Work

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

Year: 2025

Type: article

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.

Source: Journal of Information Systems Engineering & Management

DOI: 10.52783/jisem.v10i49s.10036

Author: Ali Hasan Taresh

Institution: Thai Nguyen University of Information and Communication Technology

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4410829970

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