{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a57f296a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:41:23.566483Z",
     "start_time": "2022-10-21T16:41:20.869077Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb5cd01a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:41:39.337919Z",
     "start_time": "2022-10-21T16:41:37.573036Z"
    }
   },
   "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>EmployeeNumber</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Age</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>...</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>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>41</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>...</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>2</td>\n",
       "      <td>No</td>\n",
       "      <td>49</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>...</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>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>37</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>...</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>4</td>\n",
       "      <td>No</td>\n",
       "      <td>33</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>...</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>5</td>\n",
       "      <td>No</td>\n",
       "      <td>27</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>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeNumber Attrition  Age     BusinessTravel  DailyRate  \\\n",
       "0               1       Yes   41      Travel_Rarely       1102   \n",
       "1               2        No   49  Travel_Frequently        279   \n",
       "2               3       Yes   37      Travel_Rarely       1373   \n",
       "3               4        No   33  Travel_Frequently       1392   \n",
       "4               5        No   27      Travel_Rarely        591   \n",
       "\n",
       "               Department  DistanceFromHome  Education EducationField  \\\n",
       "0                   Sales                 1          2  Life Sciences   \n",
       "1  Research & Development                 8          1  Life Sciences   \n",
       "2  Research & Development                 2          2          Other   \n",
       "3  Research & Development                 3          4  Life Sciences   \n",
       "4  Research & Development                 2          1        Medical   \n",
       "\n",
       "   EmployeeCount  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0              1  ...                         1            80   \n",
       "1              1  ...                         4            80   \n",
       "2              1  ...                         2            80   \n",
       "3              1  ...                         3            80   \n",
       "4              1  ...                         4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"/Users/rishavdas/Downloads/Data/HR_Employee_Attrition-1.xlsx\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7c2cb8",
   "metadata": {},
   "source": [
    "## Continuos Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c74b459c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:41:57.114952Z",
     "start_time": "2022-10-21T16:41:57.087540Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in dataset.select_dtypes('object').columns:\n",
