TASK_5_Sales_Prediction_using_Python_And_Machine_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "***IMPORTING NECESSARY LIBRARIES AND LOAD DATA***"
      ],
      "metadata": {
        "id": "BaRGPnsQ2n4e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFe-HZgTNR9g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6192c92b-de6e-44fc-eebb-a54176007dce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0     TV  Radio  Newspaper  Sales\n",
              "0             1  230.1   37.8       69.2   22.1\n",
              "1             2   44.5   39.3       45.1   10.4\n",
              "2             3   17.2   45.9       69.3    9.3\n",
              "3             4  151.5   41.3       58.5   18.5\n",
              "4             5  180.8   10.8       58.4   12.9\n",
              "..          ...    ...    ...        ...    ...\n",
              "195         196   38.2    3.7       13.8    7.6\n",
              "196         197   94.2    4.9        8.1    9.7\n",
              "197         198  177.0    9.3        6.4   12.8\n",
              "198         199  283.6   42.0       66.2   25.5\n",
              "199         200  232.1    8.6        8.7   13.4\n",
              "\n",
              "[200 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09583b7e-8653-49f3-a784-4aee12aea6e8\" 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>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09583b7e-8653-49f3-a784-4aee12aea6e8')\"\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-09583b7e-8653-49f3-a784-4aee12aea6e8 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-09583b7e-8653-49f3-a784-4aee12aea6e8');\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-da13af23-ea41-4eb5-85c8-333170cab6d3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da13af23-ea41-4eb5-85c8-333170cab6d3')\"\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-da13af23-ea41-4eb5-85c8-333170cab6d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9e52d55c-8ee4-49ff-a6fb-c2db6d8f3791\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9e52d55c-8ee4-49ff-a6fb-c2db6d8f3791 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.217456565710478,\n        \"min\": 1.6,\n        \"max\": 27.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          11.4,\n          21.2,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "df=pd.read_csv('/content/Advertising.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***EXPLORATORY DATA ANALYSIS***"
      ],
      "metadata": {
        "id": "hfQdMYQQ2y-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fhyw9OFBQ-iw",
        "outputId": "5c8a0f84-649e-4d52-a395-b6c786fc030a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
              "0           1  230.1   37.8       69.2   22.1\n",
              "1           2   44.5   39.3       45.1   10.4\n",
              "2           3   17.2   45.9       69.3    9.3\n",
              "3           4  151.5   41.3       58.5   18.5\n",
              "4           5  180.8   10.8       58.4   12.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-77820177-dc28-4a50-933a-969e8e4a48ae\" 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>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</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-77820177-dc28-4a50-933a-969e8e4a48ae')\"\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-77820177-dc28-4a50-933a-969e8e4a48ae 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-77820177-dc28-4a50-933a-969e8e4a48ae');\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-e2e12e5d-1672-4159-ae96-1f96a6032ffc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2e12e5d-1672-4159-ae96-1f96a6032ffc')\"\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-e2e12e5d-1672-4159-ae96-1f96a6032ffc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.217456565710478,\n        \"min\": 1.6,\n        \"max\": 27.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          11.4,\n          21.2,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lU9TNFThRBLX",
        "outputId": "5d1ab4bf-5caa-4b03-a7e9-03663288076a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0     TV  Radio  Newspaper  Sales\n",
              "195         196   38.2    3.7       13.8    7.6\n",
              "196         197   94.2    4.9        8.1    9.7\n",
              "197         198  177.0    9.3        6.4   12.8\n",
              "198         199  283.6   42.0       66.2   25.5\n",
              "199         200  232.1    8.6        8.7   13.4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b00eaa36-c8e8-43da-bc53-fbf12a8ae068\" 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>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>13.4</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-b00eaa36-c8e8-43da-bc53-fbf12a8ae068')\"\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-b00eaa36-c8e8-43da-bc53-fbf12a8ae068 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-b00eaa36-c8e8-43da-bc53-fbf12a8ae068');\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-c7670aab-0470-4f08-aa6a-911828c9bfbf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c7670aab-0470-4f08-aa6a-911828c9bfbf')\"\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-c7670aab-0470-4f08-aa6a-911828c9bfbf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 196,\n        \"max\": 200,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          197,\n          200,\n          198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.75375682148518,\n        \"min\": 38.2,\n        \"max\": 283.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          94.2,\n          232.1,\n          177.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.997656078313472,\n        \"min\": 3.7,\n        \"max\": 42.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          8.6,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.61782582499928,\n        \"min\": 6.4,\n        \"max\": 66.2,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.1,\n          8.7,\n          6.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.951618516575834,\n        \"min\": 7.6,\n        \"max\": 25.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.7,\n          13.4,\n          12.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Handling missing data and dropping duplicated data_"
      ],
      "metadata": {
        "id": "x4cAMmNB25Cw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Vl4jm9G4S1t7",
        "outputId": "4a1d8ffa-6d45-40ef-e873-c5439c50026d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "TV            0\n",
              "Radio         0\n",
              "Newspaper     0\n",
              "Sales         0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radio</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Newspaper</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "id": "5yYwvWcfS86-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f369237c-27e8-4b8b-81fc-13a31eb0fe2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper  Sales\n",
              "0    230.1   37.8       69.2   22.1\n",
              "1     44.5   39.3       45.1   10.4\n",
              "2     17.2   45.9       69.3    9.3\n",
              "3    151.5   41.3       58.5   18.5\n",
              "4    180.8   10.8       58.4   12.9\n",
              "..     ...    ...        ...    ...\n",
              "195   38.2    3.7       13.8    7.6\n",
              "196   94.2    4.9        8.1    9.7\n",
              "197  177.0    9.3        6.4   12.8\n",
              "198  283.6   42.0       66.2   25.5\n",
              "199  232.1    8.6        8.7   13.4\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5919a2c0-a785-4758-89f9-11348a534cfa\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5919a2c0-a785-4758-89f9-11348a534cfa')\"\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-5919a2c0-a785-4758-89f9-11348a534cfa 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-5919a2c0-a785-4758-89f9-11348a534cfa');\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-18f8afed-5165-426b-8912-ca6c4b5fd481\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-18f8afed-5165-426b-8912-ca6c4b5fd481')\"\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-18f8afed-5165-426b-8912-ca6c4b5fd481 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_335af2cd-fa36-4b4b-bae6-bf286f078e65\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_335af2cd-fa36-4b4b-bae6-bf286f078e65 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.217456565710478,\n        \"min\": 1.6,\n        \"max\": 27.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          11.4,\n          21.2,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "wCs3Hacy8JGY",
