Employee-Attrition-using-Survival-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98a86ad8",
   "metadata": {},
   "source": [
    "This project aims to study and investigate Employee Attrition using the statistical Survival Analysis method.\n",
    "The dataset used in this project is IBM HR Analytics Employee Attrition & Performance available for download on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f30ec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "import lifelines\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4a6ac865",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3df22d34",
   "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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82c5c979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
      "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
      "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
      "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
      "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
      "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
      "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
      "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
      "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
      "\n",
      "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
      "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
      "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
      "std        602.024335                 1.093082    20.329428        0.711561   \n",
      "min          1.000000                 1.000000    30.000000        1.000000   \n",
      "25%        491.250000                 2.000000    48.000000        2.000000   \n",
      "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
      "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
      "max       2068.000000                 4.000000   100.000000        4.000000   \n",
      "\n",
      "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
      "count  1470.000000  ...               1470.000000         1470.0   \n",
      "mean      2.063946  ...                  2.712245           80.0   \n",
      "std       1.106940  ...                  1.081209            0.0   \n",
      "min       1.000000  ...                  1.000000           80.0   \n",
      "25%       1.000000  ...                  2.000000           80.0   \n",
      "50%       2.000000  ...                  3.000000           80.0   \n",
      "75%       3.000000  ...                  4.000000           80.0   \n",
      "max       5.000000  ...                  4.000000           80.0   \n",
      "\n",
      "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
      "count       1470.000000        1470.000000            1470.000000   \n",
      "mean           0.793878          11.279592               2.799320   \n",
      "std            0.852077           7.780782               1.289271   \n",
      "min            0.000000           0.000000               0.000000   \n",
      "25%            0.000000           6.000000               2.000000   \n",
      "50%            1.000000          10.000000               3.000000   \n",
      "75%            1.000000          15.000000               3.000000   \n",
      "max            3.000000          40.000000               6.000000   \n",
      "\n",
      "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
      "count      1470.000000     1470.000000         1470.000000   \n",
      "mean          2.761224        7.008163            4.229252   \n",
      "std           0.706476        6.126525            3.623137   \n",
      "min           1.000000        0.000000            0.000000   \n",
      "25%           2.000000        3.000000            2.000000   \n",
      "50%           3.000000        5.000000            3.000000   \n",
      "75%           3.000000        9.000000            7.000000   \n",
      "max           4.000000       40.000000           18.000000   \n",
      "\n",
      "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
      "count              1470.000000           1470.000000  \n",
      "mean                  2.187755              4.123129  \n",
      "std                   3.222430              3.568136  \n",
      "min                   0.000000              0.000000  \n",
      "25%                   0.000000              2.000000  \n",
      "50%                   1.000000              3.000000  \n",
      "75%                   3.000000              7.000000  \n",
      "max                  15.000000             17.000000  \n",
      "\n",
      "[8 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "#exploring data\n",
    "\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8f22db30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     1233\n",
      "Yes     237\n",
      "Name: Attrition, dtype: int64\n",
      "===================================\n",
      "Travel_Rarely        1043\n",
      "Travel_Frequently     277\n",
      "Non-Travel            150\n",
      "Name: BusinessTravel, dtype: int64\n",
      "===================================\n",
      "Research & Development    961\n",
      "Sales                     446\n",
      "Human Resources            63\n",
      "Name: Department, dtype: int64\n",
      "===================================\n",
      "Life Sciences       606\n",
      "Medical             464\n",
      "Marketing           159\n",
      "Technical Degree    132\n",
      "Other                82\n",
      "Human Resources      27\n",
      "Name: EducationField, dtype: int64\n",
      "===================================\n",
      "Male      882\n",
      "Female    588\n",
      "Name: Gender, dtype: int64\n",
      "===================================\n",
      "Sales Executive              326\n",
      "Research Scientist           292\n",
      "Laboratory Technician        259\n",
      "Manufacturing Director       145\n",
      "Healthcare Representative    131\n",
      "Manager                      102\n",
      "Sales Representative          83\n",
      "Research Director             80\n",
      "Human Resources               52\n",
      "Name: JobRole, dtype: int64\n",
      "===================================\n",
      "Married     673\n",
      "Single      470\n",
      "Divorced    327\n",
      "Name: MaritalStatus, dtype: int64\n",
      "===================================\n",
      "Y    1470\n",
      "Name: Over18, dtype: int64\n",
      "===================================\n",
      "No     1054\n",
      "Yes     416\n",
      "Name: OverTime, dtype: int64\n",
      "===================================\n"
     ]
    }
   ],
   "source": [
    "#exploring categorical variables\n",
    "\n",
    "res_categorical=[]\n",
    "for c in df.columns:\n",
    "    if df[c].dtype==object:\n",
    "        res_categorical.append(c)\n",
    "        print(df[c].value_counts())\n",
    "        print(f\"===================================\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43713ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age : Minimum: 18, Maximum: 60 \n",
      "===================================\n",
      "DailyRate : Minimum: 102, Maximum: 1499 \n",
      "===================================\n",
      "DistanceFromHome : Minimum: 1, Maximum: 29 \n",
      "===================================\n",
      "Education : Minimum: 1, Maximum: 5 \n",
      "===================================\n",
      "EmployeeCount : Minimum: 1, Maximum: 1 \n",
      "===================================\n",
      "EmployeeNumber : Minimum: 1, Maximum: 2068 \n",
      "===================================\n",
      "EnvironmentSatisfaction : Minimum: 1, Maximum: 4 \n",
      "===================================\n",
      "HourlyRate : Minimum: 30, Maximum: 100 \n",
      "===================================\n",
      "JobInvolvement : Minimum: 1, Maximum: 4 \n",
      "===================================\n",
      "JobLevel : Minimum: 1, Maximum: 5 \n",
      "===================================\n",
      "JobSatisfaction : Minimum: 1, Maximum: 4 \n",
      "===================================\n",
      "MonthlyIncome : Minimum: 1009, Maximum: 19999 \n",
      "===================================\n",
      "MonthlyRate : Minimum: 2094, Maximum: 26999 \n",
      "===================================\n",
      "NumCompaniesWorked : Minimum: 0, Maximum: 9 \n",
      "===================================\n",
      "PercentSalaryHike : Minimum: 11, Maximum: 25 \n",
      "===================================\n",
      "PerformanceRating : Minimum: 3, Maximum: 4 \n",
