model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>MaintenanceStaff</th>\n",
       "      <th>Gymnasium</th>\n",
       "      <th>SwimmingPool</th>\n",
       "      <th>LandscapedGardens</th>\n",
       "      <th>JoggingTrack</th>\n",
       "      <th>...</th>\n",
       "      <th>LiftAvailable</th>\n",
       "      <th>BED</th>\n",
       "      <th>VaastuCompliant</th>\n",
       "      <th>Microwave</th>\n",
       "      <th>GolfCourse</th>\n",
       "      <th>TV</th>\n",
       "      <th>DiningTable</th>\n",
       "      <th>Sofa</th>\n",
       "      <th>Wardrobe</th>\n",
       "      <th>Refrigerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Uttam Nagar</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000000</td>\n",
       "      <td>1350</td>\n",
       "      <td>Sarita Vihar</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2500000</td>\n",
       "      <td>435</td>\n",
       "      <td>Uttam Nagar</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800000</td>\n",
       "      <td>900</td>\n",
       "      <td>Dwarka Mor</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area          Location  No. of Bedrooms  Resale  \\\n",
       "0  10500000  1200  Sector 10 Dwarka                2       1   \n",
       "1   6000000  1000       Uttam Nagar                3       0   \n",
       "2  15000000  1350      Sarita Vihar                2       1   \n",
       "3   2500000   435       Uttam Nagar                2       0   \n",
       "4   5800000   900        Dwarka Mor                3       0   \n",
       "\n",
       "   MaintenanceStaff  Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  \\\n",
       "0                 0          1             0                  0             1   \n",
       "1                 0          0             0                  0             0   \n",
       "2                 0          0             0                  0             0   \n",
       "3                 0          0             0                  0             0   \n",
       "4                 0          0             0                  0             0   \n",
       "\n",
       "   ...  LiftAvailable  BED  VaastuCompliant  Microwave  GolfCourse  TV  \\\n",
       "0  ...              1    0                1          0           0   0   \n",
       "1  ...              0    0                0          0           0   0   \n",
       "2  ...              0    0                0          0           0   0   \n",
       "3  ...              0    0                1          0           0   0   \n",
       "4  ...              1    0                0          0           0   0   \n",
       "\n",
       "   DiningTable  Sofa  Wardrobe  Refrigerator  \n",
       "0            0     0         0             0  \n",
       "1            0     0         0             0  \n",
       "2            0     0         0             0  \n",
       "3            0     0         0             0  \n",
       "4            0     0         0             0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train=pd.read_csv('Delhi.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>MaintenanceStaff</th>\n",
       "      <th>Gymnasium</th>\n",
       "      <th>SwimmingPool</th>\n",
       "      <th>LandscapedGardens</th>\n",
       "      <th>JoggingTrack</th>\n",
       "      <th>...</th>\n",
       "      <th>BED</th>\n",
       "      <th>VaastuCompliant</th>\n",
       "      <th>Microwave</th>\n",
       "      <th>GolfCourse</th>\n",
       "      <th>TV</th>\n",
       "      <th>DiningTable</th>\n",
       "      <th>Sofa</th>\n",
       "      <th>Wardrobe</th>\n",
       "      <th>Refrigerator</th>\n",
       "      <th>PSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>10500000</td>\n",
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       "      <th>1</th>\n",
       "      <td>6000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Uttam Nagar</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000000</td>\n",
       "      <td>1350</td>\n",
       "      <td>Sarita Vihar</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2500000</td>\n",
       "      <td>435</td>\n",
       "      <td>Uttam Nagar</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800000</td>\n",
       "      <td>900</td>\n",
       "      <td>Dwarka Mor</td>\n",
       "      <td>3</td>\n",
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       "      <td>6444.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area          Location  No. of Bedrooms  Resale  \\\n",
       "0  10500000  1200  Sector 10 Dwarka                2       1   \n",
       "1   6000000  1000       Uttam Nagar                3       0   \n",
       "2  15000000  1350      Sarita Vihar                2       1   \n",
       "3   2500000   435       Uttam Nagar                2       0   \n",
       "4   5800000   900        Dwarka Mor                3       0   \n",
       "\n",
       "   MaintenanceStaff  Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  \\\n",
       "0                 0          1             0                  0             1   \n",
       "1                 0          0             0                  0             0   \n",
       "2                 0          0             0                  0             0   \n",
       "3                 0          0             0                  0             0   \n",
       "4                 0          0             0                  0             0   \n",
       "\n",
       "   ...  BED  VaastuCompliant  Microwave  GolfCourse  TV  DiningTable  Sofa  \\\n",
