Machine learning-based hotel occupancy prediction

The online booking system has tremendously facilitated people's ability to travel and make reservations because of the growth of the Internet. However, the unpredictability of travel led to frequent changes in reservations. Frequent order changes can lead to many problems, such as hotels not being able to get order changes in a timely manner and more in-demand customers not being able to stay, which can lead to lower profits and occupancy rates. In addition to this, there are a number of subjective, such as changes in the trip, reasons for work, and reasons for family, and objective, such as weather changes, natural disasters, and Transportation issues, factors that make it more difficult to predict the occupancy rate. In recent years, machine learning has become an increasingly valuable tool for researchers to analyze data. Based on these, this paper summarizes the machine learning algorithms related to hotel occupancy probability prediction and analyzes and compares them. Finally, it gives an outlook on the research of the hotel occupancy rate prediction.