Employee Attrition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14c7a9c0",
   "metadata": {},
   "source": [
    "# Employee Attrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d7229d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5679f5ba",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ae31fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f05c506a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv(\"C:/Users/tudua/Desktop/ML Experiment/Unified Mentor/4. Employee Attrition/Attrition data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ef3813d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EmployeeID', 'Age', 'Attrition', 'BusinessTravel', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'MonthlyIncome',\n",
       "       'NumCompaniesWorked', 'Over18', 'PercentSalaryHike', 'StandardHours',\n",
       "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
       "       'YearsAtCompany', 'YearsSinceLastPromotion', 'YearsWithCurrManager',\n",
       "       'EnvironmentSatisfaction', 'JobSatisfaction', 'WorkLifeBalance',\n",
       "       'JobInvolvement', 'PerformanceRating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa9f3879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4410, 29)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "def1c873",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4410 entries, 0 to 4409\n",
      "Data columns (total 29 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   EmployeeID               4410 non-null   int64  \n",
      " 1   Age                      4410 non-null   int64  \n",
      " 2   Attrition                4410 non-null   object \n",
      " 3   BusinessTravel           4410 non-null   object \n",
      " 4   Department               4410 non-null   object \n",
      " 5   DistanceFromHome         4410 non-null   int64  \n",
      " 6   Education                4410 non-null   int64  \n",
      " 7   EducationField           4410 non-null   object \n",
      " 8   EmployeeCount            4410 non-null   int64  \n",
      " 9   Gender                   4410 non-null   object \n",
      " 10  JobLevel                 4410 non-null   int64  \n",
      " 11  JobRole                  4410 non-null   object \n",
      " 12  MaritalStatus            4410 non-null   object \n",
      " 13  MonthlyIncome            4410 non-null   int64  \n",
      " 14  NumCompaniesWorked       4391 non-null   float64\n",
      " 15  Over18                   4410 non-null   object \n",
      " 16  PercentSalaryHike        4410 non-null   int64  \n",
      " 17  StandardHours            4410 non-null   int64  \n",
      " 18  StockOptionLevel         4410 non-null   int64  \n",
      " 19  TotalWorkingYears        4401 non-null   float64\n",
      " 20  TrainingTimesLastYear    4410 non-null   int64  \n",
      " 21  YearsAtCompany           4410 non-null   int64  \n",
      " 22  YearsSinceLastPromotion  4410 non-null   int64  \n",
      " 23  YearsWithCurrManager     4410 non-null   int64  \n",
      " 24  EnvironmentSatisfaction  4385 non-null   float64\n",
      " 25  JobSatisfaction          4390 non-null   float64\n",
      " 26  WorkLifeBalance          4372 non-null   float64\n",
      " 27  JobInvolvement           4410 non-null   int64  \n",
      " 28  PerformanceRating        4410 non-null   int64  \n",
      "dtypes: float64(5), int64(16), object(8)\n",
      "memory usage: 999.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "05ce38a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
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       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
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       "      <td>No</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
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       "      <td>Research &amp; Development</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeID  Age Attrition     BusinessTravel              Department  \\\n",
       "0           1   51        No      Travel_Rarely                   Sales   \n",
       "1           2   31       Yes  Travel_Frequently  Research & Development   \n",
       "2           3   32        No  Travel_Frequently  Research & Development   \n",
       "3           4   38        No         Non-Travel  Research & Development   \n",
       "4           5   32        No      Travel_Rarely  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  Gender  ...  \\\n",
       "0                 6          2  Life Sciences              1  Female  ...   \n",
       "1                10          1  Life Sciences              1  Female  ...   \n",
       "2                17          4          Other              1    Male  ...   \n",
       "3                 2          5  Life Sciences              1    Male  ...   \n",
       "4                10          1        Medical              1    Male  ...   \n",
       "\n",
       "   TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
       "0                1.0                     6              1   \n",
       "1                6.0                     3              5   \n",
       "2                5.0                     2              5   \n",
       "3               13.0                     5              8   \n",
       "4                9.0                     2              6   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager EnvironmentSatisfaction  \\\n",
       "0                        0                     0                     3.0   \n",
       "1                        1                     4                     3.0   \n",
