HR-Analysis-Prediction-Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HR Analysis, Prediciton and Visualization\n",
    "\n",
    "## Breakdown of this notebook:\n",
    "1) **Importing the Libraries**\n",
    "\n",
    "2) **Loading the dataset**\n",
    "\n",
    "3) **Understanding the Attrition Problem and the Dataset**\n",
    "\n",
    "4) **Data Visualization:**   \n",
    "   - Plot a correlation map for all numeric variables\n",
    "   - Overtime\n",
    "   - Marital Status\n",
    "   - Job Role\n",
    "   - Gender\n",
    "   - Education Field\n",
    "   - Department\n",
    "   - Buisness Travel\n",
    "   - Relation between Overtime and Age\n",
    "   - Total Working Years\n",
    "   - Education Level\n",
    "   - Number of Companies Worked\n",
    "   - Distance from Home\n",
    "\n",
    "5) **Prediction of Attrition**: LGBM Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "#print(os.listdir(\"../input\"))\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the Attrition Problem and the Dataset\n",
    "\n",
    "HR Analytics helps us with interpreting organizational data. It finds out the people-related trends in the data and helps the HR Department take the appropriate steps to keep the organization running smoothly and profitably.Attrition is a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with. \n",
    " \n",
    " Interestingly, machine learning models can be deployed to predict potential attrition cases, thereby helping the appropriate HR Personnel take the necessary steps to retain the employee."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</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>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
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       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
       "count  1470.000000  ...               1470.000000         1470.0   \n",
       "mean      2.063946  ...                  2.712245           80.0   \n",
       "std       1.106940  ...                  1.081209            0.0   \n",
       "min       1.000000  ...                  1.000000           80.0   \n",
       "25%       1.000000  ...                  2.000000           80.0   \n",
       "50%       2.000000  ...                  3.000000           80.0   \n",
       "75%       3.000000  ...                  4.000000           80.0   \n",
       "max       5.000000  ...                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the correlation map for all the numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(data.corr(), annot=True, linewidth=.5, fmt = '.1f', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### We'd now eplore the number of people based on different criteria to see which criteria is the dominat one for attrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Overtime')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count of people doing overtime\n",
    "\n",
    "sns.countplot(data['OverTime'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.title('Overtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the Count of people doing overtime < the count of people not doing overtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Marital Status')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count of people based on their maritial status \n",
    "\n",
    "sns.countplot(data['MaritalStatus'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10, 10)\n",
    "plt.title('Marital Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Job Role')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot based on thier Job Role\n",
    "\n",
    "sns.countplot(data['JobRole'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20,14)\n",
    "plt.title('Job Role')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on their Gender\n",
    "sns.countplot(data['Gender'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.title('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Education Field')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on their field of education\n",
    "sns.countplot(data['EducationField'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.title('Education Field')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Department')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on the department in which they work\n",
    "sns.countplot(data['Department'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.title('Department')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Business travel')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on their Travel Frequency\n",
    "sns.countplot(data['BusinessTravel'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.title('Business travel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a20856ad0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysing the age group of people doing overtime\n",
    "\n",
    "sns.catplot(x='OverTime', y='Age', kind='swarm', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TotalWorkingYears', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count of people based on their total woring years\n",
    "\n",
    "plt.subplots(figsize=(15,5))\n",
    "sns.countplot(data.TotalWorkingYears)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on their Education\n",
    "\n",
    "plt.subplots(figsize=(15,5))\n",
    "sns.countplot(data.Education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NumCompaniesWorked', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on the number of companies they have worked for\n",
    "\n",
    "sns.countplot(data.NumCompaniesWorked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DistanceFromHome', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count of people based on the distance from home they are working\n",
    "\n",
    "plt.subplots(figsize=(18,5))\n",
    "sns.countplot(data.DistanceFromHome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# necessary imports\n",
    "import time\n",
    "import math\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sci\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "py.init_notebook_mode(connected=True)\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "# import lightgbm as lgb\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from hyperopt import hp, tpe, Trials, STATUS_OK\n",
    "from hyperopt import fmin\n",
    "\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('fivethirtyeight') \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibm_df = data \n",
    "ibm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe with custom indices\n",
    "\n",
    "description = pd.DataFrame(index=['observations(rows)', 'percent missing', 'dtype', 'range'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing empty lists\n",
    "\n",
    "numerical = []\n",
    "categorical = []\n",
    "\n",
    "for col in ibm_df.columns:\n",
    "    obs = ibm_df[col].size\n",
    "    \n",
    "    # Calculating the number of missing values\n",
    "    p_nan = round(ibm_df[col].isna().sum()/obs, 2)\n",
    "    num_nan = f'{p_nan}% ({ibm_df[col].isna().sum()}/{obs})'\n",
    "    \n",