    "    dataset[i] = dataset[i].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a397324",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T15:45:49.997835Z",
     "start_time": "2022-10-21T15:45:49.953572Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2940 entries, 0 to 2939\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   EmployeeNumber            2940 non-null   int64   \n",
      " 1   Attrition                 2940 non-null   category\n",
      " 2   Age                       2940 non-null   int64   \n",
      " 3   BusinessTravel            2940 non-null   category\n",
      " 4   DailyRate                 2940 non-null   int64   \n",
      " 5   Department                2940 non-null   category\n",
      " 6   DistanceFromHome          2940 non-null   int64   \n",
      " 7   Education                 2940 non-null   int64   \n",
      " 8   EducationField            2940 non-null   category\n",
      " 9   EmployeeCount             2940 non-null   int64   \n",
      " 10  EnvironmentSatisfaction   2940 non-null   int64   \n",
      " 11  Gender                    2940 non-null   category\n",
      " 12  HourlyRate                2940 non-null   int64   \n",
      " 13  JobInvolvement            2940 non-null   int64   \n",
      " 14  JobLevel                  2940 non-null   int64   \n",
      " 15  JobRole                   2940 non-null   category\n",
      " 16  JobSatisfaction           2940 non-null   int64   \n",
      " 17  MaritalStatus             2940 non-null   category\n",
      " 18  MonthlyIncome             2940 non-null   int64   \n",
      " 19  MonthlyRate               2940 non-null   int64   \n",
      " 20  NumCompaniesWorked        2940 non-null   int64   \n",
      " 21  Over18                    2940 non-null   category\n",
      " 22  OverTime                  2940 non-null   category\n",
      " 23  PercentSalaryHike         2940 non-null   int64   \n",
      " 24  PerformanceRating         2940 non-null   int64   \n",
      " 25  RelationshipSatisfaction  2940 non-null   int64   \n",
      " 26  StandardHours             2940 non-null   int64   \n",
      " 27  StockOptionLevel          2940 non-null   int64   \n",
      " 28  TotalWorkingYears         2940 non-null   int64   \n",
      " 29  TrainingTimesLastYear     2940 non-null   int64   \n",
      " 30  WorkLifeBalance           2940 non-null   int64   \n",
      " 31  YearsAtCompany            2940 non-null   int64   \n",
      " 32  YearsInCurrentRole        2940 non-null   int64   \n",
      " 33  YearsSinceLastPromotion   2940 non-null   int64   \n",
      " 34  YearsWithCurrManager      2940 non-null   int64   \n",
      "dtypes: category(9), int64(26)\n",
      "memory usage: 624.6 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529fb384",
   "metadata": {},
   "source": [
    "## Attrition vs Employees Earning vs Salary Hike "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b141b135",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T15:48:46.134228Z",
     "start_time": "2022-10-21T15:48:46.127612Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = dataset[['DailyRate', 'MonthlyIncome', 'PercentSalaryHike', \n",
    "               'HourlyRate', 'MonthlyRate']].columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10fbe358",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T15:48:49.214521Z",
     "start_time": "2022-10-21T15:48:49.207797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DailyRate',\n",
