Airbnb_Amsterdam_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import Normalize\n",
    "%matplotlib inline\n",
    "from numpy.random import rand\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = pd.read_csv('listings.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>access</th>\n",
       "      <th>interaction</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_thumbnail_url</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>street</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>minimum_minimum_nights</th>\n",
       "      <th>maximum_minimum_nights</th>\n",
       "      <th>minimum_maximum_nights</th>\n",
       "      <th>maximum_maximum_nights</th>\n",
       "      <th>minimum_nights_avg_ntm</th>\n",
       "      <th>maximum_nights_avg_ntm</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818</td>\n",
       "      <td>https://www.airbnb.com/rooms/2818</td>\n",
       "      <td>20190808195507</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>I'm renting a bedroom (room overlooking the ga...</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi I'm r...</td>\n",
       "      <td>none</td>\n",
       "      <td>Indische Buurt (\"Indies Neighborhood\") is a ne...</td>\n",
       "      <td>From week 38 to week 47 maintenance work to th...</td>\n",
       "      <td>The neighbourhood is well served by 24 hours p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Please: - Leave your shoes in the entrance  - ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/10272854/8...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3159</td>\n",
       "      <td>https://www.airbnb.com/users/show/3159</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>2008-09-24</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>Upon arriving in Amsterdam, one can imagine as...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>Oostelijk Havengebied - Indische Buurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.36575</td>\n",
       "      <td>4.94142</td>\n",
       "      <td>f</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{Internet,Wifi,\"Paid parking off premises\",\"Bu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$59.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1,500.00</td>\n",
       "      <td>$200.00</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>1</td>\n",
       "      <td>$20.00</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>89</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>267</td>\n",
       "      <td>30</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>98.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{Amsterdam,\" NL Zip Codes 2\",\" Amsterdam\",\" NL\"}</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20168</td>\n",
       "      <td>https://www.airbnb.com/rooms/20168</td>\n",
       "      <td>20190808195507</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>Studio with private bathroom in the centre 1</td>\n",
       "      <td>Cozy studio on your own private floor, 100% in...</td>\n",
       "      <td>For those who like all facets of city life. In...</td>\n",
       "      <td>Cozy studio on your own private floor, 100% in...</td>\n",
       "      <td>none</td>\n",
       "      <td>Located just in between famous central canals....</td>\n",
       "      <td>Check-in time from 2pm till 10pm (But it doesn...</td>\n",
       "      <td>No need to use any transport! All is within a ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No curfew, free entrance 27/7 with your own ke...</td>\n",
       "      <td>This studio/room takes entire floor and has it...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/69979664/3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59484</td>\n",
       "      <td>https://www.airbnb.com/users/show/59484</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>2009-12-02</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>Secondary phone nr. + (Phone number hidden by ...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/user/579c8...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/user/579c8...</td>\n",
       "      <td>Grachtengordel</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>Grachtengordel</td>\n",
       "      <td>Centrum-Oost</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>1017</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.36509</td>\n",
       "      <td>4.89354</td>\n",
       "      <td>t</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,Wifi,\"Paid parking off premises\",...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$80.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>124</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>283</td>\n",
       "      <td>67</td>\n",
       "      <td>2010-03-02</td>\n",
       "      <td>2019-07-26</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{Amsterdam,\" NL Zip Codes 2\",\" Amsterdam\",\" NL\"}</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                         listing_url       scrape_id last_scraped  \\\n",
       "0   2818   https://www.airbnb.com/rooms/2818  20190808195507   2019-08-09   \n",
       "1  20168  https://www.airbnb.com/rooms/20168  20190808195507   2019-08-09   \n",
       "\n",
       "                                           name  \\\n",
       "0      Quiet Garden View Room & Super Fast WiFi   \n",
       "1  Studio with private bathroom in the centre 1   \n",
       "\n",
       "                                             summary  \\\n",
       "0           Quiet Garden View Room & Super Fast WiFi   \n",
       "1  Cozy studio on your own private floor, 100% in...   \n",
       "\n",
       "                                               space  \\\n",
       "0  I'm renting a bedroom (room overlooking the ga...   \n",
       "1  For those who like all facets of city life. In...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi I'm r...                none   \n",
       "1  Cozy studio on your own private floor, 100% in...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  Indische Buurt (\"Indies Neighborhood\") is a ne...   \n",
       "1  Located just in between famous central canals....   \n",
       "\n",
       "                                               notes  \\\n",
       "0  From week 38 to week 47 maintenance work to th...   \n",
       "1  Check-in time from 2pm till 10pm (But it doesn...   \n",
       "\n",
       "                                             transit access  \\\n",
       "0  The neighbourhood is well served by 24 hours p...    NaN   \n",
       "1  No need to use any transport! All is within a ...    NaN   \n",
       "\n",
       "                                         interaction  \\\n",
       "0                                                NaN   \n",
       "1  No curfew, free entrance 27/7 with your own ke...   \n",
       "\n",
       "                                         house_rules  thumbnail_url  \\\n",
       "0  Please: - Leave your shoes in the entrance  - ...            NaN   \n",
       "1  This studio/room takes entire floor and has it...            NaN   \n",
       "\n",
       "   medium_url                                        picture_url  \\\n",
       "0         NaN  https://a0.muscache.com/im/pictures/10272854/8...   \n",
       "1         NaN  https://a0.muscache.com/im/pictures/69979664/3...   \n",
       "\n",
       "   xl_picture_url  host_id                                 host_url  \\\n",
       "0             NaN     3159   https://www.airbnb.com/users/show/3159   \n",
       "1             NaN    59484  https://www.airbnb.com/users/show/59484   \n",
       "\n",
       "   host_name  host_since                              host_location  \\\n",
       "0     Daniel  2008-09-24  Amsterdam, Noord-Holland, The Netherlands   \n",
       "1  Alexander  2009-12-02  Amsterdam, Noord-Holland, The Netherlands   \n",
       "\n",
       "                                          host_about host_response_time  \\\n",
       "0  Upon arriving in Amsterdam, one can imagine as...     within an hour   \n",
       "1  Secondary phone nr. + (Phone number hidden by ...     within an hour   \n",
       "\n",
       "  host_response_rate  host_acceptance_rate host_is_superhost  \\\n",
       "0               100%                   NaN                 t   \n",
       "1               100%                   NaN                 f   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...   \n",
       "1  https://a0.muscache.com/im/pictures/user/579c8...   \n",
       "\n",
       "                                    host_picture_url host_neighbourhood  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...     Indische Buurt   \n",
       "1  https://a0.muscache.com/im/pictures/user/579c8...     Grachtengordel   \n",
       "\n",
       "   host_listings_count  host_total_listings_count  \\\n",
       "0                  1.0                        1.0   \n",
       "1                  2.0                        2.0   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0  ['email', 'phone', 'reviews', 'jumio', 'offlin...                    t   \n",
       "1  ['email', 'phone', 'reviews', 'jumio', 'offlin...                    t   \n",
       "\n",
       "  host_identity_verified                                 street  \\\n",
       "0                      f  Amsterdam, North Holland, Netherlands   \n",
       "1                      f  Amsterdam, North Holland, Netherlands   \n",
       "\n",
       "    neighbourhood                  neighbourhood_cleansed  \\\n",
       "0  Indische Buurt  Oostelijk Havengebied - Indische Buurt   \n",
       "1  Grachtengordel                            Centrum-Oost   \n",
       "\n",
       "   neighbourhood_group_cleansed       city          state zipcode     market  \\\n",
       "0                           NaN  Amsterdam  North Holland     NaN  Amsterdam   \n",
       "1                           NaN  Amsterdam  North Holland    1017  Amsterdam   \n",
       "\n",
       "           smart_location country_code      country  latitude  longitude  \\\n",
       "0  Amsterdam, Netherlands           NL  Netherlands  52.36575    4.94142   \n",
       "1  Amsterdam, Netherlands           NL  Netherlands  52.36509    4.89354   \n",
       "\n",
       "  is_location_exact property_type     room_type  accommodates  bathrooms  \\\n",
       "0                 f     Apartment  Private room             2        1.5   \n",
       "1                 t     Townhouse  Private room             2        1.0   \n",
       "\n",
       "   bedrooms  beds  bed_type  \\\n",
       "0       1.0   2.0  Real Bed   \n",
       "1       1.0   1.0  Real Bed   \n",
       "\n",
       "                                           amenities  square_feet   price  \\\n",
       "0  {Internet,Wifi,\"Paid parking off premises\",\"Bu...          NaN  $59.00   \n",
       "1  {TV,Internet,Wifi,\"Paid parking off premises\",...          NaN  $80.00   \n",
       "\n",
       "  weekly_price monthly_price security_deposit cleaning_fee  guests_included  \\\n",
       "0          NaN     $1,500.00          $200.00       $60.00                1   \n",
       "1          NaN           NaN              NaN          NaN                2   \n",
       "\n",
       "  extra_people  minimum_nights  maximum_nights  minimum_minimum_nights  \\\n",
       "0       $20.00               3              15                       3   \n",
       "1        $0.00               1            1000                       1   \n",
       "\n",
       "   maximum_minimum_nights  minimum_maximum_nights  maximum_maximum_nights  \\\n",
       "0                       3                      15                      15   \n",
       "1                       4                    1000                    1000   \n",
       "\n",
       "   minimum_nights_avg_ntm  maximum_nights_avg_ntm calendar_updated  \\\n",
       "0                     3.0                    15.0            today   \n",
       "1                     3.4                  1000.0            today   \n",
       "\n",
       "  has_availability  availability_30  availability_60  availability_90  \\\n",
       "0                t                8               18               43   \n",
       "1                t                0                8               34   \n",
       "\n",
       "   availability_365 calendar_last_scraped  number_of_reviews  \\\n",
       "0                89            2019-08-09                267   \n",
       "1               124            2019-08-09                283   \n",
       "\n",
       "   number_of_reviews_ltm first_review last_review  review_scores_rating  \\\n",
       "0                     30   2009-03-30  2019-08-05                  98.0   \n",
       "1                     67   2010-03-02  2019-07-26                  88.0   \n",
       "\n",
       "   review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                    10.0                       10.0                   10.0   \n",
       "1                     9.0                       10.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                         10.0                    10.0                 10.0   \n",
       "1                         10.0                    10.0                  9.0   \n",
       "\n",
       "  requires_license license                                jurisdiction_names  \\\n",
       "0                f     NaN  {Amsterdam,\" NL Zip Codes 2\",\" Amsterdam\",\" NL\"}   \n",
       "1                f     NaN  {Amsterdam,\" NL Zip Codes 2\",\" Amsterdam\",\" NL\"}   \n",
       "\n",
       "  instant_bookable is_business_travel_ready          cancellation_policy  \\\n",
       "0                t                        f  strict_14_with_grace_period   \n",
       "1                f                        f  strict_14_with_grace_period   \n",
       "\n",
       "  require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                             f                                f   \n",
       "1                             f                                f   \n",
       "\n",
       "   calculated_host_listings_count  \\\n",
       "0                               1   \n",
       "1                               2   \n",
       "\n",
       "   calculated_host_listings_count_entire_homes  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  \\\n",
       "0                                             1   \n",
       "1                                             2   \n",
       "\n",
       "   calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "0                                            0               2.12  \n",
       "1                                            0               2.46  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_columns', None)\n",
    "pd.set_option('max_rows', 100)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20677, 106)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping initial columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some columns do not provide any value to our analysis and therefore are dropped from the outset. An example is urls and scrapping information among others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_drop_cols = ['listing_url', 'scrape_id', 'last_scraped', 'name', 'summary', 'space', 'description', 'neighborhood_overview', 'notes', 'transit', 'access', 'interaction', 'house_rules', 'thumbnail_url', 'medium_url', 'picture_url', 'xl_picture_url', 'host_id', 'host_url', 'host_name', 'host_location', 'host_about', 'host_thumbnail_url', 'host_picture_url', 'host_neighbourhood', 'host_verifications', 'calendar_last_scraped','is_business_travel_ready']\n",
    "df = data.drop(initial_drop_cols, axis = 1)\n",
    "df.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20677, 77)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null vales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experiences_offered                                 0\n",
       "host_since                                         12\n",
       "host_response_time                               8541\n",
       "host_response_rate                               8541\n",
       "host_acceptance_rate                            20677\n",
       "host_is_superhost                                  12\n",
       "host_listings_count                                12\n",
       "host_total_listings_count                          12\n",
       "host_has_profile_pic                               12\n",
       "host_identity_verified                             12\n",
       "street                                              0\n",
       "neighbourhood                                     831\n",
       "neighbourhood_cleansed                              0\n",
       "neighbourhood_group_cleansed                    20677\n",
       "city                                                4\n",
       "state                                             219\n",
       "zipcode                                           886\n",
       "market                                             40\n",
       "smart_location                                      0\n",
       "country_code                                        0\n",
       "country                                             0\n",
       "latitude                                            0\n",
       "longitude                                           0\n",
       "is_location_exact                                   0\n",
       "property_type                                       0\n",
       "room_type                                           0\n",
       "accommodates                                        0\n",
       "bathrooms                                           6\n",
       "bedrooms                                            8\n",
       "beds                                                8\n",
       "bed_type                                            0\n",
       "amenities                                           0\n",
       "square_feet                                     20291\n",
       "price                                               0\n",
       "weekly_price                                    18086\n",
       "monthly_price                                   19269\n",
       "security_deposit                                 6202\n",
       "cleaning_fee                                     3721\n",
       "guests_included                                     0\n",
       "extra_people                                        0\n",
       "minimum_nights                                      0\n",
       "maximum_nights                                      0\n",
       "minimum_minimum_nights                              0\n",
       "maximum_minimum_nights                              0\n",
       "minimum_maximum_nights                              0\n",
       "maximum_maximum_nights                              0\n",
       "minimum_nights_avg_ntm                              0\n",
       "maximum_nights_avg_ntm                              0\n",
       "calendar_updated                                    0\n",
       "has_availability                                    0\n",
       "availability_30                                     0\n",
       "availability_60                                     0\n",
       "availability_90                                     0\n",
       "availability_365                                    0\n",
       "number_of_reviews                                   0\n",
       "number_of_reviews_ltm                               0\n",
       "first_review                                     2593\n",
       "last_review                                      2593\n",
       "review_scores_rating                             2816\n",
       "review_scores_accuracy                           2827\n",
       "review_scores_cleanliness                        2825\n",
       "review_scores_checkin                            2837\n",
       "review_scores_communication                      2830\n",
       "review_scores_location                           2835\n",
       "review_scores_value                              2835\n",
       "requires_license                                    0\n",
       "license                                         20652\n",
       "jurisdiction_names                                 37\n",
       "instant_bookable                                    0\n",
       "cancellation_policy                                 1\n",
       "require_guest_profile_picture                       0\n",
       "require_guest_phone_verification                    0\n",
       "calculated_host_listings_count                      0\n",
       "calculated_host_listings_count_entire_homes         0\n",
       "calculated_host_listings_count_private_rooms        0\n",
       "calculated_host_listings_count_shared_rooms         0\n",
       "reviews_per_month                                2593\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, if all rows or a great majority of of rows that do not provide any meaningful value are missing, they should be dropped.I will keep the weekly and monthly price for now to compare with the daily price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['host_acceptance_rate','neighbourhood_group_cleansed','square_feet','requires_license','license'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20677, 72)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Target Variable from dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the Target Variable 'Price' is separated from the data for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "2818      $59.00\n",
       "20168     $80.00\n",
       "25428    $125.00\n",
       "27886    $150.00\n",
       "28871     $75.00\n",
       "Name: price, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the columns with prices have dollar signs attached to them therefore the data is not numerical. Removing the $-signs and turning them to numerical after filling in missing values with zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_cols = ['price','weekly_price','monthly_price','security_deposit','cleaning_fee','extra_people']\n",
    "for col in price_cols:\n",
    "    df[col] = df[col].str.replace(',', '')\n",
    "    df[col] = df[col].str.replace('$', '')\n",
    "    df[col].fillna(0, inplace=True)\n",
    "    df[col] = df[col].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "2818      59.0\n",
       "20168     80.0\n",
       "25428    125.0\n",
       "27886    150.0\n",
       "28871     75.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = df['price']\n",
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekly_price'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BpLjOuk4pCAAAQCkQrHPUqcc6DIzBiwAAACVBsM5R5K5xVjMfVQ0DljQHAAAoCYJ1jtoNXpTiuawZvAgAAFAOBOsctZtuT4qDNYMXAQAAyoFgnaMocgVtakEqYcDgRQAAgJIgWOfI1X7wYiVk8CIAAEBZEKxz1GnwYiUw1SkFAQAAKAWCdY6iyGVtBy8yKwgAAEBZEKxzFM9jPfH+KqUgAAAApUGwzlHUYbq9kFIQAACA0iBY58g7rLxYCQPVmBUEAACgFAjWOYrc285jXQ1ZIAYAAKAsCNY56lwKEqhGsAYAACgFgnWOOg5eDIwFYgAAAEqCYJ0j9w7T7VEKAgAAUBoE6xxFLoUd5rFm8CIAAEA5EKxz1GnwIkuaAwAAlAfBOkedpttjHmsAAIDyIFjnKJ4VZOL9VZY0BwAAKA2CdY6iFIMX6bEGAAAoh1TB2sxuMLPtZrbDzO4eZ7+Z2T3J/m1mdlWnY83so2b2bNL+i2a2MNm+0syGzOyJ5GtDFjc6EyKXgjZd1pWAGmsAAICy6BiszSyU9DFJN0paI+n9ZrZmTLMbJa1Ovm6XdG+KY78p6c3u/lZJP5T0wZbzPe/uVyZfd0z15mZaFLUvBamEAfNYAwAAlESaHuurJe1w953uPiLpQUnrx7RZL+lTHtskaaGZLWt3rLt/w93ryfGbJK3I4H5mFVf7wYuV0Fh5EQAAoCTSBOvlkna3vN6TbEvTJs2xkvQrkv625fUqM/u+mX3bzK5NcY2zUqfBi5WABWIAAADKopKizXjRcGwanKhNx2PN7EOS6pI+k2zaJ+lCdz9oZj8i6Utmdrm7Hxtz3O2Ky0504YUXdryJmRBFHQYvBoEakXdcoREAAACzX5oe6z2SLmh5vULS3pRt2h5rZrdKepekX3J3lyR3H3b3g8n3j0t6XtKlYy/K3e9z97Xuvra/vz/FbZx98TzWE++vhvHOGjODAAAAFF6aYL1Z0mozW2VmXZJulrRxTJuNkm5JZgdZJ+mou+9rd6yZ3SDpDyS9290Hmycys/5k0KPM7GLFAyJ3TusuZ0jDvcMCMfHHTzkIAABA8XUsBXH3upndJelhSaGk+939KTO7I9m/QdJDkm6StEPSoKTb2h2bnPrPJHVL+mZSBrEpmQHkOkl/ZGZ1SQ1Jd7j7oaxu+GxyV9sSj9Ee6yjSHIVn67IAAACQgzQ11nL3hxSH59ZtG1q+d0l3pj022X7JBO2/IOkLaa5rtkszeFESi8QAAACUACsv5ijqVAoSxh8/y5oDAAAUH8E6Rx0HL9JjDQAAUBoE6xxFHabRqzR7rAnWAAAAhUewzlHkHVZebPZYUwoCAABQeATrnETxtNztBy+GzWBNjzUAAEDREaxzkuRqBW2SdSWZx7rWoMcaAACg6AjWORntsW7TplkKwgIxAAAAxUewzkkzWLcfvMiS5gAAAGVBsM5JmlKQ6uisIJSCAAAAFB3BOidR1HnwYkgpCAAAQGkQrHPS7INuVwpSbZaCEKwBAAAKj2CdkzQ91s1ZQSgFAQAAKD6CdU5OzWM9cbIOA+axBgAAKAuCdU5GBy+26bGusqQ5AABAaRCsc5Kmx/rUyouUggAAABQdwTonzeqOtvNYN0tB6LEGAAAoPIJ1Tk71WE/cptIsBaHHGgAAoPAI1jlJUwpSDVh5EQAAoCwI1jlJM3iRBWIAAADKg2Cdk3SDF+OPv8Y81gAAAIVHsM5JmsGLzZUX6bEGAAAoPoJ1TjzF4EUWiAEAACgPgnVOmlk5aJOsqwGlIAAAAGVBsM5Js8a6TSWIgsAUGKUgAAAAZUCwzsno4EW1SdaSKkHAdHsAAAAlQLDOSXPNl3alIFK8rHmdUhAAAIDCI1jnJM3gRSle1pzBiwAAAMVHsM5Jmun2pHgua5Y0BwAAKD6CdU6iyfRYU2MNAABQeATrnKRZeVGSqmFAKQgAAEAJEKxz4qOlIO3bhQGDFwEAAMqAYJ2TZo912LHG2lSjxxoAAKDwCNY5ST14MTA1qLEGAAAoPIJ1TtJPt8esIAAAAGVAsM5JI/XgRWPlRQAAgBIgWOdkMoMXG9RYAwAAFB7BOifpBy8GqjErCAAAQOERrHOSdvBiNWRJcwAAgDIgWOck7eDFMGCBGAAAgDIgWOekmZWDDsm6ygIxAAAApUCwzkmzxrrT4MVKaKozKwgAAEDhEaxzEqWcbo95rAEAAMohVbA2sxvMbLuZ7TCzu8fZb2Z2T7J/m5ld1elYM/uomT2btP+imS1s2ffBpP12M7t+ujc5E5pZuUOHddxjTY01AABA4XUM1mYWSvqYpBslrZH0fjNbM6bZjZJWJ1+3S7o3xbHflPRmd3+rpB9K+mByzBpJN0u6XNINkj6enKdQ3F2BpVnSPKAUBAAAoATS9FhfLWmHu+909xFJD0paP6bNekmf8tgmSQvNbFm7Y939G+5eT47fJGlFy7kedPdhd39B0o7kPIUSeedQLUmVwCgFAQAAKIE0wXq5pN0tr/ck29K0SXOsJP2KpL+dxPvNelHSY90JgxcBAADKIU2wHi8ejk2CE7XpeKyZfUhSXdJnJvF+MrPbzWyLmW05cODAOIfMrLgUpHOyrrLyIgAAQCmkCdZ7JF3Q8nqFpL0p27Q91sxulfQuSb/kzRVV0r2f3P0+d1/r7mv7+/tT3MbZFXnnGUEkKQxMDQYvAgAAFF6aYL1Z0mozW2VmXYoHFm4c02ajpFuS2UHWSTrq7vvaHWtmN0j6A0nvdvfBMee62cy6zWyV4gGRj03jHmdE5N5xDmspLgWpEawBAAAKr9KpgbvXzewuSQ9LCiXd7+5Pmdkdyf4Nkh6SdJPigYaDkm5rd2xy6j+T1C3pm8kgv03ufkdy7s9Jelpxicid7t7I7I7PkrQ91tUgYOVFAACAEugYrCXJ3R9SHJ5bt21o+d4l3Zn22GT7JW3e78OSPpzm2mYrTzl4MQxMkUtR5B2XPwcAAMDsxcqLOYlSD16M27BIDAAAQLERrHMSz2PduV0ljH8EzGUNAABQbATrnKTtsa4E9FgDAACUAcE6J2kHL44GaxaJAQAAKDSCdU489XR7SSkIM4MAAAAUGsE6J1HkClPM8kEpCAAAQDkQrHMy6cGLlIIAAAAUGsE6J67JTbdXY1YQAACAQiNY5ySK0g1ebJaLNCgFAQAAKDSCdU6itIMXg/hHUGPwIgAAQKERrHOSdrq90ZUXqbEGAAAoNIJ1TuIFYjq3C5kVBAAAoBQI1jnxlCsvVpnHGgAAoBQI1jmZ9MqL9FgDAAAUGsE6J6kHL4YEawAAgDIgWOfEU/dYUwoCAABQBgTrnKQdvNjssa4xKwgAAEChEaxzEpeCpO+xZoEYAACAYiNY5yQevNi53akaa0pBAAAAioxgnZMo7XR7ozXW9FgDAAAUGcE6J2kHL4b0WAMAAJQCwTonkbuCFJ9uNWDwIgAAQBkQrHMSRSkHL4YMXgQAACgDgnVO0g5eDEd7rCkFAQAAKDKCdU5cKQcvsvIiAABAKRCscxJFKQcvJj3WlIIAAAAUG8E6J/ECMZ3bNafboxQEAACg2AjWOUk73V4QmAJjHmsAAICiI1jnJF4gJl3bShBQYw0AAFBwBOscuLtcSjXdnhQva16nFAQAAKDQCNY5aHY+pykFkaRKYPRYAwAAFBzBOgeRxyE5dSlIGLCkOQAAQMERrHPgU+mxZvAiAABAoRGsczDZHutqGKhGsAYAACg0gnUOmsE67eDFMDA1KAUBAAAoNIJ1DkYHL6bssq6EphqDFwEAAAqNYJ2DSZeCBAHT7QEAABQcwToHo4MXNZlSEHqsAQAAioxgnYPRHuuUn241NAYvAgAAFBzBOgdRNLnBi8xjDQAAUHwE6xycmsc6XfuQeawBAAAKj2Cdg1ODF9Ml62rIkuYAAABFlypYm9kNZrbdzHaY2d3j7DczuyfZv83Mrup0rJm9z8yeMrPIzNa2bF9pZkNm9kTytWG6N3m2NTNy6lIQZgUBAAAovEqnBmYWSvqYpJ+StEfSZjPb6O5PtzS7UdLq5OsaSfdKuqbDsU9K+jlJfz7O2z7v7ldO/bZm1mSn26sE9FgDAAAUXZoe66sl7XD3ne4+IulBSevHtFkv6VMe2yRpoZkta3esuz/j7tszu5NZ5FSNdfoFYqixBgAAKLY0wXq5pN0tr/ck29K0SXPseFaZ2ffN7Ntmdu14DczsdjPbYmZbDhw4kOKUZ8+ke6zDQDVmBQEAACi0NMF6vHg4tnt1ojZpjh1rn6QL3f1tkn5f0gNmNv+Mk7jf5+5r3X1tf39/h1OeXc1gnb7GmgViAAAAii5NsN4j6YKW1ysk7U3ZJs2xp3H3YXc/mHz/uKTnJV2a4jpnjWiypSBBQCkIAABAwaUJ1pslrTazVWbWJelmSRvHtNko6ZZkdpB1ko66+76Ux57GzPqTQY8ys4sVD4jcOam7mmGTLQWJV16kFAQAAKDIOs4K4u51M7tL0sOSQkn3u/tTZnZHsn+DpIck3SRph6RBSbe1O1aSzOy9kv5UUr+kr5nZE+5+vaTrJP2RmdUlNSTd4e6HsrzpvE128GJIKQgAAEDhdQzWkuTuDykOz63bNrR875LuTHtssv2Lkr44zvYvSPpCmuuarSbfYx3QYw0AAFBwrLyYg6kMXmQeawAAgGIjWOdgtBQkZZd1yJLmAAAAhUewzkGzXjp1KQhLmgMAABQewToHzb7n1KUgoSlyKaLXGgAAoLAI1jmY9MqLSUPKQQAAAIqLYJ2DaLQUJG2PdfxjqLOsOQAAQGERrHMw2Xmsmz3WNVZfBAAAKCyCdQ6mWgrCIjEAAADFRbDOQTMfpx+8mJSCMDMIAABAYRGsczD5lReTUhB6rAEAAAqLYJ0D98kNXgyD+MfQoMYaAACgsAjWOYgmOXjxVI81pSAAAABFRbDOweQHLzZrrOmxBgAAKCqCdQ4mO3gxHF0ghh5rAACAoqrM9AWUkafosX7g0V2j3z+775gk6Wvb9mnr7qPjtv/Fay7M7gIBAACQOXqsczBaCpKyFiRgHmsAAIDCI1jnYLQUJGX75iBHcjUAAEBxEaxzELnLNPkaa3qsAQAAiotgnQP39GUgkpTMtjdaQgIAAIDiIVjnIIo89VR70qkQHtFjDQAAUFgE6xxE7qnLQKRTNdYNeqwBAAAKi2Cdg0jpF4eRWnqsydUAAACFRbDOQVwKMpkaawYvAgAAFB3BOgfu6WcEkU7NCkKNNQAAQHERrHMQuY/O9JFGwKwgAAAAhUewzkE0yR7r0ZUXCdYAAACFRbDOgfvkptujxhoAAKD4CNY5aPjkBi+ypDkAAEDxEaxzwOBFAACA1x+CdQ6iSZaCBMlPgRprAACA4iJY5yByTa0UhB5rAACAwiJY52CygxcDM5nosQYAACgygnUOIvfRKfTSCgOjxxoAAKDACNY5iFyaXKyOe63J1QAAAMVFsM5BNMnp9qR4ACPzWAMAABQXwToH7pp8KYgZNdYAAAAFRrDOQRS5JtlhrYAaawAAgEIjWOdgKqUgoZkieqwBAAAKi2CdA5cmNd2eFPdYU2MNAABQXATrHETRFAYvMisIAABAoRGsczDZlRclqUKPNQAAQKGlCtZmdoOZbTezHWZ29zj7zczuSfZvM7OrOh1rZu8zs6fMLDKztWPO98Gk/XYzu346NzgTIp/84MWeaqChWiOfCwIAAEDuOgZrMwslfUzSjZLWSHq/ma0Z0+xGSauTr9sl3Zvi2Ccl/Zyk74x5vzWSbpZ0uaQbJH08OU9hTKXHurerooHhek5XBAAAgLyl6bG+WtIOd9/p7iOSHpS0fkyb9ZI+5bFNkhaa2bJ2x7r7M+6+fZz3Wy/pQXcfdvcXJO1IzlMY7j7pwYt93aEGR+ixBgAAKKo0wXq5pN0tr/ck29K0SXPsVN5vVpvKdHt9XRUNjtSZcg8AAKCg0gTr8RLi2PQ3UZs0x07l/WRmt5vZFjPbcuDAgQ6nPLsil2yypSDdFUUuDdeinK4KAAAAeUoTrPdIuqDl9QpJe1O2SXPsVN5P7n6fu69197X9/f0dTnl2TakUpCsuIx8Yoc4aAACgiNIE682SVpvZKjPrUjywcOOYNhsl3ZLMDrJO0lF335fy2GdO9VkAAB8ESURBVLE2SrrZzLrNbJXiAZGPTeKeZlxjCoMX+7orksQARgAAgIKqdGrg7nUzu0vSw5JCSfe7+1Nmdkeyf4OkhyTdpHig4aCk29odK0lm9l5JfyqpX9LXzOwJd78+OffnJD0tqS7pTncv1Kg+n8J0e71JjzUDGAEAAIqpY7CWJHd/SHF4bt22oeV7l3Rn2mOT7V+U9MUJjvmwpA+nubbZKHJXMMlaEHqsAQAAio2VF3MQ+eQ/2L6uJFjTYw0AAFBIBOsc+BR6rLsqgaqhaZAeawAAgEIiWOdgKisvSsnqi8wKAgAAUEgE6xxE0eQHL0rxlHsDw5SCAAAAFBHBOmPuLtfUeqz7uumxBgAAKCqCdcaaS0ROdoEYKZ5yj+n2AAAAiolgnbEoiqP1lHusGbwIAABQSATrjCW5esqDF4frkeqNKOOrAgAAQN4I1hmLPE7WUxq82M3qiwAAAEVFsM6YT6PH+tQiMZSDAAAAFA3BOmPNHuspDV5MeqyZcg8AAKB4CNYZO1UKMvUe60F6rAEAAAqHYJ2x6Qxe7OtOSkGYGQQAAKBwCNYZ82mUgsypJqUgDF4EAAAoHIJ1xqbTYx0GpjnVkB5rAACAAiJYZ2x08OIUP9m+blZfBAAAKCKCdcamM3hRigcwMt0eAABA8RCsMzadUhBJ6u2uaJDp9gAAAAqHYJ2x6QxelKS+rpAeawAAgAIiWGdsuj3WfUmPdTOgAwAAoBgI1hmLomaN9dSO7+0K1XDXcD3K8KoAAACQN4J1xk6Vgky9x1pikRgAAICiIVhnrDHdUpAuFokBAAAoIoJ1xtynWwoS91gP0mMNAABQKATrjGUxeFESM4MAAAAUDME6Y1EG0+1J0gBzWQMAABQKwTpj0x282FUJVAlMg/RYAwAAFArBOmPTLQUxM/V2hfRYAwAAFAzBOmPRNAcvSnGdNTXWAAAAxUKwzthoj/VUi6wl9XVVmMcaAACgYAjWGRutsZ7GOXq7Qw0yjzUAAEChEKwzFk1z8KKU9FhTCgIAAFAoBOuMZVEK0tsd6mQtUqN5MgAAAMx6BOuMTXflRSnusZbElHsAAAAFQrDOWBTFf06rFKS5+iJT7gEAABQGwTpjkaa38qIk9TZXX6THGgAAoDAI1hmLomYpyPQGL0piZhAAAIACIVhn7NTKi1M/R1930mPNXNYAAACFQbDOmGcw3V5v0mNNKQgAAEBxEKwzdqrHeurBOgxMPdWAwYsAAAAFQrDOWJTBdHtSXGfNdHsAAADFQbDOWBY91lI8M8ggPdYAAACFkSpYm9kNZrbdzHaY2d3j7DczuyfZv83Mrup0rJktNrNvmtlzyZ+Lku0rzWzIzJ5IvjZkcaNny6klzad3nr5uljUHAAAoko7B2sxCSR+TdKOkNZLeb2ZrxjS7UdLq5Ot2SfemOPZuSd9y99WSvpW8bnre3a9Mvu6Y6s3NBHeXaXrT7UlxKQizggAAABRHmh7rqyXtcPed7j4i6UFJ68e0WS/pUx7bJGmhmS3rcOx6SZ9Mvv+kpPdM815mhcinXwYixVPuDYw0RmcZAQAAwOyWJlgvl7S75fWeZFuaNu2OPdfd90lS8uc5Le1Wmdn3zezbZnbteBdlZreb2RYz23LgwIEUt3F2RO7THrgoxVPuNSLXSD2a/skAAACQuzTBeryYOLYbdaI2aY4da5+kC939bZJ+X9IDZjb/jJO43+fua919bX9/f4dTnj3uUjDdAmu1LBLD6osAAACFkCZY75F0QcvrFZL2pmzT7tj9SbmIkj9flSR3H3b3g8n3j0t6XtKlaW5mNojcpz1wUTq1rDl11gAAAMWQJlhvlrTazFaZWZekmyVtHNNmo6RbktlB1kk6mpR3tDt2o6Rbk+9vlfRlSTKz/mTQo8zsYsUDIndO+Q7PsshdNm5H/eT0dsfBmrmsAQAAiqHSqYG7183sLkkPSwol3e/uT5nZHcn+DZIeknSTpB2SBiXd1u7Y5NQfkfQ5M/tVSbskvS/Zfp2kPzKzuqSGpDvc/VAmd3sWRFmVgnRRCgIAAFAkHYO1JLn7Q4rDc+u2DS3fu6Q70x6bbD8o6SfG2f4FSV9Ic12zkWdVCtJNKQgAAECRsPJixqIom+n2uiuB+rpC7T82nMFVAQAAIG8E64xlNXjRzLR80RztOTw4/ZMBAAAgdwTrjMXzWGeQrCWtWNSrA8eHNVynzhoAAGC2I1hnLF55MZtzrVg0Ry5p75GT2ZwQAAAAuSFYZywevJhdj7UkykEAAAAKgGCdsbjHOptgPbe7ooVzqtpzeCiT8wEAACA/BOuMZTV4sWn5ojl6+QjBGgAAYLYjWGfMXZkNXpTicpBDAyM6NDCS2TkBAACQPYJ1xrLusV6xaI4kadueI9mdFAAAAJkjWGcsynDwoiQtXzhHJmnbnqOZnRMAAADZI1hP0XC9oX/554/os4/tOm17lHEpSE811NK53fRYAwAAzHIE6ynqroT64f7j+sHLp/ckZ10KIsXlIFv3HJW7Z3tiAAAAZIZgPQ0XLenTSwcHTtvmLgUZJ+vli+bowPFhvXKMhWIAAABmK4L1NKxc0qsXXzt98ZZ8eqzjhWK27qbOGgAAYLYiWE/DRUv6tPfokIbrjdFtkbtM2SbrZQt6VAmMOmsAAIBZjGA9DauW9sld2n3oVK91HqUg1TDQZefNY2YQAACAWYxgPQ0XLYlLNFrLQRpR9qUgknTFBQu1bc8RRREDGAEAAGYjgvU0rFzSJ0l6sWUAY9YrLzZdsWKBjp2sn/ZeAAAAmD0I1tOwqK9LC+ZUTwu7eQxelKS3rlgoiYViAAAAZiuC9TStXNKrlw6eKgXJeuXFptXnzFVPNdBWBjACAADMSgTrabpoSd8ZpSB5BOtKGOjN5y+gxxoAAGCWIlhP08olvXr58JDqUSQpv1IQKS4HeWrvUdUbUT5vAAAAgCkjWE/TyqV9ilw6PFCTJEU5DV6UpCsuWKCTtUjPvnI8l/MDAABg6gjW03RRMjPIwYFhSfn2WL/9DUtUCUwbt+7N5w0AAAAwZQTraVqZzGV98MSIpPxqrCXpnHk9uv7y8/S5Lbt1stbofAAAAADOGoL1NC3u69K87spZ6bGWpA+8/SIdGazRaw0AADDLEKynycy0cmnfaI91XtPtNV2zarEuPXeuPr3ppdzeAwAAAJNHsM7ARUt6dXDgVClIXoMXpfjcH1h3kbbtOaondjOnNQAAwGxBsM7AyiV9OjI4okbkuZeCSNJ73rZcfV2h/voReq0BAABmC4J1Bkan3BscUeRSkHOyntdT1XuvWq6vbNurQ0lPOQAAAGYWwToDzZlBXjseD2DMsRJk1AfWrdRIPdJ/37I7/zcDAACYAe6uT296SfuODs30paRSmekLKIPmXNYHTsTBOo/Biw88uuuMbSuX9GnDt59XX3fljPf8xWsuzPwaAAAAzqZte47q337pST2970L9h/e+ZaYvpyN6rDOwdG6XuiqBDhzPL1iPZ93Fi3V4sKYf7mclRgAAUD5feuJlSdJDP9inkXo0w1fTGcE6A2ampX1dLT3WZ+d915w/X/O6K3rk+YOK3M/OmwIAAJwFjcj1la37dO78bh0ZrOk7Pzww05fUEcE6I4vndo/WWJ+tHutKEOjqixfruVdP6E8eekaff3yPnnz5qIbrrMoIAACK7ZHnD+q1E8P60M+s0aLeqr5cgMXxqLHOyJK+Lj35chxoz1KuliS987JztLSvW8+8ckxP7zuq7+06rDAw/XD/cX34PW/JfYYSAACAPHz5iZc1r7uin15zrh574aA+//geDQzX1dc9e+Pr7L2yglnS1zX6/dnqsW6+1xUXLNQVFyxUI3K9dHBAT+w+os8+tlsrFvXqzndectauBQAAIK3hekMnRyIt6K2ese9kraGvP/mKrn/zeeqphlp/5XJ9etMufePpV/Tet62YgatNh1KQjCyZ2z36/dkM1q3CwHRx/1y9923L9e4rztf/843t+qcdr83ItQAAAEwkilwf+MRj+sn/+u1x1+T4h2df1fHhutZfeb4k6UcuXKTlC+foy0/M7nIQgnVGlsxt7bGewQtRPJjyT37uLbq4f65++8Hva/+xkzN7QQAAAC0++ciLeuyFQzpwfFj/7itPnbH/y0/s1dK53Xr7xUskxYvvvfvK8/WPz72mg8lkEbMRwToj87or6grjj9NmqMe6VV93RRt++SoNjjR01wPfU60x+6eoAQAAxXey1tBXtu7V0Mj4kym8dHBA/+nr2/WOy/r1uz+5Wl9+Yq+++fT+0f1Hh2r6++2v6mevWKZKeCqqrr/yfDUi10M/2Jf7PUwVwTojZjbaaz3TPdZNl5wzT3/yc2/R5hcP66MPb5/pywEAACU3MFzXr35ys37rs9/XLfc/qmMna6ftjyLXH3xhmypB/H/Xf/Mdl+iN583Th774Ax0djNs+/OQrGqlHWn/l8tOOfeN583XZufP0pVlcDpIqWJvZDWa23cx2mNnd4+w3M7sn2b/NzK7qdKyZLTazb5rZc8mfi1r2fTBpv93Mrp/uTZ4ti/uawXrmk/UDj+7SA4/u0sBwQ+suXqz7vrNT79vwiG75xGO67S8369c+uVl3/PXj+g9fe0af3vTShOc5PDCiHa+yAA0AAGXm7hocqbdt8/hLh/Uz9/yj3n/fJu149cQZ+48MjuiXP/GoNu08pFvffpGe2H1E779vk15rKd34zGO7tGnnIX3oZ96kZQvmqKsS6D+/7wodHBjRH3/taUnSl7e+rIuW9OqKFQvOeI/1bztfj790WLsPDU7zjvPRcVYQMwslfUzST0naI2mzmW1096dbmt0oaXXydY2keyVd0+HYuyV9y90/kgTuuyX9gZmtkXSzpMslnS/p78zsUnef9ZMzL+mLBzDOglx9mpvevEyHB2rauvuIGu6KIlfrcjIL5lT1wmsDetdbl+mKFQv13Ksn9K1n9+vvn3lV39t1WJFLV69arLveeYmuXb30tFKX104M67OP7tKDm3erqxLon1/ar3dc1q91Fy9RTzU8+zcLAMDrxO5Dgzo0MKLLz59/WslEk7vrBy8f1d89vV/LF83RDZcvO2MGjpF6pK9u26v7vrNT2/cf10+vOVe3X3exfuSixaNtjp2s6aNf365PP/qSzp3Xoz2Hh3TTf/tH/cY73qDffOcb1F0J9erxk7rlE49p54EBffyXrtL1l5+nd77xHN3x6cf1LzY8ok//2jWK3PWRh57RtauX6l/+6AWj53/z8gX6jX/+Bv3ZP+zQ1SsX65+eP6jfeucl45bW/uxbz9d/+vp2bdy6d1bOfGbeYcU+M3u7pP/b3a9PXn9Qktz9T1ra/Lmk/+nun01eb5f0DkkrJzq22cbd95nZsuT4y8ae38weTs7xyETXuHbtWt+yZcsUbj87Dzy6S5tfPKQvfv9l/eLVF+rNy8/8V9Zs4e6KPP6Pafv+4/rBniN6/sCARhqRertCDSY1UW9ZvkDvfOM5mtdd0Se++4JeOXZSb12xQHe+8xKdN79Hn3zkRX116z6NNCJdu3qpwsD0yPMHNVyP1FMNdM2qJVq2oEc91VDdlUDd1VBzqqHOnd+t8xb0aNmCOTpvfo/MpB2vntDT+47p2X3H9cy+Y2q4603nzdObls3Xm5bN12XnzVPkrj2Hh7T70KD2HB7SvqMntbivqhWLenXBol6tWDRHC3urOjZU1yvHTmr/sZN65dhJnaw1dM68bp07v0fnzu9R/7xuhWY6OlTTocERHR4Y0eHBmroqgRb3dmnx3C4t7u3SnK5Qw/WGjgzWdHhwRIcHahocqWvBnKoW9nZpUW9VC+ZUFQamgZGGjgyO6MhgTUcGazKL/8GyqK9LC+dU1dsVyl06PlzX8ZM1HT9Z18BwXXO6Qs3vqWp+T1VzeyoKA9NIPRptc/xkXfUo0ryequb3VDSvp6qeavzLc6jW0ImTdR1LztVVCTS3u6L5PVX1dYeqhIGGRho6ODCsQwMjOjgwoqGRhhb2VrWkr1uL++J7qISBhusNDQw3NDBc18BIXYGZ+rormttVGT2Xu2u4HmlwJG7X/Dn3dlXU2xX/jM1MUdRsV9dQrSF3aU5X/LPvqYYKk1qpKHKdrDd0shZpuN5QJQjUUw3UUw1VbfkLotaINFyPNFxrqBG5uiuhuquBusJgdJ721nOdrDUUBqaeSqierrhd85dzrRHvP1mLVGtE6qrE79dTCUb/Umre58laQ8P1SO4ava7mPUrximDxuRoaaUTjXr+7q9ZoXltDURSfq7sSn6v1+lvfMwxM3dVAPZVQ1dDGvf56FKkrTK5/zOfaPNdQraHA7Izrj3umGjoxXNeJ4bqGRhrqqYaa11NRX3dFvdVQQWCjn8VwLdLJekORe/y5Vs+8/pP1hoZr0ej1N9+zEpy6/nrzZ1mPr787jH+WrZ/rSD3SQHJdgyMNhUE8ZqS3q6K53ZXR+6w1Ig3VGjo5En/+XZVg9BmrjvlZDo3En4Uk9SS/h1qvv56ca6gW30M1TM7V8vy0Pv9DtYYaDVdPV9xuTjUcfX7qjUiDtUb8niPxs9jbFY7+N9B6roHkHkcakeZUw9F2zfdsnmtwuKGBpCexr6ui3u5QvdVT/12erEU6MRz/HhiqxT/Lvu4wbtsVjp5rYLih48M1nRiuq95wze2uaG5P/Ll2V4LR31HHhmo6OlTTsZM1dVcCLZhT1fw58e+77kqoWiNKfj/VdGyoruF6Q3N74t8985LzSdLASENHh2o6Mjiio0M1VYL4XPHv0Kp6qvG5Die/Ow8PjOjEcF3zeqpa1Bv/nl3YW1UlMB0fruvQiREdHBjWwRMjity1uK9bS+Z2aUlfl+b3VFWPXAcHhnXgePx18MSI5vZU1D+vW/1zu3XO/G7NqYY6OlTTK8dO6pWj8dfgSEPnzO/WsgXx3xHnzOuRJO07OqSXDw9pz5Eh7T0ypJ5qqOUL52j5ojlasXCOls7t1sGBEb3w2oB2HjihF14b0KvHh7V84Rxd3N+nVUv7dPHSuequBtrx6gn9cP9xbX/luLbvj/8v8GXnztNl583TpefO0yXnzNVrJ4a1dfdRbd1zRE/sOqLt+4/rwsW9ujKZUvfKCxbqvAU9euyFg/r29gP6znOv6YXXBiRJ83sq+merl+q61f267tJ+HTtZ01e27tVXtu7Trpae3Wpoum51v951xTKtu3iJNj6xV3/5v17UK8dOavU5c/Vjb1iiLz2xV0eHarrqwoW6/bqLJUn/18an9OrxYd369pX619dfpqGRhv74q09r49a9ekN/n37vpy7VRx/ergPHh/X/3bJWP37J0tH33PziIf3KX27W/DlVLVvQo2f2HdPDv3edVizqPS2bDNcb+tk//a6ee/WE3KW/+/3rdMk588bNMb9w7z/p+Mm6Hv6968bdnzcze9zd1467L0Ww/gVJN7j7ryWvPyDpGne/q6XNVyV9xN2/m7z+lqQ/UBysxz3WzI64+8KWcxx290Vm9meSNrn7p5Ptn5D0t+7++YmucbYE652vndBf/OML+sC6i/SmZfNn9Homa2ikoWdeOaaXDg5oxaJeXXbuPM2fc+pftfVGpO/vOqJvP3dgdFqcrkqgqy5cpLdfvET98+Le+loj0s4DA/rh/uN6/sAJDdUaqjdcUfIXyXjMpOZj2FMNdNl58xWatP2V4xoYaZzRpqkSmOrR6RvDwNSIOj3Tkknq0EzV0FRrdF4qfrzrGO9c9cjPuIexusJAIx0GmlYCk0sd7zPNuczi83W6z65KoHojavuZBSZVw2DCn3PrueRqe21hYKOff7v77KrEYWakzXuaSd2VoOO5qqEpMOt4/d2VQFESmDtd/0i9/WeW5rMILG6X5vrNrO1n0bz+Woefpdmp56fdM5vl9ac5V/P661H7c1UCUxhk87NM/VwnwTrN9Xf6XCuBKQg6/yzT/HdpyfV3Olc1jH93dvq9mOb3SmBxSWSa34tZ/Y5N87s/7bmanaSdfl8HdvrfI11hoKVzu7T/+PBp19L691dXGOgN58yVJD3/6olxP8uuSqDLz5+vN543Ty8dHNS2PUd1Yvj0Eo2eaqB1Fy/Rdav7tXRet7773AF954ev6ZWWWcDCwPRjb1iin33r+frpy8/VrkOD+srWvfratn3ae/RUux97wxL9+nUX6x2X9svMNDhS13/fskd/8d2d2n1oSJL0pmXz9Sc/9xZdecHC067jf25/Vf/2S09qz+Ehze+p6K9+5WpddeEijfXky0d16/2PxeUe73mzPrDuonE/0627j+i9H/9fetOy+frab187bhtJ+utHXtT/+eWn9Le/c+2M5K12wTrNAjHjFTaMfdwmapPm2Km8n8zsdkm3Jy9PJD3gM2mppNck6d/P8IWcTc9J+puMzznTP8gcjD4bwDh4PtAOz0eBPHcW22yX9MkOz8dOSZ/ucJ6XJH02RZuv/27na/qRf9e5zS3/UbqlQ5sXJNnvdD7Xmv/YuU1Oxv+XgdIF6z2SLmh5vULS2OGYE7XpanPsfjNb1lIK8uok3k/ufp+k+1Jc/1lhZlsm+tcLXt94NtAOzwfa4flAOzwfs0+aWUE2S1ptZqvMrEvxwMKNY9pslHRLMjvIOklH3X1fh2M3Sro1+f5WSV9u2X6zmXWb2SrFAyIfm+L9AQAAAGdFxx5rd6+b2V2SHpYUSrrf3Z8yszuS/RskPSTpJkk7JA1Kuq3dscmpPyLpc2b2q5J2SXpfcsxTZvY5SU9Lqku6swgzggAAAOD1rePgRaRjZrcn5SnAaXg20A7PB9rh+UA7PB+zD8EaAAAAyABLmgMAAAAZIFhPU6fl3lE+ZnaBmf2DmT1jZk+ZxZMCmdliM/ummT2X/Lmo5ZgPJs/IdjO7vmX7j5jZD5J999h4y0yhkMwsNLPvJ/P883xglJktNLPPm9mzye+Rt/N8QJLM7PeSv1eeNLPPmlkPz0axEKynwU4t2X6jpDWS3m/xkuwot7qk/93d3yRpnaQ7k5/73ZK+5e6rJX0rea1k382SLpd0g6SPJ8+OJN2reD721cnXDWfzRpCr35H0TMtrng80/TdJX3f3N0q6QvFzwvPxOmdmyyX9tqS17v5mxZM+3CyejUIhWE/P1ZJ2uPtOdx+R9KCk9TN8TciZu+9z9+8l3x9X/JficsU/+08mzT4p6T3J9+slPejuw+7+guLZc65O5m+f7+6PeDzY4VMtx6DAzGyFpJ+R9Bctm3k+IDObL+k6SZ+QJHcfcfcj4vlArCJpjplVJPUqXseDZ6NACNbTs1zS7pbXe5JteJ0ws5WS3ibpUUnnJvO3K/nznKTZRM/J8uT7sdtRfP+vpH8jqXW9Yp4PSNLFkg5I+sukVOgvzKxPPB+ve+7+sqT/rHgK4n2K1wT5hng2CoVgPT1TWbIdJWFmcyV9QdLvuvuxdk3H2eZttqPAzOxdkl5198fTHjLONp6P8qpIukrSve7+NkkDSv7X/gR4Pl4nktrp9ZJWSTpfUp+Z/XK7Q8bZxrMxwwjW05Nq+XWUj5lVFYfqz7j7/0g270/+F5ySP19Ntk/0nOxJvh+7HcX245LebWYvKi4P+9/M7NPi+UBsj6Q97v5o8vrzioM2zwd+UtIL7n7A3WuS/oekHxPPRqEQrKcnzXLvKJlkdPUnJD3j7v+lZddGSbcm398q6cst2282s24zW6V4IMljyf/SO25m65Jz3tJyDArK3T/o7ivcfaXi3wl/7+6/LJ4PSHL3VyTtNrPLkk0/oXilYZ4P7JK0zsx6k5/pTygew8OzUSAdlzTHxDos2Y7y+nFJH5D0AzN7Itn2f0j6iKTPmdmvKv4F+T5JcvenzOxziv/yrEu6090byXG/IemvJM2R9LfJF8qJ5wNNvyXpM0mHzE5Jtynu6OL5eB1z90fN7POSvqf4Z/19SfdJmiuejcJg5UUAAAAgA5SCAAAAABkgWAMAAAAZIFgDAAAAGSBYAwAAABkgWAMAAAAZIFgDwOuQmf2Rmf3kTF8HAJQJ0+0BwOuMmYUt890CADJCjzUAlIiZrTSzZ83sk2a2zcw+n6zk9qKZ/aGZfVfS+8zsr8zsF5JjftTM/snMtprZY2Y2z8xCM/uomW1OzvOvZvjWAGDWI1gDQPlcJuk+d3+rpGOSfjPZftLd/5m7P9hsmKz+9zeSfsfdr5D0k5KGJP2qpKPu/qOSflTSryfLJgMAJkCwBoD/v507dsUojOI4/v3Vq5SNzSALFqtBmf0LsjAb/DUGKaPFbpGVQUpI2A12ZZE6lmvRy/S86b2+n+ncpzOc8dd57r3981xVF119BKx19fGQ3iXgpaquAKrqtao+gHVgK8kNcAnMAAujHVuSxtvgrweQJDX3/eOZr+e3Ib0Z0v91vltVpy0Hk6Q+c2MtSf0zl2S1qzeB8196n4DZJCsA3fvVA+AU2Eky0Z0vJpka5dCSNO4M1pLUP4/AdpI7YBrY/6mxqt6BDWAvyS1wBkwCh8ADcJ3kHjjAW05J+pW/25OkHkkyD5xU1fIfjyJJ/44ba0mSJKkBN9aSJElSA26sJUmSpAYM1pIkSVIDBmtJkiSpAYO1JEmS1IDBWpIkSWrAYC1JkiQ18AmKQF5BvqXQ4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of price (dependent) variable\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.distplot(target, hist=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20677.000000\n",
       "mean       159.078686\n",
       "std        162.464267\n",
       "min          0.000000\n",
       "25%         99.000000\n",
       "50%        130.000000\n",
       "75%        180.000000\n",
