sklearn-real_estate .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple linear regression - Exercise Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are given a real estate dataset. \n",
    "\n",
    "The data is located in the file: 'real_estate_price_size.csv'. \n",
    "\n",
    "We are expected to create a simple linear regression using the data and perform the following tasks:\n",
    "\n",
    "-  Create a scatter plot (with or without a regression line)\n",
    "-  Calculate the R-squared\n",
    "-  Display the intercept and coefficient(s)\n",
    "-  Using the model make a prediction about an apartment with size 750 sq.ft.\n",
    "\n",
    "Note: The dependent variable is 'price', while the independent variable is 'size'.\n",
    "\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 seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'D:\\Stu\\Data Science\\Exercises Regression\\real_estate_price_size.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['size']\n",
    "y = data['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.xlabel('Size',fontsize=20)\n",
    "plt.ylabel('Price',fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform the inputs into a matrix (2D object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_matrix = x.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(x_matrix,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the R-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7447391865847586"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_matrix,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101912.60180122906"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([223.17874259])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions\n",
    "\n",
    "You find an apartment online with a size of 750 sq.ft.\n",
    "\n",
    "All else equal what should be its price according to the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([269296.65874718])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(750)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}