Real estate case study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression - Housing case study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#supress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the file\n",
    "housing_df = pd.read_csv('Housing.csv')\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataframe\n",
    "housing_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      "price               545 non-null int64\n",
      "area                545 non-null int64\n",
      "bedrooms            545 non-null int64\n",
      "bathrooms           545 non-null int64\n",
      "stories             545 non-null int64\n",
      "mainroad            545 non-null object\n",
      "guestroom           545 non-null object\n",
      "basement            545 non-null object\n",
      "hotwaterheating     545 non-null object\n",
      "airconditioning     545 non-null object\n",
      "parking             545 non-null int64\n",
      "prefarea            545 non-null object\n",
      "furnishingstatus    545 non-null object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# info of dataframe\n",
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>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>10%</th>\n",
       "      <td>2.835000e+06</td>\n",
       "      <td>3000.000000</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>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>90%</th>\n",
       "      <td>7.350000e+06</td>\n",
       "      <td>7980.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>1.054200e+07</td>\n",
       "      <td>12543.600000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</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",
       "10%    2.835000e+06   3000.000000    2.000000    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",
       "90%    7.350000e+06   7980.000000    4.000000    2.000000    3.000000   \n",
       "99%    1.054200e+07  12543.600000    5.000000    3.000000    4.000000   \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",
       "10%      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "90%      2.000000  \n",
       "99%      3.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe dataframe\n",
    "housing_df.describe(percentiles = [0.10,0.25,0.50,0.75,0.90,0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xc06b978>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pair plot of numerical variable\n",
    "sns.pairplot(data = housing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd2a9128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#categorical variables\n",
    "plt.figure(figsize = (20,8))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "sns.boxplot(x ='mainroad', y = 'price', data = housing_df)\n",
    "plt.subplot(2,3,2)\n",
    "sns.boxplot(x ='guestroom', y = 'price', data = housing_df)\n",
    "plt.subplot(2,3,3)\n",
    "sns.boxplot(x ='basement', y = 'price', data = housing_df)\n",
    "plt.subplot(2,3,4)\n",
    "sns.boxplot(x ='hotwaterheating', y = 'price', data = housing_df)\n",
    "plt.subplot(2,3,5)\n",
    "sns.boxplot(x ='airconditioning', y = 'price', data = housing_df)\n",
    "plt.subplot(2,3,6)\n",
    "sns.boxplot(x ='prefarea', y = 'price', data = housing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed78c88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x ='furnishingstatus', y = 'price', data = housing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xedf8550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(x ='furnishingstatus', y = 'price', hue = 'airconditioning',data = housing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf1112b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(housing_df.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see high correlation between price, area and bathrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preparation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting yes to 1 and 'No' to 0\n",
    "varaible_list = ['mainroad','guestroom','basement','hotwaterheating','airconditioning', 'prefarea']\n",
    "\n",
    "def binary_map(x):\n",
    "    return x.map({'yes':1 , 'no':0})\n",
    "\n",
    "housing_df[varaible_list] = housing_df[varaible_list].apply(binary_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
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       "      <td>7420</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "  furnishingstatus  \n",
       "0        furnished  \n",
       "1        furnished  \n",
       "2   semi-furnished  \n",
       "3        furnished  \n",
       "4        furnished  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummy variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>semi-furnished</th>\n",
       "      <th>unfurnished</th>\n",
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      ],
      "text/plain": [
       "   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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us create a dummy variable for furnishing status as 3 level values\n",
    "\n",
    "status = pd.get_dummies(housing_df['furnishingstatus'], drop_first = True)\n",
    "status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_df = pd.concat([housing_df,status], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\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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       "    <tr>\n",
