jupyterNotebook/BodyFat.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHHhkRjW1G4t",
        "outputId": "f3fc7d57-2c87-463e-92fd-24830efca24b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pycaret\n",
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            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, Werkzeug, tsdownsample, scipy, scikit-base, schemdraw, retrying, orjson, jedi, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, plotly-resampler, category-encoders, tbats, pycaret\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "VswXDqSn1cff"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/bodyfat.csv\")"
      ],
      "metadata": {
        "id": "p77VPY3S1eIi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "e6TxxOFxxjN8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/bodyfat.csv\")"
      ],
      "metadata": {
        "id": "p3Ktpadcx315"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dL0cN8WPyEqQ",
        "outputId": "c24e2515-ade4-432a-de0a-745cbdf0c375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  \\\n",
              "0   1.0708     12.3   23  154.25   67.75  36.2   93.1     85.2   94.5   59.0   \n",
              "1   1.0853      6.1   22  173.25   72.25  38.5   93.6     83.0   98.7   58.7   \n",
              "2   1.0414     25.3   22  154.00   66.25  34.0   95.8     87.9   99.2   59.6   \n",
              "3   1.0751     10.4   26  184.75   72.25  37.4  101.8     86.4  101.2   60.1   \n",
              "4   1.0340     28.7   24  184.25   71.25  34.4   97.3    100.0  101.9   63.2   \n",
              "\n",
              "   Knee  Ankle  Biceps  Forearm  Wrist  \n",
              "0  37.3   21.9    32.0     27.4   17.1  \n",
              "1  37.3   23.4    30.5     28.9   18.2  \n",
              "2  38.9   24.0    28.8     25.2   16.6  \n",
              "3  37.3   22.8    32.4     29.4   18.2  \n",
              "4  42.2   24.0    32.2     27.7   17.7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c319593-15f6-4005-8bcc-0ccdc2b705e2\" 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>Density</th>\n",
              "      <th>BodyFat</th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Neck</th>\n",
              "      <th>Chest</th>\n",
              "      <th>Abdomen</th>\n",
              "      <th>Hip</th>\n",
              "      <th>Thigh</th>\n",
              "      <th>Knee</th>\n",
              "      <th>Ankle</th>\n",
              "      <th>Biceps</th>\n",
              "      <th>Forearm</th>\n",
              "      <th>Wrist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0708</td>\n",
              "      <td>12.3</td>\n",
              "      <td>23</td>\n",
              "      <td>154.25</td>\n",
              "      <td>67.75</td>\n",
              "      <td>36.2</td>\n",
              "      <td>93.1</td>\n",
              "      <td>85.2</td>\n",
              "      <td>94.5</td>\n",
              "      <td>59.0</td>\n",
              "      <td>37.3</td>\n",
              "      <td>21.9</td>\n",
              "      <td>32.0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>17.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0853</td>\n",
              "      <td>6.1</td>\n",
              "      <td>22</td>\n",
              "      <td>173.25</td>\n",
              "      <td>72.25</td>\n",
              "      <td>38.5</td>\n",
              "      <td>93.6</td>\n",
              "      <td>83.0</td>\n",
              "      <td>98.7</td>\n",
              "      <td>58.7</td>\n",
              "      <td>37.3</td>\n",
              "      <td>23.4</td>\n",
              "      <td>30.5</td>\n",
              "      <td>28.9</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0414</td>\n",
              "      <td>25.3</td>\n",
              "      <td>22</td>\n",
              "      <td>154.00</td>\n",
              "      <td>66.25</td>\n",
              "      <td>34.0</td>\n",
              "      <td>95.8</td>\n",
              "      <td>87.9</td>\n",
              "      <td>99.2</td>\n",
              "      <td>59.6</td>\n",
              "      <td>38.9</td>\n",
              "      <td>24.0</td>\n",
              "      <td>28.8</td>\n",
              "      <td>25.2</td>\n",
              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0751</td>\n",
              "      <td>10.4</td>\n",
              "      <td>26</td>\n",
              "      <td>184.75</td>\n",
              "      <td>72.25</td>\n",
              "      <td>37.4</td>\n",
              "      <td>101.8</td>\n",
              "      <td>86.4</td>\n",
              "      <td>101.2</td>\n",
              "      <td>60.1</td>\n",
              "      <td>37.3</td>\n",
              "      <td>22.8</td>\n",
              "      <td>32.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0340</td>\n",
              "      <td>28.7</td>\n",
              "      <td>24</td>\n",
              "      <td>184.25</td>\n",
              "      <td>71.25</td>\n",
              "      <td>34.4</td>\n",
              "      <td>97.3</td>\n",
              "      <td>100.0</td>\n",
              "      <td>101.9</td>\n",
              "      <td>63.2</td>\n",
              "      <td>42.2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.7</td>\n",
              "      <td>17.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "        document.querySelector('#df-2c319593-15f6-4005-8bcc-0ccdc2b705e2 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-2c319593-15f6-4005-8bcc-0ccdc2b705e2');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </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",
