Churn_Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lP6JLo1tGNBg"
   },
   "source": [
    "# Artificial Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWZyYmS_UE_L"
   },
   "source": [
    "### Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "MxkJoQBkUIHC"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1E0Q3aoKUCRX"
   },
   "source": [
    "## Part 1 - Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cKWAkFVGUU0Z"
   },
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "OqvCSUP9s3jr"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Deep learning\\\\Neural network\\\\ANN'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "RpW8Lw0-s8S3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WinError 3] The system cannot find the path specified: '/content/drive/MyDrive/Training and Development /deep learning/Deep Learning/ANN'\n",
      "D:\\Deep learning\\Neural network\\ANN\n"
     ]
    }
   ],
   "source": [
    "cd \"/content/drive/MyDrive/Training and Development /deep learning/Deep Learning/ANN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "flJscr02s_jS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Deep learning\\\\Neural network\\\\ANN'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "cMox40uctrRR"
   },
   "outputs": [
    {
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       "  <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",
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       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
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       "      <td>502</td>\n",
       "      <td>France</td>\n",
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       "      <td>42</td>\n",
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       "      <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>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\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",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\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>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\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",
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       "      <td>15628319</td>\n",
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       "      <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 × 14 columns</p>\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",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \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",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \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",
       "...               ...     ...  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv(\"D:\\\\Deep learning\\\\Neural network\\\\ANN\\\\Churn_Modelling.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "nTcIJyyqAYvA"
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>619</td>\n",
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       "      <td>42</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>709</td>\n",
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       "      <td>772</td>\n",
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       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10 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  \n",
       "0             1               1        101348.88  \n",
       "1             0               1        112542.58  \n",
       "2             1               0        113931.57  \n",
       "3             0               0         93826.63  \n",
       "4             1               1         79084.10  \n",
       "...         ...             ...              ...  \n",
       "9995          1               0         96270.64  \n",
       "9996          1               1        101699.77  \n",
       "9997          0               1         42085.58  \n",
       "9998          1               0         92888.52  \n",
       "9999          1               0         38190.78  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dataset.drop([\"RowNumber\",\t\"CustomerId\",\t\"Surname\",\"Exited\"],axis=1)\n",
    "x\n",
    "y=dataset[\"Exited\"]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "44cMK6M2_Pm0"
   },
   "outputs": [],
   "source": [
    "# X = dataset.iloc[:, 3:-1].values\n",
    "# y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "VYP9cQTWbzuI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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  \n",
      "0             1               1        101348.88  \n",
      "1             0               1        112542.58  \n",
      "2             1               0        113931.57  \n",
      "3             0               0         93826.63  \n",
      "4             1               1         79084.10  \n",
      "...         ...             ...              ...  \n",
      "9995          1               0         96270.64  \n",
      "9996          1               1        101699.77  \n",
      "9997          0               1         42085.58  \n",
      "9998          1               0         92888.52  \n",
      "9999          1               0         38190.78  \n",
      "\n",
      "[10000 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "38vKGE6Nb2RR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       0\n",
