Notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Project Data Science : HR Attrition Rate Analytics"
      ],
      "metadata": {
        "id": "PCAPKlaXZzPw"
      },
      "id": "PCAPKlaXZzPw"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Business Understanding"
      ],
      "metadata": {
        "id": "gAwsLuSsnXou"
      },
      "id": "gAwsLuSsnXou"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building machine learning model to help Human Resource (HR) Department predict Attrition Rate"
      ],
      "metadata": {
        "id": "o5QZRhJZnZLK"
      },
      "id": "o5QZRhJZnZLK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import necessary libraries"
      ],
      "metadata": {
        "id": "beMgXnimaZJQ"
      },
      "id": "beMgXnimaZJQ"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install psycopg2-binary scikit-learn==1.2.2 joblib==1.3.1\n",
        "\n",
        "import scipy\n",
        "import joblib\n",
        "import psycopg2\n",
        "import sqlalchemy\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "2CO0kj2mabZ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4e4c9b0-af54-4603-f4f6-b8a28494034c"
      },
      "id": "2CO0kj2mabZ3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: psycopg2-binary in /usr/local/lib/python3.10/dist-packages (2.9.9)\n",
            "Requirement already satisfied: scikit-learn==1.2.2 in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: joblib==1.3.1 in /usr/local/lib/python3.10/dist-packages (1.3.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (1.11.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (3.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Understanding"
      ],
      "metadata": {
        "id": "GFyc642jqO6r"
      },
      "id": "GFyc642jqO6r"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset used in this machine learning project is [IBM HR Analytics Employee Attrition & Performance](https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset/). The dataset consists of 1470 records data with 35 features."
      ],
      "metadata": {
        "id": "yEvams_9qV_E"
      },
      "id": "yEvams_9qV_E"
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Field                   | Description                                   |\n",
        "|-------------------------|-----------------------------------------------|\n",
        "| EmployeeId              | Employee Identifier                           |\n",
        "| Attrition               | Did the employee attrition? (0=no, 1=yes)    |\n",
        "| Age                     | Age of the employee                           |\n",
        "| BusinessTravel          | Travel commitments for the job                |\n",
        "| DailyRate               | Daily salary                                  |\n",
        "| Department              | Employee Department                          |\n",
        "| DistanceFromHome        | Distance from work to home (in km)           |\n",
        "| Education               | 1-Below College, 2-College, 3-Bachelor, 4-Master, 5-Doctor |\n",
        "| EducationField          | Field of Education                           |\n",
        "| EnvironmentSatisfaction | 1-Low, 2-Medium, 3-High, 4-Very High        |\n",
        "| Gender                  | Employee's gender                            |\n",
        "| HourlyRate              | Hourly salary                                |\n",
        "| JobInvolvement          | 1-Low, 2-Medium, 3-High, 4-Very High        |\n",
        "| JobLevel                | Level of job (1 to 5)                        |\n",
        "| JobRole                 | Job Roles                                    |\n",
        "| JobSatisfaction         | 1-Low, 2-Medium, 3-High, 4-Very High        |\n",
        "| MaritalStatus           | Marital Status                               |\n",
        "| MonthlyIncome           | Monthly salary                               |\n",
        "| MonthlyRate             | Monthly rate                                 |\n",
        "| NumCompaniesWorked      | Number of companies worked at                |\n",
        "| Over18                  | Over 18 years of age?                       |\n",
        "| OverTime                | Overtime?                                    |\n",
        "| PercentSalaryHike       | The percentage increase in salary last year  |\n",
        "| PerformanceRating       | 1-Low, 2-Good, 3-Excellent, 4-Outstanding    |\n",
        "| RelationshipSatisfaction| 1-Low, 2-Medium, 3-High, 4-Very High        |\n",
        "| StandardHours           | Standard Hours                               |\n",
        "| StockOptionLevel        | Stock Option Level                          |\n",
        "| TotalWorkingYears       | Total years worked                           |\n",
        "| TrainingTimesLastYear   | Number of training attended last year        |\n",
        "| WorkLifeBalance         | 1-Low, 2-Good, 3-Excellent, 4-Outstanding    |\n",
        "| YearsAtCompany          | Years at Company                             |\n",
        "| YearsInCurrentRole      | Years in the current role                    |\n",
        "| YearsSinceLastPromotion | Years since the last promotion               |\n",
        "| YearsWithCurrManager    | Years with the current manager               |\n"
      ],
      "metadata": {
        "id": "cySoGZAuqQRc"
      },
      "id": "cySoGZAuqQRc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation"
      ],
      "metadata": {
        "id": "ifOPDH2ee77S"
      },
      "id": "ifOPDH2ee77S"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data preparation follows a series of steps that starts with collecting the right data, followed by cleaning, labeling, and then validation and visualization."
