to download project abstract


Generally, Air pollution refers to the release of pollutants into the air that are
detrimental to human health and the planet. 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 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 analyse 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
supervised classification machine learning algorithms. Additionally, to compare
and discuss the performance of various machine learning algorithms from the
given transport traffic department dataset with evaluation classification report,
identify the confusion matrix and to categorizing data from priority and the result
shows that the effectiveness of the proposed machine learning algorithm
technique can be compared with best accuracy with precision, Recall and F1

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