Machine Learning Algorithms for Forecasting Air Quality Index: A Predictive Analysis in theTaj Trapezium Zone (TTZ) of Agra

Clean air is vital for sustaining life, and its quality directly impacts health. As industrialization progresses and populations grow, air pollution has become increasingly prevalent, emerging as a significant societal challenge. Air pollution has wide-ranging negative impacts on human health, including increased risk of early death and various ailments such as skin irritation., pulmonary infections, respiratory ailments, pneumonia, lung cancer, and cardiac complications. The purpose of this study is to use machine learning algorithms to predict the Taj Trapezium Zone’s air quality index. Such predictions can inform preventive action to mitigate air pollution. The study compares four algorithmic approaches: Light Gradient Boosting Machine (LightGBM), categorical boosting (Catboost), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). This algorithm’s performance is evaluated based on several parameters, including R-SQUARE, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and ((MAE).