kaggle-logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1628, 29)\n"
     ]
    },
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1614</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>842</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>689</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age  Attrition BusinessTravel              Department  \\\n",
       "0   1   30          0     Non-Travel  Research & Development   \n",
       "1   2   36          0  Travel_Rarely  Research & Development   \n",
       "2   3   55          1  Travel_Rarely                   Sales   \n",
       "3   4   39          0  Travel_Rarely  Research & Development   \n",
       "4   5   37          0  Travel_Rarely  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeNumber  \\\n",
       "0                 2          3        Medical             571   \n",
       "1                12          4  Life Sciences            1614   \n",
       "2                 2          1        Medical             842   \n",
       "3                24          1  Life Sciences            2014   \n",
       "4                 3          3          Other             689   \n",
       "\n",
       "   EnvironmentSatisfaction  ... PerformanceRating  StockOptionLevel  \\\n",
       "0                        3  ...                 3                 0   \n",
       "1                        3  ...                 3                 2   \n",
       "2                        3  ...                 3                 0   \n",
       "3                        1  ...                 3                 0   \n",
       "4                        3  ...                 3                 1   \n",
       "\n",
       "  TotalWorkingYears  TrainingTimesLastYear YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                12                      2             11                   7   \n",
       "1                 7                      2              3                   2   \n",
       "2                12                      3              9                   7   \n",
       "3                18                      2              7                   7   \n",
       "4                10                      2             10                   7   \n",
       "\n",
       "   YearsSinceLastPromotion YearsWithCurrManager  CommunicationSkill  Behaviour  \n",
       "0                        6                    7                   4          1  \n",
       "1                        1                    1                   2          1  \n",
       "2                        7                    3                   5          1  \n",
       "3                        1                    7                   4          1  \n",
       "4                        7                    8                   1          1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv(\"train.csv\")\n",
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(470, 28)\n"
     ]
    },
    {
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       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
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       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>827</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>972</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   1   28  Travel_Rarely  Research & Development                 9   \n",
       "1   2   31  Travel_Rarely                   Sales                 6   \n",
       "2   3   37  Travel_Rarely  Research & Development                 6   \n",
       "3   4   42  Travel_Rarely  Research & Development                 1   \n",
       "4   5   45     Non-Travel  Research & Development                 4   \n",
       "\n",
       "   Education EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  \\\n",
       "0          3        Medical             377                        4    Male   \n",
       "1          4        Medical             653                        1    Male   \n",
       "2          3        Medical             474                        3    Male   \n",
       "3          2  Life Sciences             827                        4  Female   \n",
       "4          2  Life Sciences             972                        3    Male   \n",
       "\n",
       "   ...  PerformanceRating StockOptionLevel  TotalWorkingYears  \\\n",
