AirBnB_Open_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c926093",
   "metadata": {},
   "source": [
    "# ABOUT DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b337065",
   "metadata": {},
   "source": [
    "New York City Airbnb Data Cleaning\n",
    "\n",
    "Airbnb, Inc is an American company that operates an online marketplace for lodging, primarily homestays for vacation rentals, and tourism activities. Based in San Francisco, California, the platform is accessible via website and mobile app. Airbnb does not own any of the listed properties; instead, it profits by receiving commission from each booking. The company was founded in 2008. Airbnb is a shortened version of its original name, AirBedandBreakfast.com.\n",
    "\n",
    "<h3>About Dataset</h3>\n",
    "<h3>Context</h3>\n",
    "\n",
    "Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in New York City\n",
    "\n",
    "<h3>Content</h3>\n",
    "\n",
    "The following Airbnb activity is included in this New York dataset:\n",
    "\n",
    "Listings, including full descriptions and average review score Reviews, including unique id for each reviewer and detailed comments Calendar, including listing id and the price and availability for that day\n",
    "\n",
    "<h3>Inspiration</h3>\n",
    "+ What can we learn about different hosts and areas?\n",
    "\n",
    "+ What can we learn from predictions? (ex: locations, prices, reviews, etc)\n",
    "\n",
    "+ Which hosts are the busiest and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae68bcf",
   "metadata": {},
   "source": [
    "# LOAD LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e186d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (3.5.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib) (1.23.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib) (4.36.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib) (9.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: seaborn in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (0.11.2)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from seaborn) (1.9.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from seaborn) (1.4.3)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from seaborn) (1.23.1)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from seaborn) (3.5.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib>=2.2->seaborn) (4.36.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib>=2.2->seaborn) (9.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from pandas>=0.23->seaborn) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\miniconda3\\envs\\yolo\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install matplotlib\n",
    "!pip install seaborn\n",
    "import os\n",
    "import sys\n",
    "import csv\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5281f5a",
   "metadata": {},
   "source": [
    "# LOAD DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1441e234",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/ADmin/Downloads/Airbnb_Open_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0a68170",
   "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>NAME</th>\n",
       "      <th>host id</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host name</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>country</th>\n",
       "      <th>...</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>last review</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>license</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001254</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>80014485718</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Madaline</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10/19/2021</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>Clean up and treat the home the way you'd like...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002102</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>52335172823</td>\n",
       "      <td>verified</td>\n",
       "      <td>Jenna</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5/21/2022</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>Pet friendly but please confirm with me if the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002403</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>78829239556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elise</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$124</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>I encourage you to use my kitchen, cooking and...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85098326012</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Garry</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7/5/2019</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003689</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>92037596077</td>\n",
       "      <td>verified</td>\n",
       "      <td>Lyndon</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11/19/2018</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>Please no smoking in the house, porch or on th...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                              NAME      host id  \\\n",
