CODSOFT_task_4_Sales_Predictions_by_Advertising_Costs.ipynb

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      "source": [
        "Import Necessary Libraries."
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as snr\n",
        "from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the data and show first 10 rows of data."
      ],
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      "source": [
        "data=pd.read_csv(\"/content/advertising.csv\")\n",
        "data.head(10)"
      ],
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              "   sno     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   12.0\n",
              "3    4  151.5   41.3       58.5   16.5\n",
              "4    5  180.8   10.8       58.4   17.9\n",
              "5    6    8.7   48.9       75.0    7.2\n",
              "6    7   57.5   32.8       23.5   11.8\n",
              "7    8  120.2   19.6       11.6   13.2\n",
              "8    9    8.6    2.1        1.0    4.8\n",
              "9   10  199.8    2.6       21.2   15.6"
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              "      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   12.0\n",
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              "    [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-24fdb0d0-32cd-4aa3-90e2-ecc54c161ada 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-24fdb0d0-32cd-4aa3-90e2-ecc54c161ada');\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-1bfc1cab-8551-4821-a463-bf4636f723f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1bfc1cab-8551-4821-a463-bf4636f723f4')\"\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-1bfc1cab-8551-4821-a463-bf4636f723f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculates statistical values and rounds them to 3 decimal places."
      ],
      "metadata": {
        "id": "pjhrTVeZhMdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.describe().round(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-QZIh0qxfh0B",
        "outputId": "1b3938ac-2737-45c3-e4e9-c832029e2070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            TV    Radio  Newspaper    Sales\n",
              "count  200.000  200.000    200.000  200.000\n",
              "mean   147.042   23.264     30.554   15.131\n",
              "std     85.854   14.847     21.779    5.284\n",
              "min      0.700    0.000      0.300    1.600\n",
              "25%     74.375    9.975     12.750   11.000\n",
              "50%    149.750   22.900     25.750   16.000\n",
              "75%    218.825   36.525     45.100   19.050\n",
              "max    296.400   49.600    114.000   27.000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e3b4a60-be19-4b91-a15c-7b3c9d194c0c\" 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>200.000</td>\n",
              "      <td>200.000</td>\n",
              "      <td>200.000</td>\n",
              "      <td>200.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>147.042</td>\n",
              "      <td>23.264</td>\n",
              "      <td>30.554</td>\n",
              "      <td>15.131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>85.854</td>\n",
              "      <td>14.847</td>\n",
              "      <td>21.779</td>\n",
              "      <td>5.284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.700</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>74.375</td>\n",
              "      <td>9.975</td>\n",
              "      <td>12.750</td>\n",
              "      <td>11.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>149.750</td>\n",
              "      <td>22.900</td>\n",
              "      <td>25.750</td>\n",
              "      <td>16.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>218.825</td>\n",
              "      <td>36.525</td>\n",
              "      <td>45.100</td>\n",
              "      <td>19.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>296.400</td>\n",
              "      <td>49.600</td>\n",
              "      <td>114.000</td>\n",
              "      <td>27.000</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-9e3b4a60-be19-4b91-a15c-7b3c9d194c0c')\"\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-9e3b4a60-be19-4b91-a15c-7b3c9d194c0c 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-9e3b4a60-be19-4b91-a15c-7b3c9d194c0c');\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-b6d7aa3b-d30c-431d-ac0c-74c5e7ff73bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6d7aa3b-d30c-431d-ac0c-74c5e7ff73bd')\"\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-b6d7aa3b-d30c-431d-ac0c-74c5e7ff73bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mark null values as True and returns sum of number of True values in each column"
      ],
      "metadata": {
        "id": "fHfP6R2ehf1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avl1sIUGhXWL",
        "outputId": "2c730eae-8535-4606-97b3-f120dc88623b"
      },
      "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"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting all information about data."
