3. Logistic Regression/3. Logistic Regression with Scikit-Learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Logistic Regression**: HR Dataset 🕵️‍♀️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the dataset of a large company, which has around 4000 employees. Every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. This level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons:\n",
    "* the former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners\n",
    "* a sizeable department has to be maintained, for the purposes of recruiting new talent\n",
    "* more often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company\n",
    "\n",
    "Hence, it is important to understand what factors cause attrition. In other words, it is important to undersand what needs to be changed in order to get most of the employees to stay. Therefore, the goal of the study is to model the probability of attrition based on certain features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Logistic Regression with Scikit-Learn**\n",
    "\n",
    "$\\qquad$ <span style=\"color:gray\"><b>0.</b> Settings </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>1.</b> Dataset </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>2.</b> Data Preprocessing </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>3.</b> Data Preparation </span><br>\n",
    "$\\qquad\\qquad$ <span style=\"color:gray\"><b>3.1.</b> FULL-set of features </span><br>\n",
    "$\\qquad\\qquad$ <span style=\"color:gray\"><b>3.2.</b> Reduced-set of features </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>4.</b> Logistic Regression with Scikit-Learn </span><br>\n",
    "$\\qquad\\qquad$ <span style=\"color:gray\"><b>4.1.</b> FULL-set of features </span><br>\n",
    "$\\qquad\\qquad$ <span style=\"color:gray\"><b>4.2.</b> Reduced-set of features </span><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **0.** Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import scikitplot.metrics as splt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import pandas as pd  \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1.** Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    DATASET INFORMATIONS\n",
    "\n",
    "    |--------------------------|------------|-------------------------------------------------------------------------------|\n",
    "    | Name                     | Data Type  | Description                                                                   |\n",
    "    |--------------------------|------------|-------------------------------------------------------------------------------|\n",
    "    | Age                      | continuous | Age of the employee                                                           |\n",
    "    | Attrition                | nominal    | Whether the employee left in the previous year or not                         |\n",
    "    | BusinessTravel           | nominal    | How frequently the employees travelled for business purposes in the last year |\n",
    "    | Department               | nominal    | Department in company                                                         |\n",
    "    | DistanceFromHome         | continuous | Distance from home in kms                                                     |\n",
    "    | Education                | continuous | Education Level                                                               |\n",
    "    | EducationField           | nominal    | Field of education                                                            |\n",
    "    | EmployeeCount            | continuous | Employee count                                                                |\n",
    "    | EmployeeID               | nominal    | Employee number/id                                                            |\n",
    "    | Gender                   | nominal    | Gender of employee                                                            |\n",
    "    | JobLevel                 | continuous | Job level at company on a scale of 1 to 5                                     |\n",
    "    | JobRole                  | nominal    | Name of job role in company                                                   |\n",
    "    | MaritalStatus            | nominal    | Marital status of the employee                                                |\n",
    "    | MonthlyIncome            | continuous | Monthly income in rupees per month                                            |\n",
    "    | NumCompaniesWorked       | continuous | Total number of companies the employee has worked for                         |\n",
    "    | Over18                   | nominal    | Whether the employee is above 18 years of age or not                          |\n",
    "    | PercentSalaryHike        | continuous | Percent salary hike for last year                                             |\n",
    "    | PerformanceRating\t       | continuous | Performance rating for last year                                              |\n",
    "    | RelationshipSatisfaction | continuous | Relationship satisfaction level                                               |\n",
    "    | StandardHours            | continuous | Standard hours of work for the employee                                       |\n",
    "    | StockOptionLevel         | continuous | Stock option level of the employee                                            |\n",
    "    | TotalWorkingYears        | continuous | Total number of years the employee has worked so far                          |\n",
    "    | TrainingTimesLastYear    | continuous | Number of times training was conducted for this employee last year            |\n",
    "    | WorkLifeBalance          | continuous | Work life balance level                                                       |\n",
    "    | YearsAtCompany           | continuous | Total number of years spent at the company by the employee                    |\n",
    "    | YearsSinceLastPromotion  | continuous | Number of years since last promotion                                          |\n",
