Customers Survival Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Survival Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If time to event has the probability density function $f(t)$ and cumulative distribution function $F(t)$, then the probability of surviving at least to time $t$ is: $Pr(T>t)=S(t)=1-F(t)$. \n",
    "\n",
    "Cumulative hazard at time t is defined as $H(t)=-ln(S(t))$ and instantaneous hazard at time $t$ is $h(t)=\\frac{dH(t)}{dt}$. The instantateous hazard can also be written as $h(t)=\\frac{f(t)}{S(t)}$\n",
    "\n",
    "The likelihood function for survival analysis is described as:\n",
    "\n",
    "$$ l(\\beta) = \\prod_{n=1}^{n} h(t_{i})^{d_{i}} S(t_{i}) $$\n",
    "where $d_i$ is the censoring variable that equals to 1 if the event is observed for individual $i$ and 0 if the event is not observed (censored) for individual $i$, $h(t_i)$ is the hazard for individual $i$ at time $t$, $H(t_i)$ is the cumulative hazard for individual $i$ at time $t$, and $S(t_i)$ is the survival probability for individual $i$ at time $t$. Note that when $d_i=0$, the contribution of the $i$'th individual to the likelihood function is just its survival probability until time $t$: S(t). If the individual has the event, the contribution to the likelihood function is given by the density function $f(t)=h(t)S(t)$.\n",
    "\n",
    "The log of likelihood is:\n",
    "\n",
    "$$ logl(\\beta) = \\sum_{i=1}^n d_i log(h(t_i)) - H(t_i) $$\n",
    "where $log$ is the natural logarithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Data/Telco-Customer-Churn.csv\")\n",
    "df.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",
      "customerID          7043 non-null object\n",
      "gender              7043 non-null object\n",
      "SeniorCitizen       7043 non-null int64\n",
      "Partner             7043 non-null object\n",
      "Dependents          7043 non-null object\n",
      "tenure              7043 non-null int64\n",
      "PhoneService        7043 non-null object\n",
      "MultipleLines       7043 non-null object\n",
      "InternetService     7043 non-null object\n",
      "OnlineSecurity      7043 non-null object\n",
      "OnlineBackup        7043 non-null object\n",
      "DeviceProtection    7043 non-null object\n",
      "TechSupport         7043 non-null object\n",
      "StreamingTV         7043 non-null object\n",
      "StreamingMovies     7043 non-null object\n",
      "Contract            7043 non-null object\n",
      "PaperlessBilling    7043 non-null object\n",
      "PaymentMethod       7043 non-null object\n",
      "MonthlyCharges      7043 non-null float64\n",
      "TotalCharges        7043 non-null object\n",
      "Churn               7043 non-null object\n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, Churn is an event which indicates whether customer exited or not. Tenure shows how long customer remained in our service. Both of these variables are very important for customer survival analysis."
   ]
  },
  {
   "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": [
    "df.Churn = labelencoder.fit_transform(df.Churn)\n",
    "df.Churn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventvar = df['Churn']\n",
    "timevar = df['tenure']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the analysis, I will need to create dummy variables for all categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</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>...</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",
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       "      <th>0</th>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <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>9237-HQITU</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</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>...</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 × 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": [
    "categorical = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n",
    "       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
    "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
    "       'PaperlessBilling', 'PaymentMethod']\n",
    "\n",
    "survivaldata = pd.get_dummies(df, columns = categorical, drop_first= True)\n",
    "survivaldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to drop variables such as customerID, tenure, Churn as they are not needed in survival data. Also, we need to add constant for survival analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "survivaldata.drop(['customerID', 'tenure', 'Churn'], axis = 1, inplace= True)\n",
    "survivaldata = st.add_constant(survivaldata, prepend=False)\n",
    "survivaldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaplan-Meier Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Kaplan-Meier method calculates the probability of survival at time 𝑡  as:\n",
    "\n",
    "$$ S(t) = \\prod_{i=1}^{t-1} (1 - \\frac{d_i}{n_i}) $$\n",
    "\n",
    "where,\n",
    "- 𝑆(𝑡) is the probability of survival until time 𝑡, \n",
    "- $𝑑_𝑖$  is the number of units that experienced the event at time 𝑡,  \n",
    "- $𝑛_𝑖$  is the number of units at risk of experiencing the event at time 𝑡.  \n",
    "\n",
    "$𝑛_𝑖$ decreases with time, as units experience the event or are censored. $\\frac{d_i}{n_i}$ is the probability of experiencing the event at time 𝑖 and $(1− \\frac{d_i}{n_i})$ is the probability of surviving at time 𝑖. \n",
    "\n",
    "Note that this method does not use any parameters, it only depends on the data on time and censoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 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",
    "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": "markdown",
   "metadata": {},
   "source": [
    "### Log-Rank Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use non-parametric method log-rank test to compare survival curves between different groups. The log-rank test assumes that the hazards of the groups are proportional. Under the null hypothesis, the probability of event across the groups is the same for all time points. \n",
    "\n",
    "To test the null hypothesis, the log-rank test calculates the difference between the observed number of events and the number of events expected in each group proportional to the size of the groups at each time point an event is observed. The log-rank test statistic for group $j$ $(k_{j})$ follows a $\\chi^2$ distribution and is calculated as:\n",
    "\n",
    "$$k_{j} = \\frac{(O_{j}-E_{j})^{2}}{var(O_{j}-E_{j})}$$\n",
    "\n",
    "$O_{j}-E_{j}$ is calculated as:\n",
    "\n",
    "$$O_{j}-E_{j} = \\sum_{i}(o_{ij}-e_{ij})$$ \n",
    "\n",
    "and $var(O_{j}-E_{j})$ is:\n",
    "\n",
    "$$var(O_{j}-E_{j}) = o_{i}\\frac{n_{ij}}{n_{i}}\\Big(1-\\frac{n_{ij}}{n_{i}}\\Big)\\frac{(n_{i}-o_{i})}{(n_{i}-1)}$$\n",
    "\n",