      ],
      "metadata": {
        "id": "a8VHdrhzi0ok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVefqwJphpSd",
        "outputId": "525ad792-f6f6-44e9-d3ac-13025cb1c4e2"
      },
      "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": [
        "Shape of data."
      ],
      "metadata": {
        "id": "c6Krgag4i9Vp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulCjA7I_i7c1",
        "outputId": "8f357cc4-885f-4a0b-9fbf-284c3f2f970d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding data into dependent and independent variables."
      ],
      "metadata": {
        "id": "AaLB2j3Vkxx7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data_new.drop(['Sales'], axis=1)\n",
        "y=data_new['Sales']\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-re9bX7jJ_v",
        "outputId": "84eb58b1-836e-4882-b95e-1cef2491fc9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    22.1\n",
              "1    10.4\n",
              "2    12.0\n",
              "3    16.5\n",
              "4    17.9\n",
              "Name: Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wTBB90C4ngo4",
        "outputId": "f8939a6a-787a-480b-bad3-3e8586ab7004"
      },
      "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"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding the cleaned data into training and testing sets."
      ],
      "metadata": {
        "id": "vmqKzHLDlWt4"
      }
    },
    {
      "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,train_size=0.8)"
      ],
      "metadata": {
        "id": "fxeNfdFPlS6i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating first Machine Learning Model 'Linear Regression'."
      ],
      "metadata": {
        "id": "-FqGJPsJoSLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linear=LinearRegression()"
      ],
      "metadata": {
        "id": "41ElPcLfoPUj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "IZ7WJWEFphd1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pY957lzQpbEJ",
        "outputId": "2b28f9c9-0027-4e95-9a71-664339e967db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model."
      ],
      "metadata": {
        "id": "bs5dT7rEp3oD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=linear.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7C8hd4xpxD_",
        "outputId": "fc215d8a-4e50-4598-e6c6-1565fd2f1c46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10.39192697 24.55352085 15.45371036 10.11138992 11.37764163 24.20419107\n",
            " 18.48913227 14.45437801 17.14480272 18.29198295 15.16304848  9.89206852\n",
            "  9.9308128  20.96721682 17.01863932 15.27152109 15.75539304 15.7539167\n",
            " 24.09601121 16.95988173 21.34991127 15.19190327  9.25140237 20.03099445\n",
            " 16.48900676 16.7799423   6.21384285 18.87148839 11.1609655  12.44063855\n",
            "  7.89423024  9.96123432  5.8929257  23.74064647 10.60055165  8.03092597\n",
            "  9.84796843  9.6226985   9.36569364 17.8851816 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check accuracy score."
      ],
      "metadata": {
        "id": "Y0R0LaqbqSbT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sk4SXZQHrSMZ",
        "outputId": "6d9f37e9-5915-4776-f89f-86d74256c89a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8718000770602493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the second machine learning model 'Polynomial regression'.\n",
        "\n"
      ],
      "metadata": {
        "id": "uLpwyj6xuctc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly = PolynomialFeatures(degree=2, include_bias=True)\n",
        "x_train_trans=poly.fit_transform(x_train)\n",
        "x_test_trans=poly.transform(x_test)\n",
        "linear=LinearRegression()"
      ],
      "metadata": {
        "id": "G5w1aVXzubOp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the Model"
      ],
      "metadata": {
        "id": "nKxIJ6yHyGqI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear.fit(x_train_trans,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4JzRfN5yqQRP",
        "outputId": "9d1076a5-f503-4eda-fe75-1f0bd92688db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model"
      ],
      "metadata": {
        "id": "x-2gh4pWyJm7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_predictions=linear.predict(x_test_trans)"
      ],
      "metadata": {
        "id": "XC_8qGQLqQA9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check Accuracy Score."
      ],
      "metadata": {
        "id": "bEapkSaOyOAu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y_test,y_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FECD6hGqmbP",
        "outputId": "b3e41ee4-faa3-4dd6-8ab0-9b4f961fc149"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9314403710799105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the third Machine Learning Model 'SVM regressor'."