       "max       8915.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a majority of the prices fall below 1000 dollars with the lowest being 0 dollars and the next several being 9 dollars. We will impute 9 dollars where there is zero and then break the data into two groups and examine each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['price'] <= 9, 'price'] = 9\n",
    "target = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_log = np.log(target)\n",
    "target_log.hist(figsize=(15,5), bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will leave as is and come back to it later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction and Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We go over feature by feature and clean the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experiences_offered                                0\n",
       "host_since                                        12\n",
       "host_response_time                              8541\n",
       "host_response_rate                              8541\n",
       "host_is_superhost                                 12\n",
       "host_listings_count                               12\n",
       "host_total_listings_count                         12\n",
       "host_has_profile_pic                              12\n",
       "host_identity_verified                            12\n",
       "street                                             0\n",
       "neighbourhood                                    831\n",
       "neighbourhood_cleansed                             0\n",
       "city                                               4\n",
       "state                                            219\n",
       "zipcode                                          886\n",
       "market                                            40\n",
       "smart_location                                     0\n",
       "country_code                                       0\n",
       "country                                            0\n",
       "latitude                                           0\n",
       "longitude                                          0\n",
       "is_location_exact                                  0\n",
       "property_type                                      0\n",
       "room_type                                          0\n",
       "accommodates                                       0\n",
       "bathrooms                                          6\n",
       "bedrooms                                           8\n",
       "beds                                               8\n",
       "bed_type                                           0\n",
       "amenities                                          0\n",
       "price                                              0\n",
       "weekly_price                                       0\n",
       "monthly_price                                      0\n",
       "security_deposit                                   0\n",
       "cleaning_fee                                       0\n",
       "guests_included                                    0\n",
       "extra_people                                       0\n",
       "minimum_nights                                     0\n",
       "maximum_nights                                     0\n",
       "minimum_minimum_nights                             0\n",
       "maximum_minimum_nights                             0\n",
       "minimum_maximum_nights                             0\n",
       "maximum_maximum_nights                             0\n",
       "minimum_nights_avg_ntm                             0\n",
       "maximum_nights_avg_ntm                             0\n",
       "calendar_updated                                   0\n",
       "has_availability                                   0\n",
       "availability_30                                    0\n",
       "availability_60                                    0\n",
       "availability_90                                    0\n",
       "availability_365                                   0\n",
       "number_of_reviews                                  0\n",
       "number_of_reviews_ltm                              0\n",
       "first_review                                    2593\n",
       "last_review                                     2593\n",
       "review_scores_rating                            2816\n",
       "review_scores_accuracy                          2827\n",
       "review_scores_cleanliness                       2825\n",
       "review_scores_checkin                           2837\n",
       "review_scores_communication                     2830\n",
       "review_scores_location                          2835\n",
       "review_scores_value                             2835\n",
       "jurisdiction_names                                37\n",
       "instant_bookable                                   0\n",
       "cancellation_policy                                1\n",
       "require_guest_profile_picture                      0\n",
       "require_guest_phone_verification                   0\n",
       "calculated_host_listings_count                     0\n",
       "calculated_host_listings_count_entire_homes        0\n",
       "calculated_host_listings_count_private_rooms       0\n",
       "calculated_host_listings_count_shared_rooms        0\n",
       "reviews_per_month                               2593\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Experience offered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "none    20677\n",
       "Name: experiences_offered, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experiences_offered'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No experience offered. Dropping column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('experiences_offered', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Host since"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers usually trust someone who has been longer in the business. This will be considered if it has an effect on the target features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "2818   2008-09-24\n",
       "Name: host_since, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to datetime\n",
    "from datetime import datetime, timedelta\n",
    "df['host_since'] = pd.to_datetime(df.host_since)\n",
    "df.host_since[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subtract for last scraped date of 2019-08-09\n",
    "df['host_days_active'] = (datetime(2019,8,9) -df.host_since).astype('timedelta64[D]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recorder so that it's the first column to stay with host group\n",
    "col_name = 'host_days_active'\n",
    "first_cols = df.pop(col_name)\n",
    "df.insert(0, col_name, first_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "4806556    1650.0\n",
       "Name: host_days_active, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 12 missing values for this column. We replace with the median since \n",
    "# from a visual inspection the median is a better representation\n",
    "median_days_active = df.host_days_active.median()\n",
    "df.host_days_active.fillna(median_days_active, inplace=True)\n",
    "df[df.host_since.isna()].host_days_active[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.host_since.fillna((datetime(2019,8,9) -timedelta(days=1650)), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Host response rate and time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two are tied together and it's apparent from the equal number of 8541 missing values. Host response rate is calculated from their response time over the past 365 days. If they respond late more than 24 hours after a guest contacts them, it is counted as late and their rate drops. We will enter Unknown for bboth of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown               8541\n",
       "within an hour        6217\n",
       "within a few hours    3016\n",
       "within a day          2570\n",
       "a few days or more     333\n",
       "Name: host_response_time, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.host_response_time.fillna('unknown', inplace=True)\n",
    "df.host_response_time.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_response_time\n",
       "within a few hours    145.0\n",
       "within a day          145.0\n",
       "a few days or more    145.0\n",
       "within an hour        135.0\n",
       "unknown               125.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('host_response_time').price.median().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.host_response_time.replace({'within a few hours': 'a few hours to days', \n",
    "                               'within a day': 'a few hours to days', \n",
    "                               'a few days or more': 'a few hours to days'},\n",
    "                              inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100%       9011\n",
       "unknown    8721\n",
       "50-89%     1804\n",
       "90-99%      809\n",
       "0-50%       332\n",
       "Name: host_response_rate, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert host_respoce_rate to interger, fill with 'Unknown' and make bins of <50%, 50-89%,90-99%, and 100%\n",
    "df.host_response_rate = df['host_response_rate'].str[:-1].astype(float)\n",
    "df.host_response_rate = pd.cut(df.host_response_rate, bins=[0, 50, 90, 99, 100], labels=['0-50%', '50-89%', '90-99%', '100%'])\n",
    "\n",
    "# Convert back to string\n",
    "df.host_response_rate = df.host_response_rate.astype('str')\n",
    "\n",
    "df.host_response_rate.replace('nan', 'unknown', inplace=True)\n",
    "df.host_response_rate.value_counts()"
   ]
  },
  {
   "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": [
    "df.groupby('host_response_rate').price.median().sort_values(ascending=True).plot(kind='bar', color=['blue', 'green', 'orange', 'yellow', 'red'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Host is superhost, host_listing_count, host_has_profile_pic, and  host_identity_verified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    17451\n",
       "t     3214\n",
       "Name: host_is_superhost, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.host_is_superhost.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X5EnACVV1LYPLNkcCRwCPMbiMMaO97GO6dZ9VVXdW1QeACQaXTL7F4Lr4Id2rLldN2ewNQ7f/0d2/Bnjr0H6fP5u5DrkeOCPJYUkOB/4A+EqSpwM/qqpLgA8BT7xny6z/PHRw8Jq79idrgU91T+m7n8E15yXAJV1Uw+A69veS/AtwRZLVDK7Tf2Uv+512H3tY9/wkpzE4g74HuLq7Tr+JwaWd7cBtU7Y5JMlNDE6WzurG3g58PMkdDP6eXc/gl8CzUlW3JvkccHM39Omqui3Ja4APJvk58FMGbxwHg4+vuzrJzqo6bbbHUbt8+wGphyTfBMb9Rab2N16WkaQGeeauA16S9wJnThn+56q6aC/bnANM/QSrG6rqvPmen7QYjLskNcjLMpLUIOMuSQ0y7pLUIOMuSQ0y7pLUoP8HGbq/AOq8P5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will imute false for the missing values since being a superhost is a badge of honor\n",
    "df['host_is_superhost'].fillna('f', inplace=True)\n",
    "df.groupby('host_is_superhost').price.median().plot(kind='bar', color=['blue', 'green'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price\n",
       "host_is_superhost       \n",
       "f                  130.0\n",
       "t                  130.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = pd.pivot_table(df, values='price', index=['host_is_superhost'], aggfunc=np.median)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "# For host_total_listings_counts, we impute median which will maintain the distribution\n",
    "df['host_total_listings_count'].fillna(df['host_total_listings_count'].median(), inplace=True)\n",
    "plt.scatter(df.host_total_listings_count, df.price);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "980.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df.host_total_listings_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these add no value to our algorithm and will be dropped. We will however retain 'host_total_listings_count'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['host_is_superhost','host_listings_count','host_has_profile_pic','host_identity_verified'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### street, neighbourhood, neighbourhood_cleansed, city, state, zipcode, market, smart_location, country_code, country, latitude, longitude, is_location_exact, and jurisdiction_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just need one feature to represent location. We will rename neighbourhood_cleansed to neighborhood, since it has no missing values and will convert it later to numeric for computational purposes. The rest we will drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['neighbourhood'] = df['neighbourhood_cleansed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['street','neighbourhood_cleansed','city','state','zipcode','market','smart_location','country_code','country','latitude','longitude','is_location_exact','jurisdiction_names'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "De Baarsjes - Oud-West                    3549\n",
       "De Pijp - Rivierenbuurt                   2560\n",
       "Centrum-West                              2328\n",
       "Centrum-Oost                              1797\n",
       "Westerpark                                1530\n",
       "Zuid                                      1464\n",
       "Oud-Oost                                  1349\n",
       "Bos en Lommer                             1180\n",
       "Oostelijk Havengebied - Indische Buurt     985\n",
       "Oud-Noord                                  625\n",
       "Watergraafsmeer                            591\n",
       "IJburg - Zeeburgereiland                   491\n",
       "Slotervaart                                404\n",
       "Noord-West                                 375\n",
       "Noord-Oost                                 285\n",
       "Buitenveldert - Zuidas                     260\n",
       "Geuzenveld - Slotermeer                    226\n",
       "De Aker - Nieuw Sloten                     154\n",
       "Osdorp                                     152\n",
       "Gaasperdam - Driemond                      141\n",
       "Bijlmer-Centrum                            116\n",
       "Bijlmer-Oost                               115\n",
       "Name: neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.neighbourhood.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bijlmer-Oost</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bijlmer-Centrum</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaasperdam - Driemond</th>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Osdorp</th>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geuzenveld - Slotermeer</th>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Aker - Nieuw Sloten</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slotervaart</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oostelijk Havengebied - Indische Buurt</th>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bos en Lommer</th>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noord-West</th>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenveldert - Zuidas</th>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noord-Oost</th>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Watergraafsmeer</th>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westerpark</th>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oud-Noord</th>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oud-Oost</th>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Baarsjes - Oud-West</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuid</th>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Pijp - Rivierenbuurt</th>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IJburg - Zeeburgereiland</th>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centrum-Oost</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centrum-West</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        price\n",
       "neighbourhood                                \n",
       "Bijlmer-Oost                             75.0\n",
       "Bijlmer-Centrum                          80.0\n",
       "Gaasperdam - Driemond                    85.0\n",
       "Osdorp                                   91.0\n",
       "Geuzenveld - Slotermeer                  99.0\n",
       "De Aker - Nieuw Sloten                  100.0\n",
       "Slotervaart                             100.0\n",
       "Oostelijk Havengebied - Indische Buurt  110.0\n",
       "Bos en Lommer                           110.0\n",
       "Noord-West                              115.0\n",
       "Buitenveldert - Zuidas                  120.0\n",
       "Noord-Oost                              120.0\n",
       "Watergraafsmeer                         125.0\n",
       "Westerpark                              125.0\n",
       "Oud-Noord                               125.0\n",
       "Oud-Oost                                129.0\n",
       "De Baarsjes - Oud-West                  130.0\n",
       "Zuid                                    140.0\n",
       "De Pijp - Rivierenbuurt                 140.0\n",
       "IJburg - Zeeburgereiland                150.0\n",
       "Centrum-Oost                            160.0\n",
       "Centrum-West                            160.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhood_median_Prices = pd.pivot_table(df, values='price', index=['neighbourhood'], aggfunc=np.median)\n",
    "neighbourhood_median_Prices = neighbourhood_median_Prices.reindex(neighbourhood_median_Prices['price'].sort_values(ascending=True).index)\n",
    "neighbourhood_median_Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bunching some neighborhoods that have same median price together\n",
    "df.neighbourhood.replace({\n",
    "    'De Aker - Nieuw Sloten': 'De Aker-Slotervaart',\n",
    "    'Slotervaart': 'De Aker-Slotervaart',\n",
    "    'Oostelijk Havengebied - Indische Buurt': 'Buurt-Lommer',\n",
    "    'Bos en Lommer': 'Buurt-Lommer',\n",
    "    'Buitenveldert - Zuidas': 'Noord-Zuidas',\n",
    "    'Noord-Oost': 'Noord-Zuidas',\n",
    "    'Watergraafsmeer': 'Water-park-Noord',\n",
    "    'Westerpark': 'Water-park-Noord',\n",
    "    'Oud-Noord': 'Water-park-Noord',\n",
    "    'Zuid': 'Zuid-Rivierenbuurt',\n",
    "    'De Pijp - Rivierenbuurt': 'Zuid-Rivierenbuurt',\n",
    "    'Centrum-Oost': 'Centrum',\n",
    "    'Centrum-West': 'Centrum'\n",
    "    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hood_median_Prices = pd.pivot_table(df, values='price', index=['neighbourhood'], aggfunc=np.median)\n",
    "hood_median_Prices = hood_median_Prices.reindex(hood_median_Prices['price'].sort_values(ascending=True).index)\n",
    "hood_median_Prices.plot(kind='bar', figsize=(15,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Property_type and room_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment                 15832\n",
       "House                      1641\n",
       "Townhouse                   671\n",
       "Bed and breakfast           549\n",
       "Loft                        392\n",
       "Boat                        351\n",
       "Condominium                 334\n",
       "Houseboat                   290\n",
       "Guest suite                 146\n",
       "Aparthotel                  119\n",
       "Serviced apartment           69\n",
       "Guesthouse                   49\n",
       "Boutique hotel               43\n",
       "Other                        39\n",
       "Villa                        39\n",
       "Hostel                       29\n",
       "Hotel                        23\n",
       "Cottage                      12\n",
       "Tiny house                   12\n",
       "Cabin                        10\n",
       "Camper/RV                     5\n",
       "Chalet                        3\n",
       "Bungalow                      3\n",
       "Casa particular (Cuba)        3\n",
       "Barn                          2\n",
       "Yurt                          2\n",
       "Earth house                   1\n",
       "Island                        1\n",
       "Nature lodge                  1\n",
       "Lighthouse                    1\n",
       "Tent                          1\n",
       "Dome house                    1\n",
       "Castle                        1\n",
       "Hut                           1\n",
       "Campsite                      1\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.property_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will group similar property types together and for the not so common types, we will group with other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.property_type.replace({\n",
    "    'Townhouse': 'House',\n",
    "    'Serviced apartment': 'Apartment',\n",
    "    'Loft': 'Apartment',\n",
    "    'Bungalow': 'House',\n",
    "    'Condominium': 'Apartment',\n",
    "    'Cottage': 'House',\n",
    "    'Villa': 'House',\n",
    "    'Tiny house': 'House',\n",
    "    'Earth house': 'House',\n",
    "    'Dome house': 'House',\n",
    "    'Chalet': 'House',\n",
    "    'Houseboat': 'Boat',\n",
    "    'Aparthotel': 'Bed and breakfast',\n",
    "    'Hotel': 'Bed and breakfast',\n",
    "    'Guesthouse': 'Bed and breakfast',\n",
    "    'Boutique hotel': 'Bed and breakfast',\n",
    "    'Hostel': 'Bed and breakfast'\n",
    "    }, inplace=True)\n",
    "\n",
    "# Remaining ones renamed other\n",
    "df.loc[~df.property_type.isin(['Apartment', 'House','Bed and breakfast','Boat']), 'property_type'] = 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment            16627\n",
       "House                 2383\n",
       "Bed and breakfast      812\n",
       "Boat                   641\n",
       "Other                  214\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.property_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('property_type').price.median().sort_values(ascending=True).plot(kind='bar', figsize=(12,6), color=['yellowgreen', 'gold', 'lightblue', 'lightcoral','blue'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    16402\n",
       "Private room        4194\n",
       "Shared room           81\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.room_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAXLUlEQVR4nO3deZhldX3n8fcHWnZFGYpFgTQxHRQdNdriggsjLmTGAYwSwAU0znRMFJUZ04GYkSQ+ZBBnEnUyxDBKYJ4QEFcwz6hga4tLBJp9E2FcWBsaGaO4IMt3/ji/Ol7aqu7q6r73VnPfr+ep55zzO9u3zrl1P2e591SqCkmSALYYdwGSpIXDUJAk9QwFSVLPUJAk9QwFSVLPUJAk9RaNu4CNsfPOO9fixYvHXYYkbVYuvfTSu6tqaqZxm3UoLF68mFWrVo27DEnarCT5/mzjvHwkSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSept1t9TkBaq5cuXs3r1anbbbTdOPvnkcZcjzZmhIA3B6tWrue2228ZdhrTBvHwkSep5pqDNws1/8a/HXcIGeeCenYBFPHDP9zeb2vd6z9XjLkELgGcKkqSeoSBJ6hkKkqSeoSBJ6nmjWRqCnbd5CHigdaXNh6EgDcG7nvbDcZcgzcvQLh8lOS3JXUmumWHcu5JUkp0H2o5PclOSG5K8Ylh1SZJmN8x7CqcDB63dmGRP4GXAzQNt+wJHAE9p85ySZMsh1iZJmsHQQqGqLgTumWHUXwPLgRpoOwQ4u6ruq6rvAjcB+w2rNknSzEb66aMkBwO3VdWVa416AnDLwPCtrU2SNEIju9GcZDvg3cDLZxo9Q1vN0EaSZcAygL322muT1SdJGu2ZwhOBvYErk3wP2AO4LMludGcGew5Muwdw+0wLqapTq2ppVS2dmpoacsmSNFlGFgpVdXVV7VJVi6tqMV0QPLOqVgPnAUck2TrJ3sAS4OJR1SZJ6gzzI6lnAf8M7JPk1iRvnm3aqroWOAe4Dvg88NaqenBYtUmSZja0ewpVdeR6xi9ea/hE4MRh1SNJWj+ffSRJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6g0tFJKcluSuJNcMtL0/ybeSXJXk00keOzDu+CQ3JbkhySuGVZckaXbDPFM4HThorbYLgKdW1dOAbwPHAyTZFzgCeEqb55QkWw6xNknSDIYWClV1IXDPWm3nV9UDbfCbwB6t/xDg7Kq6r6q+C9wE7Des2iRJMxvnPYXfAz7X+p8A3DIw7tbWJkkaobGEQpJ3Aw8AZ043zTBZzTLvsiSrkqxas2bNsEqUpIk08lBIcjTwSuB1VTX9xn8rsOfAZHsAt880f1WdWlVLq2rp1NTUcIuVpAkz0lBIchDwx8DBVfXTgVHnAUck2TrJ3sAS4OJR1iZJgkXDWnCSs4ADgJ2T3AqcQPdpo62BC5IAfLOq3lJV1yY5B7iO7rLSW6vqwWHVJkma2dBCoaqOnKH5o+uY/kTgxGHVI0laP7/RLEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqGQqSpJ6hIEnqDS0UkpyW5K4k1wy07ZTkgiQ3tu7jBsYdn+SmJDckecWw6pIkzW6YZwqnAwet1XYcsKKqlgAr2jBJ9gWOAJ7S5jklyZZDrE2SNIOhhUJVXQjcs1bzIcAZrf8M4NCB9rOr6r6q+i5wE7DfsGqTJM1s1PcUdq2qOwBad5fW/gTgloHpbm1tvyLJsiSrkqxas2bNUIuVpEmzUG40Z4a2mmnCqjq1qpZW1dKpqakhlyVJk2XUoXBnkt0BWveu1n4rsOfAdHsAt4+4NkmaeKMOhfOAo1v/0cC5A+1HJNk6yd7AEuDiEdcmSRNv0bAWnOQs4ABg5yS3AicAJwHnJHkzcDNwGEBVXZvkHOA64AHgrVX14LBqkyTNbGihUFVHzjLqwFmmPxE4cVj1SJLWb6HcaJYkLQCGgiSpZyhIknqGgiSpZyhIknqGgiSpZyhIknpD+56CNs7y5ctZvXo1u+22GyeffPK4y5E0IQyFBWr16tXcdttt4y5D0oTx8pEkqTcxZwrP+qP/Pe4SNsij7/4xWwI33/3jzar2S99/1LhLkLQRPFOQJPUMBUlSb2IuH21uHtpq+4d1JWkU5hwKSX4NWFJVX0yyLbCoqn48vNIm20+WvHzcJUiaQHO6fJTkPwKfAP6uNe0BfGZYRUmSxmOu9xTeCuwP/Aigqm4EdhlWUZKk8ZhrKNxXVb+YHkiyCKjhlCRJGpe5hsJXkvwJsG2SlwEfBz4735UmOTbJtUmuSXJWkm2S7JTkgiQ3tu7j5rt8SdL8zDUUjgPWAFcDvw/8H+BP57PCJE8A3g4sraqnAlsCR7R1rKiqJcCKNixJGqG5fvpoW+C0qvpfAEm2bG0/3Yj1bpvkfmA74HbgeOCANv4MYCXwx/NcviRpHuZ6prCCLgSmbQt8cT4rrKrbgP8G3AzcAfxLVZ0P7FpVd7Rp7sAb2ZLGYPny5Rx11FEsX7583KWMxVxDYZuqund6oPVvN58VtnsFhwB7A48Htk/y+g2Yf1mSVUlWrVmzZj4lSNKspp9QvHr16nGXMhZzDYWfJHnm9ECSZwE/m+c6Xwp8t6rWVNX9wKeA5wN3Jtm9LX934K6ZZq6qU6tqaVUtnZqammcJkqSZzPWewjuBjye5vQ3vDhw+z3XeDDw3yXZ0wXIgsAr4CXA0cFLrnjvP5UuS5mlOoVBVlyR5ErAPEOBb7Sh/g1XVRUk+AVwGPABcDpwK7ACck+TNdMFx2HyWL0mav3WGQpKXVNWXkvzOWqOWJKGqPjWflVbVCcAJazXfR3fWIEkak/WdKbwY+BLw72cYV3T3AyRJjxDrDIWqOiHJFsDnquqcEdUkSRqT9X76qKoeAt42glokSWM214+kXpDkXUn2bM8o2inJTkOtTJI0cnP9SOrv0d1D+MO12n9905YjSRqnuYbCvnSB8AK6cPgq8OFhFSVJGo+5hsIZdP9g50Nt+MjW9rvDKEqSNB5zDYV9qurpA8NfTnLlMAqSJI3PXG80X57kudMDSZ4DfH04JUmSxmWuZwrPAY5KcnMb3gu4PsnVQFXV04ZSnSRppOYaCgcNtQpJ0oIw1wfifX/YhUiSxm+u9xQkSRPAUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVJvLKGQ5LFJPpHkW0muT/K89j8aLkhyY+s+bhy1SdIkG9eZwgeBz1fVk4CnA9cDxwErqmoJsKINS5JGaOShkOQxwIuAjwJU1S+q6ofAIXSP46Z1Dx11bZI06cZxpvDrwBrg75NcnuQjSbYHdq2qOwBad5cx1CZJE22uD8Tb1Ot8JnBMVV2U5INswKWiJMuAZQB77bXXcCqUtEnt/z/2H3cJc7bVD7diC7bglh/eslnV/fVjNs1/MxjHmcKtwK1VdVEb/gRdSNyZZHeA1r1rppmr6tSqWlpVS6empkZSsCRNipGHQlWtBm5Jsk9rOhC4DjgPOLq1HQ2cO+raJGnSjePyEcAxwJlJtgK+A7yJLqDOSfJm4GbgsDHVJkkTayyhUFVXAEtnGHXgqGuRJP2S32iWJPUMBUlSz1CQJPXGdaNZkhak2q54iIeo7WrcpYyFoSBJA+7f//5xlzBWXj6SJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSb2yhkGTLJJcn+ac2vFOSC5Lc2LqPG1dtkjSpxnmm8A7g+oHh44AVVbUEWNGGJUkjNJZQSLIH8O+Ajww0HwKc0frPAA4ddV2SNOnGdabwAWA58NBA265VdQdA6+4yjsIkaZKNPBSSvBK4q6ounef8y5KsSrJqzZo1m7g6SZps4zhT2B84OMn3gLOBlyT5B+DOJLsDtO5dM81cVadW1dKqWjo1NTWqmiVpIow8FKrq+Krao6oWA0cAX6qq1wPnAUe3yY4Gzh11bZI06RbS9xROAl6W5EbgZW1YkjRCi8a58qpaCaxs/T8ADhxnPZI06RbSmYIkacwMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPUMBUlSz1CQJPVGHgpJ9kzy5STXJ7k2yTta+05JLkhyY+s+btS1SdKkG8eZwgPAf66qJwPPBd6aZF/gOGBFVS0BVrRhSdIIjTwUquqOqrqs9f8YuB54AnAIcEab7Azg0FHXJkmTbqz3FJIsBn4LuAjYtarugC44gF3GV5kkTaaxhUKSHYBPAu+sqh9twHzLkqxKsmrNmjXDK1CSJtBYQiHJo+gC4cyq+lRrvjPJ7m387sBdM81bVadW1dKqWjo1NTWagiVpQozj00cBPgpcX1V/NTDqPODo1n80cO6oa5OkSbdoDOvcH3gDcHWSK1rbnwAnAeckeTNwM3DYGGqTpIk28lCoqq8BmWX0gaOsRZL0cH6jWZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQZLUW3ChkOSgJDckuSnJceOuR5ImyYIKhSRbAv8T+G1gX+DIJPuOtypJmhwLKhSA/YCbquo7VfUL4GzgkDHXJEkTI1U17hp6SV4DHFRV/6ENvwF4TlW9bWCaZcCyNrgPcMPICx2dnYG7x12E5s39t/l6pO+7X6uqqZlGLBp1JeuRGdoellpVdSpw6mjKGa8kq6pq6bjr0Py4/zZfk7zvFtrlo1uBPQeG9wBuH1MtkjRxFlooXAIsSbJ3kq2AI4DzxlyTJE2MBXX5qKoeSPI24AvAlsBpVXXtmMsap4m4TPYI5v7bfE3svltQN5olSeO10C4fSZLGyFCQJPUMhfVI8mCSK5Jck+TjSbabZbpvzHP5i5O8duOqnFwD+2f6Z52PRklyQJLnDwy/JclRG7H+09v3a0YmyaOSXDrPed+Y5PGbuqZhSPLuJNcmuart2+e09u8l2XkE61+ZZOI+lrqgbjQvUD+rqmcAJDkTeAvwV9Mjk2xZVQ9W1fNnW8B6LAZeC/zjfGZOErp7Qw/Nc/2bu37/zNEBwL3ANwCq6sMzTZRkUVU9sPHlDcULaPXPwxuBa1jgH/VO8jzglcAzq+q+FgJbbYLlbtL9Ov33v6mWtxB4prBhvgr8Rjva/HKSfwSuBkhyb+t+LMm/nZ6hHUm+up0RfDXJZe1nOkROAl7YjoSOTbJlkvcnuaQdIf3+2kW0ZV2f5BTgMmDPNs81Sa5OcnibLrO0H5DkK0nOSfLtJCcleV2Si9t0TxzmRhyFdjT5521bX53kSUkW04X6sW17vzDJnyV5V5tnZZK/TPIV4B1JntW206VJvpBk91lW96Ik30jynemzho3d9kmmknyyvQ4uSbL/wPoOAj7XpvtMq+/adN/2n/79703y39vvv6It7zXAUuDM9vtvuym3+Sa2O3B3Vd0HUFV3V9VgkB0zuG8BkuzX9sPlrbtPa39jurP8zwLnJ9k+yWltu16e5JA23bZJzm5/dx8DZtw+7bX1niRfAw5LcmSr45ok7xuYbrb2e5O8r+23L7a6V7bXz8GbeDtuuKryZx0/wL2tuwg4F/gDuqPNnwB7zzDdq4AzWv9WwC10L67tgG1a+xJgVes/APingeUsA/609W8NrBpcT2tfDDwEPLcNvxq4gO5jvLsCN9P9Uc3WfgDww9a/NXAb8OdtWe8APjDu7b4B++dB4IqBn8Nb+/eAY1r/HwIfaf1/BrxrYP5+GFgJnNL6H0V3ND7Vhg+n+4j02us/Hfg43QHWvnTP7lrXPpnTtqc7c3xB698LuH5gnRcD27X+nVp3W7ozgH/Vhgt4Xet/D/A3A7/j0nHvtzns1x3a/vw2cArw4oFxs+3bxwCLWv9LgU+2/jfSfTF2elv9JfD61v/Yto7tgf80vY+BpwEPzLSt2vqXt/7Ht307Rfce8SXg0NnaB/bNb7f+TwPnt9fb04Erxr3tvXy0ftsmuaL1fxX4KPB84OKq+u4M038O+FCSremO6C6sqp8l2RH4myTPoHsj+81Z1vdy4Gn55XXqHelCZO11fb+qvtn6XwCcVd1p7J3tSPfZ62j/EXBJVd0BkOT/0r0woTvz+Tfr3ywLxrouH32qdS8FfmeOy/tY6+4DPBW4IAl0b+53zDLPZ6q7fHddkl1b28Zu+5cC+7Z1AzwmyaOBRwP3VNVPW/vbk7yq9e9J91r5Ad1Bw/Tv8g8D22KzUFX3JnkW8EK6bfKxJMdV1eltkpn27Y7AGUmW0L3xPmpgkRdU1T2t/+XAwdNniMA2dMH7IuBDbf1XJblqHSVOb9tnAyurag30l5hf1NY/U/tngF8An2/zXw3cV1X3J7ma7oBvrAyF9fuVN532h/qTmSauqp8nWQm8gu7o8qw26ljgTrqjgS2An8+yvtAdBX1hPXUNrn+mZ0atqx3gvoH+hwaGH+KR87qY/p0eZO6/0/R2DXBtVT1vA9YzPd9gd33Tz7bttwCeV1U/G5wxye/SfbmTJAfQhcfzquqn7XW3zSzr3Oy+kNQCdSWwsr1hHk13ZgYz79v3Al+uqle1S4UrBxa39t/Lq6vqYQ/TbH/Xc91Og6+Tmaxr/99f7TSBgf1fVQ8lGfvfnvcUhuNs4E10RznTb+47Ane0I8o30B15AvyY7uhv2heAP0jyKIAkv5lk+/Ws70Lg8HT3I6bojkguXkf7JFt7e8/mBmAq3Q3P6U/8PGUD1rOx2/58YPDpwNMHJv39BLrX1P9rgfAk4LkD828BTJ9tvhb4Wuuf6+8/Vkn2aUf8054BfH89s+1IdzkOuktGs/kC3T2JtHX9Vmu/EHhda3sq3SWk9bkIeHGSndP9P5gjga+so33BMxSG43y6N4EvVvd/IaC7Lnp0km/SXTqaPtK4CnggyZVJjgU+AlwHXJbkGuDvWP9R7qfbcq6ku3a5vKpWr6P9kWTbPPwjqSetZ/rPAq9q075wtonafnsN8L4kV9Jd396QT5ht7LZ/O7C03fS8DnhLe3NZUlXfatN8HljULnO8F/jmwPw/AZ6S7qOrLwH+orWfDnx4M7jRvAPdpaDr2u+3L939n3U5GfivSb7OLw+6ZvJeuktLV7W/sfe29r8FdmjrW84cQrxdBjwe+DLdvr6sqs6drX19y1sIfMyFtJlI8gK6G6RvmcO091bVDiMoS48whoL0CGQoaL4MBUlSz3sKkqSeoSBJ6hkK0ogkOTTJvuOuQ1oXQ0ETLZ1R/R0cSvfRSmnBMhQ0cfKrDxT8aIb8MMF0D0A8GHh/+47AE5NcNjB+SftOwfQD197Xlnlxkt9o7et6SJ60SYz9K9XSmOxD963zFXRPTn06sDNwSZIL6b6o9owZ2mltTwbuAb5D90C2/ZK8AzgGeOfaK6uqbyQ5j+7hh58ASPIvSZ5RVVe0Wk4fmOVHbZlHAR+ge4z0B4G/rqqvJdmL7pu5T95kW0TCMwVNrukHCvYPrquqO+keRfDsdbRDe6BddY91XvuBdos3oIaPAG9q31Q+nIf/T42zBrrTz196Kd1DFa8AzuOXD8mTNhnPFDSpNuaBZpvqYYKfBE6gewzGpVX1g4FxNUP/jA/JkzYlzxQ06Ub5MMGHPYyuqn5Odwnob4G/X2vawwe6/9z6Z3tInrTJGAqadKN8mODZwB+l+29f0zekz6Q7Ezh/rWm3TnIR3T/eOba1/cpD8jayHulX+JgLaYzS/aOXHavqvwy0fY/uP37dPbbCNLG8pyCNSZJPA0+ke7S1tCB4piBtYkneDRy2VvPHq+rEcdQjbQhDQZLU80azJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSev8f+WBNmGhLsgUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df.room_type,df.price, estimator=np.median);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bathrooms, bedrooms, and beds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing missing values with median\n",
    "features = ['bathrooms','bedrooms','beds']\n",
    "for feat in features:\n",
    "    df[feat].fillna(df[feat].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     15937\n",
       "1.5      3272\n",
       "2.0       966\n",
       "2.5       252\n",
       "3.0        81\n",
       "0.5        75\n",
       "3.5        42\n",
       "0.0        28\n",
       "4.0        15\n",
       "5.0         3\n",
       "4.5         2\n",
       "8.0         2\n",
       "15.0        1\n",
       "5.5         1\n",
       "Name: bathrooms, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bathrooms.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_plot(feat, figsize=(8,4)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    df.groupby(feat).price.median().plot(kind='bar')\n",
    "    plt.title(feat)\n",
    "    plt.xticks(rotation=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IMAYAgEowBgCASjAGAIBKMAYAgEowBgCASjAGAIBKMAYAgEowBgCASjAGAIBKMAYAgEowBgCASjAGAIBKMAYAgEowBgCASjAGAIBKMAYAgEowBgCASjAGAIDqAMF4Zs6dmV+cmVtm5uaZ+ZZN+5kzc8PMvG/z/JRd+7x8Zm6dmffOzIuOsgAAADgMB7lifG/1bWutL6heUL1sZp5dXVnduNY6v7pxs95m22XVc6qLq1fOzBlHMXgAADgsDxqM11p3rrXetln+g+qW6pnVpdW1m27XVi/eLF9avWatdc9a6wPVrdWFhz1wAAA4TCceSueZOVk9r3pz9fS11p21E55n5mmbbs+s3rRrt9s3bXuPdUV1RdV55533UMcNp83JK19/5Oe47epLjvwcAMADO/Cb72bmM6ofr751rfWxB+q6T9u6X8Na16y1LlhrXXDWWWcddBgAAHAkDhSMZ+Zx7YTiH15r/cSm+cMzc/Zm+9nVXZv226tzd+1+TnXH4QwXAACOxkE+lWKqH6huWWv9o12brq8u3yxfXr1uV/tlM/OEmXlWdX71lsMbMgAAHL6DzDH+suobqnfNzNs3bd9RXV1dNzMvrT5YvaRqrXXzzFxXvaedT7R42VrrE4c+cgAAOEQPGozXWv93+88brrroFPtcVV31CMYFAACnlTvfAQBAgjEAAFSCMQAAVIIxAABUgjEAAFSCMQAAVIIxAABUB7vBBzwkJ698/Wk5z21XX3JazgMAfGpwxRgAABKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKCqE4/2ANhx8srXH/k5brv6kiM/BwDAtnLFGAAAEowBAKASjAEAoBKMAQCgOkAwnplXzcxdM/PuXW1nzswNM/O+zfNTdm17+czcOjPvnZkXHdXAAQDgMB3kivG/rC7e03ZldeNa6/zqxs16M/Ps6rLqOZt9XjkzZxzaaAEA4Ig8aDBea/1y9ZE9zZdW126Wr61evKv9NWute9ZaH6hurS48pLECAMCRebifY/z0tdadVWutO2fmaZv2Z1Zv2tXv9k3b/czMFdUVVeedd95DHsDp+Nzf8tm/AACfKg77zXezT9var+Na65q11gVrrQvOOuusQx4GAAA8NA83GH94Zs6u2jzftWm/vTp3V79zqjse/vAAAOD0eLjB+Prq8s3y5dXrdrVfNjNPmJlnVedXb3lkQwQAgKP3oHOMZ+ZHq6+snjozt1d/p7q6um5mXlp9sHpJ1Vrr5pm5rnpPdW/1srXWJ45o7AAAcGgeNBivtb7uFJsuOkX/q6qrHsmgAADgdHPnOwAASDAGAIBKMAYAgEowBgCA6uHf+Q4AAPa1rXcodsUYAAASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKA6wmA8MxfPzHtn5taZufKozgMAAIfhSILxzJxR/dPqz1fPrr5uZp59FOcCAIDDcFRXjC+sbl1rvX+t9afVa6pLj+hcAADwiB1VMH5m9aFd67dv2gAA4DFp1lqHf9CZl1QvWmv95c36N1QXrrX+2q4+V1RXbFY/v3rvoQ/k/p5a/c5pOM/pcpzqOU611PGq5zjVUsernuNUSx2veo5TLXW86jlOtdTxqud01fI5a62z9ttw4ohOeHt17q71c6o7dndYa11TXXNE59/XzNy01rrgdJ7zKB2neo5TLXW86jlOtdTxquc41VLHq57jVEsdr3qOUy11vOp5LNRyVFMpfq06f2aeNTOPry6rrj+icwEAwCN2JFeM11r3zsxfrX6+OqN61Vrr5qM4FwAAHIajmkrRWutnqp85quM/TKd16sZpcJzqOU611PGq5zjVUsernuNUSx2veo5TLXW86jlOtdTxqudRr+VI3nwHAADbxi2hAQCgYxqMH+x21LPjH2+2v3Nmnv9ojPMgZuZVM3PXzLz7FNu3qZZzZ+YXZ+aWmbl5Zr5lnz5bUc/MPHFm3jIz79jU8t379NmKWnabmTNm5tdn5qf32bY19czMbTPzrpl5+8zctM/2ramlamaePDOvnZnf2Pz/+dI927einpn5/M3X5L7Hx2bmW/f02Ypa7jMzf2PzM+DdM/OjM/PEPdu3pp6Z+ZZNHTfv/bpstj+ma9nv9+XMnDkzN8zM+zbPTznFvg+YGx4Np6jnJZuvzydn5pSf3vBYq+cUtfyDzc+0d87MT87Mk0+x7+mtZa11rB7tvNnv31WfWz2+ekf17D19/kL1s9VUL6je/GiP+wHq+Yrq+dW7T7F9m2o5u3r+Zvkzq3+7rV+bzfg+Y7P8uOrN1Qu2sZY9Y/6b1Y9UP73l32u3VU99gO1bU8tmvNdWf3mz/Pjqydtcz2bMZ1S/3c7niW5lLe3cuOoD1adt1q+rvnEb66m+sHp39entvP/o31Tnb1Mt+/2+rP5+deVm+crqe0/xvfiAueExVM8XtHPvhzdWF5xiv8dcPaeo5c9VJzbL3/tY+docxyvGB7kd9aXVv1o73lQ9eWbOPt0DPYi11i9XH3mALttUy51rrbdtlv+guqX73xFxK+rZjO8PN6uP2zz2TtjfilruMzPnVJdU33+KLltVz4PYmlpm5s+280vlB6rWWn+61vronm5bU88uF1X/bq31m3vat62WE9WnzcyJdkLlHXu2b0s9X1C9aa31x2ute6tfqv7inj6P6VpO8fvy0nb+sGzz/OJ9dj1Ibjjt9qtnrXXLWuvBboj2mKvnFLW8YfO9VvWmdu55sddpr+U4BuOD3I76ON2yeitrmZmT1fPaudK629bUs5l28PbqruqGtdbW1rLxiurbq0+eYvs21bOqN8zMW2fnLpt7bVMtn1vdXb16M83l+2fmSXv6bFM997ms+tF92remlrXWb1X/sPpgdWf1+2utN+zpti31vLv6ipn57Jn59HauDp+7p8+21LLb09dad9bOxZnqafv02ca6Hsg21vM/tvNqxF6nvZbjGIxnn7a9V/IO0mdbbF0tM/MZ1Y9X37rW+tjezfvs8pisZ631ibXWc9v5K/fCmfnCPV22ppaZ+erqrrXWWx+o2z5tj8l6qi9baz2/+vPVy2bmK/Zs36ZaTrTzEuQ/W2s9r/qjdl4S3m2b6ml2bvz0NdWP7bd5n7bHZC2b+aqXVs+qnlE9aWb+0t5u++z6mKtnrXVLOy9n31D9XDsvWd+7p9tW1PIwHLe6tqqemfnOdr7Xfni/zfu0HWktxzEYP+jtqA/YZ1tsVS0z87h2QvEPr7V+Yp8uW1VP1eZl7TdWF+/ZtE21fFn1NTNzWzsvVb1wZn5oT5+tqWetdcfm+a7qJ9t5OW63ramlnbHevusVide2E5T39tmWemrnD5a3rbU+vM+2barlq6oPrLXuXmt9vPqJ6r/c02dr6llr/cBa6/lrra9o52Xv9+3psjW17PLh+6Z7bJ7v2qfPNtb1QLamnpm5vPrq6uvXZlLxHqe9luMYjA9yO+rrq/9h8w7bF7Tz8tedp3ugh2RrapmZaWee5C1rrX90im5bUc/MnHXfO2hn5tPa+QX5G3u6bUUtVWutl6+1zllrnWzn/8wvrLX2Xvnainpm5kkz85n3LbfzBo+9n+qyFbVUrbV+u/rQzHz+pumi6j17um1NPRtf1/7TKGq7avlg9YKZ+fTNz7eL2nnvxG5bU8/MPG3zfF71td3/a7Q1texyfXX5Zvny6nX79DlIbtgmW1HPzFxc/a3qa9Zaf3yKbqe/llO9K2+bH+3Mjfq37byT8Ts3bd9cffNmeap/utn+rk7xzs7HwqOdH0x3Vh9v5y+nl25xLV/ezksg76zevnn8hW2sp/qi6tc3tby7+tvb/H22p7avbPOpFNtYTztzct+xedy87T8DNuN9bnXT5vvtp6qnbGs97bxB7Xerz9rVtpW1bMb73e38Ufzu6gerJ2xrPdWvtPNH1zuqi7bta3OK35efXd3YztXvG6szN32fUf3Mrn3vlxse7ccp6vmLm+V7qg9XP78N9ZyillvbmT98Xx7454+FWtz5DgAAOp5TKQAA4CETjAEAIMEYAAAqwRgAACrBGAAAKsEY4MjMzMmZ2fsZyke+LwAPj2AMsEVm5sSjPQaA40owBjhaJ2bm2pl558y8dnOXtC+ZmV+ambfOzM/vumXtl8zMO2bmV6uX3XeAmfnGmfmxmfm/qjfMzJkz81ObY75pZr5o0+9U7X93M4Y3zMxtM/O1M/P3Z+ZdM/Nzm1u1NzNXz8x7Nvv/w9P/TwXw6BKMAY7W51fXrLW+qPpYO4H3/6z+27XWl1Svqq7a9H119dfXWl+6z3G+tLp8rfXCdu629uubY35H9a82fU7VXvV51SXVpdUPVb+41vrPqj+pLpmZM9u5q9ZzNvt/z6FUD7BFBGOAo/Whtdb/s1n+oepF1RdWN8zM26vvqs6Zmc+qnrzW+qVN3x/cc5wb1lof2Sx/+X3b11q/UH32Zv9TtVf97Frr4+3cyveM6uc27e+qTrYT2v999f0z87XVHx9G8QDbxFw1gKO19qz/QXXz3qvCM/Pkffru9ke7u5/iPKdqr7qnaq31yZn5+FrrvvZPVifWWvfOzIXVRdVl1V+tXvgA4wE4dlwxBjha583MfSH466o3VWfd1zYzj5uZ56y1Plr9/sx8+abv1z/AMX/5vu0z85XV76y1PvYA7Q9qZj6j+qy11s9U31o99+AlAhwPrhgDHK1bqstn5vuq97Uzv/jnq3+8meZwonpFdXP1TdWrZuaPN31O5e9Wr56Zd7Yz5eHyB2k/iM+sXjczT2znyvPfeAj7AhwL8/+/mgYAAJ+6TKUAAIAEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqOr/A8GjJEqBe/YGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in features:\n",
    "    feature_plot(feature, figsize=(12,6))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bed_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed         20498\n",
       "Pull-out Sofa      119\n",
       "Futon               40\n",
       "Airbed              14\n",
       "Couch                6\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bed_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "sns.barplot(df.bed_type,df.price, estimator=np.median);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Amenities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature will not be considered for now and will be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('amenities', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Minimum and maximum nights group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only minimum_nights and maximum_nights should be kept. The rest add no value to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('minimum_minimum_nights', axis=1, inplace=True)\n",
    "df.drop('maximum_minimum_nights', axis=1, inplace=True)\n",
    "df.drop('minimum_maximum_nights', axis=1, inplace=True)\n",
    "df.drop('maximum_maximum_nights', axis=1, inplace=True)\n",
    "df.drop('minimum_nights_avg_ntm', axis=1, inplace=True)\n",