       "0  ...                  4                1                  5   \n",
       "1  ...                  4                2                 13   \n",
       "2  ...                  3                2                 13   \n",
       "3  ...                  3                1                  8   \n",
       "4  ...                  3                0                  9   \n",
       "\n",
       "  TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                     3               5                   2   \n",
       "1                     4               7                   7   \n",
       "2                     2               7                   7   \n",
       "3                     4               4                   3   \n",
       "4                     5               9                   7   \n",
       "\n",
       "  YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  Behaviour  \n",
       "0                       0                     4                   5          1  \n",
       "1                       5                     7                   3          1  \n",
       "2                       6                     7                   4          1  \n",
       "3                       0                     2                   5          1  \n",
       "4                       0                     8                   2          1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv(\"test.csv\")\n",
    "print(test.shape)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain=train[\"Attrition\"]\n",
    "train1=train.drop(\"Attrition\",axis=1)\n",
    "tt=train.drop(\"Attrition\",axis=1)\n",
    "df=tt.append(test,sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>571</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1614</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>842</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>689</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   1   30     Non-Travel  Research & Development                 2   \n",
       "1   2   36  Travel_Rarely  Research & Development                12   \n",
       "2   3   55  Travel_Rarely                   Sales                 2   \n",
       "3   4   39  Travel_Rarely  Research & Development                24   \n",
       "4   5   37  Travel_Rarely  Research & Development                 3   \n",
       "\n",
       "   Education EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  \\\n",
       "0          3        Medical             571                        3  Female   \n",
       "1          4  Life Sciences            1614                        3  Female   \n",
       "2          1        Medical             842                        3    Male   \n",
       "3          1  Life Sciences            2014                        1    Male   \n",
       "4          3          Other             689                        3    Male   \n",
       "\n",
       "   ...  PerformanceRating StockOptionLevel  TotalWorkingYears  \\\n",
       "0  ...                  3                0                 12   \n",
       "1  ...                  3                2                  7   \n",
       "2  ...                  3                0                 12   \n",
       "3  ...                  3                0                 18   \n",
       "4  ...                  3                1                 10   \n",
       "\n",
       "  TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                     2              11                   7   \n",
       "1                     2               3                   2   \n",
       "2                     3               9                   7   \n",
       "3                     2               7                   7   \n",
       "4                     2              10                   7   \n",
       "\n",
       "  YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  Behaviour  \n",
       "0                       6                     7                   4          1  \n",
       "1                       1                     1                   2          1  \n",
       "2                       7                     3                   5          1  \n",
       "3                       1                     7                   4          1  \n",
