Prediction of Hotel Booking Cancellation using CRISP-DM

Abstract

Online travel sales continue to increase every year. Recorded in 2019, digital transactions related to online travel reached USD 755.4 billion. One of the supports of the travel business is the tourism and hospitality industry. The online reservation system is one of the most attractive solutions in the hospitality industry. Cancellation of hotel bookings or reservations through the online system is currently one of the problems in the hotel management system. When the reservation has been canceled, the hotel will be harmed. Therefore, predicting whether a booking will be canceled or not using the help of data science is needed so that the hotel can minimize lost profits. Therefore, by using datasets related to hotel booking requests, a predictive analysis using the CRISP-DM framework is conducted. By first performing some data preparation processes, this study uses a tree-based algorithm to perform the prediction. The experiment produced that Random Forest model has the best value with an accuracy value of 0.8725 and it is found that the time difference between booking is made and arrival time is the most influential feature in predicting the level of cancellation.