    "$o_{ij}$ is the observed number of events in group $j$ at time $i$ and $e_{ij}$ is the expected number of events in group $j$ at time $i$, which is calculated as $e_{ij} = \\frac{n_{ij}}{n_i}{o_{i}}$. Note that $\\frac{n_{ij}}{n_i}$ is the proportion of units in group $j$ at risk of event at time $i$ ($n_{ij}$) to the number of units in all groups at risk of event at time $i$ ($n_{i}$) and ${o_{i}}$ is the observed number of events in all groups at time $i$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing multiple groups, we first calculate the pairwise log-rank test scores between each of the $k-1$ groups, and write them as a vector of log-rank statistics, $\\bf{Z}$ which has $k - 1$ elements. We can leave any of one of the statistics out, because the $k$ covariances are linearly dependent on each other (the sum of log-rank statistics is 0, $\\sum k_{j}=0$.\n",
    "\n",
    "The test statistic for the hypothesis that there is no difference in survival times of $k$ groups is calculated as: \n",
    "\n",
    "$$logrankstatistic = \\bf{Z} {\\sum}^{-1} \\bf{Z}'$$ \n",
    "\n",
    "which has a $\\chi^2$ distribution, where ${\\sum}^{-1}$ is the inverse of the $k-1$ by $k-1$ variance-covariance matrix of $\\bf{Z}$, which has variance of $k_{j}$ on its diagonal elements and $covar(k_{jg})$ on its off-diagonal elements.\n",
    "\n",
    "The variance of observed number of events in group $j$ is calculated as $var(O_{j}-E_{j})$ as demonstrated above. The covariance between the observed number of events in group $j$ and $g$ is calculated as:\n",
    "\n",
    "$$covar(k_{jg})=o_{i}\\frac{(n_{ij}n_{ig})}{(n_{i}n_{i})}\\frac{(n_{i}-o_{i})}{(n_{i}-1)}$$\n",
    "\n",
    "Note that rejecting the null hypothesis means that the survival times of groups do not come from the same distribution, it does not specify which group's survival time is different. The following plots and test statistics compare the groups in the dataset in terms of the different explanatory variables. Astatistically significant log-rank test statistic indicates that we can reject the null hypothesis that time to survival in all groups come from the same distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic    p  -log2(p)\n",
      "           0.53 0.47      1.09\n"
     ]
    },
    {
     "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": [
    "male = (survivaldata['gender_Male'] == 1)\n",
    "female = (survivaldata['gender_Male'] == 0)\n",
    "\n",
    "plt.figure()\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": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         109.49 <0.005     82.71\n"
     ]
    },
    {
     "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 = (survivaldata['SeniorCitizen_1'] == 1)\n",
    "no_SeniorCitizen = (survivaldata['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": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         423.54 <0.005    310.21\n"
     ]
    },
    {
     "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 = (survivaldata['Partner_Yes'] == 1)\n",
    "no_partner = (survivaldata['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": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         232.70 <0.005    172.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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gGImUHcWtYMPKHdvbtsK68rSWx2jIon44JiaPdEZIpTsvI5moySTdvg9PHG5Xkc17NFdIGgU8QzAkdaKZzZE0Figzs6nAYOCPkoyg+Sgj92xOKvHDfst6eP32HdsFNEIpH0RZNyqZVMkjk7WORN5c5VxqWZ2nkA0p5ynUxbtTdjQpVTYnDR5d9ZgG3qS0syWbh5EoE/MykqmpuQqqd5JHSRRReKe5yxdRO5p37aSw4NmqayZ9/iG0LsxJbw3JzkwcdUkUqaSbSKIkDvDk4erHk0Jdlf09aE5KHKHUsr3XHvJQlMQB6SWPdPo1Mj0sN5moySMVTy67Jk8K6YhvToKgSWntEtirW9XjvPZQMBKTx85smoqiLslkZzZpgSePhsaTQjrim5MAFr8AH74GjZvu2Jes9uA1h4KRzRpGOuoyixyqJo9MJYmaeH9Iw+JJIVOi1B685tDg7KwaRlSZnEUeVZSks3r9Zk7o0yFrMbjMyYcZzQ1D4vyG9n2DUUqV1i6pPgmu8jyvPRSsdIfcRpGJOR3pNldFFXV9q80V23yORwPjNYV0JA5lTdbvsHcv6P69qvs8UTjyrxaSTNSaSdQ5HpnoIAdPMPXhNYVsiq89JNYcIEgSAAefUXV/fI3D7bKyNfEvk6LUTKJOBsxkzOlOIozCE07AawqZ4KOW3E6Qy4l/yUSd45HtDvFMaegd697RvDP5qCWXJ6KOrooiE3M8Mt0hnusEk25TWD4kE08KuRal9uD9Di6PZaLvI5Md4tkecZXNhJMPo7S8TyHXUo1aWvle+PNu1fP27lW11lFZlicKt5NF6fvI1gKHyWRzxFV9FkuMItkorXyoPSST1ZqCpCHAOIJVUu82s+sTHu8M3APsER4z2sz+XVuZBVNTSCa+9vDB09Vv8lNZkxhyfdX9G1YGNwpyLs8ka67Kx9FUqdRnDaxMrnmVzUSR8+YjSUXAB8BRBDfcmQGcbmZz446ZAMwys9sl9QT+bWZdayu3oJNCYt/D1s2wZ5cd21GHt3rNweWxQhhyG0XU5dghc4kjMVFkMknkQ/NRf2ChmS0OA5oMDAXmxh1jQMvw91Yk3JmtwUn8IE+c8FYyuPo5yZqZvInJ5bFMTfzLdTKJ0vSV6ft4JD7fTE2YrItsJoUOwPK47XLg0IRjxgDTJF0EfA1I6HUNSBoJjATo3LlzxgPNmWr9DgdD2/2r1h4Sm5l8DoQrMOl+0831LPIo0k0cURNFLvoistl8dApwjJn9NNw+C+hvZhfFHXNpGMOfJX0L+BtwYHh7zqQKuvkoisQmJqjazORNTM7VWbpDdXfmqrrL1nxJl9bN+P3xB9R6brp9EfnQfFQOdIrb7kj15qGfAEMAzOw1ScVAG2BVFuPKb8k+yOObmUoGV3/cm5icq1WmaiuZShLJahhjn5rDsjVfMvapObF9UZqYoPpM7/rUJrKZFGYA3SV1Az4CTgOGJxzzIXAkMEnS/kAxsDqLMRWmKstq1KOJ6bNlvnCfc3WwM/tHBn6jDbCj9rBszZfAp5E6qBPLTrYcSKOmzVsSQbaHpH4fuJlguOlEM7tW0ligzMymhiOO7gKaE3Q6/8bMptVWZoNvPooi3SamksHwzSFV9322DJoU79j2JOFcxk2d/RFtWxSnPjBOZc2hS+tmVfanOwR2UK+Sxdu+XPeNVMdldfJaOOfg3wn7fh/3+1xgYDZjaJDq08SUODeiZHDVROG1CecyrsXuTeo88S+x5gDZn2QHPqO54ahrExMkTxQlg5PXJhITRbLre+JwLqlk7fupZogn63eoqYM6ajNTFL72UUOVqokJqieKle8F/+59YOrySwZXTR4+69q5estUM1OymkNeNB+5HEr2rX3Bs7XPi0hWm0gmWQ1j717R4vIahXMZldjMVFMTU1SeFHYlqRJFsmanZJLVMJIt7lcyuHpTlE+yc65GiX0PUUYtRbkhUtDEFI03H7mqojQ7JaqpvwKqN0X5JDvnIsvUgoNjn5rDtCt+4M1HLg1Rmp2gaqL45pDqNYLaOrZTTbJLxpOH2wWl00GdTJfW0ZOI1xRcelKt+JpM1I7tksE+n8K5OojSQe0dzS67Ej+QU9UmoHqNIuowWYg2nyIZTx7O1UnKpCDpQDN7b2cE4wrYzmh2SjUyqmRw6jkWniRcA5ROB3VNUjYfSXoZ2A2YBDxgZp+ndaUM8eajApeJZqdkojZFJTZDgScK1+Ak66A+8Vv7Ldy2aX33VOdG6lOQ1B04FzgFeBP4u5k9m1649eNJoYFJZ7RTMlFHQJUMTt1fAZ4oXIPTqPhrH2zfvLFHquMidzSHt9f8IXAL8AUg4Ldm9s/6BFpXnhR2AdlKFPXp2E7GE4crIBlLCpJ6Az8GjgOeBf5mZm9Jag+8ZmY1/k+VNAQYR7BK6t1mdn3C438BDg83mwHtzGyP2uLxpLCLSpYoEmVyPkXJ4OqJIpGPiHIFJJNJ4UWC5a2nmNmmhMfOMrN/1HBeEfABcBTBDXdmAKeHK6MmO/4i4GAzO7e2eDwpuBple+JdopLB3l/hCkYm77z2z8QPfkkXm9m4mhJCqD+w0MwWh+dMBoYCSZMCcDpwVYR4nEsuygioVMNkIXrHdrorzHqicHksSk3hLTPrm7BvlpkdnOK8k4EhCfdoPtTMRiU5tgvwOtDRzLYleXwkMBKgc+fO/ZYtW1b7s3KuJvnYX+FJwu0E9a4pSDqd4PaZ3SRNjXuoBbAmSgxJ9tWUgU4jaJ6qlhAAzGwCMAGC5qMI13YuuXTmUyTjE/FcA1Vb89GrwMdAG+DPcfvXA+9EKLsc6BS33RFYUcOxpwEXRijTucyLmigSZWoiXsng1J3a4BPx3E6RtbWPJDUm6Gg+EviIoKN5uJnNSTiuB/AM0M0iBOMdzS5vZHP9p2RKBnvHtktbJpqPXjazQZLWU7XZR4CZWcvaCjazCkmjCD7wi4CJZjZH0ligzMwqm6ROByZHSQjO5ZVsrf+UjHdsu53EV0l1LpvysWM7GU8cDV7UmkKNSUHSXrWdaGZr04ytXjwpuIKXTrNTomxPxANPFA1MJpLCEoJmo6SjiMyspH4hpseTgmtwdvb6T8mUDPahsw1cvZNCvvKk4HYJ2UwUieqzwmwynjzyUiZqCvuZ2XxJfZM9bmZv1TPGtHhScLusTDQ7JVOfFWaT8RpGXspEUphgZiMlPZ/kYTOzI+obZDo8KTgXylRtIpn6dGwn8hpGXvDmI+d2RZlYTTaZTPdXJLNhJfQ6uW5xucgylhQkFQM/BwYRdDy/BNxhZptrPTFLPCk4V0/52F8B3uyUZZlMCg8TLG1xX7jrdGBPMzul3lGmwZOCc1mQraao+gyd9WGyGZXJpPC2mR2Uat/O4knBuZ0k101RJYO9NpFBmbyfwixJA8zs9bDgQ4FX6hugcy7PRfmwzcQKs1A9UaxdsuPYeInl+rIeGVfb6KN3CfoQmgA9gA/D7S7AXDOLsIJX5nlNwbk8l4kaxtOjg8SwV7fayykZHG1ZD08UGakpHJ/BeJxzu4pM1DBKBqcuI2ptAnzZ8TqIPCRVUjsgln7N7MMI5wwBxhGsknq3mV2f5JhTgTEEtZC3zWx4bWV6TcG5BurdKdB87+jHR61NgHdik8E+BUknENxkpz2wiqD5aB5wQIrzioDxwFEEN9yZIWmqmc2NO6Y7cAUw0Mw+CxOPc25XVNyq9vtpJyoZHK3cZDWKKLWJypgacKJIJkpH8x+AAcB/zexgSYcTDEtNpT+w0MwWA0iaDAwF5sYdcx4w3sw+AzCzVXUJ3jnXgNT1/hTJOqyTqaxRPD16x76Swek1OyXTwBJHlKSw1czWSGokqZGZPS/p/yKc1wFYHrddDhyacMw3ASS9QtDENMbMno4SuHOugYtym9QoQ2JLBlfdjnrvbIg23LaBJY4oSeFzSc0JZjLfL2kVUBHhvKRLbie5fndgMME9nF+SdKCZfV6lIGkkMBKgc+fOES7tnGuQsnW3u5oSRaKSwZlLHHmaKKIkhaHAZuAS4AygFTA2wnnlQKe47Y7AiiTHvG5mW4Elkt4nSBIz4g8yswnABAg6miNc2zm3K4hSm4DUzU5Rl+yIkjigevJIljgSY8wTkUYfSdqHoI/AgBlm9kmEcxoDHwBHAh8RfNAPN7M5cccMAU43s3MktQFmAX3MbE1N5froI+dcndV1ZFMydbmfNtR9djZktfaQydFHPwV+D/yPoEnoVkljzWxibeeZWYWkUcAzBP0FE81sjqSxQJmZTQ0fO1rSXGAbcFltCcE559JS15FNyUTt2E627HjUPow8mE8RZe2j94FvV35YS2oNvGpmPXZCfNV4TcE5V29RZl1D9hYFrJxfMaTa1K2qMlibyOTaR+UEq6RWWk/VUUXOOVdYon6opjPaKVGyGkayYbKQF30RNSYFSZeGv34EvCHpCYI+haHAm1mNyjnn8kGU0U7JpDPxLlkzU8ng6gll6+asjmSqrabQIvx3UfhT6YmMXNk55wpNpmoYmVwpFjJae6jL2kctCO7NvCFjV0+D9yk45wpOOjcxqmltp5LBad1nIpOjjw4E/gHsFW5/CpwdP7TUOedcLZLVMFLNgi4ZXH1f1JFM9ag5ROlongBcambPA0gaDNwFfDvtqzrn3K4ucZgspDfJLlkzU5J+hz2LaRklrChJ4WuVCQHAzKZL+lqUwp1zztUgnbWdahrJlChJs1RRIxVFCStKUlgs6UqCJiSAM4ElUQp3zjlXB4mJIlUTU6Uoq8BGFCUpnAtcDfwz3H4R+HFaV3POORddqiYmiL4KbES1JoXwRjm/NbNfpFW6c8659EXpoI6yCuza6I07tSYFM9smqV/k0pxzzmVXOh3UT48GUq5jCkRrPpolaSrwCLCxcqeZ/bPmU5xzzmVFOsNb6yBKUtgLWAMcEbfP2NHH4JxzLp/tVQK8FunQKEnhMjP7NJ04wvsljCNYOvtuM7s+4fERwI0E6ysB3GZmd6dzLeec22WlWhq8/0jg/khF1bYg3g+AicBWSduBU83s1agxhp3U44GjCFZanSFpqpnNTTj0ITMbFbVc55xzCSIs3Ldtu22PUlRtNYVrge+Y2XxJhwI3AN+tQ5j9gYVmthhA0mSCFVYTk4JzzrlMStLvsN2IlBQa1fJYhZnNBzCzN9ixampUHah634XycF+iH0l6R9IUSZ2SPI6kkZLKJJWtXr26jmE455yLqraaQru4eypU2zazm1KUrST7EpdkfRJ40My2SDofuIeqHdqV15pAsAYTpaWl0ZZ1dc45V2e11RTuIqgdVP4kbqdSDsR/8+8IrIg/wMzWmNmWuOv5nAjnnMuCdVuq3EGzRjXWFMzs6nrGMAPoLqkbweii04Dh8QdI+rqZfRxungDMq+c1nXPO1UOUIalpMbMKSaOAZwiGpE40szmSxgJlZjYV+IWkE4AKYC0wIlvxOOecSy3yndfyhd95zTnn6i7qnddq61Nwzjm3i6lt8tqlNT0GkUYfOeecKzC19SnUdV6Cc865ApfN0UfOOecKTMrRR5KKgZ8ABwDFlfvN7NwsxuWccy4HonQ0/wPYBzgGeIFgElqkSRDOOecKS5SksK+ZXQlsNLN7gOOAXtkNyznnXC5ESQpbw38/l3Qg0AromrWInHPO5UyUGc0TJO0JXAlMBZqHvzvnnGtgoiSFv5vZNoL+hJIsx+Occy6HojQfLZE0QdKRkpIth+2cc66BiJIUegD/BS4Elkq6TdKg7IblnHMuF1ImBTPbZGYPm9lJQB+gJUFTUkqShkh6X9JCSaNrOe5kSSYp5WJNzjnnsifSgniSvivpr8BbBBPYTo1wThEwHjgW6AmcLqlnkuNaAL8A3qhD3M4557IgZVKQtAS4BHgJONDMTjWzRyOU3R9YaGaLzewrYDIwNMlxfwBuADZHD9s551w2RBl9dJCZfZFG2R2A5XHb5cCh8QdIOhjoZGZPSfp1TQVJGgmMBOjcuXMaoTjnnIuitqWzf2NmNwDXSqp2Jx4z+0WKspONVIqVI6kR8Bci3G3NzCYAEyC4yU6q451zzqWntppC5f2S073NWTnQKW67I7AibrsFcCAwPRzpug8wVdIJZua3VnPOuRyobensJ8Nf3zGzWWm0CBPRAAAZYElEQVSUPQPoLqkb8BFwGjA8rvx1QJvKbUnTgV97QnDOudyJMvroJknzJf1B0gFRCzazCmAU8AxBreNhM5sjaaykE9KM1znnXBbJLHUTvaR9CIahDiOYp/CQmV2T5diSKi0ttbIyr0w451xdSJppZinngkWap2Bmn5jZLcD5wGzg9/WMzznnXB6KMk9hf0ljJL0H3Aa8StBp7JxzroGJtEoq8CBwtJmtSHWwc865wlVrUgiXqlhkZuN2UjzOOedyqNbmo/A+Cq0l7baT4nHOOZdDUZqPlgGvSJoKbKzcaWY3ZS0q55xzORElKawIfxoRzEJ2zjnXQKVMCmZ29c4IxDnnXO6lTAqSniduIbtKZnZEViJyzjmXM1Gaj+KXtC4GfgRUZCcc55xzuRSl+Whmwq5XJEW6HadzzrnCEqX5aK+4zUZAP4Jlrp1zzjUwUZqPZhL0KYig2WgJ8JMohUsaAowDioC7zez6hMfPBy4EtgEbgJFmNjdy9M455zIqSvNRt3QKDmdDjweOIrjhzgxJUxM+9B8wszvC408AbgKGpHM955xz9VfjjGZJh4RLZlduny3pCUm3JDQp1aQ/sNDMFpvZV8BkYGj8AQn3fv4aSUY5Oeec23lqW+biTuArAEmHAdcD9wLrCO+XnEIHYHncdnm4rwpJF0paBNwAJL3vs6SRksokla1evTrCpZ1zzqWjtqRQZGZrw9+HARPM7FEzuxLYN0LZSrIv2XyH8Wb2DeBy4HfJCjKzCWZWamalbdu2jXBp55xz6ag1KUiq7HM4Evhf3GNROqjLgU5x2x0JlsuoyWTghxHKdc45lyW1JYUHgRckPQFsAl4CkLQvQRNSKjOA7pK6hausngZMjT9AUve4zeOABXWI3TnnXIbV+I3fzK6V9BzwdWCa7biZcyPgolQFm1mFpFHAMwRDUiea2RxJY4EyM5sKjJL0PWAr8BlwTv2ejnPOufrQjs/6wlBaWmplZWW5DsM55wqKpJlmVprquJT3aHbOObfr8KTgnHMuxpOCc865GE8KzjnnYjwpOOeci/Gk4JxzLsaTgnPOuRhPCs4552I8KTjnnIvxpOCccy7Gk4JzzrkYTwrOOedispoUJA2R9L6khZJGJ3n8UklzJb0j6TlJXbIZj3POudplLSlIKgLGA8cCPYHTJfVMOGwWUGpmvYEpBLfkdM45lyPZrCn0Bxaa2WIz+4rgzmpD4w8ws+fN7Mtw83WCu7M555zLkWwmhQ7A8rjt8nBfTX4C/CfZA5JGSiqTVLZ69eoMhuiccy5eNpOCkuxLekcfSWcCpcCNyR43swlmVmpmpW3bts1giM455+LVeDvODCgHOsVtdwRWJB4U3o7z/wHfNbMtWYzHOedcCtmsKcwAukvqJmk34DRgavwBkg4G7gROMLNVWYzFOedcBFlLCmZWAYwCngHmAQ+b2RxJYyWdEB52I9AceETSbElTayjOOefcTpDN5iPM7N/AvxP2/T7u9+9l4jpbt26lvLyczZs3Z6I457KiuLiYjh070qRJk1yH4lyNspoUdpby8nJatGhB165dkZL1bzuXW2bGmjVrKC8vp1u3brkOx7kaNYhlLjZv3kzr1q09Ibi8JYnWrVt7bdblvQaRFABPCC7v+XvUFYIGkxScc87VnyeFDGnevHmV7UmTJjFq1KiMXmPMmDH86U9/ymiZUU2fPp3jjz8+7fOvu+66DEbjnMsWTwpup/Ck4FxhaBCjj+Jd/eQc5q74IqNl9mzfkqt+cEDa5z/55JNcc801fPXVV7Ru3Zr777+fvffemxdeeIGLL74YCNqbX3zxRVq0aFHl3GuvvZZ7772XTp060bZtW/r16wfAokWLuPDCC1m9ejXNmjXjrrvuYr/99mPEiBEUFxczZ84cVq5cyU033cTxxx/Ptm3bGD16NNOnT2fLli1ceOGF/OxnP2P69OmMGTOGNm3a8N5779GvXz/uu+8+JPH0009zySWX0KZNG/r27RuLaePGjVx00UW8++67VFRUMGbMGIYOHcqkSZOYOnUqX375JYsWLeLEE0/khhtuYPTo0WzatIk+ffpwwAEHMGHCBE499VTKy8vZtm0bV155JcOGDUv79XXOZU6DSwq5UvmhV2nt2rWccEIwR2/QoEG8/vrrSOLuu+/mhhtu4M9//jN/+tOfGD9+PAMHDmTDhg0UFxdXKXPmzJlMnjyZWbNmUVFRQd++fWNJYeTIkdxxxx10796dN954g5///Of873//A2Dp0qW88MILLFq0iMMPP5yFCxdy77330qpVK2bMmMGWLVsYOHAgRx99NACzZs1izpw5tG/fnoEDB/LKK69QWlrKeeedx//+9z/23XffKh/a1157LUcccQQTJ07k888/p3///nzve8GUk9mzZzNr1iyaNm1Kjx49uOiii7j++uu57bbbmD17NgCPPvoo7du351//+hcA69aty8afxDmXhgaXFOrzjb4+dt9999iHHgR9CmVlZUAwj2LYsGF8/PHHfPXVV7Fx6gMHDuTSSy/ljDPO4KSTTqJjx6orh7/00kuceOKJNGvWDCCWZDZs2MCrr77KKaecEjt2y5Ydy0adeuqpNGrUiO7du1NSUsL8+fOZNm0a77zzDlOmTAGCD+IFCxaw22670b9//9i1+/Tpw9KlS2nevDndunWje/fuAJx55plMmDABgGnTpjF16tRY/8bmzZv58MMPATjyyCNp1aoVAD179mTZsmV06hS/BBb06tWLX//611x++eUcf/zxfOc730nvRXfOZZz3KewEF110EaNGjeLdd9/lzjvvjI1VHz16NHfffTebNm1iwIABzJ8/v9q5yYYxbt++nT322IPZs2fHfubNm1fjOZIwM2699dbY8UuWLInVFJo2bRo7tqioiIqKihqvDcFErEcffTRW1ocffsj+++9fa1nxvvnNbzJz5kx69erFFVdcwdixY5O/cM65nc6Twk6wbt06OnQIbiVxzz33xPYvWrSIXr16cfnll1NaWlotKRx22GE89thjbNq0ifXr1/Pkk08C0LJlS7p168YjjzwCBB/Sb7/9duy8Rx55hO3bt7No0SIWL15Mjx49OOaYY7j99tvZunUrAB988AEbN26sMeb99tuPJUuWsGjRIgAefPDB2GPHHHMMt956K2bBSuizZs1K+Ro0adIkdu0VK1bQrFkzzjzzTH7961/z1ltvpTzfObdzNLjmo3w0ZswYTjnlFDp06MCAAQNYsmQJADfffDPPP/88RUVF9OzZk2OPPbbKeX379mXYsGH06dOHLl26VGlmuf/++7ngggu45ppr2Lp1K6eddhoHHXQQAD169OC73/0uK1eu5I477qC4uJif/vSnLF26lL59+2JmtG3blscff7zGmIuLi5kwYQLHHXccbdq0YdCgQbz33nsAXHnllVxyySX07t0bM6Nr16489dRTtb4GI0eOpHfv3vTt25ezzz6byy67jEaNGtGkSRNuv/32tF5X51zmqfLbXlYKl4YA44Ai4G4zuz7h8cOAm4HewGlmNiVVmaWlpVbZVl9p3rx5seaLXd2IESM4/vjjOfnkk3MdikvC36suVyTNNLPSVMdlrflIUhEwHjgW6AmcLqlnwmEfAiOAB7IVh3POueiy2XzUH1hoZosBJE0GhgJzKw8ws6XhY9uzGMcuZdKkSbkOwTlXwLLZ0dwBWB63XR7uc845l6eymRSSjWdMqwND0khJZZLKVq9eXc+wnHPO1SSbSaEciJ+11BFYkU5BZjbBzErNrLRt27YZCc4551x12UwKM4DukrpJ2g04DfB7MDvnXB7LWlIwswpgFPAMMA942MzmSBor6QQASYdIKgdOAe6UNCdb8WRbUVFRbMG3gw46iJtuuont2/On/3z69Om8+uqrSR8r5CW56ytxyfO6mDRpEitWpFX5dS5vZXXympn9G/h3wr7fx/0+g6BZqeDFr320atUqhg8fzrp167j66qtzHFlg+vTpNG/enG9/+9u5DqXBmDRpEgceeCDt27fPdSjOZUzDm9H8n9HwybuZLXOfXnDs9amPC7Vr144JEyZwyCGHMGbMGLZs2cIFF1xAWVkZjRs35qabbuLwww+vcTnrjz/+mGHDhvHFF19QUVHB7bffXm3RuK5du3LOOefw5JNPsnXrVh555BH2228/1q5dy7nnnsvixYtp1qwZEyZMoGXLltxxxx0UFRVx3333ceutt1Yrb+7cuQwePJgPP/yQSy65hF/84hcA/PCHP2T58uVs3ryZiy++mJEjR3L77bezZMkSbrjhBiD4cJw5cya33nor9913H7fccgtfffUVhx56KH/9618pKiqqcq10luR+7LHH2LJlC0uWLGH48OFcddVVADVer3nz5lx88cU89dRT7L777jzxxBPsvffesfMrKioYMmRIlbhuvPFGHn74YbZs2cKJJ57I1VdfzdKlSzn22GMZNGgQr776Kh06dOCJJ57gX//6F2VlZZxxxhnsvvvuvPbaa1x99dVMnTqVxo0bc/TRR+es9uVcffjaR1lSUlLC9u3bWbVqFePHjwfg3Xff5cEHH+Scc85h8+bN/O1vf4stZz1jxgzuuusulixZwgMPPMAxxxzD7Nmzefvtt6ssyR2vTZs2vPXWW1xwwQWxD6CrrrqKgw8+mHfeeYfrrruOs88+m65du3L++efzy1/+ktmzZyddlXT+/Pk888wzvPnmm1x99dWxdYomTpzIzJkzKSsr45ZbbmHNmjWcfPLJ/POf/4yd+9BDDzFs2DDmzZvHQw89xCuvvMLs2bMpKiri/vvvr3KdzZs3c9555/Hkk0/y0ksv8cknn8Qeq1ySe8aMGTz//PNcdtllsfWZ3nzzTe6//35mz57NI488QllZWa3X27hxIwMGDODtt9/msMMO46677gLg4osv5oILLmDGjBnss88+sWtPmzaNBQsW8OabbzJ79mxmzpzJiy++CMCCBQu48MILmTNnDnvssQePPvooJ598MqWlpbGYNm3axGOPPcacOXN45513+N3vfleHd4tz+aPh1RTq8I0+2yqXEHn55Ze56KKLgGChuS5duvDBBx/UuJz1IYccwrnnnsvWrVv54Q9/WGNSOOmkkwDo169f7EP65Zdf5tFHHwXgiCOOYM2aNZHuV3DcccfRtGlTmjZtSrt