BreatheSafe: Predicting Air Pollution Using Machine Learning and Django

This study delves into the development of AirCast, a suite of predictive systems designed to estimate air pollution levels through supervised learning techniques. Utilizing historical data on air quality alongside meteorological and environmental parameters, AirCast anticipates pollutant levels, including PM2.5, PM10, and NO2. Techniques such as linear regression, decision trees, and neural networks uncover data relationships. Effectiveness is gauged via performance indicators, showcasing its accuracy. The findings highlight AirCast as an essential tool for environmental oversight and health strategy planning, enabling proactive measures to combat urban air pollution.