notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b3a32fb-5c06-439e-9282-c7328a53e9ec",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "470b2ff5-15c2-417b-9399-4b9f86fbcd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db26fb7-b1d1-4248-974d-e27119e24560",
   "metadata": {},
   "source": [
    "## 1.Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c80a2b3-6219-448f-b46e-0bd9d75a06cf",
   "metadata": {},
   "source": [
    "Airbnb is a company that connects hosts with potential guests searching for a good place to stay during travels.\n",
    "\n",
    "The places offered has a lot of informations that help the guest to find the better place to rent.\n",
    "\n",
    "Some of the variables used to determine if we are going to rent or not a place are the price and the rating past guest gave.\n",
    "\n",
    "For the hosts, knowing the price of other housings with similar characteristics, help them to offer their places for a good price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8471794a-8737-455b-894a-cddd8e90f54a",
   "metadata": {},
   "source": [
    "## 2.Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6490329a-f669-4945-a080-9510f358c37e",
   "metadata": {},
   "source": [
    "The listings.csv contains the data about the accommodations in the city\n",
    "\n",
    "Some columns have boolean values as strings ('t' and 'f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f31b22bf-1739-48d3-828a-493d65750b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel_list = pd.read_csv('dataset/melbourne/listings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "245e5eb0-bd08-4e05-bf20-5ee7f54a1530",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mel_list.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fc453a5-387d-4f8d-9529-8a7861e9d044",
   "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>0</th>\n",
       "      <td>9835</td>\n",
       "      <td>https://www.airbnb.com/rooms/9835</td>\n",
       "      <td>20210410122555</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>Beautiful Room &amp; House</td>\n",
       "      <td>&lt;b&gt;The space&lt;/b&gt;&lt;br /&gt;House: Clean, New, Moder...</td>\n",
       "      <td>Very safe! Family oriented. Older age group.</td>\n",
       "      <td>https://a0.muscache.com/pictures/44620/5a5815c...</td>\n",
       "      <td>33057</td>\n",
       "      <td>https://www.airbnb.com/users/show/33057</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10803</td>\n",
       "      <td>https://www.airbnb.com/rooms/10803</td>\n",
       "      <td>20210410122555</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>Room in Cool Deco Apartment in Brunswick East</td>\n",
       "      <td>A large air conditioned room with queen spring...</td>\n",
       "      <td>This hip area is a crossroads between two grea...</td>\n",
       "      <td>https://a0.muscache.com/pictures/31323790/90b6...</td>\n",
       "      <td>38901</td>\n",
       "      <td>https://www.airbnb.com/users/show/38901</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12936</td>\n",
       "      <td>https://www.airbnb.com/rooms/12936</td>\n",
       "      <td>20210410122555</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>St Kilda 1BR+BEACHSIDE+BALCONY+WIFI+AC</td>\n",
       "      <td>RIGHT IN THE HEART OF ST KILDA! It doesn't get...</td>\n",
       "      <td>A stay at our apartment means you can enjoy so...</td>\n",
       "      <td>https://a0.muscache.com/pictures/59701/2e8cdaf...</td>\n",
       "      <td>50121</td>\n",
       "      <td>https://www.airbnb.com/users/show/50121</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38271</td>\n",
       "      <td>https://www.airbnb.com/rooms/38271</td>\n",
       "      <td>20210410122555</td>\n",
       "      <td>2021-04-14</td>\n",
       "      <td>Melbourne - Old Trafford Apartment</td>\n",
       "      <td>Please note: No booking will be accepted with ...</td>\n",
       "      <td>Our street is quiet &amp; secluded but within walk...</td>\n",
       "      <td>https://a0.muscache.com/pictures/1182791/3bf4b...</td>\n",
       "      <td>164193</td>\n",
       "      <td>https://www.airbnb.com/users/show/164193</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41836</td>\n",
       "      <td>https://www.airbnb.com/rooms/41836</td>\n",
       "      <td>20210410122555</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>CLOSE TO CITY &amp; MELBOURNE AIRPORT</td>\n",
       "      <td>Easy to travel from and to the Airport; quiet ...</td>\n",
       "      <td>The neighbours are quiet and friendly,  please...</td>\n",
       "      <td>https://a0.muscache.com/pictures/569696dd-1ad0...</td>\n",
       "      <td>182833</td>\n",
       "      <td>https://www.airbnb.com/users/show/182833</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                         listing_url       scrape_id last_scraped  \\\n",
       "0   9835   https://www.airbnb.com/rooms/9835  20210410122555   2021-04-13   \n",
       "1  10803  https://www.airbnb.com/rooms/10803  20210410122555   2021-04-11   \n",
       "2  12936  https://www.airbnb.com/rooms/12936  20210410122555   2021-04-11   \n",
       "3  38271  https://www.airbnb.com/rooms/38271  20210410122555   2021-04-14   \n",
       "4  41836  https://www.airbnb.com/rooms/41836  20210410122555   2021-04-13   \n",
       "\n",
       "                                            name  \\\n",
       "0                         Beautiful Room & House   \n",
       "1  Room in Cool Deco Apartment in Brunswick East   \n",
       "2         St Kilda 1BR+BEACHSIDE+BALCONY+WIFI+AC   \n",
       "3             Melbourne - Old Trafford Apartment   \n",
       "4              CLOSE TO CITY & MELBOURNE AIRPORT   \n",
       "\n",
       "                                         description  \\\n",
       "0  <b>The space</b><br />House: Clean, New, Moder...   \n",
       "1  A large air conditioned room with queen spring...   \n",
       "2  RIGHT IN THE HEART OF ST KILDA! It doesn't get...   \n",
       "3  Please note: No booking will be accepted with ...   \n",
       "4  Easy to travel from and to the Airport; quiet ...   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0       Very safe! Family oriented. Older age group.   \n",
       "1  This hip area is a crossroads between two grea...   \n",
       "2  A stay at our apartment means you can enjoy so...   \n",
       "3  Our street is quiet & secluded but within walk...   \n",
       "4  The neighbours are quiet and friendly,  please...   \n",
       "\n",
       "                                         picture_url  host_id  \\\n",
       "0  https://a0.muscache.com/pictures/44620/5a5815c...    33057   \n",
       "1  https://a0.muscache.com/pictures/31323790/90b6...    38901   \n",
