Treffer: Prediction of Air Quality Using Supervised Machine Learning Approach

Generally, Air pollution refers to the release of pollutants into the air that are detrimental to human health and the planet as a whole. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Hence, air quality evaluation and prediction has become an important research area. The aim is to investigate machine learning based techniques for air quality forecasting by prediction results in best accuracy. The analysis of datasets by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of air quality pollution by accuracy calculation. To propose a machine learning-based method to accurately predict the Air Quality Index value by prediction results in the form of best accuracy from comparing supervise classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given transport traffic department data set with evaluation of GUI based user interface air quality prediction by attributes. IndexTerms: Data preprocessing,Training ML models,Saving/loading models,Deploying the model via a Flask web application,python, GUI results.