    "df.drop('maximum_nights_avg_ntm', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GYGAtSZIkDcDAWpIkSRqAgbUkSZI0AANrSZIkaQAG1pIkSdIADKwlSZKkARhYS5IkSQMwsJYkSZIGYGAtSZIkDcDAWpIkSRqAgbUkSZI0AANrSZIkaQAG1pIkSdIADKwlSZKkARhYS5IkSQMwsJYkSdpMTZ9xMtNnnLypi7HFMLCWJEmSBmBgLUmSJA3AwFqSJEkawIQD64iYFhHnRcT36v09IuLUiLi8fu/WW/aIiJgdEbMi4rkT3bYkSZI0WQwxY/0u4NLe+xnAaa21fYHT6j0R8XDgUGA/4CDgmIiYNsD2JUmSpE1uQoF1ROwNvAD4fC/5YGBmvZ4JvKiX/rXW2orW2lXAbOCAiWxfkiRJmiwmOmP9L8DfAWt7afdqrc0HqN97VvpewNzecvMqTZIkSZryxh1YR8RfAAtba+ds6CrrSGvryfuNEXF2RJy9aNGi8RZRkiRJ2mgmMmP9FOCFETEH+BrwrIj4MrAgIu4DUL8X1vLzgPv11t8buG5dGbfWjm2t7d9a23+PPfaYQBElSZKkjWPcgXVr7YjW2t6ttenkhxJ/3Fp7FXAScFgtdhjwnXp9EnBoRGwbEfsA+wK/HnfJJUmSpElk67sgz6OAEyPidcA1wCEArbWLI+JE4BJgNfC21tqau2D7kiRJ0kY3SGDdWvsJ8JN6fSNw4HqWOxI4cohtSpIkSZOJ/3lRkiRJGoCBtSRJkjQAA2tJkiRpAAbWkiRJ0gAMrCVJkqQBGFhLkiRJAzCwliRJkgZgYC1JkiQNwMBakiRJGoCBtSRJkjQAA2tJ0pQ1fcbJTJ9x8qYuhiQBBtaSJEnSIAysJUmSpAEYWEuSJEkDMLCWJEmSBmBgLUmSJA3AwFqSJEkagIG1JEmSNAADa0mSJGkABtaSJEnSAAysJUmSpAEYWEuSJEkDMLCWJEmSBmBgLUmSJA3AwFqSJEkagIG1JEmSNAADa0mSJGkABtaSJEnSAAysJUmSpAEYWEuSJEkDMLCWJEmSBmBgLUmSJA3AwFqSJEkagIG1JEmSNAADa0mSJGkABtaSJEnSAAysJUmSpAEYWEuSJEkDMLCWJEmSBmBgLUmSJA3AwFqSpAmaPuNkps84eVMXQ9ImtvWmLoAkSdJk1b9gmnPUCzZhSTQVOGMtSZIkDcDAWpIkSRqAgbUkSZI0AANrSZIkaQAG1pIkSdIADKwlSdJG5dcTanNlYC1J0p1gUChpffwea0mD8LteJa2LY4O2JM5YS5IkSQMwsJYkSZIGYGAtSZIkDWDcgXVE3C8iTo+ISyPi4oh4V6XfIyJOjYjL6/duvXWOiIjZETErIp47xA5IkiRJk8FEZqxXA3/TWnsY8CTgbRHxcGAGcFprbV/gtHpP/e1QYD/gIOCYiJg2kcJLkiRJk8W4A+vW2vzW2rn1ejFwKbAXcDAwsxabCbyoXh8MfK21tqK1dhUwGzhgvNuXJEmSJpNBnrGOiOnAY4FfAfdqrc2HDL6BPWuxvYC5vdXmVZokSZI05U34e6wjYifgP4F3t9ZujYj1LrqOtLaePN8IvBHg/ve//0SLKEnaCPy+4k2rq/87qvsNXU4bh8dj8zKhwDoitiGD6hNaa9+q5AURcZ/W2vyIuA+wsNLnAffrrb43cN268m2tHQscC7D//vuvM/iW7moOdpK0aY13HJ6s47cXn5u/iXwrSADHAZe21j7Z+9NJwGH1+jDgO730QyNi24jYB9gX+PV4ty8NyX9RLEmSJmoiM9ZPAV4N/CYizq+09wNHASdGxOuAa4BDAFprF0fEicAl5DeKvK21tmYC25c0BU3WmaQtgbNlmswcG7Q5GHdg3Vr7Get+bhrgwPWscyRw5Hi3KUnS5s4AU5q6/M+LkiRJ0gAMrCVJkqQBGFhLkqTNnh9S18Yw4e+xliRtXH4IUZImJ2esJUmSpAEYWEuSJEkD8FEQbVJT/Zb2VC+/JGny8SsXpy4D682AHVCSRtY1JjpO3jlOGkjj46MgkjY5P60vSdocOGMtSdLAnPGVtkwG1luwqXRrdCqVdbKwzkasC01WU6ltToaybskXLBtS/0PWz6ao68nQxiZqSgTWm0NFT2XWv7TpbMmBhNbPdiFNTlMisNaWxRPGiHWx8XgBufFsaF17TKTJz356ewbWk9imaKzj3aYB4MRZh3+c9XPXsF4laTgG1tJmwBkDTVYG7pK2JAbWE+AJY/IxwJw461C2AUmTxV39oc2hx7spG1hPpYF/MpR1slwEbOy6mCz7LUnSVDcZ4pkNLcemOv9P2cB6Y9vQAzRZGp00lhcZmgjHNkm6YwbWk8SQgftdHUBtyQGawcWdY31Jmzf7+KazJZ+LJzP/pbmkO+S/HJck6Y45Y826r7i9Cp+4u7oOp/oxmurlvytNlpmYyXCHaDIbsg37+Yu7huOMJoN19bfNtQ86Yy0NzNldSVuyLWEM3BL2UePjjLW0BdtcZwykzYn9VJo6NuvA+o/detjUg9NkKceWyJOUNgUfjZo4+66kyW6zCawdcCVJksZnS7g43xh8xlqSJEkawGYzYy1pyzNZZ1gmyx20yVo/krS5MrCWdKdNlsBxvKZ6+SUNw4tPDc3AWtJmxROlpMnAC/gtk89YSxuB33kqTU32XUl3hjPW0hTjjKx013CGURrGlnyecsZakiRJGoAz1pIkSXjXQhPnjLUkSZI0AGesJUnaCJwNlTZ/zlhLkiRJAzCwliRJkgZgYC1JkiQNwGesJWkTWdd3vW7J3/8qjYfPrmtDbYzx1RlrSZIkaQAG1pIkSdIADKwlSZImaPqMk2/3WIq2TAbWkiRJ0gAMrCVJkqQBGFhLkiRJAzCwlqQ7wecoJUnrY2AtSZIkDcDAWpIkSRqAgbUkSZI0AANrSZIkaQAG1pIkSdIADKwlSZKkARhYS5IkSQPY6IF1RBwUEbMiYnZEzNjY25ckSZLuChs1sI6IacBngecBDwdeEREP35hlkCRJku4KG3vG+gBgdmvtytbaSuBrwMEbuQySJEnS4DZ2YL0XMLf3fl6lSZIkSVNatNY23sYiDgGe21p7fb1/NXBAa+0dY5Z7I/DGevsQYBawO3DDmCw3JG28622K/KdSWe/q/C3r1Mzfsk7N/KdSWe/q/C3r1Mzfsk7N/CdbWR/QWtuDiWitbbQf4MnAD3vvjwCO2MB1zx5P2njX2xT5T6WyWhdTs6zWxdQsq3VhXUz1sloXU7OsW3JdjPdnYz8K8j/AvhGxT0TcDTgUOGkjl0GSJEka3NYbc2OttdUR8Xbgh8A04PjW2sUbswySJEnSXWGjBtYArbX/Bv57HKseO8608a63KfKfSmW9q/O3rFMzf8s6NfOfSmW9q/O3rFMzf8s6NfOfzGUdl4364UVJkiRpc+W/NJckSZKGMNSnIO+qH+B4YCFwUb2/H3A6cClwMfAuYDvg18AFlfaPvfWnAecB36v3c4DfAOdTnwIFdgW+CfwWuAK4rP5+PnAr8G7gPZX3RcBXa5vvAm4CVgPze9v8MrASWAGcWu9vAZYDa4H9qwyrK+3bVYbzK20ZcApw397+zwca+ZUw5wFrarnza9mFwHXkVxNeDNwMrKpl5tRy/9VLOxv4i6q3pVWOi4CHAT8FllT5fws8ov62osrw8ToO11Ta8joePwUW1ft5wBnAWfW362vdRwFX9coxD3hNHdPrK7/5wOeABZXXsqrnxcCVVd55wDl1vG6r5WYBe1T64srrCuDewOW1TCNv+WxX21leP7+t9bptXg/8qOr6gl759wKu7ZX/evKfHP260lf0jtfNvWWurte31e/ryH+Y9GjgF5W2ANi5jvGtteypwOF1TBvw02pjH6syL6tt7dpr79cyaisfqvdLyTb4fOBxlfctZDt4Za8ebgHOJdvMJVWPy6scN/Tq8JLaXteuu2O0K6M+1pXjmtq3lVWO8+v9db11P1rvl/bq6QDgqcCNtdxi4EBgei+/JcCLqw679vla4DNVh916f86ony+s5Q4Cjqrtdfv//lpmQeV3JfCVXhmW1TK/7R3LVXU8fsOo3a0GvlfbWlV1cwEwk1Hfup5sm93Y0vXxvYAfMGpjN5Ft7Lx6v6zyuBaYXXV2bR3Lz/bynwXsBnyhd9w+UcftNEZ990JgRpV1eeV1CvBjcrxqVZbnk+11UaWvqWV2YzTmra7yXF5/W1nLrq7ydO2lVdkXVz4NOLrSbuttdzbZL69kNF7+oPZxfqWtqvdd3itq/a7/ra1y3EqOYzdW3rf1tvHD2v/FvbTZte7ayufW+ttqsq2uqDyW9l6vrHKs6v0s75V1TS+/3/XKt7T2ofvbPPJ/PizurbOUHEt/Vnl1aVdy+/FtZaXNrzy7fnc5o/PSqvp9FdkOl1dat2y3D7fUcmvqd9f+uraztHcMZtc+9fNaXmmrGPWNNb1j2aqst/XKuooca7p2vbp+fkf2u26cvbGW/2Qt26q8K3v7uZLsW93+dsfgcvIf1HX9d20d/9eQ7bv19vmGOh5dO+7a07J6f20vfVWt+7ve8V1dabdWfXfjXtdmbqzXi8m+vohRn1hLttn39MrVan9fQo5N/WW7frCE0Xjd/X1Nbbvrv11es6tsy3ppcxn1o27Z68esd3bt28ox5fp6bb9L6459dxy7PtS1hTW9/Nf01ru0jlM/bTbZrvvbXMJobO7Kd1vvmKzprdud0/p9txvzltbfzyXH7svI9nYROXZ1MeGek+1bQcbji+QJsLMa+JvW2sOAJwFvAx4IPKu19mjgMcBBEfGkWv5d5AHq+7PW2mNaa/vX+08DP2itPZQMLA9orT0GeDxZ2T8H3gns31p7BBm8/A3wBuBl5NcI3j0i9q38tgL+jWwUpwFBnuyvAc7s7deTyYN9GfnVgzPIYOIK8sB+oJY7DNip1oc8uJ8Brqhyfhj432RQ9qjW2n7kAPHEyus/gW+Rgfp7Ku0DZICwI/kv5vcgA7EZ5ED34Xp/zyrbB8ig+GfAIcA+ZFCyY617b7Ij/0m97wKo+cBzyMa5GngAedI+AtizlptDXqxcVGVcTJ48n9da266WAzi56utVtd6xta3nA3cHtgE+UnV8ZNXZNmTQcygZFJwJ/CnZTt5Uy9y96u4yYN96fzUZCJ1DXoBcSHbmR5DB0RG1ztVkEPZBsp3dgxw839pa27W3zFnkoPbSSruVbCOfr218mxxY/hY4sY7B6WT7eSr57TkLGTm11v02OUAcUekfJNvfst6y55AXVT+tzzh8lbwQ+Cl5XD9HDuh7k+3j5FrvNODIOganAdsCzyAvmF5Tyywjv/dz+zoeXTleSR7Prs3OBN7fWtuh2mwj+8cute7HyQDttfX+EDLY/i/guCrDe4Hnkn3jZ621uwH/lzzWJ5Pt88yq/4cC76v1/g14AdnPf0UGqNeQx/sg4Lu13M7k8b2KDJLvSV6EPAv4Ui3zcrK9La7fO5FtcVvg+1WWHcjB+um9OjmVDHAPBo5qrW1LHs8vk2PLwWQbW0peSD0R+Ieqi1PJ/rYrcI9KW1TbnlN1dSnZRt5A9oEdyXZwLNl+nlh189SIeD7ZT3evPO4J/B3wyHp/Kdn+ngz8Ozkmrajj9XmyXxzN6EJgRm3rRPIkeiTZXp4AHFdl/G61g9eSbf96sk/+r6rvZeQ4sR85lt1YaYeS4989GI2X+5CBzv8Af0WOK/8G/KSOy9XkCfL/1Xb3r2P9v8nx62/IsWo7sh28rI77B+rYdWl7kuPsJVXn7+T2wc/OZLuNqq/nAn9f+35oleuzwDHkCf6ZVbbTap9n1X5eQo4La2sb8xgFx1dWfV1Wyyypev273no3kuPqW+vYLSSDq1OAf651V5B9/v11PLsLwwurnPtX2q1kwLSmyr1dLf+oKteTyc9mfQk4gVHgen3ltYJsa5dUuX5I+leyjTy7jtXNleeyOoYvJfvmrCrHqxmdM7tg571V5904G7XOa+vvi8nxay15bnptLfs9Rhc/l9VxPo28ID+z8t+5yvbv5Hh5MaOx8RAy0FxDxgfvqPe7VRl+Ro4FT6+8lpKfI7uZHM+/XMdtGtk/ribHhyNrme2Bp1Q9vbOWW0Kep1aR49VHybZ/A3keOZ/si/uSbWl57fPqKlMXLC6q5bsLju7C9H2MgkbtLhIAABx5SURBVO3D6uf6qj9qP75Hntvm1j7dSPaHLgD+aeVzBvCKqvdTyHb+8V7aGcA3al9eVWX4Ojlm/1WlX1bb/WfygmQNec64iezHt1RenyH7b3c8l9X7/ev1VZX+fOB1tX43dh1KtsFtap/eVHVyWKU9qLX2cOCXwNNbaw8GHl7bfmXFjI9prfXPw+s06QPr1tqZZEV37+e31s6t14vJjrNXa21JLbJN/bSI2Js8oX5+fflHxM5khziu8lzZWru5/nwgGYReSw4m20fE1uTgux3wy9baqWTjvY3sqJAB+Zfq9UzyoJ9LHsRuPz5R60EeyL1baz/o7euOuVg7k+zIC8iOANkxl46poxcCi1prKyrtpF5eLyODqZt6u74LecK7H3Bm1eUFZKd4EjCz0s4hG9m3WmuzyAY/B9i2tXZMa211LXcZsHNr7dbecdmp9vFTZONfBdyNPCH3j9+25KB2VGvtxkpb2h1ncpDZjhwQGnl8LyVPJtuTg+M2dQz+jOxUMyvt5ir/OVX+II9la62d1FpbXcstA3Zrrd3KqA1tX2X+FPAPXXX3juPv2xo5iB/VK991Y5Y5sNJ2rvfbVt10F3Ofr/cvJ09MH671Z9Y+HdDVW7mEvCD6fO3j3tXe39wrK7W9Ln9qmd3JQRGyHWxPDj6QQdxL6vXzavtdObYj+0NfNyMC1Y7r9YfJk//6PsRxd+DTvfa6sJfelWsheQL9X5X2A7KdPxh4e6WdCDyIvACYxWgWY1/yJAl5YXwfsp8/sVeutWQb+nUttx0ZODyAbIvdDOLO5Iw4ZL+8hvzHVWeSx/Uysv0eXPV0IDmQb08e663IMWNBrf+fNY4sIAfurYFPkAF4kO1gK2Cb3nizgNuPQfclT65ryH52BjkrD/D5atffIoPVM1prF9Q+n0ue8H7UWltcy10JrG2tLaj3Z1Q9Lan96I9vDyHHjC/V3+8HvIgc826pn+PJNrsVGfwuJYPePWv708g2S2vtmNq3bYBTWmsrWmufItt31P5cXPt5C9k+LiX74Jm97Z5V5bmE7FuQ7XEVY8ZUsv0EOR52d1Quqvrvpy0kL2Y6D65tbwd8rdruw8jjf7fW2s8YBTIHVLleQgZnFzGacHgcOa6uZHQO3qXqmNrHX5HnnevJfr9NLXN5lX1Nb73rqsw7ke31+tqPJ5HnivMrv6e31j5Ojo9fqbx2qvp9W6V1QfnvyAD+CnKG7mIysHtbbf8HjO4m/bq293gyuH4zOZnTXXD+jgwSb659XlTrPbby6sbP06qeumP2kirbN8m2f2nV13fI8/oqMkDambyAgRy/tiUDt0a2rReRfWh15XU82Ue3qrr+WvX1OVXPJ1Zex5Nj4Fuq7Ne21i5vrX226uJfyDa3urV2CvA08qLkmtqvy8nz6fW1fiPb06LeMV1R+/g6cox5TpXrXHKy6tY6XtPIce5E8gJid/Jc+lCy/95CXrxsU+nTyMB1OXAvRn3mbmTbnMmoHa1orX278nkc2YaeRE6eXEa2kRuAHVtr7660tcD9a79m1X632u7dWmsfYtT/diXbwppKW1t1cx3ZLrvz+9XkxcxWtexT6ti+jdHM9zPIWKY/091dCDXyIifIvvQfvbxWkePxvRjdieh+X1HlvrGO+18CqyJi29ZadxfizhnqkY278oe89XvRetKvqcqfRg4gS4CP1N+/SXb2ZzJ6FOQqstGeQ/53x8eQA8MXySvDz5MNCLJjvb1ev6vyXkReqXcD6j3Jxr0U+Nda9uZ+mclBZzo5aPyEnPmml/Zd4FWVdnQdyG4gfmGV4yKy43e39+eRneZ48sr5EnJQ/BV5YnxC5X8lo0deHsboMYZryQDi52RAMJ3R4xY3j6nfm3p1/ktyoNh5zHFYCry+tw+rq0yvImcKp1fa9Cr/HLKzLyE76PnAP9YxWA48s5f/y8gO0AWJ3a236xjdJl5CDsyLq/5/3xZqv7r2sYa8aIDbt5nZ5CzJ+dz+tvGryWPe3WLbvcq5svK6sOr/fEZX8teQgUmX9wnkwLMfo1vji6v+byBnYp7J6JblYm7fZleS7fh8Ro+C9Nv2gqrns8hB55l1PHavY3AdOXhcQ56UXlLrLKu67B6BOZc8US0n+8nq+tsba5trGM0MHbmO/nRBlWMB2RbPqf27mjyRrCDb7fG1T/PJAfAysr12M3Ur65g9r7Z1GtkufsnoFt8XK+1bY97PJ2d8+336arLNXVb1cV7le1Dt/xJGj0ydR57U59c+nEueBLrb1LeR/b3rN8eTQUS/3xxPBv5LGbWTmyq9e4yjG0duIi8AuoBjSR237zO69XwF2ca6Mejm+rmMPAF2t21vq23dkwwkusdjunHqTLK9frmXtkPldVu9/0itc3PVwzXkDOJqcvb751WG6VVH3QzuzeTJem5vzFtBBiUX1TFZXutdW3X7RHL2+dbK/x96ff7rtfz+5Gzmj8i+380GfoucLZtZ+/9dsu19nWzv3aNOl9bx6Y+p86tO5zIaU09l9PhFl3Yuo9vpx5Nt/UuM+vjx5J2N7jG5M8ix4egq10JGY+9xVRdXVj5Hk8Fd9xjCSrJ/d48B/I4cHx7GqM1fSwatP2c0Bq6qv7+j0rpb4f9Tx+bdVVfdIwl/V9t+d9Xl66tss+uYraxjcm5vnS+RbePGKtvNvfPc8lqnG2ePZ/QIVjemdo9p3Fp//wijx1u6MXQJo0dzlpFtrjuOa6u+n1Bl6ILUuVWf15F3JVfXMWxksHdO1UNXJ4ur7t/I6FG77lGGWeSF6Vqy36ypY7+K0cXlIkbn1uPIPnRTlf9ccvw9g2wTr6plzmV0rvoYo8cgFlfZVpLj5s2M2vYaso9+olf231ZZ3s2o362onzm13/swehxlMTnB8qNK+zo5Lndt5LZabjWjfngzOeGytvb1k4we47kG+FWvb66sZQ+vv93CaIyY24sLllXdHs1o3LiNHI+PJu9arCTPjwvIPnJW5XVulf+lZMC8rLbZjQe31U83RvykyrK00s6vst9cfzuBjO+6R5WuJY/11b362pXROetccqb9LEaPD/8D9aUff+xn0s9Yr09E7EQOmO+uWdI1LW8x7w0cEBFvBRa21s4Zs+pTWmuPI0/abyM7yeOAz7XWHkseqBn1D2xeCHwjInYjT6L7kDNFO5JBzUfIAXkmebBWj2NX9qj1Tqj3Hycb4glkJ/p7csa073Pk7Fv3vNEnyCBxGnmi7R4ngJzR+Gq9fgvwT5X/e8iO/9fk7aeLydt2K+H29dtttNL2I5/TvLWXdhY5uBxX759MBsNfI2dxP1J5/Y7sXJ8jO9RScubjQ+SMxJ5kp/4A8IVIO5GzEV+tbb6TrOuXVdlWMHoe8oHUTE6/LQBb9drHL4D9IuIRvbRPVT2dV+/3JAfl08hHDR5UeXUzBMeQV7+7k8H+cVX+E8kT0MLa38fWes8kH+t4EzlbeA+y4/6AvHV2EHm7axdGV+Jdnf8FOZt4zpi0rm2/itFzmQ8iT7yd55AXWvcjT6DTyFm3Z1Sdnka2g+5Z+ka2la3JYH8J1U8i4unA4uo7FwKHVFrXn37I6ELsWnLG+HlVPx8gL2AfQJ4Yt65j/w2yT0EG/D+s+tmbHKg/UfXczcTBqJ13/XXFmPdryBmqx5Ht7JvkIPrAKtPba7lGPq5yL/KEumPt72PIk+U3qi7mVr1+uOWjJ2eQJ7+/JoPn15BBWtdvunHjFHL26DFk8HoGOQO1LbcfR+5GtuWHVtpWledO5Cx7N9P2PUZj0NerXGeQj38sJh8zOb3261SybXWz1N049ShGQUKXdnHt71n1/smV10XkxckNjJ63/dPa723JQHYrenfhGM0w963rjkUXdP8HOes3dozue2CV9U1kW+2enb2wt82duf0Yuprs0z8m+8eTa79PIG+p78LoLgVkG3s0GXT1XUq23dm1nV3ISYKbyXF5PqNxYQE57r61V64dGI29XV3sQl4gUmWbT54DXkW2j2lkfZ/AKBg/nwzMPkEGO5eT4/hvyTZ8QZXnA1UvXUC2I3lB9gJyXFhM3nWY3lv2uCrLlbVfJ5LBUvc89mfJPv0x8rGs64DlEfGI3nrHUeNs7eNZ5IXhLuSF6rfJQGgeeWzuR54zX1/5zSYnJbYm2/UsciZ516rXN5Nj6rerXn9HznAvJz+P8CXyDsltjO5S7UCeex5U6d0zvseT5/1p5HH/NTlbfAsZ6AXZ1/asY7C26ms38jG77ty6Fdk270Y+KvZU8vGqP6196Nrix8i+M41sG7dVPcxi1IceXNu5mJylXUGeE17IaJZ/JaNHUPv9rvtMw55kX/r9uWMdururt5DjyLR63/XDX5AXcmvJc1s3Jqwhz1ljdX37JPL4Qx6P9S03VpCPdtxIjr1fZHQu7u4o7kj2//fXOtszGg863RhB5dEq7e5kPQY5tj6EDMA/zCgGuhtZh08h7zj8DTXBUOe1X5Cfn3tk/f1p5MXiH7epZ6PHM2NNNpAfAu9dz/IfJIOGeeTV3PVk4/vymOU+RM4wzOmlPY28hXIweWsS8hmr43rLvAY4Zkz5FpHP1VIH7Qnkyek+9X46fzhj3T0rtMPYfSWDkMvJAaWbyVtNDnL37i3X/T4DuKqXzxVVhtXkYyaQHapbPsir9t/XJdnBf13lPb3SuvJ3y83ulX8b8kpuDjmQ3e64kIHdakYf1OnKv3dvm115fljbfm+v/PchA5TFtc425CDVLRPArb19/kzlPwu4T6V9jAxCu2V+Qgax76v3h5Gd58NdWq8NfbzKPKd+upnHe/eW+xR5cvwBNcte695AnjC3JgeNf6r6j94y3QzLHLKNrmJ022oh2Ta6mYo5jJ6Ru6jWW8Tow0a/ZfShoO4W2UryxNXPv/swyu/7xJg6fDAZGL+vq0dG/WRWrw6PXUcdHkle0S/s1dnafp1VXkfWMX1mL+0Gsj1GL205t++br2AU5HXH93DywqNb5jzyYmJOr1zPJmc6useY5jBql3N7676hyvsD8mLoaWRA0IA9euPDqnp9MNk++/3m8Ep7AzWz1xszzqgydGV/B6PnBLtydR9K/kpv3fdWfXUXcAvI2etjqj6CfDbxrWPy/zSwYEzbP57RONXVz0e7tEr/Z/J55O6O23Ty5HfdmPHtsqrvWfXzWbJddWPGbWQgfhGjD3pP5w/HwXPqmB7dK8MZ5PG/mrx4gxwT1lZaN6N+EdnOd+itt7D2q0v7JTluPaDK0N196vpL92Goxb20a8gx8MW1jeNq36bX7yPq9WIy8L2itrWQnBX7WOXVjb0/JE/qq8kT+tHUXbHeWNaqrDdW/rdWOc+ofb6M0Ux8V4/dOL669vm9vbTug42/7h2TD9bfftern1Nqf0+uOrqI7I9n9fbzolpuKaNna2fVMflc7e/C2s/TqrwfruV+WMucSvb9brx/MrcfP+eQwWY3fl5R9XFEL+3GOpZzGH3Ybk1vf79T6T8nA+D/rnLNIi9AvkKO6zfUcp+r/A9h9OHRv6h6eVGlLSb73BG9dnhNrf+lXpudQ7bjHXrH/O+q3t5Sdd5dtJ9I9t+l5Mzo58j2tqj2r2t7N9S2T691P8uo391W9dzvd92HS28i+3F3gfSL2tbPGI3j3Qdru364gAzuuw9mbkW2ve5OxpxeH1tJTh4dTrbl6ZXXudQd7l7a2ax7xvoSRhfx3czxU2qZFWRbWDkmbTGj8aD78OHV5MXQZxjdPe/nNY/RjPpaRvFUd2eiy+89ta/LyMk4yIvAi3vH+HB649T6fqbcjHVEBDnIXdpa+2Sl7RERu9br7ckT6adaa3u31qaTD63/GHhTRNy9ltuRDPx+AcyNiIfUJg4kD/grGM02XAM8KSJ2qO0fCFwaEd2H6u5Lzpp0y59EDqKQJ6/vrGM/DiKvxK9urS2ttH17i7wQuKC1tid5NXwZ2SAex+1nhrqB/xTy6o6IeDB5JfZw8vmpebXsdeSMNuRVdjeLcyl5xfq/yQ+WrAa2qfrtyn9cLTevdxy+X/v+BLIxHkde3X2ytvEQMkj7f621e/TK/ylGx68r/y5kgPTJXvk/Qnaecyuf48iO1T17/WKyAxERO5DPJ3+fHNAOq7bwUuBXXfsgB4sDgOsi4iXk87svI9vM3IjYt9eGdgdOrjb0MHIgmlFl7draC8kB9YfAsyqte9b+BvL5uNWMvhnlGbXMS2q9x1X+ryAHtjfUsfgvss1+kWzL08l2+T8tP0D7enIQPgT4cWvtoa21aa21rcn2uayOzeN7feAyckCfRc7m/Jgc2K6OiLtHxFaMAtoryHb8erKf7MPoHzttRd4+vDwiXlx1eCg5a3Qh8Ce1ze7Rl3dldf2+z927yvKsSnsheYKcX/WzI6MPDF0fEQ+pss0gT5gLyItSyJm+23r9d7favyVkkP9Csv/8stZ9bpVtCdmeru2t+5La91+Q/ePAOkaN0QeoXw/cWH3/FeRdma7fnEQOzl8lZ6pXR8QDe2NGF7S9vtIOJ0/2V5J9dZ86bscDj++NN4eSfedJ5Ozjb8l+NJfs04cA/x/ZR24k2/79u/L1xqlta7++GhEvr+P2lqqjU6vt37/y6i7KINvRLsAvKq9ufNuDbCffqbRta7m/JgPnVeQYQB2DbkY4qGeLI+LPGT3S8JyI2DYi9iFn/7YBPttaO6vGy7VkUPAAso/8JzkrtRZYU+s9rMrwotba0oh4FnlyXUVeCG1Lzty/tPZxXtXpvDFpLyJnvS6oMr+EvHW/BzkGHVrLX9Udtxq3pjH6JgyARVWufcm7ECsYfdZlIfmh92l1XFbUcjeT7Ww22ccfTs5IfoXRIxJ/WXk8ixwDtyKDh0/30rZhdIfo9XWM3lVlvK5XZ39KBkt7k8d+V3L8fTOjD4/OJT/ceil5J667eLknGcAcQvb9R1d9LyHH0OXk3ZjjyEB6eq27b5XhYLKPnV15PYBsj5Dt4kayHb+Q0Yz4V8njfDEZRP0TOYt4Onm+6B67u5y8s/sz8gJ5b/IRgUPIi+8gx7RDyXG3+9aL59cxeXsdg23J8erQiNiv6mcvcrJhv+qnz2d0p63fFg8g2+mfVD43kueFZ5PtYBXZfr5Vx2EFOU49tpbdlbzgeViV9RCyLcyvdb9Nnvd3IQPpLs81ZLvevur3oXXc7wV8p8bYbsaaiDih6vc9lf6zqpd9GH0WYGmN9/vU+1/Wdql1tq5yrex9ecTW5Hg81p+Rbez/VF6PBL7YWjuLjKW2Jj9QfE3ty861/Im1TGfbOkYryDvYO1fazFpuGjlGbEfOes8iL/ZmVz3OrfFld/Li7URyPHtm5f/ntSwRsQ35RQYXrWN/bu+OIu9N/UN2oq4RzSNvzTeyE3dff/JW8gruwtrpD4zJ45nkFU03UHZfy/f39ffHkJ3xQjKguS/ZqHfp5fGPjL565Ut18H7KaAatm318HdmRVlQ5l5MzMr9j9MGq7uqpP7P4E3Lw6r6up/u0a3//V5MD41Vjlju3lumer7qanDXoPjDQlevU3noryKCqe72cDFi6Twt3z3B1zx515ey28dteWvf1S92+dVfN3QcKumO1kmy8rbfMLVW3jdHV4lLyCr5Vvc0lB8nG6OvFuk8Ad1+LtqKOx6Nqe/2v23sqGbx05V9Czsas6JV/EaPb3svJQfYndby7dnUTOVB3X6O0vOrhWbV/N1XawirvhZV2UpXrsl7Z55GPE72r0ucyuuV8T7LN3FbH8dWMrrJXMLpz0K1zC/BvY9p794z1l8i7ClcwmtV4KjnA3EqeYI7t1eEickbngtrnJYxmr64ZU4dnjqnD+eSFWr+P3VL5dF/D1X2N4SW9+rqOvKXdHdvltW+PJ2dTu6+muowMnJ9RZe/K+/xat3ueeWGVpytX94G6fj9fSp7svtc7JvPJgOqcKtuttR/dBV535+WZZD9ZU9s9qo7b3mTfuKKO20cYtaebalufrbxW1Pbuze3HliXk+HMBo68AnEsGHP9Y+V1bx/VnZB9YWtv8Ve1bl/+55C3cSxjdrbiJbD/d14wtY/RIWfeVW1eSs0Vjv/brOnI86r6Srhvf3sntx7y1ZL/8WS+tm1lcO+b9qjHbWFH7019uGbf/sNJKMlhY0Utr3H5M7ZbrLma6cW4OozG1W3YB2dfHpnV3ebqyncvoeeHug0/dONMva//O0GpGY8nSXl79/enKtqRX1tVkW+t/9WB3p6n/FYVryf7Z5dHl3X3YfXGvjru0lb0ydmVazu2PxeLevnRjw2Ky7f2I0WcxuuewV5LnnVW9/Fczep54ef3uzknd4w3ds9hLa/9XMTrXL2D0dbZdPV7B7ceNmeQ5+wZGz0X3vxJwSa3TjSFdWS8n+8vSXlmvJB/16NrfKnIMW8zoGeUVvbq7sPb5d726774y9mYyQF7T2/erqyzd10B29dqNLd03fnTtvd+Oz+T2/W4teY7sPovR7483MWoHq8bktYbRzHwb87N0HWlrxmxz7N/XriN9XWkb8rOu9cb28dar/7Hrjl1u7M/qXl139bKs934VeR6/uOroYvJi7cL6uZi8cJ12R3Gr/3lRkiRJGsCUexREkiRJmowMrCVJkqQBGFhLkiRJAzCwliRJkgZgYC1JkiQNwMBakiRJGoCBtSRtQhHxwoiYsanLsT4R8eaIeM0dLHN4RBy9nr+9f13pkrQ58nusJUkTEhGHk/8a+e3r+NuS1tpOG79UkrTxOWMtSRsoIqZHxG8j4vMRcVFEnBARz46IsyLi8og4oH5+HhHn1e+H1LrvjYjj6/Uja/0d+rO9EfHFiPhcRJweEVdGxDMi4viIuDQivtgrx5Le65d2f9vQ9dezb0si4siIuCAifhkR96r0D0XE++r1EyLiwoj4RUR8LCL6/973vhHxg6qHj9byRwHbR8T5VVc7RsTJtY2L6l+rS9Jmw8Baku6cB5H/2vZRwEOBvyL/Tfz7gPeT/5786a21xwIfAP651vsX4EER8WLgC8CbWmtL15H/bsCzgPeQ/1r8U8B+wCMj4jEbUL7xrr8j8MvW2qPJf6H8hnUs8wXgza21J5P/ErjvMcDLgUcCL4+I+7XWZgDLWmuPaa29EjgIuK619ujW2iOAH2zA/kjSlGFgLUl3zlWttd+01tYCFwOntXym7jfAdGAX4Bs1m9sFtdTyhwNfAs5orZ21nvy/28tvwZhtTd+A8o13/ZXA9+r1OWOXjYhdgbu31n5eSV8Zs/5prbVbWmvLgUuAB6xjG78Bnh0RH4mIp7XWbtmA/ZGkKcPAWpLunBW912t779cCWwP/BJxeM7J/CWzXW35fYAlw3w3Iv593P3+A/odj+vlv6PrrsqqNPnSzZh3Lxh9Zt7/d9a1Pa+0y4PFkgP1/I+IDd5CnJE0pBtaSNKxdgGvr9eFdYkTsQj5C8nTgnhHx0glsY0FEPCwitgJePIF8Nlhr7SZgcUQ8qZIO3cBVV0XENgARcV9gaWvty8DHgccNX1JJ2nT+2OyFJOnO+ygwMyLeC/y4l/4p4JjW2mUR8Trg9Ig4c5zbmEE+tjEXuAjYWN+68Trg3yPiNuAnwIY8ynEscGFEnAv8B/CxiFgLrALeclcVVJI2Bb9uT5K0QSJip9bakno9A7hPa+1dm7hYkjRpOGMtSdpQL4iII8hzx9X0HnWRJDljLUlblIj4FbDtmORXt9Z+synKI0mbEwNrSZIkaQB+K4gkSZI0AANrSZIkaQAG1pIkSdIADKwlSZKkARhYS5IkSQP4/wGwGScSdXQ86QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nights = ['minimum_nights', 'maximum_nights']\n",
    "for night in nights:\n",
    "    feature_plot(night, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum nights mode:  0    2\n",
      "dtype: int64\n",
      "Maximum nights mode:  0    1125\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum nights mode: \", df.minimum_nights.mode())\n",
    "print(\"Maximum nights mode: \", df.maximum_nights.mode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no pattern in these two columns relating to the median price and will therefore be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('minimum_nights', axis=1, inplace=True)\n",
    "df.drop('maximum_nights', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calendar updated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will be droppEd since it has a high correlation with availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('calendar_updated', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Availability group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Has_avaliability is all 't' so we will drop it. In the city of Amsterdam, rental for entire home/apartment is limtied to 30 nights per calendar year as of 1 Janury, 2019. This does not apply to other types of rentals. However, there is still room to extend the limit if one obtains the necessary permits. Since this will be captured in the 'room_type\" feature, we will drop the entire group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('has_availability', axis=1, inplace=True)\n",
    "df.drop('availability_30', axis=1, inplace=True)\n",
    "df.drop('availability_60', axis=1, inplace=True)\n",
    "df.drop('availability_90', axis=1, inplace=True)\n",
    "df.drop('availability_365', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Review group"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "As with other important features, we will impute 'no review' for all mssing values except 'last_review' which will not be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.first_review = pd.to_datetime(df.first_review)\n",
    "#df.first_review.fillna('no reviews', inplace=True)\n",
    "\n",
    "#df.drop(['last_review'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pot the distributions of the review columns\n",
    "review_scores = list(df.columns[df.columns.str.startswith(\"review_scores\") == True])\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "for i, var_name in enumerate(review_scores):\n",
    "    ax = fig.add_subplot(3,3,i+1)\n",
    "    df[var_name].hist(bins=10,ax=ax)\n",
    "    ax.set_title(var_name)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost all reviews are above the 60th percentile. We will form an ordinal category with this in mind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bin and mpute missing rows with 'none'\n",
    "df.review_scores_rating = pd.cut(df['review_scores_rating'], \n",
    "                                 [0, 79, 94, 100],\n",
    "                                labels=['0-79/100', '80-94/100', '95-100/100'])\n",
    "# Create label for missing rows\n",
    "df.review_scores_rating.replace(np.nan,'no review', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_scores.pop(0)\n",
    "\n",
    "for feat in review_scores:\n",
    "    df[feat] = pd.cut(df[feat], \n",
    "                      [0, 8, 9, 10],\n",
    "                      labels=['0-8/10', '9/10', '10/10'])\n",
    "    # Create label for missing rows\n",
    "    df[feat].replace(np.nan,'no review', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reviews_per_month.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cancellation_policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "strict_14_with_grace_period    8003\n",
       "moderate                       7492\n",
       "flexible                       5043\n",
       "super_strict_60                 112\n",
       "super_strict_30                  26\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cancellation_policy.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in with flexible since it's the most reasonable and the host has 365 availability\n",
    "df.cancellation_policy.fillna('flexible', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Require_guest_profile_pisture and require_guest_phone_verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    20450\n",
       "t      227\n",
       "Name: require_guest_profile_picture, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.require_guest_profile_picture.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    20378\n",
       "t      299\n",
       "Name: require_guest_phone_verification, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.require_guest_phone_verification.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate host listing counts group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section has some data that will be useful when doing our Exploratory Data Analysis. It is possible that we might drop more features, but tha's what EDA is all about."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the trend for Airbnb in Amsterdam for the period in the data and see how review affected the trend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hosts joining vs first reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First host joined : 2008-09-24\n",
      "Last host joined  : 2019-08-08\n"
     ]
    }
   ],
   "source": [
    "# First host\n",
    "print(f\"First host joined : {min(df.host_since).strftime('%Y-%m-%d')}\")\n",
    "# Last host\n",
    "print(f\"Last host joined  : {max(df.host_since).strftime('%Y-%m-%d')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "df.set_index('host_since').resample('MS').size().plot(label='Hosts joining Airbnb', color='blue')\n",
    "df.set_index('first_review').resample('MS').size().plot(label='Listings first review', color='green')\n",
    "plt.title('Amsterdam hosts joining Airbnb verses first reviews for each month')\n",
    "plt.legend()\n",
    "plt.xlim('2008-09-01', '2019-08-01')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that while the number of first reviews are going up, the number of hosts joining started declining in the ffirst half of 2016.\n",
    "\n",
    "Now we drop the first_review and last_review columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['first_review'], axis=1, inplace=True)\n",
    "df.drop(['last_review'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split DataFrame into numerical and categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20677 entries, 2818 to 37590988\n",
      "Data columns (total 38 columns):\n",
      "host_days_active                                20677 non-null float64\n",
      "host_since                                      20677 non-null datetime64[ns]\n",
      "host_response_time                              20677 non-null object\n",
      "host_response_rate                              20677 non-null object\n",
      "host_total_listings_count                       20677 non-null float64\n",
      "neighbourhood                                   20677 non-null object\n",
      "property_type                                   20677 non-null object\n",
      "room_type                                       20677 non-null object\n",
      "accommodates                                    20677 non-null int64\n",
      "bathrooms                                       20677 non-null float64\n",
      "bedrooms                                        20677 non-null float64\n",
      "beds                                            20677 non-null float64\n",
      "bed_type                                        20677 non-null object\n",
      "price                                           20677 non-null float64\n",
      "weekly_price                                    20677 non-null float64\n",
      "monthly_price                                   20677 non-null float64\n",
      "security_deposit                                20677 non-null float64\n",
      "cleaning_fee                                    20677 non-null float64\n",
      "guests_included                                 20677 non-null int64\n",
      "extra_people                                    20677 non-null float64\n",
      "number_of_reviews                               20677 non-null int64\n",
      "number_of_reviews_ltm                           20677 non-null int64\n",
      "review_scores_rating                            20677 non-null object\n",
      "review_scores_accuracy                          20677 non-null object\n",
      "review_scores_cleanliness                       20677 non-null object\n",
      "review_scores_checkin                           20677 non-null object\n",
      "review_scores_communication                     20677 non-null object\n",
      "review_scores_location                          20677 non-null object\n",
      "review_scores_value                             20677 non-null object\n",
      "instant_bookable                                20677 non-null object\n",
      "cancellation_policy                             20677 non-null object\n",
      "require_guest_profile_picture                   20677 non-null object\n",
      "require_guest_phone_verification                20677 non-null object\n",
      "calculated_host_listings_count                  20677 non-null int64\n",
      "calculated_host_listings_count_entire_homes     20677 non-null int64\n",
      "calculated_host_listings_count_private_rooms    20677 non-null int64\n",
      "calculated_host_listings_count_shared_rooms     20677 non-null int64\n",
      "reviews_per_month                               20677 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(12), int64(8), object(17)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw=df.copy()\n",
    "df.drop('host_since',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save all categorical columns in list\n",
    "categorical_columns = [col for col in df.columns.values if df[col].dtype == 'object']\n",
    "\n",
    "# dataframe with categorical features\n",
    "df_cat = df[categorical_columns]\n",
    "# dataframe with numerical features\n",
    "df_num = df.drop(categorical_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>host_days_active</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "      <td>20677.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1613.156309</td>\n",
       "      <td>7.007400</td>\n",
       "      <td>2.879335</td>\n",
       "      <td>1.158534</td>\n",
       "      <td>1.454225</td>\n",
       "      <td>1.828892</td>\n",
       "      <td>159.079122</td>\n",
       "      <td>103.130241</td>\n",
       "      <td>175.769454</td>\n",
       "      <td>157.643227</td>\n",
       "      <td>32.688591</td>\n",
       "      <td>1.459351</td>\n",
       "      <td>11.330415</td>\n",
       "      <td>23.290323</td>\n",
       "      <td>6.998597</td>\n",
       "      <td>4.925134</td>\n",
       "      <td>4.492431</td>\n",
       "      <td>0.410988</td>\n",
       "      <td>0.021715</td>\n",
       "      <td>0.863869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>716.940797</td>\n",
       "      <td>37.718876</td>\n",
       "      <td>1.314935</td>\n",
       "      <td>0.380045</td>\n",
       "      <td>0.894728</td>\n",
       "      <td>1.407402</td>\n",
       "      <td>162.463853</td>\n",
       "      <td>315.802268</td>\n",
       "      <td>762.047849</td>\n",
       "      <td>328.680729</td>\n",
       "      <td>27.348466</td>\n",
       "      <td>0.935662</td>\n",
       "      <td>23.150310</td>\n",
       "      <td>48.619730</td>\n",
       "      <td>14.239550</td>\n",
       "      <td>22.883678</td>\n",
       "      <td>22.710906</td>\n",
       "      <td>1.082794</td>\n",
       "      <td>0.237805</td>\n",
       "      <td>1.303439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>1.000000</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>1148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2152.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3971.000000</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>8915.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>4736.000000</td>\n",
       "      <td>531.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>33.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_days_active  host_total_listings_count  accommodates  \\\n",
       "count      20677.000000               20677.000000  20677.000000   \n",
       "mean        1613.156309                   7.007400      2.879335   \n",
       "std          716.940797                  37.718876      1.314935   \n",
       "min            1.000000                   0.000000      1.000000   \n",
       "25%         1148.000000                   1.000000      2.000000   \n",
       "50%         1650.000000                   1.000000      2.000000   \n",
       "75%         2152.000000                   2.000000      4.000000   \n",
       "max         3971.000000                 980.000000     30.000000   \n",
       "\n",
       "          bathrooms      bedrooms          beds         price  weekly_price  \\\n",
       "count  20677.000000  20677.000000  20677.000000  20677.000000  20677.000000   \n",
       "mean       1.158534      1.454225      1.828892    159.079122    103.130241   \n",
       "std        0.380045      0.894728      1.407402    162.463853    315.802268   \n",
       "min        0.000000      0.000000      0.000000      9.000000      0.000000   \n",
       "25%        1.000000      1.000000      1.000000     99.000000      0.000000   \n",
       "50%        1.000000      1.000000      1.000000    130.000000      0.000000   \n",
       "75%        1.000000      2.000000      2.000000    180.000000      0.000000   \n",
       "max       15.000000     12.000000     32.000000   8915.000000   6000.000000   \n",
       "\n",
       "       monthly_price  security_deposit  cleaning_fee  guests_included  \\\n",
       "count   20677.000000      20677.000000  20677.000000     20677.000000   \n",
       "mean      175.769454        157.643227     32.688591         1.459351   \n",
       "std       762.047849        328.680729     27.348466         0.935662   \n",
       "min         0.000000          0.000000      0.000000         1.000000   \n",
       "25%         0.000000          0.000000     10.000000         1.000000   \n",
       "50%         0.000000          0.000000     30.000000         1.000000   \n",
       "75%         0.000000        250.000000     50.000000         2.000000   \n",
       "max     20000.000000       4736.000000    531.000000        16.000000   \n",
       "\n",
       "       extra_people  number_of_reviews  number_of_reviews_ltm  \\\n",
       "count  20677.000000       20677.000000           20677.000000   \n",
       "mean      11.330415          23.290323               6.998597   \n",
       "std       23.150310          48.619730              14.239550   \n",
       "min        0.000000           0.000000               0.000000   \n",
       "25%        0.000000           2.000000               0.000000   \n",
       "50%        0.000000           9.000000               2.000000   \n",
       "75%       20.000000          23.000000               7.000000   \n",
       "max      280.000000         767.000000             161.000000   \n",
       "\n",
       "       calculated_host_listings_count  \\\n",
       "count                    20677.000000   \n",
       "mean                         4.925134   \n",
       "std                         22.883678   \n",
       "min                          1.000000   \n",
       "25%                          1.000000   \n",
       "50%                          1.000000   \n",
       "75%                          1.000000   \n",
       "max                        208.000000   \n",
       "\n",
       "       calculated_host_listings_count_entire_homes  \\\n",
       "count                                 20677.000000   \n",
       "mean                                      4.492431   \n",
       "std                                      22.710906   \n",
       "min                                       0.000000   \n",
       "25%                                       1.000000   \n",
       "50%                                       1.000000   \n",
       "75%                                       1.000000   \n",
       "max                                     206.000000   \n",
       "\n",
       "       calculated_host_listings_count_private_rooms  \\\n",
       "count                                  20677.000000   \n",
       "mean                                       0.410988   \n",
       "std                                        1.082794   \n",
       "min                                        0.000000   \n",
       "25%                                        0.000000   \n",
       "50%                                        0.000000   \n",
       "75%                                        1.000000   \n",
       "max                                       15.000000   \n",
       "\n",
       "       calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "count                                 20677.000000       20677.000000  \n",
       "mean                                      0.021715           0.863869  \n",
       "std                                       0.237805           1.303439  \n",
       "min                                       0.000000           0.000000  \n",
       "25%                                       0.000000           0.140000  \n",
       "50%                                       0.000000           0.430000  \n",
       "75%                                       0.000000           1.000000  \n",
       "max                                       8.000000          33.670000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using describe function in numeric dataframe \n",
    "df_num.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>Buurt-Lommer</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>95-100/100</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20168</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>Centrum</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>80-94/100</td>\n",
       "      <td>9/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>9/10</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25428</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>Centrum</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>95-100/100</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27886</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>Centrum</td>\n",
       "      <td>Boat</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>95-100/100</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28871</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>Centrum</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>95-100/100</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>10/10</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      host_response_time host_response_rate neighbourhood property_type  \\\n",
       "id                                                                        \n",
       "2818      within an hour               100%  Buurt-Lommer     Apartment   \n",
       "20168     within an hour               100%       Centrum         House   \n",
       "25428     within an hour               100%       Centrum     Apartment   \n",
       "27886     within an hour               100%       Centrum          Boat   \n",
       "28871     within an hour               100%       Centrum     Apartment   \n",
       "\n",
       "             room_type  bed_type review_scores_rating review_scores_accuracy  \\\n",
       "id                                                                             \n",
       "2818      Private room  Real Bed           95-100/100                  10/10   \n",
       "20168     Private room  Real Bed            80-94/100                   9/10   \n",
       "25428  Entire home/apt  Real Bed           95-100/100                  10/10   \n",
       "27886     Private room  Real Bed           95-100/100                  10/10   \n",
       "28871     Private room  Real Bed           95-100/100                  10/10   \n",
       "\n",
       "      review_scores_cleanliness review_scores_checkin  \\\n",
       "id                                                      \n",
       "2818                      10/10                 10/10   \n",
       "20168                     10/10                 10/10   \n",
       "25428                     10/10                 10/10   \n",
       "27886                     10/10                 10/10   \n",
       "28871                     10/10                 10/10   \n",
       "\n",
       "      review_scores_communication review_scores_location review_scores_value  \\\n",
       "id                                                                             \n",
       "2818                        10/10                  10/10               10/10   \n",
       "20168                       10/10                  10/10                9/10   \n",
       "25428                       10/10                  10/10               10/10   \n",
       "27886                       10/10                  10/10               10/10   \n",
       "28871                       10/10                  10/10               10/10   \n",
       "\n",
       "      instant_bookable          cancellation_policy  \\\n",
       "id                                                    \n",
       "2818                 t  strict_14_with_grace_period   \n",
       "20168                f  strict_14_with_grace_period   \n",
       "25428                f  strict_14_with_grace_period   \n",
       "27886                t  strict_14_with_grace_period   \n",
       "28871                f                     moderate   \n",
       "\n",
       "      require_guest_profile_picture require_guest_phone_verification  \n",
       "id                                                                    \n",
       "2818                              f                                f  \n",
       "20168                             f                                f  \n",
       "25428                             f                                f  \n",
       "27886                             f                                f  \n",
       "28871                             f                                f  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing 5 head observation in categorical dataframe\n",
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df_num.drop('price', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing skewness' for numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import skew\n",
    "# check for skewness over 75%\n",
    "df_num_skew = df_num.apply(lambda x: skew(x.dropna()))\n",
    "df_num_skew = df_num_skew[df_num_skew > .75]\n",
    "\n",
    "# apply log + 1 transformation for all numeric features with skewnes over .75\n",
    "df_num[df_num_skew.index] = np.log1p(df_num[df_num_skew.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_total_listings_count                        9.712629\n",
       "accommodates                                     3.035133\n",
       "bathrooms                                        5.457308\n",
       "bedrooms                                         1.921748\n",
       "beds                                             5.885202\n",
       "weekly_price                                     4.266217\n",
       "monthly_price                                    6.285889\n",
       "security_deposit                                 6.973125\n",
       "cleaning_fee                                     2.188632\n",
       "guests_included                                  4.911856\n",
       "extra_people                                     4.473722\n",
       "number_of_reviews                                5.413640\n",
       "number_of_reviews_ltm                            3.844777\n",
       "calculated_host_listings_count                   7.680603\n",
       "calculated_host_listings_count_entire_homes      7.687529\n",
       "calculated_host_listings_count_private_rooms     6.355184\n",
       "calculated_host_listings_count_shared_rooms     23.782424\n",
       "reviews_per_month                                3.800501\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of variables with skewness over 0.75\n",
    "df_num_skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing t/f columns with 0/1\n",
    "df_cat.replace({'t': 1, 'f': 0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding host_response_time\n",
    "df_cat.host_response_time.replace({'a few hours to days':2,'within an hour':1,'unknown':0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding host_response_rate\n",
    "df_cat.host_response_rate.replace({'0-50%':4,'100%':3,'50-89%':2,'90-99%':1,'unknown':0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding neighbourhoods\n",
    "neighbourhood_list = list(df.groupby('neighbourhood').price.median().sort_values(ascending=True).index)\n",
    "neighbourhood_dict = {}\n",
    "\n",
    "for i,neighbourhood in enumerate(neighbourhood_list):\n",
    "    neighbourhood_dict[neighbourhood] = i\n",
    "    \n",
    "df_cat.neighbourhood.replace(neighbourhood_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bijlmer-Oost': 0,\n",
       " 'Bijlmer-Centrum': 1,\n",
       " 'Gaasperdam - Driemond': 2,\n",
       " 'Osdorp': 3,\n",
       " 'Geuzenveld - Slotermeer': 4,\n",
       " 'De Aker-Slotervaart': 5,\n",
       " 'Buurt-Lommer': 6,\n",
       " 'Noord-West': 7,\n",
       " 'Noord-Zuidas': 8,\n",
       " 'Water-park-Noord': 9,\n",
       " 'Oud-Oost': 10,\n",
       " 'De Baarsjes - Oud-West': 11,\n",
       " 'Zuid-Rivierenbuurt': 12,\n",
       " 'IJburg - Zeeburgereiland': 13,\n",
       " 'Centrum': 14}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhood_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['property_type',\n",
       " 'room_type',\n",
       " 'bed_type',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'review_scores_cleanliness',\n",
       " 'review_scores_checkin',\n",
       " 'review_scores_communication',\n",
       " 'review_scores_location',\n",
       " 'review_scores_value',\n",