       " 'MonthlyIncome',\n",
       " 'PercentSalaryHike',\n",
       " 'HourlyRate',\n",
       " 'MonthlyRate']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab4d9b79",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T15:53:20.331357Z",
     "start_time": "2022-10-21T15:53:20.327445Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0bb0687",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T15:53:23.943591Z",
     "start_time": "2022-10-21T15:53:23.131134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for i, variable in enumerate(cols):\n",
    "    plt.subplot(3, 2, i+1)\n",
    "    sns.boxplot(dataset['Attrition'], dataset[variable], palette='viridis')\n",
    "    plt.tight_layout()\n",
    "    plt.title(variable)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e23dd9f",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Daily Rate is lower for those who got attrited \n",
    "2. Mean of the monthly income for those who left is half og mean income for those who are working in the organization \n",
    "3. So income is major contributors \n",
    "4. as per EDA-PART-II,we have observed that higher Hike will reduce the Attrition rate given Monthly income is lesser than Avg\n",
    "5. Hourly Rate, Monthly Rate are not impact because these are secondary income soruces "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4b2150",
   "metadata": {},
   "source": [
    "## Attrition vs Experience in the Company "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28fd873d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:17:40.873708Z",
     "start_time": "2022-10-21T16:17:40.868448Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = dataset[['YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager',\n",
    "               'TrainingTimesLastYear']].columns.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d04b689",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:21:17.650436Z",
     "start_time": "2022-10-21T16:21:16.851791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "for i, variable in enumerate(cols):\n",
    "    plt.subplot(3, 2, i+1)\n",
    "    sns.boxplot(dataset['Attrition'], dataset[variable], palette='magma')\n",
    "    plt.tight_layout()\n",
    "    plt.title(variable)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c12b966",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:42:01.765028Z",
     "start_time": "2022-10-21T16:42:01.758800Z"
    }
   },
   "outputs": [],
   "source": [
    "def continuos_univariate_analysis(data,\n",
    "                                  feature,\n",
    "                                  figsize=(12, 8),\n",
    "                                  kde=False,\n",
    "                                  bins=None):\n",
    "    f1, (ax_box,\n",
    "         ax_hist) = plt.subplots(nrows=2,\n",
    "                                 sharex=True,\n",
    "                                 gridspec_kw={'height_ratios': (0.25, 0.75)},\n",
    "                                 figsize=figsize)\n",
    "    sns.color_palette(\"viridis\", as_cmap=True)\n",
    "    sns.boxplot(data=data,\n",
    "                x=feature,\n",
    "                ax=ax_box,\n",
    "                showmeans=True,\n",
    "                color='yellow')\n",
    "    sns.histplot(data=data,\n",
    "                 x=feature,\n",
    "                 ax=ax_hist,\n",
    "                 showmeans=True,\n",