       "2                        0                     3                     2.0   \n",
       "3                        7                     5                     4.0   \n",
       "4                        0                     4                     4.0   \n",
       "\n",
       "   JobSatisfaction  WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "0              4.0              2.0               3                  3  \n",
       "1              2.0              4.0               2                  4  \n",
       "2              2.0              1.0               3                  3  \n",
       "3              4.0              3.0               2                  3  \n",
       "4              1.0              3.0               3                  3  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a079e67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
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       "      <th>PerformanceRating</th>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4408</th>\n",
       "      <td>4409</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>9</td>\n",
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       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "      EmployeeID  Age Attrition BusinessTravel              Department  \\\n",
       "4405        4406   42        No  Travel_Rarely  Research & Development   \n",
       "4406        4407   29        No  Travel_Rarely  Research & Development   \n",
       "4407        4408   25        No  Travel_Rarely  Research & Development   \n",
       "4408        4409   42        No  Travel_Rarely                   Sales   \n",
       "4409        4410   40        No  Travel_Rarely  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  Gender  ...  \\\n",
       "4405                 5          4        Medical              1  Female  ...   \n",
       "4406                 2          4        Medical              1    Male  ...   \n",
       "4407                25          2  Life Sciences              1    Male  ...   \n",
       "4408                18          2        Medical              1    Male  ...   \n",
       "4409                28          3        Medical              1    Male  ...   \n",
       "\n",
       "      TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
       "4405               10.0                     5              3   \n",
       "4406               10.0                     2              3   \n",
       "4407                5.0                     4              4   \n",
       "4408               10.0                     2              9   \n",
       "4409                NaN                     6             21   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager EnvironmentSatisfaction  \\\n",
       "4405                        0                     2                     4.0   \n",
       "4406                        0                     2                     4.0   \n",
       "4407                        1                     2                     1.0   \n",
       "4408                        7                     8                     4.0   \n",
       "4409                        3                     9                     1.0   \n",
       "\n",
       "      JobSatisfaction  WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "4405              1.0              3.0               3                  3  \n",
       "4406              4.0              3.0               2                  3  \n",
       "4407              3.0              3.0               3                  4  \n",
       "4408              1.0              3.0               2                  3  \n",
       "4409              3.0              NaN               4                  3  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9218a5bc",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4391.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4401.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4385.000000</td>\n",
       "      <td>4390.000000</td>\n",
       "      <td>4372.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2205.500000</td>\n",
       "      <td>36.923810</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>65029.312925</td>\n",
       "      <td>2.694830</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.279936</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "      <td>2.723603</td>\n",
       "      <td>2.728246</td>\n",
       "      <td>2.761436</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>3.153741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1273.201673</td>\n",
       "      <td>9.133301</td>\n",
       "      <td>8.105026</td>\n",
       "      <td>1.023933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.106689</td>\n",
       "      <td>47068.888559</td>\n",
       "      <td>2.498887</td>\n",
       "      <td>3.659108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.782222</td>\n",
       "      <td>1.288978</td>\n",
       "      <td>6.125135</td>\n",
       "      <td>3.221699</td>\n",
       "      <td>3.567327</td>\n",
       "      <td>1.092756</td>\n",
       "      <td>1.101253</td>\n",
       "      <td>0.706245</td>\n",
       "      <td>0.711400</td>\n",
