Data_Cleaning_Listings_AirbnbFlorence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "utility-luther",
   "metadata": {},
   "source": [
    "# Data Cleaning and Pre-processing\n",
    "\n",
    "### Airbnb Listings from the city of Florence\n",
    "\n",
    "\n",
    "**Open-source data** from: <br>\n",
    "http://insideairbnb.com/get-the-data.html (data compiled: 12 July, 2021)\n",
    "\n",
    "The raw dataset contains **11,364 listings** and **5,593** hosts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-ecology",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [Data Cleaning and Characterization](#chapter1)\n",
    "    * [Data Dictionary](#section1.1)\n",
    "    * [Handling Missing Values](#section1.2)\n",
    "    * [Convert Columns to Appropriate Datatype](#section1.3)\n",
    "    * [Select the Most Important Amenities](#section1.4)\n",
    "* [EDA and Data Viz](#chapter2)\n",
    "* [Data Pre-processing](#chapter3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifth-thirty",
   "metadata": {},
   "source": [
    "### IMPORT LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adopted-amber",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "import datetime\n",
    "import re\n",
    "\n",
    "from itertools import chain\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f5e541-9197-41dd-bced-e25755d80c53",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents) \n",
    "<a id='chapter1'></a>\n",
    "\n",
    "### DATA CLEANING AND CHARACTERIZATION\n",
    "\n",
    " - What data types are in each column?\n",
    " - How can we describe the data?\n",
    " - Convert columns to their correct datatype\n",
    " - Drop columns not relevant to the problem (Example, URL, host picture, etc.)\n",
    " - Impute null values, either with a meaningful value or a \"NA\" string to convert later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "macro-analysis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df = pd.read_csv('data/listings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "electronic-diagnosis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'description',\n",
       "       'neighborhood_overview', 'picture_url', 'host_id', 'host_url',\n",
       "       'host_name', 'host_since', 'host_location', 'host_about',\n",
       "       'host_response_time', 'host_response_rate', 'host_acceptance_rate',\n",
       "       'host_is_superhost', 'host_thumbnail_url', 'host_picture_url',\n",
       "       'host_neighbourhood', 'host_listings_count',\n",
       "       'host_total_listings_count', 'host_verifications',\n",
       "       'host_has_profile_pic', 'host_identity_verified', 'neighbourhood',\n",
       "       'neighbourhood_cleansed', 'neighbourhood_group_cleansed', 'latitude',\n",
       "       'longitude', 'property_type', 'room_type', 'accommodates', 'bathrooms',\n",
       "       'bathrooms_text', 'bedrooms', 'beds', 'amenities', 'price',\n",
       "       'minimum_nights', 'maximum_nights', 'minimum_minimum_nights',\n",
       "       'maximum_minimum_nights', 'minimum_maximum_nights',\n",
       "       'maximum_maximum_nights', 'minimum_nights_avg_ntm',\n",
       "       'maximum_nights_avg_ntm', 'calendar_updated', 'has_availability',\n",
       "       'availability_30', 'availability_60', 'availability_90',\n",
       "       'availability_365', 'calendar_last_scraped', 'number_of_reviews',\n",
       "       'number_of_reviews_ltm', 'number_of_reviews_l30d', 'first_review',\n",
       "       'last_review', 'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'license', 'instant_bookable',\n",
       "       'calculated_host_listings_count',\n",
       "       'calculated_host_listings_count_entire_homes',\n",
       "       'calculated_host_listings_count_private_rooms',\n",
       "       'calculated_host_listings_count_shared_rooms', 'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "supposed-account",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>license</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6688</th>\n",
       "      <td>28827610</td>\n",
       "      <td>https://www.airbnb.com/rooms/28827610</td>\n",
       "      <td>20210712150741</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>Cozy room in Florence</td>\n",
       "      <td>Snuggle up in a comfy bed and settle in for a ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/pictures/9fdd900e-7c84...</td>\n",
       "      <td>217201511</td>\n",
       "      <td>https://www.airbnb.com/users/show/217201511</td>\n",
       "      <td>...</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.44</td>\n",
       "      <td>4.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                            listing_url       scrape_id  \\\n",
       "6688  28827610  https://www.airbnb.com/rooms/28827610  20210712150741   \n",
       "\n",
       "     last_scraped                   name  \\\n",
       "6688   2021-07-13  Cozy room in Florence   \n",
       "\n",
       "                                            description neighborhood_overview  \\\n",
       "6688  Snuggle up in a comfy bed and settle in for a ...                   NaN   \n",
       "\n",
       "                                            picture_url    host_id  \\\n",
       "6688  https://a0.muscache.com/pictures/9fdd900e-7c84...  217201511   \n",
       "\n",
       "                                         host_url  ...  \\\n",
       "6688  https://www.airbnb.com/users/show/217201511  ...   \n",
       "\n",
       "     review_scores_communication review_scores_location review_scores_value  \\\n",
       "6688                        4.69                   4.44                4.69   \n",
       "\n",
       "     license instant_bookable calculated_host_listings_count  \\\n",
       "6688     NaN                f                              1   \n",
       "\n",
       "     calculated_host_listings_count_entire_homes  \\\n",
       "6688                                           0   \n",
       "\n",
       "     calculated_host_listings_count_private_rooms  \\\n",
       "6688                                            0   \n",
       "\n",
       "     calculated_host_listings_count_shared_rooms reviews_per_month  \n",
       "6688                                           1              0.52  \n",
       "\n",
       "[1 rows x 74 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.id == 28827610]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "referenced-hebrew",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances: 11364\n",
      "Number of attributes: 74\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of instances: {df.shape[0]}')\n",
    "print(f'Number of attributes: {df.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smaller-ceramic",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents) \n",
    "<a class=\"anchor\" id='section1.1'></a>\n",
    "\n",
    "### Data Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "racial-metropolitan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11364 entries, 0 to 11363\n",
      "Data columns (total 74 columns):\n",
      " #   Column                                        Non-Null Count  Dtype  \n",
      "---  ------                                        --------------  -----  \n",
      " 0   id                                            11364 non-null  int64  \n",
      " 1   listing_url                                   11364 non-null  object \n",
      " 2   scrape_id                                     11364 non-null  int64  \n",
      " 3   last_scraped                                  11364 non-null  object \n",
      " 4   name                                          11362 non-null  object \n",
      " 5   description                                   11167 non-null  object \n",
      " 6   neighborhood_overview                         7273 non-null   object \n",
      " 7   picture_url                                   11364 non-null  object \n",
      " 8   host_id                                       11364 non-null  int64  \n",
      " 9   host_url                                      11364 non-null  object \n",
      " 10  host_name                                     11114 non-null  object \n",
      " 11  host_since                                    11114 non-null  object \n",
      " 12  host_location                                 11102 non-null  object \n",
      " 13  host_about                                    7217 non-null   object \n",
      " 14  host_response_time                            7610 non-null   object \n",
      " 15  host_response_rate                            7610 non-null   object \n",
      " 16  host_acceptance_rate                          7581 non-null   object \n",
      " 17  host_is_superhost                             11114 non-null  object \n",
      " 18  host_thumbnail_url                            11114 non-null  object \n",
      " 19  host_picture_url                              11114 non-null  object \n",
      " 20  host_neighbourhood                            8947 non-null   object \n",
      " 21  host_listings_count                           11114 non-null  float64\n",
      " 22  host_total_listings_count                     11114 non-null  float64\n",
      " 23  host_verifications                            11364 non-null  object \n",
      " 24  host_has_profile_pic                          11114 non-null  object \n",
      " 25  host_identity_verified                        11114 non-null  object \n",
      " 26  neighbourhood                                 7273 non-null   object \n",
      " 27  neighbourhood_cleansed                        11364 non-null  object \n",
      " 28  neighbourhood_group_cleansed                  0 non-null      float64\n",
      " 29  latitude                                      11364 non-null  float64\n",
      " 30  longitude                                     11364 non-null  float64\n",
      " 31  property_type                                 11364 non-null  object \n",
      " 32  room_type                                     11364 non-null  object \n",
      " 33  accommodates                                  11364 non-null  int64  \n",
      " 34  bathrooms                                     0 non-null      float64\n",
      " 35  bathrooms_text                                11355 non-null  object \n",
      " 36  bedrooms                                      10914 non-null  float64\n",
      " 37  beds                                          11285 non-null  float64\n",
      " 38  amenities                                     11364 non-null  object \n",
      " 39  price                                         11364 non-null  object \n",
      " 40  minimum_nights                                11364 non-null  int64  \n",
      " 41  maximum_nights                                11364 non-null  int64  \n",
      " 42  minimum_minimum_nights                        11364 non-null  int64  \n",
      " 43  maximum_minimum_nights                        11364 non-null  int64  \n",
      " 44  minimum_maximum_nights                        11364 non-null  int64  \n",
      " 45  maximum_maximum_nights                        11364 non-null  int64  \n",
      " 46  minimum_nights_avg_ntm                        11364 non-null  float64\n",
      " 47  maximum_nights_avg_ntm                        11364 non-null  float64\n",
      " 48  calendar_updated                              0 non-null      float64\n",
      " 49  has_availability                              11364 non-null  object \n",
      " 50  availability_30                               11364 non-null  int64  \n",
      " 51  availability_60                               11364 non-null  int64  \n",
      " 52  availability_90                               11364 non-null  int64  \n",
      " 53  availability_365                              11364 non-null  int64  \n",
      " 54  calendar_last_scraped                         11364 non-null  object \n",
      " 55  number_of_reviews                             11364 non-null  int64  \n",
      " 56  number_of_reviews_ltm                         11364 non-null  int64  \n",
      " 57  number_of_reviews_l30d                        11364 non-null  int64  \n",
      " 58  first_review                                  8641 non-null   object \n",
      " 59  last_review                                   8641 non-null   object \n",
      " 60  review_scores_rating                          8641 non-null   float64\n",
      " 61  review_scores_accuracy                        8560 non-null   float64\n",
      " 62  review_scores_cleanliness                     8560 non-null   float64\n",
      " 63  review_scores_checkin                         8557 non-null   float64\n",
      " 64  review_scores_communication                   8557 non-null   float64\n",
      " 65  review_scores_location                        8557 non-null   float64\n",
      " 66  review_scores_value                           8555 non-null   float64\n",
      " 67  license                                       438 non-null    object \n",
      " 68  instant_bookable                              11364 non-null  object \n",
      " 69  calculated_host_listings_count                11364 non-null  int64  \n",
      " 70  calculated_host_listings_count_entire_homes   11364 non-null  int64  \n",
      " 71  calculated_host_listings_count_private_rooms  11364 non-null  int64  \n",
      " 72  calculated_host_listings_count_shared_rooms   11364 non-null  int64  \n",
      " 73  reviews_per_month                             8641 non-null   float64\n",
      "dtypes: float64(19), int64(21), object(34)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "greenhouse-wallpaper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>...</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>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.136400e+04</td>\n",
       "      <td>1.136400e+04</td>\n",
       "      <td>1.136400e+04</td>\n",
       "      <td>11114.000000</td>\n",
       "      <td>11114.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8560.000000</td>\n",
       "      <td>8557.00000</td>\n",
       "      <td>8557.000000</td>\n",
       "      <td>8557.000000</td>\n",
       "      <td>8555.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>11364.000000</td>\n",
       "      <td>8641.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.451295e+07</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>9.336482e+07</td>\n",
       "      <td>28.126417</td>\n",
       "      <td>28.126417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.773970</td>\n",
       "      <td>11.253741</td>\n",
       "      <td>3.746744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.761133</td>\n",
       "      <td>4.84293</td>\n",
       "      <td>4.836304</td>\n",
       "      <td>4.809875</td>\n",
       "      <td>4.697973</td>\n",
       "      <td>16.666315</td>\n",
       "      <td>11.218585</td>\n",
       "      <td>5.329285</td>\n",
       "      <td>0.009944</td>\n",
       "      <td>1.240854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.548873e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.067000e+08</td>\n",
       "      <td>121.756857</td>\n",
       "      <td>121.756857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009675</td>\n",