        "outputId": "cdc2d35a-40c2-407f-9921-7417d2125c0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TV           0\n",
              "Radio        0\n",
              "Newspaper    0\n",
              "Sales        0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radio</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Newspaper</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45CyngW93Gtc",
        "outputId": "4b4a3cad-25b5-4d61-fde0-ffb4564f4d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Oowp_pqSeVxF",
        "outputId": "af2a766d-5178-4ca4-c0da-1d0cc65ea8c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper  Sales\n",
              "0    230.1   37.8       69.2   22.1\n",
              "1     44.5   39.3       45.1   10.4\n",
              "2     17.2   45.9       69.3    9.3\n",
              "3    151.5   41.3       58.5   18.5\n",
              "4    180.8   10.8       58.4   12.9\n",
              "..     ...    ...        ...    ...\n",
              "195   38.2    3.7       13.8    7.6\n",
              "196   94.2    4.9        8.1    9.7\n",
              "197  177.0    9.3        6.4   12.8\n",
              "198  283.6   42.0       66.2   25.5\n",
              "199  232.1    8.6        8.7   13.4\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4f08ab2-9f54-42d8-9724-f898e42428d0\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4f08ab2-9f54-42d8-9724-f898e42428d0')\"\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-e4f08ab2-9f54-42d8-9724-f898e42428d0 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-e4f08ab2-9f54-42d8-9724-f898e42428d0');\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-253f8588-2175-408c-92f4-fa3cb2a5db1e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-253f8588-2175-408c-92f4-fa3cb2a5db1e')\"\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-253f8588-2175-408c-92f4-fa3cb2a5db1e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fb1bb140-82fb-44a4-96fe-836b7e8eead5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fb1bb140-82fb-44a4-96fe-836b7e8eead5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.217456565710478,\n        \"min\": 1.6,\n        \"max\": 27.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          11.4,\n          21.2,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "MoAw6HYNZbTp",
        "outputId": "11d07c13-61f7-40fb-ba3a-407d54bf55ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TV           float64\n",
              "Radio        float64\n",
              "Newspaper    float64\n",
              "Sales        float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radio</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Newspaper</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5WQYxV50abew",
        "outputId": "faaefe69-5ee3-40fc-9126-2d8cf04ac935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               TV       Radio   Newspaper       Sales\n",
              "count  200.000000  200.000000  200.000000  200.000000\n",
              "mean   147.042500   23.264000   30.554000   14.022500\n",
              "std     85.854236   14.846809   21.778621    5.217457\n",
              "min      0.700000    0.000000    0.300000    1.600000\n",
              "25%     74.375000    9.975000   12.750000   10.375000\n",
              "50%    149.750000   22.900000   25.750000   12.900000\n",
              "75%    218.825000   36.525000   45.100000   17.400000\n",
              "max    296.400000   49.600000  114.000000   27.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18a30a13-efe1-40ad-8498-cb50441f3318\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>147.042500</td>\n",
              "      <td>23.264000</td>\n",
              "      <td>30.554000</td>\n",
              "      <td>14.022500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>85.854236</td>\n",
              "      <td>14.846809</td>\n",
              "      <td>21.778621</td>\n",
              "      <td>5.217457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>74.375000</td>\n",
              "      <td>9.975000</td>\n",
              "      <td>12.750000</td>\n",
              "      <td>10.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>149.750000</td>\n",
              "      <td>22.900000</td>\n",
              "      <td>25.750000</td>\n",
              "      <td>12.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>218.825000</td>\n",
              "      <td>36.525000</td>\n",
              "      <td>45.100000</td>\n",
              "      <td>17.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>296.400000</td>\n",
              "      <td>49.600000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>27.000000</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-18a30a13-efe1-40ad-8498-cb50441f3318')\"\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-18a30a13-efe1-40ad-8498-cb50441f3318 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-18a30a13-efe1-40ad-8498-cb50441f3318');\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-b07e3500-7d9b-4fc8-8d78-37de5387a1b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b07e3500-7d9b-4fc8-8d78-37de5387a1b7')\"\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-b07e3500-7d9b-4fc8-8d78-37de5387a1b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93.12930693433862,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          147.0425,\n          149.75,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.62946191825954,\n        \"min\": 0.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          23.264000000000006,\n          22.9,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67.53295876114069,\n        \"min\": 0.3,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.553999999999995,\n          25.75,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66.68380930502316,\n        \"min\": 1.6,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.0225,\n          12.9,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mOwzmb03vUF",
        "outputId": "8336785b-9d50-4fc0-e87c-cb9da52dd8a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   TV         200 non-null    float64\n",
            " 1   Radio      200 non-null    float64\n",
            " 2   Newspaper  200 non-null    float64\n",
            " 3   Sales      200 non-null    float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 6.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***FEATURE ENGINEERING***"
      ],
      "metadata": {
        "id": "b--6U7ur4EgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VeT94xJmAaVE",
        "outputId": "0aa95c6a-159b-4f16-f793-dafe09621fea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper\n",
              "0    230.1   37.8       69.2\n",
              "1     44.5   39.3       45.1\n",
              "2     17.2   45.9       69.3\n",
              "3    151.5   41.3       58.5\n",
              "4    180.8   10.8       58.4\n",
              "..     ...    ...        ...\n",
              "195   38.2    3.7       13.8\n",
              "196   94.2    4.9        8.1\n",
              "197  177.0    9.3        6.4\n",
              "198  283.6   42.0       66.2\n",
              "199  232.1    8.6        8.7\n",
              "\n",
              "[200 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ef893e7-8209-459f-a89c-86fa205ca9ca\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>38.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>13.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>94.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>177.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ef893e7-8209-459f-a89c-86fa205ca9ca')\"\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-1ef893e7-8209-459f-a89c-86fa205ca9ca 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-1ef893e7-8209-459f-a89c-86fa205ca9ca');\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-190149a4-054c-43c9-a4b3-a21a1159b34f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-190149a4-054c-43c9-a4b3-a21a1159b34f')\"\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-190149a4-054c-43c9-a4b3-a21a1159b34f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e90d9449-a03b-4457-8543-c4a27dd09246\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e90d9449-a03b-4457-8543-c4a27dd09246 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "WyYhg2jEBFak",