      ],
      "metadata": {
        "id": "2J1LE6y7abed"
      },
      "id": "2J1LE6y7abed"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gathering Data"
      ],
      "metadata": {
        "id": "0ZYbT8SsfBL2"
      },
      "id": "0ZYbT8SsfBL2"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"employee_data.csv\", encoding='windows-1252')"
      ],
      "metadata": {
        "id": "G3kRqGdAadS5"
      },
      "id": "G3kRqGdAadS5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Assessing Data"
      ],
      "metadata": {
        "id": "AKUE2S4yafAA"
      },
      "id": "AKUE2S4yafAA"
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "1XPGhXQwckHq",
        "outputId": "35f468c7-58d5-4c1c-d767-b8fea71c94b4"
      },
      "id": "1XPGhXQwckHq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   EmployeeId  Age  Attrition     BusinessTravel  DailyRate  \\\n",
              "0           1   38        NaN  Travel_Frequently       1444   \n",
              "1           2   37        1.0      Travel_Rarely       1141   \n",
              "2           3   51        1.0      Travel_Rarely       1323   \n",
              "3           4   42        0.0  Travel_Frequently        555   \n",
              "4           5   40        NaN      Travel_Rarely       1194   \n",
              "\n",
              "               Department  DistanceFromHome  Education EducationField  \\\n",
              "0         Human Resources                 1          4          Other   \n",
              "1  Research & Development                11          2        Medical   \n",
              "2  Research & Development                 4          4  Life Sciences   \n",
              "3                   Sales                26          3      Marketing   \n",
              "4  Research & Development                 2          4        Medical   \n",
              "\n",
              "   EmployeeCount  EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  \\\n",
              "0              1                        4    Male          88               3   \n",
              "1              1                        1  Female          61               1   \n",
              "2              1                        1    Male          34               3   \n",
              "3              1                        3  Female          77               3   \n",
              "4              1                        3  Female          98               3   \n",
              "\n",
              "   JobLevel                    JobRole  JobSatisfaction MaritalStatus  \\\n",
              "0         1            Human Resources                2       Married   \n",
              "1         2  Healthcare Representative                2       Married   \n",
              "2         1         Research Scientist                3       Married   \n",
              "3         4            Sales Executive                2       Married   \n",
              "4         1         Research Scientist                3       Married   \n",
              "\n",
              "   MonthlyIncome  MonthlyRate  NumCompaniesWorked Over18 OverTime  \\\n",
              "0           2991         5224                   0      Y      Yes   \n",
              "1           4777        14382                   5      Y       No   \n",
              "2           2461        10332                   9      Y      Yes   \n",
              "3          13525        14864                   5      Y       No   \n",
              "4           2001        12549                   2      Y       No   \n",
              "\n",
              "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                 11                  3                         2   \n",
              "1                 15                  3                         1   \n",
              "2                 12                  3                         3   \n",
              "3                 14                  3                         4   \n",
              "4                 14                  3                         2   \n",
              "\n",
              "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0             80                 1                  7                      2   \n",
              "1             80                 0                 15                      2   \n",
              "2             80                 3                 18                      2   \n",
              "3             80                 1                 23                      2   \n",
              "4             80                 3                 20                      2   \n",
              "\n",
              "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                3               6                   2   \n",
              "1                1               1                   0   \n",
              "2                4              10                   0   \n",
              "3                4              20                   4   \n",
              "4                3               5                   3   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                        1                     2  \n",
              "1                        0                     0  \n",
              "2                        2                     7  \n",
              "3                        4                     8  \n",
              "4                        0                     2  "
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3644dc3a-0636-4d84-ab0d-50e60081dd88 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3644dc3a-0636-4d84-ab0d-50e60081dd88');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ab8f1953-3f23-4ec2-940c-5378afd337ae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab8f1953-3f23-4ec2-940c-5378afd337ae')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab8f1953-3f23-4ec2-940c-5378afd337ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "DC8arL8echnr",
        "outputId": "eab35ec8-69a0-4f7f-d2c0-ee03ca9a1507"
      },
      "id": "DC8arL8echnr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        EmployeeId          Age    Attrition    DailyRate  DistanceFromHome  \\\n",
              "count  1470.000000  1470.000000  1058.000000  1470.000000       1470.000000   \n",
              "mean    735.500000    36.923810     0.169187   802.485714          9.192517   \n",
              "std     424.496761     9.135373     0.375094   403.509100          8.106864   \n",
              "min       1.000000    18.000000     0.000000   102.000000          1.000000   \n",
              "25%     368.250000    30.000000     0.000000   465.000000          2.000000   \n",
              "50%     735.500000    36.000000     0.000000   802.000000          7.000000   \n",
              "75%    1102.750000    43.000000     0.000000  1157.000000         14.000000   \n",
              "max    1470.000000    60.000000     1.000000  1499.000000         29.000000   \n",
              "\n",
              "         Education  EmployeeCount  EnvironmentSatisfaction   HourlyRate  \\\n",
              "count  1470.000000         1470.0              1470.000000  1470.000000   \n",
              "mean      2.912925            1.0                 2.721769    65.891156   \n",
              "std       1.024165            0.0                 1.093082    20.329428   \n",
              "min       1.000000            1.0                 1.000000    30.000000   \n",
              "25%       2.000000            1.0                 2.000000    48.000000   \n",
              "50%       3.000000            1.0                 3.000000    66.000000   \n",
              "75%       4.000000            1.0                 4.000000    83.750000   \n",
              "max       5.000000            1.0                 4.000000   100.000000   \n",
              "\n",
              "       JobInvolvement     JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
              "count     1470.000000  1470.000000      1470.000000    1470.000000   \n",
              "mean         2.729932     2.063946         2.728571    6502.931293   \n",
              "std          0.711561     1.106940         1.102846    4707.956783   \n",
              "min          1.000000     1.000000         1.000000    1009.000000   \n",
              "25%          2.000000     1.000000         2.000000    2911.000000   \n",
              "50%          3.000000     2.000000         3.000000    4919.000000   \n",
              "75%          3.000000     3.000000         4.000000    8379.000000   \n",
              "max          4.000000     5.000000         4.000000   19999.000000   \n",
              "\n",
              "        MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
              "count   1470.000000         1470.000000        1470.000000        1470.000000   \n",
              "mean   14313.103401            2.693197          15.209524           3.153741   \n",
              "std     7117.786044            2.498009           3.659938           0.360824   \n",
              "min     2094.000000            0.000000          11.000000           3.000000   \n",
              "25%     8047.000000            1.000000          12.000000           3.000000   \n",
              "50%    14235.500000            2.000000          14.000000           3.000000   \n",
              "75%    20461.500000            4.000000          18.000000           3.000000   \n",
              "max    26999.000000            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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99f702f0-48ca-4907-b70e-87246900efd2\" class=\"colab-df-container\">\n",
              "    <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>EmployeeId</th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</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>1058.000000</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>1470.0</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>735.500000</td>\n",
              "      <td>36.923810</td>\n",
              "      <td>0.169187</td>\n",
              "      <td>802.485714</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.0</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>424.496761</td>\n",
              "      <td>9.135373</td>\n",
              "      <td>0.375094</td>\n",
              "      <td>403.509100</td>\n",
              "      <td>8.106864</td>\n",
              "      <td>1.024165</td>\n",
              "      <td>0.0</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>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</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>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>368.250000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>465.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</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>735.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>802.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.0</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>1102.750000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1157.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</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>1470.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1499.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</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>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-99f702f0-48ca-4907-b70e-87246900efd2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-99f702f0-48ca-4907-b70e-87246900efd2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f5a7aa9c-de2f-422a-a917-e8a3071ca9d5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5a7aa9c-de2f-422a-a917-e8a3071ca9d5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5a7aa9c-de2f-422a-a917-e8a3071ca9d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFUjyKmdc03Q",
        "outputId": "d6ce27cd-208c-4d34-f096-3893fba6f915"
      },
      "id": "YFUjyKmdc03Q",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93AOTwUffS-h",
        "outputId": "09a941e7-0a37-4a9b-8864-75cb6daff0ce"
      },
      "id": "93AOTwUffS-h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   EmployeeId                1470 non-null   int64  \n",
            " 1   Age                       1470 non-null   int64  \n",
            " 2   Attrition                 1058 non-null   float64\n",
            " 3   BusinessTravel            1470 non-null   object \n",
            " 4   DailyRate                 1470 non-null   int64  \n",
            " 5   Department                1470 non-null   object \n",
            " 6   DistanceFromHome          1470 non-null   int64  \n",
            " 7   Education                 1470 non-null   int64  \n",
            " 8   EducationField            1470 non-null   object \n",
            " 9   EmployeeCount             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: float64(1), int64(26), object(8)\n",
            "memory usage: 402.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBH3YOpVar4h",
        "outputId": "8945f895-6858-4078-b084-be40f3a04fd2"
      },
      "id": "OBH3YOpVar4h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmployeeId                    0\n",
              "Age                           0\n",
              "Attrition                   412\n",
              "BusinessTravel                0\n",
              "DailyRate                     0\n",
              "Department                    0\n",
              "DistanceFromHome              0\n",
              "Education                     0\n",
              "EducationField                0\n",
              "EmployeeCount                 0\n",
              "EnvironmentSatisfaction       0\n",
              "Gender                        0\n",
              "HourlyRate                    0\n",
              "JobInvolvement                0\n",
              "JobLevel                      0\n",
              "JobRole                       0\n",
              "JobSatisfaction               0\n",
              "MaritalStatus                 0\n",
              "MonthlyIncome                 0\n",
              "MonthlyRate                   0\n",
              "NumCompaniesWorked            0\n",
              "Over18                        0\n",
              "OverTime                      0\n",
              "PercentSalaryHike             0\n",
              "PerformanceRating             0\n",
              "RelationshipSatisfaction      0\n",
              "StandardHours                 0\n",
              "StockOptionLevel              0\n",
              "TotalWorkingYears             0\n",
              "TrainingTimesLastYear         0\n",
              "WorkLifeBalance               0\n",
              "YearsAtCompany                0\n",
              "YearsInCurrentRole            0\n",
              "YearsSinceLastPromotion       0\n",
              "YearsWithCurrManager          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- There is a **missing value** in the Attrition feature (column), so handling is required at the data cleansing stage.\n",
        "- Features (column) EmployeeCount, Over18, and StandardHours only have one type of value. So this feature can be ignored (**dropout**) because it does not provide additional information (insight)."
