Simple_Linear_Regression_Fuel(X).ipynb

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      "name": "Simple Linear Regression_Fuel(X).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMpwc+fDU4gOsgZUbSanIw1",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lonewolf235/ML-Models/blob/main/Simple_Linear_Regression_Fuel(X).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Simple Regression with CO2 emission just independent variable is Fuel Consumption"
      ],
      "metadata": {
        "id": "4FtY69uVS1nI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "mNTEY-MAFdCP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NAlgTnulF4RD",
        "outputId": "5ed066da-e81d-48da-d6ee-0b41be44d876"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "        style=\"border:none\" />\n",
              "     <output id=\"result-bd76a161-9d2b-4f17-b07e-515cb89ca9b6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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          "name": "stdout",
          "text": [
            "Saving ML_Dataset_CO2Emission.csv to ML_Dataset_CO2Emission.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('ML_Dataset_CO2Emission.csv')\n",
        "print(dataset.shape)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "-2OK28xMG4Nc",
        "outputId": "20fe469f-574c-4108-b281-e03d4579d79d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1067, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Engine_Size</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Fuel_Consumption</th>\n",
              "      <th>CO2_Emissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
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              "      <td>9.9</td>\n",
              "      <td>196</td>\n",
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              "      <th>1</th>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>11.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>12.7</td>\n",
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              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
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              "      <td>244</td>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-95e66354-6c6b-453a-9887-17e5a71c3964 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-95e66354-6c6b-453a-9887-17e5a71c3964');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Engine_Size  Cylinders  Fuel_Consumption  CO2_Emissions\n",
              "0          2.0          4               9.9            196\n",
              "1          2.4          4              11.2            221\n",
              "2          1.5          4               6.0            136\n",
              "3          3.5          6              12.7            255\n",
              "4          3.5          6              12.1            244"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "auEMqpVqH3MP",
        "outputId": "450acfe1-d2dd-4ed4-e049-de64728ae708"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d3986ac0-bebd-4f6a-8028-9d63c525d5b7\">\n",
              "    <div 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>Engine_Size</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Fuel_Consumption</th>\n",
              "      <th>CO2_Emissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.346298</td>\n",
              "      <td>5.794752</td>\n",
              "      <td>13.296532</td>\n",
              "      <td>256.228679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.415895</td>\n",
              "      <td>1.797447</td>\n",
              "      <td>4.101253</td>\n",
              "      <td>63.372304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>108.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>207.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.400000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>251.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>15.550000</td>\n",
              "      <td>294.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.400000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>30.200000</td>\n",
              "      <td>488.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3986ac0-bebd-4f6a-8028-9d63c525d5b7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d3986ac0-bebd-4f6a-8028-9d63c525d5b7 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-d3986ac0-bebd-4f6a-8028-9d63c525d5b7');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Engine_Size    Cylinders  Fuel_Consumption  CO2_Emissions\n",
              "count  1067.000000  1067.000000       1067.000000    1067.000000\n",
              "mean      3.346298     5.794752         13.296532     256.228679\n",
              "std       1.415895     1.797447          4.101253      63.372304\n",
              "min       1.000000     3.000000          4.600000     108.000000\n",
              "25%       2.000000     4.000000         10.250000     207.000000\n",
              "50%       3.400000     6.000000         12.600000     251.000000\n",
              "75%       4.300000     8.000000         15.550000     294.000000\n",
              "max       8.400000    12.000000         30.200000     488.000000"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.Fuel_Consumption.values\n",
        "Y = dataset.CO2_Emissions.values\n",
        "X=X.reshape(-1,1)\n",
        "Y=Y.reshape(-1,1)"
      ],
      "metadata": {
        "id": "r5fEvZGRH74S"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('Fuel Consumption')\n",
        "plt.ylabel('CO2 Emissions')\n",
        "plt.scatter(X,Y,color='red',marker='.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "NnZMuwvQJH2x",
        "outputId": "1ec7731d-a03b-45cc-ec74-97a2406be86b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f22700b2390>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X,Y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "J1DSb3EmKc0V"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(Xtrain,Ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAaqy75eMRSD",