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              "      --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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "      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",
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              "      border-color: transparent;\n",
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              "    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-c57b65f0-71cf-44eb-9ecb-1b63d33af651 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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "-bUMqLC_y1hQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc1bba0d-5ddf-454c-c470-397bf5b9b714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Density    0\n",
              "BodyFat    0\n",
              "Age        0\n",
              "Weight     0\n",
              "Height     0\n",
              "Neck       0\n",
              "Chest      0\n",
              "Abdomen    0\n",
              "Hip        0\n",
              "Thigh      0\n",
              "Knee       0\n",
              "Ankle      0\n",
              "Biceps     0\n",
              "Forearm    0\n",
              "Wrist      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "pS44Zkq41Eim"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# separating the data and labels\n",
        "X = df.drop(columns = 'BodyFat', axis=1)\n",
        "Y = df['BodyFat']"
      ],
      "metadata": {
        "id": "zlCsPTae1ax1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WpxUiq2410sC",
        "outputId": "97f16820-afab-4ffe-c6ba-9d88685e300f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Density  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  Knee  \\\n",
              "0   1.0708   23  154.25   67.75  36.2   93.1     85.2   94.5   59.0  37.3   \n",
              "1   1.0853   22  173.25   72.25  38.5   93.6     83.0   98.7   58.7  37.3   \n",
              "2   1.0414   22  154.00   66.25  34.0   95.8     87.9   99.2   59.6  38.9   \n",
              "3   1.0751   26  184.75   72.25  37.4  101.8     86.4  101.2   60.1  37.3   \n",
              "4   1.0340   24  184.25   71.25  34.4   97.3    100.0  101.9   63.2  42.2   \n",
              "\n",
              "   Ankle  Biceps  Forearm  Wrist  \n",
              "0   21.9    32.0     27.4   17.1  \n",
              "1   23.4    30.5     28.9   18.2  \n",
              "2   24.0    28.8     25.2   16.6  \n",
              "3   22.8    32.4     29.4   18.2  \n",
              "4   24.0    32.2     27.7   17.7  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Density</th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.0708</td>\n",
              "      <td>23</td>\n",
              "      <td>154.25</td>\n",
              "      <td>67.75</td>\n",
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              "      <td>17.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0853</td>\n",
              "      <td>22</td>\n",
              "      <td>173.25</td>\n",
              "      <td>72.25</td>\n",
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              "      <td>28.9</td>\n",
              "      <td>18.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0414</td>\n",
              "      <td>22</td>\n",
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              "      <td>87.9</td>\n",
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              "      <td>25.2</td>\n",
              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0751</td>\n",
              "      <td>26</td>\n",
              "      <td>184.75</td>\n",
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              "      <td>60.1</td>\n",
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              "      <td>32.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0340</td>\n",
              "      <td>24</td>\n",
              "      <td>184.25</td>\n",
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              "      <td>34.4</td>\n",
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              "      <td>101.9</td>\n",
              "      <td>63.2</td>\n",
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              "      <td>32.2</td>\n",
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              "      <td>17.7</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        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-ee63759e-1a4d-4292-bc43-617f6701b954\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee63759e-1a4d-4292-bc43-617f6701b954')\"\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-ee63759e-1a4d-4292-bc43-617f6701b954 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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "metadata": {
        "id": "vG2zJSyQ36Vo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U_8raOEm7hxN",
        "outputId": "74124743-0c67-4a63-f86d-1b936a4d285d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Density  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  Knee  \\\n",
              "16    1.0333   34  195.75   71.00  38.9  101.9     96.4  105.2   64.8  40.8   \n",
              "64    1.0264   57  205.50   70.00  40.1  105.3    105.5  108.3   65.0  41.2   \n",
              "234   1.0403   60  157.75   67.50  40.4   97.2     93.3   94.0   54.3  35.7   \n",