    "    | YearsWithCurrManager     | continuous | Number of years under current manager                                         |\n",
    "    |--------------------------|------------|-------------------------------------------------------------------------------|\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusinessTravel</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <td>Sales</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationField</th>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Other</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Medical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeCount</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeID</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>Sales Executive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>Married</td>\n",
       "      <td>Single</td>\n",
       "      <td>Married</td>\n",
       "      <td>Married</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>131160</td>\n",
       "      <td>41890</td>\n",
       "      <td>193280</td>\n",
       "      <td>83210</td>\n",
       "      <td>23420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Over18</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardHours</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                                 0                       1  \\\n",
       "Age                                             51                      31   \n",
       "Attrition                                       No                     Yes   \n",
       "BusinessTravel                       Travel_Rarely       Travel_Frequently   \n",
       "Department                                   Sales  Research & Development   \n",
       "DistanceFromHome                                 6                      10   \n",
       "Education                                        2                       1   \n",
       "EducationField                       Life Sciences           Life Sciences   \n",
       "EmployeeCount                                    1                       1   \n",
       "EmployeeID                                       1                       2   \n",
       "Gender                                      Female                  Female   \n",
       "JobLevel                                         1                       1   \n",
       "JobRole                  Healthcare Representative      Research Scientist   \n",
       "MaritalStatus                              Married                  Single   \n",
       "MonthlyIncome                               131160                   41890   \n",
       "NumCompaniesWorked                             1.0                     0.0   \n",
       "Over18                                           Y                       Y   \n",
       "PercentSalaryHike                               11                      23   \n",
       "StandardHours                                    8                       8   \n",
       "StockOptionLevel                                 0                       1   \n",
       "TotalWorkingYears                              1.0                     6.0   \n",
       "TrainingTimesLastYear                            6                       3   \n",
       "YearsAtCompany                                   1                       5   \n",
       "YearsSinceLastPromotion                          0                       1   \n",
       "YearsWithCurrManager                             0                       4   \n",
       "\n",
       "                                              2                       3  \\\n",
       "Age                                          32                      38   \n",
       "Attrition                                    No                      No   \n",
       "BusinessTravel                Travel_Frequently              Non-Travel   \n",
       "Department               Research & Development  Research & Development   \n",
       "DistanceFromHome                             17                       2   \n",
       "Education                                     4                       5   \n",
       "EducationField                            Other           Life Sciences   \n",
       "EmployeeCount                                 1                       1   \n",
       "EmployeeID                                    3                       4   \n",
       "Gender                                     Male                    Male   \n",
       "JobLevel                                      4                       3   \n",
       "JobRole                         Sales Executive         Human Resources   \n",
       "MaritalStatus                           Married                 Married   \n",
       "MonthlyIncome                            193280                   83210   \n",
       "NumCompaniesWorked                          1.0                     3.0   \n",
       "Over18                                        Y                       Y   \n",
       "PercentSalaryHike                            15                      11   \n",
       "StandardHours                                 8                       8   \n",
       "StockOptionLevel                              3                       3   \n",
       "TotalWorkingYears                           5.0                    13.0   \n",
       "TrainingTimesLastYear                         2                       5   \n",
       "YearsAtCompany                                5                       8   \n",
       "YearsSinceLastPromotion                       0                       7   \n",
       "YearsWithCurrManager                          3                       5   \n",
       "\n",
       "                                              4  \n",
       "Age                                          32  \n",
       "Attrition                                    No  \n",
       "BusinessTravel                    Travel_Rarely  \n",
       "Department               Research & Development  \n",
       "DistanceFromHome                             10  \n",
       "Education                                     1  \n",
