Airbnb data of NYC
Hospitality & Tourism
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About
This dataset contains information about Airbnb listings in New York City, including features like room type, pricing, neighbourhood, and reviews. It is designed to predict factors like price and availability based on various attributes of the listing. The dataset provides a useful resource for building models focused on rental pricing and demand prediction in the tourism and hospitality industry.
Dataset Features:
- Id: Unique identifier for the listing.
- Name: Name or title of the listing.
- Host id: Unique identifier for the host.
- Hostname: Name of the host.
- Neighbourhood group: The broader geographic area, e.g., Brooklyn, Manhattan.
- Neighbourhood: Specific neighbourhood within the group, e.g., Kensington, Midtown.
- Latitude: Latitude of the listing's location.
- Longitude: Longitude of the listing's location.
- Room type: Type of the room offered (e.g., entire home/apartment, private room, and shared room).
- Price: Price per night for the listing. (range of 10 to 1000)
- Minimum nights: Minimum number of nights required for booking.
- Number of reviews: Total number of reviews received by the listing.
- Last review: Date of the most recent review.
- Reviews per month: Average number of reviews per month.
- Calculated host listings: Number of listings a host has on the platform.
- Availability: Number of days the listing is available for booking in a year.
Usage:
This dataset is ideal for training and testing machine learning models in the following areas:
- Predicting rental prices based on location, room type, and other factors.
- Analysing availability patterns and trends.
- Evaluating the impact of reviews on pricing and demand.
- Comparing the performance of different neighbourhoods in terms of rental prices and reviews.
Coverage:
The dataset provides insights into Airbnb listings across various neighbourhoods in New York City, covering diverse room types, pricing strategies, and host characteristics. This makes it suitable for predictive modelling, pricing analysis, and market trend exploration.
License:
CC0 (Public Domain)
Who can use it:
This dataset is intended for data scientists, machine learning practitioners, researchers, and students interested in exploring predictive analytics in the real estate and hospitality sectors.
How to use it:
- Develop models to predict pricing and availability based on listing attributes.
- Analyse the effect of host behaviour and reviews on rental success.
- Explore trends in pricing across different neighbourhoods and room types.
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