Internship_Project.ipynb

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      "source": [
        "# Mileage Prediction - Regression Analysis"
      ],
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        "id": "5NfnhKijGhLs"
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      "source": [
        "###**Objective :**\n",
        "To develop an AI/ML project using NumPy, pandas, Matplotlib, and Seaborn to predict vehicle mileage (MPG) through regression analysis. Key goals include data preprocessing, feature selection, model training, evaluation, and potentially deployment for accurate MPG estimation, benefiting the automotive industry."
      ],
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      "source": [
        "###**Data Source :**\n",
        "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition."
      ],
      "metadata": {
        "id": "HVmGRNONJR3a"
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      "cell_type": "markdown",
      "source": [
        "###**Import Library**"
      ],
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
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        "id": "wf3adKcAMLBk"
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      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "###**Import Data**"
      ],
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      }
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/MPG.csv')\n",
        "df.head()"
      ],
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              "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
              "0  18.0          8         307.0       130.0    3504          12.0   \n",
              "1  15.0          8         350.0       165.0    3693          11.5   \n",
              "2  18.0          8         318.0       150.0    3436          11.0   \n",
              "3  16.0          8         304.0       150.0    3433          12.0   \n",
              "4  17.0          8         302.0       140.0    3449          10.5   \n",
              "\n",
              "   model_year origin                       name  \n",
              "0          70    usa  chevrolet chevelle malibu  \n",
              "1          70    usa          buick skylark 320  \n",
              "2          70    usa         plymouth satellite  \n",
              "3          70    usa              amc rebel sst  \n",
              "4          70    usa                ford torino  "
            ],
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      "source": [
        "df.nunique()"
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        "outputId": "2090953f-8adb-4417-e509-79fed6d435d0"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             129\n",
              "cylinders         5\n",
              "displacement     82\n",
              "horsepower       93\n",
              "weight          351\n",
              "acceleration     95\n",
              "model_year       13\n",
              "origin            3\n",
              "name            305\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
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      "source": [
        "###**Data Preprocessing**"
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      "execution_count": 4,
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 398 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           398 non-null    float64\n",
            " 1   cylinders     398 non-null    int64  \n",
            " 2   displacement  398 non-null    float64\n",
            " 3   horsepower    392 non-null    float64\n",
            " 4   weight        398 non-null    int64  \n",
            " 5   acceleration  398 non-null    float64\n",
            " 6   model_year    398 non-null    int64  \n",
            " 7   origin        398 non-null    object \n",
            " 8   name          398 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 28.1+ KB\n"
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        "df.describe()"
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      "execution_count": 5,
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            "text/plain": [
              "              mpg   cylinders  displacement  horsepower       weight  \\\n",
              "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
              "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
              "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
              "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
              "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
              "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
              "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
              "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
              "\n",
              "       acceleration  model_year  \n",
              "count    398.000000  398.000000  \n",
              "mean      15.568090   76.010050  \n",
              "std        2.757689    3.697627  \n",
              "min        8.000000   70.000000  \n",
              "25%       13.825000   73.000000  \n",
