EDA on HRData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NF-0lLPndMlL"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "98qCO202dMlP",
    "outputId": "8252678c-ae24-4a11-852b-c553a8a386fa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2  Female          94               3         2   \n",
       "1                        3    Male          61               2         2   \n",
       "2                        4    Male          92               2         1   \n",
       "3                        4  Female          56               3         1   \n",
       "4                        1    Male          40               3         1   \n",
       "\n",
       "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0        Sales Executive                4        Single           5993   \n",
       "1     Research Scientist                2       Married           5130   \n",
       "2  Laboratory Technician                3        Single           2090   \n",
       "3     Research Scientist                3       Married           2909   \n",
       "4  Laboratory Technician                2       Married           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        19479                   8      Y      Yes                 11   \n",
       "1        24907                   1      Y       No                 23   \n",
       "2         2396                   6      Y      Yes                 15   \n",
       "3        23159                   1      Y      Yes                 11   \n",
       "4        16632                   9      Y       No                 12   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         1             80   \n",
       "1                  4                         4             80   \n",
       "2                  3                         2             80   \n",
       "3                  3                         3             80   \n",
       "4                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                  8                      0   \n",
       "1                 1                 10                      3   \n",
       "2                 0                  7                      3   \n",
       "3                 0                  8                      3   \n",
       "4                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\hp\\Downloads\\Decision Tree\\HR-Employee-Attrition.csv\")\n",
    "pd.set_option('display.max_columns',None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ced9CRyMdMlS"
   },
   "source": [
    "## BASIC CHECKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s_I_oe5LdMlV",
    "outputId": "fd723fc0-4f91-4d02-cbb7-7f3baa091d90"
   },
   "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>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>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</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",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</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>2.728571</td>\n",
       "      <td>6502.931293</td>\n",
       "      <td>14313.103401</td>\n",
       "      <td>2.693197</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>3.153741</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>1.102846</td>\n",
       "      <td>4707.956783</td>\n",
       "      <td>7117.786044</td>\n",
       "      <td>2.498009</td>\n",
       "      <td>3.659938</td>\n",
       "      <td>0.360824</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>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</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>2.000000</td>\n",
       "      <td>2911.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</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>3.000000</td>\n",
       "      <td>4919.000000</td>\n",
       "      <td>14235.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</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>4.000000</td>\n",
       "      <td>8379.000000</td>\n",
       "      <td>20461.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</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>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>26999.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</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",
