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Project 08 · Machine learning

Machine Learning Model · Fraud with K-means

✅ published

Unsupervised K-means clustering applied to credit card fraud detection.

Python
SPSS
Machine Learning
K-means
Clustering
Fraud

CONTEXT AND OBJECTIVE

Explore whether an unsupervised technique can separate transactional behaviors compatible with fraud patterns.

DATA AND DIAGNOSIS

  • Transactional variables are used to build behavioral groups.
  • K-means is applied to segment observations without using a prior label.
  • Cluster profiles are read to identify segments with atypical signals.

DELIVERY AND LEARNING

The delivery compares an SPSS Modeler implementation and a Python version, showing the value of clustering as an exploratory tool for fraud.