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Peer Feedback String Normalisation and Missing-Value Fill
x402
Peer Feedback String Normalisation and 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 2026-11-02).
Normalises strings and fills missing values (zero, mean, or median) for peer feedback records containing review_count and actioned_comments fields, across up to 200 rows in 2 independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Peer feedback — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied peer feedback records (review_count, actioned_comments). 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 analytical reports covering the submitted peer feedback rows (up to 200): normalised string fields and explicitly filled numeric values for the chosen field (review_count or actioned_comments) using the selected method (zero, mean, or median).
Example use cases
Median fill for missing review counts
“I've got a batch of 150 peer feedback records across several course groups and some review_count fields are null — can you normalise the strings and fill those missing values using median so I get clean data back in 2 reports?”
Zero-fill missing actioned comments
“My peer feedback dataset has blank actioned_comments for several rows dated in October 2026. Please normalise the strings and fill those missing actioned_comments with zero across all rows, and give me 2 reports.”
Mean imputation before dashboard export
“Before I export our course group feedback to the dashboard, can you run string normalisation and fill any missing review_count values using mean imputation on these 80 rows? I need the 2 analytical reports your service produces.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-23-037?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have peer feedback records (review_count, actioned_comments) with missing or unnormalised values and need lightweight, offline string normalisation plus explicit numeric imputation (zero, mean, or median) for up to 200 rows at a very low cost — without requiring live lookups, ML predictions, or external data enrichment.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, course_group) causes row rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside ±1,000,000,000,000 boundary are rejected
- Invalid method or field enum value causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
Reviews
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