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Online hotel booking became increasingly popular as time passed, and with its popularity, the data that can be collected based on customer actions has increased. This data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from Booking.com and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. This result allows accommodation owners to predict the room price to keep the rooms fully occupied.

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Hotel’s Price Prediction Based on Country Specific Data

  • 会议论文

  • 发表日期：

    2025年

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  • Andrei Bălan,
  • Paul Ştefan Popescu,
  • Marian Cristian Mihăescu

摘要

Online hotel booking became increasingly popular as time passed, and with its popularity, the data that can be collected based on customer actions has increased. This data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from Booking.com and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. This result allows accommodation owners to predict the room price to keep the rooms fully occupied.