Churn Prediction.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Telco Customer Churn Prediction\n",
    "\n",
    "<table><tr><td><img src = \"CC.png\" width = 518 height = 300 align></img></td></tr></table>\n",
    "\n",
    "* Churn Prediction is  a **Key Predictor** of the Long Term Sucess or Failure of Business.\n",
    "\n",
    "* Churn | Attrition : Customers Who Left using **Company Product** or **Service** within the Last Month.\n",
    "\n",
    "* Customer Retention should be a Top Priority of any Business for keepin the Existing Loyal Customers.\n",
    "\n",
    "* A Company should determine the Customers more at Risk and take **Preventive Measures.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sweetviz as sv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data\n",
    "\n",
    "* Each Row Represents a Customer. \n",
    "\n",
    "* Each Column Represents Customer’s Attributes.\n",
    "\n",
    "**1.Demographic Data :**\n",
    "\n",
    "* Age : Age of a Customer.\n",
    "\n",
    "* Gender : Male | Female.\n",
    "\n",
    "* Senior Citizen : 1 | 0.\n",
    "\n",
    "* Partner or Single : Yes | No.\n",
    "\n",
    "* Dependent or Independent : Yes | No.\n",
    "\n",
    "**2.Services of Company :**\n",
    "\n",
    "* Phone Service : Yes | No.\n",
    "\n",
    "* Multiple Lines : Yes | No | No Phone Service.\n",
    "\n",
    "* Intenet Service : DSL | Fibre Optics | No. \n",
    "\n",
    "* Online Security : Yes | No | No Internet Service.\n",
    "\n",
    "* Device Protection : Yes | No | No Internet Service.\n",
    "\n",
    "* Tech Support : Yes | No | No Internet Service.\n",
    "\n",
    "* TV Streaming : Yes | No | No Internet Service.\n",
    "\n",
    "* Movies Streaming : Yes | No | No Internet Service.\n",
    "\n",
    "**3.Accounts Information :**\n",
    "\n",
    "* Contract : Month to Month | Two Year | One Year.\n",
    "\n",
    "* Payment Method : Electronic Check | Mailed Check | Bank Transfer (Automatic) | Credit Card (Automatic).\n",
    "\n",
    "* Paperless Billing : Yes | No.\n",
    "\n",
    "* Monthly Charges\n",
    "\n",
    "* Total Charges\n",
    "\n",
    "* Tenures : Length of Tenure in Months.\n",
    "\n",
    "**4.Target :**\n",
    "\n",
    "* Churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n0  7590-VHVEG  Female              0     Yes         No       1           No   \n1  5575-GNVDE    Male              0      No         No      34          Yes   \n2  3668-QPYBK    Male              0      No         No       2          Yes   \n3  7795-CFOCW    Male              0      No         No      45           No   \n4  9237-HQITU  Female              0      No         No       2          Yes   \n\n      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n0  No phone service             DSL             No  ...               No   \n1                No             DSL            Yes  ...              Yes   \n2                No             DSL            Yes  ...               No   \n3  No phone service             DSL            Yes  ...              Yes   \n4                No     Fiber optic             No  ...               No   \n\n  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n0          No          No              No  Month-to-month              Yes   \n1          No          No              No        One year               No   \n2          No          No              No  Month-to-month              Yes   \n3         Yes          No              No        One year               No   \n4          No          No              No  Month-to-month              Yes   \n\n               PaymentMethod MonthlyCharges  TotalCharges Churn  \n0           Electronic check          29.85         29.85    No  \n1               Mailed check          56.95        1889.5    No  \n2               Mailed check          53.85        108.15   Yes  \n3  Bank transfer (automatic)          42.30       1840.75    No  \n4           Electronic check          70.70        151.65   Yes  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customerID</th>\n      <th>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>DeviceProtection</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7590-VHVEG</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5575-GNVDE</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>56.95</td>\n      <td>1889.5</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3668-QPYBK</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7795-CFOCW</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9237-HQITU</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# Data Set :\n",
    "data = pd.read_csv('Customer Churn.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7043 entries, 0 to 7042\nData columns (total 21 columns):\n #   Column            Non-Null Count  Dtype  \n---  ------            --------------  -----  \n 0   customerID        7043 non-null   object \n 1   gender            7043 non-null   object \n 2   SeniorCitizen     7043 non-null   int64  \n 3   Partner           7043 non-null   object \n 4   Dependents        7043 non-null   object \n 5   tenure            7043 non-null   int64  \n 6   PhoneService      7043 non-null   object \n 7   MultipleLines     7043 non-null   object \n 8   InternetService   7043 non-null   object \n 9   OnlineSecurity    7043 non-null   object \n 10  OnlineBackup      7043 non-null   object \n 11  DeviceProtection  7043 non-null   object \n 12  TechSupport       7043 non-null   object \n 13  StreamingTV       7043 non-null   object \n 14  StreamingMovies   7043 non-null   object \n 15  Contract          7043 non-null   object \n 16  PaperlessBilling  7043 non-null   object \n 17  PaymentMethod     7043 non-null   object \n 18  MonthlyCharges    7043 non-null   float64\n 19  TotalCharges      7043 non-null   object \n 20  Churn             7043 non-null   object \ndtypes: float64(1), int64(2), object(18)\nmemory usage: 660.3+ KB\n"
