This paper examines the utilization of machine learning methods to predict the values of valuable metals, specifically gold, silver, palladium, and platinum, from 2017 to 2023. Accurate price prediction for these commodities is tough yet crucial for investors and stakeholders due to their volatile nature, influenced by macroeconomic, geopolitical, and market-specific factors. We utilize historical price data to create and assess various machine-learning models to improve predicting accuracy. It utilizes machine learning techniques, specifically the Gaussian Mixture Model (GMM), to accurately collect and analyze the patterns present in the data. The study entails thorough data preprocessing, which encompasses cleaning and normalization, and models undergo training and validation through cross-validation techniques. Their performance is assessed using metrics such as Entropy, Log-Likelihood, Normalized Entropy Criterion (NEC), Integrated Completed Likelihood (ICL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Our research shows that machine learning models offer higher forecasting capabilities. In addition, the prices of precious metals saw a substantial rise during the COVID-19 pandemic due to increased demand for secure investments, industrial usage, favorable monetary policies, worries about inflation, and a devalued US dollar. The pandemic underscored the dual nature of precious metals as both valuable metals and commodities used in industries, leading to an increase in their price during this time. The paper provides advice for investors and policymakers on how to utilize machine learning-driven insights to make well-informed decisions in the precious metals market.
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