Female_Attrition_Model_Shadow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "125befda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\S K PANKAJ\\Desktop\\people_analytics\n"
     ]
    }
   ],
   "source": [
    "%cd \"C:\\Users\\S K PANKAJ\\Desktop\\people_analytics\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f22f5eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d98f3e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
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       "      <th>DailyRate</th>\n",
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       "      <th>RelationshipSatisfaction</th>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>27</td>\n",
       "      <td>No</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
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      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "hr_data = pd.read_csv(\"HR-Employee-Attrition.csv\")\n",
    "hr_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7b3a0db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EmployeeNumber              0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values check\n",
    "hr_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b199ab30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                   object\n",
       "BusinessTravel              object\n",
       "DailyRate                    int64\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EmployeeCount                int64\n",
       "EmployeeNumber               int64\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "HourlyRate                   int64\n",
       "JobInvolvement               int64\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "MonthlyRate                  int64\n",
       "NumCompaniesWorked           int64\n",
       "Over18                      object\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "RelationshipSatisfaction     int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "WorkLifeBalance              int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the structure of dataset\n",
    "hr_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c18d418",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.001330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050263</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>-0.008548</td>\n",
       "      <td>-0.004607</td>\n",
       "      <td>-0.019582</td>\n",
       "      <td>-0.024106</td>\n",
       "      <td>-0.026716</td>\n",
       "      <td>-0.020123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>0.029820</td>\n",
       "      <td>0.046135</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.042438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006888</td>\n",
       "      <td>-0.008278</td>\n",
       "      <td>0.042861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034297</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021523</td>\n",
       "      <td>-0.005533</td>\n",
       "      <td>-0.015338</td>\n",
       "      <td>-0.014617</td>\n",
       "      <td>-0.021355</td>\n",
       "      <td>0.008717</td>\n",
       "      <td>-0.024184</td>\n",
       "      <td>0.025976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.002966</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.101589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018519</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.027853</td>\n",
       "      <td>-0.012630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.013984</td>\n",
       "      <td>0.782208</td>\n",
       "      <td>-0.018191</td>\n",
       "      <td>0.037818</td>\n",
       "      <td>0.534739</td>\n",
       "      <td>0.389447</td>\n",
       "      <td>0.353885</td>\n",
       "      <td>0.375281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>-0.004892</td>\n",
       "      <td>0.030571</td>\n",
       "      <td>-0.003669</td>\n",
       "      <td>-0.011296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.046247</td>\n",
       "      <td>-0.006784</td>\n",
       "      <td>-0.071335</td>\n",
       "      <td>-0.021476</td>\n",
       "      <td>-0.001944</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010690</td>\n",
       "      <td>-0.020185</td>\n",
       "      <td>-0.005779</td>\n",
       "      <td>-0.019459</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>-0.002305</td>\n",
       "      <td>-0.018214</td>\n",
       "      <td>-0.027656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>0.497855</td>\n",
       "      <td>0.007707</td>\n",
       "      <td>-0.017014</td>\n",
       "      <td>0.094961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014829</td>\n",
       "      <td>-0.006259</td>\n",
       "      <td>-0.015794</td>\n",
       "      <td>-0.015271</td>\n",
       "      <td>0.950300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005408</td>\n",
       "      <td>0.772893</td>\n",
       "      <td>-0.021736</td>\n",
       "      <td>0.030683</td>\n",
       "      <td>0.514285</td>\n",
       "      <td>0.363818</td>\n",
       "      <td>0.344978</td>\n",
       "      <td>0.344079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>0.028051</td>\n",
       "      <td>-0.032182</td>\n",
       "      <td>0.027473</td>\n",
       "      <td>-0.026084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012648</td>\n",
       "      <td>0.037600</td>\n",
       "      <td>-0.015297</td>\n",
       "      <td>-0.016322</td>\n",
       "      <td>0.039563</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.034323</td>\n",
       "      <td>0.026442</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.023655</td>\n",
       "      <td>-0.012815</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>-0.036746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>0.299635</td>\n",
       "      <td>0.038153</td>\n",
       "      <td>-0.029251</td>\n",