       "0  ...    0                1          0           0   0            0     0   \n",
       "1  ...    0                0          0           0   0            0     0   \n",
       "2  ...    0                0          0           0   0            0     0   \n",
       "3  ...    0                1          0           0   0            0     0   \n",
       "4  ...    0                0          0           0   0            0     0   \n",
       "\n",
       "   Wardrobe  Refrigerator           PSF  \n",
       "0         0             0   8750.000000  \n",
       "1         0             0   6000.000000  \n",
       "2         0             0  11111.111111  \n",
       "3         0             0   5747.126437  \n",
       "4         0             0   6444.444444  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['PSF']=df_train['Price']/df_train['Area']\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sector 10 Dwarka', 'Uttam Nagar', 'Sarita Vihar', 'Dwarka Mor',\n",
       "       'Sector 7 Dwarka', 'Sector 6 Dwarka', 'Sector 5 Dwarka',\n",
       "       'Sector 23 Rohini', 'Mayur Vihar II', 'Sector 24 Rohini',\n",
       "       'Sector 11 Dwarka', 'Sector 23 Dwarka', 'Sector 12 Dwarka',\n",
       "       'West End', 'Sector 9 Rohini', 'Mundka', 'Sector 13 Rohini',\n",
       "       'Jamia Nagar', 'Sector 19 Dwarka', 'Sector 17 Dwarka', 'Bindapur',\n",
       "       'Sector-18 Dwarka', 'Vasant Kunj', 'Shastri Nagar',\n",
       "       'Sector-8 Rohini', 'Sector 9 Dwarka', 'Shanti Park Dwarka',\n",
       "       'Govindpuri', 'Sector 22 Dwarka', 'Matiala', 'Saket',\n",
       "       'Mahavir Enclave', 'Burari', 'Shahdara', 'Babarpur', 'Khanpur',\n",
       "       'Sector 13 Dwarka', 'Mansa Ram Park', 'Green Park', 'Kalkaji',\n",
       "       'Sector 4 Dwarka', 'DLF Phase 5', 'Sector 3 Dwarka',\n",
       "       'Chittaranjan Park', 'Chattarpur', 'Greater Kailash',\n",
       "       'Sector-14 Rohini', 'Paschim Vihar', 'Pitampura',\n",
       "       'Sector 18B Dwarka', 'Sector 2 Dwarka', 'Jasola',\n",
       "       'Pochanpur Colony', 'Palam', 'Saidabad', 'Budh Vihar',\n",
       "       'Sector 25 Rohini', 'Sector 18A Dwarka', 'Sewak Park',\n",
       "       'Sector 23B Dwarka', 'Rohini sector 24', 'Sector 28 Rohini',\n",
       "       'Rohini Sector 9', 'Rohini Extension', 'nawada', 'Alaknanda',\n",
       "       'Sector 22 Rohini', 'Lajpat Nagar', 'South Extension 2',\n",
       "       'Sector 16B Dwarka', 'Sheikh Sarai', 'Sidhartha Nagar',\n",
       "       'Sector-D Vasant Kunj', 'Hauz Khas', 'Kalkaji Extension',\n",
       "       'Greater kailash 1', 'Lajpat Nagar III', 'Safdarjung Enclave',\n",
       "       'Greater Kailash II', 'Sainik Farms', 'Sector 20 Rohini',\n",
       "       'greater kailash Enclave 1', 'DLF Farms', 'Mehrauli', 'Mahipalpur',\n",
       "       'mayur vihar phase 1', 'Sarvodaya Enclave', 'Karol Bagh',\n",
       "       'West Sagarpur', 'Ashok Vihar', 'Sector 21 Dwarka',\n",
       "       'East of Kailash', 'Khirki Extension', 'Dashrath Puri',\n",
       "       'SULTANPUR', 'Patparganj', 'Kaushambi', 'Shakurbasti',\n",
       "       'Hari Nagar', 'Siri Fort', 'Katwaria Sarai', 'Mayur Vihar',\n",
       "       'Nasirpur', 'Bharat Vihar', 'Nanak Heri Village', 'Vikas Puri',\n",
       "       'Madhu Vihar', 'New Ashok Nagar', 'Naraina',\n",
       "       'Tagore Garden Extension', 'New Friends Colony',\n",
       "       'Sector 14 Rohini', 'Dabri', 'R.K. Puram', 'Vijay Mandal Enclave',\n",
       "       'Sector 8 Dwarka', 'Badarpur', 'Masjid moth', 'Punjabi Bagh',\n",
       "       'Gulmohar Enclave', 'Jasola Vihar Sector 8 Road', 'Bharthal',\n",
       "       'Sheikh Sarai Phase2 New Delhi', 'Abul Fazal Enclave Jamia Nagar',\n",
       "       'Ramesh Nagar', 'Shaheen Bagh Jamia Nagar',\n",
       "       'Batla House Jamia Nagar', 'Sector 1A Dwarka', 'Zakir Nagar',\n",
       "       'i p extension patparganj', 'Neb Sarai',\n",
       "       'Jogabai Extension New Delhi', 'Jor bagh', 'Delhi',\n",
       "       'dwarka sector 12', 'Moti Nagar', 'Press Enclave',\n",
       "       'Kailash Colony', 'Karampura', 'Civil Lines', 'Khanpur Deoli',\n",
       "       'Naraina Vihar', 'Block GH5', 'West Punjabi Bagh',\n",
       "       '3 Dwarka Sector 9 Road', 'Dwarka 11 Sector',\n",
       "       'Block UU Uttari Pitampura', 'Siddharth Enclave', 'Lalita Park',\n",
       "       'Mathura Road Sarita Vihar', 'Azad Apartments', 'Sadulajab',\n",
       "       'Rajouri Garden', 'Sector 1 Dwarka', 'Vasant Kunj Marg', 'Munirka',\n",
       "       'Paryavaran Complex', 'Meera Bagh', 'Kundli', 'Hauz Khas Enclave',\n",
       "       'dwarka sector 13', 'Rohini sector 16', 'Som Vihar',\n",
       "       'Mayur Vihar Phase 2', 'Block SD Delhi', 'Model Town',\n",
       "       'Duggal Colony', 'Shivalik', 'Faridabad Gurgaon Road',\n",
       "       '86 Sector 21 Road', '1 Lane Johri Farm',\n",
       "       'Bamnoli Sector 28 Dwarka', 'Sector24', 'IP Extension',\n",
       "       'Anand Lok', 'Devli', 'Inderpuri', 'MBS Nagar Block WZ', 'Block A',\n",
       "       'New Ashok Nagar Block E', 'Pocket 12',\n",
       "       'Alaknanda Gangotri Enclave', 'Sector 19B', 'Roop Nagar',\n",
       "       'Kamla Nagar', 'param puri', 'Om Nagar', 'Delhi Meerut Expressway',\n",
       "       'Road Number 1', 'Pitampura Vaishali', 'KMP Expressway',\n",
       "       'Pratap Nagar', 'Pratap Vihar', 'Chhatarpur Extension', 'Block H',\n",
       "       'Block C', 'vikaspuri', 'Block C4H', 'Block D', 'Rana Pratap Bagh',\n",
       "       'Malka Ganj', 'Shakti Nagar', 'Azadpur', 'Panchsheel Enclave',\n",
