Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression\n",
    "# By Giriraj Vaishnav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages and classes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Temperature     Revenue\n",
      "0      24.566884  534.799028\n",
      "1      26.005191  625.190122\n",
      "2      27.790554  660.632289\n",
      "3      20.595335  487.706960\n",
      "4      11.503498  316.240194\n",
      "..           ...         ...\n",
      "495    22.274899  524.746364\n",
      "496    32.893092  755.818399\n",
      "497    12.588157  306.090719\n",
      "498    22.362402  566.217304\n",
      "499    28.957736  655.660388\n",
      "\n",
      "[500 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Read the IceCreamData.csv file\n",
    "IceCream=pd.read_csv('IceCreamData.csv')\n",
    "print(IceCream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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>Temperature</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.566884</td>\n",
       "      <td>534.799028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.005191</td>\n",
       "      <td>625.190122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.790554</td>\n",
       "      <td>660.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.595335</td>\n",
       "      <td>487.706960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.503498</td>\n",
       "      <td>316.240194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature     Revenue\n",
       "0    24.566884  534.799028\n",
       "1    26.005191  625.190122\n",
       "2    27.790554  660.632289\n",
       "3    20.595335  487.706960\n",
       "4    11.503498  316.240194"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print first 5 data\n",
    "IceCream.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>Temperature</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>22.274899</td>\n",
       "      <td>524.746364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>32.893092</td>\n",
       "      <td>755.818399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12.588157</td>\n",
       "      <td>306.090719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>22.362402</td>\n",
       "      <td>566.217304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>28.957736</td>\n",
       "      <td>655.660388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature     Revenue\n",
       "495    22.274899  524.746364\n",
       "496    32.893092  755.818399\n",
       "497    12.588157  306.090719\n",
       "498    22.362402  566.217304\n",
       "499    28.957736  655.660388"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print last 5 data\n",
    "IceCream.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>Temperature</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.232225</td>\n",
       "      <td>521.570777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.096388</td>\n",
       "      <td>175.404751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.122258</td>\n",
       "      <td>405.558681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.392791</td>\n",
       "      <td>529.368565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.740674</td>\n",
       "      <td>642.257922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature      Revenue\n",
       "count   500.000000   500.000000\n",
       "mean     22.232225   521.570777\n",
       "std       8.096388   175.404751\n",
       "min       0.000000    10.000000\n",
       "25%      17.122258   405.558681\n",
       "50%      22.392791   529.368565\n",
       "75%      27.740674   642.257922\n",
       "max      45.000000  1000.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print mathematical description\n",
    "IceCream.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Temperature  500 non-null    float64\n",
      " 1   Revenue      500 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Print information of Dataset\n",
    "IceCream.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the data into “Attributes” and “labels”\n",
    "X = IceCream[['Temperature']]\n",
    "y = IceCream['Revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split 80% of the data to the training set while 20% of the data to test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Linear Regression model and fit it\n",
    "regressor =LinearRegression(fit_intercept=True)\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Model Coeff (m) = [21.5133908]\n",
      "Linear Model Coeff (b) = 43.73357869209303\n"
     ]
    }
   ],
   "source": [
    "# Getting Results\n",
    "print('Linear Model Coeff (m) =' , regressor.coef_)\n",
    "print('Linear Model Coeff (b) =' , regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[698.3385558  653.32331149 664.73027451 450.5192845  665.47469743\n",
      " 441.36861407 584.06540609 623.82532723 667.48717467 468.72433832\n",
      " 546.82733151 443.41191785 622.95162777 377.64639971 367.0607334\n",
      " 945.67057977 893.79551974 694.45445099 546.05047608 420.58523672\n",
      " 391.08500303 597.0141581  283.23582775 655.50055011 380.98796154\n",
      " 412.31810124 371.05055651 510.23910289 479.70270426 456.68206658\n",
      " 640.1157508  281.65224383 314.1894674  470.01363777 559.72453055\n",
      " 539.75091165 307.72368191 508.65180339 571.43237276 732.25599161\n",
      " 440.44010989 494.39422767 567.56536766 443.94181482 914.46632525\n",
      " 603.19341879 541.83315574 199.94980451 694.04258508 351.09960842\n",
      " 189.49123987 576.80689646 216.55393778 468.15141951 461.80905978\n",
      " 448.43970076 494.89418532 801.3758273  331.24527072 540.42751209\n",
      " 661.1953557  526.66690494 360.66507037 451.46656256 621.57729407\n",
      " 254.83395119 290.1749214  525.5900171  656.68802152 663.1062835\n",
      " 740.96627734 184.48524774 593.42653041 148.41501952 485.97744998\n",
      " 611.03624804 664.50658946 473.51664017 785.34682628 422.11909846\n",
      " 169.76879503 820.72328003 434.39990573 325.82688811 660.07484042\n",
      " 586.46853445 415.89811147 651.95510136 865.22669518 265.88922879\n",
      " 577.32110608  43.73357869 901.24435059 621.87554173 759.07316169\n",
      " 465.78060018 758.74558525 711.30125473 394.45680968 559.53716333]\n"
     ]
    }
   ],
   "source": [
    "# Predicting the data\n",
    "y_predict=regressor.predict(X_test)\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Revenue Generated vs. Temperature @Ice Cream Stand (Training)')"
      ]
     },
     "execution_count": 41,
     "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": [
    "# Scatter plot on Training Data\n",
    "plt.scatter(X_train,y_train,color='blue')\n",
    "plt.plot(X_train,regressor.predict(X_train),color='red')\n",
    "plt.ylabel('Revenue [$]')\n",
    "plt.xlabel('Temperatur [degC]')\n",
    "plt.title('Revenue Generated vs. Temperature @Ice Cream Stand (Training)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Revenue Generated vs. Temperature @Ice Cream Stand (Training)')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot on Testing Data\n",
    "plt.scatter(X_test,y_test,color='blue')\n",
    "plt.plot(X_test,regressor.predict(X_test),color='red')\n",
    "plt.ylabel('Revenue [$]')\n",
    "plt.xlabel('Temperatur [degC]')\n",
    "plt.title('Revenue Generated vs. Temperature @Ice Cream Stand (Training)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------0---------\n",
      "[43.73357869]\n",
      "--------35----------\n",
      "[796.70225678]\n",
      "--------55----------\n",
      "[1226.97007282]\n"
     ]
    }
   ],
   "source": [
    "# Prediction the revenve using Temperature Value directly\n",
    "print('---------0---------')\n",
    "Temp = -0\n",
    "Revenue = regressor.predict([[Temp]])\n",
    "print(Revenue)\n",
    "print('--------35----------')\n",
    "Temp = 35\n",
    "Revenue = regressor.predict([[Temp]])\n",
    "print(Revenue)\n",
    "print('--------55----------')\n",
    "Temp = 55\n",
    "Revenue = regressor.predict([[Temp]])\n",
    "print(Revenue)"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}