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       "      <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",
       "      <td>0</td>\n",
       "      <td>0</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  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_df.drop(['furnishingstatus'], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>0</td>\n",
       "      <td>0</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",
       "   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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the data into Test Train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(housing_df, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rescaling the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "#applying the scaler only to below variables\n",
    "num_var= ['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'parking']\n",
    "\n",
    "df_train[num_var] = scaler.fit_transform(df_train[num_var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
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       "      <th>359</th>\n",
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       "      <th>28</th>\n",
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       "      <td>0.538015</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
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       "      <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",
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       "      <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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the values are in the range 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf21fb38>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let us check the correlation of train data\n",
    "plt.figure(figsize = (10,8))\n",
    "sns.heatmap(df_train.corr(),annot = True, cmap = 'YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see high correlation between price and area, price and bathrooms, bedrooms with bathrooms and stories and many more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dividing X and Y for model building:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train.pop('price')\n",
    "X_train = df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building a linear model:\n",
    "\n",
    "We will be using two methods:\n",
    "    1. using statsmodels.api\n",
    "    2. using RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: using statsmodels.api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let us build by bottom-up approach for i.e. we will start by building the model with just one variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#area\n",
    "X_train_sm = sm.add_constant(X_train[['area']])\n",
    "\n",
    "lr_1 = sm.OLS(y_train, X_train_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    0.126894\n",
       "area     0.462192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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, 01 Oct 2019   Prob (F-statistic):           3.15e-29\n",
      "Time:                        17:50:02   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(lr_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable 'Area' just explains 28% variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train_sm.iloc[:,1], y_train)\n",
    "plt.plot(X_train_sm.iloc[:,1],0.126894 + 0.462192*X_train_sm.iloc[:,1], 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though the line is passing through the data, we see that 'area' could explain only 28% variance. So let us add another variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#area and bathrooms\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[['area', 'bathrooms']])\n",
    "\n",
    "lr_2 = sm.OLS(y_train, X_train_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const        0.104589\n",
       "area         0.398396\n",
       "bathrooms    0.298374\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_2.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.480\n",
      "Model:                            OLS   Adj. R-squared:                  0.477\n",
      "Method:                 Least Squares   F-statistic:                     174.1\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           2.51e-54\n",
      "Time:                        17:50:02   Log-Likelihood:                 288.24\n",
      "No. Observations:                 381   AIC:                            -570.5\n",
      "Df Residuals:                     378   BIC:                            -558.6\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1046      0.011      9.384      0.000       0.083       0.127\n",
      "area           0.3984      0.033     12.192      0.000       0.334       0.463\n",
      "bathrooms      0.2984      0.025     11.945      0.000       0.249       0.347\n",
      "==============================================================================\n",
      "Omnibus:                       62.839   Durbin-Watson:                   2.157\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              168.790\n",
      "Skew:                           0.784   Prob(JB):                     2.23e-37\n",
      "Kurtosis:                       5.859   Cond. No.                         6.17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjusted R-squared increased from 28.1% to 47.7%. Let us add one more variable and check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#area ,bathrooms and bedrooms\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[['area','bedrooms','bathrooms']])\n",
