A security-first blockchain implementation that utilizes Unsupervised Machine Learning to vet transactions before they are committed to the immutable ledger.
- Anomaly Detection: Powered by an Isolation Forest model to identify high-risk transaction patterns (Amount vs. Time vs. Wallet Age).
- Cryptographic Integrity: SHA-256 hashing ensures that the ledger and the associated ML risk scores remain tamper-proof.
- Real-Time Dashboard: Built with Streamlit for live monitoring and interactive "stress-testing" of the security engine.
- Language: Python 3.11+
- ML Library: Scikit-Learn (Isolation Forest)
- Web UI: Streamlit
- Environment: Fedora Linux / PyCharm