Week3_Linear_Regression_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Week3_Linear_Regression_Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZKx6Kg5HdZa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv(\"https://raw.githubusercontent.com/SiP-AI-ML/LessonMaterials/master/Salary_Data.csv\")\n",
        "x = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:,1].values"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPHVNBX1HjJJ",
        "colab_type": "code",
        "colab": {}
      },
      "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=1/3, random_state=0)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v25dpzAZHoNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "80bf7032-6028-419b-b679-794db6d03118"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(x_train, y_train)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioB5Tc07HrGc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor.predict(x_test)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYDLBdpwHv2L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "79fc68b5-0361-43a8-e3b7-a9dd9566050b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(x_train, y_train, color = 'red')\n",
        "\n",
        "plt.plot(x_train, regressor.predict(x_train), color='blue')\n",
        "plt.title('salary vs experience (training set)')\n",
        "plt.xlabel('experience', color='green')\n",
        "plt.ylabel('salary', color='orange')\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJM_nbhjH1W0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "1d646edd-4adc-4933-cd36-7ababef949b6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(x_test, y_test, color = 'red')\n",
        "\n",
        "plt.plot(x_train, regressor.predict(x_train), color='blue')\n",
        "plt.title('Salary vs. Experience (test set)')\n",
        "plt.xlabel('experience', color='orange')\n",
        "plt.ylabel('salary', color='green')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}