Logistic regression model_Decision tree model_Random forest model and Plotting ROC curves.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df71062f",
   "metadata": {},
   "source": [
    "# Logistic regression model , Decision tree model , Random forest model and Plotting ROC curves "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a69402d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a4cf116",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
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       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
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       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>2</td>\n",
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       "<p>5 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the data\n",
    "\n",
    "hr_data = pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
    "hr_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd1f43c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n",
      "None\n",
      "Age                         0\n",
      "Attrition                   0\n",
      "BusinessTravel              0\n",
      "DailyRate                   0\n",
      "Department                  0\n",
      "DistanceFromHome            0\n",
      "Education                   0\n",
      "EducationField              0\n",
      "EmployeeCount               0\n",
      "EmployeeNumber              0\n",
      "EnvironmentSatisfaction     0\n",
      "Gender                      0\n",
      "HourlyRate                  0\n",
      "JobInvolvement              0\n",
      "JobLevel                    0\n",
      "JobRole                     0\n",
      "JobSatisfaction             0\n",
      "MaritalStatus               0\n",
      "MonthlyIncome               0\n",
      "MonthlyRate                 0\n",
      "NumCompaniesWorked          0\n",
      "Over18                      0\n",
      "OverTime                    0\n",
      "PercentSalaryHike           0\n",
      "PerformanceRating           0\n",
      "RelationshipSatisfaction    0\n",
      "StandardHours               0\n",
      "StockOptionLevel            0\n",
      "TotalWorkingYears           0\n",
      "TrainingTimesLastYear       0\n",
      "WorkLifeBalance             0\n",
      "YearsAtCompany              0\n",
      "YearsInCurrentRole          0\n",
      "YearsSinceLastPromotion     0\n",
      "YearsWithCurrManager        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning \n",
    "# Exploratory Data Analysis (EDA)\n",
    "\n",
    "print(hr_data.info())\n",
    "print(hr_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d55d22dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will change the datatype of our target variable column (change from object to category)\n",
    "hr_data['Attrition'] = hr_data['Attrition'].astype('category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30713996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   Age                       1470 non-null   int64   \n",
      " 1   Attrition                 1470 non-null   category\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: category(1), int64(26), object(8)\n",
      "memory usage: 392.2+ KB\n"
     ]
    }
   ],
   "source": [
    "hr_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc271581",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition  DailyRate  DistanceFromHome  Education  EmployeeCount  \\\n",
       "0   41       Yes       1102                 1          2              1   \n",
       "1   49        No        279                 8          1              1   \n",
       "2   37       Yes       1373                 2          2              1   \n",
       "3   33        No       1392                 3          4              1   \n",
       "4   27        No        591                 2          1              1   \n",
       "\n",
       "   EmployeeNumber  EnvironmentSatisfaction  HourlyRate  JobInvolvement  ...  \\\n",
       "0               1                        2          94               3  ...   \n",
       "1               2                        3          61               2  ...   \n",
       "2               4                        4          92               2  ...   \n",
       "3               5                        4          56               3  ...   \n",
       "4               7                        1          40               3  ...   \n",
       "\n",
       "   MaritalStatus_Single  OverTime_Yes  JobRole_Human Resources  \\\n",
       "0                     1             1                        0   \n",
       "1                     0             0                        0   \n",
       "2                     1             1                        0   \n",
       "3                     0             1                        0   \n",
       "4                     0             0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                0   \n",
       "1                              0                0   \n",
       "2                              1                0   \n",
       "3                              0                0   \n",
       "4                              1                0   \n",
       "\n",
       "  JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                              0                          0   \n",
       "1                              0                          0   \n",
       "2                              0                          0   \n",
       "3                              0                          0   \n",
       "4                              0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             0  \n",
       "3                             0  \n",
       "4                             0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy Variables: Concept by which we can convert our non-numerical columns into a numerical column.\n",
    "# If your categorical column has N unique values, we can still live with N-1 columns.\n",
    "\n",
    "cat_col = ['BusinessTravel','Department','EducationField','Gender','MaritalStatus','OverTime','JobRole']\n",
    "\n",
    "hr_final = pd.get_dummies(hr_data, columns=cat_col, drop_first=True)\n",
    "\n",
    "hr_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29b4e0bc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
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       "<p>1470 rows × 49 columns</p>\n",
