Multiple+Linear+Regression+-+Housing+Case+Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression\n",
    "## Housing Case Study\n",
    "\n",
    "#### Problem Statement:\n",
    "\n",
    "Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.\n",
    "\n",
    "Essentially, the company wants —\n",
    "\n",
    "\n",
    "- To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc.\n",
    "\n",
    "- To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc.\n",
    "\n",
    "- To know the accuracy of the model, i.e. how well these variables can predict house prices.\n",
    "\n",
    "**So interpretation is important!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Reading and Understanding the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supress Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"Housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of the dataset\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the various aspects of the housing dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      "price               545 non-null int64\n",
      "area                545 non-null int64\n",
      "bedrooms            545 non-null int64\n",
      "bathrooms           545 non-null int64\n",
      "stories             545 non-null int64\n",
      "mainroad            545 non-null object\n",
      "guestroom           545 non-null object\n",
      "basement            545 non-null object\n",
      "hotwaterheating     545 non-null object\n",
      "airconditioning     545 non-null object\n",
      "parking             545 non-null int64\n",
      "prefarea            545 non-null object\n",
      "furnishingstatus    545 non-null object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.4+ KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Visualising the Data\n",
    "\n",
    "understanding the data\n",
    "- If there is some obvious multicollinearity going on\n",
    "- identify if some predictors directly have a strong association with the outcome variable\n",
    "\n",
    "We'll visualise our data using `matplotlib` and `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising Numeric Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising Categorical Variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "plt.subplot(2,3,1)\n",
    "sns.boxplot(x = 'mainroad', y = 'price', data = housing)\n",
    "plt.subplot(2,3,2)\n",
    "sns.boxplot(x = 'guestroom', y = 'price', data = housing)\n",
    "plt.subplot(2,3,3)\n",
    "sns.boxplot(x = 'basement', y = 'price', data = housing)\n",
    "plt.subplot(2,3,4)\n",
    "sns.boxplot(x = 'hotwaterheating', y = 'price', data = housing)\n",
    "plt.subplot(2,3,5)\n",
    "sns.boxplot(x = 'airconditioning', y = 'price', data = housing)\n",
    "plt.subplot(2,3,6)\n",
    "sns.boxplot(x = 'furnishingstatus', y = 'price', data = housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualise some of these categorical features parallely by using the `hue` argument. Below is the plot for `furnishingstatus` with `airconditioning` as the hue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "sns.boxplot(x = 'furnishingstatus', y = 'price', hue = 'airconditioning', data = housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " in order to fit a regression line, we would need numerical values and not string. Hence, we need to convert them to 1s and 0s, where 1 is a 'Yes' and 0 is a 'No'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of variables to map\n",
    "\n",
    "varlist =  ['mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'prefarea']\n",
    "\n",
    "# Defining the map function\n",
    "def binary_map(x):\n",
    "    return x.map({'yes': 1, \"no\": 0})\n",
    "\n",
    "# Applying the function to the housing list\n",
    "housing[varlist] = housing[varlist].apply(binary_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
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       "      <td>furnished</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "  furnishingstatus  \n",
       "0        furnished  \n",
       "1        furnished  \n",
       "2   semi-furnished  \n",
       "3        furnished  \n",
       "4        furnished  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the housing dataframe now\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable `furnishingstatus` has three levels. We need to convert these levels into integer as well. \n",
    "\n",
    "For this, we will use `dummy variables`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the dummy variables for the feature 'furnishingstatus' and store it in a new variable - 'status'\n",
    "status = pd.get_dummies(housing['furnishingstatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
       "      <th>furnished</th>\n",
       "      <th>semi-furnished</th>\n",
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      "text/plain": [
       "   furnished  semi-furnished  unfurnished\n",
       "0          1               0            0\n",
       "1          1               0            0\n",
       "2          0               1            0\n",
       "3          1               0            0\n",
       "4          1               0            0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check what the dataset 'status' looks like\n",
