Fraud Detection of Credit Card Transactions

This workflow shows an overview of different outlier detection techniques for identifying fraudulent credit card transactions. After accessing the credit card fraud detection dataset, the data is partitioned (train set, validation set and test set) and normalized. For each technique, both performance metrics and predictions are output. The seven different techniques are:

Quartiles, Distribution and Clustering (DBSCAN)
Isolation Forest and Autoencoder
- For the Autoencoder, make sure to select the proper Conda environment for Keras under "Preferences > Python Deep Learning". For more info and installation guidance, check the pertinent docs.
Logistic Regression and Random Forest

Important: The performance of the techniques is evaluated on the same test set and, given the heavily imbalanced dataset, this is reported in terms of Recall and Precision.

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