Predictive Approach for Unemployment Management Using Data-Driven Techniques

This study presents the modelling and implementation of an integrated system for unemployment prediction and employment tracking in Nigeria using data-driven techniques. The unemployment prediction system was initially developed using a linear regression model trained on five years of data from the National Youth Service Corps (NYSC) covering graduates of Higher Education Institutions (HEIs). In parallel, an Employment Tracking System (ETS) was developed using the Feed Forward Neural Network (FFNN) architecture trained using fingerprint data from the Federal Ministry of Labour, Employment, and Productivity (FMLEP) and both systems were implemented using MATLAB’s regression and neural network toolboxes and validated through rigorous testing and evaluation metrics. The FFNN algorithm was introduced and reconfigured to train on the NYSC dataset. The result of the applied FFNN demonstrated performance coefficient of determination with an R2 of 0.99624, a Mean Square Error (MSE) of 0.0025165 and a Root Mean Square Error (RMSE) of 0.050165, showing a significant improvement in prediction accuracy. The ETS effectively classifies individuals as employed or unemployed based on biometric inputs. The integrated model provides a reliable platform for forecasting unemployment trends and tracking employment status, offering valuable insights for policy formulation and labour market planning in Nigeria.