Survival Analysis.ipynb

{
 "cells": [
  {
   "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",
    "from scipy.stats import norm\n",
    "import statsmodels.api as st\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "#Lifelines is a survival analysis package\n",
    "from lifelines import KaplanMeierFitter\n",
    "from lifelines.statistics import multivariate_logrank_test   \n",
    "from lifelines.statistics import logrank_test\n",
    "from lifelines import CoxPHFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('datasets_13996_18858_WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data 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 \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5174\n",
       "1    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Churn = labelencoder.fit_transform(data.Churn)\n",
    "data.Churn.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Event is defined as the time at which the customer unsubscribe a marketing channel. \n",
    "Time of origin is defined as the time at which the customer starts the service / subscription of a marketing channel.\n",
    "Time scale could be months, or weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventvar = data['Churn']\n",
    "timevar = data['tenure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>customerID</th>\n",
       "      <th>tenure</th>\n",
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       "      <th>Churn</th>\n",
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       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_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",
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       "      <td>7590-VHVEG</td>\n",
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       "      <td>29.85</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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>3668-QPYBK</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
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       "      <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>7795-CFOCW</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
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       "      <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 × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  tenure  MonthlyCharges TotalCharges  Churn  gender_Male  \\\n",
       "0  7590-VHVEG       1           29.85        29.85      0            0   \n",
       "1  5575-GNVDE      34           56.95       1889.5      0            1   \n",
       "2  3668-QPYBK       2           53.85       108.15      1            1   \n",
       "3  7795-CFOCW      45           42.30      1840.75      0            1   \n",
       "4  9237-HQITU       2           70.70       151.65      1            0   \n",
       "\n",
       "   SeniorCitizen_1  Partner_Yes  Dependents_Yes  PhoneService_Yes  ...  \\\n",
       "0                0            1               0                 0  ...   \n",
       "1                0            0               0                 1  ...   \n",
       "2                0            0               0                 1  ...   \n",
       "3                0            0               0                 0  ...   \n",
       "4                0            0               0                 1  ...   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0                                0                0   \n",
       "1                                0                0   \n",
       "2                                0                0   \n",
       "3                                0                0   \n",
       "4                                0                0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                  0                  0                     1   \n",
       "1                  1                  0                     0   \n",
       "2                  0                  0                     1   \n",
       "3                  1                  0                     0   \n",
       "4                  0                  0                     1   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the analysis, I will need to create dummy variables for all categorical variables.\n",
    "categorical = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n",
    "       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
    "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
    "       'PaperlessBilling', 'PaymentMethod']\n",
    "\n",
    "survival_data = pd.get_dummies(data, columns = categorical, drop_first= True)\n",
    "survival_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>SeniorCitizen_1</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>...</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",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</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",
       "      <td>...</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</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>...</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</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>...</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</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",
       "      <td>...</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MonthlyCharges TotalCharges  gender_Male  SeniorCitizen_1  Partner_Yes  \\\n",
       "0           29.85        29.85            0                0            1   \n",
       "1           56.95       1889.5            1                0            0   \n",
       "2           53.85       108.15            1                0            0   \n",
       "3           42.30      1840.75            1                0            0   \n",
       "4           70.70       151.65            0                0            0   \n",
       "\n",
       "   Dependents_Yes  PhoneService_Yes  MultipleLines_No phone service  \\\n",
       "0               0                 0                               1   \n",
       "1               0                 1                               0   \n",
       "2               0                 1                               0   \n",
       "3               0                 0                               1   \n",
       "4               0                 1                               0   \n",