    }
   ],
   "source": [
    "# Data Set Information :\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis : EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "customerID          False\ngender              False\nSeniorCitizen       False\nPartner             False\nDependents          False\ntenure              False\nPhoneService        False\nMultipleLines       False\nInternetService     False\nOnlineSecurity      False\nOnlineBackup        False\nDeviceProtection    False\nTechSupport         False\nStreamingTV         False\nStreamingMovies     False\nContract            False\nPaperlessBilling    False\nPaymentMethod       False\nMonthlyCharges      False\nTotalCharges        False\nChurn               False\ndtype: bool"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "data.isna().any() # Any Missing Data ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['CustomerID',\n 'Gender',\n 'SeniorCitizen',\n 'Partner',\n 'Dependents',\n 'Tenure',\n 'PhoneService',\n 'MultipleLines',\n 'InternetService',\n 'OnlineSecurity',\n 'OnlineBackup',\n 'DeviceProtection',\n 'TechSupport',\n 'StreamingTV',\n 'StreamingMovies',\n 'Contract',\n 'PaperlessBilling',\n 'PaymentMethod',\n 'MonthlyCharges',\n 'TotalCharges',\n 'Churn']"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# Not Important, but just trying to Keep Everything in Same Pace...\n",
    "\n",
    "col = list(data.columns) # Making a List of Column Names to Change Each Column Names to Camel Case.\n",
    "col[0] = 'CustomerID'\n",
    "col[1] = 'Gender'\n",
    "col[5] = 'Tenure'\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['CustomerID', 'Gender', 'SeniorCitizen', 'Partner', 'Dependents',\n       'Tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n      dtype='object')"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "data.columns = col # Changing New Name instead of Old Names \n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   CustomerID  Gender  SeniorCitizen Partner Dependents  Tenure PhoneService  \\\n0  7590-VHVEG  Female              0     Yes         No       1           No   \n1  5575-GNVDE    Male              0      No         No      34          Yes   \n2  3668-QPYBK    Male              0      No         No       2          Yes   \n3  7795-CFOCW    Male              0      No         No      45           No   \n4  9237-HQITU  Female              0      No         No       2          Yes   \n\n      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n0  No phone service             DSL             No  ...               No   \n1                No             DSL            Yes  ...              Yes   \n2                No             DSL            Yes  ...               No   \n3  No phone service             DSL            Yes  ...              Yes   \n4                No     Fiber optic             No  ...               No   \n\n  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n0          No          No              No  Month-to-month              Yes   \n1          No          No              No        One year               No   \n2          No          No              No  Month-to-month              Yes   \n3         Yes          No              No        One year               No   \n4          No          No              No  Month-to-month              Yes   \n\n               PaymentMethod MonthlyCharges  TotalCharges Churn  \n0           Electronic check          29.85         29.85    No  \n1               Mailed check          56.95        1889.5    No  \n2               Mailed check          53.85        108.15   Yes  \n3  Bank transfer (automatic)          42.30       1840.75    No  \n4           Electronic check          70.70        151.65   Yes  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CustomerID</th>\n      <th>Gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>Tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>DeviceProtection</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7590-VHVEG</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5575-GNVDE</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>56.95</td>\n      <td>1889.5</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3668-QPYBK</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7795-CFOCW</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9237-HQITU</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Binary Feature: ['Gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'PaperlessBilling', 'Churn']\n"
    }
   ],
   "source": [
    "# Find Binary Feature | Variables : Features with only two Values (Labels | Classes) :\n",
    "binary_features = []\n",
    "\n",
    "for col in data.columns :\n",
    "    if data[col].value_counts().shape[0] == 2 :\n",
    "        binary_features.append(col)\n",
    "\n",
    "print(f'Binary Feature: {binary_features}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remaining are Features with Multiple Values : Ordinal, Nomianal \n",
    "\n",
    "## Binary Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 2, ncols = 3, figsize = (20,10))\n",
    "\n",
    "sns.countplot(x = 'Gender', data = data, hue = 'Gender', ax = axes[0,0])\n",
    "sns.countplot(x = 'SeniorCitizen', data = data, hue = 'SeniorCitizen', ax = axes[0,1])\n",
    "sns.countplot(x = 'Partner', data = data, hue = 'Partner', ax = axes[0,2])\n",
    "sns.countplot(x = 'Dependents', data = data, hue = 'Dependents', ax = axes[1,0])\n",
    "sns.countplot(x = 'PhoneService', data = data, hue = 'PhoneService', ax = axes[1,1])\n",