    "    # Defining the criteria for numerical and categorical values \n",
    "    dtype= 'categorical' if ibm_df[col].dtype == object else 'numerical'\n",
    "    \n",
    "    # Preparing the list of numerical and cateogrical columns\n",
    "    numerical.append(col) if dtype=='numerical' else categorical.append(col)\n",
    "    \n",
    "    # The range of values in the columns\n",
    "    rng = f'{len(ibm_df[col].unique())} labels' if dtype=='categorical' else f'{ibm_df[col].min()}-{ibm_df[col].max()}'\n",
    "    \n",
    "    description[col] = [obs,num_nan, dtype, rng]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'DailyRate',\n",
       " 'DistanceFromHome',\n",
       " 'Education',\n",
       " 'EmployeeCount',\n",
       " 'EmployeeNumber',\n",
       " 'EnvironmentSatisfaction',\n",
       " 'HourlyRate',\n",
       " 'JobInvolvement',\n",
       " 'JobLevel',\n",
       " 'JobSatisfaction',\n",
       " 'MonthlyIncome',\n",
       " 'MonthlyRate',\n",
       " 'NumCompaniesWorked',\n",
       " 'PercentSalaryHike',\n",
       " 'PerformanceRating',\n",
       " 'RelationshipSatisfaction',\n",
       " 'StandardHours',\n",
       " 'StockOptionLevel',\n",
       " 'TotalWorkingYears',\n",
       " 'TrainingTimesLastYear',\n",
       " 'WorkLifeBalance',\n",
       " 'YearsAtCompany',\n",
       " 'YearsInCurrentRole',\n",
       " 'YearsSinceLastPromotion',\n",
       " 'YearsWithCurrManager']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Attrition',\n",
       " 'BusinessTravel',\n",
       " 'Department',\n",
       " 'EducationField',\n",
       " 'Gender',\n",
       " 'JobRole',\n",
       " 'MaritalStatus',\n",
       " 'Over18',\n",
       " 'OverTime']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>observations(rows)</th>\n",
       "      <td>1470</td>\n",
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       "      <td>1470</td>\n",
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       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent missing</th>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "      <td>0.0% (0/1470)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtype</th>\n",
       "      <td>numerical</td>\n",
       "      <td>categorical</td>\n",
       "      <td>categorical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>categorical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>categorical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>...</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>18-60</td>\n",
       "      <td>2 labels</td>\n",
       "      <td>3 labels</td>\n",
       "      <td>102-1499</td>\n",
       "      <td>3 labels</td>\n",
       "      <td>1-29</td>\n",
       "      <td>1-5</td>\n",
       "      <td>6 labels</td>\n",
       "      <td>1-1</td>\n",
       "      <td>1-2068</td>\n",
       "      <td>...</td>\n",
       "      <td>1-4</td>\n",
       "      <td>80-80</td>\n",
       "      <td>0-3</td>\n",
       "      <td>0-40</td>\n",
       "      <td>0-6</td>\n",
       "      <td>1-4</td>\n",
       "      <td>0-40</td>\n",
       "      <td>0-18</td>\n",
       "      <td>0-15</td>\n",
       "      <td>0-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Age      Attrition BusinessTravel  \\\n",
       "observations(rows)           1470           1470           1470   \n",
       "percent missing     0.0% (0/1470)  0.0% (0/1470)  0.0% (0/1470)   \n",
       "dtype                   numerical    categorical    categorical   \n",
       "range                       18-60       2 labels       3 labels   \n",
       "\n",
       "                        DailyRate     Department DistanceFromHome  \\\n",
       "observations(rows)           1470           1470             1470   \n",
       "percent missing     0.0% (0/1470)  0.0% (0/1470)    0.0% (0/1470)   \n",
       "dtype                   numerical    categorical        numerical   \n",
       "range                    102-1499       3 labels             1-29   \n",
       "\n",
       "                        Education EducationField  EmployeeCount  \\\n",
       "observations(rows)           1470           1470           1470   \n",
       "percent missing     0.0% (0/1470)  0.0% (0/1470)  0.0% (0/1470)   \n",
       "dtype                   numerical    categorical      numerical   \n",
       "range                         1-5       6 labels            1-1   \n",
       "\n",
       "                   EmployeeNumber  ... RelationshipSatisfaction  \\\n",
       "observations(rows)           1470  ...                     1470   \n",
       "percent missing     0.0% (0/1470)  ...            0.0% (0/1470)   \n",
       "dtype                   numerical  ...                numerical   \n",
       "range                      1-2068  ...                      1-4   \n",
       "\n",
       "                    StandardHours StockOptionLevel TotalWorkingYears  \\\n",
       "observations(rows)           1470             1470              1470   \n",
       "percent missing     0.0% (0/1470)    0.0% (0/1470)     0.0% (0/1470)   \n",
       "dtype                   numerical        numerical         numerical   \n",
       "range                       80-80              0-3              0-40   \n",
       "\n",
       "                   TrainingTimesLastYear WorkLifeBalance YearsAtCompany  \\\n",
       "observations(rows)                  1470            1470           1470   \n",
       "percent missing            0.0% (0/1470)   0.0% (0/1470)  0.0% (0/1470)   \n",
       "dtype                          numerical       numerical      numerical   \n",
       "range                                0-6             1-4           0-40   \n",
       "\n",
       "                   YearsInCurrentRole YearsSinceLastPromotion  \\\n",
       "observations(rows)               1470                    1470   \n",
       "percent missing         0.0% (0/1470)           0.0% (0/1470)   \n",
       "dtype                       numerical               numerical   \n",
       "range                            0-18                    0-15   \n",
       "\n",
       "                   YearsWithCurrManager  \n",
       "observations(rows)                 1470  \n",
       "percent missing           0.0% (0/1470)  \n",
       "dtype                         numerical  \n",
       "range                              0-17  \n",
       "\n",
       "[4 rows x 35 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>3</th>\n",
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       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing Employee counts and StandardHours from numerical columns\n",
    "\n",
    "numerical.remove('EmployeeCount')\n",
    "numerical.remove('StandardHours')\n",
    "\n",
    "display(description)\n",
    "display(ibm_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a method for calculating accuracy, serach time, hyperparameter for different models \n",
    "\n",
    "def org_results(trials, hyperparams, model_name):\n",
    "    fit_idx = -1\n",
    "    \n",
    "    for idx, fit in enumerate(trials):\n",
    "        hyp = fit['misc']['vals']\n",
    "        xgb_hyp = {key:[val] for key, val in hyperparams.items()}\n",
