EDA HR WIth Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f82b1226",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as snp\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6915f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\5 PROJECTS\\3. EDA\\HR-Employee-Attrition.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0529e6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a8e31f4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</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>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</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>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</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>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</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>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</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>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</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",
       "</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",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2  Female          94               3         2   \n",
       "1                        3    Male          61               2         2   \n",
       "2                        4    Male          92               2         1   \n",
       "3                        4  Female          56               3         1   \n",
       "4                        1    Male          40               3         1   \n",
       "\n",
       "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0        Sales Executive                4        Single           5993   \n",
       "1     Research Scientist                2       Married           5130   \n",
       "2  Laboratory Technician                3        Single           2090   \n",
       "3     Research Scientist                3       Married           2909   \n",
       "4  Laboratory Technician                2       Married           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        19479                   8      Y      Yes                 11   \n",
       "1        24907                   1      Y       No                 23   \n",
       "2         2396                   6      Y      Yes                 15   \n",
       "3        23159                   1      Y      Yes                 11   \n",
       "4        16632                   9      Y       No                 12   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         1             80   \n",
       "1                  4                         4             80   \n",
       "2                  3                         2             80   \n",
       "3                  3                         3             80   \n",
       "4                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                  8                      0   \n",
       "1                 1                 10                      3   \n",
       "2                 0                  7                      3   \n",
       "3                 0                  8                      3   \n",
       "4                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9bebb78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f87a6dd",
   "metadata": {
    "scrolled": true
   },
   "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",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b16fdd70",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4f5b6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['Attrition', 'DailyRate', 'EmployeeCount',\n",
    "       'EmployeeNumber', 'EnvironmentSatisfaction','HourlyRate',\n",
    "       'JobInvolvement', 'JobLevel'], inplace = True)\n",
    "df.drop(columns = ['JobSatisfaction','MonthlyRate','Over18', 'PercentSalaryHike', 'PerformanceRating',\n",
    "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
    "        'TrainingTimesLastYear', 'WorkLifeBalance',\n",
    "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
    "       'YearsWithCurrManager'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff0f8f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>9</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   41      Travel_Rarely                   Sales                 1   \n",
       "1   49  Travel_Frequently  Research & Development                 8   \n",
       "2   37      Travel_Rarely  Research & Development                 2   \n",
       "3   33  Travel_Frequently  Research & Development                 3   \n",
       "4   27      Travel_Rarely  Research & Development                 2   \n",
       "\n",
       "   Education EducationField  Gender                JobRole MaritalStatus  \\\n",
       "0          2  Life Sciences  Female        Sales Executive        Single   \n",
       "1          1  Life Sciences    Male     Research Scientist       Married   \n",
       "2          2          Other    Male  Laboratory Technician        Single   \n",
       "3          4  Life Sciences  Female     Research Scientist       Married   \n",
       "4          1        Medical    Male  Laboratory Technician       Married   \n",
       "\n",
       "   MonthlyIncome  NumCompaniesWorked OverTime  TotalWorkingYears  \n",
       "0           5993                   8      Yes                  8  \n",
       "1           5130                   1       No                 10  \n",
       "2           2090                   6      Yes                  7  \n",
       "3           2909                   1      Yes                  8  \n",
       "4           3468                   9       No                  6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0fadec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.BusinessTravel = df.BusinessTravel.str.replace('_', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a865600",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'DistanceFromHome' : 'DistFrmHome', 'NumCompaniesWorked': 'Num_Comp_Wk', 'Education': 'Edu_level', 'TotalWorkingYears': 'TotalWkYears'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38fc5000",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OverTime_cat'] =df['OverTime'].map({'Yes': 1, 'No':0}) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9125241",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e365636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37df295",
   "metadata": {},
   "source": [
    "seem to be correlation between Age and Monthly income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5cffdc49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 18)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.max(), df.Age.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb4301c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Age']<=25, 'Age_Group'] = 'Young Adult'\n",
    "df.loc[df['Age'].between(26, 40), 'Age_Group'] = 'Adult'\n",
    "df.loc[df['Age']>=41, 'Age_Group'] = 'Older Adult'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef6fa7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Which Age range earns the most\n",
    "\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.barplot(x = 'Age_Group',  y ='MonthlyIncome', data = df )\n",
    "plt.title('Total Monthly Income of Each Age Group', fontsize = 15)\n",
    "plt.xlabel('Age_Group', fontsize = 15)\n",
    "plt.ylabel('MonthlyIncome', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f92006c",
   "metadata": {},
   "source": [
    "Older Adult earns more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a174025",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2. Which departments earns the most\n",
    "\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.barplot(x='Department', y = 'MonthlyIncome', data = df)\n",
    "plt.title('Total Monthly Income of Each Department', fontsize = 15)\n",
    "plt.xlabel('Department', fontsize = 15)\n",
    "plt.ylabel('MonthlyIncome', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d1deb51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. How many single older age do we have in the dataset\n",
    "\n",
    "len(df[(df.Age_Group == 'Older Adult') & (df.MaritalStatus == 'Single')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3badf78a",
   "metadata": {},
   "source": [
    "We have 126 older adult that are single in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d77fe445",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      882\n",
       "Female    588\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. count of gender and their average monthly income\n",
    "\n",
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182206ff",
   "metadata": {},
   "source": [
    "We have 882 male and 588 female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66314486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    6686.566327\n",
       "Male      6380.507937\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Gender')['MonthlyIncome'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a72a0d4",
   "metadata": {},
   "source": [
    "Approximately 6686.56 and 6380.50 is total Monthly earning for male and female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7698c4d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Edu_level\n",
       "5    8277.645833\n",
       "4    6832.402010\n",
       "3    6517.263986\n",
       "2    6226.645390\n",
       "1    5640.570588\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Lets know if education level gets in the way of better income\n",
    "\n",
    "df.groupby('Edu_level')['MonthlyIncome'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6a995e",
   "metadata": {},
   "source": [
    "Yes, it does. People with high education levels have higher mean Monthl income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cba0262d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BusinessTravel\n",
       "Travel Rarely        6619.707574\n",
       "Non-Travel           6261.946667\n",
       "Travel Frequently    6193.725632\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. Averge income of people on frequent business trips\n",
    "\n",
    "df.groupby('BusinessTravel')['MonthlyIncome'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e335648",
   "metadata": {},
   "source": [
    "The Average monthly income of people of on frequently business trip is approximately $6193.73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90565eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverTime\n",
       "Yes    6548.548077\n",
       "No     6484.926945\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#7. does overtime affect imcome\n",
    "\n",
    "df.groupby('OverTime')['MonthlyIncome'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30df8b62",
   "metadata": {},
   "source": [
    "Yes, it does. People that work over time tends to have higher average income than those that dont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f05aff65",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. education field with highest total working years\n",
    "\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.barplot(x='EducationField', y = 'TotalWkYears', data = df)\n",
    "plt.title('Total Working Year of People in Each Education Field', fontsize = 15)\n",
    "plt.ylabel('Total Workin Years', fontsize = 15)\n",
    "plt.xlabel('EducationField', fontsize = 15)\n",
    "plt.xticks (rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b7c139c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b9c36170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9. jobrole with highest experience in terms of Number company worked\n",
    "\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.barplot(x='JobRole', y = 'Num_Comp_Wk', data = df)\n",
    "plt.title('Experience Level Based on the Number of Companies Worked In', fontsize = 15)\n",
    "plt.ylabel('Number Companies Worked In', fontsize = 15)\n",
    "plt.xlabel('JobRole', fontsize = 15)\n",
    "plt.xticks (rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28307da4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
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