Welcome to the AnomalyDetection.ml GitHub repository! This software provides a powerful and efficient solution for analyzing Enterprise System transactions, such as sales orders or purchase orders, to detect anomalies and outliers using machine learning techniques. The software is implemented in Python, containerized with Docker, and offers RESTful APIs via FastAPI for seamless integration into your system landscape.
Detecting anomalies in enterprise system transactions is crucial to maintaining data integrity, security, and operational efficiency. This software employs state-of-the-art machine learning algorithms to analyze transactional data and identify outliers that might indicate fraudulent activities, errors, or unusual patterns.
- Machine Learning: Leverages advanced machine learning techniques to accurately identify anomalies in system transactions.
- FastAPI: Provides a RESTful API interface for seamless integration.
- Dockerized: Containerized with Docker for easy deployment and scalability.
- Customizable: Easily configurable to suit your specific enterprise data and requirements.
- Scalable: Capable of handling large volumes of transactions for real-time analysis.
- User-Friendly: Well-documented codebase and API endpoints for straightforward usage.
- Docker: Install Docker & Docker-compose
- Python (if using outside of Docker): Python 3.9+
- Clone this repository to your local machine:
git clone https://github.com/fabigr8/DeepScan.git- Navigate to the project directory:
cd DeepScan- Build and run the Docker container:
docker-compose up --builddepending on the system sudo rights are needed.
sudo docker-compose up --buildplease have a look in our documentation, for more details and informations.
Access the API documentation by opening your web browser and visiting: http://localhost:8000/docs
You can adjust the configuration settings by modifying the config.yaml file. Tune parameters related to data preprocessing, model selection to suit your enterprise's needs.
We welcome contributions to enhance the functionality and usability of this outlier detection software. If you find any issues or have ideas for improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.
Disclaimer: This software is provided as-is and may require additional customization to fit your specific enterprise requirements. Use at your own discretion.
For questions, support, or inquiries, contact open issues.