       "       'Janakpuri', 'Geetanjali Enclave', 'Kalkaji Govindpuri',\n",
       "       'Ali Gaon', 'Noida', 'Mall Road', 'North Campus', 'Block F',\n",
       "       'Shalimar Bagh', 'DLF Golf Course', 'Park End', 'South Delhi',\n",
       "       'Sahibabad', 'F Block', 'Block 18', 'Sector-7 Rohini',\n",
       "       '10 Sector Dwarka', 'Sector 6 Rohini', 'Dwarka More',\n",
       "       'Dwarka Sector3', 'Prithviraj Road', 'Sector 23',\n",
       "       'Vasundhara Enclave', 'Om Enclave Mithapur Extension',\n",
       "       'West Enclave', 'Khari Baoli Road', 'Chhattarpur Enclave Phase1',\n",
       "       'Rama Park', 'Mayur Vihar 2 Phase', 'Sector 15 Rohini',\n",
       "       '92 Sector 22 Road', 'Sector 10', 'Sector 4 Rohini',\n",
       "       'Dwarka New Delhi 110075', 'Sunder Nagar', 'CVD Line',\n",
       "       'Vijay Nagar', 'Malviya Nagar', 'Molar band Extension',\n",
       "       'Gurgaon Delhi Expressway', 'Okhla Village', 'Mayur Vihar III',\n",
       "       'Vasant Vihar', 'joshi road', 'Krishna Nagar', 'Sector 62',\n",
       "       'Faridabad Road', 'Aurungzeb Road', 'Okhla', 'Mandi House',\n",
       "       'Sector 37 Rohini', 'Vishal Enclave', 'Sector 112',\n",
       "       '85 Sector 22 Road', 'Sushant Lok Phase 1 Barakhamba',\n",
       "       'Sector 21 Dwarka New Delhi', 'Chanakya Puri', 'Mathura Road',\n",
       "       'Sector 108', '75 Sector 22 Road', '75 Noida Road', 'Vaishali',\n",
       "       'Pratap Khand', 'Sangam Vihar', '47 Sector 22 Road',\n",
       "       'Munirka Vihar', 'Ghitorni', 'Sector 4', 'Sector 53',\n",
       "       'Shankar Vihar', 'Krishna Park Jawahar Park', 'Nanak Pura',\n",
       "       'Green Park Extension', 'Noida Road', '48 Sector 22 Road',\n",
       "       'Sector 33 Rohini', 'Munirka Marg', 'Pocket3 Sector24',\n",
       "       'Panchsheel Park', 'Shakti Vihar School Road',\n",
       "       'Residential FlatVasant Vihar', '79 Sector 23 Road',\n",
       "       'Kundli Manesar Palwal Expressway', 'Phase 2 Chattarpur Enclave',\n",
       "       'Om Vihar', 'Anarkali Colony', 'Nawada Extension',\n",
       "       'Lajpat Nagar IV', 'New Manglapuri', 'MG Road Ghitorni', 'Tigri',\n",
       "       'Dharampura', 'Ansal Satbari', '78 Sector 6 Road',\n",
       "       'Sector C Vasant Kunj', 'CD Block Dakshini Pitampura',\n",
       "       '59 Sector 22 Road', 'KarKardooma', 'Nirman Vihar', 'Preet Vihar',\n",
       "       'Gagan Vihar', 'Anand Niketan', 'Pushpanjali Enclave',\n",
       "       'Sector 14 Dwarka', 'Block Wb Ganesh Nagar II', 'Block E',\n",
       "       '72 Sector 23 Road', 'Aurobindo Marg', 'Kirti Nagar',\n",
       "       'Rajpur Road', 'Residential FlatShakurbasti',\n",
       "       'Residential FlatRajouri Garden', '77 Sector 6 Road',\n",
       "       '82 Sector 22 Road', 'Dwarka Road', 'South Anarkali Delhi',\n",
       "       'South Extension Part 1', 'Defence Colony', 'Ashram New',\n",
       "       'Mansarovar garden', 'Golf Links', 'Connaught Place', 'Westend',\n",
       "       'Raju Parak Extension', 'Sector 26 Dwarka', 'Swasthya Vihar',\n",
       "       'Anand Vihar', 'Pontoon Bridge', 'Hastsal',\n",
       "       'Sector 8 Dwarka Block B', 'Transit Camp',\n",
       "       'Residential FlatSeelamPur', 'Rajpur', 'Meenakshi Garden',\n",
       "       'Dwarka sec 6'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dist_plot (feature, color, position=121):\n",
    "    plt.figure(figsize=(15,4))\n",
    "    plt.subplot(position)\n",
    "    sns.boxplot(x=feature, data=df_train, color=color)\n",
    "    plt.subplot(position+1)\n",
    "    sns.distplot(df_train[feature], color=color)\n",
    "dist_plot('Price', 'red')\n",
    "dist_plot('Area', 'grey')\n",
    "dist_plot('PSF', 'teal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_psf_outliers = np.percentile(df_train.PSF, [5,90])\n",
    "price_outliers = np.percentile(df_train.Price, [5,90])\n",
    "area_outliers = np.percentile(df_train.Area, [5,90])\n",
    "# create filters based on outliers\n",
    "price_psf_filter = (df_train.PSF > price_psf_outliers[0]) & (df_train.PSF < price_psf_outliers[1])\n",
    "price_filter = (df_train.Price > price_outliers[0]) & (df_train.Price < price_outliers[1])\n",
    "area_filter = (df_train.Area > area_outliers[0]) & (df_train.Area < area_outliers[1])\n",
    "# apply filters\n",
    "df_train = df_train[(price_psf_filter) & (price_filter) & (area_filter)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dist_plot (feature, color, position=121):\n",
    "    plt.figure(figsize=(15,4))\n",
    "    plt.subplot(position)\n",
    "    sns.boxplot(x=feature, data=df_train, color=color)\n",
    "    plt.subplot(position+1)\n",
    "    sns.distplot(df_train[feature], color=color)\n",
    "dist_plot('Price', 'red')\n",
    "dist_plot('Area', 'grey')\n",
    "dist_plot('PSF', 'teal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Area', 'Location', 'No. of Bedrooms', 'Resale',\n",
       "       'MaintenanceStaff', 'Gymnasium', 'SwimmingPool', 'LandscapedGardens',\n",
       "       'JoggingTrack', 'RainWaterHarvesting', 'IndoorGames', 'ShoppingMall',\n",
       "       'Intercom', 'SportsFacility', 'ATM', 'ClubHouse', 'School',\n",
       "       '24X7Security', 'PowerBackup', 'CarParking', 'StaffQuarter',\n",
       "       'Cafeteria', 'MultipurposeRoom', 'Hospital', 'WashingMachine',\n",
       "       'Gasconnection', 'AC', 'Wifi', 'Children'splayarea', 'LiftAvailable',\n",
       "       'BED', 'VaastuCompliant', 'Microwave', 'GolfCourse', 'TV',\n",
