Survival_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66db62bc",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89334460",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, ExponentialFitter\n",
    "from Exponential import ExponentialAFTFitter\n",
    "from lifelines.utils import k_fold_cross_validation\n",
    "import seaborn as sns\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb452233",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf28583",
   "metadata": {},
   "source": [
    "# Reading and transforming the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b404d981",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'telco.csv'\n",
    "raw_data = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1211aef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_data(data):\n",
    "    data = data.copy()\n",
    "    data.drop(['ID'], axis=1, inplace=True)\n",
    "    cols = ['region', 'retire', 'marital', 'ed', 'gender', 'voice', 'internet', 'custcat', 'churn', 'forward']\n",
    "    data = data.copy()\n",
    "    data = pd.get_dummies(data, columns=cols, drop_first=True)\n",
    "    data = data.rename(columns={'churn_Yes': 'churn'})\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3abe281f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>region_Zone 2</th>\n",
       "      <th>region_Zone 3</th>\n",
       "      <th>retire_Yes</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>custcat_E-service</th>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <th>custcat_Total service</th>\n",
       "      <th>churn</th>\n",
       "      <th>forward_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <td>9</td>\n",
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       "      <td>10</td>\n",
       "      <td>39</td>\n",
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       "      <td>27</td>\n",
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       "      <th>996</th>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>67</td>\n",
       "      <td>59</td>\n",
       "      <td>40</td>\n",
       "      <td>944</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</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>998</th>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tenure  age  address  income  region_Zone 2  region_Zone 3  retire_Yes  \\\n",
       "0        13   44        9      64              1              0           0   \n",
       "1        11   33        7     136              0              1           0   \n",
       "2        68   52       24     116              0              1           0   \n",
       "3        33   33       12      33              1              0           0   \n",
       "4        23   30        9      30              1              0           0   \n",
       "..      ...  ...      ...     ...            ...            ...         ...   \n",
       "995      10   39        0      27              0              1           0   \n",
       "996       7   34        2      22              0              0           0   \n",
       "997      67   59       40     944              0              1           0   \n",
       "998      70   49       18      87              0              1           0   \n",
       "999      50   36        7      39              0              1           0   \n",
       "\n",
       "     marital_Unmarried  ed_Did not complete high school  \\\n",
       "0                    0                                0   \n",
       "1                    0                                0   \n",
       "2                    0                                1   \n",
       "3                    1                                0   \n",
       "4                    0                                1   \n",
       "..                 ...                              ...   \n",
       "995                  1                                0   \n",
       "996                  1                                0   \n",
       "997                  1                                0   \n",
       "998                  1                                0   \n",
       "999                  0                                0   \n",
       "\n",
       "     ed_High school degree  ed_Post-undergraduate degree  ed_Some college  \\\n",
       "0                        0                             0                0   \n",
       "1                        0                             1                0   \n",
       "2                        0                             0                0   \n",
       "3                        1                             0                0   \n",
       "4                        0                             0                0   \n",
       "..                     ...                           ...              ...   \n",
       "995                      0                             0                1   \n",
       "996                      0                             1                0   \n",
       "997                      0                             1                0   \n",
       "998                      1                             0                0   \n",
       "999                      0                             0                1   \n",
       "\n",
       "     gender_Male  voice_Yes  internet_Yes  custcat_E-service  \\\n",
       "0              1          0             0                  0   \n",
       "1              1          1             0                  0   \n",
       "2              0          0             0                  0   \n",
       "3              0          0             0                  0   \n",
       "4              1          0             0                  0   \n",
       "..           ...        ...           ...                ...   \n",
       "995            0          0             0                  0   \n",
       "996            0          0             0                  0   \n",
       "997            0          1             1                  0   \n",
       "998            0          1             0                  0   \n",
       "999            0          0             1                  1   \n",
       "\n",
       "     custcat_Plus service  custcat_Total service  churn  forward_Yes  \n",
       "0                       0                      0      1            1  \n",
       "1                       0                      1      1            1  \n",
       "2                       1                      0      0            0  \n",
       "3                       0                      0      1            0  \n",
       "4                       1                      0      0            1  \n",
       "..                    ...                    ...    ...          ...  \n",
       "995                     0                      0      0            0  \n",
       "996                     0                      0      0            0  \n",
       "997                     0                      1      0            1  \n",
       "998                     1                      0      0            1  \n",
       "999                     0                      0      1            0  \n",
       "\n",
       "[1000 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = process_data(raw_data)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8b357c",
   "metadata": {},
   "source": [
    "# Fitting AFT Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7f047f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "weibull_model = WeibullAFTFitter()\n",
    "log_norm_model = LogNormalAFTFitter()\n",
    "log_logistic_model = LogLogisticAFTFitter()\n",
    "exponential_model = ExponentialAFTFitter()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5144c5f2",
   "metadata": {},
   "source": [
    "### Weibull "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d9d3aab",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>274</td>\n",
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       "      <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 20:15:06 UTC</td>\n",
