churn_predict_johnson.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer churn prediction using ANN | Deep Learning Tutorial 18 (Tensorflow2.0, Keras & Python) \n",
    "### YouTube Tutorial:\n",
    "### Link\n",
    "<https://youtu.be/MSBY28IJ47U?si=nfn8pauvttwRnkpF>\n",
    "\n",
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('customer_churn.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>173</th>\n",
       "      <td>0691-JVSYA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>53</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>94.85</td>\n",
       "      <td>5000.2</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4352</th>\n",
       "      <td>0595-ITUDF</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>64</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</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>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>91.80</td>\n",
       "      <td>5960.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3159</th>\n",
       "      <td>5150-ITWWB</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>94.85</td>\n",
       "      <td>335.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>2676-ISHSF</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</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>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.30</td>\n",
       "      <td>20.3</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5904</th>\n",
       "      <td>9402-CXWPL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>70</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>98.90</td>\n",
       "      <td>6838.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure   \n",
       "173   0691-JVSYA  Female              0     Yes         No      53  \\\n",
       "4352  0595-ITUDF    Male              0     Yes        Yes      64   \n",
       "3159  5150-ITWWB    Male              1      No         No       3   \n",
       "6499  2676-ISHSF    Male              0      No         No       1   \n",
       "5904  9402-CXWPL  Female              0      No         No      70   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...   \n",
       "173           Yes            No     Fiber optic                   No  ...  \\\n",
       "4352          Yes           Yes     Fiber optic                  Yes  ...   \n",
       "3159          Yes           Yes     Fiber optic                   No  ...   \n",
       "6499          Yes            No              No  No internet service  ...   \n",
       "5904          Yes           Yes     Fiber optic                   No  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV   \n",
       "173                   Yes                   No                  Yes  \\\n",
       "4352                  Yes                   No                   No   \n",
       "3159                   No                   No                  Yes   \n",
       "6499  No internet service  No internet service  No internet service   \n",
       "5904                   No                   No                  Yes   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling   \n",
       "173                   Yes        One year              Yes  \\\n",
       "4352                   No  Month-to-month              Yes   \n",
       "3159                  Yes  Month-to-month              Yes   \n",
       "6499  No internet service  Month-to-month               No   \n",
       "5904                  Yes        One year               No   \n",
       "\n",
       "                  PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "173   Bank transfer (automatic)          94.85        5000.2   Yes  \n",
       "4352           Electronic check          91.80        5960.5    No  \n",
       "3159           Electronic check          94.85        335.75    No  \n",
       "6499               Mailed check          20.30          20.3    No  \n",
       "5904           Electronic check          98.90        6838.6    No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### drop columns (CustomerID, Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('customerID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes # show all column data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.TotalCharges.values # original data type is object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# df.TotalCharges = pd.to_numeric(df.TotalCharges, errors='coerce')\n",
    "nullDF = df[pd.to_numeric(df.TotalCharges, errors='coerce').isnull()] # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "      <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>OnlineBackup</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>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",
       "      <td>No phone service</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</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>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>20.25</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "      <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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nullDF # show missing values in TotalCharges column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nullDF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                 Female\n",
