Applied Machine Learning: A Creation of Sustainable Development Progress Index Based on Circular Economy

Global achievements in economic growth, poverty reduction and improved welfare have been counterbalanced by an increasing strain on the biosphere in the last decade. With technology improvement, "big data" will become the dominant scientific paradigm increasing predictive strengthen and visibility. This study focuses on evaluating the relationship among five identified capital resources using unsupervised machine learning clustering analysis to construct pattern recognition then performed forest-based classification and regression with data collected from 32 OECD nations to create a Sustainable Development Progress Index (SDPI). The proposed SDPI ratings provides a clear outlook in ways for countries to balance resource demand and economic development.