prague_airbnb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries and initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_l = pd.read_csv('data/listings.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<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",
       "      <th>gmaps_result</th>\n",
       "      <th>centre_transit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23163</td>\n",
       "      <td>https://www.airbnb.com/rooms/23163</td>\n",
       "      <td>20200229181007</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>Quiet 1BR directly in the Centre, Air-conditio...</td>\n",
       "      <td>Unique and elegant apartment rental in Prague,...</td>\n",
       "      <td>Unique and elegant apartment rental in Prague ...</td>\n",
       "      <td>Unique and elegant apartment rental in Prague,...</td>\n",
       "      <td>none</td>\n",
       "      <td>Check my guidebook for this apartment to disco...</td>\n",
       "      <td>To help you with your first steps in Prague, I...</td>\n",
       "      <td>The closest METRO station is called „Narodni t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Of course, you can contact me anytime during y...</td>\n",
       "      <td>Thank you for treating the property like it wa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/01bbe32c-3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5282</td>\n",
       "      <td>https://www.airbnb.com/users/show/5282</td>\n",
       "      <td>Klara</td>\n",
       "      <td>2008-12-17</td>\n",
       "      <td>Prague, Prague, Czech Republic</td>\n",
       "      <td>Hello, \\r\\nglad to see that you are interested...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>97%</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/user/b7309...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/user/b7309...</td>\n",
       "      <td>Josefov</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Prague, Czech Republic, Czech Republic</td>\n",
       "      <td>Staré Město</td>\n",
       "      <td>Praha 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prague</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>11000</td>\n",
       "      <td>Prague</td>\n",
       "      <td>Prague, Czech Republic</td>\n",
       "      <td>CZ</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>50.08229</td>\n",
       "      <td>14.41552</td>\n",
       "      <td>t</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,Wifi,\"Air conditioning\",Kitchen,\"...</td>\n",
       "      <td>775.0</td>\n",
       "      <td>$3,717.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2,546.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>$636.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>5.6</td>\n",
       "      <td>365.0</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>288</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-09-20</td>\n",
       "      <td>2019-07-27</td>\n",
       "      <td>98.0</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>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>69</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>{'destination_addresses': ['Staroměstské nám. ...</td>\n",
       "      <td>9.883333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                         listing_url       scrape_id last_scraped  \\\n",
       "0  23163  https://www.airbnb.com/rooms/23163  20200229181007   2020-03-01   \n",
       "\n",
       "                                                name  \\\n",
       "0  Quiet 1BR directly in the Centre, Air-conditio...   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Unique and elegant apartment rental in Prague,...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Unique and elegant apartment rental in Prague ...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Unique and elegant apartment rental in Prague,...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  Check my guidebook for this apartment to disco...   \n",
       "\n",
       "                                               notes  \\\n",
       "0  To help you with your first steps in Prague, I...   \n",
       "\n",
       "                                             transit access  \\\n",
       "0  The closest METRO station is called „Narodni t...    NaN   \n",
       "\n",
       "                                         interaction  \\\n",
       "0  Of course, you can contact me anytime during y...   \n",
       "\n",
       "                                         house_rules  thumbnail_url  \\\n",
       "0  Thank you for treating the property like it wa...            NaN   \n",
       "\n",
       "   medium_url                                        picture_url  \\\n",
       "0         NaN  https://a0.muscache.com/im/pictures/01bbe32c-3...   \n",
       "\n",
       "   xl_picture_url  host_id                                host_url host_name  \\\n",
       "0             NaN     5282  https://www.airbnb.com/users/show/5282     Klara   \n",
       "\n",
       "   host_since                   host_location  \\\n",
       "0  2008-12-17  Prague, Prague, Czech Republic   \n",
       "\n",
       "                                          host_about host_response_time  \\\n",
       "0  Hello, \\r\\nglad to see that you are interested...     within an hour   \n",
       "\n",
       "  host_response_rate host_acceptance_rate host_is_superhost  \\\n",
       "0                97%                 100%                 t   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/pictures/user/b7309...   \n",
       "\n",
       "                                    host_picture_url host_neighbourhood  \\\n",
       "0  https://a0.muscache.com/im/pictures/user/b7309...            Josefov   \n",
       "\n",
       "   host_listings_count  host_total_listings_count  \\\n",
       "0                 69.0                       69.0   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0  ['email', 'phone', 'reviews', 'jumio', 'govern...                    t   \n",
       "\n",
       "  host_identity_verified                                  street  \\\n",
       "0                      t  Prague, Czech Republic, Czech Republic   \n",
       "\n",
       "  neighbourhood neighbourhood_cleansed  neighbourhood_group_cleansed    city  \\\n",
       "0   Staré Město                Praha 1                           NaN  Prague   \n",
       "\n",
       "            state zipcode  market          smart_location country_code  \\\n",
       "0  Czech Republic   11000  Prague  Prague, Czech Republic           CZ   \n",
