survival_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "903cd332",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, \\\n",
    "                      ExponentialFitter, GeneralizedGammaFitter\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a18d14",
   "metadata": {},
   "source": [
    "#### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "087d9b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read csv file\n",
    "telco = pd.read_csv('telco.csv')\n",
    "telco.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a71c63eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical variables to dummy variables\n",
    "cat_cols = ['retire', 'region', 'marital', 'ed', 'gender', 'voice', 'internet', 'forward', 'custcat', 'churn']\n",
    "df = telco.copy()\n",
    "df = pd.get_dummies(df, columns=cat_cols, drop_first=True)\n",
    "df = df.rename(columns={'churn_Yes': 'churn'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ec426de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>retire_Yes</th>\n",
       "      <th>region_Zone 2</th>\n",
       "      <th>region_Zone 3</th>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <th>ed_High school degree</th>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <th>ed_Some college</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>voice_Yes</th>\n",
       "      <th>internet_Yes</th>\n",
       "      <th>forward_Yes</th>\n",
       "      <th>custcat_E-service</th>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <th>custcat_Total service</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
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      ],
      "text/plain": [
       "   tenure  age  address  income  retire_Yes  region_Zone 2  region_Zone 3  \\\n",
       "0      13   44        9      64           0              1              0   \n",
       "1      11   33        7     136           0              0              1   \n",
       "2      68   52       24     116           0              0              1   \n",
       "3      33   33       12      33           0              1              0   \n",
       "4      23   30        9      30           0              1              0   \n",
       "\n",
       "   marital_Unmarried  ed_Did not complete high school  ed_High school degree  \\\n",
       "0                  0                                0                      0   \n",
       "1                  0                                0                      0   \n",
       "2                  0                                1                      0   \n",
       "3                  1                                0                      1   \n",
       "4                  0                                1                      0   \n",
       "\n",
       "   ed_Post-undergraduate degree  ed_Some college  gender_Male  voice_Yes  \\\n",
       "0                             0                0            1          0   \n",
       "1                             1                0            1          1   \n",
       "2                             0                0            0          0   \n",
       "3                             0                0            0          0   \n",
       "4                             0                0            1          0   \n",
       "\n",
       "   internet_Yes  forward_Yes  custcat_E-service  custcat_Plus service  \\\n",
       "0             0            1                  0                     0   \n",
       "1             0            1                  0                     0   \n",
       "2             0            0                  0                     1   \n",
       "3             0            0                  0                     0   \n",
       "4             0            1                  0                     1   \n",
       "\n",
       "   custcat_Total service  churn  \n",
       "0                      0      1  \n",
       "1                      1      1  \n",
       "2                      0      0  \n",
       "3                      0      1  \n",
       "4                      0      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caaf376",
   "metadata": {},
   "source": [
    "# Parametric Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa811cba",
   "metadata": {},
   "source": [
    "#### Build the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9ddba6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.GeneralizedGammaFitter:\"GeneralizedGamma_estimate\", fitted with 1000 total observations, 726 right-censored observations>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the Weibull AFT model\n",
    "weibull_aft = WeibullAFTFitter()\n",
    "weibull_aft.fit(df, duration_col='tenure', event_col='churn')\n",
    "\n",
    "# Fit the Log-Normal AFT model\n",
    "lognormal_aft = LogNormalAFTFitter()\n",
    "lognormal_aft.fit(df, duration_col='tenure', event_col='churn')\n",
    "\n",
    "# Fit the Log-Logistic AFT model\n",
    "loglogistic_aft = LogLogisticAFTFitter()\n",
    "loglogistic_aft.fit(df, duration_col='tenure', event_col='churn')\n",
    "\n",
    "# Fit the Exponential AFT model\n",
    "exponential_aft = ExponentialFitter()\n",
    "exponential_aft.fit(df['tenure'], df['churn'])\n",
    "\n",
    "# Fit the Generalized Gamma AFT model\n",
    "generalizedgamma_aft = GeneralizedGammaFitter()\n",
    "generalizedgamma_aft.fit(df['tenure'], df['churn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a95105",
   "metadata": {},
   "source": [
    "#### Compare the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b36e908",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weibull AFT model:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.WeibullAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'churn'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1462.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 18:57:01 UTC</td>\n",
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       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">lambda_</th>\n",
       "      <th>address</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>18.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.12</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>0.98</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.28</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>0.74</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.83</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>12.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.78</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.67</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>18.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.06</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_High school degree</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Some college</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_Yes</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>25.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>10.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 2</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 3</th>\n",
       "      <td>0.12</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retire_Yes</th>\n",
       "      <td>0.17</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.52</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.78</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.31</td>\n",
       "      <td>9.48</td>\n",
       "      <td>27.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.25</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>79.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.17</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.42</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>10.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>2964.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>288.52 on 18 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>165.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{19}{*}{lambda_} & address & 0.04 & 1.04 & 0.01 & 0.02 & 0.06 & 1.02 & 1.06 & 0.00 & 4.69 & 0.00 & 18.47 \\\\\n",
       " & age & 0.03 & 1.03 & 0.01 & 0.01 & 0.04 & 1.01 & 1.04 & 0.00 & 4.12 & 0.00 & 14.69 \\\\\n",
       " & custcat_E-service & 0.98 & 2.66 & 0.16 & 0.67 & 1.28 & 1.96 & 3.61 & 0.00 & 6.28 & 0.00 & 31.44 \\\\\n",
       " & custcat_Plus service & 0.74 & 2.10 & 0.19 & 0.36 & 1.12 & 1.44 & 3.06 & 0.00 & 3.83 & 0.00 & 12.95 \\\\\n",
       " & custcat_Total service & 1.00 & 2.71 & 0.21 & 0.58 & 1.41 & 1.78 & 4.11 & 0.00 & 4.67 & 0.00 & 18.35 \\\\\n",
       " & ed_Did not complete high school & 0.44 & 1.55 & 0.19 & 0.06 & 0.82 & 1.06 & 2.27 & 0.00 & 2.25 & 0.02 & 5.37 \\\\\n",
       " & ed_High school degree & 0.32 & 1.38 & 0.15 & 0.03 & 0.61 & 1.03 & 1.83 & 0.00 & 2.19 & 0.03 & 5.14 \\\\\n",
       " & ed_Post-undergraduate degree & 0.22 & 1.25 & 0.19 & -0.15 & 0.60 & 0.86 & 1.82 & 0.00 & 1.17 & 0.24 & 2.05 \\\\\n",
       " & ed_Some college & 0.25 & 1.29 & 0.14 & -0.03 & 0.54 & 0.97 & 1.71 & 0.00 & 1.75 & 0.08 & 3.66 \\\\\n",
       " & forward_Yes & -0.10 & 0.91 & 0.15 & -0.39 & 0.19 & 0.68 & 1.21 & 0.00 & -0.67 & 0.51 & 0.98 \\\\\n",
       " & gender_Male & 0.00 & 1.00 & 0.10 & -0.20 & 0.21 & 0.82 & 1.23 & 0.00 & 0.04 & 0.97 & 0.05 \\\\\n",
       " & income & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.12 & 0.26 & 1.92 \\\\\n",
       " & internet_Yes & -0.77 & 0.46 & 0.14 & -1.04 & -0.50 & 0.35 & 0.61 & 0.00 & -5.59 & 0.00 & 25.40 \\\\\n",
       " & marital_Unmarried & -0.35 & 0.71 & 0.10 & -0.55 & -0.14 & 0.58 & 0.87 & 0.00 & -3.32 & 0.00 & 10.13 \\\\\n",
       " & region_Zone 2 & -0.06 & 0.94 & 0.13 & -0.31 & 0.19 & 0.73 & 1.21 & 0.00 & -0.49 & 0.63 & 0.67 \\\\\n",
       " & region_Zone 3 & 0.12 & 1.12 & 0.13 & -0.13 & 0.36 & 0.87 & 1.44 & 0.00 & 0.91 & 0.36 & 1.46 \\\\\n",
       " & retire_Yes & 0.17 & 1.19 & 0.52 & -0.85 & 1.19 & 0.43 & 3.30 & 0.00 & 0.33 & 0.74 & 0.43 \\\\\n",
       " & voice_Yes & -0.34 & 0.72 & 0.15 & -0.63 & -0.04 & 0.53 & 0.96 & 0.00 & -2.26 & 0.02 & 5.38 \\\\\n",
       " & Intercept & 2.78 & 16.14 & 0.27 & 2.25 & 3.31 & 9.48 & 27.47 & 0.00 & 10.25 & 0.00 & 79.48 \\\\\n",
       "rho_ & Intercept & 0.17 & 1.19 & 0.05 & 0.07 & 0.27 & 1.08 & 1.32 & 0.00 & 3.42 & 0.00 & 10.66 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.WeibullAFTFitter: fitted with 1000 total observations, 726 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'churn'\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1462.17\n",
       "         time fit was run = 2023-04-30 18:57:01 UTC\n",
       "\n",
       "---\n",
       "                                          coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param   covariate                                                                                                                               \n",
       "lambda_ address                           0.04       1.04       0.01             0.02             0.06                 1.02                 1.06\n",
       "        age                               0.03       1.03       0.01             0.01             0.04                 1.01                 1.04\n",
       "        custcat_E-service                 0.98       2.66       0.16             0.67             1.28                 1.96                 3.61\n",
       "        custcat_Plus service              0.74       2.10       0.19             0.36             1.12                 1.44                 3.06\n",
       "        custcat_Total service             1.00       2.71       0.21             0.58             1.41                 1.78                 4.11\n",
       "        ed_Did not complete high school   0.44       1.55       0.19             0.06             0.82                 1.06                 2.27\n",
       "        ed_High school degree             0.32       1.38       0.15             0.03             0.61                 1.03                 1.83\n",
       "        ed_Post-undergraduate degree      0.22       1.25       0.19            -0.15             0.60                 0.86                 1.82\n",
       "        ed_Some college                   0.25       1.29       0.14            -0.03             0.54                 0.97                 1.71\n",
       "        forward_Yes                      -0.10       0.91       0.15            -0.39             0.19                 0.68                 1.21\n",
       "        gender_Male                       0.00       1.00       0.10            -0.20             0.21                 0.82                 1.23\n",
       "        income                            0.00       1.00       0.00            -0.00             0.00                 1.00                 1.00\n",
       "        internet_Yes                     -0.77       0.46       0.14            -1.04            -0.50                 0.35                 0.61\n",
       "        marital_Unmarried                -0.35       0.71       0.10            -0.55            -0.14                 0.58                 0.87\n",