              "50%       15.500000   76.000000  \n",
              "75%       17.175000   79.000000  \n",
              "max       24.800000   82.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
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              "      <td>398.000000</td>\n",
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              "      <td>398.000000</td>\n",
              "      <td>392.000000</td>\n",
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              "      <td>398.000000</td>\n",
              "      <td>398.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23.514573</td>\n",
              "      <td>5.454774</td>\n",
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              "      <td>104.469388</td>\n",
              "      <td>2970.424623</td>\n",
              "      <td>15.568090</td>\n",
              "      <td>76.010050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.815984</td>\n",
              "      <td>1.701004</td>\n",
              "      <td>104.269838</td>\n",
              "      <td>38.491160</td>\n",
              "      <td>846.841774</td>\n",
              "      <td>2.757689</td>\n",
              "      <td>3.697627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>1613.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>104.250000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>2223.750000</td>\n",
              "      <td>13.825000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>148.500000</td>\n",
              "      <td>93.500000</td>\n",
              "      <td>2803.500000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>76.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>3608.000000</td>\n",
              "      <td>17.175000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46.600000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>455.000000</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>5140.000000</td>\n",
              "      <td>24.800000</td>\n",
              "      <td>82.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6547730d-44a3-43aa-ae60-893fe732fb9e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "id": "725D1xTWNpX2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "2c58c541-2046-4628-e6e6-6f4e0c1984e8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
              "mpg           1.000000  -0.775396     -0.804203   -0.778427 -0.831741   \n",
              "cylinders    -0.775396   1.000000      0.950721    0.842983  0.896017   \n",
              "displacement -0.804203   0.950721      1.000000    0.897257  0.932824   \n",
              "horsepower   -0.778427   0.842983      0.897257    1.000000  0.864538   \n",
              "weight       -0.831741   0.896017      0.932824    0.864538  1.000000   \n",
              "acceleration  0.420289  -0.505419     -0.543684   -0.689196 -0.417457   \n",
              "model_year    0.579267  -0.348746     -0.370164   -0.416361 -0.306564   \n",
              "\n",
              "              acceleration  model_year  \n",
              "mpg               0.420289    0.579267  \n",
              "cylinders        -0.505419   -0.348746  \n",
              "displacement     -0.543684   -0.370164  \n",
              "horsepower       -0.689196   -0.416361  \n",
              "weight           -0.417457   -0.306564  \n",
              "acceleration      1.000000    0.288137  \n",
              "model_year        0.288137    1.000000  "
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              "\n",
              "<div id=\"df-cb7bf49f-6a1f-472d-9207-b0205f5cc8cf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb7bf49f-6a1f-472d-9207-b0205f5cc8cf')\"\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-cb7bf49f-6a1f-472d-9207-b0205f5cc8cf 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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Remove Missing Values**"
      ],
      "metadata": {
        "id": "p5oa7SSLNv1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZATjSuvGN1xn",
        "outputId": "9ed435f8-ec0b-48c3-bd8c-49cdbb4a2502"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 392 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           392 non-null    float64\n",
            " 1   cylinders     392 non-null    int64  \n",
            " 2   displacement  392 non-null    float64\n",
            " 3   horsepower    392 non-null    float64\n",
            " 4   weight        392 non-null    int64  \n",
            " 5   acceleration  392 non-null    float64\n",
            " 6   model_year    392 non-null    int64  \n",
            " 7   origin        392 non-null    object \n",
            " 8   name          392 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 30.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Data Visualization**"
      ],
      "metadata": {
        "id": "XOz81MKeODlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df, x_vars = ['displacement', 'horsepower', 'weight', 'acceleration', 'mpg'], y_vars = ['mpg']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "te2L1OvHOI3t",