    "        if hyp == xgb_hyp:\n",
    "            fit_idx = idx\n",
    "            break\n",
    "    \n",
    "    train_time = str(trials[-1]['refresh_time'] - trials[0]['book_time'])\n",
    "    acc = round(trials[fit_idx]['result']['accuracy'], 3)\n",
    "    train_auc = round(trials[fit_idx]['result']['train auc'], 3)\n",
    "    test_auc = round(trials[fit_idx]['result']['test auc'], 3)\n",
    "    \n",
    "    results = {\n",
    "        'model': model_name,\n",
    "        'parameter search time': train_time,\n",
    "        'accuracy': acc,\n",
    "        'test auc score': test_auc,\n",
    "        'training auc score': train_auc,\n",
    "        'parameters': hyperparams\n",
    "    }\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_data = ibm_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
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       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Attrition_Yes</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
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       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>...</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
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       "      <th>JobRole_Research Director</th>\n",
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       "      <th>JobRole_Sales Executive</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition_Yes  BusinessTravel_Travel_Frequently  \\\n",
       "0              1                                 0   \n",
       "1              0                                 1   \n",
       "2              1                                 0   \n",
       "3              0                                 1   \n",
       "4              0                                 0   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Research & Development  \\\n",
       "0                             1                                  0   \n",
       "1                             0                                  1   \n",
       "2                             1                                  1   \n",
       "3                             0                                  1   \n",
       "4                             1                                  1   \n",
       "\n",
       "   Department_Sales  EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                 1                             1                         0   \n",
       "1                 0                             1                         0   \n",
       "2                 0                             0                         0   \n",
       "3                 0                             1                         0   \n",
       "4                 0                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  ...  JobRole_Laboratory Technician  \\\n",
       "0                                0  ...                              0   \n",
       "1                                0  ...                              0   \n",
       "2                                0  ...                              1   \n",
       "3                                0  ...                              0   \n",
       "4                                0  ...                              1   \n",
       "\n",
       "   JobRole_Manager  JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                0                               0                          0   \n",
       "1                0                               0                          0   \n",
       "2                0                               0                          0   \n",
       "3                0                               0                          0   \n",
       "4                0                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "0                             0                      0                     1   \n",
       "1                             0                      1                     0   \n",
       "2                             0                      0                     1   \n",
       "3                             0                      1                     0   \n",
       "4                             0                      1                     0   \n",
       "\n",
       "   OverTime_Yes  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_dummy = pd.get_dummies(lgb_data[categorical], drop_first=True) # creating dummy values for categoriccal columns\n",
    "lgb_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Attrition_Yes</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
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       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</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",
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      "text/plain": [
       "   Attrition_Yes  BusinessTravel_Travel_Frequently  \\\n",
       "0              1                                 0   \n",
       "1              0                                 1   \n",
       "2              1                                 0   \n",
       "3              0                                 1   \n",
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       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Research & Development  \\\n",
       "0                             1                                  0   \n",
       "1                             0                                  1   \n",
       "2                             1                                  1   \n",
       "3                             0                                  1   \n",
       "4                             1                                  1   \n",
       "\n",
       "   Department_Sales  EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                 1                             1                         0   \n",
       "1                 0                             1                         0   \n",
       "2                 0                             0                         0   \n",
       "3                 0                             1                         0   \n",
       "4                 0                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  ...  RelationshipSatisfaction  \\\n",
       "0                                0  ...                         1   \n",
       "1                                0  ...                         4   \n",
       "2                                0  ...                         2   \n",
       "3                                0  ...                         3   \n",
       "4                                0  ...                         4   \n",
       "\n",
       "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0             80                 0                  8                      0   \n",
       "1             80                 1                 10                      3   \n",
       "2             80                 0                  7                      3   \n",