       "</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  JobSatisfaction  MonthlyIncome   MonthlyRate  \\\n",
       "count  1470.000000      1470.000000    1470.000000   1470.000000   \n",
       "mean      2.063946         2.728571    6502.931293  14313.103401   \n",
       "std       1.106940         1.102846    4707.956783   7117.786044   \n",
       "min       1.000000         1.000000    1009.000000   2094.000000   \n",
       "25%       1.000000         2.000000    2911.000000   8047.000000   \n",
       "50%       2.000000         3.000000    4919.000000  14235.500000   \n",
       "75%       3.000000         4.000000    8379.000000  20461.500000   \n",
       "max       5.000000         4.000000   19999.000000  26999.000000   \n",
       "\n",
       "       NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "count         1470.000000        1470.000000        1470.000000   \n",
       "mean             2.693197          15.209524           3.153741   \n",
       "std              2.498009           3.659938           0.360824   \n",
       "min              0.000000          11.000000           3.000000   \n",
       "25%              1.000000          12.000000           3.000000   \n",
       "50%              2.000000          14.000000           3.000000   \n",
       "75%              4.000000          18.000000           3.000000   \n",
       "max              9.000000          25.000000           4.000000   \n",
       "\n",
       "       RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
       "count               1470.000000         1470.0       1470.000000   \n",
       "mean                   2.712245           80.0          0.793878   \n",
       "std                    1.081209            0.0          0.852077   \n",
       "min                    1.000000           80.0          0.000000   \n",
       "25%                    2.000000           80.0          0.000000   \n",
       "50%                    3.000000           80.0          1.000000   \n",
       "75%                    4.000000           80.0          1.000000   \n",
       "max                    4.000000           80.0          3.000000   \n",
       "\n",
       "       TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "count        1470.000000            1470.000000      1470.000000   \n",
       "mean           11.279592               2.799320         2.761224   \n",
       "std             7.780782               1.289271         0.706476   \n",
       "min             0.000000               0.000000         1.000000   \n",
       "25%             6.000000               2.000000         2.000000   \n",
       "50%            10.000000               3.000000         3.000000   \n",
       "75%            15.000000               3.000000         3.000000   \n",
       "max            40.000000               6.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "count     1470.000000         1470.000000              1470.000000   \n",
       "mean         7.008163            4.229252                 2.187755   \n",
       "std          6.126525            3.623137                 3.222430   \n",
       "min          0.000000            0.000000                 0.000000   \n",
       "25%          3.000000            2.000000                 0.000000   \n",
       "50%          5.000000            3.000000                 1.000000   \n",
       "75%          9.000000            7.000000                 3.000000   \n",
       "max         40.000000           18.000000                15.000000   \n",
       "\n",
       "       YearsWithCurrManager  \n",
       "count           1470.000000  \n",
       "mean               4.123129  \n",
       "std                3.568136  \n",
       "min                0.000000  \n",
       "25%                2.000000  \n",
       "50%                3.000000  \n",
       "75%                7.000000  \n",
       "max               17.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DV4pBmCldMlV"
   },
   "source": [
    "* Here two columns(Employ_count,Standard_Hours) have std==0 so ,it means they have unique values it means they are not able to use in analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PD16s9FqdMlW",
