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This study explores the effectiveness of supervised machine learning algorithms, particularly gradient boosting, in predicting urban air quality using a comprehensive dataset over five years. The results indicate that gradient boosting outperforms other methods, providing valuable insights for public health and city planning. The research highlights the potential of machine learning to enhance air quality forecasting by modeling complex environmental interactions.

Author
fmfelix
Language
EN

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