    "status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's drop the first column from status df using 'drop_first = True'\n",
    "\n",
    "status = pd.get_dummies(housing['furnishingstatus'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the results to the original housing dataframe\n",
    "\n",
    "housing = pd.concat([housing, status], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>furnished</td>\n",
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      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "  furnishingstatus  semi-furnished  unfurnished  \n",
       "0        furnished               0            0  \n",
       "1        furnished               0            0  \n",
       "2   semi-furnished               1            0  \n",
       "3        furnished               0            0  \n",
       "4        furnished               0            0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's see the head of our dataframe.\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop 'furnishingstatus' as we have created the dummies for it\n",
    "\n",
    "housing.drop(['furnishingstatus'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "   semi-furnished  unfurnished  \n",
       "0               0            0  \n",
       "1               0            0  \n",
       "2               1            0  \n",
       "3               0            0  \n",
       "4               0            0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Splitting the Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# We specify this so that the train and test data set always have the same rows, respectively\n",
    "np.random.seed(0)\n",
    "df_train, df_test = train_test_split(housing, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescaling the Features \n",
    "\n",
    "Min-Max scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply scaler() to all the columns except the 'yes-no' and 'dummy' variables\n",
    "num_vars = ['area', 'bedrooms', 'bathrooms', 'stories', 'parking','price']\n",
    "\n",
    "df_train[num_vars] = scaler.fit_transform(df_train[num_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
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       "      <th>prefarea</th>\n",
       "      <th>semi-furnished</th>\n",
       "      <th>unfurnished</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
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       "      <td>0.155227</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>0.403379</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>0.115628</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.538015</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price      area  bedrooms  bathrooms   stories  mainroad  guestroom  \\\n",
       "359  0.169697  0.155227       0.4        0.0  0.000000         1          0   \n",
       "19   0.615152  0.403379       0.4        0.5  0.333333         1          0   \n",
       "159  0.321212  0.115628       0.4        0.5  0.000000         1          1   \n",
       "35   0.548133  0.454417       0.4        0.5  1.000000         1          0   \n",
       "28   0.575758  0.538015       0.8        0.5  0.333333         1          0   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning   parking  prefarea  \\\n",
       "359         0                0                0  0.333333         0   \n",
       "19          0                0                1  0.333333         1   \n",
       "159         1                0                1  0.000000         0   \n",
       "35          0                0                1  0.666667         0   \n",
       "28          1                1                0  0.666667         0   \n",
       "\n",
       "     semi-furnished  unfurnished  \n",
       "359               0            1  \n",
       "19                1            0  \n",
       "159               0            0  \n",
       "35                0            0  \n",
       "28                0            1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
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       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>semi-furnished</th>\n",
       "      <th>unfurnished</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
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       "      <td>381.000000</td>\n",
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       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>381.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.260333</td>\n",
       "      <td>0.288710</td>\n",
       "      <td>0.386352</td>\n",
       "      <td>0.136483</td>\n",
       "      <td>0.268591</td>\n",
       "      <td>0.855643</td>\n",
       "      <td>0.170604</td>\n",
       "      <td>0.351706</td>\n",
       "      <td>0.052493</td>\n",
       "      <td>0.299213</td>\n",
       "      <td>0.242345</td>\n",
       "      <td>0.233596</td>\n",
       "      <td>0.414698</td>\n",
       "      <td>0.325459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.157607</td>\n",
       "      <td>0.181420</td>\n",
       "      <td>0.147336</td>\n",
       "      <td>0.237325</td>\n",
       "      <td>0.295001</td>\n",
       "      <td>0.351913</td>\n",
       "      <td>0.376657</td>\n",
       "      <td>0.478131</td>\n",
       "      <td>0.223313</td>\n",
