Predicting Air Quality with Machine Learning

The prediction of air quality is a critical issue in environmental sciencehaving important policy and public health ramifications Advancements in machine learning recently have created new opportunities for accurate and timely air quality forecasting. This paper presents a comprehensive study on the application of or algorithms to predict air quality indices. We utilize a diverse dataset encompassing meteorological data and pollutant concentrations apply advanced preprocessing techniques, and evaluate several algorithms, including. Our findings show that machine learning models can significantly outperform traditional statistical methods in air quality prediction, offering more reliable predictions and enabling proactive measures to mitigate pollution impacts.