/fraud-detection-sample

Primary LanguageJupyter Notebook

Credit Card Fraud Detection Sample

This project is based on Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

How to run

To run this project in MacOS, you need to get some dependencies installed with HomeBrew. First install brew, and then install the following packages:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install \
    cython \
    freetype \
    git
    graphviz \
    libomp \
    pkg-config

This project use Pipenv to manage dependencies. Install it using pip:

pip install --user pipenv

Then, create a virtual environment and install dependencies:

pipenv install

The code can be run from a terminal. To run the code, first activate the virtual environment:

pipenv shell

And then you can each step, one by one:

python dataset_generation.py
python feature_transformation.py
python logistic_regression.py
python convolutional_neural_network.py

Or using Jupyter Lab. A browser will be opened, and you can open notebook.ipynb and run it:

pipenv run jupyter lab

The notebook can be also imported in Google Collab.

Development

pipenv shell

# Lint and format code:
isort --profile=black *.py
black *.py
pylint -E *.py

# Clear the Jupyter Notebook output:
jupyter nbconvert --clear-output --inplace *.ipynb