       "SeniorCitizen                               0\n",
       "Partner                                   Yes\n",
       "Dependents                                Yes\n",
       "tenure                                      0\n",
       "PhoneService                               No\n",
       "MultipleLines                No phone service\n",
       "InternetService                           DSL\n",
       "OnlineSecurity                            Yes\n",
       "OnlineBackup                               No\n",
       "DeviceProtection                          Yes\n",
       "TechSupport                               Yes\n",
       "StreamingTV                               Yes\n",
       "StreamingMovies                            No\n",
       "Contract                             Two year\n",
       "PaperlessBilling                          Yes\n",
       "PaymentMethod       Bank transfer (automatic)\n",
       "MonthlyCharges                          52.55\n",
       "TotalCharges                                 \n",
       "Churn                                      No\n",
       "Name: 488, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[488] # show the row with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check for empty strings , remove than missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df[\"TotalCharges\"]!=' '] \n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Covnert TotalCharges data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_404579/973151263.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1.TotalCharges = pd.to_numeric(df1.TotalCharges)\n"
     ]
    }
   ],
   "source": [
    "df1.TotalCharges = pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.TotalCharges.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Customer no churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</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>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>Yes</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>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>No</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>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>No</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>6</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>89.10</td>\n",
       "      <td>1949.40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>29.75</td>\n",
       "      <td>301.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7037</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>72</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>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>21.15</td>\n",
       "      <td>1419.40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <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>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5163 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService   \n",
       "0     Female              0     Yes         No       1           No  \\\n",
       "1       Male              0      No         No      34          Yes   \n",
       "3       Male              0      No         No      45           No   \n",
       "6       Male              0      No        Yes      22          Yes   \n",
       "7     Female              0      No         No      10           No   \n",
       "...      ...            ...     ...        ...     ...          ...   \n",
       "7037  Female              0      No         No      72          Yes   \n",
       "7038    Male              0     Yes        Yes      24          Yes   \n",
       "7039  Female              0     Yes        Yes      72          Yes   \n",
       "7040  Female              0     Yes        Yes      11           No   \n",
       "7042    Male              0      No         No      66          Yes   \n",
       "\n",
       "         MultipleLines InternetService       OnlineSecurity   \n",
       "0     No phone service             DSL                   No  \\\n",
       "1                   No             DSL                  Yes   \n",
       "3     No phone service             DSL                  Yes   \n",
       "6                  Yes     Fiber optic                   No   \n",
       "7     No phone service             DSL                  Yes   \n",
       "...                ...             ...                  ...   \n",
       "7037                No              No  No internet service   \n",
       "7038               Yes             DSL                  Yes   \n",
       "7039               Yes     Fiber optic                   No   \n",
       "7040  No phone service             DSL                  Yes   \n",
       "7042                No     Fiber optic                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport   \n",