        "outputId": "35b7c380-fba0-49ce-a036-cb1bbdcba49a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x250 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x = 'displacement', y = 'mpg', data = df);"
      ],
      "metadata": {
        "id": "HPbnOIFiPKTT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "7fe1381f-d7d9-4b49-c0ab-345b6e1d6829"
      },
      "execution_count": 9,
      "outputs": [
        {
          "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": [
        "###**Define Target Variable y and Feature X**"
      ],
      "metadata": {
        "id": "JsqdhpHFQAC0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "831TK0jpQHdx",
        "outputId": "9f44aab8-0724-4d51-e61e-e33c5462a83e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
              "       'acceleration', 'model_year', 'origin', 'name'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['mpg']\n",
        "y.shape"
      ],
      "metadata": {
        "id": "eYtYxMprQNm9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76fe2e57-56b2-437b-f9f1-bf3a06f91510"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(392,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['displacement', 'horsepower', 'weight', 'acceleration']]\n",
        "X.shape"
      ],
      "metadata": {
        "id": "yaa5qc8EQU7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c615d95-546b-4069-c0da-19dc55cf2238"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(392, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "ieiQcmo5UMlt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "d0008cb2-3222-491f-c837-7c632a4443e2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     displacement  horsepower  weight  acceleration\n",
              "0           307.0       130.0    3504          12.0\n",
              "1           350.0       165.0    3693          11.5\n",
              "2           318.0       150.0    3436          11.0\n",
              "3           304.0       150.0    3433          12.0\n",
              "4           302.0       140.0    3449          10.5\n",
              "..            ...         ...     ...           ...\n",
              "393         140.0        86.0    2790          15.6\n",
              "394          97.0        52.0    2130          24.6\n",
              "395         135.0        84.0    2295          11.6\n",
              "396         120.0        79.0    2625          18.6\n",
              "397         119.0        82.0    2720          19.4\n",
              "\n",
              "[392 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f7a2341-1d49-4bc3-b02f-5bedd33c7d13\" 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>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693</td>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449</td>\n",
              "      <td>10.5</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>393</th>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790</td>\n",
              "      <td>15.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130</td>\n",
              "      <td>24.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295</td>\n",
              "      <td>11.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625</td>\n",
              "      <td>18.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>392 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-5f7a2341-1d49-4bc3-b02f-5bedd33c7d13')\"\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-5f7a2341-1d49-4bc3-b02f-5bedd33c7d13 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-5f7a2341-1d49-4bc3-b02f-5bedd33c7d13');\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-12d96508-df6f-42bd-8b1f-2bd920f2e7a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12d96508-df6f-42bd-8b1f-2bd920f2e7a8')\"\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-12d96508-df6f-42bd-8b1f-2bd920f2e7a8 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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Scaling Data**"
      ],
      "metadata": {
        "id": "iiv0H3fFiAPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "X = ss.fit_transform(X)\n",
        "X"
      ],
      "metadata": {
        "id": "PFQFrzfLiGw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9c22bd1-b39b-4fe0-8a65-01a06fcc7292"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.07728956,  0.66413273,  0.62054034, -1.285258  ],\n",
              "       [ 1.48873169,  1.57459447,  0.84333403, -1.46672362],\n",
              "       [ 1.1825422 ,  1.18439658,  0.54038176, -1.64818924],\n",
              "       ...,\n",
              "       [-0.56847897, -0.53247413, -0.80463202, -1.4304305 ],\n",
              "       [-0.7120053 , -0.66254009, -0.41562716,  1.11008813],\n",
              "       [-0.72157372, -0.58450051, -0.30364091,  1.40043312]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(X).describe()"
      ],
      "metadata": {
        "id": "5NNElJ-HiaS7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9f33f390-0c27-40ef-dd8f-17dcfe2cb725"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0             1             2             3\n",
              "count  3.920000e+02  3.920000e+02  3.920000e+02  3.920000e+02\n",