       "      <td>0.126317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001251</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.022157</td>\n",
       "      <td>0.015012</td>\n",
       "      <td>0.142501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030075</td>\n",
       "      <td>0.237639</td>\n",
       "      <td>-0.066054</td>\n",
       "      <td>-0.008366</td>\n",
       "      <td>-0.118421</td>\n",
       "      <td>-0.090754</td>\n",
       "      <td>-0.036814</td>\n",
       "      <td>-0.110319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>0.003634</td>\n",
       "      <td>0.022704</td>\n",
       "      <td>0.040235</td>\n",
       "      <td>-0.011111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012944</td>\n",
       "      <td>-0.031701</td>\n",
       "      <td>-0.009062</td>\n",
       "      <td>-0.017205</td>\n",
       "      <td>-0.034730</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007528</td>\n",
       "      <td>-0.020608</td>\n",
       "      <td>-0.005221</td>\n",
       "      <td>-0.003280</td>\n",
       "      <td>-0.035991</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>-0.022154</td>\n",
       "      <td>-0.011985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerformanceRating</th>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>-0.024539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020359</td>\n",
       "      <td>-0.029548</td>\n",
       "      <td>-0.002172</td>\n",
       "      <td>-0.029071</td>\n",
       "      <td>-0.021222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003506</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>-0.015579</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.034986</td>\n",
       "      <td>0.017896</td>\n",
       "      <td>0.022827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>0.053535</td>\n",
       "      <td>0.007846</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.069861</td>\n",
       "      <td>0.007665</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.034297</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.045952</td>\n",
       "      <td>0.024054</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>0.019367</td>\n",
       "      <td>-0.015123</td>\n",
       "      <td>0.033493</td>\n",
       "      <td>-0.000867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardHours</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>0.037510</td>\n",
       "      <td>0.042143</td>\n",
       "      <td>0.044872</td>\n",
       "      <td>0.018422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062227</td>\n",
       "      <td>0.003432</td>\n",
       "      <td>0.050263</td>\n",
       "      <td>0.021523</td>\n",
       "      <td>0.013984</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.011274</td>\n",
       "      <td>0.004129</td>\n",
       "      <td>0.015058</td>\n",
       "      <td>0.050818</td>\n",
       "      <td>0.014352</td>\n",
       "      <td>0.024698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>0.680381</td>\n",
       "      <td>0.014515</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.148280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014365</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>-0.005533</td>\n",
       "      <td>0.782208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.460365</td>\n",
       "      <td>0.404858</td>\n",
       "      <td>0.459188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>-0.019621</td>\n",
       "      <td>0.002453</td>\n",
       "      <td>-0.036942</td>\n",
       "      <td>-0.025100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023603</td>\n",
       "      <td>-0.019359</td>\n",
       "      <td>-0.008548</td>\n",
       "      <td>-0.015338</td>\n",
       "      <td>-0.018191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011274</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028072</td>\n",
       "      <td>0.003569</td>\n",
       "      <td>-0.005738</td>\n",
       "      <td>-0.002067</td>\n",
       "      <td>-0.004096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <td>-0.021490</td>\n",
       "      <td>-0.037848</td>\n",
       "      <td>-0.026556</td>\n",
       "      <td>0.009819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.027627</td>\n",
       "      <td>-0.004607</td>\n",
       "      <td>-0.014617</td>\n",
       "      <td>0.037818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004129</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.028072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012089</td>\n",
       "      <td>0.049856</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.002759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>0.311309</td>\n",
       "      <td>-0.034055</td>\n",
       "      <td>0.009508</td>\n",
       "      <td>0.069114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011240</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>-0.019582</td>\n",
       "      <td>-0.021355</td>\n",
       "      <td>0.534739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015058</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.003569</td>\n",
       "      <td>0.012089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758754</td>\n",
       "      <td>0.618409</td>\n",
       "      <td>0.769212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <td>0.212901</td>\n",
       "      <td>0.009932</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>0.060236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008416</td>\n",
       "      <td>0.018007</td>\n",
       "      <td>-0.024106</td>\n",
       "      <td>0.008717</td>\n",
       "      <td>0.389447</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050818</td>\n",
       "      <td>0.460365</td>\n",
       "      <td>-0.005738</td>\n",
       "      <td>0.049856</td>\n",
       "      <td>0.758754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548056</td>\n",