       "2  https://a0.muscache.com/pictures/59701/2e8cdaf...    50121   \n",
       "3  https://a0.muscache.com/pictures/1182791/3bf4b...   164193   \n",
       "4  https://a0.muscache.com/pictures/569696dd-1ad0...   182833   \n",
       "\n",
       "                                   host_url  ... review_scores_communication  \\\n",
       "0   https://www.airbnb.com/users/show/33057  ...                        10.0   \n",
       "1   https://www.airbnb.com/users/show/38901  ...                        10.0   \n",
       "2   https://www.airbnb.com/users/show/50121  ...                        10.0   \n",
       "3  https://www.airbnb.com/users/show/164193  ...                        10.0   \n",
       "4  https://www.airbnb.com/users/show/182833  ...                         9.0   \n",
       "\n",
       "  review_scores_location review_scores_value license instant_bookable  \\\n",
       "0                    9.0                 9.0     NaN                f   \n",
       "1                   10.0                10.0     NaN                f   \n",
       "2                   10.0                 9.0     NaN                f   \n",
       "3                   10.0                10.0     NaN                f   \n",
       "4                    9.0                 9.0     NaN                f   \n",
       "\n",
       "  calculated_host_listings_count calculated_host_listings_count_entire_homes  \\\n",
       "0                              1                                           0   \n",
       "1                              1                                           0   \n",
       "2                             10                                          10   \n",
       "3                              1                                           1   \n",
       "4                              2                                           0   \n",
       "\n",
       "  calculated_host_listings_count_private_rooms  \\\n",
       "0                                            1   \n",
       "1                                            1   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            2   \n",
       "\n",
       "  calculated_host_listings_count_shared_rooms reviews_per_month  \n",
       "0                                           0              0.03  \n",
       "1                                           0              1.44  \n",
       "2                                           0              0.32  \n",
       "3                                           0              1.24  \n",
       "4                                           0              1.25  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mel_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38621013-f986-4f3c-a08b-f4eae23254e8",
   "metadata": {},
   "source": [
    "## 3.Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab520830-bb2c-42c1-bb7b-09d1bc9857f7",
   "metadata": {},
   "source": [
    "Only 2.5% of the host_listings_count data is different from the host_total_listings_count data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef35a9ea-bd3d-4495-aca7-8dc68deb8962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025375039897861475"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings_count_diff = sum(mel_list['host_listings_count'] != mel_list['host_total_listings_count'])\n",
    "listings_count_percentage = listings_count_diff / mel_list.shape[0]\n",
    "listings_count_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1f7e31-c43e-462a-b689-9d32fb58cb67",
   "metadata": {},
   "source": [
    "Aproximately 99% of the has_availability data has the value true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78f0eec9-a9a0-4b74-ba76-b51beef53cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t    18728\n",
      "f       70\n",
      "Name: has_availability, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9962761995957017"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(mel_list['has_availability'].value_counts())\n",
    "availability_percentage = mel_list['has_availability'].value_counts()[0] / mel_list.shape[0]\n",
    "availability_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885ca363-9248-41d2-a133-80fda4c74e14",
   "metadata": {},
   "source": [
    "Some columns are not useful for the analysis I'm going to do.\n",
    "\n",
    "Some columns have only ids, others have urls.\n",
    "\n",
    "Columns with long text were not used because nlp algorithms had to be used to make them useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "501792b2-3fa3-4c8a-b8ab-c5ade20b29aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Getting the useful columns\n",
    "df = mel_list.copy()\n",
    "\n",
    "df = df.drop(['listing_url','id','scrape_id','last_scraped','name',\n",
    "              'description','neighborhood_overview','picture_url',\n",
    "              'host_url','host_name', 'host_about','host_thumbnail_url',\n",
    "              'host_picture_url','latitude','longitude','minimum_minimum_nights',\n",
    "              'maximum_minimum_nights','minimum_maximum_nights','maximum_maximum_nights',\n",
    "              'minimum_nights_avg_ntm','maximum_nights_avg_ntm','has_availability',\n",
    "              'calendar_last_scraped','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','host_total_listings_count'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f306540-7d4c-4d4b-9135-03acd75b27da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the price from string to float\n",
    "df['price'] = df['price'].replace({'\\$':'', ',':''}, regex=True).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd4b76e0-5372-443d-8c15-3c78430d56dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>number_of_reviews_l30d</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>license</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.879800e+04</td>\n",
       "      <td>18321.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18798.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17950.000000</td>\n",
       "      <td>18569.000000</td>\n",
       "      <td>18798.000000</td>\n",
       "      <td>18798.000000</td>\n",
       "      <td>18798.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18798.000000</td>\n",
       "      <td>14265.000000</td>\n",
       "      <td>14227.000000</td>\n",
       "      <td>14231.000000</td>\n",
       "      <td>14223.000000</td>\n",
       "      <td>14229.000000</td>\n",
       "      <td>14224.000000</td>\n",
       "      <td>14222.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.114555e+08</td>\n",
       "      <td>10.298346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.365890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.622340</td>\n",
       "      <td>1.941246</td>\n",
       "      <td>168.025960</td>\n",