       "4                       7                     8                   1          1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         False\n",
       "Age                        False\n",
       "BusinessTravel             False\n",
       "Department                 False\n",
       "DistanceFromHome           False\n",
       "Education                  False\n",
       "EducationField             False\n",
       "EmployeeNumber             False\n",
       "EnvironmentSatisfaction    False\n",
       "Gender                     False\n",
       "JobInvolvement             False\n",
       "JobRole                    False\n",
       "JobSatisfaction            False\n",
       "MaritalStatus              False\n",
       "MonthlyIncome              False\n",
       "NumCompaniesWorked         False\n",
       "OverTime                   False\n",
       "PercentSalaryHike          False\n",
       "PerformanceRating          False\n",
       "StockOptionLevel           False\n",
       "TotalWorkingYears          False\n",
       "TrainingTimesLastYear      False\n",
       "YearsAtCompany             False\n",
       "YearsInCurrentRole         False\n",
       "YearsSinceLastPromotion    False\n",
       "YearsWithCurrManager       False\n",
       "CommunicationSkill         False\n",
       "Behaviour                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                          int64\n",
       "Age                         int64\n",
       "BusinessTravel             object\n",
       "Department                 object\n",
       "DistanceFromHome            int64\n",
       "Education                   int64\n",
       "EducationField             object\n",
       "EmployeeNumber              int64\n",
       "EnvironmentSatisfaction     int64\n",
       "Gender                     object\n",
       "JobInvolvement              int64\n",
       "JobRole                    object\n",
       "JobSatisfaction             int64\n",
       "MaritalStatus              object\n",
       "MonthlyIncome               int64\n",
       "NumCompaniesWorked          int64\n",
       "OverTime                   object\n",
       "PercentSalaryHike           int64\n",
       "PerformanceRating           int64\n",
       "StockOptionLevel            int64\n",
       "TotalWorkingYears           int64\n",
       "TrainingTimesLastYear       int64\n",
       "YearsAtCompany              int64\n",
       "YearsInCurrentRole          int64\n",
       "YearsSinceLastPromotion     int64\n",
       "YearsWithCurrManager        int64\n",
       "CommunicationSkill          int64\n",
       "Behaviour                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    843\n",
       "1    785\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Attrition.value_counts(normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6792.187425860024\n",
      "4704.484076433121\n"
     ]
    }
   ],
   "source": [
    "print(train[train.Attrition==0][\"MonthlyIncome\"].mean())\n",
    "print(train[train.Attrition==1][\"MonthlyIncome\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the mean salary of employess who left company is 4704 and for employees who did not leave the company is 4704."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>499.385528</td>\n",
       "      <td>37.480427</td>\n",
       "      <td>8.860024</td>\n",
       "      <td>2.925267</td>\n",
       "      <td>1030.077106</td>\n",
       "      <td>2.767497</td>\n",
       "      <td>2.763938</td>\n",
       "      <td>2.795967</td>\n",
       "      <td>6792.187426</td>\n",
       "      <td>2.676157</td>\n",
       "      <td>...</td>\n",
       "      <td>3.151839</td>\n",
       "      <td>0.838671</td>\n",
       "      <td>11.846975</td>\n",
       "      <td>2.883749</td>\n",
       "      <td>7.406880</td>\n",
       "      <td>4.519573</td>\n",
       "      <td>2.201661</td>\n",
       "      <td>4.302491</td>\n",
       "      <td>2.995255</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1152.896815</td>\n",
       "      <td>33.675159</td>\n",
       "      <td>10.675159</td>\n",
       "      <td>2.840764</td>\n",
       "      <td>969.745223</td>\n",
       "      <td>2.515924</td>\n",
       "      <td>2.528662</td>\n",
       "      <td>2.471338</td>\n",
       "      <td>4704.484076</td>\n",
