SurvivalModelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3c3e2211-0f44-4591-b714-dd67c6d8f5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b4e3c69a-dc89-4b2c-9b8c-406832c297b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifelines import CoxPHFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4ae1ff76-ad93-4bd5-b799-59be44b08ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Fancy_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4f75ee6b-c5a2-4d7f-a052-05409e35f8ff",
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>RobustOutlier</th>\n",
       "      <th>Ease_of_Payment</th>\n",
       "      <th>FamilyResponsibility</th>\n",
       "      <th>TotalServices</th>\n",
       "      <th>CommunicationServices</th>\n",
       "      <th>OnlineServices</th>\n",
       "      <th>EntertainmentServices</th>\n",
       "      <th>Loyalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  tenure  PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "0              0       1                 1           29.85         29.85   \n",
       "1              0      34                 0           56.95       1889.50   \n",
       "2              0       2                 1           53.85        108.15   \n",
       "3              0      45                 0           42.30       1840.75   \n",
       "4              0       2                 1           70.70        151.65   \n",
       "\n",
       "   Churn  RobustOutlier  Ease_of_Payment  FamilyResponsibility  TotalServices  \\\n",
       "0      0          False                2                     1              2   \n",
       "1      0          False                1                     0              4   \n",
       "2      1          False                1                     0              4   \n",
       "3      0          False                4                     0              4   \n",
       "4      1          False                2                     0              2   \n",
       "\n",
       "   CommunicationServices  OnlineServices  EntertainmentServices  Loyalty  \n",
       "0                      0               1                      0        0  \n",
       "1                      1               1                      0        1  \n",
       "2                      1               1                      0        0  \n",
       "3                      0               1                      0        1  \n",
       "4                      1               0                      0        0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "29fca88c-30c0-42a7-b40a-ea7ac6352236",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['TotalServices']\n",
    "df.drop(['RobustOutlier','TotalServices'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "daeacb88-a1d3-4e84-989e-4babaa0eb056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a train-test split\n",
    "X_train, X_test, _, _ = train_test_split(\n",
    "    df,y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e9422fda-59f0-4d24-b656-b04f78995001",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</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>robust variance</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>4696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-8049.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-10 12:57:09 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;\">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>SeniorCitizen</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.16</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.33</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>302.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</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>-27.67</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>557.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ease_of_Payment</th>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.74</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>35.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilyResponsibility</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CommunicationServices</th>\n",
       "      <td>-0.67</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>15.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineServices</th>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.68</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>45.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EntertainmentServices</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.22</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>30.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loyalty</th>\n",
       "      <td>-1.87</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-16.76</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>207.00</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>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>16118.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>3875.16 on 10 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\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",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "SeniorCitizen & -0.05 & 0.95 & 0.06 & -0.17 & 0.06 & 0.85 & 1.07 & 0.00 & -0.87 & 0.39 & 1.38 \\\\\n",