       " 'cancellation_policy']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating list of remaining categorical columns\n",
    "cat_columns = list([col for col in df_cat.columns.values if df_cat[col].dtype == 'object'])\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a multiple encoder\n",
    "def categorical_encoder(column):\n",
    "    entry_list = list(df.groupby(column).price.median().sort_values(ascending=True).index)\n",
    "    entry_dict = {}\n",
    "    \n",
    "    for i,entry in enumerate(entry_list):\n",
    "        entry_dict[entry] = i\n",
    "        \n",
    "    df_cat[column].replace(entry_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding remaining categorical columns\n",
    "for col in cat_columns:\n",
    "    categorical_encoder(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "2818    1\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.room_type[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "37581675    1\n",
       "37582501    2\n",
       "37584197    0\n",
       "37584917    0\n",
       "37586354    1\n",
       "37586530    2\n",
       "37590988    0\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.cancellation_policy[-7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_joined = pd.concat([df_num, df_cat], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>host_days_active</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>3971.0</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.916291</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.313887</td>\n",
       "      <td>5.303305</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>5.590987</td>\n",
       "      <td>3.433987</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.137833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20168</th>\n",
       "      <td>3537.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.648974</td>\n",
       "      <td>4.219508</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.241269</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25428</th>\n",
       "      <td>3549.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>5.707110</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.148420</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27886</th>\n",
       "      <td>3426.0</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.698268</td>\n",
       "      <td>7.824446</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.278115</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.134623</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28871</th>\n",
       "      <td>3375.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.214608</td>\n",
       "      <td>7.579168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.648974</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.278152</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37584197</th>\n",
       "      <td>2267.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37584917</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37586354</th>\n",
       "      <td>2135.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.303305</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37586530</th>\n",
       "      <td>806.0</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.525453</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37590988</th>\n",
       "      <td>1406.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</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>20677 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          host_days_active  host_total_listings_count  accommodates  \\\n",
       "id                                                                    \n",
       "2818                3971.0                   0.693147      1.098612   \n",
       "20168               3537.0                   1.098612      1.098612   \n",
       "25428               3549.0                   1.098612      1.386294   \n",
       "27886               3426.0                   0.693147      1.098612   \n",
       "28871               3375.0                   1.386294      1.098612   \n",
       "...                    ...                        ...           ...   \n",
       "37584197            2267.0                   1.386294      1.098612   \n",
       "37584917               1.0                   0.000000      1.609438   \n",
       "37586354            2135.0                   0.000000      1.098612   \n",
       "37586530             806.0                   2.197225      1.098612   \n",
       "37590988            1406.0                   1.386294      1.098612   \n",
       "\n",
       "          bathrooms  bedrooms      beds  weekly_price  monthly_price  \\\n",
       "id                                                                     \n",
       "2818       0.916291  0.693147  1.098612      0.000000       7.313887   \n",
       "20168      0.693147  0.693147  0.693147      0.000000       0.000000   \n",
       "25428      0.693147  0.693147  0.693147      6.478510       7.601402   \n",
       "27886      0.693147  0.693147  0.693147      6.698268       7.824446   \n",
       "28871      0.693147  0.693147  0.693147      6.214608       7.579168   \n",
       "...             ...       ...       ...           ...            ...   \n",
       "37584197   0.693147  0.693147  0.693147      0.000000       0.000000   \n",
       "37584917   0.693147  1.098612  1.098612      0.000000       0.000000   \n",
       "37586354   0.693147  0.693147  0.693147      0.000000       0.000000   \n",
       "37586530   0.693147  0.693147  1.098612      0.000000       0.000000   \n",
       "37590988   0.693147  0.000000  0.693147      0.000000       0.000000   \n",
       "\n",
       "          security_deposit  cleaning_fee  guests_included  extra_people  \\\n",
       "id                                                                        \n",
       "2818              5.303305      4.110874         0.693147      3.044522   \n",
       "20168             0.000000      0.000000         1.098612      0.000000   \n",
       "25428             5.707110      3.713572         1.098612      2.397895   \n",
       "27886             0.000000      0.000000         0.693147      0.000000   \n",
       "28871             0.000000      0.000000         0.693147      0.000000   \n",
       "...                    ...           ...              ...           ...   \n",
       "37584197          0.000000      0.000000         0.693147      0.000000   \n",
       "37584917          0.000000      0.000000         0.693147      0.000000   \n",
       "37586354          5.303305      3.044522         0.693147      2.772589   \n",
       "37586530          5.525453      3.688879         0.693147      0.000000   \n",
       "37590988          0.000000      0.000000         0.693147      0.000000   \n",
       "\n",
       "          number_of_reviews  number_of_reviews_ltm  \\\n",
       "id                                                   \n",
       "2818               5.590987               3.433987   \n",
       "20168              5.648974               4.219508   \n",
       "25428              1.386294               1.098612   \n",
       "27886              5.278115               3.737670   \n",
       "28871              5.648974               4.605170   \n",
       "...                     ...                    ...   \n",
       "37584197           0.000000               0.000000   \n",
       "37584917           0.000000               0.000000   \n",
       "37586354           0.000000               0.000000   \n",
       "37586530           0.000000               0.000000   \n",
       "37590988           0.000000               0.000000   \n",
       "\n",
       "          calculated_host_listings_count  \\\n",
       "id                                         \n",
       "2818                            0.693147   \n",
       "20168                           1.098612   \n",
       "25428                           1.098612   \n",
       "27886                           0.693147   \n",
       "28871                           1.386294   \n",
       "...                                  ...   \n",
       "37584197                        0.693147   \n",
       "37584917                        0.693147   \n",
       "37586354                        0.693147   \n",
       "37586530                        2.197225   \n",
       "37590988                        1.386294   \n",
       "\n",
       "          calculated_host_listings_count_entire_homes  \\\n",
       "id                                                      \n",
       "2818                                         0.000000   \n",
       "20168                                        0.000000   \n",
       "25428                                        1.098612   \n",
       "27886                                        0.000000   \n",
       "28871                                        0.000000   \n",
       "...                                               ...   \n",
       "37584197                                     0.000000   \n",
       "37584917                                     0.693147   \n",
       "37586354                                     0.000000   \n",
       "37586530                                     1.945910   \n",
       "37590988                                     0.000000   \n",
       "\n",
       "          calculated_host_listings_count_private_rooms  \\\n",
       "id                                                       \n",
       "2818                                          0.693147   \n",
       "20168                                         1.098612   \n",
       "25428                                         0.000000   \n",
       "27886                                         0.693147   \n",
       "28871                                         1.386294   \n",
       "...                                                ...   \n",
       "37584197                                      0.000000   \n",
       "37584917                                      0.000000   \n",
       "37586354                                      0.693147   \n",
       "37586530                                      1.098612   \n",
       "37590988                                      1.386294   \n",
       "\n",
       "          calculated_host_listings_count_shared_rooms  reviews_per_month  \\\n",
       "id                                                                         \n",
       "2818                                         0.000000           1.137833   \n",
       "20168                                        0.000000           1.241269   \n",
       "25428                                        0.000000           0.148420   \n",
       "27886                                        0.000000           1.134623   \n",
       "28871                                        0.000000           1.278152   \n",
       "...                                               ...                ...   \n",
       "37584197                                     0.693147           0.000000   \n",
       "37584917                                     0.000000           0.000000   \n",
       "37586354                                     0.000000           0.000000   \n",
       "37586530                                     0.000000           0.000000   \n",
       "37590988                                     0.000000           0.000000   \n",
       "\n",
       "          host_response_time  host_response_rate  neighbourhood  \\\n",
       "id                                                                \n",
       "2818                       1                   3              6   \n",
       "20168                      1                   3             14   \n",
       "25428                      1                   3             14   \n",
       "27886                      1                   3             14   \n",
       "28871                      1                   3             14   \n",
       "...                      ...                 ...            ...   \n",
       "37584197                   0                   0             11   \n",
       "37584917                   0                   0              9   \n",
       "37586354                   0                   0              8   \n",
       "37586530                   2                   1             14   \n",
       "37590988                   2                   2             12   \n",
       "\n",
       "          property_type  room_type  bed_type  review_scores_rating  \\\n",
       "id                                                                   \n",
       "2818                  1          1         4                     2   \n",
       "20168                 3          1         4                     0   \n",
       "25428                 1          2         4                     2   \n",
       "27886                 4          1         4                     2   \n",
       "28871                 1          1         4                     2   \n",
       "...                 ...        ...       ...                   ...   \n",
       "37584197              1          0         4                     3   \n",
       "37584917              3          2         4                     3   \n",
       "37586354              1          1         4                     3   \n",
       "37586530              4          2         4                     3   \n",
       "37590988              2          1         4                     3   \n",
       "\n",
       "          review_scores_accuracy  review_scores_cleanliness  \\\n",
       "id                                                            \n",
       "2818                           2                          2   \n",
       "20168                          0                          2   \n",
       "25428                          2                          2   \n",
       "27886                          2                          2   \n",
       "28871                          2                          2   \n",
       "...                          ...                        ...   \n",
       "37584197                       3                          3   \n",
       "37584917                       3                          3   \n",
       "37586354                       3                          3   \n",
       "37586530                       3                          3   \n",
       "37590988                       3                          3   \n",
       "\n",
       "          review_scores_checkin  review_scores_communication  \\\n",
       "id                                                             \n",
       "2818                          2                            2   \n",
       "20168                         2                            2   \n",
       "25428                         2                            2   \n",
       "27886                         2                            2   \n",
       "28871                         2                            2   \n",
       "...                         ...                          ...   \n",
       "37584197                      3                            3   \n",
       "37584917                      3                            3   \n",
       "37586354                      3                            3   \n",
       "37586530                      3                            3   \n",
       "37590988                      3                            3   \n",
       "\n",
       "          review_scores_location  review_scores_value  instant_bookable  \\\n",
       "id                                                                        \n",
       "2818                           2                    1                 1   \n",
       "20168                          2                    2                 0   \n",
       "25428                          2                    1                 0   \n",
       "27886                          2                    1                 1   \n",
       "28871                          2                    1                 0   \n",
       "...                          ...                  ...               ...   \n",
       "37584197                       3                    3                 1   \n",
       "37584917                       3                    3                 1   \n",
       "37586354                       3                    3                 0   \n",
       "37586530                       3                    3                 0   \n",
       "37590988                       3                    3                 0   \n",
       "\n",
       "          cancellation_policy  require_guest_profile_picture  \\\n",
       "id                                                             \n",
       "2818                        2                              0   \n",
       "20168                       2                              0   \n",
       "25428                       2                              0   \n",
       "27886                       2                              0   \n",
       "28871                       1                              0   \n",
       "...                       ...                            ...   \n",
       "37584197                    0                              0   \n",
       "37584917                    0                              0   \n",
       "37586354                    1                              0   \n",
       "37586530                    2                              0   \n",
       "37590988                    0                              0   \n",
       "\n",
       "          require_guest_phone_verification  \n",
       "id                                          \n",
       "2818                                     0  \n",
       "20168                                    0  \n",
       "25428                                    0  \n",
       "27886                                    0  \n",
       "28871                                    0  \n",
       "...                                    ...  \n",
       "37584197                                 0  \n",
       "37584917                                 0  \n",
       "37586354                                 0  \n",
       "37586530                                 0  \n",
       "37590988                                 0  \n",
       "\n",
       "[20677 rows x 36 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MinMaxScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "newdata=pd.DataFrame(scaler.fit_transform(df_joined), index= df_joined.index, columns=list(df_joined.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_days_active</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>host_days_active</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045815</td>\n",
       "      <td>0.005189</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.057228</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.259269</td>\n",
       "      <td>0.212707</td>\n",
       "      <td>0.062476</td>\n",
       "      <td>0.101065</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>0.074318</td>\n",
       "      <td>0.218086</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>-0.061217</td>\n",
       "      <td>-0.013360</td>\n",
       "      <td>-0.065402</td>\n",
       "      <td>-0.080552</td>\n",
       "      <td>-0.065061</td>\n",
       "      <td>0.025731</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.023841</td>\n",
       "      <td>-0.035693</td>\n",
       "      <td>0.081453</td>\n",
       "      <td>-0.033276</td>\n",
       "      <td>-0.014305</td>\n",
       "      <td>-0.026500</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.013319</td>\n",
       "      <td>-0.017052</td>\n",
       "      <td>-0.074925</td>\n",
       "      <td>-0.022693</td>\n",
       "      <td>-0.209416</td>\n",
       "      <td>0.098442</td>\n",
       "      <td>0.146070</td>\n",
       "      <td>0.170137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <td>-0.045815</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045892</td>\n",
       "      <td>0.042380</td>\n",
       "      <td>0.015884</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>-0.074637</td>\n",
       "      <td>-0.031693</td>\n",
       "      <td>0.089624</td>\n",
       "      <td>0.085214</td>\n",
       "      <td>-0.020687</td>\n",
       "      <td>0.053516</td>\n",
       "      <td>-0.051297</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.954043</td>\n",
       "      <td>0.839711</td>\n",
       "      <td>0.304252</td>\n",
       "      <td>0.397220</td>\n",
       "      <td>0.008856</td>\n",
       "      <td>0.104446</td>\n",
       "      <td>0.057704</td>\n",
       "      <td>0.078368</td>\n",
       "      <td>-0.009220</td>\n",
       "      <td>-0.021164</td>\n",
       "      <td>0.013954</td>\n",
       "      <td>-0.083162</td>\n",
       "      <td>-0.075605</td>\n",
       "      <td>-0.036996</td>\n",
       "      <td>-0.097184</td>\n",
       "      <td>-0.108236</td>\n",
       "      <td>0.012164</td>\n",
       "      <td>-0.014555</td>\n",
       "      <td>0.200689</td>\n",
       "      <td>0.212330</td>\n",
       "      <td>-0.002876</td>\n",
       "      <td>0.017334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accommodates</th>\n",
       "      <td>0.005189</td>\n",
       "      <td>0.045892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337743</td>\n",
       "      <td>0.669051</td>\n",
       "      <td>0.802256</td>\n",
       "      <td>0.030102</td>\n",
       "      <td>0.005023</td>\n",
       "      <td>0.143160</td>\n",
       "      <td>0.189487</td>\n",
       "      <td>0.393854</td>\n",
       "      <td>0.239867</td>\n",
       "      <td>-0.051273</td>\n",
       "      <td>-0.034476</td>\n",
       "      <td>0.032278</td>\n",
       "      <td>0.141668</td>\n",
       "      <td>-0.196238</td>\n",
       "      <td>-0.042760</td>\n",
       "      <td>-0.072334</td>\n",
       "      <td>0.070594</td>\n",
       "      <td>0.056522</td>\n",
       "      <td>0.018396</td>\n",
       "      <td>0.231379</td>\n",
       "      <td>0.285798</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>-0.004206</td>\n",
       "      <td>-0.030625</td>\n",
       "      <td>-0.031123</td>\n",
       "      <td>0.002614</td>\n",
       "      <td>-0.002868</td>\n",
       "      <td>-0.009838</td>\n",
       "      <td>0.009989</td>\n",
       "      <td>-0.030630</td>\n",
       "      <td>0.113363</td>\n",
       "      <td>0.002964</td>\n",
       "      <td>0.010712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.042380</td>\n",
       "      <td>0.337743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.378098</td>\n",
       "      <td>0.366059</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>-0.010883</td>\n",
       "      <td>0.059632</td>\n",
       "      <td>0.097220</td>\n",
       "      <td>0.152312</td>\n",
       "      <td>0.055508</td>\n",
       "      <td>-0.061666</td>\n",
       "      <td>-0.017821</td>\n",
       "      <td>0.033103</td>\n",
       "      <td>0.051880</td>\n",
       "      <td>-0.025674</td>\n",
       "      <td>-0.019611</td>\n",
       "      <td>-0.036095</td>\n",
       "      <td>0.071242</td>\n",
       "      <td>0.086636</td>\n",
       "      <td>0.040940</td>\n",
       "      <td>0.176621</td>\n",
       "      <td>0.067172</td>\n",
       "      <td>0.025201</td>\n",
       "      <td>0.056626</td>\n",
       "      <td>0.029408</td>\n",
       "      <td>0.040329</td>\n",
       "      <td>0.028309</td>\n",
       "      <td>0.035524</td>\n",
       "      <td>0.055675</td>\n",
       "      <td>0.019600</td>\n",
       "      <td>-0.010740</td>\n",
       "      <td>0.071856</td>\n",
       "      <td>0.018043</td>\n",
       "      <td>0.020090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.057228</td>\n",
       "      <td>0.015884</td>\n",
       "      <td>0.669051</td>\n",
       "      <td>0.378098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.704059</td>\n",
       "      <td>0.045051</td>\n",
       "      <td>-0.004108</td>\n",
       "      <td>0.127578</td>\n",
       "      <td>0.168515</td>\n",
       "      <td>0.279861</td>\n",
       "      <td>0.124882</td>\n",
       "      <td>-0.100880</td>\n",
       "      <td>-0.093058</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.099219</td>\n",
       "      <td>-0.175267</td>\n",
       "      <td>-0.017090</td>\n",
       "      <td>-0.152166</td>\n",
       "      <td>0.047443</td>\n",
       "      <td>0.020628</td>\n",
       "      <td>-0.017318</td>\n",
       "      <td>0.250608</td>\n",
       "      <td>0.236904</td>\n",
       "      <td>0.060951</td>\n",
       "      <td>0.045357</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>0.007085</td>\n",
       "      <td>0.024891</td>\n",
       "      <td>0.026976</td>\n",
       "      <td>-0.002273</td>\n",
       "      <td>0.007761</td>\n",
       "      <td>-0.092346</td>\n",
       "      <td>0.097464</td>\n",
       "      <td>0.018011</td>\n",
       "      <td>0.017006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beds</th>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>0.802256</td>\n",
       "      <td>0.366059</td>\n",
       "      <td>0.704059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044532</td>\n",
       "      <td>0.011439</td>\n",
       "      <td>0.131652</td>\n",
       "      <td>0.155153</td>\n",
       "      <td>0.358539</td>\n",
       "      <td>0.196129</td>\n",
       "      <td>-0.034531</td>\n",
       "      <td>-0.017236</td>\n",
       "      <td>0.043506</td>\n",
       "      <td>0.108603</td>\n",
       "      <td>-0.121287</td>\n",
       "      <td>-0.008669</td>\n",
       "      <td>-0.058078</td>\n",
       "      <td>0.070891</td>\n",
       "      <td>0.061050</td>\n",
       "      <td>0.008946</td>\n",
       "      <td>0.302714</td>\n",
       "      <td>0.191445</td>\n",
       "      <td>0.047880</td>\n",
       "      <td>0.003971</td>\n",
       "      <td>-0.028394</td>\n",
       "      <td>-0.014922</td>\n",
       "      <td>0.015406</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>-0.000890</td>\n",
       "      <td>0.013663</td>\n",
       "      <td>-0.016590</td>\n",
       "      <td>0.113332</td>\n",
       "      <td>0.012757</td>\n",
       "      <td>0.016192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly_price</th>\n",
       "      <td>0.259269</td>\n",
       "      <td>-0.074637</td>\n",
       "      <td>0.030102</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.045051</td>\n",
       "      <td>0.044532</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.619870</td>\n",
       "      <td>0.065038</td>\n",
       "      <td>0.045784</td>\n",
       "      <td>0.126574</td>\n",
       "      <td>0.072931</td>\n",
       "      <td>0.212522</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>-0.082117</td>\n",
       "      <td>-0.052318</td>\n",
       "      <td>-0.066030</td>\n",
       "      <td>-0.035926</td>\n",
       "      <td>-0.024667</td>\n",
       "      <td>-0.000710</td>\n",
       "      <td>-0.009971</td>\n",
       "      <td>0.037719</td>\n",
       "      <td>0.016445</td>\n",
       "      <td>0.049574</td>\n",
       "      <td>-0.031049</td>\n",
       "      <td>-0.036665</td>\n",
       "      <td>-0.047169</td>\n",
       "      <td>-0.046210</td>\n",
       "      <td>-0.011880</td>\n",
       "      <td>-0.021912</td>\n",
       "      <td>-0.047564</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-0.105904</td>\n",
       "      <td>0.074521</td>\n",
       "      <td>0.073803</td>\n",
       "      <td>0.081113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_price</th>\n",
       "      <td>0.212707</td>\n",
       "      <td>-0.031693</td>\n",
       "      <td>0.005023</td>\n",
       "      <td>-0.010883</td>\n",
       "      <td>-0.004108</td>\n",
       "      <td>0.011439</td>\n",
       "      <td>0.619870</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057859</td>\n",
       "      <td>0.041349</td>\n",
       "      <td>0.099478</td>\n",
       "      <td>0.086956</td>\n",
       "      <td>0.203607</td>\n",
       "      <td>0.043152</td>\n",
       "      <td>-0.040114</td>\n",
       "      <td>-0.033363</td>\n",
       "      <td>-0.018767</td>\n",
       "      <td>-0.021109</td>\n",
       "      <td>0.031956</td>\n",
       "      <td>0.032401</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>0.043201</td>\n",
       "      <td>-0.004782</td>\n",
       "      <td>0.005559</td>\n",
       "      <td>-0.037516</td>\n",
       "      <td>-0.038132</td>\n",
       "      <td>-0.041589</td>\n",
       "      <td>-0.038593</td>\n",
       "      <td>-0.018411</td>\n",
       "      <td>-0.019864</td>\n",
       "      <td>-0.027454</td>\n",
       "      <td>0.006308</td>\n",
       "      <td>-0.057839</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.073027</td>\n",
       "      <td>0.105614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>security_deposit</th>\n",
       "      <td>0.062476</td>\n",
       "      <td>0.089624</td>\n",
       "      <td>0.143160</td>\n",
       "      <td>0.059632</td>\n",
       "      <td>0.127578</td>\n",
       "      <td>0.131652</td>\n",
       "      <td>0.065038</td>\n",
       "      <td>0.057859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.410419</td>\n",
       "      <td>0.184835</td>\n",
       "      <td>0.191924</td>\n",
       "      <td>0.089540</td>\n",
       "      <td>0.009948</td>\n",
       "      <td>0.082264</td>\n",
       "      <td>0.152013</td>\n",
       "      <td>-0.118704</td>\n",
       "      <td>0.009475</td>\n",
       "      <td>-0.035248</td>\n",
       "      <td>0.009844</td>\n",
       "      <td>0.013585</td>\n",
       "      <td>0.055382</td>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.173744</td>\n",
       "      <td>0.005434</td>\n",
       "      <td>-0.078832</td>\n",
       "      <td>-0.068716</td>\n",
       "      <td>-0.053870</td>\n",
       "      <td>-0.106044</td>\n",
       "      <td>-0.089747</td>\n",
       "      <td>-0.068067</td>\n",
       "      <td>-0.040277</td>\n",
       "      <td>-0.104652</td>\n",
       "      <td>0.255310</td>\n",
       "      <td>0.060197</td>\n",
       "      <td>0.065516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cleaning_fee</th>\n",
       "      <td>0.101065</td>\n",
       "      <td>0.085214</td>\n",
       "      <td>0.189487</td>\n",
       "      <td>0.097220</td>\n",
       "      <td>0.168515</td>\n",
       "      <td>0.155153</td>\n",
       "      <td>0.045784</td>\n",
       "      <td>0.041349</td>\n",
       "      <td>0.410419</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196080</td>\n",
       "      <td>0.195698</td>\n",
       "      <td>0.115080</td>\n",
       "      <td>0.046213</td>\n",
       "      <td>0.068916</td>\n",
       "      <td>0.163550</td>\n",
       "      <td>-0.157601</td>\n",
       "      <td>-0.015546</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.043726</td>\n",
       "      <td>0.049202</td>\n",
       "      <td>0.074090</td>\n",
       "      <td>0.011576</td>\n",
       "      <td>0.227967</td>\n",
       "      <td>0.020790</td>\n",
       "      <td>-0.090258</td>\n",
       "      <td>-0.093437</td>\n",
       "      <td>-0.094041</td>\n",
       "      <td>-0.127663</td>\n",
       "      <td>-0.117245</td>\n",
       "      <td>-0.083180</td>\n",
       "      <td>-0.075646</td>\n",
       "      <td>-0.105500</td>\n",
       "      <td>0.275487</td>\n",
       "      <td>0.017697</td>\n",
       "      <td>0.024368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guests_included</th>\n",
       "      <td>0.110619</td>\n",
       "      <td>-0.020687</td>\n",
       "      <td>0.393854</td>\n",
       "      <td>0.152312</td>\n",
       "      <td>0.279861</td>\n",
       "      <td>0.358539</td>\n",
       "      <td>0.126574</td>\n",
       "      <td>0.099478</td>\n",
       "      <td>0.184835</td>\n",
       "      <td>0.196080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.576632</td>\n",
       "      <td>0.159283</td>\n",
       "      <td>0.078846</td>\n",
       "      <td>-0.030682</td>\n",
       "      <td>0.010674</td>\n",
       "      <td>-0.080699</td>\n",
       "      <td>-0.026095</td>\n",
       "      <td>0.042832</td>\n",
       "      <td>0.061653</td>\n",
       "      <td>0.069042</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>0.097133</td>\n",
       "      <td>0.107338</td>\n",
       "      <td>0.015878</td>\n",
       "      <td>-0.081572</td>\n",
       "      <td>-0.100141</td>\n",
       "      <td>-0.079998</td>\n",
       "      <td>-0.064812</td>\n",
       "      <td>-0.075977</td>\n",
       "      <td>-0.092878</td>\n",
       "      <td>-0.025450</td>\n",
       "      <td>-0.026397</td>\n",
       "      <td>0.179980</td>\n",
       "      <td>0.069472</td>\n",
       "      <td>0.072688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extra_people</th>\n",
       "      <td>0.074318</td>\n",
       "      <td>0.053516</td>\n",
       "      <td>0.239867</td>\n",
       "      <td>0.055508</td>\n",
       "      <td>0.124882</td>\n",
       "      <td>0.196129</td>\n",
       "      <td>0.072931</td>\n",
       "      <td>0.086956</td>\n",
       "      <td>0.191924</td>\n",
       "      <td>0.195698</td>\n",
       "      <td>0.576632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.148941</td>\n",
       "      <td>0.085853</td>\n",
       "      <td>0.061346</td>\n",
       "      <td>0.044225</td>\n",
       "      <td>0.046885</td>\n",
       "      <td>0.084821</td>\n",
       "      <td>0.076488</td>\n",
       "      <td>0.073448</td>\n",
       "      <td>0.066457</td>\n",
       "      <td>-0.017785</td>\n",
       "      <td>0.030254</td>\n",
       "      <td>-0.015525</td>\n",
       "      <td>-0.006670</td>\n",
       "      <td>-0.098945</td>\n",
       "      <td>-0.108964</td>\n",
       "      <td>-0.094806</td>\n",
       "      <td>-0.085807</td>\n",
       "      <td>-0.091559</td>\n",
       "      <td>-0.097666</td>\n",
       "      <td>-0.048680</td>\n",
       "      <td>-0.001728</td>\n",
       "      <td>0.152332</td>\n",
       "      <td>0.049007</td>\n",
       "      <td>0.048237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_reviews</th>\n",
       "      <td>0.218086</td>\n",
       "      <td>-0.051297</td>\n",
       "      <td>-0.051273</td>\n",
       "      <td>-0.061666</td>\n",
       "      <td>-0.100880</td>\n",
       "      <td>-0.034531</td>\n",
       "      <td>0.212522</td>\n",
       "      <td>0.203607</td>\n",
       "      <td>0.089540</td>\n",
       "      <td>0.115080</td>\n",
       "      <td>0.159283</td>\n",
       "      <td>0.148941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>-0.066986</td>\n",
       "      <td>-0.159309</td>\n",
       "      <td>0.172905</td>\n",
       "      <td>-0.029425</td>\n",
       "      <td>0.709267</td>\n",
       "      <td>0.213486</td>\n",
       "      <td>0.282490</td>\n",
       "      <td>0.055972</td>\n",
       "      <td>0.016297</td>\n",
       "      <td>-0.209502</td>\n",
       "      <td>-0.046381</td>\n",
       "      <td>-0.358322</td>\n",
       "      <td>-0.363652</td>\n",
       "      <td>-0.354162</td>\n",
       "      <td>-0.336919</td>\n",
       "      <td>-0.353440</td>\n",
       "      <td>-0.376117</td>\n",
       "      <td>-0.212092</td>\n",
       "      <td>0.013572</td>\n",
       "      <td>0.211378</td>\n",
       "      <td>0.072759</td>\n",
       "      <td>0.093453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <td>0.012918</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>-0.034476</td>\n",
       "      <td>-0.017821</td>\n",
       "      <td>-0.093058</td>\n",
       "      <td>-0.017236</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.043152</td>\n",
       "      <td>0.009948</td>\n",
       "      <td>0.046213</td>\n",
       "      <td>0.078846</td>\n",
       "      <td>0.085853</td>\n",
       "      <td>0.727878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009651</td>\n",
       "      <td>-0.148563</td>\n",
       "      <td>0.261861</td>\n",
       "      <td>-0.011670</td>\n",
       "      <td>0.824998</td>\n",
       "      <td>0.382597</td>\n",
       "      <td>0.488956</td>\n",
       "      <td>0.040633</td>\n",
       "      <td>0.071186</td>\n",
       "      <td>-0.294586</td>\n",
       "      <td>-0.005444</td>\n",
       "      <td>-0.188597</td>\n",
       "      <td>-0.206070</td>\n",
       "      <td>-0.194319</td>\n",
       "      <td>-0.183651</td>\n",
       "      <td>-0.199387</td>\n",
       "      <td>-0.162543</td>\n",
       "      <td>-0.119876</td>\n",
       "      <td>0.144605</td>\n",
       "      <td>0.138721</td>\n",
       "      <td>0.030328</td>\n",
       "      <td>0.044523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <td>-0.061217</td>\n",
       "      <td>0.954043</td>\n",
       "      <td>0.032278</td>\n",
       "      <td>0.033103</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.043506</td>\n",
       "      <td>-0.082117</td>\n",
       "      <td>-0.040114</td>\n",
       "      <td>0.082264</td>\n",
       "      <td>0.068916</td>\n",
       "      <td>-0.030682</td>\n",
       "      <td>0.061346</td>\n",
       "      <td>-0.066986</td>\n",
       "      <td>-0.009651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877598</td>\n",
       "      <td>0.331984</td>\n",
       "      <td>0.445975</td>\n",
       "      <td>0.001016</td>\n",
       "      <td>0.100110</td>\n",
       "      <td>0.055172</td>\n",
       "      <td>0.070587</td>\n",
       "      <td>-0.011485</td>\n",
       "      <td>-0.020980</td>\n",
       "      <td>0.014433</td>\n",
       "      <td>-0.087744</td>\n",
       "      <td>-0.078292</td>\n",
       "      <td>-0.037018</td>\n",
       "      <td>-0.103096</td>\n",
       "      <td>-0.114024</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>-0.018611</td>\n",
       "      <td>0.218551</td>\n",
       "      <td>0.181688</td>\n",
       "      <td>-0.006916</td>\n",
       "      <td>0.007657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <td>-0.013360</td>\n",
       "      <td>0.839711</td>\n",
       "      <td>0.141668</td>\n",
       "      <td>0.051880</td>\n",
       "      <td>0.099219</td>\n",
       "      <td>0.108603</td>\n",
       "      <td>-0.052318</td>\n",
       "      <td>-0.033363</td>\n",
       "      <td>0.152013</td>\n",
       "      <td>0.163550</td>\n",
       "      <td>0.010674</td>\n",
       "      <td>0.044225</td>\n",
       "      <td>-0.159309</td>\n",
       "      <td>-0.148563</td>\n",
       "      <td>0.877598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.134266</td>\n",
       "      <td>0.347029</td>\n",
       "      <td>-0.177999</td>\n",
       "      <td>0.058139</td>\n",
       "      <td>-0.017876</td>\n",
       "      <td>0.081708</td>\n",
       "      <td>-0.079001</td>\n",
       "      <td>0.397574</td>\n",
       "      <td>0.033241</td>\n",
       "      <td>-0.041662</td>\n",
       "      <td>-0.039217</td>\n",
       "      <td>-0.019653</td>\n",
       "      <td>-0.082393</td>\n",
       "      <td>-0.078277</td>\n",
       "      <td>0.022416</td>\n",
       "      <td>-0.011171</td>\n",
       "      <td>0.107637</td>\n",
       "      <td>0.188503</td>\n",
       "      <td>-0.010804</td>\n",
       "      <td>-0.000381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <td>-0.065402</td>\n",