       "      <td>0.714365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0.216513</td>\n",
       "      <td>-0.033229</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009019</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>-0.026716</td>\n",
       "      <td>-0.024184</td>\n",
       "      <td>0.353885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014352</td>\n",
       "      <td>0.404858</td>\n",
       "      <td>-0.002067</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.618409</td>\n",
       "      <td>0.548056</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.510224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>0.202089</td>\n",
       "      <td>-0.026363</td>\n",
       "      <td>0.014406</td>\n",
       "      <td>0.069065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009197</td>\n",
       "      <td>-0.004999</td>\n",
       "      <td>-0.020123</td>\n",
       "      <td>0.025976</td>\n",
       "      <td>0.375281</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024698</td>\n",
       "      <td>0.459188</td>\n",
       "      <td>-0.004096</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.769212</td>\n",
       "      <td>0.714365</td>\n",
       "      <td>0.510224</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Age  DailyRate  DistanceFromHome  Education  \\\n",
       "Age                       1.000000   0.010661         -0.001686   0.208034   \n",
       "DailyRate                 0.010661   1.000000         -0.004985  -0.016806   \n",
       "DistanceFromHome         -0.001686  -0.004985          1.000000   0.021042   \n",
       "Education                 0.208034  -0.016806          0.021042   1.000000   \n",
       "EmployeeCount                  NaN        NaN               NaN        NaN   \n",
       "EmployeeNumber           -0.010145  -0.050990          0.032916   0.042070   \n",
       "EnvironmentSatisfaction   0.010146   0.018355         -0.016075  -0.027128   \n",
       "HourlyRate                0.024287   0.023381          0.031131   0.016775   \n",
       "JobInvolvement            0.029820   0.046135          0.008783   0.042438   \n",
       "JobLevel                  0.509604   0.002966          0.005303   0.101589   \n",
       "JobSatisfaction          -0.004892   0.030571         -0.003669  -0.011296   \n",
       "MonthlyIncome             0.497855   0.007707         -0.017014   0.094961   \n",
       "MonthlyRate               0.028051  -0.032182          0.027473  -0.026084   \n",
       "NumCompaniesWorked        0.299635   0.038153         -0.029251   0.126317   \n",
       "PercentSalaryHike         0.003634   0.022704          0.040235  -0.011111   \n",
       "PerformanceRating         0.001904   0.000473          0.027110  -0.024539   \n",
       "RelationshipSatisfaction  0.053535   0.007846          0.006557  -0.009118   \n",
       "StandardHours                  NaN        NaN               NaN        NaN   \n",
       "StockOptionLevel          0.037510   0.042143          0.044872   0.018422   \n",
       "TotalWorkingYears         0.680381   0.014515          0.004628   0.148280   \n",
       "TrainingTimesLastYear    -0.019621   0.002453         -0.036942  -0.025100   \n",
       "WorkLifeBalance          -0.021490  -0.037848         -0.026556   0.009819   \n",
       "YearsAtCompany            0.311309  -0.034055          0.009508   0.069114   \n",
       "YearsInCurrentRole        0.212901   0.009932          0.018845   0.060236   \n",
       "YearsSinceLastPromotion   0.216513  -0.033229          0.010029   0.054254   \n",
       "YearsWithCurrManager      0.202089  -0.026363          0.014406   0.069065   \n",
       "\n",
       "                          EmployeeCount  EmployeeNumber  \\\n",
       "Age                                 NaN       -0.010145   \n",
       "DailyRate                           NaN       -0.050990   \n",
       "DistanceFromHome                    NaN        0.032916   \n",
       "Education                           NaN        0.042070   \n",
       "EmployeeCount                       NaN             NaN   \n",
       "EmployeeNumber                      NaN        1.000000   \n",
       "EnvironmentSatisfaction             NaN        0.017621   \n",
       "HourlyRate                          NaN        0.035179   \n",
       "JobInvolvement                      NaN       -0.006888   \n",
       "JobLevel                            NaN       -0.018519   \n",
       "JobSatisfaction                     NaN       -0.046247   \n",
       "MonthlyIncome                       NaN       -0.014829   \n",
       "MonthlyRate                         NaN        0.012648   \n",
       "NumCompaniesWorked                  NaN       -0.001251   \n",
       "PercentSalaryHike                   NaN       -0.012944   \n",
       "PerformanceRating                   NaN       -0.020359   \n",
       "RelationshipSatisfaction            NaN       -0.069861   \n",
       "StandardHours                       NaN             NaN   \n",
       "StockOptionLevel                    NaN        0.062227   \n",
       "TotalWorkingYears                   NaN       -0.014365   \n",
       "TrainingTimesLastYear               NaN        0.023603   \n",
       "WorkLifeBalance                     NaN        0.010309   \n",
       "YearsAtCompany                      NaN       -0.011240   \n",
       "YearsInCurrentRole                  NaN       -0.008416   \n",
       "YearsSinceLastPromotion             NaN       -0.009019   \n",
       "YearsWithCurrManager                NaN       -0.009197   \n",
       "\n",
       "                          EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "Age                                      0.010146    0.024287        0.029820   \n",
       "DailyRate                                0.018355    0.023381        0.046135   \n",
