Air Quality Prediction using Supervised Regression Model

This paper explores patterns in Beijing’s Particulate Matter 2.5[7] concentration and forecasts future concentrations. Air quality has been an enormous health concern in recent decades as the place has become further industrialized and more and more of its citizens have begun driving automobiles. The occurenece of air pollution takes place in the following ways. 1. release and generation of pollutants from their source. 2. carry of pollutants in the atmosphere. 3. penetrating and negatively impacting human health and ecosystems. We tend to minimise the effects of these emissions as there is no practical, economical or technical method for zero emissions. PM 2.5 is especially dangerous because it can pass through the human body’s natural filters and enter the lungs. Health concerns related to PM 2.5 include heart and lung disease, asthma, bronchitis, and other respiratory problems. Machine learning, as one of the most accepted techniques, is capable to efficiently train a model using regression models to predict the hourly air pollution concentration [1]. Following six regressors chosen for this problem were Linear Regression, K-Nearnest Neighbor, Stochastic Gradient Descent, Decision Tree, Random Forest and Multi-layer Perceptron. Although performance of all models was comparable, Multi-layer Perceptron Algorithm model successfully bring about better accuracy and true positive rate with 95.4 accuracy.