       "      <td>0.016270</td>\n",
       "      <td>2.053520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.378772</td>\n",
       "      <td>0.30137</td>\n",
       "      <td>0.324095</td>\n",
       "      <td>0.298208</td>\n",
       "      <td>0.366419</td>\n",
       "      <td>43.609462</td>\n",
       "      <td>32.118969</td>\n",
       "      <td>30.227874</td>\n",
       "      <td>0.159168</td>\n",
       "      <td>1.391153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.446900e+04</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>1.565100e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.726430</td>\n",
       "      <td>11.159190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.140529e+07</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>1.090685e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.768610</td>\n",
       "      <td>11.245602</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>4.81000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>4.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.349237e+07</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>4.289194e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.772550</td>\n",
       "      <td>11.253984</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.880000</td>\n",
       "      <td>4.93000</td>\n",
       "      <td>4.940000</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.775198e+07</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>1.572704e+08</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.777997</td>\n",
       "      <td>11.261450</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.098406e+07</td>\n",
       "      <td>2.021071e+13</td>\n",
       "      <td>4.117103e+08</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.830530</td>\n",
       "      <td>11.331620</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     scrape_id       host_id  host_listings_count  \\\n",
       "count  1.136400e+04  1.136400e+04  1.136400e+04         11114.000000   \n",
       "mean   2.451295e+07  2.021071e+13  9.336482e+07            28.126417   \n",
       "std    1.548873e+07  0.000000e+00  1.067000e+08           121.756857   \n",
       "min    2.446900e+04  2.021071e+13  1.565100e+04             0.000000   \n",
       "25%    1.140529e+07  2.021071e+13  1.090685e+07             1.000000   \n",
       "50%    2.349237e+07  2.021071e+13  4.289194e+07             3.000000   \n",
       "75%    3.775198e+07  2.021071e+13  1.572704e+08             8.000000   \n",
       "max    5.098406e+07  2.021071e+13  4.117103e+08          1507.000000   \n",
       "\n",
       "       host_total_listings_count  neighbourhood_group_cleansed      latitude  \\\n",
       "count               11114.000000                           0.0  11364.000000   \n",
       "mean                   28.126417                           NaN     43.773970   \n",
       "std                   121.756857                           NaN      0.009675   \n",
       "min                     0.000000                           NaN     43.726430   \n",
       "25%                     1.000000                           NaN     43.768610   \n",
       "50%                     3.000000                           NaN     43.772550   \n",
       "75%                     8.000000                           NaN     43.777997   \n",
       "max                  1507.000000                           NaN     43.830530   \n",
       "\n",
       "          longitude  accommodates  bathrooms  ...  review_scores_cleanliness  \\\n",
       "count  11364.000000  11364.000000        0.0  ...                8560.000000   \n",
       "mean      11.253741      3.746744        NaN  ...                   4.761133   \n",
       "std        0.016270      2.053520        NaN  ...                   0.378772   \n",
       "min       11.159190      0.000000        NaN  ...                   0.000000   \n",
       "25%       11.245602      2.000000        NaN  ...                   4.700000   \n",
       "50%       11.253984      4.000000        NaN  ...                   4.880000   \n",
       "75%       11.261450      4.000000        NaN  ...                   4.990000   \n",
       "max       11.331620     16.000000        NaN  ...                   5.000000   \n",
       "\n",
       "       review_scores_checkin  review_scores_communication  \\\n",
       "count             8557.00000                  8557.000000   \n",
       "mean                 4.84293                     4.836304   \n",
       "std                  0.30137                     0.324095   \n",
       "min                  0.00000                     0.000000   \n",
       "25%                  4.81000                     4.810000   \n",
       "50%                  4.93000                     4.940000   \n",
       "75%                  5.00000                     5.000000   \n",
       "max                  5.00000                     5.000000   \n",
       "\n",
       "       review_scores_location  review_scores_value  \\\n",
       "count             8557.000000          8555.000000   \n",
       "mean                 4.809875             4.697973   \n",
       "std                  0.298208             0.366419   \n",
       "min                  0.000000             0.000000   \n",
       "25%                  4.730000             4.610000   \n",
       "50%                  4.920000             4.790000   \n",
       "75%                  5.000000             4.900000   \n",
       "max                  5.000000             5.000000   \n",
       "\n",
       "       calculated_host_listings_count  \\\n",
       "count                    11364.000000   \n",
       "mean                        16.666315   \n",
       "std                         43.609462   \n",
       "min                          1.000000   \n",
       "25%                          1.000000   \n",
       "50%                          3.000000   \n",
       "75%                          8.000000   \n",
       "max                        225.000000   \n",
       "\n",
       "       calculated_host_listings_count_entire_homes  \\\n",
       "count                                 11364.000000   \n",
       "mean                                     11.218585   \n",
       "std                                      32.118969   \n",
       "min                                       0.000000   \n",
       "25%                                       1.000000   \n",
       "50%                                       1.000000   \n",
       "75%                                       6.000000   \n",
       "max                                     204.000000   \n",
       "\n",
       "       calculated_host_listings_count_private_rooms  \\\n",
       "count                                  11364.000000   \n",
       "mean                                       5.329285   \n",
       "std                                       30.227874   \n",
       "min                                        0.000000   \n",
       "25%                                        0.000000   \n",
       "50%                                        0.000000   \n",
       "75%                                        1.000000   \n",
       "max                                      217.000000   \n",
       "\n",
       "       calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "count                                 11364.000000        8641.000000  \n",
       "mean                                      0.009944           1.240854  \n",
       "std                                       0.159168           1.391153  \n",
       "min                                       0.000000           0.010000  \n",
       "25%                                       0.000000           0.210000  \n",
       "50%                                       0.000000           0.760000  \n",
       "75%                                       0.000000           1.870000  \n",
       "max                                       4.000000          30.710000  \n",
       "\n",
       "[8 rows x 40 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7d981b2c-98e7-4496-9365-52ea83532041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 5593 unique host ID\n"
     ]
    }
   ],
   "source": [
    "print(f'The dataset contains {df.host_id.nunique()} unique host ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb1b4c1f-f816-48a5-bfbc-27c4eb1f1f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      2.031825\n",
       "std       5.453443\n",
       "amin      1.000000\n",
       "amax    225.000000\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean, standard deviation, min, max of listings per host\n",
    "\n",
    "df.groupby('host_id').id.count().agg([np.mean,np.std,np.min,np.max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "reflected-texas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     34\n",
       "int64      21\n",
       "float64    19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how data types are distributed. We have to check if each data type makes sense\n",
    "\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "southwest-salem",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['listing_url', 'last_scraped', 'name', 'description',\n",
       "       'neighborhood_overview', 'picture_url', 'host_url', 'host_name',\n",
       "       'host_since', 'host_location', 'host_about', 'host_response_time',\n",
       "       'host_response_rate', 'host_acceptance_rate', 'host_is_superhost',\n",
       "       'host_thumbnail_url', 'host_picture_url', 'host_neighbourhood',\n",
       "       'host_verifications', 'host_has_profile_pic', 'host_identity_verified',\n",
       "       'neighbourhood', 'neighbourhood_cleansed', 'property_type', 'room_type',\n",
       "       'bathrooms_text', 'amenities', 'price', 'has_availability',\n",
       "       'calendar_last_scraped', 'first_review', 'last_review', 'license',\n",
       "       'instant_bookable'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "employed-current",
   "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>listing_url</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>...</th>\n",
       "      <th>room_type</th>\n",
       "      <th>bathrooms_text</th>\n",
       "      <th>amenities</th>\n",
       "      <th>price</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>license</th>\n",
       "      <th>instant_bookable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.airbnb.com/rooms/24469</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>Fortezza/City Centre Modern Apt 2+2</td>\n",
       "      <td>Apartment, perfect for 2 people, wide, bright ...</td>\n",
       "      <td>In 10/15 minutes walking or with only one stop...</td>\n",
       "      <td>https://a0.muscache.com/pictures/fd822ef3-4f6d...</td>\n",
       "      <td>https://www.airbnb.com/users/show/99178</td>\n",
       "      <td>Benedetta And Lorenzo</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>Florence, Toscana, Italy</td>\n",
       "      <td>...</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1 bath</td>\n",
       "      <td>[\"Coffee maker\", \"Refrigerator\", \"Dedicated wo...</td>\n",
       "      <td>$70.00</td>\n",
       "      <td>t</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.airbnb.com/rooms/24470</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>Fortezza/City Centre Modern Apt 2+1</td>\n",
       "      <td>Apartment, perfect for 2 people, can accommoda...</td>\n",
       "      <td>In 10/15 minutes walking you will reach the hi...</td>\n",
       "      <td>https://a0.muscache.com/pictures/cfa220db-5c70...</td>\n",
       "      <td>https://www.airbnb.com/users/show/99178</td>\n",
       "      <td>Benedetta And Lorenzo</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>Florence, Toscana, Italy</td>\n",
       "      <td>...</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1 bath</td>\n",
       "      <td>[\"Luggage dropoff allowed\", \"Paid parking on p...</td>\n",
       "      <td>$70.00</td>\n",
       "      <td>t</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2019-04-21</td>\n",
       "      <td>2010-10-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.airbnb.com/rooms/24471</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>Fortezza/City Centre Modern Apt 4+2</td>\n",
       "      <td>Perfect, big apartment for families or groups ...</td>\n",
       "      <td>In 10/15 minutes walking you will reach the hi...</td>\n",
       "      <td>https://a0.muscache.com/pictures/31756e2d-098a...</td>\n",
       "      <td>https://www.airbnb.com/users/show/99178</td>\n",
       "      <td>Benedetta And Lorenzo</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>Florence, Toscana, Italy</td>\n",
       "      <td>...</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1.5 baths</td>\n",
       "      <td>[\"Coffee maker\", \"Refrigerator\", \"Dedicated wo...</td>\n",
       "      <td>$135.00</td>\n",
       "      <td>t</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.airbnb.com/rooms/24472</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>Fortezza/City Centre Modern Apt 4+2</td>\n",
       "      <td>Santa Maria Novella, perfect apartment for fam...</td>\n",
       "      <td>In 10/15 minutes walking or with only one stop...</td>\n",
       "      <td>https://a0.muscache.com/pictures/130826/15c567...</td>\n",
       "      <td>https://www.airbnb.com/users/show/99178</td>\n",
       "      <td>Benedetta And Lorenzo</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>Florence, Toscana, Italy</td>\n",
       "      <td>...</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1 bath</td>\n",
       "      <td>[\"Paid parking on premises\", \"Fire extinguishe...</td>\n",
       "      <td>$120.00</td>\n",
       "      <td>t</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2011-06-28</td>\n",
       "      <td>2012-04-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.airbnb.com/rooms/31840</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>A Residenza Martin  Classic room</td>\n",
       "      <td>Nice, private and quiet double room, classic s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/pictures/09da8cb8-47df...</td>\n",
       "      <td>https://www.airbnb.com/users/show/380378</td>\n",
       "      <td>Officina 360</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>Florence, Tuscany, Italy</td>\n",
       "      <td>...</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1 private bath</td>\n",
       "      <td>[\"Keypad\", \"Coffee maker\", \"Refrigerator\", \"Ca...</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>t</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>2013-04-21</td>\n",
       "      <td>2019-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          listing_url last_scraped  \\\n",
       "0  https://www.airbnb.com/rooms/24469   2021-07-13   \n",
       "1  https://www.airbnb.com/rooms/24470   2021-07-13   \n",
       "2  https://www.airbnb.com/rooms/24471   2021-07-13   \n",
       "3  https://www.airbnb.com/rooms/24472   2021-07-13   \n",
       "4  https://www.airbnb.com/rooms/31840   2021-07-12   \n",
       "\n",
       "                                  name  \\\n",
       "0  Fortezza/City Centre Modern Apt 2+2   \n",
       "1  Fortezza/City Centre Modern Apt 2+1   \n",
