Predicting Tourist Spending Behavior Using Bayesian Networks

Understanding tourist spending behavior is crucial for optimizing tourism revenue and enhancing visitor experiences. Predicting spending patterns enables stakeholders to develop data-driven strategies for targeted marketing and resource allocation. However, traditional predictive models often struggle with handling uncertainty, complex dependencies among variables, and limited adaptability to dynamic tourism trends. To address these challenges, we propose a Bayesian Network-Based Tourist Spending Prediction System (BN-TSP), which utilizes probabilistic graphical models to capture intricate relationships among demographic and behavioral factors. Our framework integrates data preprocessing, dependency structure learning, and probabilistic inference to enhance prediction accuracy. The BN-TSP leverages Bayesian inference techniques to quantify uncertainty and provide more interpretable insights into spending behaviors. The proposed system is designed to support tourism policymakers, businesses, and marketers by offering precise predictions of visitor expenditures based on dynamic factors such as age, income, travel purpose, and activity preferences. By employing Bayesian Networks, our approach improves adaptability, reduces prediction errors, and enhances decision-making in tourism management. Experimental results demonstrate that BN-TSP outperforms conventional machine learning models, offering higher accuracy in predicting tourist spending behavior. The findings suggest that Bayesian Networks provide a robust framework for understanding spending patterns, facilitating better tourism planning and economic forecasting.