Machine_Learning_Pipeline.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/Jatansahu/GOLDDIGGER_GROUP12_PROJECT_01/blob/main/Machine_Learning_Pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ML Pipiline**"
      ],
      "metadata": {
        "id": "kMKbOLCBwKxp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![ml_pipeline.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "YwF0LfEjv8FF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1.Loading Required Libraries"
      ],
      "metadata": {
        "id": "e-p8aO5vRfPm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from sklearn.preprocessing import LabelEncoder,StandardScaler\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "import warnings\n",
        "warnings.filterwarnings(action = 'ignore')\n",
        "from sklearn.preprocessing import LabelEncoder,StandardScaler\n",
        "from sklearn.linear_model import Lasso,LinearRegression,ElasticNet,Ridge\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score,cross_val_predict\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
        "import xgboost\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "# import optuna"
      ],
      "metadata": {
        "id": "ufqPdDS_RhaH"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.Load Dataset"
      ],
      "metadata": {
        "id": "t93RwgXRSm6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total = pd.read_csv(\"https://raw.githubusercontent.com/Jatansahu/GOLDDIGGER_GROUP12_PROJECT_01/main/Preprocessed_data/total.csv\")\n",
        "inc = pd.read_csv(\"https://raw.githubusercontent.com/Jatansahu/GOLDDIGGER_GROUP12_PROJECT_01/main/Preprocessed_data/income.csv\")\n",
        "race = pd.read_csv(\"https://raw.githubusercontent.com/Jatansahu/GOLDDIGGER_GROUP12_PROJECT_01/main/Preprocessed_data/race.csv\")\n",
        "edu = pd.read_csv(\"https://raw.githubusercontent.com/Jatansahu/GOLDDIGGER_GROUP12_PROJECT_01/main/Preprocessed_data/education.csv\")"
      ],
      "metadata": {
        "id": "TXoyxQTjRWU9"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3.ML Pipeline"
      ],
      "metadata": {
        "id": "fMeT6G0ER0oA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Total"
      ],
      "metadata": {
        "id": "VqNZaH2qiQO-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Splitting"
      ],
      "metadata": {
        "id": "JKAjqsiuoXRi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "1pI8uZ-niW9W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "kdS8I397hBlX",
        "outputId": "7bc55a37-41b5-429f-eb81-afd7361a7bcc"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      yearend  locationid  sample_size  stratificationid1  data_value  \\\n",
              "0        2011           1       7304.0                  1        32.0   \n",
              "1        2011           1       7304.0                  1        34.7   \n",
              "2        2011           1       7231.0                  0        43.8   \n",
              "3        2011           1       7135.0                  0        24.3   \n",
              "4        2011           1       7191.0                  2        42.4   \n",
              "...       ...         ...          ...                ...         ...   \n",
              "1727     2016          55       5270.0                  2        20.0   \n",
              "1728     2016          56       4483.0                  2        23.1   \n",
              "1729     2016          66       1578.0                  2        29.6   \n",
              "1730     2016          72       5790.0                  2        41.7   \n",
              "1731     2016          78       1265.0                  2        26.1   \n",
              "\n",
              "      classid_FV  classid_OWS  classid_PA  questionid_Q018  questionid_Q019  \\\n",
              "0              0            1           0                0                0   \n",
              "1              0            1           0                0                0   \n",
              "2              1            0           0                1                0   \n",
              "3              1            0           0                0                1   \n",
              "4              0            0           1                0                0   \n",
              "...          ...          ...         ...              ...              ...   \n",
              "1727           0            0           1                0                0   \n",
              "1728           0            0           1                0                0   \n",
              "1729           0            0           1                0                0   \n",
              "1730           0            0           1                0                0   \n",
              "1731           0            0           1                0                0   \n",
              "\n",
              "      questionid_Q036  questionid_Q037  questionid_Q043  questionid_Q044  \\\n",
              "0                   1                0                0                0   \n",
              "1                   0                1                0                0   \n",
              "2                   0                0                0                0   \n",
              "3                   0                0                0                0   \n",
              "4                   0                0                1                0   \n",
              "...               ...              ...              ...              ...   \n",
              "1727                0                0                0                0   \n",
              "1728                0                0                0                0   \n",
              "1729                0                0                0                0   \n",
              "1730                0                0                0                0   \n",
              "1731                0                0                0                0   \n",
              "\n",
              "      questionid_Q045  questionid_Q046  questionid_Q047  \n",
              "0                   0                0                0  \n",
              "1                   0                0                0  \n",
              "2                   0                0                0  \n",
              "3                   0                0                0  \n",
              "4                   0                0                0  \n",
              "...               ...              ...              ...  \n",
              "1727                0                0                1  \n",
              "1728                0                0                1  \n",
              "1729                0                0                1  \n",
              "1730                0                0                1  \n",
              "1731                0                0                1  \n",
              "\n",
              "[1732 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75d43558-7826-4127-a346-8374b4bb093e\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>data_value</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7304.0</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7304.0</td>\n",
              "      <td>1</td>\n",
              "      <td>34.7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7231.0</td>\n",
              "      <td>0</td>\n",
              "      <td>43.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7135.0</td>\n",
              "      <td>0</td>\n",
              "      <td>24.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7191.0</td>\n",
              "      <td>2</td>\n",
              "      <td>42.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1727</th>\n",
              "      <td>2016</td>\n",
              "      <td>55</td>\n",
              "      <td>5270.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1728</th>\n",
              "      <td>2016</td>\n",
              "      <td>56</td>\n",
              "      <td>4483.0</td>\n",
              "      <td>2</td>\n",
              "      <td>23.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1729</th>\n",
              "      <td>2016</td>\n",
              "      <td>66</td>\n",
              "      <td>1578.0</td>\n",
              "      <td>2</td>\n",
              "      <td>29.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1730</th>\n",
              "      <td>2016</td>\n",
              "      <td>72</td>\n",
              "      <td>5790.0</td>\n",
              "      <td>2</td>\n",
              "      <td>41.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1731</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>2</td>\n",
              "      <td>26.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1732 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75d43558-7826-4127-a346-8374b4bb093e')\"\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-75d43558-7826-4127-a346-8374b4bb093e 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-75d43558-7826-4127-a346-8374b4bb093e');\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-0016eb52-f63a-4836-bb3f-e6d9b2aff4f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0016eb52-f63a-4836-bb3f-e6d9b2aff4f8')\"\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-0016eb52-f63a-4836-bb3f-e6d9b2aff4f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_to_move = total.pop('data_value')\n",
        "total['data_value']= column_to_move"
      ],
      "metadata": {
        "id": "ljtBH0aaeiv5"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = total.iloc[:,:16]\n",
