Prediction of Air Quality Data with Feature Selection and Machine Learning Algorithms

Abstract

Predicting air quality is essential for managing public health and the environment. This study looks into how well machine learning (ML) algorithms forecast air quality data and incorporates feature selection strategies for better model performance. Air pollution is one of the biggest issues facing humanity today. Every year, air pollution causes millions of deaths due to direct or indirect effects. These days, there is a critical need for effective measures to mitigate the negative impacts of air pollution. Usually, the answers to the issues with air pollution are hasty decisions that don't end up helping. It is vital to concentrate efforts on the pollutants that cause the majority of air pollution in order to create an efficient counter-strategy. In many industrial and urban regions today, monitoring and safeguarding air quality has emerged as one of the government's top priorities. The combustion of fossil fuels, traffic and weather conditions, and industrial parameters