• M.Prabu
• N.Prabhakaran

Air pollution is a dangerous threat to both human health and the planet. It involves the release of harmful pollutants into the air that cause damage to animals, crops, and forests. To combat this issue, machine learning techniques are being used to predict air quality from pollutants in the transport sector. Consequently, air quality evaluation and prediction have become crucial research areas with the goal of achieving the most accurate results. To achieve this goal, a dataset analysis using supervised machine learning techniques (SMLT) is necessary. This analysis includes variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning and preparation, and data visualization. The analysis provides a comprehensive guide to sensitivity analysis of model parameters with regards to performance in predicting air quality pollution by accuracy calculation. In this study, we propose a machine learning-based method that accurately predicts the Air Quality Index value by comparing supervised classification machine learning algorithms and selecting the best accuracy prediction results. Furthermore, we compare and discuss the performance of various machine learning algorithms using the given transport traffic department dataset. Additionally, we evaluate a GUI-based user interface for air quality prediction based on attributes.

Pollutant, Machine Learning, SMLT, Visualization.

"AIR QUALITY PREDICTION USING SUPERVISED MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.83-86, June-2019, Available :http://www.jetir.org/papers/JETIR1907W12.pdf

"AIR QUALITY PREDICTION USING SUPERVISED MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp83-86, June-2019, Available at : http://www.jetir.org/papers/JETIR1907W12.pdf