       "PaperlessBilling & 0.21 & 1.23 & 0.07 & 0.08 & 0.34 & 1.08 & 1.41 & 0.00 & 3.16 & 0.00 & 9.33 \\\\\n",
       "MonthlyCharges & 0.06 & 1.07 & 0.00 & 0.06 & 0.07 & 1.06 & 1.07 & 0.00 & 20.33 & 0.00 & 302.85 \\\\\n",
       "TotalCharges & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -27.67 & 0.00 & 557.58 \\\\\n",
       "Ease_of_Payment & -0.18 & 0.84 & 0.03 & -0.23 & -0.13 & 0.80 & 0.88 & 0.00 & -6.74 & 0.00 & 35.85 \\\\\n",
       "FamilyResponsibility & -0.22 & 0.81 & 0.05 & -0.32 & -0.11 & 0.73 & 0.90 & 0.00 & -4.02 & 0.00 & 14.06 \\\\\n",
       "CommunicationServices & -0.67 & 0.51 & 0.16 & -0.98 & -0.36 & 0.38 & 0.70 & 0.00 & -4.24 & 0.00 & 15.44 \\\\\n",
       "OnlineServices & -0.44 & 0.64 & 0.06 & -0.56 & -0.33 & 0.57 & 0.72 & 0.00 & -7.68 & 0.00 & 45.79 \\\\\n",
       "EntertainmentServices & -0.45 & 0.64 & 0.07 & -0.60 & -0.31 & 0.55 & 0.73 & 0.00 & -6.22 & 0.00 & 30.90 \\\\\n",
       "Loyalty & -1.87 & 0.15 & 0.11 & -2.09 & -1.65 & 0.12 & 0.19 & 0.00 & -16.76 & 0.00 & 207.00 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 4696 total observations, 3442 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'Churn'\n",
       "          robust variance = True\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 4696\n",
       "number of events observed = 1254\n",
       "   partial log-likelihood = -8049.10\n",
       "         time fit was run = 2024-11-10 12:57:09 UTC\n",
       "\n",
       "---\n",
       "                       coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                              \n",
       "SeniorCitizen         -0.05      0.95      0.06           -0.17            0.06                0.85                1.07\n",
       "PaperlessBilling       0.21      1.23      0.07            0.08            0.34                1.08                1.41\n",
       "MonthlyCharges         0.06      1.07      0.00            0.06            0.07                1.06                1.07\n",
       "TotalCharges          -0.00      1.00      0.00           -0.00           -0.00                1.00                1.00\n",
       "Ease_of_Payment       -0.18      0.84      0.03           -0.23           -0.13                0.80                0.88\n",
       "FamilyResponsibility  -0.22      0.81      0.05           -0.32           -0.11                0.73                0.90\n",
       "CommunicationServices -0.67      0.51      0.16           -0.98           -0.36                0.38                0.70\n",
       "OnlineServices        -0.44      0.64      0.06           -0.56           -0.33                0.57                0.72\n",
       "EntertainmentServices -0.45      0.64      0.07           -0.60           -0.31                0.55                0.73\n",
       "Loyalty               -1.87      0.15      0.11           -2.09           -1.65                0.12                0.19\n",
       "\n",
       "                       cmp to      z      p  -log2(p)\n",
       "covariate                                            \n",
       "SeniorCitizen            0.00  -0.87   0.39      1.38\n",
       "PaperlessBilling         0.00   3.16 <0.005      9.33\n",
       "MonthlyCharges           0.00  20.33 <0.005    302.85\n",
       "TotalCharges             0.00 -27.67 <0.005    557.58\n",
       "Ease_of_Payment          0.00  -6.74 <0.005     35.85\n",
       "FamilyResponsibility     0.00  -4.02 <0.005     14.06\n",
       "CommunicationServices    0.00  -4.24 <0.005     15.44\n",
       "OnlineServices           0.00  -7.68 <0.005     45.79\n",
       "EntertainmentServices    0.00  -6.22 <0.005     30.90\n",
       "Loyalty                  0.00 -16.76 <0.005    207.00\n",
       "---\n",
       "Concordance = 0.92\n",
       "Partial AIC = 16118.20\n",
       "log-likelihood ratio test = 3875.16 on 10 df\n",
       "-log2(p) of ll-ratio test = inf"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph = CoxPHFitter()\n",
    "cph.fit(X_train, duration_col='tenure', event_col='Churn',robust=True)\n",
    "\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eb494119-19cf-4fe0-a3b5-f015d19efa46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c9a3613b-8fea-495c-8697-f54edb9bfe29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2314, 12)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6d4b1368-413c-478a-9488-fcab7765ae8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def risk_period(preds,threshold=0.5):\n",
    "    indices = preds.index\n",
    "    \n",
    "    def fun(x):\n",
    "        for i in range(len(x)):\n",
    "            if x.iloc[i] <= threshold:\n",
    "                return indices[i]\n",
    "        return 72\n",
    "    \n",
    "    return pd.DataFrame({\"Expected Time to Churn\" : preds.apply(fun,axis=0)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e38788d7-db44-45f0-95a3-d04adbf42566",
   "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>Expected Time to Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5719</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6150</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4747</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3772</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4696 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Expected Time to Churn\n",
