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\n",
    "\n",
    "Let us first import NumPy and Pandas and read the housing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Supress Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"Housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Visualising the Data\n",
    "\n",
    "Let's now spend some time doing what is arguably the most important step - **understanding the data**.\n",
    "- If there is some obvious multicollinearity going on, this is the first place to catch it\n",
    "- Here's where you'll also 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": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising Numeric Variables\n",
    "\n",
    "Let's make a pairplot of all the numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OzpiJPsPC5hVNmDQ+lHFTvPGc6Vk3SiNdXVQUhtpIQPobFSKvXDdFXt0sVYd1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SM3NZbB/ahHuOHMfI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5ofHV1ZZxJtzNfZZxeKYcvqbPU2M13+JrGMm6nb41+I+8tmUu52rm6PH1rbhk48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\nrkJggg==\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\n",
    "\n",
    "As you might have noticed, there are a few categorical variables as well. Let's make a boxplot for some of these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RdQgAJpbhEQBAT83OznYdoXNfX1pKklzw5Mn+XVwR9wcAumN4BADQUwsLC11H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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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JUgbsE6Z+lWsUTLl0xNLRl6BaDLdROZKkbJmEqV8tLS2sfuA+9ti+PetQAHjL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\nTkSuQmCC\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": [
    "- You can see that your dataset has many columns with values as 'Yes' or 'No'.\n",
    "\n",
    "- But 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": 19,
   "metadata": {
    "collapsed": true
   },
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.0\n",
      "1    0.5\n",
      "2    1.0\n",
      "3    1.0\n",
      "4    0.5\n",
      "5    1.0\n",
      "Name: Response, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Reading the input\n",
    "# import ast,sys\n",
    "# input_str = sys.stdin.read()\n",
    "# input_list = ast.literal_eval(input_str)\n",
    "# Storing the names in a variable 'name'\n",
    "name =['Reetesh', 'Shruti', 'Kaustubh', 'Vikas', 'Mahima', 'Akshay']\n",
    "# Storing the responses in a variable 'repsonse'\n",
    "response = ['No', 'Maybe', 'yes', 'Yes', 'maybe', 'Yes']\n",
    "\n",
    "# Importing pandas and converting the read lists to a dataframe. You can print\n",
    "# the dataframe and run the code to see what it will look like\n",
    "import pandas as pd \n",
    "df = pd.DataFrame({'Name': name,'Response': response})\n",
    "\n",
    "# Write your code here\n",
    "def tri_map(x):\n",
    "    # return x.map(x.lower)\n",
    "    x = x.map(lambda x : x.lower())\n",
    "    return x.map({'yes':1.0,\"no\":0.0,\"maybe\": 0.5})\n",
    "# Print the final DataFrame\n",
    "print(tri_map(df['Response']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <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": 20,
     "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 something called `dummy variables`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>furnished</th>\n",
