Abstract- Due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the air pollution concentrations to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of air pollution depends on a variety of factors, such as meteorology and the concentration of the other pollutants in urban areas. The aim to investigate machine learning –based techniques for the prediction of air pollution results with the best accuracy. The analysis of the dataset by supervised machine learning techniques(SMLT) captures several information like variable identification and analysis techniques like univariable analysis, bi-variate, and multivariate analysis. Compare and discuss the performance of various machine learning algorithm from the given pollution dataset with evaluation techniques.
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DOI:
10.17148/IJARCCE.2023.12590