       "DistanceFromHome                        -0.016075    0.031131        0.008783   \n",
       "Education                               -0.027128    0.016775        0.042438   \n",
       "EmployeeCount                                 NaN         NaN             NaN   \n",
       "EmployeeNumber                           0.017621    0.035179       -0.006888   \n",
       "EnvironmentSatisfaction                  1.000000   -0.049857       -0.008278   \n",
       "HourlyRate                              -0.049857    1.000000        0.042861   \n",
       "JobInvolvement                          -0.008278    0.042861        1.000000   \n",
       "JobLevel                                 0.001212   -0.027853       -0.012630   \n",
       "JobSatisfaction                         -0.006784   -0.071335       -0.021476   \n",
       "MonthlyIncome                           -0.006259   -0.015794       -0.015271   \n",
       "MonthlyRate                              0.037600   -0.015297       -0.016322   \n",
       "NumCompaniesWorked                       0.012594    0.022157        0.015012   \n",
       "PercentSalaryHike                       -0.031701   -0.009062       -0.017205   \n",
       "PerformanceRating                       -0.029548   -0.002172       -0.029071   \n",
       "RelationshipSatisfaction                 0.007665    0.001330        0.034297   \n",
       "StandardHours                                 NaN         NaN             NaN   \n",
       "StockOptionLevel                         0.003432    0.050263        0.021523   \n",
       "TotalWorkingYears                       -0.002693   -0.002334       -0.005533   \n",
       "TrainingTimesLastYear                   -0.019359   -0.008548       -0.015338   \n",
       "WorkLifeBalance                          0.027627   -0.004607       -0.014617   \n",
       "YearsAtCompany                           0.001458   -0.019582       -0.021355   \n",
       "YearsInCurrentRole                       0.018007   -0.024106        0.008717   \n",
       "YearsSinceLastPromotion                  0.016194   -0.026716       -0.024184   \n",
       "YearsWithCurrManager                    -0.004999   -0.020123        0.025976   \n",
       "\n",
       "                          JobLevel  ...  RelationshipSatisfaction  \\\n",
       "Age                       0.509604  ...                  0.053535   \n",
       "DailyRate                 0.002966  ...                  0.007846   \n",
       "DistanceFromHome          0.005303  ...                  0.006557   \n",
       "Education                 0.101589  ...                 -0.009118   \n",
       "EmployeeCount                  NaN  ...                       NaN   \n",
       "EmployeeNumber           -0.018519  ...                 -0.069861   \n",
       "EnvironmentSatisfaction   0.001212  ...                  0.007665   \n",
       "HourlyRate               -0.027853  ...                  0.001330   \n",
       "JobInvolvement           -0.012630  ...                  0.034297   \n",
       "JobLevel                  1.000000  ...                  0.021642   \n",
       "JobSatisfaction          -0.001944  ...                 -0.012454   \n",
       "MonthlyIncome             0.950300  ...                  0.025873   \n",
       "MonthlyRate               0.039563  ...                 -0.004085   \n",
       "NumCompaniesWorked        0.142501  ...                  0.052733   \n",
       "PercentSalaryHike        -0.034730  ...                 -0.040490   \n",
       "PerformanceRating        -0.021222  ...                 -0.031351   \n",
       "RelationshipSatisfaction  0.021642  ...                  1.000000   \n",
       "StandardHours                  NaN  ...                       NaN   \n",
       "StockOptionLevel          0.013984  ...                 -0.045952   \n",
       "TotalWorkingYears         0.782208  ...                  0.024054   \n",
       "TrainingTimesLastYear    -0.018191  ...                  0.002497   \n",
       "WorkLifeBalance           0.037818  ...                  0.019604   \n",
       "YearsAtCompany            0.534739  ...                  0.019367   \n",
       "YearsInCurrentRole        0.389447  ...                 -0.015123   \n",
       "YearsSinceLastPromotion   0.353885  ...                  0.033493   \n",
       "YearsWithCurrManager      0.375281  ...                 -0.000867   \n",
       "\n",
       "                          StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
       "Age                                 NaN          0.037510           0.680381   \n",
       "DailyRate                           NaN          0.042143           0.014515   \n",
       "DistanceFromHome                    NaN          0.044872           0.004628   \n",
       "Education                           NaN          0.018422           0.148280   \n",
       "EmployeeCount                       NaN               NaN                NaN   \n",
       "EmployeeNumber                      NaN          0.062227          -0.014365   \n",
       "EnvironmentSatisfaction             NaN          0.003432          -0.002693   \n",
       "HourlyRate                          NaN          0.050263          -0.002334   \n",
       "JobInvolvement                      NaN          0.021523          -0.005533   \n",
       "JobLevel                            NaN          0.013984           0.782208   \n",
       "JobSatisfaction                     NaN          0.010690          -0.020185   \n",
       "MonthlyIncome                       NaN          0.005408           0.772893   \n",
       "MonthlyRate                         NaN         -0.034323           0.026442   \n",
       "NumCompaniesWorked                  NaN          0.030075           0.237639   \n",
       "PercentSalaryHike                   NaN          0.007528          -0.020608   \n",
       "PerformanceRating                   NaN          0.003506           0.006744   \n",
