This project leverages XGBoost, a powerful machine learning algorithm, to predict air pollution levels. It takes into account various data points such as temperature, humidity, pollutant levels, and wind speed to train the model. The project is aimed at forecasting pollutant levels for different geographical locations, which can be used by environmental agencies for pollution management.
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