4,000 Indian hotels on Goibibo
Dataset Description
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:
addressarea- The sub-city region that this hotel is located in, geographically.citycountry- AlwaysIndia.crawl_dateguest_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_categoryhotel_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.latitudelocalitylongitudepageurlpoint_of_interest- Nearby locations of interest.property_nameproperty_type- The type of property. Usually a hotel.provinceqts- Crawl timestamp.query_time_stamp- Copy ofqts.review_count_by_category- Reviews for the hotel, broken across several different categories.room_arearoom_countroom_facilitiesroom_typesimilar_hotelsite_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_ratingsitename- Alwaysgoibibo.comstateuniq_id
Acknowledgements
This dataset was created by PromptCloud's in-house web-crawling service.
Inspiration
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Try exploring some of the amenity categories. What do you see?
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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?
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What can you discover by drilling down further into hotels in different regions?
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