       "\n",
       "          country  latitude  longitude is_location_exact property_type  \\\n",
       "0  Czech Republic  50.08229   14.41552                 t     Apartment   \n",
       "\n",
       "         room_type  accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "0  Entire home/apt             4        1.0       1.0   2.0  Real Bed   \n",
       "\n",
       "                                           amenities  square_feet      price  \\\n",
       "0  {TV,Internet,Wifi,\"Air conditioning\",Kitchen,\"...        775.0  $3,717.00   \n",
       "\n",
       "  weekly_price monthly_price security_deposit cleaning_fee  guests_included  \\\n",
       "0          NaN           NaN        $2,546.00          NaN                2   \n",
       "\n",
       "  extra_people  minimum_nights  maximum_nights  minimum_minimum_nights  \\\n",
       "0      $636.00               1             365                       1   \n",
       "\n",
       "   maximum_minimum_nights  minimum_maximum_nights  maximum_maximum_nights  \\\n",
       "0                       7                     365                     365   \n",
       "\n",
       "   minimum_nights_avg_ntm  maximum_nights_avg_ntm calendar_updated  \\\n",
       "0                     5.6                   365.0            today   \n",
       "\n",
       "  has_availability  availability_30  availability_60  availability_90  \\\n",
       "0                t               19               40               70   \n",
       "\n",
       "   availability_365 calendar_last_scraped  number_of_reviews  \\\n",
       "0               288            2020-03-01                 19   \n",
       "\n",
       "   number_of_reviews_ltm first_review last_review  review_scores_rating  \\\n",
       "0                      3   2010-09-20  2019-07-27                  98.0   \n",
       "\n",
       "   review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                     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",
       "\n",
       "  requires_license license  jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN                 NaN                t   \n",
       "\n",
       "  is_business_travel_ready cancellation_policy require_guest_profile_picture  \\\n",
       "0                        f            moderate                             f   \n",
       "\n",
       "  require_guest_phone_verification  calculated_host_listings_count  \\\n",
       "0                                f                              69   \n",
       "\n",
       "   calculated_host_listings_count_entire_homes  \\\n",
       "0                                           68   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  \\\n",
       "0                                             0   \n",
       "\n",
       "   calculated_host_listings_count_shared_rooms  reviews_per_month  \\\n",
       "0                                            0               0.17   \n",
       "\n",
       "                                        gmaps_result  centre_transit_time  \n",
       "0  {'destination_addresses': ['Staroměstské nám. ...             9.883333  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these columns were chosen as valuable for our analysis as features which could affect our target variable \"price\"\n",
    "# \n",
    "cols_to_keep = ['host_since', 'host_response_time', 'host_is_superhost', 'neighbourhood_cleansed', 'room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds', 'price', 'security_deposit', 'cleaning_fee', 'guests_included', 'extra_people', 'number_of_reviews', 'review_scores_rating', 'cancellation_policy', 'centre_transit_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf = df_l[cols_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "security_deposit          0.280220\n",
       "cleaning_fee              0.194849\n",
       "host_response_time        0.187157\n",
       "review_scores_rating      0.137157\n",
       "beds                      0.008173\n",
       "bedrooms                  0.001442\n",
       "bathrooms                 0.000481\n",
       "host_is_superhost         0.000069\n",
       "host_since                0.000069\n",
       "cancellation_policy       0.000000\n",
       "price                     0.000000\n",
       "guests_included           0.000000\n",
       "accommodates              0.000000\n",
       "room_type                 0.000000\n",
       "neighbourhood_cleansed    0.000000\n",
       "extra_people              0.000000\n",
       "number_of_reviews         0.000000\n",
       "centre_transit_time       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ration of null values in our variables (our target price doesn't have any nulls)\n",
    "features = df_lf.isnull().sum() / df_lf.shape[0]\n",
    "features.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>extra_people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$3,717.00</td>\n",
       "      <td>$2,546.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$2,493.00</td>\n",
       "      <td>$2,546.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$2,540.00</td>\n",
       "      <td>$5,092.00</td>\n",
       "      <td>$458.00</td>\n",
       "      <td>$255.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$2,032.00</td>\n",
       "      <td>$3,819.00</td>\n",
       "      <td>$458.00</td>\n",
       "      <td>$204.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$1,039.00</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>$350.00</td>\n",
       "      <td>$0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price security_deposit cleaning_fee extra_people\n",
       "0  $3,717.00        $2,546.00          NaN      $636.00\n",
       "1  $2,493.00        $2,546.00          NaN      $636.00\n",
       "2  $2,540.00        $5,092.00      $458.00      $255.00\n",
       "3  $2,032.00        $3,819.00      $458.00      $204.00\n",
       "4  $1,039.00            $0.00      $350.00        $0.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# these variables have prices encoded as strings with dollar signs\n",
    "df_lf[['price', 'security_deposit', 'cleaning_fee', 'extra_people']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_prices(df, cols):\n",
    "    \"\"\"Function for converting strings with a dollar sign to floats\n",
    "\n",
    "    Parameters:\n",
    "    df (DataFrame): Description of arg1,\n",
    "    cols (list): Column names which are going to be affected\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: New dataframe with converted values\n",
