Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Air pollution is a major environmental issue and machine learning techniques play an important role in analyzing forecasting these data sets. quality outcome of the complex interaction several factors involving chemical reactions, meteorological parameters, emissions from natural anthropogenic sources. In this paper, we propose efficient combined technique that takes benefits statistical to predict/forecast Quality Pollution particular regions. This work also indicates prediction performance varies over different regions/cities India. We used time series analysis, regression Ada-boosting anticipate PM 2.5 concentration levels locations throughout Hyderabad on annual basis, depending numerous atmospheric surface parameters like wind speed, air temperature, pressure, so on. Dataset for investigation taken Kaggle experimented with proposed method comparison results our experiments are then plotted.