用贝叶斯模型预测酒店预订取消，提升管理效率。
Hotel Booking Cancellation Prediction Using Applied Bayesian Models
- 采用贝叶斯逻辑回归与贝塔-二项模型，基于17个特征分析数据。
- 在5000条样本上，逻辑回归准确率高于贝塔-二项模型。
- 特殊需求和停车资源是取消最强预测因子，适合酒店运营优化。
本研究应用贝叶斯模型预测酒店预订取消，这是影响酒店资源配置、收入与客户满意度的关键问题。基于包含36,285条记录和17个特征的Kaggle数据集，实现了贝叶斯逻辑回归与贝塔-二项模型。在12个特征和5,000条随机抽样数据上，逻辑回归模型的预测准确率优于贝塔-二项模型。关键预测因子包括成人数量、儿童数量、入住时长、提前预订天数、停车空间、房型及特殊需求。通过留一交叉验证（LOO-CV）评估，模型预测结果与实际观测高度一致，验证了其稳健性。特殊需求与停车可用性被确认为最强烈的取消预测因素。该贝叶斯方法为提升酒店预订管理与运营效率提供了有效工具。
原文摘要 · Abstract (English)
This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 features and 5,000 randomly selected observations, outperformed the Beta-Binomial model in predictive accuracy. Key predictors included the number of adults, children, stay duration, lead time, car parking space, room type, and special requests. Model evaluation using Leave-One-Out Cross-Validation (LOO-CV) confirmed strong alignment between observed and predicted outcomes, demonstrating the model's robustness. Special requests and parking availability were found to be the strongest predictors of cancellation. This Bayesian approach provides a valuable tool for improving booking management and operational efficiency in the hotel industry.
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