S&P 500 Stock Prediction with LSTM model

This project aims to predict the stock prices of the S&P 500 using LSTM model. The model is trained on historical stock data and utilizes multiple features to improve prediction accuracy.

Project Structure

• stockprediction-multiplefeature.ipynb: Jupyter Notebook containing the code for data preprocessing, model training, and prediction.
• .weights.h5: Model weights file that will be created after you train the model.
• logs/: Directory for storing log files generated during model training.

Requirements

To install the required packages, run:

pip install -r requirements.txt

Usage

1. Ensure you have all the required dependencies installed.
2. Run the Jupyter Notebook stockprediction-multiplefeature.ipynb to train the model and make predictions.

.gitignore

*.weights.h5
logs/

Installation

To install the necessary packages, use the following command:

pip install -r requirements.txt

Contributing

Feel free to contribute to this project by creating pull requests or submitting issues.

License

This project is licensed under the MIT License.