CustomerChurnANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "2d813dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "ef7cfaa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/admin/Documents/datasets/customer_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "5a415b75",
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "152b0e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "e5db6b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop customerID column because it is useless\n",
    "df.drop('customerID',axis='columns',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "eb898b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "7fb52a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the rows which has totalCharges = \" \" Otherwise it will throw a error in pd.to_numeric()\n",
    "\n",
    "df1 = df[df.TotalCharges != ' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "97f6a5e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# converting totalCharges column data type from object to float\n",
    "df1 = df1.astype({'TotalCharges' : 'float'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "82b5d73a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "cd030147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 20)"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "d2ccfa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unique values for each column \n",
    "def print_unique_values_column(df):\n",
    "    for column in df: \n",
    "        print(f'{df[column].dtypes} : {column} : {df[column].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "24d004d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object : gender : ['Female' 'Male']\n",
      "int64 : SeniorCitizen : [0 1]\n",
      "object : Partner : ['Yes' 'No']\n",
      "object : Dependents : ['No' 'Yes']\n",
      "int64 : tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "object : PhoneService : ['No' 'Yes']\n",
      "object : MultipleLines : ['No phone service' 'No' 'Yes']\n",
      "object : InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "object : OnlineSecurity : ['No' 'Yes' 'No internet service']\n",
      "object : OnlineBackup : ['Yes' 'No' 'No internet service']\n",
      "object : DeviceProtection : ['No' 'Yes' 'No internet service']\n",
      "object : TechSupport : ['No' 'Yes' 'No internet service']\n",
      "object : StreamingTV : ['No' 'Yes' 'No internet service']\n",
      "object : StreamingMovies : ['No' 'Yes' 'No internet service']\n",
      "object : Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "object : PaperlessBilling : ['Yes' 'No']\n",
      "object : PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "float64 : MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "float64 : TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "object : Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_values_column(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "d3ba6c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us replace all the value of 'No Internet Service' and 'No Phone Service' with No\n",
    "\n",
    "df1.replace('No internet service','No',inplace=True)\n",
    "df1.replace('No phone service','No',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "3a9ec75f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object : gender : ['Female' 'Male']\n",
      "int64 : SeniorCitizen : [0 1]\n",
      "object : Partner : ['Yes' 'No']\n",
      "object : Dependents : ['No' 'Yes']\n",
      "int64 : tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "object : PhoneService : ['No' 'Yes']\n",
      "object : MultipleLines : ['No' 'Yes']\n",
      "object : InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "object : OnlineSecurity : ['No' 'Yes']\n",
      "object : OnlineBackup : ['Yes' 'No']\n",
      "object : DeviceProtection : ['No' 'Yes']\n",
      "object : TechSupport : ['No' 'Yes']\n",
      "object : StreamingTV : ['No' 'Yes']\n",
      "object : StreamingMovies : ['No' 'Yes']\n",
      "object : Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "object : PaperlessBilling : ['Yes' 'No']\n",
      "object : PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "float64 : MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "float64 : TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "object : Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_values_column(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "373e1c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting values of column which has Yes and No to 1 and 0 . \n",
    "\n",
    "yes_no_column = {'Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection',\n",
    "                'TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn'}\n",
    "\n",
    "for column in yes_no_column:\n",
    "    df1[column].replace({'Yes' : 1 , 'No' : 0},inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "2043c9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner               int64\n",
       "Dependents            int64\n",
       "tenure                int64\n",
       "PhoneService          int64\n",
       "MultipleLines         int64\n",
       "InternetService      object\n",
       "OnlineSecurity        int64\n",
       "OnlineBackup          int64\n",
       "DeviceProtection      int64\n",
       "TechSupport           int64\n",
       "StreamingTV           int64\n",
       "StreamingMovies       int64\n",
       "Contract             object\n",
       "PaperlessBilling      int64\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "6783a72f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object : gender : ['Female' 'Male']\n",
      "int64 : SeniorCitizen : [0 1]\n",
      "int64 : Partner : [1 0]\n",
      "int64 : Dependents : [0 1]\n",
      "int64 : tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "int64 : PhoneService : [0 1]\n",