      "===================================\n",
      "RelationshipSatisfaction : Minimum: 1, Maximum: 4 \n",
      "===================================\n",
      "StandardHours : Minimum: 80, Maximum: 80 \n",
      "===================================\n",
      "StockOptionLevel : Minimum: 0, Maximum: 3 \n",
      "===================================\n",
      "TotalWorkingYears : Minimum: 0, Maximum: 40 \n",
      "===================================\n",
      "TrainingTimesLastYear : Minimum: 0, Maximum: 6 \n",
      "===================================\n",
      "WorkLifeBalance : Minimum: 1, Maximum: 4 \n",
      "===================================\n",
      "YearsAtCompany : Minimum: 0, Maximum: 40 \n",
      "===================================\n",
      "YearsInCurrentRole : Minimum: 0, Maximum: 18 \n",
      "===================================\n",
      "YearsSinceLastPromotion : Minimum: 0, Maximum: 15 \n",
      "===================================\n",
      "YearsWithCurrManager : Minimum: 0, Maximum: 17 \n",
      "===================================\n"
     ]
    }
   ],
   "source": [
    "#exploring numerical variables\n",
    "\n",
    "res_numerical=[]\n",
    "for c in df.columns:\n",
    "    if df[c].dtype!=object:\n",
    "        print(f\"{c} : Minimum: {min((df[c].unique()))}, Maximum: {max((df[c].unique()))} \")\n",
    "        res_numerical.append(c)\n",
    "        print(f\"===================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fa8b4491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    0\n",
      "2    1\n",
      "3    0\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "7    0\n",
      "8    0\n",
      "9    0\n",
      "Name: Attrition, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "#Converting Attrition variable into binary variable\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "df['Attrition']=encoder.fit_transform(df['Attrition'])\n",
    "\n",
    "print (df['Attrition'][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "139449b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this project I use the non-parametric Kaplan-Meier Estimator to estimate the survival function.\n",
    "\n",
    "from lifelines import KaplanMeierFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "15875166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J8ubQiHhZRHS1ADxMdj2YnJedEVRRJuswm+xc3Z5SWpsHc7ex8fmr5GGyZt9d8zR216JQrf8ka0K9Kr9O7hoRR0RE14/s/wAOj4hTI2K3iHg/WT/G81Pjb0/zNWDviDgnP9Z7yFpqSn2TLPj6eWTdJ3aO7J6i5+Q/7Kr1LbLv7+siYt/ImoDfkdfCAXyF7Mfj9/Nr8YSIOCAivh4RuxT8nOUaXuZSdvuiX5Jdc6aw8bX5abIuKR/N83lvsu+xigF6ROwTEadHxNSIeGV+3XstL11z1QAGlK3tBl66pcI8slu0dJINnlgGkFK6mawm5ItkfW7eSvZlVpV8P78ka8YrD6gq+QHZr8+X5e9LfYrsy+5/yGqdFlHHjdFTSjeQXUiWkN0a5y95OvchGwjQ5bPA/WT9Y24g6y81p4ZDVfwsKaWufmG7k/Xnu4/sS+NZNm1W7tomkQVA15L9wu5K99vJOt5DdkE8jqzD+4Nkg4E+llK6JV/+c7IRmb8ku3B+vobP06P8fH+A7LYbc8lu0PxZsuCka531ZE1J7ydrTvpTPr+a/Pg62SCUr5EFhHtTfXm8hazVZHZ+vEQWpGyY14PPkPUre5gszxomrxn5ANkAmxsiu0dqb9u8mFJalqq412udaVpC9j9/KtlAsuvyRd8lK/93keXDUVWWyVptdK5ys7uZ153zyX603JunsZoa7G6llP5MNgBkO7Kycg9ZeV6XL59F1uryIbLrxNfIgrGz6j1mD2n5A9nI4SPJbpd2Ktn1sHSdJWT/E8vIzsc8sr7cO5Gdx2qP9TjZwKxnyb4f5pLdMizy5Q+SXStHkV0bHyDry7sZWYVEI/VVmbuc7Hvp/ry7CbDh+nQEWR/Y+8gqES6g5/xbQZbv15F9h15Klu9nV5kWVaHrVh+SJElSXayhlCRJUiEGlJIkSSrEgFKSJEmFGFBKkiSpkH67D+WKFSsc/SNJktTmxowZs8nTFqyhlCRJUiEGlJIkSSpkQAeUnZ2dzU5CyzJvemb+VGbeVGbeVGbeVGbeVGbe9KyV8mdAB5SSJEnqewaUkiRJKqTfRnlLkiRVklJi5cqVrF+/fsO8kSNHsmLFiiamqrX1Vf50dHQwatQoIjYZzF2RAaUkSWq6lStXMmLECIYPH75h3ogRIxg5cmQTU9Xa+ip/1qxZw8qVKxk9enTV2/Ta5B0Rl0bE0oi4v8LyiIhvRMT8iLgvIl5fQ5olSZJYv379RsGkmmf48OEb1RRXo5o+lJcB03tYPgOYmL8+Bvx3TSmQJElSW+s1oEwp3Q481cMqhwFXpMxvga0i4uWNSqAkSZJaW6TU+xMRI2JnYGZK6TXdLJsJnJtSuiOfvgX4Qkrp7tL1Sh+92Er3TZIkSc03cuRItttuu6amYZdddmHBggUA3HzzzZx++un8+Mc/5oc//CHnn38+v/nNb5gwYQIAF198MWeccQa/+tWv2GOPPQof+4YbbmCXXXbhVa96FQDnnXcee++9N/vvv3+h/a5YsYJrr72W4447rqbt/v73v7N69eoN0xMnTtzwvrtHLzZiUE53Q4B6jFJLE9WXtvreorq222nUEC7YZysOGDdwOwJ3dnb223loR+ZPZeZNZeZNZeZNZeZNZsWKFRsNMKn3O7yS5ceN63WdiGDkyJHcdtttnHbaafzsZz9jwoQJDB06lEmTJjFz5kw+97nPATBr1ixe/epXN2xgzK9//WumT5/O6173OgDOOOOMXrdZvXp1r8desmQJV1xxBR//+MdrSs+WW27JjjvuWPX6jbgP5UKg9IjjgcUN2G/TPLpyHZ+8a3mzkyFJkvrZXXfdxSc+8QmuueaaDbWRAG9/+9uZNWsWAI888ghbbrkl2267bY/7mj17NgcffDD7778/H/rQh1i5ciUAZ555JlOnTmWfffbhtNNO43e/+x033HADp59+Ovvttx8PP/wwH//4x7nuuusA2H333fnyl7/MwQcfzLRp07jnnnt473vfy9SpU7n00kuBbJT8u971Lvbff3/22WcffvnLXwJw1lln8fDDD7Pffvtx+umnA/CNb3yDAw44gH322YevfOUrDcm3RtRQXg+cFBFXA1OBFSmlxxuw36Z6dOW6ZidBkiT1oxdeeIGjjz6amTNnsttuu220bMstt2TcuHE88MADzJo1i/e85z1cddVVFff15JNP8tWvfpWf//znbLHFFlxwwQVceOGFfOxjH2PmzJnMmTOHiGD58uVstdVWzJgxg+nTp3PYYYd1u79x48Zx00038cUvfpF//dd/5cYbb2TFihVMmzaND3/4w4wcOZLvf//7bLnlljz55JMcdNBBHHrooZxxxhk8+OCD3HHHHUAW5D700EPMnj2blBJHHXUUd955J/vuu2+hvOs1oIyIHwLTgLERsRA4AxgGkFK6CJgFHArMB1YBtTXSS5IktYBhw4YxdepUrrzySs4777xNlh9++OFce+213HLLLVx33XU9BpRz5sxh3rx5vO1tbwPgxRdfZMqUKYwePZoRI0Zw8sknc8ghhzB9ek830nnJjBkzAJg0aRLPPfcco0ePZtiwYYwcOZLly5ezxRZbcPbZZ3PnnXfS0dHB448/ztKlSzfZz+zZs5k9ezZvfvObAXjuued46KGH+j6gTCkd1cvyBJxYKBV95OuTVrPF2OoHnB9/+9N9mBpJklSt5ceNq6qPYCN1dHTwve99j3e/+92cf/75fOYzn9lo+fTp0zn99NPZc8892XLLLXvcV0qJAw44gO9+97ubLJs9eza33XYbP/3pT/nOd77DL37xi17TNmLEiA1p7HoPWb/PdevWcc0117Bs2TJuu+02hg0bxu67777RoJrSdH3605+ueZBObwb0k3L22WY9E3fdvOr1DSglSRrcNt98c370ox8xY8YMtttuOz74wQ9uWLbZZptx1llnseuuu/a6nylTpvC5z32OBQsWsMsuu7Bq1SoWL17MDjvswPPPP88hhxzClClT2HPPPQEYNWoUzz77bN3pfuaZZxg7dizDhg3j9ttv57HHHgNg9OjRG+33wAMP5JxzzuGII45g1KhRLF68mGHDhhUeYT+gA0pJkqRabb311vzkJz/h0EMP3WTgzeGHH17VPsaOHcuFF17IRz7yEV544QUATjvtNEaNGsXRRx+9ofawa1DM4YcfzimnnMLFF1/MFVdcUXOa3//+93PkkUcybdo0dt999w19QLfZZhve9KY3sffee3PQQQdx9tlnM2/ePA455BAAtthiC7797W8XDiirug9lI5Teh7K/1HorhvJbFFRzi4F25W0qemb+VGbeVGbeVGbeVGbeZFasWMGYMWM2mtffTd7tpi/zp7vz0aW7+1A24rZBkiRJGsRs8pYkSSrgwAMP3NCs3eXiiy9m8uTJTUpR/zOg7EE9d+l/2WYdXPzmrQf0U3YkSdJLbrnllmYnoels8m6wpc+v9yk7kiTVqKOjgzVr1jQ7GQLWrFlDR0dtIaI1lCV2GjWkIU/I8Sk7kiTVZtSoUaxcuZLnn39+w7xnnnmm1/s9DmZ9lT8dHR2MGjWqpm0MKEtcsM9WfPKu5QaEkiT1s4hg9OjRG81bunQpO+64Y5NS1PpaKX8MKEscMG4k9x6xAz95aBXbbz6kpm3f+atlG03/5KFVNW0/ZngHB+9ov0tJktR+DCi7MWZ4B0tWFaulrPWpO2NHdvCd/R3MI0mS2o8BZTfqqSks+tjGZauzwTz3HrFDof1IkiT1N0d5N8hOo2prIu+OfTclSVI7soayQRo1oMe+l5Ikqd0YUDZIvQN6ygfz2PdSkiS1G5u821xX30tJkqRmsYaywWodIT52ZAfLVq8vdEz7XkqSpGYyoGywWvszbjuyoyl9L597qoOJhY4oSZKUMaBsskb1vaz1Ruz3ro2a1pckSarEgHKAKA8we7P1sOFM3nm1g3kkSVJhBpQtohFP56nF0y+GN1KXJEkNYUDZImrte3n2H58p3O/y0ZXrau57Cd77UpIkbcyAsk016kbqtfa9BPq1JlWSJLU+A8o21ajBPJIkSUUZUA5y9QSY247oYO7TLzJ562E1bWdTuSRJA5MBZZvr78E8AE++sJ4fzF/FJW/ZpqbtbCqXJGlgMqBsc80YzAOw5PliT/eRJEkDhwHlINOowTxQe3O5TeWSJA1MBpSDTNdgnm/euYDX7TKupm2LDuixqVySpIGpo9kJUPvYfrPixcWmckmSBh5rKFW1EyeP4sK5KwsHhbXWdI4d2cG2Izt8TKQkSS3KgHKQGj001dyU/IrNh3DOlDGsXpfYaXT1RadoU/my1ev56O1Pc+5eY2razr6XkiT1DwPKQWqfbdYzcdfN69q21sc1br9ZR+FazWWr19f8VB/7XkqS1D/sQ6k+d+LkUQ3pfylJklqTNZSqWa03U29UU3l/3aYI4LmnOphY81aSJA1OBpSqWb39EmttKi+q3tsUAdy7NvogRZIkDUy2Q6pleZsiSZLagwGlWpZ9LyVJag82eavftEvfS4Cthw1n8s6rvfelJElVMKBUv2mXvpcAT78YfPKu5dx7xA79fmxJktqN7YkacBrVTP7oSu9jKUlSNar65o2I6RExLyLmR8Sp3SwfExG/iIh7I2JuRBzX+KRK1bHvpSRJ/avXJu+IGAJcCBwMLATmRMT1KaUHSlY7EXggpfTOiNgOmBcRV6WU1vRJqjWo9FffSyj+mEhJkgajar5t9wLmp5QWAETE1cBhQGlAmYDRERHAKOApYG2D06pBqpl9L2vdh88PlyQNRpFS6nmFiPcB01NKx+fTxwBTU0onlawzGrgeeDUwGvinlNIvS/ezYsWKDQfq7Oxs2AeQKrlx6RDGDu+5fJc74f6Ng8GLXrO6pu0XrQ5G9GNr++ihiX228V6bkqS+NXHiS8+PGzNmzCZP/6imhrK7R4aUf0u/DbgHeCuwK3BTRPxvSumZ3hLVlzo7O/vtWO1mMOTNvR2r2H7zIbVtdP/GTd7jxo+vafNxtR2tsCWr1jFx18379ZiDoezUy7ypzLypzLypzLzpWSvlTzUB5UJgx5Lp8cDisnWOA85NWXXn/Ih4mKy28vcNSaVUh1r7Xraj1etSXU37Ns1LkhqpmoByDjAxIiYAi4AjgaPL1vkbcCDwvxGxPfAqYEEjEyrVqp6A6fjbn+6DlPSdWgcddXn02bV19zF97qkOWuP3sCSpVfT6bZRSWhsRJwE3AkOAS1NKcyPihHz5RcDZwGUR8WeyJvIvpJQcLqu2V+uo7+036+DEyaPYc+zwPkpRY9QbiAL8fk1YKypJ2khV3yoppVnArLJ5F5W8Xwwc0tikSe1nyfPr+dLd3XYd7hPNCGDHjUy1902l/lpRA1FJan0+elEqsdOoIW31hJwlz6/nwrkrueQt2zQ7Kb3q7+Z5A1FJ6j8GlFKJC/bZik/etbztgsqB2jQP9QeiA31AliS1EgNKqcQB40Zy7xE78M07F/C6Xaq/CdCflq3hwrkrWfJ8e9wTsp1qNutV7wh4sHZTkmplQCk1wJ5jh/drcHb8bU8VDl7rqdkEGDtsOJ8YuablazeLDDyqt5ndEfCSBisDSqkNnTh5VNNqRJe92DHgazfrDUYdAS9psDKglNpQvTWijajZhIHfb7Ne9Y6At7+npHZnQCkNIs2s2az3lkqDIRD1iUeS2p0BpTSIFOnr2ajazVoNhkDUkeyS2p0BpdSN0UNTXV/Wq9elQoNBWlkzazfr4Uj2nlm7KamRBuY3n1TQPtusZ+Kum9e83U2Pre7XWqP+DGC7ajcXLVzIuPHjq96umbdUqncke7vUbhY599ZuSmokA0qpgfq7xqfe2qn+VG8ze7MD0YFeuylJjWRAKaklteNI9na5Ryc4EEhSYxlQSm1szPAO+3qW8R6d1XEgkKRGGpjfKNIgUW9NUTs0lder2SPZvUenpMHIgFIahOqt2QRYtDqo/inn7aVZtZvt1Gez3qbyJ5YO4d6OgftDpogieWMXBLUKA0ppECryBfTNJxqYkBbTzH6b7VKzWW9T+drh9T1FaDAokjd2QVCrMKCUVBPv0bkpazYlDXYD8+ouqc/09z062yEQrfcenY2q2dTg5Wh9tYrWvkpLGjAcQLSpdnv6kFqPo/XVKgwoJbW0gXxrpHr7bJb3tRzITwOS1B5a+2oradCzZrNv2P9SUiMZUEoakIrcGqnVaze336yjYU8DkqRGaN0rpiQVUGTAQavXbtr3UlKrMaCUpDZT5GlARftf2vdyYHB0uBrNgFKSytTbXD6QnyLUxb6XA4Ojw9VoBpSSVKbeGph2eIpQI/pf2tQuqZwBpSQ1SDs8Rcj+l5L6ggGlJDVIvU8R6s9BQI2