              "70    1.0439   62  167.50   71.50  35.5   97.6     91.5   98.5   56.6  38.6   \n",
              "178   1.0477   38  187.25   69.25  38.0  102.7     92.7  101.9   64.7  39.5   \n",
              "\n",
              "     Ankle  Biceps  Forearm  Wrist  \n",
              "16    23.1    36.2     30.8   17.3  \n",
              "64    24.7    35.3     31.1   18.4  \n",
              "234   21.0    31.3     28.7   18.3  \n",
              "70    22.4    31.5     27.3   18.6  \n",
              "178   24.7    34.8     30.3   18.1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b01ec522-9a29-47c5-a500-889097d21179\" 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>Density</th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Neck</th>\n",
              "      <th>Chest</th>\n",
              "      <th>Abdomen</th>\n",
              "      <th>Hip</th>\n",
              "      <th>Thigh</th>\n",
              "      <th>Knee</th>\n",
              "      <th>Ankle</th>\n",
              "      <th>Biceps</th>\n",
              "      <th>Forearm</th>\n",
              "      <th>Wrist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.0333</td>\n",
              "      <td>34</td>\n",
              "      <td>195.75</td>\n",
              "      <td>71.00</td>\n",
              "      <td>38.9</td>\n",
              "      <td>101.9</td>\n",
              "      <td>96.4</td>\n",
              "      <td>105.2</td>\n",
              "      <td>64.8</td>\n",
              "      <td>40.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>36.2</td>\n",
              "      <td>30.8</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1.0264</td>\n",
              "      <td>57</td>\n",
              "      <td>205.50</td>\n",
              "      <td>70.00</td>\n",
              "      <td>40.1</td>\n",
              "      <td>105.3</td>\n",
              "      <td>105.5</td>\n",
              "      <td>108.3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>41.2</td>\n",
              "      <td>24.7</td>\n",
              "      <td>35.3</td>\n",
              "      <td>31.1</td>\n",
              "      <td>18.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>1.0403</td>\n",
              "      <td>60</td>\n",
              "      <td>157.75</td>\n",
              "      <td>67.50</td>\n",
              "      <td>40.4</td>\n",
              "      <td>97.2</td>\n",
              "      <td>93.3</td>\n",
              "      <td>94.0</td>\n",
              "      <td>54.3</td>\n",
              "      <td>35.7</td>\n",
              "      <td>21.0</td>\n",
              "      <td>31.3</td>\n",
              "      <td>28.7</td>\n",
              "      <td>18.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>1.0439</td>\n",
              "      <td>62</td>\n",
              "      <td>167.50</td>\n",
              "      <td>71.50</td>\n",
              "      <td>35.5</td>\n",
              "      <td>97.6</td>\n",
              "      <td>91.5</td>\n",
              "      <td>98.5</td>\n",
              "      <td>56.6</td>\n",
              "      <td>38.6</td>\n",
              "      <td>22.4</td>\n",
              "      <td>31.5</td>\n",
              "      <td>27.3</td>\n",
              "      <td>18.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>1.0477</td>\n",
              "      <td>38</td>\n",
              "      <td>187.25</td>\n",
              "      <td>69.25</td>\n",
              "      <td>38.0</td>\n",
              "      <td>102.7</td>\n",
              "      <td>92.7</td>\n",
              "      <td>101.9</td>\n",
              "      <td>64.7</td>\n",
              "      <td>39.5</td>\n",
              "      <td>24.7</td>\n",
              "      <td>34.8</td>\n",
              "      <td>30.3</td>\n",
              "      <td>18.1</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-b01ec522-9a29-47c5-a500-889097d21179')\"\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-b01ec522-9a29-47c5-a500-889097d21179 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-b01ec522-9a29-47c5-a500-889097d21179');\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-cb060aee-1d39-431a-93b5-0a4d8bcbec27\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb060aee-1d39-431a-93b5-0a4d8bcbec27')\"\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-cb060aee-1d39-431a-93b5-0a4d8bcbec27 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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opajYWno12pG",
        "outputId": "06218afd-a3f6-48d3-aea8-f0f6a90848d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    12.3\n",
              "1     6.1\n",
              "2    25.3\n",
              "3    10.4\n",
              "4    28.7\n",
              "Name: BodyFat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "DBsLywHu136t",
        "outputId": "f46f2304-ca46-4d0a-bc1d-a4931411c4c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler(copy=True, with_mean=True, with_std=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler(copy=True, with_mean=True, with_std=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_train=scaler.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "JUcVRVuM2P2e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "uNbdxuEC2gfj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "##cross validation\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "qAhuvO482hz5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regression=LinearRegression()\n",
