metadata
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
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])