Air Quality Prediction and Analysis using Machine Learning

The rapid urbanization and industrialization of modern societies have led to a significant increase in air pollution levels, posing severe threats to human health and the environment. This study presents an in-depth analysis of air quality, employing predictive modeling techniques to forecast pollutant concentrations and assess their impact. The research focuses on integrating advanced machine learning algorithms and environmental data to develop accurate and timely air quality predictions. The aim of the work was to study the issue of measuring the air quality in the room. Measurement and evaluation of the CO
2
level was chosen as an indicator of deteriorated air quality. Other variables studied were temperature and relative humidity. In order to maintain high air quality in the living room, in our geographical conditions, it is necessary to find a suitable compromise between the intensity of ventilation and the prevention of heat loss.