The "AirBnB Booking Analysis" project focuses on exploring and analyzing the booking data of AirBnB rentals to gain valuable insights into the trends, patterns, and factors influencing the booking process. Airbnb is a popular online marketplace that connects travelers with hosts who offer unique lodging options around the world. I have conducted an exploratory data analysis (EDA) of Airbnb NYC 2019 data to gain insights into the Airbnb market in New York City.
By conducting exploratory data analysis (EDA) on this data, our goal is to understand the preferences of travelers and hosts, identify peak booking seasons, and discover interesting correlations between various attributes and booking trends. It empowers travelers to make informed choices when selecting AirBnB accommodations that best align with their preferences and requirements.
The dataset used in this project is sourced from here. It comprises a comprehensive collection of information related to Airbnb bookings, including:
UNDERSTANDING THE VARIABLES:
ID:- It's a unique id of the House/apartment.
Name:- Name of the listing House/apartment.
Host ID:- Host ID is the government-approved ID for each individual.
Neighborhood groups:- Neighbourhood groups are the cluster of neighborhoods in the area.
Neighborhood:- Area
Latitude:- It shows the latitude point of the site.
Longitude:- It shows the longitude point of the site.
Room type:- There are three types of rooms on Airbnb
>> Entire House/Apartment
>> Private Room
>> Shared Room
Price:- Prices are in terms of dollars($) and the total price of an Airbnb reservation is based on the cost determined by either the Host or Airbnb Co.
Minimum Nights:- Minimum night is a criteria for booking that guests have to pay to book that House or apartment.
Number of reviews:- counts of review which is submitted by guests.
Last review:- Latest review as per the dataset.
Review per month:- Number of reviews per month host got.
Calculated host listings count:- Amount of listing per host.
Availibility_365:- It shows the total number of days the listings are available during the year.
To run this project on your local machine, follow these steps:
Copy the colab file into your drive.
Run the colab file to gain insights.
In conclusion, the Airbnb project has been a successful venture for both the company and its users. The platform has revolutionized the way people travel, offering a more affordable and unique way to stay in a city, as well as providing a source of income for hosts. The company has faced challenges, such as regulatory issues and competition from traditional hotels, but has continued to grow and expand globally.
Here are some more points:-
- The people who prefer to stay in an Entire home or Apartment are going to stay a bit longer in that particular Neighbourhood only.
- People who prefer to stay in a Private room won't stay longer compared to a Home or Apartment.
- Most people prefer to pay less price.
- If there are more Reviews for a particular Neighbourhood group that means that a place is a tourist place.
- If people are not staying more than one night means they are travelers or maybe a businessman/employee to visit a particular area for meetings.
- The most popular area for the hotel listing is Manhattan.
- The most popular location for hotel listing is Williamsburg.
- The most popular price range for hotel booking is 0 to 50 dollars.
- Availability of the room can be 365 days.
- The popular figure of Minimum night stays for the hotel is 1 day.
I hope this all information is sufficient to get started with your "AirBnB Booking Analysis" EDA project. If you require any further assistance or have questions, please feel free to reach out to me.
Happy exploring and analyzing the fascinating world of AirBnB bookings! 🚅: 🏘️: