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Feature Availability Analytics — Exact Row Deduplication + IQR Outlier Flags
x402
Feature Availability Analytics — Exact Row Deduplication + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Deduplicates feature availability records and flags IQR-based outliers for populated_features or expected_features columns across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two reports: (1) an exact row deduplication report identifying duplicate records across the submitted rows, and (2) an IQR outlier flagging report identifying rows where the selected feature field (populated_features or expected_features) falls beyond 1.5× the interquartile range. No external data is fetched; all analysis is local.
Example use cases
Feature completeness audit for ML pipeline
“I'm auditing my ML pipeline's feature availability logs — here are 120 rows with row_id, dataset_group, date, populated_features, and expected_features. Can you deduplicate exact rows and flag any IQR outliers in the populated_features column?”
Detecting anomalous expected feature counts
“I've got feature availability records across two dataset groups for the past quarter — about 80 rows. Run deduplication and IQR outlier detection on the expected_features column and tell me which rows look suspicious.”
Data hygiene check before model training
“Before I feed this feature availability data into a model, I need to clean it up — can you remove exact duplicate rows and flag any outliers in populated_features using the IQR method? I have around 50 records with date, dataset_group, populated_features, and expected_features.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-049?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both exact row deduplication and IQR-based outlier detection on feature availability data in a single API call. It is ideal for data quality checks on feature engineering pipelines where populated_features and expected_features are tracked over time. Prefer it over generic analytics tools when your dataset is under 200 rows and you want two focused analytical reports (deduplication + outlier flags) without external data lookups or model inference.
Side effects
- Fewer than 1 row submitted — rejected by schema minItems constraint
- More than 200 rows submitted — rejected by schema maxItems constraint
- Invalid date format (not YYYY-MM-DD) — schema pattern validation failure
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — schema validation error
- Null values in the selected numeric field may reduce outlier detection accuracy
- All rows identical — deduplication collapses to one row, outlier report may be trivial
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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