🚨This was a project based on real data from a hotel in Portugal.
 Life is rife with uncertainty, and reserving a hotel stay for a
travel or business trip is no exception. Given the unpredictable nature
of circumstances, customers may need to cancel or reschedule their
visit, causing potential stress for both customers and the hotel. To
address this challenge, a contingency plan becomes necessary. This
project aims to provide assistance and ideas for developing backup plans
specifically tailored for hotels.
 Efficiently managing cancellations and rescheduling is crucial from
a hotel’s business perspective. By employing machine learning methods,
it becomes possible to analyze customer cancellation patterns and
utilize this information to create a robust plan. This project focuses
on predicting the probability of reservation cancellations, allowing for
proactive adjustments. By utilizing this information, improvements can
be made to the booking system to optimize hotel occupancy. This model
serves as an initial step toward maximizing the hotel’s revenue by
effectively managing cancellations and optimizing room occupancy.
[Fig. XGB Classification features by importance]
For more detailed analysis and code, please check my Colab notebook
HERE(Colab)