       "2  Fortezza/City Centre Modern Apt 4+2   \n",
       "3  Fortezza/City Centre Modern Apt 4+2   \n",
       "4     A Residenza Martin  Classic room   \n",
       "\n",
       "                                         description  \\\n",
       "0  Apartment, perfect for 2 people, wide, bright ...   \n",
       "1  Apartment, perfect for 2 people, can accommoda...   \n",
       "2  Perfect, big apartment for families or groups ...   \n",
       "3  Santa Maria Novella, perfect apartment for fam...   \n",
       "4  Nice, private and quiet double room, classic s...   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  In 10/15 minutes walking or with only one stop...   \n",
       "1  In 10/15 minutes walking you will reach the hi...   \n",
       "2  In 10/15 minutes walking you will reach the hi...   \n",
       "3  In 10/15 minutes walking or with only one stop...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         picture_url  \\\n",
       "0  https://a0.muscache.com/pictures/fd822ef3-4f6d...   \n",
       "1  https://a0.muscache.com/pictures/cfa220db-5c70...   \n",
       "2  https://a0.muscache.com/pictures/31756e2d-098a...   \n",
       "3  https://a0.muscache.com/pictures/130826/15c567...   \n",
       "4  https://a0.muscache.com/pictures/09da8cb8-47df...   \n",
       "\n",
       "                                   host_url              host_name  \\\n",
       "0   https://www.airbnb.com/users/show/99178  Benedetta And Lorenzo   \n",
       "1   https://www.airbnb.com/users/show/99178  Benedetta And Lorenzo   \n",
       "2   https://www.airbnb.com/users/show/99178  Benedetta And Lorenzo   \n",
       "3   https://www.airbnb.com/users/show/99178  Benedetta And Lorenzo   \n",
       "4  https://www.airbnb.com/users/show/380378           Officina 360   \n",
       "\n",
       "   host_since             host_location  ...        room_type  bathrooms_text  \\\n",
       "0  2010-03-26  Florence, Toscana, Italy  ...  Entire home/apt          1 bath   \n",
       "1  2010-03-26  Florence, Toscana, Italy  ...  Entire home/apt          1 bath   \n",
       "2  2010-03-26  Florence, Toscana, Italy  ...  Entire home/apt       1.5 baths   \n",
       "3  2010-03-26  Florence, Toscana, Italy  ...  Entire home/apt          1 bath   \n",
       "4  2011-02-07  Florence, Tuscany, Italy  ...     Private room  1 private bath   \n",
       "\n",
       "                                           amenities    price  \\\n",
       "0  [\"Coffee maker\", \"Refrigerator\", \"Dedicated wo...   $70.00   \n",
       "1  [\"Luggage dropoff allowed\", \"Paid parking on p...   $70.00   \n",
       "2  [\"Coffee maker\", \"Refrigerator\", \"Dedicated wo...  $135.00   \n",
       "3  [\"Paid parking on premises\", \"Fire extinguishe...  $120.00   \n",
       "4  [\"Keypad\", \"Coffee maker\", \"Refrigerator\", \"Ca...   $65.00   \n",
       "\n",
       "  has_availability calendar_last_scraped first_review last_review license  \\\n",
       "0                t            2021-07-13   2019-09-27  2019-09-27     NaN   \n",
       "1                t            2021-07-13   2019-04-21  2010-10-22     NaN   \n",
       "2                t            2021-07-13          NaN         NaN     NaN   \n",
       "3                t            2021-07-13   2011-06-28  2012-04-11     NaN   \n",
       "4                t            2021-07-12   2013-04-21  2019-12-20     NaN   \n",
       "\n",
       "  instant_bookable  \n",
       "0                t  \n",
       "1                t  \n",
       "2                t  \n",
       "3                t  \n",
       "4                t  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "premium-sugar",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop irrelevant object types that are not useful for the analysis, without losing much of information.\n",
    "\n",
    "drop_obj_cols = [\n",
    "                 'listing_url','last_scraped',\n",
    "                 'name', 'description',\n",
    "                 'picture_url','host_url',\n",
    "                 'neighborhood_overview',\n",
    "                 'host_name','host_since',\n",
    "                 'host_location','host_about',\n",
    "                 'host_thumbnail_url','host_picture_url',\n",
    "                 'host_neighbourhood','calendar_last_scraped',\n",
    "                 'neighbourhood', 'has_availability',\n",
    "                 'calendar_last_scraped', 'license',\n",
    "                 'host_verifications','property_type'\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "early-moscow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['host_listings_count', 'host_total_listings_count',\n",
       "       'neighbourhood_group_cleansed', 'latitude', 'longitude', 'bathrooms',\n",
       "       'bedrooms', 'beds', 'minimum_nights_avg_ntm', 'maximum_nights_avg_ntm',\n",
       "       'calendar_updated', 'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['float64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "centered-brave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>minimum_nights_avg_ntm</th>\n",
       "      <th>maximum_nights_avg_ntm</th>\n",
       "      <th>calendar_updated</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>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.00</td>\n",
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       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.78202</td>\n",
       "      <td>11.24399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.78202</td>\n",
       "      <td>11.24399</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.78202</td>\n",
       "      <td>11.24399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.77709</td>\n",
       "      <td>11.25216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.85</td>\n",
       "      <td>4.91</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.71</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_listings_count  host_total_listings_count  \\\n",
       "0                  5.0                        5.0   \n",
       "1                  5.0                        5.0   \n",
       "2                  5.0                        5.0   \n",
       "3                  5.0                        5.0   \n",
       "4                 32.0                       32.0   \n",
       "\n",
       "   neighbourhood_group_cleansed  latitude  longitude  bathrooms  bedrooms  \\\n",
       "0                           NaN  43.78210   11.24392        NaN       1.0   \n",
       "1                           NaN  43.78202   11.24399        NaN       1.0   \n",
       "2                           NaN  43.78202   11.24399        NaN       2.0   \n",
       "3                           NaN  43.78202   11.24399        NaN       2.0   \n",
       "4                           NaN  43.77709   11.25216        NaN       1.0   \n",
       "\n",
       "   beds  minimum_nights_avg_ntm  maximum_nights_avg_ntm  calendar_updated  \\\n",
       "0   1.0                     2.0                   365.0               NaN   \n",
       "1   1.0                     2.0                   365.0               NaN   \n",
       "2   2.0                     2.0                   365.0               NaN   \n",
       "3   2.0                     2.0                   365.0               NaN   \n",
       "4   1.0                     1.3                    99.0               NaN   \n",
       "\n",
       "   review_scores_rating  review_scores_accuracy  review_scores_cleanliness  \\\n",
       "0                  5.00                    5.00                       5.00   \n",
       "1                  4.67                    5.00                       5.00   \n",
       "2                   NaN                     NaN                        NaN   \n",
       "3                  3.00                    5.00                       5.00   \n",
       "4                  4.65                    4.73                       4.87   \n",
       "\n",
       "   review_scores_checkin  review_scores_communication  review_scores_location  \\\n",
       "0                   5.00                         5.00                     4.0   \n",
       "1                   5.00                         5.00                     4.0   \n",
       "2                    NaN                          NaN                     NaN   \n",
       "3                   5.00                         5.00                     4.0   \n",
       "4                   4.85                         4.91                     4.9   \n",
       "\n",
       "   review_scores_value  reviews_per_month  \n",
       "0                 5.00               0.05  \n",
       "1                 5.00               0.11  \n",
       "2                  NaN                NaN  \n",
       "3                 4.50               0.02  \n",
       "4                 4.71               1.19  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['float64']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "marine-apparatus",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_float_cols = [\n",
    "                   'neighbourhood_group_cleansed',\n",
    "                   'bathrooms','calendar_updated',\n",
    "                   'minimum_nights_avg_ntm',\n",
    "                   'maximum_nights_avg_ntm'\n",
    "                   ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "interracial-accident",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'scrape_id', 'host_id', 'accommodates', 'minimum_nights',\n",
       "       'maximum_nights', 'minimum_minimum_nights', 'maximum_minimum_nights',\n",
       "       'minimum_maximum_nights', 'maximum_maximum_nights', 'availability_30',\n",
       "       'availability_60', 'availability_90', 'availability_365',\n",
       "       'number_of_reviews', 'number_of_reviews_ltm', 'number_of_reviews_l30d',\n",
       "       'calculated_host_listings_count',\n",
       "       'calculated_host_listings_count_entire_homes',\n",
       "       'calculated_host_listings_count_private_rooms',\n",
       "       'calculated_host_listings_count_shared_rooms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['int64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "twelve-corps",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>accommodates</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>...</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>number_of_reviews_l30d</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24470</td>\n",
       "      <td>20210712150741</td>\n",
       "      <td>99178</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
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       "      <td>60</td>\n",
       "      <td>90</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24471</td>\n",
       "      <td>20210712150741</td>\n",
       "      <td>99178</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24472</td>\n",
       "      <td>20210712150741</td>\n",
       "      <td>99178</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>365</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31840</td>\n",
       "      <td>20210712150741</td>\n",
       "      <td>380378</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
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       "      <td>38</td>\n",
       "      <td>60</td>\n",
       "      <td>301</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id       scrape_id  host_id  accommodates  minimum_nights  \\\n",
       "0  24469  20210712150741    99178             4               2   \n",
       "1  24470  20210712150741    99178             3               2   \n",
       "2  24471  20210712150741    99178             6               2   \n",
       "3  24472  20210712150741    99178             6               2   \n",
       "4  31840  20210712150741   380378             2               1   \n",
       "\n",
       "   maximum_nights  minimum_minimum_nights  maximum_minimum_nights  \\\n",
       "0             365                       2                       2   \n",
       "1             365                       2                       2   \n",
       "2             365                       2                       2   \n",
       "3             365                       2                       2   \n",
       "4              99                       1                       2   \n",
       "\n",
       "   minimum_maximum_nights  maximum_maximum_nights  ...  availability_60  \\\n",
       "0                     365                     365  ...               60   \n",
       "1                     365                     365  ...               60   \n",
       "2                     365                     365  ...               60   \n",
       "3                     365                     365  ...               60   \n",
       "4                      99                      99  ...               38   \n",
       "\n",
       "   availability_90  availability_365  number_of_reviews  \\\n",
       "0               90               365                  1   \n",
       "1               90               365                  3   \n",
       "2               90               365                  0   \n",
       "3               90               365                  2   \n",
       "4               60               301                119   \n",
       "\n",
       "   number_of_reviews_ltm  number_of_reviews_l30d  \\\n",
       "0                      0                       0   \n",
       "1                      0                       0   \n",
       "2                      0                       0   \n",
       "3                      0                       0   \n",
       "4                      1                       0   \n",
       "\n",
       "   calculated_host_listings_count  \\\n",
       "0                               4   \n",
       "1                               4   \n",
       "2                               4   \n",
       "3                               4   \n",
       "4                              26   \n",
       "\n",
       "   calculated_host_listings_count_entire_homes  \\\n",
       "0                                            4   \n",
       "1                                            4   \n",
       "2                                            4   \n",
       "3                                            4   \n",
       "4                                           22   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             4   \n",
       "\n",
       "   calculated_host_listings_count_shared_rooms  \n",
       "0                                            0  \n",
       "1                                            0  \n",
       "2                                            0  \n",
       "3                                            0  \n",
       "4                                            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['int64']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bottom-occupation",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_int_cols = [\n",
    "                 'scrape_id','host_id',\n",
    "                 'minimum_minimum_nights','maximum_minimum_nights',\n",
    "                 'minimum_maximum_nights','maximum_maximum_nights',\n",
    "                 'availability_30', 'availability_60', \n",