       "      <td>0.360742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10090.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1103.250000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2205.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>49190.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3307.750000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>83800.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4410.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>199990.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        EmployeeID          Age  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  4410.000000  4410.000000       4410.000000  4410.000000         4410.0   \n",
       "mean   2205.500000    36.923810          9.192517     2.912925            1.0   \n",
       "std    1273.201673     9.133301          8.105026     1.023933            0.0   \n",
       "min       1.000000    18.000000          1.000000     1.000000            1.0   \n",
       "25%    1103.250000    30.000000          2.000000     2.000000            1.0   \n",
       "50%    2205.500000    36.000000          7.000000     3.000000            1.0   \n",
       "75%    3307.750000    43.000000         14.000000     4.000000            1.0   \n",
       "max    4410.000000    60.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "          JobLevel  MonthlyIncome  NumCompaniesWorked  PercentSalaryHike  \\\n",
       "count  4410.000000    4410.000000         4391.000000        4410.000000   \n",
       "mean      2.063946   65029.312925            2.694830          15.209524   \n",
       "std       1.106689   47068.888559            2.498887           3.659108   \n",
       "min       1.000000   10090.000000            0.000000          11.000000   \n",
       "25%       1.000000   29110.000000            1.000000          12.000000   \n",
       "50%       2.000000   49190.000000            2.000000          14.000000   \n",
       "75%       3.000000   83800.000000            4.000000          18.000000   \n",
       "max       5.000000  199990.000000            9.000000          25.000000   \n",
       "\n",
       "       StandardHours  ...  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count         4410.0  ...        4401.000000            4410.000000   \n",
       "mean             8.0  ...          11.279936               2.799320   \n",
       "std              0.0  ...           7.782222               1.288978   \n",
       "min              8.0  ...           0.000000               0.000000   \n",
       "25%              8.0  ...           6.000000               2.000000   \n",
       "50%              8.0  ...          10.000000               3.000000   \n",
       "75%              8.0  ...          15.000000               3.000000   \n",
       "max              8.0  ...          40.000000               6.000000   \n",
       "\n",
       "       YearsAtCompany  YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "count     4410.000000              4410.000000           4410.000000   \n",
       "mean         7.008163                 2.187755              4.123129   \n",
       "std          6.125135                 3.221699              3.567327   \n",
       "min          0.000000                 0.000000              0.000000   \n",
       "25%          3.000000                 0.000000              2.000000   \n",
       "50%          5.000000                 1.000000              3.000000   \n",
       "75%          9.000000                 3.000000              7.000000   \n",
       "max         40.000000                15.000000             17.000000   \n",
       "\n",
       "       EnvironmentSatisfaction  JobSatisfaction  WorkLifeBalance  \\\n",
       "count              4385.000000      4390.000000      4372.000000   \n",
       "mean                  2.723603         2.728246         2.761436   \n",
       "std                   1.092756         1.101253         0.706245   \n",
       "min                   1.000000         1.000000         1.000000   \n",
       "25%                   2.000000         2.000000         2.000000   \n",
       "50%                   3.000000         3.000000         3.000000   \n",
       "75%                   4.000000         4.000000         3.000000   \n",
       "max                   4.000000         4.000000         4.000000   \n",
       "\n",
       "       JobInvolvement  PerformanceRating  \n",
       "count     4410.000000        4410.000000  \n",
       "mean         2.729932           3.153741  \n",
       "std          0.711400           0.360742  \n",
       "min          1.000000           3.000000  \n",
       "25%          2.000000           3.000000  \n",
       "50%          3.000000           3.000000  \n",
       "75%          3.000000           3.000000  \n",
       "max          4.000000           4.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866e550a",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1286d6cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null value counts:\n",
      "EmployeeID                  0\n",
      "Age                         0\n",
      "Attrition                   0\n",
      "BusinessTravel              0\n",
      "Department                  0\n",
      "DistanceFromHome            0\n",
      "Education                   0\n",
      "EducationField              0\n",
      "EmployeeCount               0\n",
      "Gender                      0\n",
      "JobLevel                    0\n",
      "JobRole                     0\n",
      "MaritalStatus               0\n",
      "MonthlyIncome               0\n",
      "NumCompaniesWorked         19\n",
      "Over18                      0\n",
      "PercentSalaryHike           0\n",
      "StandardHours               0\n",
      "StockOptionLevel            0\n",
      "TotalWorkingYears           9\n",
      "TrainingTimesLastYear       0\n",
      "YearsAtCompany              0\n",