       "EducationField                          Medical  \n",
       "EmployeeCount                                 1  \n",
       "EmployeeID                                    5  \n",
       "Gender                                     Male  \n",
       "JobLevel                                      1  \n",
       "JobRole                         Sales Executive  \n",
       "MaritalStatus                            Single  \n",
       "MonthlyIncome                             23420  \n",
       "NumCompaniesWorked                          4.0  \n",
       "Over18                                        Y  \n",
       "PercentSalaryHike                            12  \n",
       "StandardHours                                 8  \n",
       "StockOptionLevel                              2  \n",
       "TotalWorkingYears                           9.0  \n",
       "TrainingTimesLastYear                         2  \n",
       "YearsAtCompany                                6  \n",
       "YearsSinceLastPromotion                       0  \n",
       "YearsWithCurrManager                          4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the dataset\n",
    "data = pd.read_csv('./dataset.csv')\n",
    "data.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'Department', 'DistanceFromHome',\n",
       "       'Education', 'EducationField', 'EmployeeCount', 'EmployeeID', 'Gender',\n",
       "       'JobLevel', 'JobRole', 'MaritalStatus', 'MonthlyIncome',\n",
       "       'NumCompaniesWorked', 'Over18', 'PercentSalaryHike', 'StandardHours',\n",
       "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
       "       'YearsAtCompany', 'YearsSinceLastPromotion', 'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4410 entries, 0 to 4409\n",
      "Data columns (total 24 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Age                      4410 non-null   int64  \n",
      " 1   Attrition                4410 non-null   object \n",
      " 2   BusinessTravel           4410 non-null   object \n",
      " 3   Department               4410 non-null   object \n",
      " 4   DistanceFromHome         4410 non-null   int64  \n",
      " 5   Education                4410 non-null   int64  \n",
      " 6   EducationField           4410 non-null   object \n",
      " 7   EmployeeCount            4410 non-null   int64  \n",
      " 8   EmployeeID               4410 non-null   int64  \n",
      " 9   Gender                   4410 non-null   object \n",
      " 10  JobLevel                 4410 non-null   int64  \n",
      " 11  JobRole                  4410 non-null   object \n",
      " 12  MaritalStatus            4410 non-null   object \n",
      " 13  MonthlyIncome            4410 non-null   int64  \n",
      " 14  NumCompaniesWorked       4391 non-null   float64\n",
      " 15  Over18                   4410 non-null   object \n",
      " 16  PercentSalaryHike        4410 non-null   int64  \n",
      " 17  StandardHours            4410 non-null   int64  \n",
      " 18  StockOptionLevel         4410 non-null   int64  \n",
      " 19  TotalWorkingYears        4401 non-null   float64\n",
      " 20  TrainingTimesLastYear    4410 non-null   int64  \n",
      " 21  YearsAtCompany           4410 non-null   int64  \n",
      " 22  YearsSinceLastPromotion  4410 non-null   int64  \n",
      " 23  YearsWithCurrManager     4410 non-null   int64  \n",
      "dtypes: float64(2), int64(14), object(8)\n",
      "memory usage: 827.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.** Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EmployeeID                  0\n",
       "Gender                      0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "NumCompaniesWorked         19\n",
       "Over18                      0\n",
       "PercentSalaryHike           0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           9\n",
       "TrainingTimesLastYear       0\n",
       "YearsAtCompany              0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null elements\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                        False\n",
       "Attrition                  False\n",
       "BusinessTravel             False\n",
       "Department                 False\n",
       "DistanceFromHome           False\n",
       "Education                  False\n",
       "EducationField             False\n",
       "EmployeeCount              False\n",
       "EmployeeID                 False\n",
       "Gender                     False\n",
       "JobLevel                   False\n",
       "JobRole                    False\n",
       "MaritalStatus              False\n",
       "MonthlyIncome              False\n",
       "NumCompaniesWorked          True\n",
       "Over18                     False\n",
       "PercentSalaryHike          False\n",
       "StandardHours              False\n",
       "StockOptionLevel           False\n",
       "TotalWorkingYears           True\n",
       "TrainingTimesLastYear      False\n",
       "YearsAtCompany             False\n",
       "YearsSinceLastPromotion    False\n",
       "YearsWithCurrManager       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4410, 24)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4382, 24)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can either fill the nulls with 0 or delete the rows. \n",
    "# In this case we can try to delete the rows with missing \n",
    "# elements since we have quite a lot of data.\n",
    "\n",
    "# Fill\n",
    "# data.fillna(0, inplace =True)\n",
    "\n",
    "# Drop\n",
    "data = data.dropna(how='any', axis=0)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4382, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We remove unnecessary information\n",
    "data.drop(['EmployeeCount','EmployeeID','StandardHours', 'Over18'], axis=1, inplace=True)\n",
    "\n",
    "#   * 'EmployeeCount' is always equal to 1 (as employees are interviewed one at a time)\n",
    "#   * 'EmployeeID' are unique values \n",
    "#   * 'StandardHours' is (basically) always 8\n",
