IBN_HR_analysis_Kaggle.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/roapple10/Python-learning-note/blob/main/IBN_HR_analysis_Kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "light-manual",
      "metadata": {
        "id": "light-manual"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "from sklearn.preprocessing import LabelEncoder,MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from xgboost import XGBClassifier\n",
        "# sns.color_palette(\"Set3\", as_cmap=True)\n",
        "sns.set_style('darkgrid')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "upper-processor",
      "metadata": {
        "id": "upper-processor"
      },
      "outputs": [],
      "source": [
        "pd.options.display.max_columns = 200\n",
        "pd.options.display.max_rows =200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "silver-guide",
      "metadata": {
        "id": "silver-guide",
        "outputId": "f909d359-ee54-409f-8744-a8dc01ba2649"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>Over18</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1102</td>\n",
              "      <td>Sales</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>279</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1373</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>1392</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>591</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1   49        No  Travel_Frequently        279  Research & Development   \n",
              "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3   33        No  Travel_Frequently       1392  Research & Development   \n",
              "4   27        No      Travel_Rarely        591  Research & Development   \n",
              "\n",
              "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences              1               1   \n",
              "1                 8          1  Life Sciences              1               2   \n",
              "2                 2          2          Other              1               4   \n",
              "3                 3          4  Life Sciences              1               5   \n",
              "4                 2          1        Medical              1               7   \n",
              "\n",
              "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                        2  Female          94               3         2   \n",
              "1                        3    Male          61               2         2   \n",
              "2                        4    Male          92               2         1   \n",
              "3                        4  Female          56               3         1   \n",
              "4                        1    Male          40               3         1   \n",
              "\n",
              "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
              "0        Sales Executive                4        Single           5993   \n",
              "1     Research Scientist                2       Married           5130   \n",
              "2  Laboratory Technician                3        Single           2090   \n",
              "3     Research Scientist                3       Married           2909   \n",
              "4  Laboratory Technician                2       Married           3468   \n",
              "\n",
              "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
              "0        19479                   8      Y      Yes                 11   \n",
              "1        24907                   1      Y       No                 23   \n",
              "2         2396                   6      Y      Yes                 15   \n",
              "3        23159                   1      Y      Yes                 11   \n",
              "4        16632                   9      Y       No                 12   \n",
              "\n",
              "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
              "0                  3                         1             80   \n",
              "1                  4                         4             80   \n",
              "2                  3                         2             80   \n",
              "3                  3                         3             80   \n",
              "4                  3                         4             80   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                 0                  8                      0   \n",
              "1                 1                 10                      3   \n",
              "2                 0                  7                      3   \n",
              "3                 0                  8                      3   \n",
              "4                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "micro-science",
      "metadata": {
        "id": "micro-science"
      },
      "source": [
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "protected-proof",
      "metadata": {
        "id": "protected-proof",
        "outputId": "1eb4cf54-6226-4b82-e6ed-d71f8652ba28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1470 entries, 0 to 1469\n",
            "Data columns (total 35 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   Age                       1470 non-null   int64 \n",
            " 1   Attrition                 1470 non-null   object\n",
            " 2   BusinessTravel            1470 non-null   object\n",
            " 3   DailyRate                 1470 non-null   int64 \n",
            " 4   Department                1470 non-null   object\n",
            " 5   DistanceFromHome          1470 non-null   int64 \n",
            " 6   Education                 1470 non-null   int64 \n",
            " 7   EducationField            1470 non-null   object\n",
            " 8   EmployeeCount             1470 non-null   int64 \n",
            " 9   EmployeeNumber            1470 non-null   int64 \n",
            " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
            " 11  Gender                    1470 non-null   object\n",
            " 12  HourlyRate                1470 non-null   int64 \n",
            " 13  JobInvolvement            1470 non-null   int64 \n",
            " 14  JobLevel                  1470 non-null   int64 \n",
            " 15  JobRole                   1470 non-null   object\n",
            " 16  JobSatisfaction           1470 non-null   int64 \n",
            " 17  MaritalStatus             1470 non-null   object\n",
            " 18  MonthlyIncome             1470 non-null   int64 \n",
            " 19  MonthlyRate               1470 non-null   int64 \n",
            " 20  NumCompaniesWorked        1470 non-null   int64 \n",
            " 21  Over18                    1470 non-null   object\n",
            " 22  OverTime                  1470 non-null   object\n",
            " 23  PercentSalaryHike         1470 non-null   int64 \n",
            " 24  PerformanceRating         1470 non-null   int64 \n",
            " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
            " 26  StandardHours             1470 non-null   int64 \n",
            " 27  StockOptionLevel          1470 non-null   int64 \n",
            " 28  TotalWorkingYears         1470 non-null   int64 \n",
            " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
            " 30  WorkLifeBalance           1470 non-null   int64 \n",
            " 31  YearsAtCompany            1470 non-null   int64 \n",
            " 32  YearsInCurrentRole        1470 non-null   int64 \n",
            " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
            " 34  YearsWithCurrManager      1470 non-null   int64 \n",
            "dtypes: int64(26), object(9)\n",
            "memory usage: 402.1+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "opponent-brief",
      "metadata": {
        "id": "opponent-brief",
        "outputId": "ccc8237f-cf88-4eec-f11d-90481db97d0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1470, 35)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "meaningful-merit",
      "metadata": {
        "id": "meaningful-merit"
      },
      "outputs": [],
      "source": [
        "numeric_cols = df.select_dtypes(include=['int64','float64']).columns.tolist()\n",
        "categorical_cols = df.select_dtypes(include=['object']).columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ordered-bridges",
      "metadata": {
        "id": "ordered-bridges",
