HR Employee Attrition.ipynb

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       "  <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",
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       "      <th>EmployeeNumber</th>\n",
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       "      <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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       "      <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",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
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       "      <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",
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       "      <td>4</td>\n",
       "      <td>80</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <td>80</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",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>27</td>\n",
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       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
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       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>59</td>\n",
       "      <td>No</td>\n",
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       "      <td>1324</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1358</td>\n",
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       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>216</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1299</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "<p>10 rows × 35 columns</p>\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",
       "5   32        No  Travel_Frequently       1005  Research & Development   \n",
       "6   59        No      Travel_Rarely       1324  Research & Development   \n",
       "7   30        No      Travel_Rarely       1358  Research & Development   \n",
       "8   38        No  Travel_Frequently        216  Research & Development   \n",
       "9   36        No      Travel_Rarely       1299  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",
       "5                 2          2  Life Sciences              1               8   \n",
       "6                 3          3        Medical              1              10   \n",
       "7                24          1  Life Sciences              1              11   \n",
       "8                23          3  Life Sciences              1              12   \n",
       "9                27          3        Medical              1              13   \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",
       "5  ...                         3            80                 0   \n",
       "6  ...                         1            80                 3   \n",
       "7  ...                         2            80                 1   \n",
       "8  ...                         2            80                 0   \n",
       "9  ...                         2            80                 2   \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",
       "5                  8                      2               2               7   \n",
       "6                 12                      3               2               1   \n",
       "7                  1                      2               3               1   \n",
       "8                 10                      2               3               9   \n",
       "9                 17                      3               2               7   \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",
       "5                  7                        3                     6  \n",
       "6                  0                        0                     0  \n",
       "7                  0                        0                     0  \n",
       "8                  7                        1                     8  \n",
       "9                  7                        7                     7  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
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   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import xgboost\n",
    "import pickle\n",
    "\n",
    "df_hr_employee = pd.read_csv(r'data\\HR Employee\\WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
    "df_hr_employee.head(10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      "Age                         1470 non-null int64\n",
      "Attrition                   1470 non-null object\n",
      "BusinessTravel              1470 non-null object\n",
      "DailyRate                   1470 non-null int64\n",
      "Department                  1470 non-null object\n",
      "DistanceFromHome            1470 non-null int64\n",
      "Education                   1470 non-null int64\n",
      "EducationField              1470 non-null object\n",
      "EmployeeCount               1470 non-null int64\n",
      "EmployeeNumber              1470 non-null int64\n",
      "EnvironmentSatisfaction     1470 non-null int64\n",
      "Gender                      1470 non-null object\n",
      "HourlyRate                  1470 non-null int64\n",
      "JobInvolvement              1470 non-null int64\n",
      "JobLevel                    1470 non-null int64\n",
      "JobRole                     1470 non-null object\n",
      "JobSatisfaction             1470 non-null int64\n",
      "MaritalStatus               1470 non-null object\n",
      "MonthlyIncome               1470 non-null int64\n",
      "MonthlyRate                 1470 non-null int64\n",
      "NumCompaniesWorked          1470 non-null int64\n",
      "Over18                      1470 non-null object\n",
      "OverTime                    1470 non-null object\n",
      "PercentSalaryHike           1470 non-null int64\n",
      "PerformanceRating           1470 non-null int64\n",
      "RelationshipSatisfaction    1470 non-null int64\n",
      "StandardHours               1470 non-null int64\n",
      "StockOptionLevel            1470 non-null int64\n",
      "TotalWorkingYears           1470 non-null int64\n",
      "TrainingTimesLastYear       1470 non-null int64\n",
      "WorkLifeBalance             1470 non-null int64\n",
      "YearsAtCompany              1470 non-null int64\n",
      "YearsInCurrentRole          1470 non-null int64\n",
      "YearsSinceLastPromotion     1470 non-null int64\n",
      "YearsWithCurrManager        1470 non-null int64\n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_hr_employee.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th></th>\n",
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       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.001686</td>\n",
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       "      <th>DailyRate</th>\n",
       "      <td>0.010661</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.016806</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.046135</td>\n",
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       "      <td>-0.034055</td>\n",
       "      <td>0.009932</td>\n",
       "      <td>-0.033229</td>\n",
       "      <td>-0.026363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>-0.001686</td>\n",
       "      <td>-0.004985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021042</td>\n",
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       "      <td>0.032916</td>\n",
       "      <td>-0.016075</td>\n",
       "      <td>0.031131</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044872</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>-0.036942</td>\n",
       "      <td>-0.026556</td>\n",
       "      <td>0.009508</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.014406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.208034</td>\n",
       "      <td>-0.016806</td>\n",
       "      <td>0.021042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042070</td>\n",
       "      <td>-0.027128</td>\n",
       "      <td>0.016775</td>\n",
       "      <td>0.042438</td>\n",
       "      <td>0.101589</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018422</td>\n",
       "      <td>0.148280</td>\n",
       "      <td>-0.025100</td>\n",
       "      <td>0.009819</td>\n",
       "      <td>0.069114</td>\n",