       "      <td>5.776732</td>\n",
       "      <td>762.236834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.570167</td>\n",
       "      <td>93.615703</td>\n",
       "      <td>9.563084</td>\n",
       "      <td>9.327665</td>\n",
       "      <td>9.673135</td>\n",
       "      <td>9.697589</td>\n",
       "      <td>9.730034</td>\n",
       "      <td>9.402756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.005809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.070621e+08</td>\n",
       "      <td>68.616043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.260281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.957532</td>\n",
       "      <td>1.546161</td>\n",
       "      <td>412.474797</td>\n",
       "      <td>31.419598</td>\n",
       "      <td>5394.975781</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543554</td>\n",
       "      <td>10.057439</td>\n",
       "      <td>0.999090</td>\n",
       "      <td>1.136722</td>\n",
       "      <td>0.895676</td>\n",
       "      <td>0.892670</td>\n",
       "      <td>0.789232</td>\n",
       "      <td>1.040459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.342168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.082000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.340579e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.987400e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.775739e+08</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.962809e+08</td>\n",
       "      <td>7755.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>16267.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>730365.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            host_id  host_listings_count  neighbourhood_group_cleansed  \\\n",
       "count  1.879800e+04         18321.000000                           0.0   \n",
       "mean   1.114555e+08            10.298346                           NaN   \n",
       "std    1.070621e+08            68.616043                           NaN   \n",
       "min    9.082000e+03             0.000000                           NaN   \n",
       "25%    2.340579e+07             1.000000                           NaN   \n",
       "50%    6.987400e+07             1.000000                           NaN   \n",
       "75%    1.775739e+08             4.000000                           NaN   \n",
       "max    3.962809e+08          7755.000000                           NaN   \n",
       "\n",
       "       accommodates  bathrooms      bedrooms          beds         price  \\\n",
       "count  18798.000000        0.0  17950.000000  18569.000000  18798.000000   \n",
       "mean       3.365890        NaN      1.622340      1.941246    168.025960   \n",
       "std        2.260281        NaN      0.957532      1.546161    412.474797   \n",
       "min        1.000000        NaN      1.000000      0.000000      0.000000   \n",
       "25%        2.000000        NaN      1.000000      1.000000     69.000000   \n",
       "50%        2.000000        NaN      1.000000      1.000000    110.000000   \n",
       "75%        4.000000        NaN      2.000000      2.000000    174.000000   \n",
       "max       16.000000        NaN     16.000000     32.000000  16267.000000   \n",
       "\n",
       "       minimum_nights  maximum_nights  ...  number_of_reviews_l30d  \\\n",
       "count    18798.000000    18798.000000  ...            18798.000000   \n",
       "mean         5.776732      762.236834  ...                0.570167   \n",
       "std         31.419598     5394.975781  ...                1.543554   \n",
       "min          1.000000        1.000000  ...                0.000000   \n",
       "25%          1.000000       60.000000  ...                0.000000   \n",
       "50%          2.000000     1125.000000  ...                0.000000   \n",
       "75%          3.000000     1125.000000  ...                0.000000   \n",
       "max       1125.000000   730365.000000  ...               62.000000   \n",
       "\n",
       "       review_scores_rating  review_scores_accuracy  \\\n",
       "count          14265.000000            14227.000000   \n",
       "mean              93.615703                9.563084   \n",
       "std               10.057439                0.999090   \n",
       "min               20.000000                2.000000   \n",
       "25%               92.000000                9.000000   \n",
       "50%               97.000000               10.000000   \n",
       "75%              100.000000               10.000000   \n",
       "max              100.000000               10.000000   \n",
       "\n",
       "       review_scores_cleanliness  review_scores_checkin  \\\n",
       "count               14231.000000           14223.000000   \n",
       "mean                    9.327665               9.673135   \n",
       "std                     1.136722               0.895676   \n",
       "min                     2.000000               2.000000   \n",
       "25%                     9.000000              10.000000   \n",
       "50%                    10.000000              10.000000   \n",
       "75%                    10.000000              10.000000   \n",
       "max                    10.000000              10.000000   \n",
       "\n",
       "       review_scores_communication  review_scores_location  \\\n",
       "count                 14229.000000            14224.000000   \n",
       "mean                      9.697589                9.730034   \n",
       "std                       0.892670                0.789232   \n",
       "min                       2.000000                2.000000   \n",
       "25%                      10.000000               10.000000   \n",
       "50%                      10.000000               10.000000   \n",
       "75%                      10.000000               10.000000   \n",
       "max                      10.000000               10.000000   \n",
       "\n",
       "       review_scores_value  license  reviews_per_month  \n",
       "count         14222.000000      0.0       14660.000000  \n",
       "mean              9.402756      NaN           1.005809  \n",
       "std               1.040459      NaN           1.342168  \n",
       "min               2.000000      NaN           0.010000  \n",
       "25%               9.000000      NaN           0.140000  \n",
       "50%              10.000000      NaN           0.510000  \n",
       "75%              10.000000      NaN           1.370000  \n",
