Customer Churn Prediction using ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d48efe4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import os\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6516080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('customer_churn.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d53dd735",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('customerID', axis='columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07238df6",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0111906e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalCharges.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81e35a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.85,  56.95,  53.85, ...,  29.6 ,  74.4 , 105.65])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MonthlyCharges.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f27576d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         29.85\n",
       "1       1889.50\n",
       "2        108.15\n",
       "3       1840.75\n",
       "4        151.65\n",
       "         ...   \n",
       "7038    1990.50\n",
       "7039    7362.90\n",
       "7040     346.45\n",
       "7041     306.60\n",
       "7042    6844.50\n",
       "Name: TotalCharges, Length: 7043, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df.TotalCharges, errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "592db220",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "      <th>Dependents</th>\n",
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       "      <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>488</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Male</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.25</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>80.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.75</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>56.05</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.00</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>73.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "488   Female              0     Yes        Yes       0           No   \n",
       "753     Male              0      No        Yes       0          Yes   \n",
       "936   Female              0     Yes        Yes       0          Yes   \n",
       "1082    Male              0     Yes        Yes       0          Yes   \n",
       "1340  Female              0     Yes        Yes       0           No   \n",
       "3331    Male              0     Yes        Yes       0          Yes   \n",
       "3826    Male              0     Yes        Yes       0          Yes   \n",
       "4380  Female              0     Yes        Yes       0          Yes   \n",
       "5218    Male              0     Yes        Yes       0          Yes   \n",
       "6670  Female              0     Yes        Yes       0          Yes   \n",
       "6754    Male              0      No        Yes       0          Yes   \n",
       "\n",
       "         MultipleLines InternetService       OnlineSecurity  \\\n",
       "488   No phone service             DSL                  Yes   \n",
       "753                 No              No  No internet service   \n",
       "936                 No             DSL                  Yes   \n",
       "1082               Yes              No  No internet service   \n",
       "1340  No phone service             DSL                  Yes   \n",
       "3331                No              No  No internet service   \n",
       "3826               Yes              No  No internet service   \n",
       "4380                No              No  No internet service   \n",
       "5218                No              No  No internet service   \n",
       "6670               Yes             DSL                   No   \n",
       "6754               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55                 No  \n",
       "753                Mailed check           20.25                 No  \n",
       "936                Mailed check           80.85                 No  \n",
       "1082               Mailed check           25.75                 No  \n",
       "1340    Credit card (automatic)           56.05                 No  \n",
       "3331               Mailed check           19.85                 No  \n",
       "3826               Mailed check           25.35                 No  \n",
       "4380               Mailed check           20.00                 No  \n",
       "5218               Mailed check           19.70                 No  \n",
       "6670               Mailed check           73.35                 No  \n",
       "6754  Bank transfer (automatic)           61.90                 No  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pd.to_numeric(df.TotalCharges, errors = 'coerce').isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f7bcbe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df.TotalCharges != ' ']\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb97ac4a",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f277fc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.TotalCharges = pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14f48482",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "972cfd05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1591a99e7c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tenure_churn_no = df1[df1.Churn == 'No'].tenure\n",
    "tenure_churn_yes = df1[df1.Churn == 'Yes'].tenure\n",
    "\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Customer Churn Prediction Visualization')\n",
    "\n",
    "plt.hist([tenure_churn_no, tenure_churn_yes], color=['green','red'], label=['Churn = No', 'Churn = Yes'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d3db79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1590ba19d60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tenure_churn_no = df1[df1.Churn == 'No'].MonthlyCharges\n",
    "tenure_churn_yes = df1[df1.Churn == 'Yes'].MonthlyCharges\n",
    "\n",
    "plt.xlabel('Monthly Charges')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Customer Churn Prediction Visualization')\n",
    "\n",
    "plt.hist([tenure_churn_no, tenure_churn_yes], color=['green','red'], label=['Churn = No', 'Churn = Yes'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ee35a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_unique_column_values(df):\n",
    "    for column in df:\n",
    "        if df[column].dtypes=='object':\n",
    "            print(f'{column}: {df[column].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "122684a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No phone service' 'No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup: ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection: ['No' 'Yes' 'No internet service']\n",
      "TechSupport: ['No' 'Yes' 'No internet service']\n",
      "StreamingTV: ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies: ['No' 'Yes' 'No internet service']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_column_values(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "847736e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.replace('No internet service', 'No', inplace = True)\n",
    "df1.replace('No phone service', 'No', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50007b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes']\n",
      "OnlineBackup: ['Yes' 'No']\n",
      "DeviceProtection: ['No' 'Yes']\n",
      "TechSupport: ['No' 'Yes']\n",
      "StreamingTV: ['No' 'Yes']\n",
      "StreamingMovies: ['No' 'Yes']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print_unique_column_values(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7fff40fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "yes_no_columns = ['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
    "                  'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
    "\n",
    "for col in yes_no_columns:\n",
    "    df1[col].replace({'Yes': 1, 'No': 0},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "025f80c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " gender: ['Female' 'Male']\n",
      " SeniorCitizen: [0 1]\n",
      " Partner: [1 0]\n",
      " Dependents: [0 1]\n",
      " 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",
      " PhoneService: [0 1]\n",
      " MultipleLines: [0 1]\n",
      " InternetService: ['DSL' 'Fiber optic' 'No']\n",
      " OnlineSecurity: [0 1]\n",
      " OnlineBackup: [1 0]\n",
      " DeviceProtection: [0 1]\n",
      " TechSupport: [0 1]\n",
      " StreamingTV: [0 1]\n",
      " StreamingMovies: [0 1]\n",
