Sample-data demonstration

What a property will sell for

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
Real Estate Valuation (UCI)
Predicts
house price of unit area
Task
Regression

Yeh, I. (2018). Real Estate Valuation [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5J30W. CC BY 4.0. Modified: split into training and held-out rows, predictions added.

STRONG CERTIFIED — AUDITABLE & REPEATABLE PREDICTIVE MODEL

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

Winner
Random Forest (best of 12 models built)
Trained on
311 rows

Certificate · holdout, 52 rows that took no part in choosing the winner

Error reduction
55.3% (90% range 43.2–66.2%)
Mean abs. error
5.783 vs 12.95 for the baseline
Baseline
Always predict the training median (38.1)

Compare · all 103 held-out rows

R²
0.6583
MAE / RMSE
4.96 / 9.21
Within ±10%
53 of 103

Run: model acd485e2-1dc1-4b78-bb11-423cdb484892 · tournament real_estate_valuation_20261028-210037 · completed 2026-10-28T21:02:50Z

Step 3

Download predictions and results

Predictions (CSV)

103 held-out rows: the feature values, the actual outcome, the prediction.

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 103 in the order the product tested them (heldout_row). These are not row numbers from the original dataset: the split shuffled the rows.

Removed from this download: X5 latitude, X6 longitude. The results above are as recorded, with every column the model used.

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