understanding_multiple_linear_regression_with_sklearn.ipynb

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    "colab": {
      "name": "understanding_multiple_linear_regression_with_sklearn.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Machine_Learning/blob/master/understanding_multiple_linear_regression_with_sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIO_M4xh1pt3",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "<h3>About this Notebook</h3>\n",
        "In this notebook, we learn how to use scikit-learn to implement Multiple linear regression. <br>\n",
        "Multiple Linear Regression involves two or more independent variables and one dependent variable.<br>\n",
        "The key point in Linear Regression is that the dependent variable must be continous and cannot be a categorical variable. However the independent variables can be categorical or continous.<br>\n",
        "We shall download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. <br>\n",
        "Then, we split our data into training and test sets, create a model using training set, <br>\n",
        "Evaluate your model using test set, and finally use model to predict unknown value\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ua2bm1Uj0L7R",
        "colab_type": "text"
      },
      "source": [
        "See Direct [link](https://drive.google.com/file/d/1Apxb-KxuagxhryXmT4UJA0U-2PgvdRer/view?usp=sharing) to The Data.\n",
        "\n",
        "Or download from my Github [repo](https://raw.githubusercontent.com/Blackman9t/Machine_Learning/master/Original_2000_2014_Fuel_Consumption_Ratings.csv)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3YnQAq75x5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing required libraries\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import pylab as pl\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXKMe5IS2tPt",
        "colab_type": "text"
      },
      "source": [
        "<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
        "\n",
        "### `FuelConsumption.csv`:\n",
        "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
        "\n",
        "- **MODELYEAR** e.g. 2014\n",
        "- **MAKE** e.g. Acura\n",
        "- **MODEL** e.g. ILX\n",
        "- **VEHICLE CLASS** e.g. SUV\n",
        "- **ENGINE SIZE** e.g. 4.7\n",
        "- **CYLINDERS** e.g 6\n",
        "- **TRANSMISSION** e.g. A6\n",
        "- **FUELTYPE** e.g. z\n",
        "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
        "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
        "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
        "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OS0F3_fT3TWO",
        "colab_type": "text"
      },
      "source": [
        "Let's import the data set from Github using the !wget command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c6OXcK89oaZ",
        "colab_type": "code",
        "outputId": "96bda918-5a00-4ee7-e724-70a4589f8039",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!wget -O fuel_data.csv https://raw.githubusercontent.com/Blackman9t/Machine_Learning/master/Original_2000_2014_Fuel_Consumption_Ratings.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-10 02:48:29--  https://raw.githubusercontent.com/Blackman9t/Machine_Learning/master/Original_2000_2014_Fuel_Consumption_Ratings.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 966474 (944K) [text/plain]\n",
            "Saving to: ‘fuel_data.csv’\n",
            "\n",
            "\rfuel_data.csv         0%[                    ]       0  --.-KB/s               \rfuel_data.csv       100%[===================>] 943.82K  --.-KB/s    in 0.06s   \n",
            "\n",
            "2019-05-10 02:48:29 (15.3 MB/s) - ‘fuel_data.csv’ saved [966474/966474]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMcAATSM5pB4",
        "colab_type": "text"
      },
      "source": [
        "### let's read the file into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsVH5tjz-232",
        "colab_type": "code",
        "outputId": "68113c52-0762-48b5-ea28-2707287f9c93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "df = pd.read_csv('fuel_data.csv')\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODEL_YEAR</th>\n",
              "      <th>MAKE</th>\n",
              "      <th>MODEL</th>\n",
              "      <th>VEHICLE_CLASS</th>\n",
              "      <th>ENGINE_SIZE(L)</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>TRANSMISSION</th>\n",
              "      <th>FUEL_TYPE</th>\n",
              "      <th>FUEL_CONSUMPTION_CITY(L/100km)</th>\n",
              "      <th>HWY_(L/100km)</th>\n",
              "      <th>COMB_(L/100km)</th>\n",
              "      <th>COMB_(mpg)</th>\n",
