https://github.com/federicobruzzone/anti-money-laundering
The project is based on the analysis of the "IBM Transactions for Anti Money Laundering" dataset published on Kaggle. The task is to implement a model which predicts whether or not a transaction is illicit, using the attribute "Is Laundering" as a label to be predicted.
https://github.com/federicobruzzone/anti-money-laundering
machine-learning machine-learning-algorithms massive-datasets pyspark
Last synced: 16 days ago
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The project is based on the analysis of the "IBM Transactions for Anti Money Laundering" dataset published on Kaggle. The task is to implement a model which predicts whether or not a transaction is illicit, using the attribute "Is Laundering" as a label to be predicted.
- Host: GitHub
- URL: https://github.com/federicobruzzone/anti-money-laundering
- Owner: FedericoBruzzone
- License: gpl-3.0
- Created: 2023-06-20T23:24:33.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-12T15:52:21.000Z (9 months ago)
- Last Synced: 2025-04-19T21:25:45.408Z (about 1 month ago)
- Topics: machine-learning, machine-learning-algorithms, massive-datasets, pyspark
- Language: Jupyter Notebook
- Homepage:
- Size: 41.6 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# Anti Money Laundering
The project is based on the analysis of the «IBM Transactions for Anti Money Laundering» dataset published on Kaggle. The task is to implement a system which predicts whether or not a transaction is illicit, using the attribute "Is Laundering" as a label to be predicted.
## Starting point working locally
**Kaggle instructions**
1. Create `.env` file using the following template:
```env
KAGGLE_USER=
KAGGLE_KEY=
KAGGLE_DATASET_LINK=ealtman2019/ibm-transactions-for-anti-money-laundering-aml
VIEW=default-viewer|code|""
VERBOSE=0|1
```
2. If you prefer to use Kaggle-style configuration, you need to create `.kaggle` folder in the root directory and add `kaggle.json` into using the following template:
```json
{
"username":"",
"key":""
}
```
**Create and start a new virtual environment**
`source create_venv.sh venv`
**Start current virtual environment**
`source venv/bin/activate`
**Deactivate the current virtual environment**
`deactivate`
### Run the `.py` file
`python3 -m src.`
### Using Virtual Environments in Jupyter Notebook and Python
**Add Virtual Environment to Jupyter Notebook**
Install ipykernel which provides the IPython kernel for Jupyter:
```pip install --user ipykernel```
Add your virtual environment to Jupyter by typing:
```python -m ipykernel install --user --name=```
This should print the following:
```Installed kernelspec myenv in /home/user/.local/share/jupyter/kernels/```
**Remove Virtual Environment from Jupyter Notebook**
List the kernel with:
```
jupyter kernelspec list
```
This should return something like:
```
Available kernels:
/home/user/.local/share/jupyter/kernels/
python3 /usr/local/share/jupyter/kernels/python3
```
To uninstall the kernel, type:
```jupyter kernelspec uninstall ```
## Contributing
Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to submit a pull request.
## License
This repository is licensed under the [GNU General Public License (GPL)](https://www.gnu.org/licenses/gpl-3.0.html). Please review the license file provided in the repository for more information regarding the terms and conditions of the GPL license.
## Contact
If you have any questions, suggestions, or feedback, do not hesitate to [contact me](https://federicobruzzone.github.io/).