Aging Level Prediction

This project utilizes machine learning models to predict global aging levels, achieving 83% accuracy. By analyzing demographic, socioeconomic, and health indicators, it identifies key predictors, such as fertility rate, hypertension treatment, GNI per capita, and urban/rural population balance. These insights inform targeted policy suggestions to mitigate aging population challenges and create a sustainable demographic balance.