        "regression.fit(X_train,Y_train)"
      ],
      "metadata": {
        "id": "XvvS8fxl2msX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "94f1e551-b1fa-42fd-ef61-3cbc63c32650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, positive=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, positive=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, positive=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse=cross_val_score(regression,X_train,Y_train,scoring='neg_mean_squared_error',cv=10)"
      ],
      "metadata": {
        "id": "Z6MnnNbm4OYA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "tj3OsowQ4YOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTIpgLWT4UrD",
        "outputId": "ca66d642-2904-4b6b-a53c-52a946630b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2.3591364076254684"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##prediction\n",
        "reg_pred=regression.predict(X_test)"
      ],
      "metadata": {
        "id": "D_Wk6jHH4Wzg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecz-OrjA4ew4",
        "outputId": "6522e993-6c63-483b-8f2f-f72a02069229"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([27.9846175 , 31.59380479, 25.21633504, 23.98886675, 22.10968122,\n",
              "       31.23413382, 19.32963673, 16.75517181, 21.65893467, 28.15695324,\n",
              "       26.62644864, 18.41663584,  9.14966304, 13.62065251,  6.17411606,\n",
              "       12.48971326, 11.09737653, 16.7890615 , 34.72234314, 20.92123024,\n",
              "       11.01005923, 37.08239151, 16.52865344, 20.61457026, 24.80873953,\n",
              "       21.10773263, 17.49237511,  3.18068923, 13.21147876, 21.30888485,\n",
              "       12.12226972, 23.53906049, 20.66301858, 45.43938369, 26.48477784,\n",
              "       15.44547977, 29.28620624, 28.06933914, 22.65584334, 35.6026791 ,\n",
              "       -4.29743281, 16.99999982, 19.93372882, 20.95981397, 10.40272792,\n",
              "       16.76816968, 31.35977088, 22.16084949,  7.04933276, 14.17892437,\n",
              "       19.76865769])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import seaborn as sns\n",
        "sns.displot(reg_pred-Y_test,kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "SiqqASKN4nQz",
        "outputId": "9c95fac0-7b24-4363-9e92-8f4f44832c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7e97d169a050>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "-m0ZQUcf4qdR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=r2_score(reg_pred,Y_test)\n"
      ],
      "metadata": {
        "id": "FYgZb-3N5BHx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fE4nnSwa5DbE",
        "outputId": "3a629d93-793d-4d49-ef86-0dc1477ac844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9903263475467049"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "e6B9M7jA5IpM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_regressor=Ridge()"
      ],
      "metadata": {
        "id": "tfbYHP-k5mml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}\n",
        "ridgecv=GridSearchCV(ridge_regressor,parameters,scoring='neg_mean_squared_error',cv=5)\n",
        "ridgecv.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "wyY7cv5z5oOA",
        "outputId": "0499cfa9-e2d6-4e3a-847d-489db5c8c361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=None, positive=False, random_state=None,\n",
              "                             solver='auto', tol=0.0001),\n",
              "             n_jobs=None,\n",
              "             param_grid={'alpha': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=None, positive=False, random_state=None,\n",
              "                             solver=&#x27;auto&#x27;, tol=0.0001),\n",
              "             n_jobs=None,\n",
              "             param_grid={&#x27;alpha&#x27;: [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch=&#x27;2*n_jobs&#x27;, refit=True, return_train_score=False,\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=None, positive=False, random_state=None,\n",
              "                             solver=&#x27;auto&#x27;, tol=0.0001),\n",
              "             n_jobs=None,\n",
              "             param_grid={&#x27;alpha&#x27;: [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch=&#x27;2*n_jobs&#x27;, refit=True, return_train_score=False,\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=0)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, positive=False,\n",
              "      random_state=None, solver=&#x27;auto&#x27;, tol=0.0001)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ridgecv.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtgomfDr5riJ",
        "outputId": "08be767b-e83b-4095-cfd3-fd002d5b8905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'alpha': 2}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ridgecv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Jb9U4xn5uHp",
        "outputId": "450323f0-0749-437d-b837-27f35e679f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2.354401558415501\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_pred=ridgecv.predict(X_test)"
      ],
      "metadata": {
        "id": "6YoAYtyb54TX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.displot(ridge_pred-Y_test,kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "Ahl6V3ay5v6j",
        "outputId": "f010166a-4df5-4b11-f53a-edc6868c5915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7e97e2606590>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_ridge=r2_score(ridge_pred,Y_test)"
