "      <td>3930530.15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5156943</th>\n",
+       "      <td>357</td>\n",
+       "      <td>19494.57</td>\n",
+       "      <td>1921089.11</td>\n",
+       "      <td>1940583.68</td>\n",
+       "      <td>75635.52</td>\n",
+       "      <td>56140.95</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5189870</th>\n",
+       "      <td>369</td>\n",
+       "      <td>141519.24</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>12853274.23</td>\n",
+       "      <td>12994793.47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1551677</th>\n",
+       "      <td>154</td>\n",
+       "      <td>635412.68</td>\n",
+       "      <td>58610.00</td>\n",
+       "      <td>694022.68</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6142173</th>\n",
+       "      <td>545</td>\n",
+       "      <td>31617.57</td>\n",
+       "      <td>10626.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         step      amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
+       "3737323   278   330218.42       20866.00       351084.42       452419.57   \n",
+       "264914     15    11647.08       30370.00        18722.92            0.00   \n",
+       "85647      10   152264.21      106589.00       258853.21       201303.01   \n",
+       "5899326   403  1551760.63           0.00            0.00      3198359.45   \n",
+       "2544263   206    78172.30     2921331.58      2999503.88       415821.90   \n",
+       "...       ...         ...            ...             ...             ...   \n",
+       "3113342   235   371042.71     1699365.85      2070408.56      4301572.86   \n",
+       "5156943   357    19494.57     1921089.11      1940583.68        75635.52   \n",
+       "5189870   369   141519.24           0.00            0.00     12853274.23   \n",
+       "1551677   154   635412.68       58610.00       694022.68            0.00   \n",
+       "6142173   545    31617.57       10626.00            0.00            0.00   \n",
+       "\n",
+       "         newbalanceDest  isFraud  isFlaggedFraud  type_CASH_IN  type_CASH_OUT  \\\n",
+       "3737323       122201.15        0               0             1              0   \n",
+       "264914             0.00        0               0             0              0   \n",
+       "85647          49038.80        0               0             1              0   \n",
+       "5899326      4750120.08        0               0             0              0   \n",
+       "2544263       337649.60        0               0             1              0   \n",
+       "...                 ...      ...             ...           ...            ...   \n",
+       "3113342      3930530.15        0               0             1              0   \n",
+       "5156943        56140.95        0               0             1              0   \n",
+       "5189870     12994793.47        0               0             0              1   \n",
+       "1551677            0.00        0               0             1              0   \n",
+       "6142173            0.00        0               0             0              0   \n",
+       "\n",
+       "         type_DEBIT  type_PAYMENT  type_TRANSFER  \n",
+       "3737323           0             0              0  \n",
+       "264914            0             1              0  \n",
+       "85647             0             0              0  \n",
+       "5899326           0             0              1  \n",
+       "2544263           0             0              0  \n",
+       "...             ...           ...            ...  \n",
+       "3113342           0             0              0  \n",
+       "5156943           0             0              0  \n",
+       "5189870           0             0              0  \n",
+       "1551677           0             0              0  \n",
+       "6142173           0             1              0  \n",
+       "\n",
+       "[100000 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#drop nameOrig and nameDest\n",
+    "data_dummies = data_dummies.drop([\"nameOrig\",\"nameDest\"], axis=1)\n",
+    "data_dummies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>step</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "      <th>type_CASH_IN</th>\n",
+       "      <th>type_CASH_OUT</th>\n",
+       "      <th>type_DEBIT</th>\n",
+       "      <th>type_PAYMENT</th>\n",
+       "      <th>type_TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.00000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>243.709070</td>\n",
+       "      <td>1.805811e+05</td>\n",
+       "      <td>8.366804e+05</td>\n",
+       "      <td>8.582234e+05</td>\n",
+       "      <td>1.104193e+06</td>\n",
+       "      <td>1.230055e+06</td>\n",
+       "      <td>0.001410</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>0.221410</td>\n",
+       "      <td>0.35334</td>\n",
+       "      <td>0.006120</td>\n",
+       "      <td>0.335640</td>\n",
+       "      <td>0.083490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>142.518613</td>\n",
+       "      <td>5.586699e+05</td>\n",
+       "      <td>2.901104e+06</td>\n",
+       "      <td>2.936799e+06</td>\n",
+       "      <td>3.223011e+06</td>\n",
+       "      <td>3.475326e+06</td>\n",
+       "      <td>0.037524</td>\n",
+       "      <td>0.003162</td>\n",
+       "      <td>0.415198</td>\n",
+       "      <td>0.47801</td>\n",
+       "      <td>0.077991</td>\n",
+       "      <td>0.472216</td>\n",
+       "      <td>0.276623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>9.200000e-01</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>156.000000</td>\n",
+       "      <td>1.350821e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>7.603086e+04</td>\n",
+       "      <td>1.393850e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.387482e+05</td>\n",