       "RelationshipSatisfaction            NaN         -0.045952           0.024054   \n",
       "StandardHours                       NaN               NaN                NaN   \n",
       "StockOptionLevel                    NaN          1.000000           0.010136   \n",
       "TotalWorkingYears                   NaN          0.010136           1.000000   \n",
       "TrainingTimesLastYear               NaN          0.011274          -0.035662   \n",
       "WorkLifeBalance                     NaN          0.004129           0.001008   \n",
       "YearsAtCompany                      NaN          0.015058           0.628133   \n",
       "YearsInCurrentRole                  NaN          0.050818           0.460365   \n",
       "YearsSinceLastPromotion             NaN          0.014352           0.404858   \n",
       "YearsWithCurrManager                NaN          0.024698           0.459188   \n",
       "\n",
       "                          TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "Age                                   -0.019621        -0.021490   \n",
       "DailyRate                              0.002453        -0.037848   \n",
       "DistanceFromHome                      -0.036942        -0.026556   \n",
       "Education                             -0.025100         0.009819   \n",
       "EmployeeCount                               NaN              NaN   \n",
       "EmployeeNumber                         0.023603         0.010309   \n",
       "EnvironmentSatisfaction               -0.019359         0.027627   \n",
       "HourlyRate                            -0.008548        -0.004607   \n",
       "JobInvolvement                        -0.015338        -0.014617   \n",
       "JobLevel                              -0.018191         0.037818   \n",
       "JobSatisfaction                       -0.005779        -0.019459   \n",
       "MonthlyIncome                         -0.021736         0.030683   \n",
       "MonthlyRate                            0.001467         0.007963   \n",
       "NumCompaniesWorked                    -0.066054        -0.008366   \n",
       "PercentSalaryHike                     -0.005221        -0.003280   \n",
       "PerformanceRating                     -0.015579         0.002572   \n",
       "RelationshipSatisfaction               0.002497         0.019604   \n",
       "StandardHours                               NaN              NaN   \n",
       "StockOptionLevel                       0.011274         0.004129   \n",
       "TotalWorkingYears                     -0.035662         0.001008   \n",
       "TrainingTimesLastYear                  1.000000         0.028072   \n",
       "WorkLifeBalance                        0.028072         1.000000   \n",
       "YearsAtCompany                         0.003569         0.012089   \n",
       "YearsInCurrentRole                    -0.005738         0.049856   \n",
       "YearsSinceLastPromotion               -0.002067         0.008941   \n",
       "YearsWithCurrManager                  -0.004096         0.002759   \n",
       "\n",
       "                          YearsAtCompany  YearsInCurrentRole  \\\n",
       "Age                             0.311309            0.212901   \n",
       "DailyRate                      -0.034055            0.009932   \n",
       "DistanceFromHome                0.009508            0.018845   \n",
       "Education                       0.069114            0.060236   \n",
       "EmployeeCount                        NaN                 NaN   \n",
       "EmployeeNumber                 -0.011240           -0.008416   \n",
       "EnvironmentSatisfaction         0.001458            0.018007   \n",
       "HourlyRate                     -0.019582           -0.024106   \n",
       "JobInvolvement                 -0.021355            0.008717   \n",
       "JobLevel                        0.534739            0.389447   \n",
       "JobSatisfaction                -0.003803           -0.002305   \n",
       "MonthlyIncome                   0.514285            0.363818   \n",
       "MonthlyRate                    -0.023655           -0.012815   \n",
       "NumCompaniesWorked             -0.118421           -0.090754   \n",
       "PercentSalaryHike              -0.035991           -0.001520   \n",
       "PerformanceRating               0.003435            0.034986   \n",
       "RelationshipSatisfaction        0.019367           -0.015123   \n",
       "StandardHours                        NaN                 NaN   \n",
       "StockOptionLevel                0.015058            0.050818   \n",
       "TotalWorkingYears               0.628133            0.460365   \n",
       "TrainingTimesLastYear           0.003569           -0.005738   \n",
       "WorkLifeBalance                 0.012089            0.049856   \n",
       "YearsAtCompany                  1.000000            0.758754   \n",
       "YearsInCurrentRole              0.758754            1.000000   \n",
       "YearsSinceLastPromotion         0.618409            0.548056   \n",
       "YearsWithCurrManager            0.769212            0.714365   \n",
       "\n",
       "                          YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "Age                                      0.216513              0.202089  \n",
       "DailyRate                               -0.033229             -0.026363  \n",
       "DistanceFromHome                         0.010029              0.014406  \n",
       "Education                                0.054254              0.069065  \n",
       "EmployeeCount                                 NaN                   NaN  \n",
       "EmployeeNumber                          -0.009019             -0.009197  \n",
       "EnvironmentSatisfaction                  0.016194             -0.004999  \n",
       "HourlyRate                              -0.026716             -0.020123  \n",
       "JobInvolvement                          -0.024184              0.025976  \n",