       "\n",
       "   MultipleLines_Yes  InternetService_Fiber optic  ...  StreamingTV_Yes  \\\n",
       "0                  0                            0  ...                0   \n",
       "1                  0                            0  ...                0   \n",
       "2                  0                            0  ...                0   \n",
       "3                  0                            0  ...                0   \n",
       "4                  0                            1  ...                0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                  0                  0                     1   \n",
       "1                  1                  0                     0   \n",
       "2                  0                  0                     1   \n",
       "3                  1                  0                     0   \n",
       "4                  0                  0                     1   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  const  \n",
       "0                           0    1.0  \n",
       "1                           1    1.0  \n",
       "2                           1    1.0  \n",
       "3                           0    1.0  \n",
       "4                           0    1.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop variables such as customerID, tenure, Churn as they are not needed in survival data.\n",
    "#Also, we need to add constant for survival analysis.\n",
    "survival_data.drop(['customerID', 'tenure', 'Churn'], axis = 1, inplace= True)\n",
    "survival_data = st.add_constant(survival_data, prepend=False)\n",
    "survival_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 30 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   MonthlyCharges                         7043 non-null   float64\n",
      " 1   TotalCharges                           7043 non-null   object \n",
      " 2   gender_Male                            7043 non-null   uint8  \n",
      " 3   SeniorCitizen_1                        7043 non-null   uint8  \n",
      " 4   Partner_Yes                            7043 non-null   uint8  \n",
      " 5   Dependents_Yes                         7043 non-null   uint8  \n",
      " 6   PhoneService_Yes                       7043 non-null   uint8  \n",
      " 7   MultipleLines_No phone service         7043 non-null   uint8  \n",
      " 8   MultipleLines_Yes                      7043 non-null   uint8  \n",
      " 9   InternetService_Fiber optic            7043 non-null   uint8  \n",
      " 10  InternetService_No                     7043 non-null   uint8  \n",
      " 11  OnlineSecurity_No internet service     7043 non-null   uint8  \n",
      " 12  OnlineSecurity_Yes                     7043 non-null   uint8  \n",
      " 13  OnlineBackup_No internet service       7043 non-null   uint8  \n",
      " 14  OnlineBackup_Yes                       7043 non-null   uint8  \n",
      " 15  DeviceProtection_No internet service   7043 non-null   uint8  \n",
      " 16  DeviceProtection_Yes                   7043 non-null   uint8  \n",
      " 17  TechSupport_No internet service        7043 non-null   uint8  \n",
      " 18  TechSupport_Yes                        7043 non-null   uint8  \n",
      " 19  StreamingTV_No internet service        7043 non-null   uint8  \n",
      " 20  StreamingTV_Yes                        7043 non-null   uint8  \n",
      " 21  StreamingMovies_No internet service    7043 non-null   uint8  \n",
      " 22  StreamingMovies_Yes                    7043 non-null   uint8  \n",
      " 23  Contract_One year                      7043 non-null   uint8  \n",
      " 24  Contract_Two year                      7043 non-null   uint8  \n",
      " 25  PaperlessBilling_Yes                   7043 non-null   uint8  \n",
      " 26  PaymentMethod_Credit card (automatic)  7043 non-null   uint8  \n",
      " 27  PaymentMethod_Electronic check         7043 non-null   uint8  \n",
      " 28  PaymentMethod_Mailed check             7043 non-null   uint8  \n",
      " 29  const                                  7043 non-null   float64\n",
      "dtypes: float64(2), object(1), uint8(27)\n",
      "memory usage: 350.9+ KB\n"
     ]
    }
   ],
   "source": [
    "survival_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a KaplanMeier object, imported from lifelines\n",
    "kmf = KaplanMeierFitter()\n",
    "#Calculate the K-M curve for all groups\n",
    "kmf.fit(timevar,event_observed = eventvar,label = \"All Customers\")\n",
    "#Plot the curve and assign labels\n",
    "plt.figure(figsize=(8,6))\n",
    "kmf.plot()\n",
    "plt.ylabel('Probability of Customer Survival')\n",
    "plt.xlabel('Tenure')\n",
    "plt.title('Kaplan-Meier Curve');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there is a sudden drop in the starting which says that after one tenure only customers starts churning rapidly and after that churning rate decreases. To deal with that we can consider giving more discounts on long-term plans and make more customers to subscribe for long term plans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male = (survival_data['gender_Male'] == 1)\n",
    "female = (survival_data['gender_Male'] == 0)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[male],event_observed = eventvar[male],label = \"Male\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[female],event_observed = eventvar[female],label = \"Female\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Gender')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[male], timevar[female], event_observed_A=eventvar[male], event_observed_B=eventvar[female])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Senior Citizen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109.49</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SeniorCitizen = (survival_data['SeniorCitizen_1'] == 1)\n",
    "no_SeniorCitizen = (survival_data['SeniorCitizen_1'] == 0)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[SeniorCitizen],event_observed = eventvar[SeniorCitizen],label = \"Senior Citizen\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[no_SeniorCitizen],event_observed = eventvar[no_SeniorCitizen],label = \"Not a Senior Citizen\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Senior Citizen')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[SeniorCitizen], timevar[no_SeniorCitizen], event_observed_A=eventvar[SeniorCitizen], event_observed_B=eventvar[no_SeniorCitizen])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>423.54</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "partner = (survival_data['Partner_Yes'] == 1)\n",