        "y = total['data_value']"
      ],
      "metadata": {
        "id": "6JQcEpUJiadB"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "yukpeSvhknmT",
        "outputId": "61646958-7258-457c-f4cc-a75f6cf764af"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   yearend  locationid  sample_size  stratificationid1  classid_FV  \\\n",
              "0     2011           1       7304.0                  1           0   \n",
              "1     2011           1       7304.0                  1           0   \n",
              "2     2011           1       7231.0                  0           1   \n",
              "3     2011           1       7135.0                  0           1   \n",
              "4     2011           1       7191.0                  2           0   \n",
              "\n",
              "   classid_OWS  classid_PA  questionid_Q018  questionid_Q019  questionid_Q036  \\\n",
              "0            1           0                0                0                1   \n",
              "1            1           0                0                0                0   \n",
              "2            0           0                1                0                0   \n",
              "3            0           0                0                1                0   \n",
              "4            0           1                0                0                0   \n",
              "\n",
              "   questionid_Q037  questionid_Q043  questionid_Q044  questionid_Q045  \\\n",
              "0                0                0                0                0   \n",
              "1                1                0                0                0   \n",
              "2                0                0                0                0   \n",
              "3                0                0                0                0   \n",
              "4                0                1                0                0   \n",
              "\n",
              "   questionid_Q046  questionid_Q047  \n",
              "0                0                0  \n",
              "1                0                0  \n",
              "2                0                0  \n",
              "3                0                0  \n",
              "4                0                0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c25c2b2f-7804-4f02-a87b-568ae1541c3f\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7304.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7304.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7231.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7135.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>7191.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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-c25c2b2f-7804-4f02-a87b-568ae1541c3f')\"\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-c25c2b2f-7804-4f02-a87b-568ae1541c3f 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-c25c2b2f-7804-4f02-a87b-568ae1541c3f');\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-fc437bb8-0164-45b3-836d-f4a0925fc86d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc437bb8-0164-45b3-836d-f4a0925fc86d')\"\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-fc437bb8-0164-45b3-836d-f4a0925fc86d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Standardization"
      ],
      "metadata": {
        "id": "BTCyPxB6rGRb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Selection"
      ],
      "metadata": {
        "id": "huBk-K62q5jO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Tuning and Model Evaluation"
      ],
      "metadata": {
        "id": "JMT0UKg5f3AH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Define a function for model evaluation\n",
        "\n",
        "# Split the dataset into training, validation, and test sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(x, y, test_size=0.25, random_state=42)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.4, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_valid = scaler.transform(X_valid)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "# Create a Polynomial Regression model with degree as a hyperparameter\n",
        "def create_polyreg(degree):\n",
        "    return make_pipeline(PolynomialFeatures(degree=degree), LinearRegression())\n",
        "\n",
        "# Step 2: Define a function for model evaluation\n",
        "def evaluate_model(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    mse = mean_squared_error(y, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    mae = mean_absolute_error(y, y_pred)\n",
        "    r2 = r2_score(y, y_pred)\n",
        "    return rmse, mae, r2\n",
        "# Step 3: Build a dictionary of regression models and hyperparameter grids\n",
        "models = {\n",
        "    \"Linear Regression\": {\n",
        "        \"model\": LinearRegression(),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 2)\": {\n",
        "        \"model\": create_polyreg(degree=2),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 3)\": {\n",
        "        \"model\": create_polyreg(degree=3),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Ridge Regression\": {\n",
        "        \"model\": Ridge(),\n",
        "        \"param_grid\": {\"alpha\": [0.01, 0.1, 1.0]}\n",
        "    },\n",
        "    \"K-Nearest Neighbors\": {\n",
        "        \"model\": KNeighborsRegressor(),\n",
        "        \"param_grid\": {\"n_neighbors\": [3, 5, 7]}\n",
        "    },\n",
        "    \"Random Forest Regression\": {\n",
        "        \"model\": RandomForestRegressor(),\n",
        "        \"param_grid\": {\n",
        "            \"n_estimators\": [100, 200, 300],  # You can adjust the number of trees\n",
        "            \"max_depth\": [5, 10, 20, 30],  # You can adjust the max depth of trees\n",
        "            \"min_samples_split\": [2, 5, 10], } # You can adjust other hyperparameters\n",
        "        }\n",
        "}\n",
        "\n",
        "# Step 4: Hyperparameter tuning and model evaluation\n",
        "results = {}\n",
        "\n",
        "for model_name, config in models.items():\n",
        "    model = config[\"model\"]\n",
        "    param_grid = config[\"param_grid\"]\n",
        "\n",
        "    # Hyperparameter tuning using GridSearchCV\n",
        "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    best_params = grid_search.best_params_  # Get the best hyperparameters\n",
        "\n",
        "    # Evaluate the best model on the validation set\n",
        "    rmse, mae, r2 = evaluate_model(best_model, X_valid, y_valid)\n",
        "\n",
        "    results[model_name] = {\n",
        "        \"Best_Model\": best_model,\n",
        "        \"Best_Params\": best_params,\n",
        "        \"RMSE\": rmse,\n",
        "        \"MAE\": mae,\n",
        "        \"R2\": r2\n",
        "    }\n",
        "\n",
        "# Step 5: Compare and select the best model based on validation results\n",
        "best_model_name = None\n",
        "best_rmse = float('inf')\n",
        "\n",
        "for model_name, metrics in results.items():\n",
        "    rmse = metrics[\"RMSE\"]\n",
        "    if rmse < best_rmse:\n",
        "        best_model_name = model_name\n",
        "        best_rmse = rmse\n",
        "\n",
        "print(\"Best Model:\", best_model_name)\n",
        "print(\"Best Hyperparameters:\", results[best_model_name][\"Best_Params\"])\n",
        "\n",
        "# Step 6: Evaluate the best model on the test set\n",
        "best_model = results[best_model_name][\"Best_Model\"]\n",
        "test_rmse, test_mae, test_r2 = evaluate_model(best_model, X_test, y_test)\n",
        "\n",
        "print(f\"Test RMSE: {test_rmse:.3f}\")\n",
        "print(f\"Test MAE: {test_mae:.3f}\")\n",
        "print(f\"Test R2 Score: {test_r2:.3f}\")\n",
        "\n",
        "# Step 7: Compare results for different models\n",
        "print(\"\\nModel Comparison:\\n\")\n",
        "for model_name, metrics in results.items():\n",
        "    print(f\"Model: {model_name}\")\n",
        "    print(f\"Best Hyperparameters: {metrics['Best_Params']}\")\n",
        "    print(f\"Train RMSE: {metrics['RMSE']:.3f}\")\n",
        "    print(f\"Train MAE: {metrics['MAE']:.3f}\")\n",
        "    print(f\"Train R2 Score: {metrics['R2']:.3f}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUt8ZiGFmrAy",
        "outputId": "de2c3e3d-15e9-400f-d25a-4d0d348a111b"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 2, 'n_estimators': 300}\n",
            "Test RMSE: 2.924\n",
            "Test MAE: 2.207\n",
            "Test R2 Score: 0.840\n",
            "\n",
            "Model Comparison:\n",
            "\n",
            "Model: Linear Regression\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 3.690\n",
            "Train MAE: 2.803\n",
            "Train R2 Score: 0.755\n",
            "\n",
            "Model: Polynomial Regression (Degree 2)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 3.660\n",
            "Train MAE: 2.787\n",
            "Train R2 Score: 0.759\n",
            "\n",
            "Model: Polynomial Regression (Degree 3)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 3.781\n",
            "Train MAE: 2.877\n",
            "Train R2 Score: 0.743\n",
            "\n",
            "Model: Ridge Regression\n",
            "Best Hyperparameters: {'alpha': 1.0}\n",
            "Train RMSE: 3.679\n",
            "Train MAE: 2.795\n",
            "Train R2 Score: 0.757\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Best Hyperparameters: {'n_neighbors': 7}\n",
