Comparative Analysis of Classification Models for Predicting Quality of Air

In recent years, the air pollution has dramatically increased in urban establishments and reached dangerous levels. Air quality is affected due to multiple factors including location, time and various uncertain variables. Predicting the air quality accurately can help in reducing its effect on health and life of people. In this paper, we have created a model to classify and predict atmospheric pollution. The model uses C5.0 and RPART algorithms which are supervised machine learning techniques based on the daily data collected by CPCB from ITO station in New Delhi. The results of our analysis performed on the dataset show the efficiency of both the algorithms in classifying and predicting large datasets.