       "3             80                 0                  8                      3   \n",
       "4             80                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_data = pd.concat([lgb_dummy, lgb_data], axis=1) # concatenating dummy columns to the original dataframe\n",
    "lgb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Attrition_Yes  BusinessTravel_Travel_Frequently  \\\n",
       "0              1                                 0   \n",
       "1              0                                 1   \n",
       "2              1                                 0   \n",
       "3              0                                 1   \n",
       "4              0                                 0   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Research & Development  \\\n",
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       "2                             1                                  1   \n",
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       "4                             1                                  1   \n",
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       "1                 0                             1                         0   \n",
       "2                 0                             0                         0   \n",
       "3                 0                             1                         0   \n",
       "4                 0                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  ...  RelationshipSatisfaction  \\\n",
       "0                                0  ...                         1   \n",
       "1                                0  ...                         4   \n",
       "2                                0  ...                         2   \n",
       "3                                0  ...                         3   \n",
       "4                                0  ...                         4   \n",
       "\n",
       "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0             80                 0                  8                      0   \n",
       "1             80                 1                 10                      3   \n",
       "2             80                 0                  7                      3   \n",
       "3             80                 0                  8                      3   \n",
       "4             80                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
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       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_data.drop(columns = categorical, inplace=True) # dropping the old categorical columns\n",
    "lgb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <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 × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition  BusinessTravel_Travel_Frequently  BusinessTravel_Travel_Rarely  \\\n",
       "0          1                                 0                             1   \n",
       "1          0                                 1                             0   \n",
       "2          1                                 0                             1   \n",
       "3          0                                 1                             0   \n",
       "4          0                                 0                             1   \n",
       "\n",
       "   Department_Research & Development  Department_Sales  \\\n",
       "0                                  0                 1   \n",
       "1                                  1                 0   \n",
       "2                                  1                 0   \n",
       "3                                  1                 0   \n",
       "4                                  1                 0   \n",
       "\n",
       "   EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                             1                         0   \n",
       "1                             1                         0   \n",
       "2                             0                         0   \n",
       "3                             1                         0   \n",
       "4                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  ...  RelationshipSatisfaction  \\\n",
       "0                                0  ...                         1   \n",
       "1                                0  ...                         4   \n",
       "2                                0  ...                         2   \n",
       "3                                0  ...                         3   \n",
       "4                                0  ...                         4   \n",
       "\n",
       "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0             80                 0                  8                      0   \n",
       "1             80                 1                 10                      3   \n",
       "2             80                 0                  7                      3   \n",
       "3             80                 0                  8                      3   \n",
       "4             80                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_data.rename(columns={'Attrition_Yes': 'Attrition'}, inplace=True) # renaming the column\n",
    "lgb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: Attrition, dtype: uint8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_df = lgb_data['Attrition'].reset_index(drop=True) #  creating the label column\n",
    "display(y_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
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       "      <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",
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       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BusinessTravel_Travel_Frequently  BusinessTravel_Travel_Rarely  \\\n",
       "0                                 0                             1   \n",
       "1                                 1                             0   \n",
       "2                                 0                             1   \n",
       "3                                 1                             0   \n",
       "4                                 0                             1   \n",
       "\n",
       "   Department_Research & Development  Department_Sales  \\\n",
       "0                                  0                 1   \n",
       "1                                  1                 0   \n",
       "2                                  1                 0   \n",
       "3                                  1                 0   \n",
       "4                                  1                 0   \n",
       "\n",
       "   EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                             1                         0   \n",
       "1                             1                         0   \n",
       "2                             0                         0   \n",
       "3                             1                         0   \n",
       "4                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Male  ...  \\\n",
