Fraud Detection Machine Learning Model

This repository creates a web application to predict if a customer is going to commit fraud. It is for academic purposes and shouldn't really be used in real life. It takes a trained machine learning model created with scikit-learn and serves it using Flask. Feel free to improve the model and/or user interface. An example of the app can be seen at https://pml-detect-fraud.herokuapp.com.

Prerequisites

All required Python packages can be found in the requirements.txt file. Additionally, the provided Makefile can be used to created a virtual environment by running make venv. You will also need a Heroku account and have installed the Heroku CLI. For more information on the Heroku CLI, go to https://devcenter.heroku.com/articles/heroku-cli#download-and-install.

Running the app locally using Flask

You may want to run the app using Flask locally before deploying it to Heroku, especially if you have made any changes to the code. To run locally:

1. clone the repository.
2. in the repository, run make deploy.
3. open the link provided in the command line.

If you are using Windows, you can:

1. create and activate the virtual environment.
2. set FLASK_APP=fraud_detection/flask_app/app.py in the command line.
3. run python -m flask run.

Alternatively, you can deploy using Docker.

1. docker build -t fraud_detection .
2. docker run -d -p 5000:5000 fraud_detection

Deploying to Heroku

Make sure your app is ready to be deployed to Heroku by running Flask locally. To deploy to Heroku:

1. clone the repository (if you haven't yet).
2. heroku login and enter your credentials.
3. heroku create or heroku create app-name where app-name is a custom app name.
4. git push heroku master.
5. heroku open or open the app online through your Heroku profile.

License

This project is distributed under the GNU General Public License. The file data/Fraud_Data.csv does not fall under the license and was obtained from Kaggle. Please see LICENSE for more information.