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  1. Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

    • Jupyter Notebook 491

      Jupyter Notebook 491 - stock-price-prediction · GitHub...

    • Python 339

      Python 339 - stock-price-prediction · GitHub Topics · GitHub

    • HTML 32

      Pull requests. This project showcases a comprehensive...

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      Most Forks - stock-price-prediction · GitHub Topics · GitHub

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  2. 12 paź 2023 · FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions.

  3. This project uses Long Short-Term Memory (LSTM) networks to predict stock prices by analyzing historical data and technical indicators. LSTM captures long-term dependencies in time series, improving prediction accuracy. The tool helps traders and investors make informed, data-driven decisions with real-time analysis and robust modeling.

  4. Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  5. A Google Colab notebook that demonstrates how to predict stock prices using deep neural networks.

  6. 26 gru 2022 · Pull requests. This project showcases a comprehensive exploratory data analysis (EDA) of Tesla stock prices using various analytical tools, including Python, R, Power BI, and Microsoft Excel. The project involves examining historical Tesla stock data, performing EDA, and predicting stock prices for January 2024.

  7. Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.

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