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KiHustle Linear Regression Predict
x402
KiHustle Linear Regression Predict is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Performs linear regression on a set of (x, y) data points and returns a predicted y value for a given x input.
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
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.
Example use cases
Sales trend extrapolation
“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.”
Temperature vs energy usage forecast
“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.”
Student score prediction
“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?”
Technical details
Endpoint
POSThttps://kihustle.tech/api/v1/linear-regression-predictWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| points | array | |
| predict_x | number |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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