Update README.md

README.md

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----
-license: mit
----
+---
+license: cc-by-4.0
+library_name: scikit-learn
+tags:
+- regression
+- tabular
+- scikit-learn
+- education
+datasets:
+- "heart-failure"
+---
+
+# Heart Failure Linear Regression Model
+
+**Author:** Ella Carlotto  
+**Created:** November 2025  
+**Library:** scikit-learn  
+**Dataset:** UCI Heart Failure Clinical Records  
+**Task:** Tabular regression on a binary target (`DEATH_EVENT`)
+
+## Model Description
+This Linear Regression model predicts the likelihood of death (`DEATH_EVENT`) based on clinical features from the UCI Heart Failure Clinical Records dataset. All features are numeric, and the model was trained as a simple baseline regression example to demonstrate packaging and upload for educational use.
+
+**Target:** `DEATH_EVENT` (0 = survived, 1 = death)  
+**Output:** Continuous score; higher values indicate higher likelihood of death.
+
+## Files Included
+- heart_failure_model.pkl — trained scikit-learn model  
+- heart_failure_config.json — model configuration metadata  
+- heart_failure_test_data.csv — held-out test dataset for evaluation  
+
+## Intended Uses and Limitations
+- For educational and demonstration purposes only  
+- Not validated for medical, diagnostic, or clinical use  
+- Do not use for real-world predictions or patient decisions  
+
+## Example Usage
+```python
+import pickle, pandas as pd
+with open("heart_failure_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+df = pd.read_csv("heart_failure_test_data.csv")
+X = df.drop(columns=["DEATH_EVENT"])
+y_pred = model.predict(X)
+print(y_pred[:5])