    "\n",
    "lr_3 = sm.OLS(y_train, X_train_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const        0.041352\n",
       "area         0.392211\n",
       "bedrooms     0.181863\n",
       "bathrooms    0.259978\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_3.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.505\n",
      "Model:                            OLS   Adj. R-squared:                  0.501\n",
      "Method:                 Least Squares   F-statistic:                     128.2\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           3.12e-57\n",
      "Time:                        17:50:02   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",
      "bedrooms       0.1819      0.041      4.396      0.000       0.101       0.263\n",
      "bathrooms      0.2600      0.026     10.033      0.000       0.209       0.311\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(lr_3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us do the other way- Let us build the model by adding all the variables to the model and drop those which are insignificant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',\n",
       "       'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea',\n",
       "       'semi-furnished', 'unfurnished'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sm = sm.add_constant(X_train)\n",
    "\n",
    "lr_4 = sm.OLS(y_train, X_train_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_4.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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:                  0.670\n",
      "Method:                 Least Squares   F-statistic:                     60.40\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           8.83e-83\n",
      "Time:                        17:50:02   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_4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, certain varaibles have p- values > 0.05. Before dropping any variables, let us check VIF as well:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "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>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
    "Let us drop variable `semi-furnished` as p-value of semi-furnished is 0.938"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the variable semi-furnished\n",
    "\n",
    "X = X_train.drop('semi-furnished', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',\n",
       "       'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea',\n",
       "       'unfurnished'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sm = sm.add_constant(X)\n",
    "lr_5 = sm.OLS(y_train, X_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.681\n",
      "Model:                            OLS   Adj. R-squared:                  0.671\n",
      "Method:                 Least Squares   F-statistic:                     65.61\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           1.07e-83\n",
      "Time:                        17:50:03   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(lr_5.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now bedrooms and basement looks insignificant. Let us check VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>basement</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features   VIF\n",
       "1          bedrooms  6.59\n",
       "4          mainroad  5.68\n",
       "0              area  4.67\n",
       "3           stories  2.69\n",
       "9           parking  2.12\n",
       "6          basement  2.01\n",
       "8   airconditioning  1.77\n",
       "2         bathrooms  1.67\n",
       "10         prefarea  1.51\n",
       "5         guestroom  1.47\n",
       "11      unfurnished  1.40\n",
       "7   hotwaterheating  1.14"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us drop bedrooms\n",
    "X = X.drop('bedrooms', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sm = sm.add_constant(X)\n",
    "lr_6 = sm.OLS(y_train, X_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.680\n",
      "Model:                            OLS   Adj. R-squared:                  0.671\n",
      "Method:                 Least Squares   F-statistic:                     71.31\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           2.73e-84\n",
      "Time:                        17:50:03   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(lr_6.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-values of all variables looks fine. Let us check VIF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
    "#### Residual Analysis of the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = lr_6.predict(X_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "residual = y_train- y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109ef5c0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(residual, bins =20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error terms are normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions Using the Final Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these variables we scaled in Train data..so let us scale the same variables in test data as well.\n",
    "num_var= ['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'parking']\n",
    "\n",
    "df_test[num_var] = scaler.transform(df_test[num_var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = df_test.pop('price')\n",
