SimpleRegressionModel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SimpleRegressionModel.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNXciT1FJqX2obNJEhN/Ad5"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzpLvM_KMGSj",
        "colab_type": "text"
      },
      "source": [
        "Importing Packages\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6X6Vid1LoPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9jTZw8mMk_3",
        "colab_type": "text"
      },
      "source": [
        "Reading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diRiBjVJMcli",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "c489bf71-7933-4790-c37f-7091e202b84d"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "import io\n",
        "df2 = pd.read_csv(io.BytesIO(uploaded['FuelConsumption.csv']))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-55538cde-eeb0-4fc3-8b54-da1566b26914\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-55538cde-eeb0-4fc3-8b54-da1566b26914\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving FuelConsumption.csv to FuelConsumption (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKxC1CzhO995",
        "colab_type": "text"
      },
      "source": [
        "Reading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V81HviX6O0fS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "b48601cc-c4a9-4d6c-98f9-fb2dde61c83f"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODELYEAR</th>\n",
              "      <th>MAKE</th>\n",
              "      <th>MODEL</th>\n",
              "      <th>VEHICLECLASS</th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>TRANSMISSION</th>\n",
              "      <th>FUELTYPE</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.5</td>\n",
              "      <td>33</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>M6</td>\n",
              "      <td>Z</td>\n",
              "      <td>11.2</td>\n",
              "      <td>7.7</td>\n",
              "      <td>9.6</td>\n",
              "      <td>29</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX HYBRID</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>AV7</td>\n",
              "      <td>Z</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.9</td>\n",
              "      <td>48</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>MDX 4WD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>25</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>RDX AWD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>10.6</td>\n",
              "      <td>27</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MODELYEAR   MAKE  ... FUELCONSUMPTION_COMB_MPG CO2EMISSIONS\n",
              "0       2014  ACURA  ...                       33          196\n",
              "1       2014  ACURA  ...                       29          221\n",
              "2       2014  ACURA  ...                       48          136\n",
              "3       2014  ACURA  ...                       25          255\n",
              "4       2014  ACURA  ...                       27          244\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skRRGJryPFfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "f825b410-120c-4d4f-c961-016f452eb0dd"
      },
      "source": [
        "#Summurize the data\n",
        "df2.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODELYEAR</th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1067.0</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\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>2014.0</td>\n",
              "      <td>3.346298</td>\n",
              "      <td>5.794752</td>\n",
              "      <td>13.296532</td>\n",
              "      <td>9.474602</td>\n",
              "      <td>11.580881</td>\n",
              "      <td>26.441425</td>\n",
              "      <td>256.228679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.415895</td>\n",
              "      <td>1.797447</td>\n",
              "      <td>4.101253</td>\n",
              "      <td>2.794510</td>\n",
              "      <td>3.485595</td>\n",
              "      <td>7.468702</td>\n",
              "      <td>63.372304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>108.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>207.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>8.800000</td>\n",
              "      <td>10.900000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>251.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>15.550000</td>\n",
              "      <td>10.850000</td>\n",
              "      <td>13.350000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>294.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>30.200000</td>\n",
              "      <td>20.500000</td>\n",
              "      <td>25.800000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>488.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       MODELYEAR   ENGINESIZE  ...  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS\n",
              "count     1067.0  1067.000000  ...               1067.000000   1067.000000\n",
              "mean      2014.0     3.346298  ...                 26.441425    256.228679\n",
              "std          0.0     1.415895  ...                  7.468702     63.372304\n",
              "min       2014.0     1.000000  ...                 11.000000    108.000000\n",
              "25%       2014.0     2.000000  ...                 21.000000    207.000000\n",
              "50%       2014.0     3.400000  ...                 26.000000    251.000000\n",
              "75%       2014.0     4.300000  ...                 31.000000    294.000000\n",
              "max       2014.0     8.400000  ...                 60.000000    488.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKOKPngtdcIB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "4a9b69dd-c5e3-41b7-bda1-bd4e443ffdf7"
      },
      "source": [
        "#Get specific column name\n",
        "cdf = df2[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "#How many we want to get so in thos case 0-8 with is 9\n",
        "cdf.head(9)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>9.6</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.9</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>11.1</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.1</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.7</td>\n",
              "      <td>6</td>\n",
              "      <td>11.1</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3.7</td>\n",
              "      <td>6</td>\n",
              "      <td>11.6</td>\n",
              "      <td>267</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
              "0         2.0          4                   8.5           196\n",
              "1         2.4          4                   9.6           221\n",
              "2         1.5          4                   5.9           136\n",
              "3         3.5          6                  11.1           255\n",
              "4         3.5          6                  10.6           244\n",
              "5         3.5          6                  10.0           230\n",
              "6         3.5          6                  10.1           232\n",
              "7         3.7          6                  11.1           255\n",
              "8         3.7          6                  11.6           267"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYkQG1-9eCO7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "3f3c6e3a-6b4d-4deb-cd4d-d700145144fe"
      },
      "source": [
        "#Let's visualize the selected set with histogram plotting\n",
        "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
        "viz.hist()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noPaJAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Te4P4KmreeI8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "ba780561-0dad-4e8a-c791-b80438c4f069"
      },
      "source": [
        "#Visualize the selected data for lineer table\n",
        "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
        "#set fuelconsuption as x-axis\n",
        "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
        "#set Emission as y-axis\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEICAYAAACwDehOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7euq4iSfyrj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "3bd7e0a0-1d81-472f-e8bc-726695a78a06"
      },
      "source": [
        "#Changed the fuelconsuption with enginesize\n",
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3A21PCL2g-O5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Creating train and test dataset\n",
        "\"\"\"\n",
        "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing.\n",
        " We create a mask to select random rows using __np.random.rand()__ function: \n",
        "\"\"\"\n",
        "#For more information about random.rand() https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.rand.html\n",
        "msk = np.random.rand(len(df2)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nwdc9aCGjPbY",
        "colab_type": "text"
      },
      "source": [
        "Simple Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzRadNjJjHgm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We are going to train this data distribution\n",
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGYN8tWHjXQG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0ab1d879-1235-4403-a53c-4b6af89406cc"
      },
      "source": [
        "#Modeling\n",
        "from sklearn import linear_model\n",
        "regr =linear_model.LinearRegression()\n",
        "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
        "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "regr.fit (train_x, train_y)\n",
        "# The coefficients\n",
        "print ('Coefficients: ', regr.coef_)\n",
        "print ('Intercept: ',regr.intercept_)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients:  [[38.93464283]]\n",
            "Intercept:  [125.18431742]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuGR7GoRjyxu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d9d01eae-41fe-4ef5-adfb-42f4257985ce"
      },
      "source": [
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Emission')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArLFb4WEj2uj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5f033873-582a-445d-d50d-f57c938581db"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
        "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "test_y_hat = regr.predict(test_x)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_hat - test_y)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_hat - test_y) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(test_y_hat , test_y) )"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error: 22.65\n",
            "Residual sum of squares (MSE): 930.41\n",
            "R2-score: 0.70\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}