Build air quality prediction systems using MATLAB's Regression Learner and Time Series Toolboxes. This project develops machine learning models for PM2.5 and ozone forecasting using meteorological and pollution data. Learn to handle spatial-temporal correlations, feature selection from sensor networks, and uncertainty quantification. Includes complete workflows for data cleaning, model validation, and deployment as early warning systems with 85-90% prediction accuracy for 24-hour forecasts.