27Vi5ciUdO3bklltu4bHHHgNg+fLlLFiwgAEDBlBSUsLrr79O9+7def/99xk4cCDjx49n5syZHHLIIUBwj4l27dpVuc78+fPTWpL7qKOOonXr1rHn/fLLL9O4ceMar7fbbrvF+ir69evHs88+C8Arr7wSe33OOussLr/88ti1p02bxsEHHwwEy5MvWLCAzp07061bt9jfoF+/fixdurTa69eyZcvYGlPHHXdcTvtJnKuPhpcU8sTixYspKiqiXbt21LS+VOVy1sccc0y1x1588UX+9a9/cdZZZ3HZZZdx9tlnVzumcpnq+CWqk12rpiWwk5UVX9706dP573//y2uvvUazZs0YPHhwbNnvYcOG8fDDD7Pffvtx4oknxpbnPuecc/jjH/9Y67VSLcndo0ePKvvfeOONGpcDr+l6TZo0iZ2TuIR3suubGVdccQU/+9nPquxfunRptddm06ZN1c5v3Lgxb775Js899xyTJ0/mtttui930yLlC4s1HWbB69WrOP/98Ro0ahSQOO+ywWLPGBx98wIcffljrctbLli2jXbt2nHfeefzkJz+p09LS8deaPn06bdq0oWXLlrRo0YL169fX6XmsW7eOPffck2bNmjF//nxef/312GMnnXQSjz/+OA8++GDsrmxHHnkkU6ZMYdWqVUBw97lly5ZVKTPdJbmfffZZ1q5dy6ZNm3j88ccZOHBgpOslGjhwIJMnTwao0rR1zDHHMHHiRDZs2ADARx99FCu3JvGv6YYNG1i3bh3f//73ufnmm6vccMm5QuI1hQypvB3n1q1bady4MWeddRaXXnopAD//+c85//zz6dWrF40bN2bSpEk0bdq0xuWsp0+fzo033kiTJk1o3rw59957b+Q4xowZw49//GN69+5Ns2bNYvdv+MEPfsDJJ5/ME088kbSjOZkhQ4Zwxx130Lt3b3r06MGAAQNij+2555707NmTuXPn0r9/fyC409o111zD0Ucfzfbt22nSpAnjx4+nS5cusfPSXZJ70KBBnHXWWSxcuJDhw4dTWhos9pjqeonGjRvH8OHDGTduHD/60Y9i+48++mjmzZvHt771LSAYqnrfffdV6ySPN2LECM4//3x23313/vOf/zB06FA2b96MmfGXv/wl5evrXD7K6tLZ2eBLZ+96Km9tetttt+U6lHrz96rLlZwvne2cc67wePORy3sjRoxgxIgRuQ7DuV1Cg6kpFFozmNv1+HvUFYIGkRSKi4tZs2aN/6dzecvMWLNmDcXFxbkOxblaNYjmo44dO1JeXo7fa8Hls+LiYjp2bBBLfbkGrEEkhSZNmtCtW7dch+GccwUvq81HkoZIel/SQkmjkzzeVNJD4eNvSOqazXicc87VLmtJQVIRMB44FugJnC6pZ8JhPwE+M7N9gb8A/5eteJxzzqWWzZpCf2ChmS02s6+AycDQhGOGAveEv08BjlSUhXqcc85lRTb7FDoAy+O2y4FDazrGzCokrQNaA5/GHyRpJDAy3Nwi6b2sRJx9bUh4bgWiUOOGwo29UOOGwo29UOOGaLHXvP5LnGwmhWTf+BPHjEY5BjObAEwAkFQWZap2PirU2As1bijc2As1bijc2As1bshs7NlsPioHOsVtdwQSb2gbO0ZSY6AVsDaLMTnnnKtFNpPCDKC7pG6SdgNOA6YmHDMVOCf8/WTgf+Yz0JxzLmey1nwU9hGMAp4BioCJZjZH0ligzMymAn8D/iFpIUEN4bQIRU/IVsw7QaHGXqhxQ+HGXqhxQ+HGXqhxQwZjL7ils51zzmVPg1j7yDnnXGZ4UnDOORdTUEkh1bIZ+ULSREmr4udTSNpL0rOSFoT/7pnLGGsiqZOk5yXNkzRH0sXh/ryOX1KxpDclvR3GfXW4v1u4hMqCcEmV3XIdazKSiiTNkvRUuF0ocS+V9K6k2ZLKwn15/V6pJGkPSVMkzQ/f79/K99gl9Qhf68qfLyRdksm4CyYpRFw2I19MAoYk7BsNPGdm3YHnwu18VAH8ysz2BwYAF4avc77HvwU4wswOAvoAQyQNIFg65S9h3J8RLK2Sjy4G5sVtF0rcAIebWZ+4cfL5/l6pNA542sz2Aw4ieP3zOnYzez98rfsA/YAvgcfIZNxmVhA/wLeAZ+K2rwCuyHVctcTbFXgvbvt94Ovh718H3s91jBGfxxPAUYUUP9AMeItgBv2nQONk76F8+SGYw/MccATwFMGkzryPO4xtKdAmYV/ev1eAlsASwsE2hRR7XKxHA69kOu6CqSmQfNmMDjmKJR17m9nHAOG/7XIcT0rhqrUHA29QAPGHTTCzgVXAs8Ai4HMzqwgPydf3zM3Ab4Dt4XZrCiNuCFYgmCZpZrgcDRTAewUoAVYDfw+b7e6W9DUKI/ZKpwEPhr9nLO5CSgqRlsRwmSGpOfAocImZfZHreKIws20WVKs7EizIuH+yw3ZuVLWTdDywysxmxu9OcmhexR1noJn1JWjWvVDSYbkOKKLGQF/gdjM7GNhInjUV1SbsYzoBeCTTZRdSUoiybEY+Wynp6wDhv6tyHE+NJDUhSAj3m9k/w90FE7+ZfQ5MJ+gT2SNcQgXy8z0zEDhB0lKClYSPIKg55HvcAJjZivDfVQRt2/0pjPdKOVBuZm+E21MIkkQhxA5BEn7LzFaG2xmLu5CSQpRlM/JZ/JIe5xC01eedcOnyvwHzzOymuIfyOn5JbSXtEf6+O/A9go7D5wmWUIE8jNvMrjCzjmbWleA9/T8zO4M8jxtA0tcktaj8naCN+z3y/L0CYGafAMsl9Qh3HQnMpQBiD53OjqYjyGTcue4sqWPHyveBDwjaiv9fruOpJc4HgY+BrQTfSH5C0E78HLAg/HevXMdZQ+yDCJoq3gFmhz/fz/f4gd7ArDDu94Dfh/tLgDeBhQRV7aa5jrWW5zAYeKpQ4g5jfDv8mVP5fzLf3ytx8fcBysL3zOPAnoUQO8FAijVAq7h9GYvbl7lwzjkXU0jNR84557LMk4JzzrkYTwrOOediPCk455yL8aTgnHMuJmt3XnOukEiqHNIHsA+wjWAZBID+ZvZVTgJzbifzIanOJZA0BthgZn/K4jUa2461jZzLG9585FwKks4J79UwW9JfJTWS1FjS55KuD+/h8JqkduHx90n6Ydz5G8J/vyfpv5ImE0y0S1p2Tp6kcyF/AzpXC0kHAicC37Zgsb3GBMtRALQCXrDgHg6vAedGKHIA8Bsz65WibOdywvsUnKvd94BDgLJgWSh2Z8cS7pvM7D/h7zOB70Qo7zUz+zBC2c7lhCcF52onYKKZXVllZ7CCaXzn8zZ2/H+qIKyFh3cMjP9/tjFV2c7lkjcfOVe7/wKnSmoDwSglSZ1TnLOU4FaJEDQPFWWwbOeyypOCc7Uws3eBq4H/SnoHmAbsneK0O4GjJL1JsBLnlgyW7VxW+ZBU55xzMV5TcM45F+NJwTnnXIwnBeecczGeFJxzzsV4UnDOORfjScE551yMJwXnnHMx/x/qZGrltM9ukwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dependents = (survivaldata['Dependents_Yes'] == 1)\n",
    "no_Dependents = (survivaldata['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": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic    p  -log2(p)\n",
      "           0.43 0.51      0.97\n"
     ]
    },
    {
     "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 = (survivaldata['PhoneService_Yes'] == 1)\n",
    "no_PhoneService = (survivaldata['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": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "          30.97 <0.005     22.34\n"
     ]
    },
    {
     "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 = (survivaldata['MultipleLines_No phone service'] == 1)\n",
    "multiLines = (survivaldata['MultipleLines_Yes'] == 1)\n",
    "no_multiLines = ((survivaldata['MultipleLines_Yes'] == 0) & (survivaldata['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(df['tenure'], df['MultipleLines'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         520.12 <0.005    375.19\n"
     ]
    },
    {
     "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 = (survivaldata['InternetService_Fiber optic'] == 1)\n",
    "No_Service = (survivaldata['InternetService_No'] == 1)\n",
    "DSL = ((survivaldata['InternetService_Fiber optic'] == 0) & (survivaldata['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(df['tenure'], df['InternetService'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online Security"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "        1013.86 <0.005    731.35\n"
     ]
    },
    {
     "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 = (survivaldata['OnlineSecurity_No internet service'] == 1)\n",
    "onlineSecurity = (survivaldata['OnlineSecurity_Yes'] == 1)\n",
    "no_onlineSecurity = ((survivaldata['OnlineSecurity_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['OnlineSecurity'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online Backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         821.34 <0.005    592.47\n"
     ]
    },
    {
     "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 = (survivaldata['OnlineBackup_No internet service'] == 1)\n",
    "onlineBackup = (survivaldata['OnlineBackup_Yes'] == 1)\n",
    "no_onlineBackup = ((survivaldata['OnlineBackup_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['OnlineBackup'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Device Protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         763.51 <0.005    550.75\n"
     ]
    },
    {
     "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 = (survivaldata['DeviceProtection_No internet service'] == 1)\n",
    "DeviceProtection = (survivaldata['DeviceProtection_Yes'] == 1)\n",
    "no_DeviceProtection = ((survivaldata['DeviceProtection_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['DeviceProtection'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tech Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         989.56 <0.005    713.82\n"
     ]
    },
    {
     "data": {
