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This thesis explores the application of the Gated Recurrent Unit (GRU) method, a type of recurrent neural network, to predict global tin prices. The study aims to develop a robust predictive model that can accurately forecast future tin prices based on historical data and various economic indicators. By leveraging the GRU's ability to capture temporal dependencies and handle sequential data, the model seeks to provide valuable insights for investors, policymakers, and industry stakeholders.