       "max              10.000000      NaN          41.250000  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a0e6370-a4e5-48f2-8d4f-87c60a55ce3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</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_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>...</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>license</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33057</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>AU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews']</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38901</td>\n",
       "      <td>2009-09-16</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0%</td>\n",
       "      <td>f</td>\n",
       "      <td>Brunswick</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "      <td>...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50121</td>\n",
       "      <td>2009-10-31</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>St Kilda</td>\n",
       "      <td>13.0</td>\n",
       "      <td>['email', 'phone', 'google', 'reviews', 'jumio...</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164193</td>\n",
       "      <td>2010-07-12</td>\n",
       "      <td>Berwick, Victoria, Australia</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>82%</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'manual_online', 'reviews',...</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>182833</td>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_id  host_since                   host_location host_response_time  \\\n",
       "0    33057  2009-08-21                              AU                NaN   \n",
       "1    38901  2009-09-16  Melbourne, Victoria, Australia                NaN   \n",
       "2    50121  2009-10-31  Melbourne, Victoria, Australia                NaN   \n",
       "3   164193  2010-07-12    Berwick, Victoria, Australia     within an hour   \n",
       "4   182833  2010-07-27  Melbourne, Victoria, Australia                NaN   \n",
       "\n",
       "  host_response_rate host_acceptance_rate host_is_superhost  \\\n",
       "0                NaN                  NaN                 f   \n",
       "1                NaN                   0%                 f   \n",
       "2                NaN                  NaN                 f   \n",
       "3               100%                  82%                 t   \n",
       "4                NaN                  NaN                 f   \n",
       "\n",
       "  host_neighbourhood  host_listings_count  \\\n",
       "0                NaN                  1.0   \n",
       "1          Brunswick                  1.0   \n",
       "2           St Kilda                 13.0   \n",
       "3                NaN                  1.0   \n",
       "4                NaN                  3.0   \n",
       "\n",
       "                                  host_verifications  ...  \\\n",
       "0                      ['email', 'phone', 'reviews']  ...   \n",
       "1  ['email', 'phone', 'reviews', 'jumio', 'govern...  ...   \n",
       "2  ['email', 'phone', 'google', 'reviews', 'jumio...  ...   \n",
       "3  ['email', 'phone', 'manual_online', 'reviews',...  ...   \n",
       "4  ['email', 'phone', 'facebook', 'reviews', 'jum...  ...   \n",
       "\n",
       "  review_scores_rating review_scores_accuracy review_scores_cleanliness  \\\n",
       "0                 90.0                    9.0                      10.0   \n",
       "1                 89.0                   10.0                       8.0   \n",
       "2                 94.0                   10.0                       9.0   \n",
       "3                 97.0                   10.0                      10.0   \n",
       "4                 94.0                   10.0                       9.0   \n",
       "\n",
       "  review_scores_checkin  review_scores_communication review_scores_location  \\\n",
       "0                  10.0                         10.0                    9.0   \n",
       "1                  10.0                         10.0                   10.0   \n",
       "2                  10.0                         10.0                   10.0   \n",
       "3                  10.0                         10.0                   10.0   \n",
       "4                  10.0                          9.0                    9.0   \n",
       "\n",
       "  review_scores_value  license  instant_bookable reviews_per_month  \n",
       "0                 9.0      NaN                 f              0.03  \n",
       "1                10.0      NaN                 f              1.44  \n",
       "2                 9.0      NaN                 f              0.32  \n",
       "3                10.0      NaN                 f              1.24  \n",
       "4                 9.0      NaN                 f              1.25  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d3b0e3-34ce-40d0-b503-c111718fe9fd",
   "metadata": {},
   "source": [
    "### Data for the machine learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b77f817a-9017-41df-8a6c-9d7d3806bf3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Probably, when the first_review is null the score is null too\n",
    "### Maybe related to the fact that the listing has no review yet\n",
    "(df['first_review'].isnull() & df['review_scores_rating'].notnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52e3ba8f-16e1-4b36-9f89-db708fd1c257",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8da3b28-f12b-40ce-86c9-617735168085",
   "metadata": {},
   "source": [
    "#### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "318e5cb6-ec93-44be-b89a-758e70c673d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Code to check the missing values\n",
    "# print((df_ml.isnull().sum() / df_ml.shape[0]).sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc64e17-e264-4d3c-9106-133464cc413b",
   "metadata": {},
   "source": [
    "**Removing data with a lot of missing values** \n",
    "\n",
    "The columns wih more then 37% of missing values were dropped, because they were difficult to fill with values, so they could introduce a lot of bias in the analysis.\n",
    "\n",
    "The first_review and last_review columns were dropped, because they had many nan values and the information of these columns, probably, were not very useful. I used, instead, the values from the column reviews_per_month to get an idea of how many reviews the listings had."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27539a46-27a3-43c5-89f5-2fc39c866cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml = df_ml.loc[:,(df_ml.isnull().sum() / df_ml.shape[0] <= .37)]\n",
    "\n",
    "df_ml.drop(['first_review','last_review'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96334f70-4b27-4bf0-84a8-4e3b27dc95fa",
   "metadata": {},
   "source": [
    "**Filling the missing values according to the information**\n",
    "\n",
    "The mean_columns below were filled with the average values because the reviews, normally, have a trend to reach the average when there are a lot of reviews for the places. I would be better to fill with the mean according to other variables, but it would be necessary to do a lot of other analysis to discover what groups to fill with which average value.  \n",
    "\n",
    "The min_columns were filled with the minimum value for each coulum, because there is no meaning of filling them with the average value (there is no half bedroom or bed). And the minimum value of the column beds is zero, so I could not fill them only with ones.\n",
    "\n",
    "The reviews_per_month column has some nan values, probably because there are no reviews yet. So I decided to fill the gaps with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa7a125d-0c21-4176-9e9a-bb8c4d962fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_columns = ['review_scores_value','review_scores_checkin','review_scores_location',\n",
    "                'review_scores_accuracy','review_scores_communication',\n",
    "                'review_scores_cleanliness','review_scores_rating']\n",
    "df_ml.fillna(df_ml[mean_columns].mean(), inplace=True)\n",
    "\n",
    "min_columns = ['bedrooms','beds']\n",
    "df_ml.fillna(df_ml[min_columns].min(), inplace=True)\n",
    "\n",
    "df_ml['reviews_per_month'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c11b0929-b9a8-47db-bc78-99511c94c1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop some rows with nan values, only 2% of the data, so it's worth dropping them.\n",
    "df_ml.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a91da9e-fbc5-4226-a44b-118ae6118d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Adjusting the boolean values\n",
    "boolean_columns = ['host_is_superhost','host_has_profile_pic','host_identity_verified','instant_bookable']\n",
    "\n",
    "df_ml[boolean_columns] = df_ml[boolean_columns].replace({'f':0, 't':1}, regex=False).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "93b0e97e-6c2b-4618-b641-d9de13cad2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Function to convert the column with array values to a dataframe with many columns\n",
    "def array_values_to_df(df, col_name):\n",
    "    \"\"\"\n",
    "    Convert a column from a dataframe with array values to a dataframe with columns\n",
    "    for each valuen in the arrays.\n",
    "    \n",
    "    Parameters:\n",
    "        df (DataFrame): The dataframe that contains the column with array values.\n",
    "        col_name (str): The name of the colum to be decomposed in columns.\n",
    "    \n",
    "    Returns:\n",
    "        DataFrame: A new dataframe containing the columns based on the values of the arrays.\n",
    "    \"\"\"\n",
    "    df_temp = pd.DataFrame()\n",
    "\n",
    "    for lst in df[col_name].tolist():\n",
    "        row = {}\n",
    "        \n",
    "        # Removing the unnecessary chars from the string, to check the values\n",
    "        for col in lst.replace('[','').replace(']','').replace('\\'','').split(', '):\n",
    "            if col not in df.columns:\n",
    "                df_temp[col] = np.nan\n",
    "\n",
    "            row[col] = 1\n",
    "\n",
    "        df_temp = df_temp.append(row, ignore_index=True)\n",
    "\n",
    "    return df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "865712dd-83ff-4c56-9a18-3604e93ba830",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_103/4200699920.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider using pd.concat instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_temp[col] = np.nan\n"
     ]
    }
   ],
   "source": [
    "### Change the columns with array values to many columns with integer value\n",
    "df_host_verifications = array_values_to_df(df_ml, 'host_verifications')\n",
    "df_amenities = array_values_to_df(df_ml, 'amenities')\n",
    "\n",
    "df_ml = pd.concat([df_ml, df_host_verifications], axis=1, ignore_index=False)\n",
    "df_ml = pd.concat([df_ml, df_amenities], axis=1, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c14fc6a-3f7b-49d8-80a2-d557c1f97f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fill the new created columns with 0\n",
    "df_ml.fillna(0, inplace=True)\n",
    "\n",
    "df_ml.drop(['host_verifications', 'amenities'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8dd37d4f-3c19-493b-8550-b9f4e3c33ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</th>\n",
       "      <th>\"SUKIN natural balance  conditioner\"</th>\n",
       "      <th>\"Sonos - 3 in the house that all connect Bluetooth sound system\"</th>\n",
       "      <th>\"Miele induction stove\"</th>\n",
       "      <th>\"miele oven\"</th>\n",
       "      <th>\"Kudos Spa conditioner\"</th>\n",
       "      <th>\"kudos body soap\"</th>\n",
       "      <th>\"Fisher &amp; Paykel Model E2491R refrigerator\"</th>\n",
       "      <th>\"Paid parking lot off premises \\u2013 AUD25 per day\"</th>\n",
       "      <th>\"HiSense Bar Fridge  refrigerator\"</th>\n",
       "      <th>\"Local Handmade Soap - All Natural body soap\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.878300e+04</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "      <td>18783.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.083577e+08</td>\n",
       "      <td>0.215141</td>\n",
       "      <td>10.040302</td>\n",
       "      <td>0.970558</td>\n",
       "      <td>0.779641</td>\n",
       "      <td>3.275515</td>\n",
       "      <td>1.549646</td>\n",
       "      <td>1.866794</td>\n",
       "      <td>163.740191</td>\n",
       "      <td>5.670021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.069924e+08</td>\n",
       "      <td>0.410931</td>\n",
       "      <td>67.786253</td>\n",
       "      <td>0.169045</td>\n",
       "      <td>0.414500</td>\n",
       "      <td>2.292585</td>\n",
       "      <td>0.963818</td>\n",
       "      <td>1.563321</td>\n",
       "      <td>411.980691</td>\n",
       "      <td>31.316293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>0.007297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.106610e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.553970e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.725970e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.962809e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7755.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>16267.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 858 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            host_id  host_is_superhost  host_listings_count  \\\n",
       "count  1.878300e+04       18783.000000         18783.000000   \n",
       "mean   1.083577e+08           0.215141            10.040302   \n",
       "std    1.069924e+08           0.410931            67.786253   \n",
       "min    0.000000e+00           0.000000             0.000000   \n",
