lesedibanyini/predictive-analytics-youth-unemployment

This project investigates the socio-economic factors contributing to youth unemployment in South Africa using machine learning techniques. By analyzing multi-sector data sources, the study evaluates how underperforming sectors, load shedding, corruption, and skills mismatch impact employment outcomes. # predictive-analytics-youth-unemployment This project investigates the socio-economic factors contributing to youth unemployment in South Africa using machine learning techniques. By analyzing multi-sector data sources, the study evaluates how underperforming sectors, load shedding, corruption, and skills mismatch impact employment outcomes.