    "\n",
    "    \"\"\"\n",
    "    for col in cols:\n",
    "        df[col] = df[col].str.replace('[$|,]', '').astype(float)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf = convert_prices(df_lf, ['price', 'security_deposit', 'cleaning_fee', 'extra_people'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>extra_people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3717.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2493.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2540.0</td>\n",
       "      <td>5092.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2032.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  security_deposit  cleaning_fee  extra_people\n",
       "0  3717.0            2546.0           NaN         636.0\n",
       "1  2493.0            2546.0           NaN         636.0\n",
       "2  2540.0            5092.0         458.0         255.0\n",
       "3  2032.0            3819.0         458.0         204.0\n",
       "4  1039.0               0.0         350.0           0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lf[['price', 'security_deposit', 'cleaning_fee', 'extra_people']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling missing values of float variables by their mean\n",
    "for col in df_lf.dtypes[df_lf.dtypes == 'float'].index.tolist():\n",
    "    df_lf[col] = df_lf[col].fillna(df_lf[col].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>extra_people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3717.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2493.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2540.0</td>\n",
       "      <td>5092.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2032.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  security_deposit  cleaning_fee  extra_people\n",
       "0  3717.0            2546.0    508.480508         636.0\n",
       "1  2493.0            2546.0    508.480508         636.0\n",
       "2  2540.0            5092.0    458.000000         255.0\n",
       "3  2032.0            3819.0    458.000000         204.0\n",
       "4  1039.0               0.0    350.000000           0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lf[['price', 'security_deposit', 'cleaning_fee', 'extra_people']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting date in host_since into relative days to present day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf['host_since'] = df_lf['host_since'].apply(lambda x: (pd.to_datetime('2020-03-01') - pd.to_datetime(x)).days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation matrix will reveal highly correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = df_lf.corr().abs()\n",
    "\n",
    "# the matrix is symmetric so we need to extract upper triangle matrix without diagonal (k = 1)\n",
    "sol = (corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "                 .stack()\n",
    "                 .sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>beds</td>\n",
       "      <td>0.744801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>bedrooms</td>\n",
       "      <td>0.647316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>beds</td>\n",
       "      <td>0.636492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>guests_included</td>\n",
       "      <td>0.523426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>bathrooms</td>\n",
       "      <td>0.367238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>beds</td>\n",
       "      <td>0.364703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>guests_included</td>\n",
       "      <td>0.353369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>beds</td>\n",
       "      <td>guests_included</td>\n",
       "      <td>0.340453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>bedrooms</td>\n",
       "      <td>0.322128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>cleaning_fee</td>\n",
       "      <td>0.297937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        level_0          level_1         0\n",
       "0  accommodates             beds  0.744801\n",
       "1  accommodates         bedrooms  0.647316\n",
       "2      bedrooms             beds  0.636492\n",
       "3  accommodates  guests_included  0.523426\n",
       "4  accommodates        bathrooms  0.367238\n",
       "5     bathrooms             beds  0.364703\n",
       "6      bedrooms  guests_included  0.353369\n",
       "7          beds  guests_included  0.340453\n",
       "8     bathrooms         bedrooms  0.322128\n",
       "9  accommodates     cleaning_fee  0.297937"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol.reset_index()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like 'beds', 'bedrooms' and 'accommodates' are all highly correlated, for interpretability we will drop the first two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf = df_lf.drop(['beds', 'bedrooms'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can encode the categorical column containing the information about superhsot to a boolean series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf['host_is_superhost'] = df_lf['host_is_superhost'].replace({'t': 1, 'f': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_since</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>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>review_scores_rating</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>centre_transit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4092.0</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Praha 1</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3717.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>2</td>\n",
       "      <td>636.0</td>\n",
       "      <td>19</td>\n",
       "      <td>98.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>9.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4092.0</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Praha 1</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2493.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>2</td>\n",
       "      <td>636.0</td>\n",
       "      <td>113</td>\n",
       "      <td>95.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>4.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3597.0</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Praha 7</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>5092.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>255.0</td>\n",
       "      <td>345</td>\n",