        "outputId": "b1d1bfd3-09d3-4b85-f6a4-94711cc3ca69"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "    <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>2</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>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>3</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>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>3</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>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>2</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  DailyRate  DistanceFromHome  Education  EmployeeCount  EmployeeNumber  \\\n",
              "0   41       1102                 1          2              1               1   \n",
              "1   49        279                 8          1              1               2   \n",
              "2   37       1373                 2          2              1               4   \n",
              "3   33       1392                 3          4              1               5   \n",
              "4   27        591                 2          1              1               7   \n",
              "\n",
              "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                        2          94               3         2   \n",
              "1                        3          61               2         2   \n",
              "2                        4          92               2         1   \n",
              "3                        4          56               3         1   \n",
              "4                        1          40               3         1   \n",
              "\n",
              "   JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0                4           5993        19479                   8   \n",
              "1                2           5130        24907                   1   \n",
              "2                3           2090         2396                   6   \n",
              "3                3           2909        23159                   1   \n",
              "4                2           3468        16632                   9   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         1   \n",
              "1                 23                  4                         4   \n",
              "2                 15                  3                         2   \n",
              "3                 11                  3                         3   \n",
              "4                 12                  3                         4   \n",
              "\n",
              "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0             80                 0                  8                      0   \n",
              "1             80                 1                 10                      3   \n",
              "2             80                 0                  7                      3   \n",
              "3             80                 0                  8                      3   \n",
              "4             80                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[numeric_cols].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "widespread-cooking",
      "metadata": {
        "id": "widespread-cooking",
        "outputId": "ea0b9c83-337b-4453-8b40-3b7b26c5b834"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Over18</th>\n",
              "      <th>OverTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Other</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Single</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Married</td>\n",
              "      <td>Y</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Attrition     BusinessTravel              Department EducationField  Gender  \\\n",
              "0       Yes      Travel_Rarely                   Sales  Life Sciences  Female   \n",
              "1        No  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
              "2       Yes      Travel_Rarely  Research & Development          Other    Male   \n",
              "3        No  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
              "4        No      Travel_Rarely  Research & Development        Medical    Male   \n",
              "\n",
              "                 JobRole MaritalStatus Over18 OverTime  \n",
              "0        Sales Executive        Single      Y      Yes  \n",
              "1     Research Scientist       Married      Y       No  \n",
              "2  Laboratory Technician        Single      Y      Yes  \n",
              "3     Research Scientist       Married      Y      Yes  \n",
              "4  Laboratory Technician       Married      Y       No  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[categorical_cols].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "signal-burke",
      "metadata": {
        "id": "signal-burke",
        "outputId": "b4dc5c3a-c118-45af-8f87-ea0992f90a95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Series([], dtype: int64)"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_counts = df[numeric_cols].isna().sum().sort_values(ascending = False)\n",
        "missing_counts[missing_counts>0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "noticed-place",
      "metadata": {
        "id": "noticed-place",
        "outputId": "4c5ed9b9-66ef-4f5f-88a2-c479169e87b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Series([], dtype: int64)"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_counts = df[categorical_cols].isna().sum().sort_values(ascending = False)\n",
        "missing_counts[missing_counts>0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "identified-fence",
      "metadata": {
        "id": "identified-fence",
        "outputId": "e9459b77-1c34-421a-9890-2a2ae478e2bd"
      },
      "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>Age</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36.923810</td>\n",
              "      <td>802.485714</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1024.865306</td>\n",
              "      <td>2.721769</td>\n",
              "      <td>65.891156</td>\n",
              "      <td>2.729932</td>\n",
              "      <td>2.063946</td>\n",
              "      <td>2.728571</td>\n",
              "      <td>6502.931293</td>\n",
              "      <td>14313.103401</td>\n",
              "      <td>2.693197</td>\n",
              "      <td>15.209524</td>\n",
              "      <td>3.153741</td>\n",
              "      <td>2.712245</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.793878</td>\n",
              "      <td>11.279592</td>\n",
              "      <td>2.799320</td>\n",
              "      <td>2.761224</td>\n",
              "      <td>7.008163</td>\n",
              "      <td>4.229252</td>\n",
              "      <td>2.187755</td>\n",
              "      <td>4.123129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.135373</td>\n",
              "      <td>403.509100</td>\n",
              "      <td>8.106864</td>\n",
              "      <td>1.024165</td>\n",
              "      <td>0.0</td>\n",
              "      <td>602.024335</td>\n",
              "      <td>1.093082</td>\n",
              "      <td>20.329428</td>\n",
              "      <td>0.711561</td>\n",
              "      <td>1.106940</td>\n",
              "      <td>1.102846</td>\n",
              "      <td>4707.956783</td>\n",
              "      <td>7117.786044</td>\n",
              "      <td>2.498009</td>\n",
              "      <td>3.659938</td>\n",
              "      <td>0.360824</td>\n",
              "      <td>1.081209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.852077</td>\n",
              "      <td>7.780782</td>\n",
              "      <td>1.289271</td>\n",
              "      <td>0.706476</td>\n",
              "      <td>6.126525</td>\n",
              "      <td>3.623137</td>\n",
              "      <td>3.222430</td>\n",
              "      <td>3.568136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>465.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>491.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2911.000000</td>\n",
              "      <td>8047.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>802.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1020.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4919.000000</td>\n",
              "      <td>14235.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>1157.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1555.750000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>83.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8379.000000</td>\n",
              "      <td>20461.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>1499.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2068.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>19999.000000</td>\n",
              "      <td>26999.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
