Predicting Gold's Glitter: A Tale of Advanced Analytics and Market Trends

Abstract

In a financial landscape, it is pivotal to forecast gold prices which are required by researchers as well as as investors.  This research paper makes use of advanced machine learning algorithms, predictive modeling approach, analysis of historical gold data, with a particular focus on linear regression technique.

In this study, we have performed a comprehensive analysis of gold price prediction, and discover the intricate patterns as well as relationships that are part of  its market dynamics. Our study describes the impact of macroeconomic factors, geopolitical events, and historical  prices on the future valuation of gold. We examine the evolving predictive accuracy over time, illustrating different machine learning models in navigating shifting market conditions. This insight holds significant implications for investors, policymakers, as well as financial analysts, providing them with enhanced tools for informed decision-making in today's complex global financial arena. We have used Linear Regression, Decision Trees and Random Forest for predicting gold prices. Root Mean Squared Error for Linear Regression, Decision Trees and Random Forest are: 30, 110 and 50 respectively. R^2 values for Linear Regression, Decision Trees and Random Forest are: 0.99, 0.94 and 0.98 respectively