      "2       1\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "9995    0\n",
      "9996    0\n",
      "9997    1\n",
      "9998    1\n",
      "9999    0\n",
      "Name: Exited, Length: 10000, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y)\n",
    "type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N6bQ0UgSU-NJ"
   },
   "source": [
    "### Encoding categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "le5MJreAbW52"
   },
   "source": [
    "Label Encoding the \"Gender\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "PxVKWXxLbczC"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "x[\"Gender\"] = le.fit_transform(x[\"Gender\"])\n",
    "x[\"Geography\"]=le.fit_transform(x[\"Geography\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "-M1KboxFb6OO"
   },
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10 columns</p>\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",
       "...           ...        ...     ...  ...     ...        ...            ...   \n",
       "9995          771          0       1   39       5       0.00              2   \n",
       "9996          516          0       1   35      10   57369.61              1   \n",
       "9997          709          0       0   36       7       0.00              1   \n",
       "9998          772          1       1   42       3   75075.31              2   \n",
       "9999          792          0       0   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0             1               1        101348.88  \n",
       "1             0               1        112542.58  \n",
       "2             1               0        113931.57  \n",
       "3             0               0         93826.63  \n",
       "4             1               1         79084.10  \n",
       "...         ...             ...              ...  \n",
       "9995          1               0         96270.64  \n",
       "9996          1               1        101699.77  \n",
       "9997          0               1         42085.58  \n",
       "9998          1               0         92888.52  \n",
       "9999          1               0         38190.78  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CUxGZezpbMcb"
   },
   "source": [
    "One Hot Encoding the \"Geography\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ydRX5VIf_Pm5"
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')\n",
    "x= np.array(ct.fit_transform(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "LiSFPI7e_Pm5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0000000e+00 0.0000000e+00 1.0000000e+00 6.0800000e+02 0.0000000e+00\n",
      " 4.1000000e+01 1.0000000e+00 8.3807860e+04 1.0000000e+00 0.0000000e+00\n",
      " 1.0000000e+00 1.1254258e+05]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vHol938cW8zd"
   },
   "source": [
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Z-TDt0Y_XEfc"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "gpUEKDTCEMZE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7000, 12)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RE_FcHyfV3TQ"
   },
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ViCrE00rV8Sk"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "MFVHgX8Go5Dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.0106279 ,  1.74067133, -0.57207153, ...,  0.64642486,\n",
       "        -1.02869732,  0.71252998],\n",
       "       [ 0.98948386, -0.57449099, -0.57207153, ...,  0.64642486,\n",
       "        -1.02869732,  0.11555262],\n",
       "       [ 0.98948386, -0.57449099, -0.57207153, ...,  0.64642486,\n",
       "        -1.02869732, -0.63701059],\n",
       "       ...,\n",
       "       [-1.0106279 ,  1.74067133, -0.57207153, ..., -1.54697021,\n",
       "        -1.02869732,  0.17080672],\n",
       "       [-1.0106279 ,  1.74067133, -0.57207153, ..., -1.54697021,\n",
       "         0.97210324, -0.24408572],\n",
       "       [ 0.98948386, -0.57449099, -0.57207153, ...,  0.64642486,\n",
       "         0.97210324,  0.09662703]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-zfEzkRVXIwF"
   },
   "source": [
    "## Part 2 - Building the ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KvdeScabXtlB"
   },
   "source": [
    "### Initializing the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "uu43CZm5Ci5s"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Activation, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "3dtrScHxXQox"
   },
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rP6urV6SX7kS"
   },
   "source": [
    "### Adding the input layer and the first hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "bppGycBXYCQr"
   },
   "outputs": [],
   "source": [
    "model.add(Dense(units=12,input_dim=12,activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BELWAc_8YJze"
   },
   "source": [
    "### Adding the second hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "JneR0u0sYRTd"
   },
   "outputs": [],
   "source": [
    "model.add(Dense(units=4,activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "82IzcmZy_Pm8"
   },
   "source": [
    "### Adding the third hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "di8iDkpd_Pm8"
   },
   "outputs": [],
   "source": [
    "model.add(Dense(units=2,activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OyNEe6RXYcU4"
   },
   "source": [
    "### Adding the output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Cn3x41RBYfvY"
   },
   "outputs": [],
