Air Pollution Prediction

Machine Learning for Environmental Forecasting

This project focuses on building predictive models to forecast urban air pollution, particularly ozone levels.
Leveraging meteorological and traffic data, the model captures temporal and spatial trends using statistical learning and time-series techniques.