Sample-data demonstration

Which applicants are a higher credit risk

This shows our AI testing process on public sample data. You can't upload your own data here.

  1. 1

    Pick a sample dataset

    Public datasets, each a real business question.

  2. 2

    See how AI tested it

    The same process, already run on that data.

  3. 3

    Download predictions and results

    Every held-back row with its actual outcome and the prediction.

Step 2

See how AI tested it

Sample data
Statlog German Credit (UCI)
Predicts
credit risk
Task
Classification

Hofmann, H. (1994). Statlog (German Credit Data) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5NC77. CC BY 4.0. Modified: split into training and held-out rows, predictions added.

MODERATE CERTIFIED — AUDITABLE & REPEATABLE PREDICTIVE MODEL

Its predictive value is MODERATE, measured on held-out rows that took no part in choosing it.

Winner
Wide NN, 30 epochs (best of 12 models built)
Trained on
900 rows

Certificate · holdout, 50 rows that took no part in choosing the winner

Balanced-accuracy lift
47.8% (90% range 24.5–69.9%)
Balanced accuracy
73.9% vs 50.0% for the baseline
Baseline
Always predict the most common training class ('good')

Compare · all 100 held-out rows

The product marks these Compare figures as diagnostic: “These results do not establish predictive performance” (outcome timing was not declared for this run).

Correct
74 of 100

Run: model fe238326-b603-4442-9713-a9e590f1ab89 · tournament german_credit_20261028-212543 · completed 2026-10-28T21:26:36Z

Step 3

Download predictions and results

Predictions (CSV)

100 held-out rows: the feature values, the actual outcome, the prediction and its confidence.

Download CSV
Results (text)

The verdict, the certificate and Compare figures, and where the data came from.

Download results

The rows are numbered 1 to 100 in the order the product tested them (heldout_row). These are not row numbers from the original dataset: the split shuffled the rows.

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