    "                 'availability_365','availability_90',\n",
    "                 'calculated_host_listings_count',\n",
    "                 'calculated_host_listings_count_entire_homes',\n",
    "                 'calculated_host_listings_count_private_rooms',\n",
    "                 'calculated_host_listings_count_shared_rooms'\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "exempt-latino",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset before dropping: (11364, 74)\n",
      "Dropping 40 columns\n",
      "Shape of the dataset after dropping: (11364, 35)\n"
     ]
    }
   ],
   "source": [
    "drop_cols = drop_obj_cols + drop_float_cols + drop_int_cols\n",
    "\n",
    "print(f\"Shape of the dataset before dropping: {df.shape}\")\n",
    "print(f\"Dropping {len(drop_cols)} columns\")\n",
    "df_cols_clean = df.drop(columns=drop_cols)\n",
    "print(f\"Shape of the dataset after dropping: {df_cols_clean.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee96e31b-955d-4e7c-9d29-5148e6b40c41",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents)  \n",
    "<a class=\"anchor\" id='section1.2'></a>\n",
    "\n",
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "durable-tooth",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for missing values\n",
    "cols_missings_perc = df_cols_clean.isnull().sum() / len(df_cols_clean)\n",
    "cols_missings = cols_missings_perc[cols_missings_perc >0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "affected-patio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PgV3z/k+TNBKYTrpQANgKOFTSFXmbUyWNIoX42cBw4DVgLHA6MKRCLR0d8y7ANySNAU4DTq3QHoCI2AT4Kn9/Ts3MrBf0u5F2JuBDwObA3IgAeD+wI3ATsDAivgcMkfRERByT2+0O7BkRD+T3dcAw4B5gFLBtbn8IKYzPq1DDOOBSUvjelZdtKekpAEnTASJiqKRH8/oHgcvy62WS3smvdwWm5+OoA57Ofe4E3A40AdMq1NLRMZeB8yNiIlDO/XamlH92dk7NzKwX9NeRdguwDHgBGJNHqNcAiyS9DiwGvgn8oF27pcD9efvRwK3As8BtwFTgMVKAnwY8I6mpo51HxHrAscBxuZ+GiBgG/CkidsrbnBMRR+dlH81NR5ICufUYWok0FT+KNMqeQ7qIeFHSoaTAvqSzk9HJMX8NuFHS54H7eS+YWzUCW+fX/5h/dnhOO9uvmZlVV38NbSS9TJpqnh8Ri0jTyK2BOCO/n9Wu2Z3AmxGxgBRyZUlvAA8BAcyT9Bhp9N3p1Likt4FXgUeB+4B5wPPAScD3I2I+sAdpqnsS8O28zzOAszro8mTgxrzNZaSLh98CkyLiYdL96Uu7OCXtj/knwNW5zzHAZu22/x5wVkT8HNgmH1elc2pmZj2sVPZzJq0H1dfXl5fsPLHWZZhZN/mJaH1DRCyWtFf75f31nnaviIjhwBUdrJol6doa1DMb2KTd4tckHdnbtbQql/2PgFmRNDY1M7huUK3LsE44tNeCpEdI95b7BEn1ta6hvVL7O+Vm1qc5sPu2fntP28zMrL9xaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEQ9t6lL+PxqxYGpuaa12CVeBnj1uPKpVg+6lzal2GmXWTv+Cnb/NI28zMrCD67Ug7IhqAVyXdUetaekJEXAiskHRdFfo6GlgEtABfkXTK2vZpZmbV129DW9LMWtdQIGcAUyQtBRzYZmZ9VCFCO4+aTyRN518DnAk0AwslTY2IXwPHSHouIo4FRgCryCPRiLgUOCi3vxJ4Bpgm6fCIOA6YKuljETECGC9pcid1XAyMzv3cLOmqiNgH+BZQApYDJwC75DqbgUZgUm5zJ7ASmAvcBVyd263Mx7cuMCtvW0cK0se7cX6+kY8Z4MeSvhUROwHfy33+DRgHbJmPfx1gI+B0YGPg48CNEfE54EZJ+0bEGGBarr+1vo8D5wDvADsAsyRd3FV9ZmZWHUW6p70KOAK4APiEpBHANjlcbgDG5+0agBmtjSJiLLCDpAOAg4HzgOeAYRExGPgkUI6ILXP/syvUMB44nnQB8FZedj0wQdI+wM+BXfP+T5M0EphOCkqArYBDJV2RtzlV0ihSiJ8NDAdeA8aSAnVIVyclIg4nBei+pOA+PiJ2B/4TuFTSfsB3gT2A3YB/k3RIrmmCpDnAo/nY3sl9lvJx1edjmA+cn3c5DPgssF+u2czMekkhRtqZgA8BmwNzIwLg/cCOwE3Awoj4HjBE0hMRcUxutzuwZ0Q8kN/XkYLnHmAUsG1ufwgpjM+rUMM44FJS+N6Vl20p6SkASdMBImKopEfz+geBy/LrZZLeya93Babn46gDns597gTcDjSRRrpd2RVYIKkMNEXEL4EPAwE8nOu6Ndc1AvhyRLxFOnevd9LnZsDrkpa3OYZLgP8GHpf0LvBu7sfMzHpJkUbaLcAy4AVgTB6hXgMskvQ6sBj4JvCDdu2WAvfn7UcDtwLPArcBU4HHSAF+GvCMpKaOdh4R6wHHAsflfhoiYhjwpzwVTUSckz/U9aeI+GhuOpIUyK3H0EqkqfhRpBHrHNJFxIuSDiUF9iXdOC9PkafGI6IO2J80/f8UsHdefkJE/CtpOv4CSV8AHidNzbfW1fa/hVeAIRGxdQfH4N+8NjOrkSKFNpJeJk3rzo+IRaRp5NYwmZHfz2rX7E7gzYhYQAr2sqQ3gIdIo9F5kh4jjb47nRqX9DbwKmkq+T5gHvA8cBLw/YiYT5qCnku6h/3tvM8zgLM66PJk0n3kBaSR+GPAb4FJEfEw8HXSqL6rc/LfwLLc5pfAf0laAnwJ+I88w3ACaTbhR8DteZ87A0NzNw8BNwKb5D7L+RhmR8QvSLMQX+uqFjMz61mlsh9ZZT2ovr6+vGTnibUuw8y6yQ9X6RsiYrGkvdovL9I97V4REcOBKzpYNUvStTWoZzZ5BNzGa5KO7O1a1kS57H8EzIqksamZwXWDal2GdcKh3Y6kR0j3lvsESfW1rmFtlEpdb2NmfYcDu28r1D1tMzOzgcyhbWZmVhAObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObetR/j4as2JpbGqudQlWgZ89bj2qVILtp86pdRlm1k3+gp++zSNtMzOzgnBom5mZFUSPhHZENETEET3Rt1UWEbdExLqr2eboiBgaEVtFxPSeqs3MzNZOj9zTljSzJ/q1rkkatwbNzgCmSFoKnFLlkszMrEq6DO2IaABOJI3KrwHOBJqBhZKmRsSvgWMkPRcRxwIjgFXACknXRcSlwEG5/ZXAM8A0SYdHxHHAVEkfi4gRwHhJkzup42JgdO7nZklXRcQ+wLeAErAcOAHYJdfZDDQCk3KbO4GVwFzgLuDq3G5lPr51gVl52zpSiD3eSS1bADOBjXIf44GXgR8BQ/J5PV/SfRHxOPAgsDsg4M/5fLwNfAo4D/gQsBmwCTAd+CywM/AFYAVwi6R9875/CYwDGoAdgC2AYcBZku6JiOfyOdgW+F4+rr/lNlvmv4N1cu2nAxsDHwdujIjPATdK2jcixgDT8jlsPUcfB84B3sn7niXp4o7OkZmZVV93p8dXAUcAFwCfkDQC2Cb/w34DKbQgBcmM1kYRMRbYQdIBwMGkgHoOGBYRg4FPAuWI2DL3P7tCDeOB40mB91Zedj0wQdI+wM+BXfP+T5M0khSAV+ZttwIOlXRF3uZUSaNIIX42MBx4DRhLCrMhFWo5D7hD0v759XDgfOBnkg4CjgVuiIh1gPcDP87LDwQeyq/XBXbL/b0l6ZP5+D8l6TPAZaSgreRtSWNJI+Wz2q37T+BSSfsB3wX2yPv7N0mH5PMyQdIc4FHS+X0HICJK+dzW5/M4Px8fpAuEzwL75fNmZma9pLuhLdJocHNgbkQ8AHwY2BG4CTgmIoYCQyQ90abd7sCeefu7SSPYYcA9wCjSaPAm4BBSGN9boYZxwKW57UZ52ZaSngKQNF3SEmCopEfz+gd5LxiXSXonv94VmJ7rOhEYShp9zwduBy4CWirUEsDDeb/3Sbop9/lgXrYceD2fL4Al+edfgN/l16uAwd1c31apzevf5J8vdLBt2xpvlTSPNBvx5Yj4IXAM6e+jI5sBr+fjgP99Hh+X9K6kv/LexZOZmfWC7oZ2C7CMFA5j8gj1GmCRpNeBxcA3gR+0a7cUuD9vPxq4FXgWuA2YCjxGCuHTgGckNXW084hYjzR6PS730xARw4A/RcROeZtzIuLovOyjuelI4Ok2x9BKpKn4UaTR4hzSRcSLkg4lTQtfUuF8PAXsnfd7UERcnpcdmJdtQ5p2Xpm37+oRI5XWNwJbRMSgiNiINC3dnXZtazwhIv6VdEvgAklfAB7nvQuAFv73fwuvAEMiYuv8vu159ONSzMxqpNsfRJP0ckRcCcyPiEGkae5b8+oZpJH0ie2a3QmMiogFwIbAbZLeiIiHSCPBKyQ9lgP4igr7fjsiXiVN464C5gHPAycB34+IFuBF4Kpc17fzFO+7wMQOujyZdA93UH4/kRSwsyLiTNL98IsqnI5L8n4/RwqxiaRR8vcj4hhgfWCypHcjokI3XZO0IiJ+BvwK+H3+0x1fAr4bEeeT7ml/jjSyvj0i/gz8kTSiBngIuBGYnPdZjohJwOx8bleRbn18ZK0OxszM1kqp7OdMWg+qr68vL9m5o+smM+uL/ES0viEiFkvaq/3yPvUY04gYTscj7lmSrq1BPbNJn+hu6zVJR/Z2LUVVLvsfAbMiaWxqZnDdoK43tJroU6Et6RHSveU+QVJ9rWsoulKp623MrO9wYPdtfoypmZlZQTi0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0rUf50fZmxdLY1FzrEqyCPvUYU+t/SiXYfuqcWpdhZt3k7wro2zzSNjMzKwiHtpmZWUH069COiIaIOKLWdfSmiLgwIqZUs/1API9mZn1Rv76nLWlmrWvoD3wezcz6hsKEdkQ0ACeSZgeuAc4EmoGFkqZGxK+BYyQ9FxHHAiOAVcAKSddFxKXAQbn9lcAzwDRJh0fEccBUSR+LiBHAeEmTO6njYmB07udmSVdFxD7At4ASsBw4Adgl19kMNAKTcps7gZXAXOAu4OrcbmU+vnWBWXnbOmCKpMc7qWULYCawUe5jfF51ZD4HmwJflnRnfv/Fduess/ZExIeAm4GJQD2wAlgKnAO8A+wAzJJ0cUe1mZlZ9RVtenwVcARwAfAJSSOAbSJiDHAD74VOAzCjtVFEjAV2kHQAcDBwHvAcMCwiBgOfBMoRsWXuf3aFGsYDx5MuAN7Ky64HJkjaB/g5sGve/2mSRgLTSRcKAFsBh0q6Im9zqqRRpBA/GxgOvAaMBU4HhlSo5TzgDkn759fD8/Llkj5BurA5OSI2Ab7awTnrrH0APwaOl/RYu30OAz4L7JfrNTOzXlK00BbwIWBzYG5EPAB8GNgRuAk4JiKGAkMkPdGm3e7Annn7u0kj2GHAPcAoYNvc/hBSGN9boYZxwKW57UZ52ZaSngKQNF3SEmCopEfz+geB3fLrZZLeya93Babnuk4EhpJG3/OB24GLgJYKtQTwcN7vfZJuyssX558rgA3o/Jx11n5sbtfRL2w+LuldSX/lvYsWMzPrBUUL7RZgGfACMCaPUK8BFkl6nRRW3wR+0K7dUuD+vP1o4FbgWeA2YCrwGCmETwOekdTU0c4jYj3gWOC43E9DRAwD/hQRO+VtzomIo/Oyj+amI4Gn2xxDK5Gm4keRRq1zSBcRL0o6FJgGXFLhfDwF7J33e1BEXJ6Xt3+kSYfnrEL7q0ij9BsjYlC7vvy4FDOzGilaaCPpZdJU8/yIWEQaFbYG4oz8fla7ZncCb0bEAlKwlyW9ATxEGm3Oy9PAw6gwNS7pbeBV4FHgPmAe8DxwEvD9iJgP7EGa6p4EfDvv8wzgrA66PJkUjAuAy0gXD78FJkXEw8DXSaP6zlxCun/9AGn6+7ud1N3ZOeu0vaSfA0+S7mGbmVkfUCr7OZPWg+rr68tLdp5Y6zLMrJv8RLS+ISIWS9qr/fLCfHq8N0XEcOCKDlbNknRtDeqZDWzSbvFrko7s7VpWV7nsfwTMiqSxqZnBde3villf4dDugKRHSPeW+wRJ9bWuYU2VSrWuwMxWhwO7byvcPW0zM7OByqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2mZmZgXh0DYzMysIh7aZmVlBOLTNzMwKwqFtPcqPtjcrjsamjr6N1/oSP8bUelSpBNtPnVPrMsysG/w9AX2fR9pmZmYF4dA2MzMriD49PR4RDcCrku6odS29JSIuBFZIuq5afQG/BI6QdNHa9mlmZrXTp0Nb0sxa19AfSHoUeLTGZZiZ2VrqtdDOo+YTSVPy1wBnAs3AQklTI+LXwDGSnouIY4ERwCryqDMiLgUOyu2vBJ4Bpkk6PCKOA6ZK+lhEjADGS5rcSR0XA6NzPzdLuioi9gG+BZSA5cAJwC65zmagEZiU29wJrATmAncBV+d2K/PxrQvMytvWAVMkPd5JLVsAM4GNch/j263/X8cs6ScRMRK4IG+yQW7zDnAz8ALwQeARSSe36WdUrmNcRDwD/AII4M/AZ3P/1wE75dfnS3qgk3N1CvAFoCX/3X2po2MzM7Pq6+172quAI0ih8wlJI4BtImIMcAPvhVYDMKO1UUSMBXaQdABwMHAe8BwwLCIGA58EyhGxZe5/doUaxgPHk8LwrbzsemCCpH2AnwO75v2fJmkkMJ10oQCwFXCopCvyNqdKGkUK8bOB4cBrwFjgdGBIhVrOA+6QtH9+PbzSMUfERsBuwOckjQbuAI7NTXYGJuY+PhURW3Wyzx2BL0vaD9gc2Bv4F+AVSQcBRwLfqXCuJgBn5PbPRkSfnq0xM+tPevsfXAEfIoXF3IgAeD8pSG4CFkbE94Ahkp6IiGNyu92BPSPigfy+DhgG3AOMArbN7Q8hBcx5FWoYB1xKCt+78rItJT0FIGk6QEQMzdPKAA8Cl+XXyyS9k1/vCkzPx1EHPJ373Am4HWgCplWoJYDv5/3el/d7YRfHvBy4OiLeBLYhjZoBfi/pjdzHi8DgTvb5iqQX8usX8na7AwfmGQeA90XEpnR8riYA/x4RlwMPk2YIzMysF/T2SLsFWEYKizF5hHoNsEjS68Bi4JvAD9q1Wwrcn7cfDdwKPAvcBkwFHiMF+GnAM5KaOtp5RKxHGpkel/tpiIhhwJ8iYqe8zTkRcXRe9tHcdCQpkFuPoZVIU/GjSKPsOaSLiBclHUoK7EsqnI+nSCNdIuKgHIRdHfP3SLMCDcCfeC80u/sYk462W0qa/h5FmiH4CfAmHZ+rSaSp9pHAHsD+3dyvmZmtpV7/lS9JL5OmmudHxCJSSLQG4oz8fla7ZncCb0bEAlKwl/Oo8iHSaHWepMdII9FOp8YlvQ28SvpQ1n3APOB54CTg+xExnxREc0nh9O28zzOAszro8mTgxrzNZaSLh98CkyLiYeDrpJFqZy4Bjsyj6a8C3+3GMf9fYFFE/II0SzG0Qv/d9V1gl3z8DwH/U+FcPQ78KiLuA14CFlVh/2Zm1g2lsp8zaT2ovr6+vGTnibUuw8y6wU9E6zsiYrGkvdov75cfIoqI4cAVHayaJenaGtQzG9ik3eLXJB3Z27X0tnLZ/xCYFUVjUzOD6wbVugyroF+GtqRHSPeW+wRJ9bWuoVZK/piaWWE4sPs+P8bUzMysIBzaZmZmBeHQNjMzKwiHtpmZWUE4tM3MzArCoW1mZlYQDm0zM7OCcGibmZkVhEPbzMysIBzaZmZmBeHQth7l76MxK47GpuZal2Bd6JfPHre+o1SC7afOqXUZZtYN/nKfvs8jbTMzs4IofGhHRENEHFHrOszMzHpa4afHJc2sdQ1mZma9oU+FdkQ0ACeSZgCuAc4EmoGFkqZGxK+BYyQ9FxHHAiOAVcAKSddFxKXAQbn9lcAzwDRJh0fEccBUSR+LiBHAeEmTO6njYmB07udmSVdFxD7At4ASsBw4Adgl19kMNAKTcps7gZXAXOAu4OrcbmU+vnWBWXnbOmCKpMc7qeX/ANcCg4FNgYsk/TQiDgcuyJv9BpgCfKqDZc8Cu0hqjIjLgKXAc8DlwDvA9cBbwKm5RoBjgFdz3cNzvRcA+wDLJX0nIjYGfi5pz47qNjOz6uuL0+OrgCNIIfEJSSOAbSJiDHADMD5v1wDMaG0UEWOBHSQdABwMnEcKp2ERMRj4JFCOiC1z/7Mr1DAeOJ50AfBWXnY9MEHSPsDPgV3z/k+TNBKYTrpQANgKOFTSFXmbUyWNIoX42aQgfA0YC5wODKlQyy7ANySNAU4DTo2I9wHfBj4taW/gj8D/6WRZZwZLOlDS/wV2zu1GAQIOA44ENpM0PJ+7vYHv8d75Px64qUL/ZmZWZX1qpJ0J+BCwOTA3IgDeD+xIComFEfE9YIikJyLimNxud2DPiHggv68DhgH3AKOAbXP7Q0hhfF6FGsYBl5LC9668bEtJTwFImg4QEUMlPZrXPwhcll8vk/ROfr0rMD0fRx3wdO5zJ+B2oAmYVqGWF4HzI2IiUM59bAaskvRSrueiiNiq/bJcY9u+Sm1eq83rl4AfRsSbpIuEh4HIP5G0Ajg/9/dGRHyYNNPgzxKYmfWivjjSbgGWAS8AY/Lo7xpgkaTXgcXAN4EftGu3FLg/bz8auJU0NXwbMBV4jBTgpwHPSGrqaOcRsR5wLHBc7qchIoYBf4qInfI250TE0XnZR3PTkaRAbj2GViJNxY8ijbLnkC4iXpR0KCmwL6lwPr4G3Cjp88D9pOB9CdgoIjbJ9VwNbNd+WUQMJ03bbx0RJeDjbfptydt9APgq6ULlX0gzCyXgKdLomoj4QETck9vNIAX4HyW9UqFuMzOrsr4Y2kh6mTTVPD8iFpGmkVsDcUZ+P6tdszuBNyNiASnYy5LeAB4ijRrnSXqMNPrudGpc0tuk+7mPAvcB84DngZOA70fEfGAP0lT3JODbeZ9nAGd10OXJwI15m8tIFw+/BSZFxMPA10mj+s78BLg6tx9DmrJuAU4B5kTEQlLI/qqTZVfkWueSbj209zrwC2AJsIAU2kOBO4BVua97gKvy9rflOm6oULOZmfWAUtmPrLLVEBEbAPOBffLFQ0X19fXlJTtP7PnCzGyt+eEqfUdELJa0V/vlffGedq/IU8dXdLBqlqRra1DPbGCTdotfk3Rkb9fSmYjYH/gucF53AhvSY0z9D4FZMTQ2NTO4blCty7AKBmxoS3qEdG+5T5BUX+sauiLpIdIH/rqtVOp6GzPrGxzYfV+fvKdtZmZmf8+hbWZmVhAObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObetR/j4as76lsam51iXYWhiwzx633lEqwfZT59S6DDPL/AU+xeaRtpmZWUF4pF1BRDQAr0q6o9a11FJETAZ+ABwATJE0rsYlmZkNSA7tCiTNrHUNfcS5wI21LsLMbKAbMKGdR80nkm4JXAOcCTQDCyVNjYhfA8dIei4ijgVGAKuAFZKui4hLgYNy+yuBZ4Bpkg6PiOOAqZI+FhEjgPHAD4FvAE25nxMkvdFJbQ8AS4FdgBLwz5JWtN+npJ/kbV8GNgYOk/R3nyrJ2/wW+AjwJrAAOAzYCDg0L/s+8EFgUO57Vm73aG43BDgWOATYCrgFuArYKSLuArYA7pR0YZcn38zMqmKg3dNeBRwBXAB8QtIIYJuIGAPcQApbgAZgRmujiBgL7CDpAOBg4DzgOWBYRAwGPgmUI2LL3P9s4Kj8cyQpIDfuoraHJI0CZgHndrTPiNgob/tjSYd0FNhtPCLpE8B6wN8kjQF+l+s5CXhF0v6kUJ4WEZu1aXcI8DPgOEk3ACuA1inxwfnYDgRO6+KYzMysigZaaAv4ELA5MDePLD8M7AjcBBwTEUOBIZKeaNNud2DPvP3dQB0wDLgHGAVsm9sfQhoZ3wtcQhqN3gscQxpxV3Jf/vkQEBX22XocXVmSf/6FFNaQLloGA7sCDwLk0f/vSKNugN/kny/kbdt7QtLbkv4GvNuNOszMrEoGWmi3AMtIgTQmj2yvARZJeh1YDHyT9KGrtpYC9+ftRwO3As8CtwFTgcdIAX4a8IykJuAEYKakg4Engcld1LZn/nlA3r6zfbYeR1cq/Yb0U6SRMhHxftIFwrIK7Vp4778V/+a1mVmNDLTQRtLLpHvS8yNiETAWeDqvnpHfz2rX7E7gzYhYQAr2ch6hto6K50l6jDQSnp3b/Ar4YUTMJ4VuVx/kasjbfhq4uMI+q+F6YNOIWAg8AHxV0ksVtl8AzCXdbzczsxoplf3IqprLU+BTJC2tdS3VVl9fX16y88Ral2FmmR+uUgwRsVjSXu2XD5hPj9daRGxHx6Pt+dXuT9IFa9KnmZn1bQ7tXiLpedKH1vpkfz2lXPaVvVlf0tjUzOC6QbUuw9bQgLunbb2r5LvgZn2KA7vYHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dC2HuXvozHrWxqbmmtdgq0FP3vcelSpBNtPnVPrMsws83cBFJtH2mZmZgXh0DYzMysIT4/XSEQMAuYCuwHnSfphN9rsDmws6cEq1/JJYDtJ11ezXzMzqy6Hdu1sDWwm6f+sRpvPAiuAqoa2pLur2Z+ZmfUMh3btXA/sFBHfBX4DLAUuB97J63YBRpNuYdwM/ARoAN6JiCWSHmnfYUSMatfH88DFQDPwB+AkYBbwLUnzI2Jv4HzgNmAXSVMj4l+B44EycAtwE3CvpI9HxH7AHGBz0kXHDZIOq/J5MTOzTviedu2cAvwOeLHNssGSDpT0f4HxpPA8CHhL0nJgJnBlR4Hdvg/gR8AMoF7SSGA5KfRnAF/I27a+ByAiPgz8MzAi/zkK2AxYGRHbAp8EXgD2BI4ghb2ZmfUSh3bfojavxwGXAvcAG61BH62j4Vsj4gHgUGC73N/wiNgEOBC4q03bjwDDgHuB+4BNgQ+RwvlTwP6kkfwY4NPAT1ejLjMzW0sO7b6lBSAi1gOOBY4jTZE3RMSwvL6rv7OW/PMV4I/AkZJGkabJ75fUQppqvxb4qaS2T1oQ8CRwcG4zE3icFM7HA6+TQv4oYD1JK9b4SM3MbLU5tPsgSW8DrwKPkka880j3pxcDp0XEwd3oowU4A5gTEQ+RpuOfyKu/D9Tnn23b/JY0yl4YEb8GdgKWS/ojMJh0b3sV8C7p3raZmfWiUtnPmbQeVF9fX16y88Ral2FmmZ+IVgwRsVjSXu2X+9PjBRQRXyFNm7c3QdKy3q6nknLZ/0iY9SWNTc0MrhtU6zJsDTm0C0jSRcBFta6jO0qlWldgZm05sIvN97TNzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2mZmZgXh0DYzMysIh7aZmVlBOLTNzMwKwqFtZmZWEA5t61H+PhqzvqWxqbnrjazP8rPHrUeVSrD9VH+Lp1lf4S/wKTaPtM3MzArCoV1DEdEQEZetxvaDI+JfeqiW2T3Rr5mZVY9Du1i2AnoktCXV90S/ZmZWPb6n3QdExKXAXsD7gackTYiIA4BvAE3AKuAE4DzgwxHxlfyd2h319QTwNPA2MAW4Adg0rz4d2AE4WtKEvP1vgMOAxyRtFRG7A1cDJWAlcCLwQ2CapF9HhICpkm6LiHnABEnLq3xKzMysAx5p1966wCpJY4D9gX0jYhvgKGA2MBL4PrAxcDHwu84CO9sQ+Jqk44BzgXslHQxMBq4F5gD7RcQ/RMTewB8kvdSm/QzgVEmjgLnA2bmOsRGxA9AIjImIDwCDHdhmZr3HI+3aKwNbRMTNwJuk0K0DLiGNrO8FlgOLgPW62afyz92B0RHxz/n9xpKaI+K/gHpgP1JIt7UrMD0iyHU8DfwncDvwCnA58EVgLHDnah2pmZmtFY+0a+9gYNs2I+P1SVPTJwAz8yj5SdJIuYXu/Z215J9LgW/mUfM/ATfl5TcAnwf2BX7Wrq2A8bnN2cAcSauAvwH/DNwNPA+cSRqBm5lZL3Fo194jwI4R8Uvgv4BngaHAr4AfRsR8YDRwI/ASsG5EXN7Nvi8G/ikiHiCF7RMAkpbl9T+V1NKuzcnAjRGxALgMeCwvvx3YQNKrwD3A+pL+sLoHa2Zma65U9iOrrAfV19eXl+w8sdZlmFnmh6sUQ0QslrRX++W+p11AETEcuKKDVbMkXdvb9VRSLvsfCbO+pLGpmcF1g2pdhq0hh3YBSXoEGFXrOrqjVKp1BWbWlgO72HxP28zMrCAc2mZmZgXh0DYzMysIh7aZmVlBOLTNzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2mZmZgXh0LYe5e+jMetbGpuaa12CrQU/e9x6VKkE20+dU+syzCzzF/gUm0faZmZmBTGgQzsiGiLiiFrXUUsRsUlEHJ9fT81f+2lmZn3QgJ4elzSz1jX0AR8FjgB+LOmyWhdjZmad6zehHRENwImk2YNrgDOBZmChpKkR8WvgGEnPRcSxwAhgFbBC0nURcSlwUG5/JfAMME3S4RFxHDBV0sciYgQwXtLkTuq4GBid+7lZ0lURsQ/wLaAELAdOAHbJdTYDjcCk3OZOYCUwF7gLuDq3W5mPb11gVt62Dpgi6fFOarkQ2B/YEJgIjAf2At4PPCVpAnAe8LGImJy3vQXYCvgUsAHwQeBySTPzKPw7wBvAS0CjpIYKfy1mZlZF/W16fBVp1HgB8AlJI4BtImIMcAMptAAagBmtjSJiLLCDpAOAg0lB9hwwLCIGA58EyhGxZe5/doUaxgPHky4A3srLrgcmSNoH+Dmwa97/aZJGAtNJFwqQAvNQSVfkbU6VNIoU4mcDw4HXgLHA6cCQLs7JU5L2J10srJI0hhTO+0bENsDFwH2Srm/X7gOSDs/HOzUvuw5okDQa+EMX+zUzsyrrb6Et4EPA5sDciHgA+DCwI3ATcExEDAWGSHqiTbvdgT3z9neTRrDDgHuAUcC2uf0hpDC+t0IN44BLc9uN8rItJT0FIGm6pCXAUEmP5vUPArvl18skvZNf7wpMz3WdCAwljb7nA7cDFwEt3TgnkC4gtoiIm4HvkkbfdRXatdb2AjA4vx4q6cn8ekEX+zUzsyrrb6HdAiwjBc2YPEK9Blgk6XVgMfBN4Aft2i0F7s/bjwZuBZ4FbiONMh8jhfBpwDOSmjraeUSsBxwLHJf7aYiIYcCfImKnvM05EXF0XvbR3HQk8HSbY2gl0lT8KNIoew7pIuJFSYcC04BLunFOII3Mt5V0HHAusD5p2r2Fjv876Og3rF+IiA/n1/t2sV8zM6uyfnNPu5WklyPiSmB+RAwiTXPfmlfPII2kT2zX7E5gVEQsII1Ab5P0RkQ8BARwhaTHcgBfUWHfb0fEq6RR6ipgHvA8cBLw/YhoAV4Ersp1fTsiSsC7pHvO7Z0M3JiPg7zNSmBWRJxJuh9+UTdOC8AjwJcj4pfA26SLkqGkae7dc39dOSUfx5vAO6QpdzMz6yWlsh9ZZd0UEacCt+YLo2nAO5IqXjTU19eXl+zc0fWImdWCH65SDBGxWNJe7Zf3u5F2b8ifou5oxD1L0rU1qGc2sEm7xa9JOrLKu/ozMC+PtF8DvlDl/s3MrAKPtK1HHX10ffm22yp92N7MelNjUzOD6wZ1vaHVVGcj7f72QTTrY0qlWldgZm05sIvNoW1mZlYQDm0zM7OCcGibmZkVhEPbzMysIBzaZmZmBeHQNjMzKwiHtpmZWUE4tM3MzArCoW1mZlYQDm3rUX31KbmNTc21LsHMbLX5C0OsR5VKsP3UObUu4+/4m47MrIg80jYzMysIh3YfFBENEXHZarYZHBHP9VBJZmbWBzi0zczMCsL3tPuu/SLiXmAIcCHwJnAx0Az8ATgJWA+4CdgY+H1rw4h4AHg5L/808D3gg8Ag4EpJsyJiD+Ca3F8jMIl0ETcLeAHYHrgF+AiwBzBH0rkRcQrwBaAFWCjpSz11AszM7H/zSLvv+itwCCl0vw3MAOoljQSWAw35zxOSDgK+2679jyUdQgrjVyTtn/ubFhGb5f5Oy/1NB67M7XYEJgKHA18Dvgjsk5cBTADOkLQf8GxE+MLPzKyXOLT7roWSypJeAt4CtgVuzaPoQ4HtgN2ARwAkLQKa2rRX/rkr8GDe5g3gd6RR91BJj+ZtHsx9ATwr6TXgL8CfJb0qqRFo/eWtCcCUiJgPDANKVTxmMzOrwKHdd+0NEBFbAYOB54AjJY0iTZPfDywF9svb7QHUtWnfkn8+BRyYt3k/sDuwDPhTRHw0bzMSeDq/7uo3qycBU/IIfQ9g/zU6OjMzW22e2uy71o+I+4ANSUE5CJgTEesArwPjSSPkH0TEQlKAv91BP9cDM/I26wNflfRSREwCvh0RJeBd3pv+7srjwK8i4mXSNP2iNT5CMzNbLaVyX31klfUL9fX15SU7d/d6oPf44Spm1pdFxGJJe7Vf7ulxMzOzgvD0uPWocrlvjmobm5oZXDeo1mWYma0Wj7StR5X66GfLHdhmVkQObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEQ9t6VE98iVxjU3P1OzUzKwB/YYj1qFIJtp86p6p99sUvIDEz6w0eaZuZmRVEvwrtiGiIiMvWov3kiKirsH5qRAxvt2xwRDy3BvvaLiI+k19fld9vEhHHr3bhayAitoqI6fn1URHxREScHhGzV6OPFT1XoZmZtefp8f/tXOBGoKmjlZLW+IKgA6OBXYA7JZ0JEBGjgCOAH1dxPx2StAI4Jb89HPgPSXcCV/f0vs3MbM30x9DeNyLmAZsD1wLLgGlAI7ASOBGoA2aRZhrqgCnAcGAr4BbgqI46joiZef1C4CZgY+D3bdbvTgq9Upt97QGcA7wD7JD3exkwFdggIh4CvphrOA/4WERMBs4Ghkt6NSJOBjaU9PUOajoCOFrShPz+N8BhwMjcbzOwUNLUiLgQ2B/YEJgI/AC4hBTawyPiFeA2SVt1cixvAtcDuwF/ANbr7C/BzMyqr19Nj2dNpNA6GjiLFDL1kkYC84HzSQH9GjAWOB0YIukGYAUwrhv7aACekHQQ8N02y2cAp0oaBcwlBS/AMOCzwH7A2ZKaScH9Y0l3tGl/MXCfpOtJFwWttXyeNAPQkTnAfhHxDxGxNylM3wW+CnxC0ghgm4gYk7d/StL+wFsAef9357oe7uJYxgKDJe0L/AewQVcnyszMqqc/hvYSSWVSAG8HvC5peV73IGmUeBcpwG8HLgJaVnMfuwGPAEhaxHvT6bsC0yPiAdLIdGhe/rikdyX9lRyW3XAD8PmI+AiwQtKfO9ooXwD8F1APTCCF7YdIMw1zcy0fBnZsbdLN/Xd0LG2P+3nghW72ZWZmVdAfQ7vtbwa/AgyJiK3z+5HA08Ao4EVJh5Kmzi/J61vo3jlZSho1ExF7kKbYIQXi+Dw6PZs0Cm5fU6uO9vX/luVQ/AtpyvyGLuq5gTQa3xf4GemWwAvAmFzLNcCiNvvojo6Ope1xDwW26WZfZmZWBf0xtNsqA5OA2RHxC+AQ4GvAb4FJEfEw8HXg0rz9AtLotNRFv98hTTkvBE4F3s7LTwZujIgFpOnvxyr08ThwZES0nY7/A7B7RJyZ388ADiRNX3dK0rL88qeSWiS9DFwJzI+IRaRp7ae7OKb2/u5YJN0OvJD7vIp0UWRmZr2kVO6JR1ZZVUTEPwEfkfSVWteypurr68tLdp5Y1T79cBUz6+8iYrGkvdov74+fHl8rEbEuMK+DVZJ0Ui/WcQlplH1kfn8E6dPg7X1L0m29VdfqKperH7KNTc0MrhtU1T7NzIrAod2OpHdI97xrXce57d7fAdzRyeZ9VqmrGw1rwIFtZgNVf7+nbWZm1m84tM3MzArCoW1mZlYQDm0zM7OCcGibmZkVhEPbzMysIBzaZmZmBeHQNjMzKwiHtpmZWUE4tM3MzArCoW09qie+j6axqbn6nZqZFYCfPW49qlSC7afO6XrD1eBv+TKzgcojbTMzs4LolyPtiGgAdpE0dQ3bTwZ+IKmpk/UzgVsk3b3GRVZZRNwI7AzMBFpIXy96i6R9q9D3x4EjJF20tn2Zmdma65ehXQXnAjcCHYZ2H3WYpC1b30TE9tXqWNKjwKPV6s/MzNZMfw7tfSNiHrA5cC2wDJgGNAIrgROBOmAW6TZBHTAFGA5sBdwCHFWh/5Mi4mzgA8DJkh6JiEuBvYD3A09JmhARBwDfIF0ArAJOkPRGRx1GxAPAUmAXoAT8c359OfAOcD2wooPjuATYOCJuB27Lba5r0+9I4GKgGfgDcFKFWYTOapgiaVxETAROBgYBt0u6sMI5MjOzKurP97SbgMOAo4GzSIFXL2kkMB84nxTQrwFjgdOBIZJuIAXjuC76XyxpNHAN0BARQ4BVksYA+5MuGrYhBf9sYCTwfWDjLvp9SNIo0sXEuXnZYEkHAj/q6DgknQK8KunI9p1FRAmY0abNcqBhDWogIrYApgIHAnsCH4iIDbvoy8zMqqQ/j7SXSCpHxApgO+D3kpbndQ+SRqdnAzsBt5NCftpq9L84/1wBbAC8BWwRETcDbwIbkkbvlwDnAfeSAnNRF/3el38+BLSGsPLPzYDXOziOSjYHtgZujQiA9Un3u1e3BoAdgSckvZXfn9VFP2ZmVkX9eaTd9jeEXwGGRMTW+f1I4GlgFPCipENJgd0agC10fW7a/wbyWGBbSceRRqfrk6aXTwBmSjoYeBKY3EW/e+afB+TtW+updByVvAL8ETgyj54vBu5fgxogTa3vEhHrAUTEf+XZBDMz6wX9eaTdVhmYBMyOiBbSveWGvHxWRJxJut/b+unoBcDciDhYUncfD/II8OWI+CXwNvAsMBT4FfDDiHiTdF+6q9BuiIgvAn8FPg/s3roizxx0dBydktQSEWcAcyJiHeB1YPya1CDp5Yi4HJgfEWXgzjajfjMz62Glck88ssrWSP4Q2BRJS/tLDfX19eUlO0+sRlf/jx+uYmb9XUQslrRX++UDZaS92iJiXTq+9ytJJ61Fv9uRfp2svflr2mdfrqFcrn7INjY1M7huUFX7NDMrAod2JyS9Q7rnXe1+n++JfvtqDaVS9ft0YJvZQNWfP4hmZmbWrzi0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbT2qJ76PprGpufqdmpkVgJ89bj2qVILtp86pap/+li8zG6g80jYzMyuIPhPaEdEQEZetRfvJEVFXYf12EfGZtexjZkR8ssL6ByJil3wsR3S1n4j4eER8pVJNfVVX58rMzKqvz4R2FZwLVPrOxtHAAWvZR7dIminpjq72I+lRSRet7f5qpCrnyszMuq+v3dPeNyLmAZsD1wLLgGlAI7ASOBGoA2aRLjjqgCnAcGAr4BbgqPadRsQgYCqwQUQ8BLwAXAM0574nAWNa+4iIzwLfBbYFNgXukvTl7h5ERFwIrAD+v0q1RsRVwBRJ4yLiGeAXQAB/Bj4LrAvcCAzNNR8kaWhEnAJ8AWgBFkr6UoVaJgInkwL2dkkXRsQJwJnA28AzwGTgBGAXSVMjYjCwVNL2EfEA8CjwEWAIcCxwCBXOt5mZ9Yy+NtJuAg4DjgbOAq4H6iWNBOYD55NC7zVgLHA6METSDaSQHNdRp5KagcuAH+cR8AzgtNzvdODKdn1sC/xS0mHACFLorYnVqXVH4MuS9iNdtOxNCtNlkg4ALgS2zNtOAM7I2z4bER1efEXEFqSLlQOBPYEPRMQw4KvAaEkjgL8AJ3VxHI9IOgT4GXBcV+fbzMx6Rl8L7SWSyqRA2A54XdLyvO5BYDfgLlKA3w5cRBptrq6hkh5t129brwJ7R8RNwDeB9dZgH7B6tb4i6YX8+gVgMLAr8BCApKXAy3n9BGBKRMwHhgGlTvrcEXhC0luSWiSdBWwBPCnpjbxNR8ffvr/ftKvLzMxqoK+Fdtvf6n0FGBIRW+f3I4GngVHAi5IOJU2dX5LXt1D5eNqu/1NEfLRdv223aQD+IukE4BukafXOgrGS1am1o99ofgLYDyAiPghslpdPIk2rjwT2APbvZP9/AHaJiPVyH/8FvAR8OCL+IW/TevyNQOu5/sdu1NbV+TYzsyrry//olknhNDsifkG6j/o14LfApIh4GPg6cGnefgEwt0K4Pg4cGRHjcr/fjogFwBmkqfj/1wdwL/CpfP/7WtJ936FrcAwVa6XzEXKrG4DtI+JB0vR4Y5tj+VVE3EcK4UUdNZb0MnA5MD/XsETS/wAXAPdHxC9JFwLXAnfnfS0E/gl4vYvaujrfZmZWZaVyTzyyyqoiIvYHNpQ0LyJ2Au6W9MFa17U66uvry0t2nljVPv1wFTPr7yJisaS92i/va58eXysRsS4wr4NVktTVh626u4/tSJ/obm++pAuqsY82ngVujogLSJ8+P7WTmo4AvtjBqm9Juq3KNZmZWY14pG096uij68u33Ta7qn02NjUzuM6/Im5m/VdnI+2+fE/b+oFSD9zxdmCb2UDl0DYzMysIh7aZmVlBOLTNzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2mZmZgXh0DYzMysIh7b1qJ54Sm5jU3P1OzUzK4B+9YUh1veUSrD91DlV7dPf8mVmA5VH2mZmZgUxoEbaEdEA7CJp6hq2nwz8QFJTJ+tnArfkt9tJur6T7Y4GFgEtwFcknbIm9dRS6zFI+lOtazEzGygGVGhXwbmk79LuMLRbSbq7i37OAKZIWgoULrCzM4ApgEPbzKyXDMTQ3jci5gGbA9cCy4BpQCOwEjgRqANmkW4f1JHCaTiwFWkkfVSlHbSO6IELgVuBDwDrA2cD/wB8HLgxIj4H3Chp34h4DJgPfBQoA0cCrwPfAfYCVgA7AJ8B/hE4h3Tx8BwwXlJLJ7UcDlyQ3/4mH8snOjjmj5MuJMbldiskbZVnD94Gtge2Bhryz9ZjGCHpnUrnw8zMqmMg3tNuAg4DjgbOAq4H6iWNJIXm+aSAfg0YC5wODJF0Ayk4x63Gvj5ICvrPAMcDG0iaAzwKjAfaht0Q4OZcx/K87yOATSUNByYC2+ZtjwO+KWkEMC+3/TsR8T7g28CnJe0N/DH30dExV/I/kg4DrgEmtz0GB7aZWe8ZiKG9RFKZFMDbAa9LWp7XPQjsBtxFCrPbgYtI955Xm6QnSSPlm4HpdH2+f5N/vgAMBnYFHs59vQwszeu/CBwUEfOB/SvUtxmwStJLuY+LgL/R8TG3V6pQl5mZ1cBADO22vzn8CjAkIrbO70cCTwOjgBclHUqaRr4kr29hNc5ZROwOvF/Sp4EvkEaqlfpp/1vNTwD75b42BnbOyycDF+aRcok0a9CRl4CNImKT3MfVwI50fMyNpGlvImIYsEmFuiodg5mZ9ZCBeE+7rTIwCZgdES3AKtI92zIwKyLOBJpJo22ABcDciDg4j9a78gxwQUS0ToV/JS9/iPSBtsldtJ8DjI2Ih0gzA38jTe8/AvwsIlYCbwD/3VFjSS0RcQowJyKaSSPmRzo55r8Af4mIRcBTpHv9lTxEuqd9qKRXu9jWzMyqoFTuiUdWWVVExC7AxyXdEhGbAk8CwyS9XePSuq2+vr68ZOeJVe3TD1cxs/4uIhZL2qv98oE+0l5tEbEu6cNf7UnSSVXe3QvA5XnEPwg4p6PAjojhwBUdtJ8l6doq12RmZjXikbb1qKOPri/fdtvsqvbZ2NTM4LpBVe3TzKwv6Wyk7Q8SWY8qlbreZnU5sM1soHJom5mZFYRD28zMrCAc2mZmZgXh0DYzMysIh7aZmVlBOLTNzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2tajeuLR9o1NzdXv1MysAPwtX9ajSiXYfuqcqvbpr+Y0s4HKI20zM7OCGPChHRENEXHZWrSfHBF1FdZPzd933adExIURMWUt+zitWvWYmVnXBnxoV8G5QKffFSnpMkmP9GI9ven8WhdgZjaQ+J52sm9EzAM2B64FlgHTgEZgJXAiUAfMIl3o1AFTgOHAVsAtwFEddRwRM/P6Z4GZQBPwLjBe0vJO2tQD5+RtnwPGA18BVki6LiJ2Aa6TNCoifgcsAHYDXgWOA94BrgN2yvWeL+mBiHgCeBp4GxBwZEQcC2wKfFnSnRFxAnBm3uYZYDKwQ/vagQZgk4iYLumUrk6wmZmtPY+0kybgMOBo4CzgeqBe0khgPmlEORx4DRgLnA4MkXQDsAIY1419jAEWA4cAFwMbV9j2OOCbkkYA84AhFbbdALgpb7sUOAn4F+AVSQcBRwLfydtuCHxN0nH5/XJJnyCF9MkRsSnwVWB07u8vub+/q13SxcCrDmwzs97j0E6WSCqTAng74PU2o+AHSaPYu0gBfjtwEdCymvu4AXgFuBs4jTRi7cwXgYMiYj6wfwf7KrV53STpwfz6ISCA3YFPRcQDwP8HvC8HMqQRdqvF+ecKUvjvCDwp6Y28vPXYV6d2MzPrIQ7tpO1vE78CDImIrfP7kaQp5VHAi5IOJU2dX5LXt9C983gksCCPbH9Cmv7uzGTgwjzSL5FmABqB1pr+sc22dRHxsfz6AOBJ0oj7ZkmjSDMDPwFWtam3Vfvfol4GfDgi/iG/bz32zmovYWZmvcb3tP9eGZgEzI6IFlLYNeTlsyLiTKCZNNqGdD95bkQcnEfrnfk18KOIeJcUnGdV2PYR4GcRsRJ4A/hv0hT5rRFxEO+NkFudExHbAc/z3ofDZuSR+hBguqSWiKh44JJeiYgLgPvzsf8emAps00ntv4uIH0n6XMWOzcysKkrlnnhklfWaiHgO2EVSY61r6Uh9fX15yc4Tq9qnH65iZv1dRCyWtFf75R5pV0FErEv6wFh7knRStdoUUblc/ZBtbGpmcF2nv2VnZtZvObSrQNI7pHvePdqmk362X9s+elKpB+56O7DNbKDyB9HMzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCAc2mZmZgXh0DYzMysIh7aZmVlBOLTNzMwKwqFtZmZWEA5t61GVvo+msam59woxM+sH/Oxx61GlEmw/dU6H6/xtXWZmq8cjbTMzs4JwaA8wEdEQEZetZpvB+Xu7zcyshhzaZmZmBeF72gPTfhFxLzAEuBB4E7gYaAb+AJwErAfcBGwM/L61YUScAnwBaAEWSvpSr1ZuZjaAeaQ9MP0VOAT4NPBtYAZQL2kksBxoyH+ekHQQ8N02bScAZ0jaD3g2InzhZ2bWSxzaA9NCSWVJLwFvAdsCt0bEA8ChwHbAbsAjAJIWAU257QRgSkTMB4YBpV6u3cxswHJoD0x7A0TEVsBg4DngSEmjSNPk9wNLgf3ydnsAdbntJGBKHpXvAezfm4WbmQ1kntocmNaPiPuADUkhPAiYExHrAK8D44EHgR9ExEJSgL+d2z4O/CoiXiZNpS/q7eLNzAYqh/YAI2kmMLODVfM6WPb5Dtp/D/hedasyM7Pu8PS4mZlZQXikbT2qXO78caWNTc0MrhvUyxWZmRWXR9rWo0oVPlvuwDYzWz0ObTMzs4JwaJuZmRWEQ9vMzKwgHNpmZmYF4dA2MzMrCIe2mZlZQTi0zczMCsKhbWZmVhAObTMzs4JwaFuPKpfT40rNzGztObStR5VKflypmVm1OLTNzMwKwqFtZmZWEA7t1RQRDRFxWTe3PW1129RCREyOiLrVbLN7RBzUUzWZmdnfc2j3rPNrXUA3nQus7o3nzwIf7oFazMysE++rdQEFtV9E3AsMAS4E1gdOBVq/PfoY4CRgk4iYDjwC7BsR84DNgWslXR8RTwBPA28DJwM/yn2+Dzhf0n0RMQaYBjQCK4ETgY8D/5HbbQtcB4wGPgZ8S9K1EXFxXrYOcLOkqzo6kIiYCGwF3AIcFRGXAgfldlcCtwEPAl8FHgXuAz4FNADvRMQSSY+s4Xk0M7PV4JH2mvkrcAjwaeDbwM7ApyWNAgQcJuli4FVJp+Q2TcBhwNHAmXnZhsDXJB1HGpX/TNJBwLHADRExCLgeqJc0EpjPe6P3/0Ma7Z6cl30eGEu6WAAYDxxPCuC3OjsQSTcAK4BxETEW2EHSAcDBwHm5xuOBb5AuKv5d0v8AM4ErHdhmZr3Hob1mFkoqS3oJeI0UyD+MiB8AHwU6uj+8RFKZFJAbtFmu/HNX0ogWScuB14HNgNfze/L63fLrJyQ1AX8B/iDpHWAVMDivHwdcCtwDbNTN49od2DMiHgDuzscxTNJzwEJgi7zczMxqwKG9ZvYGiIitgA+QRs7jgH8hjWpbp8lLbdqUO+mrJf98Cjgw97sNsDHwKjAkIrbO24wkTadX6o+IWI80Wj+ONEXeEBHDKhxPC+m/haXA/XnGYDRwK/BsROwLfIR00fBv7dqYmVkv8T+6a2b9iLgPuIMU1L8AlgALSKE9NG/3u4j4UTf7vAQYHREPAj8FJueR9CRgdkT8gjQl/7WuOpL0NinwHyXdg54HPF+hyQJgLnAn8GZELAAWky4M1gFuIN1L/xLw+YjYK68/LSIO7ubxmZnZWiqVy50O2MzWWn19fXn27Nm1LsPMrFAiYrGkvdov96fHB4iImEz6QFl7/yHp4Z7ab+uzx/0oUzOztefQHiAkXU/6JHqv8rPHzcyqx/e0zczMCsKhbWZmVhAObTMzs4JwaJuZmRWEP4hmPerJJ598JSL+p9Z1mJkVTIcPxPLvaZuZmRWEp8fNzMwKwqFtZmZWEA5tMzOzgnBom5mZFYRD28zMrCD8K1+2xiJiHWA68DHgbeBfJP2+zfrPAF8B3gW+L2lGV236cu15+W+A1/JmyyRN6NXC36uvy/MYERsAPwMmSlraV859rm2168/LCnH+I+I44EygGXgMOCWvKsT576h+SS0FOv+fBaaSvl74eknf60v//a8Nj7RtbRwFDJa0H+l/kG+0roiIOuCbwKHASGByRGxVqU0v67SOzmqPiMEAkkblPzX5Bys7igrnMX/n+YPAB7vbppdVrKWj+oty/iNifWAacLCk/YEPAIdXalMDndbSWf0FOv+DgMuAQ4D9gC9FxGaV2hSJQ9vWxgjgbgBJvwTafvfrrsDvJa2S9A6wEDiwiza9aU1q/xiwQUTMi4j7ImLf3i66ja7O43rA0cDS1WjTm9ak/qKc/7eB/SX9Lb9/H9DYRZvetib1F+L8S2oGdpX0GrApUALerNSmSBzatjaG8N5UGUBzRLyvk3VvkK7YK7XpTWtS+9+A/wQOA6YAN9WodujiPEr6haQXVqdNL1uT+gtx/iW1SPozQET8K7AhaZq/EOe/Qv2FOP8Akt6NiHrgt6QZm6au2hSFQ9vWxuvA+9u8X0fSu52sez/wly7a9KY1qf1p4EeSypKeBlYCW/dCrR1Zk/PYV879mtZSmPMfEetExH8CY4DPSip31aaXrUn9hTn/AJJmA9sA6wLju9OmCBzatjZ+AXwKIE+VPd5m3VPAThGxSUSsCxwEPNxFm960JrWfSL4PFhFDSVfuL/Zm0W2syXnsK+d+TWsp0vn/LjAYOKrNNHORzn9H9Rfi/EfEkIiYHxHrSWoB/gq0VGpTJH72uK2xNp/G/CjpvtEE4B+BDSVd3+YT2OuQPoH9nY7atH4yuAC1rwvMBLYjfSr1HEkP9Xbt3am/zXYPAFPafXq8puc+17Um9Rfi/AO/zn8W5DoBvgXc3r5NXzz/dF7/HApw/vP/v5OBiaRp8ceAf80194nzvzYc2mZmZgXh6XEzM7OCcGibmZkVhEPbzMysIBzaZmZmBeHQNjMzKwiHtpmZWUE4tM3MzArCoW1mZlYQ/z8KzzPwsucNnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_missings.sort_values(ascending=True).plot(kind='barh', figsize=(6,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "roman-affair",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean['host_acceptance_rate'] = df_cols_clean['host_acceptance_rate'].str.rstrip(\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "close-snowboard",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/loredanafattorini/miniforge3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x151b2e070>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Txg9h5gn13PKL19nQ0FyCGkZPgSciMsC9va2Je597i/ecPprxw6o7fU8ikeCfrzyNbK7Ad5988wjX8MhQ4ImIDHC3Pe6kkgmuv2BSl+8bP6yaa6aN4yfPrxmQrTwFnojIAPbHTbt4+OX1zLtgEvW13Z+B+YlZx5MvFLj9twOvlafAExEZwO5atIqKsiQ3zJzco/ePH1bN+88axwMvrmFnUybi2h1ZCjwRkQFq2+5WfvaHdVx15jiG1ZT3uNxHz5tIazbPgy+tjbB2R54CT0RkgLr3ubfJZPNcP2PiIZU7ZUwdZ04Ywn3PvU2hUIimciWgwBMRGYDacnl+9OxbXHTiCI4fWXvI5T987rH8aesefv/mtghqVxoKPBGRAei3yzezZVcrH5l+7GGVf8/poxlSnea+5wfOhegKPBGRAeiBF9cworaCi23EYZWvTKd47+ljePz1Texqaevj2pWGAk9EZIDZ3NjCb30LV585jrLU4e/mZ08dQ2s2z8Jlm/qwdqWjwBMRGWAe/MNacvkCH5g2rlfLOXPCUMYPq+LnL6/ro5qVlgJPRGQAKRQK/PTFtZwzcRiTRwzq1bISiQRXTBnLopVb2byrpY9qWDqR3B7IzJLA7cAUoBW4wd1XFs1/L/CPQBa4y93vCKcvARrCt61y97lR1E9EZKB6ftV2Vm3dwycvPr5Pljd76hi+/duVPPrKBubN6HposqNdVPfDmw1Uuvt5ZjYduA24AsDM0sA3gLOBPcAiM3sE2Ang7rMiqpOIyIB3/4trGFRRxmWnjeqT5R0/spZTRtfx8Mvr+n3gRdWlOQN4DMDdnwWmFc07GVjp7jvcPQM8A8wkaA1Wm9lCM3siDEoREemhxpY2frl0A++dMobq8r5rz8yeOoZX1jawauuePltmKUQVeHXs65oEyJlZ2UHm7QIGA03ArcClwE3AvUVlRESkG//18npa2vJ88Ozxfbrc900ZSyIBP1/Sv09eiSrwGoHiS/uT7p49yLxagu7MFcA97l5w9xXANmB0RPUTERlwHnhxDSeNqmXKuMF9utxRgyuZPmk4D7+8rl8PNRZV4C0CLgMIuyaXFs17AzjBzIaZWTlwIfB7YB7BsT7MbAxBS3BDRPUTERlQlq1v4NW1DXzw7PEkEok+X/7sqWNYva2JV9Y2dP/mo1RUgbcAaDGzxQQnqNxsZnPM7GPu3gZ8DvgVQdDd5e7rgDuBIWb2DHA/MK+oVSgiIl144IU1lJcluXLq2EiW/65TR1OeSvJwP74mL5JjZO6eJzgOV2x50fxHgEc6lMkAc6Koj4jIQNbSlmPBknW8+9RRDKnu+W2ADsXgqjQXnzSCR17ZwD9cdnKvRnAplf5XYxER2c9/v7aBxpZsn5+s0tHsM8aydXcri/vpHRQUeCIi/dxPnl/DscOrmT5peKTrufikkdRWlPHwy+sjXU9UFHgiIv3Yn7bs5rlV2/nAtPEkk31/skqxynSKd506il8t20hLWy7SdUVBgSci0o898OJaUskE7z+rdwNF99TsqWPZ3Zrl12/0vzsoKPBERPqpTDbPgy+t5WIbyTF1lUdkndMnD2dkbUW/7NZU4ImI9FO/XLqBrbtb+fC5E47YOlPJBO+bMoYnfTM7mzJHbL19QYEnItIPFQoF7lq0iskjarjoxMO7q/nhuuKMsbTlCvxy6cYjut7eUuCJiPRDL721g1fXNjDvgkmRn6zS0alj65g8oqbfja2pwBMR6YfufGYVg6vSXHVmNCOrdCWRCE6SeX71dt7csvuIr/9wKfBERPqZFZt28diyjfyP6cf26W2ADsX7zxpHWTLB/S+sKcn6D4cCT0Skn/nmb/5IdTrF9SW8IevI2kr+7OSR/OyltWSy+ZLV41Ao8ERE+pE/btrFL5Zu4LoLJjK0JppxM3vq2nMmsG1Phsdf7x/X5CnwRET6kdsWrqA6neKGGZNLXRUuPGEEY4dU8Z+/X13qqvSIAk9EpJ9YtHIrjy3byF/NOq7krTsIrsmbe8FEnl+1nVfX7ix1dbqlwBMR6Qfacnm++sgyxg+r4oaZpW/dtfvg2eMZVFHGnc+sKnVVuqXAExHpB+58ZhUrNu3mi+85hcp0qtTV2au2Ms21Z4/n0Vc3sH5nc6mr0yUF3lGioSnD2h1NBzwa+tnQPSLS95atb+C2hc6l7ziGvzjlmFJX5wDXXTCRBPDdJ98sdVW6VJoLOOQAu1qzPL1i6wHTLzyxnsER3cFYRI5+zZkcN9//MkOry/mXq04nkeh+VJWGpgy7WrMHTG+N6JY+44ZWc+054/nx829z48zJTBheHcl6ekstPBGRo1QuX+AzP1nCHzfv5tZrpjCshyeqtH+B7vjI5AqR1fVT7zyBVDLB//3NisjW0VsKPBGRo1ChUOB/P/o6C1/fxJcvP4ULj/AA0YfqmLpKrjt/IguWrOO1dQ2lrk6nFHgiIkeZfKHAE8s3c/fi1cy7YBLXXVC6EVUOxScuPp7hNRX83UNLyeaOvtFXFHgiIkeR5kyOR19Zz9J1DfzVrOP40uUnl7pKPTa4Ks1X3ncKS9c1cPfi1aWuzgEUeCIiR4FCocDCZRu557m3WL29iVk2kr9910k9OknlaPKe00bzzpNGcutCZ9n6o6trU4EnIlJC+XyBp1Zs4dr5z/KxH71ERVmSa6eN54zxQ0pdtcOSSCT4t6tPZ3BVmpvueemouiu6LksQETnCcvkCL6zaxmPLNvLrNzazdkczw2vK+eu/OJE1beOoruj5rrmzSxCiuvygp0bUVvDdj5zFB7//ez7+o5f4f3PPLtltjIqVvgYiIgNYoVBgU2MrKzbt4rX1Dbywajsvrt7BrtYsCWBifQ0fmDaOU8cOpiyZZNX2Q7t0oLNreKdOGNJ3G3CYzpwwlFuvmcLN97/MdXe9wF1zz2bQIQR5FBR4JZbPF2huy7GzKUNrNkc6lSTZz/rspfQOdqFxbUWZBi6IWCabZ2dThq27M6zf2cy6nc2s39nM2p3NrNvRzJtbdrOrZd/f5thh1Vx80khOGV1LIpGgtjK93/LyhQKNzW0ArN3RtHd6WRI6u+1cqVtzXbnijLEkEwk+e//LXPHtZ/j2nDM5eXRdyeqjwItYWy7Pmu1NrNq6Z7/H6q172LYnQ2snn+B0KkFVOsXgqjSPv76R40YM4tjh1UyqH8TE+mrGDK4imVQoyj79daSeKIN6x55Wtu4Ovki2ZPO0ZfNkcnnSyQTpshSZbJ7WbC58Dh6ZbJ6Wthyt4XNzJkdLNkdLW57mthytbcHPO5oybN+TobG5jT2ZAwOnPJVkzJBKxgyp4oozxjCitoLG5iyj6iqpCVs5UycMYcnbOw8o25YrsLEhGJOy+G96sPcfDa25rrx3yhiG15TzmftfZvZ3FnHjzMl8/KLJBwT9kRBJ4JlZErgdmAK0Aje4+8qi+e8F/hHIAne5+x3dlTmaZXN5Nja2sHprE6u27mbV3uc9rNnRTC6/r4ticFWaSfU1nDt5OCNrK6gqT1GVTtHclmP5hl1kcsE/5p5MjobmDCu37Gbxm9v2C8bysiTHDqtmYn0Nk+trmFhfw8ThNUyqr+GYuop+d1ZXKfSnFlGhUKA1m6c5k6O5LUdTJhfsjMMdclsuz/Y9GVZs2kUykSCRIHgGhtWk2b4nQ1kySTqVoCyVpCyZoCyVOGAaQDZfIJvL05YrkM3nyeYKtIWvW7O5vaHQms3R2pbf/3UYGg1NGXZngiDJhCGTzxcoEBy7SgDBRzRBazbHtt0Z2j+ywbwEI2srqCxPQSFo8RTan8PfR/vrvcHUtn9ANWdynX6ZPBSJBFSUJaksS1FelqQiHfxcUZakKp3imLpKJtbXUFOeorq8jJqKMmZZPVMnDKW+pmK/L6VrdzR1+oUkLs4/vp5ffnomX3v0db7925X86Nm3uHLqWGZPHctpYweTOkJf4KNq4c0GKt39PDObDtwGXAFgZmngG8DZwB5gkZk9Apx/sDJRy+cLZHJ5svkCbdk8bfngH7wtm2d3a5bGljYam9uf29i2J+i6CB4tbGxs2S/UqtIpJtXX8I4xg7n89DFMCkNpcn3NQe9hdbB/iAtPrGfskCo2Nbbyp627Wb21idXb9rUSn1qxhUzRP3ZVOsWxw6sZN7SaodVphtaUM6Q6zdDqcoZUpalIJylPpcLnJOVl4SOV3PsPum+HBAkS++2MKJoOwc4nX4AC4XPxzil8Dn417fPbpwXzi3dk+6btW1b7sjsus3jZuXyBQqFArlAIfw6mta8nl9+3zny+QK4A2/e08ubmPUF92besCcOqqKkoI19g7066fSecTLAvTMJA2RcwwXuKwyaZSFCgQFuuQCabD4OjPQQK+37O5veGV3NRkLW0h1s2R+FwR4T63WGW66X2UE0nk6RSCWory6goS9G+XysUCH7v+QJNmRz5cAPDjwo7mzN7u/eLf8/tv/f233NF+PmtLi/bG0iV6SQVZUGIb96VCeqxN+iTnDa2lqp0Genwc58uS1KRSlKZTpBKBq8ry1KkUwky2TzPrdpxwPYdrLVlx9SSyeZZ37D/XQOO5m7HI2VEbQXf+tBUPjZzMt97+k3ue+5t7l68msFVaaZOGMI7xtTx8YuOoy7Cll9UgTcDeAzA3Z81s2lF804GVrr7DgAzewaYCZzXRZlI7GnN8s7bnmRTY+shlUunEowaXMmYwVWcO2kYY4ZUMWZIFRPrq5lcP6jPW1mJRLC+UYMrOf+4/efl8gXW72xm9bYgAFeFgbh2RxOvrWtjR1Pn3aZycKlkgtTeHey+nXPHID5UZclwx5tKUB4+p1PBznlQZZqqdJL6QeVhq7+MVDLYqVemk1Skg56A6vIk6VSwU69Mp/bumFvacixd17jvSwdBiJ8yuo6h1eVk8/u32opbcrl8gbZ8npZMjmy+sLf11x5a5akEyWQQLBVFX5Iq00lSHaanU0ny+TzPr96537Z31R3X2fQLT6xn3NADByA+2BmJhxJK7dOb2/Ldvv9Quwub2/IseXN7p+uUwGnjBvOdOWeysynDUyu2sGjlVpaua+Slt97iwhNGcO7k4ZGtO6rAqwOKrzjMmVmZu2c7mbcLGNxNmU4tW7Zsq5m91dvKVhxGmc3h4+XervwIOJztk0B7riXCR28vXM0DmfAh0p3X+OURXuOPj/D69vfRvln9sQebEVXgNQK1Ra+TRcHVcV4tsLObMp1y96N7NFURETlqRDXSyiLgMoDweNzSonlvACeY2TAzKwcuBH7fTRkREZFeSRQO+2j4wRWdcXk6QU/QXOBMYJC7zy86SzNJcJbmdzor4+7L+7xyIiISS5EEnoiIyNFGg0eLiEgsKPBERCQWNLRYifXnEWa6Ew4ycBcwkeDqiFuA14G7Cc74fw34pLv3+wsFzWwk8BJwCcEIQncz8Lbx74D3AeUEn9mnGEDbGX5e/5Pg85oDbmSA/S3N7Fzg39x9lpkdTyfbZmY3Ah8n2PZb3P3RklW4j6mFV3qzCUeYAb5AMMLMQPERYJu7zwTeDXwb+DrwxXBagiM0mk6Uwh3l94H24TUG4jbOIhgN6QLgImA8A287LwPK3P184GvAPzGAttHMPg/8AKgMJx2wbWY2Cvg0wd/5UuBfzGzAXMqrwCu9/UalASIfYeYI+inwpaLXWeAsgpYBwH8Df36kKxWBW4HvAevD1wNxGy8luFRoAfAI8CgDbztXAGVhr0sd0MbA2sY3gauKXne2becAi9y91d0bgJUEZ84PCAq80ut0hJlSVaYvuftud99lZrXAg8AXgYS7t58a3D7KTr9lZtcBW9z9V0WTB9Q2huoJvoxdA9wE3EswOMRA2s7dBN2Zy4E7gG8ygP6W7v4zghBv19m2HWwkrAFBgVd6hzzCTH9iZuOB3wI/cvf7CEbXatc+yk5/Ng+4xMyeBM4AfgiMLJo/ELYRYBvwK3fPuLsDLey/IxwI23kzwTaeSHBM/T8Jjle2GwjbWKyz/8WDjYQ1ICjwSm/AjjBjZscAC4G/dfe7wslLwuNBEBzXK9F4/n3D3S9094vcfRbB0KofBf57IG1j6BngXWaWMLMxQA3wmwG2nTvY17rZDqQZYJ/XDjrbtueBmWZWaWaDCQb7f61E9etzA6LrrJ9bQNBCWMy+UWkGir8HhgJfMrP2Y3mfAb4ZDiv3BkFX50Dz18AdA2kb3f1RM7uQYIeYBD4JrGJgbec3gLvM7HcELbu/B15kYG1jsQM+p+6eM7NvEoRfEvgHd28pZSX7kkZaERGRWFCXpoiIxIICT0REYkGBJyIisaDAExGRWFDgiYhILCjwJPbM7Doz+9delP9YOJ5myZnZleF1ckcdMzstvLRBpCQUeCK99/dAqtSVCH2GYHioo9HVwCmlroTEly48FwlMN7OFwAjguwQXVd9CMITWNoIhxNLA/QRfFNMEY0qeA4wCfkJw54sDmNmpBCPTJ4EhwKfdfbGZXQ/8FUFYPuzuXznItGuAzxHcsuYZd/+CmX0FOIlgGLOhwKcIhvo6A/ihmc0Avkow/mUt8Ia7zw3LTQrLHQvc7O6/MrPLgS+HVV4SbttMgjsG5AgGHv64uxePxVi8jU8CW8K6XE0wFuUQgjE47wD+C7gOyJjZH4Cqni5bpK+ohScSaCO4I8CVBGMqzgeucveLCEaU/yJBuDUQDMP0aaDO3e8ENgLXdrHsdwB/7e5/ThB8c8P7532BIFTOAgab2YSDTPsq8GfuPgMYa2aXhMttcvd3EtyG6Tvu/gv2DW9WCexw90sIbusz3czGhuVa3f3dBK3Bm8PByr8NvMfdzwbWEtz+546i38E6gsDqyn3hNh4H/MTd/wK4HPicu68juPfa14EXDmPZIr2mFp5I4A/uXjCzjcAEYGW4kwZ4Gvhn4PPACcDDBAF5Sw+XvY5geLVmgtZWIzAZeM3d2++hd3M4lmrHaecQtDp/aWaE5SeH858AcPdl4X3MijUDI83sxwR3ARhE0CqFoAUHsIYgGOsJwnFzuLyvhYE8GnggXG8VwbioXfHweSPwWTO7KtzWjsc3RxzGskV6TS08kUDxGHtbgTozGx2+vojgXmmzgA1hy+UWghCEYNT5rv6Xvgl82d3/kmBw8ARBN95J7TfXNLMHgQ2dTNtEEEyXhANUfwt4LlzuWeH7TiUI1eK6vBsY7+4fIjjGWBWut+O2AmwGhpjZsHB53yS4Tc5a4Ipwvf9EcNeLrrSPvv+/gN+7+0cI7omYKJqfJPj9HuqyRXpNLTyRAxWAG4GHzCxPMIr+deH0+83sswTHnr4Wvv93BC2wi4vuL1bsHuBhM9tEsKOvd/ctZvZvwFNmVgAecfe3DjLt6+G0FLAaeCBc7lQz+w3BnQtuDKctJrhF0fsIWpXPAq3An4BOz95097yZfQL4hZnlCFqALxB0ef4ivCFqI0FXaU88AnzXzD5McPwzG4b4S8D/IRio+HCXLXLYNHi0SD8Unnyy0d2/V+q6iPQXauGJ9IHwFiudHYdyd//4ka5PFMITaH7Yyayn3P3LnUwXOaqohSciIrGgk1ZERCQWFHgiIhILCjwREYkFBZ6IiMSCAk9ERGJBgSciIrHw/wH/p8zlVV5rDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "df_cols_clean['host_acceptance_rate'] = pd.to_numeric(df_cols_clean.host_acceptance_rate)\n",