       "25%    2.106610e+07           0.000000             1.000000   \n",
       "50%    6.553970e+07           0.000000             1.000000   \n",
       "75%    1.725970e+08           0.000000             4.000000   \n",
       "max    3.962809e+08           1.000000          7755.000000   \n",
       "\n",
       "       host_has_profile_pic  host_identity_verified  accommodates  \\\n",
       "count          18783.000000            18783.000000  18783.000000   \n",
       "mean               0.970558                0.779641      3.275515   \n",
       "std                0.169045                0.414500      2.292585   \n",
       "min                0.000000                0.000000      0.000000   \n",
       "25%                1.000000                1.000000      2.000000   \n",
       "50%                1.000000                1.000000      2.000000   \n",
       "75%                1.000000                1.000000      4.000000   \n",
       "max                1.000000                1.000000     16.000000   \n",
       "\n",
       "           bedrooms          beds         price  minimum_nights  ...  \\\n",
       "count  18783.000000  18783.000000  18783.000000    18783.000000  ...   \n",
       "mean       1.549646      1.866794    163.740191        5.670021  ...   \n",
       "std        0.963818      1.563321    411.980691       31.316293  ...   \n",
       "min        0.000000      0.000000      0.000000        0.000000  ...   \n",
       "25%        1.000000      1.000000     65.000000        1.000000  ...   \n",
       "50%        1.000000      1.000000    108.000000        2.000000  ...   \n",
       "75%        2.000000      2.000000    170.000000        3.000000  ...   \n",
       "max       16.000000     32.000000  16267.000000     1125.000000  ...   \n",
       "\n",
       "       \"SUKIN natural balance  conditioner\"  \\\n",
       "count                          18783.000000   \n",
       "mean                               0.000053   \n",
       "std                                0.007297   \n",
       "min                                0.000000   \n",
       "25%                                0.000000   \n",
       "50%                                0.000000   \n",
       "75%                                0.000000   \n",
       "max                                1.000000   \n",
       "\n",
       "       \"Sonos - 3 in the house that all connect Bluetooth sound system\"  \\\n",
       "count                                       18783.000000                  \n",
       "mean                                            0.000053                  \n",
       "std                                             0.007297                  \n",
       "min                                             0.000000                  \n",
       "25%                                             0.000000                  \n",
       "50%                                             0.000000                  \n",
       "75%                                             0.000000                  \n",
       "max                                             1.000000                  \n",
       "\n",
       "       \"Miele induction stove\"  \"miele oven\"  \"Kudos Spa conditioner\"  \\\n",
       "count             18783.000000  18783.000000             18783.000000   \n",
       "mean                  0.000053      0.000053                 0.000053   \n",
       "std                   0.007297      0.007297                 0.007297   \n",
       "min                   0.000000      0.000000                 0.000000   \n",
       "25%                   0.000000      0.000000                 0.000000   \n",
       "50%                   0.000000      0.000000                 0.000000   \n",
       "75%                   0.000000      0.000000                 0.000000   \n",
       "max                   1.000000      1.000000                 1.000000   \n",
       "\n",
       "       \"kudos body soap\"  \"Fisher & Paykel Model E2491R refrigerator\"  \\\n",
       "count       18783.000000                                 18783.000000   \n",
       "mean            0.000053                                     0.000053   \n",
       "std             0.007297                                     0.007297   \n",
       "min             0.000000                                     0.000000   \n",
       "25%             0.000000                                     0.000000   \n",
       "50%             0.000000                                     0.000000   \n",
       "75%             0.000000                                     0.000000   \n",
       "max             1.000000                                     1.000000   \n",
       "\n",
       "       \"Paid parking lot off premises \\u2013 AUD25 per day\"  \\\n",
       "count                                       18783.000000      \n",
       "mean                                            0.000053      \n",
       "std                                             0.007297      \n",
       "min                                             0.000000      \n",
       "25%                                             0.000000      \n",
       "50%                                             0.000000      \n",
       "75%                                             0.000000      \n",
       "max                                             1.000000      \n",
       "\n",
       "       \"HiSense Bar Fridge  refrigerator\"  \\\n",
       "count                        18783.000000   \n",
       "mean                             0.000053   \n",
       "std                              0.007297   \n",
       "min                              0.000000   \n",
       "25%                              0.000000   \n",
       "50%                              0.000000   \n",
       "75%                              0.000000   \n",
       "max                              1.000000   \n",
       "\n",
       "       \"Local Handmade Soap - All Natural body soap\"  \n",
       "count                                   18783.000000  \n",
       "mean                                        0.000053  \n",
       "std                                         0.007297  \n",
       "min                                         0.000000  \n",
       "25%                                         0.000000  \n",
       "50%                                         0.000000  \n",
       "75%                                         0.000000  \n",
       "max                                         1.000000  \n",
       "\n",
       "[8 rows x 858 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4d2b8f2-b883-4ce6-83d1-f9a0e1ef1e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>...</th>\n",
       "      <th>\"SUKIN natural balance  conditioner\"</th>\n",