       "      <th>semi-furnished</th>\n",
       "      <th>unfurnished</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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check what the dataset 'status' looks like\n",
    "status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you don't need three columns. You can drop the `furnished` column, as the type of furnishing can be identified with just the last two columns where — \n",
    "- `00` will correspond to `furnished`\n",
    "- `01` will correspond to `unfurnished`\n",
    "- `10` will correspond to `semi-furnished`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "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": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add the results to the original housing dataframe\n",
    "\n",
    "housing = pd.concat([housing, status], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
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       "      <td>semi-furnished</td>\n",
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       "      <td>1</td>\n",
       "      <td>furnished</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
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       "      <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",
       "      <td>0</td>\n",
       "      <td>0</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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's see the head of our dataframe.\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>0</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",
       "   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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Splitting the Data into Training and Testing Sets\n",
    "\n",
    "As you know, the first basic step for regression is performing a train-test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "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",
    "As you saw in the demonstration for Simple Linear Regression, scaling doesn't impact your model. Here we can see that except for `area`, all the columns have small integer values. So it is extremely important to rescale the variables so that they have a comparable scale. If we don't have comparable scales, then some of the coefficients as obtained by fitting the regression model might be very large or very small as compared to the other coefficients. This might become very annoying at the time of model evaluation. So it is advised to use standardization or normalization so that the units of the coefficients obtained are all on the same scale. As you know, there are two common ways of rescaling:\n",
    "\n",
    "1. Min-Max scaling \n",
    "2. Standardisation (mean-0, sigma-1) \n",
    "\n",
    "This time, we will use MinMax scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "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": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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",
       "      <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",
       "      <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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4nE6ezcghp7aB6EAzD6UNJcISgEvDp3lFfLC/0KexdzVrgJ2Hpw/CqBRLdxXx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\n8+e0W16XX8CBJUupOZxN1CUX0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i7O5mZ2zJ9PxJgxhA0f3hUhHrMTIZfvF/3Aiq9WAZDU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EHS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\nAElFTkSuQmCC\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": [
