| Name | mlex-lib JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | Money Laundering Expert System - A machine learning framework for financial fraud detection |
| upload_time | 2025-07-30 11:20:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2024 Diego Pinheiro
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
machine-learning
fraud-detection
financial
neural-networks
gru
lstm
rnn
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
pandas
ipykernel
matplotlib
numpy
scikit-learn
networkx
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Money Laundering Expert System (MLEX)
A comprehensive machine learning framework for financial fraud detection and money laundering prevention.
## Features
- **Neural Network Models**: GRU, LSTM, and RNN implementations optimized for sequence data
- **Evaluation Framework**: Comprehensive evaluation metrics and visualization tools
- **Data Processing**: Advanced preprocessing and feature engineering capabilities
- **Model Pipeline**: End-to-end machine learning pipelines for fraud detection
- **Visualization**: Interactive plotting and analysis tools
## Installation
```bash
pip install mlex-lib
```
## Quick Start
```python
import pandas as pd
import numpy as np
from mlex.models import GRU, LSTM, RNN
from mlex.utils import DataReader, FeatureStratifiedSplit
from mlex.evaluation import StandardEvaluator, F1MaxThresholdStrategy
# Load and preprocess data
reader = DataReader('path/to/your/data.csv', target_columns=['fraud_label'])
X = reader.fit_transform()
y = reader.get_target()
# Split data
splitter = FeatureStratifiedSplit(column_to_stratify='account_id', test_proportion=0.3)
splitter.fit(X, y)
X_train, y_train, X_test, y_test = splitter.transform(X, y)
# Train model
model = GRU(
target_column='fraud_label',
validation_data=(X_test, y_test),
input_size=10,
hidden_size=64,
epochs=50
)
model.fit(X_train, y_train)
# Evaluate
scores = model.score_samples(X_test)
evaluator = StandardEvaluator("fraud_detection", F1MaxThresholdStrategy())
evaluator.evaluate(y_test, [], scores)
print(evaluator.summary())
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
If you use MLEX in your research, please cite:
```bibtex
@software{mlex2024,
title={Money Laundering Expert System (MLEX)},
author={Pinheiro, Diego},
year={2024},
url={https://github.com/IoTDataAtelier/mlex}
}
```
## Support
- **Issues**: [GitHub Issues](https://github.com/IoTDataAtelier/mlex/issues)
- **Discussions**: [GitHub Discussions](https://github.com/IoTDataAtelier/mlex/discussions)
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