       "        region_Zone 2                    -0.06       0.94       0.13            -0.31             0.19                 0.73                 1.21\n",
       "        region_Zone 3                     0.12       1.12       0.13            -0.13             0.36                 0.87                 1.44\n",
       "        retire_Yes                        0.17       1.19       0.52            -0.85             1.19                 0.43                 3.30\n",
       "        voice_Yes                        -0.34       0.72       0.15            -0.63            -0.04                 0.53                 0.96\n",
       "        Intercept                         2.78      16.14       0.27             2.25             3.31                 9.48                27.47\n",
       "rho_    Intercept                         0.17       1.19       0.05             0.07             0.27                 1.08                 1.32\n",
       "\n",
       "                                          cmp to     z      p   -log2(p)\n",
       "param   covariate                                                       \n",
       "lambda_ address                             0.00  4.69 <0.005      18.47\n",
       "        age                                 0.00  4.12 <0.005      14.69\n",
       "        custcat_E-service                   0.00  6.28 <0.005      31.44\n",
       "        custcat_Plus service                0.00  3.83 <0.005      12.95\n",
       "        custcat_Total service               0.00  4.67 <0.005      18.35\n",
       "        ed_Did not complete high school     0.00  2.25   0.02       5.37\n",
       "        ed_High school degree               0.00  2.19   0.03       5.14\n",
       "        ed_Post-undergraduate degree        0.00  1.17   0.24       2.05\n",
       "        ed_Some college                     0.00  1.75   0.08       3.66\n",
       "        forward_Yes                         0.00 -0.67   0.51       0.98\n",
       "        gender_Male                         0.00  0.04   0.97       0.05\n",
       "        income                              0.00  1.12   0.26       1.92\n",
       "        internet_Yes                        0.00 -5.59 <0.005      25.40\n",
       "        marital_Unmarried                   0.00 -3.32 <0.005      10.13\n",
       "        region_Zone 2                       0.00 -0.49   0.63       0.67\n",
       "        region_Zone 3                       0.00  0.91   0.36       1.46\n",
       "        retire_Yes                          0.00  0.33   0.74       0.43\n",
       "        voice_Yes                           0.00 -2.26   0.02       5.38\n",
       "        Intercept                           0.00 10.25 <0.005      79.48\n",
       "rho_    Intercept                           0.00  3.42 <0.005      10.66\n",
       "---\n",
       "Concordance = 0.78\n",
       "AIC = 2964.34\n",
       "log-likelihood ratio test = 288.52 on 18 df\n",
       "-log2(p) of ll-ratio test = 165.96"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\n",
      "Log-Normal AFT model:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.LogNormalAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'churn'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1457.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 18:57:01 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">mu_</th>\n",
       "      <th>address</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.78</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>17.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>1.07</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>31.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>0.92</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.29</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>15.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.20</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <td>0.37</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_High school degree</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Some college</th>\n",
       "      <td>0.27</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_Yes</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>23.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.94</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 2</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retire_Yes</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.36</td>\n",
       "      <td>10.61</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.94</td>\n",
       "      <td>5.98</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>50.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>28.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>2954.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>291.01 on 18 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>167.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{19}{*}{mu_} & address & 0.04 & 1.04 & 0.01 & 0.03 & 0.06 & 1.03 & 1.06 & 0.00 & 4.78 & 0.00 & 19.11 \\\\\n",
       " & age & 0.03 & 1.03 & 0.01 & 0.02 & 0.05 & 1.02 & 1.05 & 0.00 & 4.50 & 0.00 & 17.19 \\\\\n",
       " & custcat_E-service & 1.07 & 2.90 & 0.17 & 0.73 & 1.40 & 2.08 & 4.06 & 0.00 & 6.25 & 0.00 & 31.21 \\\\\n",
       " & custcat_Plus service & 0.92 & 2.52 & 0.22 & 0.50 & 1.35 & 1.65 & 3.85 & 0.00 & 4.29 & 0.00 & 15.75 \\\\\n",
       " & custcat_Total service & 1.20 & 3.32 & 0.25 & 0.71 & 1.69 & 2.03 & 5.42 & 0.00 & 4.79 & 0.00 & 19.16 \\\\\n",
       " & ed_Did not complete high school & 0.37 & 1.45 & 0.20 & -0.02 & 0.77 & 0.98 & 2.16 & 0.00 & 1.85 & 0.06 & 3.97 \\\\\n",
       " & ed_High school degree & 0.32 & 1.37 & 0.16 & -0.00 & 0.64 & 1.00 & 1.89 & 0.00 & 1.94 & 0.05 & 4.24 \\\\\n",
       " & ed_Post-undergraduate degree & -0.03 & 0.97 & 0.22 & -0.47 & 0.40 & 0.62 & 1.50 & 0.00 & -0.15 & 0.88 & 0.19 \\\\\n",
       " & ed_Some college & 0.27 & 1.31 & 0.17 & -0.05 & 0.60 & 0.95 & 1.82 & 0.00 & 1.65 & 0.10 & 3.33 \\\\\n",
       " & forward_Yes & -0.20 & 0.82 & 0.18 & -0.55 & 0.15 & 0.58 & 1.17 & 0.00 & -1.10 & 0.27 & 1.88 \\\\\n",
       " & gender_Male & 0.05 & 1.05 & 0.11 & -0.17 & 0.28 & 0.84 & 1.32 & 0.00 & 0.45 & 0.65 & 0.62 \\\\\n",
       " & income & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.52 & 0.13 & 2.95 \\\\\n",
       " & internet_Yes & -0.77 & 0.46 & 0.14 & -1.05 & -0.49 & 0.35 & 0.61 & 0.00 & -5.38 & 0.00 & 23.65 \\\\\n",
       " & marital_Unmarried & -0.46 & 0.63 & 0.12 & -0.68 & -0.23 & 0.51 & 0.80 & 0.00 & -3.94 & 0.00 & 13.60 \\\\\n",
       " & region_Zone 2 & -0.10 & 0.91 & 0.14 & -0.38 & 0.18 & 0.69 & 1.20 & 0.00 & -0.68 & 0.50 & 1.01 \\\\\n",
       " & region_Zone 3 & 0.05 & 1.05 & 0.14 & -0.23 & 0.33 & 0.80 & 1.38 & 0.00 & 0.34 & 0.73 & 0.45 \\\\\n",
       " & retire_Yes & 0.02 & 1.02 & 0.44 & -0.85 & 0.89 & 0.43 & 2.44 & 0.00 & 0.05 & 0.96 & 0.06 \\\\\n",
       " & voice_Yes & -0.43 & 0.65 & 0.17 & -0.76 & -0.10 & 0.47 & 0.90 & 0.00 & -2.57 & 0.01 & 6.61 \\\\\n",
       " & Intercept & 2.36 & 10.61 & 0.29 & 1.79 & 2.94 & 5.98 & 18.84 & 0.00 & 8.07 & 0.00 & 50.37 \\\\\n",
       "sigma_ & Intercept & 0.28 & 1.32 & 0.05 & 0.19 & 0.37 & 1.20 & 1.44 & 0.00 & 6.00 & 0.00 & 28.87 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.LogNormalAFTFitter: fitted with 1000 total observations, 726 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'churn'\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1457.01\n",
       "         time fit was run = 2023-04-30 18:57:01 UTC\n",
       "\n",
       "---\n",
       "                                         coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param  covariate                                                                                                                               \n",
       "mu_    address                           0.04       1.04       0.01             0.03             0.06                 1.03                 1.06\n",
       "       age                               0.03       1.03       0.01             0.02             0.05                 1.02                 1.05\n",
       "       custcat_E-service                 1.07       2.90       0.17             0.73             1.40                 2.08                 4.06\n",
       "       custcat_Plus service              0.92       2.52       0.22             0.50             1.35                 1.65                 3.85\n",
       "       custcat_Total service             1.20       3.32       0.25             0.71             1.69                 2.03                 5.42\n",
       "       ed_Did not complete high school   0.37       1.45       0.20            -0.02             0.77                 0.98                 2.16\n",
       "       ed_High school degree             0.32       1.37       0.16            -0.00             0.64                 1.00                 1.89\n",
       "       ed_Post-undergraduate degree     -0.03       0.97       0.22            -0.47             0.40                 0.62                 1.50\n",
       "       ed_Some college                   0.27       1.31       0.17            -0.05             0.60                 0.95                 1.82\n",
       "       forward_Yes                      -0.20       0.82       0.18            -0.55             0.15                 0.58                 1.17\n",
       "       gender_Male                       0.05       1.05       0.11            -0.17             0.28                 0.84                 1.32\n",
       "       income                            0.00       1.00       0.00            -0.00             0.00                 1.00                 1.00\n",
       "       internet_Yes                     -0.77       0.46       0.14            -1.05            -0.49                 0.35                 0.61\n",
       "       marital_Unmarried                -0.46       0.63       0.12            -0.68            -0.23                 0.51                 0.80\n",
       "       region_Zone 2                    -0.10       0.91       0.14            -0.38             0.18                 0.69                 1.20\n",
       "       region_Zone 3                     0.05       1.05       0.14            -0.23             0.33                 0.80                 1.38\n",
       "       retire_Yes                        0.02       1.02       0.44            -0.85             0.89                 0.43                 2.44\n",
       "       voice_Yes                        -0.43       0.65       0.17            -0.76            -0.10                 0.47                 0.90\n",
       "       Intercept                         2.36      10.61       0.29             1.79             2.94                 5.98                18.84\n",
       "sigma_ Intercept                         0.28       1.32       0.05             0.19             0.37                 1.20                 1.44\n",
       "\n",
       "                                         cmp to     z      p   -log2(p)\n",
       "param  covariate                                                       \n",
       "mu_    address                             0.00  4.78 <0.005      19.11\n",
       "       age                                 0.00  4.50 <0.005      17.19\n",
       "       custcat_E-service                   0.00  6.25 <0.005      31.21\n",
       "       custcat_Plus service                0.00  4.29 <0.005      15.75\n",
       "       custcat_Total service               0.00  4.79 <0.005      19.16\n",
       "       ed_Did not complete high school     0.00  1.85   0.06       3.97\n",
       "       ed_High school degree               0.00  1.94   0.05       4.24\n",
       "       ed_Post-undergraduate degree        0.00 -0.15   0.88       0.19\n",
       "       ed_Some college                     0.00  1.65   0.10       3.33\n",
       "       forward_Yes                         0.00 -1.10   0.27       1.88\n",
       "       gender_Male                         0.00  0.45   0.65       0.62\n",
       "       income                              0.00  1.52   0.13       2.95\n",
       "       internet_Yes                        0.00 -5.38 <0.005      23.65\n",
       "       marital_Unmarried                   0.00 -3.94 <0.005      13.60\n",
       "       region_Zone 2                       0.00 -0.68   0.50       1.01\n",
       "       region_Zone 3                       0.00  0.34   0.73       0.45\n",
       "       retire_Yes                          0.00  0.05   0.96       0.06\n",
       "       voice_Yes                           0.00 -2.57   0.01       6.61\n",
       "       Intercept                           0.00  8.07 <0.005      50.37\n",
       "sigma_ Intercept                           0.00  6.00 <0.005      28.87\n",
       "---\n",
       "Concordance = 0.79\n",
       "AIC = 2954.02\n",
       "log-likelihood ratio test = 291.01 on 18 df\n",
       "-log2(p) of ll-ratio test = 167.66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\n",
