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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])
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