      " Contract: ['Month-to-month' 'One year' 'Two year']\n",
      " PaperlessBilling: [1 0]\n",
      " PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      " MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      " TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      " Churn: [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "    print(f' {col}: {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb2f024a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['gender'].replace({'Female': 1, 'Male': 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f5633ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "456598ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
       "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.get_dummies(data=df1, columns=['InternetService', 'Contract', 'PaymentMethod'])\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3fb316b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</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",
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       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
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       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "309        0              0        0           1      48             1   \n",
       "1132       1              1        1           0      11             1   \n",
       "6098       0              0        1           1      21             1   \n",
       "3377       0              0        1           0      68             1   \n",
       "5621       1              0        1           0       3             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "309               1               1             0                 1  ...   \n",
       "1132              1               0             0                 1  ...   \n",
       "6098              1               1             0                 0  ...   \n",
       "3377              1               0             0                 0  ...   \n",
       "5621              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "309                     1                            0                   0   \n",
       "1132                    0                            1                   0   \n",
       "6098                    1                            0                   0   \n",
       "3377                    0                            0                   1   \n",
       "5621                    0                            1                   0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "309                         0                  1                  0   \n",
       "1132                        1                  0                  0   \n",
       "6098                        0                  1                  0   \n",
       "3377                        0                  0                  1   \n",
       "5621                        1                  0                  0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "309                                         0   \n",
       "1132                                        1   \n",
       "6098                                        1   \n",
       "3377                                        0   \n",
       "5621                                        0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "309                                       1                               0   \n",
       "1132                                      0                               0   \n",
       "6098                                      0                               0   \n",
       "3377                                      1                               0   \n",
       "5621                                      1                               0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "309                            0  \n",
       "1132                           0  \n",
       "6098                           0  \n",
       "3377                           0  \n",
       "5621                           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0cbcd64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                       int64\n",
       "SeniorCitizen                                int64\n",
       "Partner                                      int64\n",
       "Dependents                                   int64\n",
       "tenure                                       int64\n",
       "PhoneService                                 int64\n",
       "MultipleLines                                int64\n",
       "OnlineSecurity                               int64\n",
       "OnlineBackup                                 int64\n",
       "DeviceProtection                             int64\n",
       "TechSupport                                  int64\n",
       "StreamingTV                                  int64\n",
       "StreamingMovies                              int64\n",
       "PaperlessBilling                             int64\n",
       "MonthlyCharges                             float64\n",
       "TotalCharges                               float64\n",
       "Churn                                        int64\n",
       "InternetService_DSL                          uint8\n",
       "InternetService_Fiber optic                  uint8\n",
       "InternetService_No                           uint8\n",
       "Contract_Month-to-month                      uint8\n",
       "Contract_One year                            uint8\n",
       "Contract_Two year                            uint8\n",
       "PaymentMethod_Bank transfer (automatic)      uint8\n",
       "PaymentMethod_Credit card (automatic)        uint8\n",
       "PaymentMethod_Electronic check               uint8\n",
       "PaymentMethod_Mailed check                   uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43010305",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_cols = ['tenure', 'MonthlyCharges','TotalCharges']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df2[scale_cols] = scaler.fit_transform(df2[scale_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65c83d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>TotalCharges</th>\n",
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       "  <tbody>\n",
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       "      <td>0.115423</td>\n",
       "      <td>0.001275</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.464789</td>\n",
       "      <td>0.385075</td>\n",
       "      <td>0.215867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.354229</td>\n",
       "      <td>0.010310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.619718</td>\n",
       "      <td>0.239303</td>\n",
       "      <td>0.210241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.521891</td>\n",
       "      <td>0.015330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>0.323944</td>\n",
       "      <td>0.662189</td>\n",
       "      <td>0.227521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.845274</td>\n",
       "      <td>0.847461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.112935</td>\n",
       "      <td>0.037809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.558706</td>\n",
       "      <td>0.033210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>0.915493</td>\n",
       "      <td>0.869652</td>\n",
       "      <td>0.787641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7032 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        tenure  MonthlyCharges  TotalCharges\n",
       "0     0.000000        0.115423      0.001275\n",
       "1     0.464789        0.385075      0.215867\n",
       "2     0.014085        0.354229      0.010310\n",
       "3     0.619718        0.239303      0.210241\n",
       "4     0.014085        0.521891      0.015330\n",
       "...        ...             ...           ...\n",
       "7038  0.323944        0.662189      0.227521\n",
       "7039  1.000000        0.845274      0.847461\n",
       "7040  0.140845        0.112935      0.037809\n",
       "7041  0.042254        0.558706      0.033210\n",
       "7042  0.915493        0.869652      0.787641\n",
       "\n",
       "[7032 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[scale_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bd63ec38",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop('Churn', axis='columns')\n",
