Summary of Hotel Booking Cancellation Prediction Using Applied Bayesian Models, by Md Asifuzzaman Jishan et al.
Hotel Booking Cancellation Prediction Using Applied Bayesian Models
by Md Asifuzzaman Jishan, Vikas Singh, Ayan Kumar Ghosh, Md Shahabub Alam, Khan Raqib Mahmud, Bijan Paul
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Bayesian models are applied to predict hotel booking cancellations, which is crucial for resource allocation, revenue, and customer satisfaction in the hospitality industry. A Kaggle dataset with 36,285 observations and 17 features is used to train Bayesian Logistic Regression and Beta-Binomial models. The logistic model outperforms the Beta-Binomial model in predictive accuracy when applied to 12 features and 5,000 randomly selected observations. Key predictors include 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) confirms strong alignment between observed and predicted outcomes, demonstrating the model’s robustness. Special requests and parking availability are found to be the strongest predictors of cancellation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hotel booking cancellations can cause problems for hotels. This study uses special math models to try to predict when people will cancel their bookings. The models use a big dataset with lots of information about hotel guests, like how many adults or children are staying and what type of room they have booked. The best model was the Bayesian Logistic Regression model, which did better than another model called Beta-Binomial. Some important things that help predict cancellations include special requests from guests and whether there is parking available at the hotel. |
Keywords
» Artificial intelligence » Alignment » Logistic regression