      ],
      "metadata": {
        "id": "ihJrnyZ9hLSL"
      },
      "id": "ihJrnyZ9hLSL"
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['EmployeeId','EmployeeCount', 'Over18', 'StandardHours'], axis=1)"
      ],
      "metadata": {
        "id": "saA521SJrjQw"
      },
      "id": "saA521SJrjQw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Cleansing"
      ],
      "metadata": {
        "id": "CzC0SDIdhhAi"
      },
      "id": "CzC0SDIdhhAi"
    },
    {
      "cell_type": "code",
      "source": [
        "# handling missing value\n",
        "missing_attrition = df[df['Attrition'].isnull()]\n",
        "missing_attrition.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "KIOhv1NYhiKp",
        "outputId": "6fc84e3d-6226-4816-b28c-4b2b6787fbe5"
      },
      "id": "KIOhv1NYhiKp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Age  Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0    38        NaN  Travel_Frequently       1444         Human Resources   \n",
              "4    40        NaN      Travel_Rarely       1194  Research & Development   \n",
              "5    29        NaN      Travel_Rarely        352         Human Resources   \n",
              "12   47        NaN      Travel_Rarely        571                   Sales   \n",
              "18   25        NaN  Travel_Frequently        772  Research & Development   \n",
              "\n",
              "    DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
              "0                  1          4          Other                        4   \n",
              "4                  2          4        Medical                        3   \n",
              "5                  6          1        Medical                        4   \n",
              "12                14          3        Medical                        3   \n",
              "18                 2          1  Life Sciences                        4   \n",
              "\n",
              "    Gender  HourlyRate  JobInvolvement  JobLevel                 JobRole  \\\n",
              "0     Male          88               3         1         Human Resources   \n",
              "4   Female          98               3         1      Research Scientist   \n",
              "5     Male          87               2         1         Human Resources   \n",
              "12  Female          78               3         2         Sales Executive   \n",
              "18    Male          77               4         2  Manufacturing Director   \n",
              "\n",
              "    JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
              "0                 2       Married           2991         5224   \n",
              "4                 3       Married           2001        12549   \n",
              "5                 2       Married           2804        15434   \n",
              "12                3       Married           4591        24200   \n",
              "18                3      Divorced           5206         4973   \n",
              "\n",
              "    NumCompaniesWorked OverTime  PercentSalaryHike  PerformanceRating  \\\n",
              "0                    0      Yes                 11                  3   \n",
              "4                    2       No                 14                  3   \n",
              "5                    1       No                 11                  3   \n",
              "12                   3      Yes                 17                  3   \n",
              "18                   1       No                 17                  3   \n",
              "\n",
              "    RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \\\n",
              "0                          2                 1                  7   \n",
              "4                          2                 3                 20   \n",
              "5                          4                 0                  1   \n",
              "12                         3                 1                 11   \n",
              "18                         3                 2                  7   \n",
              "\n",
              "    TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
              "0                       2                3               6   \n",
              "4                       2                3               5   \n",
              "5                       3                3               1   \n",
              "12                      4                2               5   \n",
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              "\n",
              "    YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc76686d-b5a4-467c-87cf-767f9ade82b9 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_attrition"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this problem, there are 412 missing values, this data is not too large when compared to the total data (1471).\n",
        "\n",
        "There are several ways to deal with missing values, namely by dropping, imputation and interpolation methods. In this problem, the **dropping** data method will be used, because losing around 39% of the data is still acceptable in this case."