      "int64 : MultipleLines : [0 1]\n",
      "object : InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "int64 : OnlineSecurity : [0 1]\n",
      "int64 : OnlineBackup : [1 0]\n",
      "int64 : DeviceProtection : [0 1]\n",
      "int64 : TechSupport : [0 1]\n",
      "int64 : StreamingTV : [0 1]\n",
      "int64 : StreamingMovies : [0 1]\n",
      "object : Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "int64 : PaperlessBilling : [1 0]\n",
      "object : PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "float64 : MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "float64 : TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "int64 : Churn : [0 1]\n"
     ]
    }
   ],
   "source": [
    "print_unique_values_column(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "1ec2f5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['gender'].replace({'Male' : 1 , 'Female' : 0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "28b966eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 : gender : [0 1]\n",
      "int64 : SeniorCitizen : [0 1]\n",
      "int64 : Partner : [1 0]\n",
      "int64 : Dependents : [0 1]\n",
      "int64 : tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "int64 : PhoneService : [0 1]\n",
      "int64 : MultipleLines : [0 1]\n",
      "object : InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "int64 : OnlineSecurity : [0 1]\n",
      "int64 : OnlineBackup : [1 0]\n",
      "int64 : DeviceProtection : [0 1]\n",
      "int64 : TechSupport : [0 1]\n",
      "int64 : StreamingTV : [0 1]\n",
      "int64 : StreamingMovies : [0 1]\n",
      "object : Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "int64 : PaperlessBilling : [1 0]\n",
      "object : PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "float64 : MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "float64 : TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "int64 : Churn : [0 1]\n"
     ]
    }
   ],
   "source": [
    "print_unique_values_column(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "704d6aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding for InternetService,Contract,PaymentMethod column\n",
    "df2 = pd.get_dummies(data=df1,columns=['InternetService','Contract','PaymentMethod'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "cfda6ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 : gender : [0 1]\n",
      "int64 : SeniorCitizen : [0 1]\n",
      "int64 : Partner : [1 0]\n",
      "int64 : Dependents : [0 1]\n",
      "int64 : tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "int64 : PhoneService : [0 1]\n",
      "int64 : MultipleLines : [0 1]\n",
      "int64 : OnlineSecurity : [0 1]\n",
      "int64 : OnlineBackup : [1 0]\n",
      "int64 : DeviceProtection : [0 1]\n",
      "int64 : TechSupport : [0 1]\n",
      "int64 : StreamingTV : [0 1]\n",
      "int64 : StreamingMovies : [0 1]\n",
      "int64 : PaperlessBilling : [1 0]\n",
      "float64 : MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "float64 : TotalCharges : [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "int64 : Churn : [0 1]\n",
      "uint8 : InternetService_DSL : [1 0]\n",
      "uint8 : InternetService_Fiber optic : [0 1]\n",
      "uint8 : InternetService_No : [0 1]\n",
      "uint8 : Contract_Month-to-month : [1 0]\n",
      "uint8 : Contract_One year : [0 1]\n",
      "uint8 : Contract_Two year : [0 1]\n",
      "uint8 : PaymentMethod_Bank transfer (automatic) : [0 1]\n",
      "uint8 : PaymentMethod_Credit card (automatic) : [0 1]\n",
      "uint8 : PaymentMethod_Electronic check : [1 0]\n",
      "uint8 : PaymentMethod_Mailed check : [0 1]\n"
     ]
    }
   ],
   "source": [
    "print_unique_values_column(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "e676f4f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling data \n",
    "\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "cols_to_scale = ['tenure','MonthlyCharges','TotalCharges']\n",
    "\n",
    "df2[cols_to_scale] = scaler.fit_transform(df2[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "7beccc9f",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.464789</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "0       0              0        1           0  0.000000             0   \n",
       "1       1              0        0           0  0.464789             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0              0               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "\n",
       "   InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                    1                            0                   0   \n",
       "1                    1                            0                   0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                        1                  0                  0   \n",
       "1                        0                  1                  0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "228eb406",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperating input and output\n",
    "\n",
    "x = df2.drop('Churn',axis='columns')\n",
    "y = df2['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "a8f0f8e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting test and train dataset\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "b39953c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5625, 26)"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "37cc7d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1407, 26)"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "5f0d5456",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_features = len(x_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "6cf60c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "b860a096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "176/176 [==============================] - 1s 2ms/step - loss: 0.5282 - accuracy: 0.7365\n",
