01_Simple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01-Simple Linear Regression",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rupam-Shil/Machine-Learning-Code-Templates/blob/master/01_Simple_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_LulNCC8z96"
      },
      "source": [
        "# Simple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpXdowrE9DxW"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63zBnj8gplYJ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JhpWJi59J1p"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldczCcckpq-Z"
      },
      "source": [
        "dataset = pd.read_csv('Salary_Data.csv')\n",
        "X = dataset.iloc[:,:-1].values\n",
        "y = dataset.iloc[:,-1].values"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDujDFY_qEgG"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyhQaTwP9RzG"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP_vfHQKqMbr"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vF6980Fqpw0",
        "outputId": "f71aec44-660c-457e-a082-50f53d785e41"
      },
      "source": [
        "print(X_train)\n",
        "print(X_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 9.6]\n",
            " [ 4. ]\n",
            " [ 5.3]\n",
            " [ 7.9]\n",
            " [ 2.9]\n",
            " [ 5.1]\n",
            " [ 3.2]\n",
            " [ 4.5]\n",
            " [ 8.2]\n",
            " [ 6.8]\n",
            " [ 1.3]\n",
            " [10.5]\n",
            " [ 3. ]\n",
            " [ 2.2]\n",
            " [ 5.9]\n",
            " [ 6. ]\n",
            " [ 3.7]\n",
            " [ 3.2]\n",
            " [ 9. ]\n",
            " [ 2. ]\n",
            " [ 1.1]\n",
            " [ 7.1]\n",
            " [ 4.9]\n",
            " [ 4. ]]\n",
            "[[ 1.5]\n",
            " [10.3]\n",
            " [ 4.1]\n",
            " [ 3.9]\n",
            " [ 9.5]\n",
            " [ 8.7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZijQwFMQ9itx"
      },
      "source": [
        "## Training the Simple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAMybDN5qwzz",
        "outputId": "3af4403a-7bfa-43fc-c10b-2573ba4d871c"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wa2T1Lq89o5H"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQWfRpKprE6-",
        "outputId": "15af742d-1061-4bd4-dbbd-fb9e3725a9d3"
      },
      "source": [
        "y_pred = regressor.predict(X_test)\n",
        "print(y_pred)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 40748.96184072 122699.62295594  64961.65717022  63099.14214487\n",
            " 115249.56285456 107799.50275317]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zSoMZ-P9v8t"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ohgm6GGdrThM",
        "outputId": "7761c862-800f-452f-edca-2a829f1ea9a4"
      },
      "source": [
        "plt.scatter(X_train, y_train, color=\"blue\")\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'red')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUX1Vhsv97ZT"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LW9IWuK5rnS2",
        "outputId": "cfe76869-ab95-4306-fc1c-e9c37b479b70"
      },
      "source": [
        "plt.scatter(X_test, y_test, color=\"blue\")\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'red')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkUW7vcwr_d8"
      },
      "source": [
        "## Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibGWAJ5wr-mM",
        "outputId": "e04eecf0-1bb4-471d-bd0d-642b19bb94fa"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(r2_score(y_test,y_pred))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.988169515729126\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}