A Data-Driven Approach for Identifying Most Visited Tourist Places Using XGBoost and Random Forest

Identifying the most visited tourist places plays a significant role in enhancing tourism development and resource allocation. Conventional approaches often struggle to provide accurate predictions due to limited data utilization and weaker model performance. In this work, we present a data-driven methodology that leverages ensemble learning techniques to effectively predict the popularity of tourist destinations. XGBoost algorithm are employed to analyze and learn patterns from tourism-related data, improving prediction accuracy and consistency. The proposed system classifies tourist locations into two categories: Most Visited Place (1) and Not Most Visited Place (0). By utilizing important features influencing tourist preferences, the model provides reliable and efficient predictions. This approach helps in better understanding travel trends and supports strategic planning in the tourism sector. Additionally, the model focuses on optimizing feature importance to enhance prediction quality and reduce unnecessary data complexity, while also minimizing overfitting and improving generalization on unseen data. The system can handle large-scale tourism datasets efficiently, ensuring scalability and consistent performance, ultimately supporting better decision-making by accurately identifying high-demand tourist locations.

Keywords: Tourist Destination Analysis, XGBoost, Most Visited Classification, Tourism Prediction, Data Analysis