Prediction of Air Quality Index Using Random Forest and Prophet Tool

The Air Quality Index (AQI) serves as a critical tool for informing the public about the negative impact of air pollution on human health. In Indian cities, where air pollution levels have been rising sharply, accurate assessment and forecasting of AQI are more important than ever. This study evaluates urban air quality and forecasts future AQI by analyzing a wide array of pollutants, including nitrogen oxides (NOx), ammonia, carbon monoxide, sulfur dioxide, ozone, benzene, toluene, PM2.5, PM10, nitric oxide, nitrogen dioxide, and xylene. We employ a Random Forest classifier for detection and use the Prophet library for forecasting. The performance of our model is demonstrated by precision, accuracy, and recall metrics, with the model achieving an accuracy score of 99.57%. This high level of accuracy indicates the model's robust capability in predicting AQI and underscores its potential utility in urban air quality management.