              "mean  -7.250436e-17 -1.812609e-16 -1.812609e-17  4.350262e-16\n",
              "std    1.001278e+00  1.001278e+00  1.001278e+00  1.001278e+00\n",
              "min   -1.209563e+00 -1.520975e+00 -1.608575e+00 -2.736983e+00\n",
              "25%   -8.555316e-01 -7.665929e-01 -8.868535e-01 -6.410551e-01\n",
              "50%   -4.153842e-01 -2.853488e-01 -2.052109e-01 -1.499869e-02\n",
              "75%    7.782764e-01  5.600800e-01  7.510927e-01  5.384714e-01\n",
              "max    2.493416e+00  3.265452e+00  2.549061e+00  3.360262e+00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbb239ad-aaa5-419e-8301-58e3dad97e6f\" 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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-7.250436e-17</td>\n",
              "      <td>-1.812609e-16</td>\n",
              "      <td>-1.812609e-17</td>\n",
              "      <td>4.350262e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.209563e+00</td>\n",
              "      <td>-1.520975e+00</td>\n",
              "      <td>-1.608575e+00</td>\n",
              "      <td>-2.736983e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.555316e-01</td>\n",
              "      <td>-7.665929e-01</td>\n",
              "      <td>-8.868535e-01</td>\n",
              "      <td>-6.410551e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-4.153842e-01</td>\n",
              "      <td>-2.853488e-01</td>\n",
              "      <td>-2.052109e-01</td>\n",
              "      <td>-1.499869e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.782764e-01</td>\n",
              "      <td>5.600800e-01</td>\n",
              "      <td>7.510927e-01</td>\n",
              "      <td>5.384714e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.493416e+00</td>\n",
              "      <td>3.265452e+00</td>\n",
              "      <td>2.549061e+00</td>\n",
              "      <td>3.360262e+00</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-dbb239ad-aaa5-419e-8301-58e3dad97e6f')\"\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-dbb239ad-aaa5-419e-8301-58e3dad97e6f 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-dbb239ad-aaa5-419e-8301-58e3dad97e6f');\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-b939abb2-523c-4bbc-99c7-f9c700219ae6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b939abb2-523c-4bbc-99c7-f9c700219ae6')\"\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-b939abb2-523c-4bbc-99c7-f9c700219ae6 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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**After Standardization Mean is Zero and Standard Deviation is One**"
      ],
      "metadata": {
        "id": "THdLU4oIih-f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Train Test Split Data**"
      ],
      "metadata": {
        "id": "v_MGy_DNivHT"
      }
    },
    {
      "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=0.7, random_state=2529)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "id": "hYlsdnf3i2UL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1f33051-56ff-4b1d-de8f-d08a19e149ff"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((274, 4), (118, 4), (274,), (118,))"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Linear Regression Model**"
      ],
      "metadata": {
        "id": "xUSny1bFjFOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "XzMqIR2EjKAA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "c82d40f3-486c-4718-ff18-319515d95b85"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "id": "0BzjFLe1kghQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4943f050-6188-4e7f-91a0-b7525a06cde2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.485738559737584"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "id": "_LxEobNPkms9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d27777a-3790-4948-ce04-877d65f7162e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.05767743, -1.68734727, -4.10787617, -0.11495177])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mileage = 23.4 - 1.05Displacement - 1.68Horsepower - 4.10Weight 0.115Acceleration + Error**"
      ],
      "metadata": {
        "id": "RHW4wcv4kvD0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Predict Test Data**"
      ],
      "metadata": {
        "id": "oj6ne9Y0lSkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "id": "AdBBQA3glXWx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f507620-f7ca-458d-ee43-b8987186c1fc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([18.51865637, 15.09305675, 14.30128789, 23.6753321 , 29.7546115 ,\n",
              "       23.68796629, 26.61066644, 24.56692437, 15.06260986, 11.94312046,\n",
              "       24.08050053, 27.96518468, 31.66130278, 31.01309132, 18.32428976,\n",
              "       19.32795009, 28.08847536, 32.1506879 , 31.15859692, 27.15792144,\n",
              "       18.82433097, 22.54580176, 26.15598115, 32.36393869, 20.74377679,\n",
              "        8.78027518, 22.19699435, 18.20614294, 25.00052718, 15.26421552,\n",
              "       23.13441082, 17.10542257,  9.87180062, 30.00790415, 20.41204655,\n",
              "       29.11860245, 24.4305187 , 21.72601835, 10.51174626, 13.12426391,\n",