       "5719                    16.0\n",
       "2821                    68.0\n",
       "229                     72.0\n",
       "6150                    43.0\n",
       "4747                    72.0\n",
       "...                      ...\n",
       "3772                    68.0\n",
       "5191                    67.0\n",
       "5226                     3.0\n",
       "5390                    72.0\n",
       "860                     24.0\n",
       "\n",
       "[4696 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = cph.predict_survival_function(X_train)\n",
    "risk_period(preds,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dea59b46-73f0-481c-ba20-5a81dd779b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot_partial_effects_on_outcome(\n",
    "    covariates=[\"CommunicationServices\"],\n",
    "    values=[0,1]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "249699a8-d4b7-49a9-a11c-72544e39843e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
      "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
      "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
      "assumption will be flagged.\n",
      "\n",
      "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
      "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
      "and looking for non-constant lines. See link [A] below for a full example.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>&lt;lifelines.CoxPHFitter: fitted with 4696 total...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>proportional_hazard_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CommunicationServices</th>\n",
       "      <th>km</th>\n",
       "      <td>5.97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>4.12</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ease_of_Payment</th>\n",
       "      <th>km</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">EntertainmentServices</th>\n",
       "      <th>km</th>\n",
       "      <td>4.43</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>3.07</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">FamilyResponsibility</th>\n",
       "      <th>km</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Loyalty</th>\n",
       "      <th>km</th>\n",
       "      <td>75.96</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>58.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>62.44</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>48.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonthlyCharges</th>\n",
       "      <th>km</th>\n",
       "      <td>117.54</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>88.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>151.69</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>113.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineServices</th>\n",
       "      <th>km</th>\n",
       "      <td>1.68</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaperlessBilling</th>\n",
       "      <th>km</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SeniorCitizen</th>\n",
       "      <th>km</th>\n",
       "      <td>5.72</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.68</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TotalCharges</th>\n",
       "      <th>km</th>\n",
       "      <td>132.79</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>99.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>35.34</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrr}\n",
       " &  & test_statistic & p & -log2(p) \\\\\n",
       "\\multirow[c]{2}{*}{CommunicationServices} & km & 5.97 & 0.01 & 6.11 \\\\\n",
       " & rank & 4.12 & 0.04 & 4.56 \\\\\n",
       "\\multirow[c]{2}{*}{Ease_of_Payment} & km & 0.12 & 0.73 & 0.45 \\\\\n",
       " & rank & 0.47 & 0.49 & 1.02 \\\\\n",
       "\\multirow[c]{2}{*}{EntertainmentServices} & km & 4.43 & 0.04 & 4.82 \\\\\n",
       " & rank & 3.07 & 0.08 & 3.65 \\\\\n",
       "\\multirow[c]{2}{*}{FamilyResponsibility} & km & 0.12 & 0.73 & 0.45 \\\\\n",
       " & rank & 0.22 & 0.64 & 0.65 \\\\\n",
       "\\multirow[c]{2}{*}{Loyalty} & km & 75.96 & 0.00 & 58.26 \\\\\n",
       " & rank & 62.44 & 0.00 & 48.37 \\\\\n",
       "\\multirow[c]{2}{*}{MonthlyCharges} & km & 117.54 & 0.00 & 88.57 \\\\\n",
       " & rank & 151.69 & 0.00 & 113.38 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineServices} & km & 1.68 & 0.20 & 2.36 \\\\\n",
       " & rank & 0.21 & 0.65 & 0.62 \\\\\n",
       "\\multirow[c]{2}{*}{PaperlessBilling} & km & 0.38 & 0.54 & 0.90 \\\\\n",
       " & rank & 0.60 & 0.44 & 1.19 \\\\\n",
       "\\multirow[c]{2}{*}{SeniorCitizen} & km & 5.72 & 0.02 & 5.90 \\\\\n",
       " & rank & 2.68 & 0.10 & 3.29 \\\\\n",
       "\\multirow[c]{2}{*}{TotalCharges} & km & 132.79 & 0.00 & 99.65 \\\\\n",
       " & rank & 35.34 & 0.00 & 28.43 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: proportional_hazard_test>\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "             model = <lifelines.CoxPHFitter: fitted with 4696 total observations, 3442 right-censored observations>\n",
       "         test_name = proportional_hazard_test\n",
       "\n",
       "---\n",