    "As you might have noticed, `area` seems to the correlated to `price` the most. Let's see a pairplot for `area` vs `price`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WATVv6ae0lLWiJ43PkwE9Z7LIm6ZxASniwKKsA2qSUzHkXV4u6nFK4/NkyiVn0r7NTytP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\nSTCgExGVBAM6EVFJMKATEZXE/wdGZAdfVXZdLQAAAABJRU5ErkJggg==\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": 48,
   "metadata": {
    "collapsed": true
   },
   "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",
    "Fit a regression line through the training data using `statsmodels`. Remember that in `statsmodels`, you 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": 54,
   "metadata": {
    "collapsed": true
   },
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    0.126894\n",
       "area     0.462192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the parameters obtained\n",
    "\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NjBmWmPtF2QR5ImkaOYIb5h3lG73iRBhLqW16C51Xfp4b5h3lOWntt++BwcqV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\nSvj/MfcjC8Q7pwwAAAAASUVORK5CYII=\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": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_lm1 =  X_train[['area','bathrooms','airconditioning']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const              0.094756\n",
      "area               0.358061\n",
      "bathrooms          0.265044\n",
      "airconditioning    0.086986\n",
      "dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.538\n",
      "Model:                            OLS   Adj. R-squared:                  0.534\n",
      "Method:                 Least Squares   F-statistic:                     146.4\n",
      "Date:                Fri, 26 Jun 2020   Prob (F-statistic):           6.61e-63\n",
      "Time:                        16:11:55   Log-Likelihood:                 310.99\n",
      "No. Observations:                 381   AIC:                            -614.0\n",
      "Df Residuals:                     377   BIC:                            -598.2\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0948      0.011      8.932      0.000       0.074       0.116\n",
      "area                0.3581      0.031     11.414      0.000       0.296       0.420\n",
      "bathrooms           0.2650      0.024     11.021      0.000       0.218       0.312\n",
      "airconditioning     0.0870      0.013      6.916      0.000       0.062       0.112\n",
      "==============================================================================\n",
      "Omnibus:                       67.166   Durbin-Watson:                   2.112\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              167.372\n",
      "Skew:                           0.863   Prob(JB):                     4.52e-37\n",
      "Kurtosis:                       5.750   Cond. No.                         6.55\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_train_lm1 =  sm.add_constant(X_train_lm1)\n",
    "lr1 = sm.OLS(y_train,X_train_lm1).fit()\n",
    "print(lr1.params)\n",