            "Train RMSE: 3.661\n",
            "Train MAE: 2.749\n",
            "Train R2 Score: 0.759\n",
            "\n",
            "Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 2, 'n_estimators': 300}\n",
            "Train RMSE: 3.242\n",
            "Train MAE: 2.371\n",
            "Train R2 Score: 0.811\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Selecting best model\n"
      ],
      "metadata": {
        "id": "nLgfzAmprS8G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation on test data"
      ],
      "metadata": {
        "id": "Wa-ErzSGraaj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model comparision\n",
        "\n",
        "Best model here is Random Forest Regression"
      ],
      "metadata": {
        "id": "_8zYokQhrown"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Models used for Evaluation***\n",
        "\n",
        "**Model 1: Linear Regression**\n",
        "Hyperparameters: None (No specific hyperparameters tuned)\n",
        "\n",
        "**Model 2: Polynomial Regression **\n",
        "Hyperparameters: degree(2, 3)\n",
        "\n",
        "**Model 3: Ridge Regression**\n",
        "Hyperparameters:\n",
        "Alpha (Regularization strength): [0.01, 0.1, 1.0]\n",
        "\n",
        "**Model 4: K-Nearest Neighbors Regression**\n",
        "Hyperparameters:\n",
        "Number of Neighbors (n_neighbors): [3, 5, 7]\n",
        "\n",
        "**Model 5: Random Forest Regression**\n",
        "Hyperparameters:\n",
        "Number of Estimators (n_estimators): [100, 200, 300]\n",
        "Maximum Depth of Trees (max_depth): [5, 10, 20, 30]\n",
        "Minimum Samples for Split (min_samples_split): [2, 5, 10]\n"
      ],
      "metadata": {
        "id": "opV0KotUBmCB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 Race"
      ],
      "metadata": {
        "id": "wXAOBSjKsPmZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Splitting"
      ],
      "metadata": {
        "id": "0m-mj4gHsPmZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "C8ARNWwZsPmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "race"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "e106e9b6-f83d-4f12-82f9-f0566e2d4a92",
        "id": "Ba77QaM7sPma"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      yearend  locationid  sample_size  data_value  classid_FV  classid_OWS  \\\n",
              "0        2011           1       1878.0        40.1           0            1   \n",
              "1        2011           1        115.0        28.6           0            1   \n",
              "2        2011           1         61.0        32.9           0            1   \n",
              "3        2011           1         58.0        27.8           0            1   \n",
              "4        2011           1       1878.0        30.1           0            1   \n",
              "...       ...         ...          ...         ...         ...          ...   \n",
              "8248     2016          66        511.0        34.6           0            0   \n",
              "8249     2016          66        203.0        21.5           0            0   \n",
              "8250     2016          78        154.0        18.3           0            0   \n",
              "8251     2016          78        820.0        24.1           0            0   \n",
              "8252     2016          78        178.0        30.3           0            0   \n",
              "\n",
              "      classid_PA  questionid_Q018  questionid_Q019  questionid_Q036  ...  \\\n",
              "0              0                0                0                1  ...   \n",
              "1              0                0                0                1  ...   \n",
              "2              0                0                0                1  ...   \n",
              "3              0                0                0                1  ...   \n",
              "4              0                0                0                0  ...   \n",
              "...          ...              ...              ...              ...  ...   \n",
              "8248           1                0                0                0  ...   \n",
              "8249           1                0                0                0  ...   \n",
              "8250           1                0                0                0  ...   \n",
              "8251           1                0                0                0  ...   \n",
              "8252           1                0                0                0  ...   \n",
              "\n",
              "      questionid_Q046  questionid_Q047  stratificationid1_0  \\\n",
              "0                   0                0                    0   \n",
              "1                   0                0                    0   \n",
              "2                   0                0                    0   \n",
              "3                   0                0                    1   \n",
              "4                   0                0                    0   \n",
              "...               ...              ...                  ...   \n",
              "8248                0                1                    0   \n",
              "8249                0                1                    1   \n",
              "8250                0                1                    0   \n",
              "8251                0                1                    0   \n",
              "8252                0                1                    0   \n",
              "\n",
              "      stratificationid1_1  stratificationid1_2  stratificationid1_3  \\\n",
              "0                       0                    1                    0   \n",
              "1                       0                    0                    1   \n",
              "2                       0                    0                    0   \n",
              "3                       0                    0                    0   \n",
              "4                       0                    1                    0   \n",
              "...                   ...                  ...                  ...   \n",
              "8248                    0                    0                    0   \n",
              "8249                    0                    0                    0   \n",
              "8250                    0                    0                    0   \n",
              "8251                    0                    1                    0   \n",
              "8252                    0                    0                    1   \n",
              "\n",
              "      stratificationid1_4  stratificationid1_5  stratificationid1_6  \\\n",
              "0                       0                    0                    0   \n",
              "1                       0                    0                    0   \n",
              "2                       0                    1                    0   \n",
              "3                       0                    0                    0   \n",
              "4                       0                    0                    0   \n",
              "...                   ...                  ...                  ...   \n",
              "8248                    1                    0                    0   \n",
              "8249                    0                    0                    0   \n",
              "8250                    0                    0                    0   \n",
              "8251                    0                    0                    0   \n",
              "8252                    0                    0                    0   \n",
              "\n",
              "      stratificationid1_7  \n",
              "0                       0  \n",
              "1                       0  \n",
              "2                       0  \n",
              "3                       0  \n",
              "4                       0  \n",
              "...                   ...  \n",
              "8248                    0  \n",
              "8249                    0  \n",
              "8250                    1  \n",
              "8251                    0  \n",
              "8252                    0  \n",
              "\n",
              "[8253 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2dcdab5-dc40-4e34-b734-d9b6ebdd8e82\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>data_value</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>...</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "      <th>stratificationid1_0</th>\n",
              "      <th>stratificationid1_1</th>\n",
              "      <th>stratificationid1_2</th>\n",
              "      <th>stratificationid1_3</th>\n",
              "      <th>stratificationid1_4</th>\n",
              "      <th>stratificationid1_5</th>\n",
              "      <th>stratificationid1_6</th>\n",
              "      <th>stratificationid1_7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1878.0</td>\n",
              "      <td>40.1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>115.0</td>\n",
              "      <td>28.6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>58.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1878.0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>2016</td>\n",
              "      <td>66</td>\n",
              "      <td>511.0</td>\n",
              "      <td>34.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2016</td>\n",
              "      <td>66</td>\n",
