kelvng/predict-stock

This repository contains Jupyter Notebook files that demonstrate how to use Long Short-Term Memory (LSTM) networks to predict stock prices.

Predicting Stock Prices Using LSTM

This repository contains Jupyter Notebook files that demonstrate how to use Long Short-Term Memory (LSTM) networks to predict stock prices.

Description

Stock price prediction is a popular application of deep learning. LSTM networks are particularly well-suited to this task because they can capture long-term dependencies and can handle variable-length input sequences.

This repository includes Jupyter Notebook files that demonstrate how to use LSTM networks to predict stock prices. The notebooks are based on the Keras deep learning library and use historical stock price data to predict future stock prices.

Dependencies

This project requires the following dependencies:

• Python 3.x
• Keras
• NumPy
• Pandas
• Matplotlib

References

This project was inspired by the following resources:

• Time Series Analysis with LSTM using Python's Keras Library by Usman Malik

• Stock Price Prediction using LSTM and RNN Model by Muhammad Ahmed

License

This project is licensed under the MIT License. See the LICENSE file for details.