              "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
              "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
              "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
              "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
              "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
              "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
              "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
              "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
              "\n",
              "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
              "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
              "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
              "std        602.024335                 1.093082    20.329428        0.711561   \n",
              "min          1.000000                 1.000000    30.000000        1.000000   \n",
              "25%        491.250000                 2.000000    48.000000        2.000000   \n",
              "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
              "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
              "max       2068.000000                 4.000000   100.000000        4.000000   \n",
              "\n",
              "          JobLevel  JobSatisfaction  MonthlyIncome   MonthlyRate  \\\n",
              "count  1470.000000      1470.000000    1470.000000   1470.000000   \n",
              "mean      2.063946         2.728571    6502.931293  14313.103401   \n",
              "std       1.106940         1.102846    4707.956783   7117.786044   \n",
              "min       1.000000         1.000000    1009.000000   2094.000000   \n",
              "25%       1.000000         2.000000    2911.000000   8047.000000   \n",
              "50%       2.000000         3.000000    4919.000000  14235.500000   \n",
              "75%       3.000000         4.000000    8379.000000  20461.500000   \n",
              "max       5.000000         4.000000   19999.000000  26999.000000   \n",
              "\n",
              "       NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
              "count         1470.000000        1470.000000        1470.000000   \n",
              "mean             2.693197          15.209524           3.153741   \n",
              "std              2.498009           3.659938           0.360824   \n",
              "min              0.000000          11.000000           3.000000   \n",
              "25%              1.000000          12.000000           3.000000   \n",
              "50%              2.000000          14.000000           3.000000   \n",
              "75%              4.000000          18.000000           3.000000   \n",
              "max              9.000000          25.000000           4.000000   \n",
              "\n",
              "       RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
              "count               1470.000000         1470.0       1470.000000   \n",
              "mean                   2.712245           80.0          0.793878   \n",
              "std                    1.081209            0.0          0.852077   \n",
              "min                    1.000000           80.0          0.000000   \n",
              "25%                    2.000000           80.0          0.000000   \n",
              "50%                    3.000000           80.0          1.000000   \n",
              "75%                    4.000000           80.0          1.000000   \n",
              "max                    4.000000           80.0          3.000000   \n",
              "\n",
              "       TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
              "count        1470.000000            1470.000000      1470.000000   \n",
              "mean           11.279592               2.799320         2.761224   \n",
              "std             7.780782               1.289271         0.706476   \n",
              "min             0.000000               0.000000         1.000000   \n",
              "25%             6.000000               2.000000         2.000000   \n",
              "50%            10.000000               3.000000         3.000000   \n",
              "75%            15.000000               3.000000         3.000000   \n",
              "max            40.000000               6.000000         4.000000   \n",
              "\n",
              "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
              "count     1470.000000         1470.000000              1470.000000   \n",
              "mean         7.008163            4.229252                 2.187755   \n",
              "std          6.126525            3.623137                 3.222430   \n",
              "min          0.000000            0.000000                 0.000000   \n",
              "25%          3.000000            2.000000                 0.000000   \n",
              "50%          5.000000            3.000000                 1.000000   \n",
              "75%          9.000000            7.000000                 3.000000   \n",
              "max         40.000000           18.000000                15.000000   \n",
              "\n",
              "       YearsWithCurrManager  \n",
              "count           1470.000000  \n",
              "mean               4.123129  \n",
              "std                3.568136  \n",
              "min                0.000000  \n",
              "25%                2.000000  \n",
              "50%                3.000000  \n",
              "75%                7.000000  \n",
              "max               17.000000  "
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[numeric_cols].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aggressive-reconstruction",
      "metadata": {
        "id": "aggressive-reconstruction",
        "outputId": "3011e0f6-7049-4665-aceb-be74abb855eb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No     1233\n",
              "Yes     237\n",
              "Name: Attrition, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Attrition'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "brazilian-morrison",
      "metadata": {
        "id": "brazilian-morrison",
        "outputId": "64570db1-3d3e-4fd3-da15-e9873623ba8d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "r = df.groupby('Attrition')['Attrition'].count()\n",
        "plt.pie(r,explode=(0.05,0.1),labels=['No','Yes'],radius=1.5, autopct ='%1.1f%%',shadow=True )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "suspended-telescope",
      "metadata": {
        "id": "suspended-telescope",
        "outputId": "7f08edbf-aa0c-48cf-f086-27489983126f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Research & Development    961\n",
              "Sales                     446\n",
              "Human Resources            63\n",
              "Name: Department, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Department'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "played-palmer",
      "metadata": {
        "id": "played-palmer",
        "outputId": "f40ae5a7-8122-4c54-b225-6f93e0079c0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Life Sciences       606\n",
              "Medical             464\n",
              "Marketing           159\n",
              "Technical Degree    132\n",
              "Other                82\n",
              "Human Resources      27\n",
              "Name: EducationField, dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['EducationField'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "processed-india",
      "metadata": {
        "id": "processed-india",
        "outputId": "c1366976-c56e-4e64-974e-bb6855b6f16f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sales Executive              326\n",
              "Research Scientist           292\n",
              "Laboratory Technician        259\n",
              "Manufacturing Director       145\n",
              "Healthcare Representative    131\n",
              "Manager                      102\n",
              "Sales Representative          83\n",
              "Research Director             80\n",
              "Human Resources               52\n",
              "Name: JobRole, dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['JobRole'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "mexican-darkness",
      "metadata": {
        "id": "mexican-darkness",
        "outputId": "2d14a175-4f39-449c-f04f-d100ed547765"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Y    1470\n",