    "no_partner = (survival_data['Partner_Yes'] == 0)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[partner],event_observed = eventvar[partner],label = \"Has partner\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[no_partner],event_observed = eventvar[no_partner],label = \"Does not have a partner\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Partner')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[partner], timevar[no_partner], event_observed_A=eventvar[partner], event_observed_B=eventvar[no_partner])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>232.70</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dependents = (survival_data['Dependents_Yes'] == 1)\n",
    "no_Dependents = (survival_data['Dependents_Yes'] == 0)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[Dependents],event_observed = eventvar[Dependents],label = \"Has dependents\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[no_Dependents],event_observed = eventvar[no_Dependents],label = \"Does not have dependents\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Dependents')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[Dependents], timevar[no_Dependents], event_observed_A=eventvar[Dependents], event_observed_B=eventvar[no_Dependents])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PhoneService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PhoneService = (survival_data['PhoneService_Yes'] == 1)\n",
    "no_PhoneService = (survival_data['PhoneService_Yes'] == 0)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[PhoneService],event_observed = eventvar[PhoneService],label = \"Has a phone service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[no_PhoneService],event_observed = eventvar[no_PhoneService],label = \"Does not have a phone service\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Phone Service')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[PhoneService], timevar[no_PhoneService], event_observed_A=eventvar[PhoneService], event_observed_B=eventvar[no_PhoneService])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultipleLines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.97</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_phone = (survival_data['MultipleLines_No phone service'] == 1)\n",
    "multiLines = (survival_data['MultipleLines_Yes'] == 1)\n",
    "no_multiLines = ((survival_data['MultipleLines_Yes'] == 0) & (survival_data['MultipleLines_No phone service'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_phone],event_observed = eventvar[no_phone],label = \"No Phone Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[multiLines],event_observed = eventvar[multiLines],label = \"Multiple Lines\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_multiLines],event_observed = eventvar[no_multiLines],label = \"Single Line\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Mutliple Lines')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['MultipleLines'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Internet Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>520.12</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fiber_optic = (survival_data['InternetService_Fiber optic'] == 1)\n",
    "No_Service = (survival_data['InternetService_No'] == 1)\n",
    "DSL = ((survival_data['InternetService_Fiber optic'] == 0) & (survival_data['InternetService_No'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[Fiber_optic],event_observed = eventvar[Fiber_optic],label = \"Fiber optic\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[No_Service],event_observed = eventvar[No_Service],label = \"No Service\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[DSL],event_observed = eventvar[DSL],label = \"DSL\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Internet Service')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['InternetService'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Security"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1013.86</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['OnlineSecurity_No internet service'] == 1)\n",
    "onlineSecurity = (survival_data['OnlineSecurity_Yes'] == 1)\n",
    "no_onlineSecurity = ((survival_data['OnlineSecurity_No internet service'] == 0) & (survival_data['OnlineSecurity_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[onlineSecurity],event_observed = eventvar[onlineSecurity],label = \"Online Security\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_onlineSecurity],event_observed = eventvar[no_onlineSecurity],label = \"No online Security\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Online Security')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['OnlineSecurity'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# online backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>0.95</td>\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['OnlineBackup_No internet service'] == 1)\n",
    "onlineBackup = (survival_data['OnlineBackup_Yes'] == 1)\n",
    "no_onlineBackup = ((survival_data['OnlineBackup_No internet service'] == 0) & (survival_data['OnlineBackup_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[onlineBackup],event_observed = eventvar[onlineBackup],label = \"Online Backup\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_onlineBackup],event_observed = eventvar[no_onlineBackup],label = \"No online Backup\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Online Backup')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['OnlineBackup'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Device Protection"
   ]
  },
  {
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>763.51</td>\n",
       "      <td>&lt;0.005</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['DeviceProtection_No internet service'] == 1)\n",
    "DeviceProtection = (survival_data['DeviceProtection_Yes'] == 1)\n",