              "      <th>CO2_EMISSIONS(g/km)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.1</td>\n",
              "      <td>35</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>M5</td>\n",
              "      <td>X</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>37</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.2TL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.2</td>\n",
              "      <td>7.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.5RL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>25</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>INTEGRA</td>\n",
              "      <td>SUBCOMPACT</td>\n",
              "      <td>1.8</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>33</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MODEL_YEAR   MAKE    MODEL VEHICLE_CLASS  ENGINE_SIZE(L)  CYLINDERS  \\\n",
              "0        2000  ACURA    1.6EL       COMPACT             1.6          4   \n",
              "1        2000  ACURA    1.6EL       COMPACT             1.6          4   \n",
              "2        2000  ACURA    3.2TL      MID-SIZE             3.2          6   \n",
              "3        2000  ACURA    3.5RL      MID-SIZE             3.5          6   \n",
              "4        2000  ACURA  INTEGRA    SUBCOMPACT             1.8          4   \n",
              "\n",
              "  TRANSMISSION FUEL_TYPE  FUEL_CONSUMPTION_CITY(L/100km)  HWY_(L/100km)  \\\n",
              "0           A4         X                             9.2            6.7   \n",
              "1           M5         X                             8.5            6.5   \n",
              "2          AS5         Z                            12.2            7.4   \n",
              "3           A4         Z                            13.4            9.2   \n",
              "4           A4         X                            10.0            7.0   \n",
              "\n",
              "   COMB_(L/100km)  COMB_(mpg)  CO2_EMISSIONS(g/km)  \n",
              "0             8.1          35                  186  \n",
              "1             7.6          37                  175  \n",
              "2            10.0          28                  230  \n",
              "3            11.5          25                  264  \n",
              "4             8.6          33                  198  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKKSXGgHJDYz",
        "colab_type": "text"
      },
      "source": [
        "Let's confirm the shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NSVg_kJJHA8",
        "colab_type": "code",
        "outputId": "99ffc409-8e43-416a-8697-42efd412133d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14343, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf5w4NAEJKx8",
        "colab_type": "text"
      },
      "source": [
        "Let's confirm the right data types exist per column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAqpsAIGJRA0",
        "colab_type": "code",
        "outputId": "a57bb4f4-226a-46db-c946-eb3a1c1aa265",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MODEL_YEAR                          int64\n",
              "MAKE                               object\n",
              "MODEL                              object\n",
              "VEHICLE_CLASS                      object\n",
              "ENGINE_SIZE(L)                    float64\n",
              "CYLINDERS                           int64\n",
              "TRANSMISSION                       object\n",
              "FUEL_TYPE                          object\n",
              "FUEL_CONSUMPTION_CITY(L/100km)    float64\n",
              "HWY_(L/100km)                     float64\n",
              "COMB_(L/100km)                    float64\n",
              "COMB_(mpg)                          int64\n",
              "CO2_EMISSIONS(g/km)                 int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJR5vzMQJsU3",
        "colab_type": "text"
      },
      "source": [
        "Let's rename some columns for brevity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fSiSJY2Jwot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.rename(columns={'FUEL_CONSUMPTION_CITY(L/100km)':'FUEL_CONS_CITY(L/100km)','CO2_EMISSIONS(g/km)':'CO2_EMISSIONS'}, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrC_u4blM5WZ",
        "colab_type": "code",
        "outputId": "10eaab02-7cfa-45cb-c7ca-7d62743a0550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODEL_YEAR</th>\n",
              "      <th>MAKE</th>\n",
              "      <th>MODEL</th>\n",
              "      <th>VEHICLE_CLASS</th>\n",
              "      <th>ENGINE_SIZE(L)</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>TRANSMISSION</th>\n",
              "      <th>FUEL_TYPE</th>\n",
