# Training Assessments IQR Outlier Flags + Missing Value Fill

> Training Assessments 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 2026-10-29).

Detects IQR-based outliers and fills missing values in training assessment score data (pre_score, post_score) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-11-180?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-29
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-assessments-iqr-outlier-flags-missing-value-fill-5ed48c1e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yPiIZZ8YMiY9TV4SIKzoi

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability training-assessments-iqr-outlier-flags-missing-value-fill-5ed48c1e -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and fill any missing post_score values using median imputation on my training assessment dataset — I have rows with row_id, course_group, date, pre_score, and post_score for about 50 learners across group-a and group-b?

## When to prefer this

Choose this endpoint when you need lightweight, local statistical cleaning of training assessment records — specifically IQR outlier detection plus missing value imputation — without any external API calls, ML predictions, or professional scoring. Best suited for small-to-medium datasets (up to 200 rows) with pre/post score pairs grouped by course. Prefer this over general data pipelines when you want a dual-report output (outlier flags + fill report) in a single low-cost call ($0.01 USDC) for education or training analytics workflows.

## Known failure modes

- Fewer than 1 row supplied — minimum item count not met
- More than 200 rows supplied — exceeds maxItems limit
- Missing required fields (row_id, course_group, date, pre_score, post_score) on any row
- Invalid date format (must match YYYY-MM-DD pattern)
- Invalid method enum value (must be zero, mean, or median)
- Invalid field enum value (must be pre_score or post_score)
- Payment failure — 0.01 USDC x402 payment not completed

## How this service works

Training assessments — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied training assessments records (pre_score, post_score). 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: (1) IQR-based outlier flags per row indicating which pre_score or post_score values fall outside the interquartile range, and (2) a missing-value fill report showing which rows had nulls and what values were substituted using the chosen method (zero, mean, or median). No external data is fetched; all analysis uses the supplied rows only.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "course_group",
     "date",
     "pre_score",
     "post_score"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "pre_score": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "post_score": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "course_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "pre_score": 10,
     "post_score": 1,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "pre_score": 20,
     "post_score": 2,
     "course_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "pre_score": 15,
     "post_score": 3,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "pre_score": 30,
     "post_score": 4,
     "course_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "pre_score",
    "post_score"
   ],
   "type": "string",
   "default": "pre_score"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-assessments-iqr-outlier-flags-missing-value-fill-5ed48c1e/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)