    "sns.countplot(x = 'PaperlessBilling', data = data, hue = 'PaperlessBilling', ax = axes[1,2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is **High Imbalance** between Senior Citizen and Phone Service.\n",
    "\n",
    "Senior Citizen's Hardly use Phone Services.\n",
    "\n",
    "There are Very Less Persons who Never use Phone Services, almost Everyone use Phone Services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "No     5174\nYes    1869\nName: Churn, dtype: int64"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "data['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding Binary Categorical Values 'Yes' and 'No' to Binary Numeric Values 1's and 0's\n",
    "data['Churn'] = np.where(data['Churn'] == 'Yes',1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to Find Relationship of Churn with Each and Every Feature of the Data Set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "           Churn\nGender          \nFemale  0.269209\nMale    0.261603",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Female</th>\n      <td>0.269209</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>0.261603</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "data.groupby('Gender')[['Churn']].mean()\n",
    "\n",
    "# Gender has no Relation with Churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average Churn Rate of Male and Female is **Almost Equal.**\n",
    "\n",
    "Gender is not Responsible for the Churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  Churn\nSeniorCitizen          \n0              0.236062\n1              0.416813",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>SeniorCitizen</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.236062</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.416813</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "data.groupby('SeniorCitizen')[['Churn']].mean()\n",
    "\n",
    "# Senior Citizens use Less Phone, Internet and Other Services.\n",
    "\n",
    "# Few Senior Citizens just use Services of Streaming TV and Movies if they have no Eye or Ear Problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            Churn\nPartner          \nNo       0.329580\nYes      0.196649",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>Partner</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.329580</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.196649</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "data.groupby('Partner')[['Churn']].mean()\n",
    "\n",
    "# Partners Communicate More with Each Other as well as they Use Other Services as well, so they Hardly Churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                 Churn\nPhoneService          \nNo            0.249267\nYes           0.267096",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>PhoneService</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.249267</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.267096</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "data.groupby('PhoneService')[['Churn']].mean()\n",
    "\n",
    "# Phone Service is a Basic Thing Customer will not Churn Due to Phone Service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     Churn\nPaperlessBilling          \nNo                0.163301\nYes               0.335651",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>PaperlessBilling</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.163301</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.335651</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "data.groupby('PaperlessBilling')[['Churn']].mean()\n",
    "\n",
    "# Not Every Customer is Comfortable with Paperless Billing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Table\n",
    "\n",
    "* Relationship between Features and Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SeniorCitizen         0         1\nGender                           \nFemale         0.239384  0.422535\nMale           0.232808  0.411150",
      "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>SeniorCitizen</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Female</th>\n      <td>0.239384</td>\n      <td>0.422535</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>0.232808</td>\n      <td>0.411150</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "pd.pivot_table(data, values = 'Churn', index = ['Gender'], columns = ['SeniorCitizen'], aggfunc = np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Dependents        No       Yes\nPartner                       \nNo          0.342378  0.213296\nYes         0.254083  0.142367",
      "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>Dependents</th>\n      <th>No</th>\n      <th>Yes</th>\n    </tr>\n    <tr>\n      <th>Partner</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.342378</td>\n      <td>0.213296</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.254083</td>\n      <td>0.142367</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "pd.pivot_table(data, values = 'Churn', index = ['Partner'], columns = ['Dependents'], aggfunc = np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remaining Categorical Data \n",
    "\n",
    "1. Internet Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.countplot('InternetService', data = data, hue='InternetService');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                    Churn\nInternetService          \nDSL              0.189591\nFiber optic      0.418928\nNo               0.074050",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>InternetService</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>DSL</th>\n      <td>0.189591</td>\n    </tr>\n    <tr>\n      <th>Fiber optic</th>\n      <td>0.418928</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>0.074050</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "data.groupby('InternetService')[['InternetService', 'Churn']].mean()\n",