      ],
      "metadata": {
        "id": "J2VKjqOHj5hb"
      },
      "id": "J2VKjqOHj5hb"
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna(subset=['Attrition'])"
      ],
      "metadata": {
        "id": "lUmnYCfU08cj"
      },
      "id": "lUmnYCfU08cj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOMxL6E9lyPh",
        "outputId": "3ee94017-a74f-4aa8-b29c-5c8ffb756905"
      },
      "id": "IOMxL6E9lyPh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1058, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling duplicate data\n",
        "print(\"Jumlah duplikasi: \", df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cHDCmUAmngY",
        "outputId": "9b01462b-4e6f-4f04-e5d5-3f2890990890"
      },
      "id": "8cHDCmUAmngY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory data analysis (EDA)"
      ],
      "metadata": {
        "id": "7RXeru4rZzbv"
      },
      "id": "7RXeru4rZzbv"
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcHYlMhoRREa",
        "outputId": "d18ac5e7-be48-4761-e2a6-d6ac33938590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1058 entries, 1 to 1469\n",
            "Data columns (total 31 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Age                       1058 non-null   int64  \n",
            " 1   Attrition                 1058 non-null   float64\n",
            " 2   BusinessTravel            1058 non-null   object \n",
            " 3   DailyRate                 1058 non-null   int64  \n",
            " 4   Department                1058 non-null   object \n",
            " 5   DistanceFromHome          1058 non-null   int64  \n",
            " 6   Education                 1058 non-null   int64  \n",
            " 7   EducationField            1058 non-null   object \n",
            " 8   EnvironmentSatisfaction   1058 non-null   int64  \n",
            " 9   Gender                    1058 non-null   object \n",
            " 10  HourlyRate                1058 non-null   int64  \n",
            " 11  JobInvolvement            1058 non-null   int64  \n",
            " 12  JobLevel                  1058 non-null   int64  \n",
            " 13  JobRole                   1058 non-null   object \n",
            " 14  JobSatisfaction           1058 non-null   int64  \n",
            " 15  MaritalStatus             1058 non-null   object \n",
            " 16  MonthlyIncome             1058 non-null   int64  \n",
            " 17  MonthlyRate               1058 non-null   int64  \n",
            " 18  NumCompaniesWorked        1058 non-null   int64  \n",
            " 19  OverTime                  1058 non-null   object \n",
            " 20  PercentSalaryHike         1058 non-null   int64  \n",
            " 21  PerformanceRating         1058 non-null   int64  \n",
            " 22  RelationshipSatisfaction  1058 non-null   int64  \n",
            " 23  StockOptionLevel          1058 non-null   int64  \n",
            " 24  TotalWorkingYears         1058 non-null   int64  \n",
            " 25  TrainingTimesLastYear     1058 non-null   int64  \n",
            " 26  WorkLifeBalance           1058 non-null   int64  \n",
            " 27  YearsAtCompany            1058 non-null   int64  \n",
            " 28  YearsInCurrentRole        1058 non-null   int64  \n",
            " 29  YearsSinceLastPromotion   1058 non-null   int64  \n",
            " 30  YearsWithCurrManager      1058 non-null   int64  \n",
            "dtypes: float64(1), int64(23), object(7)\n",
            "memory usage: 264.5+ KB\n"
          ]
        }
      ],
      "id": "NcHYlMhoRREa"
    },
    {
      "cell_type": "markdown",
      "source": [
        "- There are 7 columns with object types, namely: BusinessTravel, Department, EducationField, Gender, JobRole, MaritalStatus, and Overtime. This column is a categorical feature (non-numerical feature).\n",
        "\n",
        "- There are 23 numeric columns with int64 data type, namely: Age, DailyRate, DistanceFromHome, Education, EnvironmentSatisfaction, HourlyRate, JobInvolvement, JobLevel, JobSatisfaction, MonthlyIncome, MonthlyRate, NumCompaniesWorked, PercentSalaryHike, PerformanceRating, RelationshipSatisfaction, StockOptionLevel, TotalWorkingYears, TrainingTimesLastYear, WorkLifeBalance, YearsAtCompany , YearsInCurrentRole, YearsSinceLastPromotion, and YearsWithCurrManager. This is a numerical feature that is the result of a physical measurement.\n",
        "\n",
        "- There is 1 numeric column with the float64 data type, namely: Attrition. This column is our feature target."
