Preparation of Quality Data for Air Pollution Forecasting

Abstract

This paper proposes a preparation of quality data for training accurate machine learning model. Data preparation is very important in machine learning. Here we are preparing the data for air pollu­tion forecast. As Air pollution forecasting has tradi­tionally been done by physical models of the atmos­phere, which are unstable and in accurate for large pe­riods of time. Since machine learning techniques are more robust to perturbations, in this paper we explore the data preparation and applications of machine learning to air pollution forecasting to potentially gen­erate more accurate predictions. A Linear Regression model is used to train the data a more accurately and predict the air pollution.