    "X_test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_sm = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_sm = X_test_sm.drop(['semi-furnished','bedrooms'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr_6.predict(X_test_sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xf8935f8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   71.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 Oct 2019</td> <th>  Prob (F-statistic):</th> <td>2.73e-84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:50:04</td>     <th>  Log-Likelihood:    </th> <td>  380.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   381</td>      <th>  AIC:               </th> <td>  -737.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   369</td>      <th>  BIC:               </th> <td>  -690.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>           <td>    0.0357</td> <td>    0.015</td> <td>    2.421</td> <td> 0.016</td> <td>    0.007</td> <td>    0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>area</th>            <td>    0.2347</td> <td>    0.030</td> <td>    7.851</td> <td> 0.000</td> <td>    0.176</td> <td>    0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>       <td>    0.1965</td> <td>    0.022</td> <td>    9.132</td> <td> 0.000</td> <td>    0.154</td> <td>    0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stories</th>         <td>    0.1178</td> <td>    0.018</td> <td>    6.654</td> <td> 0.000</td> <td>    0.083</td> <td>    0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mainroad</th>        <td>    0.0488</td> <td>    0.014</td> <td>    3.423</td> <td> 0.001</td> <td>    0.021</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guestroom</th>       <td>    0.0301</td> <td>    0.014</td> <td>    2.211</td> <td> 0.028</td> <td>    0.003</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>basement</th>        <td>    0.0239</td> <td>    0.011</td> <td>    2.183</td> <td> 0.030</td> <td>    0.002</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hotwaterheating</th> <td>    0.0864</td> <td>    0.022</td> <td>    4.014</td> <td> 0.000</td> <td>    0.044</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>airconditioning</th> <td>    0.0665</td> <td>    0.011</td> <td>    5.895</td> <td> 0.000</td> <td>    0.044</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parking</th>         <td>    0.0629</td> <td>    0.018</td> <td>    3.501</td> <td> 0.001</td> <td>    0.028</td> <td>    0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prefarea</th>        <td>    0.0596</td> <td>    0.012</td> <td>    5.061</td> <td> 0.000</td> <td>    0.036</td> <td>    0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unfurnished</th>     <td>   -0.0323</td> <td>    0.010</td> <td>   -3.169</td> <td> 0.002</td> <td>   -0.052</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>97.661</td> <th>  Durbin-Watson:     </th> <td>   2.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 325.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.130</td> <th>  Prob(JB):          </th> <td>2.20e-71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.923</td> <th>  Cond. No.          </th> <td>    10.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.680\n",
       "Model:                            OLS   Adj. R-squared:                  0.671\n",
       "Method:                 Least Squares   F-statistic:                     71.31\n",
       "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           2.73e-84\n",
       "Time:                        17:50:04   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",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_6.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the equation of our best fitted line is:\n",
    "\n",
    "𝑝𝑟𝑖𝑐𝑒=0.234×𝑎𝑟𝑒𝑎+0.1965×𝑏𝑎𝑡ℎ𝑟𝑜𝑜𝑚𝑠+0.11×𝑠𝑡𝑜𝑟𝑖𝑒𝑠+0.05×𝑚𝑎𝑖𝑛𝑟𝑜𝑎𝑑+0.03×𝑔𝑢𝑒𝑠𝑡𝑟𝑜𝑜𝑚+0.02×basement+\n",
    "0.086×ℎ𝑜𝑡𝑤𝑎𝑡𝑒𝑟ℎ𝑒𝑎𝑡𝑖𝑛𝑔+0.066×𝑎𝑖𝑟𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑖𝑛𝑔+0.0629×𝑝𝑎𝑟𝑘𝑖𝑛𝑔+0.0596×𝑝𝑟𝑒𝑓𝑎𝑟𝑒𝑎−0.0323×𝑢𝑛𝑓𝑢𝑟𝑛𝑖𝑠ℎ𝑒𝑑"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: using RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the data into Test Train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(housing_df, train_size = 0.7, test_size =0.3, random_state =100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 14)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164, 14)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list = ['price','area', 'bedrooms','bathrooms', 'stories','parking']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df_train[var_list] = scaler.fit_transform(df_train[var_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dividing X and Y for model building:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train.pop('price')\n",
    "X_train = df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "rfe = RFE(lm,10)\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('area', True, 1),\n",
       " ('bedrooms', True, 1),\n",
       " ('bathrooms', True, 1),\n",
       " ('stories', True, 1),\n",
       " ('mainroad', True, 1),\n",
       " ('guestroom', True, 1),\n",
       " ('basement', False, 3),\n",
       " ('hotwaterheating', True, 1),\n",
       " ('airconditioning', True, 1),\n",
       " ('parking', True, 1),\n",
       " ('prefarea', True, 1),\n",
       " ('semi-furnished', False, 4),\n",
       " ('unfurnished', False, 2)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',\n",