    "outputId": "df150095-4d64-45c8-c085-a4917a6abfeb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1233</td>\n",
       "      <td>1043</td>\n",
       "      <td>961</td>\n",
       "      <td>606</td>\n",
       "      <td>882</td>\n",
       "      <td>326</td>\n",
       "      <td>673</td>\n",
       "      <td>1470</td>\n",
       "      <td>1054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attrition BusinessTravel              Department EducationField Gender  \\\n",
       "count       1470           1470                    1470           1470   1470   \n",
       "unique         2              3                       3              6      2   \n",
       "top           No  Travel_Rarely  Research & Development  Life Sciences   Male   \n",
       "freq        1233           1043                     961            606    882   \n",
       "\n",
       "                JobRole MaritalStatus Over18 OverTime  \n",
       "count              1470          1470   1470     1470  \n",
       "unique                9             3      1        2  \n",
       "top     Sales Executive       Married      Y       No  \n",
       "freq                326           673   1470     1054  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=\"O\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yqckl5kLdMlX"
   },
   "source": [
    "* HERE IN OVER18 COLUMN WE ONLY HAVE 1 UNIQUE VALUE SO IT MEANS NO ANALYSIS DONE ON IT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TX76JxFqdMlX",
    "outputId": "96584d34-4a45-4d76-aa64-34dee53822a1"
   },
   "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": "markdown",
   "metadata": {
    "id": "yWOIZstkdMlY"
   },
   "source": [
    "# BI-VARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-MDdAZudMlZ"
   },
   "source": [
    "### CREATING NEW DATA FRAME OF CATAGORICAL COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L0hNeuxgdMlZ",
    "outputId": "462d87d6-24ea-4593-b2b8-18e9f7c6d510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition : ['Yes' 'No']\n",
      "====================================\n",
      "BusinessTravel : ['Travel_Rarely' 'Travel_Frequently' 'Non-Travel']\n",
      "====================================\n",
      "Department : ['Sales' 'Research & Development' 'Human Resources']\n",
      "====================================\n",
      "EducationField : ['Life Sciences' 'Other' 'Medical' 'Marketing' 'Technical Degree'\n",
      " 'Human Resources']\n",
      "====================================\n",
      "Gender : ['Female' 'Male']\n",
      "====================================\n",
      "JobRole : ['Sales Executive' 'Research Scientist' 'Laboratory Technician'\n",
      " 'Manufacturing Director' 'Healthcare Representative' 'Manager'\n",
      " 'Sales Representative' 'Research Director' 'Human Resources']\n",
      "====================================\n",
      "MaritalStatus : ['Single' 'Married' 'Divorced']\n",
      "====================================\n",
      "Over18 : ['Y']\n",
      "====================================\n",
      "OverTime : ['Yes' 'No']\n",
      "====================================\n"
     ]
    }
   ],
   "source": [
    "categorical_col = []\n",
    "for column in data.columns:\n",
    "    if data[column].dtype == object:\n",
    "        categorical_col.append(column)\n",
    "        print(f\"{column} : {data[column].unique()}\")\n",
    "        print(\"====================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N3-NcsYZdMla",
    "outputId": "f38c5a90-e8b2-4efd-e0d9-2dfea27cfbd2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Attrition',\n",
       " 'BusinessTravel',\n",
       " 'Department',\n",
       " 'EducationField',\n",
       " 'Gender',\n",
       " 'JobRole',\n",
       " 'MaritalStatus',\n",
       " 'Over18',\n",
       " 'OverTime']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d2UeKtDbdMla"
   },
   "outputs": [],
   "source": [
    "data1=data[['Attrition',\n",
    " 'BusinessTravel',\n",
    " 'Department',\n",
    " 'EducationField',\n",
    " 'Gender',\n",
    " 'JobRole',\n",
    " 'MaritalStatus',\n",
    " 'Over18',\n",
    " 'OverTime']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KhObN4CNdMlb"
   },
   "source": [
    "## ANALYSIS ON CATAGORICAL COLUMNS W.R.T TARGET COLUMN(ATTRITION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8vMiFtc9dMlb",
