<![CDATA[This study investigates predictive analytics applications in the hospitality sector, specifically employing the XGBoost algorithm to predict room selection patterns based on guest data. Analysis of 900 booking records revealed that three variables—"Length of Stay," "Rating," and "Guest Type"—exhibited the strongest predictive power for room preferences. The implementation achieved 85% classification accuracy, revealing subtle correlations between customer characteristics and accommodation choices. Our findings suggest that hotels can leverage similar analytical frameworks to refine inventory management strategies, develop targeted promotional campaigns, and streamline operational workflows. The investigation also identified methodological limitations regarding class distribution in the dataset, suggesting that enhanced feature selection techniques could potentially reduce error rates in subsequent modeling approaches. This work contributes to the growing body of evidence demonstrating how advanced data analytics can drive competitive advantage and sustainability initiatives within tourism enterprises.]]>
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