       "0  1001254                Clean & quiet apt home by the park  80014485718   \n",
       "1  1002102                             Skylit Midtown Castle  52335172823   \n",
       "2  1002403               THE VILLAGE OF HARLEM....NEW YORK !  78829239556   \n",
       "3  1002755                                               NaN  85098326012   \n",
       "4  1003689  Entire Apt: Spacious Studio/Loft by central park  92037596077   \n",
       "\n",
       "  host_identity_verified host name neighbourhood group neighbourhood  \\\n",
       "0            unconfirmed  Madaline            Brooklyn    Kensington   \n",
       "1               verified     Jenna           Manhattan       Midtown   \n",
       "2                    NaN     Elise           Manhattan        Harlem   \n",
       "3            unconfirmed     Garry            Brooklyn  Clinton Hill   \n",
       "4               verified    Lyndon           Manhattan   East Harlem   \n",
       "\n",
       "        lat      long        country  ... service fee minimum nights  \\\n",
       "0  40.64749 -73.97237  United States  ...       $193            10.0   \n",
       "1  40.75362 -73.98377  United States  ...        $28            30.0   \n",
       "2  40.80902 -73.94190  United States  ...       $124             3.0   \n",
       "3  40.68514 -73.95976  United States  ...        $74            30.0   \n",
       "4  40.79851 -73.94399  United States  ...        $41            10.0   \n",
       "\n",
       "  number of reviews last review  reviews per month review rate number  \\\n",
       "0               9.0  10/19/2021               0.21                4.0   \n",
       "1              45.0   5/21/2022               0.38                4.0   \n",
       "2               0.0         NaN                NaN                5.0   \n",
       "3             270.0    7/5/2019               4.64                4.0   \n",
       "4               9.0  11/19/2018               0.10                3.0   \n",
       "\n",
       "  calculated host listings count  availability 365  \\\n",
       "0                            6.0             286.0   \n",
       "1                            2.0             228.0   \n",
       "2                            1.0             352.0   \n",
       "3                            1.0             322.0   \n",
       "4                            1.0             289.0   \n",
       "\n",
       "                                         house_rules license  \n",
       "0  Clean up and treat the home the way you'd like...     NaN  \n",
       "1  Pet friendly but please confirm with me if the...     NaN  \n",
       "2  I encourage you to use my kitchen, cooking and...     NaN  \n",
       "3                                                NaN     NaN  \n",
       "4  Please no smoking in the house, porch or on th...     NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47fb47ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 102599\n",
      "Number of columns: 26\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of records: {}\".format(df.shape[0]))\n",
    "print(\"Number of columns: {}\".format(df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54412bb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102599 entries, 0 to 102598\n",
      "Data columns (total 26 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   id                              102599 non-null  int64  \n",
      " 1   NAME                            102349 non-null  object \n",
      " 2   host id                         102599 non-null  int64  \n",
      " 3   host_identity_verified          102310 non-null  object \n",
      " 4   host name                       102193 non-null  object \n",
      " 5   neighbourhood group             102570 non-null  object \n",
      " 6   neighbourhood                   102583 non-null  object \n",
      " 7   lat                             102591 non-null  float64\n",
      " 8   long                            102591 non-null  float64\n",
      " 9   country                         102067 non-null  object \n",
      " 10  country code                    102468 non-null  object \n",
      " 11  instant_bookable                102494 non-null  object \n",
      " 12  cancellation_policy             102523 non-null  object \n",
      " 13  room type                       102599 non-null  object \n",
      " 14  Construction year               102385 non-null  float64\n",
      " 15  price                           102352 non-null  object \n",
      " 16  service fee                     102326 non-null  object \n",
      " 17  minimum nights                  102190 non-null  float64\n",
      " 18  number of reviews               102416 non-null  float64\n",
      " 19  last review                     86706 non-null   object \n",
      " 20  reviews per month               86720 non-null   float64\n",
      " 21  review rate number              102273 non-null  float64\n",
      " 22  calculated host listings count  102280 non-null  float64\n",
      " 23  availability 365                102151 non-null  float64\n",
      " 24  house_rules                     50468 non-null   object \n",
      " 25  license                         2 non-null       object \n",
