Fraud Detection of Credit Card Transactions with Snowflake
This workflow shows an overview of different outlier detection techniques for identifying fraudulent credit card transactions. After connecting to a Snowflake database and loading credit card transaction data, the workflow partitions the data (train set, validation set and test set) and normalizes it. For each technique, both performance metrics and predictions are output. The six different techniques are:
Quartiles, Distribution and Clustering (DBSCAN)
Isolation Forest
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.