       "      <td>97.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>16.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3597.0</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Praha 1</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>204.0</td>\n",
       "      <td>246</td>\n",
       "      <td>99.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>13.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3585.0</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Praha 3</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>411</td>\n",
       "      <td>96.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>20.683333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_since host_response_time  host_is_superhost neighbourhood_cleansed  \\\n",
       "0      4092.0     within an hour                1.0                Praha 1   \n",
       "1      4092.0     within an hour                1.0                Praha 1   \n",
       "2      3597.0     within an hour                1.0                Praha 7   \n",
       "3      3597.0     within an hour                1.0                Praha 1   \n",
       "4      3585.0     within an hour                1.0                Praha 3   \n",
       "\n",
       "         room_type  accommodates  bathrooms   price  security_deposit  \\\n",
       "0  Entire home/apt             4        1.0  3717.0            2546.0   \n",
       "1  Entire home/apt             4        1.0  2493.0            2546.0   \n",
       "2  Entire home/apt             6        1.0  2540.0            5092.0   \n",
       "3  Entire home/apt             4        1.5  2032.0            3819.0   \n",
       "4  Entire home/apt             2        1.0  1039.0               0.0   \n",
       "\n",
       "   cleaning_fee  guests_included  extra_people  number_of_reviews  \\\n",
       "0    508.480508                2         636.0                 19   \n",
       "1    508.480508                2         636.0                113   \n",
       "2    458.000000                4         255.0                345   \n",
       "3    458.000000                3         204.0                246   \n",
       "4    350.000000                1           0.0                411   \n",
       "\n",
       "   review_scores_rating cancellation_policy  centre_transit_time  \n",
       "0                  98.0            moderate             9.883333  \n",
       "1                  95.0            moderate             4.766667  \n",
       "2                  97.0            moderate            16.900000  \n",
       "3                  99.0            moderate            13.450000  \n",
       "4                  96.0            moderate            20.683333  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding other categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['host_response_time',\n",
       " 'neighbourhood_cleansed',\n",
       " 'room_type',\n",
       " 'cancellation_policy']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols= df_lf.dtypes[df_lf.dtypes == 'object'].index.tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_cat(df, col, new_value_name = 'Other', threshold = 0.01):\n",
    "    \"\"\"Function which will group underrepresented categories into a new 'Other'\n",
    "       category, one hot encodes the column and appends it to the original dataframe\n",
    "\n",
    "    Parameters:\n",
    "    df (DataFrame): Input dataframe\n",
    "    col (string): column which we want to transform\n",
    "    new_value_name (string): name of a new column which is going to be created after grouping underrepresented categories together\n",
    "    threshold (float): what's the minimal share of a category in a feature not to be grouped into the 'Other' category\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: New dataframe with column transformed (grouping + OHE) and the original column dropped\n",
    "\n",
    "    \"\"\"\n",
    "    new_col = col + '_new'\n",
    "    df_g = df.groupby(col)[df.columns[0]].count().reset_index()\n",
    "    df_g['ratio'] = df_g[df.columns[0]] / df_lf.shape[0]\n",
    "    df_g[new_col] = df_g.apply(lambda x: x[col] if (x['ratio'] > threshold) else new_value_name, axis = 1)\n",
    "    print(df_g.groupby(new_col)['ratio'].sum())\n",
    "    \n",
    "    s = df.merge(df_g[[col, new_col]], on = col)[new_col]\n",
    "    ohe = pd.get_dummies(s, prefix = col)\n",
    "    return df.merge(ohe, left_index = True, right_index = True).drop(col, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "host_response_time_new\n",
      "Other                 0.011882\n",
      "within a day          0.050618\n",
      "within a few hours    0.069712\n",
      "within an hour        0.680632\n",
      "Name: ratio, dtype: float64\n",
      "neighbourhood_cleansed_new\n",
      "Other       0.153866\n",
      "Praha 1     0.291762\n",
      "Praha 10    0.053485\n",
      "Praha 2     0.159527\n",
      "Praha 3     0.136967\n",
      "Praha 5     0.088044\n",
      "Praha 7     0.056612\n",
      "Praha 8     0.059654\n",
      "Name: ratio, dtype: float64\n",
      "room_type_new\n",
      "Entire home/apt    0.782425\n",
      "Other              0.048331\n",
      "Private room       0.169159\n",
      "Name: ratio, dtype: float64\n",
      "cancellation_policy_new\n",
      "Other                          0.011829\n",
      "flexible                       0.247486\n",
      "moderate                       0.360287\n",
      "strict_14_with_grace_period    0.380313\n",
      "Name: ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# after applying on the original dataframe we can see the shares of individual categories after transformation\n",
    "for col in cat_cols:    \n",
    "    df_lf = group_cat(df_lf, col, threshold = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_since</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>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>review_scores_rating</th>\n",
       "      <th>centre_transit_time</th>\n",
       "      <th>host_response_time_Other</th>\n",
       "      <th>host_response_time_within a day</th>\n",
       "      <th>host_response_time_within a few hours</th>\n",
       "      <th>host_response_time_within an hour</th>\n",
       "      <th>neighbourhood_cleansed_Other</th>\n",
       "      <th>neighbourhood_cleansed_Praha 1</th>\n",
       "      <th>neighbourhood_cleansed_Praha 10</th>\n",
       "      <th>neighbourhood_cleansed_Praha 2</th>\n",
       "      <th>neighbourhood_cleansed_Praha 3</th>\n",
       "      <th>neighbourhood_cleansed_Praha 5</th>\n",