              "Name: Over18, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Over18'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eastern-median",
      "metadata": {
        "id": "eastern-median"
      },
      "outputs": [],
      "source": [
        "# Reassign target\n",
        "df['Attrition'] = df['Attrition'].map({\"No\":0,\"Yes\":1})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "motivated-columbia",
      "metadata": {
        "id": "motivated-columbia",
        "outputId": "8ec0dfa6-b04b-42cb-c54c-2d7ad721166b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    1233\n",
              "1     237\n",
              "Name: Attrition, dtype: int64"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Attrition'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "infrared-drama",
      "metadata": {
        "id": "infrared-drama"
      },
      "outputs": [],
      "source": [
        "# Drop useless feat\n",
        "df = df.drop(columns=['StandardHours','EmployeeCount','Over18'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "flush-option",
      "metadata": {
        "id": "flush-option"
      },
      "source": [
        "### Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "copyrighted-pound",
      "metadata": {
        "id": "copyrighted-pound",
        "outputId": "9b054965-3be9-4f84-96f8-7f5e1df7c65d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\gl3055109\\Anaconda3_1\\envs\\myenv\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Age', ylabel='Density'>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,axes = plt.subplots(1,2,figsize=(16,4))\n",
        "sns.boxplot(ax=axes[0],palette=\"Set3\",x=df['Age'])\n",
        "sns.distplot(ax=axes[1],color=\"Green\",a=df['Age'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fabulous-article",
      "metadata": {
        "id": "fabulous-article",
        "outputId": "89f45192-23f8-4a83-bc4d-46d75a872cb4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\gl3055109\\Anaconda3_1\\envs\\myenv\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='MonthlyIncome', ylabel='Density'>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,axes = plt.subplots(1,2,figsize=(16,4))\n",
        "sns.boxplot(ax=axes[0],palette=\"Set3\",x=df['MonthlyIncome'])\n",
        "sns.distplot(ax=axes[1],color=\"Green\",a=df['MonthlyIncome'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dynamic-provider",
      "metadata": {
        "id": "dynamic-provider",
        "outputId": "6782d7d1-7419-42fa-a25a-3c5faeb56ccd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "81"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['MonthlyIncome']>17500])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "studied-convention",
      "metadata": {
        "id": "studied-convention",
        "outputId": "06539528-5206-48a5-cb3b-2ae97bd86dd4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\gl3055109\\Anaconda3_1\\envs\\myenv\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='TotalWorkingYears', ylabel='Density'>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(16,4))\n",
        "sns.boxplot(ax=axes[0],palette=\"Set3\", x=df['TotalWorkingYears'])\n",
        "sns.distplot(ax=axes[1],color=\"Green\", a=df[\"TotalWorkingYears\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "twelve-arcade",
      "metadata": {
        "id": "twelve-arcade",
        "outputId": "f5784cb0-bf2e-4f6d-cc8c-dcfdc01efbb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['TotalWorkingYears']>28])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "powered-wedding",
      "metadata": {
        "id": "powered-wedding",
        "outputId": "a9abfeda-202c-4fd5-8f21-8d96b14d7960"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\gl3055109\\Anaconda3_1\\envs\\myenv\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='YearsAtCompany', ylabel='Density'>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(16,4))\n",
        "sns.boxplot(ax=axes[0],palette=\"Set3\", x=df['YearsAtCompany'])\n",
        "sns.distplot(ax=axes[1],color=\"Green\", a=df[\"YearsAtCompany\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adjustable-network",
      "metadata": {
        "id": "adjustable-network",
        "outputId": "da891842-f0f3-4982-9207-6c515298eb99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "104"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['YearsAtCompany']>18])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cooperative-peter",
      "metadata": {
        "id": "cooperative-peter",
        "outputId": "3021bbbb-6887-4f64-e922-873d1692fb1c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\gl3055109\\Anaconda3_1\\envs\\myenv\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='YearsSinceLastPromotion', ylabel='Density'>"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(16,4))\n",
        "sns.boxplot(ax=axes[0],palette=\"Set3\", x=df['YearsSinceLastPromotion'])\n",
        "sns.distplot(ax=axes[1],color=\"Green\", a=df[\"YearsSinceLastPromotion\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "little-sally",
      "metadata": {
        "id": "little-sally",
        "outputId": "f5c86a47-6088-45ca-885b-9a8faf09df55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "107"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['YearsSinceLastPromotion']>7])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "parallel-baptist",
      "metadata": {
        "id": "parallel-baptist",
        "outputId": "8af32d4f-86c0-482d-8ba1-5f954cef2685"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1470"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "directed-assembly",
      "metadata": {
        "id": "directed-assembly"
      },
      "outputs": [],
      "source": [
        "df = df[df['YearsSinceLastPromotion']<7]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "impressed-juvenile",
      "metadata": {
        "id": "impressed-juvenile",
        "outputId": "d25b0817-2164-45b8-8725-aed8e544f6ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1287"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "classical-florida",
      "metadata": {
        "id": "classical-florida",
        "outputId": "0c661c91-c595-48ec-de53-66c9fd740447"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(df.corr(),annot=True,cmap='YlGnBu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "apparent-shuttle",
      "metadata": {
        "id": "apparent-shuttle",
        "outputId": "8cc01f10-a39e-406e-af1b-561863cd0362"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='BusinessTravel', ylabel='count'>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(x='BusinessTravel',palette = 'Set3', hue='Attrition', data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "reasonable-thing",
      "metadata": {
        "id": "reasonable-thing",
        "outputId": "ee713622-0ecf-4084-f279-0a5be74c095b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Department', ylabel='count'>"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.countplot(x='Department',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "iraqi-girlfriend",
      "metadata": {
        "id": "iraqi-girlfriend",
        "outputId": "de8294d1-fc81-4b5e-8a33-e634dadf84ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='EducationField', ylabel='count'>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.countplot(x='EducationField',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "recognized-commodity",
      "metadata": {
        "id": "recognized-commodity",
        "outputId": "8b2da1b2-799a-4882-bc7d-cf2644ad831a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Gender', ylabel='count'>"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.countplot(x='Gender',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "spectacular-racing",
      "metadata": {
        "id": "spectacular-racing",
        "outputId": "da153c53-a728-4afd-922b-47eb76e5fc33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='JobRole', ylabel='count'>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,4))\n",
        "sns.countplot(x='JobRole',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "senior-dictionary",
      "metadata": {
        "id": "senior-dictionary",
        "outputId": "d5a1f4cf-1c52-40d2-cebb-10a3b44c4b57"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='MaritalStatus', ylabel='count'>"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.countplot(x='MaritalStatus',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "polar-words",
      "metadata": {
        "id": "polar-words",
        "outputId": "e5028aab-88aa-4ae6-bb4d-6f1eeb574f36"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='OverTime', ylabel='count'>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.countplot(x='OverTime',hue='Attrition',palette=\"Set3\",data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "confirmed-dominican",