       "0                     Yes                   No                   No  \\\n",
       "1                      No                  Yes                   No   \n",
       "3                      No                  Yes                  Yes   \n",
       "6                     Yes                   No                   No   \n",
       "7                      No                   No                   No   \n",
       "...                   ...                  ...                  ...   \n",
       "7037  No internet service  No internet service  No internet service   \n",
       "7038                   No                  Yes                  Yes   \n",
       "7039                  Yes                  Yes                   No   \n",
       "7040                   No                   No                   No   \n",
       "7042                   No                  Yes                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies        Contract   \n",
       "0                      No                   No  Month-to-month  \\\n",
       "1                      No                   No        One year   \n",
       "3                      No                   No        One year   \n",
       "6                     Yes                   No  Month-to-month   \n",
       "7                      No                   No  Month-to-month   \n",
       "...                   ...                  ...             ...   \n",
       "7037  No internet service  No internet service        Two year   \n",
       "7038                  Yes                  Yes        One year   \n",
       "7039                  Yes                  Yes        One year   \n",
       "7040                   No                   No  Month-to-month   \n",
       "7042                  Yes                  Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges   \n",
       "0                 Yes           Electronic check           29.85  \\\n",
       "1                  No               Mailed check           56.95   \n",
       "3                  No  Bank transfer (automatic)           42.30   \n",
       "6                 Yes    Credit card (automatic)           89.10   \n",
       "7                  No               Mailed check           29.75   \n",
       "...               ...                        ...             ...   \n",
       "7037              Yes  Bank transfer (automatic)           21.15   \n",
       "7038              Yes               Mailed check           84.80   \n",
       "7039              Yes    Credit card (automatic)          103.20   \n",
       "7040              Yes           Electronic check           29.60   \n",
       "7042              Yes  Bank transfer (automatic)          105.65   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "1          1889.50    No  \n",
       "3          1840.75    No  \n",
       "6          1949.40    No  \n",
       "7           301.90    No  \n",
       "...            ...   ...  \n",
       "7037       1419.40    No  \n",
       "7038       1990.50    No  \n",
       "7039       7362.90    No  \n",
       "7040        346.45    No  \n",
       "7042       6844.50    No  \n",
       "\n",
       "[5163 rows x 20 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1[\"Churn\"] == \"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenureNoChurn =df1[df1[\"Churn\"] == \"No\"].tenure # show tenure for customers who did not churn \n",
    "tenureYesChurn = df1[df1[\"Churn\"]== \"Yes\"].tenure # show tenure for customers who churned\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"tenure\")\n",
    "plt.ylabel(\"Number Of Customers\")\n",
    "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
    "\n",
    "plt.hist([tenureYesChurn, tenureNoChurn], rwidth=0.95, color=['green','red'],label=['Churn=Yes','Churn=No'])\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcNoChurn = df1[df1[\"Churn\"] == \"No\"].MonthlyCharges\n",
    "mcYesChurn = df1[df1[\"Churn\"] == \"Yes\"].MonthlyCharges\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Monthly Charges\")\n",
    "plt.ylabel(\"Number Of Customers\")\n",
    "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
    "\n",
    "plt.hist([mcYesChurn, mcNoChurn], rwidth=0.95, color=['green','red'],label=['Churn=Yes','Churn=No'])\n",
    "plt.legend();\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine unique value in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\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: ['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",
      "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: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "    print(f'{col}: {df1[col].unique()}') # show unique values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printUniqueValues(df):\n",
    "    for col in df:\n",
    "        if df[col].dtype == 'object': # only print data type object\n",