              "      <th>FUEL_CONS_CITY(L/100km)</th>\n",
              "      <th>HWY_(L/100km)</th>\n",
              "      <th>COMB_(L/100km)</th>\n",
              "      <th>COMB_(mpg)</th>\n",
              "      <th>CO2_EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.1</td>\n",
              "      <td>35</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>M5</td>\n",
              "      <td>X</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>37</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.2TL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.2</td>\n",
              "      <td>7.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.5RL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>25</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>INTEGRA</td>\n",
              "      <td>SUBCOMPACT</td>\n",
              "      <td>1.8</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>33</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MODEL_YEAR   MAKE    MODEL VEHICLE_CLASS  ENGINE_SIZE(L)  CYLINDERS  \\\n",
              "0        2000  ACURA    1.6EL       COMPACT             1.6          4   \n",
              "1        2000  ACURA    1.6EL       COMPACT             1.6          4   \n",
              "2        2000  ACURA    3.2TL      MID-SIZE             3.2          6   \n",
              "3        2000  ACURA    3.5RL      MID-SIZE             3.5          6   \n",
              "4        2000  ACURA  INTEGRA    SUBCOMPACT             1.8          4   \n",
              "\n",
              "  TRANSMISSION FUEL_TYPE  FUEL_CONS_CITY(L/100km)  HWY_(L/100km)  \\\n",
              "0           A4         X                      9.2            6.7   \n",
              "1           M5         X                      8.5            6.5   \n",
              "2          AS5         Z                     12.2            7.4   \n",
              "3           A4         Z                     13.4            9.2   \n",
              "4           A4         X                     10.0            7.0   \n",
              "\n",
              "   COMB_(L/100km)  COMB_(mpg)  CO2_EMISSIONS  \n",
              "0             8.1          35            186  \n",
              "1             7.6          37            175  \n",
              "2            10.0          28            230  \n",
              "3            11.5          25            264  \n",
              "4             8.6          33            198  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJKqAnfiKTZv",
        "colab_type": "text"
      },
      "source": [
        "Let's select some features for MLR prediction. But first lets plot the correlation of variables in the data set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-w8eq5K2KhVc",
        "colab_type": "code",
        "outputId": "dd3957a0-4810-4812-d578-eb9dd651f749",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "corr_data = df.corr()\n",
        "\n",
        "sns.set_style('ticks')\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title('Variables correlation figures in data set', fontsize=16)\n",
        "\n",
        "sns.heatmap(corr_data, annot=True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UKIGGhkaOHePMMplxvrr/lJQUnj9/nuU5ftPz9npe5v579uxJ27Ztc3xcdg4dOoSuri6L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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iShuP1wtMC1A",
        "colab_type": "text"
      },
      "source": [
        "Let's select the following four variables as our independent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD5EGQepMLZJ",
        "colab_type": "code",
        "outputId": "1398d53b-69e6-4243-8b15-da011f9fd905",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "independent = df[['ENGINE_SIZE(L)','CYLINDERS','FUEL_CONS_CITY(L/100km)','COMB_(L/100km)']]\n",
        "\n",
        "independent.head()"
      ],
      "execution_count": 9,
      "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>ENGINE_SIZE(L)</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUEL_CONS_CITY(L/100km)</th>\n",
              "      <th>COMB_(L/100km)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>12.2</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>13.4</td>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.8</td>\n",
              "      <td>4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ENGINE_SIZE(L)  CYLINDERS  FUEL_CONS_CITY(L/100km)  COMB_(L/100km)\n",
              "0             1.6          4                      9.2             8.1\n",
              "1             1.6          4                      8.5             7.6\n",
              "2             3.2          6                     12.2            10.0\n",
              "3             3.5          6                     13.4            11.5\n",