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       "      <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",
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       "  <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 20:15:06 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"
    }
   ],
   "source": [
    "weibull = weibull_model.fit(data, duration_col='tenure', event_col='churn')\n",
    "weibull_prediction = weibull.predict_survival_function(data).T\n",
    "weibull_prediction_avg = weibull_prediction.mean()\n",
    "weibull.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5ab9db",
   "metadata": {},
   "source": [
    "### Log Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fd469a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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 20:15:06 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 20:15:06 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"
    }
   ],
   "source": [
    "log_norm = log_norm_model.fit(data, duration_col='tenure', event_col='churn')\n",
    "log_norm_prediction = log_norm.predict_survival_function(data).T\n",
    "log_norm_prediction_avg = log_norm_prediction.mean()\n",
    "log_norm.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a6a5a3",
   "metadata": {},
   "source": [
    "### Log Logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e633488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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 20:15:07 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",
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       "<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 20:15:07 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"
    }
   ],
   "source": [
    "log_logistic = log_logistic_model.fit(data, duration_col='tenure', event_col='churn')\n",
    "log_logistic_prediction = log_logistic.predict_survival_function(data).T\n",
    "log_logistic_prediction_avg = log_logistic_prediction.mean()\n",
    "log_logistic.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefe3b33",
   "metadata": {},
   "source": [
    "### Exponential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "940eb3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.ExponentialAFTFitter</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>-1507.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 20:15:07 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=\"18\" valign=\"top\">lambda_</th>\n",
       "      <th>age</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.54</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>156.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.60</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>-0.35</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.07</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_Zone 3</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.10</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retire_Yes</th>\n",
       "      <td>-1.04</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>0.09</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Did not complete high school</th>\n",
       "      <td>1.04</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.87</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.92</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>20.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_High school degree</th>\n",
       "      <td>0.78</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Post-undergraduate degree</th>\n",
       "      <td>0.52</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed_Some college</th>\n",
       "      <td>0.73</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>17.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>1.40</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.23</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>52.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>1.08</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.15</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>21.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.28</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.27</td>\n",
       "      <td>5.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.42</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>23.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_Yes</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>3050.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>199.09 on 17 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>107.50</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]{18}{*}{lambda_} & age & 0.09 & 1.09 & 0.01 & 0.07 & 0.10 & 1.08 & 1.10 & 0.00 & 14.54 & 0.00 & 156.61 \\\\\n",
       " & address & 0.02 & 1.02 & 0.01 & -0.00 & 0.04 & 1.00 & 1.04 & 0.00 & 1.74 & 0.08 & 3.60 \\\\\n",
       " & income & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.35 & 0.72 & 0.47 \\\\\n",
       " & region_Zone 2 & 0.25 & 1.29 & 0.14 & -0.02 & 0.52 & 0.98 & 1.69 & 0.00 & 1.82 & 0.07 & 3.88 \\\\\n",
       " & region_Zone 3 & 0.43 & 1.54 & 0.14 & 0.16 & 0.70 & 1.17 & 2.01 & 0.00 & 3.10 & 0.00 & 9.03 \\\\\n",
       " & retire_Yes & -1.04 & 0.35 & 0.61 & -2.24 & 0.15 & 0.11 & 1.16 & 0.00 & -1.71 & 0.09 & 3.52 \\\\\n",
       " & marital_Unmarried & -0.18 & 0.83 & 0.12 & -0.41 & 0.05 & 0.66 & 1.05 & 0.00 & -1.57 & 0.12 & 3.09 \\\\\n",
       " & ed_Did not complete high school & 1.04 & 2.83 & 0.21 & 0.63 & 1.45 & 1.87 & 4.28 & 0.00 & 4.92 & 0.00 & 20.13 \\\\\n",
       " & ed_High school degree & 0.78 & 2.19 & 0.16 & 0.47 & 1.10 & 1.60 & 2.99 & 0.00 & 4.89 & 0.00 & 19.91 \\\\\n",
       " & ed_Post-undergraduate degree & 0.52 & 1.67 & 0.22 & 0.09 & 0.94 & 1.09 & 2.57 & 0.00 & 2.36 & 0.02 & 5.77 \\\\\n",
       " & ed_Some college & 0.73 & 2.07 & 0.16 & 0.42 & 1.04 & 1.52 & 2.82 & 0.00 & 4.62 & 0.00 & 17.99 \\\\\n",
       " & gender_Male & 0.15 & 1.17 & 0.12 & -0.07 & 0.38 & 0.93 & 1.47 & 0.00 & 1.32 & 0.19 & 2.43 \\\\\n",
       " & voice_Yes & -0.33 & 0.72 & 0.17 & -0.66 & 0.00 & 0.52 & 1.00 & 0.00 & -1.94 & 0.05 & 4.27 \\\\\n",
       " & internet_Yes & -0.56 & 0.57 & 0.14 & -0.85 & -0.28 & 0.43 & 0.76 & 0.00 & -3.88 & 0.00 & 13.20 \\\\\n",
       " & custcat_E-service & 1.40 & 4.06 & 0.17 & 1.07 & 1.74 & 2.91 & 5.68 & 0.00 & 8.23 & 0.00 & 52.22 \\\\\n",
       " & custcat_Plus service & 1.08 & 2.95 & 0.21 & 0.67 & 1.49 & 1.95 & 4.46 & 0.00 & 5.15 & 0.00 & 21.85 \\\\\n",
       " & custcat_Total service & 1.28 & 3.60 & 0.24 & 0.82 & 1.75 & 2.27 & 5.73 & 0.00 & 5.42 & 0.00 & 23.99 \\\\\n",
       " & forward_Yes & 0.05 & 1.05 & 0.16 & -0.27 & 0.37 & 0.76 & 1.44 & 0.00 & 0.29 & 0.77 & 0.38 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.ExponentialAFTFitter: 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 = -1507.44\n",
       "         time fit was run = 2023-04-30 20:15:07 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_ age                               0.09       1.09       0.01             0.07             0.10                 1.08                 1.10\n",
       "        address                           0.02       1.02       0.01            -0.00             0.04                 1.00                 1.04\n",
       "        income                           -0.00       1.00       0.00            -0.00             0.00                 1.00                 1.00\n",
       "        region_Zone 2                     0.25       1.29       0.14            -0.02             0.52                 0.98                 1.69\n",
       "        region_Zone 3                     0.43       1.54       0.14             0.16             0.70                 1.17                 2.01\n",
       "        retire_Yes                       -1.04       0.35       0.61            -2.24             0.15                 0.11                 1.16\n",
       "        marital_Unmarried                -0.18       0.83       0.12            -0.41             0.05                 0.66                 1.05\n",
       "        ed_Did not complete high school   1.04       2.83       0.21             0.63             1.45                 1.87                 4.28\n",
       "        ed_High school degree             0.78       2.19       0.16             0.47             1.10                 1.60                 2.99\n",
       "        ed_Post-undergraduate degree      0.52       1.67       0.22             0.09             0.94                 1.09                 2.57\n",
       "        ed_Some college                   0.73       2.07       0.16             0.42             1.04                 1.52                 2.82\n",
       "        gender_Male                       0.15       1.17       0.12            -0.07             0.38                 0.93                 1.47\n",
       "        voice_Yes                        -0.33       0.72       0.17            -0.66             0.00                 0.52                 1.00\n",
       "        internet_Yes                     -0.56       0.57       0.14            -0.85            -0.28                 0.43                 0.76\n",
       "        custcat_E-service                 1.40       4.06       0.17             1.07             1.74                 2.91                 5.68\n",
       "        custcat_Plus service              1.08       2.95       0.21             0.67             1.49                 1.95                 4.46\n",
       "        custcat_Total service             1.28       3.60       0.24             0.82             1.75                 2.27                 5.73\n",
       "        forward_Yes                       0.05       1.05       0.16            -0.27             0.37                 0.76                 1.44\n",
       "\n",
       "                                          cmp to     z      p   -log2(p)\n",
       "param   covariate                                                       \n",
       "lambda_ age                                 0.00 14.54 <0.005     156.61\n",
       "        address                             0.00  1.74   0.08       3.60\n",
       "        income                              0.00 -0.35   0.72       0.47\n",
       "        region_Zone 2                       0.00  1.82   0.07       3.88\n",
       "        region_Zone 3                       0.00  3.10 <0.005       9.03\n",
       "        retire_Yes                          0.00 -1.71   0.09       3.52\n",
       "        marital_Unmarried                   0.00 -1.57   0.12       3.09\n",
       "        ed_Did not complete high school     0.00  4.92 <0.005      20.13\n",
       "        ed_High school degree               0.00  4.89 <0.005      19.91\n",
       "        ed_Post-undergraduate degree        0.00  2.36   0.02       5.77\n",
       "        ed_Some college                     0.00  4.62 <0.005      17.99\n",
       "        gender_Male                         0.00  1.32   0.19       2.43\n",
       "        voice_Yes                           0.00 -1.94   0.05       4.27\n",
       "        internet_Yes                        0.00 -3.88 <0.005      13.20\n",
       "        custcat_E-service                   0.00  8.23 <0.005      52.22\n",
       "        custcat_Plus service                0.00  5.15 <0.005      21.85\n",
       "        custcat_Total service               0.00  5.42 <0.005      23.99\n",
       "        forward_Yes                         0.00  0.29   0.77       0.38\n",
       "---\n",
       "AIC = 3050.87\n",
       "log-likelihood ratio test = 199.09 on 17 df\n",
       "-log2(p) of ll-ratio test = 107.50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exponential = exponential_model.fit(data, duration_col='tenure', event_col='churn')\n",
    "exponential_prediction = exponential.predict_survival_function(data).T\n",
    "exponential_prediction_avg = exponential_prediction.mean()\n",
    "exponential.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19dc070",
   "metadata": {},
   "source": [
    "### Compare the 4 models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f35dad9c",
   "metadata": {},
   "source": [
    "AIC (Akaike information criterion) - The AIC is a measure of the relative quality of a model compared to other models. It takes into account both the goodness of fit of the model and the number of parameters used in the model. Lower AIC values indicate better fitting models. The difference in AIC between two models can be used to compare their relative performance, with a lower difference indicating that one model is a better fit than the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "956fe62d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exponential AIC: 3050.873920498042\n",
      "Log-Normal AIC: 2954.0240102517123\n",
      "Log-Logistic AIC: 2956.2085614433336\n",
      "Weibull AIC: 2964.34324808388\n",
      "\n",
      "The best model based on AIC scores is: \u001b[1mLog-normal\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "print(f'Exponential AIC: {exponential.AIC_}')\n",
    "print(f'Log-Normal AIC: {log_norm.AIC_}')\n",
    "print(f'Log-Logistic AIC: {log_logistic.AIC_}')\n",
    "print(f'Weibull AIC: {weibull.AIC_}')\n",
    "\n",
    "scores = {'Exponential': exponential.AIC_, 'Log-normal': log_norm.AIC_, 'Log-logistic': log_logistic.AIC_, 'Weibull': weibull.AIC_}\n",
    "print(f'\\nThe best model based on AIC scores is: \\033[1m{min(scores, key=scores.get)}\\033[0m')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b773dd2",
   "metadata": {},
   "source": [
    "Except the AIC score and the plots, there are some other important factors to consider when choosing the preferred model. \n",
    "\n",
    "For example, the complexity of the model is another important factor which is described by the number of the parameters the model has. Log-Normal and Log-Logistic models have both three parameters, while weibull has two and exponential has one parameter. So, exponential model is the least complex model from these four.\n",
    "\n",
    "Another factor can be the hazard rate. For instance, the Weibull model may be preferred over the exponential model if the data exhibits a non-constant hazard rate, as the Weibull model can capture both increasing and decreasing hazard rates.\n",
    "\n",
    "However, assuming that I am interested in best performing model, I will concentrate on AIC score and choose Log-normal as the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621057c0",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c000829c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "plt.plot(weibull_prediction_avg, label='Weibull')\n",
    "plt.plot(log_norm_prediction_avg, label='LogNorm')\n",
    "plt.plot(log_logistic_prediction_avg, label='LogLogistic')\n",
    "plt.plot(exponential_prediction_avg, label='Exponential')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1d0ed2",
   "metadata": {},
   "source": [
    "### Keep significant features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c4c390",
   "metadata": {},
   "source": [
    "Taking those features as significant that are 99% significant. Here is the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11f6c150",
   "metadata": {},
   "outputs": [],
   "source": [
    "significant_columns = [\"address\", \"age\", \"internet_Yes\", \"marital_Unmarried\", \"tenure\", \"churn\", \"custcat_E-service\", \"custcat_Plus service\", \"custcat_Total service\", \"voice_Yes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c609882e",
   "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>internet_Yes</th>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <th>tenure</th>\n",
       "      <th>churn</th>\n",
       "      <th>custcat_E-service</th>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <th>custcat_Total service</th>\n",
       "      <th>voice_Yes</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>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>40</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     address  age  internet_Yes  marital_Unmarried  tenure  churn  \\\n",
       "0          9   44             0                  0      13      1   \n",
       "1          7   33             0                  0      11      1   \n",
       "2         24   52             0                  0      68      0   \n",
       "3         12   33             0                  1      33      1   \n",