       "      <td>0.304252</td>\n",
       "      <td>-0.196238</td>\n",
       "      <td>-0.025674</td>\n",
       "      <td>-0.175267</td>\n",
       "      <td>-0.121287</td>\n",
       "      <td>-0.066030</td>\n",
       "      <td>-0.018767</td>\n",
       "      <td>-0.118704</td>\n",
       "      <td>-0.157601</td>\n",
       "      <td>-0.080699</td>\n",
       "      <td>0.046885</td>\n",
       "      <td>0.172905</td>\n",
       "      <td>0.261861</td>\n",
       "      <td>0.331984</td>\n",
       "      <td>-0.134266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151693</td>\n",
       "      <td>0.329692</td>\n",
       "      <td>0.093508</td>\n",
       "      <td>0.150805</td>\n",
       "      <td>-0.015575</td>\n",
       "      <td>0.125672</td>\n",
       "      <td>-0.777985</td>\n",
       "      <td>-0.029264</td>\n",
       "      <td>-0.107591</td>\n",
       "      <td>-0.092304</td>\n",
       "      <td>-0.045147</td>\n",
       "      <td>-0.055547</td>\n",
       "      <td>-0.085801</td>\n",
       "      <td>-0.025903</td>\n",
       "      <td>-0.024688</td>\n",
       "      <td>0.233862</td>\n",
       "      <td>-0.011052</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>0.023472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <td>-0.080552</td>\n",
       "      <td>0.397220</td>\n",
       "      <td>-0.042760</td>\n",
       "      <td>-0.019611</td>\n",
       "      <td>-0.017090</td>\n",
       "      <td>-0.008669</td>\n",
       "      <td>-0.035926</td>\n",
       "      <td>-0.021109</td>\n",
       "      <td>0.009475</td>\n",
       "      <td>-0.015546</td>\n",
       "      <td>-0.026095</td>\n",
       "      <td>0.084821</td>\n",
       "      <td>-0.029425</td>\n",
       "      <td>-0.011670</td>\n",
       "      <td>0.445975</td>\n",
       "      <td>0.347029</td>\n",
       "      <td>0.151693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011263</td>\n",
       "      <td>0.020930</td>\n",
       "      <td>-0.016544</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.014850</td>\n",
       "      <td>-0.140840</td>\n",
       "      <td>-0.018791</td>\n",
       "      <td>-0.056475</td>\n",
       "      <td>-0.048102</td>\n",
       "      <td>-0.045581</td>\n",
       "      <td>-0.068478</td>\n",
       "      <td>-0.059171</td>\n",
       "      <td>-0.021658</td>\n",
       "      <td>-0.037559</td>\n",
       "      <td>0.130749</td>\n",
       "      <td>0.051027</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>-0.001958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviews_per_month</th>\n",
       "      <td>-0.065061</td>\n",
       "      <td>0.008856</td>\n",
       "      <td>-0.072334</td>\n",
       "      <td>-0.036095</td>\n",
       "      <td>-0.152166</td>\n",
       "      <td>-0.058078</td>\n",
       "      <td>-0.024667</td>\n",
       "      <td>0.031956</td>\n",
       "      <td>-0.035248</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.042832</td>\n",
       "      <td>0.076488</td>\n",
       "      <td>0.709267</td>\n",
       "      <td>0.824998</td>\n",
       "      <td>0.001016</td>\n",
       "      <td>-0.177999</td>\n",
       "      <td>0.329692</td>\n",
       "      <td>0.011263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282152</td>\n",
       "      <td>0.404856</td>\n",
       "      <td>0.029837</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>-0.384239</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>-0.225056</td>\n",
       "      <td>-0.231200</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.203756</td>\n",
       "      <td>-0.222897</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>-0.156286</td>\n",
       "      <td>0.198448</td>\n",
       "      <td>0.094890</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.031997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_response_time</th>\n",
       "      <td>0.025731</td>\n",
       "      <td>0.104446</td>\n",
       "      <td>0.070594</td>\n",
       "      <td>0.071242</td>\n",
       "      <td>0.047443</td>\n",
       "      <td>0.070891</td>\n",
       "      <td>-0.000710</td>\n",
       "      <td>0.032401</td>\n",
       "      <td>0.009844</td>\n",
       "      <td>0.043726</td>\n",
       "      <td>0.061653</td>\n",
       "      <td>0.073448</td>\n",
       "      <td>0.213486</td>\n",
       "      <td>0.382597</td>\n",
       "      <td>0.100110</td>\n",
       "      <td>0.058139</td>\n",
       "      <td>0.093508</td>\n",
       "      <td>0.020930</td>\n",
       "      <td>0.282152</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.823657</td>\n",
       "      <td>0.026197</td>\n",
       "      <td>0.051574</td>\n",
       "      <td>-0.053755</td>\n",
       "      <td>0.008077</td>\n",
       "      <td>-0.005662</td>\n",
       "      <td>-0.027420</td>\n",
       "      <td>-0.022821</td>\n",
       "      <td>-0.010988</td>\n",
       "      <td>-0.022597</td>\n",
       "      <td>0.003325</td>\n",
       "      <td>-0.005166</td>\n",
       "      <td>-0.031442</td>\n",
       "      <td>0.109908</td>\n",
       "      <td>0.024024</td>\n",
       "      <td>0.033776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_response_rate</th>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.057704</td>\n",
       "      <td>0.056522</td>\n",
       "      <td>0.086636</td>\n",
       "      <td>0.020628</td>\n",
       "      <td>0.061050</td>\n",
       "      <td>-0.009971</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>0.013585</td>\n",
       "      <td>0.049202</td>\n",
       "      <td>0.069042</td>\n",
       "      <td>0.066457</td>\n",
       "      <td>0.282490</td>\n",
       "      <td>0.488956</td>\n",
       "      <td>0.055172</td>\n",
       "      <td>-0.017876</td>\n",
       "      <td>0.150805</td>\n",
       "      <td>-0.016544</td>\n",
       "      <td>0.404856</td>\n",
       "      <td>0.823657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>0.075714</td>\n",
       "      <td>-0.122163</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>-0.013919</td>\n",
       "      <td>-0.037357</td>\n",
       "      <td>-0.024319</td>\n",
       "      <td>-0.021505</td>\n",
       "      <td>-0.027540</td>\n",
       "      <td>-0.008275</td>\n",
       "      <td>-0.012989</td>\n",
       "      <td>0.094113</td>\n",
       "      <td>0.121049</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.041554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <td>0.023841</td>\n",
       "      <td>0.078368</td>\n",
       "      <td>0.018396</td>\n",
       "      <td>0.040940</td>\n",
       "      <td>-0.017318</td>\n",
       "      <td>0.008946</td>\n",
       "      <td>0.037719</td>\n",
       "      <td>0.043201</td>\n",
       "      <td>0.055382</td>\n",
       "      <td>0.074090</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>-0.017785</td>\n",
       "      <td>0.055972</td>\n",
       "      <td>0.040633</td>\n",
       "      <td>0.070587</td>\n",
       "      <td>0.081708</td>\n",
       "      <td>-0.015575</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.029837</td>\n",
       "      <td>0.026197</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044086</td>\n",
       "      <td>0.049254</td>\n",
       "      <td>0.012043</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>-0.005333</td>\n",
       "      <td>-0.004965</td>\n",
       "      <td>-0.010096</td>\n",
       "      <td>-0.015551</td>\n",
       "      <td>0.273050</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.020531</td>\n",
       "      <td>0.081384</td>\n",
       "      <td>0.012037</td>\n",
       "      <td>0.031389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <td>-0.035693</td>\n",
       "      <td>-0.009220</td>\n",
       "      <td>0.231379</td>\n",
       "      <td>0.176621</td>\n",
       "      <td>0.250608</td>\n",
       "      <td>0.302714</td>\n",
       "      <td>0.016445</td>\n",
       "      <td>-0.004782</td>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.011576</td>\n",
       "      <td>0.097133</td>\n",
       "      <td>0.030254</td>\n",
       "      <td>0.016297</td>\n",
       "      <td>0.071186</td>\n",
       "      <td>-0.011485</td>\n",
       "      <td>-0.079001</td>\n",
       "      <td>0.125672</td>\n",
       "      <td>0.014850</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>0.051574</td>\n",
       "      <td>0.075714</td>\n",
       "      <td>-0.044086</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.111371</td>\n",
       "      <td>0.023080</td>\n",
       "      <td>0.023069</td>\n",
       "      <td>0.006353</td>\n",
       "      <td>0.012515</td>\n",
       "      <td>0.038235</td>\n",
       "      <td>0.018768</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.017402</td>\n",
       "      <td>0.063238</td>\n",
       "      <td>0.017640</td>\n",
       "      <td>-0.002845</td>\n",
       "      <td>-0.002172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <td>0.081453</td>\n",
       "      <td>-0.021164</td>\n",
       "      <td>0.285798</td>\n",
       "      <td>0.067172</td>\n",
       "      <td>0.236904</td>\n",
       "      <td>0.191445</td>\n",
       "      <td>0.049574</td>\n",
       "      <td>0.005559</td>\n",
       "      <td>0.173744</td>\n",
       "      <td>0.227967</td>\n",
       "      <td>0.107338</td>\n",
       "      <td>-0.015525</td>\n",
       "      <td>-0.209502</td>\n",
       "      <td>-0.294586</td>\n",
       "      <td>-0.020980</td>\n",
       "      <td>0.397574</td>\n",
       "      <td>-0.777985</td>\n",
       "      <td>-0.140840</td>\n",
       "      <td>-0.384239</td>\n",
       "      <td>-0.053755</td>\n",
       "      <td>-0.122163</td>\n",
       "      <td>0.049254</td>\n",
       "      <td>-0.111371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062903</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.054382</td>\n",
       "      <td>0.019346</td>\n",
       "      <td>0.014711</td>\n",
       "      <td>0.039441</td>\n",
       "      <td>0.018342</td>\n",
       "      <td>0.008052</td>\n",
       "      <td>-0.178256</td>\n",
       "      <td>0.066141</td>\n",
       "      <td>-0.013546</td>\n",
       "      <td>-0.018456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_type</th>\n",
       "      <td>-0.033276</td>\n",
       "      <td>0.013954</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>0.025201</td>\n",
       "      <td>0.060951</td>\n",
       "      <td>0.047880</td>\n",
       "      <td>-0.031049</td>\n",
       "      <td>-0.037516</td>\n",
       "      <td>0.005434</td>\n",
       "      <td>0.020790</td>\n",
       "      <td>0.015878</td>\n",
       "      <td>-0.006670</td>\n",
       "      <td>-0.046381</td>\n",
       "      <td>-0.005444</td>\n",
       "      <td>0.014433</td>\n",
       "      <td>0.033241</td>\n",
       "      <td>-0.029264</td>\n",
       "      <td>-0.018791</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>0.008077</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>0.012043</td>\n",
       "      <td>0.023080</td>\n",
       "      <td>0.062903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020365</td>\n",
       "      <td>0.021183</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>0.002911</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.017266</td>\n",
       "      <td>-0.004238</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>-0.010782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_rating</th>\n",
       "      <td>-0.014305</td>\n",
       "      <td>-0.083162</td>\n",
       "      <td>-0.004206</td>\n",
       "      <td>0.056626</td>\n",
       "      <td>0.045357</td>\n",
       "      <td>0.003971</td>\n",
       "      <td>-0.036665</td>\n",
       "      <td>-0.038132</td>\n",
       "      <td>-0.078832</td>\n",
       "      <td>-0.090258</td>\n",
       "      <td>-0.081572</td>\n",
       "      <td>-0.098945</td>\n",
       "      <td>-0.358322</td>\n",
       "      <td>-0.188597</td>\n",
       "      <td>-0.087744</td>\n",
       "      <td>-0.041662</td>\n",
       "      <td>-0.107591</td>\n",
       "      <td>-0.056475</td>\n",
       "      <td>-0.225056</td>\n",
       "      <td>-0.005662</td>\n",
       "      <td>-0.013919</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>0.023069</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.020365</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.669125</td>\n",
       "      <td>0.673861</td>\n",
       "      <td>0.576509</td>\n",
       "      <td>0.597615</td>\n",
       "      <td>0.511925</td>\n",
       "      <td>0.386083</td>\n",
       "      <td>-0.054133</td>\n",
       "      <td>-0.092969</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>-0.011134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <td>-0.026500</td>\n",
       "      <td>-0.075605</td>\n",
       "      <td>-0.030625</td>\n",
       "      <td>0.029408</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>-0.028394</td>\n",
       "      <td>-0.047169</td>\n",
       "      <td>-0.041589</td>\n",
       "      <td>-0.068716</td>\n",
       "      <td>-0.093437</td>\n",
       "      <td>-0.100141</td>\n",
       "      <td>-0.108964</td>\n",
       "      <td>-0.363652</td>\n",
       "      <td>-0.206070</td>\n",
       "      <td>-0.078292</td>\n",
       "      <td>-0.039217</td>\n",
       "      <td>-0.092304</td>\n",
       "      <td>-0.048102</td>\n",
       "      <td>-0.231200</td>\n",
       "      <td>-0.027420</td>\n",
       "      <td>-0.037357</td>\n",
       "      <td>-0.005333</td>\n",
       "      <td>0.006353</td>\n",
       "      <td>0.054382</td>\n",
       "      <td>0.021183</td>\n",
       "      <td>0.669125</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.623487</td>\n",
       "      <td>0.563587</td>\n",
       "      <td>0.581427</td>\n",
       "      <td>0.494935</td>\n",
       "      <td>0.406683</td>\n",
       "      <td>-0.047932</td>\n",
       "      <td>-0.106695</td>\n",
       "      <td>-0.015996</td>\n",
       "      <td>-0.018409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.036996</td>\n",
       "      <td>-0.031123</td>\n",
       "      <td>0.040329</td>\n",
       "      <td>0.007085</td>\n",
       "      <td>-0.014922</td>\n",
       "      <td>-0.046210</td>\n",
       "      <td>-0.038593</td>\n",
       "      <td>-0.053870</td>\n",
       "      <td>-0.094041</td>\n",
       "      <td>-0.079998</td>\n",
       "      <td>-0.094806</td>\n",
       "      <td>-0.354162</td>\n",
       "      <td>-0.194319</td>\n",
       "      <td>-0.037018</td>\n",
       "      <td>-0.019653</td>\n",
       "      <td>-0.045147</td>\n",
       "      <td>-0.045581</td>\n",
       "      <td>-0.209010</td>\n",
       "      <td>-0.022821</td>\n",
       "      <td>-0.024319</td>\n",
       "      <td>-0.004965</td>\n",
       "      <td>0.012515</td>\n",
       "      <td>0.019346</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>0.673861</td>\n",
       "      <td>0.623487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.538637</td>\n",
       "      <td>0.561582</td>\n",
       "      <td>0.537425</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>-0.025931</td>\n",
       "      <td>-0.082566</td>\n",
       "      <td>-0.005965</td>\n",
       "      <td>-0.009310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <td>-0.013319</td>\n",
       "      <td>-0.097184</td>\n",
       "      <td>0.002614</td>\n",
       "      <td>0.028309</td>\n",
       "      <td>0.024891</td>\n",
       "      <td>0.015406</td>\n",
       "      <td>-0.011880</td>\n",
       "      <td>-0.018411</td>\n",
       "      <td>-0.106044</td>\n",
       "      <td>-0.127663</td>\n",
       "      <td>-0.064812</td>\n",
       "      <td>-0.085807</td>\n",
       "      <td>-0.336919</td>\n",
       "      <td>-0.183651</td>\n",
       "      <td>-0.103096</td>\n",
       "      <td>-0.082393</td>\n",
       "      <td>-0.055547</td>\n",
       "      <td>-0.068478</td>\n",
       "      <td>-0.203756</td>\n",
       "      <td>-0.010988</td>\n",
       "      <td>-0.021505</td>\n",
       "      <td>-0.010096</td>\n",
       "      <td>0.038235</td>\n",
       "      <td>0.014711</td>\n",
       "      <td>0.002911</td>\n",
       "      <td>0.576509</td>\n",
       "      <td>0.563587</td>\n",
       "      <td>0.538637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.661247</td>\n",
       "      <td>0.482797</td>\n",
       "      <td>0.414532</td>\n",
       "      <td>-0.040295</td>\n",
       "      <td>-0.119208</td>\n",
       "      <td>-0.003069</td>\n",
       "      <td>-0.006835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_communication</th>\n",
       "      <td>-0.017052</td>\n",
       "      <td>-0.108236</td>\n",
       "      <td>-0.002868</td>\n",
       "      <td>0.035524</td>\n",
       "      <td>0.026976</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>-0.021912</td>\n",
       "      <td>-0.019864</td>\n",
       "      <td>-0.089747</td>\n",
       "      <td>-0.117245</td>\n",
       "      <td>-0.075977</td>\n",
       "      <td>-0.091559</td>\n",
       "      <td>-0.353440</td>\n",
       "      <td>-0.199387</td>\n",
       "      <td>-0.114024</td>\n",
       "      <td>-0.078277</td>\n",
       "      <td>-0.085801</td>\n",
       "      <td>-0.059171</td>\n",
       "      <td>-0.222897</td>\n",
       "      <td>-0.022597</td>\n",
       "      <td>-0.027540</td>\n",
       "      <td>-0.015551</td>\n",
       "      <td>0.018768</td>\n",
       "      <td>0.039441</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.597615</td>\n",
       "      <td>0.581427</td>\n",
       "      <td>0.561582</td>\n",
       "      <td>0.661247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.492480</td>\n",
       "      <td>0.443763</td>\n",
       "      <td>-0.044948</td>\n",
       "      <td>-0.111931</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.004089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_location</th>\n",
       "      <td>-0.074925</td>\n",
       "      <td>0.012164</td>\n",
       "      <td>-0.009838</td>\n",
       "      <td>0.055675</td>\n",
       "      <td>-0.002273</td>\n",
       "      <td>-0.000890</td>\n",
       "      <td>-0.047564</td>\n",
       "      <td>-0.027454</td>\n",
       "      <td>-0.068067</td>\n",
       "      <td>-0.083180</td>\n",
       "      <td>-0.092878</td>\n",
       "      <td>-0.097666</td>\n",
       "      <td>-0.376117</td>\n",
       "      <td>-0.162543</td>\n",
       "      <td>0.013030</td>\n",
       "      <td>0.022416</td>\n",
       "      <td>-0.025903</td>\n",
       "      <td>-0.021658</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>0.003325</td>\n",
       "      <td>-0.008275</td>\n",
       "      <td>0.273050</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.018342</td>\n",
       "      <td>0.017266</td>\n",
       "      <td>0.511925</td>\n",
       "      <td>0.494935</td>\n",
       "      <td>0.537425</td>\n",
       "      <td>0.482797</td>\n",
       "      <td>0.492480</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461098</td>\n",
       "      <td>0.038881</td>\n",
       "      <td>-0.083615</td>\n",
       "      <td>-0.003922</td>\n",
       "      <td>-0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_scores_value</th>\n",
       "      <td>-0.022693</td>\n",
       "      <td>-0.014555</td>\n",
       "      <td>0.009989</td>\n",
       "      <td>0.019600</td>\n",
       "      <td>0.007761</td>\n",
       "      <td>0.013663</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.006308</td>\n",
       "      <td>-0.040277</td>\n",
       "      <td>-0.075646</td>\n",
       "      <td>-0.025450</td>\n",
       "      <td>-0.048680</td>\n",
       "      <td>-0.212092</td>\n",
       "      <td>-0.119876</td>\n",
       "      <td>-0.018611</td>\n",
       "      <td>-0.011171</td>\n",
       "      <td>-0.024688</td>\n",
       "      <td>-0.037559</td>\n",
       "      <td>-0.156286</td>\n",
       "      <td>-0.005166</td>\n",
       "      <td>-0.012989</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.017402</td>\n",
       "      <td>0.008052</td>\n",
       "      <td>-0.004238</td>\n",
       "      <td>0.386083</td>\n",
       "      <td>0.406683</td>\n",
       "      <td>0.499612</td>\n",
       "      <td>0.414532</td>\n",
       "      <td>0.443763</td>\n",
       "      <td>0.461098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011018</td>\n",
       "      <td>-0.056893</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>-0.004330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant_bookable</th>\n",
       "      <td>-0.209416</td>\n",
       "      <td>0.200689</td>\n",
       "      <td>-0.030630</td>\n",
       "      <td>-0.010740</td>\n",
       "      <td>-0.092346</td>\n",
       "      <td>-0.016590</td>\n",
       "      <td>-0.105904</td>\n",
       "      <td>-0.057839</td>\n",
       "      <td>-0.104652</td>\n",
       "      <td>-0.105500</td>\n",
       "      <td>-0.026397</td>\n",
       "      <td>-0.001728</td>\n",
       "      <td>0.013572</td>\n",
       "      <td>0.144605</td>\n",
       "      <td>0.218551</td>\n",
       "      <td>0.107637</td>\n",
       "      <td>0.233862</td>\n",
       "      <td>0.130749</td>\n",
       "      <td>0.198448</td>\n",
       "      <td>-0.031442</td>\n",
       "      <td>0.094113</td>\n",
       "      <td>0.020531</td>\n",
       "      <td>0.063238</td>\n",
       "      <td>-0.178256</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>-0.054133</td>\n",
       "      <td>-0.047932</td>\n",
       "      <td>-0.025931</td>\n",
       "      <td>-0.040295</td>\n",
       "      <td>-0.044948</td>\n",
       "      <td>0.038881</td>\n",
       "      <td>0.011018</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031970</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>-0.017308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cancellation_policy</th>\n",
       "      <td>0.098442</td>\n",
       "      <td>0.212330</td>\n",
       "      <td>0.113363</td>\n",
       "      <td>0.071856</td>\n",
       "      <td>0.097464</td>\n",
       "      <td>0.113332</td>\n",
       "      <td>0.074521</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.255310</td>\n",
       "      <td>0.275487</td>\n",
       "      <td>0.179980</td>\n",
       "      <td>0.152332</td>\n",
       "      <td>0.211378</td>\n",
       "      <td>0.138721</td>\n",
       "      <td>0.181688</td>\n",
       "      <td>0.188503</td>\n",
       "      <td>-0.011052</td>\n",
       "      <td>0.051027</td>\n",
       "      <td>0.094890</td>\n",
       "      <td>0.109908</td>\n",
       "      <td>0.121049</td>\n",
       "      <td>0.081384</td>\n",
       "      <td>0.017640</td>\n",
       "      <td>0.066141</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>-0.092969</td>\n",
       "      <td>-0.106695</td>\n",
       "      <td>-0.082566</td>\n",
       "      <td>-0.119208</td>\n",
       "      <td>-0.111931</td>\n",
       "      <td>-0.083615</td>\n",
       "      <td>-0.056893</td>\n",
       "      <td>-0.031970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038465</td>\n",
       "      <td>0.053715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <td>0.146070</td>\n",
       "      <td>-0.002876</td>\n",
       "      <td>0.002964</td>\n",
       "      <td>0.018043</td>\n",
       "      <td>0.018011</td>\n",
       "      <td>0.012757</td>\n",
       "      <td>0.073803</td>\n",
       "      <td>0.073027</td>\n",
       "      <td>0.060197</td>\n",
       "      <td>0.017697</td>\n",
       "      <td>0.069472</td>\n",
       "      <td>0.049007</td>\n",
       "      <td>0.072759</td>\n",
       "      <td>0.030328</td>\n",
       "      <td>-0.006916</td>\n",
       "      <td>-0.010804</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.024024</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.012037</td>\n",
       "      <td>-0.002845</td>\n",
       "      <td>-0.013546</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>-0.015996</td>\n",
       "      <td>-0.005965</td>\n",
       "      <td>-0.003069</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>-0.003922</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>0.038465</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.768700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <td>0.170137</td>\n",
       "      <td>0.017334</td>\n",
       "      <td>0.010712</td>\n",
       "      <td>0.020090</td>\n",
       "      <td>0.017006</td>\n",
       "      <td>0.016192</td>\n",
       "      <td>0.081113</td>\n",
       "      <td>0.105614</td>\n",
       "      <td>0.065516</td>\n",
       "      <td>0.024368</td>\n",
       "      <td>0.072688</td>\n",
       "      <td>0.048237</td>\n",
       "      <td>0.093453</td>\n",
       "      <td>0.044523</td>\n",
       "      <td>0.007657</td>\n",
       "      <td>-0.000381</td>\n",
       "      <td>0.023472</td>\n",
       "      <td>-0.001958</td>\n",
       "      <td>0.031997</td>\n",
       "      <td>0.033776</td>\n",
       "      <td>0.041554</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>-0.002172</td>\n",
       "      <td>-0.018456</td>\n",
       "      <td>-0.010782</td>\n",
       "      <td>-0.011134</td>\n",
       "      <td>-0.018409</td>\n",
       "      <td>-0.009310</td>\n",
       "      <td>-0.006835</td>\n",
       "      <td>0.004089</td>\n",
       "      <td>-0.000103</td>\n",
       "      <td>-0.004330</td>\n",
       "      <td>-0.017308</td>\n",
       "      <td>0.053715</td>\n",
       "      <td>0.768700</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              host_days_active  \\\n",
       "host_days_active                                      1.000000   \n",
       "host_total_listings_count                            -0.045815   \n",
       "accommodates                                          0.005189   \n",
       "bathrooms                                             0.006178   \n",
       "bedrooms                                              0.057228   \n",
       "beds                                                  0.000723   \n",
       "weekly_price                                          0.259269   \n",
       "monthly_price                                         0.212707   \n",
       "security_deposit                                      0.062476   \n",
       "cleaning_fee                                          0.101065   \n",
       "guests_included                                       0.110619   \n",
       "extra_people                                          0.074318   \n",
       "number_of_reviews                                     0.218086   \n",
       "number_of_reviews_ltm                                 0.012918   \n",
       "calculated_host_listings_count                       -0.061217   \n",
       "calculated_host_listings_count_entire_homes          -0.013360   \n",
       "calculated_host_listings_count_private_rooms         -0.065402   \n",
       "calculated_host_listings_count_shared_rooms          -0.080552   \n",
       "reviews_per_month                                    -0.065061   \n",
       "host_response_time                                    0.025731   \n",
       "host_response_rate                                    0.005714   \n",
       "neighbourhood                                         0.023841   \n",
       "property_type                                        -0.035693   \n",
       "room_type                                             0.081453   \n",
       "bed_type                                             -0.033276   \n",
       "review_scores_rating                                 -0.014305   \n",
       "review_scores_accuracy                               -0.026500   \n",
       "review_scores_cleanliness                            -0.033534   \n",
       "review_scores_checkin                                -0.013319   \n",
       "review_scores_communication                          -0.017052   \n",
       "review_scores_location                               -0.074925   \n",
       "review_scores_value                                  -0.022693   \n",
       "instant_bookable                                     -0.209416   \n",
       "cancellation_policy                                   0.098442   \n",
       "require_guest_profile_picture                         0.146070   \n",
       "require_guest_phone_verification                      0.170137   \n",
       "\n",
       "                                              host_total_listings_count  \\\n",
       "host_days_active                                              -0.045815   \n",
       "host_total_listings_count                                      1.000000   \n",
       "accommodates                                                   0.045892   \n",
       "bathrooms                                                      0.042380   \n",
       "bedrooms                                                       0.015884   \n",
       "beds                                                           0.057971   \n",
       "weekly_price                                                  -0.074637   \n",
       "monthly_price                                                 -0.031693   \n",
       "security_deposit                                               0.089624   \n",
       "cleaning_fee                                                   0.085214   \n",
       "guests_included                                               -0.020687   \n",
       "extra_people                                                   0.053516   \n",
       "number_of_reviews                                             -0.051297   \n",
       "number_of_reviews_ltm                                          0.003994   \n",
       "calculated_host_listings_count                                 0.954043   \n",
       "calculated_host_listings_count_entire_homes                    0.839711   \n",
       "calculated_host_listings_count_private_rooms                   0.304252   \n",
       "calculated_host_listings_count_shared_rooms                    0.397220   \n",
       "reviews_per_month                                              0.008856   \n",
       "host_response_time                                             0.104446   \n",
       "host_response_rate                                             0.057704   \n",
       "neighbourhood                                                  0.078368   \n",
       "property_type                                                 -0.009220   \n",
       "room_type                                                     -0.021164   \n",
       "bed_type                                                       0.013954   \n",
       "review_scores_rating                                          -0.083162   \n",
       "review_scores_accuracy                                        -0.075605   \n",
       "review_scores_cleanliness                                     -0.036996   \n",
       "review_scores_checkin                                         -0.097184   \n",
       "review_scores_communication                                   -0.108236   \n",
       "review_scores_location                                         0.012164   \n",
       "review_scores_value                                           -0.014555   \n",
       "instant_bookable                                               0.200689   \n",
       "cancellation_policy                                            0.212330   \n",
       "require_guest_profile_picture                                 -0.002876   \n",
       "require_guest_phone_verification                               0.017334   \n",
       "\n",
       "                                              accommodates  bathrooms  \\\n",
       "host_days_active                                  0.005189   0.006178   \n",
       "host_total_listings_count                         0.045892   0.042380   \n",
       "accommodates                                      1.000000   0.337743   \n",
       "bathrooms                                         0.337743   1.000000   \n",
       "bedrooms                                          0.669051   0.378098   \n",
       "beds                                              0.802256   0.366059   \n",
       "weekly_price                                      0.030102   0.000458   \n",
       "monthly_price                                     0.005023  -0.010883   \n",
       "security_deposit                                  0.143160   0.059632   \n",
       "cleaning_fee                                      0.189487   0.097220   \n",
       "guests_included                                   0.393854   0.152312   \n",
       "extra_people                                      0.239867   0.055508   \n",
       "number_of_reviews                                -0.051273  -0.061666   \n",
       "number_of_reviews_ltm                            -0.034476  -0.017821   \n",
       "calculated_host_listings_count                    0.032278   0.033103   \n",
       "calculated_host_listings_count_entire_homes       0.141668   0.051880   \n",
       "calculated_host_listings_count_private_rooms     -0.196238  -0.025674   \n",
       "calculated_host_listings_count_shared_rooms      -0.042760  -0.019611   \n",
       "reviews_per_month                                -0.072334  -0.036095   \n",
       "host_response_time                                0.070594   0.071242   \n",
       "host_response_rate                                0.056522   0.086636   \n",
       "neighbourhood                                     0.018396   0.040940   \n",
       "property_type                                     0.231379   0.176621   \n",
       "room_type                                         0.285798   0.067172   \n",
       "bed_type                                          0.058593   0.025201   \n",
       "review_scores_rating                             -0.004206   0.056626   \n",
       "review_scores_accuracy                           -0.030625   0.029408   \n",
       "review_scores_cleanliness                        -0.031123   0.040329   \n",
       "review_scores_checkin                             0.002614   0.028309   \n",
       "review_scores_communication                      -0.002868   0.035524   \n",
       "review_scores_location                           -0.009838   0.055675   \n",
       "review_scores_value                               0.009989   0.019600   \n",
       "instant_bookable                                 -0.030630  -0.010740   \n",
       "cancellation_policy                               0.113363   0.071856   \n",
       "require_guest_profile_picture                     0.002964   0.018043   \n",
       "require_guest_phone_verification                  0.010712   0.020090   \n",
       "\n",
       "                                              bedrooms      beds  \\\n",
       "host_days_active                              0.057228  0.000723   \n",
       "host_total_listings_count                     0.015884  0.057971   \n",
       "accommodates                                  0.669051  0.802256   \n",
       "bathrooms                                     0.378098  0.366059   \n",
       "bedrooms                                      1.000000  0.704059   \n",
       "beds                                          0.704059  1.000000   \n",
       "weekly_price                                  0.045051  0.044532   \n",
       "monthly_price                                -0.004108  0.011439   \n",
       "security_deposit                              0.127578  0.131652   \n",
       "cleaning_fee                                  0.168515  0.155153   \n",
       "guests_included                               0.279861  0.358539   \n",
       "extra_people                                  0.124882  0.196129   \n",
       "number_of_reviews                            -0.100880 -0.034531   \n",
       "number_of_reviews_ltm                        -0.093058 -0.017236   \n",
       "calculated_host_listings_count                0.007257  0.043506   \n",
       "calculated_host_listings_count_entire_homes   0.099219  0.108603   \n",
       "calculated_host_listings_count_private_rooms -0.175267 -0.121287   \n",
       "calculated_host_listings_count_shared_rooms  -0.017090 -0.008669   \n",
       "reviews_per_month                            -0.152166 -0.058078   \n",
       "host_response_time                            0.047443  0.070891   \n",
       "host_response_rate                            0.020628  0.061050   \n",
       "neighbourhood                                -0.017318  0.008946   \n",
       "property_type                                 0.250608  0.302714   \n",
       "room_type                                     0.236904  0.191445   \n",
       "bed_type                                      0.060951  0.047880   \n",
       "review_scores_rating                          0.045357  0.003971   \n",
       "review_scores_accuracy                        0.009580 -0.028394   \n",
       "review_scores_cleanliness                     0.007085 -0.014922   \n",
       "review_scores_checkin                         0.024891  0.015406   \n",
       "review_scores_communication                   0.026976  0.004845   \n",
       "review_scores_location                       -0.002273 -0.000890   \n",
       "review_scores_value                           0.007761  0.013663   \n",
       "instant_bookable                             -0.092346 -0.016590   \n",
       "cancellation_policy                           0.097464  0.113332   \n",
       "require_guest_profile_picture                 0.018011  0.012757   \n",
       "require_guest_phone_verification              0.017006  0.016192   \n",
       "\n",
       "                                              weekly_price  monthly_price  \\\n",
       "host_days_active                                  0.259269       0.212707   \n",
       "host_total_listings_count                        -0.074637      -0.031693   \n",
       "accommodates                                      0.030102       0.005023   \n",
       "bathrooms                                         0.000458      -0.010883   \n",
       "bedrooms                                          0.045051      -0.004108   \n",
       "beds                                              0.044532       0.011439   \n",
       "weekly_price                                      1.000000       0.619870   \n",
       "monthly_price                                     0.619870       1.000000   \n",
       "security_deposit                                  0.065038       0.057859   \n",
       "cleaning_fee                                      0.045784       0.041349   \n",
       "guests_included                                   0.126574       0.099478   \n",
       "extra_people                                      0.072931       0.086956   \n",
       "number_of_reviews                                 0.212522       0.203607   \n",
       "number_of_reviews_ltm                             0.005164       0.043152   \n",
       "calculated_host_listings_count                   -0.082117      -0.040114   \n",
       "calculated_host_listings_count_entire_homes      -0.052318      -0.033363   \n",
       "calculated_host_listings_count_private_rooms     -0.066030      -0.018767   \n",
       "calculated_host_listings_count_shared_rooms      -0.035926      -0.021109   \n",
       "reviews_per_month                                -0.024667       0.031956   \n",