              "4             1.8          4                     10.0             8.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hicHkEWsZorD",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize the relationship between each independent variable and the dependent variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xko4SHCZxeV",
        "colab_type": "code",
        "outputId": "d7626aa4-0fc8-4ca4-e1ef-4e02ef8c41aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        }
      },
      "source": [
        "fig = plt.figure(figsize=(20,12))\n",
        "\n",
        "plt.suptitle('Correlation of Independent Variables and The Dependent Variable', fontsize=16, y=0.95)\n",
        "ax = fig.add_subplot(221)\n",
        "ax1 = fig.add_subplot(222)\n",
        "ax2 = fig.add_subplot(223)\n",
        "ax3 = fig.add_subplot(224)\n",
        "\n",
        "# Plotting Engine_Size and Co2_emissions\n",
        "ax.scatter(x=independent['ENGINE_SIZE(L)'],y= df['CO2_EMISSIONS'], color='navy')\n",
        "ax.set_title('Engine_Size and CO2_Emissions', fontsize=14)\n",
        "ax.set_xlabel('Engine_Size', fontsize=12)\n",
        "ax.set_ylabel('CO2_Emissions', fontsize=12)\n",
        "\n",
        "# Plotting Cylinders and Co2_emissions\n",
        "ax1.scatter(x=independent['CYLINDERS'],y= df['CO2_EMISSIONS'], color='green')\n",
        "ax1.set_title('Cylinders and CO2_Emissions', fontsize=14)\n",
        "ax1.set_xlabel('Cylinders', fontsize=12)\n",
        "ax1.set_ylabel('CO2_Emissions', fontsize=12)\n",
        "\n",
        "# Plotting FUEL_CONS_CITY(L/100km) and Co2_emissions\n",
        "ax2.scatter(x=independent['FUEL_CONS_CITY(L/100km)'],y= df['CO2_EMISSIONS'], color='red')\n",
        "ax2.set_title('FUEL_CONS_CITY(L/100km) and CO2_Emissions', fontsize=14)\n",
        "ax2.set_xlabel('FUEL_CONS_CITY(L/100km)', fontsize=12)\n",
        "ax2.set_ylabel('CO2_Emissions', fontsize=12)\n",
        "\n",
        "# Plotting COMB_(L/100km) and Co2_emissions\n",
        "ax3.scatter(x=independent['COMB_(L/100km)'],y= df['CO2_EMISSIONS'], color='skyblue')\n",
        "ax3.set_title('COMB_(L/100km) and CO2_Emissions', fontsize=14)\n",
        "ax3.set_xlabel('COMB_(L/100km)', fontsize=12)\n",
        "ax3.set_ylabel('CO2_Emissions', fontsize=12)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'CO2_Emissions')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K+YYPH65wTlNSUkSTJk2Ep6enQr64uDjRuHFjsWXLFinNzs5ONG3aVLx580ZKi4qKEjKZ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\nL+3hsHksHJ76S3w21TnxDa+90qMnFSdCoJ07zfAjXnxIFxRjzm0/m9H69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LAAACAASURB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\n",
            "text/plain": [
              "<Figure size 1440x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YipMOJJdNcTE",
        "colab_type": "text"
      },
      "source": [
        "We don't need to normalize the variables for Multiple Linear Regression, so let's just view the variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wqIQDgAS8Lf",
        "colab_type": "code",
        "outputId": "58143d9d-29f7-4554-8f12-6058d244e350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "X = independent.values\n",
        "\n",
        "X"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.6,  4. ,  9.2,  8.1],\n",
              "       [ 1.6,  4. ,  8.5,  7.6],\n",
              "       [ 3.2,  6. , 12.2, 10. ],\n",
              "       ...,\n",
              "       [ 3. ,  6. , 11.9, 10.3],\n",
              "       [ 3.2,  6. , 11.4,  9.8],\n",
              "       [ 3.2,  6. , 13.3, 11.2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IwdG8MXvXYu4",
        "colab_type": "text"
      },
      "source": [
        "Define the target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5t1B6F7IXXoa",
        "colab_type": "code",
        "outputId": "74eaff6c-3265-45d2-978f-bf3904440d36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "Y = df[['CO2_EMISSIONS']].values\n",
        "\n",
        "Y"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[186],\n",
              "       [175],\n",
              "       [230],\n",
              "       ...,\n",
              "       [237],\n",
              "       [225],\n",
              "       [258]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXjnA5UJWcyd",
        "colab_type": "text"
      },
      "source": [
        "### Creating train and test dataset\n",
        "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
        "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
        "\n",
