experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "import pickle\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1 - Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"Churn_Modelling.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Drop irrelevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>...</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>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\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",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France    Male   39       5       0.00              2   \n",
       "9996          516    France    Male   35      10   57369.61              1   \n",
       "9997          709    France  Female   36       7       0.00              1   \n",
       "9998          772   Germany    Male   42       3   75075.31              2   \n",
       "9999          792    France  Female   28       4  130142.79              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",
       "...         ...             ...              ...     ...  \n",
       "9995          1               0         96270.64       0  \n",
       "9996          1               1        101699.77       0  \n",
       "9997          0               1         42085.58       1  \n",
       "9998          1               0         92888.52       1  \n",
       "9999          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(['RowNumber','CustomerId','Surname'],axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label encoding the \"Gender\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>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>Spain</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>France</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>France</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>Spain</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>...</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>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France       0   42       2       0.00              1   \n",
       "1             608     Spain       0   41       1   83807.86              1   \n",
       "2             502    France       0   42       8  159660.80              3   \n",
       "3             699    France       0   39       1       0.00              2   \n",
       "4             850     Spain       0   43       2  125510.82              1   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France       1   39       5       0.00              2   \n",
       "9996          516    France       1   35      10   57369.61              1   \n",
       "9997          709    France       0   36       7       0.00              1   \n",
       "9998          772   Germany       1   42       3   75075.31              2   \n",
       "9999          792    France       0   28       4  130142.79              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",
       "...         ...             ...              ...     ...  \n",
       "9995          1               0         96270.64       0  \n",
       "9996          1               1        101699.77       0  \n",
       "9997          0               1         42085.58       1  \n",
       "9998          1               0         92888.52       1  \n",
       "9999          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder_gender=LabelEncoder()\n",
    "data['Gender']=label_encoder_gender.fit_transform(data['Gender'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One hot encoding 'Geography' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehot_encoder_geo=OneHotEncoder()\n",
    "geo_encoder=onehot_encoder_geo.fit_transform(data[['Geography']]).toarray()\n",
    "geo_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Geography_France', 'Geography_Germany', 'Geography_Spain'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_encoder_geo.get_feature_names_out(['Geography'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Geography_France  Geography_Germany  Geography_Spain\n",
       "0                  1.0                0.0              0.0\n",
       "1                  0.0                0.0              1.0\n",
       "2                  1.0                0.0              0.0\n",
       "3                  1.0                0.0              0.0\n",
       "4                  0.0                0.0              1.0\n",
       "...                ...                ...              ...\n",
       "9995               1.0                0.0              0.0\n",
       "9996               1.0                0.0              0.0\n",
       "9997               1.0                0.0              0.0\n",
       "9998               0.0                1.0              0.0\n",
       "9999               1.0                0.0              0.0\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_encoded_df=pd.DataFrame(geo_encoder,columns=onehot_encoder_geo.get_feature_names_out(['Geography']))\n",
    "geo_encoded_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Combine one hot encoder columns with the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619       0   42       2       0.00              1          1   \n",
       "1          608       0   41       1   83807.86              1          0   \n",
       "2          502       0   42       8  159660.80              3          1   \n",
       "3          699       0   39       1       0.00              2          0   \n",
       "4          850       0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1        101348.88       1               1.0   \n",
       "1               1        112542.58       0               0.0   \n",
       "2               0        113931.57       1               1.0   \n",
       "3               0         93826.63       0               1.0   \n",
       "4               1         79084.10       0               0.0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                0.0              0.0  \n",
       "1                0.0              1.0  \n",
       "2                0.0              0.0  \n",
       "3                0.0              0.0  \n",
