Prediction of Air Quality Index Using Supervised Machine Learning Algorithms

This paper uses various machine learning algorithms to predict the Air Quality Index used to control pollution to avoid significant health concerns. Air Quality Index shows the quality of air pollution. The major pollutants are particulate matters, nitrous oxide (NO2), Sulphur dioxide (SO2) and carbon monoxide (CO). Earlier techniques such as probability and statistics are measured to forecast air quality, but these methods are very complex to predict. Machine-learning algorithms are a better approach to predicting air pollution levels to overcome difficulties in previous techniques. Various Machine Learning algorithms are random forest regression, support vector regression and Linear Regression. The accuracy of several models is measured by the root mean square error (RMSE) technique.