Using Docker and Kubernetes to produce a scalable fraud detection API
In this report a simple logistic regression model is used to classify credit card transactions as fraudulent or not. A Recall of 0.8 and Precision of 0.7 is obtained for a false positive rate of 0.0005. However, for a model to be useful from a business perspective an understanding of how to deploy the model in the real world is important. Docker and Kubernetes are investigated for this purpose.
