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Feature Availability Analytics — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill
x402
Feature Availability Analytics — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Computes a secondary-measure weighted mean and fills explicit missing values for feature availability records (populated_features, expected_features) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill 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
Returns two independent analytical reports: (1) a secondary-measure weighted mean computed over the specified field (populated_features or expected_features) across supplied rows, and (2) a version of the dataset with explicit missing values filled using the chosen method (zero, mean, or median). Reports are computed locally with no external lookups.
Example use cases
Dataset completeness audit by group
“I've got daily records across two dataset groups with populated_features and expected_features columns — some values are missing. Can you compute the weighted mean and fill in the gaps using zero fill on the expected_features field for all 200 rows?”
Feature coverage trend analysis
“I need to analyze my feature availability data from October 2026 — about 50 rows across groups A and B — and get a weighted mean of populated_features with median imputation for any missing entries.”
Pipeline data quality check
“Before I hand off this dataset to the model team, can you run a secondary-measure weighted mean plus missing-value fill using mean imputation on the populated_features field across these 120 rows of feature availability records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined analytical pass over feature availability records that simultaneously computes a weighted mean and handles missing values, especially when working with paired populated/expected feature count data grouped by date and dataset group. Prefer this over generic stats tools when you want both imputation and weighted aggregation in a single cheap call ($0.01 USDC for 2 reports on up to 200 rows) with no external data dependencies.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- All values null for selected field — weighted mean undefined
- Invalid fill method enum value — rejected with schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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