nqbinhcs/AI4VN2022-Air-Quality-Forecasting-Challenge

Implements a multi-stage forecasting pipeline combining feature engineering, data preprocessing, and ensemble modeling techniques to predict air quality indicators from temporal sensor data. The solution leverages gradient boosting and deep learning architectures trained on structured meteorological and pollution datasets, with reproducible results via provided training scripts and packaged utilities. Includes preprocessed competition data and modular components designed for custom dataset integration and hyperparameter experimentation.

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