      "image/png": 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qCxYcXiV10HRnyaQJfSZUVJH4aMVWdv/k/c0fWzzBJBoncBweSVWtcD8HjjzcSf7eOvg4xndc74NuU1Up5Rxygsjiam0jzn+c2YP3q58fcU4K91NUi/l6Zw0AOtT/mdLyBhcTXCoH4lqZGeVyr6oYgERkoap2jZmuMgSFXQd2cUOHG0LnsmpkcVbzsyqieKUSbzDx4w0w3iARjTd4xKNB3gfU2xwZTOrscALF3gZt/S4BYPt+2BmwpcobYMKDS2lU9WCyfNNuANoek1XBJSmuPINVeQagVPGrM05eWbh/94mx0iU1KIhIH+BJIAN4QVVHep4/DhgPNMbZuvM3qppXUp5+QWHJD0s4ru7hmcjtGrXjgR4PJOx1pANvgAlaC4lVw4iXX6CIV5AAE82upj3Y0eJwMKm5ZwOHPM1s8faHpKN4R9klW7zBKlVrPamoZ07r1YX7dh0fK13SgoKIZADfAhfibKgzHxigqsvC0rwBvKuqL4rIecAgVb22pHy9QeGNb9/g5WUvU72as7Dd+t3rOabOMbzzy3cS/prSSby1kMab/0v1gyUMdYoio3B/sQl8QfxceIijs6L3hUD8AcYvmHiDBPgHiiCqUjBJtniCVVlqPVUxmKRCUOgOjFDV3u7xPQCq+vewNEuB3qqaJyKCs9FOXd8MXd6gADBtzTQa13aWq3hk3iMcPHSwygeFcufdwwLg4P7ItaR8fLVhJ/sOFJSYJl5NNrsTBF31flwBBKtx+AUPLwsmFassc4sgNZvQkmnGPZcGCgrJHH3UHNgQdpyHs8NauEXA5ThNTL8CskTkKFXdVpobZdXIIv8nZ3TOwUMHqSalb2s2ZeQ3KW7lTNizpcTLOjcImH+AAFPMCf2AfoePv53O7uWzOFR4qMTL6v24gjo7llPn+09Ldbv8xt3Z0rRXzHRHbM+j+q63QsflPTckVZV24EVZh4Mbf8kMCn7jE73Vkj8DT4vI9cB/ge+BYj8bRWQIMAQgOzu7WKbhncpvrHiD3T/vjrfMJpESOXs6QIDxFR5MTuxD1ol9Yl/jrlNVqu7oLUuo9+MKTtizIHba1r0gvBw71kHG3GipD6vkM9LLawhwvMEk3c24J1i6ZAaFPCIXDmoBbAxPoKobgV8DiMiRwOWqWmwkvaqOAcaA03yUrAKbFBbvl6E3mASpcZzYJ/JLO4hoq+V6FS2E6N3xL8j9dqxz9gApUsmCRFbt6oEmj4ZL5LBu40hmUJgPtBGRVjg1gP7A1eEJRKQRsF1VDwH34IxEKrNCLWTammkR59JlmKpJMO+XZtAaR2mbq4IGkmir5QYJKK17Fa9hhAcJSOtAEc+8gaCrEHhZMIkumXs0F4jIMOB9nCGp41V1qYg8CCxQ1alAL+DvIqI4zUcJ2bM5b3ce4xdHxpd2jdpZUDDBvzCDBI94+jm8wSPRNQy/QBFEmgaTeCegxbOkTVWR9pPXvLxDVMGGqZok8RtxFUQ8wcSvhgHQpH1kuta9St/0BU4wqR5jJFWaBo6ySOQE04qWEpPXkiFWUIDIIapgw1RNivEGk0QECYgeKIJo3St2MAkSOKBKBo90EHSZi0q5IF74EFVwhqkC1s9gUkOQfo5YgcKvDyNoU5RXtH6N1r0i7xE0cFWxJqzKplIGBe8X/Rsr3mD7/u0RtQcgInAYU2GCzvGIJ1AEEa3WEaQDvHWv4vcsba2nSCUfXZUuKmVQ8LNxz0YemfdIxLl2jdpxSatLKqhExpQgSKCIp9nJT7y1jtKMnPJq3St2MAlS47DAkXBVIihc0voStu/fHnFu/e71oWYlY9JCeQ2vhWC1jkQ3V3m17hWsnyOepio/FmCAStrR7Mev8/lA4QH+0OkPoXPWx2AqpfIcJRVE0FoIJG50VRBBO9KDSMEAU6U7mv1E63wODxTWx2AqpfKcDR5EvLWQRDdXeSUyAKZx/0iVqSl42T4MxpRS0BpHRdYw/CR7qG4QfrWQcg4UVlOIwdvPYH0MxsSQyNngfspzdFUQiayZ+L2uFF2mpMoGhStOvII61etE7MNwoPCAzWUwpqzSsbnKTyKDSetewYbuxhNME6zKBgU/W/ZtsTWTjKkoiZjUl0iJCiZBJweC8/oquC+iSgeF8M7ndo3acaAwchf59bvXW+3BmIoS58ZNvioymPjVOLavOZw2XJC5GkkOFEntaBaRPji7qmUAL6jqSM/z2cCLQH03zd2qOq1YRmES1dHsx2/Y6vrd68nOitzYxzqkjUkzqTYsd/rdTmBo2CryfOtesWsne7ZATr+S0/io8I5mEckAngEuxNlwZ76ITFXVZWHJhgOvq+qzInIKMA1omawyldbpx3h3D7UOaWPSUqL6OYKKFUxa9yp+LmhfhLeJCRJae0hm81E3YJWqrgYQkclAXyA8KChQ131cD8/ObOXNO5ehXaN2nFD/BFpktQid8+uQtuYkYyqpZHWaB1lapDR9EQnsoE5mUGgObAg7zgO8P71HADNE5GagDnBBEssTk98Xu7c/AYp3SFtntDEmQjyd5mXpi0igZAYF8Tnn7cAYAExU1X+KSHfgJRFp727PeTgjkSHAEIDs7GzKk1/tIbxD2jqjjTEx+dU4Yq3Z5FebKOqLmH535PkmOTGL0KBWqFWmRMkMCnnAsWHHLSjePPRboA+Aqs4VkVpAI2BreCJVHQOMAaejOVkF9uP9Yr+k1SURHdJFK6/astzGmFKpVa/0Q25b9yp+rqj20PmaEm+XUU0yghQrmUFhPtBGRFoB3wP9gas9adYD5wMTRaQtUAtIu2/T9bvX27LcxpjSSWTtIYGSFhRUtUBEhgHv4ww3Ha+qS0XkQWCBqk4FbgfGishtOE1L12saLMZk8xuMMUnhrT2U5/wKV5VdEC+RbH6DMSYp/OZXeAOF35yH1r2K1Sgatz9ndf7eQ8fHumWVntGcLNHmN9hQVmNMqQRpYmrdK/K4LAv5YUEhIYLObwDbv8EYU0beJqZmnaFx28O1h5KGsgZgQSEBgs5v8HZIn9TgpGJprPZgjClRrNpD1M7ozYGyt6BQTrxNSut3rwfgl21+GXHeag/GmIRr2BqYGyipBYUkidWkVNQZ7R3KarUHY0zCdRsCTAqU1IJCksRqUorWGQ1WezDGlFICh7JaUChH3vkNfp3RQWoPVnMwxkQIsNZS4aHI5YOiiRkU3LWIlpSieCYK7xe5tzM6aO0hb3eeTYwzxkTn0xl9SElMUACeE5EawETgFVXdWarCmaiCDmX11h5OP+Z0eh3bKyIva2IyxiRCzKCgqj1FpA0wGFggIvOACao6M+mlq+SCDGWNNmrJGxQOFNjEOGNM2QXqU1DVlSIyHFgAjAY6i4gA96rqW8ksYFUT76glb+3Bag7GmHC7DrA7SLogfQodgEHA/wAzgUtV9UsRaYYz8NWCQgKVZdRSeFDw1hzAag/GmNiC1BSeBsbi1Ap+Kjqpqhvd2kNUItIHeBJnldQXVHWk5/lRwLnu4RHA0apavxTlrxLiGbVk/Q7GmHgECQpvqepL4SdE5I+q+qT3vCdNBvAMcCHOhjvzRWSqqob2aFbV28LS3wx0Lu0LqApKO2ppxY4VrNixgi82fRFx3ibGGWNiCRIUBgJPeM5dj1MDKEk3YJWqrgYQkclAX2BZlPQDgPsDlKfKi9XvMHvD7GIBIdrEOO/wVgsSxlRtUYOCiAzA2SmtlYhMDXsqC9gWIO/mwIaw4zygeIO4c6/jgFbAhwHyrfJi9Tv0OrZXsaajoB3UNgfCmKqtpJrCHGATzp7J/ww7vxv4OkDe4nMu2o4+/YEpqlrom5HIEGAIQHZ2tl+SKs9bezhQcCCi38Gvg9qvmcn6Ioyp2qIGBVVdB6wDuseZdx5wbNhxC2BjlLT9gaEllGUMMAacndfiLE+lFqvfwa/24G1mCjoHAqz2YExlFXU7ThH51J24tpvIX/gCqKrWLTFjkUzgW+B84HtgPnC1qi71pDsJZx/nVkH2Z07F7ThT0Sfff8LunyOHJXtrD17RthH1qz3k7c6jZmbN0LEFCWNSm4gsVNWusdKVVFPo6f6dFU8BVLVARIbhfOFnAONVdamIPAgsUNWifooBwOQgAcEEF3Tjn3BBm5iK0lpfhDGVT0k1hYYlXaiq25NSohisphC/eGoPfiOZVuxYAUQOcQ1SmwALFMZUlDLXFICFOM1G0TqMW8dZNlNB/L6MP/n+kxI7qMvSF+EXbKzT2pjUFrWmkKqsppBc1hdhTOVU5pqCiJysqt+ISBe/51X1y7IU0KSm8u6LsJqDMamlpOajP+HMDfinz3MKnJeUEpmU450DAZG1hyBNTGAL9xmTDqz5yMTF28wUq4kJ/JuZrIPamPKRiI7mooxqATcBPXFqCJ8Az6nq/jKX0qQt7xe0t8MaYs+qDtrEBLZGkzHlJWZNQURex1na4mX31ACggapekeSy+bKaQvqI1WkddLgr+M+LsNqEMcEFrSkECQqLVLVjrHPlxYJCepu2ZhqNazeO+rzNizAmORLWfAR8JSJnqOrnbsanA5+VtYCmaoq1cF+QTmtrdjImeUqa0bwYpw+hOnASsN49Pg5Ypqrty6uQ4aymULnEqjn4iTayKTsrm7u63VXitVabMFVVmZuP3D0OonJXUS13FhQql3gmy/kpzQQ6LwsUpipIxIJ4EV/6InI0UCsBZTMmJMjSGxA7UJRmAp3ftdbsZIwjSEfzZTgT2JoBW3Gaj5araruYmYv0wdm2MwN4QVVH+qS5EhiB0zS1SFWvLilPqylUTfHMi/BrZvKyZidTVSR09BHO7OVZqtpZRM4FBqjqkBjXZeDsp3AhzoY7893rloWlaQO8DpynqjtE5GhV3VpSvhYUDKRGs1P+T/lc0uqSUt3PmIqSyNFHB1V1m4hUE5FqqvqRiDwS+zK6AatUdbVboMlAX2BZWJrfAc+o6g6AWAHBmCLxrNHkpyzblPot0eFltQmTboIEhZ0iciTOTOZJIrIVKAhwXXNgQ9hxHuD9H3gigIh8htPENEJVpwfI25hiYq3R5CfRQ2C9bME/k26CNB/VAfbj7KtwDVAPmKSq22JcdwXQW1VvcI+vBbqp6s1had4FDgJX4uzh/AnQXlV3evIagrM4H9nZ2aeuW1chA59MGvJrZvKyjYZMVZCwPgU3s6Y4zUEKzFfVzQGu6Y7zy7+3e3wPgKr+PSzNc8DnqjrRPf4AuFtV50fL1/oUTKIlYq5E0OU5wD9QeFngMImWyAXxbgD+CnyIU1t4SkQeVNXxMS6dD7QRkVbA90B/wDuy6G2ctZQmikgjnOak1bHKZEwiJaLZKehS4eC/I52XNTuZihKk+WgF0KOouUhEjgLmqOpJJV7opL0EeAKnv2C8qj4sIg8CC1R1qogIznDXPkAh8LCqTi4pT6spmPIQzxBYr2gjm7ys2cmUh0QOSf0AuFhVf3aPawDTVPWChJS0lCwomIoQzxDYIPMkytLsZEHClEYituP8k/vwe+ALEXkHp0+hLzAvIaU0Jk0EmXkdZHE/r7I0O3lnXYMFClN2Ja19dH9JF6rqA0kpUQxWUzCpKhUm1FkntokmoaOP3AyzAFXVPWUtXFlYUDDpJFGrwAYdAhuE9VdUTYnsU2gPvAQ0dE/9AAxU1aVlLmUcLCiYdJKIeRJQtiGwQVigqPwSGRTmAH9R1Y/c417A31S1RyIKWloWFExlk6hO7GQHCgsS6c224zQmjSWz2cmP7TtR+SVyQbzVInIfThMSwG+ANWUpnDGmZMlax8lP0LWd/O5tk+wqnyA1hQbAA0BP99R/gQeKVjYtb1ZTMFVVIibU+bHtTauGhDQfuXsijFTVOxJZuLKwoGCMI1FDYP0kcja2BYnUkJDmI1UtFJFTE1csY0yiJGorUz9++0x42SS7yilI89E/gTbAG8DeovOq+lZyi+bPagrGlE6ihsV6JXqSnQWK5EpkR3NDYBvOlpxFFKiQoGCMKZ0gX7Tx1DDKsmuddVqnriBB4Q5V/SHpJTHGVJh4tjcNMtopWhOTH+/2plZzqBglrX10KTAeZ2e0Q8CVqjqnVJmL9AGexFk6+wVVHel5/nrgUZxF9wCeVtUXSsrTmo+MKR/luXw4FK9RWBNTYiWi+ehh4CxV/Ua7HNIWAAAgAElEQVRETgf+AZxTigJkAM8AF+LszzxfRKaq6jJP0tdUdVjQfI0x5cP75ZuoJiY/fjUKv3y9ndYWJBKvpKBQoKrfAKjqF+6CeKXRDVilqqsBRGQyzrLb3qBgjEkDiWpi8lNUo3hk3iOhc0H6ImxkU+KVFBSODttTodixqj4eI+/mwIaw4zzA72fD5SJyNvAtcJuqbvAmEJEhwBCA7OzY1VBjTPmIZ+a1H2+NwmZZV5yk7acgIlcAvVX1Bvf4WqCbqt4cluYoYI+qHhCR3+P0W5znn6PD+hSMSW2J6Isoy/Lh1hfhL+H7KcRRgO7ACFXt7R7fA6Cqf4+SPgPYrqr1SsrXgoIx6SVRM6/9RjYFWYoDbJY1pEZQyMRpEjofZ3TRfODq8H0YROQYVd3kPv4VcJeqnlFSvhYUjEl/iQgUZZk8l/9TPpe0uiTwvSqDRE5ei4uqFojIMOB9nCGp41V1qYg8CCxQ1anALSJyGVAAbAeuT1Z5jDGpI55Oa6+gk+eK0oYHCu+cCKiatQc/SaspJIvVFIypnJLVF2ErvjrK3HzkGXlUTIDRR0lhQcGYqiFRfRF+zUxBd6OrTH0RiWg+Ku28BGOMSZggq8AGCRLxDneFqjkvwpqPjDFpKxX2t/ZrdvJKhcCRyD2aawG/BdoBtYrOq+rgshYyHhYUjDElSeb+1kGbnbxSYbRTIkcfvQR8A/QGHgSuAZaXrXjGGJMcydrfujTNTl7pNNopSFA4QVWvEJG+qvqiiLyCM8zUGGNSTiKGu0LxQBFtZFNR2pKk03IcQYLCQffvnSLSHtgMtExaiYwxJsG8tYd4RjH51Sb8FvLz41ebSNXaQ5CgMEZEGgD3AVOBI93HxhiTFhKxDLifRO5lDalRewjS0ZyhqoXlVJ6YrKPZGJMMiZoX4VWa5TiSOS8ikR3Na0RkOvAa8KGm2xhWY4wJIFHzIrz8ahNBaw8VUXMIUlOoDVwK9AdOBf4PmKyqnya/eMVZTcEYU1GSOcsakrslacJqCqr6E/A68Lrbt/Ak8DHOInfGGFNlJGpkU9DaQ0X0OwRaJVVEzgGuAi7GWQL7yoDX9cEJIhnAC6o6Mkq6fsAbwGmqatUAY0za8JsX4eWtTUQbyRREskctxQwKIrIGyMWpLdyhqnuDZOxumvMMcCHOVpzzRWSqqi7zpMsCbgG+KJ6LMcaktiBfxkFrE/HsUw3F12QqS5AIUlPoqKo/xpF3N2CVqq4GEJHJQF9gmSfd/wL/AP4cxz2MMSblBZll7W1SCjoxDhLbQR01KIjInar6D+BhESnWG62qt8TIuzmwIew4D4h41SLSGThWVd8VkahBQUSGAEMAsrOzoyUzxpiUFKQvwtukFG1iXLzrLwVVUk2haH2jeNv4xedcKLiISDVgFAF2W1PVMcAYcEYfxVkeY4xJG4neWa7aEdXqBrlv1KCgqv/nPvxaVb8KkplHHnBs2HELYGPYcRbQHpgtIgBNgakicpl1NhtjKrtYTUpBFumD4KOWpJoEGjEaZJ7CR8AxOKODJqvq0kAZi2QC3wLnA9/jjFq6Otr1IjIb+HOsgGDzFIwxlVU8y34H3Vmue5vuqwt2FxwfK78g8xTOFZGmOMNQx4hIXeA1VX0oxnUFIjIMZ0XVDGC8qi4VkQeBBao6Nda9jTGmKoln4b6ydFD7KdXOayKSA9wJXKWqNeK6YxlZTcEYU1XEW3MAuKvbXRHnE1ZTEJG2OBPX+gHbgMnA7aUqpTHGmHITZDnvaILMU5gAvApcpKobYyU2xhiTGPHsIlfSEhpBlBgU3FnJ36nqk4FzNMYYkxBBVm6F2KOWHpn3CF/yZaB7lhgUVLVQRI4SkRqq+nOgHI0xxiRNohbliyZI89E64DMRmQqE1j1S1ccTVgpjjDFxizVq6disY/0u8xUkKGx0/1TDmXBmjDEmhXhrD96aw9Vtr+YpngqUV5B5Cg+UomzGGGPSWJAhqR8RtmZREVU9LyklMsYYU2GCNB+Fr15aC7gcKEhOcYwxxpSV78Y/yqEg1wZpPlroOfWZiHwcuHTGGGPKld8IJT2kiQkKItIw7LAacCrOiqbGGGMqmSDNRwtx+hQEp9loDfDbZBbKGGNMxQjSfNQq3sxFpA/wJM4qqS+o6kjP878HhgKFwB5giHcPZ2OMMWV36KdDu4OkqxbtCRE5zV0yu+h4oIi8IyKjPU1K0a7PAJ4BLgZOAQaIyCmeZK+oao6qdsLZp9kmxBljTAWKGhSA54GfAUTkbGAk8C9gF+7WmDF0A1ap6mp3iYzJQN/wBKr6Y9hhHXyGvhpjjCk/JTUfZajqdvfxVcAYVX0TeFNEcgPk3RzYEHacBxRbvk9EhgJ/AmoAvnMfRGQIMAQgOzvbL4kxxpgEKKmmkOFuqQnOlpofhj0XpINafM75TYJ7RlWPB+4ChvtlpKpjVLWrqnZt3Lh0G04YY4wJrqQv91eBj0XkB+An4BMAETkBpwkpljwgfBWmFjhrKEUzGXg2QL7GGGOSJGpQUNWHReQD4Bhghh7et7MacHOAvOcDbUSkFfA90B+4OjyBiLRR1ZXu4f8AKzHGGFNhYu2n8LnPuW+DZKyqBSIyDHgfZ0jqeFVdKiIPAgtUdSowTEQuAA4CO4DrSvsCjDHGJE6QvoG4qeo0YJrn3F/DHv8xmfc3xhhTOiV1NBtjjKliLCgYY4wJsaBgjDEmxIKCMcaYkKR2NJeXgwcPkpeXx/79+yu6KKaC1apVixYtWlC9evWKLooxaalSBIW8vDyysrJo2bIlIn4TqU1VoKps27aNvLw8WrWKe3FfY6q0StF8tH//fo466igLCFWciHDUUUdZjdGYMqgUQQGwgGAA+xwYU1aVJigYY4wpOwsKCSIi3H777aHjxx57jBEjRgS+fuLEiQwbNqzENGvXruWVV16Jt4il8vbbb7Nsmf8meCtWrKBXr1506tSJtm3bMmTIkITcc+PGjfTr1y8heRlj4mNBIUFq1qzJW2+9xQ8//JC0e8QTFAoLC+O6V0lB4ZZbbuG2224jNzeX5cuXc/PNQdZHjF2eZs2aMWXKlFKX1RiTOJVi9FG4B/5vKcs2/hg7YSmc0qwu91/arsQ0mZmZDBkyhFGjRvHwww9HPLdu3ToGDx5Mfn4+jRs3ZsKECSVuFnT99ddTt25dFixYwObNm/nHP/5Bv379uPvuu1m+fDmdOnXiuuuu45ZbbuHuu+9m9uzZHDhwgKFDh3LjjTcye/ZsHnjgAY455hhyc3OZNm0aF198MT179mTOnDk0b96cd955h9q1a/Pdd98xdOhQ8vPzOeKIIxg7dizbt29n6tSpfPzxxzz00EO8+eabHH/88aHybdq0iRYtWoSOc3JyAOcLP0h5Lr30Uo477jhuuukmAEaMGEFWVhaXX345v/jFL1iyZAmFhYXcddddvP/++4gIv/vd77j55ptZuHAhf/rTn9izZw+NGjVi4sSJHHPMMaX+NzXG+EtqTUFE+ojIChFZJSJ3+zz/JxFZJiJfi8gHInJcMsuTbEOHDmXSpEns2hW53cSwYcMYOHAgX3/9Nddccw233HJLzLw2bdrEp59+yrvvvsvddztv3ciRIznrrLPIzc3ltttuY9y4cdSrV4/58+czf/58xo4dy5o1awCYN28eDz/8cOjX/sqVKxk6dChLly6lfv36vPnmmwAMGTKEp556ioULF/LYY49x00030aNHDy677DIeffRRcnNzIwICwG233cZ5553HxRdfzKhRo9i5cydA4PL079+f1157LZTf66+/zhVXXBFxjzFjxrBmzRq++uqr0Pt28OBBbr75ZqZMmcLChQsZPHgwf/nLXwL/+xhjYktaTUFEMoBngAtxNtyZLyJTVTW8TeIroKuq7hORPwD/wNn6M26xftEnU926dRk4cCCjR4+mdu3aofNz587lrbfeAuDaa6/lzjvvjJnXL3/5S6pVq8Ypp5zCli1bfNPMmDGDr7/+OtTksmvXLlauXEmNGjXo1q1bxFj9Vq1a0alTJwBOPfVU1q5dy549e5gzZ07EF/KBAwdilm3QoEH07t2b6dOn88477/D888+zaNGiwOXp3LkzW7duZePGjeTn59OgQQOys7NZu3Zt6B6zZs3i97//PZmZzke0YcOGLFmyhCVLlnDhhRcCTs3EagnGJFYym4+6AatUdTWAiEwG+gKhoKCqH4Wl/xz4TRLLUy5uvfVWunTpwqBBg6KmCTJssmbNmqHHh/c3iqSqPPXUU/Tu3Tvi/OzZs6lTp07U/DIyMvjpp584dOgQ9evXJzc3yJbbkZo1a8bgwYMZPHgw7du3Z8mSJaUqT79+/ZgyZQqbN2+mf//+vq/N+z6pKu3atWPu3LmlLq8xJphkNh81BzaEHee556L5LfCe3xMiMkREFojIgvz8/AQWMfEaNmzIlVdeybhx40LnevToweTJkwGYNGkSPXv2jCvvrKwsdu/eHTru3bs3zz77LAcPHgTg22+/Ze/evYHzq1u3Lq1ateKNN94AnC/dRYsW+d4r3PTp00P33Lx5M9u2baN58+alKk///v2ZPHkyU6ZM8R1xdNFFF/Hcc89RUFAAwPbt2znppJPIz88PBYWDBw+ydOnSwK/XGBNbMoOC389h35+8IvIboCvwqN/zqjpGVbuqatfGjRsnsIjJcfvtt0eMQho9ejQTJkygQ4cOvPTSSzz55JNx5duhQwcyMzPp2LEjo0aN4oYbbuCUU06hS5cutG/fnhtvvDH0JRrUpEmTGDduHB07dqRdu3a88847gPOl/eijj9K5c2e+++67iGtmzJhB+/bt6dixI7179+bRRx+ladOmpSpPu3bt2L17N82bN/dtArrhhhvIzs6mQ4cOdOzYkVdeeYUaNWowZcoU7rrrLjp27EinTp2YM2dOqV6vMaZkEq1poswZi3QHRqhqb/f4HgBV/bsn3QXAU8A5qro1Vr5du3bVBQsWRJxbvnw5bdu2TVTRTZqzz4MxxYnIQlXtGitdMmsK84E2ItJKRGoA/YGp4QlEpDPwPHBZkIBgjDEmuZIWFFS1ABgGvA8sB15X1aUi8qCIXOYmexQ4EnhDRHJFZGqU7IwxxpSDpE5eU9VpwDTPub+GPb4gmfc3xhhTOrbMhTHGmBALCsYYY0IsKBhjjAmxoJAA27Zto1OnTnTq1ImmTZvSvHnz0PHPP/8cOJ/hw4fzxBNPxEw3duxYcnJy6NixIzk5Obz77rtlKX6ZjB8/ns2bN1fY/Y0xiVXpVkmtCEcddVRoqYgRI0Zw5JFH8uc//zkp91q3bh2PPvooCxcuDM063rZtW1LuFUtBQQHjx4+nS5cuNG3atELKYIxJrMoXFN67GzYvTmyeTXPg4pFxXfriiy/yzDPP8PPPP9OjRw+efvppqlWrxn/+8x/uu+8+CgsLadKkCTNmzABg8eLFnHPOOWzYsIHbb7+doUOHRuS3ZcsW6tatG1pLKCsri6ysLAB69uzJ008/TadOndi8eTM9e/Zk1apVvPDCC/znP/9h3759rF27lmuvvZbhw4ezatUq+vbtS5cuXcjNzaVt27a8+OKL1K5dm5kzZ3LHHXdQWFjIGWecwTPPPEONGjVo0aIFN954I9OnT+eWW24hNzeXq666itq1azNv3jxq1KhRhjfaGFPRrPkoiZYsWcK///1v5syZQ25uLgUFBUyePJnNmzfzhz/8gX//+98sWrQotC4SOOsFzZw5k88//5y//vWvxTal6dKlC/Xr16dVq1YMHjw4cNPRvHnzmDx5Ml9++SWvvPJKqGazbNkyhg4dyuLFi6lVqxbPP/88+/btY/Dgwbz55pssXryYffv2MWbMmFBederU4bPPPuOqq66iU6dOvPbaa+Tm5lpAMKYSqHw1hTh/0SfDrFmzmD9/Pl27OjPLf/rpJ4499lhq167Nueeey3HHOdtHNGzYMHTNL37xC2rUqMHRRx9Nw4YNyc/Pj2iayczMZObMmXzxxRd8+OGHoV/rw4cPL7EsvXv3pkGDBoCzLPenn35Knz59aNWqFWeccQYAv/nNbxgzZgxnnXUWbdq0Ce2jMHDgQMaNGxfaLvSqq8q0urkxJoVZTSGJVJXBgweTm5tLbm4uK1as4L777vNdFrqId4lrvwXlRIQzzjiDe++9l1deeSW0YU5mZiaHDh0CYP/+/cWu8Tv2Ox9rPSzvMtjGmMrDgkISXXDBBbz++uuhFVO3bdvG+vXrOfPMM/nwww9Zt24d4CwLHVReXl7E/ge5ubmhGkfLli1ZuHAhQLG9jmfMmMHOnTvZt28f77zzDmeeeSYAa9asYf78+QC8+uqr9OzZk1NOOYWVK1eyevVqAF5++WXOOecc3/KUtMS2MSb9WFBIopycHO6//34uuOACOnTowEUXXcSWLVto0qQJzz77LH379qVjx45cc801gfM8ePAgt912GyeffDIdO3bkrbfeYtSoUQDccccdPPnkk/To0YMdO3ZEXNezZ0+uvvpqOnfuzIABA0K7sLVr146xY8fSoUMH9u7dy5AhQzjiiCMYN24cv/71r8nJyaFmzZr87ne/8y3PoEGDuOGGG0o9/NYYk5qStnR2stjS2aX3wgsvsGTJkmJzIFatWkW/fv3i2nktldnnwZjiUmHpbESkj4isEJFVInK3z/Nni8iXIlIgIsW33zLGGFOukjb6SEQygGeAC3G24pwvIlNVdVlYsvXA9UByZnoZwNnFzM8JJ5xQ6WoJxpiySeaQ1G7AKlVdDSAik4G+QCgoqOpa97lDSSyHMcaYgJLZfNQc2BB2nOeeKzURGSIiC0RkQX5+fkIKZ4wxprhkBgW/gfhx9Wqr6hhV7aqqXRs3blzGYhljjIkmmUEhDzg27LgFsDGJ9zPGGFNGyQwK84E2ItJKRGoA/YFKuweziHD77beHjh977DFGjBgR6NoJEyaEltquUaMGOTk5dOrUibvvLjZgK6YWLVqwc+fOEtPs2bOH/v37k5OTQ/v27TnrrLPYt29fqe+VCNu3b+e5556rkHsbY4pLWlBQ1QJgGPA+sBx4XVWXisiDInIZgIicJiJ5wBXA8yKyNFnlSbaaNWvy1ltvhWYvl8agQYNCS2E0a9aMjz76iNzcXEaOTM46TqNGjSI7O5vFixezZMkSxo4dS/Xq1ZNyr5IUFBRYUDAmxSR1QTxVnQZM85z7a9jj+TjNSgnzyLxH+Gb7N4nMkpMbnsxd3e4qMU1mZiZDhgxh1KhRPPzwwxHPrVu3jsGDB5Ofn0/jxo2ZMGEC2dnZge69Z88ehg0bxrJlyzh48CAPPvggl156KQUFBdxxxx3MnDmTatWq8fvf/56bbroJgCeeeIJ33nmHwsJCpkyZwoknnhiR56ZNmzjppJMOv76TTwaKT2YbOXIkBQUFDB8+nJ49e9K1a1e++OIL9uzZw4QJE+jatSvDhw9nw4YNrF+/nry8PO655x4GDx7MoUOH+POf/8yMGTMQEe6//3769evHrFmzGDlyJI0aNWLp0qWcdNJJrFixgk6dOtGnT5+kBUJjTDCVb5XUCjR06FA6dOjAnXfeGXF+2LBhDBw4kOuuu47x48dzyy238PbbbwfK88EHH6RPnz5MnDiRHTt2cPrpp3PhhRcyduxYNm7cyKJFi8jIyIhYP6lJkyZ89dVXjB49mscff7zYL/Hf/va39OnTh9dee43zzz+f6667jhNOOCFmWQ4cOMDcuXP58MMPueGGG0LBY/HixcyZM4cff/yRLl268D//8z/Mnj2bZcuWsWjRIvLz8znttNM4++yzAfj8889ZtmwZ2dnZrFq1ilWrVtl8CWNSRKULCrF+0SdT3bp1GThwIKNHj6Z27dqh83PnzuWtt94C4Nprry0WNEoyY8YM3nvvvdAv6P3797N+/XpmzZrFrbfeSkZGBhC5/Pavf/1rAE499VSmTZtWLM9TTz2V1atXM2PGDGbNmkXXrl2ZN28e1aqV3Jo4YMAAAM477zy2bt3Knj17AGcp7lq1alGrVi3OPvts5s+fz6effsrVV19NRkYGTZs2pWfPnixYsIAaNWrQvXv3wDUlY0z5qnRBoaLdeuutdOnShUGDBkVNE23ZbD+qyttvvx3a2yD8fKzlt6MtvQ3O6qaXX345l19+OarKe++9R9++fUNLb4MTgDIzD39EErX8ti29bUzqslVSE6xhw4ZceeWVjBs3LnSuR48eod3VJk2aRM+ePQPn17t3b0aPHh06/uqrrwC46KKLePbZZ0M7s5Vm+e1PP/00NELpwIEDLF++nOOOO46mTZuyceNGduzYwf79+/nPf/4Tcd1rr70GwOzZs2nSpEnoy/3tt9/mwIED/PDDD3zyySd07dqVs88+m8mTJ1NYWMiWLVv47LPPQpsNhbOlt41JLRYUkuD222+PGIU0evRoJkyYQIcOHXjppZd48sknA+d1//33s2/fPnJycmjXrl1omOuNN95I06ZN6dChAx07duT1118PnOfKlSs566yzyMnJoUuXLnTv3p2+fftSq1Yt7r33Xk477TQuu+wyTjnllIjr6tatS48ePbj55psZO3Zs6Pxpp53GxRdfTPfu3XnggQdo0qQJ/fr1Cy3vfcEFF/D4449z9NFHFytLkyZN6Nq1Kzk5OXENwTXGJJYtnW0C6dmzJ08//XRoH4Yiw4cPp1GjRtx6660VVLLi7PNgTHEpsXS2McaY9GIdzSaQTz/91Pf8Qw89VM4lMcYkU6WpKaRbM5hJDvscGFM2lSIo1KpVi23bttkXQhWnqmzbto1atWpVdFGMSVuVovmoRYsW5OXlYXstmFq1atGiRUJXTjGmSqkUQaF69eq0atWqoothjDFpL6nNRyLSR0RWiMgqESk2CF1EaorIa+7zX4hIy2SWxxhjTMmSFhREJAN4BrgYOAUYICKneJL9FtihqicAo4BHklUeY4wxsSWzptANWKWqq1X1Z2Ay0NeTpi/wovt4CnC+lGZhIGOMMQmVzD6F5sCGsOM84PRoaVS1QER2AUcBETvViMgQYIh7eEBEliSlxMnXCM9rSxPpWm5I37Kna7khfcueruWGYGU/LkhGyQwKfr/4vWNGg6RBVccAYwBEZEGQqdqpKF3Lnq7lhvQte7qWG9K37Olabkhs2ZPZfJQHHBt23ALYGC2NiGQC9YDgy30aY4xJqGQGhflAGxFpJSI1gP7AVE+aqcB17uN+wIdqM9CMMabCJK35yO0jGAa8D2QA41V1qYg8CCxQ1anAOOAlEVmFU0PoHyDrMckqczlI17Kna7khfcueruWG9C17upYbElj2tFs62xhjTPJUirWPjDHGJIYFBWOMMSFpFRRiLZuRKkRkvIhsDZ9PISINRWSmiKx0/25QkWWMRkSOFZGPRGS5iCwVkT+651O6/CJSS0Tmicgit9wPuOdbuUuorHSXVKlR0WX1IyIZIvKViLzrHqdLudeKyGIRyRWRBe65lP6sFBGR+iIyRUS+cT/v3VO97CJykvteF/35UURuTWS50yYoBFw2I1VMBPp4zt0NfKCqbYAP3ONUVADcrqptgTOAoe77nOrlPwCcp6odgU5AHxE5A2fplFFuuXfgLK2Siv4ILA87TpdyA5yrqp3Cxsmn+melyJPAdFU9GeiI8/6ndNlVdYX7XncCTgX2Af8mkeVW1bT4A3QH3g87vge4p6LLVUJ5WwJLwo5XAMe4j48BVlR0GQO+jneAC9Op/MARwJc4M+h/ADL9PkOp8gdnDs8HwHnAuziTOlO+3G7Z1gKNPOdS/rMC1AXW4A62Saeyh5X1IuCzRJc7bWoK+C+b0byCyhKPJqq6CcD9++gKLk9M7qq1nYEvSIPyu00wucBWYCbwHbBTVQvcJKn6mXkCuBM45B4fRXqUG5wVCGaIyEJ3ORpIg88K0BrIBya4zXYviEgd0qPsRfoDr7qPE1budAoKgZbEMIkhIkcCbwK3quqPFV2eIFS1UJ1qdQucBRnb+iUr31KVTER+AWxV1YXhp32SplS5w5ypql1wmnWHisjZFV2ggDKBLsCzqtoZ2EuKNRWVxO1jugx4I9F5p1NQCLJsRirbIiLHALh/b63g8kQlItVxAsIkVX3LPZ025VfVncBsnD6R+u4SKpCan5kzgctEZC3OSsLn4dQcUr3cAKjqRvfvrTht291Ij89KHpCnql+4x1NwgkQ6lB2cIPylqm5xjxNW7nQKCkGWzUhl4Ut6XIfTVp9y3KXLxwHLVfXxsKdSuvwi0lhE6ruPawMX4HQcfoSzhAqkYLlV9R5VbaGqLXE+0x+q6jWkeLkBRKSOiGQVPcZp415Cin9WAFR1M7BBRE5yT50PLCMNyu4awOGmI0hkuSu6s6SUHSuXAN/itBX/paLLU0I5XwU2AQdxfpH8Fqed+ANgpft3w4ouZ5Sy98RpqvgayHX/XJLq5Qc6AF+55V4C/NU93xqYB6zCqWrXrOiylvAaegHvpku53TIucv8sLfo/meqflbDydwIWuJ+Zt4EG6VB2nIEU24B6YecSVm5b5sIYY0xIOjUfGWOMSTILCsYYY0IsKBhjjAmxoGCMMSbEgoIxxpiQpO28Zkw6EZGiIX0ATYFCnGUQALqp6s8VUjBjypkNSTXGQ0RGAHtU9bEk3iNTD69tZEzKsOYjY2IQkevcvRpyReT/iUg1EckUkZ0iMtLdw2GuiBztpn9ZRH4Zdv0e9+8LRGSWiEzGmWjnm3eFvEhjXPYBNKYEItIe+BXQQ53F9jJxlqMAqAd8rM4eDnOBwQGyPAO4U1VzYuRtTIWwPgVjSnYBcBqwwFkWitocXsL9J1V9z328EDgrQH5zVXV9gLyNqRAWFIwpmQDjVfW+iJPOCqbhnc+FHP7/VIBbC3d3DAz/f7Y3Vt7GVCRrPjKmZLOAK0WkETijlEQkO8Y1a3G2SgSneSgjgXkbk1QWFIwpgaouBh4AZonI18AMoDqRkh0AAABMSURBVEmMy54HLhSReTgrcR5IYN7GJJUNSTXGGBNiNQVjjDEhFhSMMcaEWFAwxhgTYkHBGGNMiAUFY4wxIRYUjDHGhFhQMMYYE/L/AfEkoNGg46CZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_internetService = (survivaldata['TechSupport_No internet service'] == 1)\n",
    "TechSupport = (survivaldata['TechSupport_Yes'] == 1)\n",
    "no_TechSupport = ((survivaldata['TechSupport_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['TechSupport'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming TV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         368.31 <0.005    265.68\n"
     ]
    },
    {
     "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 = (survivaldata['StreamingTV_No internet service'] == 1)\n",
    "StreamingTV = (survivaldata['StreamingTV_Yes'] == 1)\n",
    "no_StreamingTV = ((survivaldata['StreamingTV_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['StreamingTV'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         378.43 <0.005    272.98\n"
     ]
    },
    {
     "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 = (survivaldata['StreamingMovies_No internet service'] == 1)\n",
    "StreamingMovies = (survivaldata['StreamingMovies_Yes'] == 1)\n",
    "no_StreamingMovies = ((survivaldata['StreamingMovies_No internet service'] == 0) & (survivaldata['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(df['tenure'], df['StreamingMovies'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 2\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "        2352.87 <0.005       inf\n"
     ]
    },
    {
     "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 = (survivaldata['Contract_One year'] == 1)\n",
    "Contract_Two_year = (survivaldata['Contract_Two year'] == 1)\n",
    "Contract_month_to_month = ((survivaldata['Contract_One year'] == 0) & (survivaldata['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(df['tenure'], df['Contract'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Payment Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 3\n",
      "             alpha = 0.95\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         865.24 <0.005    619.58\n"
     ]
    },
    {
     "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 = (survivaldata['PaymentMethod_Credit card (automatic)'] == 1)\n",
    "electronic_check = (survivaldata['PaymentMethod_Electronic check'] == 1)\n",
    "mailed_check = (survivaldata['PaymentMethod_Mailed check'] == 1)\n",
    "automatic_Bank_Transfer = ((survivaldata['PaymentMethod_Credit card (automatic)'] == 0) & (survivaldata['PaymentMethod_Electronic check'] == 0) & (survivaldata['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(df['tenure'], df['PaymentMethod'], df['Churn'], alpha = 0.95)\n",
    "twoplusgroups_logrank.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paperless Billing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "               t_0 = -1\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      " test_statistic      p  -log2(p)\n",
      "         189.51 <0.005    140.82\n"
     ]
    },
    {
     "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 = (survivaldata['PaperlessBilling_Yes'] == 1)\n",
    "no_PaperlessBilling = (survivaldata['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": "markdown",
   "metadata": {},
   "source": [
    "## Survival Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datapreparation(filepath):\n",
    "    \n",
    "    df = pd.read_csv(filepath)\n",
    "    df.drop([\"customerID\"], inplace = True, axis = 1)\n",
    "    \n",
    "    df.TotalCharges = df.TotalCharges.replace(\" \",np.nan)\n",
    "    df.TotalCharges.fillna(0, inplace = True)\n",
    "    df.TotalCharges = df.TotalCharges.astype(float)\n",
    "    \n",
    "    cols1 = ['Partner', 'Dependents', 'PaperlessBilling', 'Churn', 'PhoneService']\n",
    "    for col in cols1:\n",
    "        df[col] = df[col].apply(lambda x: 0 if x == \"No\" else 1)\n",
    "   \n",
    "    df.gender = df.gender.apply(lambda x: 0 if x == \"Male\" else 1)\n",
    "    df.MultipleLines = df.MultipleLines.map({'No phone service': 0, 'No': 0, 'Yes': 1})\n",
    "    \n",
    "    cols2 = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "    for col in cols2:\n",
    "        df[col] = df[col].map({'No internet service': 0, 'No': 0, 'Yes': 1})\n",
    "    \n",
    "    df = pd.get_dummies(df, columns=['InternetService', 'Contract', 'PaymentMethod'], drop_first=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       1              0        1           0       1             0   \n",
       "1       0              0        0           0      34             1   \n",
       "2       0              0        0           0       2             1   \n",
       "3       0              0        0           0      45             0   \n",
       "4       1              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0              0               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "2              0               1             1                 0  ...   \n",