       "0                                0            0  ...   \n",
       "1                                0            1  ...   \n",
       "2                                0            1  ...   \n",
       "3                                0            0  ...   \n",
       "4                                0            1  ...   \n",
       "\n",
       "   RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "0                         1             80                 0   \n",
       "1                         4             80                 1   \n",
       "2                         2             80                 0   \n",
       "3                         3             80                 0   \n",
       "4                         4             80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0                1               6   \n",
       "1                 10                      3                3              10   \n",
       "2                  7                      3                3               0   \n",
       "3                  8                      3                3               8   \n",
       "4                  6                      3                3               2   \n",
       "\n",
       "   YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                   4                        0                     5  \n",
       "1                   7                        1                     7  \n",
       "2                   0                        0                     0  \n",
       "3                   7                        3                     0  \n",
       "4                   2                        2                     2  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df = lgb_data.drop(columns='Attrition') # selecting the feature columns\n",
    "x_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(x_df, y_df, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Training Data\n"
     ]
    },
    {
     "data": {
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       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
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       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
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       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BusinessTravel_Travel_Frequently  BusinessTravel_Travel_Rarely  \\\n",
       "26                                   1                             0   \n",
       "1079                                 0                             1   \n",
       "93                                   1                             0   \n",
       "826                                  0                             1   \n",
       "1166                                 1                             0   \n",
       "\n",
       "      Department_Research & Development  Department_Sales  \\\n",
       "26                                    1                 0   \n",
       "1079                                  1                 0   \n",
       "93                                    1                 0   \n",
       "826                                   0                 0   \n",
       "1166                                  1                 0   \n",
       "\n",
       "      EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "26                               1                         0   \n",
       "1079                             1                         0   \n",
       "93                               0                         0   \n",
       "826                              0                         0   \n",
       "1166                             0                         0   \n",
       "\n",
       "      EducationField_Medical  EducationField_Other  \\\n",
       "26                         0                     0   \n",
       "1079                       0                     0   \n",
       "93                         1                     0   \n",
       "826                        0                     0   \n",
       "1166                       1                     0   \n",
       "\n",
       "      EducationField_Technical Degree  Gender_Male  ...  \\\n",
       "26                                  0            0  ...   \n",
       "1079                                0            0  ...   \n",
       "93                                  0            1  ...   \n",
       "826                                 0            1  ...   \n",
       "1166                                0            1  ...   \n",
       "\n",
       "      RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "26                           2             80                 0   \n",
       "1079                         4             80                 0   \n",
       "93                           3             80                 1   \n",
       "826                          4             80                 1   \n",
       "1166                         2             80                 1   \n",
       "\n",
       "      TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "26                   10                      5                3   \n",
       "1079                  9                      3                3   \n",
       "93                   21                      5                2   \n",
       "826                   7                      2                4   \n",
       "1166                 23                      3                3   \n",
       "\n",
       "      YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "26                10                   2                        6   \n",
       "1079               2                   0                        2   \n",
       "93                10                   9                        9   \n",
       "826                7                   6                        5   \n",
       "1166               2                   2                        2   \n",
       "\n",
       "      YearsWithCurrManager  \n",
       "26                       7  \n",
       "1079                     2  \n",
       "93                       5  \n",
       "826                      0  \n",
       "1166                     2  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Test Data\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Male</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",
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       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BusinessTravel_Travel_Frequently  BusinessTravel_Travel_Rarely  \\\n",
       "878                                 0                             0   \n",
       "303                                 0                             1   \n",
       "996                                 0                             1   \n",
       "511                                 0                             1   \n",
       "229                                 0                             1   \n",
       "\n",
       "     Department_Research & Development  Department_Sales  \\\n",
       "878                                  0                 0   \n",
