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Hotel Booking Cancellation Analytics on Imbalanced Data

In: Tourism Analytics Before and After COVID-19

Author

Listed:
  • Cai Yuxuan

    (Nanyang Technological University)

  • Hsu Tuan-Chun

    (Nanyang Technological University)

  • Jin Zhuofan

    (Nanyang Technological University)

  • Tan Chian Wen Melvin

    (Nanyang Technological University)

  • Vivek Goyal

    (Nanyang Technological University)

  • Zheng Yijun

    (Nanyang Technological University)

Abstract

In this study, we look at the impact of COVID on different travel sectors and how can we contribute to the recovery of this sector using Analytics. We focus our study on reducing the cancellation of hotel bookings in the Portugal region. We performed data cleaning, processing, and visualization to gain an understanding of this problem and observe how travelling plans evolve over the years. We created a prediction model for hotel room cancellation and applied different machine learning algorithms to predict possible cancellations. We also looked at feature engineering and saw how these models can be used to find out important features that contribute towards the prediction of room cancellations. We further analyze these factors in more depth to understand what exactly is leading to hotel room cancellations. This study also proposes recommendations for hotel operators to adopt in the future when the pandemic situation improves to recover from losses in the past period.

Suggested Citation

  • Cai Yuxuan & Hsu Tuan-Chun & Jin Zhuofan & Tan Chian Wen Melvin & Vivek Goyal & Zheng Yijun, 2023. "Hotel Booking Cancellation Analytics on Imbalanced Data," Springer Books, in: Yok Yen Nguwi (ed.), Tourism Analytics Before and After COVID-19, pages 97-117, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9369-5_7
    DOI: 10.1007/978-981-19-9369-5_7
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