Bank Customer Chun Rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Logistics Regresiion for Modelling Churn for Bank Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries and modules.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the data as pandas data frame\n",
    "data = pd.read_csv(\"churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>15592531</td>\n",
       "      <td>Bartlett</td>\n",
       "      <td>822</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>15656148</td>\n",
       "      <td>Obinna</td>\n",
       "      <td>376</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>15792365</td>\n",
       "      <td>He</td>\n",
       "      <td>501</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>15592389</td>\n",
       "      <td>H?</td>\n",
       "      <td>684</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "5          6    15574012       Chu          645     Spain    Male   44   \n",
       "6          7    15592531  Bartlett          822    France    Male   50   \n",
       "7          8    15656148    Obinna          376   Germany  Female   29   \n",
       "8          9    15792365        He          501    France    Male   44   \n",
       "9         10    15592389        H?          684    France    Male   27   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "5       8  113755.78              2          1               0   \n",
       "6       7       0.00              2          1               1   \n",
       "7       4  115046.74              4          1               0   \n",
       "8       4  142051.07              2          0               1   \n",
       "9       2  134603.88              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  \n",
       "5        149756.71       1  \n",
       "6         10062.80       0  \n",
       "7        119346.88       1  \n",
       "8         74940.50       0  \n",
       "9         71725.73       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the first rows of the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows and variable\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Listing out the columns of the data \n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber            int64\n",
       "CustomerId           int64\n",
       "Surname             object\n",
       "CreditScore          int64\n",
       "Geography           object\n",
       "Gender              object\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "Exited               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data types of each variables\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for the present of missing data\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a69fe0048>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the missing data \n",
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicate data\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          10000\n",
       "CustomerId         10000\n",
       "Surname             2932\n",
       "CreditScore          460\n",
       "Geography              3\n",
       "Gender                 2\n",
       "Age                   70\n",
       "Tenure                11\n",
       "Balance             6382\n",
       "NumOfProducts          4\n",
       "HasCrCard              2\n",
       "IsActiveMember         2\n",
       "EstimatedSalary     9999\n",
       "Exited                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get unique count for each variable\n",
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6ab29a88>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical representation of the gender using bar chart\n",
    "data['Gender'].value_counts().plot(kind='bar',y='value_column',title='Frequency Distribution Of Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6a214fc8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Exited'].value_counts().plot(kind='bar',y='value_column',title='Frequency Distribution Of Exited')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6a514ac8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical representation of the geography using bar chart\n",
    "data['Geography'].value_counts().plot(kind='bar',y='value_column',title='Frequency Distribution Of Geography')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping non relevant variable for the modeling\n",
    "data.drop(['RowNumber', 'CustomerId', 'Surname'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>822</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>376</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>501</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>684</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "5          645     Spain    Male   44       8  113755.78              2   \n",
       "6          822    France    Male   50       7       0.00              2   \n",
       "7          376   Germany  Female   29       4  115046.74              4   \n",
       "8          501    France    Male   44       4  142051.07              2   \n",
       "9          684    France    Male   27       2  134603.88              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  \n",
       "5          1               0        149756.71       1  \n",
       "6          1               1         10062.80       0  \n",
       "7          1               0        119346.88       1  \n",
       "8          0               1         74940.50       0  \n",
       "9          1               1         71725.73       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data first ten rows\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
       "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
       "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
       "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
       "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
       "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
       "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
       "\n",
       "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
       "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
       "mean       0.70550        0.515100    100090.239881      0.203700  \n",
       "std        0.45584        0.499797     57510.492818      0.402769  \n",
       "min        0.00000        0.000000        11.580000      0.000000  \n",
       "25%        0.00000        0.000000     51002.110000      0.000000  \n",
