Predicting Air Quality Based on Multiclass Machine Learning Techniques

Abstract

India is among the most polluted nations with severe environmental implications of pollution increase in several bas cities. For the past few years’ Indian cities have witnessed an alarming drop in the Air Quality (AQ) which is a result of the rapid economic growth. The lives of billions of people are deeply affected by Air Pollution (AP) every year. The economic sectors that source poor urban AQ include transportation, agriculture, construction, forestry, and logging, emitting dangerous gases and particulates such as NO₂, NH₃, SO₃, and Benzene. This research attempts to implement four different classifiers independently on the AQ dataset. The four classifiers that are being implemented are the Multiclass Decision Jungle (MDJ), Multiclass Logistic Regression (MLR), Multiclass Decision Forest (MDF), and Multiclass Neural Network (MNN). This project objective is to identify the most suitable among the four classification models in building the best model for AQ. The performance of a model is judged by accuracy, precision, and recall. Likewise, about other three modes MDF performs best, where it obtained 99.96% accuracies, 98.91% precision, and 99.76% recall.