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Cryptocurrency is a popular digital currency due to its security and peer-to-peer transferability. Predicting cryptocurrency prices is crucial for investors and traders to make informed decisions on buying, selling, or holding cryptocurrencies based on their expected value, potential risks, and returns. This study aims to identify the optimal model for predicting the prices of cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), using Deephaven for Data curation. The study involves extracting data from both cryptocurrencies by Deephaven and selecting the most correlating parameters through time lag adjustment. We use correlating cryptocurrency data to train models, such as Artificial Neural Networks (ANN), Long-Short Term Memories (LSTM), and Gated Recurrent Units (GRU). Where the trial-and-error technique was applied for selecting optimized hyper-parameters for each model. The models are then …