       "       'hotwaterheating', 'airconditioning', 'parking', 'prefarea'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "support_col = X_train.columns[rfe.support_]\n",
    "support_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['basement', 'semi-furnished', 'unfurnished'], dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discarded_col = X_train.columns[~rfe.support_]\n",
    "discarded_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model using statsmodel using supported columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe = X_train[support_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe_sm = sm.add_constant(X_train_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_rfe = sm.OLS(y_train,X_train_rfe_sm ).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const              0.002721\n",
       "area               0.236257\n",
       "bedrooms           0.066102\n",
       "bathrooms          0.198169\n",
       "stories            0.097722\n",
       "mainroad           0.055649\n",
       "guestroom          0.038136\n",
       "hotwaterheating    0.089673\n",
       "airconditioning    0.071079\n",
       "parking            0.063739\n",
       "prefarea           0.064326\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_rfe.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.669\n",
      "Model:                            OLS   Adj. R-squared:                  0.660\n",
      "Method:                 Least Squares   F-statistic:                     74.89\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           1.28e-82\n",
      "Time:                        17:50:05   Log-Likelihood:                 374.65\n",
      "No. Observations:                 381   AIC:                            -727.3\n",
      "Df Residuals:                     370   BIC:                            -683.9\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0027      0.018      0.151      0.880      -0.033       0.038\n",
      "area                0.2363      0.030      7.787      0.000       0.177       0.296\n",
      "bedrooms            0.0661      0.037      1.794      0.074      -0.006       0.139\n",
      "bathrooms           0.1982      0.022      8.927      0.000       0.155       0.242\n",
      "stories             0.0977      0.019      5.251      0.000       0.061       0.134\n",
      "mainroad            0.0556      0.014      3.848      0.000       0.027       0.084\n",
      "guestroom           0.0381      0.013      2.934      0.004       0.013       0.064\n",
      "hotwaterheating     0.0897      0.022      4.104      0.000       0.047       0.133\n",
      "airconditioning     0.0711      0.011      6.235      0.000       0.049       0.093\n",
      "parking             0.0637      0.018      3.488      0.001       0.028       0.100\n",
      "prefarea            0.0643      0.012      5.445      0.000       0.041       0.088\n",
      "==============================================================================\n",
      "Omnibus:                       86.105   Durbin-Watson:                   2.098\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              286.069\n",
      "Skew:                           0.992   Prob(JB):                     7.60e-63\n",
      "Kurtosis:                       6.753   Cond. No.                         13.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_rfe.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable `bedrooms` is insignificant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe_1 = X_train_rfe.drop(['bedrooms'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe_new = sm.add_constant(X_train_rfe_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_rfe_1 = sm.OLS(y_train,X_train_rfe_new).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.666\n",
      "Model:                            OLS   Adj. R-squared:                  0.658\n",
      "Method:                 Least Squares   F-statistic:                     82.37\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           6.67e-83\n",
      "Time:                        17:50:05   Log-Likelihood:                 373.00\n",
      "No. Observations:                 381   AIC:                            -726.0\n",
      "Df Residuals:                     371   BIC:                            -686.6\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0242      0.013      1.794      0.074      -0.002       0.051\n",
      "area                0.2367      0.030      7.779      0.000       0.177       0.297\n",
      "bathrooms           0.2070      0.022      9.537      0.000       0.164       0.250\n",
      "stories             0.1096      0.017      6.280      0.000       0.075       0.144\n",
      "mainroad            0.0536      0.014      3.710      0.000       0.025       0.082\n",
      "guestroom           0.0390      0.013      2.991      0.003       0.013       0.065\n",
      "hotwaterheating     0.0921      0.022      4.213      0.000       0.049       0.135\n",
      "airconditioning     0.0710      0.011      6.212      0.000       0.049       0.094\n",
      "parking             0.0669      0.018      3.665      0.000       0.031       0.103\n",
      "prefarea            0.0653      0.012      5.513      0.000       0.042       0.089\n",
      "==============================================================================\n",
      "Omnibus:                       91.542   Durbin-Watson:                   2.107\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              315.402\n",