       "      <td>2.853503</td>\n",
       "      <td>...</td>\n",
       "      <td>3.159236</td>\n",
       "      <td>0.535032</td>\n",
       "      <td>7.910828</td>\n",
       "      <td>2.611465</td>\n",
       "      <td>4.681529</td>\n",
       "      <td>2.643312</td>\n",
       "      <td>1.885350</td>\n",
       "      <td>2.745223</td>\n",
       "      <td>3.286624</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id        Age  DistanceFromHome  Education  \\\n",
       "Attrition                                                        \n",
       "0           499.385528  37.480427          8.860024   2.925267   \n",
       "1          1152.896815  33.675159         10.675159   2.840764   \n",
       "\n",
       "           EmployeeNumber  EnvironmentSatisfaction  JobInvolvement  \\\n",
       "Attrition                                                            \n",
       "0             1030.077106                 2.767497        2.763938   \n",
       "1              969.745223                 2.515924        2.528662   \n",
       "\n",
       "           JobSatisfaction  MonthlyIncome  NumCompaniesWorked  ...  \\\n",
       "Attrition                                                      ...   \n",
       "0                 2.795967    6792.187426            2.676157  ...   \n",
       "1                 2.471338    4704.484076            2.853503  ...   \n",
       "\n",
       "           PerformanceRating  StockOptionLevel  TotalWorkingYears  \\\n",
       "Attrition                                                           \n",
       "0                   3.151839          0.838671          11.846975   \n",
       "1                   3.159236          0.535032           7.910828   \n",
       "\n",
       "           TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  \\\n",
       "Attrition                                                              \n",
       "0                       2.883749        7.406880            4.519573   \n",
       "1                       2.611465        4.681529            2.643312   \n",
       "\n",
       "           YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  \\\n",
       "Attrition                                                                      \n",
       "0                         2.201661              4.302491            2.995255   \n",
       "1                         1.885350              2.745223            3.286624   \n",
       "\n",
       "           Behaviour  \n",
       "Attrition             \n",
       "0                1.0  \n",
       "1                1.0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Attrition').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees having age around 33 are more likely to leave company.\n",
    "Employees having distance from home around 10 km have high chance of Attrition.\n",
    "Employees having TotalWorkingYears around 8 years have high attrition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Research & Development :\n",
      "Research Scientist           341\n",
      "Laboratory Technician        310\n",
      "Manufacturing Director       121\n",
      "Healthcare Representative    110\n",
      "Manager                       50\n",
      "Research Director             47\n",
      "Name: JobRole, dtype: int64\n",
      "\n",
      " Sales :\n",
      "Sales Executive         365\n",
      "Sales Representative    172\n",
      "Manager                  31\n",
      "Name: JobRole, dtype: int64\n",
      "\n",
      " Human Resources :\n",
      "Human Resources    72\n",
      "Manager             9\n",
      "Name: JobRole, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for dept in train['Department'].unique():\n",
    "    print('\\n', dept, ':')\n",
    "    print(train[train['Department']==dept]['JobRole'].value_counts())#for analyzing the job role in each department"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to train data 52% employees did not leave the company while 48% employees left company.\n",
    "Now  we will do EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df900d5288>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 408.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"Department\", data=train, aspect=1, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R&D department has highest attrition among all three departments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df901876c8>"