    "ax = sns.distplot(df_cols_clean['host_acceptance_rate'])\n",
    "\n",
    "plt.axvline(x=df_cols_clean.host_acceptance_rate.mean(), linewidth=3, color='g', label=\"mean\", alpha=0.5)\n",
    "plt.axvline(x=df_cols_clean.host_acceptance_rate.median(), linewidth=3, color='y', label=\"median\", alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "defensive-lithuania",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/loredanafattorini/miniforge3/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x151b23ee0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cols_clean['host_response_rate'] = df_cols_clean['host_response_rate'].str.rstrip(\"%\")\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "df_cols_clean['host_response_rate'] = pd.to_numeric(df_cols_clean.host_response_rate)\n",
    "ax = sns.distplot(df_cols_clean.host_response_rate)\n",
    "\n",
    "plt.axvline(x=df_cols_clean.host_response_rate.mean(), linewidth=3, color='g', label=\"mean\", alpha=0.5)\n",
    "plt.axvline(x=df_cols_clean.host_response_rate.median(), linewidth=3, color='y', label=\"median\", alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "transparent-church",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean.drop(df_cols_clean[df_cols_clean.accommodates == 0].index, inplace = True)\n",
    "df_cols_clean.drop(df_cols_clean[df_cols_clean.beds == 0].index, inplace = True)\n",
    "df_cols_clean.drop(df_cols_clean[df_cols_clean.beds.isnull() == True].index, inplace = True)\n",
    "df_cols_clean.drop(df_cols_clean[df_cols_clean.bathrooms_text.isnull() == True].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "first-platform",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing values for host_acceptance_rate with the median since the distribution is left skewed\n",
    "df_cols_clean['host_acceptance_rate'].fillna(df_cols_clean['host_acceptance_rate'].median(), inplace = True)\n",
    "\n",
    "# Fill the missing values for host_acceptance_rate with the median since the distribution is left skewed\n",
    "df_cols_clean['host_response_rate'].fillna(df_cols_clean['host_response_rate'].median(), inplace = True)\n",
    "\n",
    "# Fill the missing values for host_response_time (categorical) with NA\n",
    "df_cols_clean['host_response_time'].fillna('NA', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "finished-comfort",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing values for review_score_* with the mean by accommodates (# of people that can be hosted)\n",
    "\n",
    "df_cols_clean['review_scores_rating'].fillna(df_cols_clean.groupby('accommodates')['review_scores_rating'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_accuracy'].fillna(df_cols_clean.groupby('accommodates')['review_scores_accuracy'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_cleanliness'].fillna(df_cols_clean.groupby('accommodates')['review_scores_cleanliness'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_checkin'].fillna(df_cols_clean.groupby('accommodates')['review_scores_checkin'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_communication'].fillna(df_cols_clean.groupby('accommodates')['review_scores_communication'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_location'].fillna(df_cols_clean.groupby('accommodates')['review_scores_location'].transform('mean'), inplace = True)\n",
    "df_cols_clean['review_scores_value'].fillna(df_cols_clean.groupby('accommodates')['review_scores_value'].transform('mean'), inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "engaging-cigarette",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values for reviews_per_month, first_review, last_review with 0, since it seems these records don't have any review.\n",
    "\n",
    "df_cols_clean['reviews_per_month'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "limiting-newark",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean['first_review'] = pd.to_datetime(df_cols_clean['first_review'], infer_datetime_format=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "laden-windows",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean['last_review'] = pd.to_datetime(df_cols_clean['last_review'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "smoking-supervisor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values for host_identity_verified with NA\n",
    "df_cols_clean.host_identity_verified.fillna('NA', inplace = True)\n",
    "\n",
    "# Fill missing values for host_has_profile_pic with NA\n",
    "df_cols_clean.host_has_profile_pic.fillna('NA', inplace = True)\n",
    "\n",
    "# Fill missing values for host_total_listing_count with the median\n",
    "df_cols_clean.host_total_listings_count.fillna(df_cols_clean.host_total_listings_count.median(), inplace = True)\n",
    "\n",
    "# Fill missing values for host_listing_count with the median\n",
    "df_cols_clean.host_listings_count.fillna(df_cols_clean.host_listings_count.median(), inplace = True)\n",
    "\n",
    "# Fill missing values for host_is_superhost with the NA\n",
    "df_cols_clean.host_is_superhost.fillna('NA', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "assisted-tongue",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing values for bedrooms with 1, since most of them are studio, suite or loft\n",
    "df_cols_clean['bedrooms'].fillna(1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "practical-realtor",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean['bathrooms'] = df_cols_clean.bathrooms_text.str.extract(r'([+-]?([0-9]*[.])?[0-9]+)', expand = False).astype(float)[0]\n",
    "df_cols_clean['bathrooms'].fillna(1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8825a240-7ddf-4331-a6b9-68e2c4e88534",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents)  \n",
    "<a class=\"anchor\" id='section1.3'></a>\n",
    "\n",
    "### Convert Columns to Appropriate Datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "naughty-purse",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column with 1 if the bathroom is shared and 0 otherwise.\n",
    "word_list = ['shared']\n",
    "df_cols_clean['bathroom_shared'] = df.bathrooms_text.str.extract(r'({})'.format('|'.join(word_list)), expand=False).str.lower().fillna('')\n",
    "df_cols_clean['bathroom_shared'] = [1 if x == 'shared' else 0 for x in df_cols_clean['bathroom_shared']]\n",
    "\n",
    "df_cols_clean = df_cols_clean.drop(columns=['bathrooms_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "delayed-universal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert price column to numeric\n",
    "df_cols_clean['price'] = [float(str(i).replace('$','').replace(',', '')) for i in df_cols_clean['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "owned-birmingham",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert first_review and last_review to datetime and create numeric columns\n",
    "df_cols_clean[['first_review','last_review']] = df_cols_clean[['first_review','last_review']].apply(pd.to_datetime)\n",
    "df_cols_clean['days_since_first_review'] = (datetime.datetime.utcnow() - df_cols_clean['first_review']).dt.days\n",
    "df_cols_clean['days_since_last_review'] = (datetime.datetime.utcnow() - df_cols_clean['last_review']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "declared-rings",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean = df_cols_clean.drop(columns=['first_review','last_review'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cbc52a0-1813-48ce-89ac-5a27eb019b26",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents) \n",
    "<a class=\"anchor\" id='section1.4'></a>\n",
    "\n",
    "### Select the Most Important Amenities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "unavailable-mambo",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lowecase amenities in lists\n",
    "df_cols_clean['amenities'] = df_cols_clean['amenities'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "standard-phone",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the words pool table and whirpool to not confuse them with the pool when selecting the amenities\n",
    "df_cols_clean['amenities'] = df_cols_clean['amenities'].str.replace('pool table','biliard')\n",
    "df_cols_clean['amenities'] = df_cols_clean['amenities'].str.replace('whirpool','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cooperative-syntax",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get an idea of what type of amenities are in the list to make a selection\n",
    "amenities_list = []\n",
    "for i in df_cols_clean['amenities']:\n",
    "    items_lower = i.lower().replace('\"', \"\")\n",
    "    tokens = items_lower.split(',')\n",
    "    amenities_list.append(tokens)\n",
    "\n",
    "ameneties_list_unique = list(set(chain(*amenities_list)))\n",
    "#ameneties_list_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "alert-block",
   "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>coffee_machine</th>\n",
       "      <th>wifi</th>\n",
       "      <th>fireplace</th>\n",
       "      <th>microwave</th>\n",
       "      <th>parking</th>\n",
       "      <th>bathroom_essentials</th>\n",
       "      <th>pool</th>\n",
       "      <th>terrace_patio_balcony</th>\n",
       "      <th>bathtub</th>\n",
       "      <th>sauna</th>\n",
       "      <th>...</th>\n",
       "      <th>pets_allowed</th>\n",
       "      <th>bbq_grill</th>\n",
       "      <th>crib</th>\n",
       "      <th>gym</th>\n",
       "      <th>bed_linens</th>\n",
       "      <th>hair_dryer</th>\n",
       "      <th>luggage_dropoff_allowed</th>\n",
       "      <th>self_checkin</th>\n",
       "      <th>cooking_basics</th>\n",
       "      <th>refrigerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   coffee_machine  wifi  fireplace  microwave  parking  bathroom_essentials  \\\n",
       "0               1     1          0          1        1                    1   \n",
       "1               0     1          0          0        1                    1   \n",
       "2               1     1          0          1        1                    1   \n",
       "3               0     1          0          0        1                    1   \n",
       "4               1     1          0          1        1                    1   \n",
       "\n",
       "   pool  terrace_patio_balcony  bathtub  sauna  ...  pets_allowed  bbq_grill  \\\n",
       "0     0                      0        0      0  ...             0          0   \n",
       "1     0                      0        0      0  ...             0          0   \n",
       "2     0                      0        0      0  ...             0          0   \n",
       "3     0                      0        0      0  ...             0          0   \n",
       "4     0                      0        0      0  ...             0          0   \n",
       "\n",
       "   crib  gym  bed_linens  hair_dryer  luggage_dropoff_allowed  self_checkin  \\\n",
       "0     1    0           0           1                        1             0   \n",
       "1     1    0           0           1                        1             0   \n",
       "2     1    0           0           1                        0             0   \n",
       "3     1    0           0           1                        0             0   \n",
       "4     0    0           1           1                        1             0   \n",
       "\n",
       "   cooking_basics  refrigerator  \n",
       "0               1             1  \n",
       "1               0             0  \n",
       "2               1             1  \n",
       "3               0             0  \n",
       "4               1             1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the most important amenities for guests based on the industry experience\n",
    "amenities_searchfor = {\n",
    "    'coffee_machine': ['coffee','espresso','nespresso'],\n",
    "    'wifi': ['wifi'],\n",
    "    'fireplace': ['fireplace','fire pit'],\n",
    "    'microwave': ['microwave','microonde oven'],\n",
    "    'parking': ['parking','garage'],\n",
    "    'bathroom_essentials': ['shampoo','soap','shower gel','body soap'],\n",
    "    'pool': ['pool'],\n",
    "    'terrace_patio_balcony': ['terrace','patio','balcony'],\n",
    "    'bathtub': ['bathtub'],\n",
    "    'sauna': ['sauna'],\n",
    "    'bikes': ['bikes'],\n",
    "    'iron': ['iron','ironing board'],\n",
    "    'garden_backyard': ['garden','backyard'],\n",
    "    'workspace': ['desk','workspace'],\n",
    "    'tv': ['tv'],\n",
    "    'air_conditioning': ['air conditioning'],\n",
    "    'pets_allowed': ['pets allowed'],\n",
    "    'bbq_grill': ['bbq'],\n",
    "    'crib': ['crib'],\n",
    "    'gym': ['gym'],\n",
    "    'bed_linens': ['bed linens'],\n",
    "    'hair_dryer': ['hair dryer'],\n",
    "    'luggage_dropoff_allowed': ['luggage dropoff allowed'],\n",
    "    'self_checkin': ['self check-in'],\n",
    "    'cooking_basics': ['cooking basics','dishes','silverware'],\n",
    "    'refrigerator': ['refrigerator', 'mini fridge']\n",
    "            }\n",
    "\n",
    "for key, value in amenities_searchfor.items():\n",