      "dtypes: float64(9), int64(2), object(15)\n",
      "memory usage: 20.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a606bcd4",
   "metadata": {},
   "source": [
    "# CLEAN DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "463b5af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 102058\n",
      "Number of columns: 26\n"
     ]
    }
   ],
   "source": [
    "# drop duplicates\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(\"Number of records: {}\".format(df.shape[0]))\n",
    "print(\"Number of columns: {}\".format(df.shape[1]))\n",
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ab390fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'NAME', 'host id', 'host_identity_verified', 'host name', 'neighbourhood group', 'neighbourhood', 'lat', 'long', 'country', 'country code', 'instant_bookable', 'cancellation_policy', 'room type', 'Construction year', 'price', 'service fee', 'minimum nights', 'number of reviews', 'last review', 'reviews per month', 'review rate number', 'calculated host listings count', 'availability 365', 'house_rules', 'license']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2204c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'name', 'host_id', 'host_identity_verified', 'host_name', 'neighbourhood_group', 'neighbourhood', 'lat', 'long', 'country', 'country_code', 'instant_bookable', 'cancellation_policy', 'room_type', 'construction_year', 'price', 'service_fee', 'minimum_nights', 'number_of_reviews', 'last_review', 'reviews_per_month', 'review_rate_number', 'calculated_host_listings_count', 'availability_365', 'house_rules', 'license']\n"
     ]
    }
   ],
   "source": [
    "# lower all the column names, change \" \" to \"_\" to all column names\n",
    "df.columns = [col.lower().replace(\" \",\"_\") for col in df.columns]\n",
    "print(df.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6202450",
   "metadata": {},
   "source": [
    "**Now we will handle MISSING DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "296508e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "name                                 250\n",
       "host_id                                0\n",
       "host_identity_verified               289\n",
       "host_name                            404\n",
       "neighbourhood_group                   29\n",
       "neighbourhood                         16\n",
       "lat                                    8\n",
       "long                                   8\n",
       "country                              532\n",
       "country_code                         131\n",
       "instant_bookable                     105\n",
       "cancellation_policy                   76\n",
       "room_type                              0\n",
       "construction_year                    214\n",
       "price                                247\n",
       "service_fee                          273\n",
       "minimum_nights                       400\n",
       "number_of_reviews                    183\n",
       "last_review                        15832\n",
       "reviews_per_month                  15818\n",
       "review_rate_number                   319\n",
       "calculated_host_listings_count       319\n",
       "availability_365                     448\n",
       "house_rules                        51842\n",
       "license                           102056\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c51fda2",
   "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>columns</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>name</td>\n",
       "      <td>0.244959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>host_id</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>host_identity_verified</td>\n",
       "      <td>0.283172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>host_name</td>\n",
       "      <td>0.395853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>neighbourhood_group</td>\n",
       "      <td>0.028415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>neighbourhood</td>\n",
       "      <td>0.015677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lat</td>\n",
       "      <td>0.007839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>long</td>\n",
       "      <td>0.007839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>country</td>\n",
       "      <td>0.521272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>country_code</td>\n",
       "      <td>0.128358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>instant_bookable</td>\n",
       "      <td>0.102883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cancellation_policy</td>\n",
       "      <td>0.074467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>room_type</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>construction_year</td>\n",
       "      <td>0.209685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>price</td>\n",
       "      <td>0.242019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>service_fee</td>\n",
       "      <td>0.267495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>minimum_nights</td>\n",
       "      <td>0.391934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>number_of_reviews</td>\n",
