Simple Linear Regression from Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIMPLE LINEAR REGRESSION \n",
    "### FROM SCRATCH\n",
    "   USING PYTHON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the dataset -- Years of Experience V/S Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing Libraries\n",
    "import numpy as np      # for working with arrays\n",
    "import pandas as pd     # for dealing with DataFrames\n",
    "import matplotlib.pyplot as plt     # for Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# loading the Training Dataset into Pandas.DataFrame\n",
    "df = pd.read_csv(\"G://machine learning//SalaryData_Train.csv\")\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>years</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  salary\n",
       "0    1.1   39343\n",
       "1    1.3   46205\n",
       "2    1.5   37731\n",
       "3    2.0   43525\n",
       "4    2.2   39891\n",
       "5    2.9   56642\n",
       "6    3.0   60150\n",
       "7    3.2   54445\n",
       "8    3.2   64445\n",
       "9    3.7   57189"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10) # prints the first 10 Rows from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 2)\n"
     ]
    }
   ],
   "source": [
    "# Printing the Shape of Dataset\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# Visualising the data\n",
    "plt.plot(df[\"years\"],df[\"salary\"],\"o\")   # creates a Scatter Plot\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary (rs)\")\n",
    "plt.legend([\"Data Points\"])\n",
    "plt.title(\"Years of Experience VS Salary Plot\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m =  9449.962321455077 \t c =  25792.20019866869\n",
      "\n",
      "The Equation of the Regression Line generated is : \n",
      "\n",
      "y =  9449.962321455077 * x  +  25792.20019866869\n"
     ]
    }
   ],
   "source": [
    "x = df[\"years\"]  # independent variable\n",
    "y = df['salary']  # dependent variable\n",
    "\n",
    "x_mean = np.mean(x)\n",
    "y_mean = np.mean(y)\n",
    "\n",
    "n = len(x)   # number of rows in the dataset\n",
    "\n",
    "# Regression Formula\n",
    "# Open this link  --  https://i.stack.imgur.com/lYevl.gif\n",
    "# Image Source -- Stack Exchange\n",
    "num = 0\n",
    "den = 0\n",
    "for i in range(n):\n",
    "        num += (x[i]-x_mean)*(y[i]-y_mean)\n",
    "        den += (x[i]-x_mean)**2\n",
    "        \n",
    "# m --> slope\n",
    "# c --> y-intercept\n",
    "# General Equation of Line --> y = m*x + c\n",
    "\n",
    "m = num/den\n",
    "c = y_mean - m*x_mean\n",
    "\n",
    "print (\"m = \",m,'\\t',\"c = \",c)\n",
    "print(\"\\nThe Equation of the Regression Line generated is : \\n\")\n",
    "print(\"y = \", m, \"*\",\"x\", \" + \", c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 36187.15875227,  38077.15121656,  39967.14368085,  44692.12484158,\n",
       "        46582.11730587,  53197.09093089,  54142.08716303,  56032.07962732,\n",
       "        56032.07962732,  60757.06078805,  62647.05325234,  63592.04948449,\n",
       "        63592.04948449,  64537.04571663,  68317.03064522,  72097.0155738 ,\n",
       "        73987.00803809,  75877.00050238,  81546.97789525,  82491.9741274 ,\n",
       "        90051.94398456,  92886.932681  , 100446.90253816, 103281.8912346 ,\n",
       "       108006.87239533, 110841.86109176, 115566.84225249, 116511.83848464,\n",
       "       123126.81210966, 125016.80457395])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = np.array(m*x+c)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "# Data Visualisation\n",
    "plt.scatter(x,y_pred,color='r')  \n",
    "plt.scatter(x,y,color='g')\n",
    "plt.plot(x,y_pred,color='b')\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary (Rs)\")\n",
    "plt.title(\"Years of Experience V/S Salary\")\n",
    "plt.legend([\"Regression Line\",\"Predicted Data Points\",\"Data Points\"])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9569566641435084\n"
     ]
    }
   ],
   "source": [
    "# Calculating the Root Mean Squared Error (RMSE)\n",
    "# Gives the Idea about : \n",
    "#  -- How far from the regression line data points are\n",
    "#  -- It tells you how concentrated the data is around the line of best fit.\n",
    "numerator = 0\n",
    "denominator = 0\n",
    "for i in range(n):\n",
    "    numerator += (y_pred[i] - y_mean)**2\n",
    "    denominator += (y[i] - y_mean)**2\n",
    "    \n",
    "rmse = numerator/denominator\n",
    "print (rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   years  salary\n",
      "0    3.3     NaN\n",
      "1    3.5     NaN\n",
      "2    7.8     NaN\n",
      "3    9.0     NaN\n",
      "4   10.0     NaN\n",
      "5    6.0     NaN\n"
     ]
    }
   ],
   "source": [
    "# Loading the Testing Dataset\n",
    "df_test = pd.read_csv(\"G:/machine learning/SalaryData_Test.csv\")\n",
    "\n",
    "# The column for Salary will be empty \n",
    "# i.e. in Python, it will be having null value.\n",
    "print(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the Regression Line for predicting the Test-Data values\n",
    "test_x = df_test[\"years\"]\n",
    "test_y = df_test[\"salary\"]\n",
    "\n",
    "test_y = m*test_x + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xd5e6ab0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 Data Graphically\n",
    "plt.plot(x,y_pred, color='b')\n",
    "plt.scatter(test_x,test_y,color='r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   years         salary\n",
      "0    3.3   56977.075859\n",
      "1    3.5   58867.068324\n",
      "2    7.8   99501.906306\n",
      "3    9.0  110841.861092\n",
      "4   10.0  120291.823413\n",
      "5    6.0   82491.974127\n"
     ]
    }
   ],
   "source": [
    "# Printing the Predicted Values\n",
    "df_test['salary'] = test_y\n",
    "print(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Made By : Karansinh Padhiar"
   ]
  }
 ],
 "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
}