       "4          9   30             0                  0      23      0   \n",
       "..       ...  ...           ...                ...     ...    ...   \n",
       "995        0   39             0                  1      10      0   \n",
       "996        2   34             0                  1       7      0   \n",
       "997       40   59             1                  1      67      0   \n",
       "998       18   49             0                  1      70      0   \n",
       "999        7   36             1                  0      50      1   \n",
       "\n",
       "     custcat_E-service  custcat_Plus service  custcat_Total service  voice_Yes  \n",
       "0                    0                     0                      0          0  \n",
       "1                    0                     0                      1          1  \n",
       "2                    0                     1                      0          0  \n",
       "3                    0                     0                      0          0  \n",
       "4                    0                     1                      0          0  \n",
       "..                 ...                   ...                    ...        ...  \n",
       "995                  0                     0                      0          0  \n",
       "996                  0                     0                      0          0  \n",
       "997                  0                     0                      1          1  \n",
       "998                  0                     1                      0          1  \n",
       "999                  1                     0                      0          0  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_data = data[significant_columns]\n",
    "dropped_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2f540de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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>-1462.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 20:15:07 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=\"9\" 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>4.84</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</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>5.75</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>1.03</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>29.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>0.82</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.85</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>1.01</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.83</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>-0.84</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>29.63</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>-0.67</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2.53</td>\n",
       "      <td>12.62</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.01</td>\n",
       "      <td>7.84</td>\n",
       "      <td>20.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.45</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>82.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.15</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>30.27</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>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>2944.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>280.83 on 8 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>183.73</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]{9}{*}{mu_} & address & 0.04 & 1.04 & 0.01 & 0.03 & 0.06 & 1.03 & 1.06 & 0.00 & 4.84 & 0.00 & 19.56 \\\\\n",
       " & age & 0.04 & 1.04 & 0.01 & 0.02 & 0.05 & 1.02 & 1.05 & 0.00 & 5.75 & 0.00 & 26.78 \\\\\n",
       " & custcat_E-service & 1.03 & 2.79 & 0.17 & 0.69 & 1.36 & 2.00 & 3.89 & 0.00 & 6.07 & 0.00 & 29.53 \\\\\n",
       " & custcat_Plus service & 0.82 & 2.28 & 0.17 & 0.49 & 1.15 & 1.63 & 3.17 & 0.00 & 4.85 & 0.00 & 19.66 \\\\\n",
       " & custcat_Total service & 1.01 & 2.75 & 0.21 & 0.60 & 1.42 & 1.83 & 4.15 & 0.00 & 4.83 & 0.00 & 19.52 \\\\\n",
       " & internet_Yes & -0.84 & 0.43 & 0.14 & -1.11 & -0.57 & 0.33 & 0.57 & 0.00 & -6.08 & 0.00 & 29.63 \\\\\n",
       " & marital_Unmarried & -0.45 & 0.64 & 0.11 & -0.67 & -0.22 & 0.51 & 0.80 & 0.00 & -3.91 & 0.00 & 13.39 \\\\\n",
       " & voice_Yes & -0.46 & 0.63 & 0.17 & -0.79 & -0.14 & 0.45 & 0.87 & 0.00 & -2.78 & 0.01 & 7.52 \\\\\n",
       " & Intercept & 2.53 & 12.62 & 0.24 & 2.06 & 3.01 & 7.84 & 20.30 & 0.00 & 10.45 & 0.00 & 82.47 \\\\\n",
       "sigma_ & Intercept & 0.28 & 1.33 & 0.05 & 0.19 & 0.37 & 1.21 & 1.45 & 0.00 & 6.15 & 0.00 & 30.27 \\\\\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 = -1462.10\n",
       "         time fit was run = 2023-04-30 20:15:07 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                     0.04       1.04       0.01             0.02             0.05                 1.02                 1.05\n",
       "       custcat_E-service       1.03       2.79       0.17             0.69             1.36                 2.00                 3.89\n",
       "       custcat_Plus service    0.82       2.28       0.17             0.49             1.15                 1.63                 3.17\n",
       "       custcat_Total service   1.01       2.75       0.21             0.60             1.42                 1.83                 4.15\n",
       "       internet_Yes           -0.84       0.43       0.14            -1.11            -0.57                 0.33                 0.57\n",
       "       marital_Unmarried      -0.45       0.64       0.11            -0.67            -0.22                 0.51                 0.80\n",
       "       voice_Yes              -0.46       0.63       0.17            -0.79            -0.14                 0.45                 0.87\n",
       "       Intercept               2.53      12.62       0.24             2.06             3.01                 7.84                20.30\n",
       "sigma_ Intercept               0.28       1.33       0.05             0.19             0.37                 1.21                 1.45\n",
       "\n",
       "                               cmp to     z      p   -log2(p)\n",
       "param  covariate                                             \n",
       "mu_    address                   0.00  4.84 <0.005      19.56\n",
       "       age                       0.00  5.75 <0.005      26.78\n",
       "       custcat_E-service         0.00  6.07 <0.005      29.53\n",
       "       custcat_Plus service      0.00  4.85 <0.005      19.66\n",
       "       custcat_Total service     0.00  4.83 <0.005      19.52\n",
       "       internet_Yes              0.00 -6.08 <0.005      29.63\n",
       "       marital_Unmarried         0.00 -3.91 <0.005      13.39\n",
       "       voice_Yes                 0.00 -2.78   0.01       7.52\n",
       "       Intercept                 0.00 10.45 <0.005      82.47\n",
       "sigma_ Intercept                 0.00  6.15 <0.005      30.27\n",
       "---\n",
       "Concordance = 0.79\n",
       "AIC = 2944.20\n",
       "log-likelihood ratio test = 280.83 on 8 df\n",
       "-log2(p) of ll-ratio test = 183.73"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_norm = log_norm_model.fit(dropped_data, duration_col='tenure', event_col='churn')\n",
    "log_norm_prediction = log_norm.predict_survival_function(dropped_data).T\n",
    "log_norm_prediction_avg = log_norm_prediction.mean()\n",
    "log_norm.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1c5f0d",
   "metadata": {},
   "source": [
    "# CLV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e9e9e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "clv_data = log_norm_prediction.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a3a24b",
   "metadata": {},
   "source": [
    "Taking some conventional numbers for margin and r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0245d665",
   "metadata": {},
   "outputs": [],
   "source": [
    "margin = 1000\n",
    "sequence = range(1,len(clv_data.columns)+1)\n",
    "r = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d886a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in sequence:\n",
