Economic Growth in Developed and Developing Countries

Collaborative empirical project using World Bank macroeconomic indicators and Human Development Index data to compare growth drivers across developed and developing countries.

Problem

Economic growth is shaped by overlapping development, institutional, investment, and macroeconomic conditions. This study asks whether machine learning methods can help classify development groups and identify which indicators are most associated with GDP growth across those groups.

Method

The project combines unsupervised and supervised learning. K-means clustering groups countries using development and macroeconomic indicators, while Random Forest regression models GDP growth and ranks predictor importance within the resulting analytical frame.

• World Bank macroeconomic indicators for country-level economic conditions.
• HDI data to represent development status and related country characteristics.
• Feature-importance interpretation to compare growth drivers across groups.

Result

The project demonstrates an applied economics workflow that combines research framing, notebook-based analysis, clustering, supervised prediction, and written interpretation. It is included here as an archived academic portfolio project.