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This research paper investigates the use of various classification techniques, including Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Linear Regression, to predict gold price movements. The study aims to enhance investment decisions by accurately forecasting whether gold prices will rise or fall, utilizing data mining techniques and performance evaluation through Rapidminer software. The findings indicate varying levels of accuracy among the algorithms, with SVM achieving a max

Author

ibnucholdun

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EN