       "JobLevel                                 0.353885              0.375281  \n",
       "JobSatisfaction                         -0.018214             -0.027656  \n",
       "MonthlyIncome                            0.344978              0.344079  \n",
       "MonthlyRate                              0.001567             -0.036746  \n",
       "NumCompaniesWorked                      -0.036814             -0.110319  \n",
       "PercentSalaryHike                       -0.022154             -0.011985  \n",
       "PerformanceRating                        0.017896              0.022827  \n",
       "RelationshipSatisfaction                 0.033493             -0.000867  \n",
       "StandardHours                                 NaN                   NaN  \n",
       "StockOptionLevel                         0.014352              0.024698  \n",
       "TotalWorkingYears                        0.404858              0.459188  \n",
       "TrainingTimesLastYear                   -0.002067             -0.004096  \n",
       "WorkLifeBalance                          0.008941              0.002759  \n",
       "YearsAtCompany                           0.618409              0.769212  \n",
       "YearsInCurrentRole                       0.548056              0.714365  \n",
       "YearsSinceLastPromotion                  1.000000              0.510224  \n",
       "YearsWithCurrManager                     0.510224              1.000000  \n",
       "\n",
       "[26 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pearson Correlation\n",
    "hr_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7208cb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = sns.heatmap(hr_data.corr(),xticklabels=True, yticklabels=True, linewidths = 2)\n",
    "#res.set_xticklabels(res.get_xmajorticklabels(), fontsize = 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a352ffbc",
   "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>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  EnvironmentSatisfaction  HourlyRate  \\\n",
       "0   41       1102                 1                        2          94   \n",
       "1   49        279                 8                        3          61   \n",
       "2   37       1373                 2                        4          92   \n",
       "3   33       1392                 3                        4          56   \n",
       "4   27        591                 2                        1          40   \n",
       "\n",
       "   JobInvolvement  JobLevel  JobSatisfaction  RelationshipSatisfaction  \\\n",
       "0               3         2                4                         1   \n",
       "1               2         2                2                         4   \n",
       "2               2         1                3                         2   \n",
       "3               3         1                3                         3   \n",
       "4               3         1                2                         4   \n",
       "\n",
       "   StockOptionLevel  TrainingTimesLastYear  \n",
       "0                 0                      0  \n",
       "1                 1                      3  \n",
       "2                 0                      3  \n",
       "3                 0                      3  \n",
       "4                 1                      3  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's remove the strongly correlated variables\n",
    "# which are those\n",
    "hr_data_uc = hr_data[['Age','DailyRate','DistanceFromHome', \n",
    "                       'EnvironmentSatisfaction', 'HourlyRate',                     \n",
    "                       'JobInvolvement', 'JobLevel',\n",
    "                       'JobSatisfaction', \n",
    "                       'RelationshipSatisfaction', \n",
    "                       'StockOptionLevel',\n",
    "                        'TrainingTimesLastYear']].copy()\n",
    "hr_data_uc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a057169f",
   "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>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Single</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition     BusinessTravel              Department EducationField  Gender  \\\n",
       "0       Yes      Travel_Rarely                   Sales  Life Sciences  Female   \n",
       "1        No  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
       "2       Yes      Travel_Rarely  Research & Development          Other    Male   \n",
       "3        No  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
       "4        No      Travel_Rarely  Research & Development        Medical    Male   \n",
       "\n",
       "                 JobRole MaritalStatus Over18 OverTime  \n",
       "0        Sales Executive        Single      Y      Yes  \n",
       "1     Research Scientist       Married      Y       No  \n",
       "2  Laboratory Technician        Single      Y      Yes  \n",
       "3     Research Scientist       Married      Y      Yes  \n",
       "4  Laboratory Technician       Married      Y       No  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copy categorical data\n",
    "hr_data_cat = hr_data[['Attrition', 'BusinessTravel','Department',\n",
    "                       'EducationField','Gender','JobRole',\n",
    "                       'MaritalStatus',\n",
    "                       'Over18', 'OverTime']].copy()\n",
    "hr_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "579789a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
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       "      <td>Single</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition     BusinessTravel              Department EducationField  \\\n",
       "0          1      Travel_Rarely                   Sales  Life Sciences   \n",
       "1          0  Travel_Frequently  Research & Development  Life Sciences   \n",