      "metadata": {
        "id": "confirmed-dominican"
      },
      "source": [
        "### Preparing data for training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "prime-external",
      "metadata": {
        "id": "prime-external"
      },
      "source": [
        "初學Python手記#3-資料前處理( Label encoding、 One hot encoding)\n",
        "原始資料是有序離散值的話 => Label Encoding\n",
        "原始資料是無序離散值的話 => One Hot Encoding (Dummies)\n",
        "https://medium.com/@PatHuang/%E5%88%9D%E5%AD%B8python%E6%89%8B%E8%A8%98-3-%E8%B3%87%E6%96%99%E5%89%8D%E8%99%95%E7%90%86-label-encoding-one-hot-encoding-85c983d63f87"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "handled-criterion",
      "metadata": {
        "id": "handled-criterion",
        "outputId": "1763bdb8-c683-45df-a24a-e7034eb4b207"
      },
      "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>Gender</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>OverTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Single</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Other</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Single</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>Female</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Married</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Male</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Married</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      BusinessTravel              Department EducationField  Gender  \\\n",
              "0      Travel_Rarely                   Sales  Life Sciences  Female   \n",
              "1  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
              "2      Travel_Rarely  Research & Development          Other    Male   \n",
              "3  Travel_Frequently  Research & Development  Life Sciences  Female   \n",
              "4      Travel_Rarely  Research & Development        Medical    Male   \n",
              "\n",
              "                 JobRole MaritalStatus OverTime  \n",
              "0        Sales Executive        Single      Yes  \n",
              "1     Research Scientist       Married       No  \n",
              "2  Laboratory Technician        Single      Yes  \n",
              "3     Research Scientist       Married      Yes  \n",
              "4  Laboratory Technician       Married       No  "
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categorical_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
        "df[categorical_cols].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wanted-thumbnail",
      "metadata": {
        "id": "wanted-thumbnail"
      },
      "outputs": [],
      "source": [
        "df['Attrition'] = LabelEncoder().fit_transform(df['Attrition'])\n",
        "df['BusinessTravel'] = LabelEncoder().fit_transform(df['BusinessTravel'])\n",
        "df['Department'] = LabelEncoder().fit_transform(df['Department'])\n",
        "df['EducationField'] = LabelEncoder().fit_transform(df['EducationField'])\n",
        "df['Gender'] = LabelEncoder().fit_transform(df['Gender'])\n",
        "df['JobRole'] = LabelEncoder().fit_transform(df['JobRole'])\n",
        "df['MaritalStatus'] = LabelEncoder().fit_transform(df['MaritalStatus'])\n",
        "df['OverTime'] = LabelEncoder().fit_transform(df['OverTime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adverse-adult",
      "metadata": {
        "id": "adverse-adult",
        "outputId": "5cbc7ee2-7828-4c16-bf9f-b6aea2503519"
      },
      "outputs": [
        {
          "data": {
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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>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</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",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1373</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "      <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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1392</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>56</td>\n",
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              "      <td>6</td>\n",
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              "      <td>8</td>\n",
              "      <td>3</td>\n",
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              "      <td>8</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>591</td>\n",
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              "      <td>2</td>\n",
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              "      <td>3</td>\n",
              "      <td>7</td>\n",
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              "      <td>2</td>\n",
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              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Attrition  BusinessTravel  DailyRate  Department  DistanceFromHome  \\\n",
              "0   41          1               2       1102           2                 1   \n",
              "1   49          0               1        279           1                 8   \n",
              "2   37          1               2       1373           1                 2   \n",
              "3   33          0               1       1392           1                 3   \n",
              "4   27          0               2        591           1                 2   \n",
              "\n",
              "   Education  EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  \\\n",
              "0          2               1               1                        2       0   \n",
              "1          1               1               2                        3       1   \n",
              "2          2               4               4                        4       1   \n",
              "3          4               1               5                        4       0   \n",
              "4          1               3               7                        1       1   \n",
              "\n",
              "   HourlyRate  JobInvolvement  JobLevel  JobRole  JobSatisfaction  \\\n",
              "0          94               3         2        7                4   \n",
              "1          61               2         2        6                2   \n",
              "2          92               2         1        2                3   \n",
              "3          56               3         1        6                3   \n",
              "4          40               3         1        2                2   \n",
              "\n",
              "   MaritalStatus  MonthlyIncome  MonthlyRate  NumCompaniesWorked  OverTime  \\\n",
              "0              2           5993        19479                   8         1   \n",
              "1              1           5130        24907                   1         0   \n",
              "2              2           2090         2396                   6         1   \n",
              "3              1           2909        23159                   1         1   \n",
              "4              1           3468        16632                   9         0   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         1   \n",
              "1                 23                  4                         4   \n",
              "2                 15                  3                         2   \n",
              "3                 11                  3                         3   \n",
              "4                 12                  3                         4   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                 0                  8                      0   \n",
              "1                 1                 10                      3   \n",
              "2                 0                  7                      3   \n",
              "3                 0                  8                      3   \n",
              "4                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 147,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "residential-wonder",
      "metadata": {
        "id": "residential-wonder",
        "outputId": "51e6b04f-f8f3-4034-bc1e-30b6c60149e7"
      },
      "outputs": [
        {
          "data": {
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              "      <td>2</td>\n",
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              "      <td>2396</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Attrition  DailyRate  DistanceFromHome  Education  EmployeeNumber  \\\n",
              "0   41          1       1102                 1          2               1   \n",
              "1   49          0        279                 8          1               2   \n",