       "       'DiningTable', 'Sofa', 'Wardrobe', 'Refrigerator', 'PSF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.rename(columns={'No. of Bedrooms':'Bedrooms', \"Children'splayarea\":'PlayArea'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_dict = {'MaintenanceStaff':2, 'Gymnasium':4, 'SwimmingPool':4,'LandscapedGardens':3, 'JoggingTrack':3, 'RainWaterHarvesting':2,'IndoorGames':3, 'ShoppingMall':2, 'Intercom':2, 'SportsFacility':3, 'ATM':2, 'ClubHouse':2, 'School':2, '24X7Security':1, 'PowerBackup':4, 'CarParking':3, 'StaffQuarter':0, 'Cafeteria':0, 'MultipurposeRoom':2, 'Hospital':3, 'WashingMachine':0, 'Gasconnection':2, 'AC':0, 'Wifi':0, 'PlayArea':3, 'LiftAvailable':0, 'BED':0, 'VaastuCompliant':0, 'Microwave':0, 'GolfCourse':0, 'TV':0, 'DiningTable':0, 'Sofa':0, 'Wardrobe':0, 'Refrigerator':0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MaintenanceStaff</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gymnasium</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SwimmingPool</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LandscapedGardens</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JoggingTrack</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features  Weight\n",
       "0   MaintenanceStaff       2\n",
       "1          Gymnasium       4\n",
       "2       SwimmingPool       4\n",
       "3  LandscapedGardens       3\n",
       "4       JoggingTrack       3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.DataFrame(feature_dict.items(), columns=['Features', 'Weight'])\n",
    "features.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:5238: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().replace(\n"
     ]
    }
   ],
   "source": [
    "features_matrix = df_train[['MaintenanceStaff', 'Gymnasium', 'SwimmingPool',\n",
    "       'LandscapedGardens', 'JoggingTrack', 'RainWaterHarvesting',\n",
    "       'IndoorGames', 'ShoppingMall', 'Intercom', 'SportsFacility', 'ATM',\n",
    "       'ClubHouse', 'School', '24X7Security', 'PowerBackup', 'CarParking',\n",
    "       'StaffQuarter', 'Cafeteria', 'MultipurposeRoom', 'Hospital',\n",
    "       'WashingMachine', 'Gasconnection', 'AC', 'Wifi', 'PlayArea',\n",
    "       'LiftAvailable', 'BED', 'VaastuCompliant', 'Microwave',\n",
    "       'GolfCourse', 'TV', 'DiningTable', 'Sofa', 'Wardrobe',\n",
    "       'Refrigerator']]\n",
    "features_matrix.replace(9, 0, inplace=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>MaintenanceStaff</th>\n",
       "      <th>Gymnasium</th>\n",
       "      <th>SwimmingPool</th>\n",
       "      <th>LandscapedGardens</th>\n",
       "      <th>JoggingTrack</th>\n",
       "      <th>...</th>\n",
       "      <th>VaastuCompliant</th>\n",
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       "      <th>Sofa</th>\n",
       "      <th>Wardrobe</th>\n",
       "      <th>Refrigerator</th>\n",
       "      <th>PSF</th>\n",
       "      <th>FeatureScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>2</td>\n",
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       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Uttam Nagar</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000000</td>\n",
       "      <td>1350</td>\n",
       "      <td>Sarita Vihar</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>11111.111111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800000</td>\n",
       "      <td>900</td>\n",
       "      <td>Dwarka Mor</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6444.444444</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15800000</td>\n",
       "      <td>1800</td>\n",
       "      <td>Sector 7 Dwarka</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>8777.777778</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area          Location  Bedrooms  Resale  MaintenanceStaff  \\\n",
       "0  10500000  1200  Sector 10 Dwarka         2       1                 0   \n",
       "1   6000000  1000       Uttam Nagar         3       0                 0   \n",
       "2  15000000  1350      Sarita Vihar         2       1                 0   \n",
       "4   5800000   900        Dwarka Mor         3       0                 0   \n",
       "5  15800000  1800   Sector 7 Dwarka         4       1                 0   \n",
       "\n",
       "   Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  ...  \\\n",
       "0          1             0                  0             1  ...   \n",
       "1          0             0                  0             0  ...   \n",
       "2          0             0                  0             0  ...   \n",
       "4          0             0                  0             0  ...   \n",
       "5          1             1                  0             1  ...   \n",
       "\n",
       "   VaastuCompliant  Microwave  GolfCourse  TV  DiningTable  Sofa  Wardrobe  \\\n",
       "0                1          0           0   0            0     0         0   \n",
       "1                0          0           0   0            0     0         0   \n",
       "2                0          0           0   0            0     0         0   \n",
       "4                0          0           0   0            0     0         0   \n",
       "5                1          0           0   0            0     0         0   \n",
       "\n",
       "   Refrigerator           PSF  FeatureScore  \n",
       "0             0   8750.000000            25  \n",
       "1             0   6000.000000             9  \n",
       "2             0  11111.111111             0  \n",
       "4             0   6444.444444             3  \n",
       "5             0   8777.777778            30  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_scores_df = np.dot(features_matrix, features['Weight'])\n",