      ],
      "metadata": {
        "id": "Bxx1wRBK5yo5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_ridge"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nAKfCUh59JO",
        "outputId": "7d748125-7df5-4146-c810-205bc643b2d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9888193701841247"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Lasso Regression\n",
        "from sklearn.linear_model import Lasso"
      ],
      "metadata": {
        "id": "0BqMdoTp6BaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso=Lasso()"
      ],
      "metadata": {
        "id": "lE8PQZep6EUq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}\n",
        "lassocv=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\n",
        "lassocv.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "QIVdrQxv6F30",
        "outputId": "dfbebe32-e459-4c26-a4af-89d12188d8be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=1000, positive=False, precompute=False,\n",
              "                             random_state=None, selection='cyclic', tol=0.0001,\n",
              "                             warm_start=False),\n",
              "             n_jobs=None,\n",
              "             param_grid={'alpha': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=1000, positive=False, precompute=False,\n",
              "                             random_state=None, selection=&#x27;cyclic&#x27;, tol=0.0001,\n",
              "                             warm_start=False),\n",
              "             n_jobs=None,\n",
              "             param_grid={&#x27;alpha&#x27;: [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch=&#x27;2*n_jobs&#x27;, refit=True, return_train_score=False,\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=1000, positive=False, precompute=False,\n",
              "                             random_state=None, selection=&#x27;cyclic&#x27;, tol=0.0001,\n",
              "                             warm_start=False),\n",
              "             n_jobs=None,\n",
              "             param_grid={&#x27;alpha&#x27;: [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
              "                                   90]},\n",
              "             pre_dispatch=&#x27;2*n_jobs&#x27;, refit=True, return_train_score=False,\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=0)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000, positive=False,\n",
              "      precompute=False, random_state=None, selection=&#x27;cyclic&#x27;, tol=0.0001,\n",
              "      warm_start=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lassocv.best_params_)\n",
        "print(lassocv.best_score_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSgFKUJ16IKA",
        "outputId": "b4c06f2d-6551-4409-ad9f-5437b36f403b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'alpha': 1}\n",
            "-3.113179309281194\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_pred=lassocv.predict(X_test)"
      ],
      "metadata": {
        "id": "u57Y_EQd6Lsu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.displot(lasso_pred-Y_test,kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "LX8dPEEO6Pdw",
        "outputId": "c3818d23-0c3d-4f10-bdd9-fe0fca0fa286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7e97d1747ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yl-AEk5Q8CnM",
        "outputId": "2049f148-bf9d-4ce7-a907-380c9b7bcf60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16     29.0\n",
              "64     32.3\n",
              "234    25.8\n",
              "70     24.3\n",
              "178    22.5\n",
              "Name: BodyFat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "JSreTovt8JGx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# This line enables inline plotting in Google Colab\n",
        "%matplotlib inline\n",
        "\n",
        "actual_values=Y_test\n",
        "predicted_values=reg_pred\n",
        "\n",
        "# Calculate the regression line\n",
        "slope, intercept = np.polyfit(actual_values, predicted_values, 1)\n",
        "regression_line = np.polyval([slope, intercept], actual_values)\n",
        "\n",
        "# Create a scatter plot with different colors\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_values, actual_values, color='blue', label='Actual Values', marker='o')\n",
        "plt.scatter(actual_values, predicted_values, color='red', label='Predicted Values', marker='x')\n",
        "plt.plot(actual_values, regression_line, color='green', label='Regression Line')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs Predicted Values (Linear Reg)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uML59gaa9gOu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "86ad9ae3-cabe-464d-caf6-7092de858621"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# This line enables inline plotting in Google Colab\n",
        "%matplotlib inline\n",
        "\n",
        "actual_values=Y_test\n",
        "predicted_values=ridge_pred\n",
        "\n",
        "# Calculate the regression line\n",
        "slope, intercept = np.polyfit(actual_values, predicted_values, 1)\n",
        "regression_line = np.polyval([slope, intercept], actual_values)\n",
        "\n",
        "# Create a scatter plot with different colors\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_values, actual_values, color='blue', label='Actual Values', marker='o')\n",