      "Log-Logistic AFT model:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.LogLogisticAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'churn'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1458.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 18:57:01 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">alpha_</th>\n",
       "      <th>address</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.42</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>16.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.67</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>1.04</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>31.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>0.86</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.13</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>14.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.20</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.08</td>\n",
       "      <td>5.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>20.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_High school degree</th>\n",
       "      <td>0.34</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Some college</th>\n",
       "      <td>0.24</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_Yes</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.80</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>25.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 2</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 3</th>\n",
       "      <td>0.11</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retire_Yes</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.33</td>\n",
       "      <td>10.33</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.89</td>\n",
       "      <td>5.95</td>\n",
       "      <td>17.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.30</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>53.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.34</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.63</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>34.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>2956.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>294.21 on 18 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>169.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{19}{*}{alpha_} & address & 0.04 & 1.04 & 0.01 & 0.02 & 0.06 & 1.02 & 1.06 & 0.00 & 4.42 & 0.00 & 16.60 \\\\\n",
       " & age & 0.03 & 1.03 & 0.01 & 0.02 & 0.05 & 1.02 & 1.05 & 0.00 & 4.67 & 0.00 & 18.36 \\\\\n",
       " & custcat_E-service & 1.04 & 2.83 & 0.17 & 0.72 & 1.36 & 2.05 & 3.91 & 0.00 & 6.30 & 0.00 & 31.62 \\\\\n",
       " & custcat_Plus service & 0.86 & 2.37 & 0.21 & 0.45 & 1.27 & 1.57 & 3.57 & 0.00 & 4.13 & 0.00 & 14.74 \\\\\n",
       " & custcat_Total service & 1.20 & 3.33 & 0.24 & 0.73 & 1.67 & 2.08 & 5.34 & 0.00 & 5.00 & 0.00 & 20.73 \\\\\n",
       " & ed_Did not complete high school & 0.43 & 1.54 & 0.20 & 0.04 & 0.82 & 1.05 & 2.28 & 0.00 & 2.18 & 0.03 & 5.11 \\\\\n",
       " & ed_High school degree & 0.34 & 1.40 & 0.15 & 0.03 & 0.64 & 1.03 & 1.89 & 0.00 & 2.17 & 0.03 & 5.04 \\\\\n",
       " & ed_Post-undergraduate degree & -0.02 & 0.98 & 0.22 & -0.45 & 0.40 & 0.64 & 1.49 & 0.00 & -0.11 & 0.91 & 0.13 \\\\\n",
       " & ed_Some college & 0.24 & 1.27 & 0.16 & -0.06 & 0.55 & 0.94 & 1.73 & 0.00 & 1.55 & 0.12 & 3.04 \\\\\n",
       " & forward_Yes & -0.19 & 0.82 & 0.17 & -0.53 & 0.14 & 0.59 & 1.15 & 0.00 & -1.14 & 0.25 & 1.98 \\\\\n",
       " & gender_Male & 0.04 & 1.04 & 0.11 & -0.18 & 0.26 & 0.84 & 1.29 & 0.00 & 0.36 & 0.72 & 0.48 \\\\\n",
       " & income & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.17 & 0.24 & 2.06 \\\\\n",
       " & internet_Yes & -0.80 & 0.45 & 0.14 & -1.07 & -0.52 & 0.34 & 0.60 & 0.00 & -5.58 & 0.00 & 25.29 \\\\\n",
       " & marital_Unmarried & -0.45 & 0.64 & 0.11 & -0.66 & -0.23 & 0.52 & 0.80 & 0.00 & -4.01 & 0.00 & 13.99 \\\\\n",
       " & region_Zone 2 & -0.05 & 0.95 & 0.14 & -0.31 & 0.22 & 0.73 & 1.24 & 0.00 & -0.35 & 0.73 & 0.46 \\\\\n",
       " & region_Zone 3 & 0.11 & 1.12 & 0.14 & -0.15 & 0.38 & 0.86 & 1.46 & 0.00 & 0.83 & 0.41 & 1.30 \\\\\n",
       " & retire_Yes & 0.06 & 1.06 & 0.48 & -0.87 & 1.00 & 0.42 & 2.71 & 0.00 & 0.13 & 0.90 & 0.16 \\\\\n",
       " & voice_Yes & -0.40 & 0.67 & 0.16 & -0.72 & -0.08 & 0.49 & 0.92 & 0.00 & -2.45 & 0.01 & 6.13 \\\\\n",
       " & Intercept & 2.33 & 10.33 & 0.28 & 1.78 & 2.89 & 5.95 & 17.93 & 0.00 & 8.30 & 0.00 & 53.08 \\\\\n",
       "beta_ & Intercept & 0.34 & 1.40 & 0.05 & 0.24 & 0.44 & 1.27 & 1.55 & 0.00 & 6.63 & 0.00 & 34.82 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.LogLogisticAFTFitter: fitted with 1000 total observations, 726 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'churn'\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1458.10\n",
       "         time fit was run = 2023-04-30 18:57:01 UTC\n",
       "\n",
       "---\n",
       "                                         coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param  covariate                                                                                                                               \n",
       "alpha_ address                           0.04       1.04       0.01             0.02             0.06                 1.02                 1.06\n",
       "       age                               0.03       1.03       0.01             0.02             0.05                 1.02                 1.05\n",
       "       custcat_E-service                 1.04       2.83       0.17             0.72             1.36                 2.05                 3.91\n",
       "       custcat_Plus service              0.86       2.37       0.21             0.45             1.27                 1.57                 3.57\n",
       "       custcat_Total service             1.20       3.33       0.24             0.73             1.67                 2.08                 5.34\n",
       "       ed_Did not complete high school   0.43       1.54       0.20             0.04             0.82                 1.05                 2.28\n",
       "       ed_High school degree             0.34       1.40       0.15             0.03             0.64                 1.03                 1.89\n",
       "       ed_Post-undergraduate degree     -0.02       0.98       0.22            -0.45             0.40                 0.64                 1.49\n",
       "       ed_Some college                   0.24       1.27       0.16            -0.06             0.55                 0.94                 1.73\n",
       "       forward_Yes                      -0.19       0.82       0.17            -0.53             0.14                 0.59                 1.15\n",
       "       gender_Male                       0.04       1.04       0.11            -0.18             0.26                 0.84                 1.29\n",
       "       income                            0.00       1.00       0.00            -0.00             0.00                 1.00                 1.00\n",
       "       internet_Yes                     -0.80       0.45       0.14            -1.07            -0.52                 0.34                 0.60\n",
       "       marital_Unmarried                -0.45       0.64       0.11            -0.66            -0.23                 0.52                 0.80\n",
       "       region_Zone 2                    -0.05       0.95       0.14            -0.31             0.22                 0.73                 1.24\n",
       "       region_Zone 3                     0.11       1.12       0.14            -0.15             0.38                 0.86                 1.46\n",
       "       retire_Yes                        0.06       1.06       0.48            -0.87             1.00                 0.42                 2.71\n",
       "       voice_Yes                        -0.40       0.67       0.16            -0.72            -0.08                 0.49                 0.92\n",
       "       Intercept                         2.33      10.33       0.28             1.78             2.89                 5.95                17.93\n",
       "beta_  Intercept                         0.34       1.40       0.05             0.24             0.44                 1.27                 1.55\n",
       "\n",
       "                                         cmp to     z      p   -log2(p)\n",
       "param  covariate                                                       \n",
       "alpha_ address                             0.00  4.42 <0.005      16.60\n",
       "       age                                 0.00  4.67 <0.005      18.36\n",
       "       custcat_E-service                   0.00  6.30 <0.005      31.62\n",
       "       custcat_Plus service                0.00  4.13 <0.005      14.74\n",
       "       custcat_Total service               0.00  5.00 <0.005      20.73\n",
       "       ed_Did not complete high school     0.00  2.18   0.03       5.11\n",
       "       ed_High school degree               0.00  2.17   0.03       5.04\n",
       "       ed_Post-undergraduate degree        0.00 -0.11   0.91       0.13\n",
       "       ed_Some college                     0.00  1.55   0.12       3.04\n",
       "       forward_Yes                         0.00 -1.14   0.25       1.98\n",
       "       gender_Male                         0.00  0.36   0.72       0.48\n",
       "       income                              0.00  1.17   0.24       2.06\n",
       "       internet_Yes                        0.00 -5.58 <0.005      25.29\n",
       "       marital_Unmarried                   0.00 -4.01 <0.005      13.99\n",
       "       region_Zone 2                       0.00 -0.35   0.73       0.46\n",
       "       region_Zone 3                       0.00  0.83   0.41       1.30\n",
       "       retire_Yes                          0.00  0.13   0.90       0.16\n",
       "       voice_Yes                           0.00 -2.45   0.01       6.13\n",
       "       Intercept                           0.00  8.30 <0.005      53.08\n",
       "beta_  Intercept                           0.00  6.63 <0.005      34.82\n",
       "---\n",
       "Concordance = 0.79\n",
       "AIC = 2956.21\n",
       "log-likelihood ratio test = 294.21 on 18 df\n",
       "-log2(p) of ll-ratio test = 169.84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\n",
      "Exponential AFT model:\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.ExponentialFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1606.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypothesis</th>\n",
       "      <td>lambda_ != 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lambda_</th>\n",
       "      <td>129.66</td>\n",
       "      <td>7.83</td>\n",
       "      <td>114.30</td>\n",
       "      <td>145.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.55</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>202.03</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>3215.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrr}\n",
       " & coef & se(coef) & coef lower 95% & coef upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "lambda_ & 129.66 & 7.83 & 114.30 & 145.01 & 0.00 & 16.55 & 0.00 & 202.03 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.ExponentialFitter:\"Exponential_estimate\", fitted with 1000 total observations, 726 right-censored observations>\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1606.98\n",
       "               hypothesis = lambda_ != 0\n",
       "\n",
       "---\n",
       "          coef   se(coef)   coef lower 95%   coef upper 95%\n",
       "lambda_ 129.66       7.83           114.30           145.01\n",
       "\n",
       "          cmp to     z      p   -log2(p)\n",
       "lambda_     0.00 16.55 <0.005     202.03\n",
       "---\n",
       "AIC = 3215.96"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\n",
      "Generalized Gamma AFT model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.GeneralizedGammaFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1602.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypothesis</th>\n",
       "      <td>mu_ != 0, ln_sigma_ != 0, lambda_ != 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mu_</th>\n",
       "      <td>4.81</td>\n",
       "      <td>0.13</td>\n",
       "      <td>4.55</td>\n",
       "      <td>5.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.31</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>956.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_sigma_</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.73</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>12.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lambda_</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>3211.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrr}\n",
       " & coef & se(coef) & coef lower 95% & coef upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "mu_ & 4.81 & 0.13 & 4.55 & 5.07 & 0.00 & 36.31 & 0.00 & 956.59 \\\\\n",
       "ln_sigma_ & 0.54 & 0.14 & 0.26 & 0.82 & 0.00 & 3.73 & 0.00 & 12.34 \\\\\n",