    "\n",
    "# Fibre Optics is Expensive, not all Customers can Afford that Service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                 MonthlyCharges\nInternetService                \nDSL                   58.102169\nFiber optic           91.500129\nNo                    21.079194",
      "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>MonthlyCharges</th>\n    </tr>\n    <tr>\n      <th>InternetService</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>DSL</th>\n      <td>58.102169</td>\n    </tr>\n    <tr>\n      <th>Fiber optic</th>\n      <td>91.500129</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>21.079194</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Monthly Charges on Internet Services\n",
    "data.groupby('InternetService')[['MonthlyCharges']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fibre Optics Connection is much more Expensive than DSL, the main Reason to Churn.\n",
    "\n",
    "# Count Plot \n",
    "\n",
    "* Remaining Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize = (20,10))\n",
    "\n",
    "sns.countplot(x = 'StreamingTV', data = data, hue='StreamingTV', ax = axes[0,0])\n",
    "sns.countplot(x = 'StreamingMovies', data = data, hue = 'StreamingMovies', ax = axes[0,1])\n",
    "sns.countplot(x = 'OnlineSecurity', data = data, hue = 'OnlineSecurity', ax = axes[0,2])\n",
    "sns.countplot(x = 'OnlineBackup', data = data, hue = 'OnlineBackup', ax = axes[1,0])\n",
    "sns.countplot(x = 'DeviceProtection', data = data, hue = 'DeviceProtection', ax = axes[1,1])\n",
    "sns.countplot(x = 'TechSupport', data = data, hue = 'TechSupport', ax = axes[1,2]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nStreamingTV                  \nNo                   0.335231\nNo internet service  0.074050\nYes                  0.300702",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>StreamingTV</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.335231</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.300702</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "data.groupby('StreamingTV')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nStreamingMovies              \nNo                   0.336804\nNo internet service  0.074050\nYes                  0.299414",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>StreamingMovies</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.336804</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.299414</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "data.groupby('StreamingMovies')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nOnlineSecurity               \nNo                   0.417667\nNo internet service  0.074050\nYes                  0.146112",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>OnlineSecurity</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.417667</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.146112</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "data.groupby('OnlineSecurity')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nOnlineBackup                 \nNo                   0.399288\nNo internet service  0.074050\nYes                  0.215315",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>OnlineBackup</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.399288</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.215315</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "data.groupby('OnlineBackup')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nDeviceProtection             \nNo                   0.391276\nNo internet service  0.074050\nYes                  0.225021",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>DeviceProtection</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.391276</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.225021</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "data.groupby('DeviceProtection')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        Churn\nTechSupport                  \nNo                   0.416355\nNo internet service  0.074050\nYes                  0.151663",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>TechSupport</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.416355</td>\n    </tr>\n    <tr>\n      <th>No internet service</th>\n      <td>0.074050</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.151663</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "data.groupby('TechSupport')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are Trying to Find the Impact of Each Feature that may be Responsible for Customer Churn.\n",
    "\n",
    "# Phone Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Yes    6361\nNo      682\nName: PhoneService, dtype: int64"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "data['PhoneService'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "No                  3390\nYes                 2971\nNo phone service     682\nName: MultipleLines, dtype: int64"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "data['MultipleLines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                 Churn\nPhoneService          \nNo            0.249267\nYes           0.267096",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>PhoneService</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.249267</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.267096</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "data.groupby('PhoneService')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     Churn\nMultipleLines             \nNo                0.250442\nNo phone service  0.249267\nYes               0.286099",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>MultipleLines</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.250442</td>\n    </tr>\n    <tr>\n      <th>No phone service</th>\n      <td>0.249267</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.286099</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "data.groupby('MultipleLines')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contract "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.countplot('Contract', data = data, hue = 'Contract');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                   Churn\nContract                \nMonth-to-month  0.427097\nOne year        0.112695\nTwo year        0.028319",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>Contract</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Month-to-month</th>\n      <td>0.427097</td>\n    </tr>\n    <tr>\n      <th>One year</th>\n      <td>0.112695</td>\n    </tr>\n    <tr>\n      <th>Two year</th>\n      <td>0.028319</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "data.groupby('Contract')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Payment Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.countplot('PaymentMethod', data = data, hue = 'PaymentMethod');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                              Churn\nPaymentMethod                      \nBank transfer (automatic)  0.167098\nCredit card (automatic)    0.152431\nElectronic check           0.452854\nMailed check               0.191067",
      "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>Churn</th>\n    </tr>\n    <tr>\n      <th>PaymentMethod</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Bank transfer (automatic)</th>\n      <td>0.167098</td>\n    </tr>\n    <tr>\n      <th>Credit card (automatic)</th>\n      <td>0.152431</td>\n    </tr>\n    <tr>\n      <th>Electronic check</th>\n      <td>0.452854</td>\n    </tr>\n    <tr>\n      <th>Mailed check</th>\n      <td>0.191067</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "data.groupby('PaymentMethod')[['Churn']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous Variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['SeniorCitizen', 'Tenure', 'MonthlyCharges', 'Churn']"
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "list(data.select_dtypes('number').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize = (20,5))\n",
    "\n",
    "sns.distplot(data['Tenure'], ax = axes[0])\n",
    "sns.distplot(data['MonthlyCharges'], ax = axes[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          Tenure  MonthlyCharges\nChurn                           \n0      37.569965       61.265124\n1      17.979133       74.441332",
      "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>Tenure</th>\n      <th>MonthlyCharges</th>\n    </tr>\n    <tr>\n      <th>Churn</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>37.569965</td>\n      <td>61.265124</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17.979133</td>\n      <td>74.441332</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "data.groupby('Churn')[['Tenure', 'MonthlyCharges']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People who have been a Customer for a Long Time tends to Stay with the Company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                   Tenure\nContract                 \nMonth-to-month  18.036645\nOne year        42.044807\nTwo year        56.735103",
      "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>Tenure</th>\n    </tr>\n    <tr>\n      <th>Contract</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Month-to-month</th>\n      <td>18.036645</td>\n    </tr>\n    <tr>\n      <th>One year</th>\n      <td>42.044807</td>\n    </tr>\n    <tr>\n      <th>Two year</th>\n      <td>56.735103</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "data.groupby('Contract')[['Tenure']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contract and Tenure are **Highly Correlated.**\n",
    "\n",
    "Customers with Long Contract Stays Long with the Company as Compared to Customers with Short Term Contracts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sweetviz\n",
    "\n",
    "* Beautiful and High Density Visualizations for Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": ":FEATURES DONE:                    |█████████████████████| [100%]   00:13  -> (00:00 left)\n:PAIRWISE DONE:                    |█████████████████████| [100%]   00:05  -> (00:00 left)\nCreating Associations graph...DONE!\nReport Report.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
    }
   ],
   "source": [
    "eda = sv.analyze([data, \"data\"],target_feat='Churn')\n",
    "\n",
    "eda.show_html('Report.html') # Exploratory Data Analysis Report for Customer Churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion** (Sweetviz) :\n",
    "\n",
    "Customer ID, Gender, Total Charges, Phone Service and Multiple Line do not give any Information regarding Churn.\n",
    "\n",
    "We can Drop this Features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   SeniorCitizen Partner Dependents  Tenure InternetService OnlineSecurity  \\\n0              0     Yes         No       1             DSL             No   \n1              0      No         No      34             DSL            Yes   \n2              0      No         No       2             DSL            Yes   \n3              0      No         No      45             DSL            Yes   \n4              0      No         No       2     Fiber optic             No   \n\n  OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n0          Yes               No          No          No              No   \n1           No              Yes          No          No              No   \n2          Yes               No          No          No              No   \n3           No              Yes         Yes          No              No   \n4           No               No          No          No              No   \n\n         Contract PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n0  Month-to-month              Yes           Electronic check           29.85   \n1        One year               No               Mailed check           56.95   \n2  Month-to-month              Yes               Mailed check           53.85   \n3        One year               No  Bank transfer (automatic)           42.30   \n4  Month-to-month              Yes           Electronic check           70.70   \n\n   Churn  \n0      0  \n1      0  \n2      1  \n3      0  \n4      1  ",