        "outputId": "d42d9fa2-ab45-4e70-acfc-c243611c707f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      22.1\n",
              "1      10.4\n",
              "2       9.3\n",
              "3      18.5\n",
              "4      12.9\n",
              "       ... \n",
              "195     7.6\n",
              "196     9.7\n",
              "197    12.8\n",
              "198    25.5\n",
              "199    13.4\n",
              "Name: Sales, Length: 200, dtype: float64"
            ],
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***DATA VISUALIZATION***"
      ],
      "metadata": {
        "id": "y3IaIB3m_brD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#TV Vs Sales Plot\n",
        "sns.regplot(x=df['TV'],y=y,color='purple')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2psHmhQyBLeF",
        "outputId": "05d45e46-ec18-4070-bc78-e10023f22dea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='TV', ylabel='Sales'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NewsPaper Vs Sales Plot\n",
        "sns.regplot(x=df['Newspaper'],y=y,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "HpBbmmB2Fm9x",
        "outputId": "56766972-3469-449b-a30d-a4a397258de6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Newspaper', ylabel='Sales'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Radio Vs Sales Plot\n",
        "sns.regplot(x=df['Radio'],y=y,color='green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "gXR3YbMJF7qx",
        "outputId": "98246d8b-f611-4bc8-8817-4c4775d9d65c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Radio', ylabel='Sales'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Splitting of training and testing data_"
      ],
      "metadata": {
        "id": "Y2bsQEat4oKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30,random_state=1)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-Dfka_PsGB3E",
        "outputId": "0bf6e21c-1de2-4257-eb4f-5586a7f6872a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper\n",
              "116  139.2   14.3       25.6\n",
              "67   139.3   14.5       10.2\n",
              "78     5.4   29.9        9.4\n",
              "42   293.6   27.7        1.8\n",
              "17   281.4   39.6       55.8\n",
              "..     ...    ...        ...\n",
              "133  219.8   33.5       45.1\n",
              "137  273.7   28.9       59.7\n",
              "72    26.8   33.0       19.3\n",
              "140   73.4   17.0       12.9\n",
              "37    74.7   49.4       45.7\n",
              "\n",
              "[140 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bcb83753-e597-470b-b733-9bc032c8ee0d\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>139.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>139.3</td>\n",
              "      <td>14.5</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>5.4</td>\n",
              "      <td>29.9</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>293.6</td>\n",
              "      <td>27.7</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>281.4</td>\n",
              "      <td>39.6</td>\n",
              "      <td>55.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>219.8</td>\n",
              "      <td>33.5</td>\n",
              "      <td>45.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>273.7</td>\n",
              "      <td>28.9</td>\n",
              "      <td>59.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>26.8</td>\n",
              "      <td>33.0</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>73.4</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>74.7</td>\n",
              "      <td>49.4</td>\n",
              "      <td>45.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcb83753-e597-470b-b733-9bc032c8ee0d')\"\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-bcb83753-e597-470b-b733-9bc032c8ee0d 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-bcb83753-e597-470b-b733-9bc032c8ee0d');\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-b0f47e44-e72c-4a63-b713-31d30ff9ad54\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0f47e44-e72c-4a63-b713-31d30ff9ad54')\"\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-b0f47e44-e72c-4a63-b713-31d30ff9ad54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bcca2276-6a7e-4db6-b36f-b7f0895447e6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bcca2276-6a7e-4db6-b36f-b7f0895447e6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 140,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86.89985933188463,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 134,\n        \"samples\": [\n          96.2,\n          156.6,\n          53.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.260685960048553,\n        \"min\": 0.0,\n        \"max\": 49.4,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          15.5,\n          41.1,\n          39.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.58206503897047,\n        \"min\": 0.9,\n        \"max\": 100.9,\n        \"num_unique_values\": 133,\n        \"samples\": [\n          0.9,\n          29.7,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "944twABlGHaZ",
        "outputId": "4b0595b5-24ce-4298-cf45-5450815df319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Newspaper\n",
              "58   210.8   49.6       37.7\n",
              "40   202.5   22.3       31.6\n",
              "34    95.7    1.4        7.4\n",
              "102  280.2   10.1       21.4\n",
              "184  253.8   21.3       30.0\n",
              "198  283.6   42.0       66.2\n",
              "95   163.3   31.6       52.9\n",
              "4    180.8   10.8       58.4\n",
              "29    70.6   16.0       40.8\n",
              "168  215.4   23.6       57.6\n",
              "171  164.5   20.9       47.4\n",
              "18    69.2   20.5       18.3\n",
              "11   214.7   24.0        4.0\n",
              "89   109.8   47.8       51.4\n",
              "110  225.8    8.2       56.5\n",
              "118  125.7   36.9       79.2\n",
              "159  131.7   18.4       34.6\n",
              "35   290.7    4.1        8.5\n",
              "136   25.6   39.0        9.3\n",
              "59   210.7   29.5        9.3\n",
              "51   100.4    9.6        3.6\n",
              "16    67.8   36.6      114.0\n",
              "44    25.1   25.7       43.3\n",
              "94   107.4   14.0       10.9\n",
              "31   112.9   17.4       38.6\n",
              "162  188.4   18.1       25.6\n",
              "38    43.1   26.7       35.1\n",
              "28   248.8   27.1       22.9\n",
              "193  166.8   42.0        3.6\n",
              "27   240.1   16.7       22.9\n",
              "47   239.9   41.5       18.5\n",
              "165  234.5    3.4       84.8\n",
              "194  149.7   35.6        6.0\n",
              "177  170.2    7.8       35.2\n",
              "176  248.4   30.2       20.3\n",
              "97   184.9   21.0       22.0\n",
              "174  222.4    3.4       13.1\n",
              "73   129.4    5.7       31.3\n",
              "69   216.8   43.9       27.2\n",
              "172   19.6   20.1       17.0\n",
              "108   13.1    0.4       25.6\n",
              "107   90.4    0.3       23.2\n",
              "189   18.7   12.1       23.4\n",
              "14   204.1   32.9       46.0\n",
              "56     7.3   28.1       41.4\n",
              "19   147.3   23.9       19.1\n",
              "114   78.2   46.8       34.5\n",
              "39   228.0   37.7       32.0\n",
              "185  205.0   45.1       19.6\n",
              "124  229.5   32.3       74.2\n",
              "98   289.7   42.3       51.2\n",
              "123  123.1   34.6       12.4\n",
              "119   19.4   16.0       22.3\n",
              "53   182.6   46.2       58.7\n",
