International Research Journal of Education and Technology

Gold stands as a cornerstone asset in the intricate world of finance, valued not only for investment diversification but also as a bedrock of economic stability. The ability to accurately predict gold prices is therefore of paramount importance to a diverse group, ranging from individual investors seeking to optimize their portfolios to financial analysts tasked with risk assessment and forecasting, and even to policymakers guiding economic strategy. This study introduces a sophisticated
machine learning (ML)-driven model meticulously designed for gold price prediction, leveraging the power of historical data. At the heart of our approach is the
Random Forest Regression algorithm, a robust and versatile ML technique lauded for its proficiency in unraveling complex, non-linear relationships inherent in financial markets.