Application of Machine Learning on Air Quality

Abstract

Air pollution can cause serious health problems and millions of people live in areas with poor air quality. Understanding air quality is an important topic of research which in turn helps understand health and global climate. In this paper, I applied machine learning techniques to the predict the air quality. Using a data set collected in urban cities with information on air pollutants, supervised learning was applied to gain insights and predict quality. Neural networks are used to determine the specific factors that impact the air quality index and the accuracy of the prediction. The research was helpful in identifying specific factors that affected air quality and accuracy of prediction improved when increasing hidden layers and epoch values within the neural networks.