        "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it’s truly an out-of-sample testing.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToYfx_KWTlhd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split  # The function to split the data set into training and testing sets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLTM-TiJXEP1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(X,Y, test_size=0.27, shuffle=True, random_state=47)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMLHPo5Hgeg9",
        "colab_type": "code",
        "outputId": "e5e57e5b-5f10-4389-d8a9-f8e5fe1f7bd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print('train_x length is',len(train_x),'\\ntest_x length is',len(test_x),'\\ntrain_y length is',len(train_y),'\\ntest_y length is',len(test_y))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_x length is 10470 \n",
            "test_x length is 3873 \n",
            "train_y length is 10470 \n",
            "test_y length is 3873\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-z4f6y4Kin9M",
        "colab_type": "text"
      },
      "source": [
        "### Multiple Regression Model\n",
        "\n",
        "In reality, there are multiple variables that predict the Co2emission. When more than one independent variable is present, the process is called multiple linear regression. For example, predicting co2emission using FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that Multiple linear regression is the extension of simple linear regression model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYSu1yQpitQQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "mlr = LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqYvgsWPjUdg",
        "colab_type": "text"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCMUAv-MjWMI",
        "colab_type": "code",
        "outputId": "b939562c-cfdd-4be8-9a5a-95cdcf02aa3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "mlr.fit(X,Y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNtUjRx3jhsU",
        "colab_type": "text"
      },
      "source": [
        "### print the coefficients of the model parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aerQt_lAjlD3",
        "colab_type": "code",
        "outputId": "f52578f9-6b3b-4021-a892-4279f6ac0e8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# The Slope or gradient\n",
        "slope = mlr.coef_\n",
        "\n",
        "# the intercept or bias unit\n",
        "intercept = mlr.intercept_\n",
        "\n",
        "print('Slope =', slope,'\\nIntercept = ',intercept)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Slope = [[ 5.5140746   5.05050613 -1.44032344 15.68854857]] \n",
            "Intercept =  [44.0302154]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4nZck9xD-Bz",
        "colab_type": "text"
      },
      "source": [
        "Therefore our MLR model equation is \n",
        "\n",
        "### $y_{hat} = b0 + b1x1 + b2x2 + b3x3 + b4x4$<br>\n",
        "<br>\n",
        "which means given our model parameters above, we can make predictions by substituting the slope and intercept values like this:-\n",
        "<br>\n",
        "\n",
        "### $y_{hat} = 44.0302154 + 5.5140746(x1) + 5.05050613(x2) -1.440323441(x3) + 15.68854857(x4)$<br>\n",
        "\n",
        "\n",
        "where:<br>\n",
        "\n",
        "$x1$ = Engine_Size<br>\n",
        "$x2$ = Cylinders<br>\n",
        "$x3$ = Fuel_Cons_City(L/100km)<br>\n",
        "$x4$ = Comb_(L/100km)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26p2Y8vIF-Uq",
        "colab_type": "text"
      },
      "source": [
        "So if we have the following details,  for an audi_car for example, we can easily predict CO2 emissions for the car below\n",
        "\n",
        "\n",
        "ENGINE_SIZE(L) = 3.2<br>\n",
        "CYLINDERS = 6<br>\n",
        "FUEL_CONS_CITY(L/100km) = 12.2<br>\n",
        "COMB_(L/100km) = 10.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvRDVxuEHyvk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ENGINE_SIZE = 3.2\n",
        "CYLINDERS = 6\n",
        "FUEL_CONS_CITY = 12.2\n",
        "COMB_L = 10.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIGDyMZxMIY3",
        "colab_type": "code",
        "outputId": "84ba67b0-a452-480e-c9d0-6ef6e80194c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# next we substitute the variables in the MLR equation\n",
        "\n",
        "acura_car_CO2 = intercept + (slope[0][0]*ENGINE_SIZE + slope[0][1]*CYLINDERS + slope[0][2]*FUEL_CONS_CITY + slope[0][3]*COMB_L)\n",
        "\n",
        "print('CO2_Emissions for audi_car =',acura_car_CO2)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CO2_Emissions for audi_car = [231.29183068]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLHswfPSUxUR",
        "colab_type": "text"
      },
      "source": [
        "the actual value in the df data set for the prediction in row 2, acura 3.2L is actually 230 emissions... our model is doing really well on the training data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slNMXBqLmKls",
