Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Simple Linear Regression</h1>\n",
    "\n",
    "- SLR: y = b0+ b1*x1\n",
    "- y is Dependent Variable, b0 is Constant, b1 is Coefficient, x1 us Independent Variable.\n",
    "- Constant is the point where the line crosses the vertical axis.\n",
    "- B1 is the Slope of the line.\n",
    "\n",
    "**Importing Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.1]\n",
      " [ 1.3]\n",
      " [ 1.5]\n",
      " [ 2. ]\n",
      " [ 2.2]\n",
      " [ 2.9]\n",
      " [ 3. ]\n",
      " [ 3.2]\n",
      " [ 3.2]\n",
      " [ 3.7]\n",
      " [ 3.9]\n",
      " [ 4. ]\n",
      " [ 4. ]\n",
      " [ 4.1]\n",
      " [ 4.5]\n",
      " [ 4.9]\n",
      " [ 5.1]\n",
      " [ 5.3]\n",
      " [ 5.9]\n",
      " [ 6. ]\n",
      " [ 6.8]\n",
      " [ 7.1]\n",
      " [ 7.9]\n",
      " [ 8.2]\n",
      " [ 8.7]\n",
      " [ 9. ]\n",
      " [ 9.5]\n",
      " [ 9.6]\n",
      " [10.3]\n",
      " [10.5]]\n"
     ]
    }
   ],
   "source": [
    "x = dataset.iloc[:, :-1].values      # iloc is index of location\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 39343.  46205.  37731.  43525.  39891.  56642.  60150.  54445.  64445.\n",
      "  57189.  63218.  55794.  56957.  57081.  61111.  67938.  66029.  83088.\n",
      "  81363.  93940.  91738.  98273. 101302. 113812. 109431. 105582. 116969.\n",
      " 112635. 122391. 121872.]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Splitting the dataset into Training Set and Test Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Dividing training and test set.\n",
    "# The best ratio is 80 - 20 for trainging and testing respectively.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[112635.  55794.  83088. 101302.  56642.  66029.  64445.  61111. 113812.\n",
      "  91738.  46205. 121872.  60150.  39891.  81363.  93940.  57189.  54445.\n",
      " 105582.  43525.  39343.  98273.  67938.  56957.]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training the SLR model on the Training Set**\n",
    "   - Regression is when you have to predict a continuous real value. \n",
    "   - Classification is when you have to predict a category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Fit and Transform\n",
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting the Test Set**\n",
    "  - y_test contain the real salary.\n",
    "  - y_prad contain the predicted salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Training Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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, reg.predict(x_train), color = \"blue\")\n",
    "plt.title(\"Salary vs Experience (Training Set)\")\n",
    "plt.xlabel(\"Year of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Test Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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, reg.predict(x_train), color = \"blue\")\n",
    "plt.title(\"Salary vs Experience (Test Set)\")\n",
    "plt.xlabel(\"Year of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}