696UklTKglCTVrPYAcyTcb1DavfrzxkFSahUdzU6AJEmqT9cgKanZrKGUpCZrh8dLNupm6mq8/jwv9d5uqF5PLB3CvR2tfV/YZnruqQ4mNjsROQNKSWqydni8pIN5BPXfbqhea4cntt98SL8es53cuzaanYQNDCglSb0qcjN1gEULFzJu/PgGpmjgqCdvHCSlVmMfSkmSJBViQClJkqRCbPKWpDZV72Ae6N8BPZIGPq8mktSm6h3MA/07oEfSwGeTtyRJkgqxhlKSpDZX66hvn7CjRrOGUpKkQcYn7KjRqgooI2J6RMyLiPkRcWqFdaZFxD0RMTcibmtsMiVJUpftNyteH+RN6tVIvZbIiBgCXAjMACYBR0XEpLJ1tgK+BbwrpTQZOKLxSZUkSZA9uagRQaXUKNX0odwLmJ9SWgAQEVcDhwEPlKxzNHBtSulvACmlpY1OqCSpcdrh+eGqrN4nF/mEHfWVaq4K44DHSqYXAlPL1tkNGBYRtwKjga+nlK5oSAolSQ3XDs8Pl9Q+qgkou3vyeOpmP28ADgQ2A34TEb9NKf21ux12dnbWlMgi+vNY7ca86Zn5U5l5U9lAz5snlg5h7fDyr4DqLFq4sMGpGTj6L282/iHRDuekHdLYPNFv15yJEyf2uLyagHIhsGPJ9HhgcTfrLEspPQc8FxG3A68Dug0oe0tUo3R2dvbbsdqNedMz86cy86aywZA393asYvvNh9S83aKFCxk3fnwfpKj99Wve3L9xk3ernxPLTc+WLVjUMtecanr0zgEmRsSEiBgOHAlcX7bOdcCbI2JoRGxO1iT+YGOTKkmSpFbUaw1lSmltRJwE3AgMAS5NKc2NiBPy5RellB6MiF8B9wHrgUtSSvf3ZcIlSZLUGqoaqpdSmgXMKpt3Udn0V4GvNi5pkiRJagfe+0GSVLV6bze0bE0wtI7tBoMieeNtnNQqLIWSpKrVe7uhzvXrmLjr5g1OzcBQJG+K3sbJZ4CrUbzNviRJqorPAFclBpSSJA0SPgNcfcWAUpKkQcJngKuv2IdSkqRBwmeAq6/4M0WSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIQaUkiRJKsSAUpIkSYX4pBxJklST/ntyzki436f0VLL1sOFM3nk1B4wb2eykWEMpSZLUjp5+MfjkXcubnQzAgFKSJPVi+80MF1rVoyvXNTsJgAGlJEnqxYmTRxlUqkf2oZQkqU2NGd7BklW111CtXpfYaXT1IcCeY4dzyVu2qfk4RS1auJBx48f3+3FbXf/1Ya2eAaUkSW3q4B3rG4zxk4dWNTglGuysv5YkSVIhBpSSJEkqxIBSkiRJhRhQSpIkqRADSkmSJBViQClJkqRCDCglSZJUiAGlJEmSCjGglCRJUiEGlJIkSSrEgFKSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIUObnQBJktS/xgzvYMmqdTVvt3pdYqfRhg7alKVCkqRB5uAdR9a13U8eWtXglGigsMlbkiRJhVQVUEbE9IiYFxHzI+LUHtabEhHrIuJ9jUuiJEmSWlmvAWVEDAEuBGYAk4CjImJShfXOA25sdCIlSZLUuqqpodwLmJ9SWpBSWgNcDRzWzXonAz8FljYwfZIkSWpx1QzKGQc8VjK9EJhaukJEjAPeA7wVmNLbDjs7O2tIYjH9eax2Y970zPypzLypzLypzLyprF3y5omlQ1g7PPXrMRctXNivx2sPGw+q6o/yM3HixB6XVxNQRjfzykvTBcAXUkrrIrpbvbZENUpnZ2e/HavdmDc9M38qM28qM28qM28qa6e8ubdjFdtvPqTfjrdo4ULGjR/fb8drG/cv22iyFcpPNQHlQmDHkunxwOKydd4IXJ0Hk2OBQyNibUrp541IpCRJklpXNQHlHGBiREwAFgFHAkeXrpBSmtD1PiIuA2YaTEqSJA0OvQaUKaW1EXES2ejtIcClKaW5EXFCvvyiPk6jJEmSWlhVT8pJKc0CZpXN6zaQTCkdWzxZkiRJahc+KUeSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIQaUkiRJKqSqG5tLkiSNGd7BklXr+u14y9YEQ/vxeKqfAaUkSarKwTuO7Nfjda5fx8RdN+/XY7aD429/utlJ2IRN3pIkSSrEgFKSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIQaUkiRJKsSAUpIkSYUYUEqSJKkQA0pJkiQVYkApSZKkQgwoJUmSVIgBpSRJkgoxoJQkSVIhBpSSJEkqxIBSkiRJhRhQSpIkqRADSkmSJBViQClJkqRCDCglSZJUiAGlJEmSCjGglCRJUiEGlJIkSSrEgFKSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKqSqgjIjpETEvIuZHxKndLP/niLgvf90VEa9rfFIlSZLUinoNKCNiCHAhMAOYBBwVEZPKVnsYeEtK6bXA2cC3G51QSZIktaZqaij3AuanlBaklNYAVwOHla6QUrorpfR0PvlbYHxjkylJkqRWFSmlnleIeB8wPaV0fD59DDA1pXRShfU/C7y6a/0uK1as2HCgzs7OoumWJEkalKbcsflG03P2W9Xnx5w4ceKG92PGjIny5UOr2McmGwHdRqERcQDwEWC/ahPVlzo7O/vtWO3GvOmZ+VOZeVOZeVOZeVOZeVOZeVPBHYs2mmyFPKomoFwI7FgyPR5YXL5SRLwWuASYkVJ6sjHJkyRJUqurpg/lHGBiREyIiOHAkcD1pStExCuBa4FjUkp/bXwyJUmS1Kp6raFMKa2NiJOAG4EhwKUppbkRcUK+/CLgS8C2wLciAmBtSumNfZdsSZIktYpqmrxJKc0CZpXNu6jk/fHA8eXbSZIkaeDzSTmSJEkqxIBSkiRJhRhQSpIkqRADSkmSJBViQClJkqRCDCglSZJUiAGlJEmSCjGglCRJUiEGlJIkSSrEgFKSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIQaUkiRJKsSAUpIkSYUYUEqSJKkQA0pJkiQVYkApSZKkQgwoJUmSVIgBpSRJkgoxoJQkSVIhBpSSJEkqxIBSkiRJhRhQSpIkqRADSkmSJBViQClJkqRCDCglSZJUiAGlJEmSCjGglCRJUiEGlJIkSSrEgFKSJEmFGFBKkiSpEANKSZIkFWJAKUmSpEIMKCVJklSIAaUkSZIKMaCUJElSIQaUkiRJKqSqgDIipkfEvIiYHxGndrM8IuIb+fL7IuL1jU+qJEmSWlGvAWVEDAEuBGYAk4CjImJS2WozgIn562PAfzc4nZIkSWpR1dRQ7gXMTyktSCmtAa4GDitb5zDgipT5LbBVRLy8wWmVJElSC4qUUs8rRLwPmJ5SOj6fPgaYmlI6qWSdmcC5KaU78ulbgC+klO7uWmfFihUbDtTZ2dnQDyFJkjTY/GF5B2/Yan2/HGvixIkb3o8ZMybKlw+tYh+bbASUR6HVrNNtovpSZ2dnvx2r3Zg3PTN/KjNvKjNvKjNvKjNvKjNvetFC+VNNk/dCYMeS6fHA4jrWkSRJ0gBUTUA5B5gYERMiYjhwJHB92TrXAx/MR3u/CViRUnq8wWmVJElSC+q1yTultDYiTgJuBIYAl6aU5kbECfnyi4BZwKHAfGAVcFzfJVmSJEmtpJo+lKSUZpEFjaXzLip5n4ATG5s0SZIktQOflCNJkqRCDCglSZJUiAGlJEmSCjGglCRJUiEGlJIkSSqk10cvNkrpoxclSZLUnrp79KI1lJIkSSrEgFKSJEmF9FuTtyRJkgYmayglSZJUiAGlJEmSChmwAWVETI+IeRExPyJObXZ6WklEPBIRf46IeyLi7manp5ki4tKIWBoR95fM2yYiboqIzvzv1s1MY7NUyJszI2JRXnbuiYhDm5nGZomIHSPifyLiwYiYGxGn5PMHfdnpIW8sO0BEjIyI30fEvXn+nJXPt+xUzhvLTi4ihkTEnyJiZj7dMuVmQPahjIghwF+Bg4GFwBzgqJTSA01NWIuIiEeAN6aUljU7Lc0WEfsDK4ErUkqvyef9J/BUSunc/MfI1imlLzQznc1QIW/OBFamlP6rmWlrtoh4OfDylNIfI2I08Afg3cCxDPKy00PevB/LDhERwBYppZURMQy4AzgFeC+WnUp5Mx3LDgAR8WngjcCWKaV3tNL31UCtodwLmJ9SWpBSWgNcDRzW5DSpBaWUbgeeKpt9GHB5/v5ysi/DQadC3ghIKT2eUvpj/v5Z4EFgHJadnvJGQMqszCeH5a+EZaenvBEQEeOBtwOXlMxumXIzUAPKccBjJdML8YJWKgG/jog/RMTHmp2YFrR9SulxyL4cgZc1OT2t5qSIuC9vEh90zXLlImJnYE/gd1h2NlKWN2DZATY0W94DLAVuSilZdnIV8gYsOwAXAJ8H1pfMa5lyM1ADyk3u4I6/ckrtm1J6PTADODFv2pSq8d/ArsAewOPA+U1NTZNFxCjgp8AnU0rPNDs9raSbvLHs5FJK61JKewDjgb0i4jVNTlLLqJA3g77sRMQ7gKUppT80Oy2VDNSAciGwY8n0eGBxk9LSclJKi/O/S4GfkXUR0EuW5P3AuvqDLW1yelpGSmlJfsFfD3yHQVx28j5ePwWuSildm8+27NB93lh2NpVSWg7cStZH0LJTojRvLDsA7Au8Kx8DcTXw1oj4Pi1UbgZqQDkHmBgREyJiOHAkcH2T09QSImKLvKM8EbEFcAhwf89bDTrXAx/K338IuK6JaWkpXReu3HsYpGUnHzzwXeDBlNL/LVk06MtOpbyx7GQiYruI2Cp/vxlwEPAXLDsV88ayAymlL6aUxqeUdiaLaWanlD5AC5Wboc06cF9KKa2NiJOAG4EhwKUppblNTlar2B74WXbNZyjwg5TSr5qbpOaJiB8C04CxEbEQOAM4F7gmIj4C/A04onkpbJ4KeTMtIvYg60LyCPAvzUpfk+0LHAP8Oe/vBfBvWHagct4cZdkB4OXA5fndSDqAa1JKMyPiN1h2KuXNlZadilrmmjMgbxskSZKk/jNQm7wlSZLUTwwoJUmSVIgBpSRJkgoxoJQkSVIhBpSSJEkqxIBS0qAVEa+MiJX5bUoave/LIuLf8/dvjoh5jT6GJLUKA0pJg0pEPBIRBwGklP6WUhqVUlrXl8dMKf1vSulVfXkMSWomA0pJkiQVYkApadCIiCuBVwK/yJu6Px8RKSKG5stvjYh/j4i78uW/iIhtI+KqiHgmIuZExM4l+3t1RNwUEU9FxLyIeH+F407LnzbUNf1IRHw2Iu6LiBUR8aOIGFmy/B0RcU9ELM/T8to+yxRJagADSkmDRkrpGLLHk70zpTQKuKab1Y4ke3TgOGBX4DfA94BtgAfJHkFJRGwB3AT8AHgZcBTwrYiYXGVy3g9MByYArwWOzff7euBSssfLbQtcDFwfESNq+7SS1H8MKCVpY99LKT2UUloB3AA8lFK6OaW0FvgxsGe+3juAR1JK30sprU0p/RH4KfC+Ko/zjZTS4pTSU8AvgD3y+R8FLk4p/S6ltC6ldDnwAvCmxnw8SWq8oc1OgCS1mCUl75/vZnpU/n4nYGpELC9ZPhS4ssrjPFHyfhXwipL9figiTi5ZPrxkuSS1HANKSYNNatB+HgNuSykd3KD9le73nJTSOQ3eryT1GZu8JQ02S4BdGrCfmcBuEXFMRAzLX1Mi4h8L7vc7wAkRMTUyW0TE2yNidAPSLEl9woBS0mDzH8BpeVN1tf0dN5FSehY4hGwQz2KyJuzzgEKDZ1JKd5P1o/wm8DQwn3zAjiS1qkipUa0/kiRJGoysoZQkSVIhBpSSJEkqxIBSkiRJhRhQSpIkqRADSkmSJBViQClJkqRCDCglSZJUiAGlJEmSCvn/ZFFGajIWDeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(durations=df['YearsAtCompany'], event_observed=df['Attrition'])\n",
    "\n",
    "kmf.survival_function_.plot(figsize=(10,5))\n",
    "plt.title('Survival Curve estimated with K-M Fitter')\n",
    "plt.show()\n",
    "\n",
    "kmf.plot_survival_function(figsize=(10,5))\n",
    "plt.title('Survival Curve estimated with K-M Fitter with confidence intervals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dbd5131e",
   "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>KM_estimate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.989116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.948192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.927792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.911342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.894026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.872931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.861611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.846275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.831850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.816995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.776815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.770500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.770500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.762795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.754127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.749354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.743923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.738019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.731711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>0.724676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.716884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.706022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.692444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.673730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.654480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31.0</th>\n",
       "      <td>0.613575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0.566377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>0.509739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.0</th>\n",
       "      <td>0.509739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>0.509739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>0.509739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          KM_estimate\n",
       "timeline             \n",
       "0.0          0.989116\n",
       "1.0          0.948192\n",
       "2.0          0.927792\n",
