Main Article Content
For the most part, Air tainting insinuates the appearance of poisons into the air that are blocking human prosperity and the planet overall. It tends to be portrayed as perhaps the most risky danger that the humankind at any point confronted. It harms creatures, crops, backwoods thus on. To prevent this issue in transport locales need to expect the quality of air from contaminations by using man-made knowledge techniques. Henceforth air quality evaluation and conjecture has become a huge investigation domain. In this paper, we are investigating machine learning based techniques for air quality forecasting by prediction of results in best accuracy. The analysis of dataset 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. The proposed method accurately predicts the Air Quality Index value by prediction of results in the form of best accuracy from comparing supervised classification machine learning algorithms. Additionally, we compared and discussed the performance of various machine learning algorithms from the given transport traffic department dataset with evaluation of GUI based user interface air quality prediction by attributes.