       "host_response_time                               -0.000710       0.032401   \n",
       "host_response_rate                               -0.009971       0.021143   \n",
       "neighbourhood                                     0.037719       0.043201   \n",
       "property_type                                     0.016445      -0.004782   \n",
       "room_type                                         0.049574       0.005559   \n",
       "bed_type                                         -0.031049      -0.037516   \n",
       "review_scores_rating                             -0.036665      -0.038132   \n",
       "review_scores_accuracy                           -0.047169      -0.041589   \n",
       "review_scores_cleanliness                        -0.046210      -0.038593   \n",
       "review_scores_checkin                            -0.011880      -0.018411   \n",
       "review_scores_communication                      -0.021912      -0.019864   \n",
       "review_scores_location                           -0.047564      -0.027454   \n",
       "review_scores_value                               0.000068       0.006308   \n",
       "instant_bookable                                 -0.105904      -0.057839   \n",
       "cancellation_policy                               0.074521       0.067433   \n",
       "require_guest_profile_picture                     0.073803       0.073027   \n",
       "require_guest_phone_verification                  0.081113       0.105614   \n",
       "\n",
       "                                              security_deposit  cleaning_fee  \\\n",
       "host_days_active                                      0.062476      0.101065   \n",
       "host_total_listings_count                             0.089624      0.085214   \n",
       "accommodates                                          0.143160      0.189487   \n",
       "bathrooms                                             0.059632      0.097220   \n",
       "bedrooms                                              0.127578      0.168515   \n",
       "beds                                                  0.131652      0.155153   \n",
       "weekly_price                                          0.065038      0.045784   \n",
       "monthly_price                                         0.057859      0.041349   \n",
       "security_deposit                                      1.000000      0.410419   \n",
       "cleaning_fee                                          0.410419      1.000000   \n",
       "guests_included                                       0.184835      0.196080   \n",
       "extra_people                                          0.191924      0.195698   \n",
       "number_of_reviews                                     0.089540      0.115080   \n",
       "number_of_reviews_ltm                                 0.009948      0.046213   \n",
       "calculated_host_listings_count                        0.082264      0.068916   \n",
       "calculated_host_listings_count_entire_homes           0.152013      0.163550   \n",
       "calculated_host_listings_count_private_rooms         -0.118704     -0.157601   \n",
       "calculated_host_listings_count_shared_rooms           0.009475     -0.015546   \n",
       "reviews_per_month                                    -0.035248      0.000313   \n",
       "host_response_time                                    0.009844      0.043726   \n",
       "host_response_rate                                    0.013585      0.049202   \n",
       "neighbourhood                                         0.055382      0.074090   \n",
       "property_type                                         0.005046      0.011576   \n",
       "room_type                                             0.173744      0.227967   \n",
       "bed_type                                              0.005434      0.020790   \n",
       "review_scores_rating                                 -0.078832     -0.090258   \n",
       "review_scores_accuracy                               -0.068716     -0.093437   \n",
       "review_scores_cleanliness                            -0.053870     -0.094041   \n",
       "review_scores_checkin                                -0.106044     -0.127663   \n",
       "review_scores_communication                          -0.089747     -0.117245   \n",
       "review_scores_location                               -0.068067     -0.083180   \n",
       "review_scores_value                                  -0.040277     -0.075646   \n",
       "instant_bookable                                     -0.104652     -0.105500   \n",
       "cancellation_policy                                   0.255310      0.275487   \n",
       "require_guest_profile_picture                         0.060197      0.017697   \n",
       "require_guest_phone_verification                      0.065516      0.024368   \n",
       "\n",
       "                                              guests_included  extra_people  \\\n",
       "host_days_active                                     0.110619      0.074318   \n",
       "host_total_listings_count                           -0.020687      0.053516   \n",
       "accommodates                                         0.393854      0.239867   \n",
       "bathrooms                                            0.152312      0.055508   \n",
       "bedrooms                                             0.279861      0.124882   \n",
       "beds                                                 0.358539      0.196129   \n",
       "weekly_price                                         0.126574      0.072931   \n",
       "monthly_price                                        0.099478      0.086956   \n",
       "security_deposit                                     0.184835      0.191924   \n",
       "cleaning_fee                                         0.196080      0.195698   \n",
       "guests_included                                      1.000000      0.576632   \n",
       "extra_people                                         0.576632      1.000000   \n",
       "number_of_reviews                                    0.159283      0.148941   \n",
       "number_of_reviews_ltm                                0.078846      0.085853   \n",
       "calculated_host_listings_count                      -0.030682      0.061346   \n",
       "calculated_host_listings_count_entire_homes          0.010674      0.044225   \n",
       "calculated_host_listings_count_private_rooms        -0.080699      0.046885   \n",
       "calculated_host_listings_count_shared_rooms         -0.026095      0.084821   \n",
       "reviews_per_month                                    0.042832      0.076488   \n",
       "host_response_time                                   0.061653      0.073448   \n",
       "host_response_rate                                   0.069042      0.066457   \n",
       "neighbourhood                                        0.011757     -0.017785   \n",
       "property_type                                        0.097133      0.030254   \n",
       "room_type                                            0.107338     -0.015525   \n",
       "bed_type                                             0.015878     -0.006670   \n",
       "review_scores_rating                                -0.081572     -0.098945   \n",
       "review_scores_accuracy                              -0.100141     -0.108964   \n",
       "review_scores_cleanliness                           -0.079998     -0.094806   \n",
       "review_scores_checkin                               -0.064812     -0.085807   \n",
       "review_scores_communication                         -0.075977     -0.091559   \n",
       "review_scores_location                              -0.092878     -0.097666   \n",
       "review_scores_value                                 -0.025450     -0.048680   \n",
       "instant_bookable                                    -0.026397     -0.001728   \n",
       "cancellation_policy                                  0.179980      0.152332   \n",
       "require_guest_profile_picture                        0.069472      0.049007   \n",
       "require_guest_phone_verification                     0.072688      0.048237   \n",
       "\n",
       "                                              number_of_reviews  \\\n",
       "host_days_active                                       0.218086   \n",
       "host_total_listings_count                             -0.051297   \n",
       "accommodates                                          -0.051273   \n",
       "bathrooms                                             -0.061666   \n",
       "bedrooms                                              -0.100880   \n",
       "beds                                                  -0.034531   \n",
       "weekly_price                                           0.212522   \n",
       "monthly_price                                          0.203607   \n",
       "security_deposit                                       0.089540   \n",
       "cleaning_fee                                           0.115080   \n",
       "guests_included                                        0.159283   \n",
       "extra_people                                           0.148941   \n",
       "number_of_reviews                                      1.000000   \n",
       "number_of_reviews_ltm                                  0.727878   \n",
       "calculated_host_listings_count                        -0.066986   \n",
       "calculated_host_listings_count_entire_homes           -0.159309   \n",
       "calculated_host_listings_count_private_rooms           0.172905   \n",
       "calculated_host_listings_count_shared_rooms           -0.029425   \n",
       "reviews_per_month                                      0.709267   \n",
       "host_response_time                                     0.213486   \n",
       "host_response_rate                                     0.282490   \n",
       "neighbourhood                                          0.055972   \n",
       "property_type                                          0.016297   \n",
       "room_type                                             -0.209502   \n",
       "bed_type                                              -0.046381   \n",
       "review_scores_rating                                  -0.358322   \n",
       "review_scores_accuracy                                -0.363652   \n",
       "review_scores_cleanliness                             -0.354162   \n",
       "review_scores_checkin                                 -0.336919   \n",
       "review_scores_communication                           -0.353440   \n",
       "review_scores_location                                -0.376117   \n",
       "review_scores_value                                   -0.212092   \n",
       "instant_bookable                                       0.013572   \n",
       "cancellation_policy                                    0.211378   \n",
       "require_guest_profile_picture                          0.072759   \n",
       "require_guest_phone_verification                       0.093453   \n",
       "\n",
       "                                              number_of_reviews_ltm  \\\n",
       "host_days_active                                           0.012918   \n",
       "host_total_listings_count                                  0.003994   \n",
       "accommodates                                              -0.034476   \n",
       "bathrooms                                                 -0.017821   \n",
       "bedrooms                                                  -0.093058   \n",
       "beds                                                      -0.017236   \n",
       "weekly_price                                               0.005164   \n",
       "monthly_price                                              0.043152   \n",
       "security_deposit                                           0.009948   \n",
       "cleaning_fee                                               0.046213   \n",
       "guests_included                                            0.078846   \n",
       "extra_people                                               0.085853   \n",
       "number_of_reviews                                          0.727878   \n",
       "number_of_reviews_ltm                                      1.000000   \n",
       "calculated_host_listings_count                            -0.009651   \n",
       "calculated_host_listings_count_entire_homes               -0.148563   \n",
       "calculated_host_listings_count_private_rooms               0.261861   \n",
       "calculated_host_listings_count_shared_rooms               -0.011670   \n",
       "reviews_per_month                                          0.824998   \n",
       "host_response_time                                         0.382597   \n",
       "host_response_rate                                         0.488956   \n",
       "neighbourhood                                              0.040633   \n",
       "property_type                                              0.071186   \n",
       "room_type                                                 -0.294586   \n",
       "bed_type                                                  -0.005444   \n",
       "review_scores_rating                                      -0.188597   \n",
       "review_scores_accuracy                                    -0.206070   \n",
       "review_scores_cleanliness                                 -0.194319   \n",
       "review_scores_checkin                                     -0.183651   \n",
       "review_scores_communication                               -0.199387   \n",
       "review_scores_location                                    -0.162543   \n",
       "review_scores_value                                       -0.119876   \n",
       "instant_bookable                                           0.144605   \n",
       "cancellation_policy                                        0.138721   \n",
       "require_guest_profile_picture                              0.030328   \n",
       "require_guest_phone_verification                           0.044523   \n",
       "\n",
       "                                              calculated_host_listings_count  \\\n",
       "host_days_active                                                   -0.061217   \n",
       "host_total_listings_count                                           0.954043   \n",
       "accommodates                                                        0.032278   \n",
       "bathrooms                                                           0.033103   \n",
       "bedrooms                                                            0.007257   \n",
       "beds                                                                0.043506   \n",
       "weekly_price                                                       -0.082117   \n",
       "monthly_price                                                      -0.040114   \n",
       "security_deposit                                                    0.082264   \n",
       "cleaning_fee                                                        0.068916   \n",
       "guests_included                                                    -0.030682   \n",
       "extra_people                                                        0.061346   \n",
       "number_of_reviews                                                  -0.066986   \n",
       "number_of_reviews_ltm                                              -0.009651   \n",
       "calculated_host_listings_count                                      1.000000   \n",
       "calculated_host_listings_count_entire_homes                         0.877598   \n",
       "calculated_host_listings_count_private_rooms                        0.331984   \n",
       "calculated_host_listings_count_shared_rooms                         0.445975   \n",
       "reviews_per_month                                                   0.001016   \n",
       "host_response_time                                                  0.100110   \n",
       "host_response_rate                                                  0.055172   \n",
       "neighbourhood                                                       0.070587   \n",
       "property_type                                                      -0.011485   \n",
       "room_type                                                          -0.020980   \n",
       "bed_type                                                            0.014433   \n",
       "review_scores_rating                                               -0.087744   \n",
       "review_scores_accuracy                                             -0.078292   \n",
       "review_scores_cleanliness                                          -0.037018   \n",
       "review_scores_checkin                                              -0.103096   \n",
       "review_scores_communication                                        -0.114024   \n",
       "review_scores_location                                              0.013030   \n",
       "review_scores_value                                                -0.018611   \n",
       "instant_bookable                                                    0.218551   \n",
       "cancellation_policy                                                 0.181688   \n",
       "require_guest_profile_picture                                      -0.006916   \n",
       "require_guest_phone_verification                                    0.007657   \n",
       "\n",
       "                                              calculated_host_listings_count_entire_homes  \\\n",
       "host_days_active                                                                -0.013360   \n",
       "host_total_listings_count                                                        0.839711   \n",
       "accommodates                                                                     0.141668   \n",
       "bathrooms                                                                        0.051880   \n",
       "bedrooms                                                                         0.099219   \n",
       "beds                                                                             0.108603   \n",
       "weekly_price                                                                    -0.052318   \n",
       "monthly_price                                                                   -0.033363   \n",
       "security_deposit                                                                 0.152013   \n",
       "cleaning_fee                                                                     0.163550   \n",
       "guests_included                                                                  0.010674   \n",
       "extra_people                                                                     0.044225   \n",
       "number_of_reviews                                                               -0.159309   \n",
       "number_of_reviews_ltm                                                           -0.148563   \n",
       "calculated_host_listings_count                                                   0.877598   \n",
       "calculated_host_listings_count_entire_homes                                      1.000000   \n",
       "calculated_host_listings_count_private_rooms                                    -0.134266   \n",
       "calculated_host_listings_count_shared_rooms                                      0.347029   \n",
       "reviews_per_month                                                               -0.177999   \n",
       "host_response_time                                                               0.058139   \n",
       "host_response_rate                                                              -0.017876   \n",
       "neighbourhood                                                                    0.081708   \n",
       "property_type                                                                   -0.079001   \n",
       "room_type                                                                        0.397574   \n",
       "bed_type                                                                         0.033241   \n",
       "review_scores_rating                                                            -0.041662   \n",
       "review_scores_accuracy                                                          -0.039217   \n",
       "review_scores_cleanliness                                                       -0.019653   \n",
       "review_scores_checkin                                                           -0.082393   \n",
       "review_scores_communication                                                     -0.078277   \n",
       "review_scores_location                                                           0.022416   \n",
       "review_scores_value                                                             -0.011171   \n",
       "instant_bookable                                                                 0.107637   \n",
       "cancellation_policy                                                              0.188503   \n",
       "require_guest_profile_picture                                                   -0.010804   \n",
       "require_guest_phone_verification                                                -0.000381   \n",
       "\n",
       "                                              calculated_host_listings_count_private_rooms  \\\n",
       "host_days_active                                                                 -0.065402   \n",
       "host_total_listings_count                                                         0.304252   \n",
       "accommodates                                                                     -0.196238   \n",
       "bathrooms                                                                        -0.025674   \n",
       "bedrooms                                                                         -0.175267   \n",
       "beds                                                                             -0.121287   \n",
       "weekly_price                                                                     -0.066030   \n",
       "monthly_price                                                                    -0.018767   \n",
       "security_deposit                                                                 -0.118704   \n",
       "cleaning_fee                                                                     -0.157601   \n",
       "guests_included                                                                  -0.080699   \n",
       "extra_people                                                                      0.046885   \n",
       "number_of_reviews                                                                 0.172905   \n",
       "number_of_reviews_ltm                                                             0.261861   \n",
       "calculated_host_listings_count                                                    0.331984   \n",
       "calculated_host_listings_count_entire_homes                                      -0.134266   \n",
       "calculated_host_listings_count_private_rooms                                      1.000000   \n",
       "calculated_host_listings_count_shared_rooms                                       0.151693   \n",
       "reviews_per_month                                                                 0.329692   \n",
       "host_response_time                                                                0.093508   \n",
       "host_response_rate                                                                0.150805   \n",
       "neighbourhood                                                                    -0.015575   \n",
       "property_type                                                                     0.125672   \n",
       "room_type                                                                        -0.777985   \n",
       "bed_type                                                                         -0.029264   \n",
       "review_scores_rating                                                             -0.107591   \n",
       "review_scores_accuracy                                                           -0.092304   \n",
       "review_scores_cleanliness                                                        -0.045147   \n",
       "review_scores_checkin                                                            -0.055547   \n",
       "review_scores_communication                                                      -0.085801   \n",
       "review_scores_location                                                           -0.025903   \n",
       "review_scores_value                                                              -0.024688   \n",
       "instant_bookable                                                                  0.233862   \n",
       "cancellation_policy                                                              -0.011052   \n",
       "require_guest_profile_picture                                                     0.012120   \n",
       "require_guest_phone_verification                                                  0.023472   \n",
       "\n",
       "                                              calculated_host_listings_count_shared_rooms  \\\n",
       "host_days_active                                                                -0.080552   \n",
       "host_total_listings_count                                                        0.397220   \n",
       "accommodates                                                                    -0.042760   \n",
       "bathrooms                                                                       -0.019611   \n",
       "bedrooms                                                                        -0.017090   \n",
       "beds                                                                            -0.008669   \n",
       "weekly_price                                                                    -0.035926   \n",
       "monthly_price                                                                   -0.021109   \n",
       "security_deposit                                                                 0.009475   \n",
       "cleaning_fee                                                                    -0.015546   \n",
       "guests_included                                                                 -0.026095   \n",
       "extra_people                                                                     0.084821   \n",
       "number_of_reviews                                                               -0.029425   \n",
       "number_of_reviews_ltm                                                           -0.011670   \n",
       "calculated_host_listings_count                                                   0.445975   \n",
       "calculated_host_listings_count_entire_homes                                      0.347029   \n",
       "calculated_host_listings_count_private_rooms                                     0.151693   \n",
       "calculated_host_listings_count_shared_rooms                                      1.000000   \n",
       "reviews_per_month                                                                0.011263   \n",
       "host_response_time                                                               0.020930   \n",
       "host_response_rate                                                              -0.016544   \n",
       "neighbourhood                                                                    0.008504   \n",
       "property_type                                                                    0.014850   \n",
       "room_type                                                                       -0.140840   \n",
       "bed_type                                                                        -0.018791   \n",
       "review_scores_rating                                                            -0.056475   \n",
       "review_scores_accuracy                                                          -0.048102   \n",
       "review_scores_cleanliness                                                       -0.045581   \n",
       "review_scores_checkin                                                           -0.068478   \n",
       "review_scores_communication                                                     -0.059171   \n",
       "review_scores_location                                                          -0.021658   \n",
       "review_scores_value                                                             -0.037559   \n",
       "instant_bookable                                                                 0.130749   \n",
       "cancellation_policy                                                              0.051027   \n",
       "require_guest_profile_picture                                                    0.001785   \n",
       "require_guest_phone_verification                                                -0.001958   \n",
       "\n",
       "                                              reviews_per_month  \\\n",
       "host_days_active                                      -0.065061   \n",
       "host_total_listings_count                              0.008856   \n",
       "accommodates                                          -0.072334   \n",
       "bathrooms                                             -0.036095   \n",
       "bedrooms                                              -0.152166   \n",
       "beds                                                  -0.058078   \n",
       "weekly_price                                          -0.024667   \n",
       "monthly_price                                          0.031956   \n",
       "security_deposit                                      -0.035248   \n",
       "cleaning_fee                                           0.000313   \n",
       "guests_included                                        0.042832   \n",
       "extra_people                                           0.076488   \n",
       "number_of_reviews                                      0.709267   \n",
       "number_of_reviews_ltm                                  0.824998   \n",
       "calculated_host_listings_count                         0.001016   \n",
       "calculated_host_listings_count_entire_homes           -0.177999   \n",
       "calculated_host_listings_count_private_rooms           0.329692   \n",
       "calculated_host_listings_count_shared_rooms            0.011263   \n",
       "reviews_per_month                                      1.000000   \n",
       "host_response_time                                     0.282152   \n",
       "host_response_rate                                     0.404856   \n",
       "neighbourhood                                          0.029837   \n",
       "property_type                                          0.068376   \n",
       "room_type                                             -0.384239   \n",
       "bed_type                                              -0.009680   \n",
       "review_scores_rating                                  -0.225056   \n",
       "review_scores_accuracy                                -0.231200   \n",
       "review_scores_cleanliness                             -0.209010   \n",
       "review_scores_checkin                                 -0.203756   \n",
       "review_scores_communication                           -0.222897   \n",
       "review_scores_location                                -0.180012   \n",
       "review_scores_value                                   -0.156286   \n",
       "instant_bookable                                       0.198448   \n",
       "cancellation_policy                                    0.094890   \n",
       "require_guest_profile_picture                          0.018316   \n",
       "require_guest_phone_verification                       0.031997   \n",
       "\n",
       "                                              host_response_time  \\\n",
       "host_days_active                                        0.025731   \n",
       "host_total_listings_count                               0.104446   \n",
       "accommodates                                            0.070594   \n",
       "bathrooms                                               0.071242   \n",
       "bedrooms                                                0.047443   \n",
       "beds                                                    0.070891   \n",
       "weekly_price                                           -0.000710   \n",
       "monthly_price                                           0.032401   \n",
       "security_deposit                                        0.009844   \n",
       "cleaning_fee                                            0.043726   \n",
       "guests_included                                         0.061653   \n",
       "extra_people                                            0.073448   \n",
       "number_of_reviews                                       0.213486   \n",
       "number_of_reviews_ltm                                   0.382597   \n",
       "calculated_host_listings_count                          0.100110   \n",
       "calculated_host_listings_count_entire_homes             0.058139   \n",
       "calculated_host_listings_count_private_rooms            0.093508   \n",
       "calculated_host_listings_count_shared_rooms             0.020930   \n",
       "reviews_per_month                                       0.282152   \n",
       "host_response_time                                      1.000000   \n",
       "host_response_rate                                      0.823657   \n",
       "neighbourhood                                           0.026197   \n",
       "property_type                                           0.051574   \n",
       "room_type                                              -0.053755   \n",
       "bed_type                                                0.008077   \n",
       "review_scores_rating                                   -0.005662   \n",
       "review_scores_accuracy                                 -0.027420   \n",
       "review_scores_cleanliness                              -0.022821   \n",
       "review_scores_checkin                                  -0.010988   \n",
       "review_scores_communication                            -0.022597   \n",
       "review_scores_location                                  0.003325   \n",
       "review_scores_value                                    -0.005166   \n",
       "instant_bookable                                       -0.031442   \n",
       "cancellation_policy                                     0.109908   \n",
       "require_guest_profile_picture                           0.024024   \n",
       "require_guest_phone_verification                        0.033776   \n",
       "\n",
       "                                              host_response_rate  \\\n",
       "host_days_active                                        0.005714   \n",
       "host_total_listings_count                               0.057704   \n",
       "accommodates                                            0.056522   \n",
       "bathrooms                                               0.086636   \n",
       "bedrooms                                                0.020628   \n",
       "beds                                                    0.061050   \n",
       "weekly_price                                           -0.009971   \n",
       "monthly_price                                           0.021143   \n",
       "security_deposit                                        0.013585   \n",
       "cleaning_fee                                            0.049202   \n",
       "guests_included                                         0.069042   \n",
       "extra_people                                            0.066457   \n",
       "number_of_reviews                                       0.282490   \n",
       "number_of_reviews_ltm                                   0.488956   \n",
       "calculated_host_listings_count                          0.055172   \n",
       "calculated_host_listings_count_entire_homes            -0.017876   \n",
       "calculated_host_listings_count_private_rooms            0.150805   \n",
       "calculated_host_listings_count_shared_rooms            -0.016544   \n",
       "reviews_per_month                                       0.404856   \n",
       "host_response_time                                      0.823657   \n",
       "host_response_rate                                      1.000000   \n",
       "neighbourhood                                           0.025374   \n",
       "property_type                                           0.075714   \n",
       "room_type                                              -0.122163   \n",
       "bed_type                                                0.008355   \n",
       "review_scores_rating                                   -0.013919   \n",
       "review_scores_accuracy                                 -0.037357   \n",
       "review_scores_cleanliness                              -0.024319   \n",
       "review_scores_checkin                                  -0.021505   \n",
       "review_scores_communication                            -0.027540   \n",
       "review_scores_location                                 -0.008275   \n",
       "review_scores_value                                    -0.012989   \n",
       "instant_bookable                                        0.094113   \n",
       "cancellation_policy                                     0.121049   \n",
       "require_guest_profile_picture                           0.033079   \n",
       "require_guest_phone_verification                        0.041554   \n",
       "\n",
       "                                              neighbourhood  property_type  \\\n",
       "host_days_active                                   0.023841      -0.035693   \n",
       "host_total_listings_count                          0.078368      -0.009220   \n",
       "accommodates                                       0.018396       0.231379   \n",
       "bathrooms                                          0.040940       0.176621   \n",
       "bedrooms                                          -0.017318       0.250608   \n",
       "beds                                               0.008946       0.302714   \n",
       "weekly_price                                       0.037719       0.016445   \n",
       "monthly_price                                      0.043201      -0.004782   \n",
       "security_deposit                                   0.055382       0.005046   \n",
       "cleaning_fee                                       0.074090       0.011576   \n",
       "guests_included                                    0.011757       0.097133   \n",
       "extra_people                                      -0.017785       0.030254   \n",
       "number_of_reviews                                  0.055972       0.016297   \n",
       "number_of_reviews_ltm                              0.040633       0.071186   \n",
       "calculated_host_listings_count                     0.070587      -0.011485   \n",
       "calculated_host_listings_count_entire_homes        0.081708      -0.079001   \n",
       "calculated_host_listings_count_private_rooms      -0.015575       0.125672   \n",
       "calculated_host_listings_count_shared_rooms        0.008504       0.014850   \n",
