AQI Predictor

Created an intelligent AQI prediction system that leverages machine learning algorithms to forecast air quality levels with high accuracy. The solution ingests data from multiple sources including weather patterns, pollution measurements, and traffic density to generate reliable predictions for up to 72 hours in advance.

Preprocessing and normalizing diverse data from multiple sources presented significant challenges. Fine-tuning the machine learning model to account for complex environmental interactions and seasonal variations required extensive experimentation and validation.

The AQI prediction system achieved 87% accuracy in forecasting air quality levels, enabling local authorities and health organizations to issue timely advisories and helping vulnerable populations plan outdoor activities more effectively.