This repository contains a Jupyter notebook for predicting stock prices using Recurrent Neural Networks (RNN). The notebook includes data preprocessing, model training, evaluation, and prediction steps.
- Data preprocessing for stock market data
- Implementation of RNN architecture
- Model training and validation
- Early stopping to prevent overfitting
- Prediction and evaluation metrics
To run the notebook, you need the following Python packages:
- Jupyter Notebook
- PyTorch
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
-
Clone the repository:
git clone https://github.com/zwayth/StockPredictor-RNN.git cd stock-prediction-rnn -
Open the Jupyter notebook:
jupyter notebook stock_prediction_RNN.ipynb
-
Follow the steps in the notebook to preprocess data, train the model, evaluate it, and make predictions.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.
This project is licensed under the MIT License