Machine learning is a branch of AI where systems learn patterns from data instead of following hand-written rules. In hotels it drives demand forecasting, dynamic pricing, personalization and fraud detection.
A machine learning model trained on booking history learns how demand responds to lead time, seasonality, events and price, then predicts outcomes for future dates. Unlike fixed rules, it adapts as new data arrives, within the limits of what it has seen.
Models inherit the flaws of their training data. A model trained through an unusual period can learn distorted patterns, and one never monitored degrades silently. The operational discipline, tracking prediction accuracy over time, matters as much as the algorithm choice.
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