              "33   265.6   20.0        0.3\n",
              "179  165.6   10.0       17.6\n",
              "181  218.5    5.4       27.4\n",
              "106   25.0   11.0       29.7\n",
              "199  232.1    8.6        8.7\n",
              "138   43.0   25.9       20.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5cd343e-f025-4203-bebf-97cc916732e4\" 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>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Newspaper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>210.8</td>\n",
              "      <td>49.6</td>\n",
              "      <td>37.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>202.5</td>\n",
              "      <td>22.3</td>\n",
              "      <td>31.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>95.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>280.2</td>\n",
              "      <td>10.1</td>\n",
              "      <td>21.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>253.8</td>\n",
              "      <td>21.3</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>283.6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>66.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>163.3</td>\n",
              "      <td>31.6</td>\n",
              "      <td>52.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>70.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>40.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>215.4</td>\n",
              "      <td>23.6</td>\n",
              "      <td>57.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>164.5</td>\n",
              "      <td>20.9</td>\n",
              "      <td>47.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>69.2</td>\n",
              "      <td>20.5</td>\n",
              "      <td>18.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>214.7</td>\n",
              "      <td>24.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>109.8</td>\n",
              "      <td>47.8</td>\n",
              "      <td>51.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>225.8</td>\n",
              "      <td>8.2</td>\n",
              "      <td>56.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>125.7</td>\n",
              "      <td>36.9</td>\n",
              "      <td>79.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>131.7</td>\n",
              "      <td>18.4</td>\n",
              "      <td>34.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>290.7</td>\n",
              "      <td>4.1</td>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>25.6</td>\n",
              "      <td>39.0</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>210.7</td>\n",
              "      <td>29.5</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>100.4</td>\n",
              "      <td>9.6</td>\n",
              "      <td>3.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>67.8</td>\n",
              "      <td>36.6</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>25.1</td>\n",
              "      <td>25.7</td>\n",
              "      <td>43.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>107.4</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>112.9</td>\n",
              "      <td>17.4</td>\n",
              "      <td>38.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>188.4</td>\n",
              "      <td>18.1</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>43.1</td>\n",
              "      <td>26.7</td>\n",
              "      <td>35.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>248.8</td>\n",
              "      <td>27.1</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>166.8</td>\n",
              "      <td>42.0</td>\n",
              "      <td>3.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>240.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>239.9</td>\n",
              "      <td>41.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>234.5</td>\n",
              "      <td>3.4</td>\n",
              "      <td>84.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>149.7</td>\n",
              "      <td>35.6</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>170.2</td>\n",
              "      <td>7.8</td>\n",
              "      <td>35.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>248.4</td>\n",
              "      <td>30.2</td>\n",
              "      <td>20.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>184.9</td>\n",
              "      <td>21.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>222.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>13.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>129.4</td>\n",
              "      <td>5.7</td>\n",
              "      <td>31.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>216.8</td>\n",
              "      <td>43.9</td>\n",
              "      <td>27.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>19.6</td>\n",
              "      <td>20.1</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>13.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>90.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>23.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>18.7</td>\n",
              "      <td>12.1</td>\n",
              "      <td>23.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>204.1</td>\n",
              "      <td>32.9</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>7.3</td>\n",
              "      <td>28.1</td>\n",
              "      <td>41.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>147.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>19.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>78.2</td>\n",
              "      <td>46.8</td>\n",
              "      <td>34.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>228.0</td>\n",
              "      <td>37.7</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>205.0</td>\n",
              "      <td>45.1</td>\n",
              "      <td>19.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>229.5</td>\n",
              "      <td>32.3</td>\n",
              "      <td>74.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>289.7</td>\n",
              "      <td>42.3</td>\n",
              "      <td>51.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>123.1</td>\n",
              "      <td>34.6</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>19.4</td>\n",
              "      <td>16.0</td>\n",
              "      <td>22.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>182.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>58.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>265.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>165.6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>218.5</td>\n",
              "      <td>5.4</td>\n",
              "      <td>27.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>25.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>29.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>232.1</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>43.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>20.5</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-a5cd343e-f025-4203-bebf-97cc916732e4')\"\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-a5cd343e-f025-4203-bebf-97cc916732e4 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-a5cd343e-f025-4203-bebf-97cc916732e4');\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-ea934bdd-3ada-4e85-a473-8b0f9f3682dd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea934bdd-3ada-4e85-a473-8b0f9f3682dd')\"\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-ea934bdd-3ada-4e85-a473-8b0f9f3682dd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5231d67c-acc4-455a-bffd-6ce3ad5e2bf6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5231d67c-acc4-455a-bffd-6ce3ad5e2bf6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_test",
              "summary": "{\n  \"name\": \"x_test\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83.20117235905336,\n        \"min\": 7.3,\n        \"max\": 290.7,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          210.8,\n          283.6,\n          222.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.956342239303426,\n        \"min\": 0.3,\n        \"max\": 49.6,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          49.6,\n          42.0,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.366904273494377,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          37.7,\n          66.2,\n          13.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "SsS6n84RGMkS",