              "2   37          1       1373                 2          2               4   \n",
              "3   33          0       1392                 3          4               5   \n",
              "4   27          0        591                 2          1               7   \n",
              "\n",
              "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                        2          94               3         2   \n",
              "1                        3          61               2         2   \n",
              "2                        4          92               2         1   \n",
              "3                        4          56               3         1   \n",
              "4                        1          40               3         1   \n",
              "\n",
              "   JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0                4           5993        19479                   8   \n",
              "1                2           5130        24907                   1   \n",
              "2                3           2090         2396                   6   \n",
              "3                3           2909        23159                   1   \n",
              "4                2           3468        16632                   9   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         1   \n",
              "1                 23                  4                         4   \n",
              "2                 15                  3                         2   \n",
              "3                 11                  3                         3   \n",
              "4                 12                  3                         4   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                 0                  8                      0   \n",
              "1                 1                 10                      3   \n",
              "2                 0                  7                      3   \n",
              "3                 0                  8                      3   \n",
              "4                 1                  6                      3   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                1               6                   4   \n",
              "1                3              10                   7   \n",
              "2                3               0                   0   \n",
              "3                3               8                   7   \n",
              "4                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        0                     5  \n",
              "1                        1                     7  \n",
              "2                        0                     0  \n",
              "3                        3                     0  \n",
              "4                        2                     2  "
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "numeric_cols = df.select_dtypes(include=['int64','float64']).columns.tolist()\n",
        "df[numeric_cols].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "specified-stevens",
      "metadata": {
        "id": "specified-stevens",
        "outputId": "938a2f5b-c5c4-4340-ad6f-0aa4b40b0904"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>DailyRate</th>\n",
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              "      <th>MonthlyRate</th>\n",
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              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</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",
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              "    <tr>\n",
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              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>279</td>\n",
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              "      <td>61</td>\n",
              "      <td>2</td>\n",
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              "      <td>23</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>1</td>\n",
              "      <td>1373</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
              "      <td>6</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>1392</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2909</td>\n",
              "      <td>23159</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
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              "      <td>40</td>\n",
              "      <td>3</td>\n",
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              "      <td>16632</td>\n",
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              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>884</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>2061</td>\n",
              "      <td>3</td>\n",
              "      <td>41</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2571</td>\n",
              "      <td>12290</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>613</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2062</td>\n",
              "      <td>4</td>\n",
              "      <td>42</td>\n",
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              "      <td>7</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
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              "      <td>27</td>\n",
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              "      <td>155</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2064</td>\n",
              "      <td>2</td>\n",
              "      <td>87</td>\n",
              "      <td>4</td>\n",
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              "      <td>2</td>\n",
              "      <td>6142</td>\n",
              "      <td>5174</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1468</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>1023</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2065</td>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5390</td>\n",
              "      <td>13243</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1469</th>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>628</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2068</td>\n",
              "      <td>2</td>\n",
              "      <td>82</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4404</td>\n",
              "      <td>10228</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1287 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Age  Attrition  DailyRate  DistanceFromHome  Education  EmployeeNumber  \\\n",
              "0      41          1       1102                 1          2               1   \n",
              "1      49          0        279                 8          1               2   \n",
              "2      37          1       1373                 2          2               4   \n",
              "3      33          0       1392                 3          4               5   \n",
              "4      27          0        591                 2          1               7   \n",
              "...   ...        ...        ...               ...        ...             ...   \n",
              "1465   36          0        884                23          2            2061   \n",
              "1466   39          0        613                 6          1            2062   \n",
              "1467   27          0        155                 4          3            2064   \n",
              "1468   49          0       1023                 2          3            2065   \n",
              "1469   34          0        628                 8          3            2068   \n",
              "\n",
              "      EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \\\n",
              "0                           2          94               3         2   \n",
              "1                           3          61               2         2   \n",
              "2                           4          92               2         1   \n",
              "3                           4          56               3         1   \n",
              "4                           1          40               3         1   \n",
              "...                       ...         ...             ...       ...   \n",
              "1465                        3          41               4         2   \n",
              "1466                        4          42               2         3   \n",
              "1467                        2          87               4         2   \n",
              "1468                        4          63               2         2   \n",
