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KiHustle Correlation Matrix Builder
x402
KiHustle Correlation Matrix Builder 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).
Computes a correlation matrix from submitted datasets to reveal statistical relationships between variables
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field indicating the processed correlation matrix and a 'status' field confirming success. The correlation matrix contains pairwise correlation coefficients between all input dataset variables, typically in the range of -1 to 1.
Example use cases
Feature selection for ML model
“I'm building a machine learning model and need to identify which features are redundant — can you compute a correlation matrix for my dataset with columns price, volume, sentiment_score, days_listed, and conversion_rate so I can drop the highly correlated ones?”
Marketing channel performance analysis
“I have monthly data for email opens, social clicks, paid impressions, and total sales — can you run a correlation matrix on it so I can see which marketing channels are most tied to revenue?”
Financial variable relationship check
“I've got a dataset with stock return, market index, interest rate, and volatility across 24 months — please build me a correlation matrix so I understand how these financial variables move together.”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/correlation-matrix-builderWhen to prefer
Use this endpoint when you need a quick, low-cost ($0.002 per call) correlation matrix computation over multiple numerical variables. It suits lightweight statistical analysis workflows where you need pairwise correlation coefficients without spinning up a full data science environment. Prefer alternatives if you need advanced statistical outputs (p-values, confidence intervals), large-scale batch processing, or detailed schema validation on inputs.
Side effects
- Malformed or empty datasets object returns a generic error response
- Non-numeric data in datasets may cause computation failure or silent errors
- Missing required 'datasets' field results in processing failure
- Very large datasets may time out or produce incomplete results
- Sparse or misaligned datasets may yield unreliable correlation values
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| datasets | object |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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