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The examples use apex0data/ecommerce-fraud-detection-synthetic-10k-sampl on Hugging Face—the ecommerce fraud dataset used in our earlier model-training workflow.

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Source facts

The source is a CSV of synthetic ecommerce transactions. Its target is is_fraud. The dataset card describes the source and lists an MIT license. The available fields are transaction_id, timestamp, user_id, transaction_amount, merchant_category, device_fingerprint, ip_address and is_fraud.

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What preparation should establish

The coding agent inspects which transaction fields are available at prediction time and how related observations should be grouped. It keeps the target out of model input and retains the original source, preparation and exact versions used by consumers. For a binary decision, the supplied hard label can become a reference over non-fraud and fraud options in an explicit order. This does not imply that the source contains calibrated probabilities. Class imbalance matters: predicting non-fraud for every row gives 95.8% accuracy on the full source. Inspect fraud precision, recall and coverage alongside aggregate accuracy. Repeated users, devices or IPs need examination before choosing a split; the agent should explain its choice.

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Reproducing the source check

The original CSV was inspected at the revision above, with SHA-256 05f63f3c49fc9a3fab5e230e8474e064052a239fb56c1aa1e10be98821e3a44c. Pin that revision when reproducing these counts. Newer source revisions may differ. Synthetic-data results demonstrate the workflow. Assess real fraud-detection performance on suitable independently held-out operational data.