Machine Learning Approaches to Ambient Air Quality Prediction

Air pollution is a significant concern in today's smart city environment. Pollution is monitored in real time by confined authorities who have the authority to assess current traffic conditions and make recommendations based on what they discover.Sensors based on the Internet of Things have been deployed, drastically altering the features of air quality prediction. To gain a better understanding of their processing time for multiple datasets, a comparative analysis of these methodologies is required. To gain a better understanding of their processing time for multiple datasets, a comparative analysis of these methodologies is required. The pollution prediction in this work was done utilizing advanced supervised approaches as well as a comparative evaluation of numerous models to select the optimum model for effectively predicting air quality. Kaggle was used to conduct experiments, and multiple datasets were used to estimate pollution levels.) Aside from these methods, the processing time of each methodology is estimated using standalone learning, and hyperparameter tweaking is fitted using Kaggle. Performance measures were analyzed to obtain the best-fit model in terms of processing time and the lowest error rate.