    "    clv_data.loc[:, i] = clv_data.loc[:, i]/((1+r/12)**(sequence[i-1]-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbec4a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>64.0</th>\n",
       "      <th>65.0</th>\n",
       "      <th>66.0</th>\n",
       "      <th>67.0</th>\n",
       "      <th>68.0</th>\n",
       "      <th>69.0</th>\n",
       "      <th>70.0</th>\n",
       "      <th>71.0</th>\n",
       "      <th>72.0</th>\n",
       "      <th>CLV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999688</td>\n",
       "      <td>0.989880</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.966893</td>\n",
       "      <td>0.954194</td>\n",
       "      <td>0.940967</td>\n",
       "      <td>0.927370</td>\n",
       "      <td>0.913526</td>\n",
       "      <td>0.899533</td>\n",
       "      <td>0.885469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363526</td>\n",
       "      <td>0.357889</td>\n",
       "      <td>0.352351</td>\n",
       "      <td>0.346911</td>\n",
       "      <td>0.341567</td>\n",
       "      <td>0.336317</td>\n",
       "      <td>0.331159</td>\n",
       "      <td>0.326092</td>\n",
       "      <td>0.321112</td>\n",
       "      <td>43421.920852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999736</td>\n",
       "      <td>0.990127</td>\n",
       "      <td>0.979439</td>\n",
       "      <td>0.967868</td>\n",
       "      <td>0.955620</td>\n",
       "      <td>0.942870</td>\n",
       "      <td>0.929761</td>\n",
       "      <td>0.916405</td>\n",
       "      <td>0.902892</td>\n",
       "      <td>0.889296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.373555</td>\n",
       "      <td>0.367869</td>\n",
       "      <td>0.362283</td>\n",
       "      <td>0.356792</td>\n",
       "      <td>0.351396</td>\n",
       "      <td>0.346093</td>\n",
       "      <td>0.340880</td>\n",
       "      <td>0.335756</td>\n",
       "      <td>0.330720</td>\n",
       "      <td>44012.968135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.991724</td>\n",
       "      <td>0.983496</td>\n",
       "      <td>0.975307</td>\n",
       "      <td>0.967152</td>\n",
       "      <td>0.959028</td>\n",
       "      <td>0.950934</td>\n",
       "      <td>0.942869</td>\n",
       "      <td>0.934834</td>\n",
       "      <td>0.926828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561230</td>\n",
       "      <td>0.555839</td>\n",
       "      <td>0.550497</td>\n",
       "      <td>0.545202</td>\n",
       "      <td>0.539956</td>\n",
       "      <td>0.534757</td>\n",
       "      <td>0.529605</td>\n",
       "      <td>0.524500</td>\n",
       "      <td>0.519441</td>\n",
       "      <td>53279.464095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.997985</td>\n",
       "      <td>0.982516</td>\n",
       "      <td>0.963596</td>\n",
       "      <td>0.942708</td>\n",
       "      <td>0.920782</td>\n",
       "      <td>0.898406</td>\n",
       "      <td>0.875956</td>\n",
       "      <td>0.853676</td>\n",
       "      <td>0.831726</td>\n",
       "      <td>0.810209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.231513</td>\n",
       "      <td>0.227024</td>\n",
       "      <td>0.222639</td>\n",
       "      <td>0.218356</td>\n",
       "      <td>0.214171</td>\n",
       "      <td>0.210084</td>\n",
       "      <td>0.206089</td>\n",
       "      <td>0.202185</td>\n",
       "      <td>34882.879434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999870</td>\n",
       "      <td>0.990870</td>\n",
       "      <td>0.981213</td>\n",
       "      <td>0.970970</td>\n",
       "      <td>0.960245</td>\n",
       "      <td>0.949137</td>\n",
       "      <td>0.937733</td>\n",
       "      <td>0.926110</td>\n",
       "      <td>0.914328</td>\n",
       "      <td>0.902440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413868</td>\n",
       "      <td>0.408046</td>\n",
       "      <td>0.402314</td>\n",
       "      <td>0.396671</td>\n",
       "      <td>0.391115</td>\n",
       "      <td>0.385646</td>\n",
       "      <td>0.380260</td>\n",
       "      <td>0.374959</td>\n",
       "      <td>0.369738</td>\n",
       "      <td>46287.751605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.996036</td>\n",
       "      <td>0.975404</td>\n",
       "      <td>0.950272</td>\n",
       "      <td>0.923094</td>\n",
       "      <td>0.895196</td>\n",
       "      <td>0.867318</td>\n",
       "      <td>0.839884</td>\n",
       "      <td>0.813132</td>\n",
       "      <td>0.787198</td>\n",
       "      <td>0.762150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187415</td>\n",
       "      <td>0.183429</td>\n",
       "      <td>0.179547</td>\n",
       "      <td>0.175765</td>\n",
       "      <td>0.172079</td>\n",
       "      <td>0.168488</td>\n",
       "      <td>0.164988</td>\n",
       "      <td>0.161575</td>\n",
       "      <td>0.158249</td>\n",
       "      <td>30970.665732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.995073</td>\n",
       "      <td>0.972135</td>\n",
       "      <td>0.944405</td>\n",
       "      <td>0.914717</td>\n",
       "      <td>0.884526</td>\n",
       "      <td>0.854605</td>\n",
       "      <td>0.825374</td>\n",
       "      <td>0.797056</td>\n",
       "      <td>0.769766</td>\n",
       "      <td>0.743549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172052</td>\n",
       "      <td>0.168286</td>\n",
       "      <td>0.164620</td>\n",
       "      <td>0.161053</td>\n",
       "      <td>0.157579</td>\n",
       "      <td>0.154197</td>\n",
       "      <td>0.150903</td>\n",
       "      <td>0.147695</td>\n",
       "      <td>0.144569</td>\n",
       "      <td>29633.260935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.991651</td>\n",
       "      <td>0.983268</td>\n",
       "      <td>0.974822</td>\n",
       "      <td>0.966311</td>\n",
       "      <td>0.957738</td>\n",
       "      <td>0.949110</td>\n",
       "      <td>0.940436</td>\n",
       "      <td>0.931725</td>\n",
       "      <td>0.922986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518455</td>\n",
       "      <td>0.512743</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.501505</td>\n",
       "      <td>0.495977</td>\n",
       "      <td>0.490511</td>\n",
       "      <td>0.485104</td>\n",
       "      <td>0.479757</td>\n",
       "      <td>0.474468</td>\n",
       "      <td>51473.533992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.999923</td>\n",
       "      <td>0.991186</td>\n",
       "      <td>0.982008</td>\n",
       "      <td>0.972409</td>\n",
       "      <td>0.962449</td>\n",
       "      <td>0.952190</td>\n",
       "      <td>0.941692</td>\n",
       "      <td>0.931008</td>\n",
       "      <td>0.920183</td>\n",
       "      <td>0.909257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440037</td>\n",
       "      <td>0.434174</td>\n",
       "      <td>0.428396</td>\n",
       "      <td>0.422700</td>\n",
       "      <td>0.417087</td>\n",
       "      <td>0.411554</td>\n",
       "      <td>0.406100</td>\n",
       "      <td>0.400725</td>\n",
       "      <td>0.395427</td>\n",
       "      <td>47681.088097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.999471</td>\n",
       "      <td>0.988800</td>\n",
       "      <td>0.976448</td>\n",
       "      <td>0.962850</td>\n",
       "      <td>0.948374</td>\n",
       "      <td>0.933304</td>\n",
       "      <td>0.917854</td>\n",
       "      <td>0.902183</td>\n",
       "      <td>0.886413</td>\n",
       "      <td>0.870636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329600</td>\n",
       "      <td>0.324161</td>\n",
       "      <td>0.318826</td>\n",
       "      <td>0.313594</td>\n",
       "      <td>0.308461</td>\n",
       "      <td>0.303426</td>\n",
       "      <td>0.298486</td>\n",
       "      <td>0.293640</td>\n",
       "      <td>0.288884</td>\n",
       "      <td>41343.286520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1.0       2.0       3.0       4.0       5.0       6.0       7.0  \\\n",
