SUSTAINABLE ROAD TOURISM ROUTE PREDICTION: AN ANALYSIS USING KAGGLE DATA AND RANDOM FOREST
DOI:
https://doi.org/10.21837/pm.v24i42.2036Keywords:
Sustainable Tourism, Road Tourism, Route Prediction, Machine Learning, Random Forest, Data AnalysisAbstract
This paper delves into the forecasting of sustainable tourist routes using an open-access Kaggle dataset comprising 1,000 route samples and 7 predictive features, employing a Random Forest approach. The analysis includes two tasks: classification of a binary Sustainability_Target, achieving perfect accuracy (100% precision, recall, F1-score) on a held-out test set, and regression of a continuous Sustainability_Score, yielding an MAE of 0.069, RMSE of 0.081, and R² of 0.683. The study is situated within the ISSTERN framework to link predictions to planning actions. Findings suggest that while binary sustainability screening is highly feasible with this dataset, continuous score prediction shows moderate performance but is heavily influenced by the binary target feature, resulting in a two‑cluster prediction pattern. Limitations include the use of simulated/open data, unknown generalizability to real-world Malaysia contexts, and potential data leakage due to perfect classification scores and the dominant importance of the binary target in regression. This paper contributes a reproducible data-driven workflow to sustainable tourism planning discourse.
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