       "      <th>neighbourhood_cleansed_Praha 7</th>\n",
       "      <th>neighbourhood_cleansed_Praha 8</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>room_type_Other</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>cancellation_policy_Other</th>\n",
       "      <th>cancellation_policy_flexible</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4092.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3717.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>2</td>\n",
       "      <td>636.0</td>\n",
       "      <td>19</td>\n",
       "      <td>98.0</td>\n",
       "      <td>9.883333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4092.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2493.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>508.480508</td>\n",
       "      <td>2</td>\n",
       "      <td>636.0</td>\n",
       "      <td>113</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.766667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3597.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>5092.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>255.0</td>\n",
       "      <td>345</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3597.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>204.0</td>\n",
       "      <td>246</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.450000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3585.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>411</td>\n",
       "      <td>96.0</td>\n",
       "      <td>20.683333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_since  host_is_superhost  accommodates  bathrooms   price  \\\n",
       "0      4092.0                1.0             4        1.0  3717.0   \n",
       "1      4092.0                1.0             4        1.0  2493.0   \n",
       "2      3597.0                1.0             6        1.0  2540.0   \n",
       "3      3597.0                1.0             4        1.5  2032.0   \n",
       "4      3585.0                1.0             2        1.0  1039.0   \n",
       "\n",
       "   security_deposit  cleaning_fee  guests_included  extra_people  \\\n",
       "0            2546.0    508.480508                2         636.0   \n",
       "1            2546.0    508.480508                2         636.0   \n",
       "2            5092.0    458.000000                4         255.0   \n",
       "3            3819.0    458.000000                3         204.0   \n",
       "4               0.0    350.000000                1           0.0   \n",
       "\n",
       "   number_of_reviews  review_scores_rating  centre_transit_time  \\\n",
       "0                 19                  98.0             9.883333   \n",
       "1                113                  95.0             4.766667   \n",
       "2                345                  97.0            16.900000   \n",
       "3                246                  99.0            13.450000   \n",
       "4                411                  96.0            20.683333   \n",
       "\n",
       "   host_response_time_Other  host_response_time_within a day  \\\n",
       "0                         0                                0   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   host_response_time_within a few hours  host_response_time_within an hour  \\\n",
       "0                                      0                                  1   \n",
       "1                                      0                                  1   \n",
       "2                                      0                                  1   \n",
       "3                                      0                                  1   \n",
       "4                                      0                                  1   \n",
       "\n",
       "   neighbourhood_cleansed_Other  neighbourhood_cleansed_Praha 1  \\\n",
       "0                             0                               1   \n",
       "1                             0                               1   \n",
       "2                             0                               1   \n",
       "3                             0                               1   \n",
       "4                             0                               1   \n",
       "\n",
       "   neighbourhood_cleansed_Praha 10  neighbourhood_cleansed_Praha 2  \\\n",
       "0                                0                               0   \n",
       "1                                0                               0   \n",
       "2                                0                               0   \n",
       "3                                0                               0   \n",
       "4                                0                               0   \n",
       "\n",
       "   neighbourhood_cleansed_Praha 3  neighbourhood_cleansed_Praha 5  \\\n",
       "0                               0                               0   \n",
       "1                               0                               0   \n",
       "2                               0                               0   \n",
       "3                               0                               0   \n",
       "4                               0                               0   \n",
       "\n",
       "   neighbourhood_cleansed_Praha 7  neighbourhood_cleansed_Praha 8  \\\n",
       "0                               0                               0   \n",
       "1                               0                               0   \n",
       "2                               0                               0   \n",
       "3                               0                               0   \n",
       "4                               0                               0   \n",
       "\n",
       "   room_type_Entire home/apt  room_type_Other  room_type_Private room  \\\n",
       "0                          1                0                       0   \n",
       "1                          1                0                       0   \n",
       "2                          1                0                       0   \n",
       "3                          1                0                       0   \n",
       "4                          1                0                       0   \n",
       "\n",
       "   cancellation_policy_Other  cancellation_policy_flexible  \\\n",
       "0                          0                             0   \n",
       "1                          0                             0   \n",
       "2                          0                             0   \n",
