 Gold price prediction using random forest regressionT Gopi Krishna (), T Sai Lakshmi Manikanta (), B Hari Rajiv (), M Kavitha (), Dharmaiah Devarapalli (), M Kalyani () and D Mythrayee () Edu - Tech Enterprise, 2025, vol. 3, 23 Abstract: The fluctuations in gold prices are significantly influenced by economic volatility, inflation rates, and geopolitical events, which are key drivers in global financial markets. Traditional forecasting models, while comprehensive, often lack the flexibility to adapt to rapid market changes. This project focuses on a Machine Learning-based approach, specifically utilizing a Random Forest Regression Model, to predict future trends in gold prices. By leveraging an AI-driven framework, this system offers a more robust and adaptive solution to real-time market shifts and economic indicators. The study synthesizes financial research and case studies on the use of Machine Learning in commodity markets, demonstrating how advanced predictive models can enhance investment strategies and mitigate financial risk. Furthermore, this project emphasizes the resilience and adaptability of Random Forest models in processing diversified financial data, offering a reliable data-driven method for determining gold prices amidst market uncertainties. Keywords: Gold price fluctuations; Forecasting models; Investment strategies; Financial risk management; Data-driven solution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cua:edutec:v:3:y:2025:i::p:23:id:23 Access Statistics for this article More articles in Edu - Tech Enterprise from Science - Tech Enterprise Alliance |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.