       "4                0.0              1.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.concat([data.drop('Geography',axis=1),geo_encoded_df],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the encoders and scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('label_encoder_gender.pkl','wb') as file:\n",
    "    pickle.dump(label_encoder_gender,file)\n",
    "\n",
    "with open('onehot_encoder_geo.pkl','wb') as file:\n",
    "    pickle.dump(onehot_encoder_geo,file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>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",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619       0   42       2       0.00              1          1   \n",
       "1          608       0   41       1   83807.86              1          0   \n",
       "2          502       0   42       8  159660.80              3          1   \n",
       "3          699       0   39       1       0.00              2          0   \n",
       "4          850       0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1        101348.88       1               1.0   \n",
       "1               1        112542.58       0               0.0   \n",
       "2               0        113931.57       1               1.0   \n",
       "3               0         93826.63       0               1.0   \n",
       "4               1         79084.10       0               0.0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                0.0              0.0  \n",
       "1                0.0              1.0  \n",
       "2                0.0              0.0  \n",
       "3                0.0              0.0  \n",
       "4                0.0              1.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Divide the dataset into indepent and dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('Exited',axis=1)\n",
    "y=data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>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>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0             619       0   42       2       0.00              1          1   \n",
       "1             608       0   41       1   83807.86              1          0   \n",
       "2             502       0   42       8  159660.80              3          1   \n",
       "3             699       0   39       1       0.00              2          0   \n",
       "4             850       0   43       2  125510.82              1          1   \n",
       "...           ...     ...  ...     ...        ...            ...        ...   \n",
       "9995          771       1   39       5       0.00              2          1   \n",
       "9996          516       1   35      10   57369.61              1          1   \n",
       "9997          709       0   36       7       0.00              1          0   \n",
       "9998          772       1   42       3   75075.31              2          1   \n",
       "9999          792       0   28       4  130142.79              1          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "0                  1        101348.88               1.0                0.0   \n",
       "1                  1        112542.58               0.0                0.0   \n",
       "2                  0        113931.57               1.0                0.0   \n",
       "3                  0         93826.63               1.0                0.0   \n",
       "4                  1         79084.10               0.0                0.0   \n",
       "...              ...              ...               ...                ...   \n",
       "9995               0         96270.64               1.0                0.0   \n",
       "9996               1        101699.77               1.0                0.0   \n",
       "9997               1         42085.58               1.0                0.0   \n",
       "9998               0         92888.52               0.0                1.0   \n",
       "9999               0         38190.78               1.0                0.0   \n",
       "\n",
       "      Geography_Spain  \n",
       "0                 0.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 1.0  \n",
       "...               ...  \n",
       "9995              0.0  \n",
       "9996              0.0  \n",
       "9997              0.0  \n",
       "9998              0.0  \n",
       "9999              0.0  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the dataset into training set and tet set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split the data in training and tetsing sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>686</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>179093.26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>632</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>119624.60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>195978.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>114739.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>85891.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6087</th>\n",
       "      <td>561</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>135637.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>153080.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6669</th>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>142147.32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39488.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>768</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>69712.74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>69381.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>682</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>706.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>735</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92220.12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>667</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>190227.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>97508.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>697</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>147910.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53581.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "9254          686       1   32       6       0.00              2          1   \n",
       "1561          632       1   42       4  119624.60              2          1   \n",
