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Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms

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  • Berigel, Muhammet
  • Boztaş, Gizem Dilan
  • Rocca, Antonella
  • Neagu, Gabriela

Abstract

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability.

Suggested Citation

  • Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:soceps:v:94:y:2024:i:c:s0038012124001204
    DOI: 10.1016/j.seps.2024.101921
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