Analysis of Influencing Factors of Tourist Attractions Accessibility based on Machine Learning Algorithm

Abstract

A place of interest that draws travellers is referred to as a tourist attraction. These locations typically have historical significance, natural or man-made beauty, and options for leisure and amusement. Precisely estimating the demand for tourism is a significant and demanding undertaking, but research on this area is limited. In this work, we proposed a novel remora-optimized adaptive XGBoost (RO-AXGBoost) to forecast various factors associated with tourist attractions. We gathered data from Kaggle. The proposed method is implemented using Python software. The performance of these methods was evaluated using metrics such as MAPE (7.24), MAE (7.321), RMSE (10.241), and R² (85.7). The finding shows that the proposed method has achieved better performance and the key factors were identified as significantly influencing accessibility in tourist attractions.