       "3.0          0.911342\n",
       "4.0          0.894026\n",
       "5.0          0.872931\n",
       "6.0          0.861611\n",
       "7.0          0.846275\n",
       "8.0          0.831850\n",
       "9.0          0.816995\n",
       "10.0         0.776815\n",
       "11.0         0.770500\n",
       "12.0         0.770500\n",
       "13.0         0.762795\n",
       "14.0         0.754127\n",
       "15.0         0.749354\n",
       "16.0         0.743923\n",
       "17.0         0.738019\n",
       "18.0         0.731711\n",
       "19.0         0.724676\n",
       "20.0         0.716884\n",
       "21.0         0.706022\n",
       "22.0         0.692444\n",
       "23.0         0.673730\n",
       "24.0         0.654480\n",
       "25.0         0.654480\n",
       "26.0         0.654480\n",
       "27.0         0.654480\n",
       "29.0         0.654480\n",
       "30.0         0.654480\n",
       "31.0         0.613575\n",
       "32.0         0.566377\n",
       "33.0         0.509739\n",
       "34.0         0.509739\n",
       "36.0         0.509739\n",
       "37.0         0.509739\n",
       "40.0         0.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.survival_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bacce98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this part, I will study the survival function for different groups of workers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c428e31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p0gllcXFxukPosFQ3jVP9NEx10zDVTcNUNw1T3TRMddO4jlQ/XTqhFBEREZG2p4RSRERERFql3a7yFhEREenMnHOUl5cTjUbTHQoA2dnZlJaWpny/Pp+P/Px8zOpczN2gJhNKM3sAuBjY65w7vp7tBtyDdyP6SuAG59y7zY5AREREpBMoLy8nKyuLzMzMdIcCQFZWFtnZ2SnfbzAYpLy8nIKCgmY/pzld3g8BFzSy/UKgKPb4LPC7Zh9dREREpJOIRqMdJplsS5mZmS1uhW0yoXTOzQcONlLkUuBh51kI9DSzQS2KQkREREQ6LXOu6RvYmNlI4LkGuryfA+5yzr0ZW34F+Lpz7u34cvF3yulIl7mLiIiINEd2djb9+vVLawyjR49m06ZNbX6cffv2UV1dXbtcVFRU+3t9d8pJxUU59Y3YbDRLjQ+qLRWvWkX14sWM/+jVZObmtMsxO4vi4uJ2ex86I9VPw1Q3DVPdNEx10zDVTcM6Wt2Ulpa2yZjFljCz2hiqq6vbLJ4ePXowbNiwZpdPRUK5HYg/4lBgZwr222o7n3+BD658ll1Lnif6yS+TPeOUdIckIiIiXUTPB3ekdH8lnxxyTM9bvnw5t912G5WVlYwaNYp7772XUCjElVdeyeuvv86KFSuYM2cOK1asYNiwYUybNo233nqL3NzclMWeinkonwGuN89soNQ5tysF+22V/3tzDeeueh6AQRX76Pvbb+F+9R3s4N40RyYiIiKSOjfddBPf//73eeutt5g4cSJ33XUX/fr1o6amhsOHD7NgwQKmT5/OggUL2Lp1K/369UtpMgnNmzbo78CZQF8z2w58D8gAcM7dDzyPN2XQBrxpgz6Z0giPQTDi6P38I2S6SML6gqVvElnzDuHLP0no3MvBr2k4RUREpPMqLS3l8OHDnHbaaQBce+21fOITnwBg5syZLFq0iLfeeovbbruNV155BeccJ598csrjaDKjcs5d08R2B9ycsohSINNvHP+/3+Svd/+Waza/iD9uSKe/pgr/3+8j8N+51HziNqJjJ6UxUhEREZG2cfLJJ7NgwQK2bdvGBz/4Qe655x7MjPPPPz/lx+qyTXS9exYw5uor+dqKc7j69fuZWbYxYbt/60Zyb7+Z0JmXUHPVZyC/R5oiFRERkc7oWMc8plJhYSGFhYW89dZbnHLKKTz22GOceuqpAJxyyinccccdnHLKKfh8Pnr16sVLL73E9773vZTH0WUTSoBsP3zvipP4ztCf8tArz/HjTY/TM1KZUCZj3rP433mD4NWfJ3zqedCC2wyJiIiItKfKykomTpwIeLeCvPnmm/nd735Xe1HOyJEjue+++wAYMWIE4CWWALNnz2bHjh307Nkz5XF16YQSwO8z7jy5D/f2uIJJb87gJxsf5eN7/ptQxldWQvYf7yT8xn+o+cSXcYNHpClaERERkYYdOnSo9vf4aYNefvnlesuvXLmy9vevfOUrfOUrX2mTuFJxlXencPOkfH56/ig+d/z/cO7Ub7Eup+7NfAJrl5L7nRvJ/OefoKYqDVGKiIiIdD5dvoUy3qUjc+if05drXzme6YV38rWtz/HN958m24Vqy1gkTOazfyXjP48TGTuJyMQTiEw8geioCRDoVtUlIiIi0izdLkM6eUAWL36wH1e+eIA7Rl7GY/1P5jfFD3HeoRUJ5SwcIrB2KYG1S+GpB3DZOUTGTz2aYA4dDb5u08ArIiIi0qBul1ACFBVm8NLF/fjoywd4b/9ALpryda7ct4hfbniEwcGSep9j1VUEli0ksGwhAK6gkPCE6UQmeQmm6z9EF/SIiIhIt9QtE0qA/jl+nrugL5+ad5C522v4Z//ZzO09hW9sfYaP7F3IqOp9jT7fykrJWDKPjCXzAIj2GUDkuOlERk/A9R9CdOBQXJ/+4PO3w6sRERERSZ9um1AC5GX4ePTsPnxtYQkPrqukLJDLt0dfzbdHX83Iqr1cF1rHDaG1DHt/Gb7Dhxrdl+/AHnxvvkDGmy/UrnP+AK7/IKL9hxAdMAQ3YCjRAUOIDoglm7pTj4iIiHQB3T6jCfiMX57ck+H5AX7wzuHa9Vty+nN7Tn9uZw7TxwW4c8ghTtu/gsCapfjXLsWqKprct0XC2K5t+HZtq7PN+f24voOIDhzqJZxDRxEpOt6bskhd5yIiIlKPIUOGsGPHjtrlRx99lKVLl/Kzn/2MBx54gJycHK65puGbHMaXT6Vun1ACmBlfnlLAkDw/t7x5iGA0cft7B8JccKCAmf3O5FsfuYQz+gfwv78e/+p3vUfxCiwUqn/nDR0zEsH2bMe3Z3vCepffg0jRZCLjJhMZP4XoiCIIZLT2JYqIiEgX96lPfSptx1ZCGecjY3KZ1ieDu5aW8dTmuvNQLt4X5MNzD3DygEy+fcIYTrtkIqFLPg7BGvwbVuErXolvzw58e7Zje3bgKytpcQxWfpjAe/8l8J43+brLzCIyZiLRcZOJjJtCZMxEyMlt7UsVERGRVsr/xJkp3V/5X+a16vl33nkn+fn53Hrrrbz77rvccsst5OXlMXv2bF5++WUWLFgAwK5du7jiiivYvHkzF198MT/84Q9bHbsSyiTjembwwJm9+cqUEHctPcyz71fXKbNgT5CL/7Of0wdl8a3pBcwekFU7nVCCynIvwdy7A9u9Hd/eHfj27PCSzSbGZB5hwRoCa96DNe8B4Hw+osPHei2Y46YQHT8F16NXq1+3iIiIdHxVVVWcdtppgHfrxdLSUi688MI65W6++WbuvvtuZs2axfe///2EbStWrGD+/PlkZWVx0kkn8dnPfpahQ4e2Ki4llA2Y1DuDRz7Qh2UHgtz5XhkvbKubWM7fVcP8XTWcPSSLb03vwYn9MhML5OYTHTWe6KjxdQ9QVRFrzdyB7dqKf8Mq/BtWNTk206JR/FvW49+yHl58EoDwcdMJz7mQ8ElzICvnmF+ziIiIdGw5OTm8+eabgHfrxSeffJKlS5cmlCkpKaGsrIxZs2YBcOWVVzJ37tza7WeccQaFhYUATJgwgW3btimhbGtT+2Ty2Dl9eHdfkB+/d5iXd9TUKfPKjhpe2bGP84dl881pBUzrm1nPnpLk5BEdOY7oyHEAhACiEXzbNuFfvwLf+hX41y/HV3KgyV0F1rxHYM17uIdzCM88i9CcC4gWTdbFPSIiIlJHVlZW7e9+v59wONzqfSqhbKYT+mXyz/P6smhPDXcuLWPezrqJ5dxt1czdVs0lI7L55vQeTOzVwotpfH6iI4q8C3HOvRycw/btwr9+Of4jCWY9V4wfYdVVZMx/noz5zxMdMITQaRcQPvU8XJ8BLX25IiIi0oTWjnlsKz179qSgoIAlS5YwY8YMnnrqqTY/phLKFpo1IIv/Oz+LN3fX8ON3D/PWnmCdMs++X81z71dz5egcvjGtB2MKj7GazXD9BxPuP5jwaRd46w6XeMll8Qr8a97D/35xvU/17dlB1pN/JvOpB4hMPMHrEj/hNMjKPrZYREREpNP4zW9+wxe+8AXy8vI47bTT6NGjR5sez5xzbXqAI0pLS9vnQHGKi4spKipqs/0755i/q4Y73i1j8b66iSWA3+Dasbl8bVoBw/NTn7/bji1kvDmXwH/n4is92Hi8OXm1XeLrXCZF48alPJ6uoq3Pnc5MddMw1U3DVDcNU900rKPVTWlpae3Yw46gurqa7Oz6G4rKy8vJz88H4Fe/+hW7d+/mJz/5SbP33dhrLSwsrDOmTi2UrWBmnDE4m9MHZfHKjhrueO8w7+1PnI8y4uCR4koe21jJDePyuG1qAYNyU3c7RjdkJMGPfo7glTfiX/k2gTdeIPDef7Fw3XkxraqCjNefI+P15ziu9wB8J51GdMxEImMm4voO1JhLERGRLuLFF1/kl7/8JZFIhGHDhnHfffe16fGUUKaAmXHO0GzOHpLF81urueO9w6w+lDjANRSFP66t4JHiCj5zXD5fnJxP3+wU3ufbHyAydTaRqbOpKS8lY+GrBN58Af/mdfUWzz64J3aVuHeleLSwt5dcjvUSzOio8bpiXEREpJO6/PLLufzyy9vteEooU8jM+OCIHC4cns2/Nldx53tlbDicmFhWR+A3K8t5cG0FN03K55ZJ+fTM8qU2kPxCQudcRuicy/Bt20TgzRcIvPVSo3Nf+koP4nv3TQLvelMROJ+P6NDRCUmmGzhMrZgiIiJShxLKNuAz44rRuVw6MofHN1byk6VlbC2PJJQpDzt+vqyMP64p5wvHF/C5iXnkZ6Q4sQSiw0YTvOZ/CF71WfwrFpPx5gv433sLizQ+RYBFo/i3bsC/dQMZrz0DgMsr8FovB4/A9RlAtO+A2M+BkFeQ8thFRESkc1BC2YYCPuNjRXlcNTqXvxZX8rNlh9lVmXij8NKg4/Z3D3PvqnKuH5fLDePzGFnQBm9LIEBk+ilEpp8C5aXsfe0FhlaW4N+4Gt/mtViw7jRIyayijMDyRbB8UZ1tLiePaJ8BuL4DYj8HJiSdrrC3WjdFRKRT8/l8BINBMjObMd90JxYMBvH5WtbIpYSyHWT6jU9NyOOasbk8sK6CXy0vY391YmJ5sCbK3SvKuWdFOWcPyeKT4/M4f1g2AV8bJGH5hZROOIH+R66cC4fx7diMb8Nq7449G1fj27O9Rbu0qgr82zfB9k31bncZGUSHjSV80hzCM87E9R/c2lchIiLSrvLz8ykvL6eqqirdoQBw+PDhNpkOyOfz1V4h3lxKKNtRTsC4eVI+nxiXyx/WVHDPijJKg4mzKTng5R01vLyjhiG5fq4fn8t1RXkMzkvhBTzJAoHaCdXDZ1/qrSsvxb9xjZdcbliNf9OaJm8L2RgLhfBvWoN/0xqynvgDkZHjCM88i/DMM3H9BqXohYiIiLQdM6OgoOMM8dq7dy/Dhg1LdxiAEsq0yM/wcduUAj41Po97V5Xz+zXlHA7WnaZzR2WEO98r46dLy7hwWDY3TsjjjMFZ+Nqj6zi/sPaqcQCiUe+e41vWY/t34zuwB9u/x/t5YA8Wqn8ezoYcuR951hO/JzJqPOEZZyq5FBER6aSUUKZRzywf3z6hB1+anM9Tm6t4YF1FnXkswZvL8rmt1Ty3tZrRBX4+OT6Pa4ty6ZPKaYea4vPhhowkPGRk3W3OYYcPYfu95NK3f3fsZ2z5wG6ssuHWTf/mdfg3rzuaXM48i/CMM5RcioiIdBJKKDuAvAwf143L47pxeby3P8iD6yr456YqKsN1Wy03lUX4f28f5vZ3D/PhkTl8ckIes/tnYum84MUMV9jbu/BmzHFE6ityaD+Bd94gsHgevvXLsQbu0FSbXD5+P5FREwjPPFPJpYiISAenhLKDmd43k+l9M7l9RiGPb6jkwXUVrCmpO8VPMApPbKriiU1VjC7w85ExuXxkTC6je3TMt9T16ls7N6aVHCCw5HUvuSxe0UhyuRb/5rVkPX4/0cEjCE+eSWTyTCLjp0BmVju/AhEREWlIx8w+hMJMH5+dmM9njstj4d4gD66t4P+2VBGM1i27qSzCXUvLuGtpGTP6ZfDRMblcNiqnfbvEW8D17EPo3MsJnXu513L59vwmk0vfzvfJ3Pk+zP0HLiOTyIRpRCbPIDx5Jm7QcE1JJCIikkZKKDs4M+PkAVmcPCCLH8+K8Ldir9Vyc1l9HcuwZF+IJftK+caiUs4Zms3VY3I5f1g2OYGOmXC5Xn3rSS5fw79+RYPPsVCQwIrFBFYsJot7ifYZQGTyTK8Fc+J0yG3ZVAciIiLSOkooO5G+2X6+MLmAW47PZ97OGh5aV8EL26rrbbUMO3hhWzUvbKumR4bxoZE5fGRMLqcNzGyfq8SPQUJyeXCfl1wueR3fhpVYtJ4XGeM7sAffvGfJmPesd8vIsccTnjyDyOSZREeOU+uliIhIG1NC2Qn5zPjAkGw+MCSbkpooT2+p4rGNlSzYU//UPYdDjr8WV/LX4kqG5Pq5akwOswLGWOfSezFPI1zvfoTOu4LQeVdARRn+1e8SWLEE/4rF+A7ubfB5Fo3iX78c//rl8OSfiYyZSM0nvkx0RFE7Ri8iItK9KKHs5Hpm+fjE+Dw+MT6P98vC/HNTFY9vrGR9af336t5RGeHuFeVADoWrdjGhZwYTegaO/uyVwcAcX8dKNPMKiMw4g8iMM7wpinZtJbB8Mf6Vi/GvXYqF6k61dIR/42pyvvc5QudcRvCKT0FOXjsGLiIi0j0ooexCRhQE+MrUAm6bks+yAyEe31jJk5ur2FtVf3dxadCxaG+QRXsTWzYLM43jemYwPpZoHtfL+zmgIySaZrjBIwgNHkHogqugphr/umX4VywhsGIxvl1b6z7FRcl86UkCS+YRvPZmwjPPUje4iIhICimh7ILMjGl9M5kWm37o9V01PL6hkue2Vtc7t2Wy0qBj4d4gC5MSzZ6ZxoSeGZw9JIvPHJdPz6yW3Ti+TWRlE5kyi8iUWQQB27/bSy7fnk9g5ZKEor6SA2Tf90PCrz9PzfVfwg0cmp6YRUREupgOkBFIWwr4jLOHZPOHM3qz/uqB/P70Xpw9JIscX9OJZbKSWKJ5x3tlTP7Hbn70zmEOVtd/tXm6uL4DCZ91CdVf+xlVX/4x0b4D65QJrHqb3G9/ksynHoRgTRqiFBER6VrUQtmN5Gf4+OiYXD46Jpd164vJHjSKtSVh1pWEWFMSZm1JiPUlYSqa0YpZFnL8fHkZ968u5zPH5XHL8fkdbt7LyLRTqDzuBDKf/SsZzz+GRY6OK7VwiMyn/0JgwUvUXP8lIpNnpjFSERGRzk0JZTflM2/M5YiCAOcPy65dH3WObeWRZiea5WHHr1aU84c1Fdw4IY9bj8+nX04HSiyzsgle+WlCp5xL1sN3E1jzXsJm396d5Pz8fwnPOIOaa2/B9e6XpkBFREQ6LyWUksBn1mii+a/NVfxmZTkHahIv9KkIO369spw/rqngUxPy+MLx+QzI7TiJpRs8guqv/5LAgpfJfOw+fKWHErYHlryOf8Vigpd9itC5l6UpShERkc5JYyilWY4kml+aUsCyqwZw+0k96Jdd9/SpijjuXVXO1H/u5huLSthV2YHGWJoRPuVcKu98mODZH8YlXelt1VVk/f1ecr7/OXqufhvbswMamVBdREREPM1qoTSzC4B7AD/wJ+fcXUnbC4G/AsNj+/y5c+7BFMcqHUR+ho9bJxdw43F5PLiukl+vKGNP0tRE1RG4f3UFD66r4PpxeXxpcgFD8jpIi2VeAcHrv0R4zgVkPfRL/FvWJ2z2b93IqK0b4anf47JziQ4dTXT4GCLDxxAdNobosNGQlZOm4EVERDqeJhNKM/MD9wLnAtuBJWb2jHNudVyxm4HVzrlLzKwfsM7MHnXO1X/rFukScgM+bp6Uz6fG5/GX9RXcvbyM3UmJZU0E/rimgr+sq+DSkTlM6JnBqAI/IwsCjOoRoFcapx6KjppA1fd+R+C1Z8n65x+xyoo6Zay6Ev+Glfg3rCQjts6Z4foP8ZLMYWOIDh9LdPgYXO/+mt9SRES6pea0UM4ENjjnNgGY2WPApUB8QumAAvNmvc4HDgL136pFupycgHHTxHxuGJfHI8UV/Gp5GTsrExPLYBT+sakKqEpYX5hpjCoIeI8eXqI5siDAqAI/Q/L8bX/fcZ+f8NkfJnLiHDIfv5+Mt15q8inmHLZnO7492wkseb12vcsrwBX0PKYwXFY2rnd/or374fr0j/3eH9e7H65XPwhouLOIiHRc5lzjU8SY2ZXABc65T8eWrwNmOeduiStTADwDTAAKgI865/4dv5/S0tLaAxUXF6fsBUjHE4zCM3sCPLQ9wJ6aY2+BzDDH4GzHiJwokwuiTO0RZWJBlLZs1MzbtoGea94mZ892cvZsI1Bd2XYHayaHEc7vQbBHb4I9ehGK/1nYm2DPvoRzC9Q6KiIibaaoqKj298LCwjp/cJqTUF4FnJ+UUM50zt0aV+ZK4FTgNmAM8BIw1Tl3+EiZ+ISyvRQXFydUgBzVHnUTjDj+tqGSXywvY1t5ai7OyfTB9L6ZzOqfyewB3s+2mP+yuLiYorFjsYN78W3diG/rBvzbNuLbuhHbuwNr4nPT3lxWNtF+g3B9BxHtPwjXbxDRfoNx/QYS7TcopWM+9blqmOqmYaqbhqluGqa6aVy66qe+hLI5/WjbgWFxy0OBnUllPgnc5bzsdIOZbcZrrVx8jLFKF5DpN24Yn8fHinL57+4a1pWE2VwWZktZhC1lYbaUhWnpjXaCUWrvP/7rld66cYWB2uTy5AFZjCrwp+ae42a4PgOI9BlAZPophI6sr6nCt20Tvm0b8W/1kkzf9o1YdVVje2tTVlONf/tm2L653u3RHr1iyeVgL9nsMwAyMuot25SCihqsT09cr75qFRUREaB5CeUSoMjMRgE7gKuBa5PKbAXOBt4wswHAeGBTKgOVzivDZ5w5OJszByeujzrH7soom8tiiebhCFvKw2w+HGZzWYSDNc2bsmd9aZj1pWEeXu91T/fL9jF7gJdcXjM2N/UX/mTlEB07iejYSUcHCkej2MG9EDqG69CcwyrL8R3Yix30Hr6D+7DYsq/0YKtD9h0+BIcP4d+4ptX7Ggvwt1/hcvOJDh5JdMhIokNjP4eMwhX2VqIpItLNNJlQOufCZnYLMBdv2qAHnHOrzOym2Pb7gduBh8xsBWDA151z+9swbukCfGYMzvMzOM/PqQOz6mwvDUbZfDjMu/tDLNxTw4K9wWZ1ne+rjvLs+9U8+341v1hWxsMf6F3v/lPK58PVc9/w5nJAdOyk+jeGQ9ih/diBvfgOHkk693kJ6P7d+PbvSkvrqFWW114BH8/lFXjJ5ZFkM/ZQoiki0nU169JR59zzwPNJ6+6P+30ncF5qQ5PurjDTx7S+mUzrm8mnJuQBsKMiwqI9NSzcG2ThniArD4WINjKc8UBNlA/P3c8vTu7J9ePy2inyFAtk4PrFxkXWt905KC/Ft3eXl1zu3YVv3y5s3058+3ZjB3Zj7ThBu1WU4V+/Av/6FYlh5vfwplkaNprosLHezyEjIbONk30REWlzmotEOpUheX4uH53L5aNzATgcjPLOviAL9gZZtCfIkn1BKpPuOR6Kwhf+W8LqQyF+NKOQgK+LtZKZQUFPogU9iY45ru72SNhr0dy3C9sXSzYP7fcS0ZaKhAlu3Ujugd1YTXXLwiw/7N1LPe5+6s58uIFDEyaNjw7TnJ4iIp2NEkrp1Hpk+jhrSDZnDfHuOx6KOlYeDPHkpip+u6o8oez9qytYVxLmwTN70zONE6q3O38A128QkX6DUrK74uJiisaM8cZ37tgSe2yO/XwfCzY/0TQXxXZtxbdrKyx6rXa9y80nOmwMkWGjcQOG4vwd5C5LTehRHYbhwyArO92hiIi0KyWU0qVk+IzpfTOZ3jeTk/pl8vk3DlEVOdoS99rOGs5+bi+PndOHosJju8pZqB0zGuk7kMjU2UfXR6PYgT2xBPP9o8nmzvexYE2zd2+V5fjXLcO/blkbBN92xgDu//5EePrJhGecSWTKLCWXItItKKGULuvDo3IYWeDnY68cZEfl0Yt5Nh6OcPZz+3jwzN6cPUR/7FPK56ttDY1MO+Xo+mgU27vTm2pp2yZ82zZ4Uy/t25W+WNuIBavJWPQaGYtew2VlE552CuGZseRS40VFpItSQild2rS+mbx6ST+ue/Ugi/cdndLncNBx1UsH+NGMQj4/MS8181ZKw3yxsZIDhxKZccbR9VUV+LZvxrd1ozdx/LZNaZ/TM5WsppqMRa+SsejVuOTyLCJTZiq5FJEuRQmldHkDcv08e2FfvvRWCX/fcPRWilEH31pcyupDIX5xck+y/Eoq211OHtGi44kWHZ84p+f+3fiOJJgpmIezXYSCuKULyCwrqXdzQnKZnXO05XKykksR6fyUUEq3kOU37jutJxN7Bfje24cTphr6a3ElG0rDPPKB3vTL6RwXf3RpPh+u/2Ai/QcTOXFOuqNpkeL16xhPkMCSeQQWv46vpP7peK26ioyFr5Cx8BVcdg6RCdNxWceQVGbnEj5xjtedrlZ2EUkjJZTSbZgZtx5fwISeGdw47yCHQ0ezyoV7g5z17D7+fk4fJvfWxTpyjMxHtGgywXGTCV5zM74NKwksfp3Aknn4Sg7U/5TqKgJL3zrmQ2a8/m/Ck2dQ87FbcYOGH/N+RERaoxvNnSLiOXdoNi9d3I/RBYmtkdsrIpz/7308s6VrjN+TNPP5iI6bQvDjt1L5q39Q+e1fEzz3cqI9+6T8UIEVS8j99qfIfPz3UFXZ9BNERFJMLZTSLY3vmcErl/TnhtcO8vquo9PZVIYd1792kNk9s+j1fv0tSm2hR6YxvmcGE3oGmNAzgxH5fvxdbQL27uxIcjluCsFrb8FXvNLrFl/yeoMtly1lkTCZz/+dwFsvEbz6JsKzz1Y3uIi0GyWU0m31yvLxz/P68O3FpfxhTUXCtoUlfihp2Z1gWu9oy2i2H8YVxhLMXko0uxSfj+j4KQTHTyF47c34Nq3Ft393y/cTjRKY/7x396H43ZfsJ/v+HxF57VlqPv4FosPHpChwEZGGKaGUbi3DZ/x0dk8m9srgqwtKCB/D3QjbQnUElh8MsfxgiKYSzUm9Mhie79fUR52Rz0907CSiYycd09PDJ59DYPE8Mh+7D9/BfQnb/OuWkfPdzxA658MEL/sk5BWkImIRkXopoRQBbhifx9jCADe8dpD91dF0h9OghhLNHhnGxF4ZHN87g0m9MpjUO8BxvTIoyNAw6S7NjPCsswhPnUXms4+S8cITWDh0dLOLkvnSU2QsfIWaqz5LeM6F4NM5ISKpp4RSJOa0gVm8ffkAluwL8v72nQwalJp7XzfFAbsqI6wrCbOmJMTaQ2EO1LQsqT0ccizcG2Th3mDC+lEF/liC6SWak3tnMKLAj0+tmV1Ldi7Bqz5DaM6FZD36GwLLFyVstrJSsh/4mdcNft0XiY45Lk2BikhXpYRSJE7PLB/nDs2muCpC0YictMWxvzrCmkNh1paEahPNdSXhFreebi6LsLkswnNbj44HzQsYE3sFmNInk6l9MpjSO4PjemVoYvcuwA0cSvVtd+FfuoCsR3+Lb9/OhO3+zWvJ/eHnCZ1+EaHTL2rXi3Zyt2/DZ8GmC3Zirnd/XO9+6Q5DJC2UUIp0QH2z/cwZ5GfOoMTJrvdXR1hbEmbtoRBrS8KsPhRi5aEQh4PNH/xZEXYs2Rdiyb6jXaMZPjiuZwZT+3iPKX287vPcgLpHOx0zItNPoXLSiWT853Eyn3sUC9YkFMmY/zwZ859v17DGt+vR0ic8eSbBK24kOqq7vGIRjxJKkU6kb7af0wb6OW3g0UTTOcf2igirDoVYeTDMqoMhVh0KseFwOOGOQI0JRY+OzXyk2FvnMxhXGGBKnwymxlozNel7J5KZRejS6wmfeh5Zf7+PwNvz0x1RtxBYsZjAisWETzqd4GWfJDp0VLpDEmkXSihFOjkzY1h+gGH5AS4YdnR9ZTjKupIwK2MJ5sqD3qOkma2ZUYfXGloS5omNRy8AyrQcbMGOVL+MBuUFfMzon8mcgZmcPiiL43tnaAxoC7i+A6m+9Yf4V71N1iO/xrdra7pD6hYCb8/H/84bhGefTfCyG3ADhqY7JJE2pYRSpIvKDfiY3jeT6X0za9c559hVGWX5wSDLD4RYFntsr4g0e79BZ9D84q1WE4kyd1s1c7d540B7ZRmnDcxizsAsTh+cxfjCgKZMaobIpJOo/NEDZLz2DP73/otVt+8ddaqrq8nOzm7XY7Ynq6yok6ybc2QseJnAolcJz7mI4KXX4/r0T1OEIm1LCaVIN2JmDM7zMzgvhwuGHb3o6EB1JCHBXHYgyKaydswaW+BQjePZ96t59n0vweyf42POwCzmDMri9EFZjCrQnJwNCgQInXs5oXMvb/dDFxcXU1RU1O7HbTfO4V+xmMwn/4x/y/qETRaNkvH6cwT+O5fQBz5E6OKP4Qp7pylQkbahhFJE6JPt56whfs4acrQFqTQYZcXBownm8gMh1pc2f1xme9lbFeXJzVU8udnrlh+S62fOoEzmDMriA0OyGZTrb2IPIilgRmTKLKomz8T/zptkPvVn/Du2JBYJh8h88Uky5v2b0LmXE7zoasjvkZ54RVJMCaWI1Ksw08dpA7MSLgAKRx1rizcwdszYdonBAZsOh3ljdw3zd9Xw3901lDYxBnRHZYTHNlbxWGzc55TeGZw3LJvzh2ZzQt8M3bpS2pYZkZPmUHXCKQQWvkrmvx7Etzdx+iYLVpP577+R8erTBC/4CL6x09MUrEjqKKEUkWYL+IwsH2QH2i8pm9Tbm5j9pon5RKKOFQdDvLHLSzDf2hOkoon7ZR65ev3ny8rok+XjnKFZXDAsm7MGZ9MzS9MiSRvx+Qmfci7hmWcRePMFMp9+GN/BvQlFrKqCrH89yKScfxD50McJnXMZZGY1sEORjk0JpYh0Gn6fMa1vJtP6ZnLr5AJCUcd7+4PM3xXkjV01LNpbQ3UjQz8P1ER5fGMVj2+swm8we0Am5w/N5rxh2bq4R9pGIED4zIsJn3IuGfOeI+O5v+IrPZRYpKqcwOP3kzH3H4Qu+TihMy+GgKboks5FCaWIdFoZPmNm/yxm9s/iq1MLqA473t4fZP6uGl7dUc07+0I01H4ZcfDf3UH+uzvId98+zPB8f21yOWdgVru2wko3kJlF6LwrCJ1xERkv/4vMf/8dqyhLKOIrOUDWI/eQ8Z/HCF56A+FTzwW//kxL56AzVUS6jOyA1Y77/Nb0HuyrivDyjhrmbqvm1R3VHA413D2+tTzCH9dW8Me1FeQFjA8MyeKi4TmcPzSL3tm6sEdSJCuH0AevJXTWh8h84R9kzH0Cq65KKOLbv4fsP/+E6L//RvCyGwjPPAt8Gp4hHZsSShHpsvrl+LlmbC7XjM0lFHUs3BPkxe3VvLitmnWl4QafVxE+OjXRka7xi4bn8MHh2Yws0NempEBuPsHLP0nw3MuoePR39H9nXp1bZPp2byP7d7cTee5vBC//FJHpp7Tr/ddFWkLfjCLSLWT4jDmDvPkqb59RyJayMHO3VfPi9mre3F1DTQNjL+O7xr+9uJSJPQNcNDyHi4ZnM62v7tojrVTQk51nX0n+1Z8l45lHyJj3HBZJ/GfHv20jOfd8m8jo4whecSORSScqsZQORwmliHRLIwsCfG5iPp+bmE9FKMrru2p4cVs1c7dXs6sy2uDzVpeEWV1Sxs+XlzEo18eFw3L44IjshOmVRFrK9exD8PovEbroajKffpjAmy9g0cTz0L9pDTk/+yqRCVOpueLTRMdNTlO0InUpoRSRbi8vwxdrdcwh6hxL94d4fms1z2+tYnVJw13juyqjPLCuggfWVVCQYYzNySJ3w74WH39gjp+PFeXygSFd99aE0jyu70Bqbvxfgh+8hsynHiRj0at1yvjXLiP3jlsJT5lF5Lhjm8MyMvo4ohOmtjZckVpKKEVE4vjMOKFfJif0y+Q7J/ZgS1mYf8eSywV7gg3eKags5Hgv5IfDwWM67pObqzhvaBY/mlHIuJ6aMqa7cwOHUfM/3yV08cfI/NcDBN79b50ygeWLCCxfdMzHqLnmZkIXXNWaMEVq6bIxEZFGjCwIcPOkfP59YT82XD2Q383pxSUjsslrg2mFXtxewyn/t5dvLCrhUE3D3e7SfUSHj6H6i3dQ+d3fET5+Rkr3nfnY7/Cveiel+5TuSwmliEgz9c72rhp/5AN92HjNIB4/pw+fGJdL/5zUfZWGHdy/uoITntzNH9eUE+5oN0+XtIiOOY7qr/2Mym/eQyRFYyfNRcm+7wfY/t0p2Z90b+ryFhE5BtkB4/xh2Zw/LJtfOceqQ2FWbdzKkKFDW7SfinCUe1aUs2BPYlf5oRrH1xaW8ue1Fdwxs5CzNb5SgOiEqVR969f417yHf/W7EA61bAfBGjJefRpz3j8qVn6Y7N98l6pv/0a3fZRWUUIpItJKPjMm984g+0CUokEt/6N8/tBsnt5Szf97u5Rt5YnzF60tCXPFiwc0vlKOMiMy8QQiE084pqe7nn3IevLPtcv+LevJevhuam78X01HJMdMXd4iImlmZnx4VA6LLxvA/zuhR73jMzW+UlIldPHHCJ9wasK6jDf+Q2Des2mKSLoCJZQiIh1ETsD4ytQC3rliANeOza2zXeMrJSV8Pqo/802iA4clrM565Nf4NqxKU1DS2SmhFBHpYAbm+rlvTi/mXdKPkwdk1tl+ZHzlaU/v5clNlVSFlVhKC+XmU/WF23FZR8fmWiRM9m++h5UeTGNg0lkpoRQR6aCm9c3k+Qv78tCZvRmW76+zfW1JmBtfP8T4x3Zxy5uHeGNXDVGn5FKaxw0ZSfVnvpGwzleyn+x7fwDhhif0F6mPEkoRkQ6sOeMrD4ccfy2u5JIX9jPlH3v4wdulrC1p4dW/0i1FZpxJ8KJrEtb51y0j8/H70xSRdFZKKEVEOoGmxlcesb0iwq9WlDP7X3s5/em93LuqnD2VkQbLiwSvvJFw0hXjmS/+k8CCl9MUkXRGzUoozewCM1tnZhvM7BsNlDnTzJaa2Sozez21YYqICBwdX/nfS/vz+Yl5jU6qvvxgiG8vLuW4J3ZzxYv7eWJjJRUhXSEuSfwBqj//XaJ9BiSsznrgZ/i2bkxTUNLZNJlQmpkfuBe4EJgIXGNmE5PK9ATuAz7knJsE6OagIiJtaFLvDO6c1ZPVHxnIP8/tw1Wjc8jx1z+HYNTBKztq+Oz8Q4x7bDefm3+Q13ZUE9FV4nJEj55U3/oDXMbReU4tWEP2r/8fVJSlMTDpLJrTQjkT2OCc2+ScCwKPAZcmlbkWeMo5txXAObc3tWGKiEh9Aj7jnKHZ/PGM3qy/xrvX+JmDs2hoeuqKsOPxjVVc9uIBJj2xm+8sLmXFwRBOF/N0e9FRE6i5/ssJ63z7dpL9+zsgqpZtaZw19SViZlcCFzjnPh1bvg6Y5Zy7Ja7M3UAGMAkoAO5xzj0cv5/S0tLaAxUXF6cqfhERqcfeGmPuPj//2RuguLLptoMxuVEu6h/m/H4RBmQpuezOhj3/CH3fnZ+wbtecS9h9xofSFJF0BEVFRbW/FxYW1vmftTkJ5VXA+UkJ5Uzn3K1xZX4LnAScDeQAC4APOufWHykTn1C2l+Li4oQKkKNUN41T/TRMddOwjlo3Kw+GeGJjJf/YVMmuysZbmgyYMyiLj47J4ZIROfTITM21mx21bjqCDlc3oSA5d34R/8Y1CaurvvxjItNOaddQOlzddDDpqp/6EsrmfFNsB+Kn0x8K7KynzAvOuQrn3H5gPjD1WAMVEZHUOb53Bj+cUcjKqwby9Pl9uGZsLvn1TD8E4ID5u2q4+c0Sxj+2mxvnHeTFbdWENN6y+8jIpPqWHxDt0Sthdfbv78B2b09TUNLRBZpRZglQZGajgB3A1XhjJuM9DfzWzAJAJjAL+FUqAxURkdbx+4wzBmdzxuBsfnFyIc9vrebxDZW8urOGSD35YlXE8eTmKp7cXEXfbB9XjMrh1IFZ+BoaoNmIXQf8rMusav2L6IKOtW5yA8bJA7LIaeCfg9ZwvftTffP3yPnJbVhs/KRVVpD96+8QOvuylB+vIX337SWwdXW7HQ8gOmIs0TETwVJfr11Zkwmlcy5sZrcAcwE/8IBzbpWZ3RTbfr9zbo2ZvQAsB6LAn5xzK9sycBEROXa5AR9Xjs7lytG57K2K8OSmKh7fWMnSA/VPiL6/Osrv11Tw+zUVx3jELFijW/rV79jrZmCOjzc/3J++2XXvpNRa0QnTCF79ebL+dm/tOv+OLfgfbr/2omFNF2kTNVd+htAlH0vT0TunZg2Occ4975wb55wb45y7I7bufufc/XFlfuacm+icO945d3cbxSsiIinWP8fP5yflM+9D/Vl0WX++MiWfoXmpT1Ak9XZXRbl3ZXmb7T903pWEZp/dZvvvqDKffQRCwXSH0anoTjkiIlJrfM8M/t+JhSy/agD/vrAv14/LpUemuv46srnbqttu52bUfOqrRIaPabtjdEBWU41/7bJ0h9GpNGcMpYiIdDM+M04dmMWpA7P46ayezN1ezQvbqikNHtt8hBXl5eTl56c4yq7hWOpm7rbq2nGvq0vCvF8WZkRBG/1Jz8qh6pv3kPHyv/Ad3Nc2x2hASWkpPQsL2+VYvk1r8b9fOzkN/mULiEye0S7H7gqUUIqISKOyA8alI3O4dGTOMe+juPggRUV9UhhV13EsdXPR8/t4a8/RLtkXt1fzmePaMGHPzSf0oevabv8N2F5cTE47TYvjf/dNcu75Tu1yYOkCgh+7VRfnNJO6vEVERDqZC4ZlJyy3abd3NxGZeELCrSd9+3Zhu7amMaLORQmliIhIJ3N+UkL5xu4aKkK6PWKrZOcSmTA9YVVg6YI0BdP5KKEUERHpZMYVBhiRf/RK/JoIvL6rJo0RdQ2RaScnLAeWKaFsLiWUIiIinYyZ1WmlVLd364Wnzk5Y9q1fARVlaYqmc1FCKSIi0gklj6N8cXs1zukWma3h+g0iMmRk7bJFowRWLE5fQJ2IEkoREZFO6NSBWeTF3XZxV2WU5Qfrv9ORNF9yt7df4yibRQmliIhIJ5TlN84cnJWwTt3erReemjSOcvliiEbSFE3noYRSRESkk9I4ytSLjp2IyyuoXbaKw/g2rE5jRJ2DEkoREZFO6ryhiQnlO/tD7K1Sa1qr+AOEp8xKWKXpg5qmhFJERKSTGpjrZ3rfjIR1L25XK2VrRZK6vf2aPqhJSihFREQ6sfOTWilfVLd3q4Unz8DZ0RTJv30ztn93GiPq+JRQioiIdGLJ4yhf3VFDMKLpg1olvwfRouMTVvmXLUxTMJ2DEkoREZFObGqfDAbkHP1zXh52vLVHd81prXDyXXM0jrJRSihFREQ6MZ9ZnYtzXlC3d6slJ5T+Ne9CTVWaoun4lFCKiIh0cvVNH6S75rSOGzyCaN+BtcsWCuFf/V4aI+rYlFCKiIh0cmcOziIz7i/65rIIGw6H0xdQV2Cmbu8WUEIpIiLSyeVn+DhtYOJdc9Tt3Xr13oZRLb/1UkIpIiLSBeiuOakXGT8Vl3W0Xn0l+/Ft3ZDGiDouJZQiIiJdQHJCuWBPkJKaaJqi6SIys4hMOjFhlV/d3vVSQikiItIFjCwIMKFnoHY54uDVHWqlbK1w0l1zArprTr2UUIqIiHQRyXfNmavbMLZaZOrshGXfprVY6cE0RdNxKaEUERHpIs5L6vZ+aXsNkaguImkN16svkZHjapfNOfzLF6Uxoo5JCaWIiEgXMat/JoWZVrt8sCbK2/uCaYyoa4jU6fbWbRiTKaEUERHpIgI+41x1e6dcnbvmrFgC4VCaoumYlFCKiIh0IcnjKDUfZetFR44jWtirdtmqK/GvW57GiDoeJZQiIiJdyDlDs/Ed7fVm9aEw28p115xW8fnqdHtr+qBESihFRES6kF5ZPmb1z0xY96K6vVstnHS1t8ZRJlJCKSIi0sXUmT5I3d6tFpl0Es5/dJ5P357t2O5taYyoY1FCKSIi0sUk3zXn9V01VIR015xWycklMmFawqqAur1rKaEUERHpYib0DDA831+7XBOB+btq0hhR1xBJvtpb3d61lFCKiIh0MWZWp9tb4yhbL3kcpX/dMqgsT1M0HYsSShERkS4oudt77rZqnNNdc1rDDRhCdNDw2mWLRPCvfDuNEXUcSihFRES6oNMGZpEbODp/0M7KKCsOajLu1kqe5FzjKD1KKEVERLqg7IBx5uCshHW62rv1Isnd3ssXQTSSpmg6DiWUIiIiXdQFyd3eGkfZapGiybjcvNplX1kJvk1r0xhRx6CEUkREpItKvq/3O/tC7KtSa1qrBAKEJ89MXKVubyWUIiIiXdWgXD9T+2TULjvgJbVStlqd2zAuU0KphFJERKQLq3O1txLKVgtPmYmzoxc8+bduxA7uTWNE6deshNLMLjCzdWa2wcy+0Ui5GWYWMbMrUxeiiIiIHKsLkrq9X91RQzCi6YNapaAn0TGTElZ190nOm0wozcwP3AtcCEwErjGziQ2U+wkwN9VBioiIyLGZ1jeD/jlH/9yXhRwL9gTTGFHXoOmDEjWnhXImsME5t8k5FwQeAy6tp9ytwJNA927zFRER6UB8ZnUuzpm7vSpN0XQddW7DuPpdCHbf21taU7Pmx7qvL3DOfTq2fB0wyzl3S1yZIcDfgA8Afwaec879M34/paWltQcqLi5O2QsQERGRxr2638/X1x6dk3J4dpQnT9JYylZxjkm/+QaZhw/Wrtp49Rc4PHZyGoNqO0VFRbW/FxYWWvL2QDP2UedJeBeKxbsb+LpzLmJWX/GGg2pLxcXF7XaszkZ10zjVT8NUNw1T3TRMddOw9qibgSOjfGf9LkJRb3lrtY+1mUPpn938a3PNYGb/rKYLplBHP2/spDnw6tO1y8P3vk/NhZe32/E7Uv00J6HcDgyLWx4K7EwqcxLwWCyZ7AtcZGZh59z/pSJIEREROXYFGT5OG5jFazuPdsle9+rBRp5RV6YP9n5iSKpD69TC004mIy6h9C9dANd/ycu+u5nm/GuyBCgys1FmlglcDTwTX8A5N8o5N9I5NxL4J/A/SiZFREQ6jvOSxlFK60WOm47LPNpq6zu4F9+2TWmMKH2aTCidc2HgFryrt9cATzjnVpnZTWZ2U1sHKCIiIq136cgcCjK6X8tZm8rMIjLxhIRV3XX6oOZ0eeOcex54Pmnd/Q2UvaH1YYmIiEgqDc7z89cP9OEPa8rZVxVt8fMDuhVKvcLTTk6YMiiwdAGhSz6WxojSo1kJpYiIiHR+ZwzO4ozB7XthTVcXmTo7Ydm3cRWUlUBBz7TEky76f0NERETkGLne/YkMH1u7bM4RWL44jRGlhxJKERERkVZIbqX0L+t+d81RQikiIiLSCnVuw7hiMYTDaYomPZRQioiIiLRCdPQEonFjJq2yAn/xivQFlAZKKEVERERaw+cnMnVWwir/0u7V7a2EUkRERKSVwlOTur272XyUSihFREREWily/Ek4v7922bdrK7Znexojal9KKEVERERaKzefyPipCau6UyulEkoRERGRFIgkdXt3p3GUSihFREREUiA8LWk+yrXLoKoyTdG0LyWUIiIiIingBg4jOmBo7bJFwvhXvZ3GiNqPEkoRERGRFKkzyXk36fZWQikiIiKSInVuw7h8IUSjaYqm/SihFBEREUmRyPgpuOzc2mVf6SF8W9anMaL2oYRSREREJFUCGUQmz0hc1Q26vZVQioiIiKRQ8jjK7jB9kBJKERERkRSKTJmFM6td9r+/Hju0P40RtT0llCIiIiIp5Hr0Ijp6QsI6fxe/a44SShEREZEUCyfdNSewrGt3eyuhFBEREUmxSPI4ypXvQLAmTdG0PSWUIiIiIikWHT6WaK++tcsWrMa/blkaI2pbSihFREREUs2MyNTuc7W3EkoRERGRNlDvbRidS1M0bUsJpYiIiEgbiEw8AZeRWbvs278b344t6QuoDSmhFBEREWkLWdlEjpuesKqrTh+khFJERESkjdTb7d0FKaEUERERaSORqbMTln3FK6H8cJqiaTtKKEVERETaiOs7kMjQ0bXL5qIEVixOY0RtQwmliIiISBtKbqXsiuMolVCKiIiItKE64yiXL4JIOE3RtA0llCIiIiJtKDp2Ii6vR+2yVZTh27A6jRGlnhJKERERkbbk8xOeOithVVe72lsJpYiIiEgbqzuOUgmliIiIiLRAePJMnO9o2uXfsQXbtyuNEaWWEkoRERGRtpZXQHTc5IRVXanbWwmliIiISDsIT0282rsrdXsroRQRERFpB8nTB/nXLIXqyvQEk2JKKEVERETagRs0nGi/wbXLFg7hX/1uGiNKHSWUIiIiIu3BrO4k511kHKUSShEREZF2Uu9tGJ1LUzSp06yE0swuMLN1ZrbBzL5Rz/aPmdny2OMtM5ua+lBFREREOrfIhKm4rOzaZV/JAXzvF6cxotRoMqE0Mz9wL3AhMBG4xswmJhXbDJzhnJsC3A78IdWBioiIiHR6GZlEjp+RsMrfBbq9m9NCORPY4Jzb5JwLAo8Bl8YXcM695Zw7FFtcCAxNbZgiIiIiXUNXHEdprol+ezO7ErjAOffp2PJ1wCzn3C0NlP8qMOFI+SNKS0trD1Rc3PmbdkVERESORaC8lMl3fzVh3Yov/YJwfo80RdS0oqKi2t8LCwsteXugGfuo8ySg3izUzM4CbgROa25Qbam4uLjdjtXZqG4ap/ppmOqmYaqbhqluGqa6aVhXrpvIqPH4N6+rXS4q20t4+okt2kdHqp/mdHlvB4bFLQ8FdiYXMrMpwJ+AS51zB1ITnoiIiEjXk3zXnEAnv2tOcxLKJUCRmY0ys0zgauCZ+AJmNhx4CrjOObc+9WGKiIiIdB2R5LvmrFwCoWCaomm9JhNK51wYuAWYC6wBnnDOrTKzm8zsplix7wJ9gPvMbKmZvd1mEYuIiIh0ctERRUQLe9cuW3UV/vXL0xhR6zRnDCXOueeB55PW3R/3+6eBTyc/T0RERETq4fMRmTob3/yj6ZV/6QIik05KY1DHTnfKEREREUmDOtMHvbeg0941RwmliIiISBpEJp2IC2TULvv27cR2bU1jRMdOCaWIiIhIOmTnEpkwLWFVYNnC9MTSSkooRURERNKkztXenfSuOUooRURERNIkPHV2wrJ//XKoKEtTNMdOCaWIiIhImrj+g4kMHlm7bNEogZVL0hfQMVJCKSIiIpJGkWlJrZRLO984SiWUIiIiImlU5zaMyxdCNJKmaI6NEkoRERGRNIoWTcLl5tcuW/lhfBvXpDGillNCKSIiIpJO/gDhKbMSVgU62dXeSihFRERE0iySfLV3J5uPUgmliIiISJqFp8zE2dG0zL9tI3ZgTxojahkllCIiIiLpll9ItGhSwqrOdLW3EkoRERGRDiCcdNecwLLOM45SCaWIiIhIB1BnHOXqd6GmOk3RtIwSShEREZEOIDpkFNG+A2qXLRTEv+bdNEbUfEooRURERDoCs7qTnHeS6YOUUIqIiIh0EJGkhNK/bCE4l6Zomk8JpYiIiEgHETluGi4zu3bZd3Afvm0b0xhR8yihFBEREekoMrOITDoxYZW/E3R7K6EUERER6UDqTB+khFJEREREWiKSdF9v36Y1cLgkPcE0kxJKERERkQ7E9e5HZERR7bI5R2D5ojRG1DQllCIiIiIdTCSp27ujj6NUQikiIiLSwdSZj3LlEgiH0xRN05RQioiIiHQw0VHjifboVbtsVRX4i1ekMaLGKaEUERER6Wh8vjoX53Tkbm8llCIiIiIdUGeaPkgJpYiIiEgHFDn+JJw/ULvs270N2709jRE1TAmliIiISEeUk0dk/JSEVYFlHbOVUgmliIiISAfVWaYPUkIpIiIi0kElj6P0r1sGVRVpiqZhSihFREREOig3YCjRQcNqly0Swb9ySRojqp8SShEREZEOrM4k50sXpimShimhFBEREenA6oyjXLYQotE0RVM/JZQiIiIiHVikaDIuJ6922VdWgm/z2jRGVJcSShEREZGOLBAgPHlm4qoOdrW3EkoRERGRDi4ydXbCsn9ZxxpHqYRSREREpIMLT5mFM6td9r9fTMbhQ2mMKJESShEREZGOrkdPomMmJq7asCJNwdSlhFJERESkE0ie5LyweHmaIqmrWQmlmV1gZuvMbIOZfaOe7WZmv45tX25mJ6Q+VBEREZHuK3kcZf6WNRCsSVM0iZpMKM3MD9wLXAhMBK4xs4lJxS4EimKPzwK/S3GcIiIiIt1adNgYor371S77Q0H8a5amL6A4zWmhnAlscM5tcs4FgceAS5PKXAo87DwLgZ5mNijFsYqIiIh0X2ZEYnfNcVnZlIybhsvJTXNQHnPONV7A7ErgAufcp2PL1wGznHO3xJV5DrjLOfdmbPkV4OvOubePlCktLa09UHFxcUpfhIiIiEh3kL13OxllJZSPGI8LZLTbcYuKimp/LywstOTtgWbso86TgOQstDll6g2qLRUXF7fbsTob1U3jVD8NU900THXTMNVNw1Q3DVPd1COuPjpS/TSny3s7MCxueSiw8xjKiIiIiEgX1JyEcglQZGajzCwTuBp4JqnMM8D1sau9ZwOlzrldKY5VRERERDqgJru8nXNhM7sFmAv4gQecc6vM7KbY9vuB54GLgA1AJfDJtgtZRERERDqS5oyhxDn3PF7SGL/u/rjfHXBzakMTERERkc5Ad8oRERERkVZRQikiIiIiraKEUkRERERaRQmliIiIiLSKEkoRERERaZUmb72YKvG3XhQRERGRzqm+Wy+qhVJEREREWkUJpYiIiIi0Srt1eYuIiIhI16QWShERERFpFSWUIiIiItIqXTahNLMLzGydmW0ws2+kO56OxMy2mNkKM1tqZm+nO550MrMHzGyvma2MW9fbzF4ys+LYz17pjDFdGqib75vZjti5s9TMLkpnjOliZsPM7DUzW2Nmq8zsi7H13f7caaRudO4AZpZtZovNbFmsfn4QW69zp+G60bkTY2Z+M3vPzJ6LLXeY86ZLjqE0Mz+wHjgX2A4sAa5xzq1Oa2AdhJltAU5yzu1PdyzpZmanA+XAw86542PrfgocdM7dFftnpJdz7uvpjDMdGqib7wPlzrmfpzO2dDOzQcAg59y7ZlYAvAN8GLiBbn7uNFI3H0HnDmZmQJ5zrtzMMoA3gS8Cl6Nzp6G6uQCdOwCY2W3ASUAP59zFHenvVVdtoZwJbHDObXLOBYHHgEvTHJN0QM65+cDBpNWXAn+J/f4XvD+G3U4DdSOAc26Xc+7d2O9lwBpgCDp3GqsbAZynPLaYEXs4dO40VjcCmNlQ4IPAn+JWd5jzpqsmlEOAbXHL29EXWjwHvGhm75jZZ9MdTAc0wDm3C7w/jkD/NMfT0dxiZstjXeLdrlsumZmNBKYDi9C5kyCpbkDnDlDbbbkU2Au85JzTuRPTQN2Azh2Au4H/BaJx6zrMedNVE8o6M7ij/3LineqcOwG4ELg51rUp0hy/A8YA04BdwC/SGk2amVk+8CTwJefc4XTH05HUUzc6d2KccxHn3DRgKDDTzI5Pc0gdRgN10+3PHTO7GNjrnHsn3bE0pKsmlNuBYXHLQ4GdaYqlw3HO7Yz93Av8C2+IgBy1JzYO7Mh4sL1pjqfDcM7tiX3hR4E/0o3PndgYryeBR51zT8VW69yh/rrRuVOXc64EmIc3RlDnTpz4utG5A8CpwIdi10A8BnzAzP5KBzpvumpCuQQoMrNRZpYJXA08k+aYOgQzy4sNlMfM8oDzgJWNP6vbeQb4ROz3TwBPpzGWDuXIF1fMZXTTcyd28cCfgTXOuV/Gber2505DdaNzx2Nm/cysZ+z3HOAcYC06dxqsG5074Jz7pnNuqHNuJF5O86pz7uN0oPMmkK4DtyXnXNjMbgHmAn7gAefcqjSH1VEMAP7lfecTAP7mnHshvSGlj5n9HTgT6Gtm24HvAXcBT5jZjcBW4Kr0RZg+DdTNmWY2DW8IyRbgc+mKL81OBa4DVsTGewF8C5070HDdXKNzB4BBwF9is5H4gCecc8+Z2QJ07jRUN4/o3GlQh/nO6ZLTBomIiIhI++mqXd4iIiIi0k6UUIqIiIhIqyihFBEREZFWUUIpIiIiIq2ihFJEREREWkUJpYh0W2Y23MzKY9OUpHrfD5nZj2K/zzGzdak+hohIR6GEUkS6FTPbYmbnADjntjrn8p1zkbY8pnPuDefc+LY8hohIOimhFBEREZFWUUIpIt2GmT0CDAeejXV1/6+ZOTMLxLbPM7Mfmdlbse3PmlkfM3vUzA6b2RIzGxm3vwlm9pKZHTSzdWb2kQaOe2bsbkNHlreY2VfNbLmZlZrZ42aWHbf9YjNbamYlsVimtFmliIikgBJKEek2nHPX4d2e7BLnXD7wRD3Frsa7deAQYAywAHgQ6A2swbsFJWaWB7wE/A3oD1wD3Gdmk5oZzkeAC4BRwBTghth+TwAewLu9XB/g98AzZpbVslcrItJ+lFCKiCR60Dm30TlXCvwH2Oice9k5Fwb+AUyPlbsY2OKce9A5F3bOvQs8CVzZzOP82jm30zl3EHgWmBZb/xng9865Rc65iHPuL0ANMDs1L09EJPUC6Q5ARKSD2RP3e1U9y/mx30cAs8ysJG57AHikmcfZHfd7JTA4br+fMLNb47Znxm0XEelwlFCKSHfjUrSfbcDrzrlzU7S/+P3e4Zy7I8X7FRFpM+ryFpHuZg8wOgX7eQ4YZ2bXmVlG7DHDzI5r5X7/CNxkZrPMk2dmHzSzghTELCLSJpRQikh3cyfwnVhXdXPHO9bhnCsDzsO7iGcnXhf2T4BWXTzjnHsbbxzlb4FDwAZiF+yIiHRU5lyqen9EREREpDtSC6WIiIiItIoSShERERFpFSWUIiIiItIqSihFREREpFWUUIqIiIhIqyihFBEREZFWUUIpIiIiIq2ihFJEREREWuX/A5XHGCWOzUcqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Environmental Satisfaction\n",
    "\n",