       "      <td>0.179310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>last_review</td>\n",
       "      <td>15.512748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>reviews_per_month</td>\n",
       "      <td>15.499030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>review_rate_number</td>\n",
       "      <td>0.312567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>calculated_host_listings_count</td>\n",
       "      <td>0.312567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>availability_365</td>\n",
       "      <td>0.438966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>house_rules</td>\n",
       "      <td>50.796606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>license</td>\n",
       "      <td>99.998040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           columns          %\n",
       "0                               id   0.000000\n",
       "1                             name   0.244959\n",
       "2                          host_id   0.000000\n",
       "3           host_identity_verified   0.283172\n",
       "4                        host_name   0.395853\n",
       "5              neighbourhood_group   0.028415\n",
       "6                    neighbourhood   0.015677\n",
       "7                              lat   0.007839\n",
       "8                             long   0.007839\n",
       "9                          country   0.521272\n",
       "10                    country_code   0.128358\n",
       "11                instant_bookable   0.102883\n",
       "12             cancellation_policy   0.074467\n",
       "13                       room_type   0.000000\n",
       "14               construction_year   0.209685\n",
       "15                           price   0.242019\n",
       "16                     service_fee   0.267495\n",
       "17                  minimum_nights   0.391934\n",
       "18               number_of_reviews   0.179310\n",
       "19                     last_review  15.512748\n",
       "20               reviews_per_month  15.499030\n",
       "21              review_rate_number   0.312567\n",
       "22  calculated_host_listings_count   0.312567\n",
       "23                availability_365   0.438966\n",
       "24                     house_rules  50.796606\n",
       "25                         license  99.998040"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the % missing values\n",
    "percentage_result = df.isnull().sum()/ len(df) * 100\n",
    "# create a DataFrame to show case the result\n",
    "percentage_result = pd.DataFrame({\"columns\": percentage_result.keys(), \"%\": percentage_result})\n",
    "percentage_result.reset_index(drop=True, inplace=True)\n",
    "percentage_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c4b5d9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# host_identity_verified, neighbourhood, country\n",
    "# we will fill null values with \"Unknown\"\n",
    "\n",
    "df['host_identity_verified'] = np.where(df['host_identity_verified'].isnull(), 'Unknown', df['host_identity_verified'])\n",
    "df['neighbourhood'] = np.where(df['neighbourhood'].isnull(), 'Unknown', df['neighbourhood'])\n",
    "df['neighbourhood_group'] = np.where(df['neighbourhood_group'].isnull(), 'Unknown', df['neighbourhood_group'])\n",
    " # Because the research is conducted in New York so we will fill null value with United States \n",
    "df['country'] = np.where(df['country'].isnull(), 'United States', df['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd5016a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# review_rate_number\n",
    "# we will fill null values with -1 (Becasue rating is from 1-5)\n",
    "df['review_rate_number'] = np.where(df['review_rate_number'].isnull(), -1, df['review_rate_number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1db687e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will exclude some unnecessaray columns from the dataset (license has too much missing value and id is not needed)\n",
    "df.drop([\"id\", \"license\"], axis = 1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2756a7c0",
   "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>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>...</th>\n",
       "      <th>price</th>\n",
       "      <th>service_fee</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>review_rate_number</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>house_rules</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>80014485718</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Madaline</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>$966</td>\n",
       "      <td>$193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10/19/2021</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>Clean up and treat the home the way you'd like...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>52335172823</td>\n",
       "      <td>verified</td>\n",
       "      <td>Jenna</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>$142</td>\n",
       "      <td>$28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5/21/2022</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>Pet friendly but please confirm with me if the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>78829239556</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Elise</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>$620</td>\n",
