A collection models for detecting anamolies transactions in bank data.
heroBrine is a project repository by FrederickPu, primarily developed using Jupyter Notebook. The repository is public and open for collaboration, forking, and issue tracking.
Note: The project description has not yet been provided. If you are the author or a contributor, please add a more detailed project description, key features, and usage instructions below.
- Written in Jupyter Notebook format for interactive code execution and documentation.
- Open-source and available for forking and contributions.
- Supports collaborative project management with GitHub issues and wiki.
To get started with this project:
-
Clone the repository:
git clone https://github.com/FrederickPu/heroBrine.git cd heroBrine -
Open Jupyter Notebooks:
- Launch Jupyter Notebook or JupyterLab in your project directory.
- Open the relevant notebook files to explore or run the code.
Feel free to update this README with more specific project details, features, and usage instructions as the project evolves.
model weights for the lstm model can be found in model_lstm.h5 and the bidirectional weights can be found in model_lstm_bi. These checkpoints can be loaded using tf.keras.models.load_model
When running the bencmarking.ipynb you may need to change the file paths for the csv files namely:
kyc = pd.read_csv('/Users/mac/Desktop/kyc.csv')
emt = pd.read_csv('/Users/mac/Desktop/emt.csv')
wire = pd.read_csv('/Users/mac/Desktop/wire.csv')
abm = pd.read_csv('/Users/mac/Desktop/abm.csv')
cheque = pd.read_csv('/Users/mac/Desktop/cheque.csv')
card = pd.read_csv('/Users/mac/Desktop/card.csv')
eft = pd.read_csv('/Users/mac/Desktop/eft.csv')
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. For major changes, open an issue first to discuss what you would like to change.
The repository currently does not specify a license. Please contact the author for usage and distribution permissions.