    "df_train['FeatureScore'] = feature_scores_df\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = df_train[['Area', 'Resale', 'Bedrooms', 'FeatureScore', 'Price','PSF']]\n",
    "sns.heatmap(corr_df.corr(method='pearson'), cmap='RdYlGn_r', linewidths=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Resale</th>\n",
       "      <th>MaintenanceStaff</th>\n",
       "      <th>Gymnasium</th>\n",
       "      <th>SwimmingPool</th>\n",
       "      <th>LandscapedGardens</th>\n",
       "      <th>JoggingTrack</th>\n",
       "      <th>RainWaterHarvesting</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_West End</th>\n",
       "      <th>Location_West Punjabi Bagh</th>\n",
       "      <th>Location_West Sagarpur</th>\n",
       "      <th>Location_Zakir Nagar</th>\n",
       "      <th>Location_greater kailash Enclave 1</th>\n",
       "      <th>Location_i p extension patparganj</th>\n",
       "      <th>Location_mayur vihar phase 1</th>\n",
       "      <th>Location_nawada</th>\n",
       "      <th>Location_param puri</th>\n",
       "      <th>Location_vikaspuri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500000</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000000</td>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800000</td>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15800000</td>\n",
       "      <td>1800</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 276 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area  Bedrooms  Resale  MaintenanceStaff  Gymnasium  \\\n",
       "0  10500000  1200         2       1                 0          1   \n",
       "1   6000000  1000         3       0                 0          0   \n",
       "2  15000000  1350         2       1                 0          0   \n",
       "4   5800000   900         3       0                 0          0   \n",
       "5  15800000  1800         4       1                 0          1   \n",
       "\n",
       "   SwimmingPool  LandscapedGardens  JoggingTrack  RainWaterHarvesting  ...  \\\n",
       "0             0                  0             1                    0  ...   \n",
       "1             0                  0             0                    0  ...   \n",
       "2             0                  0             0                    0  ...   \n",
       "4             0                  0             0                    0  ...   \n",
       "5             1                  0             1                    1  ...   \n",
       "\n",
       "   Location_West End  Location_West Punjabi Bagh  Location_West Sagarpur  \\\n",
       "0                  0                           0                       0   \n",
       "1                  0                           0                       0   \n",
       "2                  0                           0                       0   \n",
       "4                  0                           0                       0   \n",
       "5                  0                           0                       0   \n",
       "\n",
       "   Location_Zakir Nagar  Location_greater kailash Enclave 1  \\\n",
       "0                     0                                   0   \n",
       "1                     0                                   0   \n",
       "2                     0                                   0   \n",
       "4                     0                                   0   \n",
       "5                     0                                   0   \n",
       "\n",
       "   Location_i p extension patparganj  Location_mayur vihar phase 1  \\\n",
       "0                                  0                             0   \n",
       "1                                  0                             0   \n",
       "2                                  0                             0   \n",
       "4                                  0                             0   \n",
       "5                                  0                             0   \n",
       "\n",
       "   Location_nawada  Location_param puri  Location_vikaspuri  \n",
       "0                0                    0                   0  \n",
       "1                0                    0                   0  \n",
       "2                0                    0                   0  \n",
       "4                0                    0                   0  \n",
       "5                0                    0                   0  \n",
       "\n",
       "[5 rows x 276 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_one_hot = pd.get_dummies(df_train, columns=['Location'])\n",
    "train_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PSF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10 Sector Dwarka</th>\n",
       "      <td>6666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Dwarka Sector 9 Road</th>\n",
       "      <td>8301.158301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75 Sector 22 Road</th>\n",
       "      <td>7505.773672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77 Sector 6 Road</th>\n",
       "      <td>4334.365325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78 Sector 6 Road</th>\n",
       "      <td>11574.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i p extension patparganj</th>\n",
       "      <td>11607.378129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mayur vihar phase 1</th>\n",
       "      <td>7700.735992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nawada</th>\n",
       "      <td>5772.095674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param puri</th>\n",