              "      <td>203.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8250</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>154.0</td>\n",
              "      <td>18.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8251</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>820.0</td>\n",
              "      <td>24.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8252</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>178.0</td>\n",
              "      <td>30.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8253 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2dcdab5-dc40-4e34-b734-d9b6ebdd8e82')\"\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-c2dcdab5-dc40-4e34-b734-d9b6ebdd8e82 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-c2dcdab5-dc40-4e34-b734-d9b6ebdd8e82');\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-6662d801-a96d-4a76-9531-09ac238f2872\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6662d801-a96d-4a76-9531-09ac238f2872')\"\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-6662d801-a96d-4a76-9531-09ac238f2872 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_to_move = race.pop('data_value')\n",
        "race['data_value']= column_to_move"
      ],
      "metadata": {
        "id": "dpUChWzSsPma"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = race.iloc[:,:23]\n",
        "y = race['data_value']"
      ],
      "metadata": {
        "id": "fyanojlzsPma"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "6965d33c-2409-4f3a-a388-84ec2386d11c",
        "id": "usHaeXjcsPma"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   yearend  locationid  sample_size  classid_FV  classid_OWS  classid_PA  \\\n",
              "0     2011           1       1878.0           0            1           0   \n",
              "1     2011           1        115.0           0            1           0   \n",
              "2     2011           1         61.0           0            1           0   \n",
              "3     2011           1         58.0           0            1           0   \n",
              "4     2011           1       1878.0           0            1           0   \n",
              "\n",
              "   questionid_Q018  questionid_Q019  questionid_Q036  questionid_Q037  ...  \\\n",
              "0                0                0                1                0  ...   \n",
              "1                0                0                1                0  ...   \n",
              "2                0                0                1                0  ...   \n",
              "3                0                0                1                0  ...   \n",
              "4                0                0                0                1  ...   \n",
              "\n",
              "   questionid_Q046  questionid_Q047  stratificationid1_0  stratificationid1_1  \\\n",
              "0                0                0                    0                    0   \n",
              "1                0                0                    0                    0   \n",
              "2                0                0                    0                    0   \n",
              "3                0                0                    1                    0   \n",
              "4                0                0                    0                    0   \n",
              "\n",
              "   stratificationid1_2  stratificationid1_3  stratificationid1_4  \\\n",
              "0                    1                    0                    0   \n",
              "1                    0                    1                    0   \n",
              "2                    0                    0                    0   \n",
              "3                    0                    0                    0   \n",
              "4                    1                    0                    0   \n",
              "\n",
              "   stratificationid1_5  stratificationid1_6  stratificationid1_7  \n",
              "0                    0                    0                    0  \n",
              "1                    0                    0                    0  \n",
              "2                    1                    0                    0  \n",
              "3                    0                    0                    0  \n",
              "4                    0                    0                    0  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34104c34-bef3-42d7-979f-50d78c7824b9\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>...</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "      <th>stratificationid1_0</th>\n",
              "      <th>stratificationid1_1</th>\n",
              "      <th>stratificationid1_2</th>\n",
              "      <th>stratificationid1_3</th>\n",
              "      <th>stratificationid1_4</th>\n",
              "      <th>stratificationid1_5</th>\n",
              "      <th>stratificationid1_6</th>\n",
              "      <th>stratificationid1_7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1878.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1878.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34104c34-bef3-42d7-979f-50d78c7824b9')\"\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-34104c34-bef3-42d7-979f-50d78c7824b9 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-34104c34-bef3-42d7-979f-50d78c7824b9');\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-8f8c3e43-254a-4951-848b-a24062cc1042\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f8c3e43-254a-4951-848b-a24062cc1042')\"\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-8f8c3e43-254a-4951-848b-a24062cc1042 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Standardization"
      ],
      "metadata": {
        "id": "NWasHuMZsPmb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Selection"
      ],
      "metadata": {
        "id": "8VzMg19tsPmb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Tuning and Model Evaluation"
      ],
      "metadata": {
        "id": "z6y4CbmXsPmj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Define a function for model evaluation\n",
        "\n",
        "# Split the dataset into training, validation, and test sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(x, y, test_size=0.25, random_state=42)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.4, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_valid = scaler.transform(X_valid)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "# Create a Polynomial Regression model with degree as a hyperparameter\n",
        "def create_polyreg(degree):\n",
        "    return make_pipeline(PolynomialFeatures(degree=degree), LinearRegression())\n",
        "\n",
        "# Step 2: Define a function for model evaluation\n",
        "def evaluate_model(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    mse = mean_squared_error(y, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    mae = mean_absolute_error(y, y_pred)\n",
        "    r2 = r2_score(y, y_pred)\n",
        "    return rmse, mae, r2\n",
        "# Step 3: Build a dictionary of regression models and hyperparameter grids\n",
        "models = {\n",
        "    \"Linear Regression\": {\n",
        "        \"model\": LinearRegression(),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 2)\": {\n",
        "        \"model\": create_polyreg(degree=2),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 3)\": {\n",
        "        \"model\": create_polyreg(degree=3),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Ridge Regression\": {\n",
        "        \"model\": Ridge(),\n",
        "        \"param_grid\": {\"alpha\": [0.01, 0.1, 1.0]}\n",
        "    },\n",
        "    \"K-Nearest Neighbors\": {\n",
        "        \"model\": KNeighborsRegressor(),\n",
        "        \"param_grid\": {\"n_neighbors\": [3, 5, 7]}\n",
        "    },\n",
        "    \"Random Forest Regression\": {\n",
        "        \"model\": RandomForestRegressor(),\n",
        "        \"param_grid\": {\n",
        "            \"n_estimators\": [100, 200, 300],  # You can adjust the number of trees\n",
        "            \"max_depth\": [5, 10, 20, 30],  # You can adjust the max depth of trees\n",
        "            \"min_samples_split\": [2, 5, 10], } # You can adjust other hyperparameters\n",
        "        }\n",
        "}\n",
        "\n",
        "# Step 4: Hyperparameter tuning and model evaluation\n",
        "results = {}\n",
        "\n",
        "for model_name, config in models.items():\n",
        "    model = config[\"model\"]\n",
        "    param_grid = config[\"param_grid\"]\n",
        "\n",
        "    # Hyperparameter tuning using GridSearchCV\n",
        "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    best_params = grid_search.best_params_  # Get the best hyperparameters\n",
        "\n",
        "    # Evaluate the best model on the validation set\n",
        "    rmse, mae, r2 = evaluate_model(best_model, X_valid, y_valid)\n",
        "\n",
        "    results[model_name] = {\n",
        "        \"Best_Model\": best_model,\n",
        "        \"Best_Params\": best_params,\n",
        "        \"RMSE\": rmse,\n",
        "        \"MAE\": mae,\n",
        "        \"R2\": r2\n",
        "    }\n",
        "\n",
        "# Step 5: Compare and select the best model based on validation results\n",
        "best_model_name = None\n",
        "best_rmse = float('inf')\n",
        "\n",
        "for model_name, metrics in results.items():\n",
        "    rmse = metrics[\"RMSE\"]\n",
        "    if rmse < best_rmse:\n",
        "        best_model_name = model_name\n",
