<![CDATA[Customer loyalty has become a critical factor for sustaining competitiveness in the hotel industry, particularly in increasingly digital and data-driven business environments. Although hotels continuously generate large volumes of transactional customer data, transforming this data into actionable insights for customer retention and marketing decision-making remains a significant challenge. This study proposes an Explainable Machine Learning Framework for hotel customer loyalty prediction using transactional behavioral data. The study used a publicly available hotel customer transaction dataset from the Mendeley Data repository, comprising 2,000 customer records. A supervised machine learning approach was employed using Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. Model performance was evaluated using Stratified K-Fold Cross-Validation and classification metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Experimental results demonstrated that all evaluated models achieved perfect classification performance, with Accuracy, Precision, Recall, F1-Score, and ROC-AUC values reaching 1.000. SHAP analysis revealed that frequency_of_bookings, days_since_last_booking, total_meal_charges, and total_revenue_generated were the primary drivers of customer loyalty prediction, while average_stay_duration showed minimal influence. The findings indicate that booking frequency and customer recency are the most influential behavioral factors affecting loyalty outcomes. From a managerial perspective, the proposed framework provides actionable insights for customer retention, customer segmentation, loyalty programs, and personalized marketing strategies. This study contributes to predictive customer analytics and Explainable Artificial Intelligence by integrating predictive accuracy with transparent interpretation of customer behavior in hotel loyalty management.]]>
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