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      ],
      "text/plain": [
       "      Age  Attrition  DailyRate  DistanceFromHome  Education  EmployeeCount  \\\n",
       "0      41          1       1102                 1          2              1   \n",
       "1      49          0        279                 8          1              1   \n",
       "2      37          1       1373                 2          2              1   \n",
       "3      33          0       1392                 3          4              1   \n",
       "4      27          0        591                 2          1              1   \n",
       "...   ...        ...        ...               ...        ...            ...   \n",
       "1465   36          0        884                23          2              1   \n",
       "1466   39          0        613                 6          1              1   \n",
       "1467   27          0        155                 4          3              1   \n",
       "1468   49          0       1023                 2          3              1   \n",
       "1469   34          0        628                 8          3              1   \n",
       "\n",
       "      EmployeeNumber  EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "0                  1                        2          94               3   \n",
       "1                  2                        3          61               2   \n",
       "2                  4                        4          92               2   \n",
       "3                  5                        4          56               3   \n",
       "4                  7                        1          40               3   \n",
       "...              ...                      ...         ...             ...   \n",
       "1465            2061                        3          41               4   \n",
       "1466            2062                        4          42               2   \n",
       "1467            2064                        2          87               4   \n",
       "1468            2065                        4          63               2   \n",
       "1469            2068                        2          82               4   \n",
       "\n",
       "      ...  MaritalStatus_Single  OverTime_Yes  JobRole_Human Resources  \\\n",
       "0     ...                     1             1                        0   \n",
       "1     ...                     0             0                        0   \n",
       "2     ...                     1             1                        0   \n",
       "3     ...                     0             1                        0   \n",
       "4     ...                     0             0                        0   \n",
       "...   ...                   ...           ...                      ...   \n",
       "1465  ...                     0             0                        0   \n",
       "1466  ...                     0             0                        0   \n",
       "1467  ...                     0             1                        0   \n",
       "1468  ...                     0             0                        0   \n",
       "1469  ...                     0             0                        0   \n",
       "\n",
       "      JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                                 0                0   \n",
       "1                                 0                0   \n",
       "2                                 1                0   \n",
       "3                                 0                0   \n",
       "4                                 1                0   \n",
       "...                             ...              ...   \n",
       "1465                              1                0   \n",
       "1466                              0                0   \n",
       "1467                              0                0   \n",
       "1468                              0                0   \n",
       "1469                              1                0   \n",
       "\n",
       "     JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                                 0                          0   \n",
       "1                                 0                          0   \n",
       "2                                 0                          0   \n",
       "3                                 0                          0   \n",
       "4                                 0                          0   \n",
       "...                             ...                        ...   \n",
       "1465                              0                          0   \n",
       "1466                              0                          0   \n",
       "1467                              1                          0   \n",
       "1468                              0                          0   \n",
       "1469                              0                          0   \n",
       "\n",
       "      JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                              0                        1   \n",
       "1                              1                        0   \n",
       "2                              0                        0   \n",
       "3                              1                        0   \n",
       "4                              0                        0   \n",
       "...                          ...                      ...   \n",
       "1465                           0                        0   \n",
       "1466                           0                        0   \n",
       "1467                           0                        0   \n",
       "1468                           0                        1   \n",
       "1469                           0                        0   \n",
       "\n",
       "      JobRole_Sales Representative  \n",
       "0                                0  \n",
       "1                                0  \n",
       "2                                0  \n",
       "3                                0  \n",
       "4                                0  \n",
       "...                            ...  \n",
       "1465                             0  \n",
       "1466                             0  \n",
       "1467                             0  \n",
       "1468                             0  \n",
       "1469                             0  \n",
       "\n",
