For predicting guest trends in a global hotel chain using machine learning, which option is most suitable?

The most suitable option for predicting guest trends in a global hotel chain using machine learning is BigQuery ML. This tool allows users to build and operationalize machine learning models directly within BigQuery, which is a fully managed data warehouse service. Given that a hotel chain would be dealing with large volumes of historical guest data, transaction records, and holistic customer information stored in BigQuery, utilizing BigQuery ML enables effective analysis and model building right where the data resides.

By leveraging SQL queries, users can easily create and train machine learning models without the need for extensive programming or data preparation. This makes it highly accessible for data analysts and other professionals who may not have a strong data science background. Additionally, because the training and predictions are done using the same platform where data is stored, this minimizes data movement and enhances security and performance.

Utilizing BigQuery ML is particularly advantageous for tasks such as customer segmentation, demand forecasting, and trend prediction, which are pivotal for maximizing occupancy rates, setting pricing strategies, and enhancing customer experiences in the hospitality sector.