simple_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l_LulNCC8z96"
   },
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xpXdowrE9DxW"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nhKd4hWx9GFt"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6JhpWJi59J1p"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f8GfYDZ59O18"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AyhQaTwP9RzG"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bxOOauiN9VpC"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZijQwFMQ9itx"
   },
   "source": [
    "## Training the Simple Linear Regression model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 882,
     "status": "ok",
     "timestamp": 1586352495803,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "B4Aj_8YJ9l7J",
    "outputId": "15363111-2a76-4774-88ad-48db22159e9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wa2T1Lq89o5H"
   },
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rn_sTJ2o9smm"
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-zSoMZ-P9v8t"
   },
   "source": [
    "## Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 763,
     "status": "ok",
     "timestamp": 1586352502760,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "IAePn_u-93tI",
    "outputId": "b860fdce-5757-4104-b6e9-0f180e3fca05"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EUX1Vhsv97ZT"
   },
   "source": [
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1097,
     "status": "ok",
     "timestamp": 1586352506966,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "Ze9vpBTf-Bol",
    "outputId": "58359ab2-7a52-4960-f49e-3d8b5ef6f7a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyP1VVwrQU8S68bmX5lftYWC",
   "name": "Simple Linear Regression",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}