        "        best_rmse = rmse\n",
        "\n",
        "print(\"Best Model:\", best_model_name)\n",
        "print(\"Best Hyperparameters:\", results[best_model_name][\"Best_Params\"])\n",
        "\n",
        "# Step 6: Evaluate the best model on the test set\n",
        "best_model = results[best_model_name][\"Best_Model\"]\n",
        "test_rmse, test_mae, test_r2 = evaluate_model(best_model, X_test, y_test)\n",
        "\n",
        "print(f\"Test RMSE: {test_rmse:.3f}\")\n",
        "print(f\"Test MAE: {test_mae:.3f}\")\n",
        "print(f\"Test R2 Score: {test_r2:.3f}\")\n",
        "\n",
        "# Step 7: Compare results for different models\n",
        "print(\"\\nModel Comparison:\\n\")\n",
        "for model_name, metrics in results.items():\n",
        "    print(f\"Model: {model_name}\")\n",
        "    print(f\"Best Hyperparameters: {metrics['Best_Params']}\")\n",
        "    print(f\"Train RMSE: {metrics['RMSE']:.3f}\")\n",
        "    print(f\"Train MAE: {metrics['MAE']:.3f}\")\n",
        "    print(f\"Train R2 Score: {metrics['R2']:.3f}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea7d134e-92ca-49f3-a09f-81e29b831339",
        "id": "p9HVbDr_sPmj"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: Polynomial Regression (Degree 2)\n",
            "Best Hyperparameters: {}\n",
            "Test RMSE: 6.335\n",
            "Test MAE: 4.925\n",
            "Test R2 Score: 0.620\n",
            "\n",
            "Model Comparison:\n",
            "\n",
            "Model: Linear Regression\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 7.413\n",
            "Train MAE: 5.855\n",
            "Train R2 Score: 0.445\n",
            "\n",
            "Model: Polynomial Regression (Degree 2)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 6.450\n",
            "Train MAE: 4.948\n",
            "Train R2 Score: 0.580\n",
            "\n",
            "Model: Polynomial Regression (Degree 3)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 6.531\n",
            "Train MAE: 4.993\n",
            "Train R2 Score: 0.569\n",
            "\n",
            "Model: Ridge Regression\n",
            "Best Hyperparameters: {'alpha': 1.0}\n",
            "Train RMSE: 7.415\n",
            "Train MAE: 5.855\n",
            "Train R2 Score: 0.445\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Best Hyperparameters: {'n_neighbors': 7}\n",
            "Train RMSE: 6.717\n",
            "Train MAE: 5.137\n",
            "Train R2 Score: 0.544\n",
            "\n",
            "Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 300}\n",
            "Train RMSE: 6.471\n",
            "Train MAE: 4.849\n",
            "Train R2 Score: 0.577\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Selecting best model\n"
      ],
      "metadata": {
        "id": "8oEBEmMNsPmk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation on test data"
      ],
      "metadata": {
        "id": "XFAuGM8PsPmk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model comparision"
      ],
      "metadata": {
        "id": "S9B6Ug9bsPmk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 Education"
      ],
      "metadata": {
        "id": "H193Y6Qatviy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Splitting"
      ],
      "metadata": {
        "id": "-oflbTnitviy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "sNZc04RXtviz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "63ee80c7-67fd-496c-84b1-1f74bef3fe4e",
        "id": "7536b-Frtviz"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      yearend  locationid  sample_size  stratificationid1  classid_FV  \\\n",
              "0        2011           1       1153.0                  2           0   \n",
              "1        2011           1       2402.0                  3           0   \n",
              "2        2011           1       1925.0                  1           0   \n",
              "3        2011           1       1812.0                  0           0   \n",
              "4        2011           1       1153.0                  2           0   \n",
              "...       ...         ...          ...                ...         ...   \n",
              "7003     2016          72       1735.0                  0           0   \n",
              "7004     2016          78        238.0                  2           0   \n",
              "7005     2016          78        394.0                  3           0   \n",
              "7006     2016          78        230.0                  1           0   \n",
              "7007     2016          78        378.0                  0           0   \n",
              "\n",
              "      classid_OWS  classid_PA  questionid_Q018  questionid_Q019  \\\n",
              "0               1           0                0                0   \n",
              "1               1           0                0                0   \n",
              "2               1           0                0                0   \n",
              "3               1           0                0                0   \n",
              "4               1           0                0                0   \n",
              "...           ...         ...              ...              ...   \n",
              "7003            0           1                0                0   \n",
              "7004            0           1                0                0   \n",
              "7005            0           1                0                0   \n",
              "7006            0           1                0                0   \n",
              "7007            0           1                0                0   \n",
              "\n",
              "      questionid_Q036  questionid_Q037  questionid_Q043  questionid_Q044  \\\n",
              "0                   1                0                0                0   \n",
              "1                   1                0                0                0   \n",
              "2                   1                0                0                0   \n",
              "3                   1                0                0                0   \n",
              "4                   0                1                0                0   \n",
              "...               ...              ...              ...              ...   \n",
              "7003                0                0                0                0   \n",
              "7004                0                0                0                0   \n",
              "7005                0                0                0                0   \n",
              "7006                0                0                0                0   \n",
              "7007                0                0                0                0   \n",
              "\n",
              "      questionid_Q045  questionid_Q046  questionid_Q047  data_value  \n",
              "0                   0                0                0        33.6  \n",
              "1                   0                0                0        32.8  \n",
              "2                   0                0                0        33.8  \n",
              "3                   0                0                0        26.4  \n",
              "4                   0                0                0        33.2  \n",
              "...               ...              ...              ...         ...  \n",
              "7003                0                0                1        33.9  \n",
              "7004                0                0                1        29.2  \n",
              "7005                0                0                1        26.9  \n",
              "7006                0                0                1        31.2  \n",
              "7007                0                0                1        16.4  \n",
              "\n",
              "[7008 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80e7a5e1-0156-4846-bd23-20df476c93be\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "      <th>data_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1153.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1812.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1153.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7003</th>\n",
              "      <td>2016</td>\n",
              "      <td>72</td>\n",
              "      <td>1735.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7004</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>238.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7005</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>394.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7006</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>230.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>31.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7007</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>378.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7008 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80e7a5e1-0156-4846-bd23-20df476c93be')\"\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-80e7a5e1-0156-4846-bd23-20df476c93be 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-80e7a5e1-0156-4846-bd23-20df476c93be');\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-8f45a802-8e22-4887-958c-7bbb6883ed06\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f45a802-8e22-4887-958c-7bbb6883ed06')\"\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-8f45a802-8e22-4887-958c-7bbb6883ed06 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_to_move = edu.pop('data_value')\n",
