DATA-DRIVEN INSIGHTS INTO MIGRATION: IDENTIFYING KEY DRIVERS OF POPULATION DECLINE IN NORTH MACEDONIA

SALI, Shpresa and LUMA-OSMANI, Shkurte and IMERI, Florinda and MEMETI, Ermira (2025) DATA-DRIVEN INSIGHTS INTO MIGRATION: IDENTIFYING KEY DRIVERS OF POPULATION DECLINE IN NORTH MACEDONIA. Journal of Natural Sciences and Mathematics of UT, 10 (19-20). pp. 401-405. ISSN 2671-3039

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Abstract

This project addresses the issue of population leaving the country, using advanced Data Mining techniques to identify the main factors that have a great impact on this worrying phenomenon. Mass migration, especially of young people and skilled labor, has become a serious challenge for economic and social development in the Balkan countries, particularly in the Republic of North Macedonia. By analyzing statistical data from sources such as state institutions like MakStat and international agencies like the World Bank, this study aims to reveal the connection between economic (unemployment, low wages) and social (education, quality of life) factors with the decision to emigrate. The methodology used in this project requires the application of advanced machine learning techniques, such as algorithms for classification, clustering, and regression analysis, to analyze behaviors that influence these migratory changes. Through these approaches, the main goal is to identify the population groups that are most at risk of leaving the country, as well as the main factors that influence this big decision. Mass migration of population has affected a lot, various sectors of North Macedonia, including the labor market, public services, the education system, and the domestic economy, making this phenomenon a serious challenge to the sustainable development of the country.

Item Type: Article
Uncontrolled Keywords: migration, population outflow, data mining, socio-economic factors, North Macedonia, predictive analysis.
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Unnamed user with email zshi@unite.edu.mk
Date Deposited: 28 Dec 2025 18:32
Last Modified: 28 Dec 2025 18:32
URI: http://eprints.unite.edu.mk/id/eprint/2335

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