       "303                                  0                 1   \n",
       "996                                  0                 1   \n",
       "511                                  1                 0   \n",
       "229                                  1                 0   \n",
       "\n",
       "     EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "878                             0                         0   \n",
       "303                             0                         0   \n",
       "996                             0                         1   \n",
       "511                             0                         0   \n",
       "229                             0                         0   \n",
       "\n",
       "     EducationField_Medical  EducationField_Other  \\\n",
       "878                       1                     0   \n",
       "303                       0                     0   \n",
       "996                       0                     0   \n",
       "511                       1                     0   \n",
       "229                       1                     0   \n",
       "\n",
       "     EducationField_Technical Degree  Gender_Male  ...  \\\n",
       "878                                0            1  ...   \n",
       "303                                1            1  ...   \n",
       "996                                0            0  ...   \n",
       "511                                0            1  ...   \n",
       "229                                0            1  ...   \n",
       "\n",
       "     RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "878                         1             80                 1   \n",
       "303                         2             80                 1   \n",
       "996                         4             80                 0   \n",
       "511                         3             80                 1   \n",
       "229                         3             80                 0   \n",
       "\n",
       "     TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "878                 10                      3                4   \n",
       "303                 10                      3                2   \n",
       "996                  6                      3                3   \n",
       "511                 13                      2                3   \n",
       "229                  4                      3                2   \n",
       "\n",
       "     YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "878               4                   3                        0   \n",
       "303               8                   7                        7   \n",
       "996               6                   2                        4   \n",
       "511               3                   2                        0   \n",
       "229               4                   3                        0   \n",
       "\n",
       "     YearsWithCurrManager  \n",
       "878                     3  \n",
       "303                     7  \n",
       "996                     4  \n",
       "511                     2  \n",
       "229                     1  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Sample Training Data')\n",
    "display(train_x.head())\n",
    "print('Sample Test Data')\n",
    "display(test_x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Training Labels\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "26      1\n",
       "1079    0\n",
       "93      0\n",
       "826     0\n",
       "1166    0\n",
       "Name: Attrition, dtype: uint8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Test Labels\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "878    0\n",
       "303    0\n",
       "996    0\n",
       "511    0\n",
       "229    1\n",
       "Name: Attrition, dtype: uint8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Sample Training Labels')\n",
    "display(train_y.head())\n",
    "print('Sample Test Labels')\n",
    "display(test_y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method to return the loss and the different accuracies for LGB model\n",
    "def lgb_objective(space, early_stopping_rounds=50):\n",
    "    \n",
    "    lgbm = LGBMClassifier(\n",
    "        learning_rate = space['learning_rate'],\n",
    "        n_estimators= int(space['n_estimators']), \n",
    "        max_depth = int(space['max_depth']),\n",
    "        num_leaves = int(space['num_leaves']),\n",
    "        colsample_bytree = space['colsample_bytree'],\n",
    "        feature_fraction = space['feature_fraction'],\n",
    "        reg_lambda = space['reg_lambda'],\n",
    "        reg_alpha = space['reg_alpha'],\n",
    "        min_split_gain = space['min_split_gain']\n",
    "    )\n",
    "    \n",
    "    lgbm.fit(train_x, train_y, \n",
    "            eval_set = [(train_x, train_y), (test_x, test_y)],\n",
    "            early_stopping_rounds = early_stopping_rounds,\n",
    "            eval_metric = 'auc',\n",
    "            verbose = False)\n",
    "    \n",
    "    predictions = lgbm.predict(test_x)\n",
    "    test_preds = lgbm.predict_proba(test_x)[:,1]\n",
    "    train_preds = lgbm.predict_proba(train_x)[:,1]\n",
    "    \n",
    "    train_auc = roc_auc_score(train_y, train_preds)\n",
    "    test_auc = roc_auc_score(test_y, test_preds)\n",
    "    accuracy = accuracy_score(test_y, predictions)  \n",
    "\n",
    "    return {'status': STATUS_OK, 'loss': 1-test_auc, 'accuracy': accuracy,\n",
    "            'test auc': test_auc, 'train auc': train_auc\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 150/150 [00:26<00:00,  5.70trial/s, best loss: 0.18406012683003203]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model': 'LightGBM',\n",
       " 'parameter search time': '0:00:26.203000',\n",
       " 'accuracy': 0.867,\n",
       " 'test auc score': 0.816,\n",
       " 'training auc score': 0.982,\n",
       " 'parameters': {'colsample_bytree': 0.33388597861971536,\n",
       "  'feature_fraction': 0.4655662224425487,\n",
       "  'learning_rate': 0.054842788505661585,\n",
       "  'max_depth': 3.0,\n",
       "  'min_split_gain': 0.009702346475502065,\n",
       "  'n_estimators': 475.0,\n",
       "  'num_leaves': 117.0,\n",
       "  'reg_alpha': 0.23657401278340243,\n",
       "  'reg_lambda': 0.4799410484539867}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trials = Trials()\n",
    "\n",
    "# defining parameters for LGB \n",
    "space = {\n",
    "    'learning_rate': hp.loguniform('learning_rate', np.log(0.001), np.log(0.3)),\n",
    "    'n_estimators': hp.quniform('n_estimators', 50, 1200, 25),\n",
    "    'max_depth': hp.quniform('max_depth', 1, 15, 1),\n",
    "    'num_leaves': hp.quniform('num_leaves', 10, 150, 1),\n",
    "    'colsample_bytree': hp.uniform('colsample_bytree', 0.3, 1.0), \n",
    "    'feature_fraction': hp.uniform('feature_fraction', .3, 1.0),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
    "    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "    'min_split_gain': hp.uniform('min_split_gain', 0.0001, 0.1)\n",
    "}\n",