    "outputId": "fc4ab8e5-f878-4141-f5e3-616118e7ba0d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20), facecolor='white')\n",
    "plotnumber = 1\n",
    "\n",
    "for column in data1:\n",
    "    if plotnumber<=16 :\n",
    "        ax = plt.subplot(4,4,plotnumber)\n",
    "        sns.countplot(x=data1[column].dropna(axis=0),hue=data.Attrition)\n",
    "        plt.xlabel(column)\n",
    "        plt.ylabel('Attrition')\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eD-OMxk6dMlc"
   },
   "source": [
    "### TARGET COLUMN ==  ATTRITION\n",
    "\n",
    "* Data of Attrition says that it has more number of \" No \" values and less number of \" Yes \".\n",
    "* It can be seen that there is big difference in counts of the values so we can say that it is IMBALANCE DATA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qSfXcgXXdMlc"
   },
   "source": [
    "### 1. IMPACT OF BUSINESS TRAVEL ON ATTRITION\n",
    "\n",
    "* Graph tells us that company has more count or more no. of employes who travels rarely. It means travel rate of company is less.\n",
    "* There are more employes which  travels rarely and are not satisfied with their job.\n",
    "* Non - traveller have least count as well as least attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OP3J2_FbdMlc"
   },
   "source": [
    "### 2. IMPACT OF DEPARTMENT ON ATTRITION\n",
    "\n",
    "* There are 3 no. of department are there --> 1. Sales , 2.Research and Development , 3. HR department\n",
    "* \"Research and Development\" department have more number of Attrition(150 employees) as compared to other two department.\n",
    "* \"HR Department\" have least Attrition with just 5 to 10 employees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6J5Utw_rdMld"
   },
   "source": [
    "### 3. IMPACT OF EDUCATION FIELD ON ATTRITION\n",
    "\n",
    "* First and foremost thing is that Employees who are from \" Life Science \" and \" Medical \" backgrounds are more as compared to other edication fields.\n",
    "* Nearly 100 number of employees are there who are from Life Science education background will leave the company   and followed by Medical education Employees.\n",
    "* As we conclude from analysis of Department and Attrition , here also HR educational background employees have least Attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "trySZDHAdMld"
   },
   "source": [
    "### 4. GENDER AND ATTRITION\n",
    "\n",
    "* Male employees are more as compared to Females\n",
    "* Males are more likely to quit the job rather than Females.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vrhvSkUTdMld"
   },
   "source": [
    "### 5. OVERTIME AND ATTRITION\n",
    "\n",
    "* As for \" Attrition yes \" , there is minor difference between the  Employees who are doing overtime and who are not doing overtime.\n",
    "* So we can say that Overtime feature is not much Attrition.\n",
    "* But we can conclude that most of employees are not doing overtime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YOBMNkwDdMle"
   },
   "source": [
    "### 6. IMPACT OF JOB ROLE ON ATTRITION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wWybz8qpdMlg",
    "outputId": "c2ca00c6-6590-4c38-e339-6db88fe8c574"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'JobRole')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10), facecolor='white')\n",
    "sns.countplot(x='JobRole',hue='Attrition',data=data1)\n",
    "plt.xlabel('JobRole',fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P0AppyYvdMlh"
   },
   "source": [
    "* There are less no. of Research Director who leaves the company.\n",
    "* Laboratory Technician , Sales Executive and Research Scientist are the top 3 job roles in which emloyees have thier Attrition \"Yes\".\n",
    "* Apart from these it can also seen that there are more number of employees in Sales Executive job role."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kulniF1RdMlh"
   },
   "source": [
    "# ANALYSIS ON CONTINUOS DATA W.R.T TARGET COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dh1lndNxdMli"
   },
   "outputs": [],
   "source": [
    "numerical_col = []\n",
    "for column in data.columns:\n",