       "[1470 rows x 49 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the Attrition Column let us convert this into numerical form by using LabelEncoder\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le= LabelEncoder()\n",
    "hr_final['Attrition'] = le.fit_transform(hr_final['Attrition'])\n",
    "hr_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68530201",
   "metadata": {},
   "outputs": [
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       "      <td>1470.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
       "count  1470.000000  ...               1470.000000         1470.0   \n",
       "mean      2.063946  ...                  2.712245           80.0   \n",
       "std       1.106940  ...                  1.081209            0.0   \n",
       "min       1.000000  ...                  1.000000           80.0   \n",
       "25%       1.000000  ...                  2.000000           80.0   \n",
       "50%       2.000000  ...                  3.000000           80.0   \n",
       "75%       3.000000  ...                  4.000000           80.0   \n",
       "max       5.000000  ...                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of DataSet\n",
    "hr_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b42c741",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Have to standardize (transform) our data: Because there are few columns which has a high range.\n",
    "# we need to scale our data. \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_scal = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b1a4977",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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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>...</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>OverTime_Yes</th>\n",
       "      <th>JobRole_Human Resources</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
       "      <th>JobRole_Manager</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>94</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>884</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2061</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>613</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2062</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>155</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2064</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>1023</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2065</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>628</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2068</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  DailyRate  DistanceFromHome  Education  EmployeeCount  \\\n",
       "0      41       1102                 1          2              1   \n",
       "1      49        279                 8          1              1   \n",
       "2      37       1373                 2          2              1   \n",
       "3      33       1392                 3          4              1   \n",
       "4      27        591                 2          1              1   \n",
       "...   ...        ...               ...        ...            ...   \n",
       "1465   36        884                23          2              1   \n",
       "1466   39        613                 6          1              1   \n",
       "1467   27        155                 4          3              1   \n",
       "1468   49       1023                 2          3              1   \n",
       "1469   34        628                 8          3              1   \n",
       "\n",
       "      EmployeeNumber  EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "0                  1                        2          94               3   \n",
       "1                  2                        3          61               2   \n",
       "2                  4                        4          92               2   \n",
       "3                  5                        4          56               3   \n",
       "4                  7                        1          40               3   \n",
       "...              ...                      ...         ...             ...   \n",
       "1465            2061                        3          41               4   \n",
       "1466            2062                        4          42               2   \n",
       "1467            2064                        2          87               4   \n",
       "1468            2065                        4          63               2   \n",
       "1469            2068                        2          82               4   \n",
       "\n",
       "      JobLevel  ...  MaritalStatus_Single  OverTime_Yes  \\\n",
       "0            2  ...                     1             1   \n",
       "1            2  ...                     0             0   \n",
       "2            1  ...                     1             1   \n",
       "3            1  ...                     0             1   \n",
       "4            1  ...                     0             0   \n",
       "...        ...  ...                   ...           ...   \n",
       "1465         2  ...                     0             0   \n",
       "1466         3  ...                     0             0   \n",
       "1467         2  ...                     0             1   \n",
       "1468         2  ...                     0             0   \n",
       "1469         2  ...                     0             0   \n",
       "\n",
       "      JobRole_Human Resources  JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                           0                              0                0   \n",
       "1                           0                              0                0   \n",
       "2                           0                              1                0   \n",
       "3                           0                              0                0   \n",
       "4                           0                              1                0   \n",
       "...                       ...                            ...              ...   \n",
       "1465                        0                              1                0   \n",
       "1466                        0                              0                0   \n",
       "1467                        0                              0                0   \n",
       "1468                        0                              0                0   \n",
       "1469                        0                              1                0   \n",