       "2          1      Travel_Rarely  Research & Development          Other   \n",
       "3          0  Travel_Frequently  Research & Development  Life Sciences   \n",
       "4          0      Travel_Rarely  Research & Development        Medical   \n",
       "\n",
       "   Gender                JobRole MaritalStatus Over18 OverTime  \n",
       "0  Female        Sales Executive        Single      Y      Yes  \n",
       "1    Male     Research Scientist       Married      Y       No  \n",
       "2    Male  Laboratory Technician        Single      Y      Yes  \n",
       "3  Female     Research Scientist       Married      Y      Yes  \n",
       "4    Male  Laboratory Technician       Married      Y       No  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let’s first replace Yes and No in Attrition with 1 and 0\n",
    "\n",
    "Num_val = {'Yes':1, 'No':0}\n",
    "hr_data_cat['Attrition'] = hr_data_cat[\"Attrition\"].apply(lambda x: Num_val[x])\n",
    "hr_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "999f3acd",
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "   Attrition  BusinessTravel_Non-Travel  BusinessTravel_Travel_Frequently  \\\n",
       "0          1                          0                                 0   \n",
       "1          0                          0                                 1   \n",
       "2          1                          0                                 0   \n",
       "3          0                          0                                 1   \n",
       "4          0                          0                                 0   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Human Resources  \\\n",
       "0                             1                           0   \n",
       "1                             0                           0   \n",
       "2                             1                           0   \n",
       "3                             0                           0   \n",
       "4                             1                           0   \n",
       "\n",
       "   Department_Research & Development  Department_Sales  \\\n",
       "0                                  0                 1   \n",
       "1                                  1                 0   \n",
       "2                                  1                 0   \n",
       "3                                  1                 0   \n",
       "4                                  1                 0   \n",
       "\n",
       "   EducationField_Human Resources  EducationField_Life Sciences  \\\n",
       "0                               0                             1   \n",
       "1                               0                             1   \n",
       "2                               0                             0   \n",
       "3                               0                             1   \n",
       "4                               0                             0   \n",
       "\n",
       "   EducationField_Marketing  ...  JobRole_Research Director  \\\n",
       "0                         0  ...                          0   \n",
       "1                         0  ...                          0   \n",
       "2                         0  ...                          0   \n",
       "3                         0  ...                          0   \n",
       "4                         0  ...                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  Over18_Y  OverTime_No  \\\n",
       "0                      0                     1         1            0   \n",
       "1                      1                     0         1            1   \n",
       "2                      0                     1         1            0   \n",
       "3                      1                     0         1            0   \n",
       "4                      1                     0         1            1   \n",
       "\n",
       "   OverTime_Yes  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data_cat = pd.get_dummies(hr_data_cat)\n",
    "hr_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9937649b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  EnvironmentSatisfaction  HourlyRate  \\\n",
       "0   41       1102                 1                        2          94   \n",
       "1   49        279                 8                        3          61   \n",
       "2   37       1373                 2                        4          92   \n",
       "3   33       1392                 3                        4          56   \n",
       "4   27        591                 2                        1          40   \n",
       "\n",
       "   JobInvolvement  JobLevel  JobSatisfaction  RelationshipSatisfaction  \\\n",
       "0               3         2                4                         1   \n",
       "1               2         2                2                         4   \n",
       "2               2         1                3                         2   \n",
       "3               3         1                3                         3   \n",
       "4               3         1                2                         4   \n",
       "\n",
       "   StockOptionLevel  ...  JobRole_Research Director  \\\n",
       "0                 0  ...                          0   \n",
       "1                 1  ...                          0   \n",
       "2                 0  ...                          0   \n",
       "3                 0  ...                          0   \n",
       "4                 1  ...                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  Over18_Y  OverTime_No  \\\n",
       "0                      0                     1         1            0   \n",
       "1                      1                     0         1            1   \n",
       "2                      0                     1         1            0   \n",
       "3                      1                     0         1            0   \n",