    "            print(f'{col}: {df[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "printUniqueValues(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_404579/1290998544.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1.replace('No internet service','No', inplace=True)\n",
      "/tmp/ipykernel_404579/1290998544.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1.replace('No phone service','No', inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df1.replace('No internet service','No', inplace=True)\n",
    "df1.replace('No phone service','No', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "printUniqueValues(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert yes or no unique values to 1 or 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_404579/592710539.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[col].replace({'Yes': 1,'No': 0},inplace=True) # replace yes and no with 1 and 0\n"
     ]
    }
   ],
   "source": [
    "# List of columns with yes, no values\n",
    "yesNOColumns = ['Partner','Dependents','PhoneService','MultipleLines',\n",
    "                'OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport',\n",
    "                'StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
    "\n",
    "for col in yesNOColumns:\n",
    "    df1[col].replace({'Yes': 1,'No': 0},inplace=True) # replace yes and no with 1 and 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### convert Gender to  1 or 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_404579/2148712592.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[\"gender\"].replace({\"Female\": 1, \"Male\": 0}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df1[\"gender\"].replace({\"Female\": 1, \"Male\": 0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"gender\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create One-hot Encoding for several columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.get_dummies(data=df1, columns=['InternetService', 'Contract', 'PaymentMethod'], dtype=int) # one hot encoding into int value\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: [1 0]\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",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "PaperlessBilling: [1 0]\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",
      "InternetService_DSL: [1 0]\n",
      "InternetService_Fiber optic: [0 1]\n",
      "InternetService_No: [0 1]\n",
      "Contract_Month-to-month: [1 0]\n",
      "Contract_One year: [0 1]\n",
      "Contract_Two year: [0 1]\n",
      "PaymentMethod_Bank transfer (automatic): [0 1]\n",
      "PaymentMethod_Credit card (automatic): [0 1]\n",
      "PaymentMethod_Electronic check: [1 0]\n",
      "PaymentMethod_Mailed check: [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df2:\n",
    "    print(f'{col}: {df2[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>4942</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</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>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>6482</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>6285</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</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>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>4 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService   \n",
       "4942       0              0        1           1       4             1  \\\n",
       "6482       0              0        0           0       1             1   \n",
       "6285       0              0        0           0      26             0   \n",
       "1467       1              0        0           0      23             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...   \n",
       "4942              1               0             0                 0  ...  \\\n",
       "6482              1               0             0                 1  ...   \n",
       "6285              0               1             0                 0  ...   \n",
       "1467              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No   \n",
       "4942                    1                            0                   0  \\\n",
       "6482                    0                            1                   0   \n",
       "6285                    1                            0                   0   \n",
       "1467                    1                            0                   0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year   \n",
       "4942                        1                  0                  0  \\\n",
       "6482                        1                  0                  0   \n",
       "6285                        0                  1                  0   \n",
       "1467                        0                  1                  0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)   \n",
       "4942                                        0  \\\n",
       "6482                                        0   \n",
       "6285                                        0   \n",
       "1467                                        0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check   \n",