      ],
      "metadata": {
        "id": "F9BC4IYNRU-B"
      },
      "id": "F9BC4IYNRU-B"
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr().round(2)\n",
        "mask = (correlation_matrix.abs() < 0.75) & (correlation_matrix.abs() > -0.75)\n",
        "correlation_matrix[mask] = np.nan\n",
        "plt.figure(figsize=(20, 15))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", cbar=False)\n",
        "plt.title('Correlation Matrix of Employee Attrition')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "id": "HTlWj6d81aHp",
        "outputId": "6e0ee7cf-2370-4e8b-8ddf-267b45d53e37"
      },
      "id": "HTlWj6d81aHp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-100-3a795f7951c3>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = df.corr().round(2)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the correlation matrix above, several correlations can be seen, such as:\n",
        "- MonthlyIncome has a strong positive correlation to JobLevel of 0.95.\n",
        "- MonthlyIncome has a strong positive correlation to TotalWorkingYears of 0.78."
      ],
      "metadata": {
        "id": "ygdrRXQ31gdg"
      },
      "id": "ygdrRXQ31gdg"
    },
    {
      "cell_type": "code",
      "source": [
        "df['Attrition'] = df['Attrition'].replace({1: 'Yes', 0: 'No'})\n",
        "\n",
        "# Calculating the number of employees who experienced attrition and who are still active\n",
        "attrition_count = df['Attrition'].value_counts()\n",
        "attrition_rate = attrition_count / len(df) * 100\n",
        "\n",
        "# Create plots\n",
        "fig, ax = plt.subplots()\n",
        "ax.pie(attrition_count, labels=attrition_count.index, autopct='%1.1f%%', startangle=90, colors=['#06D6A0', '#EF476F'])\n",
        "ax.axis('equal')\n",
        "ax.set_title('Attrition Rate')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "y7oEQRvThjdx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "4445702f-608f-4de6-aa30-ceb3f6f2bdf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "id": "y7oEQRvThjdx"
    },
    {
      "cell_type": "code",
      "source": [
        "attritionByOverTime = df.groupby(by='OverTime').agg({'Attrition': 'count'}).reset_index()\n",
        "attritionByOverTime_sorted = attritionByOverTime.sort_values(by='Attrition', ascending=False)\n",
        "\n",
        "attritionByOverTime_sorted['OverTime'] = attritionByOverTime_sorted['OverTime'].replace({0: 'No', 1: 'Yes'})\n",
        "\n",
        "plt.pie(attritionByOverTime_sorted['Attrition'], labels=attritionByOverTime_sorted['OverTime'], autopct='%1.1f%%', startangle=90, colors=['#06D6A0', '#EF476F'])\n",
        "plt.axis('equal')\n",
        "plt.title('Attrition Percentage by OverTime')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "lNWbdNC90tz6",
        "outputId": "14608c30-1dbc-45fd-bf9c-f8d5a5a52074"
      },
      "id": "lNWbdNC90tz6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attritionByAge = df.groupby(by='Age').agg({'Attrition': 'count'}).reset_index()\n",
        "\n",
        "attritionByAge_sorted = attritionByAge.sort_values(by='Attrition', ascending=False)\n",
        "\n",
        "plt.figure(figsize=(15, 6))\n",
        "sns.barplot(x='Age', y='Attrition', data=attritionByAge_sorted, palette='viridis')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Attrition Count')\n",
        "plt.title('Attrition Count by Age')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "DihFAV4hvswW",
        "outputId": "18229832-cb4b-49d2-94bc-815bf331839d"
      },
      "id": "DihFAV4hvswW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-103-d6ae9caaffe8>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Age', y='Attrition', data=attritionByAge_sorted, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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eYw7jfjVq1MhhY47EWVevXtUCBQo4bMxhHhveuXNHGzdurP369VNVx4w3zJeyXrhwweHjDXNWeow3zFn//vuvw8cb5qzEr5ujxhvmrH/++UeDgoI0KCjIYeMNc5bqw/FG+/btHTbeMGcl/mx31HjDnHX16lUtXbq09uzZ06HjDXPemTNnNGvWrLp06VLLOnuOOSIjIy1nvqo+/DysWrWqhoeHq6paHeMtWrSwGoukxTNfNEv8i6Kqunv3bm3evLm++uqrWqZMGZ03b55lXWhoqBYpUkSnTJlil6yvvvpKTSaT5bRPb29v7d27t6o+PLW7WLFieuPGDZsvMbh3757euXNHIyIiLMv279+v+fPn165du1qWmQ+qIUOGaOvWrVO1T8llqVoXZc6cOWMZxJ47d0779++vNWrUsHkOq5RkrVixQsuXL295PGTIEG3VqlWS5z61WaoPL6M9e/asqv7fczh16lStXbt2qgZfj8oKCQnRVq1aqerDSxgKFSqkCxYsUG9vb23Tpo3lDwJ7ZH322Wf6008/Wd6gzc9Xhw4dtGPHjjbnPCorMjJSK1eurCaTyXKqtdmGDRu0cuXKqZrb7HGvl3mfrl+/rmXLltURI0ak6YPoUVk7duzQVatWafXq1fXGjRuWY2Pnzp1asmRJPXXqlF2yVq5cqYULF9b8+fOrj4+PDhkyxLKuXr16OmLEiFTumerNmzf1xIkTlr6uXbtWTSaT/vTTT6r68HfNfIwvWbJEc+fOneqB+c2bN/X48ePJPi9xcXG6bNkyq4FsXFycLly4MFUDTXPW6dOnrZ5L83Fw7949LVy4sO7bt8+ybvfu3TbnJM46efKk1fLg4GB97bXXrP642b17t5pMpjRlnThxQv/991+rAUTNmjUtkw337t1bvb291cPDQ5ctW5bqS7qM+3XixAl9/vnn9cUXX0wy99FXX32lefPm1UuXLqXqdy3xfqk+nBPG09NTq1atarlswuzkyZNqMpl0/fr1adov43No7ndYWJhmz55dP/zww1S1b8xK/Pu1fft2LVWqlLZr1059fX2tilrBwcGaLVu2VE/kbM4yv17jxo1Td3d3HT16tM6cOVNz5sypffr0UVXV6dOna926dfXevXuper3MU0u0a9dOd+3apaoPC5B58+a1+5gjcdbff/9tWZ74M95eYw5zVtu2ba3eGxJn2WvMkXi/9uzZk2SdPcccibMS/2F75coVu485kjs2VFU//fRTu485kjs2IiMjtVKlSnYfczzqOVS1/5gjcdbOnTsty7dv366rV6+265gjuedw5cqVWqhQIbuPOQ4cOKCtWrXSe/fuqerD3yVHjTcOHDigLVu2tGQlZu/xhnm/7t+/b3XpqiPGG4/aL0eMN8z7ZfxSwBHjDfN+mS8dPHjwoMPGG8b92rt3r8PGG496Dh0x3jD+fv3xxx8OG28kl2e+ZNfeY44jR45oy5YtdevWrZas69evq7+/v7744otW4zjVh5e91q9f3+bP5OQ800WzU6dO6WeffaYhISFWy+/du6dxcXEaGBioy5cvtyyPiYnRF198UWfNmqWqtlWAk8uKj4/XBQsWaM2aNbV58+b6ySefWNZ9+eWXWrVqVZsPpmPHjmmzZs20atWq6ufnp4sWLVLVh3N/LF26VPPmzasdO3bUmJgYS7Hp9ddf1y5dumhsbKxNeY/KSq6Ns2fPaqNGjdRkMqmXl1eSQaG9statW6dlypRRVbWcKZh4oGbv/TLr1auXvvnmmzZ/g/OoLNWHH9oNGzbUggULaoECBSwDs23btmmBAgVsnoA1uazEbyLGN5qEhATt2LGj1YS99sjavXu3VqhQQf39/fX333+3vOkNHTpUGzZsaPO39Cl9vWJjY7Vnz54aGBiY6m+Wk8tK/Jr/9ttvSc46GDZsmNaoUUNv3bqVpqyFCxda1t29e1f//vtvq2/vo6KitHnz5ql6f1J9+EFUtWpVrVixombJkkUnTpyoUVFROnDgQPXw8NC1a9dabf/rr79q2bJlU/WNW+Isd3d3/eijj5K8/8TGxury5cstA9lBgwapm5ubzYNmc1aFChXUw8NDP/roI42Pj7e8/8XGxmpoaKj6+fnpiRMnVPXhe4fJZLJ5wvLk9ivx75j5GEhISND4+Hjdvn27VqpUKVXzBSXOMu+XeYD66quv6q+//qqDBg1SPz8/vXDhgr777rtqMpmsPtNSk5UlSxadMGGCqj4cQP7444+W3yfz6/fVV19pxYoVUzXXjfE5NGd9/fXX6ubmplWqVLEUEFQfzpNUt27dVH2rndxzmPj1Mv9/2LBhWrdu3TSdLWp8DsePH6+qD4tbERERWrNmTd2+fbuq/t+Z7/Xq1dMffvghTVnu7u46efJkvXjxok6aNEmLFy+ugYGBVoPy0aNHa2BgYKr3bfPmzerm5qZNmjTR7t27W76tXrZsmebOnVvbtm1rlzFHclmPKmaldcyROKtx48bavXt3qznazPtijzHHk/bLuG+qqR9zPC7LPAdYvnz57DLmSC4r8R/sxj+o0zLmSC7LXPTZvXu3li1bVgsXLmyXMUdyWYlfL/Pniz3GHMllJX4O165dq2XLlrXaPrVjjsRZjRs31m7dulmKRvfu3dOdO3da7WdaxhzmCdCHDx9uWRYbG6sDBgxQDw8PqzPBVNM23kguy/wZnDjbHuMNc1bi+apV/+/3NyYmxm7jjUftl5n5TsX2GG8klxUdHa0JCQn66quv6rp16+w23jBmmffp+PHj+uOPP1r+VrHHeONRWXPnzlU3NzetXLmy3cYbjzo2zJnm93J7jDeM+2U+1q9fv64RERFao0YNu403EucZ52n/6KOPtHjx4lqnTh27jDnMNwtJbj7KXbt2qZ+fnzZs2FD//fdfy/HQq1cvfemll5L8jZsaz2zR7PTp0+rj46Mmk0lHjhxpNUlcfHy83rt3T2vXrq1jx47V27dv6927d3Xs2LFasGBBm+/y87gs1YcFrcSXLqiqDhgwQDt27KgPHjxI8QfRsWPHNE+ePDp48GBdvHixDhkyRLNkyWIZwEZGRuqaNWu0UKFCGhAQoG3bttVOnTqpl5eX1aUOaclKfHlLYtHR0dqlSxf18fFJckmAPbNWr16tderU0VGjRqm7u3uSgae99ysmJkbHjBmjefPmtXwA2isrNjZWx4wZo40aNbIa1KqqzR8Otu6XObtgwYKWSSnTmmU+DuPj4/Xo0aNatWpVLVKkiFauXFlbt26tuXLl0oMHDzpkv8y/Q+fOnVOTyZTkMg17ZYWHh+tzzz2n9evX17Fjx2qvXr00T548dtuvR506fefOHR0xYoTmz5/f6gPe1rxhw4bpsWPH9LPPPlOTyaRXrlzRK1euaO/evTVLliw6Z84cvXr1qj548EBHjBihlStXTlUxMLms5CYujouL0yVLlqjJZNLcuXMnmVjcHlkJCQl67do19fPz03PnzumHH36o3t7eqSrspyQrsREjRmjDhg3t9hyaB8NTp05Vk8lk9Yev6sPBWGrfpx6VldyZ0O+++6526NAhVQXw5LIuXLigDx480E8++URdXFy0e/fuum3bNg0NDdUxY8ZosWLFUvUlQkqPww0bNmj27Nl15cqVNmU8Kcv8HF6/fl1LlChheV+KiYnR8ePHa6FChWweOBuzpk6dqi4uLpbJ8W/fvm25hMGsT58+2qtXL42JiUn1mYFt2rTRr7/+WqtVq6avvvqq5SzBVatWably5bRMmTJpGnM8Kuu1116zXCKX+FhMy5jDlqxVq1alacxh636lZczxuCzzFzAjR47UNm3aWN5rUzvmeFyWuRBjLFqkdszxuKzjx4+r6sO7CjZq1EgLFy6cpjHH47KSe73SMuZISdbt27e1bNmyWq9evTSNOR6V9eqrr1qOjcTvDWkZcxw6dEi9vLysJiVXfXis3bhxQ/v372+38cajspL7Azqt442UZCUkJOj169fTPN5IaVZiqR1vPCrL/HfrzJkz1WQyqa+vb5rHG4/Ketz7T2rHG497DhMSEvTzzz+323jDluMwreONJ71eERERdhtvJJdnPu7MXxTcv3/fLmOOe/fuabNmzSyXcKs+vOLhwIEDlvHN0aNHtVy5clqqVCmtVauWvvzyy+rt7W11gkFaPJNFs3v37umbb76pb7zxhs6aNUtNJpO+//77SYpZy5cvV5PJpKVLl9batWtr0aJFbb7eNyVZiQ+YEydO6HvvvafZs2e36bTgmzdvarNmzZLcPrZRo0ZW1+mrPvyw++CDD/Stt97SAQMG2DygTElW4n2Kj4/XL7/8Ul1dXW1+/mzNMr9mqfnAszXrjz/+0A4dOmihQoUcsl+qD69DN54JaeyHPbISt7dhwwZt3bq1+vr6Ovz1+uabb3TcuHH68ccf23wpQWqOwzt37ujAgQOt7jplr6zE8280btxYAwMD9ZVXXnHI71fiQfmBAwe0b9++6ufnl6o/2K5fv64NGjSwmi8kISFBg4KCdPfu3Xr48GHds2ePzp49W93d3dXf318rVaqk+fLls/n4eFRW8+bNdefOnXrgwAGrokVcXJz26tVLs2fPbvnjx55Z5g/aqKgoLV++vDZt2lTd3d1tfu+wdb/OnDmjY8aMsfl9/klZf/31lx46dEgXLlyoH3zwgeUPp9Selv6kLPO8Ion3a+zYsZorVy6ruX7SkhUUFKQ7d+7UgwcP6vnz53XdunX63HPPaYECBbRs2bKp+ly29fVSfTgvRv369TU+Pt6m998nZe3bt09v376t8+bNU5PJpNWrV9eGDRvqc889Z7f9CgoK0r/++kv37dtnOeZVH54B/8EHH2iOHDlsfr3M4uLi9Nq1a1q6dGm9fPmy/vzzz1qzZk3t1auXNmzYUDt16qR37tzRYcOGpXrM8aSs3r17a926dbVDhw6q+vC9OLVjjpRmmS/bScsf2bbu14YNG1I95nhS1ltvvaUvvvii1q1bN9k/CFNTTE3pfv3++++pHnOkZL/q1q2r3bp1U9WHZ5Ckdsxh637FxsamesyRkqzatWtr165d9dixY9qwYcNUjzls3a99+/alesxhnpc1KCjIkvnee+9pixYttFy5cvrll1/q5s2b9YsvvkjzeONRWS1bttSAgAD9/PPPrYo6aRlvpCTLfKl8VFSUVqhQIdXjDVv36+zZs6kebzwuq0yZMjpz5kydO3euDh482PIFcmrHGynZr8Svy9mzZ1M93nhU1ksvvaRly5bVGTNm6LFjx3T16tX63HPPqa+vb6rHG7a+