   "source": [
    "model.add(Dense(units=1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JT4u2S1_Y4WG"
   },
   "source": [
    "## Part 3 - Training the ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MpYLtA1kA3Ff"
   },
   "source": [
    "### Summarize the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "hQ_iHyWp_Pm-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 12)                156       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 52        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 10        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 221 (884.00 Byte)\n",
      "Trainable params: 221 (884.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8GWlJChhY_ZI"
   },
   "source": [
    "### Compiling the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "fG3RrwDXZEaS"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['accuracy'])\n",
    "# ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0QR_G5u7ZLSM"
   },
   "source": [
    "### Training the ANN on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "nHZ-LKv_ZRb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "55/55 [==============================] - 2s 4ms/step - loss: 0.7464 - accuracy: 0.6020\n",
      "Epoch 2/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.6802 - accuracy: 0.7463\n",
      "Epoch 3/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.6594 - accuracy: 0.7851\n",
      "Epoch 4/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.6452 - accuracy: 0.7950\n",
      "Epoch 5/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.6329 - accuracy: 0.7971\n",
      "Epoch 6/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.6216 - accuracy: 0.7970\n",
      "Epoch 7/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.6111 - accuracy: 0.7971\n",
      "Epoch 8/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.6013 - accuracy: 0.7973\n",
      "Epoch 9/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5922 - accuracy: 0.7974\n",
      "Epoch 10/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5836 - accuracy: 0.7981\n",
      "Epoch 11/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.5755 - accuracy: 0.7990\n",
      "Epoch 12/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5676 - accuracy: 0.8003\n",
      "Epoch 13/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.5601 - accuracy: 0.8013\n",
      "Epoch 14/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5528 - accuracy: 0.8044\n",
      "Epoch 15/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.5458 - accuracy: 0.8063\n",
      "Epoch 16/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.5380 - accuracy: 0.8087\n",
      "Epoch 17/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5233 - accuracy: 0.8220\n",
      "Epoch 18/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.5135 - accuracy: 0.8289\n",
      "Epoch 19/20\n",
      "55/55 [==============================] - 0s 3ms/step - loss: 0.5059 - accuracy: 0.8301\n",
      "Epoch 20/20\n",
      "55/55 [==============================] - 0s 4ms/step - loss: 0.4991 - accuracy: 0.8293\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, batch_size = 128, epochs = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "XDAn8nXdKxBp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x21e42db4730>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "5vUpEFKeLI-I"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 3.3020067e-01,  3.0513754e-02, -5.1668173e-01, -1.4961587e-01,\n",
       "          1.4668445e-01, -2.6966879e-01, -7.0448160e-02, -1.0967846e-02,\n",
       "          2.7428165e-01,  9.0976462e-02,  3.5347709e-01, -2.8358454e-02],\n",
       "        [-4.3782735e-01,  1.1767892e-01, -1.4098930e-01,  4.0854129e-01,\n",
       "         -3.5738045e-01,  1.8776740e-01,  1.6244014e-01,  5.2211744e-01,\n",
       "         -3.2733485e-02,  6.5042579e-05,  3.7644681e-01, -3.1280375e-01],\n",
       "        [ 1.6259484e-01, -1.4213465e-01, -4.8283902e-01,  2.2753952e-01,\n",
       "          1.6741295e-01, -1.6512372e-01, -6.2667167e-01, -1.7866574e-01,\n",
       "          3.2326853e-01, -1.2858503e-01, -1.8817370e-01,  4.1984943e-01],\n",
       "        [-1.9388556e-01, -2.9061967e-01, -5.7688713e-01,  1.3728277e-01,\n",
       "          3.0265641e-01,  8.9636773e-02,  5.3511930e-01,  1.4793627e-01,\n",
       "          2.4949524e-01, -9.9091843e-02,  1.4572945e-02,  4.1693631e-01],\n",
       "        [-9.3222089e-02,  1.8205942e-01, -9.7897120e-02,  1.6340956e-01,\n",
       "         -3.2710922e-01,  3.7616678e-02, -1.7514171e-01, -2.1666265e-01,\n",
       "          2.9473844e-01,  1.6727430e-01, -2.5875807e-01,  2.6686320e-01],\n",
       "        [-6.8620312e-01, -1.5447320e-01, -2.7062383e-01,  2.9458436e-01,\n",
       "          4.7117326e-02, -1.7356507e-02,  6.7301102e-02,  8.3265352e-01,\n",
       "          6.9624895e-01,  9.9424340e-02, -6.3514337e-02,  1.4438914e-01],\n",
       "        [-5.4040354e-01,  6.1511405e-02,  2.6329908e-01, -1.6097429e-01,\n",
       "          6.7355871e-02,  3.7522927e-01, -2.8630143e-01, -5.5516575e-02,\n",
       "         -1.7854723e-01, -5.8418103e-02,  3.9871320e-01,  2.5297931e-01],\n",
       "        [-3.0657035e-01, -1.4937176e-01,  2.8208238e-01, -4.1478637e-01,\n",
       "         -1.8077491e-01,  1.4789857e-01,  4.0471846e-01,  1.3376391e-01,\n",
       "         -1.2277682e-02,  5.9955871e-01, -4.8330021e-01,  5.6690425e-01],\n",
