• Prathipati Silpa Chaitanya
• Galla Devanshi
• Vallepu Anusha
• Navuluri Anitha
• Meduri Lahya Priya

Air pollution is an international concern with respect to health and environmental issues, and therefore, an efficient monitoring and predictive approach must be designed. This paper presents the utilization of SVM algorithms for air quality prediction based on a robust dataset that features historical pollution records and relevant meteorological variables. The data was sourced from a number of monitoring stations, focusing on key pollutants such as PM2.5, PM10, NO2, and CO. The strengths of SVM in classification and regression were aimed at the development of predictive models that were capable of identifying pollution trends and categorizing air quality levels. These models, across check, utilized performance metrics such as accuracy, precision, and F1 score to establish an overall increased superiority of SVM over the traditionally used methods in regression and classification.

Air quality prediction, Meteorological variables, Real-time data integration, Hybrid models, Predictive models ,SVM(Support Vector Machine), Historical pollution data

"PREDICTION OF AIR POLLUTION USING SUPPORT VECTOR MACHINE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.406-414, November-2024, Available :http://www.jetir.org/papers/JETIRGO06042.pdf

"PREDICTION OF AIR POLLUTION USING SUPPORT VECTOR MACHINE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp406-414, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06042.pdf