      "Epoch 2/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4379 - accuracy: 0.7915\n",
      "Epoch 3/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4254 - accuracy: 0.7968\n",
      "Epoch 4/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4204 - accuracy: 0.7989\n",
      "Epoch 5/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4180 - accuracy: 0.8027\n",
      "Epoch 6/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4157 - accuracy: 0.8018\n",
      "Epoch 7/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4132 - accuracy: 0.8060\n",
      "Epoch 8/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4127 - accuracy: 0.8071\n",
      "Epoch 9/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4114 - accuracy: 0.8076\n",
      "Epoch 10/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4107 - accuracy: 0.8071\n",
      "Epoch 11/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4100 - accuracy: 0.8105\n",
      "Epoch 12/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4086 - accuracy: 0.8096\n",
      "Epoch 13/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4078 - accuracy: 0.8096\n",
      "Epoch 14/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4072 - accuracy: 0.8108\n",
      "Epoch 15/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4063 - accuracy: 0.8107\n",
      "Epoch 16/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4065 - accuracy: 0.8105\n",
      "Epoch 17/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4053 - accuracy: 0.8124\n",
      "Epoch 18/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4051 - accuracy: 0.8110\n",
      "Epoch 19/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4041 - accuracy: 0.8108\n",
      "Epoch 20/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4036 - accuracy: 0.8107\n",
      "Epoch 21/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4028 - accuracy: 0.8103\n",
      "Epoch 22/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4021 - accuracy: 0.8114\n",
      "Epoch 23/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4012 - accuracy: 0.8137\n",
      "Epoch 24/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4013 - accuracy: 0.8133\n",
      "Epoch 25/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4011 - accuracy: 0.8135\n",
      "Epoch 26/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.4001 - accuracy: 0.8116\n",
      "Epoch 27/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3996 - accuracy: 0.8119\n",
      "Epoch 28/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3990 - accuracy: 0.8123\n",
      "Epoch 29/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3989 - accuracy: 0.8124\n",
      "Epoch 30/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3983 - accuracy: 0.8105\n",
      "Epoch 31/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3980 - accuracy: 0.8151\n",
      "Epoch 32/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3973 - accuracy: 0.8116\n",
      "Epoch 33/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3965 - accuracy: 0.8128\n",
      "Epoch 34/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3963 - accuracy: 0.8126\n",
      "Epoch 35/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3952 - accuracy: 0.8149\n",
      "Epoch 36/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3947 - accuracy: 0.8153\n",
      "Epoch 37/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3945 - accuracy: 0.8155\n",
      "Epoch 38/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3940 - accuracy: 0.8149\n",
      "Epoch 39/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3931 - accuracy: 0.8148\n",
      "Epoch 40/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3932 - accuracy: 0.8135\n",
      "Epoch 41/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3927 - accuracy: 0.8133\n",
      "Epoch 42/1000\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3925 - accuracy: 0.8137\n",
      "Epoch 43/1000\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3918 - accuracy: 0.8148\n",
      "Epoch 44/1000\n",
      "176/176 [==============================] - 2s 9ms/step - loss: 0.3911 - accuracy: 0.8119\n",
      "Epoch 45/1000\n",
      "176/176 [==============================] - 1s 7ms/step - loss: 0.3908 - accuracy: 0.8146\n",
      "Epoch 46/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3912 - accuracy: 0.8151\n",
      "Epoch 47/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3902 - accuracy: 0.8137\n",
      "Epoch 48/1000\n",
      "176/176 [==============================] - 1s 4ms/step - loss: 0.3903 - accuracy: 0.8133\n",
      "Epoch 49/1000\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3899 - accuracy: 0.8139\n",
      "Epoch 50/1000\n",
      "176/176 [==============================] - 1s 4ms/step - loss: 0.3890 - accuracy: 0.8156\n",
      "Epoch 51/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3889 - accuracy: 0.8176\n",
      "Epoch 52/1000\n",
      "176/176 [==============================] - 1s 6ms/step - loss: 0.3885 - accuracy: 0.8181\n",
      "Epoch 53/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3884 - accuracy: 0.8156\n",
      "Epoch 54/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3877 - accuracy: 0.8167\n",
      "Epoch 55/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3880 - accuracy: 0.8172\n",
      "Epoch 56/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3871 - accuracy: 0.8156\n",
      "Epoch 57/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3874 - accuracy: 0.8158\n",
      "Epoch 58/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3866 - accuracy: 0.8180\n",
      "Epoch 59/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3862 - accuracy: 0.8183\n",
