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