       "reviews_per_month                                  0.029837       0.068376   \n",
       "host_response_time                                 0.026197       0.051574   \n",
       "host_response_rate                                 0.025374       0.075714   \n",
       "neighbourhood                                      1.000000      -0.044086   \n",
       "property_type                                     -0.044086       1.000000   \n",
       "room_type                                          0.049254      -0.111371   \n",
       "bed_type                                           0.012043       0.023080   \n",
       "review_scores_rating                               0.003606       0.023069   \n",
       "review_scores_accuracy                            -0.005333       0.006353   \n",
       "review_scores_cleanliness                         -0.004965       0.012515   \n",
       "review_scores_checkin                             -0.010096       0.038235   \n",
       "review_scores_communication                       -0.015551       0.018768   \n",
       "review_scores_location                             0.273050       0.002147   \n",
       "review_scores_value                                0.000184       0.017402   \n",
       "instant_bookable                                   0.020531       0.063238   \n",
       "cancellation_policy                                0.081384       0.017640   \n",
       "require_guest_profile_picture                      0.012037      -0.002845   \n",
       "require_guest_phone_verification                   0.031389      -0.002172   \n",
       "\n",
       "                                              room_type  bed_type  \\\n",
       "host_days_active                               0.081453 -0.033276   \n",
       "host_total_listings_count                     -0.021164  0.013954   \n",
       "accommodates                                   0.285798  0.058593   \n",
       "bathrooms                                      0.067172  0.025201   \n",
       "bedrooms                                       0.236904  0.060951   \n",
       "beds                                           0.191445  0.047880   \n",
       "weekly_price                                   0.049574 -0.031049   \n",
       "monthly_price                                  0.005559 -0.037516   \n",
       "security_deposit                               0.173744  0.005434   \n",
       "cleaning_fee                                   0.227967  0.020790   \n",
       "guests_included                                0.107338  0.015878   \n",
       "extra_people                                  -0.015525 -0.006670   \n",
       "number_of_reviews                             -0.209502 -0.046381   \n",
       "number_of_reviews_ltm                         -0.294586 -0.005444   \n",
       "calculated_host_listings_count                -0.020980  0.014433   \n",
       "calculated_host_listings_count_entire_homes    0.397574  0.033241   \n",
       "calculated_host_listings_count_private_rooms  -0.777985 -0.029264   \n",
       "calculated_host_listings_count_shared_rooms   -0.140840 -0.018791   \n",
       "reviews_per_month                             -0.384239 -0.009680   \n",
       "host_response_time                            -0.053755  0.008077   \n",
       "host_response_rate                            -0.122163  0.008355   \n",
       "neighbourhood                                  0.049254  0.012043   \n",
       "property_type                                 -0.111371  0.023080   \n",
       "room_type                                      1.000000  0.062903   \n",
       "bed_type                                       0.062903  1.000000   \n",
       "review_scores_rating                           0.064127  0.020365   \n",
       "review_scores_accuracy                         0.054382  0.021183   \n",
       "review_scores_cleanliness                      0.019346  0.009939   \n",
       "review_scores_checkin                          0.014711  0.002911   \n",
       "review_scores_communication                    0.039441  0.002119   \n",
       "review_scores_location                         0.018342  0.017266   \n",
       "review_scores_value                            0.008052 -0.004238   \n",
       "instant_bookable                              -0.178256  0.020403   \n",
       "cancellation_policy                            0.066141  0.004992   \n",
       "require_guest_profile_picture                 -0.013546 -0.000116   \n",
       "require_guest_phone_verification              -0.018456 -0.010782   \n",
       "\n",
       "                                              review_scores_rating  \\\n",
       "host_days_active                                         -0.014305   \n",
       "host_total_listings_count                                -0.083162   \n",
       "accommodates                                             -0.004206   \n",
       "bathrooms                                                 0.056626   \n",
       "bedrooms                                                  0.045357   \n",
       "beds                                                      0.003971   \n",
       "weekly_price                                             -0.036665   \n",
       "monthly_price                                            -0.038132   \n",
       "security_deposit                                         -0.078832   \n",
       "cleaning_fee                                             -0.090258   \n",
       "guests_included                                          -0.081572   \n",
       "extra_people                                             -0.098945   \n",
       "number_of_reviews                                        -0.358322   \n",
       "number_of_reviews_ltm                                    -0.188597   \n",
       "calculated_host_listings_count                           -0.087744   \n",
       "calculated_host_listings_count_entire_homes              -0.041662   \n",
       "calculated_host_listings_count_private_rooms             -0.107591   \n",
       "calculated_host_listings_count_shared_rooms              -0.056475   \n",
       "reviews_per_month                                        -0.225056   \n",
       "host_response_time                                       -0.005662   \n",
       "host_response_rate                                       -0.013919   \n",
       "neighbourhood                                             0.003606   \n",
       "property_type                                             0.023069   \n",
       "room_type                                                 0.064127   \n",
       "bed_type                                                  0.020365   \n",
       "review_scores_rating                                      1.000000   \n",
       "review_scores_accuracy                                    0.669125   \n",
       "review_scores_cleanliness                                 0.673861   \n",
       "review_scores_checkin                                     0.576509   \n",
       "review_scores_communication                               0.597615   \n",
       "review_scores_location                                    0.511925   \n",
       "review_scores_value                                       0.386083   \n",
       "instant_bookable                                         -0.054133   \n",
       "cancellation_policy                                      -0.092969   \n",
       "require_guest_profile_picture                            -0.006785   \n",
       "require_guest_phone_verification                         -0.011134   \n",
       "\n",
       "                                              review_scores_accuracy  \\\n",
       "host_days_active                                           -0.026500   \n",
       "host_total_listings_count                                  -0.075605   \n",
       "accommodates                                               -0.030625   \n",
       "bathrooms                                                   0.029408   \n",
       "bedrooms                                                    0.009580   \n",
       "beds                                                       -0.028394   \n",
       "weekly_price                                               -0.047169   \n",
       "monthly_price                                              -0.041589   \n",
       "security_deposit                                           -0.068716   \n",
       "cleaning_fee                                               -0.093437   \n",
       "guests_included                                            -0.100141   \n",
       "extra_people                                               -0.108964   \n",
       "number_of_reviews                                          -0.363652   \n",
       "number_of_reviews_ltm                                      -0.206070   \n",
       "calculated_host_listings_count                             -0.078292   \n",
       "calculated_host_listings_count_entire_homes                -0.039217   \n",
       "calculated_host_listings_count_private_rooms               -0.092304   \n",
       "calculated_host_listings_count_shared_rooms                -0.048102   \n",
       "reviews_per_month                                          -0.231200   \n",
       "host_response_time                                         -0.027420   \n",
       "host_response_rate                                         -0.037357   \n",
       "neighbourhood                                              -0.005333   \n",
       "property_type                                               0.006353   \n",
       "room_type                                                   0.054382   \n",
       "bed_type                                                    0.021183   \n",
       "review_scores_rating                                        0.669125   \n",
       "review_scores_accuracy                                      1.000000   \n",
       "review_scores_cleanliness                                   0.623487   \n",
       "review_scores_checkin                                       0.563587   \n",
       "review_scores_communication                                 0.581427   \n",
       "review_scores_location                                      0.494935   \n",
       "review_scores_value                                         0.406683   \n",
       "instant_bookable                                           -0.047932   \n",
       "cancellation_policy                                        -0.106695   \n",
       "require_guest_profile_picture                              -0.015996   \n",
       "require_guest_phone_verification                           -0.018409   \n",
       "\n",
       "                                              review_scores_cleanliness  \\\n",
       "host_days_active                                              -0.033534   \n",
       "host_total_listings_count                                     -0.036996   \n",
       "accommodates                                                  -0.031123   \n",
       "bathrooms                                                      0.040329   \n",
       "bedrooms                                                       0.007085   \n",
       "beds                                                          -0.014922   \n",
       "weekly_price                                                  -0.046210   \n",
       "monthly_price                                                 -0.038593   \n",
       "security_deposit                                              -0.053870   \n",
       "cleaning_fee                                                  -0.094041   \n",
       "guests_included                                               -0.079998   \n",
       "extra_people                                                  -0.094806   \n",
       "number_of_reviews                                             -0.354162   \n",
       "number_of_reviews_ltm                                         -0.194319   \n",
       "calculated_host_listings_count                                -0.037018   \n",
       "calculated_host_listings_count_entire_homes                   -0.019653   \n",
       "calculated_host_listings_count_private_rooms                  -0.045147   \n",
       "calculated_host_listings_count_shared_rooms                   -0.045581   \n",
       "reviews_per_month                                             -0.209010   \n",
       "host_response_time                                            -0.022821   \n",
       "host_response_rate                                            -0.024319   \n",
       "neighbourhood                                                 -0.004965   \n",
       "property_type                                                  0.012515   \n",
       "room_type                                                      0.019346   \n",
       "bed_type                                                       0.009939   \n",
       "review_scores_rating                                           0.673861   \n",
       "review_scores_accuracy                                         0.623487   \n",
       "review_scores_cleanliness                                      1.000000   \n",
       "review_scores_checkin                                          0.538637   \n",
       "review_scores_communication                                    0.561582   \n",
       "review_scores_location                                         0.537425   \n",
       "review_scores_value                                            0.499612   \n",
       "instant_bookable                                              -0.025931   \n",
       "cancellation_policy                                           -0.082566   \n",
       "require_guest_profile_picture                                 -0.005965   \n",
       "require_guest_phone_verification                              -0.009310   \n",
       "\n",
       "                                              review_scores_checkin  \\\n",
       "host_days_active                                          -0.013319   \n",
       "host_total_listings_count                                 -0.097184   \n",
       "accommodates                                               0.002614   \n",
       "bathrooms                                                  0.028309   \n",
       "bedrooms                                                   0.024891   \n",
       "beds                                                       0.015406   \n",
       "weekly_price                                              -0.011880   \n",
       "monthly_price                                             -0.018411   \n",
       "security_deposit                                          -0.106044   \n",
       "cleaning_fee                                              -0.127663   \n",
       "guests_included                                           -0.064812   \n",
       "extra_people                                              -0.085807   \n",
       "number_of_reviews                                         -0.336919   \n",
       "number_of_reviews_ltm                                     -0.183651   \n",
       "calculated_host_listings_count                            -0.103096   \n",
       "calculated_host_listings_count_entire_homes               -0.082393   \n",
       "calculated_host_listings_count_private_rooms              -0.055547   \n",
       "calculated_host_listings_count_shared_rooms               -0.068478   \n",
       "reviews_per_month                                         -0.203756   \n",
       "host_response_time                                        -0.010988   \n",
       "host_response_rate                                        -0.021505   \n",
       "neighbourhood                                             -0.010096   \n",
       "property_type                                              0.038235   \n",
       "room_type                                                  0.014711   \n",
       "bed_type                                                   0.002911   \n",
       "review_scores_rating                                       0.576509   \n",
       "review_scores_accuracy                                     0.563587   \n",
       "review_scores_cleanliness                                  0.538637   \n",
       "review_scores_checkin                                      1.000000   \n",
       "review_scores_communication                                0.661247   \n",
       "review_scores_location                                     0.482797   \n",
       "review_scores_value                                        0.414532   \n",
       "instant_bookable                                          -0.040295   \n",
       "cancellation_policy                                       -0.119208   \n",
       "require_guest_profile_picture                             -0.003069   \n",
       "require_guest_phone_verification                          -0.006835   \n",
       "\n",
       "                                              review_scores_communication  \\\n",
       "host_days_active                                                -0.017052   \n",
       "host_total_listings_count                                       -0.108236   \n",
       "accommodates                                                    -0.002868   \n",
       "bathrooms                                                        0.035524   \n",
       "bedrooms                                                         0.026976   \n",
       "beds                                                             0.004845   \n",
       "weekly_price                                                    -0.021912   \n",
       "monthly_price                                                   -0.019864   \n",
       "security_deposit                                                -0.089747   \n",
       "cleaning_fee                                                    -0.117245   \n",
       "guests_included                                                 -0.075977   \n",
       "extra_people                                                    -0.091559   \n",
       "number_of_reviews                                               -0.353440   \n",
       "number_of_reviews_ltm                                           -0.199387   \n",
       "calculated_host_listings_count                                  -0.114024   \n",
       "calculated_host_listings_count_entire_homes                     -0.078277   \n",
       "calculated_host_listings_count_private_rooms                    -0.085801   \n",
       "calculated_host_listings_count_shared_rooms                     -0.059171   \n",
       "reviews_per_month                                               -0.222897   \n",
       "host_response_time                                              -0.022597   \n",
       "host_response_rate                                              -0.027540   \n",
       "neighbourhood                                                   -0.015551   \n",
       "property_type                                                    0.018768   \n",
       "room_type                                                        0.039441   \n",
       "bed_type                                                         0.002119   \n",
       "review_scores_rating                                             0.597615   \n",
       "review_scores_accuracy                                           0.581427   \n",
       "review_scores_cleanliness                                        0.561582   \n",
       "review_scores_checkin                                            0.661247   \n",
       "review_scores_communication                                      1.000000   \n",
       "review_scores_location                                           0.492480   \n",
       "review_scores_value                                              0.443763   \n",
       "instant_bookable                                                -0.044948   \n",
       "cancellation_policy                                             -0.111931   \n",
       "require_guest_profile_picture                                    0.007336   \n",
       "require_guest_phone_verification                                 0.004089   \n",
       "\n",
       "                                              review_scores_location  \\\n",
       "host_days_active                                           -0.074925   \n",
       "host_total_listings_count                                   0.012164   \n",
       "accommodates                                               -0.009838   \n",
       "bathrooms                                                   0.055675   \n",
       "bedrooms                                                   -0.002273   \n",
       "beds                                                       -0.000890   \n",
       "weekly_price                                               -0.047564   \n",
       "monthly_price                                              -0.027454   \n",
       "security_deposit                                           -0.068067   \n",
       "cleaning_fee                                               -0.083180   \n",
       "guests_included                                            -0.092878   \n",
       "extra_people                                               -0.097666   \n",
       "number_of_reviews                                          -0.376117   \n",
       "number_of_reviews_ltm                                      -0.162543   \n",
       "calculated_host_listings_count                              0.013030   \n",
       "calculated_host_listings_count_entire_homes                 0.022416   \n",
       "calculated_host_listings_count_private_rooms               -0.025903   \n",
       "calculated_host_listings_count_shared_rooms                -0.021658   \n",
       "reviews_per_month                                          -0.180012   \n",
       "host_response_time                                          0.003325   \n",
       "host_response_rate                                         -0.008275   \n",
       "neighbourhood                                               0.273050   \n",
       "property_type                                               0.002147   \n",
       "room_type                                                   0.018342   \n",
       "bed_type                                                    0.017266   \n",
       "review_scores_rating                                        0.511925   \n",
       "review_scores_accuracy                                      0.494935   \n",
       "review_scores_cleanliness                                   0.537425   \n",
       "review_scores_checkin                                       0.482797   \n",
       "review_scores_communication                                 0.492480   \n",
       "review_scores_location                                      1.000000   \n",
       "review_scores_value                                         0.461098   \n",
       "instant_bookable                                            0.038881   \n",
       "cancellation_policy                                        -0.083615   \n",
       "require_guest_profile_picture                              -0.003922   \n",
       "require_guest_phone_verification                           -0.000103   \n",
       "\n",
       "                                              review_scores_value  \\\n",
       "host_days_active                                        -0.022693   \n",
       "host_total_listings_count                               -0.014555   \n",
       "accommodates                                             0.009989   \n",
       "bathrooms                                                0.019600   \n",
       "bedrooms                                                 0.007761   \n",
       "beds                                                     0.013663   \n",
       "weekly_price                                             0.000068   \n",
       "monthly_price                                            0.006308   \n",
       "security_deposit                                        -0.040277   \n",
       "cleaning_fee                                            -0.075646   \n",
       "guests_included                                         -0.025450   \n",
       "extra_people                                            -0.048680   \n",
       "number_of_reviews                                       -0.212092   \n",
       "number_of_reviews_ltm                                   -0.119876   \n",
       "calculated_host_listings_count                          -0.018611   \n",
       "calculated_host_listings_count_entire_homes             -0.011171   \n",
       "calculated_host_listings_count_private_rooms            -0.024688   \n",
       "calculated_host_listings_count_shared_rooms             -0.037559   \n",
       "reviews_per_month                                       -0.156286   \n",
       "host_response_time                                      -0.005166   \n",
       "host_response_rate                                      -0.012989   \n",
       "neighbourhood                                            0.000184   \n",
       "property_type                                            0.017402   \n",
       "room_type                                                0.008052   \n",
       "bed_type                                                -0.004238   \n",
       "review_scores_rating                                     0.386083   \n",
       "review_scores_accuracy                                   0.406683   \n",
       "review_scores_cleanliness                                0.499612   \n",
       "review_scores_checkin                                    0.414532   \n",
       "review_scores_communication                              0.443763   \n",
       "review_scores_location                                   0.461098   \n",
       "review_scores_value                                      1.000000   \n",
       "instant_bookable                                         0.011018   \n",
       "cancellation_policy                                     -0.056893   \n",
       "require_guest_profile_picture                            0.000392   \n",
       "require_guest_phone_verification                        -0.004330   \n",
       "\n",
       "                                              instant_bookable  \\\n",
       "host_days_active                                     -0.209416   \n",
       "host_total_listings_count                             0.200689   \n",
       "accommodates                                         -0.030630   \n",
       "bathrooms                                            -0.010740   \n",
       "bedrooms                                             -0.092346   \n",
       "beds                                                 -0.016590   \n",
       "weekly_price                                         -0.105904   \n",
       "monthly_price                                        -0.057839   \n",
       "security_deposit                                     -0.104652   \n",
       "cleaning_fee                                         -0.105500   \n",
       "guests_included                                      -0.026397   \n",
       "extra_people                                         -0.001728   \n",
       "number_of_reviews                                     0.013572   \n",
       "number_of_reviews_ltm                                 0.144605   \n",
       "calculated_host_listings_count                        0.218551   \n",
       "calculated_host_listings_count_entire_homes           0.107637   \n",
       "calculated_host_listings_count_private_rooms          0.233862   \n",
       "calculated_host_listings_count_shared_rooms           0.130749   \n",
       "reviews_per_month                                     0.198448   \n",
       "host_response_time                                   -0.031442   \n",
       "host_response_rate                                    0.094113   \n",
       "neighbourhood                                         0.020531   \n",
       "property_type                                         0.063238   \n",
       "room_type                                            -0.178256   \n",
       "bed_type                                              0.020403   \n",
       "review_scores_rating                                 -0.054133   \n",
       "review_scores_accuracy                               -0.047932   \n",
       "review_scores_cleanliness                            -0.025931   \n",
       "review_scores_checkin                                -0.040295   \n",
       "review_scores_communication                          -0.044948   \n",
       "review_scores_location                                0.038881   \n",
       "review_scores_value                                   0.011018   \n",
       "instant_bookable                                      1.000000   \n",
       "cancellation_policy                                  -0.031970   \n",
       "require_guest_profile_picture                        -0.028277   \n",
       "require_guest_phone_verification                     -0.017308   \n",
       "\n",
       "                                              cancellation_policy  \\\n",
       "host_days_active                                         0.098442   \n",
       "host_total_listings_count                                0.212330   \n",
       "accommodates                                             0.113363   \n",
       "bathrooms                                                0.071856   \n",
       "bedrooms                                                 0.097464   \n",
       "beds                                                     0.113332   \n",
       "weekly_price                                             0.074521   \n",
       "monthly_price                                            0.067433   \n",
       "security_deposit                                         0.255310   \n",
       "cleaning_fee                                             0.275487   \n",
       "guests_included                                          0.179980   \n",
       "extra_people                                             0.152332   \n",
       "number_of_reviews                                        0.211378   \n",
       "number_of_reviews_ltm                                    0.138721   \n",
       "calculated_host_listings_count                           0.181688   \n",
       "calculated_host_listings_count_entire_homes              0.188503   \n",
       "calculated_host_listings_count_private_rooms            -0.011052   \n",
       "calculated_host_listings_count_shared_rooms              0.051027   \n",
       "reviews_per_month                                        0.094890   \n",
       "host_response_time                                       0.109908   \n",
       "host_response_rate                                       0.121049   \n",
       "neighbourhood                                            0.081384   \n",
       "property_type                                            0.017640   \n",
       "room_type                                                0.066141   \n",
       "bed_type                                                 0.004992   \n",
       "review_scores_rating                                    -0.092969   \n",
       "review_scores_accuracy                                  -0.106695   \n",
       "review_scores_cleanliness                               -0.082566   \n",
       "review_scores_checkin                                   -0.119208   \n",
       "review_scores_communication                             -0.111931   \n",
       "review_scores_location                                  -0.083615   \n",
       "review_scores_value                                     -0.056893   \n",
       "instant_bookable                                        -0.031970   \n",
       "cancellation_policy                                      1.000000   \n",
       "require_guest_profile_picture                            0.038465   \n",
       "require_guest_phone_verification                         0.053715   \n",
       "\n",
       "                                              require_guest_profile_picture  \\\n",
       "host_days_active                                                   0.146070   \n",
       "host_total_listings_count                                         -0.002876   \n",
       "accommodates                                                       0.002964   \n",
       "bathrooms                                                          0.018043   \n",
       "bedrooms                                                           0.018011   \n",
       "beds                                                               0.012757   \n",
       "weekly_price                                                       0.073803   \n",
       "monthly_price                                                      0.073027   \n",
       "security_deposit                                                   0.060197   \n",
       "cleaning_fee                                                       0.017697   \n",
       "guests_included                                                    0.069472   \n",
       "extra_people                                                       0.049007   \n",
       "number_of_reviews                                                  0.072759   \n",
       "number_of_reviews_ltm                                              0.030328   \n",
       "calculated_host_listings_count                                    -0.006916   \n",
       "calculated_host_listings_count_entire_homes                       -0.010804   \n",
       "calculated_host_listings_count_private_rooms                       0.012120   \n",
       "calculated_host_listings_count_shared_rooms                        0.001785   \n",
       "reviews_per_month                                                  0.018316   \n",
       "host_response_time                                                 0.024024   \n",
       "host_response_rate                                                 0.033079   \n",
       "neighbourhood                                                      0.012037   \n",
       "property_type                                                     -0.002845   \n",
       "room_type                                                         -0.013546   \n",
       "bed_type                                                          -0.000116   \n",
       "review_scores_rating                                              -0.006785   \n",
       "review_scores_accuracy                                            -0.015996   \n",
       "review_scores_cleanliness                                         -0.005965   \n",
       "review_scores_checkin                                             -0.003069   \n",
       "review_scores_communication                                        0.007336   \n",
       "review_scores_location                                            -0.003922   \n",
       "review_scores_value                                                0.000392   \n",
       "instant_bookable                                                  -0.028277   \n",
       "cancellation_policy                                                0.038465   \n",
       "require_guest_profile_picture                                      1.000000   \n",
       "require_guest_phone_verification                                   0.768700   \n",
       "\n",
       "                                              require_guest_phone_verification  \n",
       "host_days_active                                                      0.170137  \n",
       "host_total_listings_count                                             0.017334  \n",
       "accommodates                                                          0.010712  \n",
       "bathrooms                                                             0.020090  \n",
       "bedrooms                                                              0.017006  \n",
       "beds                                                                  0.016192  \n",
       "weekly_price                                                          0.081113  \n",
       "monthly_price                                                         0.105614  \n",
       "security_deposit                                                      0.065516  \n",
       "cleaning_fee                                                          0.024368  \n",
       "guests_included                                                       0.072688  \n",
       "extra_people                                                          0.048237  \n",
       "number_of_reviews                                                     0.093453  \n",
       "number_of_reviews_ltm                                                 0.044523  \n",
       "calculated_host_listings_count                                        0.007657  \n",
       "calculated_host_listings_count_entire_homes                          -0.000381  \n",
       "calculated_host_listings_count_private_rooms                          0.023472  \n",
       "calculated_host_listings_count_shared_rooms                          -0.001958  \n",
       "reviews_per_month                                                     0.031997  \n",
       "host_response_time                                                    0.033776  \n",
       "host_response_rate                                                    0.041554  \n",
       "neighbourhood                                                         0.031389  \n",
       "property_type                                                        -0.002172  \n",
       "room_type                                                            -0.018456  \n",
       "bed_type                                                             -0.010782  \n",
       "review_scores_rating                                                 -0.011134  \n",
       "review_scores_accuracy                                               -0.018409  \n",
       "review_scores_cleanliness                                            -0.009310  \n",
       "review_scores_checkin                                                -0.006835  \n",
       "review_scores_communication                                           0.004089  \n",
       "review_scores_location                                               -0.000103  \n",
       "review_scores_value                                                  -0.004330  \n",
       "instant_bookable                                                     -0.017308  \n",
       "cancellation_policy                                                   0.053715  \n",
       "require_guest_profile_picture                                         0.768700  \n",
       "require_guest_phone_verification                                      1.000000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=newdata\n",
    "df1.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = df1.corr()\n",
    "\n",
    "mask = np.zeros_like(corr_df)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "# Set up the matplotlib figure\n",
    "#f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "#cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "plt.figure(figsize=(15,12))\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr_df, mask=mask, cmap='RdYlGn', vmax=1.0, vmin=-1, linewidths=2.5, cbar_kws={\"shrink\": .8})\n",
    "\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train  (16541, 36)\n",
      "x_test  (4136, 36)\n",
      "y_train  (16541,)\n",
