Abstract: Air pollution includes things like dangerous gases and tiny particulate matter (PM2.5) that deteriorate air quality. This has developed into a crucial area for scientific research and a significant social issue that has an impact on the lives of the general public. As a result, multiple experts and academics at various R&D centres, institutions, and elsewhere are conducting extensive research on PM2.5 pollutant predictions. The authors provided a range of machine learning methods, such as linear regression and random forest models, in this scenario to forecast PM2.5 pollutants in polluted cities. This experiment is carried out with Python 3.7.3 and Jupyter Notebook. Observed to be more dependable models are random forest and based on results for the MAE, MAPE, and RMSE metrics among the models.

Keywords: Pollution detection, Pollution prediction, Logistic regression, Linear regression, Auto regression

PDF | DOI: 10.17148/IJARCCE.2023.12240

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