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

Tourist attractions, defined by their cultural importance, aesthetic appeal, and recreational possibilities, are critical to the tourism industry. However, precisely evaluating tourism needs remains a difficult task, and research in this field is scarce. This research introduces an innovative remora-optimized adaptive XGBoost (RO-AXGBoost) model for predicting accessibility factors for tourist attractions. Data was obtained from Kaggle, and the suggested method was executed in Python. The RO-AXGBoost model's effectiveness was assessed utilizing metrics like Mean Absolute Percentage Error (MAPE) of 7.24, Mean Absolute Error (MAE) of 7.321, Root Mean Square Error (RMSE) of 10.241, and R-squared (R²) of 85.7%. The results show that the RO-AXGBoost model surpasses conventional approaches by effectively discovering important determinants that have an important impact on the accessibility of tourist attractions.

@article{Liu2024,
  title     = {Analysis of Influencing Factors of Tourist Attractions Accessibility Based on Machine Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Na Liu and Hai Zhang},
  doi       = {10.14569/IJACSA.2024.0151073},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151073}
}