       "                            test_statistic      p  -log2(p)\n",
       "CommunicationServices km              5.97   0.01      6.11\n",
       "                      rank            4.12   0.04      4.56\n",
       "Ease_of_Payment       km              0.12   0.73      0.45\n",
       "                      rank            0.47   0.49      1.02\n",
       "EntertainmentServices km              4.43   0.04      4.82\n",
       "                      rank            3.07   0.08      3.65\n",
       "FamilyResponsibility  km              0.12   0.73      0.45\n",
       "                      rank            0.22   0.64      0.65\n",
       "Loyalty               km             75.96 <0.005     58.26\n",
       "                      rank           62.44 <0.005     48.37\n",
       "MonthlyCharges        km            117.54 <0.005     88.57\n",
       "                      rank          151.69 <0.005    113.38\n",
       "OnlineServices        km              1.68   0.20      2.36\n",
       "                      rank            0.21   0.65      0.62\n",
       "PaperlessBilling      km              0.38   0.54      0.90\n",
       "                      rank            0.60   0.44      1.19\n",
       "SeniorCitizen         km              5.72   0.02      5.90\n",
       "                      rank            2.68   0.10      3.29\n",
       "TotalCharges          km            132.79 <0.005     99.65\n",
       "                      rank           35.34 <0.005     28.43"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "1. Variable 'SeniorCitizen' failed the non-proportional test: p-value is 0.0167.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['SeniorCitizen', ...]` in\n",
      "the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "2. Variable 'MonthlyCharges' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'MonthlyCharges' might be incorrect. That is, there\n",
      "may be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'MonthlyCharges' using pd.cut, and then specify it in\n",
      "`strata=['MonthlyCharges', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "3. Variable 'TotalCharges' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'TotalCharges' might be incorrect. That is, there\n",
      "may be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'TotalCharges' using pd.cut, and then specify it in\n",
      "`strata=['TotalCharges', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "4. Variable 'CommunicationServices' failed the non-proportional test: p-value is 0.0145.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['CommunicationServices',\n",
      "...]` in the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "5. Variable 'EntertainmentServices' failed the non-proportional test: p-value is 0.0354.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['EntertainmentServices',\n",
      "...]` in the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "6. Variable 'Loyalty' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice: with so few unique values (only 3), you can include `strata=['Loyalty', ...]` in the call\n",
      "in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "---\n",
      "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
      "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
      "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
      "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
      "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.check_assumptions(X_train,p_value_threshold=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6635656f-a288-4a69-8d0d-26c4671c239b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</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>robust variance</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[CommunicationServices, EntertainmentServices,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>4696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-6036.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-10 12:57:15 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;\">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>SeniorCitizen</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.27</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.43</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>305.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</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>-28.87</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>606.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ease_of_Payment</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>16.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilyResponsibility</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.90</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>OnlineServices</th>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.86</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>60.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>12087.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>3007.47 on 7 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\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",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "SeniorCitizen & 0.02 & 1.02 & 0.06 & -0.09 & 0.14 & 0.92 & 1.15 & 0.00 & 0.43 & 0.67 & 0.59 \\\\\n",