    "    if data[column].dtype == \"int64\" and len(data[column].unique())>= 10:   # for continuos features we use unique>=10\n",
    "        \n",
    "        numerical_col.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HXk3h5fAdMli",
    "outputId": "36c8511d-49ca-4847-bee7-6575ae1b05af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'DailyRate',\n",
       " 'DistanceFromHome',\n",
       " 'EmployeeNumber',\n",
       " 'HourlyRate',\n",
       " 'MonthlyIncome',\n",
       " 'MonthlyRate',\n",
       " 'NumCompaniesWorked',\n",
       " 'PercentSalaryHike',\n",
       " 'TotalWorkingYears',\n",
       " 'YearsAtCompany',\n",
       " 'YearsInCurrentRole',\n",
       " 'YearsSinceLastPromotion',\n",
       " 'YearsWithCurrManager']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y86z-EmfdMlj"
   },
   "source": [
    "### GRAPHICAL REPRESENTATION OF CONTINUOS DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rvCXf_NNdMlk"
   },
   "outputs": [],
   "source": [
    "data2=data[['Age',\n",
    " 'DailyRate',\n",
    " 'DistanceFromHome',\n",
    " 'EmployeeNumber',\n",
    " 'HourlyRate',\n",
    " 'MonthlyIncome',\n",
    " 'MonthlyRate',\n",
    " 'NumCompaniesWorked',\n",
    " 'PercentSalaryHike',\n",
    " 'TotalWorkingYears',\n",
    " 'YearsAtCompany',\n",
    " 'YearsInCurrentRole',\n",
    " 'YearsSinceLastPromotion',\n",
    " 'YearsWithCurrManager']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KjEgP_MndMlk",
    "outputId": "84b02e04-150f-4ac7-b2ed-fac940fbf572"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,25), facecolor='white')\n",
    "plotnumber = 1\n",
    "\n",
    "for column in data2:\n",
    "    if plotnumber<=16 :\n",
    "        ax = plt.subplot(4,4,plotnumber)\n",
    "        sns.histplot(x=data2[column].dropna(axis=0)\n",
    "                        ,hue=data.Attrition)\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "        plt.ylabel('Attrition',fontsize=20)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Asj5YKOwdMlk"
   },
   "source": [
    "### 1. IMPACT OF AGE ON ATTRITION\n",
    "\n",
    "* Employees in age of 25 to 35 are more likey to leave the job.\n",
    "* After 35 age , the distribution tells us that \" Higher the Age Lesser will be Attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ABh0ufm8dMll"
   },
   "source": [
    "### 2. DISTANCE FROM HOME AND ATTRITION\n",
    "\n",
    "* Employees who has distance range of 0 - 10 km , are more likely to leave the job.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0AVrTN4dMll"
   },
   "source": [
    "### 3. HOW MONTHLY INCOME GIVES TRENDS W.R.T ATTRITION\n",
    "\n",
    "* Higher the Monthly Income give rise to less Attrition ( means Attrition \" No \" )\n",
    "* Employees who have their Income aprox. 2500 are more likely to quit their job,because 2500 is the least range of income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yqoFtUsOdMll"
   },
   "source": [
    "### 4. IMPACT OF NO. OF COMPANIES WORKED \n",
    "\n",
    "* Only that Employees(no. of emp. = 100) who worked with 1 company before have most  Attririon (Attrition \" Yes \"), rest have similiar data ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bc4mDqGvdMll"
   },
   "source": [
    "### 5. HOW SALARY HIKE IS IMPACTING THE ATTRITION\n",
    "\n",
    "* Higher the salary prcentage hike , Lesser the Attririon (\" No\")."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t3zHk__YdMll"
   },
   "source": [
    "### 6. YEARS AT THE COMAPNY \n",
    "\n",
    "* Freshers have higher data of  \" Attrition Yes \" that is of 75 no. of workers.\n",
    "* Apart from this Employees who ranges from 1 to 10 year working on this company are also likely to quit thier job.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UQHZ06cMdMlm"
   },
   "source": [
    "### 7. YEARS WITH CURRENT MANAGER AND ATTRITION\n",
    "\n",
    "* Employees who have 10 years or more  of relationship with their Manager have minor Attrition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oO6mailEdMlm"
   },
   "source": [
    "# ANALYSIS  OF  DISCRETE  DATA  W.R.T  TARGET  COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7Zc7ouy7dMlm"
   },
   "outputs": [],
   "source": [
    "discrete_col = []\n",
    "for column in data.columns:\n",
