Stock Market Prediction using LSTM

Overview

This project aims to predict stock market prices using LSTM neural networks. LSTM networks are well-suited for time series data like stock prices due to their ability to capture long-term dependencies. By leveraging historical stock data, this model attempts to forecast future prices, enabling users to make informed investment decisions.

Methodology

1. Data Preprocessing: Raw stock market data is preprocessed to handle missing values, normalize the data, and create suitable input-output sequences for training the LSTM model.
2. Model Architecture: LSTM neural network architecture is employed to learn patterns from historical stock data. The model consists of multiple LSTM layers followed by fully connected layers for prediction.
3. Training: The model is trained using historical stock data, optimizing performance metrics such as Mean Squared Error (MSE) or Mean Absolute Error (MAE).
4. Evaluation: The trained model is evaluated on test data to assess its predictive accuracy. Metrics such as Root Mean Squared Error (RMSE) are computed to quantify the model's performance.
5. Prediction: Finally, the model is utilized to make predictions on unseen data, providing insights into future stock price movements.

Usage

1. Data Collection: Obtain historical stock market data for the desired stocks using APIs or data sources.
2. Preprocessing: Preprocess the data to handle missing values, scale features, and create input-output sequences.
3. Training: Train the LSTM model using the preprocessed data.
4. Evaluation: Evaluate the model's performance using test data and relevant evaluation metrics.
5. Prediction: Utilize the trained model to predict future stock prices.

Repository Structure

• data/: Contains raw and preprocessed data files.
• models/: Stores trained LSTM models.
• notebooks/: Jupyter notebooks for data preprocessing, model training, and evaluation.
• src/: Source code for data preprocessing, model definition, and prediction.
• requirements.txt: Lists dependencies required to run the project.
• README.md: Detailed instructions, usage guidelines, and project overview.

Dependencies

• Python 3.x
• TensorFlow
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook (optional, for visualization and experimentation)

Contributions

Contributions to improve the model's accuracy, efficiency, or usability are welcome. Please fork the repository, make your changes, and submit a pull request outlining your modifications.

License

This project is licensed under the MIT License, allowing for unrestricted use, modification, and distribution.

Acknowledgements

This project builds upon existing research and implementations in the field of stock market prediction and LSTM neural networks. We acknowledge the contributions of researchers, developers, and open-source communities whose work has laid the foundation for this project.