A Machine Learning Decision Support Framework for Industrial Engineering Purposes

Abstract

Data is currently one of the most critical and influential emerging assets. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data is ever actually analyzed for value creation [1]. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen's framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.