      ]
     },
     "execution_count": 100,
     "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": [
    "sns.distplot(train['MonthlyIncome'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of employees have income around 5000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df90216fc8>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sM58Kq+o7VbU/XToexOyrrM6ctg06pUuyKsm6JOs2btw4n7IkSdugP0k4CfgkcBNwblXduLXfM2QY9h7AjUk+x8QqelX18qE/UlXf6EfSHQwsT7Ks/wMmT9tmTuk2JFlG97zRXbN812pgNcDKlSu369BDSdoRbWnY9Biq6iKGrb66WUMC6O3z+eIkuwH/0YfPo4GfoxtY8Gm6lfzOoVvg6IL+I2v7/X/qj3+qqgwYSZpSQ54DuiLJDwMrqurSJI+hW3t8S/YA1vQPLO1Ed4r2iSRfBM5J8i66KX3O6vufBXwkyXq6M59XzePvkSTtILYYQEl+DVgF7Ao8nW6wwAeAQ+f6XFVdBxwwS/stdPeDNm3/FnD0oKolSTu8IcOwTwQOAe4FqKqbgd3HLEqSNP2GBNCDVfXtmZ1+gID3ZiRJ22RIAF2R5C3Ao5O8BDgPuHDcsiRJ025IAJ0KbASuB36dbtjdW8csSpK0uCX5YJI7tmWygiGj4B7uF6G7ku7S25ccHi1Ji8fzTjl7u/43+erTjxvyXNGHgT8Dzp7v7wxZkvtldFPonNH/2Pokh8/3ByVJO76q+gdmmSxgawx5EPU9wIuqaj1AkqcDfwf87235YUnS0jbkHtAdM+HTu4VuclFJkuZts2dASV7Zb96Y5CLgXLp7QEfTzYQqSdK8zXUJ7hcmtr8O/Gy/vRF44mgVSZKWhM0GUFW9diELkSTtOJJ8FHgh8OQkG4DTquqsuT/1/YbMBfc04GRg38n+W7McgyRpPAOHTW9XVXXstn7HkFFwf0s3U/WFwMPb+oOSJMGwAPpWVZ0xeiWSpCVlSAC9P8lpwN8DD840VtXnR6tKkjT1hgTQfsBrgBfzvUtw1e9LkjQvQwLoFcCPTC7JIEnSthoyE8IXgOVjFyJJWlqGnAE9BfjnJFfx/feAHIYtSZq3IQF02uhVSJKWnCHrAV2xEIVIkpaWITMh3Ec36g1gF+ARwP1V9fgxC5MkTbchZ0CPm9xPchRw0GgVSZKWhCGj4L5PVf0tPgMkSdpGQy7BvXJidydgJd+7JCdJ0rwMGQU3uS7QQ8BXgSNHqUaStGQMuQfkukCSpO1uriW53zbH56qq3jlCPZKkJWKuM6D7Z2l7LHAC8CTAAJIkzdtcS3K/Z2Y7yeOANwCvBc4B3rO5z0mSNMSc94CS7Ar8NvBqYA1wYFXdvRCFSZKm21z3gE4HXgmsBvarqm8uWFWSpKk314OovwM8FXgrcHuSe/vXfUnuXZjyJEnTaq57QFs9S4IkSUMZMpKkJgwgSVITBpAkqYnRAijJ3kk+neSmJDcmeUPfvmuSS5Lc3L8/sW9PkjOSrE9yXZIDx6pNktTemGdADwG/U1XPAg4GTkzybOBU4LKqWgFc1u8DHA6s6F+rgDNHrE2S1NhoAVRVX6uqz/fb9wE3AXvSzaS9pu+2Bjiq3z4SOLs6nwWWJ9ljrPokSW0tyD2gJPsCBwBXAk+pqq9BF1LA7n23PYHbJj62oW/b9LtWJVmXZN3GjRvHLFuSNKIh6wFtkyQ/BHwM+M2qujfJZrvO0vYDC99V1Wq62RlYuXKlC+Nph3LrO/ZrXUJDp7QuQIvMqGdASR5BFz5/VVV/0zd/febSWv9+R9++Adh74uN7AbePWZ8kqZ0xR8EFOAu4qareO3FoLXB8v308cMFE+3H9aLiDgXtmLtVJkqbPmJfgDgFeA1yf5Nq+7S3Au4Fzk5wA3Aoc3R+7CDgCWA88QLf0gyRpSo0WQFX1GWa/rwNw6Cz9CzhxrHokSYuLMyFIkpowgCRJTRhAkqQmDCBJUhMGkCSpCQNIktSEASRJasIAkiQ1YQBJkpowgCRJTRhAkqQmDCBJUhMGkCSpCQNIktSEASRJasIAkiQ1YQBJkpowgCRJTRhAkqQmDCBJUhMGkCSpCQNIktSEASRJasIAkiQ1YQBJkpowgCRJTRhAkqQmDCBJUhMGkCSpCQNIktSEASRJasIAkiQ1YQBJkpowgCRJTRhAkqQmDCBJUhOjBVCSDya5I8kNE227Jrkkyc39+xP79iQ5I8n6JNclOXCsuiRJi8OYZ0AfBg7bpO1U4LKqWgFc1u8DHA6s6F+rgDNHrEuStAiMFkBV9Q/AXZs0Hwms6bfXAEdNtJ9dnc8Cy5PsMVZtkqT2Fvoe0FOq6msA/fvuffuewG0T/Tb0bT8gyaok65Ks27hx46jFSpLGs1gGIWSWtpqtY1WtrqqVVbVyt912G7ksSdJYFjqAvj5zaa1/v6Nv3wDsPdFvL+D2Ba5NkrSAFjqA1gLH99vHAxdMtB/Xj4Y7GLhn5lKdJGk6LRvri5N8FHgh8OQkG4DTgHcD5yY5AbgVOLrvfhFwBLAeeAB47Vh1SZIWh9ECqKqO3cyhQ2fpW8CJY9UiSVp8FssgBEnSEmMASZKaMIAkSU0YQJKkJgwgSVITBpAkqQkDSJLUhAEkSWrCAJIkNWEASZKaMIAkSU0YQJKkJgwgSVITBpAkqQkDSJLUhAEkSWrCAJIkNWEASZKaMIAkSU0YQJKkJgwgSVITBpAkqQkDSJLUhAEkSWrCAJIkNWEASZKaMIAkSU0YQJKkJgwgSVITBpAkqQkDSJLUhAEkSWrCAJIkNWEASZKaMIAkSU0YQJKkJgwgSVITiyqAkhyW5EtJ1ic5tXU9kqTxLJoASrIz8OfA4cCzgWOTPLttVZKksSyaAAIOAtZX1S1V9W3gHODIxjVJkkaSqmpdAwBJfgk4rKr+S7//GuAnquqkTfqtAlb1u88AvrSghU6PJwN3ti5CS4r/zM3PnVV1WOsixrCsdQETMkvbD6RjVa0GVo9fznRLsq6qVrauQ0uH/8xpU4vpEtwGYO+J/b2A2xvVIkka2WIKoKuAFUmelmQX4FXA2sY1SZJGsmguwVXVQ0lOAj4J7Ax8sKpubFzWNPMyphaa/8zp+yyaQQiSpKVlMV2CkyQtIQaQJKkJA2jKJakk75nYf2OS329YkqZQOp9JcvhE2zFJLm5ZlxY3A2j6PQi8MsmTWxei6VXdzeTfAN6b5FFJHgv8AXBi28q0mBlA0+8hutFHv7XpgSQ/nOSyJNf17/ssfHmaFlV1A3Ah8GbgNODsqvpKkuOTfC7JtUn+IslOSZYl+UiS65PckOT1batXC4tmGLZG9efAdUn+eJP2P6P7j8SaJK8DzgCOWvDqNE3eDnwe+DawMsmPA68Afqp/1GI13TN+XwGeXFX7ASRZ3qpgtWMALQFVdW+Ss4HXA/8+cegngVf22x8BNg0oaatU1f1J/hr4ZlU9mOTngOcD65IAPBq4je55v2ckeT9wEfD3rWpWOwbQ0vGndP/P9ENz9PGhMG0PD/cv6OZ4/GBV/d6mnZI8h275ldcDv8j3JhnWEuE9oCWiqu4CzgVOmGj+P3SXQwBeDXxmoevS1LsUOGZmEEySJyXZJ8ludA/Cn0d3v+jAlkWqDc+Alpb3AJPLW7we+GCSU4CNwGubVKWpVVXXJ3k7cGmSnYD/oBst9x3grHTX5Ypu4IKWGKfikSQ14SU4SVITBpAkqQkDSJLUhAEkSWrCAJIkNWEAaeok2SvJBUluTvKVJO/vl3nflu/cr5/L7NokdyX5l3770iRPTXL+9qpfWiochq2p0j9XciVwZlV9KMnOdJOx3lVVp2zD9y6rqof67Q8Dn6gqQ0faBp4Badq8GPhWVX0IoKq+QzcT+OuSXJXkx2Y6Jrk8yfOSPDbJB/vj1yQ5sj/+n5Ocl+RC5pirLMm+SW6Y+MzfJrmwP0s6Kclv99/72SS79v2enuTiJFcn+cckzxzvfxJpcTKANG1+DLh6sqGq7gVuBT4BHAOQZA/gqVV1NfBfgU9V1fOBFwGn9+vZQDdh6/FV9eKtqOHHgV8BDqJbE+eBqjoA+CfguL7PauDkqnoe8EbgL7b2D5V2dE7Fo2kzM7XLbO2XA2fSzT12DHBef+ylwMuTvLHffxQwszbSJf08elvj01V1H3Bfknvo1sgBuB54TpIfAn4KOK+fIRrgkVv5G9IOzwDStLmRbmbl70ryeGBv4Crg3/pZmH8Z+PWZLsAvVtWXNvncTwD3z6OGBye2H57Yf5ju37mdgG9U1f7z+G5pangJTtPmMuAxSY4D6AchvAf4cFU9AJwDvAl4QlVd33/mk8DJ/QAGkhwwZoH9JcF/SXJ0/3tJ8twxf1NajAwgTZXqhnW+Ajg6yc3Al4FvAW/pu5xPtwTFuRMfeyfwCLpVY2/o98f2auCEJF+gO2s7cgF+U1pUHIYtSWrCMyBJUhMGkCSpCQNIktSEASRJasIAkiQ1YQBJkpowgCRJTfx/EaHSTlyr7tQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 408.