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Deep learning project for stock market prediction using historical price data. Compares single-stock and multi-stock time-series models (e.g., LSTM/Transformer) to test whether incorporating data from multiple correlated stocks improves prediction accuracy. Includes preprocessing, training, and evaluation pipelines in PyTorch.
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