       "50%        1.00000        1.000000    100193.915000      0.000000  \n",
       "75%        1.00000        1.000000    149388.247500      0.000000  \n",
       "max        1.00000        1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the descriptive analysis of the numerical variable \n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Geography</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5014</td>\n",
       "      <td>5457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Geography Gender\n",
       "count      10000  10000\n",
       "unique         3      2\n",
       "top       France   Male\n",
       "freq        5014   5457"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computing the descriptive analysis of the categorical variable\n",
    "data.describe(include=[object])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUTLIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6a595348>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Detection of outlier in age variable\n",
    "sns.boxplot(data[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6a514c88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Detection of outlier ib Tenure variable\n",
    "sns.boxplot(data[\"Tenure\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6aa6e508>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f8G80T4aN5x+Br9cn4QL4ckQ8WbfXsOfR7yrgvIjYTHNs+ndKKQ/Vz3+QJvjfLKX8eJy1bK/Xc1Od4/3AyRP7ikjj82xokpTER8CSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJ/h9PSo10Kv0alQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Detection of outlier in credit score variable\n",
    "sns.boxplot(data[\"CreditScore\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6aadb208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution for the age variable\n",
    "sns.distplot(data[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6ac89e08>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution for the credit score variable\n",
    "sns.distplot(data[\"CreditScore\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BIVARIATE ANALYSIS"
   ]
  },
  {
   "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>Exited</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3404</td>\n",
       "      <td>1139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4559</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Exited     0     1\n",
       "Gender            \n",
       "Female  3404  1139\n",
       "Male    4559   898"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabular frequency of Gender against Exit\n",
    "pd.crosstab(data[\"Gender\"],data[\"Exited\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Effect of Gender on Exited ')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Graphical representation of Gender against Exit\n",
    "cross_tab = pd.crosstab(data[\"Gender\"],data[\"Exited\"])\n",
    "cross_tab.plot.bar(rot=0)\n",
    "plt.title(\"Effect of Gender on Exited \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a6adfab48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation values between numerical variables\n",
    "correlation_matrix = data.corr().round(2)\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.heatmap(data=correlation_matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ENCODING THE CATEGORICAL COLUMNS INTO NUMERICAL COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting categorical columns to numerical columns\n",
    "encoder = LabelEncoder()\n",
    "data[\"Gender\"] = encoder.fit_transform(data[\"Gender\"])\n",
    "data[\"Geography\"] = encoder.fit_transform(data[\"Geography\"])"
   ]
  },
  {
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>645</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619          0       0   42       2       0.00              1   \n",
       "1          608          2       0   41       1   83807.86              1   \n",
       "2          502          0       0   42       8  159660.80              3   \n",
       "3          699          0       0   39       1       0.00              2   \n",
       "4          850          2       0   43       2  125510.82              1   \n",
       "5          645          2       1   44       8  113755.78              2   \n",
       "6          822          0       1   50       7       0.00              2   \n",
       "7          376          1       0   29       4  115046.74              4   \n",
       "8          501          0       1   44       4  142051.07              2   \n",
       "9          684          0       1   27       2  134603.88              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  \n",
       "5          1               0        149756.71       1  \n",
       "6          1               1         10062.80       0  \n",
       "7          1               0        119346.88       1  \n",
       "8          0               1         74940.50       0  \n",
       "9          1               1         71725.73       0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data first ten rows\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL BUILDING USING LOGISTICS REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning dependent and independent variables to our data\n",
    "x = data.drop([\"Exited\"], axis=1)\n",
    "y = data[\"Exited\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the data into training and testing data \n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.75, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardizing data\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "x_trained_scaler = scaler.transform(X_train)\n",
    "x_test_scaler = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building Logistics regression on data\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(x_trained_scaler,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making prediction using the logistics regression model\n",
    "prediction=logmodel.predict(x_test_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5749  215]\n",
      " [1235  301]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.96      0.89      5964\n",
      "           1       0.58      0.20      0.29      1536\n",
      "\n",
      "    accuracy                           0.81      7500\n",
      "   macro avg       0.70      0.58      0.59      7500\n",
      "weighted avg       0.77      0.81      0.77      7500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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