       "        [ 1.9164713e-01,  3.1739613e-01, -2.1199217e-02,  7.3232430e-01,\n",
       "          3.4572609e-02, -2.2223109e-01,  1.2113570e-02, -3.0971444e-01,\n",
       "         -4.2107007e-01, -4.4509289e-01,  5.5492043e-01, -3.6210442e-01],\n",
       "        [-1.7779996e-01, -3.6004272e-01,  6.5210991e-02,  2.6141569e-01,\n",
       "          2.7536741e-01, -3.7530422e-01, -2.6048121e-01,  5.3572711e-03,\n",
       "          5.9657138e-02,  3.5763073e-01, -4.0038615e-02,  9.4952799e-02],\n",
       "        [ 1.1626187e-02,  3.3472714e-01, -1.8736883e-01,  2.5211579e-01,\n",
       "         -2.1199410e-01, -1.9083180e-02,  1.4237410e-01, -6.5636837e-01,\n",
       "         -7.1568489e-01,  2.2176249e-01,  4.1332695e-01, -2.0867117e-02],\n",
       "        [-3.2581627e-01,  6.5639280e-03, -2.4883261e-01,  8.9719640e-03,\n",
       "         -1.7329434e-01, -2.6612082e-01, -1.9962181e-01, -1.9000979e-02,\n",
       "          3.5930085e-01,  4.0700546e-01,  1.8569572e-01, -4.3623468e-01]],\n",
       "       dtype=float32),\n",
       " array([ 0.1962107 , -0.08620632, -0.05613145,  0.03585587, -0.10634839,\n",
       "        -0.06733645,  0.13414086, -0.06500506, -0.05971632,  0.03318596,\n",
       "         0.14631297,  0.13780905], dtype=float32),\n",
       " array([[-0.44643623, -0.1396592 , -0.25506824,  0.154688  ],\n",
       "        [-0.4696568 ,  0.3308473 ,  0.7410175 ,  0.150289  ],\n",
       "        [-0.5402781 ,  0.3965211 ,  0.36668128,  0.6251552 ],\n",
       "        [ 0.0920279 , -0.4234002 ,  0.24145433, -0.3769748 ],\n",
       "        [-0.596391  ,  0.34093976, -0.08454707, -0.36879134],\n",
       "        [ 0.34527332,  0.41964993,  0.65245247,  0.30212745],\n",
       "        [-0.17583847, -0.44283137,  0.41249824,  0.23323311],\n",
       "        [-0.32357523,  0.76853   , -0.12073862, -0.42854056],\n",
       "        [-0.32217056,  0.3845179 , -0.10907497, -0.5726297 ],\n",
       "        [-0.4537049 , -0.5184547 ,  0.11836473,  0.3789551 ],\n",
       "        [-0.26930988, -0.7168977 ,  0.5299528 ,  0.37012774],\n",
       "        [-0.20024458, -0.26145682,  0.58661383,  0.02473213]],\n",
       "       dtype=float32),\n",
       " array([ 0.        , -0.08765172,  0.16233304,  0.15861848], dtype=float32),\n",
       " array([[-0.8688929 ,  0.84912515],\n",
       "        [-0.9053061 ,  0.73424774],\n",
       "        [-0.35483503, -0.15472007],\n",
       "        [-0.87429166, -0.56582576]], dtype=float32),\n",
       " array([ 0.        , -0.08838606], dtype=float32),\n",
       " array([[-1.3524988],\n",
       "        [ 1.2148752]], dtype=float32),\n",
       " array([-0.86520106], dtype=float32)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tJj5k2MxZga3"
   },
   "source": [
    "## Part 4 - Making the predictions and evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84QFoqGYeXHL"
   },
   "source": [
    "### Predicting the result of a single observation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CGRo3eacgDdC"
   },
   "source": [
    "\n",
    "\n",
    "Use our ANN model to predict if the customer with the following informations will leave the bank:\n",
    "\n",
    "Credit Score: 600\n",
    "\n",
    "Geography: France\n",
    "\n",
    "Gender: Male\n",
    "\n",
    "Age: 40 years old\n",
    "\n",
    "Tenure: 3 years\n",
    "\n",
    "Balance: \\$ 60000\n",
    "\n",
    "Number of Products: 2\n",
    "\n",
    "Does this customer have a credit card ? Yes\n",
    "\n",
    "Is this customer an Active Member: Yes\n",
    "\n",
    "Estimated Salary: \\$ 50000\n",
    "\n",
    "So, should we say goodbye to that customer ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QJn053b_-9Is"
   },
   "outputs": [],
   "source": [
    "model.save('mymodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uNSnbz95_14r"
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model=load_model('mymodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZMx2JucW6Cwr"
   },
   "outputs": [],
   "source": [
    "print(model.predict(sc.transform([[1, 0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]]))> 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZhU1LTgPg-kH"
   },
   "source": [
    "**Solution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wGjx94g2n7OV"
   },
   "source": [
    "Therefore, our ANN model predicts that this customer stays in the bank!\n",
    "\n",
    "**Important note 1:** Notice that the values of the features were all input in a double pair of square brackets. That's because the \"predict\" method always expects a 2D array as the format of its inputs. And putting our values into a double pair of square brackets makes the input exactly a 2D array.\n",
    "\n",
    "**Important note 2:** Notice also that the \"France\" country was not input as a string in the last column but as \"1, 0, 0\" in the first three columns. That's because of course the predict method expects the one-hot-encoded values of the state, and as we see in the first row of the matrix of features X, \"France\" was encoded as \"1, 0, 0\". And be careful to include these values in the first three columns, because the dummy variables are always created in the first columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LmGPve7pCGSB"
   },
   "source": [
    "## Great Work!!"
   ]
  }
 ],
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