       "lambda_ & 0.12 & 0.32 & -0.50 & 0.75 & 1.00 & -2.74 & 0.01 & 7.35 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.GeneralizedGammaFitter:\"GeneralizedGamma_estimate\", fitted with 1000 total observations, 726 right-censored observations>\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1602.53\n",
       "               hypothesis = mu_ != 0, ln_sigma_ != 0, lambda_ != 1\n",
       "\n",
       "---\n",
       "            coef   se(coef)   coef lower 95%   coef upper 95%\n",
       "mu_         4.81       0.13             4.55             5.07\n",
       "ln_sigma_   0.54       0.14             0.26             0.82\n",
       "lambda_     0.12       0.32            -0.50             0.75\n",
       "\n",
       "            cmp to     z      p   -log2(p)\n",
       "mu_           0.00 36.31 <0.005     956.59\n",
       "ln_sigma_     0.00  3.73 <0.005      12.34\n",
       "lambda_       1.00 -2.74   0.01       7.35\n",
       "---\n",
       "AIC = 3211.06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# Print summary statistics for each model\n",
    "print(\"Weibull AFT model:\")\n",
    "print(weibull_aft.print_summary())\n",
    "print(\"\\nLog-Normal AFT model:\")\n",
    "print(lognormal_aft.print_summary())\n",
    "print(\"\\nLog-Logistic AFT model:\")\n",
    "print(loglogistic_aft.print_summary())\n",
    "print(\"\\nExponential AFT model:\")\n",
    "print(exponential_aft.print_summary())\n",
    "print(\"\\nGeneralized Gamma AFT model:\")\n",
    "print(generalizedgamma_aft.print_summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af3293a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Survival Time\n",
      "Weibull: 38.84764928679419\n",
      "Log-Normal: 38.953910678318564\n",
      "Log-Logistic: 36.110654725804906\n",
      "Exponential: 89.87133805042757\n",
      "Generalized Gamma: 113.92378959218682\n"
     ]
    }
   ],
   "source": [
    "print('Median Survival Time')\n",
    "print(f'Weibull: {weibull_aft.median_survival_time_}')\n",
    "print(f'Log-Normal: {lognormal_aft.median_survival_time_}')\n",
    "print(f'Log-Logistic: {loglogistic_aft.median_survival_time_}')\n",
    "print(f'Exponential: {exponential_aft.median_survival_time_}')\n",
    "print(f'Generalized Gamma: {generalizedgamma_aft.median_survival_time_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ff9a3d",
   "metadata": {},
   "source": [
    "To compare the models, we can calculate the AIC values for each model and select the one with the lowest AIC value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6e4a905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weibull_aic=2964.343248083881\n",
      "lognormal_aic=2954.0240102517123\n",
      "loglogistic_aic=2956.2085614433336\n",
      "exponential_aic=3215.9608134637247\n",
      "generalizedgamma_aic=3211.061199344495\n"
     ]
    }
   ],
   "source": [
    "models_aic={}\n",
    "\n",
    "weibull_aic = weibull_aft.AIC_\n",
    "print(f'{weibull_aic=}')\n",
    "models_aic['Weibull']=weibull_aic\n",
    "\n",
    "lognormal_aic = lognormal_aft.AIC_\n",
    "print(f'{lognormal_aic=}')\n",
    "models_aic['Log-Normal']=lognormal_aic\n",
    "\n",
    "loglogistic_aic = loglogistic_aft.AIC_\n",
    "print(f'{loglogistic_aic=}')\n",
    "models_aic['Log-Logistic']=loglogistic_aic\n",
    "\n",
    "exponential_aic = exponential_aft.AIC_\n",
    "print(f'{exponential_aic=}')\n",
    "models_aic['Exponential']=exponential_aic\n",
    "\n",
    "generalizedgamma_aic = generalizedgamma_aft.AIC_\n",
    "print(f'{generalizedgamma_aic=}')\n",
    "models_aic['Generalized Gamma']=generalizedgamma_aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c4d8ad2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model with the minimum AIC value is Log-Normal with AIC = 2954.0240102517123\n"
     ]
    }
   ],
   "source": [
    "# Find the model with the minimum AIC\n",
    "min_aic_model = min(models_aic, key=models_aic.get)\n",
    "min_aic_value = models_aic[min_aic_model]\n",
    "print(f\"The model with the minimum AIC value is {min_aic_model} with AIC = {min_aic_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebb3c29",
   "metadata": {},
   "source": [
    "#### Visualize all the curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f2d54ff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict survival function for each model\n",
    "weibull_sf = weibull_aft.predict_survival_function(df)\n",
    "lognormal_sf = lognormal_aft.predict_survival_function(df)\n",
    "loglogistic_sf = loglogistic_aft.predict_survival_function(df)\n",
    "exponential_sf = exponential_aft.survival_function_\n",
    "generalizedgamma_sf = generalizedgamma_aft.survival_function_\n",
    "\n",
    "# Plot the survival function\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(weibull_sf.index, weibull_sf.mean(1), label='Weibull')\n",
    "plt.plot(lognormal_sf.index, lognormal_sf.mean(1), label='Log-Normal')\n",
    "plt.plot(loglogistic_sf.index, loglogistic_sf.mean(1), label='Log-Logistic')\n",
    "plt.plot(exponential_sf.index, exponential_sf.mean(1), label='Exponential')\n",
    "plt.plot(generalizedgamma_sf.index, generalizedgamma_sf.mean(1), label='Generalized Gamma')\n",
    "\n",
    "plt.xlabel('Tenure (months)')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.title('Survival Curves')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a36295",
   "metadata": {},
   "source": [
    "#### Choose Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7ee69f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model: LogNormalAFTFitter\n"
     ]
    }
   ],
   "source": [
    "# select the best model\n",
    "models = [weibull_aft, lognormal_aft, loglogistic_aft, exponential_aft, generalizedgamma_aft]\n",
    "best_model = models[np.argmin([m.AIC_ for m in models])]\n",
    "print('Best model:', type(best_model).__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f84292b6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.LogNormalAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'churn'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-1457.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 18:57:01 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">mu_</th>\n",
       "      <th>address</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.78</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>17.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>1.07</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>31.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>0.92</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.29</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>15.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.20</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <td>0.37</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_High school degree</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Some college</th>\n",
       "      <td>0.27</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_Yes</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>23.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.94</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 2</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retire_Yes</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.36</td>\n",
       "      <td>10.61</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.94</td>\n",
       "      <td>5.98</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>50.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>28.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>2954.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>291.01 on 18 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>167.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{19}{*}{mu_} & address & 0.04 & 1.04 & 0.01 & 0.03 & 0.06 & 1.03 & 1.06 & 0.00 & 4.78 & 0.00 & 19.11 \\\\\n",
       " & age & 0.03 & 1.03 & 0.01 & 0.02 & 0.05 & 1.02 & 1.05 & 0.00 & 4.50 & 0.00 & 17.19 \\\\\n",
       " & custcat_E-service & 1.07 & 2.90 & 0.17 & 0.73 & 1.40 & 2.08 & 4.06 & 0.00 & 6.25 & 0.00 & 31.21 \\\\\n",
       " & custcat_Plus service & 0.92 & 2.52 & 0.22 & 0.50 & 1.35 & 1.65 & 3.85 & 0.00 & 4.29 & 0.00 & 15.75 \\\\\n",
       " & custcat_Total service & 1.20 & 3.32 & 0.25 & 0.71 & 1.69 & 2.03 & 5.42 & 0.00 & 4.79 & 0.00 & 19.16 \\\\\n",
       " & ed_Did not complete high school & 0.37 & 1.45 & 0.20 & -0.02 & 0.77 & 0.98 & 2.16 & 0.00 & 1.85 & 0.06 & 3.97 \\\\\n",
       " & ed_High school degree & 0.32 & 1.37 & 0.16 & -0.00 & 0.64 & 1.00 & 1.89 & 0.00 & 1.94 & 0.05 & 4.24 \\\\\n",
       " & ed_Post-undergraduate degree & -0.03 & 0.97 & 0.22 & -0.47 & 0.40 & 0.62 & 1.50 & 0.00 & -0.15 & 0.88 & 0.19 \\\\\n",
       " & ed_Some college & 0.27 & 1.31 & 0.17 & -0.05 & 0.60 & 0.95 & 1.82 & 0.00 & 1.65 & 0.10 & 3.33 \\\\\n",
       " & forward_Yes & -0.20 & 0.82 & 0.18 & -0.55 & 0.15 & 0.58 & 1.17 & 0.00 & -1.10 & 0.27 & 1.88 \\\\\n",
       " & gender_Male & 0.05 & 1.05 & 0.11 & -0.17 & 0.28 & 0.84 & 1.32 & 0.00 & 0.45 & 0.65 & 0.62 \\\\\n",
       " & income & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.52 & 0.13 & 2.95 \\\\\n",
       " & internet_Yes & -0.77 & 0.46 & 0.14 & -1.05 & -0.49 & 0.35 & 0.61 & 0.00 & -5.38 & 0.00 & 23.65 \\\\\n",
       " & marital_Unmarried & -0.46 & 0.63 & 0.12 & -0.68 & -0.23 & 0.51 & 0.80 & 0.00 & -3.94 & 0.00 & 13.60 \\\\\n",
       " & region_Zone 2 & -0.10 & 0.91 & 0.14 & -0.38 & 0.18 & 0.69 & 1.20 & 0.00 & -0.68 & 0.50 & 1.01 \\\\\n",
       " & region_Zone 3 & 0.05 & 1.05 & 0.14 & -0.23 & 0.33 & 0.80 & 1.38 & 0.00 & 0.34 & 0.73 & 0.45 \\\\\n",
       " & retire_Yes & 0.02 & 1.02 & 0.44 & -0.85 & 0.89 & 0.43 & 2.44 & 0.00 & 0.05 & 0.96 & 0.06 \\\\\n",
       " & voice_Yes & -0.43 & 0.65 & 0.17 & -0.76 & -0.10 & 0.47 & 0.90 & 0.00 & -2.57 & 0.01 & 6.61 \\\\\n",
       " & Intercept & 2.36 & 10.61 & 0.29 & 1.79 & 2.94 & 5.98 & 18.84 & 0.00 & 8.07 & 0.00 & 50.37 \\\\\n",
       "sigma_ & Intercept & 0.28 & 1.32 & 0.05 & 0.19 & 0.37 & 1.20 & 1.44 & 0.00 & 6.00 & 0.00 & 28.87 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.LogNormalAFTFitter: fitted with 1000 total observations, 726 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'churn'\n",
       "   number of observations = 1000\n",
       "number of events observed = 274\n",
       "           log-likelihood = -1457.01\n",
       "         time fit was run = 2023-04-30 18:57:01 UTC\n",
       "\n",
       "---\n",
       "                                         coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param  covariate                                                                                                                               \n",
       "mu_    address                           0.04       1.04       0.01             0.03             0.06                 1.03                 1.06\n",
       "       age                               0.03       1.03       0.01             0.02             0.05                 1.02                 1.05\n",
       "       custcat_E-service                 1.07       2.90       0.17             0.73             1.40                 2.08                 4.06\n",
       "       custcat_Plus service              0.92       2.52       0.22             0.50             1.35                 1.65                 3.85\n",
       "       custcat_Total service             1.20       3.32       0.25             0.71             1.69                 2.03                 5.42\n",
       "       ed_Did not complete high school   0.37       1.45       0.20            -0.02             0.77                 0.98                 2.16\n",
       "       ed_High school degree             0.32       1.37       0.16            -0.00             0.64                 1.00                 1.89\n",
       "       ed_Post-undergraduate degree     -0.03       0.97       0.22            -0.47             0.40                 0.62                 1.50\n",
       "       ed_Some college                   0.27       1.31       0.17            -0.05             0.60                 0.95                 1.82\n",
       "       forward_Yes                      -0.20       0.82       0.18            -0.55             0.15                 0.58                 1.17\n",
       "       gender_Male                       0.05       1.05       0.11            -0.17             0.28                 0.84                 1.32\n",
       "       income                            0.00       1.00       0.00            -0.00             0.00                 1.00                 1.00\n",
       "       internet_Yes                     -0.77       0.46       0.14            -1.05            -0.49                 0.35                 0.61\n",
       "       marital_Unmarried                -0.46       0.63       0.12            -0.68            -0.23                 0.51                 0.80\n",
       "       region_Zone 2                    -0.10       0.91       0.14            -0.38             0.18                 0.69                 1.20\n",