    "    if data[column].dtype == \"int64\" and len(data[column].unique()) <= 10:\n",
    "        discrete_col.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZCuRh_RSdMlm"
   },
   "outputs": [],
   "source": [
    "data3=data[['Education',\n",
    " 'EmployeeCount',\n",
    " 'EnvironmentSatisfaction',\n",
    " 'JobInvolvement',\n",
    " 'JobLevel',\n",
    " 'JobSatisfaction',\n",
    " 'NumCompaniesWorked',\n",
    " 'PerformanceRating',\n",
    " 'RelationshipSatisfaction',\n",
    " 'StandardHours',\n",
    " 'StockOptionLevel',\n",
    " 'TrainingTimesLastYear',\n",
    " 'WorkLifeBalance']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CzSmDiJ8dMlm"
   },
   "source": [
    "## GRAPHICAL REPRESENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jdrtMIZRdMln",
    "outputId": "595848da-e674-4ec1-f234-3ccc632e70bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vb2+FhYVp+/btysjIsE9fcvLkSX3xxReaMGGCbrjhBnl6eio1NVVDhw6191E23dH5BWZHr89RUVE6dOiQbrrpJrVs2VKdOnW64Guxa9cuDRs2TEePHtXatWvt03MUFRUpKipKO3fuVL9+/TRx4kSdPXtWH374oe688059/fXXeuaZZ+ztmKapMWPGKCUlRV26dNEf/vAHnTlzRq+//rp27959yXMCOEPZB1tlRd8dO3bopptu0qFDhzR06FCNGjVKP//8s1avXq2BAwfq3Xff1bBhwyq0M3/+fG3YsEG33nqrIiMjdeTIEfsYs7JxWpm///3vmjx5si6//HLdfvvt8vPz06ZNmzRv3jy999572rJlS7nxZJnRo0fryy+/1C233KLbbrtNfn5+5daPGzdO27Zt07Bhw9SoUSOtWrVK8fHxatSokf773/8qOTlZw4cP15AhQ7RmzRrNmTNHzZo10/Tp08u144oc/MEHH+j222/X6dOndfPNN+uOO+5QUVGRdu3apeeee06TJ0+2b7t+/XqNGjXKnqNtNptycnL073//Wx988IE2btxY7WmhGjVqVO7nQ4cO6aGHHtK1116r6OhotW3bVnl5eXrvvfc0bNgwvfbaa7r33nslnRtvz5o1q8LfANK5vz+q4pVXXtGaNWsUExOjQYMGadu2bXrrrbe0a9cuZWRklLuOb968WTfddJNKS0s1atQodenSRbt371ZkZKSioqKqddz1ggm4CUnmr9+Sn3/+uSnJDAoKMvPy8uzLi4uLzeHDh5uSzL/85S/l9unYsaMpybzuuuvMU6dO2ZcXFhaanTt3NiWZn3zyySX7rsySJUtMSebIkSPNEydOlFs3a9YsU5K5cOFC+7J//etfpiTzkUceqdDWc889Z0oyX3zxRfuyb7/91mzUqJHZpUsXMycnp9z2H3/8senh4WHedttt5ZYPGjSoSrEDqHusyos7duwwGzVqZBqGYf72t78133nnHTMrK+uisRYVFZkFBQUVlmdnZ5v+/v5m9+7dKz2+QYMGVdre/v37TUlmXFycfdmaNWtMSebDDz9cYfvTp0+bv/zyS4Xj7tixY6Xt79u3r9I2oqKiTC8vrwo5+JprrjElmc8++2yF/QoKCsyTJ0/af75UXq4srmeffdaUZN5yyy1mcXGxfXl+fr79/G3ZssW+vOz1kWTOnj27XFvr16+3twXUFZLMgIAA0zRN8+2337aPt85X9rtw/u/Ixo0bTUnmrFmzKm23st+3svGcp6enuWnTJvvy0tJS88YbbzQlmb6+vuY///nPcvtNnDjRlGSuXr3avmzZsmX2/FodZ8+eNbt3725KqtDPihUrTElmt27dzNLSUvvysrFmy5YtzT179tiXnzp1yuzRo4fp4eFhtmrV6oLHtHPnzgqvTXXHzNXNnWX5sF+/fmZhYaF9+bFjx8wuXbqYHh4e5a5lpln5taHs2P/whz+YJSUl9uUlJSWVnpd+/fqZjRs3NvPz8yvE++tr1Z/+9CdTkvn888/bl3300UemJHP58uWmaZpmeHi4GRYWZl9/9uxZs02bNqaHh4d56NAh0zRrdn0eMmSIeezYsQqxlh33xo0bTdM0zQ0bNpgtW7Y0/f39zYyMjHLbxsXFmZLMefPmlVt+8uRJc+jQoaZhGOXeA2+++aYpyRwwYEC5a9j55/9C12igJi70t/eGDRtMwzBMwzDMrKwss7i42OzSpYvZpEmTcnnNNE0zNzfXvOKKK8z27duXy19lvzPNmjUzd+zYUWn/FxqnZWVlmY0bNzZbtGhhfvPNN+XWTZ482ZRk3nfffZW21bt370rHwWXrw8PDzcOHD9uXf//992ajRo1MHx8fMzg4uFzuPHz4sNm6dWuzTZs25a53pun8HFxQUGC2bNnSbNSoUYXX3DTPjevLHDp0yPTx8TFbt25tfv311+W22717t3n55Zebffv2Lbe87Pz8+ppdUlJiDh061J7nz3fq1Kly/ZYpKioye/bsafr6+laozVzsbwDTvPh1pkWLFuZ///vfcuvuuOMOU5L51ltv2ZeVlpaaNpvNlGSuXbu23PZ/+9vf7O/zsvzdEFCVgtuo7EJz7733mpLMv//97xW2//bbb00PDw+zU6dO5ZaXDdQ2b95cYZ+yPygmTJhwyb4rExoaanp5eZW7OJQpKSkxW7dubV599dX2ZSdOnDC9vb3N9u3blxsMm6Zp9uzZ02zUqFG5C9HDDz9sSjLff//9Svu/7bbbTE9Pz3JFFArMQP1lZV586623zPbt29tjkGS2atXKvO2228w1a9ZU6zgefPBBU5J54MCBCsfnSIH5T3/6U5X6vdTgsjLvvPOOKclMTk62L0tPTzclmaGhoeWKPRfiSIHZZrOZhmFU+IPGNE3zH//4hynJvOeee+zLyl6fjh07Vri+mKZpdujQwWzduvUlYwXcxfkFZtP8vw91Pv30U/uy2i4w//a3v62wfXJysinJvP766yus27RpU4UPdebNm2dKMseOHVvVQzVN0zQ/++wzU5J5zTXXVLp+4MCBFQq8ZX/8/vnPf66w/VNPPWVKMu++++4K65YuXWpKMpcuXVpuuSPXhgupLHea5v/lww0bNlTY58knnzQlme+991655b++NpSWlpqtWrUy27dvX6HYYprnijGGYZi33367fVm/fv3MZs2a2Yu/F/Pxxx9X+FBuxowZpiTzxx9/NE3TNB977DHTw8PD/jdARkaGvWhUpibX518Xi8ucX2B+4403zEaNGpkhISEVrqc///yz6enpWS6e85XF+9hjj9mXlX3wkJaWVmH7svNPgRnOUDaunDVrljlr1izz8ccfN0ePHm16enqaksypU6eapmmaq1evNiWZjz76aKXtLFy40JRkfvDBB/ZlZb8zld2MUOZC47RnnnnmguPMQ4cOmS1atDCbNm1arqBd1tb5H3BV1tfHH39cYV1kZKQpyUxKSqqwbsKECaakS97gUaa2cvBf//pXU5I5ZcqUS/ZZ9vq//PLLla4vq22cX3wuOz+DBg2yn/8HH3zQ/oFrjx49Kv1g8ELmz59f6YehNSkwz5w5s8L2aWlpplT+xsFPP/3UlGRGRkZW2L60tNS88sorG1yBmSky4NZ27NghSZV+veDKK69UYGCg9u/fryNHjpSbJ9TLy0vXXntthX3Kvhazc+fOasdy4sQJ7dq1S23atLF/5eLXmjRpom+++cb+82WXXaYxY8botdde04cffmj/+s5//vMfff311xo5cmS5B9iUzVP6ySefVDqn1E8//aTS0lJ99913CgsLq/YxAKj7XJUXx4wZo5EjR2rjxo367LPPtHPnTn322WdavXq1Vq9erfHjx5ebS1mStmzZokWLFumLL77QTz/9VGFet9zc3BpN8TNo0CAFBAQoISFBO3bs0LBhw3TdddcpNDRUnp6e1Wrr4MGDmjdvnlJTU3Xw4EGdPHmyQqxltm7dKkkaOnSo/Wvxteno0aPat2+fAgICKn3oSNm5ruzadaFjDwoKqjD3NVCXzJ8/X9dee60effRR++9gbQsPD6+wrOyhT5WNswICAiSdmw+0pi6Wy8uWl+XeG264ody62oy7uteG6uTOS8UcFBQkSTp8+HCl+5T57rvvdOjQIXXt2rXcFA/nu+yyy8qNwe+66y498sgj6tGjh8aNG6dBgwbpuuuuU9u2bSvse91116lJkyb69NNPVVxcrEaNGiktLU09evRQu3btJEmRkZF6/vnntWnTJt12222Vzr/s6PW5adOm6tOnz0Vfg0WLFiklJUXXXXed1qxZI19f33Lrv/zyS5WWll5wPvLi4mJJKvca7dixQx4eHho4cGCF7S82XRVQW8rm2jcMQz4+PvYpiX77299K+r+/jQ8cOFDp+7psrvpvvvmmwjQZ/fv3r3Y8F/sd9vX1Vd++fbV582bt3btXV111VbX6q0ne7tixo325s3Nw2fX2lltuuejxSP93fnbt2lXp+Smbau+bb75Rjx49yq375JNP7M8sKRMaGqpNmzZV+vyXr7/+Ws8//7w2b96svLw8nTp1qtz6Cx23I6r6WpVdHyvLoR4eHrr22murNd1gfUCBGW6tbCJ9f3//Stf7+/vr4MGDKioqKpeI2rRpU+kf3O3bty/XbnUcPnxYpmmqoKCgyg+ekc49bOq1115TcnKy/cKXnJwsSYqLiyu3bdkDWJ5//vmLtnns2LHqhA6gHnFlXmzUqJFuuukm3XTTTZKk0tJSvfPOO5o4caKWLVumkSNH6rbbbpMkvfvuu4qNjVXTpk0VHR2tLl266PLLL5eHh4c2bdqkTz75pMYPDWrZsqW2bt2qWbNmac2aNfrwww/tx/b73/9ef/7znyvM21aZ//3vf+rfv78OHz6s66+/XjfddJO8vb3l6emprKwsJScnl4u17IFfZYP92laVc3p+HOerbB5A6VzRqLKHwwB1xTXXXKPY2FitWrVKb731lsaOHVvrfVT2R2zZA/outq6sWCf93+9ndf+4rcnvfW3EXaY614bq5s7zVZaryuIqLS2tdJ8yZePjzMzMi47Bzx8fT5s2TW3atNErr7yiF198UQsXLpRhGBo0aJCef/75cgWEpk2b6tprr9XGjRu1fft29erVS//5z3/KzTM6cOBAeXl5KTU1Vbfddpt9/uWy+Y8lx6/Pfn5+FR4y9mubN2+WaZoaMmRIheLy+a/Rl19+WelNKpW9RkeOHFGrVq0qvW6WnX/AmcxKHiR6vrL39dtvv33R7Sr729iR93BN8vKl+quNvO2KHFydMW/Z+Tn/YYWVqez8zJo1S7Nnz9bZs2eVm5urv/71r3rxxRc1ZswYrVu3rtwNHVu3blVUVJRKSko0ZMgQxcTEqGXLlvLw8FBGRoZSUlJq9cGkVX2tyt4vZR9E/tqFltdnFJjh1sqS7Y8//qguXbpUWJ+Xl1duuzI///yzSktLKwyYf/zxx0q3r04sffv2tX+6WRXXXnutunbtqjVr1qioqEiXX365li9frjZt2lT4pLWsjyNHjqhly5bVjhFA/WdlXvT09NSYMWO0e/duPfPMM0pLS7MXmJ944gk1btxY6enpCgkJKbff/fffX+EuBUcFBgYqKSlJpmlqz549SktL0+LFizVnzhydPXv2gg83Od+CBQtUWFioJUuWlHvAoCQtX77c/iFgmbKBZm3eHXG+889pZS50ToH6bu7cuUpJSdGf/vQnjRw5stJtyv4ILSkpqXR9UVHRBT+IqQ1ldy6lp6dXuDP1Ytzl974614bq5s7aUhbDyJEj9e9//7vK+40fP17jx49XUVGRPv/8c7377rt6/fXXNXToUO3du7fc3cxRUVHauHGj0tLSVFhYqNLS0nIPSW3RooXCwsKUlpam0tJSffrpp2rSpImuu+66CnFW9/p8qeKyJCUlJSkhIUFPPfWUzp49qzlz5pRbX9bm1KlTtWDBgku2V7bPoUOH7Hdtn+9C70vAlcre1ykpKYqJianWvlX5vbpQfz/++KN69uxZYf3F8rIj/VWXK3Lw+WPe3r17X3Tbstdh165dl/wWxoV4eHgoKChIixYt0g8//KBVq1bp5Zdf1pQpU+zbPPPMMzp58qQ2btxY4dsVZeMEK5TVa/Lz8ytdf6Hl9Vntf88TqEV9+/aVJG3atKnCun379iknJ0edOnWq8IdDSUmJPv/88wr7lLVT1m51NG/eXD179tTXX3+tQ4cOVWvfuLg4nTp1Sm+99ZY++OAD/fzzz7rzzjsrDOYGDBggSfr000+rHR+AhsEd8mKLFi0klb/zZN++ferRo0eF4vLZs2f12WefVdqOh4fHJe9cuxDDMNSzZ089+OCD2rBhgyRp9erVVdp33759ks498fvXKiuEl+XmDz/8sEp3BZcVaqp6bC1atFCXLl2Um5tr/7rn+TZu3ChJ1X4KN1DX2Ww2/f73v9f+/fv10ksvVbpN2d2c2dnZFdbt27fPoW+tVUenTp1044036tSpU5f8Bpok+11WF8vlkut+76tzbahu7qwt3bt3l4+Pj7Zu3VrpXdiX4uPjo2HDhum1117ThAkTdOjQIW3evLncNmVTXaSlpSktLU2GYVQoZERGRmrPnj1677339Msvv+jaa6/VZZddZl/v6PW5qsewYcMGXX/99Xr66af1xz/+sdz6/v37y8PDo1p/Q/Tr1++C1+gLvS8BV3L138YX+x0uKipSRkaGmjZtWmGs6yquyMFlr/m6deuqvG1tnZ/58+erSZMmmjNnjn755Rf78n379qlVq1aVTt1zoeP29PR0+G+Mqip7v1SWQ8+ePVvptbW+o8AMtzZx4kRJ5z61KigosC8vLS3Vo48+qrNnz2rSpEmV7vunP/2p3FclDh06ZJ+37Z577nEonmnTpunMmTOaOHFipV+NOXz4cKV3N48fP14eHh5atmyZli1bJkkVPnWUpD/84Q9q1KiRpk6dWul8PWfOnKH4DDRwrsiLy5cv14YNGyotpv7444/2r8KdPy9ocHCwMjMz9cMPP9iXmaap2bNna8+ePZXG07p160qLQhfy9ddfV3o3QNmyZs2aVamd4OBgSRX/gPjwww/1j3/8o8L2YWFhuvbaa5WRkaF58+ZVWF9YWFhuLrjWrVtLOjdPXlVNnDhRpmnqscceKzcg/vnnn+13ZZede6AhefLJJ+Xj46O//OUvlX7Ntnv37mrZsqVSUlL0008/2ZefPHmy3B1QzvTiiy+qZcuWmjt3rubPn1/p3dQHDx7UuHHj7HNWXnfdderWrZs+++wzrVq1qty2q1at0qeffqorr7yy0rkda1tVrw3VzZ21xcvLSw8++KDy8vI0ZcqUCnOOSufuLDz/WrNx48ZKv35f9h759fXi6quvVosWLfTFF19o7dq1uuqqq9SqVaty25Td0fzkk09KKj//slSz63NVtGjRQuvXr9eQIUP0/PPP66GHHrKv8/Pz01133aX09HQ9/fTTlRZWvv/+e+3fv9/+c9m5nTlzZrlr2PnnH7DSiBEj1KVLFy1e/P/Yu/ewKOv8/+OvW0EMD4AKSgPiYcqzoqBYmopEmRl4YD1kianLZu1mWq1ubnloS6zNctPdll1StFZT2qRM7aCpm6XGKp3Mwg0UiAhRrDwC3r8//Dq/CFQYB2YGno/r8rrkM/fhfQ/wmnve3PO5l2vTpk2VLvPRRx/p1KlTDtnfXXfdJU9PT73wwgu2Zu5Fjz32mH744Qfddddd8vLycsj+qqs2Mjg+Pl7NmzfX3/72twp/iJPKz+V/zz33yNfXVwsWLNDevXsrLHv+/Plq/bGqbdu2+vWvf62ioiI9++yztvF27drp2LFj+vTTT8stn5ycbJsu75datmypwsLCSl8vHGXAgAHq2LGj3n///QoN+aSkpHo3/7LEFBlwcTfeeKN+//vf6+mnn1b37t0VFxenJk2aaPPmzfr88881cOBAPfLIIxXWCwwM1NmzZ9W9e3fFxMSopKREqampys/P13333VfhZikXVdb0veivf/2rpkyZov/+97/661//qo4dO+rWW29V27ZtdezYMWVlZWnnzp2655579OKLL5ZbNzg4WJGRkdq6das8PDzUo0ePSq8W7Ny5s1566SVNmTJF3bp107Bhw3T99derpKRER44c0X/+8x/5+/vr4MGD1XsiAdQZtZGLe/bs0dKlS9WmTRsNHDhQ7du3lyRlZWXprbfe0unTpxUbG6u4uDjbOjNnztS9996r3r17a8yYMfL09NSuXbt04MAB3XHHHXrzzTcr1BQVFaW1a9fqjjvuUJ8+feTp6alBgwZdMqPfffddPfLII7rhhht0/fXXKyAgQLm5uUpLS1ODBg0qPe7K3HfffVqxYoV+9atfKS4uTtdee60+//xzbdmyRWPHjtWrr75aYZ2XX35ZQ4YM0aOPPqrXXntNQ4YMkWmayszM1DvvvKODBw/aTvyjoqK0fv16jR49WsOHD9c111yjkJAQ3X333Zes6eGHH9bmzZuVlpamXr16afjw4Tp16pTWr1+v77//Xr///e9rpdEEuJoWLVro0UcfrXDF5kWenp6aMWOGnnjiCfXu3VujRo1SaWmp3n33XV177bW2myjVpC5duujtt99WXFycHn74YS1dulRRUVG69tprdfLkSX3yySfatWuXDMPQ7NmzJV34FEZKSoqio6M1btw4xcbGqnPnzvrqq6+0YcMGNWvWTKtWraqRG4v+XHVeG+zJTkd57LHH9Mknn+jFF1/Um2++qaFDh8pisej7779XZmamdu3apSeffNJ2I6lRo0apadOm6t+/v9q1ayfTNPWf//xHH3/8scLCwsrNnSxdaGIPGjRIb731ljIzMzVr1qwKNQwYMECenp767LPPJFVsMNv7+lwd3t7e2rhxo8aMGaO//OUvOnPmjF588UUZhqFly5YpMzNTjz/+uFavXq2BAweqdevW+vbbb/Xll1/q448/1po1a2yv6RMmTNCrr76qN954Q927d1dsbKzt+9+3b1/973//u6pagavl6empf//737r11lt1++2368Ybb1RoaKi8vb2Vk5Ojjz/+WN98843y8/OrfJHB5bRr107PP/+87r//fvXp00djx46Vv7+/duzYoY8++kidO3eu9EKD2lIbGdyqVSv961//UlxcnCIjI3XbbbepZ8+e+uGHH/Tpp58qJyfH9oeqli1bKjU1VaNGjVL//v0VFRWlbt26yTAM5eTk6KOPPqpwEcaVPProo0pOTtZzzz2n3/3ud2rVqpUefPBBvf322xo4cKDGjh0rHx8fpaen64MPPrDdq+GXoqKi9PHHH2vYsGEaNGiQvLy81KtXL91xxx1X/Rxd1KBBA/3zn//UsGHDFBMTozFjxqhjx4769NNP9e677+q2226rMJ90nWcCLqC0tNSUZHp6elb6+Jo1a8wBAwaYTZs2Nb28vMyuXbuaf/rTn8zTp09XWDYkJMQMCQkxi4uLzfvuu8+89tprzUaNGpmdO3c2ly5dap4/f77COpKu+O/48eO25d98803z9ttvN/39/U1PT0+zdevWZt++fc25c+eaX375ZaXHsHr1atu2/vznP1/2+fj000/N+Ph4s23btmajRo1MPz8/s1u3bmZCQoK5devWcssOHjzY5FcZqHucmYtHjhwxly1bZo4cOdK8/vrrzWbNmpmenp5mmzZtzNtuu81cvXq1WVZWVmE/K1asMHv16mV6e3ubLVu2NEeOHGl++umn5rx580xJ5vvvv19u+YKCAnPChAlmQECA2aBBA1OSOW/ePNM0TTMrK8uUZMbHx9uWP3DggDlz5kwzLCzMbNWqldmoUSMzJCTEHDNmjLlr165LHndldu3aZUZGRpq+vr5m06ZNzQEDBpivv/66+f7775er4+eOHj1q/v73vzevv/5608vLy/Tx8TF79eplPvroo+bJkydty5WWlpp/+MMfzPbt25seHh6mJHPw4MFXrOv06dPmk08+aXbr1s1s3Lixra5//etfFZat7Pn5OV4b4G4kmRaLpdLHzpw5Y7Zr1852HlVSUlLu8fPnz5uLFi0yO3ToYHp6eprBwcHmI488Yp48ebLS37cVK1aYkswVK1ZU2NflMuBKv3c//vijuWTJEnPIkCGmv7+/6eHhYTZv3tzs06ePOWfOHPObb76psM7BgwfNu+66y2zTpo3p4eFhtmnTxpw4caJ58ODBCsteKkvtPSZ7zpmrm52Xy6JL1fzLzLzo/Pnz5qpVq8yhQ4eafn5+pqenp3nttdeaAwYMMJ988knzyJEjtmX/9re/mSNHjjTbt29vXnPNNaafn58ZGhpqLl682Pzhhx8qrWfJkiW2n7E333yz0mUGDBhgSjKbN29ulpaWVrqMPa/Pl3Kp7/nZs2fNUaNG2X4eL74mnz171nzhhRfMG264wWzevLnZqFEjMzg42Bw6dKj53HPPmUePHq2wnQULFpjt27e3vaY++uij5pkzZy75fQCu1sXfs6oqKCgwZ8+ebXbr1s2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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,25), facecolor='white')\n",
    "plotnumber = 1\n",
    "\n",
    "for column in data3:\n",
    "    if plotnumber<=16 :\n",
    "        ax = plt.subplot(4,4,plotnumber)\n",
    "        sns.countplot(x=data3[column].dropna(axis=0)\n",
    "                        ,hue=data.Attrition)\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "        plt.ylabel('Attrition',fontsize=20)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kmhyMyb1dMln"
   },
   "source": [
    "### 1. IMPACT OF EMVIRONMENT AND JOB SATISFACTION   ON ATTRITION\n",
    "\n",
    "* Increase in rate of Environment as well as Job Satisfaction gives rise to increase in Attrition \" No \"(means not willing to quit).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jlvJLMiWdMln"
   },
   "source": [
    "### 2. HOW JOB LEVEL IS AFFECTING ATTRITION\n",
    "\n",
    "* Increase in job level , Decrease in chances of leaving the company for Employees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H_nUzC2odMln"
   },
   "source": [
    "### 3. JOB INVOLVEMENT IMPACTING ON ATTRITION\n",
    "\n",
    "* The employees who involved in job more than sufficient are more likely to quit.\n",
    "* Somehow,there are some emp. who are not involved fully in there job but they are also like to quit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1TKhDwPVdMln"
   },
   "source": [
    "### 4 . IMPACT OF STOCK OPTION LEVEL ON ATTRITION\n",
    "\n",
    "* For the employees who're  not having stock option are likely to quit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zDlC0exWdMln"
   },
   "source": [
    "### 5. PERFORMANCE RATING AND ATTRITION\n",
    "\n",
    "* On an average , most of employees are moderately performed.\n",
    "* However employes having less Performance rating are more likely to quit ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ykT2VGu-dMlo"
   },
   "outputs": [],
   "source": []
  }
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