        "plt.scatter(actual_values, predicted_values, color='red', label='Predicted Values', marker='x')\n",
        "plt.plot(actual_values, regression_line, color='green', label='Regression Line')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs Predicted Values (Ridge Reg)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "tyjfytRRBUH9",
        "outputId": "3897fd5f-6f96-4ac5-ae47-7d11567f033d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "%matplotlib inline\n",
        "\n",
        "actual_values=Y_test\n",
        "predicted_values=lasso_pred\n",
        "\n",
        "# Calculate the regression line\n",
        "slope, intercept = np.polyfit(actual_values, predicted_values, 1)\n",
        "regression_line = np.polyval([slope, intercept], actual_values)\n",
        "\n",
        "# Create a scatter plot with different colors\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_values, actual_values, color='blue', label='Actual Values', marker='o')\n",
        "plt.scatter(actual_values, predicted_values, color='red', label='Predicted Values', marker='x')\n",
        "plt.plot(actual_values, regression_line, color='green', label='Regression Line')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs Predicted Values (Lasso Reg)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "HRKMJe_VBbiu",
        "outputId": "e637bab9-db7b-41f6-ccb3-b823e9242ea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index = np.arange(len(actual_values))\n",
        "bar_width = 0.4\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(index, actual_values, bar_width, label='Actual Values')\n",
        "plt.bar(index + bar_width, predicted_values, bar_width, label='Predicted Values')\n",
        "\n",
        "plt.xlabel('Index')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Actual vs Predicted Values(Linear Reg)')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "Dv5PbLpm_mxf",
        "outputId": "bfc690cd-89c1-4c38-e89e-1d8ae362a749"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries and train your model\n",
        "import joblib\n",
        "\n",
        "\n",
        "\n",
        "# Save the model to a file\n",
        "joblib.dump(lassocv, 'model.pkl')\n"
      ],
      "metadata": {
        "id": "lpOJZcCu_2kZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "584eca10-b010-44e4-a96c-a75e7e982ed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor"
      ],
      "metadata": {
        "id": "3uXBJy9pO6Ht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_reg = KNeighborsRegressor(n_neighbors=3)"
      ],
      "metadata": {
        "id": "5yyxB0Qf2Pgk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_fold = 5\n",
        "cv_scores = cross_val_score(knn_reg, X_train, Y_train, cv=k_fold)\n",
        "\n",
        "\n",
        "print(\"Cross-Validation Scores:\")\n",
        "print(cv_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwE-x57g2R4r",
        "outputId": "35c67e7f-1aeb-4587-d333-b5f7d9ecabc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores:\n",
            "[0.83351211 0.85248806 0.74294553 0.80355621 0.77640864]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_accuracy = np.mean(cv_scores)\n",
        "std_deviation = np.std(cv_scores)\n",
        "print(f\"Mean Accuracy: {mean_accuracy:.2f}\")\n",
        "print(f\"Standard Deviation: {std_deviation:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3tECj652Zf7",
        "outputId": "f5a6794f-5ba2-4c65-bb7a-422dfd76b6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Accuracy: 0.80\n",
            "Standard Deviation: 0.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_reg.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "bEggjr0-23-J",
        "outputId": "1650ec5e-d2eb-4d9e-cfba-5c0dd744fff0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
              "                    weights='uniform')"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(algorithm=&#x27;auto&#x27;, leaf_size=30, metric=&#x27;minkowski&#x27;,\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
              "                    weights=&#x27;uniform&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(algorithm=&#x27;auto&#x27;, leaf_size=30, metric=&#x27;minkowski&#x27;,\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
              "                    weights=&#x27;uniform&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_pred=knn_reg.predict(X_test)"
      ],
      "metadata": {
        "id": "1e5ZxcoC3Nmn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "%matplotlib inline\n",
        "\n",
        "actual_values=Y_test\n",
        "predicted_values=knn_pred\n",
        "\n",
        "# Calculate the regression line\n",
        "slope, intercept = np.polyfit(actual_values, predicted_values, 1)\n",
        "regression_line = np.polyval([slope, intercept], actual_values)\n",
        "\n",
        "# Create a scatter plot with different colors\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_values, actual_values, color='blue', label='Actual Values', marker='o')\n",
        "plt.scatter(actual_values, predicted_values, color='red', label='Predicted Values', marker='x')\n",
        "plt.plot(actual_values, regression_line, color='green', label='Regression Line')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Actual vs Predicted Values (KNN)')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "1PFWF1nz3cKB",
        "outputId": "8e4a46b6-bebc-4925-9d34-418e7595fe01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "biKXz0Jh3lcB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}