      "Epoch 60/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3858 - accuracy: 0.8178\n",
      "Epoch 61/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3854 - accuracy: 0.8196\n",
      "Epoch 62/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3849 - accuracy: 0.8162\n",
      "Epoch 63/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3856 - accuracy: 0.8164\n",
      "Epoch 64/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3849 - accuracy: 0.8172\n",
      "Epoch 65/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3842 - accuracy: 0.8203\n",
      "Epoch 66/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3846 - accuracy: 0.8169\n",
      "Epoch 67/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3837 - accuracy: 0.8172\n",
      "Epoch 68/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3836 - accuracy: 0.8174\n",
      "Epoch 69/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3842 - accuracy: 0.8172\n",
      "Epoch 70/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3831 - accuracy: 0.8172\n",
      "Epoch 71/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3829 - accuracy: 0.8178\n",
      "Epoch 72/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3827 - accuracy: 0.8158\n",
      "Epoch 73/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3821 - accuracy: 0.8176\n",
      "Epoch 74/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3822 - accuracy: 0.8190\n",
      "Epoch 75/1000\n",
      "176/176 [==============================] - 1s 4ms/step - loss: 0.3822 - accuracy: 0.8172\n",
      "Epoch 76/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3820 - accuracy: 0.8176\n",
      "Epoch 77/1000\n",
      "176/176 [==============================] - 1s 5ms/step - loss: 0.3817 - accuracy: 0.8178\n",
      "Epoch 78/1000\n",
      "176/176 [==============================] - 1s 7ms/step - loss: 0.3817 - accuracy: 0.8196\n",
      "Epoch 79/1000\n",
      "176/176 [==============================] - 1s 4ms/step - loss: 0.3808 - accuracy: 0.8160\n",
      "Epoch 80/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3803 - accuracy: 0.8192\n",
      "Epoch 81/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3805 - accuracy: 0.8188\n",
      "Epoch 82/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3802 - accuracy: 0.8185\n",
      "Epoch 83/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3801 - accuracy: 0.8178\n",
      "Epoch 84/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3793 - accuracy: 0.8199\n",
      "Epoch 85/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3797 - accuracy: 0.8197\n",
      "Epoch 86/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3799 - accuracy: 0.8208\n",
      "Epoch 87/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3789 - accuracy: 0.8181\n",
      "Epoch 88/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3795 - accuracy: 0.8208\n",
      "Epoch 89/1000\n",
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     ]
    },
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    },
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    },
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     ]
    },
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      "Epoch 956/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3440 - accuracy: 0.8340\n",
      "Epoch 957/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3452 - accuracy: 0.8340\n",
      "Epoch 958/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8350\n",
      "Epoch 959/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3437 - accuracy: 0.8334\n",
      "Epoch 960/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3436 - accuracy: 0.8345\n",
      "Epoch 961/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3450 - accuracy: 0.8359\n",
      "Epoch 962/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3445 - accuracy: 0.8313\n",
      "Epoch 963/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3436 - accuracy: 0.8332\n",
      "Epoch 964/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.8354\n",
      "Epoch 965/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.8334\n",
      "Epoch 966/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8348\n",
      "Epoch 967/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.8336\n",
      "Epoch 968/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8332\n",
      "Epoch 969/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3445 - accuracy: 0.8327\n",
      "Epoch 970/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3442 - accuracy: 0.8336\n",
      "Epoch 971/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8357\n",
      "Epoch 972/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3441 - accuracy: 0.8357\n",
      "Epoch 973/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3440 - accuracy: 0.8347\n",
      "Epoch 974/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3431 - accuracy: 0.8352\n",
      "Epoch 975/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3442 - accuracy: 0.8338\n",
      "Epoch 976/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3439 - accuracy: 0.8368\n",
      "Epoch 977/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8372\n",
      "Epoch 978/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3445 - accuracy: 0.8343\n",
      "Epoch 979/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8311\n",
      "Epoch 980/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8356\n",
      "Epoch 981/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3438 - accuracy: 0.8336\n",
      "Epoch 982/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3425 - accuracy: 0.8350\n",
      "Epoch 983/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3432 - accuracy: 0.8350\n",
      "Epoch 984/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3442 - accuracy: 0.8320\n",