       "PaperlessBilling & 0.27 & 1.31 & 0.07 & 0.14 & 0.40 & 1.15 & 1.50 & 0.00 & 4.00 & 0.00 & 13.94 \\\\\n",
       "MonthlyCharges & 0.08 & 1.09 & 0.00 & 0.08 & 0.09 & 1.08 & 1.10 & 0.00 & 20.43 & 0.00 & 305.77 \\\\\n",
       "TotalCharges & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -28.87 & 0.00 & 606.52 \\\\\n",
       "Ease_of_Payment & -0.12 & 0.89 & 0.03 & -0.17 & -0.07 & 0.84 & 0.94 & 0.00 & -4.43 & 0.00 & 16.67 \\\\\n",
       "FamilyResponsibility & -0.21 & 0.81 & 0.05 & -0.32 & -0.10 & 0.73 & 0.90 & 0.00 & -3.83 & 0.00 & 12.95 \\\\\n",
       "OnlineServices & -0.51 & 0.60 & 0.06 & -0.62 & -0.40 & 0.54 & 0.67 & 0.00 & -8.86 & 0.00 & 60.06 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 4696 total observations, 3442 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'Churn'\n",
       "          robust variance = True\n",
       "                   strata = ['CommunicationServices', 'EntertainmentServices', 'Loyalty']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 4696\n",
       "number of events observed = 1254\n",
       "   partial log-likelihood = -6036.74\n",
       "         time fit was run = 2024-11-10 12:57:15 UTC\n",
       "\n",
       "---\n",
       "                      coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                             \n",
       "SeniorCitizen         0.02      1.02      0.06           -0.09            0.14                0.92                1.15\n",
       "PaperlessBilling      0.27      1.31      0.07            0.14            0.40                1.15                1.50\n",
       "MonthlyCharges        0.08      1.09      0.00            0.08            0.09                1.08                1.10\n",
       "TotalCharges         -0.00      1.00      0.00           -0.00           -0.00                1.00                1.00\n",
       "Ease_of_Payment      -0.12      0.89      0.03           -0.17           -0.07                0.84                0.94\n",
       "FamilyResponsibility -0.21      0.81      0.05           -0.32           -0.10                0.73                0.90\n",
       "OnlineServices       -0.51      0.60      0.06           -0.62           -0.40                0.54                0.67\n",
       "\n",
       "                      cmp to      z      p  -log2(p)\n",
       "covariate                                           \n",
       "SeniorCitizen           0.00   0.43   0.67      0.59\n",
       "PaperlessBilling        0.00   4.00 <0.005     13.94\n",
       "MonthlyCharges          0.00  20.43 <0.005    305.77\n",
       "TotalCharges            0.00 -28.87 <0.005    606.52\n",
       "Ease_of_Payment         0.00  -4.43 <0.005     16.67\n",
       "FamilyResponsibility    0.00  -3.83 <0.005     12.95\n",
       "OnlineServices          0.00  -8.86 <0.005     60.06\n",
       "---\n",
       "Concordance = 0.90\n",
       "Partial AIC = 12087.47\n",
       "log-likelihood ratio test = 3007.47 on 7 df\n",
       "-log2(p) of ll-ratio test = inf"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph = CoxPHFitter()\n",
    "cph.fit(X_train, duration_col='tenure', event_col='Churn',strata = ['CommunicationServices','EntertainmentServices','Loyalty'],robust=True)\n",
    "\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eef8d651-adf8-4c99-91e1-e68642f2a77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "785b68a7-0665-44ad-b16a-1ef964e4f2a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
      "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
      "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
      "assumption will be flagged.\n",
      "\n",
      "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
      "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
      "and looking for non-constant lines. See link [A] below for a full example.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>&lt;lifelines.CoxPHFitter: fitted with 4696 total...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>proportional_hazard_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ease_of_Payment</th>\n",
       "      <th>km</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">FamilyResponsibility</th>\n",
       "      <th>km</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonthlyCharges</th>\n",
       "      <th>km</th>\n",
       "      <td>152.65</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>114.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>122.99</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>92.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineServices</th>\n",
       "      <th>km</th>\n",
       "      <td>15.05</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>23.29</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaperlessBilling</th>\n",
       "      <th>km</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SeniorCitizen</th>\n",
       "      <th>km</th>\n",
       "      <td>1.94</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>6.32</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TotalCharges</th>\n",
       "      <th>km</th>\n",
