Employee_Churn_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMGpvvhHOEvuCGQG+/4IAjG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rishav-pred/Emplyoee-Churn-Analyis/blob/main/Employee_Churn_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8CbsjyntZYP0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('HR_comma_sep.csv')"
      ],
      "metadata": {
        "id": "CG4s-ZyXZkxa"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ojrKc5TxaIXT",
        "outputId": "8eb84f8f-20de-4104-aada-7e4590387e54"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
              "0                0.38             0.53               2                   157   \n",
              "1                0.80             0.86               5                   262   \n",
              "2                0.11             0.88               7                   272   \n",
              "3                0.72             0.87               5                   223   \n",
              "4                0.37             0.52               2                   159   \n",
              "\n",
              "   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n",
              "0                   3              0     1                      0      sales   \n",
              "1                   6              0     1                      0      sales   \n",
              "2                   4              0     1                      0      sales   \n",
              "3                   5              0     1                      0      sales   \n",
              "4                   3              0     1                      0      sales   \n",
              "\n",
              "   salary  \n",
              "0     low  \n",
              "1  medium  \n",
              "2  medium  \n",
              "3     low  \n",
              "4     low  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82601e98-a42b-4dd1-bf0e-7e0dc417a601\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>left</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>Department</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>157</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>5</td>\n",
              "      <td>262</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.88</td>\n",
              "      <td>7</td>\n",
              "      <td>272</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.87</td>\n",
              "      <td>5</td>\n",
              "      <td>223</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2</td>\n",
              "      <td>159</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82601e98-a42b-4dd1-bf0e-7e0dc417a601')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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",
              "      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-82601e98-a42b-4dd1-bf0e-7e0dc417a601 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-82601e98-a42b-4dd1-bf0e-7e0dc417a601');\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-92b85efb-2590-4f8d-b3bd-5f8d8f70f46b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-92b85efb-2590-4f8d-b3bd-5f8d8f70f46b')\"\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-92b85efb-2590-4f8d-b3bd-5f8d8f70f46b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14999,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24863065106114257,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.83,\n          0.13,\n          0.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17116911062327533,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.66,\n          0.44,\n          0.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 96,\n        \"max\": 310,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          118,\n          112,\n          222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Department\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"marketing\",\n          \"accounting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\",\n          \"medium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_df = df.select_dtypes(include=['number'])\n",
        "correlation_matrix = numeric_df.corr()\n",
        "\n",
        "print(correlation_matrix)"
      ],
      "metadata": {
        "id": "DIohUsada65b",
        "outputId": "a029d4d2-b4b2-4518-91af-cb493ca3dab3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                       satisfaction_level  last_evaluation  number_project  \\\n",
            "satisfaction_level               1.000000         0.105021       -0.142970   \n",
            "last_evaluation                  0.105021         1.000000        0.349333   \n",
            "number_project                  -0.142970         0.349333        1.000000   \n",
            "average_montly_hours            -0.020048         0.339742        0.417211   \n",
            "time_spend_company              -0.100866         0.131591        0.196786   \n",
            "Work_accident                    0.058697        -0.007104       -0.004741   \n",
            "left                            -0.388375         0.006567        0.023787   \n",
            "promotion_last_5years            0.025605        -0.008684       -0.006064   \n",
            "\n",
            "                       average_montly_hours  time_spend_company  \\\n",
            "satisfaction_level                -0.020048           -0.100866   \n",
            "last_evaluation                    0.339742            0.131591   \n",
            "number_project                     0.417211            0.196786   \n",
            "average_montly_hours               1.000000            0.127755   \n",
            "time_spend_company                 0.127755            1.000000   \n",
            "Work_accident                     -0.010143            0.002120   \n",
            "left                               0.071287            0.144822   \n",
            "promotion_last_5years             -0.003544            0.067433   \n",
            "\n",
            "                       Work_accident      left  promotion_last_5years  \n",
            "satisfaction_level          0.058697 -0.388375               0.025605  \n",
            "last_evaluation            -0.007104  0.006567              -0.008684  \n",
            "number_project             -0.004741  0.023787              -0.006064  \n",
            "average_montly_hours       -0.010143  0.071287              -0.003544  \n",
            "time_spend_company          0.002120  0.144822               0.067433  \n",
            "Work_accident               1.000000 -0.154622               0.039245  \n",
            "left                       -0.154622  1.000000              -0.061788  \n",
            "promotion_last_5years       0.039245 -0.061788               1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(numeric_df.drop('left', axis=1), numeric_df['left'], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "vlOZCF_3aqic"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "mm8YHKo4bWNg",
        "outputId": "2956b97e-d555-44dd-f547-c104f7c4ad13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "id": "4r1exccebb5p",
        "outputId": "a58675ac-28eb-45cf-d3df-3ad430e61b3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7566666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = model.predict(x_test)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_predicted)"
      ],
      "metadata": {
        "id": "B1mEqogmblHB"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mr6vqtV2kGoJ",
        "outputId": "437b22d4-3f9d-4853-c80f-c6a5bbf70008",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, y_predicted)"
      ],
      "metadata": {
        "id": "xeL5QrKBkIN4",
        "outputId": "82b8e2d8-30a9-4855-d6e0-074c9cfe7a3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7566666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_model = RandomForestClassifier()\n",
        "rf_model.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "flhR6aLupfB6",
        "outputId": "55a69a98-c423-4d64-89f2-4e3eb09400df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model.predict(x_test)"
      ],
      "metadata": {
        "id": "Suj4FYe0pqtW",
        "outputId": "ba45a8c8-bc3a-4faa-c839-6e203572b951",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, rf_model.predict(x_test))"
      ],
      "metadata": {
        "id": "5c_Htm2apz8x",
        "outputId": "2b01397e-beae-4add-aed7-0c84b9b8fca2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9886666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LydUHtuLp2kK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}