       "      <td>$124</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>I encourage you to use my kitchen, cooking and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>85098326012</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Garry</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>$368</td>\n",
       "      <td>$74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7/5/2019</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>92037596077</td>\n",
       "      <td>verified</td>\n",
       "      <td>Lyndon</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>$204</td>\n",
       "      <td>$41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11/19/2018</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>Please no smoking in the house, porch or on th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               name      host_id  \\\n",
       "0                Clean & quiet apt home by the park  80014485718   \n",
       "1                             Skylit Midtown Castle  52335172823   \n",
       "2               THE VILLAGE OF HARLEM....NEW YORK !  78829239556   \n",
       "3                                               NaN  85098326012   \n",
       "4  Entire Apt: Spacious Studio/Loft by central park  92037596077   \n",
       "\n",
       "  host_identity_verified host_name neighbourhood_group neighbourhood  \\\n",
       "0            unconfirmed  Madaline            Brooklyn    Kensington   \n",
       "1               verified     Jenna           Manhattan       Midtown   \n",
       "2                Unknown     Elise           Manhattan        Harlem   \n",
       "3            unconfirmed     Garry            Brooklyn  Clinton Hill   \n",
       "4               verified    Lyndon           Manhattan   East Harlem   \n",
       "\n",
       "        lat      long        country country_code  ...  price service_fee  \\\n",
       "0  40.64749 -73.97237  United States           US  ...  $966        $193    \n",
       "1  40.75362 -73.98377  United States           US  ...  $142         $28    \n",
       "2  40.80902 -73.94190  United States           US  ...  $620        $124    \n",
       "3  40.68514 -73.95976  United States           US  ...  $368         $74    \n",
       "4  40.79851 -73.94399  United States           US  ...  $204         $41    \n",
       "\n",
       "  minimum_nights  number_of_reviews last_review reviews_per_month  \\\n",
       "0           10.0                9.0  10/19/2021              0.21   \n",
       "1           30.0               45.0   5/21/2022              0.38   \n",
       "2            3.0                0.0         NaN               NaN   \n",
       "3           30.0              270.0    7/5/2019              4.64   \n",
       "4           10.0                9.0  11/19/2018              0.10   \n",
       "\n",
       "   review_rate_number  calculated_host_listings_count availability_365  \\\n",
       "0                 4.0                             6.0            286.0   \n",
       "1                 4.0                             2.0            228.0   \n",
       "2                 5.0                             1.0            352.0   \n",
       "3                 4.0                             1.0            322.0   \n",
       "4                 3.0                             1.0            289.0   \n",
       "\n",
       "                                         house_rules  \n",
       "0  Clean up and treat the home the way you'd like...  \n",
       "1  Pet friendly but please confirm with me if the...  \n",
       "2  I encourage you to use my kitchen, cooking and...  \n",
       "3                                                NaN  \n",
       "4  Please no smoking in the house, porch or on th...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d87f16d",
   "metadata": {},
   "source": [
    "# EDA (EXPLORATORY DATA ANALYSIS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6909b53",
   "metadata": {},
   "source": [
    "### COUNTRY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "840209e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States    102058\n",
      "Name: country, dtype: int64\n",
      "United States    100.0\n",
      "Name: country, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['country'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['country'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca4c490",
   "metadata": {},
   "source": [
    "### HOST_IDENTITY_VERIFIED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "291b1ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unconfirmed    50944\n",
      "verified       50825\n",
      "Unknown          289\n",
      "Name: host_identity_verified, dtype: int64\n",
      "unconfirmed    49.916714\n",
      "verified       49.800114\n",
      "Unknown         0.283172\n",
      "Name: host_identity_verified, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['host_identity_verified'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['host_identity_verified'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "981a0a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.xlabel(\"feature: {}\".format(\"host_identity_verified\"))\n",