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"OverTime\", data=train, aspect=1, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Employees who has done overtime left company than employees who has done no overtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df90274b48>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1488.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"JobRole\", data=train, aspect=4, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Most Employees having jobroles like laboratory technician,sales executive,Research Scientist and sales representative left the company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df90049088>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1488.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"Gender\", data=train, aspect=4, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Males employees left company compare to female employees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df902e7b08>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 408.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"MaritalStatus\", data=train, aspect=1, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of employees left companies are single."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df90300cc8>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ojVX13z22vTlwdCt0LeD4qvpiksuAY5O8HbgQOLKtfyTwn0kW0/V8njvVRiVJg9sRWFxVVwK074ruSXedQG+9Bo2rqp/T9VB6q6qLgYdN0X4lXfGT238L7D2TfUiSBvHHi8aaJcAjZ7oRx4KTJM1Ur4vGlscAkiTN1B8vGmtGLyjrbdoASrJWku/PdKOSpHntPGDbJPdNsi7dOfsZnaaB5QRQVf0B+F6SrVesRknSfNMGC3g5cBrwA7qLzC6d6Xb6XISwOXBpku8yche9qnr6THcmSVr1lnfZ9DhU1Sn0u/vqMvUJoLeuzA4kSZpKn+8BnZ3kPsC2VXVGkrvR3XtckqQV1mcw0pfQjU79kda0BfD5cRYlSZr/+lyGfSDwaOAmgKq6Ath0nEVJkua/PgH0u6q6bWKmDRTqGG2SpJXSJ4DOTvJmYP0kTwQ+A3xhvGVJkua7PgF0ELAUuAR4Kd1ld28ZZ1GSpLktyVFJrl2ZwQr6XAX3hyRHA+fSHXr7kbdJkKS54+GvP2aVvieff9i+fb5X9Ang34FjVnQ/fa6CewrdrbQPbztbnGT3Fd2hJGn1V1VfZyVvGtrni6jvAx5fVYsBkvwF8CXgyyuzY0nSmq3POaBrJ8KnuRK4dkz1SJLWEMvsASV5Zpu8NMkpwPF054D2phsJVZLmnIe/foVPScyq8w/bd+gSBjfdIbinjUz/Anhcm14K3GtsFUmS1gjLDKCqetFsFiJJWn0k+TSwM3DvJEuAQ6rqyJlsY7kXISS5L/AKYJvR9b0dgyTNDT0vm16lqmqfld1Gn6vgPg8cSTf6wR9WdoeSJEG/APptVR0+9kokSWuUPgH0r0kOAb4C/G6isaouGFtVkqR5r08AbQe8AHgCdxyCqzYvSdIK6RNAzwDuN3pLBkmSVlafkRC+B2w07kIkSWuWPj2gzYAfJjmPPz0H5GXYkqQV1ieADhl7FZKkNU6f+wGdPRuFSJLWLH1GQriZ7qo3gHWBuwC3VNUG4yxMkjS/9ekB3XN0PslewI5jq0iStEbocxXcn6iqz+N3gCRJK6nPIbhnjsyuBSzkjkNykiStkD5XwY3eF+h24CfAnmOpRpK0xuhzDsj7AkmSVrnpbsl98DTPq6r65zHUI0laQ0zXA7plira7A/sDmwAGkCRphU13S+73TUwnuSfwKuBFwLHA+5b1PEmS+pj2HFCSjYHXAM8HjgZ2qKrrZ6MwSdL8tszvASU5DDgPuBnYrqoOnUn4JNkqydeS/CDJpUle1do3TnJ6kiva471ae5IcnmRxkouT7LCSv5skaQ6b7ouorwX+HHgLcE2Sm9rPzUlu6rHt24HXVtWDgJ2AA5M8GDgIOLOqtgXObPMAuwPbtp8DgA+t0G8kSVotTHcOaMajJEx6/s+Bn7fpm5P8ANiC7jtEO7fVjgbOAt7Y2o+pqgK+k2SjJJu37UiS5pmVCpm+kmwDPAw4F9hsIlTa46ZttS2Aq0eetqS1Td7WAUkWJVm0dOnScZYtSRqjsQdQknsAnwVeXVXTHbrLFG13GvKnqo6oqoVVtXDBggWrqkxJ0iwbawAluQtd+Hyqqj7Xmn+RZPO2fHPg2ta+BNhq5OlbAteMsz5J0nDGFkBJAhwJ/KCq/mVk0cnAfm16P+CkkfZ929VwOwE3ev5HkuavPoORrqhHAy8ALklyUWt7M/Au4Pgk+wNXAXu3ZacAewCLgd/QfelVkjRPjS2Aquocpj6vA7DLFOsXcOC46pEkzS2zchWcJEmTGUCSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBrDN0AZJWD1e9bbuhS+jp9UMXoJ7sAUmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBjG2AEpyVJJrk3x/pG3jJKcnuaI93qu1J8nhSRYnuTjJDuOqS5I0N4yzB/QJYLdJbQcBZ1bVtsCZbR5gd2Db9nMA8KEx1iVJmgPGFkBV9XXgV5Oa9wSObtNHA3uNtB9Tne8AGyXZfFy1SZKGN9vngDarqp8DtMdNW/sWwNUj6y1pbXeS5IAki5IsWrp06ViLlSSNz1y5CCFTtNVUK1bVEVW1sKoWLliwYMxlSZLGZbYD6BcTh9ba47WtfQmw1ch6WwLXzHJtkqRZNNsBdDKwX5veDzhppH3fdjXcTsCNE4fqJEnz0zrj2nCSTwM7A/dOsgQ4BHgXcHyS/YGrgL3b6qcAewCLgd8ALxpXXZKkuWFsAVRV+yxj0S5TrFvAgeOqRZI098yVixAkSWuYsfWA5pqr3rbd0CUs19YHXzJ0CZI0a+wBSZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgZhAEmSBmEASZIGYQBJkgaxztAF6A4Pf/0xQ5fQy/mH7Tt0CZLmAXtAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBzKkASrJbkh8lWZzkoKHrkSSNz5wJoCRrAx8EdgceDOyT5MHDViVJGpc5E0DAjsDiqrqyqm4DjgX2HLgmSdKYpKqGrgGAJM8Cdquqv2vzLwAeWVUvn7TeAcABbfYBwI9mtdDxujdw3dBFaFr+G8198+3f6Lqq2m3oIsZhLt0RNVO03Skdq+oI4IjxlzP7kiyqqoVD16Fl899o7vPfaPUxlw7BLQG2GpnfErhmoFokSWM2lwLoPGDbJPdNsi7wXODkgWuSJI3JnDkEV1W3J3k5cBqwNnBUVV06cFmzbV4eWpxn/Dea+/w3Wk3MmYsQJElrlrl0CE6StAYxgCRJgzCAekhSSd43Mv+6JIeugu3+Y5KL2s/vR6ZfubLbnmafuyb5/Li2L0l9GUD9/A54ZpJ7r8qNVtU7qmr7qtoeuHViuqoOH10vyZy5WGQoSTYZCej/TvKzkfl1x7TPc5JsP83yJUkuSXJxkq8l2WpZ667Avj+ZZK9Vtb2VqGMuv+4TdTxyHHWsjCQvTvJnI/PT/k5rKgOon9vprqz5h8kLktwnyZntTejMJFu39k8kOTzJt5Jc2UZ66K29Ab0vydeA/5tkpyTfTnJhkm8m2battyjJA0aed06Shya5R6vhu+05T1upV2BgVfXLkbD+MPD+kcC+DSCd2f4//diq+mvgW8CbZ/LENv7hnDbHX/eJOs4dXTBHPrC9GPiz5a61hjOA+vsg8PwkG05q/3fgmPYm9ClgtPeyOfAY4KnAu1Zgn38B7FJVbwB+ADymqh4G/DPw9rbOccCzAZJsCWxSVd8DDgZOraodgScA70ty1xWoYU5Lcv8k30/yYeACYPMkR7RgvjTJwW29pyX5r5Hn7ZrkxDa9ewv3C5Icl+TuK1DKt4EtRrb/hSTntxomhpdaJ8kNSd6e5LvAjkkekeTstu6Xk2w26fd7cpLPjMzvnuT4FahvlZpDr/vods9IcixwYWvbr30AuyjJf0yEZJK/S3J5krOSfCzJB1r7n/Q6k/x6ZPqgtq2LR363idfgyPY7fznJXZM8B9geOC6TeopJXprksJH5v0/ynhX9vVd7VeXPcn6AX7fHtwH/BLwOOLS1XQfcpU3fhW7cJoBPAM8f2cbNffYxMv/JSc+/D3AS8P2Jn5H2i9v0a4G3tumLgEva40XAVcBfArsCnx/6NV3Jf49Dgde16fsDfwAeMbJ84/a4DvANutHV1wWuBtZvyz5K92XnTYGzgbu19n8E3tymzwG2n6aOJcBGbfrfgBdPUcPdgMuAe7V6CnhmW7YeXc/p3m3++cARI//+e9F9SPwR3QcLgOOB3X3d//h/+1utbVfg18DWbf4hwOeBddr8EcDz6EZY+SmwSavtO8AHRl/zyX+TwB7Af9ANF7YWcCrwqPYa/A+wXVvvc8Bzp/odJuaBewI/Hqnru8CDh/6bGupnLnRVVycfoPu09/Fp1hn9YtXvRqYDkOQdwFMAqjusMZ1bRqbfAZxWVf+R5P50fwRU1U+T/DrdrSueA7xwZH97VdWPRzc4cYhwnvlxVZ03Mr9Pkv3p3gj/nO4P/LIkpwNPSXIS8GTg1cAT6d4ov5UEujelc2aw72+0XsvPgdF7WP1Dkqe36S3perMXAbcBJ7b2BwF/BZzR9r023ZvrH1XVH1oP4nlJPgU8HNhnBvWN05Cv+2Or6oZJbd+uqqva9K7AI4BFbfvr0wXhbcCZVfVLgNabXN7fxJPobhNzYZu/B92HuWvpRvC/pLWfD2wz3Yaq6uYkXwd2T3Il8Puqumw5+5+3DKAZqKpftf+w+wNHteZv0X2i+0+6T7DT/hFV1T/SfdqbqQ2Bn7XpF05adhzwJmC9kf/MpwGvBF4FkORhVXUh89MfgzrdubFXATtW1Q1JPglMHHo8ju7f7jd0b1a3pHt3OrWqXrCC+34s3ZvaMcAhwBuS7Ar8DbBTVd2a5JyRGm6t9tGX7kPCxVX12OXs4yjgsxO/Q1X9fgVrXdWGfN2nrYfutT2qqv5pdIV052KX9e3722mnJdKdn5t4fwzw9qo6ctK27s+ffsj8Pf3eUz8GvAb4CdN/mJ33PAc0c++jG+59wiuBFyW5GHgB7Q1/DN4NHJbkm1Ms+wzd4YXRcwNvBe6W7mqhS+kOn6wJNgBuBm5KsjndJ+4JZwKPpHszPK61fQt4XJL7ASS5e3sz7a2qfkP3qf7FSTai+7DwqxY+f0X3SXwqlwFbJNmx7Xvdtv7k7V9Nd6j3ILpDu3PRrL/uy3EG8Oy0K1fTXc23Nd0ht12SbNzOzYxeHPQTuh4mwDPoeqTQfZjbf+IcVZIts/wrYm+mO9x2J1X1Tboe8d7c8XqskewB9VBV9xiZ/gXdcf2J+Z/QneSf/JwXLmsby9tHm//bSfPn0HX7J7xlZNk13PHHMtF2C/CSKfZzBt0f53x1Ad0b+/eBK4E/BnZ14w1+mS6sn9/aftEOGx03crL4zcAVM9lpVS1pFwv8PfB+4IAk3wN+CJy7jOf8rn0iPzzJPen+Ht8HTDUG4n8BG1TV5TOpaxYN8rovS1VdkuStdIc316I7V/OyqjovydvpgugaYNHI0z4CnJTkicBXaL2bqjolyQOB77TDeTe332U6Hwc+luRWupttTnYC8MCqunGFf8l5wLHgpNVAu9rs21V19NC1zCfprlB8SFW9epb3eyrwzqo6ezb3O9d4CE6a45JcRHf3308PXYtWTjsUeDlw/ZoePmAPSFquJIu48+Hq563JVy/NBl/3+c8AkiQNwkNwkqRBGECSpEEYQJqzcsctKr7Xxgt71Apu52VJ9l3V9U3axwdbrZcluTV3jNQ8o0FoZ7jPv5sYx0xaHfk9IM1lt04MV5TkycA7gcfNdCNV9eFVXdgU+zgQIMk2wBeXNcxSknWq6vZx1yOtDuwBaXWxAXA9QJKdk3xxYkGSf0/ywjb9rtYLuTjJe1vboUle16bPSvLudCMbX57ksa197SSHJTmvPfelrX3zJF9vvZnvJ3lsW/cTbf6SJHe6TceodLfIeEcbA+zlSfZMcm6622R8JcmmbZs/TbJBe07S3cbj3kk2S/K5dCNNfzfJTqv81ZUGYA9Ic9n67Tswd6W7tcWdRpwYlWRjuiFUHlhV1YbFmco6VbVjkj3oxm/blW6YmBur6hFJ1gO+meQrwDPpBoF9R7rxwe5GN6rxFlX1kLbfZe1n1AZV9Tdt/XsBJ7caXwa8tqre2EJ1T7pxBR8FXF5V1yU5DnhPVX1noodFN9qztFozgDSXjR6C+1/AMUmme+O9Cfgt3RAoX6J7o57K59rj6OjFTwL+euSczYbAtsB5wFFJ7kJ3G4uL0o1ifL8k/wZ8iW7YluU5dmR6a+D4dHfMXA+YGF7nOOANdAH0XO4YJ2xX4OWhsTEAAAGQSURBVAFtGBiAeyVZv8c+pTnNQ3BaLVTVt+kGgV3AyKjFzV3bOrfTjbv1Wbp76Zy6jM1NjGA8OnpxgFfUHXfZvG9VfaWqvk43svXPgP9Msm9VXQ88FDgLOJBudOPlGR2p+YN0dxbdDvg/3DFq9DeAByfZBHg6d9y2IXSjTE/UtkVV3dpjn9KcZgBptdAGg1wb+CXdDcUenGS9dHeo3aWtcw9gw6o6hW506uXdb2nUacDft54OSf4y3QjN9wGuraqPAkcCO6QbCXmtqvos3Q0Kd5jhr7Mh8LN0XZr9JhrbbRpOorvv1PdG7ndzBl3QTbwWM/m9pDnLQ3CayybOAUHXC9iv3Qvn6nT3ZbqYbvTkifsc3ZNuNOO7tvWnvThgko/RHY67oAXDUrpe1M7A65P8D90dN/elu/X2x9Nu8Ux3L6aZOJSud7OE7o6Ym48sO47u9t6jo6EfCHwoyYvo/ma/xkggSasrh+KRJA3CQ3CSpEEYQJKkQRhAkqRBGECSpEEYQJKkQRhAkqRBGECSpEH8f2sn0hmz/jwiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 408.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"BusinessTravel\", data=train, aspect=1, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most employees who left companies do business travel rarely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df9038ce08>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1128.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"TotalWorkingYears\", data=train, aspect=3, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Employees after working one year leave company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df90e4ee88>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1128.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.axes_style('whitegrid')\n",