       "3                          0                             0   \n",
       "4                          0                             0   \n",
       "\n",
       "   cancellation_policy_moderate  \\\n",
       "0                             1   \n",
       "1                             1   \n",
       "2                             1   \n",
       "3                             1   \n",
       "4                             1   \n",
       "\n",
       "   cancellation_policy_strict_14_with_grace_period  \n",
       "0                                                0  \n",
       "1                                                0  \n",
       "2                                                0  \n",
       "3                                                0  \n",
       "4                                                0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping rows with remaining null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lf.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf = df_lf.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the 'price' variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07f230d090>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAVN0lEQVR4nO3de4xc51nH8d8zl93ZmVl7d+3Edpqkdg1NyAWSktipUqWNaJrWUhX+aAW0EhUQiqCggsQfRVWrir9KK6QUkAoBRbSAaKFchCKh1I1QIb2kiYtzaZOSNImTNI7teu29zMzuzpx5+eOc8V48Ozvr7p5nd873I0WePTuz+747zs/PPuc977EQggAA6ct5DwAAsooABgAnBDAAOCGAAcAJAQwATgrrefLu3bvD/v37N2koADCYjh079uMQwmUrj68rgPfv36/HH39840YFABlgZie6HacFAQBOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAE/cAnpub8x4CALhwDeAzZ87oAx/4gJ555hnPYQCAC9cAPn36tKIo0okTXW8YCgADzTWAZ2dnJUnnz5/3HAYAuHAN4Hq9LokABpBNrgFcq9UkSefOnfMcBgC42BIV8CQBDCCDtkQFPHmOFgSA7NkSATx1/rxCCJ5DAYDUbYkWRHNhXo1Gw3MoAJC6LbEMTWIlBIDscW5B1BUsHgIBDCBrXAN4plZTVJqQxFI0ANnj2wOu1dQq7ZJEBQwge1wDuFGvKyqNS5ajAgaQOW4B3Gw21Wo1FQoj0lCFAAaQOW4B3FkD3M6XFBUrmpqa8hoKALhwD+CQH1YrX9HZyUmvoQCAC7cA7lyE0S4Mqz1U5XJkAJmzBSrgktrFqmamuBwZQLZsiQCOilVFUbTsyjgAGHTuAdwulNQuViRxMQaAbHHvAYf8sNrFqiQuxgCQLc4VsMUBPBQHMBUwgCzxDeDCsGRGBQwgk3xbEIWSpLgNoVyeAAaQKa4VcJQfjj8wk4aqtCAAZIpvAOeGL3zcKhDAALLFLYBnZmsKSQtCkqJihavhAGSKawXczi9WwFGRChhAtriehAv5xQq4XaxodmZaURR5DQkAUuUSwCEEzTXqahcWK+B2saoQgqanpz2GBACpcwngRqOhEMKKCpiLMQBki0sAL92Ip6M9FO8HwVpgAFnhEsCLewFfXAETwACywrkCXtIDLpQliR4wgMzYMhVwyA9LltPMzIzHkAAgdS4B3Nl4fWkFLDOpOEIAA8gM1wp46Uk4KW5D0IIAkBWuPeClLQhJivIlKmAAmeFXAefyUq6w7HhUKGuKChhARvhVwCuqX0kKhRFNT1MBA8gGtwBe2f+VpHZhRLXZWW5PDyAT3FoQ0dIVEIl2oaxWq6n5+XmHUQFAulwCeGZ2tmsAh+JI/HlOxAHIAKcWRH2VFkR8NRwBDCALnC7EWL0HLHE5MoBscNqOcvlewB2dAKYCBpAFqQdwq9VSc2G+awUcCGAAGZJ6AHfbiKeDHjCALEk9gLttRXlBLi/lhwhgAJngGMAXV8CSpGKZAAaQCW4B3O0knCRFBbakBJANqQdw55ZDnVsQrdTKsx8EgGxIPYA7dz1eLYBDYYQd0QBkgk8A5wrdT8IpXglBCwJAFvi0IIaq8S2IumgXRtRo1BVFUcojA4B0uVTAzUL39oOUXA0XwoWTdQAwqFIP4LOT5xQVK6t+PhS5PT2AbHCpgFc7ASexHwSA7Eg1gFutlmqzM2oPEcAAkGoAT01NSVp9CZokBfaDAJARqQbwWmuAJSpgANnhEsBRjxZEyA9LliOAAQy8LVcBy0wqsh8EgMGXagAv7gOx+jI0Kb4ajmVoAAZd+hVwcUTKFXo+L8qXqIABDLzUA7hn+yERFcpsyANg4KUewK1C7/aDFO+IxpaUAAZdqgEcX4a8dgXcLoyoNjurEEIKowIAHylfiHG+51VwHe1CWa1WU/Pz8ymMCgB8pBbAjUZDC/PzffWAQ5GLMQAMvtQCuK81wAluTw8gCxwCeO2TcO1CfMdkAhjAIEstgC9chNFnD1gigAEMttQr4H5WQQQ25AGQAekGsOUubDfZS7tQlvJFvfjiiymMDAB8pNuCGKqsejPOZXJ5Ncau0f888g01m83NHxwAOEi1Am710X7omNt9g+q1WR0/fnwTRwUAflIL4LOT5xT1uBvySgs7D0rFsr7+9a9v4qgAwE9qATzZ50Y8F+Tyqo//jL797UfVaDQ2b2AA4CSVAI6iSLPTU4qG1l4DvNTc7hvVbC7o0Ucf3aSRAYCfVAJ4ZmZG7XZ7fRWwpOboVQqlnbQhAAykVAJ48U4Y6wtgmak+cYP+9/jxC3dUBoBBkUoAr2cfiJXmdt+g0G7rc5/7nB5++GGdOnVqo4cHAC563xtog9TrdUmLezysR6u8R7V9t+nYk0/q2LFjkqSDB39K73//+3To0CHl8/kNHSsApCWVAO6m+tJDGpo+IWvNKRRKCvlhNSt7Nbv/7oueO/vGd2n26ruUb5zR8NQLeu61x/XpT39ae/ddoSPvebduu+027dmzZ83vOTc3p1qtpvHxceVyqW6FDAAXcQvg0pmnVCm2ddfdd+no0aOaq8+pUH9dkrqGsMwUlS9XvXy56nsPaXjyWbVOflMPPPCAHnjgAV119dW65s1vVqlUUqkUV9q1Wk21Wk2Tk+f06o9+pPPnJiVJ+UJBl112ua7Yt1d798b/jY2NyVa5Sq9zfLvdoWO7jns1lzKfzfgZDNrPdaVWq6WFhQWFEFQqlTb1t8zVfpZb7WdcqVR00003bfjPYs0ANrMPS/qwJF199dUb9o0ttHTXXXfr3nvvVQhBDz744DpenNP8rus0v+s65etnNHriIb3y8gt65eWXe76sWd6rqDSuoekTev3ka3r95Gs/4SwAZMW9996r9773vRv6NdcM4BDC/ZLul6Rbbrllw/45ikoTOnr0qEII+trXviZJapW7tyC6KdROqXzyWxo596wULWhoeFgH9h/QyEhcAYcQVK/XNTM7q8nJc5qeOq9i/XUVkyrbcjlNTOzSFfv2qlqtysyWVcD9/Mu82uPO81Z7zlqvX+013aw1jvWM+ycZ32bNYa35XOprLlU/X7PXz3Ir6PUzkhb/7q78eD3vb7/PWfl9ex3v9+v2Y71/d/fs2aM777zzkr5XL24tiIUdb9TMtPTvX31EoTCmMBr3gHuKmhqafknlU49p+PzzGhou6c533qnDhw/rxhtv1NDQ0KovrdfrOnnypGZnZ7Vnzx7t3r1bhYLb9AHAL4D7rXQlaeT0dzVy5gkVZ1+TQqTq6A7d88EP6siRI6pW+1vaVi6XdfDgwUsdLgBsuFQCeHh4WJKUixYUrfO1ubnz2vHCg7ryqqt06K57dOONN+r666+/8DUBYLtKJYDHx8clSbnm7LpfWzr7tCTpk5/4RF9LzQBgu0hlMezY2Fj8zS4hgMuTT+uaa64lfAEMnPQC2Ey5hfUFcKF2Svnaab3jHW/fpJEBgJ9UAjifz6s6ukO5Zm1dryudfUqWy+n222/fpJEBgJ/UrscdHxtbXwsiBJXPfk8333yzdu7cuXkDAwAnqQXwrolxFdYRwMWZl2XzU3r7HXds4qgAwE96FfD4uAqt/gO49OOnVRwa1uHDhzdxVADgJ9UA1sKs1M/mGqGt8rnv67bDhzQyMrL5gwMAB6kF8NjYmNSOZNHcms+1VkNqNnTttdemMDIA8JFuBaz+1gLnWvEG7jt27NjUMQGAp3QrYKmvtcC5Vnwb+tHR0U0dEwB4Si2AJyYmJEn5PtYC55pxAPe70Q4AbEfpV8B9tCAsqYBpQQAYZKkFcKVSUaFQVG5hZs3ndnrAtCAADLLUAtjMtGNsrK/LkXOthnK5PEvQAAy0VG8NvGt8vO8WRCW5TRAADKpUA3hiYlzFVj8VcJ32A4CBl2oAj/W5IU+u2dDYTk7AARhsqQZwfDlyXWr3vjFRod2gAgYw8NIPYIULqxxWk2sRwAAGn0MAr7EWOASpWWcNMICBl3oPWFLPtcDWbkrtiAoYwMBzqoBXXwlhXIQBICNcKuB8jxYEG/EAyIpUA3hoaEgj5UrPHdFyTSpgANmQagBLa68FNipgABmRegDvmhjvqwXBKggAgy71AI5vzrn6SbjOGmH2AgYw6FwC2Hr1gFsNlUojKhQKKY4KANLnEsCKFmTRQtfPW6uhKv1fABngchJOWv1quBxXwQHICL8AXqUNkY8a2jFK/xfA4Es9gDvLyyya6/r5fNSgAgaQCakHcLlcjr9xq3sAW5Od0ABkQ+oBXKlUJEkWzV/8ydCWWnMEMIBMcAzgiytgroIDkCWpB3CxWFShWOzagmAjHgBZknoAS9LISLlrCyLHVpQAMsQlgMuVSvcKuEkFDCA7XAJ4tFLpWgEbG/EAyBCXAK5Uysp1OQlHCwJAlrgEcLVaVaHdrQfcUC6X18jIiMOoACBdPj3gcnnVZWiValVm5jAqAEiXUwuiIrW6r4Kg/QAgK/wCOIpvP79sMM2GxnZyAg5ANri1IKSLr4YrtNkHAkB2+FXAknIrlqLlWgQwgOzwrYCXXowRgsRm7AAyxG0ZmqRla4GtHfeEqYABZIVzBbzYgrAmF2EAyBbXHvDSk3Cde8SNj497DAkAUudaAS9tQeSbNUmL94wDgEHnF8Bmy1oQnQqYAAaQFS4BnMvlVBouLauAOwG8c+dOjyEBQOpcAliSRsrlFRVwTZXqqIrFoteQACBVbgFcrVaWn4RbmKX9ACBT/AK4Ull+Eq5V08Q4AQwgOxwr4KryS/YELrRmWYIGIFPcArhcLi/uBRGCbIEABpAtbgFcqVQu7AVh7QUpatIDBpAprhWwWvNSCMolF2FQAQPIEtcKWKEtazeVW+AiDADZ4xvAirekZB8IAFnk24JQvCEPlyEDyCL3CjgXzSnXrCmXy7EVJYBMcQ9ga80r15xVdXSH8vm813AAIHXuAZyL5pVnDTCADPLvAbfmlG/VtGuCAAaQLe4VsEVzKrRqnIADkDluATw0NKR8Pq9ca06aZyc0ANnjFsBmptJIWfn581KI6AEDyBy3AJbiNkR+7qwk1gADyB7XAK5WKiokAUwFDCBrfAO4WpG1W5IIYADZ496C6KAFASBrtkQA5wuFZWEMAFngGsCdizF27hyTmXkOBQBStyUq4An6vwAyaEsE8Dh3QwaQQVuiBcEKCABZtCUqYFZAAMgiKmAAcOIawBMTE5KkPXv2eA4DAFwUPL/5lVdeqfvuu0/79+/3HAYAuHANYEk6cOCA9xAAwIVrCwIAsowABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADixEEL/TzY7I+nEBn7/3ZJ+vIFfb6vKwjyzMEeJeQ6atOb5xhDCZSsPriuAN5qZPR5CuMVtACnJwjyzMEeJeQ4a73nSggAAJwQwADjxDuD7nb9/WrIwzyzMUWKeg8Z1nq49YADIMu8KGAAyiwAGACcuAWxm7zazH5jZ82b2MY8xrJeZvWRmT5nZcTN7PDk2YWZHzey55M/xJc//o2R+PzCzu5cc//nk6zxvZn9mZpYcHzazLyfHHzWz/SnN6wEzO21mTy85lsq8zOxDyfd4zsw+5DDPT5nZj5L39LiZHRmAeV5lZv9lZs+Y2ffM7KPJ8YF6T3vMc3u9pyGEVP+TlJf0Q0lvkjQk6QlJ16U9jksY90uSdq849hlJH0sef0zSnySPr0vmNSzpQDLffPK570h6qyST9J+S3pMc/x1Jf5k8/mVJX05pXndIeoukp9Ocl6QJSS8kf44nj8dTnuenJP1hl+du53nuk/SW5PGopP9L5jNQ72mPeW6r99SjAj4k6fkQwgshhAVJX5J0j8M4NsI9kr6QPP6CpF9ccvxLIYT5EMKLkp6XdMjM9knaEUL4VojfyS+ueE3na31F0i90/iXeTCGE/5Y0ueJwGvO6W9LREMJkCOGcpKOS3r3xM4ytMs/VbOd5ngwhfDd5PCPpGUlv0IC9pz3muZotOU+PAH6DpFeWfPyqev/gtoog6atmdszMPpwc2xNCOCnFfyEkXZ4cX22Ob0gerzy+7DUhhJakKUm7NmEe/UhjXlvl78HvmtmTSYui82v5QMwz+ZX5ZkmPaoDf0xXzlLbRe+oRwN2quu2wFu72EMJbJL1H0kfM7I4ez11tjr3mvh1+Lhs5r60w389LOijpJkknJf1pcnzbz9PMqpL+RdLvhxCmez21y7FtM9cu89xW76lHAL8q6aolH18p6TWHcaxLCOG15M/Tkv5NcSvlVPIrjJI/TydPX22OryaPVx5f9hozK0jaqf5/Zd5oaczL/e9BCOFUCCEKIbQl/bXi91Q9xrYt5mlmRcWh9A8hhH9NDg/ce9ptntvuPd2MBvkazfOC4qb1AS2ehLs+7XGsc8wVSaNLHn9Tcc/ns1p+YuMzyePrtbzh/4IWG/6PSbpNiw3/I8nxj2h5w/+fUpzffi0/ObXp81J8AuNFxScxxpPHEynPc9+Sx3+guEe4reeZjOuLku5bcXyg3tMe89xW72kq/4N3+eEdUXzW8oeSPu4xhnWO903Jm/eEpO91xqy4H/SwpOeSPyeWvObjyfx+oOSsanL8FklPJ5/7Cy1ejViS9M+KTw58R9KbUprbPyr+Va2p+F/230hrXpJ+PTn+vKRfc5jn30l6StKTkv5jxf+823Web1P86/CTko4n/x0ZtPe0xzy31XvKpcgA4IQr4QDACQEMAE4IYABwQgADgBMCGACcEMDY9szsj83snd7jANaLZWjY1swsH0KIvMcBXAoqYGxZZrbfzJ41sy8km6t8xczKFu/N/Ekze0TS+83sb83sfclrbjWzb5rZE2b2HTMbNbO8mX3WzB5Lvs5vOU8NkEQAY+u7RtL9IYSflTSteI9WSZoLIbwthPClzhPNbEjSlyV9NITwc5LeKamh+Kq3qRDCrZJulfSbZnYgzUkA3RDA2OpeCSF8I3n894ovQZXioF3pGkknQwiPSVIIYTrE2wi+S9KvmtlxxVsW7pL005s7bGBtBe8BAGtYeZKi83Gty3Oty/M7x38vhPDQRg4M+ElRAWOru9rM3po8/hVJj/R47rOSrjCzWyUp6f8WJD0k6beT7QtlZm82s8pmDhroBwGMre4ZSR8ysycVbwP4+dWeGOJbXP2SpD83sycU3yqmJOlvJH1f0nctvinnX4nf/rAFsAwNW1Zyq5kHQwg3OA8F2BRUwADghAoYAJxQAQOAEwIYAJwQwADghAAGACcEMAA4+X/Oj2ShnOKh2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df_lf['price'], color = '#1e88e5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are some suspicious outlier prices upwards of 50000CZK/day, we are going to drop the listings in the top 10 percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = df_lf['price'].quantile(.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lf = df_lf[df_lf['price'] < cutoff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07c8c58450>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df_lf['price'], color = '#1e88e5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.model_selection\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from xgboost import plot_importance\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using XGBoost regression model to predict the price variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_lf['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_lf.drop('price', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_set = [(X_train, y_train), (X_test, y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = XGBRegressor(n_estimators=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:1294.94885\tvalidation_1-rmse:1283.37463\n",
      "Multiple eval metrics have been passed: 'validation_1-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-rmse hasn't improved in 50 rounds.\n",
      "[1]\tvalidation_0-rmse:1012.61523\tvalidation_1-rmse:1008.48602\n",
      "[2]\tvalidation_0-rmse:833.32642\tvalidation_1-rmse:836.44873\n",
      "[3]\tvalidation_0-rmse:723.22479\tvalidation_1-rmse:736.59875\n",
      "[4]\tvalidation_0-rmse:654.87335\tvalidation_1-rmse:678.65912\n",
      "[5]\tvalidation_0-rmse:612.51044\tvalidation_1-rmse:644.02972\n",
      "[6]\tvalidation_0-rmse:584.36151\tvalidation_1-rmse:625.37573\n",
      "[7]\tvalidation_0-rmse:566.53381\tvalidation_1-rmse:615.52918\n",
      "[8]\tvalidation_0-rmse:549.73291\tvalidation_1-rmse:604.79474\n",
      "[9]\tvalidation_0-rmse:538.33490\tvalidation_1-rmse:597.98505\n",
      "[10]\tvalidation_0-rmse:531.90198\tvalidation_1-rmse:595.17926\n",
      "[11]\tvalidation_0-rmse:526.41248\tvalidation_1-rmse:594.90881\n",
      "[12]\tvalidation_0-rmse:524.13648\tvalidation_1-rmse:594.10248\n",
      "[13]\tvalidation_0-rmse:521.02368\tvalidation_1-rmse:594.19928\n",
      "[14]\tvalidation_0-rmse:515.70618\tvalidation_1-rmse:591.49085\n",
      "[15]\tvalidation_0-rmse:512.75623\tvalidation_1-rmse:590.48023\n",
      "[16]\tvalidation_0-rmse:509.43442\tvalidation_1-rmse:589.94354\n",
      "[17]\tvalidation_0-rmse:504.60031\tvalidation_1-rmse:589.24756\n",
      "[18]\tvalidation_0-rmse:503.91000\tvalidation_1-rmse:589.62488\n",
      "[19]\tvalidation_0-rmse:501.05273\tvalidation_1-rmse:590.02252\n",
      "[20]\tvalidation_0-rmse:499.19110\tvalidation_1-rmse:589.80591\n",
      "[21]\tvalidation_0-rmse:495.74264\tvalidation_1-rmse:590.00226\n",
      "[22]\tvalidation_0-rmse:490.50430\tvalidation_1-rmse:589.38940\n",
      "[23]\tvalidation_0-rmse:487.82471\tvalidation_1-rmse:589.07904\n",
      "[24]\tvalidation_0-rmse:487.19928\tvalidation_1-rmse:589.83716\n",
      "[25]\tvalidation_0-rmse:485.77039\tvalidation_1-rmse:589.39288\n",
      "[26]\tvalidation_0-rmse:484.55023\tvalidation_1-rmse:589.07532\n",
      "[27]\tvalidation_0-rmse:481.14887\tvalidation_1-rmse:588.38019\n",
      "[28]\tvalidation_0-rmse:477.47165\tvalidation_1-rmse:587.68048\n",
      "[29]\tvalidation_0-rmse:474.21103\tvalidation_1-rmse:587.50598\n",
      "[30]\tvalidation_0-rmse:468.88770\tvalidation_1-rmse:587.15228\n",
      "[31]\tvalidation_0-rmse:467.70438\tvalidation_1-rmse:587.28033\n",
      "[32]\tvalidation_0-rmse:466.25970\tvalidation_1-rmse:586.93512\n",
      "[33]\tvalidation_0-rmse:465.23444\tvalidation_1-rmse:586.76343\n",
      "[34]\tvalidation_0-rmse:463.47983\tvalidation_1-rmse:586.55951\n",
      "[35]\tvalidation_0-rmse:461.72058\tvalidation_1-rmse:586.23328\n",
      "[36]\tvalidation_0-rmse:457.11850\tvalidation_1-rmse:585.64056\n",
      "[37]\tvalidation_0-rmse:456.28928\tvalidation_1-rmse:585.79797\n",
      "[38]\tvalidation_0-rmse:454.66019\tvalidation_1-rmse:586.31116\n",
      "[39]\tvalidation_0-rmse:451.84512\tvalidation_1-rmse:585.94775\n",
      "[40]\tvalidation_0-rmse:449.80029\tvalidation_1-rmse:585.83209\n",
      "[41]\tvalidation_0-rmse:446.13113\tvalidation_1-rmse:585.48779\n",
      "[42]\tvalidation_0-rmse:444.02725\tvalidation_1-rmse:585.15241\n",
      "[43]\tvalidation_0-rmse:441.92056\tvalidation_1-rmse:584.23456\n",
      "[44]\tvalidation_0-rmse:438.18970\tvalidation_1-rmse:583.40552\n",
      "[45]\tvalidation_0-rmse:435.69293\tvalidation_1-rmse:582.73230\n",
      "[46]\tvalidation_0-rmse:432.80008\tvalidation_1-rmse:583.26843\n",
      "[47]\tvalidation_0-rmse:430.21918\tvalidation_1-rmse:583.52094\n",