      "YearsSinceLastPromotion     0\n",
      "YearsWithCurrManager        0\n",
      "EnvironmentSatisfaction    25\n",
      "JobSatisfaction            20\n",
      "WorkLifeBalance            38\n",
      "JobInvolvement              0\n",
      "PerformanceRating           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for null values\n",
    "null_values = df.isnull()\n",
    "\n",
    "# Summarize null values\n",
    "null_counts = null_values.sum()\n",
    "\n",
    "# Display null counts\n",
    "print(\"Null value counts:\")\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08cc4284",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove/ drop null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8ebc5f3",
   "metadata": {},
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    EmployeeID  Age Attrition     BusinessTravel              Department  \\\n",
       "0            1   51        No      Travel_Rarely                   Sales   \n",
       "1            2   31       Yes  Travel_Frequently  Research & Development   \n",
       "2            3   32        No  Travel_Frequently  Research & Development   \n",
       "3            4   38        No         Non-Travel  Research & Development   \n",
       "4            5   32        No      Travel_Rarely  Research & Development   \n",
       "5            6   46        No      Travel_Rarely  Research & Development   \n",
       "6            7   28       Yes      Travel_Rarely  Research & Development   \n",
       "7            8   29        No      Travel_Rarely  Research & Development   \n",
       "8            9   31        No      Travel_Rarely  Research & Development   \n",
       "9           10   25        No         Non-Travel  Research & Development   \n",
       "10          11   45        No      Travel_Rarely  Research & Development   \n",
       "12          13   55        No      Travel_Rarely  Research & Development   \n",
       "13          14   47       Yes         Non-Travel  Research & Development   \n",
       "14          15   28        No      Travel_Rarely  Research & Development   \n",
       "15          16   37        No      Travel_Rarely  Research & Development   \n",
       "16          17   21        No      Travel_Rarely  Research & Development   \n",
       "17          18   37        No         Non-Travel  Research & Development   \n",
       "18          19   35        No      Travel_Rarely                   Sales   \n",
       "19          20   38        No      Travel_Rarely  Research & Development   \n",
       "20          21   26        No  Travel_Frequently  Research & Development   \n",
       "\n",
       "    DistanceFromHome  Education EducationField  EmployeeCount  Gender  ...  \\\n",
       "0                  6          2  Life Sciences              1  Female  ...   \n",
       "1                 10          1  Life Sciences              1  Female  ...   \n",
       "2                 17          4          Other              1    Male  ...   \n",
       "3                  2          5  Life Sciences              1    Male  ...   \n",
       "4                 10          1        Medical              1    Male  ...   \n",
       "5                  8          3  Life Sciences              1  Female  ...   \n",
       "6                 11          2        Medical              1    Male  ...   \n",
       "7                 18          3  Life Sciences              1    Male  ...   \n",
       "8                  1          3  Life Sciences              1    Male  ...   \n",
       "9                  7          4        Medical              1  Female  ...   \n",
       "10                17          2        Medical              1    Male  ...   \n",
       "12                14          4  Life Sciences              1  Female  ...   \n",
       "13                 1          1        Medical              1    Male  ...   \n",
       "14                 1          3  Life Sciences              1    Male  ...   \n",
       "15                 1          3  Life Sciences              1    Male  ...   \n",
       "16                 3          2  Life Sciences              1    Male  ...   \n",
       "17                 1          3        Medical              1    Male  ...   \n",
       "18                 7          4  Life Sciences              1    Male  ...   \n",
       "19                 8          3  Life Sciences              1  Female  ...   \n",
       "20                 1          4          Other              1    Male  ...   \n",
       "\n",
       "    TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
       "0                 1.0                     6              1   \n",
       "1                 6.0                     3              5   \n",
       "2                 5.0                     2              5   \n",
       "3                13.0                     5              8   \n",
       "4                 9.0                     2              6   \n",
       "5                28.0                     5              7   \n",
       "6                 5.0                     2              0   \n",
       "7                10.0                     2              0   \n",
       "8                10.0                     2              9   \n",
       "9                 6.0                     2              6   \n",
       "10               21.0                     2             20   \n",
       "12               37.0                     2             36   \n",
       "13               10.0                     4             10   \n",
       "14                5.0                     2              5   \n",