    "y = df2['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30b29a1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2027655f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5625, 26)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81e3eb96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1407, 26)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90c8f195",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>5664</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.126761</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2621</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.985915</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1327</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.169014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    <tr>\n",
       "      <th>2773</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>1936</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.704225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5387</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>4331</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.985915</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "5664       1              1        0           0  0.126761             1   \n",
       "101        1              0        1           1  0.000000             1   \n",
       "2621       0              0        1           0  0.985915             1   \n",
       "392        1              1        0           0  0.014085             1   \n",
       "1327       0              0        1           0  0.816901             1   \n",
       "3607       1              0        0           0  0.169014             1   \n",
       "2773       0              0        1           0  0.323944             0   \n",
       "1936       1              0        1           0  0.704225             1   \n",
       "5387       0              0        0           0  0.042254             0   \n",
       "4331       0              0        0           0  0.985915             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "5664              0               0             0                 1  ...   \n",
       "101               0               0             0                 0  ...   \n",
       "2621              0               0             1                 1  ...   \n",
       "392               0               0             0                 0  ...   \n",
       "1327              1               0             0                 1  ...   \n",
       "3607              0               1             0                 0  ...   \n",
       "2773              0               0             0                 1  ...   \n",
       "1936              0               1             1                 0  ...   \n",
       "5387              0               0             0                 0  ...   \n",
       "4331              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "5664                    0                            1                   0   \n",
       "101                     0                            0                   1   \n",
       "2621                    1                            0                   0   \n",
       "392                     1                            0                   0   \n",
       "1327                    0                            1                   0   \n",
       "3607                    1                            0                   0   \n",
       "2773                    1                            0                   0   \n",
       "1936                    1                            0                   0   \n",
       "5387                    1                            0                   0   \n",
       "4331                    0                            0                   1   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "5664                        1                  0                  0   \n",
       "101                         1                  0                  0   \n",
       "2621                        0                  0                  1   \n",
       "392                         1                  0                  0   \n",
       "1327                        0                  1                  0   \n",
       "3607                        0                  1                  0   \n",
       "2773                        1                  0                  0   \n",
       "1936                        0                  1                  0   \n",
       "5387                        1                  0                  0   \n",
       "4331                        0                  0                  1   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "5664                                        0   \n",
       "101                                         0   \n",
       "2621                                        0   \n",
       "392                                         0   \n",
       "1327                                        1   \n",
       "3607                                        0   \n",
       "2773                                        0   \n",
       "1936                                        1   \n",
       "5387                                        0   \n",
       "4331                                        1   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "5664                                      1                               0   \n",
       "101                                       0                               1   \n",
       "2621                                      1                               0   \n",
       "392                                       0                               1   \n",
       "1327                                      0                               0   \n",
       "3607                                      0                               0   \n",
       "2773                                      0                               1   \n",
       "1936                                      0                               0   \n",
       "5387                                      0                               1   \n",
       "4331                                      0                               0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "5664                           0  \n",
       "101                            0  \n",
       "2621                           0  \n",
       "392                            0  \n",
       "1327                           0  \n",
       "3607                           1  \n",
       "2773                           0  \n",
       "1936                           0  \n",
       "5387                           0  \n",
       "4331                           0  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ad85997c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(20, input_shape=(26,), activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs= 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b0bfa72e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 724us/step - loss: 0.4446 - accuracy: 0.7811\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4445789158344269, 0.7810945510864258]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f4d620b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 674us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.19890621],\n",
       "       [0.368203  ],\n",
       "       [0.02091769],\n",
       "       [0.7666836 ],\n",
       "       [0.5483208 ]], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = model.predict(X_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "749d6fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2660    0\n",
       "744     0\n",
       "5579    1\n",
       "64      1\n",
       "3287    1\n",
       "816     1\n",
       "2670    0\n",
       "5920    0\n",
       "1023    0\n",
       "6087    0\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "227c3350",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 48,
   "id": "342b7ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 1, 1, 1, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "04d2b576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Actual')"
      ]
     },
     "execution_count": 49,
     "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": [
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred)\n",
    "\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c74923f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.82      0.85      1083\n",
      "           1       0.52      0.65      0.58       324\n",
      "\n",
      "    accuracy                           0.78      1407\n",
      "   macro avg       0.70      0.74      0.72      1407\n",
      "weighted avg       0.80      0.78      0.79      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}