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Hotel booking cancellation and machine learning
- 1 Peking University
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Abstract
In recent years, machine learning has emerged as a powerful tool with widespread applications across various domains due to its ability to process and analyze vast amounts of data. This study explores the application of machine learning techniques in predicting hotel booking cancellations using Property Management System (PMS) data. The research involves a comprehensive process, including data cleaning, feature engineering, feature selection, and model development. Feature selection and dimensionality reduction using Principal Component Analysis (PCA) and Lasso regression identified key predictive features, facilitating the rapid creation of neural network models. A diverse set of machine learning and deep learning models, such as Logistic Regression, Decision Tree, Random Forest, XGBoost, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM), were employed. All models achieved accuracies exceeding 80%, with neural networks nearing 100%. These results highlight the efficacy of these models in predicting cancellations across different hotels, revealing consistent cancellation patterns. The study demonstrates the potential of machine learning to optimize hotel management by accurately forecasting booking cancellations, thereby reducing uncertainty and increasing revenue. Future work may focus on exploring more advanced feature engineering techniques and models to further enhance prediction accuracy and generalizability.
Keywords
hotel booking, machine learning, cancellation forecasting
[1]. Zhu, X. (2022). Qualitative research on the life meaning experience of adolescent depression patients with non-suicidal self-injury. Zhejiang University of Traditional Chinese Medicine.
[2]. Hazarika, S., Li, H., Wang, K. C., Shen, H. W., & Chou, C. S. (2019). NNVA: Neural network assisted visual analysis of yeast cell polarization simulation. IEEE Transactions on Visualization and Computer Graphics, 26(1), 34-44.
[3]. Grimaldo, A. I., & Novak, J. (2020). Combining machine learning with visual analytics for explainable forecasting of energy demand in prosumer scenarios. Procedia Computer Science, 175, 525-532.
[4]. Wang, Q., Chen, Z., Wang, Y., & Qu, H. (2021). A survey on ML4VIS: Applying machine learning advances to data visualization. IEEE Transactions on Visualization and Computer Graphics, 28(12), 5134-5153.
[5]. Ndiaye, B. M., Balde, M. A., & Seck, D. (2020). Visualization and machine learning for forecasting of COVID-19 in Senegal. arXiv preprint arXiv:2008.03135.
[6]. Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N. J., Xie, X., & Li, Z. (2018, April). DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 2018 World Wide Web Conference (pp. 167-176).
[7]. Smart, S., Wu, K., & Szafir, D. A. (2019). Color crafting: Automating the construction of designer quality color ramps. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1215-1225.
[8]. Wu, A., Tong, W., Dwyer, T., Lee, B., Isenberg, P., & Qu, H. (2020). Mobilevisfixer: Tailoring web visualizations for mobile phones leveraging an explainable reinforcement learning framework. IEEE Transactions on Visualization and Computer Graphics, 27(2), 464-474.
[9]. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1701-1708).
[10]. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).
[11]. Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3313-3332.
[12]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.
[13]. Chatzimparmpas, A., Martins, R. M., Jusufi, I., & Kerren, A. (2020). A survey of surveys on the use of visualization for interpreting machine learning models. Information Visualization, 19(3), 207-233.
[14]. Alharbi, M., & Laramee, R. S. (2018, September). SoS TextVis: A Survey of Surveys on Text Visualization. In CGVC (pp. 143-152).
[15]. Caicedo-Torres, W., & Payares, F. (2016). A machine learning model for occupancy rates and demand forecasting in the hospitality industry. In Advances in Artificial Intelligence-IBERAMIA 2016: 15th Ibero-American Conference on AI, San José, Costa Rica, November 23-25, 2016, Proceedings 15 (pp. 201-211). Springer International Publishing.
[16]. Antonio, N., De Almeida, A., & Nunes, L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies, 13(2), 25-39.
[17]. Antonio, N., de Almeida, A., & Nunes, L. (2017, December). Predicting hotel bookings cancellation with a machine learning classification model. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1049-1054). IEEE.
[18]. Satu, M. S., Ahammed, K., & Abedin, M. Z. (2020, December). Performance analysis of machine learning techniques to predict hotel booking cancellations in hospitality industry. In 2020 23rd International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
[19]. Adil, M., Ansari, M. F., Alahmadi, A., Wu, J. Z., & Chakrabortty, R. K. (2021). Solving the problem of class imbalance in the prediction of hotel cancelations: A hybridized machine learning approach. Processes, 9(10), 1713.
[20]. Sánchez-Medina, A. J., & Eleazar, C. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546.
[21]. Lee, M., Mu, X., & Zhang, Y. (2020). A machine learning approach to improving forecasting accuracy of hotel demand: A comparative analysis of neural networks and traditional models. Issues in Information Systems, 21(1).
Cite this article
Sun,J. (2025). Hotel booking cancellation and machine learning. Advances in Engineering Innovation,15,45-62.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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