Predictive Analysis of Iraq's Development Road and Trade Balance Using Machine Learning

Abstract

This scholarly article examines the utilization of machine learning algorithms, with a particular emphasis on Random Forests, to forecast the trade balance of Iraq, which is a critical economic indicator anticipated to fluctuate as a consequence of the execution of the Development Road Project. By leveraging datasets sourced from the World Bank and the International Monetary Fund (IMF), vital economic indicators such as gross domestic product (GDP), export and import volumes, foreign direct investment (FDI), and public sector expenditure were utilized to construct a Random Forest classification model.