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Stock Market Prediction Using Deep Learning and Stream lit

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LSTM outperformed traditional regression methods in predicting stock market trends with accuracy and reliability.
The predictive model achieved effective error metrics, notably lower Mean Squared Error and Root Mean Squared Error.
The framework includes a user-friendly Streamlit interface that supports dataset upload and real-time predictions.
This approach highlights the potential of deep learning in enhancing financial forecasting applications.

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This framework predicts stock trends using LSTM networks and Linear Regression, suggesting new forecasting techniques.

Tabassum et al. (2025) studied this question.

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