       "      <td>8001.895329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vikaspuri</th>\n",
       "      <td>8611.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   PSF\n",
       "Location                              \n",
       "10 Sector Dwarka           6666.666667\n",
       "3 Dwarka Sector 9 Road     8301.158301\n",
       "75 Sector 22 Road          7505.773672\n",
       "77 Sector 6 Road           4334.365325\n",
       "78 Sector 6 Road          11574.074074\n",
       "...                                ...\n",
       "i p extension patparganj  11607.378129\n",
       "mayur vihar phase 1        7700.735992\n",
       "nawada                     5772.095674\n",
       "param puri                 8001.895329\n",
       "vikaspuri                  8611.111111\n",
       "\n",
       "[235 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_pivot = pd.pivot_table(data=df_train, index='Location', aggfunc='mean', values='PSF')\n",
    "location_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PSF</th>\n",
       "      <th>LocationPremium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Safdarjung Enclave</th>\n",
       "      <td>16666.666667</td>\n",
       "      <td>6.862745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kailash Colony</th>\n",
       "      <td>16363.636364</td>\n",
       "      <td>6.737968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector-D Vasant Kunj</th>\n",
       "      <td>15517.241379</td>\n",
       "      <td>6.389452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector 14 Dwarka</th>\n",
       "      <td>15384.615385</td>\n",
       "      <td>6.334842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector24</th>\n",
       "      <td>15294.117647</td>\n",
       "      <td>6.297578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghitorni</th>\n",
       "      <td>2676.056338</td>\n",
       "      <td>1.101906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector 1 Dwarka</th>\n",
       "      <td>2615.384615</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushant Lok Phase 1 Barakhamba</th>\n",
       "      <td>2555.555556</td>\n",
       "      <td>1.052288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anand Niketan</th>\n",
       "      <td>2500.000000</td>\n",
       "      <td>1.029412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector 1A Dwarka</th>\n",
       "      <td>2428.571429</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         PSF  LocationPremium\n",
       "Location                                                     \n",
       "Safdarjung Enclave              16666.666667         6.862745\n",
       "Kailash Colony                  16363.636364         6.737968\n",
       "Sector-D Vasant Kunj            15517.241379         6.389452\n",
       "Sector 14 Dwarka                15384.615385         6.334842\n",
       "Sector24                        15294.117647         6.297578\n",
       "...                                      ...              ...\n",
       "Ghitorni                         2676.056338         1.101906\n",
       "Sector 1 Dwarka                  2615.384615         1.076923\n",
       "Sushant Lok Phase 1 Barakhamba   2555.555556         1.052288\n",
       "Anand Niketan                    2500.000000         1.029412\n",
       "Sector 1A Dwarka                 2428.571429         1.000000\n",
       "\n",
       "[235 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_pivot['LocationPremium'] = location_pivot['PSF'] / location_pivot['PSF'].min()\n",
    "location_pivot.sort_values('LocationPremium', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
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       "      <th>Bedrooms</th>\n",
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       "      <th>Refrigerator</th>\n",
       "      <th>PSF</th>\n",
       "      <th>FeatureScore</th>\n",
       "      <th>LocationPremium</th>\n",
       "      <th>LogPremium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500000</td>\n",
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       "      <th>1</th>\n",
       "      <td>10500000</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>16200000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10125.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17000000</td>\n",
       "      <td>1900</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>28</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15500000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9687.500000</td>\n",
       "      <td>14</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area          Location  Bedrooms  Resale  MaintenanceStaff  \\\n",
       "0  10500000  1200  Sector 10 Dwarka         2       1                 0   \n",
       "1  10500000  1200  Sector 10 Dwarka         2       1                 0   \n",
       "2  16200000  1600  Sector 10 Dwarka         3       1                 0   \n",
       "3  17000000  1900  Sector 10 Dwarka         3       1                 0   \n",
       "4  15500000  1600  Sector 10 Dwarka         3       1                 0   \n",
       "\n",
       "   Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  ...  GolfCourse  \\\n",
       "0          1             0                  0             1  ...           0   \n",
       "1          1             0                  0             1  ...           0   \n",
       "2          0             0                  0             0  ...           0   \n",
       "3          1             1                  0             1  ...           0   \n",