       "      <td>0.458515</td>\n",
       "      <td>0.285933</td>\n",
       "      <td>0.423674</td>\n",
       "      <td>0.493318</td>\n",
       "      <td>0.469162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.155227</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.221212</td>\n",
       "      <td>0.234424</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.345455</td>\n",
       "      <td>0.398099</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price        area    bedrooms   bathrooms     stories    mainroad  \\\n",
       "count  381.000000  381.000000  381.000000  381.000000  381.000000  381.000000   \n",
       "mean     0.260333    0.288710    0.386352    0.136483    0.268591    0.855643   \n",
       "std      0.157607    0.181420    0.147336    0.237325    0.295001    0.351913   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.151515    0.155227    0.200000    0.000000    0.000000    1.000000   \n",
       "50%      0.221212    0.234424    0.400000    0.000000    0.333333    1.000000   \n",
       "75%      0.345455    0.398099    0.400000    0.500000    0.333333    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        guestroom    basement  hotwaterheating  airconditioning     parking  \\\n",
       "count  381.000000  381.000000       381.000000       381.000000  381.000000   \n",
       "mean     0.170604    0.351706         0.052493         0.299213    0.242345   \n",
       "std      0.376657    0.478131         0.223313         0.458515    0.285933   \n",
       "min      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "25%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "50%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "75%      0.000000    1.000000         0.000000         1.000000    0.333333   \n",
       "max      1.000000    1.000000         1.000000         1.000000    1.000000   \n",
       "\n",
       "         prefarea  semi-furnished  unfurnished  \n",
       "count  381.000000      381.000000   381.000000  \n",
       "mean     0.233596        0.414698     0.325459  \n",
       "std      0.423674        0.493318     0.469162  \n",
       "min      0.000000        0.000000     0.000000  \n",
       "25%      0.000000        0.000000     0.000000  \n",
       "50%      0.000000        0.000000     0.000000  \n",
       "75%      0.000000        1.000000     1.000000  \n",
       "max      1.000000        1.000000     1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check the correlation coefficients to see which variables are highly correlated\n",
    "\n",
    "plt.figure(figsize = (16, 10))\n",
    "sns.heatmap(df_train.corr(), annot = True, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " `area` seems to the correlated to `price` the most. Let's see a pairplot for `area` vs `price`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,6])\n",
    "plt.scatter(df_train.area, df_train.price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we pick `area` as the first variable and we'll try to fit a regression line to that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividing into X and Y sets for the model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train.pop('price')\n",
    "X_train = df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Building a linear model\n",
    "\n",
    " in `statsmodels`, we need to explicitly fit a constant using `sm.add_constant(X)` because if we don't perform this step, `statsmodels` fits a regression line passing through the origin, by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Add a constant\n",
    "X_train_lm = sm.add_constant(X_train[['area']])\n",
    "\n",
    "# Create a first fitted model\n",
    "lr = sm.OLS(y_train, X_train_lm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    0.126894\n",
       "area     0.462192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the parameters obtained\n",
    "\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualise the data with a scatter plot and the fitted regression line\n",
    "plt.scatter(X_train_lm.iloc[:, 1], y_train)\n",
    "plt.plot(X_train_lm.iloc[:, 1], 0.127 + 0.462*X_train_lm.iloc[:, 1], 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.283\n",
      "Model:                            OLS   Adj. R-squared:                  0.281\n",
      "Method:                 Least Squares   F-statistic:                     149.6\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           3.15e-29\n",
      "Time:                        13:02:46   Log-Likelihood:                 227.23\n",
      "No. Observations:                 381   AIC:                            -450.5\n",
      "Df Residuals:                     379   BIC:                            -442.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1269      0.013      9.853      0.000       0.102       0.152\n",
      "area           0.4622      0.038     12.232      0.000       0.388       0.536\n",
      "==============================================================================\n",
      "Omnibus:                       67.313   Durbin-Watson:                   2.018\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              143.063\n",
      "Skew:                           0.925   Prob(JB):                     8.59e-32\n",
