HR_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading & Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "           ...           RelationshipSatisfaction StandardHours  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "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,
   "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,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns of dataset\n",
    "hr_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take a peek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this exercise, our aim is predict the employee attrition. It is important to see which variables are contibuting the most in attrition. But before that we need to know if the variable are any where correlated i.e Data Exploration\n",
    "\n",
    "There are many continuous variables where the we can have a look at their distribution and create a grid of pairplot but that would be too much as there are so mnay variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets create a dataframe with only numeric variables\n",
    "hr_data_num = hr_data[['Age','DailyRate','DistanceFromHome', 'Education','EmployeeNumber', \n",
    "                       'EnvironmentSatisfaction', 'HourlyRate',                     \n",
    "                       'JobInvolvement', 'JobLevel','JobSatisfaction','MonthlyIncome', \n",
    "                        'MonthlyRate', 'NumCompaniesWorked','PercentSalaryHike', 'PerformanceRating', \n",
    "                       'RelationshipSatisfaction', 'StockOptionLevel',\n",
    "                       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
    "                       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
    "                       'YearsWithCurrManager']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20be3e2bd30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20be3b96f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize= (10,10), dpi=100)\n",
    "sns.heatmap(hr_data_num.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's remove the strongly correlated variables\n",
    "hr_data_uc = hr_data_num[['Age','DailyRate','DistanceFromHome', \n",
    "                       'EnvironmentSatisfaction', 'HourlyRate',                     \n",
    "                       'JobInvolvement', 'JobLevel','JobSatisfaction', \n",
    "                       'RelationshipSatisfaction', 'StockOptionLevel',\n",
    "                        'TrainingTimesLastYear']].copy()\n",
    "\n",
    "hr_data_uc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copy categorical data\n",
    "hr_data_cat = hr_data[['Attrition', 'BusinessTravel','Department',\n",
    "                       'EducationField','Gender','JobRole','MaritalStatus',\n",
    "                       'Over18', 'OverTime']].copy()\n",
    "hr_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
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       "      <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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 31,
   "metadata": {},
   "outputs": [
    {
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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": 31,
     "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": 34,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  Education  EmployeeNumber  \\\n",
       "0   41       1102                 1          2               1   \n",
       "1   49        279                 8          1               2   \n",
       "2   37       1373                 2          2               4   \n",
       "3   33       1392                 3          4               5   \n",
       "4   27        591                 2          1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2          94               3         2   \n",
       "1                        3          61               2         2   \n",
       "2                        4          92               2         1   \n",
       "3                        4          56               3         1   \n",
       "4                        1          40               3         1   \n",
       "\n",
       "   JobSatisfaction      ...       JobRole_Research Director  \\\n",
       "0                4      ...                               0   \n",
       "1                2      ...                               0   \n",
       "2                3      ...                               0   \n",
       "3                3      ...                               0   \n",
       "4                2      ...                               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 54 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data_final = pd.concat([hr_data_num, hr_data_cat], axis=1)\n",
    "hr_data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Natasha Sharma\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "target = hr_data_final['Attrition']\n",
    "features = hr_data_final.drop('Attrition', axis = 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.4, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8418367346938775"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "test_pred = model.predict(X_test)\n",
    "accuracy_score(y_test, test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20be6bf9b38>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20be57dc4a8>"
      ]
     },
     "metadata": {},
     "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": "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}