       "      <td>94.24</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>71.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>4.47</td>\n",
       "      <td>0.03</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrr}\n",
       " &  & test_statistic & p & -log2(p) \\\\\n",
       "\\multirow[c]{2}{*}{Ease_of_Payment} & km & 0.88 & 0.35 & 1.53 \\\\\n",
       " & rank & 0.70 & 0.40 & 1.31 \\\\\n",
       "\\multirow[c]{2}{*}{FamilyResponsibility} & km & 0.10 & 0.75 & 0.42 \\\\\n",
       " & rank & 0.00 & 0.96 & 0.06 \\\\\n",
       "\\multirow[c]{2}{*}{MonthlyCharges} & km & 152.65 & 0.00 & 114.08 \\\\\n",
       " & rank & 122.99 & 0.00 & 92.53 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineServices} & km & 15.05 & 0.00 & 13.22 \\\\\n",
       " & rank & 23.29 & 0.00 & 19.46 \\\\\n",
       "\\multirow[c]{2}{*}{PaperlessBilling} & km & 0.27 & 0.60 & 0.74 \\\\\n",
       " & rank & 0.37 & 0.55 & 0.88 \\\\\n",
       "\\multirow[c]{2}{*}{SeniorCitizen} & km & 1.94 & 0.16 & 2.61 \\\\\n",
       " & rank & 6.32 & 0.01 & 6.39 \\\\\n",
       "\\multirow[c]{2}{*}{TotalCharges} & km & 94.24 & 0.00 & 71.60 \\\\\n",
       " & rank & 4.47 & 0.03 & 4.86 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: proportional_hazard_test>\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "             model = <lifelines.CoxPHFitter: fitted with 4696 total observations, 3442 right-censored observations>\n",
       "         test_name = proportional_hazard_test\n",
       "\n",
       "---\n",
       "                           test_statistic      p  -log2(p)\n",
       "Ease_of_Payment      km              0.88   0.35      1.53\n",
       "                     rank            0.70   0.40      1.31\n",
       "FamilyResponsibility km              0.10   0.75      0.42\n",
       "                     rank            0.00   0.96      0.06\n",
       "MonthlyCharges       km            152.65 <0.005    114.08\n",
       "                     rank          122.99 <0.005     92.53\n",
       "OnlineServices       km             15.05 <0.005     13.22\n",
       "                     rank           23.29 <0.005     19.46\n",
       "PaperlessBilling     km              0.27   0.60      0.74\n",
       "                     rank            0.37   0.55      0.88\n",
       "SeniorCitizen        km              1.94   0.16      2.61\n",
       "                     rank            6.32   0.01      6.39\n",
       "TotalCharges         km             94.24 <0.005     71.60\n",
       "                     rank            4.47   0.03      4.86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "1. Variable 'SeniorCitizen' failed the non-proportional test: p-value is 0.0119.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['SeniorCitizen', ...]` in\n",
      "the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "2. Variable 'MonthlyCharges' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'MonthlyCharges' might be incorrect. That is, there\n",
      "may be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'MonthlyCharges' using pd.cut, and then specify it in\n",
      "`strata=['MonthlyCharges', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "3. Variable 'TotalCharges' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'TotalCharges' might be incorrect. That is, there\n",
      "may be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'TotalCharges' using pd.cut, and then specify it in\n",
      "`strata=['TotalCharges', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "4. Variable 'OnlineServices' failed the non-proportional test: p-value is <5e-05.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['OnlineServices', ...]` in\n",
      "the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "---\n",
      "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
      "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
      "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
      "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
      "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.check_assumptions(X_train,p_value_threshold=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d8558a71-9288-40a5-a5d5-8969d56efc25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</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>robust variance</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[SeniorCitizen, CommunicationServices, Enterta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>4696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-5967.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-10 12:57:39 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;\">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>PaperlessBilling</th>\n",
       "      <td>0.30</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>19.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ease_of_Payment</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.03</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>49.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilyResponsibility</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.25</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>52.47</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>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>11943.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>175.35 on 4 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>120.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\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",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "PaperlessBilling & 0.30 & 1.35 & 0.07 & 0.16 & 0.43 & 1.17 & 1.54 & 0.00 & 4.25 & 0.00 & 15.48 \\\\\n",
