This project uses machine learning models to predict the Air Quality Index (AQI) based on historical air quality data. By applying algorithms like XGBoost and LightGBM, the system can predict AQI values, helping users and organizations plan activities around predicted pollution levels. The project includes preprocessing data, selecting relevant features, and training multiple models for accurate AQI predictions.
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