        "edu['data_value']= column_to_move"
      ],
      "metadata": {
        "id": "lH7JOgcTtviz"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = edu.iloc[:,:16]\n",
        "y = edu['data_value']"
      ],
      "metadata": {
        "id": "fWefyeTRtviz"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "52d7e41e-e1fc-4f53-acec-8b149d9e6763",
        "id": "YcTJcXZ3tvi0"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   yearend  locationid  sample_size  stratificationid1  classid_FV  \\\n",
              "0     2011           1       1153.0                  2           0   \n",
              "1     2011           1       2402.0                  3           0   \n",
              "2     2011           1       1925.0                  1           0   \n",
              "3     2011           1       1812.0                  0           0   \n",
              "4     2011           1       1153.0                  2           0   \n",
              "\n",
              "   classid_OWS  classid_PA  questionid_Q018  questionid_Q019  questionid_Q036  \\\n",
              "0            1           0                0                0                1   \n",
              "1            1           0                0                0                1   \n",
              "2            1           0                0                0                1   \n",
              "3            1           0                0                0                1   \n",
              "4            1           0                0                0                0   \n",
              "\n",
              "   questionid_Q037  questionid_Q043  questionid_Q044  questionid_Q045  \\\n",
              "0                0                0                0                0   \n",
              "1                0                0                0                0   \n",
              "2                0                0                0                0   \n",
              "3                0                0                0                0   \n",
              "4                1                0                0                0   \n",
              "\n",
              "   questionid_Q046  questionid_Q047  \n",
              "0                0                0  \n",
              "1                0                0  \n",
              "2                0                0  \n",
              "3                0                0  \n",
              "4                0                0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8e04a78-b004-4fab-8218-ed7948546d98\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1153.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1812.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1153.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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-e8e04a78-b004-4fab-8218-ed7948546d98')\"\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-e8e04a78-b004-4fab-8218-ed7948546d98 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-e8e04a78-b004-4fab-8218-ed7948546d98');\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-060b685e-fce5-4dac-a4f8-d18519c4edf4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-060b685e-fce5-4dac-a4f8-d18519c4edf4')\"\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-060b685e-fce5-4dac-a4f8-d18519c4edf4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Standardization"
      ],
      "metadata": {
        "id": "7B4wlbuytvi0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Selection"
      ],
      "metadata": {
        "id": "lx9I7dEAtvi0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Tuning and Model Evaluation"
      ],
      "metadata": {
        "id": "q1sjU1butvi0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Define a function for model evaluation\n",
        "\n",
        "# Split the dataset into training, validation, and test sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(x, y, test_size=0.25, random_state=42)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.4, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_valid = scaler.transform(X_valid)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "# Create a Polynomial Regression model with degree as a hyperparameter\n",
        "def create_polyreg(degree):\n",
        "    return make_pipeline(PolynomialFeatures(degree=degree), LinearRegression())\n",
        "\n",
        "# Step 2: Define a function for model evaluation\n",
        "def evaluate_model(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    mse = mean_squared_error(y, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    mae = mean_absolute_error(y, y_pred)\n",
        "    r2 = r2_score(y, y_pred)\n",
        "    return rmse, mae, r2\n",
        "# Step 3: Build a dictionary of regression models and hyperparameter grids\n",
        "models = {\n",
        "    \"Linear Regression\": {\n",
        "        \"model\": LinearRegression(),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 2)\": {\n",
        "        \"model\": create_polyreg(degree=2),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 3)\": {\n",
        "        \"model\": create_polyreg(degree=3),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Ridge Regression\": {\n",
        "        \"model\": Ridge(),\n",
        "        \"param_grid\": {\"alpha\": [0.01, 0.1, 1.0]}\n",
        "    },\n",
        "    \"K-Nearest Neighbors\": {\n",
        "        \"model\": KNeighborsRegressor(),\n",
        "        \"param_grid\": {\"n_neighbors\": [3, 5, 7]}\n",
        "    },\n",
        "    \"Random Forest Regression\": {\n",
        "        \"model\": RandomForestRegressor(),\n",
        "        \"param_grid\": {\n",
        "            \"n_estimators\": [100, 200, 300],  # You can adjust the number of trees\n",
        "            \"max_depth\": [5, 10, 20, 30],  # You can adjust the max depth of trees\n",
        "            \"min_samples_split\": [2, 5, 10], } # You can adjust other hyperparameters\n",
        "        }\n",
        "}\n",
        "\n",
        "# Step 4: Hyperparameter tuning and model evaluation\n",
        "results = {}\n",
        "\n",
        "for model_name, config in models.items():\n",
        "    model = config[\"model\"]\n",
        "    param_grid = config[\"param_grid\"]\n",
        "\n",
        "    # Hyperparameter tuning using GridSearchCV\n",
        "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    best_params = grid_search.best_params_  # Get the best hyperparameters\n",
        "\n",
        "    # Evaluate the best model on the validation set\n",
        "    rmse, mae, r2 = evaluate_model(best_model, X_valid, y_valid)\n",
        "\n",
        "    results[model_name] = {\n",
        "        \"Best_Model\": best_model,\n",
        "        \"Best_Params\": best_params,\n",
        "        \"RMSE\": rmse,\n",
        "        \"MAE\": mae,\n",
        "        \"R2\": r2\n",
        "    }\n",
        "\n",
        "# Step 5: Compare and select the best model based on validation results\n",
        "best_model_name = None\n",
        "best_rmse = float('inf')\n",
        "\n",
        "for model_name, metrics in results.items():\n",
        "    rmse = metrics[\"RMSE\"]\n",
        "    if rmse < best_rmse:\n",
        "        best_model_name = model_name\n",
        "        best_rmse = rmse\n",
        "\n",
        "print(\"Best Model:\", best_model_name)\n",
        "print(\"Best Hyperparameters:\", results[best_model_name][\"Best_Params\"])\n",
        "\n",
        "# Step 6: Evaluate the best model on the test set\n",
        "best_model = results[best_model_name][\"Best_Model\"]\n",
        "test_rmse, test_mae, test_r2 = evaluate_model(best_model, X_test, y_test)\n",
        "\n",
        "print(f\"Test RMSE: {test_rmse:.3f}\")\n",
        "print(f\"Test MAE: {test_mae:.3f}\")\n",
        "print(f\"Test R2 Score: {test_r2:.3f}\")\n",
        "\n",
        "# Step 7: Compare results for different models\n",
        "print(\"\\nModel Comparison:\\n\")\n",
        "for model_name, metrics in results.items():\n",
        "    print(f\"Model: {model_name}\")\n",
        "    print(f\"Best Hyperparameters: {metrics['Best_Params']}\")\n",
        "    print(f\"Train RMSE: {metrics['RMSE']:.3f}\")\n",
        "    print(f\"Train MAE: {metrics['MAE']:.3f}\")\n",
        "    print(f\"Train R2 Score: {metrics['R2']:.3f}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b1c0281-6ca6-445d-ff74-44f0096f5ba1",
        "id": "di654TkPtvi0"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 100}\n",
            "Test RMSE: 3.679\n",
            "Test MAE: 2.681\n",
            "Test R2 Score: 0.848\n",
            "\n",
            "Model Comparison:\n",
            "\n",
            "Model: Linear Regression\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 6.995\n",
            "Train MAE: 5.510\n",
            "Train R2 Score: 0.499\n",
            "\n",
            "Model: Polynomial Regression (Degree 2)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 4.902\n",
            "Train MAE: 3.775\n",
            "Train R2 Score: 0.754\n",
            "\n",
            "Model: Polynomial Regression (Degree 3)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 4.540\n",
            "Train MAE: 3.462\n",
            "Train R2 Score: 0.789\n",
            "\n",
            "Model: Ridge Regression\n",
            "Best Hyperparameters: {'alpha': 1.0}\n",
            "Train RMSE: 6.997\n",
            "Train MAE: 5.508\n",
            "Train R2 Score: 0.499\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Best Hyperparameters: {'n_neighbors': 7}\n",