       "3              0               1             0                 1  ...   \n",
       "4              0               0             0                 0  ...   \n",
       "\n",
       "   MonthlyCharges  TotalCharges  Churn  InternetService_Fiber optic  \\\n",
       "0           29.85         29.85      0                            0   \n",
       "1           56.95       1889.50      0                            0   \n",
       "2           53.85        108.15      1                            0   \n",
       "3           42.30       1840.75      0                            0   \n",
       "4           70.70        151.65      1                            1   \n",
       "\n",
       "   InternetService_No  Contract_One year  Contract_Two year  \\\n",
       "0                   0                  0                  0   \n",
       "1                   0                  1                  0   \n",
       "2                   0                  0                  0   \n",
       "3                   0                  1                  0   \n",
       "4                   0                  0                  0   \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 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_df = datapreparation(\"C:/Data/Telco-Customer-Churn.csv\")\n",
    "regression_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival Regression Ananlysis using Cox Proportional Hazard model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.CoxPHFitter: fitted with 7043 observations, 5174 censored>\n",
      "      duration col = 'tenure'\n",
      "         event col = 'Churn'\n",
      "number of subjects = 7043\n",
      "  number of events = 1869\n",
      "partial log-likelihood = -12659.69\n",
      "  time fit was run = 2020-09-22 14:53:48 UTC\n",
      "\n",
      "---\n",
      "                                       coef exp(coef)  se(coef)      z      p  -log2(p)  lower 0.95  upper 0.95\n",
      "gender                                 0.04      1.04      0.05   0.85   0.40      1.33       -0.05        0.13\n",
      "SeniorCitizen                          0.03      1.04      0.06   0.61   0.54      0.88       -0.08        0.15\n",
      "Partner                               -0.18      0.84      0.06  -3.23 <0.005      9.67       -0.29       -0.07\n",
      "Dependents                            -0.09      0.91      0.07  -1.31   0.19      2.40       -0.23        0.05\n",
      "PhoneService                           0.83      2.29      0.47   1.75   0.08      3.63       -0.10        1.76\n",
      "MultipleLines                          0.09      1.09      0.13   0.69   0.49      1.03       -0.16        0.33\n",
      "OnlineSecurity                        -0.21      0.81      0.13  -1.60   0.11      3.20       -0.47        0.05\n",
      "OnlineBackup                          -0.06      0.95      0.13  -0.44   0.66      0.60       -0.31        0.19\n",
      "DeviceProtection                       0.09      1.09      0.13   0.69   0.49      1.03       -0.16        0.34\n",
      "TechSupport                           -0.08      0.92      0.13  -0.64   0.52      0.93       -0.34        0.17\n",
      "StreamingTV                            0.28      1.32      0.24   1.19   0.23      2.10       -0.18        0.74\n",
      "StreamingMovies                        0.29      1.33      0.24   1.22   0.22      2.16       -0.18        0.75\n",
      "PaperlessBilling                       0.15      1.16      0.06   2.65   0.01      6.95        0.04        0.26\n",
      "MonthlyCharges                         0.01      1.01      0.02   0.57   0.57      0.82       -0.03        0.06\n",
      "TotalCharges                          -0.00      1.00      0.00 -39.16 <0.005       inf       -0.00       -0.00\n",
      "InternetService_Fiber optic            1.02      2.77      0.58   1.76   0.08      3.67       -0.12        2.15\n",
      "InternetService_No                    -2.34      0.10      0.60  -3.93 <0.005     13.51       -3.51       -1.17\n",
      "Contract_One year                     -1.27      0.28      0.10 -12.55 <0.005    117.58       -1.46       -1.07\n",
      "Contract_Two year                     -3.70      0.02      0.20 -18.32 <0.005    246.60       -4.10       -3.31\n",
      "PaymentMethod_Credit card (automatic) -0.01      0.99      0.09  -0.13   0.90      0.16       -0.19        0.17\n",
      "PaymentMethod_Electronic check         0.39      1.47      0.07   5.31 <0.005     23.13        0.24        0.53\n",
      "PaymentMethod_Mailed check             0.51      1.67      0.09   5.87 <0.005     27.74        0.34        0.68\n",
      "---\n",
      "Concordance = 0.93\n",
      "Log-likelihood ratio test = 5986.69 on 22 df, -log2(p)=inf\n"
     ]
    }
   ],
   "source": [
    "cph = CoxPHFitter()\n",
    "cph.fit(regression_df, duration_col='tenure', event_col='Churn')\n",
    "\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285636735265471"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "cph.plot(ax = ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = regression_df.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "cph.predict_cumulative_hazard(test_id).plot(ax = ax, color = 'red')\n",
    "plt.axvline(x=test_id.tenure.values[0], color = 'blue', linestyle='--')\n",
    "plt.legend(labels=['Hazard','Current Position'])\n",
    "ax.set_xlabel('Tenure', size = 10)\n",
    "ax.set_ylabel('Cumulative Hazard', size = 10)\n",
    "ax.set_title('Cumulative Hazard Over Time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "cph.predict_survival_function(test_id).plot(ax = ax, color = 'red')\n",
    "plt.axvline(x=test_id.tenure.values[0], color = 'blue', linestyle='--')\n",
    "plt.legend(labels=['Survival Function','Current Position'])\n",
    "ax.set_xlabel('Tenure', size = 10)\n",
    "ax.set_ylabel('Survival Probability', size = 10)\n",
    "ax.set_title('Survival Probability Over Time');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(cph, open('survivemodel.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Lifetime Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate customer lifetime value, I would multiply the Monthly charges the customer is paying to Telcom and the expected life time of the customer. \n",
    "\n",
    "I utilize the survival function of a customer to calculate its expected life time. I would like to be little bit conservative and consider the customer is churned when the survival probability of him is 10%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LTV(info):\n",
    "    life = cph.predict_survival_function(info).reset_index()\n",
    "    life.columns = ['Tenure', 'Probability']\n",
    "    max_life = life.Tenure[life.Probability > 0.1].max()\n",
    "    \n",
    "    LTV = max_life * info['MonthlyCharges'].values[0]\n",
    "    return LTV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LTV of a testid is: 922.25 dollars.\n"
     ]
    }
   ],
   "source": [
    "print('LTV of a testid is:', LTV(test_id), 'dollars.')"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}