       "0    0.999688  0.989880  0.978868  0.966893  0.954194  0.940967  0.927370   \n",
       "1    0.999736  0.990127  0.979439  0.967868  0.955620  0.942870  0.929761   \n",
       "2    0.999999  0.991724  0.983496  0.975307  0.967152  0.959028  0.950934   \n",
       "3    0.997985  0.982516  0.963596  0.942708  0.920782  0.898406  0.875956   \n",
       "4    0.999870  0.990870  0.981213  0.970970  0.960245  0.949137  0.937733   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995  0.996036  0.975404  0.950272  0.923094  0.895196  0.867318  0.839884   \n",
       "996  0.995073  0.972135  0.944405  0.914717  0.884526  0.854605  0.825374   \n",
       "997  0.999991  0.991651  0.983268  0.974822  0.966311  0.957738  0.949110   \n",
       "998  0.999923  0.991186  0.982008  0.972409  0.962449  0.952190  0.941692   \n",
       "999  0.999471  0.988800  0.976448  0.962850  0.948374  0.933304  0.917854   \n",
       "\n",
       "          8.0       9.0      10.0  ...      64.0      65.0      66.0  \\\n",
       "0    0.913526  0.899533  0.885469  ...  0.363526  0.357889  0.352351   \n",
       "1    0.916405  0.902892  0.889296  ...  0.373555  0.367869  0.362283   \n",
       "2    0.942869  0.934834  0.926828  ...  0.561230  0.555839  0.550497   \n",
       "3    0.853676  0.831726  0.810209  ...  0.236111  0.231513  0.227024   \n",
       "4    0.926110  0.914328  0.902440  ...  0.413868  0.408046  0.402314   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "995  0.813132  0.787198  0.762150  ...  0.187415  0.183429  0.179547   \n",
       "996  0.797056  0.769766  0.743549  ...  0.172052  0.168286  0.164620   \n",
       "997  0.940436  0.931725  0.922986  ...  0.518455  0.512743  0.507093   \n",
       "998  0.931008  0.920183  0.909257  ...  0.440037  0.434174  0.428396   \n",
       "999  0.902183  0.886413  0.870636  ...  0.329600  0.324161  0.318826   \n",
       "\n",
       "         67.0      68.0      69.0      70.0      71.0      72.0           CLV  \n",
       "0    0.346911  0.341567  0.336317  0.331159  0.326092  0.321112  43421.920852  \n",
       "1    0.356792  0.351396  0.346093  0.340880  0.335756  0.330720  44012.968135  \n",
       "2    0.545202  0.539956  0.534757  0.529605  0.524500  0.519441  53279.464095  \n",
       "3    0.222639  0.218356  0.214171  0.210084  0.206089  0.202185  34882.879434  \n",
       "4    0.396671  0.391115  0.385646  0.380260  0.374959  0.369738  46287.751605  \n",
       "..        ...       ...       ...       ...       ...       ...           ...  \n",
       "995  0.175765  0.172079  0.168488  0.164988  0.161575  0.158249  30970.665732  \n",
       "996  0.161053  0.157579  0.154197  0.150903  0.147695  0.144569  29633.260935  \n",
       "997  0.501505  0.495977  0.490511  0.485104  0.479757  0.474468  51473.533992  \n",
       "998  0.422700  0.417087  0.411554  0.406100  0.400725  0.395427  47681.088097  \n",
       "999  0.313594  0.308461  0.303426  0.298486  0.293640  0.288884  41343.286520  \n",
       "\n",
       "[1000 rows x 73 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_data[\"CLV\"] = margin * clv_data.sum(axis = 1)\n",
    "clv_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f97f6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[\"CLV\"] = clv_data.CLV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec8ccb8",
   "metadata": {},
   "source": [
    "From the above results we can see that those customers that are more probably staying with the company are having higher CLV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd4de1c",
   "metadata": {},
   "source": [
    "# Visualizing CLV vs. Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0be7e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431a91dde0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cdb8165d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431cd79900>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 542.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='ed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e9b1ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431cc738e0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='retire')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d50cce31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431d491e40>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 461.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='custcat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53c2e8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431d55ddb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0026df86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431d4b0e80>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 449x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='marital')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4512f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431cfcd990>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='internet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6a04275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431cdda110>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='forward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d1f34b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431a91dc90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='voice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c431536e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1431ccdd6c0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=raw_data, kind='kde', x='CLV', hue='churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1401df06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 CLV\n",
      "gender              \n",
      "Female  41126.506961\n",
      "Male    41326.642952\n",
      "                CLV\n",
      "voice              \n",
      "No     42575.461690\n",
      "Yes    38127.142462\n",
      "                  CLV\n",
      "forward              \n",
      "No       39698.658898\n",
      "Yes      42790.978870\n",
      "                   CLV\n",
      "internet              \n",
      "No        44663.921886\n",
      "Yes       35314.059816\n",
      "                    CLV\n",
      "marital                \n",
      "Married    43569.470093\n",
      "Unmarried  38923.336531\n",
      "                 CLV\n",
      "region              \n",
      "Zone 1  41306.111183\n",
      "Zone 2  41722.324987\n",
      "Zone 3  40660.896214\n",
      "                        CLV\n",
      "custcat                    \n",
      "Basic service  34882.570279\n",
      "E-service      44558.848716\n",
      "Plus service   46759.868046\n",
      "Total service  38710.236686\n",
      "                 CLV\n",
      "retire              \n",
      "No      40703.694514\n",
      "Yes     51756.420700\n",
      "                                       CLV\n",
      "ed                                        \n",
      "College degree                38500.886353\n",
      "Did not complete high school  45159.631326\n",
      "High school degree            42163.405680\n",
      "Post-undergraduate degree     38203.215072\n",
      "Some college                  40091.343647\n"
     ]
    }
   ],
   "source": [
    "print(raw_data.groupby(\"gender\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"voice\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"forward\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"internet\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"marital\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"region\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"custcat\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"retire\")[[\"CLV\"]].mean())\n",
    "print(raw_data.groupby(\"ed\")[[\"CLV\"]].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6210b150",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbf222e",
   "metadata": {},
   "source": [
    "Customer Lifetime Value (CLV) is an important metric that helps businesses understand the value of a customer over their entire relationship with the company.    \n",
    "\n",