    "                 color='crest',\n",
    "                 bins=bins,\n",
    "                 kde=kde) if bins else sns.histplot(\n",
    "                     data=data, x=feature, ax=ax_hist, kde=kde, color='blue')\n",
    "    ax_hist.axvline(data[feature].mean(), color='cyan', linestyle='--')\n",
    "    ax_hist.axvline(data[feature].median(), color='orange', linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2008f42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:43:22.876562Z",
     "start_time": "2022-10-21T16:43:22.580621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuos_univariate_analysis(dataset[dataset['TotalWorkingYears']<=25], 'TotalWorkingYears', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "717d22cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:45:16.929074Z",
     "start_time": "2022-10-21T16:45:16.921369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1044"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['TotalWorkingYears']<=7]['EmployeeNumber'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb95679e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T16:54:49.384158Z",
     "start_time": "2022-10-21T16:54:49.361014Z"
    }
   },
   "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>EmployeeNumber</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>782</td>\n",
       "      <td>3585.777494</td>\n",
       "      <td>4.621483</td>\n",
       "      <td>3.429668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>262</td>\n",
       "      <td>2984.992366</td>\n",
       "      <td>3.595420</td>\n",
       "      <td>2.297710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           EmployeeNumber  MonthlyIncome  TotalWorkingYears  YearsAtCompany\n",
       "Attrition                                                                  \n",
       "No                    782    3585.777494           4.621483        3.429668\n",
       "Yes                   262    2984.992366           3.595420        2.297710"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['TotalWorkingYears'] <= 7].groupby(['Attrition']).agg({\n",
    "    'EmployeeNumber':\n",
    "    'count',\n",
    "    'MonthlyIncome':\n",
    "    'mean',\n",
    "    'TotalWorkingYears':\n",
    "    'mean',\n",
    "    'YearsAtCompany':\n",
    "    'mean'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6e230e",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Avg continuity is half of those who are working in the organization \n",
    "2. Those who are leaving the organization is typically having 1-7 years of experience \n",
    "3. Those who are experienced are not so frequently leaving the organization \n",
    "4. Those who are working in the organization for 2 years on Avg with lower income in its sample are more likely to leave the organization \n",
    "5. Those who are working in the current role for longer period of time tends to leave the organization \n",
    "6. Those who are not promoted more than 2 years are more impactful \n",
    "7. Training has no relation with Attrition "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b511d7e",
   "metadata": {},
   "source": [
    "## Discrete bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef7a7f7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:09:06.550427Z",
     "start_time": "2022-10-21T17:09:06.542624Z"
    }
   },
   "outputs": [],
   "source": [