       "15                7.0                     2              5   \n",
       "16                3.0                     3              3   \n",
       "17               15.0                     2              5   \n",
       "18               10.0                     5              7   \n",
       "19                8.0                     5              8   \n",
       "20                6.0                     3              6   \n",
       "\n",
       "    YearsSinceLastPromotion  YearsWithCurrManager EnvironmentSatisfaction  \\\n",
       "0                         0                     0                     3.0   \n",
       "1                         1                     4                     3.0   \n",
       "2                         0                     3                     2.0   \n",
       "3                         7                     5                     4.0   \n",
       "4                         0                     4                     4.0   \n",
       "5                         7                     7                     3.0   \n",
       "6                         0                     0                     1.0   \n",
       "7                         0                     0                     1.0   \n",
       "8                         7                     8                     2.0   \n",
       "9                         1                     5                     2.0   \n",
       "10                        4                    10                     3.0   \n",
       "12                        4                    13                     4.0   \n",
       "13                        9                     9                     1.0   \n",
       "14                        0                     4                     4.0   \n",
       "15                        0                     1                     3.0   \n",
       "16                        1                     0                     4.0   \n",
       "17                        0                     2                     1.0   \n",
       "18                        6                     2                     2.0   \n",
       "19                        7                     7                     1.0   \n",
       "20                        1                     4                     3.0   \n",
       "\n",
       "    JobSatisfaction  WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "0               4.0              2.0               3                  3  \n",
       "1               2.0              4.0               2                  4  \n",
       "2               2.0              1.0               3                  3  \n",
       "3               4.0              3.0               2                  3  \n",
       "4               1.0              3.0               3                  3  \n",
       "5               2.0              2.0               3                  3  \n",
       "6               3.0              1.0               3                  4  \n",
       "7               2.0              3.0               3                  4  \n",
       "8               4.0              3.0               3                  4  \n",
       "9               1.0              3.0               3                  3  \n",
       "10              4.0              3.0               2                  3  \n",
       "12              1.0              3.0               3                  3  \n",
       "13              2.0              2.0               2                  3  \n",
       "14              4.0              2.0               3                  3  \n",
       "15              4.0              4.0               3                  3  \n",
       "16              3.0              4.0               2                  3  \n",
       "17              4.0              3.0               3                  3  \n",
       "18              2.0              2.0               2                  3  \n",
       "19              1.0              3.0               3                  3  \n",
       "20              2.0              1.0               3                  3  \n",
       "\n",
       "[20 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d052cda",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeID                 0\n",
       "Age                        0\n",
       "Attrition                  0\n",
       "BusinessTravel             0\n",
       "Department                 0\n",
       "DistanceFromHome           0\n",
       "Education                  0\n",
       "EducationField             0\n",
       "EmployeeCount              0\n",
       "Gender                     0\n",
       "JobLevel                   0\n",
       "JobRole                    0\n",
       "MaritalStatus              0\n",
       "MonthlyIncome              0\n",
       "NumCompaniesWorked         0\n",
       "Over18                     0\n",
       "PercentSalaryHike          0\n",
       "StandardHours              0\n",
       "StockOptionLevel           0\n",
       "TotalWorkingYears          0\n",
       "TrainingTimesLastYear      0\n",
       "YearsAtCompany             0\n",
       "YearsSinceLastPromotion    0\n",
       "YearsWithCurrManager       0\n",
       "EnvironmentSatisfaction    0\n",
       "JobSatisfaction            0\n",
       "WorkLifeBalance            0\n",
       "JobInvolvement             0\n",
       "PerformanceRating          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9ba674a",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4404</th>\n",