        "outputId": "2211a422-a656-4dec-80ef-727f5d2084e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "116    12.2\n",
              "67     13.4\n",
              "78      5.3\n",
              "42     20.7\n",
              "17     24.4\n",
              "       ... \n",
              "133    19.6\n",
              "137    20.8\n",
              "72      8.8\n",
              "140    10.9\n",
              "37     14.7\n",
              "Name: Sales, Length: 140, dtype: float64"
            ],
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>20.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>24.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>19.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>20.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>14.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V4YkOFTmGN_k",
        "outputId": "18a24ad7-4145-4e2a-bd14-22ab2081abb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58     23.8\n",
              "40     16.6\n",
              "34      9.5\n",
              "102    14.8\n",
              "184    17.6\n",
              "198    25.5\n",
              "95     16.9\n",
              "4      12.9\n",
              "29     10.5\n",
              "168    17.1\n",
              "171    14.5\n",
              "18     11.3\n",
              "11     17.4\n",
              "89     16.7\n",
              "110    13.4\n",
              "118    15.9\n",
              "159    12.9\n",
              "35     12.8\n",
              "136     9.5\n",
              "59     18.4\n",
              "51     10.7\n",
              "16     12.5\n",
              "44      8.5\n",
              "94     11.5\n",
              "31     11.9\n",
              "162    14.9\n",
              "38     10.1\n",
              "28     18.9\n",
              "193    19.6\n",
              "27     15.9\n",
              "47     23.2\n",
              "165    11.9\n",
              "194    17.3\n",
              "177    11.7\n",
              "176    20.2\n",
              "97     15.5\n",
              "174    11.5\n",
              "73     11.0\n",
              "69     22.3\n",
              "172     7.6\n",
              "108     5.3\n",
              "107     8.7\n",
              "189     6.7\n",
              "14     19.0\n",
              "56      5.5\n",
              "19     14.6\n",
              "114    14.6\n",
              "39     21.5\n",
              "185    22.6\n",
              "124    19.7\n",
              "98     25.4\n",
              "123    15.2\n",
              "119     6.6\n",
              "53     21.2\n",
              "33     17.4\n",
              "179    12.6\n",
              "181    12.2\n",
              "106     7.2\n",
              "199    13.4\n",
              "138     9.6\n",
              "Name: Sales, dtype: float64"
            ],
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>23.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>14.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>25.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>16.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>17.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>14.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>11.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>15.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>18.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>10.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>11.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>14.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>19.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>15.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>23.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>11.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>11.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>20.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>22.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>21.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>22.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>19.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>25.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>6.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>21.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>17.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>13.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***TRAINING AND EVALUATION OF MODELS***"
      ],
      "metadata": {
        "id": "Xa16H9sqXJjk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear Regression,Random Forest Regressor,Decision Tree Regressor,Gradient Boosting Regressor,SVR and KNN**"
      ],
      "metadata": {
        "id": "qwFvKUaEXSPL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error,mean_absolute_percentage_error, r2_score\n",
        "from sklearn.ensemble import RandomForestRegressor # Import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "# List of regressor models\n",
        "regressors = {\n",
        "    \"Linear Regression\": LinearRegression(),\n",
        "    \"Decision Tree\": DecisionTreeRegressor(),\n",
        "    \"Random Forest\": RandomForestRegressor(),\n",
        "    \"Gradient Boosting\": GradientBoostingRegressor(),\n",
        "    \"SVR\": SVR(),\n",
        "    \"K-Neighbors\": KNeighborsRegressor()\n",
        "}\n",
        "\n",
        "# Loop through each regressor\n",
        "for name, model in regressors.items():\n",
        "    model.fit(x_train, y_train)  # Fit the model\n",
        "    y_pred = model.predict(x_test)  # Predict on test data\n",
        "\n",
        "    # Calculate performance metrics\n",
        "    mae = mean_absolute_error(y_test, y_pred)\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "    # Print the metrics\n",
        "    print(f\"{name} Performance:\")\n",
        "    print(f\"MAE: {mae:.4f}\")\n",
        "    print(f\"MSE: {mse:.4f}\")\n",
        "    print(f\"MAPE: {mape:.4f}\")\n",
        "    print(f\"R²: {r2:.4f}\")\n",
        "    print(\"-\" * 30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4KayFr8gsQ8",
        "outputId": "d753b0ad-3291-457f-df98-4c3a46b6642c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Performance:\n",
            "MAE: 1.0548\n",
            "MSE: 1.9289\n",
            "MAPE: 0.0855\n",
            "R²: 0.9225\n",
            "------------------------------\n",
            "Decision Tree Performance:\n",
            "MAE: 0.8600\n",
            "MSE: 1.3420\n",
            "MAPE: 0.0653\n",
            "R²: 0.9461\n",
            "------------------------------\n",
            "Random Forest Performance:\n",
            "MAE: 0.5126\n",
            "MSE: 0.4044\n",
            "MAPE: 0.0402\n",
            "R²: 0.9837\n",
            "------------------------------\n",
            "Gradient Boosting Performance:\n",
            "MAE: 0.4597\n",
            "MSE: 0.3481\n",
            "MAPE: 0.0337\n",
            "R²: 0.9860\n",
            "------------------------------\n",
            "SVR Performance:\n",
            "MAE: 1.5281\n",
            "MSE: 4.2200\n",
            "MAPE: 0.1080\n",
            "R²: 0.8304\n",
            "------------------------------\n",
            "K-Neighbors Performance:\n",
            "MAE: 1.0193\n",
            "MSE: 1.9379\n",
            "MAPE: 0.0740\n",
            "R²: 0.9221\n",
            "------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Decision Tree': DecisionTreeRegressor(random_state=1),\n",
        "    'Random Forest': RandomForestRegressor(random_state=1),\n",
        "    'Gradient Boosting': GradientBoostingRegressor(random_state=1),\n",
        "    'SVR': SVR(),\n",
        "    'K-Neighbors': KNeighborsRegressor()\n",
        "}\n",
        "results = []\n",
        "for name, model in models.items():\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred_train = model.predict(x_train)\n",
        "    y_pred_test = model.predict(x_test)\n",
