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This repository contains an implementation of a Stock Market Prediction model using Long Short-Term Memory (LSTM) networks in Python. The goal of this project is to forecast stock prices based on historical data, leveraging the powerful capabilities of LSTM, a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks
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Go to fileThis repository contains an implementation of a Stock Market Prediction model using Long Short-Term Memory (LSTM) networks in Python. The goal of this project is to forecast stock prices based on historical data, leveraging the powerful capabilities of LSTM, a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks.
The implemented LSTM model utilizes historical stock market data to make predictions about future price movements. By incorporating sequential information and patterns from past stock prices, the model aims to provide insights that can aid in making informed investment decisions.
To run the code in this repository, ensure you have the following:
To utilize this repository, clone the project and follow the instructions provided in the documentation. You can train the LSTM model using your own dataset or experiment with the included sample datasets.
Contributions to this project are welcome. If you identify issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
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This repository contains an implementation of a Stock Market Prediction model using Long Short-Term Memory (LSTM) networks in Python. The goal of this project is to forecast stock prices based on historical data, leveraging the powerful capabilities of LSTM, a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks