README.md

metadata

title: Pricing Optimization API
emoji: 🚗
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false
license: mit

Pricing Optimization API

This is a production-ready FastAPI service that serves your scikit-learn Pipeline. It exposes:

• POST /predict — batch predictions
• GET /docs — interactive Swagger UI
• GET / — simple health check

Why this setup?

• The saved artifact RF_model.joblib contains both preprocessing and the model, so serving is consistent with training.
• The API accepts mixed types (strings/ints/bools/floats) and converts them into a pandas DataFrame with the exact column names used at training, allowing ColumnTransformer to work reliably.

Expected input format

• Body: {"input": [[f1, f2, ..., fN], ...]} — 2D list (batch of rows).
• Order matters: the order must match feature_names saved in your bundle.
• Booleans must be JSON booleans (true/false) — not strings.

Example

{
  "input": [[
    "Peugeot",
    174631,
    120,
    "diesel",
    "black",
    "convertible",
    true,
    true,
    false,
    false,
    false,
    false,
    true
  ]]
}