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Tutoring Sessions IQR Outlier Flags + Missing Value Fill
x402
Tutoring Sessions IQR Outlier Flags + 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 2026-10-23).
Detects IQR-based outliers and fills missing values in tutoring session data (session_minutes, session_count) for up to 200 rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tutoring sessions — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied tutoring sessions records (session_minutes, session_count). 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 analytical reports per call: (1) IQR outlier flags per row indicating which session_minutes or session_count values fall outside the interquartile range, and (2) explicit missing-value fills for null entries using the selected method (zero, mean, or median). Results cover up to 200 input rows and are computed locally without external lookups.
Example use cases
Flag anomalous tutoring durations by group
“I have 80 rows of tutoring session logs with course_group, date, and session_minutes. Can you flag any session_minutes values that are IQR outliers and fill any missing ones using the median?”
Impute missing session counts with mean
“Some rows in my tutoring dataset are missing session_count values. Can you run a missing-value fill using the mean method on the session_count field across my 120 tutoring records, and also flag any outliers?”
Data quality check before reporting
“Before I submit my weekly education report, can you check my 200 tutoring session rows for IQR-based outliers in session_minutes and fill any nulls with zero so I have a clean dataset?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-16-180?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, deterministic statistical data quality analysis on tutoring session records — specifically IQR outlier detection combined with missing value imputation — without requiring external data sources, ML models, or live lookups. Ideal for pre-processing education datasets before reporting or dashboarding, especially when working with up to 200 rows and needing both reports in a single low-cost API call.
Side effects
- Exceeding 200 rows returns an error or truncation
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, course_group, date) causes validation failure
- Numeric values outside bounds (±1,000,000,000,000) may be rejected
- Empty rows array returns an error
- Invalid enum value for method or field causes schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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