       "      <td>0.060236</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>0.069065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeCount</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>EmployeeNumber</th>\n",
       "      <td>-0.010145</td>\n",
       "      <td>-0.050990</td>\n",
       "      <td>0.032916</td>\n",
       "      <td>0.042070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017621</td>\n",
       "      <td>0.035179</td>\n",
       "      <td>-0.006888</td>\n",
       "      <td>-0.018519</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062227</td>\n",
       "      <td>-0.014365</td>\n",
       "      <td>0.023603</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>-0.011240</td>\n",
       "      <td>-0.008416</td>\n",
       "      <td>-0.009019</td>\n",
       "      <td>-0.009197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>0.010146</td>\n",
       "      <td>0.018355</td>\n",
       "      <td>-0.016075</td>\n",
       "      <td>-0.027128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017621</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049857</td>\n",
       "      <td>-0.008278</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003432</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>-0.019359</td>\n",
       "      <td>0.027627</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.018007</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>-0.004999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HourlyRate</th>\n",
       "      <td>0.024287</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>0.031131</td>\n",
       "      <td>0.016775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035179</td>\n",
       "      <td>-0.049857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042861</td>\n",
       "      <td>-0.027853</td>\n",
       "      <td>...</td>\n",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hr_employee.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values check\n",
    "df_hr_employee.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition\n",
       "No     1233\n",
       "Yes     237\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hr_employee.groupby('Attrition').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping below columns which does not have any meaning to our model.\n",
    "#EmployeeCount = Variable that just counts employees, No predictive Value\n",
    "#EmployeeNumber = Unique ID for employee, No Predictive Value\n",
    "#Over18 = Only Yes, No Predictive Value\n",
    "#StandardHours = All were 80, No Predictive Value\n",
    "#StandardWeeklyHours = All were 40, No Predictive Value\n",
    "df_hr_employee = df_hr_employee.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Even we need to drop below features as they are highly correlated with other features.\n",
    "#Age - JobLevel, MonthlyIncome, NumCompaniesWorked, TotalWorkingYears,\n",
    "#JobLevel - MonthlyIncome, TotalWorkingYears, YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithcurrManager\n",
    "#TotalWorkingYears - YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithcurrManager\n",
    "#Based on my understanding, removed few features listed above.\n",
    "\n",
    "df_hr_employee = df_hr_employee.drop(['JobLevel', 'MonthlyIncome','YearsAtCompany','YearsInCurrentRole', 'YearsSinceLastPromotion','YearsWithCurrManager'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_hr_employee.drop(['Attrition'], axis=1)\n",
    "y = df_hr_employee.Attrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Yes\n",
       "1     No\n",
       "2    Yes\n",
       "3     No\n",
       "4     No\n",
       "Name: Attrition, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>...</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>OverTime_No</th>\n",
       "      <th>OverTime_Yes</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>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  Education  EnvironmentSatisfaction  \\\n",
       "0   41       1102                 1          2                        2   \n",
       "1   49        279                 8          1                        3   \n",
       "2   37       1373                 2          2                        4   \n",
       "3   33       1392                 3          4                        4   \n",
       "4   27        591                 2          1                        1   \n",
       "\n",
       "   HourlyRate  JobInvolvement  JobSatisfaction  MonthlyRate  \\\n",
       "0          94               3                4        19479   \n",
       "1          61               2                2        24907   \n",
       "2          92               2                3         2396   \n",
       "3          56               3                3        23159   \n",
       "4          40               3                2        16632   \n",
       "\n",
       "   NumCompaniesWorked  ...  JobRole_Manufacturing Director  \\\n",
       "0                   8  ...                               0   \n",
       "1                   1  ...                               0   \n",
       "2                   6  ...                               0   \n",
       "3                   1  ...                               0   \n",
       "4                   9  ...                               0   \n",
       "\n",
       "   JobRole_Research Director  JobRole_Research Scientist  \\\n",
       "0                          0                           0   \n",
       "1                          0                           1   \n",
       "2                          0                           0   \n",
       "3                          0                           1   \n",
       "4                          0                           0   \n",
       "\n",
       "   JobRole_Sales Executive  JobRole_Sales Representative  \\\n",
       "0                        1                             0   \n",
       "1                        0                             0   \n",
       "2                        0                             0   \n",
       "3                        0                             0   \n",
       "4                        0                             0   \n",
       "\n",
       "   MaritalStatus_Divorced  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "0                       0                      0                     1   \n",
       "1                       0                      1                     0   \n",
       "2                       0                      0                     1   \n",
       "3                       0                      1                     0   \n",
       "4                       0                      1                     0   \n",
       "\n",
       "   OverTime_No  OverTime_Yes  \n",
       "0            0             1  \n",
       "1            1             0  \n",
       "2            0             1  \n",
       "3            0             1  \n",
       "4            1             0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We need to convert Categorical values to dummy values\n",
    "x = pd.get_dummies(x)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "plt.figure(figsize=(15,15))\n",
    "heat_map = sb.heatmap(x.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.872739 (0.035818)\n",
      "LDA: 0.869817 (0.035224)\n",
      "KNN: 0.815391 (0.032323)\n",
      "CART: 0.775557 (0.050367)\n",
      "NB: 0.811479 (0.035795)\n",
      "SVM: 0.838721 (0.038264)\n",
      "RF: 0.842604 (0.039392)\n"
     ]
    }
   ],
   "source": [
    "rf_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators': 1000,\n",
    "#     'warm_start': True, \n",
    "    'max_features': 0.3,\n",
    "    'max_depth': 4,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features' : 'sqrt',\n",
    "    'random_state' : 1,\n",
    "    'verbose': 0\n",
    "}\n",
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "models.append(('RF', RandomForestClassifier(**rf_params)))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = model_selection.KFold(n_splits=10, random_state=1)\n",
    "\tcv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring='accuracy')\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "\tprint(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "# Compare Algorithms\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}