       "      <th>\"Sonos - 3 in the house that all connect Bluetooth sound system\"</th>\n",
       "      <th>\"Miele induction stove\"</th>\n",
       "      <th>\"miele oven\"</th>\n",
       "      <th>\"Kudos Spa conditioner\"</th>\n",
       "      <th>\"kudos body soap\"</th>\n",
       "      <th>\"Fisher &amp; Paykel Model E2491R refrigerator\"</th>\n",
       "      <th>\"Paid parking lot off premises \\u2013 AUD25 per day\"</th>\n",
       "      <th>\"HiSense Bar Fridge  refrigerator\"</th>\n",
       "      <th>\"Local Handmade Soap - All Natural body soap\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33057.0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>AU</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manningham</td>\n",
       "      <td>Private room in house</td>\n",
       "      <td>Private room</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38901.0</td>\n",
       "      <td>2009-09-16</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>Private room in apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50121.0</td>\n",
       "      <td>2009-10-31</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Port Phillip</td>\n",
       "      <td>Entire apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164193.0</td>\n",
       "      <td>2010-07-12</td>\n",
       "      <td>Berwick, Victoria, Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Casey</td>\n",
       "      <td>Entire apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>182833.0</td>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>Melbourne, Victoria, Australia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Darebin</td>\n",
       "      <td>Private room in house</td>\n",
       "      <td>Private room</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 864 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    host_id  host_since                   host_location  host_is_superhost  \\\n",
       "0   33057.0  2009-08-21                              AU                0.0   \n",
       "1   38901.0  2009-09-16  Melbourne, Victoria, Australia                0.0   \n",
       "2   50121.0  2009-10-31  Melbourne, Victoria, Australia                0.0   \n",
       "3  164193.0  2010-07-12    Berwick, Victoria, Australia                1.0   \n",
       "4  182833.0  2010-07-27  Melbourne, Victoria, Australia                0.0   \n",
       "\n",
       "   host_listings_count  host_has_profile_pic  host_identity_verified  \\\n",
       "0                  1.0                   0.0                     0.0   \n",
       "1                  1.0                   1.0                     1.0   \n",
       "2                 13.0                   1.0                     1.0   \n",
       "3                  1.0                   1.0                     1.0   \n",
       "4                  3.0                   1.0                     1.0   \n",
       "\n",
       "  neighbourhood_cleansed              property_type        room_type  ...  \\\n",
       "0             Manningham      Private room in house     Private room  ...   \n",
       "1               Moreland  Private room in apartment     Private room  ...   \n",
       "2           Port Phillip           Entire apartment  Entire home/apt  ...   \n",
       "3                  Casey           Entire apartment  Entire home/apt  ...   \n",
       "4                Darebin      Private room in house     Private room  ...   \n",
       "\n",
       "   \"SUKIN natural balance  conditioner\"  \\\n",
       "0                                   0.0   \n",
       "1                                   0.0   \n",
       "2                                   0.0   \n",
       "3                                   0.0   \n",
       "4                                   0.0   \n",
       "\n",
       "  \"Sonos - 3 in the house that all connect Bluetooth sound system\"  \\\n",
       "0                                                0.0                 \n",
       "1                                                0.0                 \n",
       "2                                                0.0                 \n",
       "3                                                0.0                 \n",
       "4                                                0.0                 \n",
       "\n",
       "   \"Miele induction stove\"  \"miele oven\"  \"Kudos Spa conditioner\"  \\\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",
       "   \"kudos body soap\"  \"Fisher & Paykel Model E2491R refrigerator\"  \\\n",
       "0                0.0                                          0.0   \n",
       "1                0.0                                          0.0   \n",
       "2                0.0                                          0.0   \n",
       "3                0.0                                          0.0   \n",
       "4                0.0                                          0.0   \n",
       "\n",
       "   \"Paid parking lot off premises \\u2013 AUD25 per day\"  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   \"HiSense Bar Fridge  refrigerator\"  \\\n",
       "0                                 0.0   \n",
       "1                                 0.0   \n",
       "2                                 0.0   \n",
       "3                                 0.0   \n",
       "4                                 0.0   \n",
       "\n",
       "   \"Local Handmade Soap - All Natural body soap\"  \n",
       "0                                            0.0  \n",
       "1                                            0.0  \n",
       "2                                            0.0  \n",
       "3                                            0.0  \n",
       "4                                            0.0  \n",
       "\n",
       "[5 rows x 864 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5c1654-6300-452c-a84a-a1c22b05c61f",
   "metadata": {},
   "source": [
    "## 4.Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957b9d84-2f3d-40c1-b509-cc66e95a1245",
   "metadata": {},
   "source": [
    "During this phase I tried to answer some important questions.\n",
    "\n",
    "Sometimes I had to return to the *Prepare Data* phase to adjust some of the data used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e9ee781-0319-4115-b42b-099e54565ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Group values to the bar charts\n",
    "def xy_grouped(columns, group, calc):\n",
    "    \"\"\"\n",
    "    Auxiliar function to group data to the bar charts ploted below.\n",
    "    It only uses the mean() and sum() functions do calculate the values for the groups.\n",
    "    \n",
    "    Parameters:\n",
    "        columns (list): A list with the columns to be used in the chart.\n",
    "        group (str): The column's name to be used as a group on the x axis.\n",
