• Sushma C V

The environment is becoming increasingly contaminated as a result of human activities such as industrialization and urbanization. Air pollution is a combination of natural and man-made chemicals that has a variety of negative effects on humans and the environment. Toxic or hazardous contaminants such as SO2, NO2, CO, PM, and toxic organics are released in significant quantities by most manufacturing activities. This paper presents a Machine Learning based methodology for gauging air quality. It goes through several phases of pre-processing, learning, and evaluation. Based on the level of dataset obtained, the ML technique Random Forest (RF) is investigated to predict air quality. Precision, recall, and F1 score are used as performance assessment indicators.

Machine Learning, Random forest, Air pollution, Air quality index.

"Air Quality Prediction Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.c815-c820, June-2021, Available :http://www.jetir.org/papers/JETIR2106385.pdf

"Air Quality Prediction Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppc815-c820, June-2021, Available at : http://www.jetir.org/papers/JETIR2106385.pdf