       "MonthlyCharges & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 4.77 & 0.00 & 19.06 \\\\\n",
       "Ease_of_Payment & -0.22 & 0.81 & 0.03 & -0.27 & -0.16 & 0.76 & 0.85 & 0.00 & -8.03 & 0.00 & 49.87 \\\\\n",
       "FamilyResponsibility & -0.50 & 0.61 & 0.06 & -0.62 & -0.38 & 0.54 & 0.68 & 0.00 & -8.25 & 0.00 & 52.47 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 4696 total observations, 3442 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'Churn'\n",
       "          robust variance = True\n",
       "                   strata = ['SeniorCitizen', 'CommunicationServices', 'EntertainmentServices', 'OnlineServices', 'Loyalty']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 4696\n",
       "number of events observed = 1254\n",
       "   partial log-likelihood = -5967.60\n",
       "         time fit was run = 2024-11-10 12:57:39 UTC\n",
       "\n",
       "---\n",
       "                      coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                             \n",
       "PaperlessBilling      0.30      1.35      0.07            0.16            0.43                1.17                1.54\n",
       "MonthlyCharges        0.01      1.01      0.00            0.01            0.01                1.01                1.01\n",
       "Ease_of_Payment      -0.22      0.81      0.03           -0.27           -0.16                0.76                0.85\n",
       "FamilyResponsibility -0.50      0.61      0.06           -0.62           -0.38                0.54                0.68\n",
       "\n",
       "                      cmp to     z      p  -log2(p)\n",
       "covariate                                          \n",
       "PaperlessBilling        0.00  4.25 <0.005     15.48\n",
       "MonthlyCharges          0.00  4.77 <0.005     19.06\n",
       "Ease_of_Payment         0.00 -8.03 <0.005     49.87\n",
       "FamilyResponsibility    0.00 -8.25 <0.005     52.47\n",
       "---\n",
       "Concordance = 0.62\n",
       "Partial AIC = 11943.21\n",
       "log-likelihood ratio test = 175.35 on 4 df\n",
       "-log2(p) of ll-ratio test = 120.01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covs = [x for x in X_train.columns if x not in ['tenure','Churn','TotalCharges']]\n",
    "\n",
    "X_new = X_train.drop(['TotalCharges'],axis=1)\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(X_new, duration_col='tenure',event_col='Churn',strata = ['SeniorCitizen','CommunicationServices','EntertainmentServices','OnlineServices','Loyalty'],robust=True)\n",
    "\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8864a203-55d5-44c1-bc88-355d9ef96852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e064b596-6070-485f-8bdc-548fba4d2591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train['MonthlyCharges'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6ad8ec46-2a3a-4939-a02f-d50597e6691a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train.boxplot(column='MonthlyCharges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b77bd9e1-70f2-4ca6-8f94-87459aeef3d9",
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Ease_of_Payment</th>\n",
       "      <th>FamilyResponsibility</th>\n",
       "      <th>CommunicationServices</th>\n",
       "      <th>OnlineServices</th>\n",
       "      <th>EntertainmentServices</th>\n",
       "      <th>Loyalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6850</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>99.65</td>\n",
       "      <td>7181.25</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6723</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>49.85</td>\n",
       "      <td>552.10</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>75.35</td>\n",
       "      <td>273.40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>31.10</td>\n",
       "      <td>419.70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6798</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>20.20</td>\n",
       "      <td>684.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>74.55</td>\n",
       "      <td>3015.75</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>25.20</td>\n",
       "      <td>387.90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5934</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>78.55</td>\n",
       "      <td>1422.65</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
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       "      SeniorCitizen  tenure  PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "6850              1      71                 1           99.65       7181.25   \n",
       "6723              0      12                 0           49.85        552.10   \n",
       "245               1       4                 0           75.35        273.40   \n",
