Predictive Analysis of Air Pollution Using Machine Learning Techniques

Abstract

Air pollution is a major source of worry for all living things. India has one of the world’s highest levels of air pollution. Rising population, unplanned growth, increased automotive traffic, stubble burning, industrial waste, fossil fuel combustion, powerplant emissions and a variety of other causes all contribute considerably to air pollution in developing countries. Particulate matter (PM) 2.5 is the most concerning of all air pollutants since it causes major health problems in individuals. Prediction and management of air quality have therefore become critical. Several machine learning algorithms were used in this work to examine dataset results. The results of our work suggest that for future predictions, logistic regression and autoregression can be efficaciously utilised for the analysis and forecasting of levels of PM2.5 in the future. Countries can lower the prevalence of strokes, and chronic and acute respiratory illnesses such as asthma, and lung cancer by reducing air pollution levels.