{"uid":"cap_qy3BGuMS6zBwE7VRzAu2M","slug":"feature-availability-analytics-exact-row-deduplication-iqr-outlier-475107fa","name":"Feature Availability Analytics — Exact Row Deduplication + IQR Outlier Flags","description":"Feature availability — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-049?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_zkgqBEZeKXlfWY6wPG1zj","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deduplicates feature availability records and flags IQR-based outliers for populated_features or expected_features columns across up to 200 rows.","exampleAgentPrompt":"I have a feature availability dataset with populated_features and expected_features for about 150 rows — can you run exact row deduplication and IQR outlier flagging on the populated_features column and give me both reports?","exampleUseCases":[{"title":"Feature completeness audit for ML pipeline","prompt":"I'm auditing my ML pipeline's feature availability logs — here are 120 rows with row_id, dataset_group, date, populated_features, and expected_features. Can you deduplicate exact rows and flag any IQR outliers in the populated_features column?"},{"title":"Detecting anomalous expected feature counts","prompt":"I've got feature availability records across two dataset groups for the past quarter — about 80 rows. Run deduplication and IQR outlier detection on the expected_features column and tell me which rows look suspicious."},{"title":"Data hygiene check before model training","prompt":"Before I feed this feature availability data into a model, I need to clean it up — can you remove exact duplicate rows and flag any outliers in populated_features using the IQR method? I have around 50 records with date, dataset_group, populated_features, and expected_features."}],"resultDescription":"Two reports: (1) an exact row deduplication report identifying duplicate records across the submitted rows, and (2) an IQR outlier flagging report identifying rows where the selected feature field (populated_features or expected_features) falls beyond 1.5× the interquartile range. No external data is fetched; all analysis is local.","failureModes":["Fewer than 1 row submitted — rejected by schema minItems constraint","More than 200 rows submitted — rejected by schema maxItems constraint","Invalid date format (not YYYY-MM-DD) — schema pattern validation failure","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — schema validation error","Null values in the selected numeric field may reduce outlier detection accuracy","All rows identical — deduplication collapses to one row, outlier report may be trivial"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication and IQR-based outlier detection on feature availability data in a single API call. It is ideal for data quality checks on feature engineering pipelines where populated_features and expected_features are tracked over time. Prefer it over generic analytics tools when your dataset is under 200 rows and you want two focused analytical reports (deduplication + outlier flags) without external data lookups or model inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:26.927Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-exact-row-deduplication-iqr-outlier-475107fa"}