{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8uTwbvL5xrV0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('/content/Position_Salaries.csv')\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1iF2SY_yMtW",
        "outputId": "212c8f8b-40fc-483c-8e40-6ffc1f744065"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Position  Level   Salary\n",
            "0   Business Analyst      1    45000\n",
            "1  Junior Consultant      2    50000\n",
            "2  Senior Consultant      3    60000\n",
            "3            Manager      4    80000\n",
            "4    Country Manager      5   110000\n",
            "5     Region Manager      6   150000\n",
            "6            Partner      7   200000\n",
            "7     Senior Partner      8   300000\n",
            "8            C-level      9   500000\n",
            "9                CEO     10  1000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi7PlTE1yMvo",
        "outputId": "d1451500-f1e0-4962-dcf5-6efda1e82857"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10 entries, 0 to 9\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Position  10 non-null     object\n",
            " 1   Level     10 non-null     int64 \n",
            " 2   Salary    10 non-null     int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 372.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,1:2].values\n",
        "y = df.iloc[:,2].values"
      ],
      "metadata": {
        "id": "rvreHNYOyMx-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder()\n",
        "\n",
        "for col in df.select_dtypes(include=['object']).columns:\n",
        "    df[col] = label_encoder.fit_transform(df[col])"
      ],
      "metadata": {
        "id": "Q2EgbvjjyM0U"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.2,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "IVqwJ0sSyM2Z"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = RandomForestRegressor(\n",
        "    n_estimators=100,\n",
        "    random_state=42,\n",
        "    oob_score=True\n",
        ")\n",
        "\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "a_82WKWtyM5B",
        "outputId": "bc5fae03-9e2c-4000-8702-ac4ee0ad0a92"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(oob_score=True, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(oob_score=True, random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(oob_score=True, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Out-of-Bag Score:\", regressor.oob_score_)\n",
        "\n",
        "y_pred = regressor.predict(X_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"R-squared:\", r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqL4oQwMyM61",
        "outputId": "0326414b-b0d8-489c-bb7a-85b8c96aefb9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Out-of-Bag Score: 0.2280694384742593\n",
            "Mean Squared Error: 616145000.0\n",
            "R-squared: 0.9878292345679013\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid = np.arange(min(X), max(X), 0.01).reshape(-1,1)\n",
        "\n",
        "plt.scatter(X, y, color='blue', label=\"Actual Data\")\n",
        "plt.plot(X_grid, regressor.predict(X_grid), color='green', label=\"Random Forest Prediction\")\n",
        "\n",
        "plt.title(\"Random Forest Regression Results\")\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jUAhcSvpy7Xw",
        "outputId": "e4a375ca-bd72-4845-804a-090f28bb1e10"
      },
      "execution_count": 12,
      "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": [
        "from sklearn.tree import plot_tree\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tree_to_plot = regressor.estimators_[0]\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "plot_tree(tree_to_plot, feature_names=df.columns.tolist(), filled=True, rounded=True, fontsize=10)\n",
        "plt.title(\"Decision Tree from Random Forest\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "rlGt6ANUy7Z0",
        "outputId": "d72f414b-8967-49c2-94b3-142a9fe89bd5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qCRuPcBUyM86"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}