       "1670          559       1   24       3  114739.92              1          1   \n",
       "6087          561       0   27       9  135637.00              1          1   \n",
       "6669          517       1   56       9  142147.32              1          0   \n",
       "...           ...     ...  ...     ...        ...            ...        ...   \n",
       "5734          768       1   54       8   69712.74              1          1   \n",
       "5191          682       0   58       1       0.00              1          1   \n",
       "5390          735       0   38       1       0.00              3          0   \n",
       "860           667       1   43       8  190227.46              1          1   \n",
       "7270          697       1   51       1  147910.30              1          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "9254               1        179093.26               1.0                0.0   \n",
       "1561               1        195978.86               0.0                1.0   \n",
       "1670               0         85891.02               0.0                0.0   \n",
       "6087               0        153080.40               1.0                0.0   \n",
       "6669               0         39488.04               1.0                0.0   \n",
       "...              ...              ...               ...                ...   \n",
       "5734               1         69381.05               1.0                0.0   \n",
       "5191               1           706.50               1.0                0.0   \n",
       "5390               0         92220.12               1.0                0.0   \n",
       "860                0         97508.04               1.0                0.0   \n",
       "7270               1         53581.14               0.0                1.0   \n",
       "\n",
       "      Geography_Spain  \n",
       "9254              0.0  \n",
       "1561              0.0  \n",
       "1670              1.0  \n",
       "6087              0.0  \n",
       "6669              0.0  \n",
       "...               ...  \n",
       "5734              0.0  \n",
       "5191              0.0  \n",
       "5390              0.0  \n",
       "860               0.0  \n",
       "7270              0.0  \n",
       "\n",
       "[8000 rows x 12 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.35649971,  0.91324755, -0.6557859 , ...,  1.00150113,\n",
       "        -0.57946723, -0.57638802],\n",
       "       [-0.20389777,  0.91324755,  0.29493847, ..., -0.99850112,\n",
       "         1.72572313, -0.57638802],\n",
       "       [-0.96147213,  0.91324755, -1.41636539, ..., -0.99850112,\n",
       "        -0.57946723,  1.73494238],\n",
       "       ...,\n",
       "       [ 0.86500853, -1.09499335, -0.08535128, ...,  1.00150113,\n",
       "        -0.57946723, -0.57638802],\n",
       "       [ 0.15932282,  0.91324755,  0.3900109 , ...,  1.00150113,\n",
       "        -0.57946723, -0.57638802],\n",
       "       [ 0.47065475,  0.91324755,  1.15059039, ..., -0.99850112,\n",
       "         1.72572313, -0.57638802]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_df = pd.DataFrame(X_test)\n",
    "\n",
    "# Save X_test to a CSV file\n",
    "X_test_df.to_csv('X_test.csv')\n",
    "\n",
    "# Save y_test to a CSV file\n",
    "y_test_df = pd.DataFrame(y_test, columns=['Exited'])\n",
    "y_test_df.to_csv('y_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scaler.pkl','wb') as file:\n",
    "    pickle.dump(scaler,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>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",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0             619       0   42       2       0.00              1          1   \n",
       "1             608       0   41       1   83807.86              1          0   \n",
       "2             502       0   42       8  159660.80              3          1   \n",
       "3             699       0   39       1       0.00              2          0   \n",
       "4             850       0   43       2  125510.82              1          1   \n",
       "...           ...     ...  ...     ...        ...            ...        ...   \n",
       "9995          771       1   39       5       0.00              2          1   \n",
       "9996          516       1   35      10   57369.61              1          1   \n",
       "9997          709       0   36       7       0.00              1          0   \n",
       "9998          772       1   42       3   75075.31              2          1   \n",
       "9999          792       0   28       4  130142.79              1          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0                  1        101348.88       1               1.0   \n",
       "1                  1        112542.58       0               0.0   \n",
       "2                  0        113931.57       1               1.0   \n",
       "3                  0         93826.63       0               1.0   \n",
       "4                  1         79084.10       0               0.0   \n",
       "...              ...              ...     ...               ...   \n",
       "9995               0         96270.64       0               1.0   \n",
       "9996               1        101699.77       0               1.0   \n",
       "9997               1         42085.58       1               1.0   \n",
       "9998               0         92888.52       1               0.0   \n",
       "9999               0         38190.78       0               1.0   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  \n",
       "0                   0.0              0.0  \n",
       "1                   0.0              1.0  \n",
       "2                   0.0              0.0  \n",
       "3                   0.0              0.0  \n",
       "4                   0.0              1.0  \n",
       "...                 ...              ...  \n",
       "9995                0.0              0.0  \n",
       "9996                0.0              0.0  \n",
       "9997                0.0              0.0  \n",
       "9998                1.0              0.0  \n",
       "9999                0.0              0.0  \n",
       "\n",