    "print(lr1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign all the feature variables to X\n",
    "X_train_lm = X_train[['area', 'bathrooms', 'stories', 'mainroad',\n",
    "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
    "       'parking', 'prefarea', 'unfurnished']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const              0.035722\n",
       "area               0.234746\n",
       "bathrooms          0.196480\n",
       "stories            0.117751\n",
       "mainroad           0.048798\n",
       "guestroom          0.030097\n",
       "basement           0.023917\n",
       "hotwaterheating    0.086403\n",
       "airconditioning    0.066533\n",
       "parking            0.062872\n",
       "prefarea           0.059643\n",
       "unfurnished       -0.032317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "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:                Fri, 26 Jun 2020   Prob (F-statistic):           2.73e-84\n",
      "Time:                        16:18:12   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": [
    "# 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": {
    "collapsed": true
   },
   "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": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all the columns of the dataframe\n",
    "\n",
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "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": 72,
   "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:                Fri, 26 Jun 2020   Prob (F-statistic):           8.83e-83\n",
      "Time:                        16:12:51   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",
    "Maybe we could drop some?\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": 83,
   "metadata": {
    "collapsed": true
   },
   "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": 85,
   "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": 85,
     "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",
    "As you can see from the summary and the VIF dataframe, some variables are still insignificant. One of these variables is, `semi-furnished` as it has a very high p-value of `0.938`. Let's go ahead and drop this variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dropping highly correlated variables and insignificant variables\n",
    "\n",
    "X = X_train.drop('semi-furnished', 1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "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": 88,
   "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:                Fri, 26 Jun 2020   Prob (F-statistic):           1.07e-83\n",
      "Time:                        16:26:27   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": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 89,
     "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\n",
    "\n",
    "As you can notice some of the variable have high VIF values as well as high p-values. Such variables are insignificant and should be dropped.\n",
    "\n",
    "As you might have noticed, the variable `bedroom` has a significantly high VIF (`6.6`) and a high p-value (`0.206`) as well. Hence, this variable isn't of much use and should be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dropping highly correlated variables and insignificant variables\n",
    "X = X.drop('bedrooms', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "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": 92,