    "    df_cols_clean[key] = df_cols_clean['amenities'].str.contains(\"|\".join(list(value)), case=False, na=False).astype(int)\n",
    "\n",
    "df_cols_clean[amenities_searchfor.keys()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "logical-eating",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop column amenities\n",
    "df_cols_clean = df_cols_clean.drop(columns=['amenities'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-england",
   "metadata": {},
   "source": [
    "### Check object data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "compatible-forge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "within an hour        48.65\n",
      "NA                    33.13\n",
      "within a few hours     8.62\n",
      "within a day           6.81\n",
      "a few days or more     2.78\n",
      "Name: host_response_time, dtype: float64\n",
      "====================================\n",
      " \n",
      "f     67.81\n",
      "t     29.92\n",
      "NA     2.27\n",
      "Name: host_is_superhost, dtype: float64\n",
      "====================================\n",
      " \n",
      "t     97.45\n",
      "NA     2.27\n",
      "f      0.27\n",
      "Name: host_has_profile_pic, dtype: float64\n",
      "====================================\n",
      " \n",
      "t     79.80\n",
      "f     17.93\n",
      "NA     2.27\n",
      "Name: host_identity_verified, dtype: float64\n",
      "====================================\n",
      " \n",
      "Centro Storico       73.58\n",
      "Campo di Marte        9.93\n",
      "Rifredi               8.05\n",
      "Isolotto Legnaia      4.28\n",
      "Gavinana Galluzzo     4.17\n",
      "Name: neighbourhood_cleansed, dtype: float64\n",
      "====================================\n",
      " \n",
      "Entire home/apt    74.80\n",
      "Private room       22.99\n",
      "Hotel room          1.90\n",
      "Shared room         0.31\n",
      "Name: room_type, dtype: float64\n",
      "====================================\n",
      " \n",
      "t    60.09\n",
      "f    39.91\n",
      "Name: instant_bookable, dtype: float64\n",
      "====================================\n",
      " \n"
     ]
    }
   ],
   "source": [
    "categorical_col = df_cols_clean.select_dtypes('object').columns.tolist()\n",
    "for c in categorical_col:\n",
    "    print(round(df_cols_clean[c].value_counts(normalize=True)*100,2))\n",
    "    print(\"====================================\")\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "resistant-wilderness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.600873\n",
       "0    0.399127\n",
       "Name: instant_bookable, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert feature instant_bookable to 1 for true and 0 for false\n",
    "# Dealing with binary variables\n",
    "\n",
    "def encode_binary_variables(data,col):\n",
    "    data[col] = data[col].replace({'t':1, 'f':0})\n",
    "    return data\n",
    "\n",
    "df_cols_clean = encode_binary_variables(df_cols_clean,'instant_bookable')\n",
    "\n",
    "df_cols_clean['instant_bookable'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "julian-moore",
   "metadata": {},
   "source": [
    "### Check numerical data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "orange-boutique",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24469       0.01\n",
      "33231880    0.01\n",
      "33189798    0.01\n",
      "33193079    0.01\n",
      "33195573    0.01\n",
      "            ... \n",
      "16064507    0.01\n",
      "16066527    0.01\n",
      "16067209    0.01\n",
      "16069119    0.01\n",
      "50984060    0.01\n",
      "Name: id, Length: 10994, dtype: float64\n",
      "====================================\n",
      " \n",
      "100.0    82.92\n",
      "96.0      2.27\n",
      "0.0       2.14\n",
      "97.0      1.67\n",
      "90.0      1.44\n",
      "99.0      1.40\n",
      "50.0      1.05\n",
      "88.0      0.70\n",
      "95.0      0.60\n",
      "67.0      0.57\n",
      "80.0      0.56\n",
      "86.0      0.54\n",
      "98.0      0.48\n",
      "94.0      0.45\n",
      "89.0      0.42\n",
      "75.0      0.31\n",
      "91.0      0.29\n",
      "93.0      0.26\n",
      "83.0      0.22\n",
      "92.0      0.19\n",
      "33.0      0.17\n",
      "60.0      0.16\n",
      "25.0      0.15\n",
      "29.0      0.12\n",
      "70.0      0.11\n",
      "87.0      0.10\n",
      "79.0      0.07\n",
      "71.0      0.07\n",
      "77.0      0.06\n",
      "68.0      0.06\n",
      "30.0      0.05\n",
      "20.0      0.05\n",
      "76.0      0.05\n",
      "59.0      0.04\n",
      "82.0      0.04\n",
      "78.0      0.04\n",
      "57.0      0.03\n",
      "73.0      0.03\n",
      "23.0      0.03\n",
      "43.0      0.02\n",
      "40.0      0.02\n",
      "38.0      0.02\n",
      "13.0      0.01\n",
      "11.0      0.01\n",
      "63.0      0.01\n",
      "17.0      0.01\n",
      "69.0      0.01\n",
      "Name: host_response_rate, dtype: float64\n",
      "====================================\n",
      " \n",
      "100.0    75.56\n",
      "99.0      4.44\n",
      "0.0       2.82\n",
      "98.0      2.20\n",
      "97.0      1.74\n",
      "         ...  \n",
      "14.0      0.01\n",
      "47.0      0.01\n",
      "44.0      0.01\n",
      "19.0      0.01\n",
      "12.0      0.01\n",
      "Name: host_acceptance_rate, Length: 77, dtype: float64\n",
      "====================================\n",
      " \n",
      "1.0      26.75\n",
      "2.0      13.13\n",
      "3.0      10.30\n",
      "0.0       8.61\n",
      "4.0       5.78\n",
      "         ...  \n",
      "131.0     0.01\n",
      "130.0     0.01\n",
      "146.0     0.01\n",
      "416.0     0.01\n",
      "204.0     0.01\n",
      "Name: host_listings_count, Length: 82, dtype: float64\n",
      "====================================\n",
      " \n",
      "1.0      26.75\n",
      "2.0      13.13\n",
      "3.0      10.30\n",
      "0.0       8.61\n",
      "4.0       5.78\n",
      "         ...  \n",
      "131.0     0.01\n",
      "130.0     0.01\n",
      "146.0     0.01\n",
      "416.0     0.01\n",
      "204.0     0.01\n",
      "Name: host_total_listings_count, Length: 82, dtype: float64\n",
      "====================================\n",
      " \n",
      "43.771450    0.23\n",
      "43.768990    0.16\n",
      "43.774220    0.15\n",
      "43.769020    0.15\n",
      "43.770100    0.14\n",
      "             ... \n",
      "43.806690    0.01\n",
      "43.796150    0.01\n",
      "43.775719    0.01\n",
      "43.796640    0.01\n",
      "43.770443    0.01\n",
      "Name: latitude, Length: 3585, dtype: float64\n",
      "====================================\n",
      " \n",
      "11.263960    0.15\n",
      "11.249230    0.15\n",
      "11.250340    0.13\n",
      "11.253940    0.13\n",
      "11.253050    0.13\n",
      "             ... \n",
      "11.232570    0.01\n",
      "11.272390    0.01\n",
      "11.236710    0.01\n",
      "11.241690    0.01\n",
      "11.255533    0.01\n",
      "Name: longitude, Length: 4831, dtype: float64\n",
      "====================================\n",
      " \n",
      "2     31.35\n",
      "4     30.06\n",
      "3     11.15\n",
      "6     10.51\n",
      "5      7.06\n",
      "1      3.27\n",
      "8      2.26\n",
      "7      1.92\n",
      "10     0.69\n",
      "9      0.51\n",
      "16     0.44\n",
      "12     0.34\n",
      "11     0.17\n",
      "14     0.15\n",
      "15     0.06\n",
      "13     0.05\n",
      "Name: accommodates, dtype: float64\n",
      "====================================\n",
      " \n",
      "1.0     65.30\n",
      "2.0     24.14\n",
      "3.0      7.61\n",
      "4.0      1.77\n",
      "5.0      0.51\n",
      "6.0      0.31\n",
      "7.0      0.18\n",
      "8.0      0.05\n",
      "9.0      0.03\n",
      "13.0     0.03\n",
      "10.0     0.02\n",
      "12.0     0.02\n",
      "50.0     0.01\n",
      "25.0     0.01\n",
      "27.0     0.01\n",
      "Name: bedrooms, dtype: float64\n",
      "====================================\n",
      " \n",
      "1.0     37.26\n",
      "2.0     30.96\n",
      "3.0     15.69\n",
      "4.0      9.09\n",
      "5.0      3.16\n",
      "6.0      1.92\n",
      "7.0      0.65\n",
      "8.0      0.43\n",
      "10.0     0.17\n",
      "13.0     0.15\n",
      "9.0      0.14\n",
      "12.0     0.11\n",
      "11.0     0.10\n",
      "16.0     0.06\n",
      "14.0     0.06\n",
      "15.0     0.05\n",
      "17.0     0.01\n",
      "25.0     0.01\n",
      "Name: beds, dtype: float64\n",
      "====================================\n",
      " \n",
      "60.0      3.91\n",
      "50.0      3.72\n",
      "70.0      3.37\n",
      "80.0      3.33\n",
      "100.0     2.73\n",
      "          ... \n",
      "525.0     0.01\n",
      "2171.0    0.01\n",
      "799.0     0.01\n",
      "303.0     0.01\n",
      "1150.0    0.01\n",
      "Name: price, Length: 533, dtype: float64\n",
      "====================================\n",
      " \n",
      "1       36.95\n",
      "2       36.93\n",
      "3       16.85\n",
      "4        2.30\n",
      "5        1.81\n",
      "7        1.67\n",
      "30       0.93\n",
      "6        0.42\n",
      "14       0.25\n",
      "28       0.21\n",
      "20       0.20\n",
      "15       0.20\n",
      "60       0.15\n",
      "10       0.15\n",
      "90       0.12\n",
      "365      0.11\n",
      "21       0.09\n",
      "120      0.08\n",
      "180      0.07\n",
      "25       0.05\n",
      "29       0.05\n",
      "100      0.05\n",
      "150      0.04\n",
      "31       0.04\n",
      "27       0.03\n",
      "260      0.03\n",
      "8        0.03\n",
      "9        0.02\n",
      "200      0.02\n",
      "270      0.02\n",
      "23       0.02\n",
      "600      0.02\n",
      "40       0.01\n",
      "80       0.01\n",
      "800      0.01\n",
      "1000     0.01\n",
      "12       0.01\n",
      "240      0.01\n",
      "93       0.01\n",
      "11       0.01\n",
      "720      0.01\n",
      "98       0.01\n",
      "18       0.01\n",
      "1120     0.01\n",
      "1100     0.01\n",
      "Name: minimum_nights, dtype: float64\n",
      "====================================\n",
      " \n",
      "1125    45.23\n",
      "30      10.81\n",
      "365      7.49\n",
      "29       6.54\n",
      "28       6.29\n",
      "        ...  \n",
      "380      0.01\n",
      "1126     0.01\n",
      "157      0.01\n",
      "350      0.01\n",
      "547      0.01\n",
      "Name: maximum_nights, Length: 133, dtype: float64\n",
      "====================================\n",
      " \n",
      "0      23.52\n",
      "1       6.90\n",
      "2       4.43\n",
      "3       3.37\n",
      "4       2.43\n",
      "       ...  \n",
      "644     0.01\n",
      "257     0.01\n",
      "503     0.01\n",
      "284     0.01\n",
      "323     0.01\n",
      "Name: number_of_reviews, Length: 442, dtype: float64\n",
      "====================================\n",
      " \n",
      "0     68.74\n",
      "1      8.76\n",
      "2      4.50\n",
      "3      2.87\n",
      "4      1.74\n",
      "      ...  \n",
      "60     0.01\n",
      "78     0.01\n",
      "79     0.01\n",
      "65     0.01\n",
      "94     0.01\n",
      "Name: number_of_reviews_ltm, Length: 74, dtype: float64\n",
      "====================================\n",
      " \n",
      "0     84.56\n",
      "1      6.68\n",
      "2      3.16\n",
      "3      2.03\n",
      "4      1.23\n",
      "5      0.89\n",
      "6      0.55\n",
      "7      0.35\n",
      "8      0.27\n",
      "9      0.10\n",
      "10     0.08\n",
      "11     0.04\n",
      "14     0.03\n",
      "12     0.03\n",
      "13     0.01\n",
      "Name: number_of_reviews_l30d, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    14.87\n",
      "4.649389     8.59\n",
      "4.670168     6.26\n",
      "4.680810     2.62\n",
      "4.677132     2.24\n",
      "            ...  \n",
      "2.330000     0.01\n",
      "3.740000     0.01\n",
      "1.500000     0.01\n",
      "4.861667     0.01\n",
      "3.630000     0.01\n",
      "Name: review_scores_rating, Length: 140, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    19.18\n",
      "4.787544     8.83\n",
      "4.809178     6.49\n",
      "4.783887     2.66\n",
      "4.880000     2.45\n",
      "            ...  \n",
      "3.170000     0.01\n",
      "4.060000     0.01\n",
      "3.860000     0.01\n",
      "4.885000     0.01\n",
      "3.880000     0.01\n",
      "Name: review_scores_accuracy, Length: 134, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    18.68\n",
      "4.766474     8.83\n",
      "4.766063     6.49\n",
      "4.759486     2.66\n",
      "4.772193     2.30\n",
      "            ...  \n",
      "1.670000     0.01\n",
      "3.950000     0.01\n",
      "3.630000     0.01\n",
      "4.020000     0.01\n",
      "4.856667     0.01\n",
      "Name: review_scores_cleanliness, Length: 149, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    24.20\n",
      "4.848954     8.84\n",
      "4.844709     6.49\n",
      "4.950000     2.71\n",
      "4.844829     2.66\n",
      "            ...  \n",
      "4.050000     0.01\n",
      "4.080000     0.01\n",
      "3.940000     0.01\n",
      "3.890000     0.01\n",
      "3.590000     0.01\n",
      "Name: review_scores_checkin, Length: 129, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    24.60\n",
      "4.837026     8.84\n",
      "4.847518     6.49\n",
      "4.833191     2.66\n",
      "4.960000     2.65\n",
      "            ...  \n",
      "3.690000     0.01\n",
      "3.860000     0.01\n",
      "3.890000     0.01\n",
      "0.000000     0.01\n",
      "2.750000     0.01\n",
      "Name: review_scores_communication, Length: 133, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    21.80\n",
      "4.778784     8.84\n",
      "4.829884     6.49\n",
      "4.785407     2.66\n",
      "4.950000     2.33\n",
      "            ...  \n",
      "4.100000     0.01\n",
      "0.000000     0.01\n",
      "4.060000     0.01\n",
      "3.920000     0.01\n",
      "2.750000     0.01\n",
      "Name: review_scores_location, Length: 134, dtype: float64\n",
      "====================================\n",
      " \n",
      "5.000000    12.62\n",
      "4.681371     8.86\n",
      "4.712798     6.49\n",
      "4.689015     2.66\n",
      "4.500000     2.42\n",
      "            ...  \n",
      "3.930000     0.01\n",
      "3.290000     0.01\n",
      "3.920000     0.01\n",
      "3.640000     0.01\n",
      "4.908333     0.01\n",
      "Name: review_scores_value, Length: 137, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    60.09\n",
      "0    39.91\n",
      "Name: instant_bookable, dtype: float64\n",
      "====================================\n",
      " \n",
      "0.00    23.52\n",
      "0.05     1.90\n",
      "0.04     1.76\n",
      "0.03     1.45\n",
      "0.11     1.17\n",
      "        ...  \n",
      "4.61     0.01\n",
      "4.44     0.01\n",
      "7.55     0.01\n",
      "6.19     0.01\n",
      "9.51     0.01\n",
      "Name: reviews_per_month, Length: 615, dtype: float64\n",
      "====================================\n",
      " \n",
      "1.0     72.67\n",
      "2.0     16.98\n",