      "Kurtosis:                       5.365   Cond. No.                         5.99\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Print a summary of the linear regression model obtained\n",
    "print(lr.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding another variable\n",
    "\n",
    "The R-squared value obtained is `0.283`. Since we have so many variables, we can clearly do better than this. So let's go ahead and add the second most highly correlated variable, i.e. `bathrooms`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign all the feature variables to X\n",
    "X_train_lm = X_train[['area', 'bathrooms']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const        0.104589\n",
       "area         0.398396\n",
       "bathrooms    0.298374\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a linear model\n",
    "\n",
    "import statsmodels.api as sm\n",
    "X_train_lm = sm.add_constant(X_train_lm)\n",
    "\n",
    "lr = sm.OLS(y_train, X_train_lm).fit()\n",
    "\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.480\n",
      "Model:                            OLS   Adj. R-squared:                  0.477\n",
      "Method:                 Least Squares   F-statistic:                     174.1\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           2.51e-54\n",
      "Time:                        13:02:47   Log-Likelihood:                 288.24\n",
      "No. Observations:                 381   AIC:                            -570.5\n",
      "Df Residuals:                     378   BIC:                            -558.6\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1046      0.011      9.384      0.000       0.083       0.127\n",
      "area           0.3984      0.033     12.192      0.000       0.334       0.463\n",
      "bathrooms      0.2984      0.025     11.945      0.000       0.249       0.347\n",
      "==============================================================================\n",
      "Omnibus:                       62.839   Durbin-Watson:                   2.157\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              168.790\n",
      "Skew:                           0.784   Prob(JB):                     2.23e-37\n",
      "Kurtosis:                       5.859   Cond. No.                         6.17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Check the summary\n",
    "print(lr.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have clearly improved the model as the value of adjusted R-squared as its value has gone up to `0.477` from `0.281`.\n",
    "Let's go ahead and add another variable, `bedrooms`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign all the feature variables to X\n",
    "X_train_lm = X_train[['area', 'bathrooms','bedrooms']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const        0.041352\n",
       "area         0.392211\n",
       "bathrooms    0.259978\n",
       "bedrooms     0.181863\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a linear model\n",
    "\n",
    "import statsmodels.api as sm\n",
    "X_train_lm = sm.add_constant(X_train_lm)\n",
    "\n",
    "lr = sm.OLS(y_train, X_train_lm).fit()\n",
    "\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.505\n",
      "Model:                            OLS   Adj. R-squared:                  0.501\n",
      "Method:                 Least Squares   F-statistic:                     128.2\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           3.12e-57\n",
      "Time:                        13:02:47   Log-Likelihood:                 297.76\n",
      "No. Observations:                 381   AIC:                            -587.5\n",
      "Df Residuals:                     377   BIC:                            -571.7\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0414      0.018      2.292      0.022       0.006       0.077\n",
      "area           0.3922      0.032     12.279      0.000       0.329       0.455\n",
      "bathrooms      0.2600      0.026     10.033      0.000       0.209       0.311\n",
      "bedrooms       0.1819      0.041      4.396      0.000       0.101       0.263\n",
      "==============================================================================\n",
      "Omnibus:                       50.037   Durbin-Watson:                   2.136\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              124.806\n",
      "Skew:                           0.648   Prob(JB):                     7.92e-28\n",
      "Kurtosis:                       5.487   Cond. No.                         8.87\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Print the summary of the model\n",
    "\n",
    "print(lr.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have improved the adjusted R-squared again. Now let's go ahead and add all the feature variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding all the variables to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
       "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
       "       'parking', 'prefarea', 'semi-furnished', 'unfurnished'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all the columns of the dataframe\n",
    "\n",
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const              0.020033\n",
       "area               0.234664\n",
       "bedrooms           0.046735\n",
       "bathrooms          0.190823\n",