XqqpH288KWvatGl6+fJlnTt3rppMJq1Ro0aqxxuPy3vppZe0TJkyOn36dKupSv79999UjzmioqK0Xr16un//fo2Li9OgoCCtWbOment7a+3atfW///2vZdsvvvhCR4wYoePHj08yVUpauKXtNgJPJxcXF6levbrkyZNHOnfuLHnz5pUuXbqIiMgHH3xguXtLp06d5LnnnpMtW7ZI3rx5JSgoSIoVK2b3LPOdKu7evSsbN26UAwcOyLZt26RixYopzomNjZXw8HDp2LGjiDy8Q4yLi4v4+/vLrVu3ROThXSNUVbJnzy6ffPKJ1Xa2SElW4rtvuLi4SNGiReXEiRNSqlQph2ZVr15d6tWrJ7NmzbLp+bM1S1XF399fKlasKJMnT5YyZcrYPSshIUF8fX2T/Xlb7m6Smv0qV66cfPrppxIQEGD3/TKZTBIfHy+urq7Su3dvm9pP7X6JPDwOs2fPLlOnThUPDw+7Z7m5PXw7LVeunPz5558SHR0tJpPJcscpe2Yl/p2tUqWKNG3aVD744APx9/e3KUvk4XPUvHlzS57Iwzu1bdiwQa5evSrh4eFSrlw5mT59uhw+fFgOHTokqip16tSRokWL2iVr/fr1EhoaKjdu3JDy5cvLmDFjpF69erJx40bZsmWL/Pnnn1K2bFmHZI0aNUrKli0rx48flzNnzsjevXulUqVKDskaO3asFCxYUEaPHp2q9/knZV29elUiIiKkUqVK0rdvX6lcubKIPLyLXGo8ab9u3rwp5cqVk7Fjx4qvr68MHTpUDh06JJs3b5by5cvbJWvDhg0SGhoqt27dkoCAAJk7d67s27fPcsfCUqVKScGCBe26X8bjUESkT58+UrFiRZs/L23J+vXXX2Xt2rXi7+8v7dq1kxIlStgla8OGDRIWFmaVVb16dVm0aJFs3rxZtm7davPrZebi4iL58uWTmjVrytGjR6Vdu3bi4eEhPXr0kKioKJkxY4blfVckdWOOlGRFR0dbPk/c3NykSJEiqRpz2JpVq1Ytef7552X27Nk2/y7bmlWiRAmpUKFCqsYcT8rq3r27REVFyZw5c8TPz09ExOoObqm5o5ot+1W2bNlUjTlSkhUVFSU9e/YUEZG33347Ve2nZr/c3NxSPeZI6X7169dPypUrJ1u2bEn1mMPW/apWrZq88MILqR5zBAYGyqVLl2T16tUyd+5ciY2NlSpVqoi/v7/MmDFDGjduLDNmzJCGDRvKyZMnUz3eeFxWsWLF5IsvvpCjR4/KuHHjpEiRImkab6Q0a8yYMeLm5ibHjh2T06dPp2q8Yct+RUZGysSJE1M93nhcVtGiReWrr76SF198Ufr16ycVKlQQkdSPN2zZr3v37smoUaNSPd54UtbMmTPl8OHD8vXXX8vOnTslJCQk1eMNW/arSJEiIpL68caTsr788ks5fvy4jBs3TlauXCkbNmxI9XjjSXn+/v7y5ZdfyrFjx2TcuHGSI0cOWbhwYarHHOHh4XLq1Cm5ceOGvP/++yIi8t///ldCQkLkzz//lDFjxki2bNmka9euMnDgwFTtyxPZrfz2lDFOmrhs2TI1mUw6bNgwy1lgMTExSc4+s3eW+fr8uLg4DQsL09jY2FTNQ6CqVt9kmU+LHDNmjOUbNrPkJsJ2VNadO3dS1X5qsswTRiY30ae9s8wZtt6kITVZ5v1Ki5RmmU9vTsuEiak5Nhx9HKbnc2g8DdmRWcnd+CA1Er8WS5cutcxFcfPmTd2yZYvWqFEjzXfXS0nW1q1btWbNmpZ5rEJDQ9N017aUZJnnlvr8889T/Q19SrMmTpyoMTExun379jTNV/G4rD///FNr1apleQ7Typb9+vPPP5NM0muvrC1btmj16tWdchw6MmvLli1Wx6Ejs4z7deXKFQ0LC7NLbvfu3S0Tgvfq1Utz586t5cqV0zfffNNqji973H00pVn28Lgs89xSaRlzpDTLfPlWWsYcKc1KfIMFR2eZ98sekzQ/KSvxXGCOPg7T8zn866+/0i0r8Y0IUiskJES7d++uWbNm1RdffNFqnrJFixZpzpw5k8yh6oisxYsXa65cufTXX39V1bSPN1KS9csvv6iqWs5kcmSW+SYAO3bsSNN443FZCxcutHoO08qW/dq8eXOaxhtPOg5z5Mhheb3Sypbj0JFZixYtsjoOHZ1n3rd169ZZtk3tmCMhIUG7dOmiAwYM0FatWunvv/9uWXfp0iV9/fXXtW/fvhobG2v5nLTX3c7NntmimVlcXJzlSTUPMN9//329cuWKDh48WNu1a5fqO0rZmtW2bdtU32kkscSDqtGjR1tOm1RV/c9//qPTpk1L0y2HM2rWZ599ZreBV0bar/R+DjPjfpGVcufPn09yyUXLli0td1XLbFnmu/jZ64/RlGTZ07OW5YxjIz2y0vv3y16DSXM7CxYs0PHjx2u/fv0sc7/+/PPPWqJECe3bt2+SeVsdmZXa+bdszXr77bfTbb/efvvtdNuv9H4O03O/MuNx+DRmXblyRUeOHKmbNm2yylZVLVmypA4bNizNGRkxyzzvkz3+Vklplj2Qlbmy7HnMp2fe3r171cvLS00mU5KbhQwdOlQbNGhg90JZYs/k5ZmJubq6iqpKQkKCdOnSRUwmk3Tr1k3WrFkjZ8+elb1794qXl1e6ZO3Zs0eyZs2a5hwXFxer0+rNp3eOGzdOJk2aJAcOHLBcSpbZstJyWrCtWZn1OSTr2cwyK1q0qOUyiISEBImJiRFvb+9UXULwNGSZL1dI7SVjtmRl1ueQrKcvKzWX2yXH3I6/v7/07NlTChQoIL/88ov4+/uLv7+/mEwmqVy5cqovT0tNlqenZ7plZdb9yqxZvF4ZI8vPz09GjBhhactkMomqyq1btyRfvnxStWrVNGdkxCzz+7w9/lZ5UpZ5egZ7eFJWlSpV0i0rs+5XembZ85hPz7waNWrIb7/9Jg0bNpRvvvlGihcvbrnMMzY2VkqXLi1xcXGSJUsWu+Ql4bBy3FMmISHBUp1s0qSJ+vj42DxhYkbKMp81MX78eO3Tp49OnTpVPTw8Un1nJ7LIIivzZyVn7NixWqRIkVRPZEwWWWRl/qyYmBidN29esnfaI4sssshKiXHjxmmpUqUsdxYmiyyyns4sR+Zt3bpV/fz8tFatWtqrVy/t1q2b5syZM9V35U4pimaJxMXF6eDBg9VkMtnt9qTOzpo0aZKaTCbNmTOn1a2AySKLLLIe54cfftD+/ftrnjx5Un1HM7LIIuvZybLnJc5kkUXWs5Gl+nDKmj59+mju3Lkd/n5IFllkOVZ65J08eVLHjBmjTZs21X79+jm8YKaqar/rUTKJ8uXLy/79+x1yqYQzsoKCgkREZOfOnVKjRg2H5ZBFFlmZJ0vk4V1Ar1+/Ltu3b7f7qdxkkUVW5suy5yXOZJFF1rORJfLwPerKlSvp9n5IFllkPd15ZcqUkY8++kjWr18vX331leXurY5kUlV1eMpTRBPNH5RZsiIjI+02LxtZZJH1bGSJPJwjwGFzA5BFFlmZMgsAbBUTEyPu7u5kkUXWU57ljLz0QNEMAAAAAAAAMODyTAAAAAAAAMCAohkAAAAAAABgQNEMAAAAAAAAMKBoBgAAAAAAABhQNAMAAAAAAAAMKJoBAAAAAAAABhTNAAAAAAAAAAOKZgAAAE+5Xbt2iaurq7Rs2dLZXQEAAMg0TKqqzu4EAAAAUu+tt94Sb29vmTdvnpw6dUr8/Pyc3SUAAICnHmeaAQAAPMXu3bsny5cvl379+knLli1lwYIFVuvXrFkjpUqVEk9PT2ncuLH873//E5PJJOHh4ZZtduzYIfXr15esWbNK4cKFZdCgQRIZGZm+OwIAAJDBUDQDAAB4iv3www8SEBAgZcqUkddff12+++47MV9IEBwcLB07dpS2bdvKoUOH5O2335bRo0db/fzZs2elefPm0qFDBzl8+LAsX75cduzYIQMGDHDG7gAAAGQYXJ4JAADwFHv++eelU6dO8u6770pcXJwULFhQVqxYIY0aNZIRI0bIunXr5MiRI5btx4wZI5MnT5bbt29Lrly55K233hJXV1f5+uuvLdvs2LFDGjZsKJGRkeLp6emM3QIAAHA6zjQDAAB4Sp06dUr27NkjXbt2FRERNzc36dy5s8ybN8+yvmbNmlY/U6tWLavHhw4dkgULFoi3t7flX1BQkCQkJEhwcHD67AgAAEAG5ObsDgAAACB15s2bJ3FxcVYT/6uqeHh4yFdffZWiNu7duydvv/22DBo0KMm6IkWK2K2vAAAATxuKZgAAAE+huLg4+f7772XatGnSrFkzq3Vt27aVpUuXSpkyZeTXX3+1Wrd3716rx9WqVZPjx49LyZIlHd5nAACApwlzmgEAADyFVq1aJZ07d5Zr165Jzpw5rdYNHz5c/vzzT/nhhx+kTJkyMnjwYOnVq5ccPHhQhg4dKpcvX5bw8HDJmTOnHD58WOrUqSNvvvmmvPXWW+Ll5SXHjx+XjRs3pvhsNQAAgMyIOc0AAACeQvPmzZOmTZsmKZiJiHTo0EH++ecfuXv3rvz444/y888/S6VKlWTOnDmWu2d6eHiIiEilSpVk69at8u+//0r9+vWlatWqMm7cOKtLPgEAAJ5FnGkGAADwDJk8ebLMnTtXLl265OyuAAAAZGjMaQYAAJCJzZ49W2rWrCl58uSRv/76S6ZOnSoDBgxwdrcAAAAyPIpmAAAAmdjp06dl0qRJcuvWLSlSpIgMHTpURo4c6exuAQAAZHhcngkAAAAAAAAYcCMAAAAAAAAAwICiGQAAAAAAAGBA0QwAAAAAAAAwoGgGAAAAAAAAGFA0AwAAAAAAAAwomgEAAAAAAAAGFM0AAAAAAAAAA4pmAAAAAAAAgAFFMwAAAAAAAMDg/wExP43EBTGsWAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data preprocessing"