+       "      <td>2.185786e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335.000000</td>\n",
+       "      <td>2.091130e+05</td>\n",
+       "      <td>1.070771e+05</td>\n",
+       "      <td>1.464169e+05</td>\n",
+       "      <td>9.605963e+05</td>\n",
+       "      <td>1.126011e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>736.000000</td>\n",
+       "      <td>3.697390e+07</td>\n",
+       "      <td>3.359321e+07</td>\n",
+       "      <td>3.388709e+07</td>\n",
+       "      <td>2.362896e+08</td>\n",
+       "      <td>2.724047e+08</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       "count  100000.000000  1.000000e+05   1.000000e+05    1.000000e+05   \n",
+       "mean      243.709070  1.805811e+05   8.366804e+05    8.582234e+05   \n",
+       "std       142.518613  5.586699e+05   2.901104e+06    2.936799e+06   \n",
+       "min         1.000000  9.200000e-01   0.000000e+00    0.000000e+00   \n",
+       "25%       156.000000  1.350821e+04   0.000000e+00    0.000000e+00   \n",
+       "50%       240.000000  7.603086e+04   1.393850e+04    0.000000e+00   \n",
+       "75%       335.000000  2.091130e+05   1.070771e+05    1.464169e+05   \n",
+       "max       736.000000  3.697390e+07   3.359321e+07    3.388709e+07   \n",
+       "\n",
+       "       oldbalanceDest  newbalanceDest        isFraud  isFlaggedFraud  \\\n",
+       "count    1.000000e+05    1.000000e+05  100000.000000   100000.000000   \n",
+       "mean     1.104193e+06    1.230055e+06       0.001410        0.000010   \n",
+       "std      3.223011e+06    3.475326e+06       0.037524        0.003162   \n",
+       "min      0.000000e+00    0.000000e+00       0.000000        0.000000   \n",
+       "25%      0.000000e+00    0.000000e+00       0.000000        0.000000   \n",
+       "50%      1.387482e+05    2.185786e+05       0.000000        0.000000   \n",
+       "75%      9.605963e+05    1.126011e+06       0.000000        0.000000   \n",
+       "max      2.362896e+08    2.724047e+08       1.000000        1.000000   \n",
+       "\n",
+       "        type_CASH_IN  type_CASH_OUT     type_DEBIT   type_PAYMENT  \\\n",
+       "count  100000.000000   100000.00000  100000.000000  100000.000000   \n",
+       "mean        0.221410        0.35334       0.006120       0.335640   \n",
+       "std         0.415198        0.47801       0.077991       0.472216   \n",
+       "min         0.000000        0.00000       0.000000       0.000000   \n",
+       "25%         0.000000        0.00000       0.000000       0.000000   \n",
+       "50%         0.000000        0.00000       0.000000       0.000000   \n",
+       "75%         0.000000        1.00000       0.000000       1.000000   \n",
+       "max         1.000000        1.00000       1.000000       1.000000   \n",
+       "\n",
+       "       type_TRANSFER  \n",
+       "count  100000.000000  \n",
+       "mean        0.083490  \n",
+       "std         0.276623  \n",
+       "min         0.000000  \n",
+       "25%         0.000000  \n",
+       "50%         0.000000  \n",
+       "75%         0.000000  \n",
+       "max         1.000000  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dummies.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation_matrix = data_dummies.corr()\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\")\n",
+    "plt.title(\"Correlation Matrix\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\maria\\AppData\\Local\\Temp\\ipykernel_10216\\1652825981.py:2: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
+      "  pd.value_counts(data_dummies['isFraud']).plot(kind = 'bar')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='isFraud'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "pd.value_counts(data_dummies['isFraud']).plot(kind = 'bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean the dataset. How are you going to integrate the time variable? Do you think the step (integer) coding in which it is given is appropriate?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "#I already cleaned before, where i used dummies for Type and dropeped 'nameOrig','nameDest'."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "features = data_dummies.drop(labels='isFraud', axis=1)\n",
+    "target = data_dummies['isFraud']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size= 0.2)\n",
+    "\n",
+    "fraud_model= LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_model.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9984"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_model.score(X_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[19953,    16],\n",
+       "       [   16,    15]], dtype=int64)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = fraud_model.predict(X_test)\n",
+    "confusion_matrix(y_test, pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.99865"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_model.score(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[19968,     1],\n",
+       "       [   10,    21]], dtype=int64)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = knn.predict(X_test)\n",
+    "confusion_matrix(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.99945"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which model worked better and how do you know?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your response here\n",
+    "#The knn has more accuracy score(0.99865 > 0.99945).\n",
+    "#array([[19953,    16],\n",
+    "#      [   16,    15]] from Linear Rgression VS \n",
+    "#array([[19968,     1],\n",
+    "#      [   10,    21]] from knn\n",
+    "#If capturing as many positive fraud as possible, Linear Regression might be considered better, although it has a lower accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  }
+ ],
+ "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}