Mentormind_internship2_Accuracy and Final decision_component_5_Akshay_gulhane (1).ipynb

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    "# HR Analytics\n",
    "An Introduction\n",
    "\n",
    "Definition - HR analytics is the process of collecting and analyzing Human Resource (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics. This method of data analysis takes data that is routinely collected by HR and correlates it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives are contributing to the organization’s goals and strategies.\n",
    "\n",
    "Need of HR Analytics - Most organizations already have data that is routinely collected, so why the need for a specialized form of analytics? Can HR not simply look at the data they already have? Unfortunately, raw data on its own cannot actually provide any useful insight. It would be like looking at a large spreadsheet full of numbers and words. Once organized, compared and analyzed, this raw data provides useful insight.They can help answer questions like:\n",
    "\n",
    "What patterns can be revealed in employee turnover?\n",
    "How long does it take to hire employees?\n",
    "What amount of investment is needed to get employees up to a fully productive speed?\n",
    "Which of our employees are most likely to leave within the year?\n",
    "Are learning and development initiatives having an impact on employee performance?\n",
    "The process of HR Analytics\n",
    "\n",
    "HR Analytics is made up of several components that feed into each other.\n",
    "\n",
    "To gain the problem-solving insights that HR Analytics promises, data must first be collected.\n",
    "The data then needs to be monitored and measured against other data, such as historical information, norms or averages.\n",
    "This helps identify trends or patterns. It is at this point that the results can be analyzed at the analytical stage.\n",
    "The final step is to apply insight to organizational decisions."
   ]
  },
  {
   "cell_type": "code",
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   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
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       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
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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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   ],
   "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>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>...</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>No</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>...</td>\n",
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       "      <td>0</td>\n",
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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": [
    {
     "data": {
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       "      <th>1467</th>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>34</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>1470 rows × 49 columns</p>\n",
       "</div>"
      ],
      "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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       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
       "count  1470.000000  ...               1470.000000         1470.0   \n",
       "mean      2.063946  ...                  2.712245           80.0   \n",
       "std       1.106940  ...                  1.081209            0.0   \n",
       "min       1.000000  ...                  1.000000           80.0   \n",
       "25%       1.000000  ...                  2.000000           80.0   \n",
       "50%       2.000000  ...                  3.000000           80.0   \n",
       "75%       3.000000  ...                  4.000000           80.0   \n",
       "max       5.000000  ...                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of DataSet\n",
    "hr_data.describe()"
   ]
  },
  {
   "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": "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\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>41</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <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",
       "      <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>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",
       "      <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",
       "      <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",
       "    <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": "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",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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>0</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  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": "bfee1cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the Accuracy of model (By Building Accuracy score, Confusion Matrix, Classification report)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "182f82bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.84\n"
     ]
    }
   ],
   "source": [
    "# Accuracy Score\n",
    "\n",
    "accuracy = accuracy_score(Y_test['Attrition'], Y_test['Attrition Predicted'])\n",
    "accuracy = np.round(accuracy,2)\n",
    "\n",
    "print('The accuracy of the model is:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84edcbf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[984,   1],\n",
       "       [188,   3]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "\n",
    "confusion_matrix(Y_test['Attrition'], Y_test['Attrition Predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb7035fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      1.00      0.91       985\n",
      "           1       0.75      0.02      0.03       191\n",
      "\n",
      "    accuracy                           0.84      1176\n",
      "   macro avg       0.79      0.51      0.47      1176\n",
      "weighted avg       0.83      0.84      0.77      1176\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification_report\n",
    "\n",
    "print(classification_report(Y_test['Attrition'], Y_test['Attrition Predicted']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ee0e906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1233\n",
       "1     237\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_final['Attrition'].value_counts()\n",
    "# there is imbalance in Attrition so balancing it by using SMOTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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: 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: 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: scikit-learn>=1.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.0.2)\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: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "#install imblearn\n",
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "805adf5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "id": "52e2ae80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
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       "    <tr>\n",
       "      <th>936</th>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
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       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "    <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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test['Pred_Attrition_smote']= model_smote.predict(X_test)\n",
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "49342b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pd= model_smote.predict(X_test)\n",
