Air Quality Index Prediction Using Machine Learning

Abstract

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies. Keywords: Machine Learning, Air Pollution, AQI.