       "4                      1                     0         1            1   \n",
       "\n",
       "   OverTime_Yes  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data_final = pd.concat([hr_data_uc, hr_data_cat], axis=1)\n",
    "hr_data_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a8afcf",
   "metadata": {},
   "source": [
    "# Random Forest - More significant factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33d79814",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "042521a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8537414965986394"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random forest\n",
    "#model building\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "target = hr_data_final['Attrition']\n",
    "features = hr_data_final.drop('Attrition', axis = 1)\n",
    "\n",
    "#create the train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.4, random_state=10)\n",
    "\n",
    "#Create the model and train\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#predict the results for test\n",
    "test_pred = model.predict(X_test)\n",
    "\n",
    "#test the accuracy\n",
    "accuracy_score(y_test, test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eef71ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "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": [
    "feat_importances = pd.Series(model.feature_importances_, index=features.columns)\n",
    "feat_importances = feat_importances.nlargest(20)\n",
    "feat_importances.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3745bde",
   "metadata": {},
   "source": [
    "# Logistic Regression - attrition risk at an employee level\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e21ea2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                  int64\n",
       "DailyRate                            int64\n",
       "DistanceFromHome                     int64\n",
       "EnvironmentSatisfaction              int64\n",
       "HourlyRate                           int64\n",
       "JobInvolvement                       int64\n",
       "JobLevel                             int64\n",
       "JobSatisfaction                      int64\n",
       "RelationshipSatisfaction             int64\n",
       "StockOptionLevel                     int64\n",
       "TrainingTimesLastYear                int64\n",
       "Attrition                            int64\n",
       "BusinessTravel_Non-Travel            uint8\n",
       "BusinessTravel_Travel_Frequently     uint8\n",
       "BusinessTravel_Travel_Rarely         uint8\n",
       "Department_Human Resources           uint8\n",
       "Department_Research & Development    uint8\n",
       "Department_Sales                     uint8\n",
       "EducationField_Human Resources       uint8\n",
       "EducationField_Life Sciences         uint8\n",
       "EducationField_Marketing             uint8\n",
       "EducationField_Medical               uint8\n",
       "EducationField_Other                 uint8\n",
       "EducationField_Technical Degree      uint8\n",
       "Gender_Female                        uint8\n",
       "Gender_Male                          uint8\n",
       "JobRole_Healthcare Representative    uint8\n",
       "JobRole_Human Resources              uint8\n",
       "JobRole_Laboratory Technician        uint8\n",
       "JobRole_Manager                      uint8\n",
       "JobRole_Manufacturing Director       uint8\n",
       "JobRole_Research Director            uint8\n",
       "JobRole_Research Scientist           uint8\n",
       "JobRole_Sales Executive              uint8\n",
       "JobRole_Sales Representative         uint8\n",
       "MaritalStatus_Divorced               uint8\n",
       "MaritalStatus_Married                uint8\n",
       "MaritalStatus_Single                 uint8\n",
       "Over18_Y                             uint8\n",
       "OverTime_No                          uint8\n",
       "OverTime_Yes                         uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0885a460",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.4, random_state=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "08e0fd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plt\n",
    "from patsy import dmatrices\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d627eed7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8707482993197279"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model = model.fit(X_train, y_train)\n",
    "\n",
    "# check the accuracy on the training set\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d6c626ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8605442176870748"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model = model.fit(X_test, y_test)\n",
    "\n",
    "# check the accuracy on the training set\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfb2210f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.90066067e-02 -3.77867821e-04  2.54705146e-02 -1.99060394e-01\n",
      "   1.75564407e-02 -1.71980791e-02 -7.51245157e-01 -1.73730469e-01\n",
      "  -1.44259264e-01 -7.84145838e-01 -9.47465599e-03 -2.92565290e-01\n",
      "   7.53801587e-01 -1.39711709e-01  8.91186728e-02 -1.42708383e-01\n",
      "   3.75114297e-01  1.22307833e-01 -5.84407718e-04  2.26070785e-01\n",
      "  -3.92989267e-01  6.14725829e-02  3.05247061e-01 -1.44222757e-01\n",
      "   4.65747344e-01 -8.31074446e-02  1.23713587e-01  3.79399772e-01\n",
      "  -4.42792387e-02 -3.25701673e-01 -1.73922067e-01  7.76840292e-02\n",
      "   6.77336706e-03  3.60964254e-01 -1.31154947e-01 -7.00744078e-02\n",
      "   5.22753942e-01  3.21524588e-01 -8.59392515e-01  1.18091710e+00]]\n",
      "[0.32587729]\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14b7d5f3",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "908b7bf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e193a1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}