Harnessing Machine Learning for Predictive Analysis of Air Quality in Pune City: A Comparative Study

With urbanization and industrialization propelling at an unprecedented rate, monitoring and predicting air quality has become an imperative task for ensuring public health and environmental safety. While traditional methods for air quality prediction often rely on deterministic models and historical patterns, the emergence of machine learning (ML) offers a promising avenue for enhancing prediction accuracy. In this study, we harness the power of multiple ML techniques, ranging from classical regression models to sophisticated deep learning architectures, to predict the Air-Quality-Index (AQI). By utilizing comprehensive datasets sourced from renowned environmental agencies, we aim to provide a comparative analysis of the performance and efficiency of each model. For this endeavor, our primary focus was on the Pune region, with datasets specifically obtained from stations located at Karve Road, Nal Stop, Swargate, Bhosari, and Pimpri. Preliminary findings suggest that certain ML techniques exhibit heightened predictive capabilities, outperforming conventional methods. This paper provides insights into the potential of ML in environmental forecasting, laying the groundwork for future research in the domain.