Context

This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 33344 hotels) that was created by extracting data from goibibo.com, a leading travel site from India.

Content

This dataset has following fields:

• address
• area - The sub-city region that this hotel is located in, geographically.
• city
• country - Always India.
• crawl_date
• guest_recommendation - How many guests that stayed here have recommended this hotels to others on the site.
• hotel_brand - The chain that owns this hotel, if this hotel is part of a chain.
• hotel_category
• hotel_description - A hotel description, as provided by the lister.
• hotel_facilities -
• hotel_star_rating - The out-of-five star rating of this hotel.
• image_count - The number of images provided with the listing.
• latitude
• locality
• longitude
• pageurl
• point_of_interest - Nearby locations of interest.
• property_name
• property_type - The type of property. Usually a hotel.
• province
• qts - Crawl timestamp.
• query_time_stamp - Copy of qts.
• review_count_by_category - Reviews for the hotel, broken across several different categories.
• room_area
• room_count
• room_facilities
• room_type
• similar_hotel
• site_review_count - The number of reviews for this hotel left on the site by users.
• site_review_rating - The overall rating for this hotel by users.
• site_stay_review_rating
• sitename - Always goibibo.com
• state
• uniq_id

Acknowledgements

This dataset was created by PromptCloud's in-house web-crawling service.

Inspiration

• Try exploring some of the amenity categories. What do you see?

• Try applying some natural language processing algorithms to the hotel descriptions. What are the some common words and phrases? How do they relate to the amenities the hotel offers?

• What can you discover by drilling down further into hotels in different regions?