• Shivam Singh
• Asit Singh

Air pollution remains a pressing global issue with profound implications for public health and the environment. Accurate air quality prediction is essential to mitigate the adverse effects of air pollution and improve public health. In this study, we explore the application of Lasso Regression, a machine learning technique, for predicting the Air Quality Index (AQI) in a specific location. The research demonstrates the efficacy of Lasso Regression in AQI prediction, offering improved predictive accuracy compared to traditional linear regression models. This research contributes to the advancement of air quality forecasting, facilitating better decision-making and air quality management strategies.

Air Pollution, Air Quality Index (AQI), Lasso Regression, Machine Learning, Prediction, Environmental Health, Public Health

"Advancing Air Quality Forecasting with Lasso Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.g67-g85, September-2023, Available :http://www.jetir.org/papers/JETIR2309627.pdf

"Advancing Air Quality Forecasting with Lasso Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppg67-g85, September-2023, Available at : http://www.jetir.org/papers/JETIR2309627.pdf