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Project 10 · Machine learning

Machine Learning Model · Fraud with DBSCAN

✅ published

Unsupervised DBSCAN clustering to detect potential credit card fraud patterns.

Python
Machine Learning
DBSCAN
Clustering
Unsupervised model
Fraud

CONTEXT AND OBJECTIVE

Detect dense groups and atypical observations that may work as exploratory fraud signals.

DATA AND DIAGNOSIS

  • DBSCAN is used to identify densities and isolated points without predefining the number of clusters.
  • Atypical patterns and transactional segment separation are evaluated.
  • The result is read as an exploratory tool, not as a definitive fraud diagnosis.

DELIVERY AND LEARNING

The delivery documents a density-based unsupervised alternative to complement other fraud detection approaches.