      "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>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>Tenure</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>Churn</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</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>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</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>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>DSL</td>\n      <td>Yes</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>Mailed check</td>\n      <td>53.85</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</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>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Fiber optic</td>\n      <td>No</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>70.70</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "# Drop Customer ID\n",
    "data.drop(['CustomerID', 'Gender', 'PhoneService', 'MultipleLines', 'TotalCharges'], axis = 1, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SeniorCitizen         int64\nPartner              object\nDependents           object\nTenure                int64\nInternetService      object\nOnlineSecurity       object\nOnlineBackup         object\nDeviceProtection     object\nTechSupport          object\nStreamingTV          object\nStreamingMovies      object\nContract             object\nPaperlessBilling     object\nPaymentMethod        object\nMonthlyCharges      float64\nChurn                 int32\ndtype: object"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "* Encoding Categorical Data to Numeric Data\n",
    "\n",
    "* Scaling Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['Partner',\n 'Dependents',\n 'InternetService',\n 'OnlineSecurity',\n 'OnlineBackup',\n 'DeviceProtection',\n 'TechSupport',\n 'StreamingTV',\n 'StreamingMovies',\n 'Contract',\n 'PaperlessBilling',\n 'PaymentMethod']"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "categorical_columns = list(data.select_dtypes('object').columns)\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   SeniorCitizen  Tenure  MonthlyCharges  Churn  Partner_Yes  Dependents_Yes  \\\n0              0       1           29.85      0            1               0   \n1              0      34           56.95      0            0               0   \n2              0       2           53.85      1            0               0   \n3              0      45           42.30      0            0               0   \n4              0       2           70.70      1            0               0   \n\n   InternetService_Fiber optic  InternetService_No  \\\n0                            0                   0   \n1                            0                   0   \n2                            0                   0   \n3                            0                   0   \n4                            1                   0   \n\n   OnlineSecurity_No internet service  OnlineSecurity_Yes  ...  \\\n0                                   0                   0  ...   \n1                                   0                   1  ...   \n2                                   0                   1  ...   \n3                                   0                   1  ...   \n4                                   0                   0  ...   \n\n   StreamingTV_No internet service  StreamingTV_Yes  \\\n0                                0                0   \n1                                0                0   \n2                                0                0   \n3                                0                0   \n4                                0                0   \n\n   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n0                                    0                    0   \n1                                    0                    0   \n2                                    0                    0   \n3                                    0                    0   \n4                                    0                    0   \n\n   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n0                  0                  0                     1   \n1                  1                  0                     0   \n2                  0                  0                     1   \n3                  1                  0                     0   \n4                  0                  0                     1   \n\n   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n0                                      0                               1   \n1                                      0                               0   \n2                                      0                               0   \n3                                      0                               0   \n4                                      0                               1   \n\n   PaymentMethod_Mailed check  \n0                           0  \n1                           1  \n2                           1  \n3                           0  \n4                           0  \n\n[5 rows x 26 columns]",