    "def discrete_bivariate_analysis(data, predictor, target):\n",
    "    count = data[predictor].nunique()\n",
    "    sorter = data[target].value_counts().index[-1]\n",
    "    tab = pd.crosstab(data[predictor], data[target], margins=True).sort_values(by=sorter, ascending=False)\n",
    "    print(tab)\n",
    "    print('#'*50)\n",
    "    tab2 = pd.crosstab(data[predictor], data[target], normalize='index').sort_values(by=sorter, ascending=False)\n",
    "    tab2.plot(kind='bar', stacked=True, figsize=(count+1, 5))\n",
    "    plt.legend(loc=\"lower left\", frameon=True)\n",
    "    plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c9c4313",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:09:08.957802Z",
     "start_time": "2022-10-21T17:09:08.674028Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition            No  Yes   All\n",
      "BusinessTravel                    \n",
      "All                2466  474  2940\n",
      "Travel_Rarely      1774  312  2086\n",
      "Travel_Frequently   416  138   554\n",
      "Non-Travel          276   24   300\n",
      "##################################################\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_bivariate_analysis(dataset, 'BusinessTravel', 'Attrition')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a900e9",
   "metadata": {},
   "source": [
    "## Observation: \n",
    "1. Travel Frequently has more attrition \n",
    "2. Those who are not travelling has lesser attrition rate \n",
    "3. 22% is highest contirbution across any feature after monthly income \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f9d4746",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:15:39.617096Z",
     "start_time": "2022-10-21T17:15:39.430501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition                 No  Yes   All\n",
      "Department                             \n",
      "All                     2466  474  2940\n",
      "Research & Development  1656  266  1922\n",
      "Sales                    708  184   892\n",
      "Human Resources          102   24   126\n",
      "##################################################\n"
     ]
    },
    {
     "data": {
      "image/png": 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sKQ49M0uKQ8/MkuLQM7OkOPTMLCkOPTNLikPPzJJSV+hJ2lfS45LmSjqlk+2SdEG+/UFJuxVfqpnZmusx9CQ1AhcD+wEjgMMljeiw237AsPxrLHBpwXWamRWinpbeHsDciHgqIt4CrgMO6rDPQcCVkbkH2FTS1gXXama2xuoJvQHAvJrl+fm6Vd3HzKx0verYR52si9XYB0ljyS5/Af4u6fE6zr8ubQEsLuJA+rcijlJphfyu/HuqX4G/qw8UdqT1UD2hNx8YVLM8EFiwGvsQEROBiatY4zojaUZENJddx/rAv6v6+PdUPfVc3k4HhkkaKqk3cBgwpcM+U4Cj8ru4ewJLI+L5gms1M1tjPbb0IqJF0gnANKARuDwiZksal2+fAEwFPgPMBV4Djl57JZuZrT5FrNT1lixJY/NLcOuBf1f18e+pehx6ZpYUD0Mzs6Q49MwsKQ49q4ukD0p6T/7545K+JWnTksuqHGnlp+k6W2flcejVkNQgqV/ZdVTUjcBySf8AXAYMBSaXW1Il7dPJuv3WeRXWpeRDT9JkSf0kbQw8Ajwu6eSy66qg1ohoAT4H/DQivgN4fHVO0nGSHgK2zWcaavt6Gniw7PpshXpGZLzbjYiIVyQdSfa84b8AM4Fzyy2rct6WdDjwFeCAfN0GJdZTNZOBW4Gzgdrp116NiBfLKck6k3xLD9hA0gbAwcCvI+JtOhk3bBwNjAb+NSKeljQUuLrkmiojIpZGxDMRcTjZsMy2v0d9JQ0utzqrlfxzepK+Rda6ewD4LDAYuDoi9iq1sAqS1AcYHBFVmyiiMvLRS+OBvwGt+eqIiJGlFWXtJB96nZHUK++/spykA4DzgN4RMVTSLsAPI+LAciurFklzgQ9FxJKya7HOJX95K6m/pMsk3ZovjyDrt7L2xpNNKPsyQETMIruDa+3NA5aWXYR1zTcy4ArgF8Bp+fIc4Jdkj2XYCi0RsVRqN3WiLxNW9hRwu6TfAW+2rYyI88sryWol39IDtoiIX5H3v+SXtcvLLamSHpZ0BNAoaZikC4G7yi6qgp4D/hPoDby35ssqwi09WCbpfeStlrb5AMstqZJOJGsNv0n2eMY04EelVlRBEXEGgKSNI2JZ2fXYypK/kZG/rvJCYEfgYaAJOCQi/ECprTJJo8m6RvpGxGBJOwPHRsTxJZdmueRDD7K7tcC2ZO/6eDx/Vs