      "Skew:                           1.044   Prob(JB):                     3.25e-69\n",
      "Kurtosis:                       6.938   Cond. No.                         10.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_rfe_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the p-values look significant . Let us check VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>0</th>\n",
       "      <td>area</td>\n",
       "      <td>4.516773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mainroad</td>\n",
       "      <td>4.263472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stories</td>\n",
       "      <td>2.120356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>parking</td>\n",
       "      <td>2.096114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.748100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.578669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.466057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guestroom</td>\n",
       "      <td>1.300287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.121364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Features       VIF\n",
       "0             area  4.516773\n",
       "3         mainroad  4.263472\n",
       "2          stories  2.120356\n",
       "7          parking  2.096114\n",
       "6  airconditioning  1.748100\n",
       "1        bathrooms  1.578669\n",
       "8         prefarea  1.466057\n",
       "4        guestroom  1.300287\n",
       "5  hotwaterheating  1.121364"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train_rfe_1.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train_rfe_1.values, i) for i in range(X_train_rfe_1.shape[1])]\n",
    "vif = vif.sort_values(by = 'VIF', ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Residual analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = lr_rfe_1.predict(X_train_rfe_new)\n",
    "\n",
    "res = y_train - y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109c6b00>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(res, bins =20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error terms are normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions Using the Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list = ['price','area', 'bedrooms','bathrooms', 'stories','parking']\n",
    "\n",
    "\n",
    "df_test[var_list] = scaler.transform(df_test[var_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = df_test.pop('price')\n",
    "X_test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = X_train_rfe_1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_new = X_test[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'bathrooms', 'stories', 'mainroad', 'guestroom',\n",
       "       'hotwaterheating', 'airconditioning', 'parking', 'prefarea'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_rfe = sm.add_constant(X_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr_rfe_1.predict(X_test_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11120d68>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.666\n",
      "Model:                            OLS   Adj. R-squared:                  0.658\n",
      "Method:                 Least Squares   F-statistic:                     82.37\n",
      "Date:                Tue, 01 Oct 2019   Prob (F-statistic):           6.67e-83\n",
      "Time:                        18:00:01   Log-Likelihood:                 373.00\n",
      "No. Observations:                 381   AIC:                            -726.0\n",
      "Df Residuals:                     371   BIC:                            -686.6\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.0242      0.013      1.794      0.074      -0.002       0.051\n",
      "area                0.2367      0.030      7.779      0.000       0.177       0.297\n",
      "bathrooms           0.2070      0.022      9.537      0.000       0.164       0.250\n",
      "stories             0.1096      0.017      6.280      0.000       0.075       0.144\n",
      "mainroad            0.0536      0.014      3.710      0.000       0.025       0.082\n",
      "guestroom           0.0390      0.013      2.991      0.003       0.013       0.065\n",
      "hotwaterheating     0.0921      0.022      4.213      0.000       0.049       0.135\n",
      "airconditioning     0.0710      0.011      6.212      0.000       0.049       0.094\n",
      "parking             0.0669      0.018      3.665      0.000       0.031       0.103\n",
      "prefarea            0.0653      0.012      5.513      0.000       0.042       0.089\n",
      "==============================================================================\n",
      "Omnibus:                       91.542   Durbin-Watson:                   2.107\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              315.402\n",
      "Skew:                           1.044   Prob(JB):                     3.25e-69\n",
      "Kurtosis:                       6.938   Cond. No.                         10.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr_rfe_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the equation of our best fitted line is:\n",
    "\n",
    "𝑝𝑟𝑖𝑐𝑒=0.234×𝑎𝑟𝑒𝑎+0.2070×𝑏𝑎𝑡ℎ𝑟𝑜𝑜𝑚𝑠+0.11×𝑠𝑡𝑜𝑟𝑖𝑒𝑠+0.05×𝑚𝑎𝑖𝑛𝑟𝑜𝑎𝑑+0.039×𝑔𝑢𝑒𝑠𝑡𝑟𝑜𝑜𝑚+ 0.092×ℎ𝑜𝑡𝑤𝑎𝑡𝑒𝑟ℎ𝑒𝑎𝑡𝑖𝑛𝑔+0.071×𝑎𝑖𝑟𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑖𝑛𝑔+0.0669×𝑝𝑎𝑟𝑘𝑖𝑛𝑔+0.0653×𝑝𝑟𝑒𝑓𝑎𝑟𝑒𝑎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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