      "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>SeniorCitizen</th>\n      <th>Tenure</th>\n      <th>MonthlyCharges</th>\n      <th>Churn</th>\n      <th>Partner_Yes</th>\n      <th>Dependents_Yes</th>\n      <th>InternetService_Fiber optic</th>\n      <th>InternetService_No</th>\n      <th>OnlineSecurity_No internet service</th>\n      <th>OnlineSecurity_Yes</th>\n      <th>...</th>\n      <th>StreamingTV_No internet service</th>\n      <th>StreamingTV_Yes</th>\n      <th>StreamingMovies_No internet service</th>\n      <th>StreamingMovies_Yes</th>\n      <th>Contract_One year</th>\n      <th>Contract_Two year</th>\n      <th>PaperlessBilling_Yes</th>\n      <th>PaymentMethod_Credit card (automatic)</th>\n      <th>PaymentMethod_Electronic check</th>\n      <th>PaymentMethod_Mailed check</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>29.85</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>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>34</td>\n      <td>56.95</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>2</td>\n      <td>53.85</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>45</td>\n      <td>42.30</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</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>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>2</td>\n      <td>70.70</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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "# Create Dummies :\n",
    "X = pd.get_dummies(data, columns = categorical_columns, drop_first = True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Min Max Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   SeniorCitizen    Tenure  MonthlyCharges  Churn  Partner_Yes  \\\n0              0  0.013889        0.115423      0            1   \n1              0  0.472222        0.385075      0            0   \n2              0  0.027778        0.354229      1            0   \n3              0  0.625000        0.239303      0            0   \n4              0  0.027778        0.521891      1            0   \n\n   Dependents_Yes  InternetService_Fiber optic  InternetService_No  \\\n0               0                            0                   0   \n1               0                            0                   0   \n2               0                            0                   0   \n3               0                            0                   0   \n4               0                            1                   0   \n\n   OnlineSecurity_No internet service  OnlineSecurity_Yes  ...  \\\n0                                   0                   0  ...   \n1                                   0                   1  ...   \n2                                   0                   1  ...   \n3                                   0                   1  ...   \n4                                   0                   0  ...   \n\n   StreamingTV_No internet service  StreamingTV_Yes  \\\n0                                0                0   \n1                                0                0   \n2                                0                0   \n3                                0                0   \n4                                0                0   \n\n   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n0                                    0                    0   \n1                                    0                    0   \n2                                    0                    0   \n3                                    0                    0   \n4                                    0                    0   \n\n   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n0                  0                  0                     1   \n1                  1                  0                     0   \n2                  0                  0                     1   \n3                  1                  0                     0   \n4                  0                  0                     1   \n\n   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n0                                      0                               1   \n1                                      0                               0   \n2                                      0                               0   \n3                                      0                               0   \n4                                      0                               1   \n\n   PaymentMethod_Mailed check  \n0                           0  \n1                           1  \n2                           1  \n3                           0  \n4                           0  \n\n[5 rows x 26 columns]",
      "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>SeniorCitizen</th>\n      <th>Tenure</th>\n      <th>MonthlyCharges</th>\n      <th>Churn</th>\n      <th>Partner_Yes</th>\n      <th>Dependents_Yes</th>\n      <th>InternetService_Fiber optic</th>\n      <th>InternetService_No</th>\n      <th>OnlineSecurity_No internet service</th>\n      <th>OnlineSecurity_Yes</th>\n      <th>...</th>\n      <th>StreamingTV_No internet service</th>\n      <th>StreamingTV_Yes</th>\n      <th>StreamingMovies_No internet service</th>\n      <th>StreamingMovies_Yes</th>\n      <th>Contract_One year</th>\n      <th>Contract_Two year</th>\n      <th>PaperlessBilling_Yes</th>\n      <th>PaymentMethod_Credit card (automatic)</th>\n      <th>PaymentMethod_Electronic check</th>\n      <th>PaymentMethod_Mailed check</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.013889</td>\n      <td>0.115423</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>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0.472222</td>\n      <td>0.385075</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0.027778</td>\n      <td>0.354229</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.625000</td>\n      <td>0.239303</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</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>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0.027778</td>\n      <td>0.521891</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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X['Tenure'] = scaler.fit_transform(data[['Tenure']])\n",
