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README.md

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+---
+language: en
+tags:
+  - sklearn
+  - diabetes
+  - regression
+  - gradient-boosting
+---
+
+# Diabetes Disease Progression Predictor
+
+A **Gradient Boosting Regressor** trained on the
+[sklearn Diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)
+to predict disease progression one year after baseline (continuous target).
+
+## Results
+
+| Metric | Value |
+|--------|-------|
+| Test MAE | **44.80** |
+| Test R² | **0.4366** |
+| CV R² (5-fold) | **0.3543** |
+
+## Feature Importances
+
+| Feature | Importance |
+|---------|-----------|
+| bmi | 0.3688 |
+| s5 | 0.2360 |
+| bp | 0.0869 |
+| s2 | 0.0763 |
+| age | 0.0559 |
+| s6 | 0.0495 |
+| s1 | 0.0474 |
+| s3 | 0.0392 |
+| s4 | 0.0277 |
+| sex | 0.0122 |
+
+## Features
+
+The model uses 10 baseline variables: age, sex, BMI, blood pressure,
+and 6 blood serum measurements (s1–s6).
+
+## Usage
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(repo_id="Soulay/diabetes-predictor", filename="model.joblib")
+model = joblib.load(model_path)
+
+# [age, sex, bmi, bp, s1, s2, s3, s4, s5, s6]  (standardised values)
+prediction = model.predict([[0.05, -0.04, 0.06, 0.02, -0.01, 0.0, -0.03, 0.04, 0.02, -0.01]])
+print(prediction)  # e.g. [152.3]
+```
+
+## Training
+
+Model trained automatically via GitHub Actions CI/CD on every push to `main`.