       "      <td>4405</td>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4405</th>\n",
       "      <td>4406</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>4407</td>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4407</th>\n",
       "      <td>4408</td>\n",
       "      <td>25</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4408</th>\n",
       "      <td>4409</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4300 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID  Age Attrition     BusinessTravel              Department  \\\n",
       "0              1   51        No      Travel_Rarely                   Sales   \n",
       "1              2   31       Yes  Travel_Frequently  Research & Development   \n",
       "2              3   32        No  Travel_Frequently  Research & Development   \n",
       "3              4   38        No         Non-Travel  Research & Development   \n",
       "4              5   32        No      Travel_Rarely  Research & Development   \n",
       "...          ...  ...       ...                ...                     ...   \n",
       "4404        4405   29        No      Travel_Rarely                   Sales   \n",
       "4405        4406   42        No      Travel_Rarely  Research & Development   \n",
       "4406        4407   29        No      Travel_Rarely  Research & Development   \n",
       "4407        4408   25        No      Travel_Rarely  Research & Development   \n",
       "4408        4409   42        No      Travel_Rarely                   Sales   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  Gender  ...  \\\n",
       "0                    6          2  Life Sciences              1  Female  ...   \n",
       "1                   10          1  Life Sciences              1  Female  ...   \n",
       "2                   17          4          Other              1    Male  ...   \n",
       "3                    2          5  Life Sciences              1    Male  ...   \n",
       "4                   10          1        Medical              1    Male  ...   \n",
       "...                ...        ...            ...            ...     ...  ...   \n",
       "4404                 4          3          Other              1  Female  ...   \n",
       "4405                 5          4        Medical              1  Female  ...   \n",
       "4406                 2          4        Medical              1    Male  ...   \n",
       "4407                25          2  Life Sciences              1    Male  ...   \n",
       "4408                18          2        Medical              1    Male  ...   \n",
       "\n",
       "      TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
       "0                   1.0                     6              1   \n",
       "1                   6.0                     3              5   \n",
       "2                   5.0                     2              5   \n",
       "3                  13.0                     5              8   \n",
       "4                   9.0                     2              6   \n",
       "...                 ...                   ...            ...   \n",
       "4404                6.0                     2              6   \n",
       "4405               10.0                     5              3   \n",
       "4406               10.0                     2              3   \n",
       "4407                5.0                     4              4   \n",
       "4408               10.0                     2              9   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager EnvironmentSatisfaction  \\\n",
       "0                           0                     0                     3.0   \n",
       "1                           1                     4                     3.0   \n",
       "2                           0                     3                     2.0   \n",
       "3                           7                     5                     4.0   \n",
       "4                           0                     4                     4.0   \n",
       "...                       ...                   ...                     ...   \n",
       "4404                        1                     5                     3.0   \n",
       "4405                        0                     2                     4.0   \n",
       "4406                        0                     2                     4.0   \n",
       "4407                        1                     2                     1.0   \n",
       "4408                        7                     8                     4.0   \n",
       "\n",
       "      JobSatisfaction  WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "0                 4.0              2.0               3                  3  \n",
       "1                 2.0              4.0               2                  4  \n",
       "2                 2.0              1.0               3                  3  \n",
       "3                 4.0              3.0               2                  3  \n",
       "4                 1.0              3.0               3                  3  \n",
       "...               ...              ...             ...                ...  \n",
       "4404              4.0              3.0               2                  3  \n",
       "4405              1.0              3.0               3                  3  \n",
       "4406              4.0              3.0               2                  3  \n",
       "4407              3.0              3.0               3                  4  \n",
       "4408              1.0              3.0               2                  3  \n",