    "ax = sns.countplot(data=df, x='host_identity_verified')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "120d1e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.xlabel(\"feature: {}\".format(\"host_identity_verified\"))\n",
    "plt.pie(df['host_identity_verified'].value_counts()*100/len(df), autopct='%.2f', startangle=90)\n",
    "plt.legend(df['host_identity_verified'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8840c2a8",
   "metadata": {},
   "source": [
    "### NEIGHBORHOOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7ce74b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bedford-Stuyvesant        7898\n",
      "Williamsburg              7732\n",
      "Harlem                    5441\n",
      "Bushwick                  4956\n",
      "Hell's Kitchen            3949\n",
      "                          ... \n",
      "Willowbrook                  3\n",
      "Gerritsen Beach              3\n",
      "Fort Wadsworth               2\n",
      "Glen Oaks                    2\n",
      "Chelsea, Staten Island       1\n",
      "Name: neighbourhood, Length: 225, dtype: int64\n",
      "Bedford-Stuyvesant        7.738737\n",
      "Williamsburg              7.576084\n",
      "Harlem                    5.331282\n",
      "Bushwick                  4.856062\n",
      "Hell's Kitchen            3.869368\n",
      "                            ...   \n",
      "Willowbrook               0.002940\n",
      "Gerritsen Beach           0.002940\n",
      "Fort Wadsworth            0.001960\n",
      "Glen Oaks                 0.001960\n",
      "Chelsea, Staten Island    0.000980\n",
      "Name: neighbourhood, Length: 225, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['neighbourhood'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['neighbourhood'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e35d344",
   "metadata": {},
   "source": [
    "### NEIGHBORHOOD_GROUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1dc76f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan        43557\n",
      "Brooklyn         41630\n",
      "Queens           13197\n",
      "Bronx             2694\n",
      "Staten Island      949\n",
      "Unknown             29\n",
      "brookln              1\n",
      "manhatan             1\n",
      "Name: neighbourhood_group, dtype: int64\n",
      "Manhattan        42.678673\n",
      "Brooklyn         40.790531\n",
      "Queens           12.930882\n",
      "Bronx             2.639675\n",
      "Staten Island     0.929863\n",
      "Unknown           0.028415\n",
      "brookln           0.000980\n",
      "manhatan          0.000980\n",
      "Name: neighbourhood_group, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['neighbourhood_group'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['neighbourhood_group'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "00f8e4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.xlabel(\"feature: {}\".format(\"neighbourhood_group\"))\n",
    "ax = sns.countplot(data=df, x='neighbourhood_group')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c7d240b",
   "metadata": {},
   "source": [
    "### REVIEW_RATE_NUMBER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "832ce98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5.0    23251\n",
      " 4.0    23200\n",
      " 3.0    23130\n",
      " 2.0    22972\n",
      " 1.0     9186\n",
      "-1.0      319\n",
      "Name: review_rate_number, dtype: int64\n",
      " 5.0    22.782143\n",
      " 4.0    22.732172\n",
      " 3.0    22.663583\n",
      " 2.0    22.508770\n",
      " 1.0     9.000764\n",
      "-1.0     0.312567\n",
      "Name: review_rate_number, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['review_rate_number'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['review_rate_number'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34d84e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4\n",
       "1         4\n",
       "2         5\n",
       "3         4\n",
       "4         3\n",
       "         ..\n",
       "102053    5\n",
       "102054    3\n",
       "102055    4\n",
       "102056    1\n",
       "102057    4\n",
       "Name: review_rate_number, Length: 102058, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change float to int\n",
    "df['review_rate_number'] = df['review_rate_number'].astype('int64',errors='ignore')\n",
    "df['review_rate_number'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc8b076e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.xlabel(\"feature: {}\".format(\"review_rate_number\"))\n",
    "ax = sns.countplot(data=df, x='review_rate_number')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601e1fac",
   "metadata": {},
   "source": [
    "### ROOM_TYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7ca9afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entire home/apt    53429\n",
      "Private room       46306\n",
      "Shared room         2208\n",
      "Hotel room           115\n",
      "Name: room_type, dtype: int64\n",