       "4          0             0                  0             0  ...           0   \n",
       "\n",
       "   TV  DiningTable  Sofa  Wardrobe  Refrigerator           PSF  FeatureScore  \\\n",
       "0   0            0     0         0             0   8750.000000            25   \n",
       "1   0            0     0         0             0   8750.000000            25   \n",
       "2   0            0     0         0             0  10125.000000             0   \n",
       "3   0            0     0         0             0   8947.368421            28   \n",
       "4   0            0     0         0             0   9687.500000            14   \n",
       "\n",
       "   LocationPremium  LogPremium  \n",
       "0         3.705317    1.309769  \n",
       "1         3.705317    1.309769  \n",
       "2         3.705317    1.309769  \n",
       "3         3.705317    1.309769  \n",
       "4         3.705317    1.309769  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.merge(df_train, location_pivot['LocationPremium'], on='Location')\n",
    "df_train['LogPremium'] = np.log(df_train['LocationPremium'])\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1200</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8750.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10500000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8750.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16200000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10125.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17000000</td>\n",
       "      <td>1900</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8947.368421</td>\n",
       "      <td>28</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15500000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Sector 10 Dwarka</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9687.500000</td>\n",
       "      <td>14</td>\n",
       "      <td>3.705317</td>\n",
       "      <td>1.309769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price  Area          Location  Bedrooms  Resale  MaintenanceStaff  \\\n",
       "0  10500000  1200  Sector 10 Dwarka         2       1                 0   \n",
       "1  10500000  1200  Sector 10 Dwarka         2       1                 0   \n",
       "2  16200000  1600  Sector 10 Dwarka         3       1                 0   \n",
       "3  17000000  1900  Sector 10 Dwarka         3       1                 0   \n",
       "4  15500000  1600  Sector 10 Dwarka         3       1                 0   \n",
       "\n",
       "   Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  ...  GolfCourse  \\\n",
       "0          1             0                  0             1  ...           0   \n",
       "1          1             0                  0             1  ...           0   \n",
       "2          0             0                  0             0  ...           0   \n",
       "3          1             1                  0             1  ...           0   \n",
       "4          0             0                  0             0  ...           0   \n",
       "\n",
       "   TV  DiningTable  Sofa  Wardrobe  Refrigerator           PSF  FeatureScore  \\\n",
       "0   0            0     0         0             0   8750.000000            25   \n",
       "1   0            0     0         0             0   8750.000000            25   \n",
       "2   0            0     0         0             0  10125.000000             0   \n",
       "3   0            0     0         0             0   8947.368421            28   \n",
       "4   0            0     0         0             0   9687.500000            14   \n",
       "\n",
       "   LocationPremium  LogPremium  \n",
       "0         3.705317    1.309769  \n",
       "1         3.705317    1.309769  \n",
       "2         3.705317    1.309769  \n",
       "3         3.705317    1.309769  \n",
       "4         3.705317    1.309769  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_train.drop(['Price','MaintenanceStaff', 'Gymnasium', 'SwimmingPool',\n",
    "       'LandscapedGardens', 'JoggingTrack', 'RainWaterHarvesting',\n",
    "       'IndoorGames', 'ShoppingMall', 'Intercom', 'SportsFacility', 'ATM',\n",
    "       'ClubHouse', 'School', '24X7Security', 'PowerBackup', 'CarParking',\n",
    "       'StaffQuarter', 'Cafeteria', 'MultipurposeRoom', 'Hospital',\n",
    "       'WashingMachine', 'Gasconnection', 'AC', 'Wifi', 'PlayArea',\n",
    "       'LiftAvailable', 'BED', 'VaastuCompliant', 'Microwave',\n",
    "       'GolfCourse', 'TV', 'DiningTable', 'Sofa', 'Wardrobe',\n",
    "       'Refrigerator','Location','Resale'], axis=1)\n",
    "y = df_train['Price']\n",
    "#X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "695132.5427246491\n",
      "688930.8984930725\n",
      "687088.256072689\n",
      "690673.3071042865\n",
      "689422.9955912938\n",
      "689161.154906218\n",
      "683405.9222444042\n",
      "703474.5330561837\n",
      "680533.0323567674\n",
      "707491.1127255216\n",
      "Mean Error 691531.3755275087\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188222.21316226595\n",
      "189587.03234344706\n",
      "206054.36934535016\n",
      "197114.2278891609\n",
      "195674.73231686084\n",
      "198044.60821561582\n",
      "166784.0754917422\n",
      "193784.94358899366\n",
      "212327.01978258413\n",
      "181630.17266869548\n",
      "Mean Error 192922.33948047162\n"
     ]
    }
   ],
   "source": [
    "#import forrest Regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = RandomForestRegressor(n_estimators=100, random_state=42,max_leaf_nodes=100)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "460650.87531394407\n",
