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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
Credit Scoring in FHE
In this directory, CreditScoring.ipynb compares scikit-learn models and Torus ML models, in term of speed and accuracy, on a credit scoring task. This notebook was inspired by an existing notebook https://www.kaggle.com/code/ajay1735/my-credit-scoring-model. The data-set hmeq.csv file comes from https://www.kaggle.com/code/ajay1735/my-credit-scoring-model/input .
Installation
- First, create a virtual env and activate it:
python3.8 -m venv .venv
source .venv/bin/activate
- Then, install required packages:
pip3 install -U pip wheel setuptools --ignore-installed
pip3 install -r requirements.txt --ignore-installed