        "    train_mse = mean_squared_error(y_train, y_pred_train)\n",
        "    test_mse = mean_squared_error(y_test, y_pred_test)\n",
        "    train_r2 = r2_score(y_train, y_pred_train)\n",
        "    test_r2 = r2_score(y_test, y_pred_test)\n",
        "    results.append({'Model': name,\n",
        "                    'Training MSE': train_mse,\n",
        "                    'Testing MSE': test_mse,\n",
        "                    'Training R2 Score': train_r2,\n",
        "                    'Testing R2 Score': test_r2})\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "GzUXVLVDAfzT",
        "outputId": "5bf6be68-f47c-4c81-de0f-df3bb0b48020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model  Training MSE  Testing MSE  Training R2 Score  \\\n",
              "0  Linear Regression      3.203117     1.928925           0.885005   \n",
              "1      Decision Tree      0.000000     1.293500           1.000000   \n",
              "2      Random Forest      0.111142     0.425007           0.996010   \n",
              "3  Gradient Boosting      0.031177     0.348098           0.998881   \n",
              "4                SVR      4.544046     4.219984           0.836865   \n",
              "5        K-Neighbors      1.580274     1.937947           0.943267   \n",
              "\n",
              "   Testing R2 Score  \n",
              "0          0.922461  \n",
              "1          0.948004  \n",
              "2          0.982915  \n",
              "3          0.986007  \n",
              "4          0.830364  \n",
              "5          0.922098  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-03ee2938-89bc-4eac-adbe-b6b7e91d09a8\" 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>Model</th>\n",
              "      <th>Training MSE</th>\n",
              "      <th>Testing MSE</th>\n",
              "      <th>Training R2 Score</th>\n",
              "      <th>Testing R2 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>3.203117</td>\n",
              "      <td>1.928925</td>\n",
              "      <td>0.885005</td>\n",
              "      <td>0.922461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.293500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.948004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.111142</td>\n",
              "      <td>0.425007</td>\n",
              "      <td>0.996010</td>\n",
              "      <td>0.982915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.031177</td>\n",
              "      <td>0.348098</td>\n",
              "      <td>0.998881</td>\n",
              "      <td>0.986007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SVR</td>\n",
              "      <td>4.544046</td>\n",
              "      <td>4.219984</td>\n",
              "      <td>0.836865</td>\n",
              "      <td>0.830364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>K-Neighbors</td>\n",
              "      <td>1.580274</td>\n",
              "      <td>1.937947</td>\n",
              "      <td>0.943267</td>\n",
              "      <td>0.922098</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-03ee2938-89bc-4eac-adbe-b6b7e91d09a8')\"\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-03ee2938-89bc-4eac-adbe-b6b7e91d09a8 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-03ee2938-89bc-4eac-adbe-b6b7e91d09a8');\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-5a80f1ad-477b-4d7f-a2c7-764373b988c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a80f1ad-477b-4d7f-a2c7-764373b988c2')\"\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-5a80f1ad-477b-4d7f-a2c7-764373b988c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d1946b6c-24c0-4c43-8d4f-ce9a5f0de52a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d1946b6c-24c0-4c43-8d4f-ce9a5f0de52a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Linear Regression\",\n          \"Decision Tree\",\n          \"K-Neighbors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9221268484753828,\n        \"min\": 0.0,\n        \"max\": 4.54404569816598,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.2031174449376514,\n          0.0,\n          1.5802742857142853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Testing MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4196811819457886,\n        \"min\": 0.348097588722134,\n        \"max\": 4.219983823796101,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.928924907466573,\n          1.2935000000000003,\n          1.9379466666666663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06900597710617343,\n        \"min\": 0.83686492196612,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.8850053786777522,\n          1.0,\n          0.9432668185931793\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Testing R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05706871652871968,\n        \"min\": 0.8303639833656692,\n        \"max\": 0.9860070818239014,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9224605706201434,\n          0.9480035477199713,\n          0.9220979115774457\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**INSIGHTS FROM THE PERFORMANCE METRICS OF THE DIFFERENT REGRESSION MODELS USED**"
      ],
      "metadata": {
        "id": "qmBzRmGL9trL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "These performance metrics provide a detailed comparison of various regression models based on their accuracy in predicting the target variable. Here are the insights for each model:\n",
        "\n",
        " 1. **Linear Regression**\n",
        "   - **Insights**: Linear Regression performs reasonably well, with a good \\(R^2\\) score indicating that about 92.25% of the variance in the data is explained by the model. However, compared to other models, it has higher error metrics, suggesting room for improvement.\n",
        "\n",
        " 2. **Decision Tree**\n",
        "\n",
        "   - **Insights**: The Decision Tree model outperforms Linear Regression, with lower error metrics and a higher \\(R^2\\) score. The model captures the data well, but it may still be prone to overfitting due to its nature.\n",
        "\n",
        "3. **Random Forest**\n",
        "   - **Insights**: Random Forest shows significant improvements, with much lower errors and a very high \\(R^2\\) of 98.17%. This indicates strong predictive performance, with the model handling variance effectively and reducing overfitting through ensemble learning.\n",
        "\n",
        "4. **Gradient Boosting**\n",
        "   - **Insights**: Gradient Boosting provides the best performance overall, with the lowest error metrics and the highest \\(R^2\\) at 98.60%. This model excels in capturing complex relationships in the data, making it the top performer among the models evaluated.\n",
        "\n",
        "\n",
        " 5. **Support Vector Regression (SVR)**\n",
        "   - **Insights**: SVR underperforms compared to the other models, with the highest error metrics and the lowest \\(R^2\\) score. It struggles to fit the data as well as the tree-based models and may not be the best choice for this dataset.\n",
        "\n",
        " 6. **K-Neighbors Regression**\n",
        "      - **Insights**: K-Neighbors performs similarly to Linear Regression, with comparable errors and \\(R^2\\) values. It captures the data moderately well but doesn't match the performance of more complex models like Random Forest and Gradient Boosting.\n",
        "\n",
        "**Overall Insights**:\n",
        "- **Gradient Boosting** is the best-performing model with the lowest errors and highest \\(R^2\\), indicating it best captures the underlying data patterns.\n",
        "- **Random Forest** is a close second, providing strong performance with slightly higher error metrics than Gradient Boosting.\n",
        "- **Decision Tree** offers good performance but is less robust than the ensemble methods.\n",
        "- **Linear Regression** and **K-Neighbors** offer moderate performance, suitable for simpler models but not as powerful as tree-based models.\n",
        "- **Support Vector Regression (SVR)** underperforms relative to the other models, making it less suitable for this particular dataset.\n",
        "\n",
        "For practical applications, **Gradient Boosting** and **Random Forest** are likely the most reliable choices for this regression task."
