Predicting Economic Resilience in Nigeria Using Machine Learning: A Framework for Policy Intervention

Abstract

Economic resilience is important in sustaining the state of the Nigerian economy from  different  distortions.  Therefore,  the  current  study  implemented  a  machine learning  method for estimating and  forecasting Nigeria's economic resilience by using  the  following  principal  macroeconomic  indicators:  GDP  growth,  inflation, exchange  rate,  unemployment,  debt-GDP  ratio,  and  foreign  reserves  through Decision  Trees  (DT)  and  Random  Forest  (RF)  algorithms.  Amongst  these,  the prediction  accuracy  was  approximately  86%  by  Random  Forest  in  terms  of predicting an economic downturn when compared to Decision Tree. Thus, GDP growth, inflation, and exchange rate variability were singled out as the key predictors of economic resilience. This implies that the policy ramifications of the machine learning  model  results  are  geared  toward  controlling  inflation,  stabilizing  the exchange rate, creating jobs, and promoting economic diversification. The results provide  data-informed  policy-making  to  support  the  resilience  features  of  the Nigerian economy.

MIJST, Vol. 13, June 2025 : 29-36

DOI: https://doi.org/10.47981/j.mijst.13(01)2025.519(29-36)