       "       region_Zone 3                     0.05       1.05       0.14            -0.23             0.33                 0.80                 1.38\n",
       "       retire_Yes                        0.02       1.02       0.44            -0.85             0.89                 0.43                 2.44\n",
       "       voice_Yes                        -0.43       0.65       0.17            -0.76            -0.10                 0.47                 0.90\n",
       "       Intercept                         2.36      10.61       0.29             1.79             2.94                 5.98                18.84\n",
       "sigma_ Intercept                         0.28       1.32       0.05             0.19             0.37                 1.20                 1.44\n",
       "\n",
       "                                         cmp to     z      p   -log2(p)\n",
       "param  covariate                                                       \n",
       "mu_    address                             0.00  4.78 <0.005      19.11\n",
       "       age                                 0.00  4.50 <0.005      17.19\n",
       "       custcat_E-service                   0.00  6.25 <0.005      31.21\n",
       "       custcat_Plus service                0.00  4.29 <0.005      15.75\n",
       "       custcat_Total service               0.00  4.79 <0.005      19.16\n",
       "       ed_Did not complete high school     0.00  1.85   0.06       3.97\n",
       "       ed_High school degree               0.00  1.94   0.05       4.24\n",
       "       ed_Post-undergraduate degree        0.00 -0.15   0.88       0.19\n",
       "       ed_Some college                     0.00  1.65   0.10       3.33\n",
       "       forward_Yes                         0.00 -1.10   0.27       1.88\n",
       "       gender_Male                         0.00  0.45   0.65       0.62\n",
       "       income                              0.00  1.52   0.13       2.95\n",
       "       internet_Yes                        0.00 -5.38 <0.005      23.65\n",
       "       marital_Unmarried                   0.00 -3.94 <0.005      13.60\n",
       "       region_Zone 2                       0.00 -0.68   0.50       1.01\n",
       "       region_Zone 3                       0.00  0.34   0.73       0.45\n",
       "       retire_Yes                          0.00  0.05   0.96       0.06\n",
       "       voice_Yes                           0.00 -2.57   0.01       6.61\n",
       "       Intercept                           0.00  8.07 <0.005      50.37\n",
       "sigma_ Intercept                           0.00  6.00 <0.005      28.87\n",
       "---\n",
       "Concordance = 0.79\n",
       "AIC = 2954.02\n",
       "log-likelihood ratio test = 291.01 on 18 df\n",
       "-log2(p) of ll-ratio test = 167.66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(best_model.print_summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7201341",
   "metadata": {},
   "source": [
    "#### Keep siginficant features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707f2e2f",
   "metadata": {},
   "source": [
    "From previous table, I define the threshold for p value to be 0.15, hence, if p < 0.15, the column will be considered statistically significnat and will be kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "326b1ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the p-values of the features\n",
    "p_values = best_model.summary['p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ffe0d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the statistically significant features (p-value < 0.15)\n",
    "sig_cols = p_values[p_values < 0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48b95a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the significant column names\n",
    "sign_cols = []\n",
    "for tuple_ in sig_cols.keys().tolist():\n",
    "    sign_cols.append(str(tuple_).split(\"'\")[-2])\n",
    "sign_cols = sign_cols[:-2]\n",
    "sign_cols.append('tenure')\n",
    "sign_cols.append('churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edede585",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset the data to include only the significant columns and the target variable\n",
    "df = df.loc[:, sign_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1216d63",
   "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>address</th>\n",
       "      <th>age</th>\n",
       "      <th>custcat_E-service</th>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <th>custcat_Total service</th>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <th>ed_High school degree</th>\n",
       "      <th>ed_Some college</th>\n",
       "      <th>income</th>\n",
       "      <th>internet_Yes</th>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <th>voice_Yes</th>\n",
       "      <th>tenure</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>44</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>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>33</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>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>52</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>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   address  age  custcat_E-service  custcat_Plus service  \\\n",
       "0        9   44                  0                     0   \n",
       "1        7   33                  0                     0   \n",
       "2       24   52                  0                     1   \n",
       "\n",
       "   custcat_Total service  ed_Did not complete high school  \\\n",
       "0                      0                                0   \n",
       "1                      1                                0   \n",
       "2                      0                                1   \n",
       "\n",
       "   ed_High school degree  ed_Some college  income  internet_Yes  \\\n",
       "0                      0                0      64             0   \n",
       "1                      0                0     136             0   \n",
       "2                      0                0     116             0   \n",
       "\n",
       "   marital_Unmarried  voice_Yes  tenure  churn  \n",
       "0                  0          0      13      1  \n",
       "1                  0          1      11      1  \n",
       "2                  0          0      68      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1463219a",
   "metadata": {},
   "source": [
    "#### Keep final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d8ade34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.LogNormalAFTFitter: fitted with 1000 total observations, 726 right-censored observations>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lognormal = LogNormalAFTFitter()\n",
    "lognormal.fit(df, duration_col='tenure', event_col='churn')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb761388",
   "metadata": {},
   "source": [
    "# CLV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d099da",
   "metadata": {},
   "source": [
    "#### CLV per customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "458f8a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_margin = 0.2\n",
    "discount_rate = 0.1\n",
    "retention_rate = 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae2fd20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "telco['CLV'] = (average_margin * (1 - discount_rate) * retention_rate) / (1 + discount_rate - retention_rate) * df['tenure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e21ae835",
   "metadata": {
    "scrolled": true
   },
   "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>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>voice</th>\n",
       "      <th>internet</th>\n",
       "      <th>forward</th>\n",
       "      <th>custcat</th>\n",
       "      <th>churn</th>\n",
       "      <th>CLV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zone 2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>Married</td>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>College degree</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Basic service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zone 3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>Married</td>\n",
       "      <td>7</td>\n",
       "      <td>136</td>\n",
       "      <td>Post-undergraduate degree</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Total service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zone 3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>Married</td>\n",
       "      <td>24</td>\n",
       "      <td>116</td>\n",
       "      <td>Did not complete high school</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Plus service</td>\n",
       "      <td>No</td>\n",
       "      <td>32.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  \\\n",
       "0  Zone 2      13   44  Married        9      64   \n",
       "1  Zone 3      11   33  Married        7     136   \n",
       "2  Zone 3      68   52  Married       24     116   \n",
       "\n",
       "                             ed retire  gender voice internet forward  \\\n",
       "0                College degree     No    Male    No       No     Yes   \n",
       "1     Post-undergraduate degree     No    Male   Yes       No     Yes   \n",
       "2  Did not complete high school     No  Female    No       No      No   \n",
       "\n",
       "         custcat churn    CLV  \n",
       "0  Basic service   Yes   6.24  \n",
       "1  Total service   Yes   5.28  \n",
       "2   Plus service    No  32.64  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telco.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652d0918",
   "metadata": {},
   "source": [
    "#### CLV within different segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "78648bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['region', 'marital', 'ed', 'retire', 'gender', 'voice', 'internet', 'forward', 'custcat', 'churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "916bd66a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 598.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 623.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 753.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 572.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x+GgZIYFW+rVrYXY5PutvF3XjtLZRFJRUMOm9DVTY7GaXJCICeMBgZIvFUuPPDofjhGO/d35txwGeeeYZ3nvvPT766CNCQ2vuUl3f+z7wwAMUFBQ4P/bt23fSc8VzVLfmDGgfTWiQppW7S3CglRk39HeO13nhm51mlyQiApgYdFq2bElAQMAJrSiHDh06obWlWnx8fK3nBwYGEhsbW+P4c889x5NPPsmiRYvo06dPo+4LEBISQmRkZI0P8XwrdhlBZ1DHliZX4vuSYsJ56krj/9rMpbvZtC/f3IJERDAx6AQHB5OamsrixYtrHF+8eDGDBg2q9TUDBw484fxFixYxYMAAgoKCnMeeffZZHn/8cb7++msGDBjQ6PuKd7LbHazcYwSdgR1jf+dscYWL+yRw6WmJ2OwOpny4idIKTTkXEXOZ2nU1efJkXn/9dWbPns22bdu45557yMjIYOLEiYDRXVQ9UwqMGVbp6elMnjyZbdu2MXv2bN544w2mTJniPOeZZ57h73//O7Nnz6Z9+/ZkZ2eTnZ3NsWPH6nxf8Q1bswopKKmgeUggfdpEmV2O33jssp60bG5sAPrCt+rCEhFzmbqF87XXXsuRI0d47LHHyMrKolevXnz55ZckJycDkJWVVWNtm5SUFL788kvuueceXnrpJRITE3nxxRcZM2aM85yXX36Z8vJyrrrqqhr3euSRR3j00UfrdF/xDWv35gKQmhxNYIDpw9H8RnSzYJ68ohd//M86Xlm6m+E94ujXLtrsskTET5m6jo430zo6nu8v723gs00HuPfCLvxlaGezy/E798zbyIINmXRs1YwvJg3RYHARMYV+zRWftb5qZ+3UZLUmmOGRS3vQOiKE3YeLmLZ4h9nliIifUtARn5RVUEJmfglWC5yW1MLscvxSi/Bgpl7ZG4DXlu1hXXquyRWJiD9S0BGftD49HzD2t2oWYupQNL82tHscY/q3xeGAKR9upqRcs7BEpGkp6IhPWqduK4/x8KU9iI8MJS2niKe//tXsckTEzyjoiE+q7iZR0DFfVFgQT40xurDmrtjLyqrVqkVEmoKCjvic0gobvxwwNl3tr2nNHuG8rq25/gxjf7i//ncTx8oqTa5IRPyFgo74nO3ZR6m0O4gOD6JtdJjZ5UiVhy7uQZsWYezPK+HJL7eZXY6I+AkFHfE5mzMLAOjdtsUpN2qVptU8JJBnrzb2wnp3VQY/7DhsckUi4g8UdMTnbNmfD6BtHzzQoI4tGTfQWIH8vvmbKSipMLkiEfF1Cjric7ZkGuNzeinoeKT7RnajfWw4WQWlPPLJz2hxdhFxJwUd8SmlFTZ2HDwKQJ+2CjqeKDw4kOeuPg2rBT7eeIAP1u4zuyQR8WEKOuJTtmUVYrM7aNk8mISoULPLkZMY0D6Ge4d3BeDhT37h1+xCkysSEV+loCM+ZUvVQORebaI0ENnD/fncjpzbpRVllXZuf2e9ppyLiFso6IhP2bK/asaVxud4PKvVwv9d25f4yFD2HC7ioQVbNF5HRFxOQUd8yraqLpCeiQo63iCmWTAzbuhHgNXCJxsPMOO7XWaXJCI+RkFHfEalzc6Og8cA6J4QYXI1UlcD2sfw6GU9AXh+8Q7mr9tvckUi4ksUdMRnpOUUUV5pp1lwAEnR4WaXI/Uw9qxk/nRuB8BYX+fHXTkmVyQivkJBR3zGtmxjWnnX+AisVg1E9jb3jejGpaclUml3MPE/6/jlQIHZJYmID1DQEZ+xLcsYn9MtIdLkSqQhrFYLz13dhzNSYjhaVsmNr6/i50yFHRFpHAUd8Rm/VgWd7go6XiskMIDXxw2gX7sW5BdXcMNrP7FqzxGzyxIRL6agIz7j16quq+7xGojszSJDg3jrljMYkBxNYWklY99YzYINGqAsIg2joCM+Ib+4nKyCUsAYoyPeLSI0iLcnnMlFPeMpt9m5Z94mHv7kZ8oqbWaXJiJeRkFHfMK2LKM1JykmjIjQIJOrEVcIDQrgpRv7c8f5HQF4a2U6l/37R+eikCIidaGgIz5h56GqGVdxas3xJQFWC38d0Y3Z4wcQ2yyY7QePMvql5fz94y3kFpWbXZ6IeAEFHfEJ1TuWd1bQ8UkXdItj0T3ncEmfBOwOePunDM5++jumfrmN/XnFZpcnIh5MQUd8ws6qFZG7xDU3uRJxl9jmIcy4oT/v3XYWvdpEUlxu45Uf9nD200u44bWf+Gj9foq0MaiI/I9AswsQcYVdh4yg07m1WnR83cCOsXx259ks2X6I135IY+WeI6zYbXwEWjfTN6kFAzvGcmZKLF3jI2jZPFg72Yv4MYtD2wU3SGFhIVFRURQUFBAZqXVbzHTkWBmp//oGiwW2/vMiwoIDzC5JmtC+3GIWbMjko/X72XvkxG6s6PAgOreOoE10GK0jQ4iLCCWmWTBhwQGEV32EBQUSHGjF4XDgAOwOB3a78bnCZqfSbnyusDmorP5st//mmPHnAKuF5iGBRIQGEhUWRNvocFo1D9FK3SImUouOeL3q1py20WEKOX4oKSacSUM7M2loZ/blFrNy9xFW7M5hw758MnKLySuuYPXeXNhrTn0hgVaSYsLplRjJGSmxnJESTcdWzdXKJNJEFHTE6+1Qt5VUSYoJJykmnGtOTwKgtMLGrkPH2H34GFkFpRwqLONgYSn5JeUUl9soKbdRXPVRYbNjsYDVYsFogLEQYIVAq5WgAAtBAVYCA4zHgVbjz8Yxi/OcSruDY6WVFJVXkltkrO1UVmln16Fj7Dp0jI83HgCgdUQIF/dJYEz/tvRqE2XeGybiBxR0xOvtqp5x1VoDkaWm0KAAerWJMi1MVNjsZOWXknakiPXpeaxKO8KGjHwOHS1jzo97mfPjXk5vH82dF3TmnM4t1coj4gYKOuL1dla16HRS0BEPExRgpV1sOO1iwzm3SysAyiptLN+Zw0cbMln4czZr9uYxbvZqzuoQw+Oje2mJBBEX0/Ry8XrVQUc/IMQbhAQGMLR7HC/d0J8f77+AW89OITjQyk97chn14jJmfLcTu11zRERcRUFHvFpBSQWHj5YB0LFVM5OrEamfuMhQ/nFJD76791yGdmtNhc3Bc4t2MH7uGvK08rOISyjoiFfbc9hozYmLDNEeV+K12kaH8/q4ATx39WmEBln5Ycdhrpq1gsz8ErNLE/F6Cjri1dJyigBIaanWHPFuFouFq1Lb8vEdg0mICmX34SKumrmCjFrWBhKRulPQEa+257ARdDq00kBk8Q3d4iOZ/+dBdGzVjKyCUsbOXuXsnhWR+lPQEa+2J8fouuqgFh3xIYktwnj3trNoGx1G+pFibpm7htIKm9lliXglBR3xasdbdBR0xLfERYby9q1nEtMsmC2ZBTzyyS9mlyTilRR0xGvZ7Q72HqkKOi3VdSW+p33LZvz7+n5YLTBv7T4+XLvP7JJEvI6CjnitAwUllFbYCQqw0DY6zOxyRNxicKeWTL6wCwCPfbaVrALNxBKpDwUd8VrVM67axYQTGKB/yuK7/nxeJ/q1a8HRskoe/GgLDocWFBSpK/10EK+lGVfiLwKsFp69qg/BAVaWbD/MF1uyzC5JxGso6IjXqm7R0Ywr8QedWkdw+/kdAXjqq181C0ukjhR0xGvt0WKB4mf+eE4H4iJD2J9Xwpsr9ppdjohXUNARr5VRNeMqOVZBR/xDeHAgfx3RDYAZS3ZRUFJhckUink9BR7xSpc3O/jxj9klybLjJ1Yg0nSv7taFLXHOOllbyn5V7zS5HxOMp6IhXyioopdLuIDjASnxkqNnliDQZq9XCHed3AuCN5WkUl1eaXJGIZ1PQEa+UkWtsdNg2Jgyr1WJyNSJN6+LeCSTHhpNXXMG7qzLMLkfEoynoiFdKr9rROTlG3VbifwIDrEw815iBNXfFXmx2rasjcjIKOuKVqlt0NBBZ/NUV/doQFRbE/rwSvt9+yOxyRDyWgo54pYxcY8ZVklp0xE+FBgVwzYC2APznp3STqxHxXAo64pXUdSUCfzgrGYClOw6TXrXcgojUpKAjXsfhcJBRHXQ0tVz8WHJsM87t0gqHA/67br/Z5Yh4JAUd8Tr5xRUcLTOm1KrrSvzdValG99WCDZnYNShZ5AQKOuJ10qsGIsdFhhAaFGByNSLmurBHHM1DAtmfV8La9DyzyxHxOAo64nWqxyIkx2jGlUhoUAAje8UDRquOiNSkoCNeZ19Vi466rUQMV/RrA8AXmw9QVqldzUV+S0FHvE66BiKL1HBWh1haR4RQWFrJit1HzC5HxKMo6IjXSc9V0BH5LavVwvCecQAs+iXb5GpEPIuCjngddV2JnGhET2OczuKtB7UlhMhvKOiIVymtsJFdWAposUCR3zqrQyyRoYHkHCtnnWZfiTgp6IhX2Z9XjMMBzUMCiWkWbHY5Ih4jKMDK0O5G99VCdV+JOCnoiFep3syzXUw4FovF5GpEPMuIqnE63247aHIlIp5DQUe8SvWMq3bqthI5weBOLQm0Wth7pNi5TYqIv1PQEa+iqeUiJxcRGkT/5GgAlu48bHI1Ip5BQUe8SvWMq3YKOiK1OrdLKwCWblfQEQEFHfEy6bnquhI5leqgs3J3DuWVdpOrETGfgo54Dbvd4RyMrH2uRGrXIyGSls2DKSq3aZq5CAo64kUOHyujvNJOgNVCYotQs8sR8UhWq4UhnY1WneW71H0loqAjXmN/ntGaEx8ZSmCA/umKnMzADrEArNqTa3IlIubTTwvxGvvzSgBo0yLM5EpEPNtZVUFn0/58issrTa5GxFwKOuI1DuQbWz+0iVbQETmVpJgwEqNCqbA5WJ+eb3Y5IqZS0BGvkZlvdF2pRUfk1CwWi7NV56c9R0yuRsRcCjriNTKru67UoiPyuxR0RAwKOuI1MvM1Rkekrn47Tqek3GZyNSLmUdARr+BwONSiI1IPSTFhxEca43Q27c83uxwR0yjoiFcoKKmgqOq3UrXoiPw+i8VCatW+V1o4UPyZgo54heqp5S2bBxMaFGByNSLeoV+7FgBsyFDQEf+loCNeQeNzROrvty06DofD5GpEzKGgI15B43NE6q9nYhTBgVbyiitIyykyuxwRUyjoiFdQi45I/QUHWunTJgqA9Rn55hYjYhIFHfEKmdr+QaRB+mtAsvg504POyy+/TEpKCqGhoaSmprJs2bJTnr906VJSU1MJDQ2lQ4cOzJo1q8bzv/zyC2PGjKF9+/ZYLBamT59+wjUeffRRLBZLjY/4+HhXflniYs4WnehwkysR8S792xlBRwOSxV+ZGnTmzZvH3XffzUMPPcSGDRsYMmQII0eOJCMjo9bz09LSGDVqFEOGDGHDhg08+OCDTJo0ifnz5zvPKS4upkOHDjz11FOnDC89e/YkKyvL+bFlyxaXf33iOuq6EmmYvkktANh56JgWDhS/FGjmzadNm8att97KhAkTAJg+fToLFy5k5syZTJ069YTzZ82aRbt27ZytNN27d2ft2rU899xzjBkzBoDTTz+d008/HYD777//pPcODAxUK46XKC6vJLeoHNBgZJH6iosMoVVECIePlrE1q9A5E0vEX5jWolNeXs66desYPnx4jePDhw9nxYoVtb5m5cqVJ5w/YsQI1q5dS0VFRb3uv3PnThITE0lJSeG6665jz549pzy/rKyMwsLCGh/SNA5UteZEhAQSFRZkcjUi3sVisTgHJG/RCsnih0wLOjk5OdhsNuLi4mocj4uLIzs7u9bXZGdn13p+ZWUlOTk5db73mWeeyVtvvcXChQt57bXXyM7OZtCgQRw5cvLN76ZOnUpUVJTzIykpqc73k8bZr6nlIo3Su60RdDZnFphciUjTM30wssViqfFnh8NxwrHfO7+246cycuRIxowZQ+/evRk2bBhffPEFAG+++eZJX/PAAw9QUFDg/Ni3b1+d7yeNo/E5Io3Tp211i46Cjvgf08botGzZkoCAgBNabw4dOnRCq021+Pj4Ws8PDAwkNja2wbU0a9aM3r17s3PnzpOeExISQkhISIPvIQ2nxQJFGqdXVdfVrsPHKCqrpFmIqcMzRZqUaS06wcHBpKamsnjx4hrHFy9ezKBBg2p9zcCBA084f9GiRQwYMICgoIaP3SgrK2Pbtm0kJCQ0+BriPmrREWmc1hGhJESF4nDALwc0vlD8i6ldV5MnT+b1119n9uzZbNu2jXvuuYeMjAwmTpwIGN1FN910k/P8iRMnkp6ezuTJk9m2bRuzZ8/mjTfeYMqUKc5zysvL2bhxIxs3bqS8vJzMzEw2btzIrl27nOdMmTKFpUuXkpaWxqpVq7jqqqsoLCxk3LhxTffFS52pRUek8aq7rzZrQLL4GVPbL6+99lqOHDnCY489RlZWFr169eLLL78kOTkZgKysrBpr6qSkpPDll19yzz338NJLL5GYmMiLL77onFoOcODAAfr16+f883PPPcdzzz3Hueeey/fffw/A/v37uf7668nJyaFVq1acddZZ/PTTT877imdRi45I4/VKjGLhLwfZqhYd8TMWh7a0bZDCwkKioqIoKCggMjLS7HJ8VoXNTte/f4XdAasfHErryFCzSxLxSt9sPciEt9bSLT6Cr+8+x+xyRJqM6bOuRE4lu6AUuwOCA6y0bK7B4CIN1SPR+IVs16FjlFVqhWTxHwo64tGq19BJbBGK1Vr3JQREpKaEqFBahAdRaXew8+Axs8sRaTIKOuLRjm/mqfE5Io1hsVjoHm+06mzN0jgd8R8KOuLRsqqCTmKUgo5IY1V3X2lAsvgTBR3xaFmFpQAkaMaVSKP1SDCCzja16IgfUdARj5ZdYASdxCjNthJprO4Jx7uuNOFW/IWCjni0rKqgE6+gI9JonVo3JyjAwtHSSudAfxFfp6AjHi27wPhmnKAxOiKNFhxopWOr5gBszz5qcjUiTUNBRzxWaYWNvOIKQC06Iq7SLT4CgO0HFXTEPyjoiMeq7rYKDw4gMlS7LYu4QpeqoLNDQUf8hIKOeKysqm6r+KhQLBYtFijiCl3jqlp01HUlfkJBRzxW9YyrBHVbibhMl6qgs+dwERU2u8nViLifgo54LOeMq0gNRBZxlTYtwmgWHEC5zU76kSKzyxFxOwUd8Vhq0RFxPavVQmdn95X2vBLfp6AjHktr6Ii4x/FxOlohWXyfgo54rOzC6jV0FHREXKmLppiLH1HQEY+VrRYdEbeobtHZcVBdV+L7FHTEI5VV2sg5Vg5oVWQRV+scZ6yOnJFbTFmlzeRqRNxLQUc80qHCMsBYsj46PMjkakR8S+uIEJoFB2CzO8g4Umx2OSJupaAjHinrNzOutFigiGtZLBY6tjZadXYfVveV+DYFHfFIzlWRIzU+R8Qdqjf33H1Ya+mIb1PQEY+kNXRE3Ktjq2aAWnTE9ynoiEdydl210EBkEXdQi474CwUd8Uhq0RFxr+oxOnsOHcPhcJhcjYj7KOiIR8oqrN7nSkFHxB2SY8OxWuBoWSWHj5aZXY6I2yjoiEfKLqheFVldVyLuEBIYQFJMOAC7NE5HfJiCjnicCpudQ1W/YWpVZBH30Tgd8QcKOuJxDh0tw+GAoAALsc2CzS5HxGc5Z14dUouO+C4FHfE41d1WcZGhWK1aLFDEXapbdPbkqEVHfJeCjnicLM24EmkSztWR1aIjPkxBRzzO8V3LNRBZxJ2qW3Qy80soKdfmnuKbFHTE46hFR6RpxDQLpkXVprl7ctSqI75JQUc8jrNFR2voiLidZl6Jr1PQEY+T5VxDR0FHxN0080p8nYKOeJzjY3QUdETcTTOvxNcp6IhHsdkdHKxaLFCrIou4n7PrSi064qMUdMSj5Bwrw2Z3EGC10CoixOxyRHyec3PPnGPY7drcU3yPgo54lOoZV60jQgjQYoEibpcUHUZQgIXSCjsHqsbHifiSBgWdtLQ0V9chAvx2M0+NzxFpCoEBVpJjqwYka+aV+KAGBZ1OnTpx/vnn8/bbb1NaWurqmsSPHV9DR+NzRJpK+6qgk3FEQUd8T4OCzqZNm+jXrx/33nsv8fHx/OlPf2L16tWurk38kGZciTS99rHhAOw9UmxyJSKu16Cg06tXL6ZNm0ZmZiZz5swhOzubs88+m549ezJt2jQOHz7s6jrFT2hVZJGml9zSaNFJV4uO+KBGDUYODAzkiiuu4IMPPuDpp59m9+7dTJkyhbZt23LTTTeRlZXlqjrFT6hFR6TpqUVHfFmjgs7atWu5/fbbSUhIYNq0aUyZMoXdu3fz3XffkZmZyejRo11Vp/iJrEINRhZpasfH6BRj0xRz8TGBDXnRtGnTmDNnDtu3b2fUqFG89dZbjBo1CqvVyE0pKSm88sordOvWzaXFim+z2x0cLDAWC9TO5SJNJyEqlKAAC+U2O9mFpbRpof9/4jsa1KIzc+ZMbrjhBjIyMvj444+55JJLnCGnWrt27XjjjTdcUqT4hyNF5ZTb7Fgsxjo6ItI0AgOsJEUb3Vfp2gpCfofFYuHjjz82u4w6a1CLzuLFi2nXrt0J4cbhcLBv3z7atWtHcHAw48aNc0mR4h+qx+e0ah5CUIDWshRpSsmx4ezJKWLvkWIGdTK7GvFkWVlZREdHm11GnTXop0nHjh3Jyck54Xhubi4pKSmNLkr8k3YtFzFP9aKBmnklvyc+Pp6QEO9pdW9Q0HE4ah+sduzYMUJD9UNKGia7UDOuRMxyfOaVgo4ve+WVV2jTpg12u73G8csuu8zZCzNz5kw6duxIcHAwXbt25T//+U+Nc/+362r//v1cd911xMTE0KxZMwYMGMCqVaucz3/22WekpqYSGhpKhw4d+Oc//0llZaX7vsj/Ua+uq8mTJwPGF/nwww8THh7ufM5ms7Fq1Sr69u3r0gLFf2hVZBHztHeupaMp5r7s6quvZtKkSSxZsoShQ4cCkJeXx8KFC/nss89YsGABd911F9OnT2fYsGF8/vnn3HzzzbRt25bzzz//hOsdO3aMc889lzZt2vDpp58SHx/P+vXrnUFq4cKF/OEPf+DFF19kyJAh7N69mz/+8Y8APPLII03yNdcr6GzYsAEwWnS2bNlCcHCw87ng4GBOO+00pkyZ4toKxW9oDR0R81RPMd97pAiHw4HFok11fVFMTAwXXXQR7777rjPofPjhh8TExDB06FDOOeccxo8fz+233w4YDRw//fQTzz33XK1B59133+Xw4cOsWbOGmJgYwNgmqtoTTzzB/fff72wt6tChA48//jh/+9vfPDPoLFmyBICbb76ZF154gcjISLcUJf5JY3REzNMmOowAq7GL+aGjZcRF6v+hr7rxxhv54x//yMsvv0xISAjvvPMO1113HQEBAWzbts3Z4lJt8ODBvPDCC7Vea+PGjfTr188Zcv7XunXrWLNmDU888YTzmM1mo7S0lOLi4ho9Q+7SoFlXc+bMcXUdIsdbdPQNVqTJBQVYaRsdRvqRYvbmFCno+LBLL70Uu93OF198wemnn86yZcuYNm2a8/n/bc07VQtfWNiphxrY7Xb++c9/cuWVV57wXFON6a1z0LnyyiuZO3cukZGRtRb8Wx999FGjCxP/4nA4NEZHxGTJsc1IP1JM+pFizuwQa3Y54iZhYWFceeWVvPPOO+zatYsuXbqQmpoKQPfu3Vm+fDk33XST8/wVK1bQvXv3Wq/Vp08fXn/9dXJzc2tt1enfvz/bt2+v0Z3V1OocdKKiopyJLioqym0FiX/KL66grNIYvBYX5T3TFkV8SfvYcH5AM6/8wY033sill17KL7/8wh/+8Afn8b/+9a9cc8019O/fn6FDh/LZZ5/x0Ucf8c0339R6neuvv54nn3ySyy+/nKlTp5KQkMCGDRtITExk4MCBPPzww1xyySUkJSVx9dVXY7Va2bx5M1u2bOFf//pXk3ytdQ46v+2uUteVuFp1a07L5sGEBAaYXI2Ifzq+lo5mXvm6Cy64gJiYGLZv384NN9zgPH755Zfzwgsv8OyzzzJp0iRSUlKYM2cO5513Xq3XCQ4OZtGiRdx7772MGjWKyspKevTowUsvvQTAiBEj+Pzzz3nsscd45plnCAoKolu3bkyYMKEpvkwALI6TLYpzCiUlJTgcDucgovT0dBYsWECPHj0YPny4y4v0RIWFhURFRVFQUKBB2S7w3a8HuWXuWnq1ieTzvwwxuxwRv/TttoPc+uZaeiZG8sUk/T8U39CgBQNHjx7NW2+9BUB+fj5nnHEGzz//PKNHj2bmzJkuLVD8Q5ZzILLG54iY5bctOg34HVjEIzUo6Kxfv54hQ4y0/9///pf4+HjS09N56623ePHFF11aoPiHbOdAZM30EDFLUkwYFgscK6vkSFG52eWIuESDgk5xcTEREREALFq0iCuvvBKr1cpZZ51Fenq6SwsU/5ClxQJFTBcSGEBi1axH7XklvqJBQadTp058/PHH7Nu3j4ULFzrH5Rw6dEjjVaRB1KIj4hnat6za8ypHA5LFNzQo6Dz88MNMmTKF9u3bc+aZZzJw4EDAaN3p16+fSwsU/1C9KrJadETMlfybrSBEfEGDVka+6qqrOPvss8nKyuK0005zHh86dChXXHGFy4oT/6DFAkU8x/FdzNWiI76hQUEHID4+nvj4+BrHzjjjjEYXJP6nsLSS4nIboO0fRMzWLsYIOvtyFXTENzQo6BQVFfHUU0/x7bffcujQIed27NX27NnjkuLEP1SPz2kRHkRYsBYLFDFT22gj6OzPU9AR39CgoDNhwgSWLl3K2LFjSUhIOOlmXyJ14Ryfo9YcEdO1q+q6yjlWTlFZJc1CGtzwL+IRGvQv+KuvvuKLL75g8ODBrq5H/JBmXIl4jsjQIFqEB5FfXMG+vGK6xWsmrXi3Bs26io6OrnWXUpGGOL6GjgYii3iCpOjqcTolJlci3m78+PFYLBaeeuqpGsc//vjjJusNalDQefzxx3n44YcpLlYfrjSeWnREPEv1gOQMDUgWFwgNDeXpp58mLy/PlPs3KOg8//zzLFy4kLi4OHr37k3//v1rfIjUR1ahVkUW8SRtY4zWVc28ElcYNmwY8fHxTJ069aTnzJ8/n549exISEkL79u15/vnnXXb/Bo3Rufzyy11WgEh21WBkteiIeAZNMfd8DoeDkgpbk983LCig3l1OAQEBPPnkk9xwww1MmjSJtm3b1nh+3bp1XHPNNTz66KNce+21rFixgttvv53Y2FjGjx/f6JobFHQeeeSRRt9YpFqWuq5EPIq6rjxfSYWNHg8vbPL7bn1sBOHB9Y8OV1xxBX379uWRRx7hjTfeqPHctGnTGDp0KP/4xz8A6NKlC1u3buXZZ591SdBpUNcVQH5+Pq+//joPPPAAubm5gLGreWZmZqOLEv9xrKySo6WVgAYji3gK52DkvGIcDofJ1YivePrpp3nzzTfZunVrjePbtm07YRb34MGD2blzJzZb41utGtSis3nzZoYNG0ZUVBR79+7ltttuIyYmhgULFpCens5bb73V6MLEP1QPRI4IDaS51usQ8QiJLcKwWqC0ws7hY2W0jlBrq6cJCwpg62MjTLlvQ51zzjmMGDGCBx98sEZLjcPhOKE7zJUBu0E/WSZPnsz48eN55plniIiIcB4fOXIkN9xwg8uKE9+nGVcinic40EpCVBiZ+SXsyy1R0PFAFoulQV1IZnvqqafo27cvXbp0cR7r0aMHy5cvr3HeihUr6NKlCwEBjV8tv0FdV2vWrOFPf/rTCcfbtGlDdnZ2o4sS/3F813J1W4l4kiTNvBI36N27NzfeeCP//ve/ncfuvfdevv32Wx5//HF27NjBm2++yYwZM5gyZYpL7tmgoBMaGkphYeEJx7dv306rVq0aXZT4D2eLjrZ/EPEo1eN0NCBZXO3xxx+v0TXVv39/PvjgA95//3169erFww8/zGOPPeaSgcjQwK6r0aNH89hjj/HBBx8ARhNaRkYG999/P2PGjHFJYeIftIaOiGfSFHNxhblz555wLDk5mdLS0hrHxowZ47b80KAWneeee47Dhw/TunVrSkpKOPfcc+nUqRMRERE88cQTrq5RfJjG6Ih4purNPdWiI96uQS06kZGRLF++nCVLlrBu3Trsdjv9+/dn2LBhrq5PfNzxfa4UdEQ8Sduqrqv9edrvSrxbvYOO3W5n7ty5fPTRR+zduxeLxUJKSgrx8fG1ThETOZXjqyJrMLKIJ6nuujpQUEJ5pZ3gwAYvuyZiqnr9y3U4HFx22WVMmDCBzMxMevfuTc+ePUlPT2f8+PFcccUV7qpTfFBJuY284gpALToinqZl82DCggJwOOBAvlp1xHvVq0Vn7ty5/PDDD3z77becf/75NZ777rvvuPzyy3nrrbe46aabXFqk+KbsqoHI4cEBRIZ633oQIr7MYrGQFBPGjoPHyMgtpn3LZmaXJNIg9WrRee+993jwwQdPCDkAF1xwAffffz/vvPNOvQp4+eWXSUlJITQ0lNTUVJYtW3bK85cuXUpqaiqhoaF06NCBWbNm1Xj+l19+YcyYMbRv3x6LxcL06dNdcl9xveNr6ISqy1PEA2mKufiCegWdzZs3c9FFF530+ZEjR7Jp06Y6X2/evHncfffdPPTQQ2zYsIEhQ4YwcuRIMjIyaj0/LS2NUaNGMWTIEDZs2MCDDz7IpEmTmD9/vvOc4uJiOnTowFNPPUV8fLxL7ivuoRlXIp4tKeb4nlci3qpeQSc3N5e4uLiTPh8XF0deXl6drzdt2jRuvfVWJkyYQPfu3Zk+fTpJSUnMnDmz1vNnzZpFu3btmD59Ot27d2fChAnccsstPPfcc85zTj/9dJ599lmuu+46QkJCXHJfcQ/njKtIDUQW8URaS0d8Qb2Cjs1mIzDw5GMpAgICqKysrNO1ysvLWbduHcOHD69xfPjw4axYsaLW16xcufKE80eMGMHatWupqKhw230BysrKKCwsrPEhjaMWHRHP5mzRydVgZPFe9RoB6nA4GD9+/ElbSsrKyup8rZycHGw22wktRHFxcSfdLys7O7vW8ysrK8nJySEhIcEt9wWYOnUq//znP3/3+lJ3Xr+Gjq0CDv8KuXugMAvKj4HDDsHNITwWYjtBqy4QEvH71xLxQNUtOhqjI96sXkFn3Lhxv3tOfWdc1bY1+6kGpp5sK/f6Dmat730feOABJk+e7PxzYWEhSUlJ9bqn1JRdWL2GjhcFnfx98MsC2LkI9q0G2++Ee4sVEk6DDudDryshrhdo4LV4ibbRRrdyQUkFBSUVRIUFmVyReAuHw8GFF15IQEAACxcurPHcyy+/zAMPPMCWLVto166d22upV9CZM2eOy27csmVLAgICTmhFOXTo0EnHAcXHx9d6fmBgILGxsW67L0BISMhJW7KkYY53XXn4GB2HA9KWwop/w65vgeOb0RESCa26QmSi8dgaAGVH4dghyNkBxw7CgQ3Gx/JpEN8bzrodel0FgcGmfUkiddEsJJCWzYPJOVbOvtxiotpEmV2SeAmLxcKcOXPo3bs3r7zyCn/6058AY1LRfffdx7///e8mCTnQwL2uXCE4OJjU1FQWL15c4/jixYsZNGhQra8ZOHDgCecvWrSIAQMGEBRUt980GnJfcb2yShs5x8oBD2/RyVwHsy+Ct0bDrm8AB7QfAhc9DXeuhfvSYcI3cM1bMHoGXPoCXDUbxn8OU3bAPVvhileg2yUQEALZW+DjP8OMAbD5Q7Dbzf4KRU4pSQOSpYGSkpJ44YUXmDJlCmlpaTgcDm699VaGDh3KGWecwahRo2jevDlxcXGMHTuWnJwc52v/+9//0rt3b8LCwoiNjWXYsGEUFRU1qA5TV2mbPHkyY8eOZcCAAQwcOJBXX32VjIwMJk6cCBjdRZmZmbz11lsATJw4kRkzZjB58mRuu+02Vq5cyRtvvMF7773nvGZ5eTlbt251Ps7MzGTjxo00b96cTp061em+4n6HCo0un5BAKy3CPbA5vOwYLP4HrJ0DOCAwFPqPg7MmQkyHul8nqg2cdp3xUZwL6+bCqlmQnw4fTYC1s41w1KqLu74SkUZJig5nQ0a+pph7GocDKkz4OwkKr1f3+7hx41iwYAE333wzY8aM4eeff2bNmjUMGDCA2267jWnTplFSUsJ9993HNddcw3fffUdWVhbXX389zzzzDFdccQVHjx5l2bJlzqEq9WVxNPSVLvLyyy/zzDPPkJWVRa9evfi///s/zjnnHADGjx/P3r17+f77753nL126lHvuuYdffvmFxMRE7rvvvhoBZe/evaSkpJxwn3PPPbfGdU5137ooLCwkKiqKgoICIiMj6/+F+7nVablc88pK2seG8/1fT1yA0lSZ62H+rcYgY4A+18GwRyHy9we710l5Efw0E5ZNg4oiCAiGCx+HM/+k8TvicZ5buJ0ZS3bxh7Pa8a/Le5tdjlQrL4InE5v+vg8egOD6rZJ96NAhevXqxZEjR/jvf//Lhg0bWLVqVY2xO/v37ycpKYnt27dz7NgxUlNT2bt3L8nJyY0u2fSg460UdBrnk42Z3PX+Rs7qEMP7fxxodjnHbf4QPrnDGGQc2QYunwkdznXPvfIz4PPJsKuqG7XrxXDFLAjVvyfxHPPWZHDf/C2c06UVb91yhtnlSDUvCjoAf//73/n444/5+eefufjii1m8eDHBwTXHKRYVFfHll18yfPhwRowYwerVqxkxYgTDhw/nqquuIjo6ukEla4MhMYXHDUR2OGDZc/Ddv4w/dxkJV8yEsIb9x6qTFu3gxg9h9auw6O+w/Qt4Yzjc8D5Et3fffUXqoXqMzn6N0fEsQeFG6DDjvg0QGBjoXIfPbrdz6aWX8vTTT59wXkJCAgEBASxevJgVK1awaNEi/v3vf/PQQw+xatWqWntsfvfeDapYpJGq19CJi/SAgcgOB3z7mDErCmDwXTD0EWMGlbtZLEaXVdsB8N4NcHgbvD4Mxi4wZmiJmKx6v6v9eSXY7Q6sVnWvegSLpUEtK56gf//+zJ8/n/bt2590EWKLxcLgwYMZPHgwDz/8MMnJySxYsKDGMi91ZdqsK/Fv1Rt6JrbwgKCz5InjIWf4E3DhY00Tcn6rTSr8cYkRbooOw5yLIeOnpq1BpBYJUaEEWi2U2+wcPFpqdjniA+644w5yc3O5/vrrWb16NXv27GHRokXccsst2Gw2Vq1axZNPPsnatWvJyMjgo48+4vDhw3Tv3r1B91PQEVNkeUrX1apX4Ydnjccjn4VBd5pXS2QijP8C2g2EsgJ4ewzsW2NePSJAYICVNlULB2YcUfeVNF5iYiI//vgjNpuNESNG0KtXL+666y6ioqKwWq1ERkbyww8/MGrUKLp06cLf//53nn/+eUaOHNmg+2kwcgNpMHLjDPjXN+QcK+Pzv5xNL7MWIduxEN69FnDAeQ/CefeZU8f/Ki+G966FtB+MRQjHfQaJfc2uSvzYH15fxfJdOTx7VR+uHqAV4cW7qEVHmpyxWKCxjo5piwUe2Q3zbwMckDoezv2bOXXUJjgcrn8fkgdDWSG8e40xQ0vEJM5FA/O0uad4HwUdaXK/XSwwppkJ2yCUF8P7NxrdQ0lnGV1WnrZ+TXAzI+y07mlsI/HONVCSb3ZV4qeSYoyuK62OLN5IQUea3IH845t51nczVpdY/A9jdlPzOLjmTc/dcyo0Em78ACISjHo/GAuV5WZXJX6onbaBEC+moCNNztSByNu/hjWvG4+vmAUR8U1fQ31EtYUbPoDg5saYnc/vNqbDizSh6inmGQo64oUUdKTJHSg43qLTpEry4NO/GI/PugM6XtC092+ohD5w9VywBMDGd2D1a2ZXJH6meozOoaNllFbYTK5GpH4UdKTJOVdFbuo1dBY/AkWHoGUXGPpw0967sTpfCMMfNx4vfFDTzqVJRYcH