MultipleLinearRegrassion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultipleLinearRegrassion.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMYmnznWQIaSFqYlIkFJPQx"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jei8XKvbn14",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing Libraries\n",
        "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": "code",
      "metadata": {
        "id": "z2XaMXJkcs0Y",
        "colab_type": "code",
        "outputId": "37abf643-e742-428e-c67d-08386e825997",
        "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
        }
      },
      "source": [
        "#Upload data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['FuelConsumption.csv']))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-501612dc-3e1a-4976-aa87-3cdc32a138fe\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-501612dc-3e1a-4976-aa87-3cdc32a138fe\">\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.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1YvzjmZc7pb",
        "colab_type": "code",
        "outputId": "24242811-d2df-426b-9dd7-cd86801a3e74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "#Let's see what data looks like\n",
        "df = pd.read_csv(\"FuelConsumption.csv\")\n",
        "df.head()"
      ],
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDSvCir0dXV-",
        "colab_type": "code",
        "outputId": "3112853d-abc5-4b3a-8505-aa60807e4051",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "#Let's get specific columns we want to use for regrassion\n",
        "\n",
        "#The column name we want to use\n",
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "#Get the row between (0-8) with is 9 row\n",
        "cdf.head(9)"
      ],
      "execution_count": 6,
      "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_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</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>9.9</td>\n",
              "      <td>6.7</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>11.2</td>\n",
              "      <td>7.7</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>6.0</td>\n",
              "      <td>5.8</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>12.7</td>\n",
              "      <td>9.1</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>12.1</td>\n",
              "      <td>8.7</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>11.9</td>\n",
              "      <td>7.7</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>11.8</td>\n",
              "      <td>8.1</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>12.8</td>\n",
              "      <td>9.0</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>13.4</td>\n",
              "      <td>9.5</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\n",
              "\n",
              "[9 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMUmWPtIeOd8",
        "colab_type": "code",
        "outputId": "5bf98a60-f70f-43e3-9d92-2699ed70acb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='red')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Co2Emmision\")\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z4F0J5y8F7gSOIqrb8H2imcrsMx+5d2ajzWtqaa6nUGbuozy1Capsjd9Th1mj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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiDtAM170YzI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Creating test dataset\n",
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPifyg5HuKmA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "dd72a5e4-8c65-4b08-fd1c-cf8f0ee0befe"
      },
      "source": [
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evU4zXXZ45kwfzrlpU/LktU2b+lcn6UJatKhyA1YBDwIvIbpT2A3cBVwBHBn2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\nAcuIobSSPSkDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3w4hJSRulIB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6989e06c-7278-4feb-8452-f07f218c6bc1"
      },
      "source": [
        "#Multiple Linear Regression\n",
        "from sklearn import linear_model\n",
        "regr = linear_model.LinearRegression()\n",
        "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
        "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "regr.fit (x, y)\n",
        "\n",
        "print ('Coefficients: ', regr.coef_)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients:  [[10.73163034  7.33091518  9.76594253]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkLOq7bbur0Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "bdd2a7b0-3821-4b73-c69f-28522b22ff88"
      },
      "source": [
        "#Prediction\n",
        "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
        "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
        "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "print(\"Residual sum of squares: %.2f\"\n",
        "      % np.mean((y_hat - y) ** 2))\n",
        "\n",
        "# Explained variance score: 1 is perfect prediction\n",
        "print('Variance score: %.2f' % regr.score(x, y))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Residual sum of squares: 581.98\n",
            "Variance score: 0.85\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}