9qSPpP4AsR8XK+vBlwXUR8utTCKkbSX4BDgCkRsWu+7uGI2LHcyqxNspe3kj7fxabhkoiIm9ZpQdW3RVvgAUTES5K2LLGeyoqIeR1u+LiPuEKSDT1WDKXqTAAOvfZaJQ2OiOcAJH0A373tzDxJHwYif6fMt4BHS67Javjy1uoi6dPAz4E78lUfBcZGxLTyqqoeSVsA/wF8iqy75Dbg235YuToceoCkzwI7ABu2rYuIH5ZXUbVIaiDrp/pvYE+yf8x3R0Qh73M1W5eSDz1JE4CNgL2BSWT/uO+NiK+XWljFSLozIj5adh1Vl0/EcCIwhJruIw/Xqw6HnvRgRIys+d4XuCkixpRdW5VI+h7wOtlolXeeP/O0Se1JeoDskZWHWDHhABFxR5c/ZOtUyjcy2ryef39N0vuBF/GY0s58Lf/+zZp1AWxTQi1V9kZEXFB2EdY1hx78Nn/Xw4/JJg+F7DLXakSE/0dQn/+Q9AOyGxi1Y2/vK68kq5Vs6EnaHZgXEWfmy33JLkkeA/69zNqqSNJRna2PiCvXdS0VtxPwZeAT1Mynly9bBSTbpyfpPuBTEfGipI8C15F1QO8CbB8Rh5RZX9XkEwy02RD4JHCff0/tSXoMGBkRb5Vdi3Uu2ZYe0FjTCX8oMDEibgRulDSrvLKqKSJOrF2WtAlwVUnlVNkDwKbACyXXYV1IOvRqZkj+JDC2ZlvKv5d6vQYMK7uICuoPPCZpOu379PzISkWk/I/7WuAOSYvJ7uD+CSB/r6unlupA0m9YMeysEdge+FV5FVXWD8ouwLqXbJ8evDN33tbAbW1zn0kaTjYtkO+21ZD0sZrFFuDZiJhfVj1VJmkrsqn1A5geEQtLLslqJB16tmok9Qd2zxfvjQj3W3Ug6Rjg+2RD9gR8jOwFSpeXWpi9w6FndZH0RbIXoN9O9o95L+DkiLihzLqqRtLjwIfbJhjIZ+W+KyK2Lbcya5Nyn56tmtOA3dtad5KagD8ADr325gOv1iy/SvaGNKsIh57Vq6HD5ewS/GKpzvwV+IukX5P16R0E3CvpJPBb0arAoWf1+r2kaWR3vSF7tnFqifVU1ZP5V5tf59/9RrSKcJ+e1S2fYv8jZH16d0bEzSWXVFmS3gtERPy97FqsPYee1UXSxmQziCyXtC3Zi5Ru9UuU2pO0I9lIlc3zVYuBoyJidnlVWS2HntVF0kyyO7abAfcAM4DXIuLIUgurGEl3AadFxB/z5Y8DZ0XEh8usy1ZwR7TVSxHxGvB54MKI+BwwouSaqmjjtsADiIjbgY3LK8c6cuhZvZS/yPpI4Hf5Ot8IW9lTkr4naUj+dTrwdNlF2QoOPavXPwOnAjdHxGxJ2wB/7P5HkvQ1oInsFaI355+PLrUia8d9erZKJG3cNk7ZbH3kyxOrS35pexnQFxgsaWfg2Ig4vtzKqqHDLDQr8dRS1eHQs3r9FPg0MAUgIh7IZ5y2zHllF2D1cehZ3SJinqTaVcvLqqVqal/xKKkPMDgiHi+xJOuCb2RYveZJ+jAQknpL+i7waNlFVY2kA4BZwO/z5V0kTSm1KGvHoWf1Gkf2ztsBZDOJ7AK4P29l48kmEH0ZICJmAUNKq8ZW4stbq0tELCZ7Rg8ASZuRhd6/llZUNbVExNIO3QBWIW7pWbckDZI0UdJvJX1d0kaSzgMeB7Ysu74KeljSEWQvnhqWvzrzrrKLshUcetaTK4EFwIXAjmTjbgeQvdv122UWVlEnAjuQvQltMtlLpv65zIKsPT+cbN2S9EBE7Fyz/DeyO5NvdvNjyZK0a0TcX3Yd1jX36VmP8v67tk6qhcBG+VRT1Lww3TLnS9oauB64zlNKVY9betYtSc8ArawIvVoREdus24qqL38F5BfJZpfuB/wyIn5UblXWxqFntpZI2gn4v8ChEdG77Hos4xsZZgWStL2k8ZJmAxeR3bkdWHJZVsMtPbMCSbqH7OVJ10fEgrLrsZU59MwK5rG31ebLW6ubpEZJ75c0uO2r7JqqxmNvq8+PrFhdJJ0I/AD4G9ndXMjmjxtZWlHVNJ5s7O3tkI29lTSkxHqsA4ee1evbwLYRsaTsQirOY28rzqFn9ZpHNqTKutdu7C3wLTz2tlJ8I8PqIukyshd8/45sXCkAEXF+aUVVkKSNgNOAMWQPdE8DzoyIN0otzN7h0LO6SPpBZ+sj4ox1XYvZmnDomRVE0lfI+z7zVY8CF0TEleVVZR25T8/qIqmJbEjVDsCGbesj4hOlFVUhko4im0LqJOA+skvb3YBzJeHgqw4/p2f1ugZ4DBgKnAE8A0wvs6CKOR74XET8MSKWRsTLEfHfwD/hafUrxaFn9XpfRFwGvB0Rd0TE14A9yy6qQvpFxDMdV+br+q3zaqxLvry1er2df39e0mfJZlP2QPoVXl/NbbaO+UaG1UXS/sCfgEFkU8f3A86ICA+xAiS9BsztbBOwTURsvI5Lsi449MwKIOkD3W2PiGfXVS3WPYee1UXSULKX3gyhplskIg4sqyaz1eE+PavXLcBlwG9YMeGA2XrHLT2ri6S/RMSHyq7DbE059Kwu+SD6YcBttB97e19pRa0nJL3Ps9NUhy9vrV47AV8GPkH7+fQ8IqMTkp4km5zhauAKYESpBdk7HHpWr8+RPXrxVtmFrA8i4oOSvgPcDRxddj22gkdkWL0eADYtu4iqknRb7WMrkvYExgHHAvuXVpitxC09q1d/4DFJ02nfp+dHVjJbtj2Ll49YORc4ICLmSDq23NKslkPP6tXpfHr2jjfzqaUGkc2WvGtE/FVSP8CjMSrEd2/NCiDpH4BTgLeAJ4H9gDuBg4CrI+LfSyzPajj0rC6SXiW7WwvQG9gAWBYRnkGkE5J2BT4F3B8Rfyi7HlvBl7dWl4h4b+2ypIPJXnVonYiI+4H7y67DVuaWnq02SfdEhOfUs/WKW3pWF0mfr1lsAJpZcblrtt5w6Fm9Dqj53EI2XfxB5ZRitvp8eWtWIEn/CIwHPkDWqBAQEbFNmXXZCg4965akC+nmMjYivrUOy6k8SY8B3wFmAsvb1nvCgerw5a31ZEbN5zPwQ8o9WRoRt5ZdhHXNLT2rm6T7I2LXsuuoIkm75R+/CDQCN+EpuCrJLT1bFf4/ZNd+0mG5ueazp+CqEIeeWQEiYu+ya7D6eGop65akVyW9IukVYGTb57b1ZddXNZLOkrRpzfJmkn5UYknWgfv0zArUWb+npPsiYreufsbWLbf0zIrVKOk9bQuS+gDv6WZ/W8fcp2dWrKuB/5L0C7IbGF8D/n+5JVktX96aFUSSgIHADmTTSgm4LSKmlVqYtePQMyuQpJkRMarsOqxr7tMzK9Y9knYvuwjrmlt6ZgWS9AgwHHgWWMaKCQdGllqYvcOhZ1ag2tdA1mp7U5qVz3dvzQpU8xrILYENSy7HOuE+PbMCSTpQ0hPA08AdZJOtetaVCnHomRXrTGBPYE