       "4942                                      0                               1  \\\n",
       "6482                                      0                               1   \n",
       "6285                                      1                               0   \n",
       "1467                                      0                               0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "4942                           0  \n",
       "6482                           0  \n",
       "6285                           0  \n",
       "1467                           1  \n",
       "\n",
       "[4 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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                          int64\n",
       "InternetService_Fiber optic                  int64\n",
       "InternetService_No                           int64\n",
       "Contract_Month-to-month                      int64\n",
       "Contract_One year                            int64\n",
       "Contract_Two year                            int64\n",
       "PaymentMethod_Bank transfer (automatic)      int64\n",
       "PaymentMethod_Credit card (automatic)        int64\n",
       "PaymentMethod_Electronic check               int64\n",
       "PaymentMethod_Mailed check                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale Min-Max data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the columns to scale   \n",
    "colToScale = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df2[colToScale] = scaler.fit_transform(df2[colToScale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: [1 0]\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [0.         0.46478873 0.01408451 0.61971831 0.09859155 0.29577465\n",
      " 0.12676056 0.38028169 0.85915493 0.16901408 0.21126761 0.8028169\n",
      " 0.67605634 0.33802817 0.95774648 0.71830986 0.98591549 0.28169014\n",
      " 0.15492958 0.4084507  0.64788732 1.         0.22535211 0.36619718\n",
      " 0.05633803 0.63380282 0.14084507 0.97183099 0.87323944 0.5915493\n",
      " 0.1971831  0.83098592 0.23943662 0.91549296 0.11267606 0.02816901\n",
      " 0.42253521 0.69014085 0.88732394 0.77464789 0.08450704 0.57746479\n",
      " 0.47887324 0.66197183 0.3943662  0.90140845 0.52112676 0.94366197\n",
      " 0.43661972 0.76056338 0.50704225 0.49295775 0.56338028 0.07042254\n",
      " 0.04225352 0.45070423 0.92957746 0.30985915 0.78873239 0.84507042\n",
      " 0.18309859 0.26760563 0.73239437 0.54929577 0.81690141 0.32394366\n",
      " 0.6056338  0.25352113 0.74647887 0.70422535 0.35211268 0.53521127]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\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",
      "PaperlessBilling: [1 0]\n",
      "MonthlyCharges: [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
      "TotalCharges: [0.0012751  0.21586661 0.01031041 ... 0.03780868 0.03321025 0.78764136]\n",
      "Churn: [0 1]\n",
      "InternetService_DSL: [1 0]\n",
      "InternetService_Fiber optic: [0 1]\n",
      "InternetService_No: [0 1]\n",
      "Contract_Month-to-month: [1 0]\n",
      "Contract_One year: [0 1]\n",
      "Contract_Two year: [0 1]\n",
      "PaymentMethod_Bank transfer (automatic): [0 1]\n",
      "PaymentMethod_Credit card (automatic): [0 1]\n",
      "PaymentMethod_Electronic check: [1 0]\n",
      "PaymentMethod_Mailed check: [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df2:\n",
    "    print(f'{col}: {df2[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define X and Y train data and Train Test data split into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop('Churn', axis=1) #drop target output label\n",
    "y = df2['Churn'] # target output label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5625, 26), (1407, 26))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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",
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       "      <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",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.816901</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a model (ANN) in Tensorflow /Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-09 16:33:17.252028: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-09 16:33:17.373164: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "        keras.layers.Dense(26, input_shape=(26,), activation='relu'), # 26 input, 26 neutron\n",
    "        keras.layers.Dense(15, activation='relu'), # 15 neutron\n",
    "        keras.layers.Dense(1, activation='sigmoid') # 1 output layer (binrary classification)\n",
    "\n",
    "])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 26)                702       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                405       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1123 (4.39 KB)\n",
      "Trainable params: 1123 (4.39 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define optimizer, loss function and metrics\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "176/176 [==============================] - 0s 607us/step - loss: 0.4925 - accuracy: 0.7589\n",