              "1469                        2          82               4         2   \n",
              "\n",
              "      JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0                   4           5993        19479                   8   \n",
              "1                   2           5130        24907                   1   \n",
              "2                   3           2090         2396                   6   \n",
              "3                   3           2909        23159                   1   \n",
              "4                   2           3468        16632                   9   \n",
              "...               ...            ...          ...                 ...   \n",
              "1465                4           2571        12290                   4   \n",
              "1466                1           9991        21457                   4   \n",
              "1467                2           6142         5174                   1   \n",
              "1468                2           5390        13243                   2   \n",
              "1469                3           4404        10228                   2   \n",
              "\n",
              "      PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                    11                  3                         1   \n",
              "1                    23                  4                         4   \n",
              "2                    15                  3                         2   \n",
              "3                    11                  3                         3   \n",
              "4                    12                  3                         4   \n",
              "...                 ...                ...                       ...   \n",
              "1465                 17                  3                         3   \n",
              "1466                 15                  3                         1   \n",
              "1467                 20                  4                         2   \n",
              "1468                 14                  3                         4   \n",
              "1469                 12                  3                         1   \n",
              "\n",
              "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                    0                  8                      0   \n",
              "1                    1                 10                      3   \n",
              "2                    0                  7                      3   \n",
              "3                    0                  8                      3   \n",
              "4                    1                  6                      3   \n",
              "...                ...                ...                    ...   \n",
              "1465                 1                 17                      3   \n",
              "1466                 1                  9                      5   \n",
              "1467                 1                  6                      0   \n",
              "1468                 0                 17                      3   \n",
              "1469                 0                  6                      3   \n",
              "\n",
              "      WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                   1               6                   4   \n",
              "1                   3              10                   7   \n",
              "2                   3               0                   0   \n",
              "3                   3               8                   7   \n",
              "4                   3               2                   2   \n",
              "...               ...             ...                 ...   \n",
              "1465                3               5                   2   \n",
              "1466                3               7                   7   \n",
              "1467                3               6                   2   \n",
              "1468                2               9                   6   \n",
              "1469                4               4                   3   \n",
              "\n",
              "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                           0                     5  \n",
              "1                           1                     7  \n",
              "2                           0                     0  \n",
              "3                           3                     0  \n",
              "4                           2                     2  \n",
              "...                       ...                   ...  \n",
              "1465                        0                     3  \n",
              "1466                        1                     7  \n",
              "1467                        0                     3  \n",
              "1468                        0                     8  \n",
              "1469                        1                     2  \n",
              "\n",
              "[1287 rows x 25 columns]"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[numeric_cols] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "circular-victorian",
      "metadata": {
        "id": "circular-victorian"
      },
      "outputs": [],
      "source": [
        "df[numeric_cols]  = MinMaxScaler().fit_transform(df[numeric_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "pressed-workplace",
      "metadata": {
        "id": "pressed-workplace",
        "outputId": "721af519-99b4-4135-f387-37f4cab2556a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>Attrition</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>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>0.547619</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.716332</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.914286</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.262814</td>\n",
              "      <td>0.698053</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.210526</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.162162</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.294118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.738095</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.126791</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000484</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.442857</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.217306</td>\n",
              "      <td>0.916001</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.263158</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.270270</td>\n",
              "      <td>0.411765</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.411765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.452381</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.910458</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.001451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.885714</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.057003</td>\n",
              "      <td>0.012126</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.184211</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</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>3</th>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.924069</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.001935</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.100190</td>\n",
              "      <td>0.845814</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.210526</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.216216</td>\n",
              "      <td>0.411765</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.350287</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.002903</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.129667</td>\n",
              "      <td>0.583738</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.157895</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.054054</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.117647</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age  Attrition  DailyRate  DistanceFromHome  Education  \\\n",
              "0  0.547619        1.0   0.716332          0.000000       0.25   \n",
              "1  0.738095        0.0   0.126791          0.250000       0.00   \n",
              "2  0.452381        1.0   0.910458          0.035714       0.25   \n",
              "3  0.357143        0.0   0.924069          0.071429       0.75   \n",
              "4  0.214286        0.0   0.350287          0.035714       0.00   \n",
              "\n",