       "[10000 rows x 13 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2 - ANN Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping,TensorBoard\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train.shape[1],)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Build ANN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model=Sequential([\n",
    "    Dense(64,activation='relu',input_shape=(X_train.shape[1],)), ## Adding first hidden layer which is Connected wwith input layer\n",
    "    Dense(32,activation='relu'), ## Adding second hidden layer\n",
    "    Dense(1,activation='sigmoid')  ## output layer\n",
    "]\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">832</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │           \u001b[38;5;34m832\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m2,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m33\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,945</span> (11.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,945\u001b[0m (11.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,945</span> (11.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,945\u001b[0m (11.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.losses.losses.BinaryCrossentropy at 0x199341585d0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt=tf.keras.optimizers.Adam(learning_rate=0.01)\n",
    "loss=tf.keras.losses.BinaryCrossentropy()\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=opt,loss=loss,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorflow_callback=TensorBoard(log_dir=log_dir,histogram_freq=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Set up Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping_callback=EarlyStopping(monitor='val_loss',patience=10,restore_best_weights=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8143 - loss: 0.4374 - val_accuracy: 0.8605 - val_loss: 0.3593\n",
      "Epoch 2/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8559 - loss: 0.3526 - val_accuracy: 0.8540 - val_loss: 0.3593\n",
      "Epoch 3/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8637 - loss: 0.3364 - val_accuracy: 0.8545 - val_loss: 0.3480\n",
      "Epoch 4/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8566 - loss: 0.3433 - val_accuracy: 0.8600 - val_loss: 0.3521\n",
      "Epoch 5/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8719 - loss: 0.3325 - val_accuracy: 0.8545 - val_loss: 0.3472\n",
      "Epoch 6/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8614 - loss: 0.3305 - val_accuracy: 0.8575 - val_loss: 0.3463\n",
      "Epoch 7/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8654 - loss: 0.3281 - val_accuracy: 0.8640 - val_loss: 0.3406\n",
      "Epoch 8/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8666 - loss: 0.3199 - val_accuracy: 0.8600 - val_loss: 0.3438\n",
      "Epoch 9/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8683 - loss: 0.3210 - val_accuracy: 0.8595 - val_loss: 0.3500\n",
      "Epoch 10/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8676 - loss: 0.3239 - val_accuracy: 0.8620 - val_loss: 0.3429\n",
      "Epoch 11/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8642 - loss: 0.3233 - val_accuracy: 0.8610 - val_loss: 0.3449\n",
      "Epoch 12/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8687 - loss: 0.3154 - val_accuracy: 0.8605 - val_loss: 0.3462\n",
      "Epoch 13/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8700 - loss: 0.3120 - val_accuracy: 0.8625 - val_loss: 0.3544\n",
      "Epoch 14/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8709 - loss: 0.3169 - val_accuracy: 0.8645 - val_loss: 0.3462\n",
      "Epoch 15/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8643 - loss: 0.3236 - val_accuracy: 0.8620 - val_loss: 0.3429\n",
      "Epoch 16/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8758 - loss: 0.3062 - val_accuracy: 0.8585 - val_loss: 0.3520\n",
      "Epoch 17/100\n",
      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8702 - loss: 0.3097 - val_accuracy: 0.8575 - val_loss: 0.3641\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(\n",
    "    X_train,y_train,validation_data=(X_test,y_test),epochs=100,\n",
    "    callbacks=[tensorflow_callback,early_stopping_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Tensorboard Extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 32184), started 0:05:31 ago. (Use '!kill 32184' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-d646e3498d24051b\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-d646e3498d24051b\");\n",
       "          const url = new URL(\"http://localhost\");\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir logs/fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step  \n",
      "[[False]\n",
      " [False]\n",
      " [False]\n",
      " ...\n",
      " [False]\n",
      " [False]\n",
      " [False]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n",
    "\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix & Class Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[1564   43]\n",
      " [ 229  164]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.97      0.92      1607\n",
      "           1       0.79      0.42      0.55       393\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.83      0.70      0.73      2000\n",
      "weighted avg       0.86      0.86      0.85      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total correct predictions that customers stay in bank = 1564\n",
    "\n",
    "Total correct predictions that customers leave the bank = 164\n",
    "\n",
    "Total incorrect predictions that customers stay in the bank = 229\n",
    "\n",
    "Total incorrect predictions that customers leave in the bank = 43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting confusion matrix\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(conf_matrix, annot=True, xticklabels=['Did not Exit', 'Exit'], yticklabels=['Did not Exit', 'Exit'])\n",
    "plt.xlabel('Predicted values')\n",
    "plt.ylabel('Actual values')\n",
    "plt.title('Confusion Matrix for ANN model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for ANN model is: 86.4%\n"
     ]
    }
   ],
   "source": [
    "score = accuracy_score(y_pred,y_test)\n",
    "print('The accuracy for ANN model is: {}%'.format(score*100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}