{"uid":"cap_V52C3BKppOLXAdvk8vVb5","slug":"kihustle-linear-regression-predict-cc13b7c9","name":"KiHustle Linear Regression Predict","description":"Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.","url":"https://kihustle.tech/api/v1/linear-regression-predict","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"points":{"type":"array","items":{"type":"object","additionalProperties":{"type":"number"}}},"predict_x":{"type":"number"}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_CKtrXlGu3xQqbtWPuCY8P","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Performs linear regression on a set of (x, y) data points and returns a predicted y value for a given x input.","exampleAgentPrompt":"I have a set of data points — (1, 2), (2, 4), (3, 5.5), (4, 8) — can you run a linear regression on them and predict the y value when x is 6?","exampleUseCases":[{"title":"Sales trend extrapolation","prompt":"I have monthly sales data: January sold 100 units, February 130, March 155, April 180 — use linear regression to predict how many units I'll sell in month 7."},{"title":"Temperature vs energy usage forecast","prompt":"Here are my data points pairing outdoor temperature to electricity usage: (60, 200), (70, 250), (80, 310), (90, 380) — predict the electricity usage when the temperature is 95 degrees."},{"title":"Student score prediction","prompt":"Given these study-hours-to-test-score pairs: (1, 50), (2, 60), (3, 70), (5, 85), what score would a student who studied for 4 hours be expected to get?"}],"resultDescription":"Returns a JSON object containing the predicted y value for the given x input based on linear regression fitted to the provided data points, along with a success status indicator.","failureModes":["Insufficient data points provided (fewer than 2 points makes regression undefined)","Non-numeric values in the points array causing parsing errors","Missing required fields (points array or predict_x) returning a validation error","Collinear or single-value x inputs causing division-by-zero in slope calculation","API returns generic {result: processed, status: success} without a meaningful numeric prediction if schema is sparse"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call linear regression prediction without setting up your own ML infrastructure. It is ideal for simple one-dimensional regression tasks where you have a small set of numeric x-y pairs and need a quick point estimate. Prefer alternatives (e.g. scikit-learn, a full ML API) for multi-variable regression, non-linear relationships, or when you need confidence intervals and model diagnostics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:02:38.319Z","isFirstParty":false}