Sample-data demonstration

How much demand you will have tomorrow

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
Bike Sharing (UCI)
Predicts
cnt (rentals)
Task
Regression

Fanaee-T, H. (2013). Bike Sharing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5W894. 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
Basic NN, 10 epochs (best of 12 models built)
Trained on
629 rows

Certificate · holdout, 51 rows that took no part in choosing the winner

Error reduction
22.8% (90% range 18.1–27.5%)
Mean abs. error
1535.0 vs 1988.0 for the baseline
Baseline
Always predict the training median (4,359)

Compare · all 102 held-out rows

R²
0.0542
MAE / RMSE
1534.32 / 1928.99
Within ±10%
32 of 102

Run: model 50e4d648-e8a8-4c41-a549-22e9f41a4fa2 · tournament bike_day_calendar_20261028-213320 · completed 2026-10-28T21:34:12Z

Step 3

Download predictions and results

Predictions (CSV)

102 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 102 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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