    "\n",
    "#  defining the hyper parameters\n",
    "lgb_hyperparams = fmin(fn = lgb_objective, \n",
    "                 max_evals = 150, \n",
    "                 trials = trials,\n",
    "                 algo = tpe.suggest,\n",
    "                 space = space\n",
    "                 )\n",
    "\n",
    "lgb_results = org_results(trials.trials, lgb_hyperparams, 'LightGBM') # calling the org_results function previously defined\n",
    "display(lgb_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dataframe that shows values based on grouping by age\n",
    "\n",
    "age = pd.DataFrame(data.groupby(\"Age\")[[\"MonthlyIncome\",\"Education\",\"JobLevel\",\"JobInvolvement\",\"PerformanceRating\",\"JobSatisfaction\",\"EnvironmentSatisfaction\",\"RelationshipSatisfaction\",\"WorkLifeBalance\",\"DailyRate\",\"MonthlyRate\"]].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>MonthlyIncome</th>\n",
       "      <th>Education</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>MonthlyRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1518.375000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>747.625000</td>\n",
       "      <td>14476.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>3.444444</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>3.222222</td>\n",
       "      <td>548.444444</td>\n",
       "      <td>15119.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2539.363636</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>2.363636</td>\n",
       "      <td>2.909091</td>\n",
       "      <td>819.454545</td>\n",
       "      <td>16480.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2491.461538</td>\n",
       "      <td>1.615385</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.769231</td>\n",
       "      <td>762.846154</td>\n",
       "      <td>17268.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2950.000000</td>\n",
       "      <td>1.812500</td>\n",
       "      <td>1.125</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>806.937500</td>\n",
       "      <td>14424.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MonthlyIncome  Education  JobLevel  JobInvolvement  PerformanceRating  \\\n",
       "Age                                                                          \n",
       "18     1518.375000   2.500000     1.000        2.875000           3.000000   \n",
       "19     2075.000000   2.111111     1.000        2.444444           3.444444   \n",
       "20     2539.363636   2.636364     1.000        2.636364           3.000000   \n",
       "21     2491.461538   1.615385     1.000        2.692308           3.153846   \n",
       "22     2950.000000   1.812500     1.125        2.750000           3.250000   \n",
       "\n",
       "     JobSatisfaction  EnvironmentSatisfaction  RelationshipSatisfaction  \\\n",
       "Age                                                                       \n",
       "18          3.250000                 2.750000                  3.125000   \n",
       "19          2.555556                 2.444444                  2.888889   \n",
       "20          2.636364                 3.181818                  2.363636   \n",
       "21          2.692308                 2.692308                  2.692308   \n",
       "22          2.937500                 2.812500                  2.250000   \n",
       "\n",
       "     WorkLifeBalance   DailyRate   MonthlyRate  \n",
       "Age                                             \n",
       "18          3.000000  747.625000  14476.000000  \n",
       "19          3.222222  548.444444  15119.666667  \n",
       "20          2.909091  819.454545  16480.909091  \n",
       "21          2.769231  762.846154  17268.923077  \n",
       "22          2.750000  806.937500  14424.125000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>Education</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>1518.375000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>747.625000</td>\n",
       "      <td>14476.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>3.444444</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>3.222222</td>\n",
       "      <td>548.444444</td>\n",
       "      <td>15119.666667</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2539.363636</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>2.363636</td>\n",
       "      <td>2.909091</td>\n",
       "      <td>819.454545</td>\n",
       "      <td>16480.909091</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>2491.461538</td>\n",
       "      <td>1.615385</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.692308</td>\n",
       "      <td>2.769231</td>\n",
       "      <td>762.846154</td>\n",
       "      <td>17268.923077</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>2950.000000</td>\n",
       "      <td>1.812500</td>\n",
       "      <td>1.125</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>806.937500</td>\n",
       "      <td>14424.125000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  MonthlyIncome  Education  JobLevel  JobInvolvement  PerformanceRating  \\\n",
       "0   18    1518.375000   2.500000     1.000        2.875000           3.000000   \n",
       "1   19    2075.000000   2.111111     1.000        2.444444           3.444444   \n",
       "2   20    2539.363636   2.636364     1.000        2.636364           3.000000   \n",
       "3   21    2491.461538   1.615385     1.000        2.692308           3.153846   \n",
       "4   22    2950.000000   1.812500     1.125        2.750000           3.250000   \n",
       "\n",
       "   JobSatisfaction  EnvironmentSatisfaction  RelationshipSatisfaction  \\\n",
       "0         3.250000                 2.750000                  3.125000   \n",
       "1         2.555556                 2.444444                  2.888889   \n",
       "2         2.636364                 3.181818                  2.363636   \n",
       "3         2.692308                 2.692308                  2.692308   \n",
       "4         2.937500                 2.812500                  2.250000   \n",
       "\n",
       "   WorkLifeBalance   DailyRate   MonthlyRate  Count  \n",
       "0         3.000000  747.625000  14476.000000      8  \n",
       "1         3.222222  548.444444  15119.666667      9  \n",
       "2         2.909091  819.454545  16480.909091     11  \n",
       "3         2.769231  762.846154  17268.923077     13  \n",
       "4         2.750000  806.937500  14424.125000     16  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new column to age\n",
    "\n",
    "age[\"Count\"] = data.Age.value_counts(dropna=False)\n",
    "age.reset_index(level=0, inplace=True)\n",
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing the counts for different ages\n",
    "plt.figure(figsize=(15,10))\n",
    "ax=sns.barplot(x=age.Age,y=age.Count)\n",
    "plt.xticks(rotation=180)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Age Counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing monthly income according to age\n",
    "plt.figure(figsize=(15,10))\n",
    "ax=sns.barplot(x=age.Age,y=age.MonthlyIncome,palette = sns.cubehelix_palette(len(age.index)))\n",