       "stories            0.108516\n",
       "mainroad           0.050441\n",
       "guestroom          0.030428\n",
       "basement           0.021595\n",
       "hotwaterheating    0.084863\n",
       "airconditioning    0.066881\n",
       "parking            0.060735\n",
       "prefarea           0.059428\n",
       "semi-furnished     0.000921\n",
       "unfurnished       -0.031006\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build a linear model\n",
    "\n",
    "import statsmodels.api as sm\n",
    "X_train_lm = sm.add_constant(X_train)\n",
    "\n",
    "lr_1 = sm.OLS(y_train, X_train_lm).fit()\n",
    "\n",
    "lr_1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.681\n",
      "Model:                            OLS   Adj. R-squared:                  0.670\n",
      "Method:                 Least Squares   F-statistic:                     60.40\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           8.83e-83\n",
      "Time:                        13:02:47   Log-Likelihood:                 381.79\n",
      "No. Observations:                 381   AIC:                            -735.6\n",
      "Df Residuals:                     367   BIC:                            -680.4\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0200      0.021      0.955      0.340      -0.021       0.061\n",
      "area                0.2347      0.030      7.795      0.000       0.175       0.294\n",
      "bedrooms            0.0467      0.037      1.267      0.206      -0.026       0.119\n",
      "bathrooms           0.1908      0.022      8.679      0.000       0.148       0.234\n",
      "stories             0.1085      0.019      5.661      0.000       0.071       0.146\n",
      "mainroad            0.0504      0.014      3.520      0.000       0.022       0.079\n",
      "guestroom           0.0304      0.014      2.233      0.026       0.004       0.057\n",
      "basement            0.0216      0.011      1.943      0.053      -0.000       0.043\n",
      "hotwaterheating     0.0849      0.022      3.934      0.000       0.042       0.127\n",
      "airconditioning     0.0669      0.011      5.899      0.000       0.045       0.089\n",
      "parking             0.0607      0.018      3.365      0.001       0.025       0.096\n",
      "prefarea            0.0594      0.012      5.040      0.000       0.036       0.083\n",
      "semi-furnished      0.0009      0.012      0.078      0.938      -0.022       0.024\n",
      "unfurnished        -0.0310      0.013     -2.440      0.015      -0.056      -0.006\n",
      "==============================================================================\n",
      "Omnibus:                       93.687   Durbin-Watson:                   2.093\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              304.917\n",
      "Skew:                           1.091   Prob(JB):                     6.14e-67\n",
      "Kurtosis:                       6.801   Cond. No.                         14.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the p-values, it looks like some of the variables aren't really significant (in the presence of other variables).\n",
    "\n",
    "We could simply drop the variable with the highest, non-significant p value. A better way would be to supplement this with the VIF information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking VIF\n",
    "\n",
    "Variance Inflation Factor or VIF, gives a basic quantitative idea about how much the feature variables are correlated with each other. It is an extremely important parameter to test our linear model. The formula for calculating `VIF` is:\n",
    "\n",
    "### $ VIF_i = \\frac{1}{1 - {R_i}^2} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for the VIF values of the feature variables. \n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>semi-furnished</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>basement</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features   VIF\n",
       "1          bedrooms  7.33\n",
       "4          mainroad  6.02\n",
       "0              area  4.67\n",
       "3           stories  2.70\n",
       "11   semi-furnished  2.19\n",
       "9           parking  2.12\n",
       "6          basement  2.02\n",
       "12      unfurnished  1.82\n",
       "8   airconditioning  1.77\n",
       "2         bathrooms  1.67\n",
       "10         prefarea  1.51\n",
       "5         guestroom  1.47\n",
       "7   hotwaterheating  1.14"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe that will contain the names of all the feature variables and their respective VIFs\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generally want a VIF that is less than 5. So there are clearly some variables we need to drop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the variable and updating the model\n",
    "\n",
    "`semi-furnished` has a very high p-value of `0.938`. Let's go ahead and drop this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping highly correlated variables and insignificant variables\n",
    "\n",
    "X = X_train.drop('semi-furnished', 1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a third fitted model\n",
    "X_train_lm = sm.add_constant(X)\n",
    "\n",
    "lr_2 = sm.OLS(y_train, X_train_lm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.681\n",