    "        calc (string): The type of calculation to be used with the grouped values.\n",
    "    \n",
    "    Returns:\n",
    "        x: The x axis to be sent to the bar chart.\n",
    "        y: The y axis to be sent to the bar chart.\n",
    "    \"\"\"\n",
    "    grouped = df[columns].groupby(group)\n",
    "    x = list(grouped.groups.keys())\n",
    "    y = []\n",
    "    \n",
    "    if calc == 'mean':\n",
    "        values = grouped.mean().values\n",
    "    else:\n",
    "        values = grouped.sum().values\n",
    "    \n",
    "    for val in values:\n",
    "        y.append(val[0])\n",
    "        \n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7945e77d-7bae-4833-9ecf-bb13b087386f",
   "metadata": {},
   "source": [
    "### What are the room types with more reviews?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3b7ad56-2565-4cc8-88b2-96faf779246a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = xy_grouped(columns=['room_type','number_of_reviews'], group='room_type', calc='sum')\n",
    "plt.bar(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c3b29e-e80a-40a7-b086-c081ef787c35",
   "metadata": {},
   "source": [
    "### Does the room’s type influence the rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d42fefe2-d4dd-42fa-9fc5-1e56956b5850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = xy_grouped(columns=['room_type','review_scores_rating'], group='room_type', calc='mean')\n",
    "plt.bar(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261c9749-9b10-4842-b0ee-cec6a3cf0cc0",
   "metadata": {},
   "source": [
    "### Does the room’s type influence the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ec62cb10-61e2-4540-9975-37e920d3ded1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = xy_grouped(columns=['room_type','price'], group='room_type', calc='mean')\n",
    "plt.bar(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344a8859-fb73-47bb-9a2a-622b51aa3145",
   "metadata": {},
   "source": [
    "### Does the price correlate with the users' ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b300f48-a278-4a2f-8d54-b34567f042c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bins = 20\n",
    "df_filtered = df[df['review_scores_rating'] >= 0][['price','review_scores_rating']]\n",
    "grouped = df_filtered.groupby(pd.cut(df_filtered['review_scores_rating'], bins=n_bins))\n",
    "\n",
    "x = []\n",
    "for group in list(grouped.groups.keys()):\n",
    "    x.append(group.right)\n",
    "\n",
    "y = []\n",
    "for val in grouped.mean().values:\n",
    "    y.append(val[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12f6539c-6bdb-48a9-a652-2f12782468b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x, bins=n_bins, weights=y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf09479f-b690-4fa9-a47d-26d7f38523f9",
   "metadata": {},
   "source": [
    "### What are the variables that influence the price more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5dfdf861-898d-4346-a230-495bb3062d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Separate the input and the output\n",
    "X = df_ml.drop(['price'], axis=1)\n",
    "y = df_ml['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e1e9e9b-093b-4302-8535-c8661ef04d2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py:3607: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._set_item(key, value)\n",
      "/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py:3599: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._set_item_frame_value(key, value)\n"
     ]
    }
   ],
   "source": [
    "### Change the float columns to the int type\n",
    "for col in X.select_dtypes(include=['float']).columns:\n",
    "    if col != 'reviews_per_month':\n",
    "        X[col] = X[col].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "41feff28-a17a-4c14-8f85-024131b01831",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Include columns to the categorical variables\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c749113-072a-421d-8d52-f5510966f24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Separate the train and test values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "38709265-4620-4134-a1a0-70dbb24bf0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=True)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Train the model\n",
    "model = LinearRegression(normalize=True)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb0e9c73-4090-435d-ba56-e87306358920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The r-squared score for the model was -5.075248099619673e+27 on 5635 values.'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Predict and score the model\n",
    "y_test_preds = model.predict(X_test) \n",
    "\"The r-squared score for the model was {} on {} values.\".format(r2_score(y_test, y_test_preds), len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c82d9654-fdfa-4e93-a64a-1b4180aed488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the variables and the importances\n",
    "data = {'Features': X.columns, 'Importance': abs(model.coef_)}\n",
    "df_plot = pd.DataFrame(data)\n",
    "\n",
    "df_plot.sort_values('Importance', ascending=True, inplace=True)\n",
    "df_plot = df_plot[-10:]\n",
    "\n",
    "plt.barh(df_plot['Features'], df_plot['Importance']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc27d841-1636-447d-aa49-7eb9dbd5ee39",
   "metadata": {},
   "source": [
    "## 5.Evaluate the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f92852-84a0-4841-a4e2-cc21d0331328",
   "metadata": {},
   "source": [
    "With the data and charts plotted, we discovered some variables that influence the prices of accommodations in the city of Melbourne.\n",
    "\n",
    "The most important variables were the room's types and the ratings the places received previously. \n",
    "\n",
    "These variables showed that guests have to pay a higher price for more private places and accommodations with higher ratings.\n",
    "\n",
    "We tried to use a Linear Regression algorithm to better describe the places according to the other data provided, bit without much success.\n",
    "\n",
    "The R² metric was negative for the experiment, indicating that the algorithm described the data worse than a horizontal line.\n",
    "\n",
    "Some actions could be done to get a better result on the Linear Regression Algorithm:\n",
    "- Exclude more columns from the dataset, like the host_since variables. They probably are inserting bias in the algorithm calculations.\n",
    "- Prepare better the free text values. There are some strange values in the dataset used by the model. The chart plotting the mpst important variables showed some."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}