       "\n",
       "      JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                                  0                          0   \n",
       "1                                  0                          0   \n",
       "2                                  0                          0   \n",
       "3                                  0                          0   \n",
       "4                                  0                          0   \n",
       "...                              ...                        ...   \n",
       "1465                               0                          0   \n",
       "1466                               0                          0   \n",
       "1467                               1                          0   \n",
       "1468                               0                          0   \n",
       "1469                               0                          0   \n",
       "\n",
       "      JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                              0                        1   \n",
       "1                              1                        0   \n",
       "2                              0                        0   \n",
       "3                              1                        0   \n",
       "4                              0                        0   \n",
       "...                          ...                      ...   \n",
       "1465                           0                        0   \n",
       "1466                           0                        0   \n",
       "1467                           0                        0   \n",
       "1468                           0                        1   \n",
       "1469                           0                        0   \n",
       "\n",
       "      JobRole_Sales Representative  \n",
       "0                                0  \n",
       "1                                0  \n",
       "2                                0  \n",
       "3                                0  \n",
       "4                                0  \n",
       "...                            ...  \n",
       "1465                             0  \n",
       "1466                             0  \n",
       "1467                             0  \n",
       "1468                             0  \n",
       "1469                             0  \n",
       "\n",
       "[1470 rows x 47 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf8ac670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building our model:\n",
    "Y = hr_final[['Attrition']]\n",
    "\n",
    "X = hr_final.drop(columns=['Attrition','Over18'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f8683c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(294, 1176, 294, 1176)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SPlit the data into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.8, random_state=555)\n",
    "\n",
    "len(X_train), len(X_test), len(Y_train), len(Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe48bfb1",
   "metadata": {},
   "source": [
    "# Building Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfb6589d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the model has run successfuly\n"
     ]
    }
   ],
   "source": [
    "# Create the model using training dataset\n",
    "\n",
    "# Create our model object\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LR = LogisticRegression()\n",
    "\n",
    "# fit the object on training data\n",
    "\n",
    "model = LR.fit(X_train,Y_train)\n",
    "model\n",
    "print( 'the model has run successfuly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cfa1260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the values on test data using your model\n",
    "\n",
    "Y_test['Attrition Predicted'] = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac1338ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Attrition Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>874</th>\n",
       "      <td>0</td>\n",
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       "      <th>274</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Attrition Predicted\n",
       "441           0                    0\n",
       "1117          0                    0\n",
       "936           0                    0\n",
       "223           0                    0\n",
       "1171          1                    0\n",
       "...         ...                  ...\n",
       "902           0                    0\n",
       "861           0                    0\n",
       "874           0                    0\n",
       "274           0                    0\n",
       "1035          0                    0\n",
       "\n",
       "[1176 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ee0e906",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1233\n",
       "1     237\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_final['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58ff44a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: imblearn in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from imblearn) (0.11.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from imbalanced-learn->imblearn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.9.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from imbalanced-learn->imblearn) (1.3.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.0.2)\n"
     ]
    }
   ],
   "source": [
    "# there is imbalance in Attrition so balancing it by using SMOTE.\n",
    "#install imblearn\n",
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "805adf5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9d97bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LETS BLANACE X_TRAIN & Y_TRAIN\n",
    "sm = SMOTE(random_state=555)\n",
    "\n",
    "X_train_new, Y_train_new = sm.fit_resample(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "656449e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "248\n",
      "248\n"
     ]
    }
   ],
   "source": [
    "print(sum(Y_train_new['Attrition']== 0))\n",
    "print(sum(Y_train_new['Attrition']== 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "739f0092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model run successfuly\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LR_smote = LogisticRegression()\n",
    "\n",
    "# fit the object on training data\n",
    "\n",
    "model_smote = LR_smote.fit(X_train_new,Y_train_new)\n",