      "Model:                            OLS   Adj. R-squared:                  0.671\n",
      "Method:                 Least Squares   F-statistic:                     65.61\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           1.07e-83\n",
      "Time:                        13:02:48   Log-Likelihood:                 381.79\n",
      "No. Observations:                 381   AIC:                            -737.6\n",
      "Df Residuals:                     368   BIC:                            -686.3\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0207      0.019      1.098      0.273      -0.016       0.058\n",
      "area                0.2344      0.030      7.845      0.000       0.176       0.293\n",
      "bedrooms            0.0467      0.037      1.268      0.206      -0.026       0.119\n",
      "bathrooms           0.1909      0.022      8.697      0.000       0.148       0.234\n",
      "stories             0.1085      0.019      5.669      0.000       0.071       0.146\n",
      "mainroad            0.0504      0.014      3.524      0.000       0.022       0.079\n",
      "guestroom           0.0304      0.014      2.238      0.026       0.004       0.057\n",
      "basement            0.0216      0.011      1.946      0.052      -0.000       0.043\n",
      "hotwaterheating     0.0849      0.022      3.941      0.000       0.043       0.127\n",
      "airconditioning     0.0668      0.011      5.923      0.000       0.045       0.089\n",
      "parking             0.0608      0.018      3.372      0.001       0.025       0.096\n",
      "prefarea            0.0594      0.012      5.046      0.000       0.036       0.083\n",
      "unfurnished        -0.0316      0.010     -3.096      0.002      -0.052      -0.012\n",
      "==============================================================================\n",
      "Omnibus:                       93.538   Durbin-Watson:                   2.092\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              303.844\n",
      "Skew:                           1.090   Prob(JB):                     1.05e-66\n",
      "Kurtosis:                       6.794   Cond. No.                         14.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Print the summary of the model\n",
    "print(lr_2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>basement</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features   VIF\n",
       "1          bedrooms  6.59\n",
       "4          mainroad  5.68\n",
       "0              area  4.67\n",
       "3           stories  2.69\n",
       "9           parking  2.12\n",
       "6          basement  2.01\n",
       "8   airconditioning  1.77\n",
       "2         bathrooms  1.67\n",
       "10         prefarea  1.51\n",
       "5         guestroom  1.47\n",
       "11      unfurnished  1.40\n",
       "7   hotwaterheating  1.14"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the VIFs again for the new model\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the Variable and Updating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping highly correlated variables and insignificant variables\n",
    "X = X.drop('bedrooms', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a second fitted model\n",
    "X_train_lm = sm.add_constant(X)\n",
    "\n",
    "lr_3 = sm.OLS(y_train, X_train_lm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.680\n",
      "Model:                            OLS   Adj. R-squared:                  0.671\n",
      "Method:                 Least Squares   F-statistic:                     71.31\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           2.73e-84\n",
      "Time:                        13:02:48   Log-Likelihood:                 380.96\n",
      "No. Observations:                 381   AIC:                            -737.9\n",
      "Df Residuals:                     369   BIC:                            -690.6\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0357      0.015      2.421      0.016       0.007       0.065\n",
      "area                0.2347      0.030      7.851      0.000       0.176       0.294\n",
      "bathrooms           0.1965      0.022      9.132      0.000       0.154       0.239\n",
      "stories             0.1178      0.018      6.654      0.000       0.083       0.153\n",
      "mainroad            0.0488      0.014      3.423      0.001       0.021       0.077\n",
      "guestroom           0.0301      0.014      2.211      0.028       0.003       0.057\n",
      "basement            0.0239      0.011      2.183      0.030       0.002       0.045\n",
      "hotwaterheating     0.0864      0.022      4.014      0.000       0.044       0.129\n",
      "airconditioning     0.0665      0.011      5.895      0.000       0.044       0.089\n",
      "parking             0.0629      0.018      3.501      0.001       0.028       0.098\n",
      "prefarea            0.0596      0.012      5.061      0.000       0.036       0.083\n",
      "unfurnished        -0.0323      0.010     -3.169      0.002      -0.052      -0.012\n",
      "==============================================================================\n",
      "Omnibus:                       97.661   Durbin-Watson:                   2.097\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              325.388\n",
