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Is your feature request related to a problem? Please describe.
Predicting hotel reservation cancellations can significantly aid in optimizing room occupancy and maximizing revenue. By accurately forecasting which bookings are likely to be canceled, hotels can adjust their inventory, manage resources more effectively, and prevent revenue loss from unoccupied rooms. This project addresses this challenge by applying machine learning models to predict cancellations, enabling hotels to make informed decisions to increase efficiency and improve operational outcomes.
Describe the solution you'd like
This project aims to build a machine learning model to predict the likelihood of hotel reservations being canceled. Utilizing classification algorithms, this project is designed to provide insights into cancellation patterns based on historical booking data, such as the number of adults, children, lead time, special requests, and other relevant features. Various models, including linear, non-linear, and ensemble methods, are compared for their performance in accurately predicting cancellations.