    "no_DeviceProtection = ((survival_data['DeviceProtection_No internet service'] == 0) & (survival_data['DeviceProtection_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[DeviceProtection],event_observed = eventvar[DeviceProtection],label = \"Device Protection\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_DeviceProtection],event_observed = eventvar[no_DeviceProtection],label = \"No Device Protection\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Device Protection')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['DeviceProtection'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tech Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>989.56</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['TechSupport_No internet service'] == 1)\n",
    "TechSupport = (survival_data['TechSupport_Yes'] == 1)\n",
    "no_TechSupport = ((survival_data['TechSupport_No internet service'] == 0) & (survival_data['TechSupport_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[TechSupport],event_observed = eventvar[TechSupport],label = \"Tech Support\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_TechSupport],event_observed = eventvar[no_TechSupport],label = \"No Tech Support\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Tech Support')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['TechSupport'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streaming TV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>368.31</td>\n",
       "      <td>&lt;0.005</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['StreamingTV_No internet service'] == 1)\n",
    "StreamingTV = (survival_data['StreamingTV_Yes'] == 1)\n",
    "no_StreamingTV = ((survival_data['StreamingTV_No internet service'] == 0) & (survival_data['StreamingTV_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[StreamingTV],event_observed = eventvar[StreamingTV],label = \"Streaming TV\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_StreamingTV],event_observed = eventvar[no_StreamingTV],label = \"No Streaming TV\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Streaming TV')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['StreamingTV'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Streaming Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.95</td>\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survival_data['StreamingMovies_No internet service'] == 1)\n",
    "StreamingMovies = (survival_data['StreamingMovies_Yes'] == 1)\n",
    "no_StreamingMovies = ((survival_data['StreamingMovies_No internet service'] == 0) & (survival_data['StreamingMovies_Yes'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \"No Internet Service\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[StreamingMovies],event_observed = eventvar[StreamingMovies],label = \"Streaming Movies\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[no_StreamingMovies],event_observed = eventvar[no_StreamingMovies],label = \"No Streaming Movies\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Streaming Movies')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['StreamingMovies'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2352.87</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Contract_One_year = (survival_data['Contract_One year'] == 1)\n",
    "Contract_Two_year = (survival_data['Contract_Two year'] == 1)\n",
    "Contract_month_to_month = ((survival_data['Contract_One year'] == 0) & (survival_data['Contract_Two year'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[Contract_One_year],event_observed = eventvar[Contract_One_year],label = \"One year Contract\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[Contract_Two_year],event_observed = eventvar[Contract_Two_year],label = \"Two year Contract\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[Contract_month_to_month],event_observed = eventvar[Contract_month_to_month],label = \"Month to month Contract\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "                 \n",
    "plt.title('Survival of customers: Contract')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['Contract'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Payment Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>865.24</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "automatic_Credit_Card = (survival_data['PaymentMethod_Credit card (automatic)'] == 1)\n",
    "electronic_check = (survival_data['PaymentMethod_Electronic check'] == 1)\n",
    "mailed_check = (survival_data['PaymentMethod_Mailed check'] == 1)\n",
    "automatic_Bank_Transfer = ((survival_data['PaymentMethod_Credit card (automatic)'] == 0) & (survival_data['PaymentMethod_Electronic check'] == 0) & (survival_data['PaymentMethod_Mailed check'] == 0))\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[automatic_Credit_Card],event_observed = eventvar[automatic_Credit_Card],label = \"Automatic Credit card Payment\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[electronic_check],event_observed = eventvar[electronic_check],label = \"Electronic Check\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "\n",
    "kmf.fit(timevar[mailed_check],event_observed = eventvar[mailed_check],label = \"Mailed_check\")\n",
    "plot3 = kmf.plot(ax = plot2)\n",
    "\n",
    "kmf.fit(timevar[automatic_Bank_Transfer],event_observed = eventvar[automatic_Bank_Transfer],label = \"Automatic Bank Transfer\")\n",
    "plot4 = kmf.plot(ax = plot3)\n",
    "                 \n",
    "plt.title('Survival of customers: PaymentMethod')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "twoplusgroups_logrank = multivariate_logrank_test(data['tenure'], data['PaymentMethod'], data['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paperless Billing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>189.51</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PaperlessBilling = (survival_data['PaperlessBilling_Yes'] == 1)\n",
    "no_PaperlessBilling = (survival_data['PaperlessBilling_Yes'] == 0)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "kmf.fit(timevar[PaperlessBilling],event_observed = eventvar[PaperlessBilling],label = \"Paperless Billing\")\n",
    "plot1 = kmf.plot(ax = ax)\n",
    "\n",
    "kmf.fit(timevar[no_PhoneService],event_observed = eventvar[no_PhoneService],label = \"No Paperless Billing\")\n",
    "plot2 = kmf.plot(ax = plot1)\n",
    "                 \n",
    "plt.title('Survival of customers: Paperless Billing')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.yticks(np.linspace(0,1,11))\n",
    "groups = logrank_test(timevar[PaperlessBilling], timevar[no_PaperlessBilling], event_observed_A=eventvar[PaperlessBilling], event_observed_B=eventvar[no_PaperlessBilling])\n",
    "groups.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}