    "plt.xticks(rotation=180)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Monthly Income\")\n",
    "plt.title(\"Monthly Income According to Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the database called income based on the job roles\n",
    "\n",
    "income= pd.DataFrame(data.groupby(\"JobRole\").MonthlyIncome.mean().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>MonthlyIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>17181.676471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Director</th>\n",
       "      <td>16033.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>7528.763359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>7295.137931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>6924.279141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MonthlyIncome\n",
       "JobRole                                 \n",
       "Manager                     17181.676471\n",
       "Research Director           16033.550000\n",
       "Healthcare Representative    7528.763359\n",
       "Manufacturing Director       7295.137931\n",
       "Sales Executive              6924.279141"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing the job roles with their monthly incomes\n",
    "plt.figure(figsize=(15,10))\n",
    "ax=sns.barplot(x=income.index,y=income.MonthlyIncome)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Job Roles\")\n",
    "plt.ylabel(\"Monthly Income\")\n",
    "plt.title(\"Job Roles with Monthly Income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>PercentSalaryHike</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</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>Healthcare Representative</th>\n",
       "      <td>15.450382</td>\n",
       "      <td>8.374046</td>\n",
       "      <td>14.068702</td>\n",
       "      <td>4.862595</td>\n",
       "      <td>2.702290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>14.807692</td>\n",
       "      <td>5.326923</td>\n",
       "      <td>8.173077</td>\n",
       "      <td>3.134615</td>\n",
       "      <td>2.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>15.046332</td>\n",
       "      <td>5.019305</td>\n",
       "      <td>7.656371</td>\n",
       "      <td>3.200772</td>\n",
       "      <td>2.718147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>15.137255</td>\n",
       "      <td>14.431373</td>\n",
       "      <td>24.549020</td>\n",
       "      <td>6.450980</td>\n",
       "      <td>2.774510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>15.593103</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>12.786207</td>\n",
       "      <td>4.972414</td>\n",
       "      <td>2.765517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Director</th>\n",
       "      <td>14.950000</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>6.287500</td>\n",
       "      <td>2.862500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>15.448630</td>\n",
       "      <td>5.113014</td>\n",
       "      <td>7.715753</td>\n",
       "      <td>3.273973</td>\n",
       "      <td>2.678082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>14.889571</td>\n",
       "      <td>7.496933</td>\n",
       "      <td>11.101227</td>\n",
       "      <td>4.855828</td>\n",
       "      <td>2.803681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>15.674699</td>\n",
       "      <td>2.915663</td>\n",
       "      <td>4.674699</td>\n",
       "      <td>2.012048</td>\n",
       "      <td>2.891566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           PercentSalaryHike  YearsAtCompany  \\\n",
       "JobRole                                                        \n",
       "Healthcare Representative          15.450382        8.374046   \n",
       "Human Resources                    14.807692        5.326923   \n",
       "Laboratory Technician              15.046332        5.019305   \n",
       "Manager                            15.137255       14.431373   \n",
       "Manufacturing Director             15.593103        7.600000   \n",
       "Research Director                  14.950000       10.937500   \n",
       "Research Scientist                 15.448630        5.113014   \n",
       "Sales Executive                    14.889571        7.496933   \n",
       "Sales Representative               15.674699        2.915663   \n",
       "\n",
       "                           TotalWorkingYears  YearsInCurrentRole  \\\n",
       "JobRole                                                            \n",
       "Healthcare Representative          14.068702            4.862595   \n",
       "Human Resources                     8.173077            3.134615   \n",
       "Laboratory Technician               7.656371            3.200772   \n",
       "Manager                            24.549020            6.450980   \n",
       "Manufacturing Director             12.786207            4.972414   \n",
       "Research Director                  21.400000            6.287500   \n",
       "Research Scientist                  7.715753            3.273973   \n",
       "Sales Executive                    11.101227            4.855828   \n",
       "Sales Representative                4.674699            2.012048   \n",
       "\n",
       "                           WorkLifeBalance  \n",
       "JobRole                                     \n",
       "Healthcare Representative         2.702290  \n",
       "Human Resources                   2.923077  \n",
       "Laboratory Technician             2.718147  \n",
       "Manager                           2.774510  \n",
       "Manufacturing Director            2.765517  \n",
       "Research Director                 2.862500  \n",
       "Research Scientist                2.678082  \n",
       "Sales Executive                   2.803681  \n",
       "Sales Representative              2.891566  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a dataframe with mean of the selected columns grouped by job role\n",
    "jobrole=pd.DataFrame(data.groupby(\"JobRole\")[\"PercentSalaryHike\",\"YearsAtCompany\",\"TotalWorkingYears\",\"YearsInCurrentRole\",\"WorkLifeBalance\"].mean())\n",
    "jobrole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating a pie chart based on the educational field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Life Sciences', 'Medical', 'Marketing', 'Technical Degree', 'Other',\n",
       "       'Human Resources'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=data.EducationField.value_counts().index\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors=[\"cyan\",\"orange\",\"hotpink\",\"green\",\"navy\",\"#9b59b6\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([606, 464, 159, 132,  82,  27])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes=data.EducationField.value_counts().values\n",
    "sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Education Field Counts')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.pie(sizes,labels=labels,colors=colors,autopct=\"%1.1f%%\")\n",
    "plt.title(\"Education Field Counts\",color=\"saddlebrown\",fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}