0TzEWNhtvwYki5dR0JEmdyDfhK6r9BWw/k3j8aUvQJAHLFRYX2fdDj1Gg70CPhwPRUfMrkj8hMVioW20BiSLd1LQkSaX1dRdV7ZK+OJe43HqeEge1DT3dTWLBS6bATEdoXA/fDQB7OpGkKZxfIq5go54FwUdaXJNPhh5/ZtwaCuEtjD2sPJmoZFw7X8gMAx2fwc/PGd2ReInNPNKvJWCjjSp0gobuUXGFOkm2eeqJN/Yywrg/AchPMb993S3uJ5wyf8Zj5c+DftWm1uP+IWk6m0gFHTEyyjoSJOqHogcFhRAVFiQ+2+4/P+g+Ai07AoDbnH//ZpK3+uh9zXgsMFHf4Syo2ZXJD7O2XWVq8HI4l0UdKRJ/XZqudsXCzx6EFa9Yjwe9igENEGwakqjnoWoJMhLg6/vN7sa8XG/7brSFoniTRR0pEk16dTy5dOgsgTapELXhu1669HCWhiLHmKBDW/D1k/Nrkh8WNuqRQOPllVSUFJhcjUidaegI02qyQYiF+yHtbONxxf8w/P2snKV9mfD2Xcbjz+bBIVZppYjvissOICWzUMAdV+Jd1HQkSZVvc9Vorunlq/4N9jKIfls6HCee+9ltvMehPg+xsrPn9wOdrvZFYmPale9uaemmIsXUdCRJlXdohPvzhadohxYV7U44DlTfLc1p1pgMIx5HQJDjSnnq181uyLxUdUDkjXzSrxJoNkFiH/JaooxOqtmGWNzEvv5fmtOtVZdYfi/4MspsPhh6HAutO5udlWeqTgXDm+H/HQ4dgjKi8BeaayWHRIF0ckQ2wlaJEOAvkX+VvXmnlpLR7yJ/hdLk6peFTnRXS06ZceOt2icPdn3W3N+6/QJsGMh7FoMH90GE74zWnv8XUWJ0dK142vYuxxy99TtddZAY1mClCGQci60HwyhUe6t1cM5Z15pvyvxIgo60mRKym3kFxuzNeLdNUZn47tQWmD8Rt7tEvfcw1NZLDB6Brw8ELK3wPdPGtPq/dWBDbB2Dvw8H8qP1XwuKgliOkDzOGO1aYsVKkuN1p68vXBkl/HnQ78YH6tmGeckD4ZeY4w9x3xh8cl6ahuj/a7E+yjoSJOpXkOnWXAAkaFu+Kdnt8OqmcbjMyeC1Q+HoEXEG5uWfjAWlk+HziMgeaDZVTWtvcuNFaPTfjh+LCrJCL4dz4ekM42p+aditxv7iWWugz1LIW2p0RK0d5nx8eUU6DQM+t9kvMd+0sVV3XWVmVeC3e7AavWjFlPxWv7xv1M8wvE1dMLcs1jgzkXGD6PQKDjtetdf31v0uAz63ggb34EFf4SJPxqtFr7uyG5jfNKvnxt/tgZCzyuqNnIdXL9uTKsVWrQzPnpeYRzL2wu/LIAt8+HgFqMrbMfXENkG+o8zQk9kgqu/Ko+SEBVKoNVCuc3OwaOlTbdfnUgj+OGvvGKW6qnlbtu1/KeXjc/9x0FIc/fcw1tc9JTxQzo/A75+wOxq3KvsKCz6B7x0phFyLAEw4FaYtNGYjdb+bNeM1YpuD2ffA39eDneshsF3QXgsFGYa3YTTe8OCP8PBrY2/l4cKDLCS2KJqz6sj6r4S76CgI03m+GKBbgg6OTuN7gWLFc64zfXX9zahkXDFK4AFNvrwqsnpK2DmYFjxItgroONQ+PMKuGQatEhy331bdYULH4PJ2+DK1yHpLOP+m96FmQPh7TFGl5cPbpWQ5FxLRwOSxTso6EiTyXLuc+WG5u51c43PnYcbLRkCyYN+s2ryXcbeX76iotRoxZkzypgmHtUObvgQxn4Erbs1XR2BIdDnarh1IUz41hikbLHCrm/grcvg1XNh6yc+tYjjb/e8EvEGCjrSZKpbdBJdvYZORakxHgUg9WbXXtvbnfcgxPeGklz45A7faGHI3QOvXWC04uCAfn+AP/8IXYabW1fbAXDNW/CXdXD6bRAYBlmb4IObYOYgY/aX3WZujS7QVmvpiJdR0JEmk5Xvpn2utn5ibH8Q2RY6X+jaa3u7wGC48jUICDHW16ne/8tb7V4Cr55vTPkObwnXvQujX/KswdYxHeDi5+CeX+Dc+4xFCA9vg//eAi+fBZs/AFul2VU2WJJzLR0FHfEOCjrSZA4UuGkw8vq3jM+p48Aa4Npr+4LW3eHCfxqPFz4Eh3eYW09DOByw8mV4+0oozYc2A2Dicuh2sdmVnVyzWDj/Qbh7s9GyFhoFOTuMxRxnDoRtn3tlC9vxriuN0RHvoKAjTaKwtIKjpcZvsdWzNlwiLx3SlwMW6HuD667ra874k7EdRmWJ0ZVSXmR2RXVXUQof3w4LHwCHHU67AcZ/4T1TucNawHn3wd0/wwX/gLBoI/DMuxFmXwQZq8yusF6Soo3/v9mFpZRWeH9XnPg+BR1pEtVTy1uEB9EsxIXLN23+wPicMgSi2rruur7GaoUrXjVWAj68DT6/xztaE45mw9yLjdlMFiuMmAqXv2zsS+VtQiONTWbv2mRsTxIYBvt+gtnD4f0bjZmDXiCmWTDhwUbLaWa+WnXE8ynoSJPIrJqK2saVrTkOB2x6z3jszwsE1lVEHFw1x1hnZvM8WDfH7IpObf86ePU8yFwLoS3gD/Nh4O3ev39ZaBQMewQmrYd+Y40A9+vnxvidxY9AuWePfbFYLJp5JV5FQUeaRPVvfi4NOpnrIHc3BIVD90tdd11f1n6w8UMW4Kv7IHO9ufWczMb3YM5IOJoFrbrBbd9BxwvMrsq1IhONvcn+vAK6XGTsoP7jdCPw7PzG7OpOSTOvxJso6EiTcLboRLsw6FS35nS/FEIiXHddXzdokrHvk63cGK9z7LDZFR1nqzQGTH88EWxl0HUU3LoYYjuaXZn7tO4ON8yD694zZg7mp8M7Y+CjP0JpodnV1UqLBoo3UdCRJrHf1S06lWXGuiQAfa51zTX9hcViTMmO6QAF++D9G4wBv2YryYN3r4aVM4w/n/NXuPYdz5o67k7dRsEdq2DgnUZ31uZ58Mo5HtnqlqQWHfEiCjrSJFw+RmfnIuMHY/N4YzaR1E9YC7jhA2Psy/7VRguKmYvZHd5uLAK4+zujK/LquXDB3/1vB/qQ5jDiCbhlobHac14avDEcVszwqNWVq8foZCjoiBfws+8iYpbqWVcu67ra9L7xuc81WjunoVp2hmv/A9YgY1fuL/9qzkysX7+E14YaKx5HtTN+yFfvGO6vks6AiT9A98uMPbQWPQTzb4EKz+gqStJgZPEiCjridmWVNg4dLQNc1KJTnAs7FhqPNduqcVLOgStfBSyw9g1Y/HDThR1bpXG/96+H8qOQfDb8cQkk9Gma+3u6sGhjS4mLnz8eRude7BFjqtpW/cJSWFpJQXGFydWInJqCjrhd9dYPoUFWYpoFN/6CWz8xfsuN7w1xPRp/PX/X60pjt28w9o/6+n73h53CLHjzUvjxBePPZ06Emz6GZi3de19vY7HA6RPgpk+M4JO5DuZcBPn7TC2rWUggLZsb/5e1FYR4OgUdcbvfTi23uGINlK2fGJ97Xtn4a4lhwC1wyf8Zj1fNMrYpcNcA5W2fwytDIGMFBEfA1W/CyKchIMg99/MF7QfDrd9AVBIc2WWsqJy319SSNMVcvIWCjrjd8anl4Y2/WHEupP1gPO4xuvHXk+MG3AKXzwRrIGz5EN66DAoyXXf9oweN6ezzboSiwxDXG/60FHpe7rp7+LKWneCWryG2MxTuhzcvg4L9ppWjzT3FWyjoiNsdb9FxwbL9v34ODpvRbeXLa6uYpe8NxgrEIVGwbxXMGgy/fNy4riy7zdh49aUzjNY4S4CxBcKEb/R3WF9RbWHcZ8bSAPnp8NblRvg3QbuqtXQ080o8nYKOuJ1LV0Wu7rZSa477dDjPGBSc2M+Ywv/hOHh7DBzcWr/r2Cpgy3/h5YHw6V+MXccTToM/fm+szuyN+1V5gsgEI+xEJcGRnfDe9aasg3R8LR3PmAkmcjIKOuJ2LlsVuSQP9nxvPO5xeeOuJacW2xFuWWQs2hcQDLu/hZkDjcDz80cnX7HXVmnsUbX4EZjeB+bfCjnbjYG0w5+ACd9pVpUrRLWFGz+sann7CT65o8mXBtBaOuItXLiNtEjtjrfoNHKMzvavjP2AWvc01oAR9woMNhbtO+16+PafsO0z2PWN8WEJgNhORhdKcDOoLDV2Gj+0FSp+84OvWWtj1tBZE43NLMV1WneH696G/1wBP//XaIEbdGeT3T65ZTMA9ucVY7M7CLB6+War4rMUdMSt7HYHWQUuatH55WPjs7qtmlZsR2M9l9w9sO5N+PULo8skZ7vx8b9CIo0NOHuMhm4XQ2BI09fsL1LOgRFPwld/M9YkSjgNUoY0ya3jI0MJDrBSbrNzIL/EOThZxNMo6IhbHTpaRoXN+G0vLqIRP/BKC4ztAUBBxywxHeDCfxofhQeM1pv8fcZqvQFB0Lw1tOwKLbv439YNZjrjj8Z+WJvfNzYC/fOPEB7j9tsGWC20jQljz+EiMnKLFXTEYynoiFtVd1vFR4YSGNCIH37bvzYWCWzVDVp3c1F10mCRicaHmM9iMRZ8zFxrrLHz6V/g2reN426WHBPOnsNF7D1SxOBOWuxRPJN+7RK3ctmMK822Ejm54GYw5g1jq4hfP4eN7zTJbZNjjXE6GUc0IFk8l4KOuJVLZlyVFxmzfsDY5FBETpTYFy54yHi88EFjmw03S441uqvSFXTEgynoiFtl5hvfABvVorN7iTGrp0UyxPV0UWUiPmjgX4zZV6UF8MVkt085dwYdTTEXD6agI27lkhadHV8Zn7uOapJxByJeKyAQRr9kdGFt/9KYIedG7WKMrqv0I0U4mngdH5G6UtARt2r0GB27zRiIDNB1pIuqEvFhcT1h8CTj8df3Q7n7WluSYsKwWKC43EbOsXK33UekMRR0xG0cDoezRSexoUEncx0U5xgrwCYPcmF1Ij5syL3GFhEF+2DZ8267TUhgAIlR1XteFbntPiKNoaAjblNYUklRuQ1oRIvO9i+Nz50vNNZqEZHfF9wMLppqPF7xb8hLd9utqreC0IBk8VQKOuI2+6sGIsc2CyYsOKBhF9lePT5H3VYi9dLtEmPlZFsZfPe4225TPSB5r4KOeCgFHXGbRg9EPrIbDv8K1kDoNMyFlYn4AYsFLqwKOFs+NFZPdoN2VUEn44i6rsQzKeiI21TvapwU3cCl4XdUDUJOHgxhLVxTlIg/SewLfa4zHi9+2C23aF+1aKCmmIunUtARt9lX9Y2v+je+etv+m2nlItIwF/wdAoJh7zLYs9Tll68eo6PVkcVTKeiI21S36LRryGZ/xbmQvsJ43PUiF1Yl4mdaJEHqeOPxkidcvohg9RidI0XlHC2tcOm1RVxBQUfcJr0xQWfXN+CwQeueEN3etYWJ+Jsh90JgKOxbBbu+demlI0KDiGkWDGjmlXgmBR1xC7vdwf5cYzByg4JO9ficLiNcWJWIn4qIh9MnGI+XPefyy1e36mRonI54IAUdcYuDR0spt9kJtFpIiAqt34vtNtj9nfFYQUfENQbeaYzVyVgJGatceulkraUjHkxBR9yiemBim+gwAgPq+c8scx2U5EFoFLQZ4IbqRPxQZAL0udZ4/ON0l166XdXMK62OLJ5IQUfcolHjc3YuMj53vMDYpFBEXGPwXYDFWHH80K8uu2x1i87eHLXoiOdR0BG32NeooLPY+Nx5uAsrEhFadoZuFxuPV7zossu2b6kxOuK5FHTELRo8tfzYIcjaaDzWasgirnf2PcbnzR9AQaZLLtkuxui6OlBQQlmlzSXXFHEVBR1xi+pBifUOOru+MT4nnAbNW7u4KhGh7QBIPhvsFfDTyy65ZMvmwTQLDsDhON6aK+IpFHTELaq/2SXVN+io20rE/c6+2/i8bi6U5Df6chaLhZRWRqvOnsMakCyeRUFHXO5YWSVHisqBem7/YKs8Pq2804VuqExEAKNbuHUPKD8GG991ySVTWjYHIC1HQUc8i4KOuFz11PLo8CAiQ4Pq/sLMtVCaD6EtjOZ1EXEPi+X4AoJrXge7vdGX7NBSLTrimRR0xOUaPBC5utuq01CwBri4KhGpoc+1EBwBubsh7ftGX65DVdeVWnTE0yjoiMs1fHxO1fo56rYScb+Q5tD3euPx6tcbfbkOVV1Xe3KONfpaIq6koCMul161OmpyfcbnHD0I2ZuNx52GuqEqETlBdffVjq8gf1+jLlW9lk7OsXIKSrSLuXgOBR1xuYyGbOZZPa08sZ+mlYs0lVZdof0QcNhh3ZxGXSoiNIjWESGAuq/EsyjoiMs1qOtK3VYi5jjjNuPzujehsqxRl0ppWT1OR91X4jkUdMSlbHYH+/PqORjZVgl7lhiPOyvoiDSprhdDRAIU58DWTxp1qQ6tqsbpaOaVeBAFHXGprIISKmwOggIsJESF1e1F+9dAaQGERUObVPcWKCI1BQRC6s3G4zVvNOpSzinm6roSD6KgIy5VPbW8bXQ4AVZL3V7k3K1c08pFTNH/JrBYYd9PkLOzwZfpoNWRxQMp6IhLNWh8zq7qbR/UbSViisiE4+PjNvynwZepHqOzN6cIu93hispEGs30oPPyyy+TkpJCaGgoqampLFu27JTnL126lNTUVEJDQ+nQoQOzZs064Zz58+fTo0cPQkJC6NGjBwsWLKjx/KOPPorFYqnxER8f79Kvy18d38yzjt1WhVmQvQWwGC06ImKO/mONzxvfA1vDpocnxYQTaLVQUmEju7DUhcWJNJypQWfevHncfffdPPTQQ2zYsIEhQ4YwcuRIMjIyaj0/LS2NUaNGMWTIEDZs2MCDDz7IpEmTmD9/vvOclStXcu211zJ27Fg2bdrE2LFjueaaa1i1alWNa/Xs2ZOsrCznx5YtW9z6tfqL6q6r5JhmdXtBjWnlrdxUlYj8rs4jILwlFB06vkp5PQUFWJ2TEDTFXDyFqUFn2rRp3HrrrUyYMIHu3bszffp0kpKSmDlzZq3nz5o1i3bt2jF9+nS6d+/OhAkTuOWWW3juueec50yfPp0LL7yQBx54gG7duvHAAw8wdOhQpk+fXuNagYGBxMfHOz9atdIPWVfYe8T45lbnzTzVbSXiGQKD4bTrjMcb3m7wZY6P09EUc/EMpgWd8vJy1q1bx/Dhw2scHz58OCtWrKj1NStXrjzh/BEjRrB27VoqKipOec7/XnPnzp0kJiaSkpLCddddx549e05Zb1lZGYWFhTU+pCaHw0Fa1SDEjq3q0KJjq4Dd1dPKh5/6XBFxv35V3Vc7vjZWK2+AFM28Eg9jWtDJycnBZrMRFxdX43hcXBzZ2dm1viY7O7vW8ysrK8nJyTnlOb+95plnnslbb73FwoULee2118jOzmbQoEEcOXLkpPVOnTqVqKgo50dSUlK9vl5/cPhoGUXlNqyWOg5G3rcaygohLMbouhIRc7XuBm1PB4cNNr/foEtoLR3xNKYPRrZYak5BdjgcJxz7vfP/9/jvXXPkyJGMGTOG3r17M2zYML744gsA3nzzzZPe94EHHqCgoMD5sW9f4/aF8UXVv8G1jQ4nJLAO08R3abdyEY/T7w/G5/X/AUf9Z04dXx1ZQUc8g2lBp2XLlgQEBJzQenPo0KETWmSqxcfH13p+YGAgsbGxpzznZNcEaNasGb1792bnzpOvHxESEkJkZGSND6lpb9U3tupvdL+resCjtn0Q8Rw9r4SgcDiyE/at+v3z/0f1GJ39ecWUVdpcXZ1IvZkWdIKDg0lNTWXx4pqj+xcvXsygQYNqfc3AgQNPOH/RokUMGDCAoKCgU55zsmuCMf5m27ZtJCQkNORLkSpp9Qk6hQfg4M+ABToNc29hIlJ3oZHQY7TxeNN79X55q+YhNA8JxO6AjKrlJkTMZGrX1eTJk3n99deZPXs227Zt45577iEjI4OJEycCRnfRTTfd5Dx/4sSJpKenM3nyZLZt28bs2bN54403mDJlivOcu+66i0WLFvH000/z66+/8vTTT/PNN99w9913O8+ZMmUKS5cuJS0tjVWrVnHVVVdRWFjIuHHjmuxr90XVXVcd6jIQubo1p00qNIt1Y1UiUm/Vs69+WQAV9VsPx2KxOL8H7NY4HfEAgWbe/Nprr+XIkSM89thjZGVl0atXL7788kuSk5MByMrKqrGmTkpKCl9++SX33HMPL730EomJibz44ouMGTPGec6gQYN4//33+fvf/84//vEPOnbsyLx58zjzzDOd5+zfv5/rr7+enJwcWrVqxVlnncVPP/3kvK80TL1adKq3fdBsKxHP034IRCTC0QOwc+HxFp46SmnZjM37C9ijXczFA1gcjgaMNhMKCwuJioqioKBA43Uwdi3v9o+vqLA5WH7f+bSNPsWsq8pyeCYFyo/BbUugTf+mK1RE6mbxw/DjC8bu5te/W6+X/vvbnTy/eAdX9m/DtGv6uqc+kToyfdaV+IbMPGPX8uBAK4m/t2t5xkoj5DRrBQl9m6Q+EamnPlXdVzsXQdHJl96oTec4Y4r5rkNq0RHzKeiIS1Q3UafENsP6e7uWO6eVDwOr/gmKeKS4HhDfB+wV8MtH9Xppp9YRgBF0tLmnmE0/ZcQlqgcdtm9Zh4UCd2rbBxGvUD0oeVP9Fg9Mjg0nKMBCcbmNAwUlbihMpO4UdMQlqpuoO7VufuoT89Lh8K9gsULHC5qgMhFpsF5XGf9XM9dCzq46vywowEqHlsb3gp3qvhKTKeiIS+yua9Cp7rZKOhPCot1clYg0SkTc8V9INs+r10s7VY/TOaigI+ZS0BGX2FW1U3GnVhGnPlHdViLe5bTrjc+b59VrS4jOratbdI66oyqROlPQkUbLLSont6gcgI6tT7GGTkUppP1gPNa2DyLeoesoCI6A/HTI+KnOL+tcNSB5h1p0xGQKOtJo1eNz2rQIIzz4FGtQpv8IFcXQPB7iezdRdSLSKMHh0OMy43E9toTo8psp5lquTcykoCONVh10Ov7e+JzfdludYod6EfEwfa41Pv/ycZ23hEiObUag1cKxskqyCuq3jYSIKynoSKM5Z1y1+r2go20fRLxS+yEQ2QbKCmDH13V6SXCg1bkdzI6DGqcj5lHQkUZzDkQ+VYvOkd2QuxusgdDhvKYpTERcw2qFPtcYj+sx+6prvDFO59dsBR0xj4KONFqdppbv+sb43G4ghGpvMBGv04AtIbpVBZ3tCjpiIgUdaZTi8koy842VT08ZdJzdVpptJeKVWneDhNPAXlnnLSG6xhu/1KhFR8ykoCONUj11tGXzEGKaBdd+UnkxpC0zHmt8joj36lO/LSGqW3R2HTpKhc3urqpETklBRxple3YhAF3jT9Gak7YUbGUQlQStujVRZSLicr2vAktA1ZYQO3/39LbRYTQPCaTC5iAtp6gJChQ5kYKONMr2bKNFp2vcKcbdbP/S+Nx1pKaVi3iz5q3rtSWExWJxrqej7isxi4KONMr2g0aLTnUT9QnsdtheNR2168gmqkpE3KZ6R/PN84z/37/DOU4nq9CdVYmclIKONEr1bIquJws6B9ZD0SFjCfnks5uwMhFxC+eWEBmw7/e3hOieoJlXYi4FHWmwnGNl5Bwrx2KBLnEnCTq/fmF87jwMAk8yWFlEvEdwOPQYbTyuw6DkrlXfG7apRUdMoqAjDbaj6je05JhwwoIDaj9p+1fG566jmqgqEXG70+q+JUT3RKPr6kBBKXlVm/+KNCUFHWmw6sGFJ23Nyd0Dh7cZszS0fo6I70g+GyLb1mlLiMjQINrFhAOwVa06YgIFHWmw6j73kw5Erh6EnDwIwqKbqCoRcTurFfpcbTyuw+yrnlWtOj9nFrizKpFaKehIg/3qXEPnJFPLndPK1W0l4nPqsSVErzZRAPxyQC060vQUdKRBKm12Z9dV9W9rNZTkQfoK43HXi5qwMhFpEvXYEqJH1feIXw6oRUeanoKONMjuw0WUVdppHhLo7H+vYec34LBBq+4Q06HpCxQR96vjlhDVvwztySmiuLzS3VWJ1KCgIw1S3dfeIyESq7WW1Y63V00r76ZuKxGfVcctIVpHhNIqIgSHA7ZlaT0daVoKOtIg1X3tPdvU0m1VUQI7qnYr1/gcEd/VvDV0Gmo8/p1BydWtOlvVfSVNTEFHGqS6r71nYtSJT+76FiqKjE0826Q2cWUi0qT6VK2p8ztbQvSq+l6xeb+CjjQtBR2pN7vdwdbqFp3aBiJv/dj43GO0NvEU8XXdLq7TlhB92iroiDkUdKTe9uUVc7SskuBAK51aN6/5ZEXp8fVzqpeJFxHfFRRWpy0h+ia1AGDnoaMUlWlAsjQdBR2pt+rxOd3iIwgK+J9/Qru/g/KjENkG2gwwoToRaXI1toQoqfWU1pGhJESFYndo4UBpWgo6Um/VTc+1js/5bbeVVf+8RPxC8tnQop2xJcS2z0562mltWwCwaX9+09QlgoKONMDGfXkA9E36n6BTWXZ8E88elzdtUSJiHqsV+v7BeLz+rZOedlpV99WmfWrRkaajoCP1YrM72FLVotM36X/2r9q9BMoKISIR2p5uQnUiYpp+NwIW2LvM2NC3FqdV/XK0cV9+09Ulfk9BR+pl16FjFJXbaBYccOJAZGe31WXqthLxN1Ftj6+ps+HtWk/p3SYKiwUy80s4fLSsCYsTf6afRlIv1d1WvdtGEfDbFZEry+HXqk081W0l4p/632R83vAO2E6cWRURGkTHVsYvSGrVkaaioCP1Uv3N6YRuqz3fGwMRm8dD0plNXpeIeIAuIyG8JRzLhl3f1HrKgGTje8fa9NymrEz8mIKO1MuGjHzg+JoYTpur1s/oebm6rUT8VWAwnFa10edJBiWnVgedvXlNVZX4Of1EkjorLq9kx0FjQ74aQae0AH6t2sSz+puciPin6u6rHV/D0ewTnj69fQwAW/YXUFpha8rKxE8p6EidbczIx+6AhKhQ4qNCjz+x9ROoLIVW3SChr2n1iYgHaNXV6L522GDjuyc8nRwbTsvmwZTb7Fo4UJqEgo7U2eq9Rp969W9kThvfMz6fdr32thKR46066+aAvWarjcViYUCy8T1kjbqvpAko6EidrU6rCjopvwk6uWmQsQKwQJ9rzClMRDxLrzEQFm1s9Llz0QlPD2hvjNNZpwHJ0gQUdKROKmx250DkM38bdDZ/YHzucB5EJjZ5XSLigYLCjrfqrHrlhKcHtD/eomO3O5qyMvFDCjpSJz9nFlBSYaNFeBCdqtbBwOGATb/pthIRqTbgVsACe5bA4R01nuqZGEmz4AAKSirYll1oTn3iNxR0pE7WVI3PGZAcg7V6ocB9qyAvDYKbQ/dLTKxORDxOdDJ0HWk8XvN6jaeCAqyc2SEWgBW7jjR1ZeJnFHSkTlanGYMGz0j5zUKB1a05PUZDcDMTqhIRj3bGbcbnje9C2dEaTw3qWBV0duc0dVXiZxR05HdV2uysSjN+6zojxfjmRHkx/LzAeKy1c0SkNinnQWxnKD8Km96v8dTAqqCzOi2XCpu96WsTv6GgI79rc2YBR0sriQoLoncbY/dhfllgbPnQIhmSzza3QBHxTFbr8Vad1a8Z4/qqdI+PJDo8iKJyG5v355tTn/gFBR35Xct2GE3LgzrGHt/Ic+1s4/OAm7Xlg4ic3GnXG+P4crbD7m+dh61Wi7NV50eN0xE30k8o+V3Ldx0G4OzOLY0DWZsgcy1Yg6DvH0ysTEQ8Xmjk8anmy6fXeGpQR+N7yg87DjdxUeJPFHTklI6VVTrXzxnSqZVxcO0c43OPy6B5K3MKExHvMfAO4xejvctg3xrn4fO7tQZgfUYe+cXlZlUnPk5BR07pp91HqLQ7aBcTTrvYcCjOhc3zjCcH3GJucSLiHaLaQp9rjcfLpzkPt2kRRte4COwOWKpWHXETBR05pe93HAJ+0221/i2oKIa43pA82MTKRMSrnH03YIHtX8LBrc7D53UzWoWX/HrInLrE5ynoyEk5HA6+2Wp88xnWvTXYKmD1q8aTZ/1ZG3iKSN217AzdLzUe/zjdefiCrkb31dIdh7FpOwhxAwUdOamfMwvJLiwlPDjAGDS47VMozIRmrYxN+0RE6mPIZOPzlv9CXjoA/ZOjiQgNJK+4gg0Z2s1cXE9BR05q8dZsAM7p3IrQQCv8+KLxxIBbISjUxMpExCsl9oMO54PDBiuM7ydBAVbOr2rV+frnbDOrEx+loCMntXhbVbdVjzjY/R1kbYSgcDjjj+YWJiLea8i9xuf1b0H+PgBG9Y4H4Kufs3E41H0lrqWgI7Xal1vMtqxCrBa4oFtrWFY1UyJ1PDSLNbU2EfFi7c+G9kPAVg5LnwbgvK6tCQ8OIDO/hE37C0wuUHyNgo7U6rPNBwA4MyWWmCPrIX25sQ7GwDtNrkxEvJrFAkMfNh5vfBdydhIaFGD8QgV8tSXLxOLEFynoSK0+2WAEndF9E+G7fxkH+14PUW1MrEpEfELSGdBlpDFW59vHABjVOwGAL7ZkqftKXEpBR07wa3Yh2w8eJTjAysiI3cZqpgHBcM7fzC5NRHzF0H+AxWrM5kxfwfldW9MsOID9eSWsTss1uzrxIQo6coJPNhqtOed1bUXU8qrWnNSboUWSiVWJiE+J63l8D6yFDxIWaOGSPokAfLhuv4mFia9R0JEabHYHn2zIBGB0ywPG5p2BYcdnSoiIuMr5D0FwBBzYAJve4+oBbQH4cksWRWWVJhcnvkJBR2r4fvshDhSU0iIskKHbHzUODroTIuJMrUtEfFDz1nDOFOPx4n+Q2spBh5bNKC638YUGJYuLKOhIDW//ZKxWenXCIUILdkNEAgy+29yiRMR3nXU7tOoGxUewfPcYY1KNVp13VmWYXJj4CgUdcdqXW8z3VTsI33Cwat2cYY9CSHPzihIR3xYYDBdXfb9ZN5dr47MJDrCyaV8+69K1JYQ0noKOOL29Kh2HA85unkWKbQ+0PQN6X2N2WSLi69oPhn5jAWi56E5G9zHW1Jn9Y5qZVYmPUNARAApKKnjnJ6OpeFzZO8bigJe9CFb9ExGRJjDiCYhsC3lp3MyngLH3VWZ+icmFibfTTzEB4D8r93KsrJKuAQcYat0AZ98DrbubXZaI+IvQKBj9bwB6bJvOwAQLNruDWd/vNrkw8XYKOkJxeSWzf9wLwO3Wj7DGddd0