Air quality assessment model

\nThis study examines the application of machine learning methods to predict air quality in Brisbane, Australia. The main attention is paid to the creation of a model capable of predicting the concentration of PM10 suspended particles based on meteorological data. In the course of the work, a statistical analysis of the factors influencing the level of pollution was carried out, and a random forest model was developed and tested. The results showed that the model is able to explain about 69% of the variation in PM10 concentration, and also identified key meteorological parameters such as air temperature and wind speed that have the greatest impact on the concentration of pollutants. The data obtained can be used to improve the monitoring and management of air quality in cities, which in the future may contribute to reducing the harmful effects of pollution on public health.\n