    "\n",
    "X['MonthlyCharges'] = scaler.fit_transform(data[['MonthlyCharges']])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    5174\n1    1869\nName: Churn, dtype: int64"
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "# Check whether Target Feature | Variable is Balanced or Imbalanced.\n",
    "data['Churn'].value_counts()\n",
    "\n",
    "# Target Feature has Imbalanced Class, therefor Resampling is Needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.countplot('Churn', data = data).set_title('Distribution of Data before Sampling');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "No : 5174 | Yes : 1869\n"
    }
   ],
   "source": [
    "X_no = X[X['Churn'] == 0]\n",
    "\n",
    "X_yes = X[X['Churn'] == 1]\n",
    "\n",
    "print(f'No : {len(X_no)} | Yes : {len(X_yes)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Upsampled Yes : 5174\n"
    }
   ],
   "source": [
    "# Yes Needs Upscaling\n",
    "X_yes_upsample = X_yes.sample(n = len(X_no), replace = True, random_state = 42)\n",
    "print(f'Upsampled Yes : {len(X_yes_upsample)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_no.append(X_yes_upsample).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.countplot('Churn', data = X).set_title('Distribution After Upsampling');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1    5174\n0    5174\nName: Churn, dtype: int64"
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "X['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Data into Independent and Dependent Variables :\n",
    "x = X.drop('Churn', axis = 1)\n",
    "y = X['Churn']\n",
    "\n",
    "# Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Modeling\n",
    "\n",
    "1. Ridge Classifier :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_classifier = RidgeClassifier().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Accuracy of Ridge Classifier on Train Set : 75.91%\n"
    }
   ],
   "source": [
    "y_prediction = ridge_classifier.predict(x_train)\n",
    "\n",
    "print(f'Accuracy of Ridge Classifier on Train Set : {accuracy_score(y_train, y_prediction)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Model Evaluation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Accuracy of Ridge Classifier on Test Set : 76.28%\n"
    }
   ],
   "source": [
    "y_prediction = ridge_classifier.predict(x_test)\n",
    "\n",
    "print(f'Accuracy of Ridge Classifier on Test Set : {accuracy_score(y_test, y_prediction)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is not very Good, so try Different Model\n",
    "\n",
    "2. Random Forest Classifier :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Accuracy of Random Forest Classifier on Train Set : 87.87%\nAccuracy of Random Forest Classifier on Test Set : 84.88%\n"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = 100, max_depth = 10).fit(x_train, y_train)\n",
    "\n",
    "y_prediction = rfc.predict(x_train)\n",
    "\n",
    "print(f'Accuracy of Random Forest Classifier on Train Set : {accuracy_score(y_train, y_prediction)*100:.2f}%')\n",
    "\n",
    "y_prediction = rfc.predict(x_test)\n",
    "\n",
    "print(f'Accuracy of Random Forest Classifier on Test Set : {accuracy_score(y_test, y_prediction)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy on Training Set is Slightly more that Represents **Overfitting.** \n",
    "\n",
    "We can Decrease the Depth of the Tree to Prevent Model from **Overfitting,** It may also Decrease Accuracy.\n",
    "\n",
    "We Must Carefully Optimize the Parameters, Parameter Tuning is Very Critical Part.\n",
    "\n",
    "We can Increase the Number of Trees which will Help the Model to be more Generalized and Reduce Overfitting.\n",
    "\n",
    "Another way is to do **Cross Validation** which Allows to Use Every Sample in Train Set and Test Set.\n",
    "\n",
    "# Grid Search Cross Validation\n",
    "\n",
    "* Improving the Model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters  = {'n_estimators' : [150, 200, 250, 300], 'max_depth' : [15, 20, 25]}\n",
    "\n",
    "gsv_rfc = GridSearchCV(estimator = rfc, param_grid = parameters, n_jobs = -1, cv = 5).fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{'max_depth': 20, 'n_estimators': 200}"
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "# Best Parameters :\n",
    "\n",
    "gsv_rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Best Accuracy Score obtained from Grid Search Cross Validation : 88.08%\n"
    }
   ],
   "source": [
    "# Best Score :\n",
    "\n",
    "print(f'Best Accuracy Score obtained from Grid Search Cross Validation : {gsv_rfc.best_score_*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Search Cross Validation has Improved the Accuracy Score of the Model.\n",
    "\n",
    "Checking the Accuracy of Model on the Entire Data Set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Best Accuracy Score obtained from Grid Search Cross Validation : 90.11%\n"
    }
   ],
   "source": [
    "gsv_rfc = GridSearchCV(estimator = rfc, param_grid = parameters, n_jobs = -1, cv = 5).fit(x,y)\n",
    "\n",
    "print(f'Best Accuracy Score obtained from Grid Search Cross Validation : {gsv_rfc.best_score_*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Have Achieved an Overall Accuracy of **90%**\n",
    "\n",
    "The Mean Cross Validated Score of The Best Estimator,\n",
    "\n",
    "Using the Grid Search Cross Validation we Improved the **Model Accuracy.**"
   ]
  }
 ]
}