       "\n",
       "[4300 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de4250d",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc99159a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Age:\n",
      "count    4300.000000\n",
      "mean       36.926977\n",
      "std         9.146517\n",
      "min        18.000000\n",
      "25%        30.000000\n",
      "50%        36.000000\n",
      "75%        43.000000\n",
      "max        60.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Descriptive statistics for age\n",
    "age_distribution = df['Age'].describe()\n",
    "\n",
    "# Print age distribution\n",
    "print(\"Distribution of Age:\")\n",
    "print(age_distribution)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e050efb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition\n",
      "No     3605\n",
      "Yes     695\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Get the counts of attrition\n",
    "attrition_counts = df['Attrition'].value_counts()\n",
    "\n",
    "# Print the counts\n",
    "print(attrition_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ba94724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition Rate:\n",
      "Attrition\n",
      "No     83.837209\n",
      "Yes    16.162791\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate attrition rate\n",
    "attrition_rate = df['Attrition'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Print attrition rate\n",
    "print(\"Attrition Rate:\")\n",
    "print(attrition_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "374f6268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition  MonthlyIncome\n",
      "No         61420            9\n",
      "           24510            9\n",
      "           34520            9\n",
      "           26100            9\n",
      "           23800            9\n",
      "           23420            9\n",
      "           44400            6\n",
      "           57690            6\n",
      "           20730            6\n",
      "           52280            6\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Group data by monthly income and attrition status and get the counts\n",
    "attrition_by_income = df.groupby(['Attrition'])['MonthlyIncome'].value_counts()\n",
    "\n",
    "# Get the top 10 counts\n",
    "top_10_counts = attrition_by_income.nlargest(10)\n",
    "\n",
    "# Print the top 10 counts\n",
    "print(top_10_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6fab67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition  MaritalStatus\n",
      "No         Married          1718\n",
      "           Single           1032\n",
      "           Divorced          855\n",
      "Yes        Single            350\n",
      "           Married           251\n",
      "           Divorced           94\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Group data by marital status and attrition status and get the counts\n",
    "attrition_by_marital_status = df.groupby(['Attrition'])['MaritalStatus'].value_counts()\n",
    "\n",
    "# Get the top 10 counts\n",
    "top_10_counts = attrition_by_marital_status.nlargest(10)\n",
    "\n",
    "# Print the top 10 counts\n",
    "print(top_10_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef6678be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition  Department            \n",
      "No         Research & Development    2364\n",
      "           Sales                     1109\n",
      "Yes        Research & Development     443\n",
      "           Sales                      198\n",
      "No         Human Resources            132\n",
      "Yes        Human Resources             54\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Group data by department and attrition status and get the counts\n",
    "attrition_by_department = df.groupby(['Attrition'])['Department'].value_counts()\n",
    "\n",
    "# Get the top 10 counts\n",
    "top_10_counts = attrition_by_department.nlargest(10)\n",
    "\n",
    "# Print the top 10 counts\n",
    "print(top_10_counts)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6298fae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition by gender:\n",
      "Male: 430\n",
      "Female: 265\n"
     ]
    }
   ],
   "source": [
    "# Group data by gender and attrition status and get the counts\n",
    "attrition_by_gender = df.groupby(['Attrition', 'Gender']).size()\n",
    "\n",
    "# Get the top 10 counts for each gender\n",
    "top_10_male = attrition_by_gender['Yes', 'Male']\n",
    "top_10_female = attrition_by_gender['Yes', 'Female']\n",
    "\n",
    "# Print the top 10 counts for each gender\n",
    "print(\"Attrition by gender:\")\n",
    "print(\"Male:\", top_10_male)\n",
    "print(\"Female:\", top_10_female)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f902ce25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 counts of attrition by education field:\n",
      "EducationField\n",
      "Life Sciences       295\n",
      "Medical             219\n",
      "Marketing            74\n",
      "Technical Degree     45\n",
      "Human Resources      32\n",
      "Other                30\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Group data by education field and attrition status and get the counts\n",
    "attrition_by_education_field = df.groupby(['Attrition', 'EducationField']).size()\n",
    "\n",
    "# Get the top 10 counts for each education field\n",
    "top_10_counts_education_field = attrition_by_education_field['Yes'].nlargest(10)\n",
    "\n",
    "# Print the top 10 counts for each education field\n",