    "EnvironmentalLowSatisfaction = ((df['EnvironmentSatisfaction'] == 1) | (df['EnvironmentSatisfaction'] == 2))\n",
    "EnvironmentalHighSatisfaction = ((df['EnvironmentSatisfaction'] == 3) | (df['EnvironmentSatisfaction'] == 4))\n",
    "\n",
    "ax = plt.subplot()\n",
    "kmf = KaplanMeierFitter()\n",
    "\n",
    "kmf.fit(durations=df[EnvironmentalLowSatisfaction]['YearsAtCompany'], \n",
    "        event_observed=df[EnvironmentalLowSatisfaction]['Attrition'], \n",
    "        label='Low')\n",
    "kmf.survival_function_.plot(figsize=(10,5), ax=ax)\n",
    "\n",
    "kmf.fit(durations=df[EnvironmentalHighSatisfaction]['YearsAtCompany'], \n",
    "        event_observed=df[EnvironmentalHighSatisfaction]['Attrition'], \n",
    "        label='High')\n",
    "kmf.survival_function_.plot(figsize=(10,5), ax=ax)\n",
    "plt.title('Survival Function based on Environmental Satisfaction')\n",
    "plt.show();\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "332925b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender\n",
    "\n",
    "female = (df['Gender'] == 'Female')\n",
    "male = (df['Gender'] == 'Male')\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "ax = plt.subplot()\n",
    "\n",
    "kmf.fit(durations=df[male]['YearsAtCompany'], event_observed=df[male]['Attrition'], label='Male')\n",
    "kmf.survival_function_.plot(figsize=(10,5), ax=ax)\n",
    "kmf.fit(durations=df[female]['YearsAtCompany'], event_observed=df[female]['Attrition'], label='Female')\n",
    "kmf.survival_function_.plot(figsize=(10,5), ax=ax)\n",
    "plt.title('Survival Function based on Gender')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fbe44b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Work-Life balance\n",
    "\n",
    "worklifelow = ((df['WorkLifeBalance'] == 1) | (df['WorkLifeBalance'] == 2))\n",
    "worklifehigh = ((df['WorkLifeBalance'] == 3) | (df['WorkLifeBalance'] == 4))\n",
    "\n",
    "ax = plt.subplot()\n",
    "kmf = KaplanMeierFitter()\n",
    "\n",
    "kmf.fit(durations=df[worklifelow]['YearsAtCompany'], \n",
    "        event_observed=df[worklifelow]['Attrition'], \n",
    "        label='Low')\n",
    "kmf.survival_function_.plot(figsize=(8,5), ax=ax)\n",
    "\n",
    "kmf.fit(durations=df[worklifehigh]['YearsAtCompany'], \n",
    "        event_observed=df[worklifehigh]['Attrition'], \n",
    "        label='High')\n",
    "kmf.survival_function_.plot(figsize=(8,5), ax=ax)\n",
    "plt.title('Survival Function based on Work Life Balance')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a2c1d0da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method StatisticalResult.print_summary of <lifelines.StatisticalResult: logrank_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "         test_name = logrank_test\n",
       "\n",
       "---\n",
       " test_statistic    p  -log2(p)\n",
       "           4.40 0.04      4.80>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.statistics import logrank_test\n",
    "\n",
    "output = logrank_test(durations_A = df[worklifelow]['YearsAtCompany'],\n",
    "                      durations_B = df[worklifehigh]['YearsAtCompany'],\n",
    "                      event_observed_A = df[worklifelow]['Attrition'],\n",
    "                      event_observed_B = df[worklifehigh]['Attrition'])\n",
    "output.print_summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b5cbf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method StatisticalResult.print_summary of <lifelines.StatisticalResult: logrank_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "         test_name = logrank_test\n",
       "\n",
       "---\n",
       " test_statistic    p  -log2(p)\n",
       "           1.79 0.18      2.47>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = logrank_test(durations_A = df[male]['YearsAtCompany'],\n",
    "                      durations_B = df[female]['YearsAtCompany'],\n",
    "                      event_observed_A = df[male]['Attrition'],\n",
    "                      event_observed_B = df[female]['Attrition'])\n",
    "output.print_summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bf3caafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing the dataframe for fitting\n",
    "columns = ['Attrition',\n",
    "                    'EnvironmentSatisfaction',\n",
    "                    'JobInvolvement',\n",
    "                    'JobLevel',\n",
    "                    'JobSatisfaction',\n",
    "                    'PercentSalaryHike',\n",
    "                    'RelationshipSatisfaction',\n",
    "                    'StockOptionLevel', \n",
    "                    'TrainingTimesLastYear', \n",
    "                    'YearsAtCompany',\n",
    "                    ]\n",
    "df = df[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fedac4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Attrition  EnvironmentSatisfaction  JobInvolvement  JobLevel  \\\n",
      "0             1                        2               3         2   \n",
      "1             0                        3               2         2   \n",
      "2             1                        4               2         1   \n",
      "3             0                        4               3         1   \n",
      "4             0                        1               3         1   \n",
      "...         ...                      ...             ...       ...   \n",
      "1465          0                        3               4         2   \n",
      "1466          0                        4               2         3   \n",
      "1467          0                        2               4         2   \n",
      "1468          0                        4               2         2   \n",
      "1469          0                        2               4         2   \n",
      "\n",
      "      JobSatisfaction  PercentSalaryHike  RelationshipSatisfaction  \\\n",
      "0                   4                 11                         1   \n",
      "1                   2                 23                         4   \n",
      "2                   3                 15                         2   \n",
      "3                   3                 11                         3   \n",
      "4                   2                 12                         4   \n",
      "...               ...                ...                       ...   \n",
      "1465                4                 17                         3   \n",
      "1466                1                 15                         1   \n",
      "1467                2                 20                         2   \n",
      "1468                2                 14                         4   \n",
      "1469                3                 12                         1   \n",
      "\n",
      "      StockOptionLevel  TrainingTimesLastYear  YearsAtCompany  \n",
      "0                    0                      0               6  \n",
      "1                    1                      3              10  \n",
      "2                    0                      3               0  \n",
      "3                    0                      3               8  \n",
      "4                    1                      3               2  \n",
      "...                ...                    ...             ...  \n",
      "1465                 1                      3               5  \n",
      "1466                 1                      5               7  \n",
      "1467                 1                      0               6  \n",
      "1468                 0                      3               9  \n",
      "1469                 0                      3               4  \n",
      "\n",
      "[1470 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d57098cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 1470 total observations, 1233 right-censored observations>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines import CoxPHFitter \n",
    "\n",
    "coxph = CoxPHFitter()\n",
    "coxph.fit(df, \n",
    "          duration_col='YearsAtCompany',\n",
    "          event_col='Attrition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "67d59145",
   "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>1</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.986432</td>\n",
       "      <td>0.954723</td>\n",
       "      <td>0.960284</td>\n",
       "      <td>0.962204</td>\n",
       "      <td>0.988253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.986432</td>\n",
       "      <td>0.832633</td>\n",
       "      <td>0.916395</td>\n",
       "      <td>0.921246</td>\n",