    "#   * 'Over18' because all the employees are >18\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3.** Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From previous analyses (EDA) we certainly want to take into account (since they were visually influential):\n",
    "\n",
    "* Business Travel\n",
    "* Department\n",
    "* Education Field\n",
    "* Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'Department', 'DistanceFromHome',\n",
       "       'Education', 'EducationField', 'Gender', 'JobLevel', 'JobRole',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'NumCompaniesWorked',\n",
       "       'PercentSalaryHike', 'StockOptionLevel', 'TotalWorkingYears',\n",
       "       'TrainingTimesLastYear', 'YearsAtCompany', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                               51\n",
       "Attrition                                         No\n",
       "BusinessTravel                         Travel_Rarely\n",
       "Department                                     Sales\n",
       "DistanceFromHome                                   6\n",
       "Education                                          2\n",
       "EducationField                         Life Sciences\n",
       "Gender                                        Female\n",
       "JobLevel                                           1\n",
       "JobRole                    Healthcare Representative\n",
       "MaritalStatus                                Married\n",
       "MonthlyIncome                                 131160\n",
       "NumCompaniesWorked                               1.0\n",
       "PercentSalaryHike                                 11\n",
       "StockOptionLevel                                   0\n",
       "TotalWorkingYears                                1.0\n",
       "TrainingTimesLastYear                              6\n",
       "YearsAtCompany                                     1\n",
       "YearsSinceLastPromotion                            0\n",
       "YearsWithCurrManager                               0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Travel_Rarely' 'Travel_Frequently' 'Non-Travel']\n",
      "['Sales' 'Research & Development' 'Human Resources']\n",
      "['Life Sciences' 'Other' 'Medical' 'Marketing' 'Technical Degree'\n",
      " 'Human Resources']\n",
      "['Female' 'Male']\n",
      "['Healthcare Representative' 'Research Scientist' 'Sales Executive'\n",
      " 'Human Resources' 'Research Director' 'Laboratory Technician'\n",
      " 'Manufacturing Director' 'Sales Representative' 'Manager']\n",
      "['Married' 'Single' 'Divorced']\n"
     ]
    }
   ],
   "source": [
    "# Convert all the Categorical data into numerical data\n",
    "print(data['BusinessTravel'].unique())\n",
    "print(data['Department'].unique())\n",
    "print(data['EducationField'].unique())\n",
    "print(data['Gender'].unique())\n",
    "print(data['JobRole'].unique())\n",
    "print(data['MaritalStatus'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical features\n",
    "labelEncoder_X = LabelEncoder()\n",
    "\n",
    "data['BusinessTravel'] = labelEncoder_X.fit_transform(data['BusinessTravel'])\n",
    "data['Department']     = labelEncoder_X.fit_transform(data['Department'])\n",
    "data['EducationField'] = labelEncoder_X.fit_transform(data['EducationField'])\n",
    "data['Gender']         = labelEncoder_X.fit_transform(data['Gender'])\n",
    "data['JobRole']        = labelEncoder_X.fit_transform(data['JobRole'])\n",
    "data['MaritalStatus']  = labelEncoder_X.fit_transform(data['MaritalStatus'])\n",
    "\n",
    "# Encode label\n",
    "label_encoder_y = LabelEncoder()\n",
    "\n",
    "data['Attrition'] = label_encoder_y.fit_transform(data['Attrition'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This encoding transforms categorical variables into numerical values, it is not one-hot-encoding.<br>\n",
    "We proceed in this way in order to visualize the correlation of the categorical variables with the target (also categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4382 entries, 0 to 4408\n",
      "Data columns (total 20 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Age                      4382 non-null   int64  \n",
      " 1   Attrition                4382 non-null   int32  \n",
      " 2   BusinessTravel           4382 non-null   int32  \n",
      " 3   Department               4382 non-null   int32  \n",
      " 4   DistanceFromHome         4382 non-null   int64  \n",
      " 5   Education                4382 non-null   int64  \n",
      " 6   EducationField           4382 non-null   int32  \n",
      " 7   Gender                   4382 non-null   int32  \n",
      " 8   JobLevel                 4382 non-null   int64  \n",
      " 9   JobRole                  4382 non-null   int32  \n",
      " 10  MaritalStatus            4382 non-null   int32  \n",
      " 11  MonthlyIncome            4382 non-null   int64  \n",
      " 12  NumCompaniesWorked       4382 non-null   float64\n",
      " 13  PercentSalaryHike        4382 non-null   int64  \n",
      " 14  StockOptionLevel         4382 non-null   int64  \n",
      " 15  TotalWorkingYears        4382 non-null   float64\n",
      " 16  TrainingTimesLastYear    4382 non-null   int64  \n",
      " 17  YearsAtCompany           4382 non-null   int64  \n",
      " 18  YearsSinceLastPromotion  4382 non-null   int64  \n",
      " 19  YearsWithCurrManager     4382 non-null   int64  \n",
      "dtypes: float64(2), int32(7), int64(11)\n",
      "memory usage: 599.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>51.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attrition</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusinessTravel</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationField</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>131160.0</td>\n",
       "      <td>41890.0</td>\n",
       "      <td>193280.0</td>\n",
       "      <td>83210.0</td>\n",
       "      <td>23420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0        1         2        3        4\n",
       "Age                          51.0     31.0      32.0     38.0     32.0\n",
