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This project addresses disaster-risk challenges by leveraging machine learning to identify technical gaps and define objectives for real-world applications. It includes a comprehensive system design that compares baseline and proposed architectures, detailing preprocessing, features, and model implementations through a Python pipeline and dashboard. The evaluation phase reports key metrics and insights, applying findings to enhance preparedness and decision support, while emphasizing the...