5EDAU+Cfy53JKslkPPrFhv5xOGNkhqiIg/AruUXJPVcJ+eWbFeltQX+BNwjaQXyN4pYhXhu7dmBZK0MfA62VXUkcAmwDWeLr46HHpmBcsfWxkWEX+QtBHQGBGvll2XZdynZ1YgSd8AbgB+lq8aANxSWkG2EoeeWbG+Cfwj8ApARDwBbFlqRdaOQ8+sWG9GxFttC5J64XeLVIpDz6xYd0j6f0AfSfsA1wO/Kbkmq+EbGWYFktQAfB0YQzbZwDRgUvgfWmU49MzWEkmbAwMj4sGya7EVfHlrViBJt0vqlwfeLOAXks4vuSyr4dAzK9YmEfEK8HngF/ksyp8quSar4dAzK1YvSVsDXwR+W3YxtjKHnlmxfkh28+LJiJguaRvgiZJrshq+kWFmSXFLz6xAkoZL+i9JD+fLIyWdXnZdtoJDz6xYPwdOBd4GyB9XOazUiqwdh55ZsTaKiHs7rPN8ehXi0DMr1mJJHyQfbyvpEOD5ckuyWr6RYVag/G7tRODDwEtkLwg60q+ArA6HntlakM+g3EA2i/KhEXFNySVZzpe3ZgXIh56dKumifHaV14CvAHPJHlS2inBLz6wAkn5Ndjl7N9lrHzcDegPfjohZJZZmHTj0zAog6aGI2Cn/3AgsBgb73RjV48tbs2K83fYhIpYDTzvwqsktPbMCSFoOLGtbBPqQ9esJiIjoV1Zt1p5Dz8yS4stbM0uKQ8/MkuLQM7OkOPTeBSQtlzRL0mxJD0g6KX8r19o+71clvf/dch5Lg0Pv3eH1iNglInYA9gE+A/xgbZ4wfxbtq8C6CKN1dR5LgEPvXSYiXgDGAico0yjpXEnTJT0o6VgASR+XdKekmyU9ImlCW+tQ0qWSZuQtxzPaji3pGUnfl/Q/wOFAM3BN3srsk28/S9Ld+c/vJmmapCcljas5zsk19ZyRrxsi6VFJP8/Pe1t+zEM6nmed/TLtXcmh9y4UEU+R/bfdkuzF00sjYndgd+Abkobmu+4B/B9gJ+CDZG/wAjgtIpqBkcDHJI2sOfwbEfGRiLgamEE2g8guEfF6vn1eRIwG/gRcARwC7En27ggkjQGG5efeBRgl6aP5zw4DLs5brC8D/xQRN3RxHrPV0qvsAmytUf59DDAybzEBbEIWLm8B9+YBiaRrgY8ANwBflDSW7O/H1sAIoO2F1b/s4bxT8u8PAX3zUQmvSnpD0qZ5PWOA+/P9+ub1PEc2imFWvn4mMGTV/shmPXPovQvlc7otB14gC78TI2Jah30+Tj7RZY3IW4HfBXaPiJckXQFsWLPPMrr3Zv69teZz23KvvJ6zI+JnHeoZ0mH/5WSjGswK5cvbdxlJTcAE4KLIhttMA46TtEG+fXg+1xvAHpKG5n15hwL/A/QjC7alkvoD+3VzuleB965iidOAr0nqm9czQNKWPfzM6pzHrFNu6b079JE0C9iA7H0MVwHn59smkV0m3idJwCLg4Hzb3cA5ZH16dwI3R0SrpPuB2cBTwJ+7Oe8VwARJrwOj6yk0Im6TtD1wd1YOfwe+RNayq+s87tezNeGxt4nKL2+/GxH7l1yK2Trly1szS4pbemaWFLf0zCwpDj0zS4pDz8yS4tAzs6Q49MwsKQ49M0vK/wJ5hF9nXOo+6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_bivariate_analysis(dataset, 'Department', 'Attrition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b8cf55b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:25:10.010100Z",
     "start_time": "2022-10-21T17:25:09.988491Z"
    }
   },
   "outputs": [],
   "source": [
    "df = dataset.groupby(['Attrition', 'Department']).agg({\n",
    "    'EmployeeNumber': 'count',\n",
    "    'MonthlyIncome': 'mean',\n",
    "    'PercentSalaryHike': 'mean',\n",
    "}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "701c45ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:25:13.495405Z",
     "start_time": "2022-10-21T17:25:13.477770Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Department</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>102</td>\n",
       "      <td>7345.980392</td>\n",
       "      <td>14.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1656</td>\n",
       "      <td>6630.326087</td>\n",
       "      <td>15.288647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Sales</td>\n",
       "      <td>708</td>\n",
       "      <td>7232.240113</td>\n",
       "      <td>15.132768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>24</td>\n",
       "      <td>3715.750000</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>266</td>\n",
       "      <td>4108.075188</td>\n",
       "      <td>15.308271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Sales</td>\n",
       "      <td>184</td>\n",
       "      <td>5908.456522</td>\n",
       "      <td>14.956522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition              Department  EmployeeNumber  MonthlyIncome  \\\n",
       "0        No         Human Resources             102    7345.980392   \n",