    "sns.catplot(\"EducationField\", data=train, aspect=3, kind='count', hue='Attrition', palette=['C1', 'C0']).set_ylabels('Number of Employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees from Medical and life sciences education Field leave company more "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_df=train1.select_dtypes(include=np.number)\n",
    "numeric_cols=list(numerical_df.columns)\n",
    "categorical_df=train1.select_dtypes(exclude=np.number)\n",
    "categorical_cols=list(categorical_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 42)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_df_dummies=pd.get_dummies(train1[categorical_cols],drop_first=True)\n",
    "final_df=pd.concat([categorical_df_dummies,numerical_df],axis=1)\n",
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(470, 42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df1=test.select_dtypes(include=np.number)\n",
    "numeric_cols1=list(numerical_df1.columns)\n",
    "categorical_df1=test.select_dtypes(exclude=np.number)\n",
    "categorical_cols1=list(categorical_df1.columns)\n",
    "categorical_df_dummies1=pd.get_dummies(test[categorical_cols1],drop_first=True)\n",
    "final_df1=pd.concat([categorical_df_dummies1,numerical_df1],axis=1)\n",
    "final_df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df1=ss.fit_transform(final_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,accuracy_score,roc_auc_score,precision_recall_curve,confusion_matrix,precision_score,confusion_matrix\n",
    "def model(mod,model_name,x_tr,y_tr,x_tes):\n",
    "    mod.fit(x_tr,y_tr)\n",
    "    pred_dt=mod.predict(x_tes)\n",
    "    pred_dt1=mod.predict(x_tr)\n",
    "    pred=mod.predict_proba(x_tes)\n",
    "    print(\"     \",model_name,\"      \")\n",
    "    print(\"Accuracy \",accuracy_score(pred_dt1,y_tr))\n",
    "    print(\"ROC_AUC  \",roc_auc_score(pred_dt1,y_tr))\n",
    "    cm=confusion_matrix(pred_dt1,y_tr)\n",
    "    print(\"Confusion Matrix  \\n\",cm)\n",
    "    print(\"                    Classification Report \\n\",classification_report(pred_dt1,y_tr))\n",
    "    df=pd.DataFrame(pred[:,1]).to_csv('prediction1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]      Logistic Regression       \n",
      "Accuracy  0.8925061425061425\n",
      "ROC_AUC   0.8938871454288911\n",
      "Confusion Matrix  \n",
      " [[776 108]\n",
      " [ 67 677]]\n",
      "                    Classification Report \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.88      0.90       884\n",
      "           1       0.86      0.91      0.89       744\n",
      "\n",
      "    accuracy                           0.89      1628\n",
      "   macro avg       0.89      0.89      0.89      1628\n",
      "weighted avg       0.89      0.89      0.89      1628\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(solver='liblinear',max_iter=500,random_state=55,C=1.5,verbose=1)\n",
    "model(lr,\"Logistic Regression\",final_df,ytrain,final_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Logistic Regression       \n",
      "Accuracy  0.964987714987715\n",
      "ROC_AUC   0.9653514518787777\n",
      "Confusion Matrix  \n",
      " [[794   8]\n",
      " [ 49 777]]\n",
      "                    Classification Report \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97       802\n",
      "           1       0.99      0.94      0.96       826\n",
      "\n",
      "    accuracy                           0.96      1628\n",
      "   macro avg       0.97      0.97      0.96      1628\n",
      "weighted avg       0.97      0.96      0.96      1628\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rand_forest = RandomForestClassifier(n_estimators=1)\n",
    "model(rand_forest,\"Logistic Regression\",final_df,ytrain,final_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel='poly',probability=True)\n",
    "model(svc,\"SVC\",final_df,ytrain,final_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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