Simple_Linear_Regression_using_scikit_learn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Simple Linear Regression using scikit-learn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMxkbI7F5wiIpNwJaXoUsYL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Simple_Linear_Regression_using_scikit_learn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Needed packages**"
      ],
      "metadata": {
        "id": "_TuBj_Q-q3_I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For more detail please viste to [Courseteach](https://coursesteach.com/mod/page/view.php?id=5699&forceview=1)"
      ],
      "metadata": {
        "id": "korFuDPMtXAE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "al-34z-RqoUj"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset**"
      ],
      "metadata": {
        "id": "-aYeZYPqrRH4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Downloading Data**\n"
      ],
      "metadata": {
        "id": "0HEZ0xkS7zC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv"
      ],
      "metadata": {
        "id": "o6dVSOZurhNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"FuelConsumption.csv\")\n",
        "\n",
        "# take a look at the dataset\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "BzmB9rgVrmol",
        "outputId": "fb79b6fe-39f0-4506-b9d3-4a270c3c9c93"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
              "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
              "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
              "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
              "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
              "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
              "\n",
              "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
              "0          AS5        Z                   9.9                  6.7   \n",
              "1           M6        Z                  11.2                  7.7   \n",
              "2          AV7        Z                   6.0                  5.8   \n",
              "3          AS6        Z                  12.7                  9.1   \n",
              "4          AS6        Z                  12.1                  8.7   \n",
              "\n",
              "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
              "0                   8.5                        33           196  \n",
              "1                   9.6                        29           221  \n",
              "2                   5.9                        48           136  \n",
              "3                  11.1                        25           255  \n",
              "4                  10.6                        27           244  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-179c48d8-a205-4a03-ae07-42f77676271b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-179c48d8-a205-4a03-ae07-42f77676271b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-179c48d8-a205-4a03-ae07-42f77676271b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-179c48d8-a205-4a03-ae07-42f77676271b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Exploration**\n"
      ],
      "metadata": {
        "id": "bgfEv7AbrtYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize the data\n",
        "df.describe()"
      ],
      "metadata": {
        "id": "nkFkjJ-T8N0W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "cdf.head(9)"
      ],
      "metadata": {
        "id": "Y1bV0oisFCAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature selection**\n"
      ],
      "metadata": {
        "id": "EMGkyS2jr7hF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "cdf.head(9)"
      ],
      "metadata": {
        "id": "lbHESu4m8WIQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
        "viz.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XqS1oeWU8awe",
        "outputId": "b26f3d98-b6ab-4683-c85b-8ac11826df0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "P8RTJQo68hZh",
        "outputId": "82432e78-8a53-498d-ac76-dcb99e224125",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "80q3J_Kh8nFS",
        "outputId": "e94e04ea-37bb-4e6d-c452-7e23cb1f9e3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Creating train and test dataset**\n"
      ],
      "metadata": {
        "id": "VTJcPBYMsGOJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ],
      "metadata": {
        "id": "ufwjBnjH8x06"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Simple Regression Model**"
      ],
      "metadata": {
        "id": "1e-B6l63sW7K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train data distribution**"
      ],
      "metadata": {
        "id": "D9x1EaUE9Df3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HmUItgwE9AFB",
        "outputId": "8a820396-e91e-46b5-d7bc-fc8f6900fbbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8B3FEPGi9Ltl",
        "outputId": "59c3446f-331e-4fc6-d249-c656f2376372"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients:  [[38.65890385]]\n",
            "Intercept:  [127.09213082]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plot outputs**"
      ],
      "metadata": {
        "id": "1r9vlLmn9UXF"
      }
    },
    {
      "cell_type": "code",
      "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\")"
      ],
      "metadata": {
        "id": "tdjyxG0_9WqW",
        "outputId": "73b27139-b57c-4f4e-f304-962a2a5c097a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Emission')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Evaluation**"
      ],
      "metadata": {
        "id": "kXtzjRfQ9fjW"
      }
    },
    {
      "cell_type": "code",
      "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) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8oMExJU9j0I",
        "outputId": "efe62bce-1137-4f39-d259-7fa625ab61c7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error: 24.19\n",
            "Residual sum of squares (MSE): 997.37\n",
            "R2-score: 0.66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Referecs**"
      ],
      "metadata": {
        "id": "c-GBeJfUtGQT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Simple Linear Regression](https://github.com/samp-suman/Machine-Learning-With-Python--IBM-Coursera/blob/master/ML0101EN-1.%20Reg-Simple-Linear-Regression-Co2-py-v1.ipynb)"
      ],
      "metadata": {
        "id": "1kyRYwFKtKmF"
      }
    }
  ]
}