Simple_Linear_Regression_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple linear regression - Exercise Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are given a real estate dataset. \n",
    "\n",
    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
    "\n",
    "The data is located in the file: 'real_estate_price_size.csv'. \n",
    "\n",
    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
    "\n",
    "In this exercise, the dependent variable is 'price', while the independent variable is 'size'.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('real_estate_price_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>price</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>234314.144</td>\n",
       "      <td>643.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228581.528</td>\n",
       "      <td>656.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281626.336</td>\n",
       "      <td>487.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>401255.608</td>\n",
       "      <td>1504.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458674.256</td>\n",
       "      <td>1275.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price     size\n",
       "0  234314.144   643.09\n",
       "1  228581.528   656.22\n",
       "2  281626.336   487.29\n",
       "3  401255.608  1504.75\n",
       "4  458674.256  1275.46"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>292289.470160</td>\n",
       "      <td>853.024200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77051.727525</td>\n",
       "      <td>297.941951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>154282.128000</td>\n",
       "      <td>479.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>234280.148000</td>\n",
       "      <td>643.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>280590.716000</td>\n",
       "      <td>696.405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>335723.696000</td>\n",
       "      <td>1029.322500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500681.128000</td>\n",
       "      <td>1842.510000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price         size\n",
       "count     100.000000   100.000000\n",
       "mean   292289.470160   853.024200\n",
       "std     77051.727525   297.941951\n",
       "min    154282.128000   479.750000\n",
       "25%    234280.148000   643.330000\n",
       "50%    280590.716000   696.405000\n",
       "75%    335723.696000  1029.322500\n",
       "max    500681.128000  1842.510000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declare the dependent and the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['price']\n",
    "x1 = data['size']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1,y)\n",
    "plt.xlabel('Size',fontsize=20)\n",
    "plt.ylabel('Price',fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   285.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 Sep 2022</td> <th>  Prob (F-statistic):</th> <td>8.13e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:42</td>     <th>  Log-Likelihood:    </th> <td> -1198.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2401.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    98</td>      <th>  BIC:               </th> <td>   2406.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td> 1.019e+05</td> <td> 1.19e+04</td> <td>    8.550</td> <td> 0.000</td> <td> 7.83e+04</td> <td> 1.26e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>size</th>  <td>  223.1787</td> <td>   13.199</td> <td>   16.909</td> <td> 0.000</td> <td>  196.986</td> <td>  249.371</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.262</td> <th>  Durbin-Watson:     </th> <td>   2.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.044</td> <th>  Jarque-Bera (JB):  </th> <td>   2.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.117</td> <th>  Prob(JB):          </th> <td>   0.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.194</td> <th>  Cond. No.          </th> <td>2.75e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.75e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.745\n",
       "Model:                            OLS   Adj. R-squared:                  0.742\n",
       "Method:                 Least Squares   F-statistic:                     285.9\n",
       "Date:                Wed, 21 Sep 2022   Prob (F-statistic):           8.13e-31\n",
       "Time:                        14:45:42   Log-Likelihood:                -1198.3\n",
       "No. Observations:                 100   AIC:                             2401.\n",
       "Df Residuals:                      98   BIC:                             2406.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.019e+05   1.19e+04      8.550      0.000    7.83e+04    1.26e+05\n",
       "size         223.1787     13.199     16.909      0.000     196.986     249.371\n",
       "==============================================================================\n",
       "Omnibus:                        6.262   Durbin-Watson:                   2.267\n",
       "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                2.938\n",
       "Skew:                           0.117   Prob(JB):                        0.230\n",
       "Kurtosis:                       2.194   Cond. No.                     2.75e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.75e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sm.add_constant(x1)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1,y)\n",
    "yhat = x1*223.1787+101900\n",
    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
    "plt.xlabel('Size', fontsize = 20)\n",
    "plt.ylabel('Price', fontsize = 20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}