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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.16406 |
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| _version_ | 1866909361439244288 |
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| author | Jishan, Md Asifuzzaman Singh, Vikas Ghosh, Ayan Kumar Alam, Md Shahabub Mahmud, Khan Raqib Paul, Bijan |
| author_facet | Jishan, Md Asifuzzaman Singh, Vikas Ghosh, Ayan Kumar Alam, Md Shahabub Mahmud, Khan Raqib Paul, Bijan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16406 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hotel Booking Cancellation Prediction Using Applied Bayesian Models Jishan, Md Asifuzzaman Singh, Vikas Ghosh, Ayan Kumar Alam, Md Shahabub Mahmud, Khan Raqib Paul, Bijan Machine Learning Artificial Intelligence 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. |
| title | Hotel Booking Cancellation Prediction Using Applied Bayesian Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.16406 |