   "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:                Fri, 26 Jun 2020   Prob (F-statistic):           2.73e-84\n",
      "Time:                        16:26:56   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": 93,
   "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": 93,
     "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": "markdown",
   "metadata": {},
   "source": [
    "As you might have noticed, dropping `semi-furnised` decreased the VIF of `mainroad` as well such that it is now under 5. But from the summary, we can still see some of them have a high p-value. `basement` for instance, has a p-value of 0.03. We should drop this variable as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X.drop('basement', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "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": 96,
   "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:                Fri, 26 Jun 2020   Prob (F-statistic):           3.13e-84\n",
      "Time:                        16:27:46   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": 97,
   "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": 97,
     "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": [
    "Now as you can see, 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": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_price = lr_4.predict(X_train_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SHK/LkQi9//rl3Li2lM8+dIiXdCGMLCIF9SJzzvG5hw6Rm5HKR25e5XU5MgdJ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\ntYiIzymoRUR87v8Dpj7XI+PTbvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SHK/LkQi9//rl3Li2lM8+dIiXdCGMLCIF9SJzzvG5hw6Rm5HKR25e5XU5MgdJ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\ntYiIzymoRUR87v8Dpj7XI+PTbvkAAAAASUVORK5CYII=\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)     \n",
    "plt.show()# 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": 104,
   "metadata": {
    "collapsed": true
   },
   "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": 105,
   "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": 105,
     "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": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test = df_test.pop('price')\n",
    "X_test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adding constant variable to test dataframe\n",
    "X_test_m4 = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "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": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Making predictions using the fourth model\n",
    "\n",
    "y_pred_m4 = lr_4.predict(X_test_m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.660134403021964"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred_m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = y"
   ]
  },
  {