      "Skew:                           1.130   Prob(JB):                     2.20e-71\n",
      "Kurtosis:                       6.923   Cond. No.                         10.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Print the summary of the model\n",
    "\n",
    "print(lr_3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>basement</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features   VIF\n",
       "3          mainroad  4.79\n",
       "0              area  4.55\n",
       "2           stories  2.23\n",
       "8           parking  2.10\n",
       "5          basement  1.87\n",
       "7   airconditioning  1.76\n",
       "1         bathrooms  1.61\n",
       "9          prefarea  1.50\n",
       "4         guestroom  1.46\n",
       "10      unfurnished  1.33\n",
       "6   hotwaterheating  1.12"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the VIFs again for the new model\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the variable and updating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop('basement', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a fourth fitted model\n",
    "X_train_lm = sm.add_constant(X)\n",
    "\n",
    "lr_4 = sm.OLS(y_train, X_train_lm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.676\n",
      "Model:                            OLS   Adj. R-squared:                  0.667\n",
      "Method:                 Least Squares   F-statistic:                     77.18\n",
      "Date:                Tue, 09 Oct 2018   Prob (F-statistic):           3.13e-84\n",
      "Time:                        13:02:49   Log-Likelihood:                 378.51\n",
      "No. Observations:                 381   AIC:                            -735.0\n",
      "Df Residuals:                     370   BIC:                            -691.7\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0428      0.014      2.958      0.003       0.014       0.071\n",
      "area                0.2335      0.030      7.772      0.000       0.174       0.293\n",
      "bathrooms           0.2019      0.021      9.397      0.000       0.160       0.244\n",
      "stories             0.1081      0.017      6.277      0.000       0.074       0.142\n",
      "mainroad            0.0497      0.014      3.468      0.001       0.022       0.078\n",
      "guestroom           0.0402      0.013      3.124      0.002       0.015       0.065\n",
      "hotwaterheating     0.0876      0.022      4.051      0.000       0.045       0.130\n",
      "airconditioning     0.0682      0.011      6.028      0.000       0.046       0.090\n",
      "parking             0.0629      0.018      3.482      0.001       0.027       0.098\n",
      "prefarea            0.0637      0.012      5.452      0.000       0.041       0.087\n",
      "unfurnished        -0.0337      0.010     -3.295      0.001      -0.054      -0.014\n",
      "==============================================================================\n",
      "Omnibus:                       97.054   Durbin-Watson:                   2.099\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              322.034\n",
      "Skew:                           1.124   Prob(JB):                     1.18e-70\n",
      "Kurtosis:                       6.902   Cond. No.                         10.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Features   VIF\n",
       "3         mainroad  4.55\n",
       "0             area  4.54\n",
       "2          stories  2.12\n",
       "7          parking  2.10\n",
       "6  airconditioning  1.75\n",
       "1        bathrooms  1.58\n",
       "8         prefarea  1.47\n",
       "9      unfurnished  1.33\n",
       "4        guestroom  1.30\n",
       "5  hotwaterheating  1.12"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the VIFs again for the new model\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " the VIFs and p-values both are within an acceptable range. So we go ahead and make our predictions using this model only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Residual Analysis of the train data\n",
    "\n",
    "So, now to check if the error terms are also normally distributed (which is infact, one of the major assumptions of linear regression), let us plot the histogram of the error terms and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_price = lr_4.predict(X_train_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Errors')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram of the error terms\n",
    "fig = plt.figure()\n",
    "sns.distplot((y_train - y_train_price), bins = 20)\n",
    "fig.suptitle('Error Terms', fontsize = 20)                  # Plot heading \n",
    "plt.xlabel('Errors', fontsize = 18)                         # X-label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8: Making Predictions Using the Final Model\n",
    "\n",
    "Now that we have fitted the model and checked the normality of error terms, it's time to go ahead and make predictions using the final, i.e. fourth model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the scaling on the test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_vars = ['area', 'bedrooms', 'bathrooms', 'stories', 'parking','price']\n",
    "\n",
    "df_test[num_vars] = scaler.transform(df_test[num_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>semi-furnished</th>\n",