       "Attrition                     0.0      1.0       0.0      0.0      0.0\n",
       "BusinessTravel                2.0      1.0       1.0      0.0      2.0\n",
       "Department                    2.0      1.0       1.0      1.0      1.0\n",
       "DistanceFromHome              6.0     10.0      17.0      2.0     10.0\n",
       "Education                     2.0      1.0       4.0      5.0      1.0\n",
       "EducationField                1.0      1.0       4.0      1.0      3.0\n",
       "Gender                        0.0      0.0       1.0      1.0      1.0\n",
       "JobLevel                      1.0      1.0       4.0      3.0      1.0\n",
       "JobRole                       0.0      6.0       7.0      1.0      7.0\n",
       "MaritalStatus                 1.0      2.0       1.0      1.0      2.0\n",
       "MonthlyIncome            131160.0  41890.0  193280.0  83210.0  23420.0\n",
       "NumCompaniesWorked            1.0      0.0       1.0      3.0      4.0\n",
       "PercentSalaryHike            11.0     23.0      15.0     11.0     12.0\n",
       "StockOptionLevel              0.0      1.0       3.0      3.0      2.0\n",
       "TotalWorkingYears             1.0      6.0       5.0     13.0      9.0\n",
       "TrainingTimesLastYear         6.0      3.0       2.0      5.0      2.0\n",
       "YearsAtCompany                1.0      5.0       5.0      8.0      6.0\n",
       "YearsSinceLastPromotion       0.0      1.0       0.0      7.0      0.0\n",
       "YearsWithCurrManager          0.0      4.0       3.0      5.0      4.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.1.** FULL-set of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3505, 19)\n",
      "(877, 19)\n",
      "(3505,)\n",
      "(877,)\n"
     ]
    }
   ],
   "source": [
    "X = data.drop('Attrition', axis=1)\n",
    "Y = data['Attrition']\n",
    "\n",
    "# Train and validation split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.2.** Reduced-set of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3505, 19)\n",
      "(877, 19)\n",
      "(3505,)\n",
      "(877,)\n"
     ]
    }
   ],
   "source": [
    "X2 = data[['BusinessTravel', 'Department', 'EducationField', 'MaritalStatus']]\n",
    "\n",
    "# Train and validation split\n",
    "X2_train, X2_test, Y_train, Y_test = train_test_split(X2, Y, test_size = 0.2, random_state=5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4.** Logistic Regression with **Scikit-Learn**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.1.** FULL-set of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 0.86\n",
      "Confusion matrix:\n",
      " [[742   5]\n",
      " [121   9]]\n"
     ]
    }
   ],
   "source": [
    "# Normalization of the features\n",
    "Scaler_X = StandardScaler()\n",
    "X_train  = Scaler_X.fit_transform(X_train)\n",
    "X_test   = Scaler_X.transform(X_test)\n",
    "\n",
    "# Fit the logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "# Make the prediction on the test\n",
    "Y_pred = model.predict(X_test)\n",
    "\n",
    "# Print accuracy and confusion matrix\n",
    "print('Accuracy on the test set: %.2f' % accuracy_score(Y_test, Y_pred))\n",
    "print('Confusion matrix:\\n', confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix\n",
    "splt.plot_confusion_matrix(Y_test, Y_pred,figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.99      0.92       747\n",
      "           1       0.64      0.07      0.13       130\n",
      "\n",
      "    accuracy                           0.86       877\n",
      "   macro avg       0.75      0.53      0.52       877\n",
      "weighted avg       0.83      0.86      0.80       877\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(Y_test, Y_pred));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ee/dOdUQJp0QhkiCxkkGslc2aypqmNm+Gp56CW28NLYoLLkh1REmjRCGSILEGpTXonGE+/zwU7ZswAU4/PVR93XffVEeVNEoUItshni4kDUpngc8/h7lzYciQ0N2U5VUgSsqKldkiqRJrL2h1IWW4Tz8Ns5kAOnWChQtDQb8cSxKgFoVIpWkVdJZauzZsRfrgg2F1dffuoT7Tb36T6shSRolCpIR4V0ZrFXQWmjgxFPH7/HO4/HL4298ysohfVVOiECkh3pXRGpDOMkuWhF3nGjaE994Lg9YCKFGIlEqD0DkkPx9atAgJ4vXXQ7KoXTvVUaUVDWaLSG764YewDWnLlqGIH0DHjkoSpVCLQnJCRSqyamV0Dnj9dbj2WlixAu64A448MtURpTW1KCQnxJrGWpKmtWa5nj2hS5fQ1TRlStimVAPWMalFITlD4w45LLqI3zHHhI2FbrwRtK96XBLaojCzDmb2uZktMLNbS3m+iZmNMbNPzWyWmZ2VyHhEJActWhRmML3wQjju3RtuuUVJogISlijMrBrwJHAm0ALobmYtSpx2J/CKux8KdAP+kah4RCTHbNoEjz0GrVrBpEnFrQqpsESm1COBBe6+EMDMXgI6A/lR5zhQNGpYB1iawHgkh5QcvNYAdY6ZNy8snPv4YzjzTHj6aWii9S6Vlciup4bA4qjjwshj0foBF5tZITAK+ENpb2Rmvc1sqplNXb58eSJilSxTcvBaA9Q5ZsGCsLr6xRfDrnNKEtsl1Z103YEh7v43MzsaeNHMWrn75uiT3H0QMAggLy9P7UcpU1FLQpVbc9C0aTBzZtiatGPHMDaxm1qRVSGRLYolQOOo40aRx6JdCbwC4O4fAzWA+gmMSbJcdJJQCyJHrFkTNhNq1w7+8pdQ1A+UJKpQIhPFFKCZmTU1s50Ig9UjSpxTAJwCYGbNCYlCfUuyXYpaEqrBlAPGjYNDDoEHHgjrIz79VGsiEiBhicLdNwLXAe8B8wizm+aaWX8z6xQ57UbgKjObCQwHerpraoJUTlHZb8kRS5bAKafAxo3wwQcweHBOlwJPpISOUbj7KMIgdfRjd0XdzweOTWQMkjuKZjmpyynLzZ4NrVuHldVvvBGK+NWqleqosppKeEhWade0rrqcstX338Mll0CbNsVF/M45R0kiCVI960lEJDZ3ePVVuO46WLkS7r47DFxL0ihRiEh6u+yysB4iLw/++9/Q7SRJpUQhIuknuohf+/ahu+n//k/1mVJEYxQikl4WLoRTT4UhQ8LxlVfCTTcpSaSQEoWIpIdNm+DRR0PX0pQpsIP+PKULpWjJSKXtWKfCfxksPz+U3pg8Gc4+OxTxa9Qo1VFJhFK2ZKTSdqxT2Y4MtmgRfPklDBsGI0cqSaQZtSgk4xStwG7XtK6K/mWyKVNgxgy46qrQili4EHbdNdVRSSnUopCMoxXYGW716jA4fdRR8Ne/FhfxU5JIW0oUkpG0AjtDjR0bprr+7W+hJaEifhlBXU8ikhyFhXDaabDPPvDhh6FGk2QEtShEJLFmzgz/NmoEb70Fs2YpSWQYJQoRSYzly6FHD2jbFj76KDx21lmwyy4pDUsqTl1PIlK13OGll+CPf4SffoJ77oGjNTstkylRSMYouR+2pKlLLoGhQ0OF12efhZYtUx2RbKe4E4WZ7eLuqxMZjEhZhk0u4PY3ZgNhxpOmxqaZzZtDAT+zMP5w+OGhRVGtWqojkypQbqIws2OAwUBtoImZHQJc7e59Eh2cSJGitRP3ndda02LTzYIFYarrJZeEMhxXXpnqiKSKxdOi+H/AGcAIAHefaWYnJDQqEbau55S/7GetnUg3GzeGIn5//jPsvLMSRBaLa9aTuy8u8dCmBMQispXoek6q45Rm5swJA9Q33wxnnBGK+l18caqjkgSJp0WxONL95GZWHbgemJfYsESCFg12Uz2ndFRQAF9/HWY3XXhhGJuQrBVPovg98HegIbAEeB/Q+IQkjGY3panJk8Piud69w3qIhQuhdu1URyVJEE/X00HufpG77+nuv3X3i4HmiQ5Mcld0klB3Uxr49Ve44YbQ1fTgg7BuXXhcSSJnxNOieBw4LI7HRCqktM2HoHgDInU5pYEPPwwzmhYuhGuugfvvDwPXklPKTBRmdjRwDLCHmd0Q9dRugCZHy3Yrq3tJLYk0UVgYBqqbNg0lOE7QZMdcFatFsRNh7cSOQHSh+J+BLokMSnKHWg5p6NNP4dBDQxG/kSOhfXuoWTPVUUkKlZko3P0j4CMzG+LuXycxJskB0bvUSZr49tuwmvqVV8K+Ee3bQ4cOqY5K0kA8YxSrzewhoCWwZYcRdz85YVFJ1tMudWnEPdRmuv56WLUKBgyAY45JdVSSRuJJFEOBl4FzCFNlLwOWJzIoyU5aaZ2mevQI6yGOPjoU8WuuSY2ytXimx9Zz92eBDe7+kbtfAag1IRWmldZpZPPm0JIAOP10+PvfYfx4JQkpVTwtig2Rf5eZ2dnAUkAdy1IpGrxOA/Pnhymvl14a6jNdfnmqI5I0F0+iGGBmdYAbCesndgP+L5FBiUgCbNwIjzwCd98NNWpoJpPErdxE4e5vR+7+BJwEYGbHJjIoEalis2aFEuDTpsF558GTT0KDBqmOSjJErAV31YALCTWe3nX3OWZ2DnA7UBM4NDkhish2KyyExYvh1Vfh/PNVxE8qJNZg9rNAL6Ae8JiZ/Qt4GHjQ3eNKEmbWwcw+N7MFZnZrGedcaGb5ZjbXzIZV9AcQkTL873/w9NPhflERvy5dlCSkwmJ1PeUBbdx9s5nVAL4B9nf3FfG8caRF8iRwGlAITDGzEe6eH3VOM+A24Fh3X2lmv63sDyLpTQvskmjVKrjjDnj8cdh//zBYvfPOUKtWqiOTDBUrUax3980A7r7WzBbGmyQijgQWuPtCADN7CegM5EedcxXwpLuvjHzOdxWKXtJa9LqJyYt+ALTALuHefz+UAS8ogGuvhfvuUxE/2W6xEsXBZjYrct+A/SPHBri7tynnvRsC0TvjFQLtSpxzIICZTSQUGuzn7u+WfCMz6w30BmjSRAu0MkV00b92TevSuW1DLbBLpMWL4eyzQyti3Dg47rhURyRZIlaiSMbKmx2BZsCJQCNgnJm1dvcfo09y90HAIIC8vDxPQlyyHUpuPKR1Ewk2bRocfjg0bgyjRsHxx4fpryJVpMzBbHf/OtYtjvdeAjSOOm4UeSxaITDC3Te4+yJgPiFxSAbTxkNJ8s03cMEFkJcXyoADnHaakoRUuXgW3FXWFKCZmTUlJIhuQI8S57wJdAf+aWb1CV1RCxMYkyRAyQ2I1JJIMHd44QX4059g9eowDqEifpJA8dR6qhR33whcB7wHzANecfe5ZtbfzDpFTnsPWGFm+cAY4OYKDphLGoiu4QSq45Rw3bpBz57QogXMmAG33QbVq6c6KslicbUozKwm0MTdP6/Im7v7KGBUicfuirrvwA2Rm2QwtSASbPPmsP7BLKyJOP546NMHdkjYdz2RLcr9LTOzjsAM4N3IcVszG5HguCRDFK2PkAT67LOwDemzz4bjyy6D665TkpCkiec3rR9hTcSPAO4+A2iasIgko2gDogTasCGMPxxyCOTnQ+3aqY5IclRcZcbd/Sfbetm/pqhmsZKD07FoA6IEmTEjrKieMSOU3Xj8cdhrr1RHJTkqnhbFXDPrAVQzs2Zm9jjwvwTHJSlUcnA6Fg1cJ8g334Tb66+HQn5KEpJC8bQo/gDcAawDhhFmKg1IZFCSehqcToEJE0I58D59oEMH+PJL2GWXVEclEleL4mB3v8Pdj4jc7nT3tQmPTCRX/PJLGJw+/nh49FFYty48riQhaSKeRPE3M5tnZn8xs1YJj0gkl7z3HrRqBf/4B1x/PUyfriJ+knbKTRTufhJhZ7vlwEAzm21mdyY8MpFst3gxnHNOaDlMmBBaE5rZJGkorgV37v4NYfOiMUBf4C40TpHRYs1sKirBIQngDlOmwJFHhiJ+o0eHKq+qzyRpLJ4Fd83NrJ+ZzQaKZjw1SnhkklCxZjZpJlOCLFsWtiFt1664iN+ppypJSNqLp0XxHPAycIa7L01wPFKF4mk1aGZTErjDkCFwww2wdi088AAce2yqoxKJW7mJwt31lyRDRZf7LkmthiS68EJ47bUwq2nwYDjwwFRHJFIhZSYKM3vF3S+MdDlFr8SOd4c7SZB4V06r1ZBCmzaFAn477AAdO8LJJ8PVV6s+k2SkWC2K6yP/npOMQCR+sVoK0dRqSJF58+DKK0MJjquugksvTXVEItulzETh7ssid/u4+y3Rz5nZA8At275KkkUthTS0YUMYf/jLX8I01zp1Uh2RSJWIpx18WimPnVnVgUj5hk0uoOvAj+OuwyRJ9OmnYUvSP/8ZzjsvtCouvDDVUYlUiVhjFNcAfYD9zGxW1FO7AhMTHZhsS3tRp7Fvv4Xvv4c334TOnVMdjUiVijVGMQwYDfwVuDXq8V/cXTvVVJGKlvRWl1MaGTcOZs+Ga68NRfwWLICaNVMdlUiVi5Uo3N2/MrNrSz5hZnWVLCovOjkU7Q7Xrmndcl+nlkSa+PlnuPVWeOqpMNW1V69Qn0lJQrJUeS2Kc4BphOmx0TsXObBfAuPKatFdSO2a1qVz24ba+CdTjBoVprkuXRoW0PXvryJ+kvVizXo6J/Kvtj2tIkUtCXUhZajFi8P4w0EHhQV07dqlOiKRpIin1tOxZlYrcv9iM3vEzPT1txI0GJ2B3GHSpHC/cWN4//1QClxJQnJIPLWengIOMbNDgBuBwcCLQPtEBpYtoscj1JLIMEuXwjXXwIgRMHYstG8PJ52U6qhEki6edRQb3d2BzsAT7v4kYYqsxCG6SqtaEhnCPdRkatEitCAeflhF/CSnxdOi+MXMbgMuAY43sx2A6okNK7uoFZFhunSBf/87tCAGD4YDDkh1RCIpFU+i6Ar0AK5w928i4xMPJTaszFZad5OkuegifueeC6efHuo0qYifSFxboX4DDAXqmNk5wFp3fyHhkWUwdTdlmDlzQtfSs8+G40suUaVXkSjltijM7EJCC2IsYS3F42Z2s7u/luDYMpq6mzLA+vXw17/CvfeGAn67757qiETSUjxdT3cAR7j7dwBmtgfwAaBEQeklONTdlAGmTYOePUNrokcPePRR2GOPVEclkpbiaVvvUJQkIlbE+bqcUNre0+puygArVsCPP8LIkTB0qJKESAzxtCjeNbP3gOGR467AqMSFlDmGTS5g8qIfaNe0rrqZMsGYMaGI3x//GAarv/gCatRIdVQiaS+eweybgYFAm8htUMmNjHJVUZeTWg9p7qefwuD0ySeHQn7r1oXHlSRE4hJrP4pmwMPA/sBs4CZ3j68edg5p17SuCvqls5Ej4fe/h2++gZtugnvuURE/kQqK1fX0HPACMA7oCDwO/C4ZQaWrkgPXGrROc4sXw/nnw8EHhw2Fjjgi1RGJZKRYXU+7uvsz7v65uz8M7JukmNJWyYFrDVqnIXf43//C/aIiflOnKkmIbIdYLYoaZnYoxftQ1Iw+dvfp5b25mXUA/g5UAwa7+/1lnHc+YbrtEe4+tQLxJ40GrjNAYWEo4vf228VF/E48MdVRiWS8WIliGfBI1PE3UccOnBzrjc2sGvAkcBpQCEwxsxHunl/ivF2B64HJFQs9uTRwncY2b4ZnnoGbb4aNG+GRR+C441IdlUjWiLVx0fbWUz4SWODuCwHM7CVCBdr8Euf9BXgAuHk7Py/hNHCdps4/P4xBnHxySBj7afNFkaqUyIVzDYHFUceFkce2MLPDgMbu/k6sNzKz3mY21cymLl++vOojlcyzcWNoSUBIFM88Ax98oCQhkgApW2EdKVf+CGEzpJjcfZC757l73h5aQSuzZsHRR4fkAHDxxdCrV6j+KiJVLpGJYgnQOOq4UeSxIrsCrYCxZvYVcBQwwszyEhiTZLJ16+Duu+Hww+Hrr1V2QyRJ4tkz2yJ7Zd8VOW5iZkfG8d5TgGZm1tTMdgK6ASOKnnT3n9y9vrvv6+77ApOATuk660lSbMoUOOww6N8funeHefPgdzm9rEckaeJpUfwDOBroHjn+hTCbKSZ33whcB7wHzANecfe5ZtbfzDpVMt6UKJoaKym0ciWsWgWjRsELL0C9eqmOSCRnxFMUsJ27H2ZmnwK4+8pIC6Fc7j6KEgUE3f2uMs49MZ73TAVNjU2RDz8MRfyuvz4U8Zs/X+U3RFIgnhbFhsiaCIct+1FsTmhUaUhTY5Poxx/DNqSnnAIDBxYX8VOSEEmJeFoUjwFvAL81s3uBLsCdCY0qDWjf6xR5662wuvrbb6FvX+jXTwlCJMXKTRTuPtTMpgGnEMp3nOvu8xIeWYoV1XVq0WA31XRKloICuOACaN4cRoyAPE2AE0kH8eyZ3QRYDYyMfszdCxIZWCqprlMSucOECXD88dCkSVg0d9RRsFNcw2AikgTxdD29QxifMKAG0BT4HGiZwLhSSoPXSVJQEPaKGD26uIjfCSekOioRKSGerqfW0ceRsht9EhZRmtDgdQJt3gxPPw233BJaFI89piJ+ImksnhbFVtx9upm1S0QwkiN+97swaH3aaTBoEOy7b6ojEpEY4hmjuCHqcAfgMGBpwiKS7LRxI+ywQ7h17QqdO0PPnqrPJJIB4llHsWvUbWfCmEXnRAYlWWbmTGjXLrQeIJTguPxyJQmRDBGzRRFZaLeru9+UpHgkm6xdCwMGwAMPQN26sNdeqY5IRCqhzERhZju6+0YzOzaZAaWKFthVsU8+gcsug88+C/8+8khIFiKScWK1KD4hjEfMMLMRwKvAr0VPuvu/ExxbUmmBXRX7+WdYswbefRfOOCPV0YjIdohn1lMNYAVhj+yi9RQOZFWiAGjRYDctsNse778Pc+fCn/4Ep54Kn3+u8hsiWSBWovhtZMbTHIoTRBFPaFSSWVauhBtugCFDoGVL6NMnJAglCZGsEGvWUzWgduS2a9T9opsI/Pvf0KIFvPgi3HYbTJ2qBCGSZWK1KJa5e/+kRSKZp6AAunWDVq3ChkKHHprqiEQkAWK1KHJmkrt2sKsAd/joo3C/SZOwudDkyUoSIlksVqI4JWlRpJiKAMbp66/hzDPhxBOLk8Vxx0H16ikNS0QSq8xE4e459RVbRQBj2LwZnngiDFRPmACPPx7KgotITqhwUUDJQeeeCyNHhvUQAwfCPvukOiIRSSIlCindhg1QrVoo4te9O3TpApdcovpMIjkonqKAkmumT4cjjwx7RkBIFJdeqiQhkqOUKKTYmjVhLcSRR8I330DjxqmOSETSQM52PakIYAmTJoXiffPnwxVXwMMPw+67pzoqEUkDOZsoVASwhF9/DeMS//lPqNMkIhKRs4kCVASQd98NRfxuvBFOOSWUBN9pp1RHJSJpJifHKHJ+JfaKFaGb6cwz4fnnYf368LiShIiUIicTRc6uxHaH114LRfyGDYM774QpU5QgRCSmnOt6KmpN5ORK7IIC6NED2rQJe0ccckiqIxKRDJBzLYqca024h8J9EFZUjx0bZjgpSYhInHImUQybXEDXgR+Tv+zn3GlNLFoEp58eBqqLivgdcwzsmHMNSRHZDjnxF2PY5AJuf2M2EIr/ZX1rYtOmUMTv9ttDGY6nnlIRPxGptJxIFEXdTfed1zo3WhKdO8M778BZZ4UyHFphLSLbIScSBeRAGfHoIn6XXBLqM/XoofpMIrLdEjpGYWYdzOxzM1tgZreW8vwNZpZvZrPM7L9mpvrVlTF1KuTlhS4mgK5d4aKLlCREpEokLFGYWTXgSeBMoAXQ3cxalDjtUyDP3dsArwEPJiqerLRmDdxyC7RrB8uXa58IEUmIRLYojgQWuPtCd18PvAR0jj7B3ce4++rI4SSgUQLjyS4ffxymuD74YCjil58P55yT6qhEJAslcoyiIbA46rgQaBfj/CuB0aU9YWa9gd4ATZpk8ThDRaxZE7Yo/eCDMP1VRCRB0mIw28wuBvKA9qU97+6DgEEAeXl5nsTQ0suoUaGI3803w8knw7x5UL16qqMSkSyXyK6nJUD0vMxGkce2YmanAncAndx9XQLjyVzffw8XXwxnnw1DhxYX8VOSEJEkSGSLYgrQzMyaEhJEN6BH9AlmdigwEOjg7t9V1QdHb0oEGbwxkTu8/DL84Q/w009w991hEZ2K+IlIEiWsReHuG4HrgPeAecAr7j7XzPqbWafIaQ8BtYFXzWyGmY2ois8u2pSoSMZuTFRQEMqBN20K06ZBv35KEiKSdAkdo3D3UcCoEo/dFXU/YVupZeymRO7w3/+GXeb22SfUaDriiLCYTkQkBXKmKGBG+PLLMIPptNOKi/gddZSShIiklBJFOti0CR55BFq3Dl1MAweqiJ+IpI20mB6b8zp2hNGjw4K5p56CRlp3KCLpIysSRUbOclq/PuwLscMO0LNnKOTXrZvqM4lI2smKrqeMm+X0ySdw+OHwj3+E4wsvDNVelSREJA1lRYsCMmSW0+rV8Oc/w6OPQoMGsP/+qY5IRKRcWZMo