    "model_smote\n",
    "print('The model run successfuly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52e2ae80",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Attrition Predicted</th>\n",
       "      <th>Pred_Attrition_smote</th>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Attrition Predicted  Pred_Attrition_smote\n",
       "441           0                    0                     0\n",
       "1117          0                    0                     0\n",
       "936           0                    0                     0\n",
       "223           0                    0                     0\n",
       "1171          1                    0                     0\n",
       "...         ...                  ...                   ...\n",
       "902           0                    0                     1\n",
       "861           0                    0                     0\n",
       "874           0                    0                     1\n",
       "274           0                    0                     1\n",
       "1035          0                    0                     1\n",
       "\n",
       "[1176 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test['Pred_Attrition_smote']= model_smote.predict(X_test)\n",
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "49342b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pd= model_smote.predict(X_test)\n",
    "Y_test_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4613eba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Attrition Predicted  Pred_Attrition_smote\n",
       "441           0                    0                     0\n",
       "1117          0                    0                     0\n",
       "936           0                    0                     0\n",
       "223           0                    0                     0\n",
       "1171          1                    0                     0\n",
       "...         ...                  ...                   ...\n",
       "902           0                    0                     1\n",
       "861           0                    0                     0\n",
       "874           0                    0                     1\n",
       "274           0                    0                     1\n",
       "1035          0                    0                     1\n",
       "\n",
       "[1176 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a5e0832",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "Log_ROC_auc = roc_auc_score(Y_test['Attrition'],model_smote.predict(X_test))\n",
    "fpr, tpr, threshold=roc_curve(Y_test['Attrition'],model_smote.predict(X_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5020db2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr ,tpr, label=\"Logistic Regression(AUC = %0.2f)\" % Log_ROC_auc)\n",
    "plt.plot([0,1], [0,1], 'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristics Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c2f426",
   "metadata": {},
   "source": [
    "The more that the ROC curve hugs the top left corner of the plot, the better the model does at classifying the data into categories.\n",
    "\n",
    "-To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the    curve.\n",
    "\n",
    "-After calculating the AUC, Logistic Regression model have 0.66% of the plot located under the curve.\n",
    "\n",
    "-The closer AUC is to 1, the better the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d393ff4",
   "metadata": {},
   "source": [
    "## Building Desicion tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "42977b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "# fitting the object on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b00d0477",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model run successfuly\n"
     ]
    }
   ],
   "source": [
    "model_dt = dt.fit(X_train_new,Y_train_new)\n",
    "model_dt\n",
    "print('The model run successfuly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1f49eec",
   "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>Attrition</th>\n",
       "      <th>Attrition Predicted</th>\n",
       "      <th>Pred_Attrition_smote</th>\n",
       "      <th>Pred_Attrition_smote_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Attrition Predicted  Pred_Attrition_smote  \\\n",
       "441           0                    0                     0   \n",
       "1117          0                    0                     0   \n",
       "936           0                    0                     0   \n",
       "223           0                    0                     0   \n",
       "1171          1                    0                     0   \n",
       "...         ...                  ...                   ...   \n",
       "902           0                    0                     1   \n",
       "861           0                    0                     0   \n",
       "874           0                    0                     1   \n",
       "274           0                    0                     1   \n",
       "1035          0                    0                     1   \n",
       "\n",
       "      Pred_Attrition_smote_dt  \n",
       "441                         0  \n",
       "1117                        0  \n",
       "936                         0  \n",
       "223                         0  \n",
       "1171                        0  \n",
       "...                       ...  \n",
       "902                         0  \n",
       "861                         1  \n",
       "874                         1  \n",
       "274                         1  \n",
       "1035                        0  \n",
       "\n",
       "[1176 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test['Pred_Attrition_smote_dt']= model_dt.predict(X_test)\n",
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3186f4d2",
   "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>Attrition</th>\n",
       "      <th>Pred_Attrition_smote_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Pred_Attrition_smote_dt\n",
       "441           0                        0\n",
       "1117          0                        0\n",
       "936           0                        0\n",
       "223           0                        0\n",
       "1171          1                        0\n",
       "...         ...                      ...\n",
       "902           0                        0\n",
       "861           0                        1\n",
