Computational Modeling of Youth Unemployment in Nigeria: A Review of Predictive Analytics and Policy Simulation Frameworks

In modern data science, computational modeling is a crucial frontier in tackling issues related to persistent structural and dynamic youth unemployment, and in Nigeria which suffers from large-scale unemployment resistant to conventional labor market reforms, solution packages like computational modeling offer a way out. The computational methods for comprehending and reducing youth unemployment in Nigeria are thoroughly reviewed in this paper. The ideas contained in the study provides academics, researchers and policymakers a thorough synthesis by critically evaluating Nigeria's data ecosystems and suggesting simulation techniques and predictive models to curb the problem from a data-driven standpoint. The analysis in this study demonstrates how Monte Carlo simulations, system dynamics, and agent-based modeling can improve policy foresight, lower implementation risks, and deal with uncertainty. The article then provides analysis, criticism, and suggestions for incorporating ethical data governance, increasing computational capability, and creating a national labor market data strategy. In this review, computational modeling is positioned as a key component of flexible, research-based modern employment policies to obtain and drive progress in Nigeria's labour economy.