      "Epoch 2/100\n",
      "176/176 [==============================] - 0s 438us/step - loss: 0.4280 - accuracy: 0.7948\n",
      "Epoch 3/100\n",
      "176/176 [==============================] - 0s 356us/step - loss: 0.4204 - accuracy: 0.8014\n",
      "Epoch 4/100\n",
      "176/176 [==============================] - 0s 428us/step - loss: 0.4160 - accuracy: 0.8011\n",
      "Epoch 5/100\n",
      "176/176 [==============================] - 0s 520us/step - loss: 0.4125 - accuracy: 0.8044\n",
      "Epoch 6/100\n",
      "176/176 [==============================] - 0s 711us/step - loss: 0.4101 - accuracy: 0.8039\n",
      "Epoch 7/100\n",
      "176/176 [==============================] - 0s 502us/step - loss: 0.4081 - accuracy: 0.8057\n",
      "Epoch 8/100\n",
      "176/176 [==============================] - 0s 815us/step - loss: 0.4058 - accuracy: 0.8091\n",
      "Epoch 9/100\n",
      "176/176 [==============================] - 0s 453us/step - loss: 0.4052 - accuracy: 0.8103\n",
      "Epoch 10/100\n",
      "176/176 [==============================] - 0s 451us/step - loss: 0.4029 - accuracy: 0.8105\n",
      "Epoch 11/100\n",
      "176/176 [==============================] - 0s 404us/step - loss: 0.4009 - accuracy: 0.8084\n",
      "Epoch 12/100\n",
      "176/176 [==============================] - 0s 466us/step - loss: 0.3999 - accuracy: 0.8132\n",
      "Epoch 13/100\n",
      "176/176 [==============================] - 0s 506us/step - loss: 0.3998 - accuracy: 0.8117\n",
      "Epoch 14/100\n",
      "176/176 [==============================] - 0s 623us/step - loss: 0.3978 - accuracy: 0.8105\n",
      "Epoch 15/100\n",
      "176/176 [==============================] - 0s 473us/step - loss: 0.3964 - accuracy: 0.8140\n",
      "Epoch 16/100\n",
      "176/176 [==============================] - 0s 412us/step - loss: 0.3943 - accuracy: 0.8103\n",
      "Epoch 17/100\n",
      "176/176 [==============================] - 0s 1ms/step - loss: 0.3946 - accuracy: 0.8130\n",
      "Epoch 18/100\n",
      "176/176 [==============================] - 0s 564us/step - loss: 0.3924 - accuracy: 0.8123\n",
      "Epoch 19/100\n",
      "176/176 [==============================] - 0s 404us/step - loss: 0.3923 - accuracy: 0.8132\n",
      "Epoch 20/100\n",
      "176/176 [==============================] - 0s 364us/step - loss: 0.3911 - accuracy: 0.8133\n",
      "Epoch 21/100\n",
      "176/176 [==============================] - 0s 447us/step - loss: 0.3906 - accuracy: 0.8153\n",
      "Epoch 22/100\n",
      "176/176 [==============================] - 0s 493us/step - loss: 0.3906 - accuracy: 0.8164\n",
      "Epoch 23/100\n",
      "176/176 [==============================] - 0s 493us/step - loss: 0.3879 - accuracy: 0.8174\n",
      "Epoch 24/100\n",
      "176/176 [==============================] - 0s 464us/step - loss: 0.3871 - accuracy: 0.8148\n",
      "Epoch 25/100\n",
      "176/176 [==============================] - 0s 394us/step - loss: 0.3860 - accuracy: 0.8187\n",
      "Epoch 26/100\n",
      "176/176 [==============================] - 0s 405us/step - loss: 0.3845 - accuracy: 0.8176\n",
      "Epoch 27/100\n",
      "176/176 [==============================] - 0s 423us/step - loss: 0.3819 - accuracy: 0.8181\n",
      "Epoch 28/100\n",
      "176/176 [==============================] - 0s 505us/step - loss: 0.3826 - accuracy: 0.8171\n",
      "Epoch 29/100\n",
      "176/176 [==============================] - 0s 500us/step - loss: 0.3817 - accuracy: 0.8210\n",
      "Epoch 30/100\n",
      "176/176 [==============================] - 0s 397us/step - loss: 0.3806 - accuracy: 0.8183\n",
      "Epoch 31/100\n",
      "176/176 [==============================] - 0s 651us/step - loss: 0.3789 - accuracy: 0.8215\n",
      "Epoch 32/100\n",
      "176/176 [==============================] - 0s 966us/step - loss: 0.3798 - accuracy: 0.8171\n",
      "Epoch 33/100\n",
      "176/176 [==============================] - 0s 407us/step - loss: 0.3770 - accuracy: 0.8212\n",
      "Epoch 34/100\n",
      "176/176 [==============================] - 0s 393us/step - loss: 0.3771 - accuracy: 0.8242\n",
      "Epoch 35/100\n",
      "176/176 [==============================] - 0s 533us/step - loss: 0.3767 - accuracy: 0.8203\n",
      "Epoch 36/100\n",
      "176/176 [==============================] - 0s 400us/step - loss: 0.3756 - accuracy: 0.8208\n",
      "Epoch 37/100\n",
      "176/176 [==============================] - 0s 399us/step - loss: 0.3742 - accuracy: 0.8213\n",
      "Epoch 38/100\n",
      "176/176 [==============================] - 0s 489us/step - loss: 0.3736 - accuracy: 0.8210\n",
      "Epoch 39/100\n",
      "176/176 [==============================] - 0s 434us/step - loss: 0.3722 - accuracy: 0.8244\n",
      "Epoch 40/100\n",
      "176/176 [==============================] - 0s 366us/step - loss: 0.3716 - accuracy: 0.8236\n",
      "Epoch 41/100\n",
