How machine learning-based competitiveness indexes analysis may help us become a wealthy nation with improved living standards: case study Mexico

In this work we analyze the competitiveness data of the IMCO (Mexican Institute for competitiveness) 2003-2022, the ten different subindexes provide us with an overview of the evolution and performance of the Mexican nation. By using machine learning techniques, we examine the subindexes (indicators) that most significantly affect the overall competitiveness score. Once these indicators have been identified, simulations are performed to obtain 6-years projections. Two scenarios are accounted: in the first, nothing is done and the future is simply predicted based on the status quo. In the latter, however, certain actions are applied to the previously determined indicators in order to enhance them. Lastly, suggestions are made in accordance with the estimates found from the scenario simulation.