[Malaysia] An application of machine learning regression to feature selection: a study of logistics performance and megatrend attributes

This project will apply feature selection techniques for identifying features that can effectively predict the Logistics Performance Index (LPI), building upon our previously published work [1].

This project aims to expand upon the earlier research by incorporating additional distinctive features, including the carbon emissions rate, fuel and renewable energy costs and consumption rates, e-commerce market size, and growth. These elements are anticipated to provide insights into logistics performance within the contemporary landscape of a global supply chain. Furthermore, the enrichment efforts extend to the utilization of the wrapper technique.

[1]  https://doi.org/10.1007/s00521-022-07266-6