      "Entire home/apt    52.351604\n",
      "Private room       45.372239\n",
      "Shared room         2.163476\n",
      "Hotel room          0.112681\n",
      "Name: room_type, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# COUNT VALUES\n",
    "print(df['room_type'].value_counts())\n",
    "# COUNT VALUES IN PERCENTAGE \n",
    "print(df['room_type'].value_counts() * 100 / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bacb8667",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W5UlW8GYQ7AEsB0gprYuIvwF75PqsisVWzrN8g/rhTbRjHDAOYJ999tlYsyVJktq1ZnvgUkpXppT6AOemlN6dUuqTH+9JKW00wKWU1qeUBgM9KXrMDtwird5EKaVrU0q1KaXabt26tUUTJEmStpiN9cABkFL6cUS8D+hdOU9K6cYWzv9iRNwNHAnsFhE1uReuJ7AyT7YS6AWsiIgaYFfghYp6vcp5mqpLkiR1WC29iOHnwH8AHwDemx+1G5mnW0Tslod3AIYCjwB3A6PzZGOBKXl4ah4nP39XSinl+qn5KtU+QF9gNjAH6Juvat2W4kKHqS3ZHkmSpDJrUQ8cRVgbkANVS+0FTMznwXUCbkkpTYuIxcCkiPh34CHg+jz99cDPI2IpsJoikJFSWhQRtwCLgXXAF1NK6wEi4kvAdKAzMD6ltGgT2idJklRKLQ1wC4H/BTzT0gWnlBYAhzRSf4LifLgN668BJzexrIuBixup/wb4TUvbJEmS1BG0NMB1BRZHxGyK73cDIKV0QlVaJUmSpCa1NMBdWM1GSJIkqeVaehXqvdVuiCRJklqmRQEuIv5OcRcFgG0p7qrwSkppl2o1TJIkSY1raQ/czvXDFbe3OqJajZIkSVLTWvQ9cJXyPU5/DRy35ZsjSZKkjWnpIdSTKkY7UXwv3GtVaZEkSZKa1dKrUD9eMbwOWEZxGFWSJEmtrKXnwH262g2RJElSy7T0Xqg9I2JyRDyXH7dHRM9qN06SJElv19KLGG6guFH83vnx37kmSZKkVtbSANctpXRDSmldfkwAulWxXZIkSWpCSwPcCxHxqYjonB+fAl6oZsMkSZLUuJYGuLOAU4C/As8Ao4Ezq9QmSZIkNaOlXyNyETA2pbQGICK6AP9BEewkSZLUilraAzeoPrwBpJRWA4dUp0mSJElqTksDXKeI2L1+JPfAtbT3TpIkSVtQS0PYD4E/RMStefxk4OLqNEmSJEnNaemdGG6MiDrgmFw6KaW0uHrNkiRJUlNafBg0BzZDmyRJUhtr6TlwkiRJaicMcJIkSSVjgJMkSSoZA5wkSVLJGOAkSZJKxgAnSZJUMgY4SZKkkjHASZIklYwBTpIkqWQMcJIkSSVjgJMkSSoZA5wkSVLJGOAkSZJKxgAnSZJUMgY4SZKkkjHASZIklYwBTpIkqWQMcJIkSSVjgJMkSSqZqgW4iOgVEXdHxOKIWBQRX831LhFxZ0QsyT93z/WIiKsiYmlELIiIQyuWNTZPvyQixlbUh0TEw3meqyIiqrU9kiRJ7UU1e+DWAf87pTQAOAL4YkQMAM4DZqaU+gIz8zjA8UDf/BgH/AyKwAd8BzgcOAz4Tn3oy9N8tmK+4VXcHkmSpHahagEupfRMSmleHv478AjQAzgRmJgnmwiMyMMnAjemwixgt4jYCzgOuDOltDqltAa4Exien9slpTQrpZSAGyuWJUmS1GG1yjlwEdEbOAT4I7BnSumZ/NRfgT3zcA9gecVsK3KtufqKRuqNrX9cRNRFRN2qVas2b2MkSZLaWNUDXETsBNwOnJNSeqnyudxzlqrdhpTStSml2pRSbbdu3aq9OkmSpKqqaoCLiG0owttNKaVf5fKz+fAn+edzub4S6FUxe89ca67es5G6JElSh1bNq1ADuB54JKX0nxVPTQXqryQdC0ypqJ+Rr0Y9AvhbPtQ6HRgWEbvnixeGAdPzcy9FxBF5XWdULEuSJKnDqqnist8PnA48HBHzc+3fgB8At0TE2cBfgFPyc78BPgIsBV4FPg2QUlodEd8D5uTpLkoprc7DXwAmADsAv80PSZKkDq1qAS6l9ADQ1PeyHdvI9An4YhPLGg+Mb6ReBwzcjGZKkiSVjndikCRJKhkDnCRJUskY4CRJkkrGACdJklQyBjhJkqSSMcBJkiSVjAFOkiSpZAxwkiRJJWOAkyRJKhkDnCRJUskY4CRJkkrGACdJklQyBjhJkqSSMcBJkiSVjAFOkiSpZAxwkiRJJWOAkyRJKhkDnCRJUskY4CRJkkrGACdJklQyBjhJkqSSMcBJkiSVjAFOkiSpZAxwkiRJJWOAkyRJKhkDnCRJUskY4CRJkkrGACdJklQyBjhJkqSSMcBJkiSVjAFOkiSpZAxwkiRJJWOAkyRJKhkDnCRJUskY4CRJkkrGACdJklQyBjhJkqSSMcBJkiSVjAFOkiSpZAxwkiRJJVO1ABcR4yPiuYhYWFHrEhF3RsSS/HP3XI+IuCoilkbEgog4tGKesXn6JRExtqI+JCIezvNcFRFRrW2RJElqT6rZAzcBGL5B7TxgZkqpLzAzjwMcD/TNj3HAz6AIfMB3gMOBw4Dv1Ie+PM1nK+bbcF2SJEkdUtUCXErpPmD1BuUTgYl5eCIwoqJ+YyrMAnaLiL2A44A7U0qrU0prgDuB4fm5XVJKs1JKCbixYlmSJEkdWmufA7dnSumZPPxXYM883ANYXjHdilxrrr6ikXqjImJcRNRFRN2qVas2bwskSZLaWJtdxJB7zlIrrevalFJtSqm2W7durbFKSZKkqmntAPdsPvxJ/vlcrq8EelVM1zPXmqv3bKQuSZLU4bV2gJsK1F9JOhaYUlE/I1+NegTwt3yodTowLCJ2zxcvDAOm5+deiogj8tWnZ1QsS5IkqUOrqdaCI+KXwNFA14hYQXE16Q+AWyLibOAvwCl58t8AHwGWAq8CnwZIKa2OiO8Bc/J0F6WU6i+M+ALFla47AL/ND0mSpA6vagEupXRaE08d28i0CfhiE8sZD4xvpF4HDNycNkqSJJWRd2KQJEkqGQOcJElSyRjgJEmSSsYAJ0mSVDIGOEmSpJIxwEmSJJWMAU6SJKlkDHCSJEklY4CTJEkqGQOcJElSyRjgJEmSSsYAJ0mSVDIGOEmSpJIxwEmSJJWMAU6SJKlkDHCSJEklY4CTJEkqGQOcJElSyRjgJEmSSsYAJ0mSVDIGOEmSpJIxwEmSJJWMAU6S1CLLly/nwx/+MAMGDOCggw7iyiuvBOAb3/gGBx54IIMGDWLkyJG8+OKLDfNccskl7L///hxwwAFMnz69oX7FFVdw0EEHMXDgQE477TRee+211t4cqdQMcJKkFqmpqeGHP/whixcvZtasWVx99dUsXryYoUOHsnDhQhYsWEC/fv245JJLAFi8eDGTJk1i0aJF3HHHHXzhC19g/fr1rFy5kquuuoq6ujoWLlzI+vXrmTRpUhtvnVQuBjhJUovstddeHHrooQDsvPPO9O/fn5UrVzJs2DBqamoAOOKII1ixYgUAU6ZM4dRTT2W77bajT58+7L///syePRuAdevW8Y9//IN169bx6quvsvfee7fNRkklZYCTJG2yZcuW8dBDD3H44Ye/pT5+/HiOP/54AFauXEmvXr0anuvZsycrV66kR48enHvuueyzzz7stdde7LrrrgwbNqxV2y+VnQFOkrRJXn75ZUaNGsWPfvQjdtlll4b6xRdfTE1NDWPGjGl2/jVr1jBlyhSefPJJnn76aV555RV+8YtfVLvZUodigJMktdg///lPRo0axZgxYzjppJMa6hMmTGDatGncdNNNRAQAPXr0YPny5Q3TrFixgh49evC73/2OPn360K1bN7bZZhtOOukkfv/737f6tkhlZoCTJLVISomzzz6b/v378/Wvf72hfscdd3DZZZcxdepUdtxxx4b6CSecwKRJk1i7di1PPvkkS5Ys4bDDDmOfffZh1qxZvPrqq6SUmDlzJv3792+LTZJKq6atGyBJKocHH3yQn//85xx88MEMHjwYgO9///t85StfYe3atQwdOhQoLmS45pprOOiggzjllFMYMGAANTU1XH311XTu3JnDDz+c0aNHc+ihh1JTU8MhhxzCuHHj2nDLpPKJlFJbt6FV1dbWprq6uk2eb8g3bqxCa9Qa5l5+Rquu76mLDm7V9WnL2ueCh9u6CZLUICLmppRqN6x7CFWSJKlkPIQqSW3o/T9+f1s3Qe/Qg19+sK2bUBpnnXUW06ZNo3v37ixcuBCAW2+9lQsvvJBHHnmE2bNnU1tbdDItW7aM/v37c8ABBwBvHpLXW9kDJ0mSqurMM8/kjjvueEtt4MCB/OpXv+Koo4562/T77bcf8+fPZ/78+Ya3JtgDJ0mSquqoo45i2bJlb6l55fHmsQdOkiS1K08++SSHHHIIH/rQh7j//vvbujntkj1wkiSp3dhrr7146qmn2GOPPZg7dy4jRoxg0aJFb7nrh+yBkyRJ7ch2223HHnvsAcCQIUPYb7/9ePzxx9u4Ve2PAU6SJLUbq1atYv369QA88cQTLFmyhHe/+91t3Kr2p/QBLiKGR8RjEbE0Is5r6/ZIkqS3Ou200zjyyCN57LHH6NmzJ9dffz2TJ0+mZ8+e/OEPf+CjH/0oxx13HAD33XcfgwYNYvDgwYwePZprrrmGLl26tPEWtD+lPgcuIjoDVwNDgRXAnIiYmlJa3LYtkyRJ9X75y182Wh85cuTbaqNGjWLUqFHVblLplb0H7jBgaUrpiZTS68Ak4MQ2bpMkSVJVlfpeqBExGhieUvpMHj8dODyl9KUNphsH1N8p+QDgsVZtaPvXFXi+rRuh0nB/UUu5r2hTuL80bt+UUrcNi6U+hNpSKaVrgWvbuh3tVUTUNXajXKkx7i9qKfcVbQr3l01T9kOoK4FeFeM9c02SJKnDKnuAmwP0jYg+EbEtcCowtY3bJEmSVFWlPoSaUloXEV8CpgOdgfEppUVt3Kwy8vCyNoX7i1rKfUWbwv1lE5T6IgZJkqStUdkPoUqSJG11DHCSJEklY4BrRyJifUTMj4iFEXFrROzYxHS/f4fL7x0Rn9y8Vqo1VOwL9Y9mbxMXEUdHxPsqxj8XEWdsxvon5O9ZbDURsU1EzHuH854ZEXtv6TZ1BBHx7YhYFBEL8r50eK4vi4iurbD+eyLCr4YoqYh4eYPxMyPiJxuZZ0REDGjBsi+MiHM3t41bq1JfxNAB/SOlNBggIm4CPgf8Z/2TEVGTUlqXUnpfE/NvTG/gk8B/vZOZIyIozpt84x2uXy3XsC+00NHAy8DvAVJK1zQ2Uf0+tNmtq44PAA++w3nPBBYCT2+x1nQAEXEk8DHg0JTS2hzYtt0Cy92i+1FEdE4prd9Sy1ObGwFMAzb7tpbt/D2rTdkD137dD+yfe1buj4ip5D+G+k9EETEpIj5aP0N9r0nuabs/IublR33g+wHwwfwp/GsR0TkiLo+IOfnT+b9u2Ii8rMci4kaKf5C98jwLI+LhiPhEni6aqB8dEfdGxJSIeCIifhARYyJidp5uv2r+Ejua3Gvy3fy6PhwRB0ZEb4qw/7X82n6w8pNt7gH5UUTUAV+NiCH5NZkbEdMjYq8mVndURPw+v26j87I263WOiG4RcXve5+ZExPsr1jcc+G2e7te5fYuiuJNK/fa/HBFX5PrMvLzRQC1wU97+HbboL73c9gKeTymtBUgpPZ9Sqgy5X67clwAi4rCI+ENEPJRf/wNy/cyImBoRdwEzI+JdETE+v8YPRcSJebod8nvTIxExGWj09cj78qVR9LqeHBGn5XYsjIhLK6Zrqv5y3hcXRcTvcrvvyfvfCVv496hG5P8Pd0Xx/2NmROwTxf+bE4DL89/jfvlxR/6bvr9+X2tmuRMi4pqI+CNwWUQMjohZeT2TI2L3PF1T9Xvy+0Rd3g/fGxG/ioglEfHvVf/FtJaUko928gBezj9rgCnA5yl6Vl4B+jQy3UhgYh7eFlhO8Wa5I7B9rvcF6vLw0cC0iuWMA/5vHt4OqKtcT673Bt4Ajsjjo4A7Kb62ZU/gKYp/Ek3VjwZezMPbUXzR8nfzsr4K/Kitf+/t8QGsB+ZXPD6R68uAL+fhLwDX5eELgXMr5m8YB+4BfpqHt6HopeuWxz9B8fU7G65/AnArxYe8ART3HG7u9W/R60zR+/uBPLwP8EjFOmcDO+bhLvnnDhQfHPbI4wkYk4cvAH5SsY21bf26tbcHsFPefx4Hfgp8qOK5pvalXYCaPPwvwO15+ExgRcVr833gU3l4t7yOdwFfr9+ngEHAusZem7z+b+bhvfO+1I3i/e8uil6cRusV+8LxeXgyMCPv3+8B5rf1776jPHj7e9FTFX93/w2MzcNnAb/OwxOA0RXLmAn0zcOHA3fl4QupeN+qmH4CRQ9e5zy+oH7fBS7izfeTpur3AJfm4a9S9MzXvzetIL+flP3hIdT2ZYeImJ+H7weuB94HzE4pPdnI9L8FroyI7Sh6L+5LKf0jInYFfhIRgyn++Po1sb5hwKB481ynXSkC34br+ktKaVYe/gDwy1Qc7ng2Iu4F3ttM/SVgTkrpGYCI+DPFGy3Aw8CHN/ZL2Uo1dwj1V/nnXOCkFi7v5vzzAGAgcGdEQBHEnmlinl+n4nD54ojYM9c293X+F2BAXjfALhGxE8W+tzql9GqufyUiRubhXhT75QsUHybqt+UXFb8LNSKl9HJEDAE+SPEa3BwR56WUJuRJGtuXdgUmRkRfipC0TcUi70wprc7Dw4AT4s1zmLanCOVHAVfl9S+IiAXNNLH+tXwvcE9KaRU0nEJyVF5/Y/VfA68Dd+T5HwbWppT+GREPU3zw1JbxlveiiDiToscb4Eje3G9+Dly24cz57/t9wK0Vf/fbtWC9t6aU1uf/Z7ullO7N9Yl5WY3WK+av/1L/h4FFFe9NT1C8p7zQgja0awa49uVt/7TzDv9KYxOnlF6LiHuA4yh6Uiblp74GPEvxSbQT8FoT6wuKT+DTN9KuRte/CdZWDL9RMf4G7oPvRP3vbz0t//3Vv4ZB8WZ25Casp36+TZm+qde5E0Vv7lv2ySgOxU7Pw0dTBL0jU0qv5n18+ybW6RdZbkQO2/cA9+RwM5aihwMa35e+B9ydUhoZxeH5eyoWV/leEMColNJjleur+CfdEpvz3vLPlLtYqNjfUkpvRITvK+1HJ+DFZj6QNmVL/d+pfC+qH+8Q+4fnwJXfzcCnKT5h138a3RV4JveenE7RywLwd2DninmnA5+PiG0AIqJfRLxrI+u7H/hEFOfPdaP4NDy7mbpax4avbVMeA7pFcXJ7/ZWfB23Cejb3dZ4BfLl+JPcSQ8X5bxT775oc3g4EjqiYvxNQ32P8SeCBPNzS7d+qRMQBuSet3mDgLxuZbVfevKf0mc1MN53iHLrI6zok1++jeG2IiIEUh1E3ZjbwoYjoGhGdgdOAe5upq334PcUtLAHGULw/QMXfY0rpJeDJiDgZGs6jfU9LV5BS+huwJiI+mEunA/c2Vd+cjSkbA1z5zQA+BPwupfR6rv0UGBsRfwIO5M1PMguA9RHxp4j4GnAdxYUR8yJiIfD/2Pgnk8l5OX+iOB/lmymlvzZT1zuzQ7z1a0R+sJHp/xsYmaf9YFMT5X1kNHBp3j/mUxzeaKnNfZ2/AtTmk44XA5/L/5j3Tyk9mqe5A6iJiEcoLryZVTH/K8BheX89huK8Fyh6lK4JL2LY0E4Uh0MX50OZAyjOO2rOZcAlEfEQzb8ffI/i8OqCiFiUxwF+BuyUX7+LKA7PNisf3joPuJti35qbUprSVH1jy1Or+TLw6bxvnU5xvhkUR4O+EcXFLftRhLuz83vOIuDETVzPWIqLIhZQfAi5aCP1rYK30pLUpiLiAxQnw3+uBdO+nFLaqRWaJUntmgFOUmkY4CSpYICTJEkqGc+BkyRJKhkDnCRJUskY4CSpCqKFN/SWpHfCACdpq5G/g6q13vdGUHxthyRtcQY4SR1aFDfcfiwibqS4r+r1UdwY/eF8B4j6YHd5I/WjI+LeiJgSxU3SfxARY6K4gfvD+TuuGltnYzf0nlfxfN/68Shu6n5ZXt7siNg/17tFxO0RMSc/3l/d35SkMukQt5OQpI3oS/Glnz2Az1HcZq4rMCci7qP4MuPBjdTJtf7AauAJipu+HxYRX6X4ItNzNlxZSun3ETEVmJZSug0gIv4WEYNTSvMp7p5yQ8Usf0spHRwRZwA/Aj4GXAlckVJ6ICL2objzQf8t8+uQVHb2wEnaGvwlpTQL+ADwy5TS+pTSsxS33nlvM3WAOSmlZ1JKa4E/U9z9BIqbZPfehDZcR/Gt9Z0p7l38XxXP/bLiZ/19av8F+ElEzKe4MfcuUdwYXJLsgZO0VdicG2NveCPsyptkb8p76O3AdyhuQTY3pfRCxXOpkeFOwBEppdc2rbmStgb2wEnamtwPfCIiOkdEN+AoihumN1XfHA039AbIQWw6xb1Cb9hg2k9U/PxDHp5BcYgWgIgYvJntkdSBGOAkbU0mAwsobox+F/DNlNJfm6lvjg1v6A1wE0XP3YwNpt0935D7q8DXcu0rQG1ELIiIxRTn7kkS4K20JKnVRMS5wK4ppfMrasuA2pTS823WMEml4zlwktQKImIysB9wTFu3RVL52QMnSZshIr4NnLxB+daU0sVt0R5JWwcDnCRJUsl4EYMkSVLJGOAkSZJKxgAnSZJUMgY4SZKkkjHASZIklYwBTpIkqWT+P2JsURgMQC04AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.xlabel(\"feature: {}\".format(\"room_type\"))\n",
    "ax = sns.countplot(data=df, x='room_type')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebc7eb6",
   "metadata": {},
   "source": [
    "# OBTAIN INSIGHTS FROM THE DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8502e7",
   "metadata": {},
   "source": [
    "### * TOP 10 NEIGHBORHOOD HAVING THE BEST AVERAGE REVIEW RATE SCORE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4cddfcc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbourhood\n",
      "Glen Oaks          4.500000\n",
      "Co-op City         4.400000\n",
      "Gerritsen Beach    4.333333\n",
      "Huguenot           4.111111\n",
      "Woodrow            4.000000\n",
      "Silver Lake        4.000000\n",
      "West Farms         4.000000\n",
      "Eltingville        4.000000\n",
      "Todt Hill          4.000000\n",
      "Rosebank           3.928571\n",
      "Name: review_rate_number, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "avg_rating_per_neighbourhood = df.groupby(['neighbourhood'])['review_rate_number'].mean().sort_values(ascending=False)[0:10]\n",
    "print(avg_rating_per_neighbourhood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c76a0b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "ax = sns.barplot(x=avg_rating_per_neighbourhood.values, y=avg_rating_per_neighbourhood.index)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33dcc0be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}