       "1880              0      14                 1           31.10        419.70   \n",
       "6798              0      35                 1           20.20        684.40   \n",
       "...             ...     ...               ...             ...           ...   \n",
       "298               0      40                 1           74.55       3015.75   \n",
       "3295              0      15                 0           25.20        387.90   \n",
       "5934              1      18                 0           78.55       1422.65   \n",
       "2614              0      17                 1           66.70       1077.05   \n",
       "6167              0       1                 0           80.50         80.50   \n",
       "\n",
       "      Churn  Ease_of_Payment  FamilyResponsibility  CommunicationServices  \\\n",
       "6850      0                4                     0                      1   \n",
       "6723      0                1                     0                      0   \n",
       "245       0                2                     0                      1   \n",
       "1880      0                1                     0                      0   \n",
       "6798      0                1                     0                      1   \n",
       "...     ...              ...                   ...                    ...   \n",
       "298       0                2                     1                      1   \n",
       "3295      0                1                     1                      1   \n",
       "5934      1                2                     1                      1   \n",
       "2614      0                1                     1                      1   \n",
       "6167      1                2                     0                      1   \n",
       "\n",
       "      OnlineServices  EntertainmentServices  Loyalty  \n",
       "6850               1                      1        1  \n",
       "6723               1                      1        2  \n",
       "245                1                      0        0  \n",
       "1880               1                      0        0  \n",
       "6798               0                      0        2  \n",
       "...              ...                    ...      ...  \n",
       "298                1                      1        0  \n",
       "3295               0                      0        2  \n",
       "5934               0                      1        0  \n",
       "2614               1                      0        0  \n",
       "6167               0                      1        0  \n",
       "\n",
       "[2314 rows x 12 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
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    }
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   "id": "6d226a83-1efe-4baf-9ecd-135876703576",
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       "      <td>0.983160</td>\n",
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       "          1934      3607      5469      185       1199      6951      5927  \\\n",
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       "5.0   0.797071  0.739443  0.833559  0.783524  0.888356  0.888921  0.783711   \n",
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       "72.0  0.477988  0.374407  0.552947  0.452051  0.680258  0.681667  0.452403   \n",
       "\n",
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       "69.0  0.270620  0.379209  0.272526  ...  0.969729  0.958783  0.963055   \n",
       "70.0  0.270620  0.379209  0.272526  ...  0.969729  0.958783  0.963055   \n",
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       "\n",
       "[72 rows x 2314 columns]"
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     },
     "execution_count": 70,
     "metadata": {},
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    }
   ],
   "source": [
    "X_test_new = X_test.drop(['TotalCharges'],axis=1)\n",
    "final_prediction = cph.predict_survival_function(X_test_new)\n",
    "final_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5e1f9ab8-60e0-4faf-8aa3-8ff70bf76121",
   "metadata": {},
   "outputs": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Expected Time to Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5469</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4866</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5692</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3801</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2314 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Expected Time to Churn\n",
       "1934                    29.0\n",
       "3607                    29.0\n",
       "5469                    72.0\n",
       "185                     29.0\n",
       "1199                    72.0\n",
       "...                      ...\n",
       "6078                    72.0\n",
       "4866                    72.0\n",
       "627                     72.0\n",
       "5692                    72.0\n",
       "3801                    72.0\n",
       "\n",
       "[2314 rows x 1 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_period(final_prediction)"
   ]
  }
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