Booking Cancellation Prediction with Classification Model
Booking cancellation prediction becomes more significant than before, which impacts decision making in the hospitality industry. In the revenue management system, with inaccurate prediction of hotel demand, overbooking and cancellation policy might lead to a negative influence on the operation of...
| Main Author: | |
|---|---|
| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2020
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| Online Access: | https://eprints.nottingham.ac.uk/61651/ |
| _version_ | 1848799896703860736 |
|---|---|
| author | He, Haokun |
| author_facet | He, Haokun |
| author_sort | He, Haokun |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Booking cancellation prediction becomes more significant than before, which impacts
decision making in the hospitality industry. In the revenue management system, with
inaccurate prediction of hotel demand, overbooking and cancellation policy might lead to a
negative influence on the operation of the hotel and reputation of the hotel.
Using PMS data and external data, addressing the booking cancellation problem as a
classification problem, the author used different algorithms and different models to get the
highest accuracy, the result was exceeding 0.89, which shows the hotel industry can predict
whether a booking is likely to be canceled with high accuracy.
Models allow the hotel industry to make different actions on overbooking and cancel booking
based on which factors were most important in the model. A high accuracy model can prevent
the enterprise from reputation and profit losing. Moreover, there will be some future research
suggestions provided in the end.
Keyword: Data science, booking cancellation, hospitality industry, machine learning,
predictive modeling, revenue management, weather, distance, classification |
| first_indexed | 2025-11-14T20:42:57Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-61651 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:42:57Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-616512022-12-13T17:04:43Z https://eprints.nottingham.ac.uk/61651/ Booking Cancellation Prediction with Classification Model He, Haokun Booking cancellation prediction becomes more significant than before, which impacts decision making in the hospitality industry. In the revenue management system, with inaccurate prediction of hotel demand, overbooking and cancellation policy might lead to a negative influence on the operation of the hotel and reputation of the hotel. Using PMS data and external data, addressing the booking cancellation problem as a classification problem, the author used different algorithms and different models to get the highest accuracy, the result was exceeding 0.89, which shows the hotel industry can predict whether a booking is likely to be canceled with high accuracy. Models allow the hotel industry to make different actions on overbooking and cancel booking based on which factors were most important in the model. A high accuracy model can prevent the enterprise from reputation and profit losing. Moreover, there will be some future research suggestions provided in the end. Keyword: Data science, booking cancellation, hospitality industry, machine learning, predictive modeling, revenue management, weather, distance, classification 2020-12-01 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/61651/1/20182625-BUSI4374%20UNNK-Booking%20Cancellation%20Prediction%20with%20Classification%20Model.pdf He, Haokun (2020) Booking Cancellation Prediction with Classification Model. [Dissertation (University of Nottingham only)] |
| spellingShingle | He, Haokun Booking Cancellation Prediction with Classification Model |
| title | Booking Cancellation Prediction with
Classification Model |
| title_full | Booking Cancellation Prediction with
Classification Model |
| title_fullStr | Booking Cancellation Prediction with
Classification Model |
| title_full_unstemmed | Booking Cancellation Prediction with
Classification Model |
| title_short | Booking Cancellation Prediction with
Classification Model |
| title_sort | booking cancellation prediction with
classification model |
| url | https://eprints.nottingham.ac.uk/61651/ |