      ],
      "metadata": {
        "id": "BzrVeZrp9UH4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=pd.DataFrame({'Actual_Value':y_test,'Predicted_Value':y_pred,'Difference':y_test-y_pred})\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-wpqm_t-lxoa",
        "outputId": "43ccc189-75f8-41eb-9c83-bd87f9c50612"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Actual_Value  Predicted_Value  Difference\n",
              "58           23.8            21.28        2.52\n",
              "40           16.6            15.30        1.30\n",
              "34            9.5             9.50        0.00\n",
              "102          14.8            14.16        0.64\n",
              "184          17.6            16.40        1.20\n",
              "198          25.5            24.52        0.98\n",
              "95           16.9            17.00       -0.10\n",
              "4            12.9            14.60       -1.70\n",
              "29           10.5             9.98        0.52\n",
              "168          17.1            16.70        0.40\n",
              "171          14.5            16.18       -1.68\n",
              "18           11.3            11.14        0.16\n",
              "11           17.4            16.12        1.28\n",
              "89           16.7            16.20        0.50\n",
              "110          13.4            15.24       -1.84\n",
              "118          15.9            17.28       -1.38\n",
              "159          12.9            12.56        0.34\n",
              "35           12.8            15.08       -2.28\n",
              "136           9.5             9.60       -0.10\n",
              "59           18.4            18.30        0.10\n",
              "51           10.7            10.82       -0.12\n",
              "16           12.5            10.84        1.66\n",
              "44            8.5             7.38        1.12\n",
              "94           11.5            11.10        0.40\n",
              "31           11.9            11.36        0.54\n",
              "162          14.9            15.82       -0.92\n",
              "38           10.1            10.08        0.02\n",
              "28           18.9            19.28       -0.38\n",
              "193          19.6            16.68        2.92\n",
              "27           15.9            15.16        0.74\n",
              "47           23.2            21.18        2.02\n",
              "165          11.9            17.76       -5.86\n",
              "194          17.3            15.52        1.78\n",
              "177          11.7            13.16       -1.46\n",
              "176          20.2            20.28       -0.08\n",
              "97           15.5            15.82       -0.32\n",
              "174          11.5            13.00       -1.50\n",
              "73           11.0            11.66       -0.66\n",
              "69           22.3            20.20        2.10\n",
              "172           7.6             7.46        0.14\n",
              "108           5.3             5.62       -0.32\n",
              "107           8.7             9.98       -1.28\n",
              "189           6.7             6.42        0.28\n",
              "14           19.0            19.72       -0.72\n",
              "56            5.5             6.90       -1.40\n",
              "19           14.6            12.78        1.82\n",
              "114          14.6            13.90        0.70\n",
              "39           21.5            21.16        0.34\n",
              "185          22.6            20.66        1.94\n",
              "124          19.7            19.30        0.40\n",
              "98           25.4            23.96        1.44\n",
              "123          15.2            14.72        0.48\n",
              "119           6.6             7.38       -0.78\n",
              "53           21.2            20.28        0.92\n",
              "33           17.4            19.26       -1.86\n",
              "179          12.6            12.70       -0.10\n",
              "181          12.2            12.94       -0.74\n",
              "106           7.2             6.66        0.54\n",
              "199          13.4            14.34       -0.94\n",
              "138           9.6            10.00       -0.40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea76d5c8-e745-46aa-91e8-7571514443a9\" 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>Actual_Value</th>\n",
              "      <th>Predicted_Value</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>23.8</td>\n",
              "      <td>21.28</td>\n",
              "      <td>2.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>16.6</td>\n",
              "      <td>15.30</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>9.5</td>\n",
              "      <td>9.50</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>14.8</td>\n",
              "      <td>14.16</td>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>17.6</td>\n",
              "      <td>16.40</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>25.5</td>\n",
              "      <td>24.52</td>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>16.9</td>\n",
              "      <td>17.00</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.9</td>\n",
              "      <td>14.60</td>\n",
              "      <td>-1.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10.5</td>\n",
              "      <td>9.98</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>17.1</td>\n",
              "      <td>16.70</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>14.5</td>\n",
              "      <td>16.18</td>\n",
              "      <td>-1.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>11.3</td>\n",
              "      <td>11.14</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17.4</td>\n",
              "      <td>16.12</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>16.7</td>\n",
              "      <td>16.20</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>13.4</td>\n",
              "      <td>15.24</td>\n",
              "      <td>-1.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>15.9</td>\n",
              "      <td>17.28</td>\n",
              "      <td>-1.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>12.9</td>\n",
              "      <td>12.56</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>12.8</td>\n",
              "      <td>15.08</td>\n",
              "      <td>-2.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>9.5</td>\n",
              "      <td>9.60</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>18.4</td>\n",
              "      <td>18.30</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>10.7</td>\n",
              "      <td>10.82</td>\n",
              "      <td>-0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>12.5</td>\n",
              "      <td>10.84</td>\n",
              "      <td>1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>8.5</td>\n",
              "      <td>7.38</td>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>11.5</td>\n",
              "      <td>11.10</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>11.9</td>\n",
              "      <td>11.36</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>14.9</td>\n",
              "      <td>15.82</td>\n",
              "      <td>-0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>10.1</td>\n",
              "      <td>10.08</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>18.9</td>\n",
              "      <td>19.28</td>\n",
              "      <td>-0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>19.6</td>\n",
              "      <td>16.68</td>\n",
              "      <td>2.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>15.9</td>\n",
              "      <td>15.16</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>23.2</td>\n",
              "      <td>21.18</td>\n",
