Gold price prediction using random forest regression

Authors

  • T Gopi Krishna Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author
  • T Sai Lakshmi Manikanta Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author
  • B Hari Rajiv Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author
  • M Kavitha Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author
  • Dharmaiah Devarapalli Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author
  • M Kalyani Department of Information Technology, PACE Institute of Technology & Sciences, ongole, India Author
  • D Mythrayee Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India Author

DOI:

https://doi.org/10.71459/edutech202523

Keywords:

Gold price fluctuations, Forecasting models, Investment strategies, Financial risk management, Data-driven solution

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.

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Published

2025-04-15

How to Cite

Gopi Krishna , T., Sai Lakshmi Manikanta , T., Hari Rajiv, B., Kavitha, M., Devarapalli , D., Kalyani, M., & Mythrayee, D. (2025). Gold price prediction using random forest regression. Edu - Tech Enterprise, 3, 23. https://doi.org/10.71459/edutech202523