This paper presents a GDP growth simulation platform, Vikalp.ai, designed for macroeconomic forecasting using machine learning techniques. Traditional economic forecasting methods often rely on linear statistical models, which may struggle to capture the -linear volatilities of global markets, typically relying on rigid linear regressions that falter during sudden economic anomalies. To address this critical gap, this paper details the development and implementation of a robust Random Forest Regression architecture capable of processing over 50 years of historical economic data across 203 countries. By analyzing concurrent indicators such as population dynamics, export and import growth, and capital investment, the study demonstrates how ensemble learning techniques can achieve an exceptional predictive accuracy of ~89.33%. Furthermore, the paper explores the system\\\'s practical application as a real-time scenario simulator, empowering policymakers, researchers, and economists to input hypothetical variables and receive high-confidence GDP growth projections instantaneously. Ultimately, this research bridges the disciplines of artificial intelligence, data science, and macroeconomics, offering a scalable, full-stack web solution to predicting the trajectory of global economies with unprecedented precision, data security, and computational speed.
Gavali et al. (Thu,) studied this question.