Fraud_Detection_Quantile_Method_Training
Fraud Detection using the Quantile Method
This workflow uses the Quantile Method to identify fraudulent transactions to check for numeric outliers in credit card data. The quantile method is a type of classification that is well suited to linearly distributed data. We first read in and process the sample data using normalization, then use a quantile-based approach to remove outliers, labeling them as potential fraud. The model's score can be checked at the end through the 'Scorer' node with addition information in the 'Extract metrics' metanode.
The steps we perform are below:
1. Read Training Data
2. Normalize Data and Remove non-outliers
3. Save Models for deployment
4. Mark Outliers and Score Model
URL: Kaggle Dataset https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
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