      "1.5      4.67\n",
      "3.0      2.64\n",
      "2.5      1.11\n",
      "4.0      0.63\n",
      "6.0      0.29\n",
      "5.0      0.29\n",
      "3.5      0.21\n",
      "0.0      0.15\n",
      "7.0      0.10\n",
      "4.5      0.09\n",
      "9.0      0.05\n",
      "8.0      0.03\n",
      "6.5      0.02\n",
      "12.0     0.02\n",
      "5.5      0.01\n",
      "11.0     0.01\n",
      "13.0     0.01\n",
      "26.0     0.01\n",
      "19.0     0.01\n",
      "27.0     0.01\n",
      "13.5     0.01\n",
      "7.5      0.01\n",
      "Name: bathrooms, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    91.87\n",
      "1     8.13\n",
      "Name: bathroom_shared, dtype: float64\n",
      "====================================\n",
      " \n",
      "586.0     0.62\n",
      "587.0     0.54\n",
      "952.0     0.54\n",
      "1681.0    0.49\n",
      "951.0     0.49\n",
      "          ... \n",
      "1281.0    0.01\n",
      "1256.0    0.01\n",
      "2610.0    0.01\n",
      "699.0     0.01\n",
      "59.0      0.01\n",
      "Name: days_since_first_review, Length: 2338, dtype: float64\n",
      "====================================\n",
      " \n",
      "587.0     1.01\n",
      "586.0     0.94\n",
      "667.0     0.68\n",
      "37.0      0.67\n",
      "585.0     0.64\n",
      "          ... \n",
      "983.0     0.01\n",
      "2190.0    0.01\n",
      "1783.0    0.01\n",
      "1293.0    0.01\n",
      "138.0     0.01\n",
      "Name: days_since_last_review, Length: 1519, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    54.07\n",
      "0    45.93\n",
      "Name: coffee_machine, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    95.91\n",
      "0     4.09\n",
      "Name: wifi, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    95.73\n",
      "1     4.27\n",
      "Name: fireplace, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    59.16\n",
      "1    40.84\n",
      "Name: microwave, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    51.22\n",
      "0    48.78\n",
      "Name: parking, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    66.1\n",
      "0    33.9\n",
      "Name: bathroom_essentials, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    98.62\n",
      "1     1.38\n",
      "Name: pool, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    78.84\n",
      "1    21.16\n",
      "Name: terrace_patio_balcony, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    93.91\n",
      "1     6.09\n",
      "Name: bathtub, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    99.95\n",
      "1     0.05\n",
      "Name: sauna, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    99.81\n",
      "1     0.19\n",
      "Name: bikes, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    73.51\n",
      "0    26.49\n",
      "Name: iron, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    90.5\n",
      "1     9.5\n",
      "Name: garden_backyard, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    68.81\n",
      "0    31.19\n",
      "Name: workspace, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    84.18\n",
      "0    15.82\n",
      "Name: tv, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    75.9\n",
      "0    24.1\n",
      "Name: air_conditioning, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    99.68\n",
      "1     0.32\n",
      "Name: pets_allowed, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    98.34\n",
      "1     1.66\n",
      "Name: bbq_grill, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    76.67\n",
      "1    23.33\n",
      "Name: crib, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    98.78\n",
      "1     1.22\n",
      "Name: gym, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    51.32\n",
      "0    48.68\n",
      "Name: bed_linens, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    90.15\n",
      "0     9.85\n",
      "Name: hair_dryer, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    78.42\n",
      "1    21.58\n",
      "Name: luggage_dropoff_allowed, dtype: float64\n",
      "====================================\n",
      " \n",
      "0    99.01\n",
      "1     0.99\n",
      "Name: self_checkin, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    63.57\n",
      "0    36.43\n",
      "Name: cooking_basics, dtype: float64\n",
      "====================================\n",
      " \n",
      "1    60.63\n",
      "0    39.37\n",
      "Name: refrigerator, dtype: float64\n",
      "====================================\n",
      " \n"
     ]
    }
   ],
   "source": [
    "numerical_col = df_cols_clean.select_dtypes(['int64','float64']).columns.tolist()\n",
    "for n in numerical_col:\n",
    "    print(round(df_cols_clean[n].value_counts(normalize=True)*100,2))\n",
    "    print(\"====================================\")\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mighty-outside",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents) \n",
    "<a id='chapter2'></a>\n",
    "\n",
    "### EDA and Data Viz\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "occasional-image",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outlier in Shared_room\n",
    "delete_row = df_cols_clean[df_cols_clean.id == 28827610].index\n",
    "df_cols_clean = df_cols_clean.drop(delete_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "consistent-lexington",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10680    $1,031.00\n",
       "Name: price, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price[(df.room_type == 'Private room') & (df.accommodates==7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "documentary-intersection",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cols_clean['price_per_person'] = df_cols_clean.price / df_cols_clean.accommodates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "american-psychology",
   "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>price_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10993.000000</td>\n",
       "      <td>10993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>124.807605</td>\n",
       "      <td>36.619745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>299.007797</td>\n",
       "      <td>84.826603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>23.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>119.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13396.000000</td>\n",
       "      <td>3499.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price  price_per_person\n",
       "count  10993.000000      10993.000000\n",
       "mean     124.807605         36.619745\n",
       "std      299.007797         84.826603\n",
       "min        9.000000          1.125000\n",
       "25%       55.000000         16.666667\n",
       "50%       78.000000         23.400000\n",
       "75%      119.000000         35.000000\n",
       "max    13396.000000       3499.500000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols_clean[['price','price_per_person']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "consecutive-mirror",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'host_response_time', 'host_response_rate',\n",
       "       'host_acceptance_rate', 'host_is_superhost', 'host_listings_count',\n",
       "       'host_total_listings_count', 'host_has_profile_pic',\n",
       "       'host_identity_verified', 'neighbourhood_cleansed', 'latitude',\n",
       "       'longitude', 'room_type', 'accommodates', 'bedrooms', 'beds', 'price',\n",
       "       'minimum_nights', 'maximum_nights', 'number_of_reviews',\n",
       "       'number_of_reviews_ltm', 'number_of_reviews_l30d',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'instant_bookable', 'reviews_per_month',\n",
       "       'bathrooms', 'bathroom_shared', 'days_since_first_review',\n",
       "       'days_since_last_review', 'coffee_machine', 'wifi', 'fireplace',\n",
       "       'microwave', 'parking', 'bathroom_essentials', 'pool',\n",
       "       'terrace_patio_balcony', 'bathtub', 'sauna', 'bikes', 'iron',\n",
       "       'garden_backyard', 'workspace', 'tv', 'air_conditioning',\n",
       "       'pets_allowed', 'bbq_grill', 'crib', 'gym', 'bed_linens', 'hair_dryer',\n",
       "       'luggage_dropoff_allowed', 'self_checkin', 'cooking_basics',\n",
       "       'refrigerator', 'price_per_person'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ahead-cooling",
   "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>accommodates</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>17.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>70.0</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>135.0</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>120.0</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accommodates  price  price_per_person\n",
       "0             4   70.0         17.500000\n",
       "1             3   70.0         23.333333\n",
       "2             6  135.0         22.500000\n",
       "3             6  120.0         20.000000\n",
       "4             2   65.0         32.500000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols_clean[['accommodates','price','price_per_person']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "adjusted-confidentiality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x151b09fd0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 466.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df_cols_clean, y=\"neighbourhood_cleansed\", hue = 'room_type', kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "amateur-hardwood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Centro Storico       8089\n",
       "Campo di Marte       1092\n",
       "Rifredi               885\n",
       "Isolotto Legnaia      469\n",
       "Gavinana Galluzzo     458\n",
       "Name: neighbourhood_cleansed, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols_clean['neighbourhood_cleansed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "supposed-madison",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "special-canyon",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pleasant-orleans",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "timely-arabic",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stopped-wildlife",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "functioning-friendly",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "conditional-squad",
   "metadata": {},
   "source": [
    "[Back to TOC](#Table-of-Contents) \n",
    "<a id='chapter3'></a>\n",
    "\n",
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diagnostic-athens",
   "metadata": {},
   "source": [
    "### Duplicate instances check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "essential-found",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>...</th>\n",
       "      <th>bbq_grill</th>\n",
       "      <th>crib</th>\n",
       "      <th>gym</th>\n",
       "      <th>bed_linens</th>\n",
       "      <th>hair_dryer</th>\n",
       "      <th>luggage_dropoff_allowed</th>\n",
       "      <th>self_checkin</th>\n",
       "      <th>cooking_basics</th>\n",
       "      <th>refrigerator</th>\n",
       "      <th>price_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, host_response_time, host_response_rate, host_acceptance_rate, host_is_superhost, host_listings_count, host_total_listings_count, host_has_profile_pic, host_identity_verified, neighbourhood_cleansed, latitude, longitude, room_type, accommodates, bedrooms, beds, price, minimum_nights, maximum_nights, number_of_reviews, number_of_reviews_ltm, number_of_reviews_l30d, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, instant_bookable, reviews_per_month, bathrooms, bathroom_shared, days_since_first_review, days_since_last_review, coffee_machine, wifi, fireplace, microwave, parking, bathroom_essentials, pool, terrace_patio_balcony, bathtub, sauna, bikes, iron, garden_backyard, workspace, tv, air_conditioning, pets_allowed, bbq_grill, crib, gym, bed_linens, hair_dryer, luggage_dropoff_allowed, self_checkin, cooking_basics, refrigerator, price_per_person]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 62 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cols_clean[df_cols_clean.duplicated() == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operational-feeling",
   "metadata": {},
   "source": [
    "### Encoding categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "criminal-browse",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_listings = pd.get_dummies(df_cols_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "careful-reset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'host_response_rate', 'host_acceptance_rate',\n",
       "       'host_listings_count', 'host_total_listings_count', 'latitude',\n",
       "       'longitude', 'accommodates', 'bedrooms', 'beds', 'price',\n",
       "       'minimum_nights', 'maximum_nights', 'number_of_reviews',\n",
       "       'number_of_reviews_ltm', 'number_of_reviews_l30d',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'instant_bookable', 'reviews_per_month',\n",
       "       'bathrooms', 'bathroom_shared', 'days_since_first_review',\n",
       "       'days_since_last_review', 'coffee_machine', 'wifi', 'fireplace',\n",
       "       'microwave', 'parking', 'bathroom_essentials', 'pool',\n",
       "       'terrace_patio_balcony', 'bathtub', 'sauna', 'bikes', 'iron',\n",
       "       'garden_backyard', 'workspace', 'tv', 'air_conditioning',\n",
       "       'pets_allowed', 'bbq_grill', 'crib', 'gym', 'bed_linens', 'hair_dryer',\n",
       "       'luggage_dropoff_allowed', 'self_checkin', 'cooking_basics',\n",
       "       'refrigerator', 'price_per_person', 'host_response_time_NA',\n",
       "       'host_response_time_a few days or more',\n",
       "       'host_response_time_within a day',\n",
       "       'host_response_time_within a few hours',\n",
       "       'host_response_time_within an hour', 'host_is_superhost_NA',\n",
       "       'host_is_superhost_f', 'host_is_superhost_t', 'host_has_profile_pic_NA',\n",
       "       'host_has_profile_pic_f', 'host_has_profile_pic_t',\n",
       "       'host_identity_verified_NA', 'host_identity_verified_f',\n",
       "       'host_identity_verified_t', 'neighbourhood_cleansed_Campo di Marte',\n",
       "       'neighbourhood_cleansed_Centro Storico',\n",
       "       'neighbourhood_cleansed_Gavinana Galluzzo',\n",
       "       'neighbourhood_cleansed_Isolotto Legnaia',\n",
       "       'neighbourhood_cleansed_Rifredi', 'room_type_Entire home/apt',\n",
       "       'room_type_Hotel room', 'room_type_Private room',\n",
       "       'room_type_Shared room'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_listings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "diagnostic-smith",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_clean_listings.to_pickle('./data/df_clean_listings.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bottom-insured",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10993, 79)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking to make sure it pickled correctly:\n",
    "df_clean_listings = pd.read_pickle('./data/df_clean_listings.pkl')\n",
    "df_clean_listings.shape"
   ]
  }
 ],
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