SimpleLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple Linear Regression\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv(r'C:\\Users\\Minal\\Desktop\\MachineLearning\\SimpleLinearRegression\\Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "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)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train.reshape(-1,1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Visualising the Training set results\n",
    "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()\n",
    "\n",
    "# Visualising the Test set results\n",
    "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": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}