Prediction of Air Quality Index Using Supervised Machine Learning

Abstract

Abstract: The proposed system depicts various strategies utilized for forecast of Air Quality Index (AQI) utilizing supervised machine learning procedures. The system examines machine learning algorithm for air quality index by computing algorithm accuracy which will bring about the best precision. Moreover, the system exhibits different machine learning ac- curacy figures from the given dataset with assessment order report which recognizes the perplexity lattice. The outcome shows the adequacy of machine learning suggested calculation method that can be contrasted and best exactness with accuracy, Recall and F1 Score. The air pollution database contains data for each state of India. Four supervised machine learning algorithms, decision tree, random forest tree, Naïve Bayes theorem and K-nearest neighbor are compared and evaluated.