    "Accelerated Failure Time (AFT) models are a type of survival analysis that is used to model the time it takes for an event to occur, such as customer churn. AFT models are useful for predicting the survival time of customers and for identifying factors that affect customer churn. \n",
    "\n",
    "\n",
    "In our study, CLV and AFT are negative correlated. Which means that the higher is the CLV, the lower is the risk of churn. \n",
    "\n",
    "The coefficients:\n",
    "\n",
    "- Positive coefficients indicate that an increase in the corresponding variable increases the expected lifetime of the customer.\n",
    "- Negative coefficients indicate that an increase in the corresponding variable decreases the expected lifetime of the customer.\n",
    "- The magnitude of the coefficient indicates the strength of the effect of the corresponding variable on the customer lifetime.\n",
    "\n",
    "Note: The coefficients are sensitive to different scales of the data (See below)\n",
    "\n",
    "\"The most valuable segments\" are those segments that provide the highest value in average to the company. The highest CLV in average have the retired people, then the people who did not complete high school. We may connect this pheonomenon with the fact that more educated people or younger people are more demanding.\n",
    "\n",
    "Some suggestions for retention:\n",
    "\n",
    "- Listen to customer feedback and respond promptly to any issues or concerns they may have. Especially for younger people.\n",
    "- Offer to loyal customers exclusive discounts, special offers, or other perks. Especially for younger people.\n",
    "- It seems the company has some problems with internet. Since the CLV for customers having internet is a problem.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6def74",
   "metadata": {},
   "source": [
    "## Retention budget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9929f50d",
   "metadata": {},
   "source": [
    "Taking some arbitrary values for retention rate and cost per customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f7a7f04d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped_data[\"CLV\"] = clv_data.CLV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "de2fbc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "retained_customers = dropped_data[dropped_data['churn'] == 0]\n",
    "retained_clv = retained_customers['CLV'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d3157443",
   "metadata": {},
   "outputs": [],
   "source": [
    "retention_rate = 0.8\n",
    "cost_per_customer = 5000\n",
    "retention_cost = len(dropped_data) * retention_rate * cost_per_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cfcde9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27470982.863718566"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_budget = retained_clv - retention_cost\n",
    "annual_budget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4294df57",
   "metadata": {},
   "source": [
    "# Scaling the features (Experiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2196c2d",
   "metadata": {},
   "source": [
    "I think for making the coefficients meaningful, it is important that all the features are all in the same scale. That's why, I will scale the data using MinMaxScaler and try the same experiment to see the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b07e62b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15c5e61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "edf7a1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled = scaler.fit_transform(dropped_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "75ad4f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = pd.DataFrame(scaled, columns = dropped_data.columns) + 10**(-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "03b38c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>internet_Yes</th>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <th>tenure</th>\n",
       "      <th>churn</th>\n",
       "      <th>custcat_E-service</th>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <th>custcat_Total service</th>\n",
       "      <th>voice_Yes</th>\n",
       "      <th>CLV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.100182e-01</td>\n",
       "      <td>4.014237e-01</td>\n",
       "      <td>3.680000e-01</td>\n",
       "      <td>5.050000e-01</td>\n",
       "      <td>4.862817e-01</td>\n",
       "      <td>2.740000e-01</td>\n",
       "      <td>2.170000e-01</td>\n",
       "      <td>2.810000e-01</td>\n",
       "      <td>2.360000e-01</td>\n",
       "      <td>3.040000e-01</td>\n",
       "      <td>7.117916e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.833942e-01</td>\n",
       "      <td>2.128613e-01</td>\n",
       "      <td>4.825027e-01</td>\n",
       "      <td>5.002252e-01</td>\n",
       "      <td>3.008424e-01</td>\n",
       "      <td>4.462321e-01</td>\n",
       "      <td>4.124089e-01</td>\n",
       "      <td>4.497124e-01</td>\n",
       "      <td>4.248347e-01</td>\n",
       "      <td>4.602128e-01</td>\n",
       "      <td>2.150341e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.454545e-02</td>\n",
       "      <td>2.372881e-01</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>2.253521e-01</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>5.526904e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.636364e-01</td>\n",
       "      <td>3.728814e-01</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.647887e-01</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>7.506186e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.272727e-01</td>\n",
       "      <td>5.593220e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.464789e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-100</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.982277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address            age   internet_Yes  marital_Unmarried  \\\n",
       "count   1.000000e+03   1.000000e+03   1.000000e+03       1.000000e+03   \n",
       "mean    2.100182e-01   4.014237e-01   3.680000e-01       5.050000e-01   \n",
       "std     1.833942e-01   2.128613e-01   4.825027e-01       5.002252e-01   \n",
       "min    1.000000e-100  1.000000e-100  1.000000e-100      1.000000e-100   \n",
       "25%     5.454545e-02   2.372881e-01  1.000000e-100      1.000000e-100   \n",
       "50%     1.636364e-01   3.728814e-01  1.000000e-100       1.000000e+00   \n",
       "75%     3.272727e-01   5.593220e-01   1.000000e+00       1.000000e+00   \n",
       "max     1.000000e+00   1.000000e+00   1.000000e+00       1.000000e+00   \n",
       "\n",
       "              tenure          churn  custcat_E-service  custcat_Plus service  \\\n",
       "count   1.000000e+03   1.000000e+03       1.000000e+03          1.000000e+03   \n",
       "mean    4.862817e-01   2.740000e-01       2.170000e-01          2.810000e-01   \n",
       "std     3.008424e-01   4.462321e-01       4.124089e-01          4.497124e-01   \n",
       "min    1.000000e-100  1.000000e-100      1.000000e-100         1.000000e-100   \n",
       "25%     2.253521e-01  1.000000e-100      1.000000e-100         1.000000e-100   \n",
       "50%     4.647887e-01  1.000000e-100      1.000000e-100         1.000000e-100   \n",
       "75%     7.464789e-01   1.000000e+00      1.000000e-100          1.000000e+00   \n",
       "max     1.000000e+00   1.000000e+00       1.000000e+00          1.000000e+00   \n",
       "\n",
       "       custcat_Total service      voice_Yes            CLV  \n",
       "count           1.000000e+03   1.000000e+03   1.000000e+03  \n",
       "mean            2.360000e-01   3.040000e-01   7.117916e-01  \n",
       "std             4.248347e-01   4.602128e-01   2.150341e-01  \n",
       "min            1.000000e-100  1.000000e-100  1.000000e-100  \n",
       "25%            1.000000e-100  1.000000e-100   5.526904e-01  \n",
       "50%            1.000000e-100  1.000000e-100   7.506186e-01  \n",
       "75%            1.000000e-100   1.000000e+00   8.982277e-01  \n",