              "   EmployeeNumber  EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
              "0        0.000000                 0.333333    0.914286        0.666667   \n",
              "1        0.000484                 0.666667    0.442857        0.333333   \n",
              "2        0.001451                 1.000000    0.885714        0.333333   \n",
              "3        0.001935                 1.000000    0.371429        0.666667   \n",
              "4        0.002903                 0.000000    0.142857        0.666667   \n",
              "\n",
              "   JobLevel  JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
              "0      0.25         1.000000       0.262814     0.698053            0.888889   \n",
              "1      0.25         0.333333       0.217306     0.916001            0.111111   \n",
              "2      0.00         0.666667       0.057003     0.012126            0.666667   \n",
              "3      0.00         0.666667       0.100190     0.845814            0.111111   \n",
              "4      0.00         0.333333       0.129667     0.583738            1.000000   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0           0.000000                0.0                  0.000000   \n",
              "1           0.857143                1.0                  1.000000   \n",
              "2           0.285714                0.0                  0.333333   \n",
              "3           0.000000                0.0                  0.666667   \n",
              "4           0.071429                0.0                  1.000000   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0          0.000000           0.210526                    0.0   \n",
              "1          0.333333           0.263158                    0.5   \n",
              "2          0.000000           0.184211                    0.5   \n",
              "3          0.000000           0.210526                    0.5   \n",
              "4          0.333333           0.157895                    0.5   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0         0.000000        0.162162            0.235294   \n",
              "1         0.666667        0.270270            0.411765   \n",
              "2         0.666667        0.000000            0.000000   \n",
              "3         0.666667        0.216216            0.411765   \n",
              "4         0.666667        0.054054            0.117647   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                 0.000000              0.294118  \n",
              "1                 0.166667              0.411765  \n",
              "2                 0.000000              0.000000  \n",
              "3                 0.500000              0.000000  \n",
              "4                 0.333333              0.117647  "
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[numeric_cols].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dangerous-blowing",
      "metadata": {
        "id": "dangerous-blowing"
      },
      "source": [
        "Automatic Feature Selection in Python\n",
        "https://hackernoon.com/automatic-feature-selection-in-python-an-essential-guide-uv3e37mk"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "injured-campus",
      "metadata": {
        "id": "injured-campus"
      },
      "source": [
        "資料前處理-資料不平衡處理方法\n",
        "做SMOTE處理\n",
        "https://ithelp.ithome.com.tw/articles/10261413?sc=rss.iron"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "raising-woman",
      "metadata": {
        "id": "raising-woman"
      },
      "outputs": [],
      "source": [
        "cols = list(df.columns)\n",
        "cols.remove('Attrition')\n",
        "sampled,target= SMOTE().fit_resample(df[cols],df['Attrition'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "absent-bowling",
      "metadata": {
        "id": "absent-bowling",
        "outputId": "f9c7fd86-bfb1-4026-c1dc-14d4ba9bfd8a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "df['Attrition'].value_counts().plot(kind='pie',colors=['lightcoral','skyblue'],autopct='%1.2f%%')\n",
        "plt.title('Attrition')\n",
        "plt.ylabel('')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "excess-split",
      "metadata": {
        "id": "excess-split",
        "outputId": "bd37a865-2b0a-4a01-d2da-f6d221478c55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "target.value_counts().plot(kind='pie',colors=['lightcoral','skyblue'],autopct='%1.2f%%')\n",
        "plt.title('Attrition')\n",
        "plt.ylabel('')\n",
        "plt.show()\n",
        "                                                          "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "realistic-health",
      "metadata": {
        "id": "realistic-health"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,Y_train,Y_test = train_test_split(sampled[cols],target,\n",
        "                                                test_size=0.3,\n",
        "                                                shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "unusual-squad",
      "metadata": {
        "id": "unusual-squad",
        "outputId": "5a8649f8-08d1-433f-868d-27d629d4b2b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Accuracy : 0.77 %\n",
            "Test Accuracy : 0.77 %\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "logistic_model = LogisticRegression(solver='liblinear',random_state=0).fit(X_train,Y_train)\n",
        "print(\"Train Accuracy : {:.2f} %\".format(accuracy_score(logistic_model.predict(X_train),Y_train)))\n",
        "print(\"Test Accuracy : {:.2f} %\".format(accuracy_score(logistic_model.predict(X_test),Y_test)))\n",
        "\n",
        "cm = confusion_matrix(Y_test,logistic_model.predict(X_test))\n",
        "classes = [\"0\",\"1\"]\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels = classes)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "plt.title(\"Confusion Matrix\")\n",
        "disp.plot(ax=ax,cmap='GnBu')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "noted-today",
      "metadata": {
        "id": "noted-today"
      },
      "source": [
        "How to choose n_estimators in RandomForestClassifier?\n",
        "https://stackoverflow.com/questions/60768008/how-to-choose-n-estimators-in-randomforestclassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "professional-machine",
      "metadata": {
        "id": "professional-machine",
        "outputId": "66af3a46-26b2-46d3-d2d4-c5848ff268f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Accuracy : 1.00 %\n",
            "Test Accuracy : 0.92 %\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "random_forest = RandomForestClassifier(n_estimators=666,\n",
        "                                      random_state=0).fit(X_train,Y_train)\n",
        "print(\"Train Accuracy : {:.2f} %\".format(accuracy_score(random_forest.predict(X_train),Y_train)))\n",
        "print(\"Test Accuracy : {:.2f} %\".format(accuracy_score(random_forest.predict(X_test),Y_test)))\n",
        "\n",
        "cm = confusion_matrix(Y_test,random_forest.predict(X_test))\n",
        "classes = [\"0\",\"1\"]\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels = classes)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "plt.title(\"Confusion Matrix\")\n",
        "disp.plot(ax=ax,cmap='GnBu')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "moved-powder",
      "metadata": {
        "id": "moved-powder",
        "outputId": "326b54a0-155e-4629-e73e-75292039d7be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[16:15:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "Train Accuracy : 1.00 %\n",
            "Test Accuracy : 0.92 %\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "model = XGBClassifier(learning_rate=0.01,n_estimators=500,use_label_encoder=False,\n",
        "                                      random_state=0).fit(X_train,Y_train)\n",
        "print(\"Train Accuracy : {:.2f} %\".format(accuracy_score(random_forest.predict(X_train),Y_train)))\n",
        "print(\"Test Accuracy : {:.2f} %\".format(accuracy_score(random_forest.predict(X_test),Y_test)))\n",
        "\n",
        "cm = confusion_matrix(Y_test,model.predict(X_test))\n",
        "classes = [\"0\",\"1\"]\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels = classes)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "plt.title(\"Confusion Matrix\")\n",
        "disp.plot(ax=ax,cmap='GnBu')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "considered-shooting",
      "metadata": {
        "id": "considered-shooting"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.5"
    },
    "colab": {
      "name": "IBN-HR-analysis-Kaggle.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}