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L. Ramesh
S. Gopinathan


Air pollution is the “world’s largest environmental health threatâ€[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10, S02, N02, C0 as well as weather conditions like temperature, pressure and humidity [2].Hence the data set is combined from various web sources like cpcb and uci repository in order to bring accuracy in the prediction and to justify whether the Quality of air is suitable or not. This prediction will be brought about with the help of some supervised machine learning algorithms and the observation and the result will state which algorithm is giving better accuracy in prediction of AQI and which one is giving less error.


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Article Details

Author Biography

L. Ramesh

Project Fellow,

Department of Computer Science,

University of Madras,

Chennai, 600025,India


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