0t6ECWFNxMKFcPXV8MADUKdOqqMSESlXxieKYZMLmLzoB9o1rZvqUGIr2lhozBg48cRURyMiEreMThTRe2Gn5ZjEyJGhcF/fvnDSSaEU+I4ZfclFJAdl9GB22u6FvXx52Ia0UycYPry4iJ+ShIhkoIxOFJBme2G7w7Bh0Lw5vPYa9O8PkyerPpOIZDR9xa1KBQVw+eVw6KHw7LPQsmWqIxIR2W4Z36JIuc2b4b33wv199oHx42HiRCUJEckaShTb44svwk5zHTrAuHHhsSOPVBE/EckqShSVsXEjPPQQtGkDM2aEbiYV8RORLJWRYxRFtZ1SVtPpnHNCd1PnzqEMx957Jz8GEZEkychEEZ0kkrZ+Yt26sEf1DjtAr15wxRVwwQWqzyQiWS8jEwUkubbTpElw5ZXw+9+H/au7dEnO54qIpAGNUcTy66/wpz/BMcfAL79As2apjkhEJOkytkWRcOPHhyJ+ixZBnz7w17/Cbmm+x4WISAIoUZRl48YwJvHRR3DCCamORkQkZZQoor35Zijid9ttoYjf3LmqzyQiOU9jFADffht2mTvvvFCjSUX8RES2yO1E4Q4vvggtWsBbb8G994YZTiriJyKyRW5/ZS4oCGsi8vLC6uqDD051RCIiaSfjWhQ//LqeyYt+qPwbbN4Mo0eH+/vsEwr4jRunJCEiUoaMSxQ/rt4AVHJHu/nzwzakZ50VZjNBaE2oiJ+ISJkyLlFAJTYr2rgRHnggFPGbPRv++U9NeRURiVNujFGcfTa8/z787nfw5JOw116pjkhEJGNkb6JYuzYsmKtWDXr3Drfzz091VCIiGScju57KNXEitG0bWg8QEoSShIhIpWRXoli1Cv74x7CJ0Nq10Lx5qiMSEcl42dP19NFHoYhfQQFcdx3cdx/Urp3qqEREMl72JAqAXXYJVV+PPTbVkYiIZI3MThT//jd89hncfju0bx+mvmpNhIhIlUroGIWZdTCzz81sgZndWsrzO5vZy5HnJ5vZvvG8b52fVoRd5s4/H954o7iIn5KEiEiVM3dPzBubVQPmA6cBhcAUoLu750ed0wdo4+6/N7NuwHnu3jXW++5Tv4HPXfMrtTeth3794MYbwzRYEREpk5lNc/e8yrw2kS2KI4EF7r7Q3dcDLwGdS5zTGXg+cv814BQzs1hvWn/Ftyzeez+YORNuvVVJQkQkwRI5RtEQWBx1XAi0K+scd99oZj8B9YDvo08ys95A78jhuhYLZs5RET8A6lPiWuUwXYtiuhbFdC2KHVTZF2bEYLa7DwIGAZjZ1Mo2n7KNrkUxXYtiuhbFdC2KmdnUyr42kV1PS4DGUceNIo+Veo6Z7QjUAVYkMCYREamgRCaKKUAzM2tqZjsB3YARJc4ZAVwWud8F+NATNbouIiKVkrCup8iYw3XAe0A14Dl3n2tm/YGp7j4CeBZ40cwWAD8Qkkl5BiUq5gyka1FM16KYrkUxXYtilb4WCZseKyIi2SG7igKKiEiVU6IQEZGY0jZRJKr8RyaK41rcYGb5ZjbLzP5rZvukIs5kKO9aRJ13vpm5mWXt1Mh4roWZXRj53ZhrZsOSHWOyxPH/SBMzG2Nmn0b+PzkrFXEmmpk9Z2bfmdmcMp43M3sscp1mmdlhcb2xu6fdjTD4/SWwH7ATMBNoUeKcPsDTkfvdgJdTHXcKr8VJwC6R+9fk8rWInLcrMA6YBOSlOu4U/l40Az4Fdo8c/zbVcafwWgwCroncbwF8leq4E3QtTgAOA+aU8fxZwGjAgKOAyfG8b7q2KBJS/iNDlXst3H2Mu6+OHE4irFnJRvH8XgD8BXgAWJvM4JIsnmtxFfCku68EcPfvkhxjssRzLRzYLXK/DrA0ifEljbuPI8wgLUtn4AUPJgG/MbMG5b1vuiaK0sp/NCzrHHffCBSV/8g28VyLaFcSvjFko3KvRaQp3djd30lmYCkQz+/FgcCBZjbRzCaZWYekRZdc8VyLfsDFZlYIjAL+kJzQ0k5F/54AGVLCQ+JjZhcDeUD7VMeSCma2A/AI0DPFoaSLHQndTycSWpnjzKy1u/+YyqBSpDswxN3/ZmZHE9ZvtXL3zakOLBOka4tC5T+KxXMtMLNTgTuATu6+LkmxJVt512JXoBUw1sy+IvTBjsjSAe14fi8KgRHuvsHdFxHK/jdLUnzJFM+1uBJ4BcDdPwZqEAoG5pq4/p6UlK6JQuU/ipV7LczsUGAgIUlkaz80lHMt3P0nd6/v7vu6+76E8ZpO7l7pYmhpLJ7/R94ktCYws/qErqiFSYwxWeK5FgXAKQBm1pyQKJYnNcr0MAK4NDL76SjgJ3dfVt6L0rLryRNX/iPjxHktHgJqA69GxvML3L1TyoJOkDivRU6I81q8B5xuZvnAJuBmd8+6Vnec1+JG4Bkz+xNhYLtnNn6xNLPhhC8H9SPjMXcD1QHc/WnC+MxZwAJgNXB5XO+bhddKRESqULp2PYmISJpQohARkZiUKEREJCYlChERiUmJQkREYlKikLRkZpvMbEbUbd8Y566qgs8bYmaLIp81PbJ6t6LvMdjMWkTu317iuf9tb4yR9ym6LnPMbKSZ/aac89tma6VUSR5Nj5W0ZGar3L12VZ8b4z2GAG+7+2tmdjrwsLu32Y732+6YyntfM3semO/u98Y4vyehgu51VR2L5A61KCQjmFntyF4b081stpltUzXWzBqY2biob9zHRx4/3cw+jrz2VTMr7w/4OOCAyGtviLzXHDP7v8hjtczsHTObGXm8a+TxsWaWZ2b3AzUjcQyNPLcq8u9LZnZ2VMxDzKyLmVUzs4fMbEpkn4Cr47gsHxMp6GZmR0Z+xk/N7H9mdlBklXJ/oGsklq6R2J8zs08i55ZWfVdka6mun66bbqXdCCuJZ0RubxCqCOwWea4+YWVpUYt4VeTfG4E7IverEWo/1Sf84a8VefwW4K5SPm8I0CVy/wJgMnA4MBuoRVj5Phc4FDgfeCbqtXUi/44lsv9FUUxR5xTFeB7wfOT+ToRKnjWB3sCdkcd3BqYCTUuJc1XUz/cq0CFyvBuwY+T+qcDrkfs9gSeiXn8fcHHk/m8I9Z9qpfq/t27pfUvLEh4iwBp3b1t0YGbVgfvM7ARgM+Gb9J7AN1GvmQI8Fzn3TXefYWbtCRvVTIyUN9mJ8E28NA+Z2Z2EGkBXEmoDveHuv0Zi+DdwPPAu8Dcze4DQXTW+Aj/XaODvZrYz0AEY5+5rIt1dbcysS+S8OoQCfotKvL6mmc2I/PzzgP9Enf+8mTUjlKioXsbnnw50MrObIsc1gCaR9xIplRKFZIqLgD2Aw919g4XqsDWiT3D3cZFEcjYwxMweAVYC/3H37nF8xs3u/lrRgZmdUtpJ7j7fwr4XZwEDzOy/7t4/nh/C3dea2VjgDKArYZMdCDuO/cHd3yvnLda4e1sz24VQ2+ha4DHCZk1j3P28yMD/2DJeb8D57v55PPGKgMYoJHPUAb6LJImTgG32BbewV/i37v4MMJiwJeQk4FgzKxpzqGVmB8b5meOBc81sFzOrReg2Gm9mewOr3f1fhIKMpe07vCHSsinNy4RibEWtEwh/9K8peo2ZHRj5zFJ52NHwj8CNVlxmv6hcdM+oU38hdMEVeQ/4g0WaVxYqD4vEpEQhmWIokGdms4FLgc9KOedEYKaZfUr4tv53d19O+MM53MxmEbqdDo7nA919OmHs4hPCmMVgd/8UaA18EukCuhsYUMrLBwGzigazS3ifsLnUBx627oSQ2PKB6WY2h1A2PmaLPxLLLMKmPA8Cf4387NGvGwO0KBrMJrQ8qkdimxs5FolJ02NFRCQmtShERCQmJQoREYlJiUJERGJSohARkZiUKEREJCYlChERiUmJQkREYvr/V5u5g2OXV5gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Area Under Curve (AUC)\n",
    "model_roc_auc = roc_auc_score(Y_test, model.predict(X_test))\n",
    "\n",
    "# Plot\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, model.predict_proba(X_test)[:,1])\n",
    "plt.plot(fpr, tpr, label='AUC (area = %0.4f)' % model_roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim(([0.0, 1.0]))\n",
    "plt.ylim(([0.0, 1.0]))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': -0.2973671260199565,\n",
       " 'BusinessTravel': -0.008804307087983345,\n",
       " 'Department': -0.14460444388665267,\n",
       " 'DistanceFromHome': 0.017987162402208993,\n",
       " 'Education': -0.057048992994832494,\n",
       " 'EducationField': -0.09318144697171321,\n",
       " 'Gender': 0.02524228570645906,\n",
       " 'JobLevel': -0.030818781325359867,\n",
       " 'JobRole': 0.0764882059957115,\n",
       " 'MaritalStatus': 0.4358624980675554,\n",
       " 'MonthlyIncome': -0.07779658224311603,\n",
       " 'NumCompaniesWorked': 0.2788739648845272,\n",
       " 'PercentSalaryHike': 0.061940756807449954,\n",
       " 'StockOptionLevel': -0.06321421414181246,\n",
       " 'TotalWorkingYears': -0.39404498186636194,\n",
       " 'TrainingTimesLastYear': -0.1474509742540277,\n",
       " 'YearsAtCompany': 0.06997491368368643,\n",
       " 'YearsSinceLastPromotion': 0.394259325607342,\n",
       " 'YearsWithCurrManager': -0.4851606882712858,\n",
       " 'Intercept': -1.8930105909393866}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Coefficients analysis\n",
    "\n",
    "coefficients = {}\n",
    "\n",
    "# Save the coefficients\n",
    "for i in range(len(model.coef_[0])):\n",
    "    coefficients[X.columns[i]] = model.coef_[0][i]\n",
    "\n",
    "# Add the intercept\n",
    "coefficients['Intercept'] = model.intercept_[0]\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.297367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusinessTravel</th>\n",
       "      <td>-0.008804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <td>-0.144604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>0.017987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>-0.057049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationField</th>\n",