      ],
      "metadata": {
        "id": "29jeKOoLtMGm"
      },
      "id": "29jeKOoLtMGm"
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifies categorical columns\n",
        "categorical_column = []\n",
        "for column in df.columns:\n",
        "    if df[column].dtype == object and len(df[column].unique()) <= 50:\n",
        "        categorical_column.append(column)"
      ],
      "metadata": {
        "id": "-TILv5xjTIxV"
      },
      "id": "-TILv5xjTIxV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the 'Attrition' column to a categorical data type and convert categorical values ​​to integers\n",
        "df['Attrition'] = df.Attrition.astype(\"category\").cat.codes\n",
        "\n",
        "# Removed the 'Attrition' column from the categorical column list after converting it to an integer\n",
        "categorical_column.remove('Attrition')"
      ],
      "metadata": {
        "id": "R8k_BcGAs8zO"
      },
      "id": "R8k_BcGAs8zO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = LabelEncoder()\n",
        "for column in categorical_column:\n",
        "    df[column] = label.fit_transform(df[column])"
      ],
      "metadata": {
        "id": "bYae9LyHs4IP"
      },
      "id": "bYae9LyHs4IP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Modelling"
      ],
      "metadata": {
        "id": "vE85gtMQZGfS"
      },
      "id": "vE85gtMQZGfS"
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Attrition'])\n",
        "y = df['Attrition']"
      ],
      "metadata": {
        "id": "BIJluGve1-Wx"
      },
      "id": "BIJluGve1-Wx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "Hwu5kf0CZMSr"
      },
      "id": "Hwu5kf0CZMSr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest = RandomForestClassifier(n_estimators=50, max_depth=16, random_state=55, n_jobs=-1)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "train_pred = random_forest.predict(X_train)\n",
        "test_pred = random_forest.predict(X_test)\n",
        "\n",
        "train_acc = accuracy_score(y_train, train_pred) * 100\n",
        "test_acc = accuracy_score(y_test, test_pred) * 100\n",
        "\n",
        "print(f\"Train Accuracy Score: {train_acc:.2f}%\")\n",
        "print(f\"Test Accuracy Score: {test_acc:.2f}%\")"
      ],
      "metadata": {
        "id": "Ex3_wf9JZVk4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fa4694e-3dcb-4790-8204-c3adeaf3f317"
      },
      "id": "Ex3_wf9JZVk4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy Score: 100.00%\n",
            "Test Accuracy Score: 84.53%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "nOZNUQ77_Nxj"
      },
      "id": "nOZNUQ77_Nxj"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report:\")\n",
        "print(\"Train:\\n\", classification_report(y_train, train_pred))\n",
        "print(\"Test:\\n\", classification_report(y_test, test_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a22tEr7Q44wQ",
        "outputId": "c79c472b-0cb2-4d64-c8f2-b9d35dd9a96c"
      },
      "id": "a22tEr7Q44wQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "Train:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       664\n",
            "           1       1.00      1.00      1.00       129\n",
            "\n",
            "    accuracy                           1.00       793\n",
            "   macro avg       1.00      1.00      1.00       793\n",
            "weighted avg       1.00      1.00      1.00       793\n",
            "\n",
            "Test:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.91       215\n",
            "           1       1.00      0.18      0.31        50\n",
            "\n",
            "    accuracy                           0.85       265\n",
            "   macro avg       0.92      0.59      0.61       265\n",
            "weighted avg       0.87      0.85      0.80       265\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--- End of code ---"
      ],
      "metadata": {
        "id": "wOXHgAKY9EHF"
      },
      "id": "wOXHgAKY9EHF"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
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