Fraud Detection by Supervised Learning

This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classify transactions as either fraudulent or not. Notice the final Rule Engine node. This node classifies all transactions with a fraud probability greater than 0.3 as fraudulent. The classification threshold is optimized using a parameter optimization loop.

External resources

• Dataset on Kaggle
• Optimization Loop
• Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques
• Random Forest
• Regularization for Logistic Regression
• Logistic Regression