        "outputId": "5470b7ba-7a2a-4315-86f6-d1f1d191516b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=reg.predict(Xtest)"
      ],
      "metadata": {
        "id": "Z8gswCXfSTi5"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coefficent=reg.coef_\n",
        "coefficent"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2XTSjtmUX-2",
        "outputId": "3c60f92a-fa9d-4bbb-cc35-aeed39df69e4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14.02686012]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "intercept=reg.intercept_\n",
        "intercept"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCp6uoL-Ujev",
        "outputId": "138efa6a-e6a3-48f9-cf90-4d42db2d8be8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([70.85941528])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Actual': Ytest.tolist(), 'Predicted': predictions.tolist()})\n",
        "df.shape"
      ],
      "metadata": {
        "id": "rij50xdoUpfm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f036eb6-57d5-452d-84da-216ce1901592"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(321, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PEk46N3GCE84",
        "outputId": "42a16b2a-0f22-4444-81cc-0479c2db6417"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b5ff734c-c2ad-40ee-a317-d5b9226e1eea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>2</th>\n",
              "      <td>[230]</td>\n",
              "      <td>[230.7656206578505]</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[212]</td>\n",
              "      <td>[220.94681857327313]</td>\n",
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              "    <tr>\n",
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              "      <td>[168]</td>\n",
              "      <td>[185.8796682712112]</td>\n",
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              "      <th>6</th>\n",
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              "    <tr>\n",
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              "      <th>9</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5ff734c-c2ad-40ee-a317-d5b9226e1eea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      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",
              "    [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-b5ff734c-c2ad-40ee-a317-d5b9226e1eea 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-b5ff734c-c2ad-40ee-a317-d5b9226e1eea');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Actual             Predicted\n",
              "0  [356]   [312.1214093586342]\n",
              "1  [209]  [218.14144654910817]\n",
              "2  [230]   [230.7656206578505]\n",
              "3  [212]  [220.94681857327313]\n",
              "4  [168]   [185.8796682712112]\n",
              "5  [292]  [281.26231709281967]\n",
              "6  [212]  [219.54413256119065]\n",
              "7  [276]    [261.624712923665]\n",
              "8  [202]  [213.93338851286074]\n",
              "9  [334]   [303.7052932861393]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u2zaR84nCPI7",
        "outputId": "776a9f8a-6c02-4644-c908-ed95c076c0ac"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>[354]</td>\n",
              "      <td>[334.5643855519538]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>[258]</td>\n",
              "      <td>[256.0139688753351]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>[267]</td>\n",
              "      <td>[258.81934089950005]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>[297]</td>\n",
              "      <td>[282.66500310490215]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>[271]</td>\n",
              "      <td>[250.40322482700518]</td>\n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f2a60e62-38b9-4a49-bfa8-e51860d85466 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-f2a60e62-38b9-4a49-bfa8-e51860d85466');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Actual             Predicted\n",
              "316  [354]   [334.5643855519538]\n",
              "317  [258]   [256.0139688753351]\n",
              "318  [267]  [258.81934089950005]\n",
              "319  [297]  [282.66500310490215]\n",
              "320  [271]  [250.40322482700518]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(Ytest, predictions))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(Ytest, predictions))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Ytest, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K89_VnfN4X0b",
        "outputId": "14f50da2-d7f3-4b43-adc5-9e407fcf5472"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 19.449369656766258\n",
            "Mean Squared Error: 814.5702636720041\n",
            "Root Mean Squared Error: 28.540677351317438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,Y,color='red')\n",
        "plt.plot(Xtest,reg.predict(Xtest),color='blue')\n",
        "plt.title('Engine_Size vs CO2_Emissions (Training set)')\n",
        "plt.xlabel('Engine_size')\n",
        "plt.ylabel('CO2_Emissions')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "fMdfYEnp44rH",
        "outputId": "3046cc08-01e6-489c-bd2b-add4db0d8e6a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Added R square accuracy"
      ],
      "metadata": {
        "id": "z5_uA8WknE1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2=r2_score(Ytest,predictions)\n",
        "print(\"Accuracy of model is \",round(r2*100,2),\"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBv3C5J3lpSi",
        "outputId": "b3d6eadd-9369-4e0f-a63c-cf4360ba0e90"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of model is  79.46 %\n"
          ]
        }
      ]
    }
  ]
}