      "[48]\tvalidation_0-rmse:429.00500\tvalidation_1-rmse:583.28467\n",
      "[49]\tvalidation_0-rmse:426.49139\tvalidation_1-rmse:582.43152\n",
      "[50]\tvalidation_0-rmse:425.35974\tvalidation_1-rmse:582.37677\n",
      "[51]\tvalidation_0-rmse:424.22473\tvalidation_1-rmse:582.74390\n",
      "[52]\tvalidation_0-rmse:423.17108\tvalidation_1-rmse:582.53699\n",
      "[53]\tvalidation_0-rmse:420.36264\tvalidation_1-rmse:581.92340\n",
      "[54]\tvalidation_0-rmse:419.39389\tvalidation_1-rmse:581.88757\n",
      "[55]\tvalidation_0-rmse:417.55777\tvalidation_1-rmse:581.84906\n",
      "[56]\tvalidation_0-rmse:417.02692\tvalidation_1-rmse:581.91138\n",
      "[57]\tvalidation_0-rmse:414.18024\tvalidation_1-rmse:581.68927\n",
      "[58]\tvalidation_0-rmse:411.98181\tvalidation_1-rmse:582.50543\n",
      "[59]\tvalidation_0-rmse:408.63989\tvalidation_1-rmse:582.93726\n",
      "[60]\tvalidation_0-rmse:407.84760\tvalidation_1-rmse:582.90546\n",
      "[61]\tvalidation_0-rmse:404.13367\tvalidation_1-rmse:583.68219\n",
      "[62]\tvalidation_0-rmse:402.48032\tvalidation_1-rmse:583.88263\n",
      "[63]\tvalidation_0-rmse:399.84100\tvalidation_1-rmse:583.85290\n",
      "[64]\tvalidation_0-rmse:398.92081\tvalidation_1-rmse:584.02039\n",
      "[65]\tvalidation_0-rmse:398.32886\tvalidation_1-rmse:584.05096\n",
      "[66]\tvalidation_0-rmse:396.35098\tvalidation_1-rmse:584.75324\n",
      "[67]\tvalidation_0-rmse:394.15503\tvalidation_1-rmse:584.65051\n",
      "[68]\tvalidation_0-rmse:390.23309\tvalidation_1-rmse:584.29486\n",
      "[69]\tvalidation_0-rmse:388.07648\tvalidation_1-rmse:584.20361\n",
      "[70]\tvalidation_0-rmse:386.37021\tvalidation_1-rmse:583.94550\n",
      "[71]\tvalidation_0-rmse:383.37863\tvalidation_1-rmse:585.07361\n",
      "[72]\tvalidation_0-rmse:381.40848\tvalidation_1-rmse:585.22382\n",
      "[73]\tvalidation_0-rmse:379.92838\tvalidation_1-rmse:585.26837\n",
      "[74]\tvalidation_0-rmse:379.04526\tvalidation_1-rmse:585.03894\n",
      "[75]\tvalidation_0-rmse:376.73413\tvalidation_1-rmse:585.06488\n",
      "[76]\tvalidation_0-rmse:375.82144\tvalidation_1-rmse:585.05841\n",
      "[77]\tvalidation_0-rmse:375.20599\tvalidation_1-rmse:585.26025\n",
      "[78]\tvalidation_0-rmse:374.37680\tvalidation_1-rmse:584.91522\n",
      "[79]\tvalidation_0-rmse:374.18118\tvalidation_1-rmse:585.05011\n",
      "[80]\tvalidation_0-rmse:373.18869\tvalidation_1-rmse:584.45435\n",
      "[81]\tvalidation_0-rmse:370.77066\tvalidation_1-rmse:584.60962\n",
      "[82]\tvalidation_0-rmse:369.71478\tvalidation_1-rmse:585.02716\n",
      "[83]\tvalidation_0-rmse:369.60327\tvalidation_1-rmse:585.10034\n",
      "[84]\tvalidation_0-rmse:368.88486\tvalidation_1-rmse:584.91718\n",
      "[85]\tvalidation_0-rmse:365.69159\tvalidation_1-rmse:585.33234\n",
      "[86]\tvalidation_0-rmse:365.22766\tvalidation_1-rmse:585.58276\n",
      "[87]\tvalidation_0-rmse:364.90573\tvalidation_1-rmse:585.83704\n",
      "[88]\tvalidation_0-rmse:363.36365\tvalidation_1-rmse:585.59552\n",
      "[89]\tvalidation_0-rmse:362.19296\tvalidation_1-rmse:585.32306\n",
      "[90]\tvalidation_0-rmse:360.97626\tvalidation_1-rmse:585.09253\n",
      "[91]\tvalidation_0-rmse:360.65225\tvalidation_1-rmse:585.01477\n",
      "[92]\tvalidation_0-rmse:359.97220\tvalidation_1-rmse:584.77374\n",
      "[93]\tvalidation_0-rmse:357.34274\tvalidation_1-rmse:583.96759\n",
      "[94]\tvalidation_0-rmse:356.52856\tvalidation_1-rmse:583.99622\n",
      "[95]\tvalidation_0-rmse:354.31393\tvalidation_1-rmse:584.35107\n",
      "[96]\tvalidation_0-rmse:353.48685\tvalidation_1-rmse:584.18140\n",
      "[97]\tvalidation_0-rmse:351.22748\tvalidation_1-rmse:584.05328\n",
      "[98]\tvalidation_0-rmse:348.56540\tvalidation_1-rmse:584.70203\n",
      "[99]\tvalidation_0-rmse:346.87198\tvalidation_1-rmse:584.85651\n",
      "[100]\tvalidation_0-rmse:344.65405\tvalidation_1-rmse:585.33051\n",
      "[101]\tvalidation_0-rmse:343.18018\tvalidation_1-rmse:584.97095\n",
      "[102]\tvalidation_0-rmse:342.58588\tvalidation_1-rmse:584.95404\n",
      "[103]\tvalidation_0-rmse:340.19763\tvalidation_1-rmse:584.69470\n",
      "[104]\tvalidation_0-rmse:339.27664\tvalidation_1-rmse:584.41614\n",
      "[105]\tvalidation_0-rmse:338.21533\tvalidation_1-rmse:584.32556\n",
      "[106]\tvalidation_0-rmse:337.64053\tvalidation_1-rmse:584.31482\n",
      "[107]\tvalidation_0-rmse:335.77982\tvalidation_1-rmse:584.40363\n",
      "Stopping. Best iteration:\n",
      "[57]\tvalidation_0-rmse:414.18024\tvalidation_1-rmse:581.68927\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# running the XGB training with early stopping to prevent overfitting\n",
    "m = gbm.fit(X_train, y_train, early_stopping_rounds=50, eval_set = eval_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4938490181350864"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the performance of the model is largely irrelevant because we mainly care about the importance of features\n",
    "m.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_n_estimators(model):\n",
    "    \n",
    "    results = model.evals_result()\n",
    "    epochs = len(results['validation_0']['rmse'])\n",
    "    x_axis = range(0, epochs)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize = (10,5))\n",
    "    ax.plot(x_axis, results['validation_0']['rmse'], label='Train')\n",
    "    ax.plot(x_axis, results['validation_1']['rmse'], label='Test')\n",
    "    ax.legend()\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.title('XGBoost RMSE')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# root square mean error on training and test datasets with increasing iterations\n",
    "plot_n_estimators(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Shapley values to understand the influence of predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    }
   ],
   "source": [
    "shap.initjs()\n",
    "explainer = shap.TreeExplainer(m, feature_perturbation = \"tree_path_dependent\")\n",
    "shap_values = explainer.shap_values(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation of SHAP plots here: [https://github.com/slundberg/shap](https://github.com/slundberg/shap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10, 10))\n",
    "shap.dependence_plot(\"centre_transit_time\", \n",
    "                     shap_values, \n",
    "                     X, \n",
    "                     ax = ax,\n",
    "                     xmax=\"percentile(99.5)\",\n",
    "                     interaction_index='accommodates')"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}