Prediction and Analysis of Air Quality Index Using Machine Learning Algorithms

Abstract

Air pollution, in general, relates to the introduction of toxins into the atmosphere that is toxic to people’s health and therefore the entire ecosystem. It has the potential to be one of the foremost dangerous risks mankind has ever encountered. It hurts cattle, livestock, and trees, among other things. To avoid this issue, machine learning (ML) algorithms must be used to forecast air quality (AQ) from pollution in the transportation field. Therefore, AQ measurement and forecasting have become a significant research subject. Here in this work, we aimed to look at ML-based approaches for AQ forecasting with the highest degree of accuracy. The entire dataset will be evaluated using the supervised ML technique to collect multiple pieces of information such as variable recognition, univariate analysis, bivariate and multivariate analysis, missed value treatments and information confirmation, information cleaning/preparing, and visualization. Our study offers a detailed guide to model parameter sensitivity analysis in terms of results in AQ emissions prediction through accuracy measurement. To suggest an ML-based approach for reliable forecasting of the air quality index (AQI) value by comparing supervised classifier findings in the form of better accuracy. To suggest a ML-based approach for reliably forecasting the AQI value by evaluating supervise classification, ML algorithm prediction outcomes in the form of best accuracy. By predicting AQI, we can recall the major factors that cause pollution and the area affected by pollutants in various places in India.