An increase in the quantity of fine particulates (PM 2.5 ) in the air is a risk to the nation's people since it can create uncontrolled repercussions such as the aggravation of cardiovascular disease and asthma. The issue of air pollution has lately surfaced as a critical concern in smart cities. The systematic technique of estimate particulate matter 2.5 using Machine Learning (ML) has received a lot of attention over the years. The main motive of the research is to employ machine learning models to find the best predicting model to forecast particulate matter PM 2.5 in air quality in smart urban. Support Vector Regression, Decision Tree and Multiple Linear regression are chosen to study the application of machine learning in this research. The outcome of the prediction from respective machine learning then will be evaluated by the performance metrics to measure performance of the models. The outcome demonstrates that Decision Tree Regression is the best fit model for our present study.
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