    "print(\"Top 10 counts of attrition by education field:\")\n",
    "print(top_10_counts_education_field)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b59dd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export DataFrame to Excel\n",
    "df.to_excel('output.xlsx', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac419d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "76082483",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a6e12f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution analysis\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(df['Age'], bins=20, kde=True)\n",
    "plt.title('Distribution of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af8f97e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfe6a9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize attrition rate\n",
    "plt.figure(figsize=(6, 6))\n",
    "df['Attrition'].value_counts().plot(kind='pie', autopct='%1.1f%%')\n",
    "plt.title('Attrition Rate')\n",
    "plt.ylabel('')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9457c691",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31f3f018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition by education field\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x=df['EducationField'], hue=df['Attrition'])\n",
    "\n",
    "plt.title('Attrition by Education Field')\n",
    "plt.xlabel('Education Field')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Attrition')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6384925f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition by gender\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x=df['Gender'], hue=df['Attrition'])\n",
    "plt.title('Attrition by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "28d219e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition by department\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x=df['Department'], hue=df['Attrition'])\n",
    "plt.title('Attrition by Department')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Attrition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "208e7f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition by Marital Status\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x=df['MaritalStatus'], hue=df['Attrition'])\n",
    "plt.title('Attrition by Marital Status')\n",
    "plt.xlabel('Marital Status')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Attrition')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "927f380c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition by Monthly Income\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='MonthlyIncome', hue='Attrition', kde=True, bins=30)\n",
    "plt.title('Attrition by Monthly Income')\n",
    "plt.xlabel('Monthly Income')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Attrition')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed9edbef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0f1b5883",
   "metadata": {},
   "source": [
    "# Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c43d8f4",
   "metadata": {},
   "source": [
    "1. Out of 4300 employees, 695 have left the company,while 3605 did not leave , resulting in a 16.2% attrition rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffea0e88",
   "metadata": {},
   "source": [
    "2. 430 male and 265 female employees contributed to the attrition statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c99386",
   "metadata": {},
   "source": [
    "3. Maximum attrition is done by employee of lower salary, as the employee salary increases attrition decreases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5748ef",
   "metadata": {},
   "source": [
    "4. Single employees exhibit the highest attrition rate compared to married and divorced employees. \n",
    "Married employees experience a lower attrition rate than single employees but a higher attrition rate than divorced employees. \n",
    "Divorced employees have the lowest attrition rate among the three marital status categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d73c76",
   "metadata": {},
   "source": [
    "5. The Research & Development department exhibits the highest attrition rate compared to the Sales and Human Resources \n",
    "departments. Sales department employees experience a higher attrition rate than those in the Human Resources department."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0ff72c",
   "metadata": {},
   "source": [
    "6. Male employees exhibit a higher attrition rate compared to female employees across departments. \n",
    "This observation suggests that there may be gender-specific factors influencing attrition, \n",
    "such as career advancement opportunities, work-life balance, or organizational culture. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90ae9ff",
   "metadata": {},
   "source": [
    "7. Employees with educational backgrounds in Life Sciences and Medical fields exhibit higher attrition rates compared to \n",
    "those from Marketing, Technical Degree, Human Resources, and Other fields. This observation suggests that there may be \n",
    "specific challenges or opportunities within each educational background that contribute to attrition rates. Understanding \n",
    "these trends can help tailor retention strategies, such as career development programs or targeted support initiatives, to \n",
    "address the unique needs of employees from different educational backgrounds and mitigate attrition effectively."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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