- Reproduce the experiment of the paper
- Add new features and data analysis for the elliptic data set
- Add new ML method to rerun the experiment
- Deanonymized the transaction and add real bitcoin transaction data
-
Elliptic Data Set
https://www.kaggle.com/ellipticco/elliptic-data-set -
Deanonymized Transactions
https://www.kaggle.com/alexbenzik/deanonymized-995-pct-of-elliptic-transactions
.
├── elliptic_bitcoin_dataset
| ├── full_data.csv
| ├── Result.csv
| ├── elliptic_txs_edgelist.csv
| ├── elliptic_txs_features.csv
| └── elliptic_txs_classes.csv
├── NMLab-Final-Antimoney-Laundry
└── txs
- Linear Regression
- Logistic Regression
- MLP
- SVM
- Random Forest
- Logistic Regression with Cross Validation
python3 -m pip install -r requirements.txt
./start.sh <options>
- -all | -A : run all script
- -raw | -R : run replication of the expriment
- -modified | -M : run with modified features
- -pca | -P : run with pca modified features
- -pcaf | -PF : run with pca modified features with feature selection
- -corrf | -CF : run with modified features with feature selection
- -stat | -S : run stat analysis
- python >=3.6
- numpy
- pandas
- scikit-learn
- seaborn
- statsmodels