chFpeh0vgDMnAvCXo/8HwPtrMtifV2xmVeLlFHSE15elkVtUTjvLQS4O2gBjXoegULPLEhF/dOFjkNCXQZWrGRiWQYXNwYzvdpldlXgxBR0/d6iwlFlLdgAwJfADAoc/YqxYKiJihsAQuHoOhERxr202YKyUvD37qMmFibdS0PFzz32xgeJK6GvZyaW9453NxiIiponpAFfPYUDALoZb12CzO3j4k5+12ac0iIKOH1u5LZ0PNhmb5/2j9Y9YRs8Ai8XkqkREgE5DYcRU/hH4H0IpY1VarnMfPpH6UNDxU8VFx/jbuz8CcH3ISlLHP6uFAUXEs5w1kaQhN3Jn4McA/PPjDRwsLDW3JvE6Cjr+qLyIx16cyb6KKBItuTx481UQ3d7sqkRETjTsn/xxQDS9LGnklVmY/PrX2O3qwpK6U9DxN8W5vDvjYd4v6IEFO89ckkxE+75mVyUiUjuLheDL/o8X+mURRik/Hgpi+htzQeN1pI4UdPxJzi5WvDSBRw6dA8CUMyM4e/A5JhclIvI7rFY6Xv0vHu+RCcCLu1sz/5XHoOyYyYWJN1DQ8Rdb/sval29lwpEbqSCQkZ3Cuf3yc82uSkSkbiwWrrppErd3MxYPvG9vX7564c+QtcnkwsTTWRyar9cghYWFREVFUVBQQGRkpNnlnNyxw7DwQb7buIO/VPyFIsIYkhLJa7cMIjQowOzqRETqxW53MHnON3y8s5wAbDwd/AZXndMfzv0bBIWZXZ54ILXo+KrKMvhpFrYZZzBzQwkTKqZQRBiDO8bw6s0KOSLinaxWC8/fPIyrTmuFjQCmlP+Rfy45TMW/z4RN88BuN7tE8TBq0Wkgj23RKS+Gje/AihfZmWvjoYpbWO0wNue8dkAS/7qiF0EByrci4t3sdgfTFu9gxhJje4hulnSeDnqN01oFwOBJ0GsMBDczuUrxBAo6DeRRQcdug73LYMuHsPUz0krCeNV2MR/YzsNGAM2CA3j40h5cMyAJixYEFBEfsvCXbO6fv5m84gos2LnYuopJgR/RJbQAeoyG066H5MFg1S94/sr0v/mXX36ZlJQUQkNDSU1NZdmyZac8f+nSpaSmphIaGkqHDh2YNWvWCefMnz+fHj16EBISQo8ePViwYEGj7+tRHA7I2wsb34WP74BpPTj85lg+WruXm45O5ILy53jPNhQbAQzvEcdXd53Dtae3U8gREZ8zomc830w+lyv7tcGBlc/tAxle/izXHL2H99dmcmTu9fB8F/jwZlg3F3L3aGq6nzG1RWfevHmMHTuWl19+mcGDB/PKK6/w+uuvs3XrVtq1a3fC+WlpafTq1YvbbruNP/3pT/z444/cfvvtvPfee4wZMwaAlStXMmTIEB5//HGuuOIKFixYwMMPP8zy5cs588wzG3Tf2jRJi07ZMTiaBYUHIC8NDm/HcehXcg9msOdoAGn2eLY4Ulhp78EuR9saL72gW2smntuRM1Ji3FObiIiH2ZZVyIvf7mThL9n8dk3BjpZMUq07SLXs5Azrr6SEl0DrnhDXA1p2hRZJEJUEUW0hNEpb4fgYU4POmWeeSf/+/Zk5c6bzWPfu3bn88suZOnXqCeffd999fPrpp2zbts15bOLEiWzatImVK1cCcO2111JYWMhXX33lPOeiiy4iOjqa9957r0H3rY3Lgs7XD0JJLpQWQGlh1ecCKMmD8pq79a6zd+aW8r9SQO1bNXRPiGREzzgu79uG9i3VNy0i/imroISP1mfy5ZYsfjlQWOO5CwPW8VrQ8yd/cUAwhMdWfcTAoLug8zA3VyzuFGjWjcvLy1m3bh33339/jePDhw9nxYoVtb5m5cqVDB8+vMaxESNG8MYbb1BRUUFQUBArV67knnvuOeGc6dOnN/i+AGVlZZSVlTn/XFBQABiBp1F++g+UFZz8+aDmEJEALdoQ2qwXeWusQDEJkcG0bxVBh1bNGJAcw4DkaKKbBVe9yNb4ukREvFQzC4xNbc3Y1NbkFpWzaV8+G6s++nW7lMKOw+HQNuMjby8U7oeCTCjNA8qg+ABQtYFol6shrvHfTyMiIjR8wCSmBZ2cnBxsNhtxcXE1jsfFxZGdnV3ra7Kzs2s9v7KykpycHBISEk56TvU1G3JfgKlTp/LPf/7zhONJSUkn/yJd4iiQBawHPnMe3QesdvOdRUR8zcfAHfV5wVPjXHJfj5i44qdMCzrV/jfhOhyOU6be2s7/3+N1uWZ97/vAAw8wefJk55/tdju5ubnExsbWO6UXFhaSlJTEvn37/Pofvt6H4/ReGPQ+HKf3wuAr70NERITZJfgt04JOy5YtCQgIOKEV5dChQye0tlSLj4+v9fzAwEBiY2NPeU71NRtyX4CQkBBCQkJqHGvRosXJv8A6iIyM9Or/uK6i9+E4vRcGvQ/H6b0w6H2QhjJtenlwcDCpqaksXry4xvHFixczaNCgWl8zcODAE85ftGgRAwYMICgo6JTnVF+zIfcVERER72Rq19XkyZMZO3YsAwYMYODAgbz66qtkZGQwceJEwOguyszM5K233gKMGVYzZsxg8uTJ3HbbbaxcuZI33njDOZsK4K677uKcc87h6aefZvTo0XzyySd88803LF++vM73FRERER/hMNlLL73kSE5OdgQHBzv69+/vWLp0qfO5cePGOc4999wa53///feOfv36OYKDgx3t27d3zJw584Rrfvjhh46uXbs6goKCHN26dXPMnz+/Xvd1t9LSUscjjzziKC0tbbJ7eiK9D8fpvTDofThO74VB74M0lraAEBEREZ9l+hYQIiIiIu6ioCMiIiI+S0FHREREfJaCjoiIiPgsBZ0m9sQTTzBo0CDCw8NPuuBgRkYGl156Kc2aNaNly5ZMmjSJ8vLypi20Cbz88sukpKQQGhpKamoqy5YtM7skt/rhhx+49NJLSUxMxGKx8PHHH9d43uFw8Oijj5KYmEhYWBjnnXcev/zyiznFutHUqVM5/fTTiYiIoHXr1lx++eVs3769xjn+8l7MnDmTPn36OBfDGzhwYI0Nif3lffhfU6dOxWKxcPfddzuP+et7IY2noNPEysvLufrqq/nzn/9c6/M2m42LL76YoqIili9fzvvvv8/8+fO59957m7hS95o3bx533303Dz30EBs2bGDIkCGMHDmSjIwMs0tzm6KiIk477TRmzJhR6/PPPPMM06ZNY8aMGaxZs4b4+HguvPBCjh49Wuv53mrp0qXccccd/PTTTyxevJjKykqGDx9OUVGR8xx/eS/atm3LU089xdq1a1m7di0XXHABo0ePdv4A95f34bfWrFnDq6++Sp8+fWoc98f3QlzEzLnt/mzOnDmOqKioE45/+eWXDqvV6sjMzHQee++99xwhISGOgoKCJqzQvc444wzHxIkTaxzr1q2b4/777zepoqYFOBYsWOD8s91ud8THxzueeuop57HS0lJHVFSUY9asWSZU2HQOHTrkAJxrWfnze+FwOBzR0dGO119/3S/fh6NHjzo6d+7sWLx4sePcc8913HXXXQ6HQ/8mpHHUouNhVq5cSa9evUhMTHQeGzFiBGVlZaxbt87EylynvLycdevWMXz48BrHhw8fzooVK0yqylxpaWlkZ2fXeE9CQkI499xzff49KSgoACAmJgbw3/fCZrPx/vvvU1RUxMCBA/3yfbjjjju4+OKLGTZsWI3j/vheiOuYvnu51JSdnX3C5qLR0dEEBwefsBGpt8rJycFms53wdcbFxfnM11hf1V93be9Jenq6GSU1CYfDweTJkzn77LPp1asX4H/vxZYtWxg4cCClpaU0b96cBQsW0KNHD+cPcH95H95//33Wr1/PmjVrTnjO3/5NiGupRccFHn30USwWyyk/1q5dW+frWSyWE445HI5aj3uz//16fPFrrC9/e0/uvPNONm/eXGO/umr+8l507dqVjRs38tNPP/HnP/+ZcePGsXXrVufz/vA+7Nu3j7vuuou3336b0NDQk57nD++FuJ5adFzgzjvv5LrrrjvlOe3bt6/TteLj41m1alWNY3l5eVRUVJzw24y3atmyJQEBASe03hw6dMhnvsb6io+PB4zfXBMSEpzHffk9+ctf/sKnn37KDz/8QNu2bZ3H/e29CA4OplOnTgAMGDCANWvW8MILL3DfffcB/vE+rFu3jkOHDpGamuo8ZrPZ+OGHH5gxY4ZzVp4/vBfiemrRcYGWLVvSrVu3U36c6reU3xo4cCA///wzWVlZzmOLFi0iJCSkxjcBbxYcHExqaiqLFy+ucXzx4sUMGjTIpKrMlZKSQnx8fI33pLy8nKVLl/rce+JwOLjzzjv56KOP+O6770hJSanxvD+9F7VxOByUlZX51fswdOhQtmzZwsaNG50fAwYM4MYbb2Tjxo106NDBb94LcT216DSxjIwMcnNzycjIwGazsXHjRgA6depE8+bNGT58OD169GDs2LE8++yz5ObmMmXKFG677TYiIyPNLd6FJk+ezNixYxkwYAADBw7k1VdfJSMjg4kTJ5pdmtscO3aMXbt2Of+clpbGxo0biYmJoV27dtx99908+eSTdO7cmc6dO/Pkk08SHh7ODTfcYGLVrnfHHXfw7rvv8sknnxAREeFs2YuKiiIsLMy5foo/vBcPPvggI0eOJCkpiaNHj/L+++/z/fff8/XXX/vV+xAREeEco1WtWbNmxMbGOo/7y3shbmDafC8/NW7cOAdwwseSJUuc56SnpzsuvvhiR1hYmCMmJsZx5513OkpLS80r2k1eeuklR3JysiM4ONjRv39/5/RiX7VkyZJa/+7HjRvncDiMKbSPPPKIIz4+3hESEuI455xzHFu2bDG3aDeo7T0AHHPmzHGe4y/vxS233OL8P9CqVSvH0KFDHYsWLXI+7y/vQ21+O73c4fDv90Iax+JwOBxNH69ERERE3E9jdERERMRnKeiIiIiIz1LQEREREZ+loCMiIiI+S0FHREREfJaCjoiIiPgsBR0RERHxWQo6IiIi4rMUdESkTrKzs/nLX/5Chw4dCAkJISkpiUsvvZRvv/0WMDaunT59eo3XXHrppQwbNqzW661cuRKLxcL69evdXbqI+DHtdSUiv2vv3r0MHjyYFi1a8Mwzz9CnTx8qKipYuHAhd9xxB7/++mutr7v11lu58sorSU9PJzk5ucZzs2fPpm/fvvTv378pvgQR8VMKOiLyu26//XYsFgurV6+mWbNmzuM9e/bklltuOenrLrnkElq3bs3cuXN55JFHnMeLi4uZN28eTz75pFvrFhFR15WInFJubi5ff/01d9xxR42QU61FixYnfW1gYCA33XQTc+fO5bfb6n344YeUl5dz4403uqNkEREnBR0ROaVdu3bhcDjo1q1bg15/yy23sHfvXr7//nvnsdmzZ3PllVcSHR3toipFRGqnoCMip1TdEmOxWBr0+m7dujFo0CBmz54NwO7du1m2bNkpu7xERFxFQUdETqlz585YLBa2bdvW4GvceuutzJ8/n8LCQubMmUNycjJDhw51YZUiIrVT0BGRU4qJiWHEiBG89NJLFBUVnfB8fn7+717jmmuuISAggHfffZc333yTm2++ucEtRCIi9WFx/HaEoIhILdLS0hg0aBAxMTE89thj9OnTh8rKShYvXszMmTPZtm0b7du35+qrrz5hgHG7du2IiYlhwoQJfPTRRxQUFJCWlka7du1M+mpExJ8o6IhInWRlZfHEE0/w+eefk5WVRatWrUhNTeWee+7hvPPOo3379qSnp5/wujlz5jB+/HhWrlzJoEGDGD58OAsXLjThKxARf6SgIyIiIj5LY3RERETEZynoiIiIiM9S0BERERGfpaAjIiIiPktBR0RERHyWgo6IiIj4LAUdERER8VkKOiIiIuKzFHRERETEZynoiIiIiM9S0BERERGfpaAjIiIiPuv/Ab6++j2OJ70/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 572.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 572.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 572.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 572.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in columns:\n",
    "    sns.displot(telco, kind='kde', x='CLV', hue=column)\n",
    "    plt.title(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "62f8e383",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              CLV\n",
      "region           \n",
      "Zone 1  16.868571\n",
      "Zone 2  16.996886\n",
      "Zone 3  17.278605 \n",
      "\n",
      "                 CLV\n",
      "marital             \n",
      "Married    18.648242\n",
      "Unmarried  15.488317 \n",
      "\n",
      "                                    CLV\n",
      "ed                                     \n",
      "College degree                15.394872\n",
      "Did not complete high school  18.875294\n",
      "High school degree            17.726551\n",
      "Post-undergraduate degree     16.676364\n",
      "Some college                  16.322297 \n",
      "\n",
      "              CLV\n",
      "retire           \n",
      "No      16.663001\n",
      "Yes     24.949787 \n",
      "\n",
      "              CLV\n",
      "gender           \n",
      "Female  17.285571\n",
      "Male    16.802981 \n",
      "\n",
      "             CLV\n",
      "voice           \n",
      "No     17.090345\n",
      "Yes    16.965789 \n",
      "\n",
      "                CLV\n",
      "internet           \n",
      "No        18.154177\n",
      "Yes       15.160435 \n",
      "\n",
      "               CLV\n",
      "forward           \n",
      "No       16.184615\n",
      "Yes      17.944990 \n",
      "\n",
      "                     CLV\n",
      "custcat                 \n",
      "Basic service  11.844812\n",
      "E-service      20.805899\n",
      "Plus service   19.237580\n",
      "Total service  16.869153 \n",
      "\n",
      "             CLV\n",
      "churn           \n",
      "No     19.424793\n",
      "Yes    10.766715 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in columns:\n",
    "    print(telco.groupby(column)[[\"CLV\"]].mean(), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a2b5e1c",
   "metadata": {},
   "source": [
    "# Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb2ead7",
   "metadata": {},
   "source": [
    "The goal is to understand the factors affecting the churn risk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b1bfec",
   "metadata": {},
   "source": [
    "#### Interpret the coefficients\n",
    "To interpret the coefficients, we can observe the CLV values for each segment and compare them to each other. For example, we can see that customers who are retired have a higher CLV than those who are not retired, and customers with a college degree have a lower CLV than those who did not complete high school or have a high school degree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c93587f",
   "metadata": {},
   "source": [
    "#### Try to find the most valuable segments (describe the personal definition of being valuable)\n",
    "To find the most valuable segments, we can identify the segments with the highest CLV values, which in this case include customers who have Plus or Total service, customers who have not churned, and customers who do not use forwarding or internet services. However, it's important to note that the definition of being valuable may vary depending on the business goals and objectives. For example, a business may prioritize customer satisfaction or revenue growth over CLV. Therefore, the most valuable segments may differ depending on the business context and goals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad031ef",
   "metadata": {},
   "source": [
    "#### How much would be your annual retention budget?\n",
    "To estimate the annual retention budget, we need to first identify how many customers are at risk of churning within a year. We can do this by analyzing the customer data provided and estimating the probability of churn over time using survival analysis techniques.\n",
    "\n",
    "Once we know how many customers are at risk of churning, we can estimate the cost of retaining them using various strategies, such as offering discounts or incentives, providing excellent customer service, or improving the quality of the product or service.\n",
    "\n",
    "The annual retention budget will depend on the cost of these retention efforts and the expected return on investment from retaining customers. In other words, we need to weigh the cost of retaining customers against the potential revenue that they would generate for the business in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f29929",
   "metadata": {},
   "source": [
    "To detect the approximate number of at-risk subscribers within a year, we can use the predict_percentile method of the LogNormalAFTFitter class from the lifelines library. The predict_percentile method predicts the time at which a certain percentile of the customers will churn. We can use this method to predict the time at which 50% of the customers will churn, and count the number of customers whose tenure is less than the predicted time.\n",
    "\n",
    "Here is the Python code to detect the approximate number of at-risk subscribers within a year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e710671",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximately 909 customers are at risk within a year.\n"
     ]
    }
   ],
   "source": [
    "# Predict the time at which 50% of the customers will churn\n",
    "at_risk_time = lognormal.predict_percentile(df, p=0.5)\n",
    "\n",
    "# Count the number of customers whose tenure is less than the predicted time\n",
    "at_risk_customers = df[df['tenure'] < at_risk_time].shape[0]\n",
    "\n",
    "print(f\"Approximately {at_risk_customers} customers are at risk within a year.\")\n",
    "\n",
    "# annual_retention_budget = at_risk_customers * retention_cost_per_customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99278385",
   "metadata": {},
   "source": [
    "Note: This is a rough estimation based on previous results, and the formula for calculating the retention budget is given as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edac68ae",
   "metadata": {},
   "source": [
    "#### What else would you suggest doing for retention?\n",
    "There are several strategies that can be used to improve retention. Some possible suggestions include improving customer service, offering loyalty programs or rewards, providing personalized offers or recommendations, simplifying the purchasing process, and improving the overall customer experience. Additionally, conducting surveys or gathering customer feedback can help identify specific pain points or areas for improvement, allowing for targeted retention efforts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a079b56c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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