      "y_test  (4136,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into train & test\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test, y_train, y_test = train_test_split(newdata, target_log, test_size = 0.20, random_state=0)\n",
    "print(\"x_train \",x_train.shape)\n",
    "print(\"x_test \",x_test.shape)\n",
    "print(\"y_train \",y_train.shape)\n",
    "print(\"y_test \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Linear Regression Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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 (uncentered):</th>      <td>   0.993</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.993</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>6.562e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 26 May 2020</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:44:47</td>     <th>  Log-Likelihood:    </th>          <td> -8764.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 16541</td>      <th>  AIC:               </th>          <td>1.760e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 16505</td>      <th>  BIC:               </th>          <td>1.788e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    36</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>host_days_active</th>                             <td>   -0.0099</td> <td>    0.020</td> <td>   -0.493</td> <td> 0.622</td> <td>   -0.049</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_total_listings_count</th>                    <td>    1.6532</td> <td>    0.089</td> <td>   18.535</td> <td> 0.000</td> <td>    1.478</td> <td>    1.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>accommodates</th>                                 <td>    1.2846</td> <td>    0.055</td> <td>   23.448</td> <td> 0.000</td> <td>    1.177</td> <td>    1.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                                    <td>    1.7510</td> <td>    0.066</td> <td>   26.608</td> <td> 0.000</td> <td>    1.622</td> <td>    1.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                                     <td>    0.3441</td> <td>    0.037</td> <td>    9.341</td> <td> 0.000</td> <td>    0.272</td> <td>    0.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beds</th>                                         <td>    0.3321</td> <td>    0.059</td> <td>    5.645</td> <td> 0.000</td> <td>    0.217</td> <td>    0.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekly_price</th>                                 <td>   -0.0081</td> <td>    0.017</td> <td>   -0.481</td> <td> 0.631</td> <td>   -0.041</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>monthly_price</th>                                <td>   -0.0060</td> <td>    0.021</td> <td>   -0.287</td> <td> 0.774</td> <td>   -0.047</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>security_deposit</th>                             <td>    0.0305</td> <td>    0.011</td> <td>    2.762</td> <td> 0.006</td> <td>    0.009</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cleaning_fee</th>                                 <td>    0.0355</td> <td>    0.015</td> <td>    2.428</td> <td> 0.015</td> <td>    0.007</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guests_included</th>                              <td>    0.1568</td> <td>    0.034</td> <td>    4.668</td> <td> 0.000</td> <td>    0.091</td> <td>    0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>extra_people</th>                                 <td>   -0.1393</td> <td>    0.014</td> <td>   -9.873</td> <td> 0.000</td> <td>   -0.167</td> <td>   -0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>number_of_reviews</th>                            <td>    0.0977</td> <td>    0.030</td> <td>    3.274</td> <td> 0.001</td> <td>    0.039</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>number_of_reviews_ltm</th>                        <td>   -0.0273</td> <td>    0.027</td> <td>   -1.005</td> <td> 0.315</td> <td>   -0.081</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calculated_host_listings_count</th>               <td>   -1.9693</td> <td>    0.171</td> <td>  -11.517</td> <td> 0.000</td> <td>   -2.304</td> <td>   -1.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calculated_host_listings_count_entire_homes</th>  <td>    0.4044</td> <td>    0.161</td> <td>    2.511</td> <td> 0.012</td> <td>    0.089</td> <td>    0.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calculated_host_listings_count_private_rooms</th> <td>    0.9954</td> <td>    0.066</td> <td>   15.023</td> <td> 0.000</td> <td>    0.866</td> <td>    1.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calculated_host_listings_count_shared_rooms</th>  <td>    1.1052</td> <td>    0.076</td> <td>   14.452</td> <td> 0.000</td> <td>    0.955</td> <td>    1.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>reviews_per_month</th>                            <td>    0.1744</td> <td>    0.049</td> <td>    3.594</td> <td> 0.000</td> <td>    0.079</td> <td>    0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_response_time</th>                           <td>    0.0316</td> <td>    0.014</td> <td>    2.229</td> <td> 0.026</td> <td>    0.004</td> <td>    0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_response_rate</th>                           <td>    0.0976</td> <td>    0.017</td> <td>    5.671</td> <td> 0.000</td> <td>    0.064</td> <td>    0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>neighbourhood</th>                                <td>    0.6709</td> <td>    0.016</td> <td>   41.744</td> <td> 0.000</td> <td>    0.639</td> <td>    0.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>property_type</th>                                <td>    0.0775</td> <td>    0.017</td> <td>    4.550</td> <td> 0.000</td> <td>    0.044</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>room_type</th>                                    <td>    1.2886</td> <td>    0.030</td> <td>   42.694</td> <td> 0.000</td> <td>    1.229</td> <td>    1.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bed_type</th>                                     <td>    1.5287</td> <td>    0.028</td> <td>   53.783</td> <td> 0.000</td> <td>    1.473</td> <td>    1.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_rating</th>                         <td>    0.0391</td> <td>    0.015</td> <td>    2.633</td> <td> 0.008</td> <td>    0.010</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_accuracy</th>                       <td>    0.0262</td> <td>    0.016</td> <td>    1.619</td> <td> 0.106</td> <td>   -0.006</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_cleanliness</th>                    <td>    0.1094</td> <td>    0.018</td> <td>    5.922</td> <td> 0.000</td> <td>    0.073</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_checkin</th>                        <td>    0.0686</td> <td>    0.017</td> <td>    4.025</td> <td> 0.000</td> <td>    0.035</td> <td>    0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_communication</th>                  <td>    0.0919</td> <td>    0.019</td> <td>    4.903</td> <td> 0.000</td> <td>    0.055</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_location</th>                       <td>    0.1531</td> <td>    0.018</td> <td>    8.662</td> <td> 0.000</td> <td>    0.118</td> <td>    0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review_scores_value</th>                          <td>   -0.0117</td> <td>    0.014</td> <td>   -0.844</td> <td> 0.399</td> <td>   -0.039</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>instant_bookable</th>                             <td>    0.0488</td> <td>    0.008</td> <td>    6.040</td> <td> 0.000</td> <td>    0.033</td> <td>    0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cancellation_policy</th>                          <td>    0.2144</td> <td>    0.018</td> <td>   12.217</td> <td> 0.000</td> <td>    0.180</td> <td>    0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>require_guest_profile_picture</th>                <td>    0.0161</td> <td>    0.047</td> <td>    0.341</td> <td> 0.733</td> <td>   -0.076</td> <td>    0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>require_guest_phone_verification</th>             <td>   -0.0392</td> <td>    0.042</td> <td>   -0.941</td> <td> 0.347</td> <td>   -0.121</td> <td>    0.042</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3576.556</td> <th>  Durbin-Watson:     </th> <td>   2.033</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>28995.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.816</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.278</td>  <th>  Cond. No.          </th> <td>    191.</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 (uncentered):                   0.993\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.993\n",
       "Method:                 Least Squares   F-statistic:                          6.562e+04\n",
       "Date:                Tue, 26 May 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        00:44:47   Log-Likelihood:                         -8764.8\n",
       "No. Observations:               16541   AIC:                                  1.760e+04\n",
       "Df Residuals:                   16505   BIC:                                  1.788e+04\n",
       "Df Model:                          36                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "================================================================================================================\n",
       "                                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------------------\n",
       "host_days_active                                -0.0099      0.020     -0.493      0.622      -0.049       0.029\n",
       "host_total_listings_count                        1.6532      0.089     18.535      0.000       1.478       1.828\n",
       "accommodates                                     1.2846      0.055     23.448      0.000       1.177       1.392\n",
       "bathrooms                                        1.7510      0.066     26.608      0.000       1.622       1.880\n",
       "bedrooms                                         0.3441      0.037      9.341      0.000       0.272       0.416\n",
       "beds                                             0.3321      0.059      5.645      0.000       0.217       0.447\n",
       "weekly_price                                    -0.0081      0.017     -0.481      0.631      -0.041       0.025\n",
       "monthly_price                                   -0.0060      0.021     -0.287      0.774      -0.047       0.035\n",
       "security_deposit                                 0.0305      0.011      2.762      0.006       0.009       0.052\n",
       "cleaning_fee                                     0.0355      0.015      2.428      0.015       0.007       0.064\n",
       "guests_included                                  0.1568      0.034      4.668      0.000       0.091       0.223\n",
       "extra_people                                    -0.1393      0.014     -9.873      0.000      -0.167      -0.112\n",
       "number_of_reviews                                0.0977      0.030      3.274      0.001       0.039       0.156\n",
       "number_of_reviews_ltm                           -0.0273      0.027     -1.005      0.315      -0.081       0.026\n",
       "calculated_host_listings_count                  -1.9693      0.171    -11.517      0.000      -2.304      -1.634\n",
       "calculated_host_listings_count_entire_homes      0.4044      0.161      2.511      0.012       0.089       0.720\n",
       "calculated_host_listings_count_private_rooms     0.9954      0.066     15.023      0.000       0.866       1.125\n",
       "calculated_host_listings_count_shared_rooms      1.1052      0.076     14.452      0.000       0.955       1.255\n",
       "reviews_per_month                                0.1744      0.049      3.594      0.000       0.079       0.270\n",
       "host_response_time                               0.0316      0.014      2.229      0.026       0.004       0.059\n",
       "host_response_rate                               0.0976      0.017      5.671      0.000       0.064       0.131\n",
       "neighbourhood                                    0.6709      0.016     41.744      0.000       0.639       0.702\n",
       "property_type                                    0.0775      0.017      4.550      0.000       0.044       0.111\n",
       "room_type                                        1.2886      0.030     42.694      0.000       1.229       1.348\n",
       "bed_type                                         1.5287      0.028     53.783      0.000       1.473       1.584\n",
       "review_scores_rating                             0.0391      0.015      2.633      0.008       0.010       0.068\n",
       "review_scores_accuracy                           0.0262      0.016      1.619      0.106      -0.006       0.058\n",
       "review_scores_cleanliness                        0.1094      0.018      5.922      0.000       0.073       0.146\n",
       "review_scores_checkin                            0.0686      0.017      4.025      0.000       0.035       0.102\n",
       "review_scores_communication                      0.0919      0.019      4.903      0.000       0.055       0.129\n",
       "review_scores_location                           0.1531      0.018      8.662      0.000       0.118       0.188\n",
       "review_scores_value                             -0.0117      0.014     -0.844      0.399      -0.039       0.015\n",
       "instant_bookable                                 0.0488      0.008      6.040      0.000       0.033       0.065\n",
       "cancellation_policy                              0.2144      0.018     12.217      0.000       0.180       0.249\n",
       "require_guest_profile_picture                    0.0161      0.047      0.341      0.733      -0.076       0.108\n",
       "require_guest_phone_verification                -0.0392      0.042     -0.941      0.347      -0.121       0.042\n",
       "==============================================================================\n",
       "Omnibus:                     3576.556   Durbin-Watson:                   2.033\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28995.766\n",
       "Skew:                           0.816   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.278   Cond. No.                         191.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Linear regression model using statsmodel OLS\n",
    "model = sm.OLS(y_train, x_train).fit() # y_train comes first\n",
    "\n",
    "# Model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use R-Squared, Adj.R-Squared, and RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(predictions, targets):\n",
    "    differences = predictions - targets                       # the DIFFERENCEs.\n",
    "    differences_squared = differences ** 2                    # the SQUAREs of ^\n",
    "    mean_of_differences_squared = differences_squared.mean()  # the MEAN of ^\n",
    "    rmse_val = np.sqrt(mean_of_differences_squared)           # ROOT of ^\n",
    "\n",
    "    return rmse_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Model', 'R-Squared Value', 'Adj.R-Squared Value', 'RMSE']\n",
    "models_report = pd.DataFrame(columns = cols)\n",
    "# Predicting the model on test data\n",
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model  R-Squared Value  Adj.R-Squared Value  \\\n",
       "0   Base Linear Regression Model         0.993062             0.993047   \n",
       "\n",
       "       RMSE  \n",
       "0  0.407614  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = pd.Series({'Model': \" Base Linear Regression Model\",\n",
    "                 'R-Squared Value' : model.rsquared,\n",
    "                 'Adj.R-Squared Value': model.rsquared_adj,\n",
    "                 'RMSE': rmse(pred, y_test)})\n",
    "\n",
    "model_report = models_report.append(report, ignore_index = True)\n",
    "model_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model with Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_const = sm.add_constant(newdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train1-test1\n",
    "x_train1,x_test1, y_train1, y_test1 = train_test_split(df_const, target_log, test_size = 0.30, random_state=0)\n",
    "# model1 using OLS \n",
    "model1 = sm.OLS(y_train1, x_train1).fit()\n",
    "# summary\n",
    "#model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Reg Model1 with constant</td>\n",
       "      <td>0.476301</td>\n",
       "      <td>0.474995</td>\n",
       "      <td>0.377733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Model  R-Squared Value  Adj.R-Squared Value  \\\n",
       "0      Base Linear Regression Model         0.993062             0.993047   \n",
       "1   Linear Reg Model1 with constant         0.476301             0.474995   \n",
       "\n",
       "       RMSE  \n",
       "0  0.407614  \n",
       "1  0.377733  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the model on test data\n",
    "pred1 = model1.predict(x_test1)\n",
    "report1 = pd.Series({'Model': \" Linear Reg Model1 with constant\",\n",
    "                 'R-Squared Value' : model1.rsquared,\n",
    "                 'Adj.R-Squared Value': model1.rsquared_adj,\n",
    "                 'RMSE': rmse(pred1, y_test1)})\n",
    "\n",
    "model1_report = model_report.append(report1, ignore_index = True)\n",
    "model1_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multicollinearity: Variance Inflation Factor(VIF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In regression, \"multicollinearity\" refers to predictors that are correlated with other predictors.  Multicollinearity occurs when your model includes multiple factors that are correlated not just to your response variable, but also to each other.\n",
    "![](https://www.displayr.com/wp-content/uploads/2018/04/Screen-Shot-2018-04-06-at-10.32.14-am.png)\n",
    "A rule of thumb for interpreting the variance inflation factor:\n",
    "\n",
    "* 1 = not correlated.\n",
    "* Between 1 and 5 = moderately correlated.\n",
    "* Greater than 5 = highly correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variance Inflation Factor\n",
      "const 283.4\n",
      "host_days_active 1.285\n",
      "host_total_listings_count 10.983\n",
      "accommodates 3.322\n",
      "bathrooms 1.218\n",
      "bedrooms 2.27\n",
      "beds 3.49\n",
      "weekly_price 1.763\n",
      "monthly_price 1.671\n",
      "security_deposit 1.289\n",
      "cleaning_fee 1.356\n",
      "guests_included 1.789\n",
      "extra_people 1.598\n",
      "number_of_reviews 3.896\n",
      "number_of_reviews_ltm 4.189\n",
      "calculated_host_listings_count 72.593\n",
      "calculated_host_listings_count_entire_homes 54.888\n",
      "calculated_host_listings_count_private_rooms 11.21\n",
      "calculated_host_listings_count_shared_rooms 1.467\n",
      "reviews_per_month 4.144\n",
      "host_response_time 3.326\n",
      "host_response_rate 3.688\n",
      "neighbourhood 1.191\n",
      "property_type 1.175\n",
      "room_type 4.527\n",
      "bed_type 1.016\n",
      "review_scores_rating 2.515\n",
      "review_scores_accuracy 2.239\n",
      "review_scores_cleanliness 2.393\n",
      "review_scores_checkin 2.135\n",
      "review_scores_communication 2.261\n",
      "review_scores_location 2.045\n",
      "review_scores_value 1.513\n",
      "instant_bookable 1.238\n",
      "cancellation_policy 1.241\n",
      "require_guest_profile_picture 2.4\n",
      "require_guest_phone_verification 2.433\n"
     ]
    }
   ],
   "source": [
    "# Printing the VIF\n",
    "print (\"\\nVariance Inflation Factor\")\n",
    "cnames = x_train1.columns\n",
    "for i in np.arange(0,len(cnames)):\n",
    "    xvars = list(cnames)\n",
    "    yvar = xvars.pop(i)\n",
    "    mod = sm.OLS(x_train1[yvar],(x_train1[xvars]))\n",
    "    res = mod.fit()\n",
    "    vif = 1/(1-(res.rsquared))\n",
    "    print (yvar,round(vif,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Variables above 100 VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cols_above_vif(cnames, thres):\n",
    "    features = []\n",
    "    for i in np.arange(0,len(cnames)):\n",
    "        xvars = list(cnames)\n",
    "        yvar = xvars.pop(i)\n",
    "        mod = sm.OLS(x_train1[yvar],(x_train1[xvars]))\n",
    "        res = mod.fit()\n",
    "        vif = 1/(1-(res.rsquared))\n",
    "        if vif >= thres:\n",
    "            features.append(yvar)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['const']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIF_100 = cols_above_vif(cnames, 100)\n",
    "VIF_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Variables above 100 VIF\n",
    "to_retain = [x for x in x_train1 if x not in VIF_100]\n",
    "x_train2 = x_train1[to_retain]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_retain = [x for x in x_test1 if x not in VIF_100]\n",
    "x_test2 = x_test1[to_retain]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model2 after removing VIF_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we keep y_train1 for training with new x_train2\n",
    "model2 = sm.OLS(y_train1,x_train2).fit()\n",
    "\n",
    "# Summary\n",
    "#model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Reg Model1 with constant</td>\n",
       "      <td>0.476301</td>\n",
       "      <td>0.474995</td>\n",
       "      <td>0.377733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Reg Model2 after VIF100 filter</td>\n",
       "      <td>0.993096</td>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.411714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Model  R-Squared Value  \\\n",
       "0            Base Linear Regression Model         0.993062   \n",
       "1         Linear Reg Model1 with constant         0.476301   \n",
       "2   Linear Reg Model2 after VIF100 filter         0.993096   \n",
       "\n",
       "   Adj.R-Squared Value      RMSE  \n",
       "0             0.993047  0.407614  \n",
       "1             0.474995  0.377733  \n",
       "2             0.993079  0.411714  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the model on test data\n",
    "pred2 = model2.predict(x_test2)\n",
    "report2 = pd.Series({'Model': \" Linear Reg Model2 after VIF100 filter\",\n",
    "                 'R-Squared Value' : model2.rsquared,\n",
    "                 'Adj.R-Squared Value': model2.rsquared_adj,\n",
    "                 'RMSE': rmse(pred2, y_test1)})\n",
    "\n",
    "model2_report = model1_report.append(report2, ignore_index = True)\n",
    "model2_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking variable having VIF above 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['host_total_listings_count',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'number_of_reviews',\n",
       " 'calculated_host_listings_count',\n",
       " 'calculated_host_listings_count_entire_homes',\n",
       " 'calculated_host_listings_count_private_rooms',\n",
       " 'neighbourhood',\n",
       " 'room_type',\n",
       " 'bed_type',\n",
       " 'review_scores_accuracy',\n",
       " 'review_scores_cleanliness',\n",
       " 'review_scores_checkin',\n",
       " 'review_scores_communication',\n",
       " 'review_scores_location']"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for vif_10\n",
    "cnames2 = x_train2.columns\n",
    "VIF_10 = cols_above_vif(cnames2, 10)\n",
    "VIF_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Variables above 10 VIF\n",
    "to_retrain3 = [x for x in x_train2 if x not in VIF_10]\n",
    "x_train3 = x_train2[to_retrain3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model3 after removing VIF above 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = sm.OLS(y_train1,x_train3).fit()\n",
    "\n",
    "# Summary\n",
    "#model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Variables above 10 VIF\n",
    "to_retest3 = [x for x in x_test2 if x not in VIF_10]\n",
    "x_test3 = x_test2[to_retest3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Reg Model1 with constant</td>\n",
       "      <td>0.476301</td>\n",
       "      <td>0.474995</td>\n",
       "      <td>0.377733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Reg Model2 after VIF100 filter</td>\n",
       "      <td>0.993096</td>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.411714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear Reg Model3  after VIF10 filter</td>\n",
       "      <td>0.951246</td>\n",
       "      <td>0.951181</td>\n",
       "      <td>1.094270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Model  R-Squared Value  \\\n",
       "0            Base Linear Regression Model         0.993062   \n",
       "1         Linear Reg Model1 with constant         0.476301   \n",
       "2   Linear Reg Model2 after VIF100 filter         0.993096   \n",
       "3   Linear Reg Model3  after VIF10 filter         0.951246   \n",
       "\n",
       "   Adj.R-Squared Value      RMSE  \n",
       "0             0.993047  0.407614  \n",
       "1             0.474995  0.377733  \n",
       "2             0.993079  0.411714  \n",
       "3             0.951181  1.094270  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the model on test data\n",
    "pred3 = model3.predict(x_test3)\n",
    "report3 = pd.Series({'Model': \" Linear Reg Model3  after VIF10 filter\",\n",
    "                 'R-Squared Value' : model3.rsquared,\n",
    "                 'Adj.R-Squared Value': model3.rsquared_adj,\n",
    "                 'RMSE': rmse(pred3, y_test1)})\n",
    "\n",
    "model3_report = model2_report.append(report3, ignore_index = True)\n",
    "model3_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking variable having VIF above 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['host_days_active',\n",
       " 'number_of_reviews_ltm',\n",
       " 'reviews_per_month',\n",
       " 'host_response_time',\n",
       " 'host_response_rate']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for vif_5\n",
    "cnames = x_train3.columns\n",
    "VIF_5 = cols_above_vif(cnames, 5)\n",
    "VIF_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Variables above 5 VIF\n",
    "to_retain = [x for x in x_train3 if x not in VIF_5]\n",
    "x_train4 = x_train3[to_retain]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model4 after removing VIF above 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4 = sm.OLS(y_train1,x_train4).fit()\n",
    "\n",
    "# Summary\n",
    "#model4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Variables above 5 VIF for x_test\n",
    "to_retain = [x for x in x_test3 if x not in VIF_5]\n",
    "x_test4 = x_test3[to_retain]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Reg Model1 with constant</td>\n",
       "      <td>0.476301</td>\n",
       "      <td>0.474995</td>\n",
       "      <td>0.377733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Reg Model2 after VIF100 filter</td>\n",
       "      <td>0.993096</td>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.411714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear Reg Model3  after VIF10 filter</td>\n",
       "      <td>0.951246</td>\n",
       "      <td>0.951181</td>\n",
       "      <td>1.094270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear Regr Model4 after VIF5 filter</td>\n",
       "      <td>0.932228</td>\n",
       "      <td>0.932163</td>\n",
       "      <td>1.297043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Model  R-Squared Value  \\\n",
       "0            Base Linear Regression Model         0.993062   \n",
       "1         Linear Reg Model1 with constant         0.476301   \n",
       "2   Linear Reg Model2 after VIF100 filter         0.993096   \n",
       "3   Linear Reg Model3  after VIF10 filter         0.951246   \n",
       "4    Linear Regr Model4 after VIF5 filter         0.932228   \n",
       "\n",
       "   Adj.R-Squared Value      RMSE  \n",
       "0             0.993047  0.407614  \n",
       "1             0.474995  0.377733  \n",
       "2             0.993079  0.411714  \n",
       "3             0.951181  1.094270  \n",
       "4             0.932163  1.297043  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the model on test data\n",
    "pred4 = model4.predict(x_test4)\n",
    "report4 = pd.Series({'Model': \" Linear Regr Model4 after VIF5 filter\",\n",
    "                 'R-Squared Value' : model4.rsquared,\n",
    "                 'Adj.R-Squared Value': model4.rsquared_adj,\n",
    "                 'RMSE': rmse(pred4, y_test1)})\n",
    "\n",
    "model4_report = model3_report.append(report4, ignore_index = True)\n",
    "model4_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing features based on insignificant variables using the P-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = x_train2\n",
    "Y = y_train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Add  accommodates                   with p-value 0.0\n",
      "Add  neighbourhood                  with p-value 0.0\n",
      "Add  room_type                      with p-value 2.90298e-260\n",
      "Add  bathrooms                      with p-value 4.51878e-81\n",
      "Add  review_scores_location         with p-value 1.01522e-66\n",
      "Add  host_response_rate             with p-value 1.09196e-59\n",
      "Add  bedrooms                       with p-value 1.35367e-41\n",
      "Add  cancellation_policy            with p-value 9.78991e-35\n",
      "Add  extra_people                   with p-value 8.02991e-29\n",
      "Add  review_scores_rating           with p-value 7.34094e-18\n",
      "Add  guests_included                with p-value 2.02278e-18\n",
      "Add  host_days_active               with p-value 2.72188e-14\n",
      "Add  calculated_host_listings_count_shared_rooms with p-value 4.07416e-06\n",
      "Add  beds                           with p-value 5.76933e-05\n",
      "Add  number_of_reviews              with p-value 0.000132758\n",
      "Add  cleaning_fee                   with p-value 0.000594284\n",
      "Add  instant_bookable               with p-value 0.00211543\n",
      "Add  bed_type                       with p-value 0.00479682\n",
      "Add  review_scores_value            with p-value 0.0130691\n",
      "Add  review_scores_cleanliness      with p-value 0.00234523\n",
      "Add  calculated_host_listings_count_private_rooms with p-value 0.0125893\n",
      "Add  host_total_listings_count      with p-value 0.00917318\n",
      "Add  calculated_host_listings_count_entire_homes with p-value 5.71586e-20\n",
      "Add  security_deposit               with p-value 0.00893914\n",
      "Add  property_type                  with p-value 0.0393528\n",
      "resulting features:\n",
      "['accommodates', 'neighbourhood', 'room_type', 'bathrooms', 'review_scores_location', 'host_response_rate', 'bedrooms', 'cancellation_policy', 'extra_people', 'review_scores_rating', 'guests_included', 'host_days_active', 'calculated_host_listings_count_shared_rooms', 'beds', 'number_of_reviews', 'cleaning_fee', 'instant_bookable', 'bed_type', 'review_scores_value', 'review_scores_cleanliness', 'calculated_host_listings_count_private_rooms', 'host_total_listings_count', 'calculated_host_listings_count_entire_homes', 'security_deposit', 'property_type']\n"
     ]
    }
   ],
   "source": [
    "def feature_selection(X, Y, \n",
    "                       initial_list=[], \n",
    "                       threshold_in=0.05, \n",
    "                       threshold_out = 0.05, \n",
    "                       verbose=True):\n",
    "    \n",
    "    included = list(initial_list)\n",
    "    while True:\n",
    "        changed=False\n",
    "        # forward step\n",
    "        excluded = list(set(X.columns)-set(included))\n",
    "        new_pval = pd.Series(index=excluded)\n",
    "        for new_column in excluded:\n",
    "            model = sm.OLS(Y, sm.add_constant((X[included+[new_column]]))).fit()\n",
    "            new_pval[new_column] = model.pvalues[new_column]\n",
    "        best_pval = new_pval.min()\n",
    "        if best_pval < threshold_in:\n",
    "            best_feature = new_pval.argmin()\n",
    "            included.append(best_feature)\n",
    "            changed=True\n",
    "            if verbose:\n",
    "                print('Add  {:30} with p-value {:.6}'.format(best_feature, best_pval))\n",
    "\n",
    "        # backward step\n",
    "        model = sm.OLS(Y, sm.add_constant(pd.DataFrame(X[included]))).fit()\n",
    "        # use all coefs except intercept\n",
    "        pvalues = model.pvalues.iloc[1:]\n",
    "        worst_pval = pvalues.max() # null if pvalues is empty\n",
    "        if worst_pval > threshold_out:\n",
    "            changed=True\n",
    "            worst_feature = pvalues.argmax()\n",
    "            included.remove(worst_feature)\n",
    "            if verbose:\n",
    "                print('Drop {:30} with p-value {:.6}'.format(worst_feature, worst_pval))\n",
    "        if not changed:\n",
    "            break\n",
    "    return included\n",
    "\n",
    "result = feature_selection(X, Y)\n",
    "\n",
    "print('resulting features:')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train5 = x_train2[result]\n",
    "x_test5 = x_test2[result]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model5 p-value method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "model5 = sm.OLS(y_train1,x_train5).fit()\n",
    "# Summary\n",
    "#model5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Model</th>\n",
       "      <th>R-Squared Value</th>\n",
       "      <th>Adj.R-Squared Value</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Linear Regression Model</td>\n",
       "      <td>0.993062</td>\n",
       "      <td>0.993047</td>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Reg Model1 with constant</td>\n",
       "      <td>0.476301</td>\n",
       "      <td>0.474995</td>\n",
       "      <td>0.377733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Reg Model2 after VIF100 filter</td>\n",
       "      <td>0.993096</td>\n",
       "      <td>0.993079</td>\n",
       "      <td>0.411714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear Reg Model3  after VIF10 filter</td>\n",
       "      <td>0.951246</td>\n",
       "      <td>0.951181</td>\n",
       "      <td>1.094270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear Regr Model4 after VIF5 filter</td>\n",
       "      <td>0.932228</td>\n",
       "      <td>0.932163</td>\n",
       "      <td>1.297043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Linear Reg Model5 after p-value filter</td>\n",
       "      <td>0.992992</td>\n",
       "      <td>0.992980</td>\n",
       "      <td>0.415761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Model  R-Squared Value  \\\n",
       "0             Base Linear Regression Model         0.993062   \n",
       "1          Linear Reg Model1 with constant         0.476301   \n",
       "2    Linear Reg Model2 after VIF100 filter         0.993096   \n",
       "3    Linear Reg Model3  after VIF10 filter         0.951246   \n",
       "4     Linear Regr Model4 after VIF5 filter         0.932228   \n",
       "5   Linear Reg Model5 after p-value filter         0.992992   \n",
       "\n",
       "   Adj.R-Squared Value      RMSE  \n",
       "0             0.993047  0.407614  \n",
       "1             0.474995  0.377733  \n",
       "2             0.993079  0.411714  \n",
       "3             0.951181  1.094270  \n",
       "4             0.932163  1.297043  \n",
       "5             0.992980  0.415761  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the model on test data\n",
    "pred5 = model5.predict(x_test5)\n",
    "report5 = pd.Series({'Model': \" Linear Reg Model5 after p-value filter\",\n",
    "                 'R-Squared Value' : model5.rsquared,\n",
    "                 'Adj.R-Squared Value': model5.rsquared_adj,\n",
    "                 'RMSE': rmse(pred5, y_test1)})\n",
    "\n",
    "model5_report = model4_report.append(report5, ignore_index = True)\n",
    "model5_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using XGBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the cleaned data\n",
    "X, y = newdata, target_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20677, 36)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Matrix converts dataset to an optimized data structure\n",
    "data_dmatrix = xgb.DMatrix(data=X,label=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train  (16541, 36)\n",
      "X_test  (4136, 36)\n",
      "y_train  (16541,)\n",
      "y_test  (4136,)\n"
     ]
    }
   ],
   "source": [
    "# Train-test-split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "print(\"X_train \",x_train.shape)\n",
    "print(\"X_test \",x_test.shape)\n",
    "print(\"y_train \",y_train.shape)\n",
    "print(\"y_test \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating the XGBoost regressor\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.3, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=10, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=10,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model fit\n",
    "xg_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.619184\n"
     ]
    }
   ],
   "source": [
    "# Predicted RMSE score\n",
    "preds = xg_reg.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-fold Cross Validation with XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.004095</td>\n",
       "      <td>0.004266</td>\n",
       "      <td>4.004092</td>\n",
       "      <td>0.009629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.609265</td>\n",
       "      <td>0.003787</td>\n",
       "      <td>3.609481</td>\n",
       "      <td>0.009988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.254199</td>\n",
       "      <td>0.003488</td>\n",
       "      <td>3.254418</td>\n",
       "      <td>0.010189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "0         4.004095        0.004266        4.004092       0.009629\n",
       "1         3.609265        0.003787        3.609481       0.009988\n",
       "2         3.254199        0.003488        3.254418       0.010189"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building a 3-fold cross validation model\n",
    "params = {\"objective\":\"reg:squarederror\",'colsample_bytree': 0.3,'learning_rate': 0.1,\n",
    "                'max_depth': 5, 'alpha': 10}\n",
    "\n",
    "# Store results in cv_results\n",
    "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
    "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=0)\n",
    "cv_results.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.357931</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.363780</td>\n",
       "      <td>0.004709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.356958</td>\n",
       "      <td>0.001961</td>\n",
       "      <td>0.362922</td>\n",
       "      <td>0.004488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.355990</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.362064</td>\n",
       "      <td>0.004654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "47         0.357931        0.001873        0.363780       0.004709\n",
       "48         0.356958        0.001961        0.362922       0.004488\n",
       "49         0.355990        0.001677        0.362064       0.004654"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize importance of features according to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.train(params=params, dtrain=data_dmatrix, num_boost_round=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": [
    "xgb.plot_importance(xg_reg)\n",
    "plt.rcParams['figure.figsize'] = [8, 10]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}