       "1        No  Research & Development            1656    6630.326087   \n",
       "2        No                   Sales             708    7232.240113   \n",
       "3       Yes         Human Resources              24    3715.750000   \n",
       "4       Yes  Research & Development             266    4108.075188   \n",
       "5       Yes                   Sales             184    5908.456522   \n",
       "\n",
       "   PercentSalaryHike  \n",
       "0          14.980392  \n",
       "1          15.288647  \n",
       "2          15.132768  \n",
       "3          13.833333  \n",
       "4          15.308271  \n",
       "5          14.956522  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95c1c78",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Stratified attrition rate across different departments are bit risky (18-20%) \n",
    "2. HR has higher risk of attrition\n",
    "3. HR is getting below salary hikes than AVg\n",
    "4. Salary is below for all the department and probably because of that we get to observe  startified attrition across departments \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "349578a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-21T17:29:54.313102Z",
     "start_time": "2022-10-21T17:29:54.277606Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">EmployeeNumber</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MonthlyIncome</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PercentSalaryHike</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>7345.980392</td>\n",
       "      <td>3715.750000</td>\n",
       "      <td>14.980392</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Development</th>\n",
       "      <td>1656</td>\n",
       "      <td>266</td>\n",
       "      <td>6630.326087</td>\n",
       "      <td>4108.075188</td>\n",
       "      <td>15.288647</td>\n",
       "      <td>15.308271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>708</td>\n",
       "      <td>184</td>\n",
       "      <td>7232.240113</td>\n",
       "      <td>5908.456522</td>\n",
       "      <td>15.132768</td>\n",
       "      <td>14.956522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       EmployeeNumber      MonthlyIncome               \\\n",
       "Attrition                          No  Yes            No          Yes   \n",
       "Department                                                              \n",
       "Human Resources                   102   24   7345.980392  3715.750000   \n",
       "Research & Development           1656  266   6630.326087  4108.075188   \n",
       "Sales                             708  184   7232.240113  5908.456522   \n",
       "\n",
       "                       PercentSalaryHike             \n",
       "Attrition                             No        Yes  \n",
       "Department                                           \n",
       "Human Resources                14.980392  13.833333  \n",
       "Research & Development         15.288647  15.308271  \n",
       "Sales                          15.132768  14.956522  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df,\n",
    "               index=['Department'],\n",
    "               columns=['Attrition'],\n",
    "               values=['EmployeeNumber', 'MonthlyIncome', 'PercentSalaryHike'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c018dd4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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