Machine Learning Based System for the Prediction and Analysis of Air Pollution

Air pollution in contemporary society presents a significant and pressing issue. The accelerating growth across commercial, social, and economic domains has led to a corresponding escalation in pollutant concentrations across various regions globally, precipitating disruptions to human life. With the increasing concern over rising pollution levels, there is a growing need for effective tools to predict and manage air quality. The primary objective of Air Quality Prediction using Machine Learning is to develop accurate and reliable models that can forecast Air Quality Index based on historical and real-time data. Machine Learning techniques can analyze vast datasets encompassing various environmental factors to forecast air quality levels. This project yields a user-friendly system that provides clear results, categorizing air quality as either good or bad based on the evaluated parameters. It could provide a comprehensive solution with the potential to make a meaningful impact on air quality management and public health.