      "176/176 [==============================] - 0s 418us/step - loss: 0.3707 - accuracy: 0.8252\n",
      "Epoch 42/100\n",
      "176/176 [==============================] - 0s 425us/step - loss: 0.3696 - accuracy: 0.8233\n",
      "Epoch 43/100\n",
      "176/176 [==============================] - 0s 543us/step - loss: 0.3715 - accuracy: 0.8226\n",
      "Epoch 44/100\n",
      "176/176 [==============================] - 0s 398us/step - loss: 0.3693 - accuracy: 0.8252\n",
      "Epoch 45/100\n",
      "176/176 [==============================] - 0s 344us/step - loss: 0.3668 - accuracy: 0.8252\n",
      "Epoch 46/100\n",
      "176/176 [==============================] - 0s 329us/step - loss: 0.3675 - accuracy: 0.8272\n",
      "Epoch 47/100\n",
      "176/176 [==============================] - 0s 475us/step - loss: 0.3665 - accuracy: 0.8260\n",
      "Epoch 48/100\n",
      "176/176 [==============================] - 0s 613us/step - loss: 0.3657 - accuracy: 0.8251\n",
      "Epoch 49/100\n",
      "176/176 [==============================] - 0s 930us/step - loss: 0.3645 - accuracy: 0.8276\n",
      "Epoch 50/100\n",
      "176/176 [==============================] - 0s 387us/step - loss: 0.3660 - accuracy: 0.8260\n",
      "Epoch 51/100\n",
      "176/176 [==============================] - 0s 334us/step - loss: 0.3627 - accuracy: 0.8260\n",
      "Epoch 52/100\n",
      "176/176 [==============================] - 0s 351us/step - loss: 0.3628 - accuracy: 0.8274\n",
      "Epoch 53/100\n",
      "176/176 [==============================] - 0s 424us/step - loss: 0.3613 - accuracy: 0.8276\n",
      "Epoch 54/100\n",
      "176/176 [==============================] - 0s 378us/step - loss: 0.3603 - accuracy: 0.8304\n",
      "Epoch 55/100\n",
      "176/176 [==============================] - 0s 398us/step - loss: 0.3607 - accuracy: 0.8295\n",
      "Epoch 56/100\n",
      "176/176 [==============================] - 0s 461us/step - loss: 0.3599 - accuracy: 0.8286\n",
      "Epoch 57/100\n",
      "176/176 [==============================] - 0s 840us/step - loss: 0.3605 - accuracy: 0.8268\n",
      "Epoch 58/100\n",
      "176/176 [==============================] - 0s 556us/step - loss: 0.3587 - accuracy: 0.8308\n",
      "Epoch 59/100\n",
      "176/176 [==============================] - 0s 439us/step - loss: 0.3587 - accuracy: 0.8318\n",
      "Epoch 60/100\n",
      "176/176 [==============================] - 0s 519us/step - loss: 0.3574 - accuracy: 0.8295\n",
      "Epoch 61/100\n",
      "176/176 [==============================] - 0s 871us/step - loss: 0.3575 - accuracy: 0.8324\n",
      "Epoch 62/100\n",
      "176/176 [==============================] - 0s 846us/step - loss: 0.3559 - accuracy: 0.8283\n",
      "Epoch 63/100\n",
      "176/176 [==============================] - 0s 563us/step - loss: 0.3561 - accuracy: 0.8293\n",
      "Epoch 64/100\n",
      "176/176 [==============================] - 0s 510us/step - loss: 0.3555 - accuracy: 0.8320\n",
      "Epoch 65/100\n",
      "176/176 [==============================] - 0s 573us/step - loss: 0.3537 - accuracy: 0.8338\n",
      "Epoch 66/100\n",
      "176/176 [==============================] - 0s 366us/step - loss: 0.3541 - accuracy: 0.8347\n",
      "Epoch 67/100\n",
      "176/176 [==============================] - 0s 629us/step - loss: 0.3542 - accuracy: 0.8322\n",
      "Epoch 68/100\n",
      "176/176 [==============================] - 0s 519us/step - loss: 0.3533 - accuracy: 0.8325\n",
      "Epoch 69/100\n",
      "176/176 [==============================] - 0s 978us/step - loss: 0.3517 - accuracy: 0.8327\n",
      "Epoch 70/100\n",
      "176/176 [==============================] - 0s 370us/step - loss: 0.3532 - accuracy: 0.8336\n",
      "Epoch 71/100\n",
      "176/176 [==============================] - 0s 398us/step - loss: 0.3521 - accuracy: 0.8313\n",
      "Epoch 72/100\n",
      "176/176 [==============================] - 0s 633us/step - loss: 0.3522 - accuracy: 0.8306\n",
      "Epoch 73/100\n",
      "176/176 [==============================] - 0s 565us/step - loss: 0.3506 - accuracy: 0.8329\n",
      "Epoch 74/100\n",
      "176/176 [==============================] - 0s 391us/step - loss: 0.3499 - accuracy: 0.8279\n",
      "Epoch 75/100\n",
      "176/176 [==============================] - 0s 711us/step - loss: 0.3489 - accuracy: 0.8361\n",
      "Epoch 76/100\n",
      "176/176 [==============================] - 0s 645us/step - loss: 0.3502 - accuracy: 0.8332\n",
      "Epoch 77/100\n",
      "176/176 [==============================] - 0s 467us/step - loss: 0.3489 - accuracy: 0.8343\n",
      "Epoch 78/100\n",
      "176/176 [==============================] - 0s 392us/step - loss: 0.3498 - accuracy: 0.8352\n",
      "Epoch 79/100\n",
      "176/176 [==============================] - 0s 442us/step - loss: 0.3477 - accuracy: 0.8366\n",
      "Epoch 80/100\n",
      "176/176 [==============================] - 0s 503us/step - loss: 0.3478 - accuracy: 0.8368\n",
      "Epoch 81/100\n",
      "176/176 [==============================] - 0s 776us/step - loss: 0.3468 - accuracy: 0.8366\n",
