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2021
Machine Learning, Deep Learning
Stock Market Prediction
LSTM and bidirectional RNN models for stock price prediction from historical market data, containerised with Docker.
Overview
Developed deep learning models using LSTM and Bidirectional RNN architectures to predict stock market prices. The project involved collecting and preprocessing historical stock data, engineering temporal features, and training sequence models to capture market trends. The final model was containerised with Docker for easy deployment and scalability.
Built with
Python · TensorFlow · LSTM · Pandas · Docker
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