            "Train RMSE: 4.250\n",
            "Train MAE: 3.246\n",
            "Train R2 Score: 0.815\n",
            "\n",
            "Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 100}\n",
            "Train RMSE: 3.739\n",
            "Train MAE: 2.720\n",
            "Train R2 Score: 0.857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Selecting best model\n"
      ],
      "metadata": {
        "id": "NOuNEqLrtvi1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation on test data"
      ],
      "metadata": {
        "id": "fbzsB5pRtvi1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model comparision"
      ],
      "metadata": {
        "id": "cFfi0ADrtvi1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4 Income"
      ],
      "metadata": {
        "id": "5lHvK6d6twsj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Splitting"
      ],
      "metadata": {
        "id": "QeudcpSJtwsk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "7tYT44XBtwsk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "c92caf5a-7190-40b7-dc1b-9f23262b4d18",
        "id": "F8SDtafDtwsk"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       yearend  locationid  sample_size  stratificationid1  data_value  \\\n",
              "0         2011           1       1112.0                  5        38.5   \n",
              "1         2011           1       1367.0                  0        34.8   \n",
              "2         2011           1        757.0                  1        35.8   \n",
              "3         2011           1        861.0                  2        32.3   \n",
              "4         2011           1        785.0                  3        34.1   \n",
              "...        ...         ...          ...                ...         ...   \n",
              "12097     2016          78        154.0                  1        28.9   \n",
              "12098     2016          78        158.0                  2        20.9   \n",
              "12099     2016          78        159.0                  3        29.8   \n",
              "12100     2016          78        212.0                  4        13.3   \n",
              "12101     2016          78        137.0                  6        25.3   \n",
              "\n",
              "       classid_FV  classid_OWS  classid_PA  questionid_Q018  questionid_Q019  \\\n",
              "0               0            1           0                0                0   \n",
              "1               0            1           0                0                0   \n",
              "2               0            1           0                0                0   \n",
              "3               0            1           0                0                0   \n",
              "4               0            1           0                0                0   \n",
              "...           ...          ...         ...              ...              ...   \n",
              "12097           0            0           1                0                0   \n",
              "12098           0            0           1                0                0   \n",
              "12099           0            0           1                0                0   \n",
              "12100           0            0           1                0                0   \n",
              "12101           0            0           1                0                0   \n",
              "\n",
              "       questionid_Q036  questionid_Q037  questionid_Q043  questionid_Q044  \\\n",
              "0                    1                0                0                0   \n",
              "1                    1                0                0                0   \n",
              "2                    1                0                0                0   \n",
              "3                    1                0                0                0   \n",
              "4                    1                0                0                0   \n",
              "...                ...              ...              ...              ...   \n",
              "12097                0                0                0                0   \n",
              "12098                0                0                0                0   \n",
              "12099                0                0                0                0   \n",
              "12100                0                0                0                0   \n",
              "12101                0                0                0                0   \n",
              "\n",
              "       questionid_Q045  questionid_Q046  questionid_Q047  \n",
              "0                    0                0                0  \n",
              "1                    0                0                0  \n",
              "2                    0                0                0  \n",
              "3                    0                0                0  \n",
              "4                    0                0                0  \n",
              "...                ...              ...              ...  \n",
              "12097                0                0                1  \n",
              "12098                0                0                1  \n",
              "12099                0                0                1  \n",
              "12100                0                0                1  \n",
              "12101                0                0                1  \n",
              "\n",
              "[12102 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e6e0e13-f033-4cce-936f-a70023cdae96\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>data_value</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1112.0</td>\n",
              "      <td>5</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1367.0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>757.0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>861.0</td>\n",
              "      <td>2</td>\n",
              "      <td>32.3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>785.0</td>\n",
              "      <td>3</td>\n",
              "      <td>34.1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12097</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>154.0</td>\n",
              "      <td>1</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12098</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>158.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12099</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>159.0</td>\n",
              "      <td>3</td>\n",
              "      <td>29.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12100</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>212.0</td>\n",
              "      <td>4</td>\n",
              "      <td>13.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12101</th>\n",
              "      <td>2016</td>\n",
              "      <td>78</td>\n",
              "      <td>137.0</td>\n",
              "      <td>6</td>\n",
              "      <td>25.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12102 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e6e0e13-f033-4cce-936f-a70023cdae96')\"\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-6e6e0e13-f033-4cce-936f-a70023cdae96 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-6e6e0e13-f033-4cce-936f-a70023cdae96');\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-a7cd9352-9dfb-40d9-8063-10ac7ca79a2a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7cd9352-9dfb-40d9-8063-10ac7ca79a2a')\"\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-a7cd9352-9dfb-40d9-8063-10ac7ca79a2a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_to_move = inc.pop('data_value')\n",
        "inc['data_value']= column_to_move"
      ],
      "metadata": {
        "id": "2nv958Ajtwsl"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = inc.iloc[:,:16]\n",
        "y = inc['data_value']"
      ],
      "metadata": {
        "id": "vqDaPPoktwsl"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "224ed574-b7d4-418b-e323-65511a6d709e",
        "id": "gTTct96Xtwsl"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   yearend  locationid  sample_size  stratificationid1  classid_FV  \\\n",
              "0     2011           1       1112.0                  5           0   \n",
              "1     2011           1       1367.0                  0           0   \n",
              "2     2011           1        757.0                  1           0   \n",
              "3     2011           1        861.0                  2           0   \n",
              "4     2011           1        785.0                  3           0   \n",
              "\n",
              "   classid_OWS  classid_PA  questionid_Q018  questionid_Q019  questionid_Q036  \\\n",
              "0            1           0                0                0                1   \n",
              "1            1           0                0                0                1   \n",
              "2            1           0                0                0                1   \n",
              "3            1           0                0                0                1   \n",
              "4            1           0                0                0                1   \n",
              "\n",
              "   questionid_Q037  questionid_Q043  questionid_Q044  questionid_Q045  \\\n",
              "0                0                0                0                0   \n",