      "Epoch 82/100\n",
      "176/176 [==============================] - 0s 491us/step - loss: 0.3477 - accuracy: 0.8388\n",
      "Epoch 83/100\n",
      "176/176 [==============================] - 0s 408us/step - loss: 0.3466 - accuracy: 0.8361\n",
      "Epoch 84/100\n",
      "176/176 [==============================] - 0s 486us/step - loss: 0.3458 - accuracy: 0.8363\n",
      "Epoch 85/100\n",
      "176/176 [==============================] - 0s 525us/step - loss: 0.3462 - accuracy: 0.8380\n",
      "Epoch 86/100\n",
      "176/176 [==============================] - 0s 493us/step - loss: 0.3449 - accuracy: 0.8354\n",
      "Epoch 87/100\n",
      "176/176 [==============================] - 0s 453us/step - loss: 0.3457 - accuracy: 0.8363\n",
      "Epoch 88/100\n",
      "176/176 [==============================] - 0s 407us/step - loss: 0.3447 - accuracy: 0.8370\n",
      "Epoch 89/100\n",
      "176/176 [==============================] - 0s 422us/step - loss: 0.3434 - accuracy: 0.8354\n",
      "Epoch 90/100\n",
      "176/176 [==============================] - 0s 434us/step - loss: 0.3439 - accuracy: 0.8352\n",
      "Epoch 91/100\n",
      "176/176 [==============================] - 0s 960us/step - loss: 0.3444 - accuracy: 0.8379\n",
      "Epoch 92/100\n",
      "176/176 [==============================] - 0s 650us/step - loss: 0.3429 - accuracy: 0.8405\n",
      "Epoch 93/100\n",
      "176/176 [==============================] - 0s 545us/step - loss: 0.3434 - accuracy: 0.8363\n",
      "Epoch 94/100\n",
      "176/176 [==============================] - 0s 489us/step - loss: 0.3434 - accuracy: 0.8348\n",
      "Epoch 95/100\n",
      "176/176 [==============================] - 0s 535us/step - loss: 0.3426 - accuracy: 0.8361\n",
      "Epoch 96/100\n",
      "176/176 [==============================] - 0s 934us/step - loss: 0.3425 - accuracy: 0.8348\n",
      "Epoch 97/100\n",
      "176/176 [==============================] - 0s 533us/step - loss: 0.3417 - accuracy: 0.8377\n",
      "Epoch 98/100\n",
      "176/176 [==============================] - 0s 830us/step - loss: 0.3414 - accuracy: 0.8364\n",
      "Epoch 99/100\n",
      "176/176 [==============================] - 0s 530us/step - loss: 0.3415 - accuracy: 0.8359\n",
      "Epoch 100/100\n",
      "176/176 [==============================] - 0s 602us/step - loss: 0.3405 - accuracy: 0.8384\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7fdf6a9c0340>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## evaluation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 473us/step - loss: 0.4887 - accuracy: 0.7747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.48871737718582153, 0.7746979594230652]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 0s 416us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.07092979],\n",
       "       [0.47398734],\n",
       "       [0.05738977],\n",
       "       ...,\n",
       "       [0.72144884],\n",
       "       [0.669818  ],\n",
       "       [0.5953714 ]], dtype=float32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp =model.predict(X_test)\n",
    "yp # output is probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision output for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = [int(round(x[0])) for x in yp] # convert probability to 0 or 1 (>0.5 = 1)\n",
    "y_pred # output is probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 1, 0, 1, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.88      0.85       999\n",
      "           1       0.64      0.52      0.57       408\n",
      "\n",
      "    accuracy                           0.77      1407\n",
      "   macro avg       0.73      0.70      0.71      1407\n",
      "weighted avg       0.77      0.77      0.77      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### accuary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuary\n",
    "round((877+213)/(877+213+122+195),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision for 0 class , Prediction for custom who did not churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(877/(877+195) ,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision for 1 class , Prediction for custom who actually churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(213/(213+122) , 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recall for 0 class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(877/(877+122) ,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recall for 1 class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(213/(213+195) ,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take this dataset for bank customer churn prediction : https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling\n",
    "\n",
    "1) Build a deep learning model to predict churn rate at bank. \n",
    "\n",
    "2) Once model is built, print classification report and analyze precision, recall and f1-score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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