        "colab_type": "text"
      },
      "source": [
        "## Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHvTjChkolaa",
        "colab_type": "text"
      },
      "source": [
        "**Ordinary Least Squares (OLS)**\n",
        "\n",
        "OLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ( y^ ) over all samples in the dataset.\n",
        "\n",
        "OLS can find the best parameters using of the following methods: - Solving the model parameters analytically using closed-form equations - Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDS2QQbCpjRT",
        "colab_type": "text"
      },
      "source": [
        "### Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xo6hRycJpmG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_hat = mlr.predict(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T86xXgkWmSCr",
        "colab_type": "text"
      },
      "source": [
        "**$R^2$ or Coefficient of determination**\n",
        "\n",
        "Returns the coefficient of determination R^2 of the prediction.\n",
        "\n",
        "**sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)**\n",
        "\n",
        "The coefficient R^2 is defined as (1 - u/v), where u is the residual\n",
        "sum of squares ((y_true - y_pred) ** 2).sum() and v is the total\n",
        "sum of squares ((y_true - y_true.mean()) ** 2).sum().\n",
        "The best possible score is 1.0 and it can be negative (because the\n",
        "model can be arbitrarily worse). A constant model that always\n",
        "predicts the expected value of y, disregarding the input features,\n",
        "would get a R^2 score of 0.0.\n",
        "\n",
        "To find the $R^2$ we use the .score() function on our model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6SuhNIPrE2B",
        "colab_type": "text"
      },
      "source": [
        "Finding the MSE, the lower the better"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klljEtnevekD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLl6Jp0VvEtA",
        "colab_type": "code",
        "outputId": "64fad1b0-71b6-4361-8a89-9e28940dbc8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "MSE = mean_squared_error(y_hat, test_y)\n",
        "\n",
        "print('Residual sum of squares or MSE: %.2f' % MSE)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Residual sum of squares or MSE: 490.70\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0TzHcNU565V",
        "colab_type": "text"
      },
      "source": [
        "Finding the RMSE, which is the square root of the MSE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0YLxkiTvzG9",
        "colab_type": "code",
        "outputId": "abcdffd9-1a13-49cf-b9b9-b5411049bae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "RMSE = MSE**0.5\n",
        "\n",
        "RMSE"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.151636734256364"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--Ed4r2Z6FWH",
        "colab_type": "text"
      },
      "source": [
        "**Finding the RMSE as a percentage of the range of the target variable.\n",
        "The lower the percentage the better the model**\n",
        "\n",
        "First we find the range of the target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1A73oV8Xv-u_",
        "colab_type": "code",
        "outputId": "de3d021b-be25-485e-c46d-570458723497",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_range = test_y.max() - test_y.min()\n",
        "y_range"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "430"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHT40k0F600N",
        "colab_type": "text"
      },
      "source": [
        "Next we compute the RMSE as a percent of the range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pCZQo_xlb1P",
        "colab_type": "code",
        "outputId": "c8524f2c-736a-4ded-d988-97608f33a3d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "error_range = (RMSE / y_range) * 100\n",
        "\n",
        "error_range"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.151543426571247"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGBcCTFi7O_4",
        "colab_type": "text"
      },
      "source": [
        "Finally let's compute the $R^2$ score or coefficient of determination of our model prediction.<br>\n",
        "The highest value is 1... So the closer to 1, the better the score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "up_ZeLfb5RUQ",
        "colab_type": "code",
        "outputId": "f7676060-0d92-4dd5-c39d-cafb0aa3e109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(test_y, y_hat)  # pass in the true values first, then the predicted values."