       "max             1.000000e+00   1.000000e+00   1.000000e+00  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3f4733ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-660.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-04-30 20:15:58 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=\"10\" valign=\"top\">mu_</th>\n",
       "      <th>CLV</th>\n",
       "      <td>52.66</td>\n",
       "      <td>7.42e+22</td>\n",
       "      <td>13.54</td>\n",
       "      <td>26.13</td>\n",
       "      <td>79.19</td>\n",
       "      <td>2.23e+11</td>\n",
       "      <td>2.46e+34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.89</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <td>-10.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.03</td>\n",
       "      <td>-24.70</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-12.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.12</td>\n",
       "      <td>-28.91</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_E-service</th>\n",
       "      <td>-3.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3.85</td>\n",
       "      <td>-11.13</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Plus service</th>\n",
       "      <td>-3.69</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.27</td>\n",
       "      <td>-10.11</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custcat_Total service</th>\n",
       "      <td>-1.13</td>\n",
       "      <td>0.32</td>\n",
       "      <td>4.34</td>\n",
       "      <td>-9.64</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1585.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_Yes</th>\n",
       "      <td>6.30</td>\n",
       "      <td>542.40</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>12.47</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.62e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_Unmarried</th>\n",
       "      <td>1.18</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.04</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>5.19</td>\n",
       "      <td>0.06</td>\n",
       "      <td>178.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_Yes</th>\n",
       "      <td>4.02</td>\n",
       "      <td>55.65</td>\n",
       "      <td>2.75</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>9.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12252.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-36.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.59</td>\n",
       "      <td>-48.92</td>\n",
       "      <td>-23.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.47</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>3.23</td>\n",
       "      <td>25.22</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.27</td>\n",
       "      <td>24.14</td>\n",
       "      <td>26.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>144.35</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>inf</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>1343.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>59.40 on 9 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>29.09</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]{10}{*}{mu_} & CLV & 52.66 & 74179116753813021655040.00 & 13.54 & 26.13 & 79.19 & 223416424842.18 & 24629081618609058337722874091012096.00 & 0.00 & 3.89 & 0.00 & 13.29 \\\\\n",
       " & address & -10.93 & 0.00 & 7.03 & -24.70 & 2.84 & 0.00 & 17.04 & 0.00 & -1.56 & 0.12 & 3.06 \\\\\n",
       " & age & -12.98 & 0.00 & 8.12 & -28.91 & 2.94 & 0.00 & 18.84 & 0.00 & -1.60 & 0.11 & 3.19 \\\\\n",
       " & custcat_E-service & -3.58 & 0.03 & 3.85 & -11.13 & 3.98 & 0.00 & 53.41 & 0.00 & -0.93 & 0.35 & 1.50 \\\\\n",
       " & custcat_Plus service & -3.69 & 0.02 & 3.27 & -10.11 & 2.72 & 0.00 & 15.18 & 0.00 & -1.13 & 0.26 & 1.95 \\\\\n",
       " & custcat_Total service & -1.13 & 0.32 & 4.34 & -9.64 & 7.37 & 0.00 & 1585.59 & 0.00 & -0.26 & 0.79 & 0.33 \\\\\n",
       " & internet_Yes & 6.30 & 542.40 & 3.15 & 0.12 & 12.47 & 1.12 & 261699.75 & 0.00 & 2.00 & 0.05 & 4.45 \\\\\n",
       " & marital_Unmarried & 1.18 & 3.26 & 2.04 & -2.82 & 5.19 & 0.06 & 178.77 & 0.00 & 0.58 & 0.56 & 0.83 \\\\\n",
       " & voice_Yes & 4.02 & 55.65 & 2.75 & -1.38 & 9.41 & 0.25 & 12252.93 & 0.00 & 1.46 & 0.14 & 2.79 \\\\\n",
       " & Intercept & -36.01 & 0.00 & 6.59 & -48.92 & -23.11 & 0.00 & 0.00 & 0.00 & -5.47 & 0.00 & 24.39 \\\\\n",
       "sigma_ & Intercept & 3.23 & 25.22 & 0.02 & 3.18 & 3.27 & 24.14 & 26.35 & 0.00 & 144.35 & 0.00 & inf \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.LogNormalAFTFitter: fitted with 1000 total observations, 0 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'churn'\n",
       "   number of observations = 1000\n",
       "number of events observed = 1000\n",
       "           log-likelihood = -660.55\n",
       "         time fit was run = 2023-04-30 20:15:58 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_    CLV                    52.66   7.42e+22      13.54            26.13            79.19             2.23e+11             2.46e+34\n",
       "       address               -10.93       0.00       7.03           -24.70             2.84                 0.00                17.04\n",
       "       age                   -12.98       0.00       8.12           -28.91             2.94                 0.00                18.84\n",
       "       custcat_E-service      -3.58       0.03       3.85           -11.13             3.98                 0.00                53.41\n",
       "       custcat_Plus service   -3.69       0.02       3.27           -10.11             2.72                 0.00                15.18\n",
       "       custcat_Total service  -1.13       0.32       4.34            -9.64             7.37                 0.00              1585.59\n",
       "       internet_Yes            6.30     542.40       3.15             0.12            12.47                 1.12             2.62e+05\n",
       "       marital_Unmarried       1.18       3.26       2.04            -2.82             5.19                 0.06               178.77\n",
       "       voice_Yes               4.02      55.65       2.75            -1.38             9.41                 0.25             12252.93\n",
       "       Intercept             -36.01       0.00       6.59           -48.92           -23.11                 0.00                 0.00\n",
       "sigma_ Intercept               3.23      25.22       0.02             3.18             3.27                24.14                26.35\n",
       "\n",
       "                               cmp to      z      p   -log2(p)\n",
       "param  covariate                                              \n",
       "mu_    CLV                       0.00   3.89 <0.005      13.29\n",
       "       address                   0.00  -1.56   0.12       3.06\n",
       "       age                       0.00  -1.60   0.11       3.19\n",
       "       custcat_E-service         0.00  -0.93   0.35       1.50\n",
       "       custcat_Plus service      0.00  -1.13   0.26       1.95\n",
       "       custcat_Total service     0.00  -0.26   0.79       0.33\n",
       "       internet_Yes              0.00   2.00   0.05       4.45\n",
       "       marital_Unmarried         0.00   0.58   0.56       0.83\n",
       "       voice_Yes                 0.00   1.46   0.14       2.79\n",
       "       Intercept                 0.00  -5.47 <0.005      24.39\n",
       "sigma_ Intercept                 0.00 144.35 <0.005        inf\n",
       "---\n",
       "Concordance = 0.79\n",
       "AIC = 1343.10\n",
       "log-likelihood ratio test = 59.40 on 9 df\n",
       "-log2(p) of ll-ratio test = 29.09"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_norm = log_norm_model.fit(scaled_data, duration_col='tenure', event_col='churn')\n",
    "log_norm_prediction = log_norm.predict_survival_function(scaled_data).T\n",
    "log_norm_prediction_avg = log_norm_prediction.mean()\n",
    "log_norm.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72e7e46",
   "metadata": {},
   "source": [
    "Here, none of the features are significant. The coefficients should work better and provide right insights for scaled data."
   ]
  }
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