Air Quality Prediction using Machine Learning

This project predicts air quality using various machine learning models, including linear regression and decision trees. The model is trained on historical data of air quality parameters like PM2.5, PM10, temperature, humidity, and wind speed. The aim is to forecast air quality levels, allowing cities to take preventive actions. The project also includes data preprocessing, feature engineering, and model evaluation.