Published Paper Details:

This article aims to forecast gold prices (GLD) using financial indicators such as the S&P 500 index (SPX), US Oil Fund (USO), Silver Trust (SLV), and the Euro to US Dollar exchange rate (EUR/USD). The target variable GLD and the features SPX, USO, SLV, and EUR/USD are included in the dataset, which dates back to January 2, 2008. In order to capture complex interactions within the financial data, we have applied a variety of machine learning methods, including decision trees, linear regression, lasso regressions, ridge regressions, and random forests. We have also used the most comprehensive set of characteristics to predict the price of gold in this article. Kaggle's dataset was used for both testing and training. The COLAB notebook was utilized to implement Python programming since it is the greatest tool and has a variety of libraries and header files that improve the accuracy and precision of the work. The model has important ramifications since it gives analysts and investors a predictive tool to help them make wise decisions. This improves risk assessment and portfolio management in the volatile gold market. This concept can also be used in the financial sector to assist more intelligent investing strategies.