              "1                0                0                0                0   \n",
              "2                0                0                0                0   \n",
              "3                0                0                0                0   \n",
              "4                0                0                0                0   \n",
              "\n",
              "   questionid_Q046  questionid_Q047  \n",
              "0                0                0  \n",
              "1                0                0  \n",
              "2                0                0  \n",
              "3                0                0  \n",
              "4                0                0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ac292f6-0c41-425d-949a-9403aa8ff241\" 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>yearend</th>\n",
              "      <th>locationid</th>\n",
              "      <th>sample_size</th>\n",
              "      <th>stratificationid1</th>\n",
              "      <th>classid_FV</th>\n",
              "      <th>classid_OWS</th>\n",
              "      <th>classid_PA</th>\n",
              "      <th>questionid_Q018</th>\n",
              "      <th>questionid_Q019</th>\n",
              "      <th>questionid_Q036</th>\n",
              "      <th>questionid_Q037</th>\n",
              "      <th>questionid_Q043</th>\n",
              "      <th>questionid_Q044</th>\n",
              "      <th>questionid_Q045</th>\n",
              "      <th>questionid_Q046</th>\n",
              "      <th>questionid_Q047</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1112.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1367.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>757.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>861.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>785.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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-6ac292f6-0c41-425d-949a-9403aa8ff241')\"\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-6ac292f6-0c41-425d-949a-9403aa8ff241 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-6ac292f6-0c41-425d-949a-9403aa8ff241');\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-e9ec416d-1e68-4fbb-aba9-b6e1093e8b1b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9ec416d-1e68-4fbb-aba9-b6e1093e8b1b')\"\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-e9ec416d-1e68-4fbb-aba9-b6e1093e8b1b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Standardization"
      ],
      "metadata": {
        "id": "-iiCBPvxtwsl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Selection"
      ],
      "metadata": {
        "id": "0wW14N1Btwsl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Tuning and Model Evaluation"
      ],
      "metadata": {
        "id": "6Gksoi_dtwsl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Define a function for model evaluation\n",
        "\n",
        "# Split the dataset into training, validation, and test sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(x, y, test_size=0.25, random_state=42)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.4, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_valid = scaler.transform(X_valid)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "# Create a Polynomial Regression model with degree as a hyperparameter\n",
        "def create_polyreg(degree):\n",
        "    return make_pipeline(PolynomialFeatures(degree=degree), LinearRegression())\n",
        "\n",
        "# Step 2: Define a function for model evaluation\n",
        "def evaluate_model(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    mse = mean_squared_error(y, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    mae = mean_absolute_error(y, y_pred)\n",
        "    r2 = r2_score(y, y_pred)\n",
        "    return rmse, mae, r2\n",
        "# Step 3: Build a dictionary of regression models and hyperparameter grids\n",
        "models = {\n",
        "    \"Linear Regression\": {\n",
        "        \"model\": LinearRegression(),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 2)\": {\n",
        "        \"model\": create_polyreg(degree=2),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Polynomial Regression (Degree 3)\": {\n",
        "        \"model\": create_polyreg(degree=3),\n",
        "        \"param_grid\": {}\n",
        "    },\n",
        "    \"Ridge Regression\": {\n",
        "        \"model\": Ridge(),\n",
        "        \"param_grid\": {\"alpha\": [0.01, 0.1, 1.0]}\n",
        "    },\n",
        "    \"K-Nearest Neighbors\": {\n",
        "        \"model\": KNeighborsRegressor(),\n",
        "        \"param_grid\": {\"n_neighbors\": [3, 5, 7]}\n",
        "    },\n",
        "    \"Random Forest Regression\": {\n",
        "        \"model\": RandomForestRegressor(),\n",
        "        \"param_grid\": {\n",
        "            \"n_estimators\": [100, 200, 300],  # You can adjust the number of trees\n",
        "            \"max_depth\": [5, 10, 20, 30],  # You can adjust the max depth of trees\n",
        "            \"min_samples_split\": [2, 5, 10], } # You can adjust other hyperparameters\n",
        "        }\n",
        "}\n",
        "\n",
        "# Step 4: Hyperparameter tuning and model evaluation\n",
        "results = {}\n",
        "\n",
        "for model_name, config in models.items():\n",
        "    model = config[\"model\"]\n",
        "    param_grid = config[\"param_grid\"]\n",
        "\n",
        "    # Hyperparameter tuning using GridSearchCV\n",
        "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    best_params = grid_search.best_params_  # Get the best hyperparameters\n",
        "\n",
        "    # Evaluate the best model on the validation set\n",
        "    rmse, mae, r2 = evaluate_model(best_model, X_valid, y_valid)\n",
        "\n",
        "    results[model_name] = {\n",
        "        \"Best_Model\": best_model,\n",
        "        \"Best_Params\": best_params,\n",
        "        \"RMSE\": rmse,\n",
        "        \"MAE\": mae,\n",
        "        \"R2\": r2\n",
        "    }\n",
        "\n",
        "# Step 5: Compare and select the best model based on validation results\n",
        "best_model_name = None\n",
        "best_rmse = float('inf')\n",
        "\n",
        "for model_name, metrics in results.items():\n",
        "    rmse = metrics[\"RMSE\"]\n",
        "    if rmse < best_rmse:\n",
        "        best_model_name = model_name\n",
        "        best_rmse = rmse\n",
        "\n",
        "print(\"Best Model:\", best_model_name)\n",
        "print(\"Best Hyperparameters:\", results[best_model_name][\"Best_Params\"])\n",
        "\n",
        "# Step 6: Evaluate the best model on the test set\n",
        "best_model = results[best_model_name][\"Best_Model\"]\n",
        "test_rmse, test_mae, test_r2 = evaluate_model(best_model, X_test, y_test)\n",
        "\n",
        "print(f\"Test RMSE: {test_rmse:.3f}\")\n",
        "print(f\"Test MAE: {test_mae:.3f}\")\n",
        "print(f\"Test R2 Score: {test_r2:.3f}\")\n",
        "\n",
        "# Step 7: Compare results for different models\n",
        "print(\"\\nModel Comparison:\\n\")\n",
        "for model_name, metrics in results.items():\n",
        "    print(f\"Model: {model_name}\")\n",
        "    print(f\"Best Hyperparameters: {metrics['Best_Params']}\")\n",
        "    print(f\"Train RMSE: {metrics['RMSE']:.3f}\")\n",
        "    print(f\"Train MAE: {metrics['MAE']:.3f}\")\n",
        "    print(f\"Train R2 Score: {metrics['R2']:.3f}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60eb266b-8a0c-49b7-e34e-eda270b98575",
        "id": "7Cb6W3Bptwsm"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 30, 'min_samples_split': 5, 'n_estimators': 300}\n",
            "Test RMSE: 3.759\n",
            "Test MAE: 2.892\n",
            "Test R2 Score: 0.820\n",
            "\n",
            "Model Comparison:\n",
            "\n",
            "Model: Linear Regression\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 5.795\n",
            "Train MAE: 4.560\n",
            "Train R2 Score: 0.578\n",
            "\n",
            "Model: Polynomial Regression (Degree 2)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 5.558\n",
            "Train MAE: 4.385\n",
            "Train R2 Score: 0.612\n",
            "\n",
            "Model: Polynomial Regression (Degree 3)\n",
            "Best Hyperparameters: {}\n",
            "Train RMSE: 5.002\n",
            "Train MAE: 3.922\n",
            "Train R2 Score: 0.686\n",
            "\n",
            "Model: Ridge Regression\n",
            "Best Hyperparameters: {'alpha': 1.0}\n",
            "Train RMSE: 5.795\n",
            "Train MAE: 4.560\n",
            "Train R2 Score: 0.578\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Best Hyperparameters: {'n_neighbors': 7}\n",
            "Train RMSE: 4.640\n",
            "Train MAE: 3.564\n",
            "Train R2 Score: 0.729\n",
            "\n",
            "Model: Random Forest Regression\n",
            "Best Hyperparameters: {'max_depth': 30, 'min_samples_split': 5, 'n_estimators': 300}\n",
            "Train RMSE: 3.756\n",
            "Train MAE: 2.883\n",
            "Train R2 Score: 0.823\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Selecting best model\n"
      ],
      "metadata": {
        "id": "celPqS2dtwsm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation on test data"
      ],
      "metadata": {
        "id": "wjWCeWAwtwsm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model comparision"
      ],
      "metadata": {
        "id": "HJDEjKubtwsm"
      }
    }
  ]
}