    "Y_test_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e7730cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.62\n"
     ]
    }
   ],
   "source": [
    "accuracy_lr = accuracy_score(Y_test['Attrition'], Y_test['Pred_Attrition_smote'])\n",
    "accuracy_lr= np.round(accuracy_lr,2)\n",
    "print('The accuracy of the model is:', accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "081acf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[591, 394],\n",
       "       [ 57, 134]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Y_test['Attrition'], Y_test['Pred_Attrition_smote'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0335e906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(Y_test['Attrition'],Y_test_pd)\n",
    "sns.heatmap(cm, cmap='Greens', annot= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4613eba7",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>441</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</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",
       "    </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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cec6818e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.60      0.72       985\n",
      "           1       0.25      0.70      0.37       191\n",
      "\n",
      "    accuracy                           0.62      1176\n",
      "   macro avg       0.58      0.65      0.55      1176\n",
      "weighted avg       0.81      0.62      0.67      1176\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test['Attrition'], Y_test['Pred_Attrition_smote']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "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": 36,
   "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": 37,
   "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>0</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                         0  \n",
       "274                         1  \n",
       "1035                        0  \n",
       "\n",
       "[1176 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "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>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_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                        0\n",
       "274           0                        1\n",
       "1035          0                        0\n",
       "\n",
       "[1176 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.drop(columns=['Attrition Predicted','Pred_Attrition_smote'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e5cec0d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "id": "1791f11c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.71\n"
     ]
    }
   ],
   "source": [
    "accuracy_dt = accuracy_score(Y_test['Attrition'], Y_test['Pred_Attrition_smote_dt'])\n",
    "accuracy_dt = np.round(accuracy_dt,2)\n",
    "print('The accuracy of the model is:', accuracy_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2075e737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[761, 224],\n",
       "       [117,  74]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Y_test['Attrition'], Y_test['Pred_Attrition_smote_dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0023d5a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(Y_test['Attrition'],Y_test_pd_dt)\n",
    "sns.heatmap(cm, cmap='Oranges', annot= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "53f454dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.77      0.82       985\n",
      "           1       0.25      0.39      0.30       191\n",
      "\n",
      "    accuracy                           0.71      1176\n",
      "   macro avg       0.56      0.58      0.56      1176\n",
      "weighted avg       0.77      0.71      0.73      1176\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test['Attrition'], Y_test['Pred_Attrition_smote_dt']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "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": 46,
   "id": "05473484",
   "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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\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>274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </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                         0                        0  \n",
       "274                         1                        1  \n",
       "1035                        0                        0  \n",
       "\n",
       "[1176 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "id": "7b9e9751",
   "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_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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.drop(columns=['Attrition Predicted','Pred_Attrition_smote','Pred_Attrition_smote_dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8fa20e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 48,
     "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": 49,
   "id": "a95f54df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.85\n"
     ]
    }
   ],
   "source": [
    "accuracy_rf = accuracy_score(Y_test['Attrition'], Y_test['Pred_Attrition_smote_rf'])\n",
    "accuracy_rf = np.round(accuracy_rf,2)\n",
    "print('The accuracy of the model is:', accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3d953c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[941,  44],\n",
       "       [137,  54]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(Y_test['Attrition'], Y_test['Pred_Attrition_smote_rf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "36ebce43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(Y_test['Attrition'],Y_test_pd_rf)\n",
    "sns.heatmap(cm, cmap='BuPu', annot= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1321a98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.96      0.91       985\n",
      "           1       0.55      0.28      0.37       191\n",
      "\n",
      "    accuracy                           0.85      1176\n",
      "   macro avg       0.71      0.62      0.64      1176\n",
      "weighted avg       0.82      0.85      0.82      1176\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test['Attrition'], Y_test['Pred_Attrition_smote_rf']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf244de7",
   "metadata": {},
   "source": [
    "# Model evaluation based on accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "898067ed",
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest classifier</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree classifier</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Score\n",
       "0  Random Forest classifier   0.85\n",
       "1  Decision Tree classifier   0.71\n",
       "2       Logistic Regression   0.62"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model' : [ 'Random Forest classifier','Decision Tree classifier','Logistic Regression'],\n",
    "    'Score' : [accuracy_rf,accuracy_dt,accuracy_lr]\n",
    "    \n",
    "})\n",
    "\n",
    "\n",
    "models.sort_values(by = 'Score', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50299d92",
   "metadata": {},
   "source": [
    "# Final decision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ebd683",
   "metadata": {},
   "source": [
    "**\"Random Forest Classifier\" Model has the Highest Accuracy Score , which indicates that it has the Highest Accuracy and is the Best Model at Correctly Classifying Observations into Categories among these Models\".**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d475f56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "037c6618",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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