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8601623559586588"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_jKMtqe8U59",
        "colab_type": "text"
      },
      "source": [
        "## Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbPTF6zJ8pTM",
        "colab_type": "text"
      },
      "source": [
        "We have seen how Multiple Linear regression works step by step using Scikit Learn Library.<br>\n",
        "\n",
        "We built a MLR Model from scratch by first determining correlated variables, selecting four of them  and shaping them into a 2D array.<br>\n",
        "Then using sklearn library to split the data into training and testing sets.<br>\n",
        "Then training the model with the tarining data set using the  fit() function,<br>\n",
        "And using the model to make predictions on the test data set that is not yet known to the model(out of sample data set)<br>\n",
        "Finally evaluating the model using MSE, RMSE and $R^2$ score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnKQ3Lvf-ASK",
        "colab_type": "text"
      },
      "source": [
        "Our Model performed quite good on the out-of-sample data set.<br>\n",
        "With an error_range of only 5% of the target variable range and a coefficient of determination of 0.86 out of 1.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCsUI9Hv-Y-U",
        "colab_type": "text"
      },
      "source": [
        "### let's plot the model performance on the Test data set using a Histplot and Regplot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oCAX0eH8iv9",
        "colab_type": "code",
        "outputId": "2af90bc4-c05f-41a0-8663-748050a8f151",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "fig = plt.figure(figsize=(20, 7))\n",
        "plt.suptitle('Distplot and Regplot for Predictions of MLR Model on Test data set', fontsize=16)\n",
        "\n",
        "ax = fig.add_subplot(121)\n",
        "ax1 = fig.add_subplot(122)\n",
        "\n",
        "# plotting the Distplot\n",
        "sns.distplot(test_y, hist=False, label='Target', color='navy', ax=ax)\n",
        "sns.distplot(y_hat, hist=False, label='Prediction', color='red', ax=ax)\n",
        "ax.set_xlabel('CO2_EMISSIONS', fontsize=14)\n",
        "ax.set_title('Distplot showing Prediction VS Actual for our MLR Model')\n",
        "\n",
        "# plotting the Regplot\n",
        "sns.regplot(test_y.squeeze(), y_hat.squeeze(), scatter_kws={\"color\": \"navy\"}, line_kws={\"color\": \"red\" ,'linewidth':3}, ax=ax1)\n",
        "ax1.annotate('y_hat = {} + {}(Engine_Size) + {}(Cylinders) + {}(Fuel_Cons) + {}(Comb_L)'.format(np.round(intercept), \n",
        "                                                             np.round(slope[0][0]), \n",
        "                                                             np.round(slope[0][1]), \n",
        "                                                             np.round(slope[0][2]),\n",
        "                                                             np.round(slope[0][3])), \n",
        "                                                             xy=(145,420), \n",
        "                                                             rotation=34, \n",
        "                                                             fontsize=11.5, color='black')\n",
        "ax1.set_xlabel('CO2_EMISSIONS', fontsize=14)\n",
        "ax1.set_title('Regplot showing Prediction for our MLR Model')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3qlSpQkpKChcuXMDIyIgJEybolM3evXupXr06FStWVIcKCiHEv4k0Igkh/pXG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\nREQkV0oiiYiIiIiIiIhIrv4fi08oIF4STX4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}