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Tourism Demand Forecasting Using Machine Learning with Cross-Model Feature Ranking
This study proposes a machine learning (ML) framework for forecasting tourist visits to a destination, leveraging feature ranking consistency across heterogeneous models. The proposed framework uses a unified stepwise feature selection pipeline to systematically determine a subset of given features
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