A Novel Approach to Reduce Air Pollution Through Machine Learning Based PM2.5 Prediction

The industrial revolution is advancing development and also side by side creating an environmental crisis for us and the next generation. AI (Artificial Intelligence) is one of the most fascinating subjects for computer scientists in this century. It is a technology that helps a machine to do tasks by itself which needs human intelligence. Air pollution is one of the causes of the environmental crisis. So, our research proposes a novel approach for reducing air pollution by making people more aware of their day-to-day life by detecting the environmental air condition with the help of machine learning by using PM2.5 prediction. We find out accuracy tests for PM2.5 using various AI algorithms such as Linear Regression Analytics, Logistics Regression Analytics, Decision Tree, SVC Algorithm, and SGD algorithm and make a prediction based on it. Later, we introduce a framework that allows us to know about the PM2.5 values in the air and then counter it with the help of PM2.5 prediction which we calculate from the PM2.5 accuracy tests.