Assessing recorded credit-card payment default

Version: 2026-09-21

The example uses the UCI Default of Credit Card Clients dataset. The saved project contains 30,000 records and expands categorical inputs before training. The positive class is default=1. Some encoded cells require mean replacement when the project is loaded.

Internal benchmark performance does not establish a suitable lending policy. Use an out-of-time population, review affordability and protected attributes, test calibration and subgroup errors, and retain human review. A model trained on existing customers may not represent new applicants.

Open the .nd project in Neural Designer. Its embedded data, parameters, saved sample roles and task report are preserved. If the original CSV path is unavailable, select the CSV included here. The source CSV may include unused columns; the embedded project defines the exact modelling schema.

The charts/ directory contains standalone HTML exported with the application chart builder. The schema and test-metrics files identify the exact inputs, outputs, evidence and source hash.

Dataset source: https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients
Retain the source attribution and its applicable dataset terms.

The original HTML expression is included unchanged. native-model.js contains the same mathematical functions extracted from it.