   "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MdTtZfa0bVUxjwANV7zek26q9CNhd0o8k3Sjp3VknknSKpNWSVm/atKlDyTWzXrdizUY+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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MdTtZfa0bVUxjwANV7zek26q9CNhd0o8k3Sjp3VknknSKpNWSVm/atKlDyTWzXrdizUY+\ndumtbNw8QQAbN0/wsUtvZcWajd1OWl/rRoBQxraoeb8L8FLgaGAh8HeSXrTThyKWR8R4RIzPmTOn\n+JSaWV84a9U6Jia3Ttk2MbmVs1at61KKZoZuVDFtAPaper838GDGMQ9FxOPA45J+DBwC3FlOEs2s\nnzy4eaKl7ZZPN0oQNwD7S5onaVfgBGBlzTGXAa+WtIuk2cDhwB0lp9PM+sReoyMtbbd8Sg8QEbEF\nOA1YRZLpXxQRayWdKunU9Jg7gP8H3AL8J3BeRNxWdlrNrD8sXjifkeGhKdtGhodYvHB+l1I0Myii\ntvq/P42Pj8fq1au7nQwz6xL3YmqPpBsjYjxrXzfaIMzMCrdowZgDQsE81YaZmWVyCcJshnAVixXN\nAcJsBqgMFKuMBagMFAMcJKxtDhBmJevEk36jgWIOENYuBwizaWolw+/Uk74HilknuJHabBpanQOo\nU1NCeKCYdYIDhNk0tJrhd+pJv6iBYivWbOSVy65i3pIreOWyqzzZXQ8r42/lKiazaWg1w99rdISN\nGfum+6RfqZ6aTtuGG7r7R1l/KwcIs2loNcNfvHD+lP/YUNyUENMdKOaG7v5R1t/KVUxm09Bq1c6i\nBWOcedzBjI2OIGBsdIQzjzu4rf/URVcxuKG7f5T1t3IJwmwa2qnaKWJKiE5UMXSq+suKV9bfygHC\nbJq6MQdQJ6oYOln9ZcUq62/lAGHWhzpRxVBEQ7eVo6y/lQOEWR/qZG8oB4T+UMbfyo3UZn3IC+RY\nGVyCMOtDrg6yMnQlQEg6Cvg/wBDJcqLLavYfQbIu9T3ppksj4u9LTaRZj3N1kHVa6QFC0hDwBeD1\nwAbgBkkrI+L2mkN/EhFvLjt9ZmaW6EYbxGHA+oi4OyKeBi4Aju1COszMrIFuBIgx4IGq9xvSbbVe\nLulmSd+VdFDWiSSdImm1pNWbNm3qRFrNzAZWN9oglLEtat7/DNgvIh6T9CZgBbD/Th+KWA4sBxgf\nH689h5mX4TSbhoYBQtI97Jx51xURL8hx2AZgn6r3ewMP1pznt1W/Xynpi5L2jIiH8qbFzLOTmk1P\nsyqmq2teu5BUB90L/DT9OUbSG+lHOa95A7C/pHmSdgVOAFZWHyDp9yQp/f2wNJ0P5zy/GdC5xXnM\nBkXDEkREnFz5XdIpwOHAKyJiQ9X2fYBVwHV5LhgRWySdln5mCPhKRKyVdGq6/1zg7cAHJG0BJoAT\nIsJVSAOgyCqhfp6d1FVj1gtaaYNYDPxtdXAAiIgHJC0FPgN8Oc+JIuJK4MqabedW/f554PMtpM1m\ngKKrhPp1dlJXjVmvaKUX097Ak3X2PUV2TySz3IquEmo2HUWvLa9ZSc+HLrzJVWPWE1oJELcDiyX9\nTvVGSSMkpYvagW5mLSm6SqjR4jyVp/SNmycIkqf0D194E3O7FCyq01NPP1SN2czSShXT3wBXAPdL\nuhL4L+B5wJuA3YA3Fp88GySdqBKqNx1FVmml0sjVjSqdrPTU6kTVmNs6rJHcJYiI+AGwAPh34NXA\nX6Y/vwccEhFXdSSFNjDKnKG02dN42VU6zdLTifuQVYr62KW3dr2qzXpHSwPlIuIO4KQOpcX63HSf\nRsucobReaaVamVU6jdIz1qH7UNbC99a/Wh5JLWkWcCCwB7A6Ih4vPFXWd4rqeVPWDKVZSzbWKrO3\nU70lJCttJp3Qz92ArRwtzcUk6S+AXwG3AFcB89PtKyR9sPjkWb/ot0Fp1Q3YsPP8L2UuvlMpeU1M\nbmUoGR86pUG9U+oFwF7vBmzlyR0gJP0PkjUcVgDvYOr/qZ8Abys2adZP+vFpdNGCMa5Z8jruXXY0\nZx9/aGZvp06r7b20NWJ7cOr09b0qnTXTShXTXwGfi4iPpms6VPs5SVdXG1D9OiitoluL73SzHWA6\nbT7u/TQYWgkQ80imx8jyODA6/eRYv6pXh+6n0ca6XfJqJzB6pPfgaCVAPATMrbNvPuC+cTNUnqfF\nstdInilPsK2UvHrlO7v30+BoJUBcDnxC0o+A+9JtIWlP4MMkbRM2w7TytJjnabSITG4mPcHmLXn1\n0nfudqnHytNKL6aPk8y5dBvwfZKBp/8I3AFsBf6+8NRZ1xXZO6mogVn91mOqkUbTgVTrpe/s3k+D\nI3cJIiIeljQOfAhYCNyVfv7zwNnVi/zYzFHk02JRVRP1rr1x8wTzllzRd1VOeUpenXpqb6dE5/am\nwZErQKQL+3wW+GZEfAr4VEdTZT2jyN5JRWVyjUYdV5dMoHH1S6/U6efRiV5izaqt6t2fstubrHty\nBYiIeFrS+4HvdDg91mOKfFosKpPLMwq6WcmkzDr9ZoEoT6DqxFN7s2qrRvenW92CrVyttEGsAQ7u\nVEKsPK2sg5C3jjyPogZm1aapnkYlk7Lq9Ju1u+Rtlyny71DRqETXS20e1j2t9GL6CPAtSfcBV0xn\nCVBJR5GMyh4CzouIZXWO+yPgeuD4iPh2u9ezHdp5ci7qabHIqonqNL1y2VUtl0zK6onTrN2llXaZ\nop/aG5Xo3FPJoLUAcTHJug+XAVsk/ZodU+gDRETs1+wk6SjsLwCvBzYAN0haGRG3Zxz3WeoPzrM2\nFNmHvZ06/HYzuUbXaqf6payR380y2m5mxI3u21mr1vX1yHgrRisB4gdMDQjtOgxYHxF3A0i6ADiW\nnVek+0vgEuCPCrjmQGmUmRaVIZVdh9+sPhxaK5mU1ROnWSDq5hQlze6beypZK91cTy7ommPAA1Xv\nNwCHVx8gaQx4K/A6HCBa0iwzLSpDKnM0bZ5rtVoyqRy7dOVaNk9MAvA7wy1NbpxLs0DU7S6j9e6b\neyoZtLEeRAGy2hVrSybnAB+NiK1S/WZISacApwDsu+++hSWwnzXLTIvKkMqsGunktZ7asm37748+\nMVl499hmGW0vZ8TuqWQtBQhJ+5OMqH45SUlgI3At8OmIWJ/zNBuAfare7w08WHPMOHBBGhz2BN4k\naUtETJnOIyKWA8sBxsfHi6j+6nvNMtOiMqQyq0Y6da12SkGdaOR3Rmy9KneAkHQEcCUwAVwB/Bfw\nPOAY4HhJR0XE1TlOdQOwv6R5JAHmBOCd1QdExLyq654P/FttcLBseTLTIjKkMqtG8lyrnQbzRiOy\nV6zZmPl5T1Rng6SVEsTnSMZCLIyIxyobJT0b+F66f7zZSSJii6TTSHonDQFfiYi1kk5N95/bQpqs\nRlkZd5lVI82u1cpTfXUgmSWxtU5v7Xqfd/dPGyTKO5xB0gTJeISVGfsWAd+KiK71gRsfH4/Vq1d3\n6/I9pZ+mkGhH7fd74uktPPrE5E7HjY2OcM2S1035XLMR2I0+D/XHXGQda9YPJN0YEZkP962UIDYA\nu9bZtyteD6JnzLQ67eqAMDp7mMee3MLktuTBpt6cTLDzU31W9VAjWaWCbvc6MitTKwHis8AnJV0X\nEduDQdol9QzgM0UnzgZPbengyAPmcMmNG7dnyFklhXpqG7HbmRSwVi/3OjIrWisB4rXAs4G7JF3P\njkbql6W/H5E2ZEMyqvo9RSbUZr6stoRvXH9/W6Mzs57q6zXgj44M89SWbblLBTOthGZWTysB4lUk\nCwP9EtgvfZG+B3h11bHucmoty6oCyvsPaXRkmGc+Y5e2ZkRd+paDtl+/30oFM729ybqrlZHU85of\nZda+6fQEevMhz+fTixpPNpx30Fq/6KVlSG1m6shIakmzSJYlfX9E/KIT17D+lvXk22ghoGYuuXEj\n4/s9t2OTBZah1dJAp8dkuHRixU8+kxBwBEmbhdkU9dZAmLtH+72k+32tgnbW6+7kmIyi1g+3/tap\nAGFWV70n32vvfqTh53afPbzTgkPV+nmwWjsL9NSbaqSI6U68YJBBdybrsxmm1aqIehl5ozGbI8ND\nnHFM0pj8kYtuzhwBXeQ8UGVXr7RTGujkmAyPGDdwgLBpaqehtJ22htrlNRd/+2Ymt+4IEsNDyswY\n28nou9H4286EhJ0ck9HNdSqsd7iKyaalnaqIemtTz66zHsPus4d3zvRqCxAZpY9269G7Ub3S7nrd\nixaMcc2S13HPsqO5ZsnrCgtgRa0fbv3NAcKmpZ2qiEULxjjzuIMZGx1BJPMYnXncwXzmuJcwPDR1\n/Y/hIW2vWqo4a9W67VNtVExui50y8HYz+qKrV1as2cgrl13FvCVX8MplV2UGqHr3pFu9hnotPdYd\nrmKyaWm3KqJRd9NmVSZ5M/B2M/oiq1daqa7qtS64vZYeK18r60FcA5wLXBQRTzU5fBvwVeChaaRt\noPVLH/SiG0rzZEp5M/B2M/oiv5PXj7B+1koV0yRJpv+gpH+QdEC9AyPx3oi4f9opHED91Ae9G1UR\neevHjzxgTubn622vKPI7uTeQ9bNWpto4QtJ84P3Au4HTJf0E+BJwaUTkn2bTGuq3p86yqyLy9t75\n4c83ZX6+3vbaa7g3kA26ltogImId8FeSPga8AzgF+CbwkKR/AZZHxN3FJ3OwDMpT53Sq0fJk4L1w\nH2fK+hH9UuVpxWqrF1NEPBURXwdOB34CzAH+BrhT0sWSfq/ANA6cTo6QLVKe3jmNPtvparReuI8z\noTdQP1V5WrFaDhCSRiT9maT/BG4gCQ6nA3sBHwBeAXyjyTmOkrRO0npJSzL2HyvpFkk3SVot6VWt\nprOf9UMf9OlmGmWMNeiV+9ipsQpl8bQbg6uVXkwHk7Q/nAQ8E7gM+GhE/LDqsC9L+hVwcYPzDAFf\nAF5PsozpDZJWRsTtVYf9AFgZESHpJcBFQN1G8Zmm26uW5alOmG47SRnVP92+jzNFL1TVWXe00gZx\nM/AgcA5JW8Mv6xy3HriuwXkOA9ZX2iokXQAcC2wPEBHxWNXxz2SAFiCqzZzPPv7QUjO0PP32V6zZ\nWHeqjHqZRu33Gp09nLl86G4jw0V8je3cl3/63NA+uFqpYvpTYL+I+GSD4EBE3BERRzY4zxjwQNX7\nDem2KSS9VdLPgSuAP8s6kaRT0iqo1Zs2Ne+Z0ut6oa63WXVCJY31ZGUaWd/rsSe3MEs7f/7xp7e4\nbrvH9EpVnZWvlW6ulxR0zYxsYecSQkR8B/iOpNcAnwL+JOOY5cBygPHx8Z4qZbTT66Nb3Vur01rv\nJlZKBllprKjNNCrnzXr6nNwWKONfwuTW6NnuvIPKVXWDqxtTbWwA9ql6vzdJ1VWmiPixpN+XtGdE\n9MXI7HZnA+1GXW9tWuuplAwapaW6d06e89ab3tt1273HVXWDqRuT9d0A7C9pnqRdgROAldUHSHqh\nlDxfSvpDYFfg4dJT2qZ2e310o1tmoxJBRXXJoF5axkZHpmQgec5bj+u2zXpD6QEiIrYApwGrgDtI\n5nZaK+lUSaemh70NuE3STSQ9no6PaLScTG9ptyTQjbreRmnK6refN415SwG1tUxFf9/pjNUwG3Rd\nmc01Iq4ErqzZdm7V758FPlt2uooynRlOody63nppHRsd4Zolr2s7jXkXBYr0Wp34vt1Y+KcdHqVs\nvUp99GDe0Pj4eKxevbrbyQCy699Hhoe6NoK2UQZUZFqrrzM6e5jHntyy07oNteoFoiK8ctlVLQW/\nbui1fys2eCTdGBHjWfu8HkQHlFUSyPPkmfUU/aELb2LpyrUsfctB24//5OVrt49LeMYuO9c8NrtW\n7XWyxjjUqrdM6HS+b7XpNPqX9VTfbxMz2mBxgOiQTvf6yFt9Uq+xePPE5JTxDE9Obsvct2jBWOa1\nFl98M5+8fC2bn5hkr9ERHn9qS0uN0rvPHuaMYw7KfY/yft/qjH2WxNaMEnKzqr4yq6Y8Stl6mZcc\n7VN5e0o1ymgqxzc7V9b+yW3Bo09Mbh/4tnki/2zvI8NDLQWHemmo/b61A/KygkOeRvAy5x7qhQkF\ni+ROATOLA8Q0des/RN4nz2YZzYObJ5qeq+in2XYy2zzft15paUhqaSbVMp/qZ9Io5V6YCcCK5QAx\nDd38D1Ev458lTbl+VgZUe55mT7GdeJptNbPN86Rd75zbIlqaSbXMp/qZMB14hWd9nXkcIKahm/8h\n6mX8WyOmBKlKBrT77J0nwas8qTZ7im0WZNrRamab50m7qIy97Kf6fp8OvMLtKTOPA8Q0dPM/RCXj\nH8qY0Kg2SC1aMMaaT7yBc44/NPNJtfYpdvfZwzxjl1l8+MKbeOWyqwC2789rdGR4+/FFDIbL86Rd\nVMY+k57qyzTT2lPM4yCmpaYcFwsAABJKSURBVBf62c9bckXmBHsC7ll2dMvna9Yvv953rlbbj7/M\ngWAedNY9HtPRnzwOokN6Yb3houfqb9Yvv9nUHFmZcpkTvXlSue7xrK8zjwPENBTxH6J6SuyhtN/+\nWAvnKTpI1SsdVLa3OjWHDRYH6JnFAWKapvMforZIXum338rArKKf2mYJsmbHqCzus3jhfBZffPOU\nKTSGZ7U2ItrM+oMDRBc1mhK7lekWaoNUZWxGOwGj3tRJ2yJp79htZJhttTuzloAys77nXkxd1Ky3\n08bNEy0PwOvk2IwgmYZja00UqawCZ2YziwNEF+VpSG41k5/u2IzRkZ3HS+TRT33dPR2EWT4OEB3W\nKDNqZQBa3ky+2diMZpnj0rccxPCs1uuM9hod6YuM19NBmOXnANFBzTKj6gFZQOagt2p5ntIbDVbK\nkzkuWjDGWX96yPZBYs3SBEmvqSMPmNMXGa+ngzDLrysBQtJRktZJWi9pScb+kyTdkr6ulXRIN9I5\nXXkyo8o0C/cuO5q7znwT9y47uu6I5TxVUo1GE9dLz4fSEdPVgasy9cPn3nFI01LOmccdzA9/vqnh\nd+2V0oWngzDLr/QAIWmIZJ3pNwIHAidKOrDmsHuA10bES4BPAcvLTWUxurE29aIFY7ztpWPbn/yH\nJN720qSXU6MR0PWe+BtN6QHJ+IdGA+ge3DzBijUbWXzxzVNKF4svvrkrQcLTQZjl140SxGHA+oi4\nOyKeBi4Ajq0+ICKujYhH07fXA3uXnMZCtJsZNcrkm1mxZiOX3Lhx+5iKrRFccuNGPr7i1qa9UetV\ntSxaMJZZkqgOWo2+69KVa3daenRyW7B05dqm36doM2l6bbNO60aAGAMeqHq/Id1Wz/uA72btkHSK\npNWSVm/atKnAJBajncxoxZqNLPj77/Gv19+/UyY/nV5M3/rpA5lzNtWqVxJoNoFdo+9abzGhVhYZ\nKoon4jPLrxsD5bIeZDPzLklHkgSIV2Xtj4jlpNVP4+PjPTfrYKujnLMmO6vIO3CuXgaftbpalkal\nm0ajxht91w9deFOua5fF00GY5dONALEB2Kfq/d7Ag7UHSXoJcB7wxoh4uKS0Fa6VzKjRyGrI34sp\nq61hqM76zNWmW9VS77vuPnuYR5/YubSQtUaFmfWObgSIG4D9Jc0DNgInAO+sPkDSvsClwLsi4s7y\nk9h5WdNSNwsA9Z7uq8+128gww0NicuuOYDAyPMTbXjrGJTdunBKAhofEM3fdhd9MTLLX6AhHHjCH\ns1at48MX3lToTJxnHHMQi79985Q0DQ+JM445aNrnNrPOKT1ARMQWSacBq4Ah4CsRsVbSqen+c4FP\nAHsAX1TSULul3nzl/ai2KqnSg2i0zpM21H+6rz3X5olJhmeJ3WcPs/mJySkZ/fh+z61b3VUvTdB8\nwsBmPA20WX/ygkFdUG/RndGRYZ7asm2naqbRkWGWvuWgzAy1qEWLemHxIzMrnxcM6jH1qpJ+MzHJ\n2ccf2tKTdlEDvzyAzMxqOUB0QaNV4FrtYVPUinJFr0xnZv3PczGVpHqqicef2sLw0NTevrVtDHmn\npihq4JcHkJlZLZcgCpLVK2nRgjFWrNnIJy9fO6XxuVFDcuVc9RqMYefG3jOPO3jKsqXVI6Lzlkbc\nkGxmtdxIXYCsAW71upZWq9cA3EojduU6V9zyy516QI0MD2WOEq4XzMxs8DRqpHYVUwHqTW/xjZ/e\n39bAt3rbN09MZl/n+vszu8dOTG7lk5dPne/I6yGYWV4OEAWol6E3K5wVNbNoo8s8+sTklMzf6yGY\nWV4OEAVop6dPowbgVlaay6M683d3VjPLywGiAO309Kk8tWdV7VRmHC1KdebfTqmlVxb7MbNyOUAU\nYNGCMUZHWp94rrJwzoK//95Ome+iBWN1V5arXicij0rmv2LNRh5/astO+xuVZtxmYTa4HCAKsvQt\nB7VVLTS5LXj0icnMzLfe2IQTD9+H4aHms7NWPP7UFj6+4lY+dumtO63BsPvs4YbrIbjNwmxwDXyA\nKKr6JGshmnbUjmHIWtzmilt+OWVm1GY2T0zyjeuze1TN3nWXUqbyMLP+M9AD5YqewbR2moy5S65o\nK13VmW/W1BvtLMBTL5zkmWLcU3CYDaaBLkF0uvqknXYJKDfzbXatTk3B4YZvs9430AGi09UnS99y\nEMOzpjYkzyKp9xdJAKmdkwmSNoN6GWaRGWmejL4Tazi74dusPwx0FVOnq0/yzG9Ub66melVd0y3d\njI2OtDzFRtFrODcquXnKD7PeMdABYvHC+ZlzKNXOqjqdeYuaZa6LFoxx1qp1O02VUS/DzFO6Edlt\nDr2y+I8bvs36Q1eqmCQdJWmdpPWSlmTsP0DSdZKekvTXnUpHs+qTsqpCWskwd2vSriHgFb//3J6e\nuruoKUbMrLNKL0FIGgK+ALwe2ADcIGllRNxeddgjwAeBRZ1OT6Mn/HpVIR+56GY+fOFNO03r3W5J\no5WqrmZj4wK45q5H2H32MM/YZRa/mdh5OvFuy1NyM7Pu60YV02HA+oi4G0DSBcCxwPYAERG/Bn4t\n6eiyEpWVwdd7sq8MUKuUKFbf98iUab1b7S7bSoa5OWPW1iyPPjHJyPAQZx9/aM8EhgqvPWHWH0pf\nD0LS24GjIuK/p+/fBRweEadlHLsUeCwi/nez805nPYgVazay+OKbmdy2414MzxLP+p1dMqfRrjWk\n7FHNrdT5Vweo0dnDRJD59F9vrYh6eqXdwcx6U6+tB5FVSdJWlJJ0iqTVklZv2rSp7QQtXbl2SnCA\nZAqMJye35po+o96UF600ui5aMMY1S17H2ccfypOT29g8UX/6jXwzMLWeBjOzat2oYtoA7FP1fm/g\nwXZOFBHLgeWQlCDaTVDt/EQVE5PbOOf4Q7c/2c+qU1KoV4LI0+haW7X1yONPMTG5rSYdO3o0LVow\nxur7HuFfr78/13dzw6+ZtasbAeIGYH9J84CNwAnAO7uQjlyqG7FbWVq00obQqPE6a6qPeqpLAp9e\nlEwF3ixIFNXw6yVKzQZT6VVMEbEFOA1YBdwBXBQRayWdKulUAEm/J2kD8FfAxyVtkPScTqVp99nZ\nXUdnaerI5XrdYj+96ODM7UDDbrJZvaTqqS0JfHrRwQ2n8ihixDN41LPZICu9kbpTpt1I/e2bM2dI\nHRkeajujrdegXGk4nrfkityNL+dk9Eaq93kB9ywrpgNYs+9gZv2t1xqpe86iBWOc9fZDMhfgmc7k\nfc0GwOVtH9h99nBmgCpjwJlHPZsNLgeI1KIFY2wroDdStWYZeJ61p0eGhzjjmIMy93VqptVqHvVs\nNrgcIKoUnRk2y8ArbRr1lg4dkhpWb3ViptVWv4OZzVwDPVlfraKngMgzYrjye9Z182T205lpNU/v\nJI96NhtcbqSu0a0unWVft16X3aJLIGbW2xo1UjtADCj3TjIzcC8my+DeSWbWjNsgelSnq5w6vZqe\nmfU/lyB6UBmjl907ycyacYD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      "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)      \n",
    "plt.show()"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall we have a decent model, but we also acknowledge that we could do better. \n",
    "\n",
    "We have a couple of options:\n",
    "1. Add new features (bathrooms/bedrooms, area/stories, etc.)\n",
    "2. Build a non-linear model"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}