      ],
      "metadata": {
        "id": "27WwDX1D0MI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "svr=SVR()"
      ],
      "metadata": {
        "id": "BTuXvoUF0LaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the Model."
      ],
      "metadata": {
        "id": "omIQpk1v_Iai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "0nsh9rra0LW4",
        "outputId": "3402f55d-3c90-4089-fca3-d1c932711a79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on Model."
      ],
      "metadata": {
        "id": "XbCRfI5a_MD2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_svr=svr.predict(x_test)\n",
        "print(pred_svr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZA33UTvH0Kxh",
        "outputId": "db9f038a-54e3-4f1e-ef1c-0c254e6b8bfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 9.25746652 21.21076316 16.65241133 10.62521675 12.14481673 21.19866212\n",
            " 18.43703947 13.73200656 16.6424632  18.06750398 15.62455506  9.52844848\n",
            " 10.73080211 20.17394417 18.00626117 15.70330798 16.69745445 16.50907103\n",
            " 20.73603465 17.72859622 19.48566479 14.96223778  8.57821796 19.73544021\n",
            " 15.99257991 16.50707194  7.83960602 18.73401208 12.16988321 13.93434059\n",
            "  8.10441854 11.46533538  7.49613839 19.89476562 11.39817881  8.01768657\n",
            "  9.48848711  9.32226031  9.94137322 17.96301502]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the accuracy score of model."
      ],
      "metadata": {
        "id": "q2THwVvdymb_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y_test, pred_svr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiLlnlkLxkaa",
        "outputId": "99d293a3-6f97-44ec-cc06-ebd0d9efd74f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8963929828568145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating Sales for new input data."
      ],
      "metadata": {
        "id": "w3gGmx6RsjpB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data={'TV':170.9,'Radio':37.6,'Newspaper':77.2}"
      ],
      "metadata": {
        "id": "aziQmaw1sBrd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index=[1]\n",
        "my_data=pd.DataFrame(new_data,index)\n",
        "my_data"
      ],
      "metadata": {
        "id": "yXezUDVjHRjL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "bcffcbe2-c49f-48e2-9f8b-2c82b2a01f21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TV  Radio  Newspaper\n",
              "1  170.9   37.6       77.2"
            ],
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f1c25fb1-6fa7-41d5-af8b-203807f26f31 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-f1c25fb1-6fa7-41d5-af8b-203807f26f31');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_predictions=linear.predict(my_data)"
      ],
      "metadata": {
        "id": "b4ZzltIMzvt7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "eabaf720-09f5-47e4-b80b-be4bdca484a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-8977068e78f7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_predictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m         \"\"\"\n\u001b[0;32m--> 354\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_intercept\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_scale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdense_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintercept_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_n_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_check_n_features\u001b[0;34m(self, X, reset)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features_in_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    390\u001b[0m                 \u001b[0;34mf\"X has {n_features} features, but {self.__class__.__name__} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;34mf\"is expecting {self.n_features_in_} features as input.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: X has 3 features, but LinearRegression is expecting 10 features as input."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data.plot.scatter(x='TV', y='Sales', figsize=(8,6), color='black')\n",
        "ax.set_xlabel('Cost for TV Advertisement')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "I82AF25cLohc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "af575052-3c93-43f5-cf01-6db53a89fc51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data.plot.scatter(x='Radio', y='Sales', figsize=(8,6), color='red')\n",
        "ax.set_xlabel('Cost for Radio Advertisement')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kmkBm_Z_N7Wu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6903518f-3d2b-4868-bc52-35768fcf71c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data.plot.scatter(x='Newspaper', y='Sales', figsize=(8,6), color='blue')\n",
        "ax.set_xlabel('Cost for Newspaper Advertisement')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "NIxPxInLR0EA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "a1d7fe71-9a8f-41d0-bec4-0f04b4cdcf29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rdPIf8pBR_Cg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}