              "       21.41938406, 19.96113872,  6.19146626, 17.79025345, 22.5493033 ,\n",
              "       29.34765021, 13.4861847 , 25.88852083, 29.40406946, 22.41841964,\n",
              "       22.07684766, 16.46575802, 24.06290693, 30.12890046, 10.11318121,\n",
              "        9.85011438, 28.07543852, 23.41426617, 20.08501128, 30.68234133,\n",
              "       20.92026393, 26.78370281, 22.9078744 , 14.15936872, 24.6439883 ,\n",
              "       26.95515832, 15.25709393, 24.11272087, 30.80980589, 14.9770217 ,\n",
              "       27.67836372, 24.2372919 , 10.92177228, 30.22858779, 30.88687365,\n",
              "       27.33992044, 31.18447082, 10.8873597 , 27.63510608, 16.49231363,\n",
              "       25.63229888, 29.49776285, 14.90393439, 32.78670687, 30.37325244,\n",
              "       30.9262743 , 14.71702373, 27.09633246, 26.69933806, 29.06424799,\n",
              "       32.45810182, 29.44846898, 31.61239999, 31.57891837, 21.46542321,\n",
              "       31.76739191, 26.28605476, 28.96419915, 31.09628395, 24.80549594,\n",
              "       18.76490961, 23.28043777, 23.04466919, 22.14143162, 15.95854367,\n",
              "       28.62870918, 25.58809869, 11.4040908 , 25.73334842, 30.83500051,\n",
              "       21.94176255, 15.34532941, 30.37399213, 28.7620624 , 29.3639931 ,\n",
              "       29.10476703, 20.44662365, 28.11466839])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Model Accuracy**"
      ],
      "metadata": {
        "id": "tg9s-6ClljSj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, r2_score\n",
        "mean_absolute_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "IWvQV6m4lnVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df130f25-1220-4325-aa12-f31e7ca45e88"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3286968643244106"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "b7CAUBmql_gV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbe04002-3282-4f0c-91ad-87ce451ee013"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14713035779536746"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "lTi_zo1zmJAD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18a555af-df91-4d9c-ba46-b1a2d7b19d72"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7031250746717691"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Polynomial Regression**"
      ],
      "metadata": {
        "id": "bxdLbenGmRpt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)\n",
        "X_train2 = poly.fit_transform(X_train)\n",
        "X_test2 = poly.fit_transform(X_test)\n",
        "lr.fit(X_train2, y_train)"
      ],
      "metadata": {
        "id": "LiNjlWIdmYBq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "ed89be16-631a-4396-f2a0-1284035c89c0"
      },
      "execution_count": 24,
      "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "id": "Siroz1x8nIZ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ae06351-f340-4c8e-b9f7-09b6f05cead5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21.27336450063766"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "id": "KQKryfZBnO0r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cceda306-46bb-4eaa-b112-40062d53f857"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.76070596, -5.00559628, -1.36884133, -0.81225214,  1.24596571,\n",
              "       -0.12475017, -0.90542822,  1.35064048, -0.17337823,  1.41680398])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_poly = lr.predict(X_test2)"
      ],
      "metadata": {
        "id": "hCB73zWMnTXZ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Model Accuracy**"
      ],
      "metadata": {
        "id": "0HII8t55nbYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, r2_score\n",
        "mean_absolute_error(y_test, y_pred_poly)"
      ],
      "metadata": {
        "id": "oLqmEp6InfN1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe199dec-811b-4321-a0d9-a11c2ba7959f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7887147720295977"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_percentage_error(y_test, y_pred_poly)"
      ],
      "metadata": {
        "id": "ftr0P4Pdn0nJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8d62fe6-59fb-4895-b2e7-1ed4329d1fe5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12074018342938687"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred_poly)"
      ],
      "metadata": {
        "id": "j7DGWC3wn9z5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bc991c5-8f84-4c07-a239-4e500249e37b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7461731314563803"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Explanation and Conclusion**\n",
        "This project involved data preprocessing to ensure data quality, data visualization for insights, and a robust prediction model for accurate vehicle mileage estimation. It holds promise for automotive applications.\n",
        "\n",
        "A well-prepared dataset, insightful visualization, and a strong prediction model make this project valuable for automotive research and design."
      ],
      "metadata": {
        "id": "cQn3GtJGsDd9"
      }
    }
  ]
}