       "874           0                        1\n",
       "274           0                        1\n",
       "1035          0                        0\n",
       "\n",
       "[1176 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.drop(columns=['Attrition Predicted','Pred_Attrition_smote'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5cec0d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pd_dt= model_dt.predict(X_test)\n",
    "Y_test_pd_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "61b3a136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting ROC\n",
    "\n",
    "Log_ROC_auc = roc_auc_score(Y_test['Attrition'],model_dt.predict(X_test))\n",
    "fpr, tpr, threshold=roc_curve(Y_test['Attrition'],model_dt.predict(X_test))\n",
    "                              \n",
    "plt.figure()\n",
    "plt.plot(fpr ,tpr, label=\"Decision tree classifier(AUC = %0.2f)\" % Log_ROC_auc)\n",
    "plt.plot([0,1], [0,1], 'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristics Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()                              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eac0101",
   "metadata": {},
   "source": [
    "The more that the ROC curve hugs the top left corner of the plot, the better the model does at classifying the data into categories.\n",
    "\n",
    "-To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the curve.\n",
    "\n",
    "-After calculating the AUC, Decision tree classifier model have 0.58% of the plot located under the curve.\n",
    "\n",
    "-The closer AUC is to 1, the better the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07fe173",
   "metadata": {},
   "source": [
    "# Building Random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ae169ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has run successfuly\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=500)\n",
    "\n",
    "#Fit the object on training data\n",
    "\n",
    "model_rf = rf.fit(X_train_new, Y_train_new)\n",
    "model_rf\n",
    "print('The model has run successfuly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "05473484",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>Attrition Predicted</th>\n",
       "      <th>Pred_Attrition_smote</th>\n",
       "      <th>Pred_Attrition_smote_dt</th>\n",
       "      <th>Pred_Attrition_smote_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>861</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Attrition Predicted  Pred_Attrition_smote  \\\n",
       "441           0                    0                     0   \n",
       "1117          0                    0                     0   \n",
       "936           0                    0                     0   \n",
       "223           0                    0                     0   \n",
       "1171          1                    0                     0   \n",
       "...         ...                  ...                   ...   \n",
       "902           0                    0                     1   \n",
       "861           0                    0                     0   \n",
       "874           0                    0                     1   \n",
       "274           0                    0                     1   \n",
       "1035          0                    0                     1   \n",
       "\n",
       "      Pred_Attrition_smote_dt  Pred_Attrition_smote_rf  \n",
       "441                         0                        0  \n",
       "1117                        0                        0  \n",
       "936                         0                        0  \n",
       "223                         0                        0  \n",
       "1171                        0                        0  \n",
       "...                       ...                      ...  \n",
       "902                         0                        0  \n",
       "861                         1                        0  \n",
       "874                         1                        0  \n",
       "274                         1                        1  \n",
       "1035                        0                        0  \n",
       "\n",
       "[1176 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test['Pred_Attrition_smote_rf']= model_rf.predict(X_test)\n",
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b9e9751",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Pred_Attrition_smote_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition  Pred_Attrition_smote_rf\n",
       "441           0                        0\n",
       "1117          0                        0\n",
       "936           0                        0\n",
       "223           0                        0\n",
       "1171          1                        0\n",
       "...         ...                      ...\n",
       "902           0                        0\n",
       "861           0                        0\n",
       "874           0                        0\n",
       "274           0                        1\n",
       "1035          0                        0\n",
       "\n",
       "[1176 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.drop(columns=['Attrition Predicted','Pred_Attrition_smote','Pred_Attrition_smote_dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8fa20e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pd_rf= model_rf.predict(X_test)\n",
    "Y_test_pd_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc145287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting ROC\n",
    "\n",
    "Log_ROC_auc = roc_auc_score(Y_test['Attrition'],model_rf.predict(X_test))\n",
    "fpr, tpr, threshold=roc_curve(Y_test['Attrition'],model_rf.predict(X_test))\n",
    "                              \n",
    "plt.figure()\n",
    "plt.plot(fpr ,tpr, label=\"Random forest classifier(AUC = %0.2f)\" % Log_ROC_auc)\n",
    "plt.plot([0,1], [0,1], 'y--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristics Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586b6ce8",
   "metadata": {},
   "source": [
    "The more that the ROC curve hugs the top left corner of the plot, the better the model does at classifying the data into categories.\n",
    "\n",
    "-To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the curve.\n",
    "\n",
    "-After calculating the AUC, Random forest classifier model have 0.63% of the plot located under the curve.\n",
    "\n",
    "-The closer AUC is to 1, the better the model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}