Decision Tree | Random Forest |
---|---|
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.
Kaggle instructions
- Create
.env
file using the following template:
KAGGLE_USER=
KAGGLE_KEY=
KAGGLE_DATASET_LINK=ealtman2019/ibm-transactions-for-anti-money-laundering-aml
VIEW=default-viewer|code|""
VERBOSE=0|1
- If you prefer to use Kaggle-style configuration, you need to create
.kaggle
folder in the root directory and addkaggle.json
into using the following template:
{
"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
python3 -m src.<file>
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=<name>
This should print the following:
Installed kernelspec myenv in /home/user/.local/share/jupyter/kernels/<name>
Remove Virtual Environment from Jupyter Notebook
List the kernel with:
jupyter kernelspec list
This should return something like:
Available kernels:
<name> /home/user/.local/share/jupyter/kernels/<name>
python3 /usr/local/share/jupyter/kernels/python3
To uninstall the kernel, type:
jupyter kernelspec uninstall <name>
Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to submit a pull request.
This repository is licensed under the GNU General Public License (GPL). Please review the license file provided in the repository for more information regarding the terms and conditions of the GPL license.
If you have any questions, suggestions, or feedback, do not hesitate to contact me.