       "      <td>0.978083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.967805</td>\n",
       "      <td>0.809508</td>\n",
       "      <td>0.861182</td>\n",
       "      <td>0.810189</td>\n",
       "      <td>0.966473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.945323</td>\n",
       "      <td>0.809508</td>\n",
       "      <td>0.827520</td>\n",
       "      <td>0.789045</td>\n",
       "      <td>0.951264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.932830</td>\n",
       "      <td>0.778306</td>\n",
       "      <td>0.780170</td>\n",
       "      <td>0.789045</td>\n",
       "      <td>0.941632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.915764</td>\n",
       "      <td>0.741487</td>\n",
       "      <td>0.733267</td>\n",
       "      <td>0.760458</td>\n",
       "      <td>0.927576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.896696</td>\n",
       "      <td>0.721424</td>\n",
       "      <td>0.683692</td>\n",
       "      <td>0.726633</td>\n",
       "      <td>0.913012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.873145</td>\n",
       "      <td>0.694475</td>\n",
       "      <td>0.556009</td>\n",
       "      <td>0.708160</td>\n",
       "      <td>0.896843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.842240</td>\n",
       "      <td>0.664989</td>\n",
       "      <td>0.532844</td>\n",
       "      <td>0.683295</td>\n",
       "      <td>0.850746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1         3         4         5         6\n",
       "0.0  1.000000  1.000000  1.000000  1.000000  1.000000\n",
       "1.0  0.986432  0.954723  0.960284  0.962204  0.988253\n",
       "2.0  0.986432  0.832633  0.916395  0.921246  0.978083\n",
       "3.0  0.967805  0.809508  0.861182  0.810189  0.966473\n",
       "4.0  0.945323  0.809508  0.827520  0.789045  0.951264\n",
       "5.0  0.932830  0.778306  0.780170  0.789045  0.941632\n",
       "6.0  0.915764  0.741487  0.733267  0.760458  0.927576\n",
       "7.0  0.896696  0.721424  0.683692  0.726633  0.913012\n",
       "8.0  0.873145  0.694475  0.556009  0.708160  0.896843\n",
       "9.0  0.842240  0.664989  0.532844  0.683295  0.850746"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Current employees\n",
    "df_new = df.loc[df['Attrition'] == 0]\n",
    "\n",
    "#Years at the company of current employees\n",
    "df_new_obs = df_new['YearsAtCompany']\n",
    "\n",
    "# Predict survival function for the employes still in the company\n",
    "predictions = coxph.predict_survival_function(df_new, \n",
    "                              conditional_after=df_new_obs)\n",
    "pd.DataFrame(predictions).head(10).iloc[:, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7e409c45",
   "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.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'YearsAtCompany'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'Attrition'</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>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-1449.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-07-15 22:53:51 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>EnvironmentSatisfaction</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>13.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.08</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>21.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>-1.02</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-10.77</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>87.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>12.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.63</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
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       "</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>Partial AIC</th>\n",
       "      <td>2914.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>249.92 on 8 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>161.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       "\\toprule\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",
       "\\midrule\n",
       "EnvironmentSatisfaction  & -0.24 &       0.79 &      0.06 &           -0.35 &           -0.12 &                 0.70 &                 0.89 &    0.00 &  -4.00 & 0.00 &     13.92 \\\\\n",
       "JobInvolvement           & -0.44 &       0.65 &      0.09 &           -0.61 &           -0.27 &                 0.55 &                 0.76 &    0.00 &  -5.08 & 0.00 &     21.31 \\\\\n",
       "JobLevel                 & -1.02 &       0.36 &      0.09 &           -1.20 &           -0.83 &                 0.30 &                 0.44 &    0.00 & -10.77 & 0.00 &     87.49 \\\\\n",
       "JobSatisfaction          & -0.22 &       0.81 &      0.06 &           -0.33 &           -0.10 &                 0.72 &                 0.90 &    0.00 &  -3.77 & 0.00 &     12.61 \\\\\n",
       "PercentSalaryHike        & -0.01 &       0.99 &      0.02 &           -0.04 &            0.03 &                 0.96 &                 1.03 &    0.00 &  -0.44 & 0.66 &      0.60 \\\\\n",
       "RelationshipSatisfaction & -0.08 &       0.92 &      0.06 &           -0.20 &            0.04 &                 0.82 &                 1.04 &    0.00 &  -1.34 & 0.18 &      2.46 \\\\\n",
       "StockOptionLevel         & -0.42 &       0.65 &      0.09 &           -0.60 &           -0.24 &                 0.55 &                 0.78 &    0.00 &  -4.63 & 0.00 &     18.04 \\\\\n",
       "TrainingTimesLastYear    & -0.17 &       0.85 &      0.05 &           -0.28 &           -0.06 &                 0.76 &                 0.94 &    0.00 &  -3.07 & 0.00 &      8.84 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 1470 total observations, 1233 right-censored observations>\n",
       "             duration col = 'YearsAtCompany'\n",
       "                event col = 'Attrition'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 1470\n",
       "number of events observed = 237\n",
       "   partial log-likelihood = -1449.16\n",
       "         time fit was run = 2022-07-15 22:53:51 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",
       "EnvironmentSatisfaction   -0.24       0.79       0.06            -0.35            -0.12                 0.70                 0.89\n",
       "JobInvolvement            -0.44       0.65       0.09            -0.61            -0.27                 0.55                 0.76\n",
       "JobLevel                  -1.02       0.36       0.09            -1.20            -0.83                 0.30                 0.44\n",
       "JobSatisfaction           -0.22       0.81       0.06            -0.33            -0.10                 0.72                 0.90\n",
       "PercentSalaryHike         -0.01       0.99       0.02            -0.04             0.03                 0.96                 1.03\n",
       "RelationshipSatisfaction  -0.08       0.92       0.06            -0.20             0.04                 0.82                 1.04\n",
       "StockOptionLevel          -0.42       0.65       0.09            -0.60            -0.24                 0.55                 0.78\n",
       "TrainingTimesLastYear     -0.17       0.85       0.05            -0.28            -0.06                 0.76                 0.94\n",
       "\n",
       "                           cmp to      z      p   -log2(p)\n",
       "covariate                                                 \n",
       "EnvironmentSatisfaction      0.00  -4.00 <0.005      13.92\n",
       "JobInvolvement               0.00  -5.08 <0.005      21.31\n",
       "JobLevel                     0.00 -10.77 <0.005      87.49\n",
       "JobSatisfaction              0.00  -3.77 <0.005      12.61\n",
       "PercentSalaryHike            0.00  -0.44   0.66       0.60\n",
       "RelationshipSatisfaction     0.00  -1.34   0.18       2.46\n",
       "StockOptionLevel             0.00  -4.63 <0.005      18.04\n",
       "TrainingTimesLastYear        0.00  -3.07 <0.005       8.84\n",
       "---\n",
       "Concordance = 0.79\n",
       "Partial AIC = 2914.33\n",
       "log-likelihood ratio test = 249.92 on 8 df\n",
       "-log2(p) of ll-ratio test = 161.93"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coxph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0e4b0191",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coxph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87d7fac9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportional hazard assumption looks okay.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coxph.check_assumptions(df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}