       "      <td>-0.093181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.025242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>-0.030819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobRole</th>\n",
       "      <td>0.076488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>0.435862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>-0.077797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>0.278874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>0.061941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>-0.063214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>-0.394045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>-0.147451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>0.069975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>0.394259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>-0.485161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-1.893011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0\n",
       "Age                     -0.297367\n",
       "BusinessTravel          -0.008804\n",
       "Department              -0.144604\n",
       "DistanceFromHome         0.017987\n",
       "Education               -0.057049\n",
       "EducationField          -0.093181\n",
       "Gender                   0.025242\n",
       "JobLevel                -0.030819\n",
       "JobRole                  0.076488\n",
       "MaritalStatus            0.435862\n",
       "MonthlyIncome           -0.077797\n",
       "NumCompaniesWorked       0.278874\n",
       "PercentSalaryHike        0.061941\n",
       "StockOptionLevel        -0.063214\n",
       "TotalWorkingYears       -0.394045\n",
       "TrainingTimesLastYear   -0.147451\n",
       "YearsAtCompany           0.069975\n",
       "YearsSinceLastPromotion  0.394259\n",
       "YearsWithCurrManager    -0.485161\n",
       "Intercept               -1.893011"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficients as DataFrame\n",
    "coefficients_dataframe = pd.DataFrame(coefficients.values()).T\n",
    "coefficients_dataframe.columns = coefficients.keys()\n",
    "coefficients_dataframe.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the coefficients\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(data = coefficients_dataframe);\n",
    "plt.title('With Intercept');\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.2.** Reduced-set of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 0.85\n",
      "Confusion matrix:\n",
      " [[747   0]\n",
      " [130   0]]\n"
     ]
    }
   ],
   "source": [
    "# Normalization of the features\n",
    "Scaler_X2 = StandardScaler()\n",
    "X2_train  = Scaler_X2.fit_transform(X2_train)\n",
    "X2_test   = Scaler_X2.transform(X2_test)\n",
    "\n",
    "# Fit the logistic regression model\n",
    "model2 = LogisticRegression()\n",
    "model2.fit(X2_train, Y_train)\n",
    "\n",
    "# Make the prediction on the test\n",
    "Y_pred = model2.predict(X2_test)\n",
    "\n",
    "# Print accuracy and confusion matrix\n",
    "print('Accuracy on the test set: %.2f' % accuracy_score(Y_test, Y_pred))\n",
    "print('Confusion matrix:\\n', confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix\n",
    "splt.plot_confusion_matrix(Y_test, Y_pred,figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      1.00      0.92       747\n",
      "           1       1.00      0.00      0.00       130\n",
      "\n",
      "    accuracy                           0.85       877\n",
      "   macro avg       0.93      0.50      0.46       877\n",
      "weighted avg       0.87      0.85      0.78       877\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(Y_test, Y_pred, zero_division=True));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Area Under Curve (AUC)\n",
    "model_roc_auc = roc_auc_score(Y_test, model2.predict(X2_test))\n",
    "\n",
    "# Plot\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, model2.predict_proba(X2_test)[:,1])\n",
    "plt.plot(fpr, tpr, label='AUC (area = %0.4f)' % model_roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim(([0.0, 1.0]))\n",
    "plt.ylim(([0.0, 1.0]))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BusinessTravel': -0.004737109370054165,\n",
       " 'Department': -0.11937997121464804,\n",
       " 'EducationField': -0.10215895255528519,\n",
       " 'MaritalStatus': 0.46907590861077103,\n",
       " 'Intercept': -1.7117429398903727}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Coefficients analysis\n",
    "\n",
    "coefficients = {}\n",
    "\n",
    "# Save the coefficients\n",
    "for i in range(len(model2.coef_[0])):\n",
    "    coefficients[X2.columns[i]] = model2.coef_[0][i]\n",
    "\n",
    "# Add the intercept\n",
    "coefficients['Intercept'] = model2.intercept_[0]\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.004737</td>\n",
       "      <td>-0.11938</td>\n",
       "      <td>-0.102159</td>\n",
       "      <td>0.469076</td>\n",
       "      <td>-1.711743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BusinessTravel  Department  EducationField  MaritalStatus  Intercept\n",
       "0       -0.004737    -0.11938       -0.102159       0.469076  -1.711743"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficients as DataFrame\n",
    "coefficients_dataframe = pd.DataFrame(coefficients.values()).T\n",
    "coefficients_dataframe.columns = coefficients.keys()\n",
    "coefficients_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the coefficients - with intercept\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(data = coefficients_dataframe);\n",
    "plt.title('With Intercept');\n",
    "\n",
    "# Plot of the coefficients - without intercept\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(data = coefficients_dataframe[['BusinessTravel', 'Department', 'EducationField', 'MaritalStatus']]);\n",
    "plt.title('Without Intercept');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.10 64-bit (system)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9805bccb659ffcb6952b8b29440036fac091f8170d71a5a402ad9a8f2988209f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}