Sample-data demonstration

Which website visitors will buy

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
Online Shoppers Purchasing Intention (UCI)
Predicts
Revenue
Task
Classification

Sakar, C. & Kastro, Y. (2018). Online Shoppers Purchasing Intention Dataset [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5F88Q. 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
10,000 rows (the same 10,000-row sample of the 11,097 training rows for every model)

Certificate · holdout, 608 rows that took no part in choosing the winner

Balanced-accuracy lift
52.4% (90% range 43.5–60.6%)
Balanced accuracy
76.2% vs 50.0% for the baseline
Baseline
Always predict the most common training class (False)

Compare · all 1,215 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
1,086 of 1,215

Run: model a3bcb3f3-32a1-48da-85b2-516228137d42 · tournament online_shoppers_20261028-210758 · completed 2026-10-28T21:11:30Z

Step 3

Download predictions and results

Predictions (CSV)

1,215 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 1,215 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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