       "      <th>unfurnished</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.263176</td>\n",
       "      <td>0.298548</td>\n",
       "      <td>0.408537</td>\n",
       "      <td>0.158537</td>\n",
       "      <td>0.268293</td>\n",
       "      <td>0.865854</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.347561</td>\n",
       "      <td>0.030488</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.205285</td>\n",
       "      <td>0.237805</td>\n",
       "      <td>0.420732</td>\n",
       "      <td>0.329268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.172077</td>\n",
       "      <td>0.211922</td>\n",
       "      <td>0.147537</td>\n",
       "      <td>0.281081</td>\n",
       "      <td>0.276007</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.397508</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.172452</td>\n",
       "      <td>0.479569</td>\n",
       "      <td>0.289322</td>\n",
       "      <td>0.427043</td>\n",
       "      <td>0.495189</td>\n",
       "      <td>0.471387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006061</td>\n",
       "      <td>-0.016367</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.142424</td>\n",
       "      <td>0.148011</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.226061</td>\n",
       "      <td>0.259724</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.346970</td>\n",
       "      <td>0.397439</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.263992</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price        area    bedrooms   bathrooms     stories    mainroad  \\\n",
       "count  164.000000  164.000000  164.000000  164.000000  164.000000  164.000000   \n",
       "mean     0.263176    0.298548    0.408537    0.158537    0.268293    0.865854   \n",
       "std      0.172077    0.211922    0.147537    0.281081    0.276007    0.341853   \n",
       "min      0.006061   -0.016367    0.200000    0.000000    0.000000    0.000000   \n",
       "25%      0.142424    0.148011    0.400000    0.000000    0.000000    1.000000   \n",
       "50%      0.226061    0.259724    0.400000    0.000000    0.333333    1.000000   \n",
       "75%      0.346970    0.397439    0.400000    0.500000    0.333333    1.000000   \n",
       "max      0.909091    1.263992    0.800000    1.500000    1.000000    1.000000   \n",
       "\n",
       "        guestroom    basement  hotwaterheating  airconditioning     parking  \\\n",
       "count  164.000000  164.000000       164.000000       164.000000  164.000000   \n",
       "mean     0.195122    0.347561         0.030488         0.353659    0.205285   \n",
       "std      0.397508    0.477654         0.172452         0.479569    0.289322   \n",
       "min      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "25%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "50%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "75%      0.000000    1.000000         0.000000         1.000000    0.333333   \n",
       "max      1.000000    1.000000         1.000000         1.000000    1.000000   \n",
       "\n",
       "         prefarea  semi-furnished  unfurnished  \n",
       "count  164.000000      164.000000   164.000000  \n",
       "mean     0.237805        0.420732     0.329268  \n",
       "std      0.427043        0.495189     0.471387  \n",
       "min      0.000000        0.000000     0.000000  \n",
       "25%      0.000000        0.000000     0.000000  \n",
       "50%      0.000000        0.000000     0.000000  \n",
       "75%      0.000000        1.000000     1.000000  \n",
       "max      1.000000        1.000000     1.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dividing into X_test and y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = df_test.pop('price')\n",
    "X_test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding constant variable to test dataframe\n",
    "X_test_m4 = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating X_test_m4 dataframe by dropping variables from X_test_m4\n",
    "\n",
    "X_test_m4 = X_test_m4.drop([\"bedrooms\", \"semi-furnished\", \"basement\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions using the fourth model\n",
    "\n",
    "y_pred_m4 = lr_4.predict(X_test_m4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9: Model Evaluation\n",
    "\n",
    "Let's now plot the graph for actual versus predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y_pred')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting y_test and y_pred to understand the spread\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(y_test, y_pred_m4)\n",
    "fig.suptitle('y_test vs y_pred', fontsize = 20)              # Plot heading \n",
    "plt.xlabel('y_test', fontsize = 18)                          # X-label\n",
    "plt.ylabel('y_pred', fontsize = 16)      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can see that the equation of our best fitted line is:\n",
    "\n",
    "$ price = 0.236  \\times  area + 0.202  \\times  bathrooms + 0.11 \\times stories + 0.05 \\times mainroad + 0.04 \\times guestroom + 0.0876 \\times hotwaterheating + 0.0682 \\times airconditioning + 0.0629 \\times parking + 0.0637 \\times prefarea - 0.0337 \\times unfurnished $\n"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}