              "      <td>2.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>11.9</td>\n",
              "      <td>17.76</td>\n",
              "      <td>-5.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>17.3</td>\n",
              "      <td>15.52</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>11.7</td>\n",
              "      <td>13.16</td>\n",
              "      <td>-1.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>20.2</td>\n",
              "      <td>20.28</td>\n",
              "      <td>-0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>15.5</td>\n",
              "      <td>15.82</td>\n",
              "      <td>-0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>11.5</td>\n",
              "      <td>13.00</td>\n",
              "      <td>-1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>11.0</td>\n",
              "      <td>11.66</td>\n",
              "      <td>-0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>22.3</td>\n",
              "      <td>20.20</td>\n",
              "      <td>2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>7.6</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>5.3</td>\n",
              "      <td>5.62</td>\n",
              "      <td>-0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>8.7</td>\n",
              "      <td>9.98</td>\n",
              "      <td>-1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>6.7</td>\n",
              "      <td>6.42</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19.72</td>\n",
              "      <td>-0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>5.5</td>\n",
              "      <td>6.90</td>\n",
              "      <td>-1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>14.6</td>\n",
              "      <td>12.78</td>\n",
              "      <td>1.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>14.6</td>\n",
              "      <td>13.90</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>21.5</td>\n",
              "      <td>21.16</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>22.6</td>\n",
              "      <td>20.66</td>\n",
              "      <td>1.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>19.7</td>\n",
              "      <td>19.30</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>25.4</td>\n",
              "      <td>23.96</td>\n",
              "      <td>1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>15.2</td>\n",
              "      <td>14.72</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>6.6</td>\n",
              "      <td>7.38</td>\n",
              "      <td>-0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>21.2</td>\n",
              "      <td>20.28</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>17.4</td>\n",
              "      <td>19.26</td>\n",
              "      <td>-1.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>12.6</td>\n",
              "      <td>12.70</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>12.2</td>\n",
              "      <td>12.94</td>\n",
              "      <td>-0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>7.2</td>\n",
              "      <td>6.66</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>13.4</td>\n",
              "      <td>14.34</td>\n",
              "      <td>-0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>9.6</td>\n",
              "      <td>10.00</td>\n",
              "      <td>-0.40</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-ea76d5c8-e745-46aa-91e8-7571514443a9')\"\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-ea76d5c8-e745-46aa-91e8-7571514443a9 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-ea76d5c8-e745-46aa-91e8-7571514443a9');\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-e437b2a5-cb69-4e6f-bf6c-dc2344fa48ca\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e437b2a5-cb69-4e6f-bf6c-dc2344fa48ca')\"\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-e437b2a5-cb69-4e6f-bf6c-dc2344fa48ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_871ee15c-0cd6-4958-9f37-2870f043f6dc\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df2')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_871ee15c-0cd6-4958-9f37-2870f043f6dc button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"Actual_Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.029745138492813,\n        \"min\": 5.3,\n        \"max\": 25.5,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          12.5,\n          21.5,\n          21.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted_Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.6095707240357,\n        \"min\": 5.62,\n        \"max\": 24.52,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          21.28,\n          24.52,\n          21.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.402740014408443,\n        \"min\": -5.859999999999998,\n        \"max\": 2.9200000000000017,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          2.5199999999999996,\n          0.9800000000000004,\n          2.0199999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***REGRESSION PLOT***"
      ],
      "metadata": {
        "id": "3gsqCNaL_t23"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Actual vs. Predicted Values Plot\n",
        "sns.regplot(x=df2['Actual_Value'],y=df2['Predicted_Value'],color='blue')"
      ],
      "metadata": {
        "id": "JF-okmbil09t",
        "outputId": "db21410a-5243-46ee-ad97-e20c8e800f6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Actual_Value', ylabel='Predicted_Value'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***INSIGHTS FROM REGRESSION PLOT***\n",
        "\n",
        "\n",
        "This regression plot provides a visual comparison between the actual values (on the x-axis) and the predicted values (on the y-axis) from a regression model. Here are some insights from the plot:\n",
        "\n",
        "1. **Improved Linearity**: The data points closely align with the diagonal line (\\(y = x\\)), indicating that the model's predictions are strongly correlated with the actual values. This suggests an even better fit compared to the previous plot, with the model making more accurate predictions across the range of values.\n",
        "\n",
        "2. **Consistent Prediction Range**: The predicted values span a range from approximately 5 to 25, closely matching the range of the actual values. This consistency implies that the model captures the variability in the data well.\n",
        "\n",
        "3. **Reduced Spread of Data Points**: The scatter of points around the diagonal line seems tighter than in the previous plot. This reduced spread suggests that the model has lower residuals (errors), meaning that the predictions are more precise for most of the data points.\n",
        "\n",
        "4. **Narrower Confidence Interval**: The shaded region around the regression line is narrow throughout the plot, indicating a high level of confidence in the predictions. This suggests that the model's performance is reliable and that there is little uncertainty in the predictions across the observed range.\n",
        "\n",
        "5. **Few Outliers**: While there are still some deviations from the diagonal line, these are less pronounced. The few points that are slightly away from the line may indicate areas where the model could be refined, but overall, outliers are minimal.\n",
        "\n",
        "Overall, this regression plot suggests that the model is performing very well, with predictions that closely match the actual values, a strong linear relationship, minimal residuals, and high confidence in the predictions."
      ],
      "metadata": {
        "id": "cTjbFFt68VTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('model2.pkl', 'wb') as file:\n",
        "    pickle.dump(\"Gradient Boosting\", file)"
      ],
      "metadata": {
        "id": "qmG1oNo3mAdw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}