      "449222.9078555408\n",
      "469320.5012898954\n",
      "450138.80030516617\n",
      "457473.8410001915\n",
      "463097.5412846756\n",
      "450295.8302145749\n",
      "452755.9224627312\n",
      "478119.90255017753\n",
      "445276.7529396971\n",
      "Mean Error 457635.2875216595\n"
     ]
    }
   ],
   "source": [
    "#import decision tree Regression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = DecisionTreeRegressor(random_state=42, max_leaf_nodes=50)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175355.0555186616\n",
      "157869.40124008158\n",
      "182946.8443464207\n",
      "176914.216651389\n",
      "184900.3222798237\n",
      "167584.82065027131\n",
      "158435.03980246824\n",
      "170698.71703315518\n",
      "185496.26513488212\n",
      "165823.31451935516\n",
      "Mean Error 172602.39971765087\n"
     ]
    }
   ],
   "source": [
    "#import gradient boosting Regression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = GradientBoostingRegressor(random_state=42, max_leaf_nodes=50)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "695145.4993426952\n",
      "688925.8398116968\n",
      "687105.255784296\n",
      "690702.1256099308\n",
      "689415.9418597224\n",
      "689173.612587022\n",
      "683432.8516496284\n",
      "703485.077773179\n",
      "680548.3520957079\n",
      "707538.1846178292\n",
      "Mean Error 691547.2741131708\n"
     ]
    }
   ],
   "source": [
    "#import ridge Regression\n",
    "from sklearn.linear_model import Ridge\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = Ridge(alpha=0.1)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95593.01996450755\n",
      "90191.93278615794\n",
      "91378.54747116238\n",
      "101814.1754658385\n",
      "93428.41947648625\n",
      "86062.58163265306\n",
      "92753.0363797693\n",
      "96035.31499556344\n",
      "89198.27129547471\n",
      "91130.16348713398\n",
      "Mean Error 92758.5462954747\n"
     ]
    }
   ],
   "source": [
    "#import XGBoost Regression\n",
    "from xgboost import XGBRegressor\n",
    "errors=[]\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = XGBRegressor(random_state=42)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2341616.37550287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2405435.990553432\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2362446.663231527\n",
      "2374437.036855566\n",
      "Mean Error 2370984.016535849\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rehan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "errors=[]\n",
    "for i in range(4):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
    "    model = MLPRegressor(hidden_layer_sizes=(100,50,20,10), random_state=42)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    error = mean_absolute_error(predictions, y_test)\n",
    "    print(error)\n",
    "    errors.append(error)\n",
    "print(f'Mean Error {np.mean(errors)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2288, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Area</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>PSF</th>\n",
       "      <th>FeatureScore</th>\n",
       "      <th>LocationPremium</th>\n",
       "      <th>LogPremium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>750</td>\n",
       "      <td>2</td>\n",
       "      <td>4666.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2.128878</td>\n",
       "      <td>0.755595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>630</td>\n",
       "      <td>3</td>\n",
       "      <td>3968.253968</td>\n",
       "      <td>0</td>\n",
       "      <td>1.782768</td>\n",
       "      <td>0.578167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3192</th>\n",
       "      <td>800</td>\n",
       "      <td>2</td>\n",
       "      <td>3511.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.291702</td>\n",
       "      <td>0.255961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>540</td>\n",
       "      <td>2</td>\n",
       "      <td>4259.259259</td>\n",
       "      <td>0</td>\n",
       "      <td>1.897019</td>\n",
       "      <td>0.640284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>978</td>\n",
       "      <td>2</td>\n",
       "      <td>5521.472393</td>\n",
       "      <td>0</td>\n",
       "      <td>1.860486</td>\n",
       "      <td>0.620838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area  Bedrooms          PSF  FeatureScore  LocationPremium  LogPremium\n",
       "243    750         2  4666.666667             3         2.128878    0.755595\n",
       "1124   630         3  3968.253968             0         1.782768    0.578167\n",
       "3192   800         2  3511.250000             0         1.291702    0.255961\n",
       "1351   540         2  4259.259259             0         1.897019    0.640284\n",
       "3303   978         2  5521.472393             0         1.860486    0.620838"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42, shuffle=True)\n",
    "model1 =  XGBRegressor(random_state=50)\n",
    "model1.fit(X_train, y_train)\n",
    "predictions = model1.predict(X_test)\n",
    "plt.scatter(x=y_test, y=predictions)\n",
    "plt.xlabel('Actual Test Prices')\n",
    "plt.ylabel('Predicted Test Prices')\n",
    "print(X_train.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pickle\n",
    "#pickle.dump(model1, open('model1.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "091af42eb7049ab4f6a2a6a8451825e7bf1e4d9100eb6688df05c26a5835087c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}