      "Epoch 985/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3429 - accuracy: 0.8340\n",
      "Epoch 986/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8364\n",
      "Epoch 987/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3436 - accuracy: 0.8338\n",
      "Epoch 988/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8357\n",
      "Epoch 989/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3450 - accuracy: 0.8332\n",
      "Epoch 990/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3438 - accuracy: 0.8340\n",
      "Epoch 991/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3449 - accuracy: 0.8352\n",
      "Epoch 992/1000\n",
      "176/176 [==============================] - 0s 3ms/step - loss: 0.3447 - accuracy: 0.8311\n",
      "Epoch 993/1000\n",
      "176/176 [==============================] - 1s 3ms/step - loss: 0.3432 - accuracy: 0.8359\n",
      "Epoch 994/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3447 - accuracy: 0.8334\n",
      "Epoch 995/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3442 - accuracy: 0.8318\n",
      "Epoch 996/1000\n",
      "176/176 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.8325\n",
      "Epoch 997/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8354\n",
      "Epoch 998/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3439 - accuracy: 0.8331\n",
      "Epoch 999/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3437 - accuracy: 0.8354\n",
      "Epoch 1000/1000\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3431 - accuracy: 0.8348\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc4ae7e6760>"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deep learning model\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(20,input_shape=(number_of_features,),activation='relu'),\n",
    "    keras.layers.Dense(1,activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile( optimizer = 'adam',\n",
    "               loss = 'binary_crossentropy',\n",
    "               metrics = ['accuracy'])\n",
    "\n",
    "model.fit(x_train,y_train,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "995fe75b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 2ms/step - loss: 0.6337 - accuracy: 0.7647\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6336769461631775, 0.764747679233551]"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluating test dataset\n",
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "a005da7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.00721643],\n",
       "       [0.04886824],\n",
       "       [0.798108  ],\n",
       "       [0.0042718 ],\n",
       "       [0.39536235]], dtype=float32)"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing prediction result in an array\n",
    "yp = model.predict(x_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "ac516814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for every prediction value > 0.5 we will store 1 else store 0 in this new array\n",
    "y_pred = []\n",
    "for element in yp:\n",
    "    if element > 0.5 :\n",
    "        y_pred.append(1)\n",
    "    else :\n",
    "        y_pred.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "2087f26c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1, 0, 0, 0, 0, 1, 0, 0]"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "f50b3cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2481    0\n",
       "6784    0\n",
       "6125    1\n",
       "3052    0\n",
       "4099    0\n",
       "3223    0\n",
       "3774    0\n",
       "3469    0\n",
       "3420    0\n",
       "1196    0\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "584c66c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "from sklearn.metrics import confusion_matrix , classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "7ec190ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.86      0.84      1033\n",
      "           1       0.56      0.51      0.54       374\n",
      "\n",
      "    accuracy                           0.76      1407\n",
      "   macro avg       0.70      0.68      0.69      1407\n",
      "weighted avg       0.76      0.76      0.76      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report \n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "4d35f423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "\n",
    "import seaborn as sn\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred)\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm,annot=True,fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "42b21340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the accuracy value in classification report\n",
    "\n",
    "accuracy = round((885+191)/(885+191+148+183),2)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "698d427a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83\n",
      "0.56\n"
     ]
    }
   ],
   "source": [
    "# truth precision is the ratio of actual truth and predicted truth \n",
    "# false precision is the ratio of actual false and predicted false\n",
    "\n",
    "zero_precision = round((885)/(885+183),2)\n",
    "print(zero_precision)\n",
    "one_precision = round((191)/(148+191),2)\n",
    "print(one_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "ef1f692c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.86\n",
      "0.51\n"
     ]
    }
   ],
   "source": [
    "# truth recall is the ratio of predicted truth which was actually true and all the truth values\n",
    "# false recall is the ratio of predicted false which was actually false and all the false values\n",
    "\n",
    "zero_recall = round(885/(885+148),2)\n",
    "print(zero_recall)\n",
    "one_recall = round(191/(191+183),2)\n",
    "print(one_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3401dffe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}