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Survey Participation Data Quality Pipeline: Record Audit + String Normalisation + Missing-Value Fill
x402
Survey Participation Data Quality Pipeline: Record Audit + String Normalisation + 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).
Runs three data-quality reports on survey participation records (respondents/invitations): record quality audit, string normalisation, and explicit missing-value fill using zero/mean/median imputation — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Survey participation — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied survey participation records (respondents, invitations). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports per call: (1) a record quality audit identifying issues such as malformed dates, out-of-range values, or structural problems; (2) string normalisation results for text fields like course_group; and (3) explicit missing-value fill results for the chosen numeric field (respondents or invitations) using the selected method (zero, mean, or median). All processing uses local analytical engines with no external lookups.
Example use cases
Pre-analysis survey data cleaning
“I have 80 rows of survey participation data with some null respondent counts and messy course group names — can you audit the record quality, normalise the strings, and fill the missing respondents field using the median so I can run my analysis cleanly?”
Invitation count imputation for course groups
“Our invitation tracking spreadsheet has gaps — some rows are missing invitation numbers. Can you run a missing-value fill on the invitations field using mean imputation across these 120 survey participation rows, and also flag any quality issues in the records?”
Data quality check before reporting
“Before I submit our monthly education survey report, can you do a quality audit on these 40 participation records, normalise all the course_group strings, and fill any blank respondent values with zero so nothing is left null in the output?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-10-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have survey participation data (respondents and invitations keyed by course group and date) that needs data quality preparation before analysis — specifically when you need all three of record auditing, string normalisation, and missing-value imputation in a single cheap call. Ideal for education or research pipelines doing pre-analysis cleaning on datasets up to 200 rows. Not suitable for live prediction, external data enrichment, or datasets larger than 200 rows per call.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes validation errors on affected rows
- Missing required fields (row_id, course_group, date) causes row-level failures
- Invalid enum value for field (must be 'respondents' or 'invitations') or method (must be 'zero', 'mean', or 'median') causes request rejection
- Empty rows array returns an error
- Payment failure (insufficient USDC balance) blocks the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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