simple_linear_regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "simple_linear_regression.ipynb",
      "provenance": [],
      "toc_visible": true,
      "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/H4xor-hash/ML/blob/main/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": "nhKd4hWx9GFt"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JhpWJi59J1p"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8GfYDZ59O18"
      },
      "source": [
        "dataset = pd.read_csv('Salary_Data.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "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": "bxOOauiN9VpC"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZijQwFMQ9itx"
      },
      "source": [
        "## Training the Simple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4Aj_8YJ9l7J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wa2T1Lq89o5H"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn_sTJ2o9smm"
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zSoMZ-P9v8t"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAePn_u-93tI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
      },
      "source": [
        "plt.scatter(X_train, y_train, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Salary vs Experience (Training set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VaThnLmY2cDT46Yo99ZGPVAaW1asHdmABBxczG0ga/HTF7po/P12cvzSPH3LxxWmcsJ13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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUX1Vhsv97ZT"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ze9vpBTf-Bol",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
      },
      "source": [
        "plt.scatter(X_test, y_test, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Salary vs Experience (Test set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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04OQ0fRLwk8jcAewiaS9gPHBbRDwTEc8CtwET0rKdIuKOiAjgJ7l9WQO59dbu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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}