Using classification techniques to predict gold price movement

Abstract

In our day to day life, predictability of gold's price is significant in many domains such as economics, trading, investment, and financial and political environments. Better investment decision could be made when gold price values are accurately predicted. The main objective of this research is to forecasts whether the price of gold will rise or decline in the near future. Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Linear Regression are all different classification algorithms that have been used in this paper to predict the gold price movement direction. The performance of each of these algorithms has been investigated while using the Rapidminer software.