Prediction of Air Quality using Supervised Machine Learning

This paper depicts various strategies utilized for forecast of Air Quality Index (AQI) utilizing supervised machine learning procedures. The point is to examine machine learning based methods for air quality index by expectation brings about best precision. Moreover, to think about and talk about the exhibition of different machine learning calculations from the given vehicle traffic office dataset with assessment order report, recognize the perplexity lattice and to classifying information from need and the outcome shows that the adequacy of machine learning suggested calculation method can be contrasted and best exactness with accuracy, Recall and F1 Score. The air pollution database contains data for each state of India. We compare six simple machine learning algorithms, logistic regression, decision tree, support vector machine, random forest tree, Naïve Bayes theorem and K-nearest neighbor.