Heart Attack Prediction in Indonesia
This repository contains five different machine learning models trained to predict heart attack risks based on an Indonesian dataset. The models are evaluated and compared in terms of accuracy and generalization performance.
📊 Models Included
- Logistic Regression (
model_logistic.pkl)
- Random Forest Classifier (GridSearch-tuned) (
model_random_forest.pkl)
- Decision Tree Classifier (GridSearch-tuned) (
model_decision_tree.pkl)
- Bagging Classifier (with best Decision Tree) (
model_bagging.pkl)
- XGBoost Classifier (GridSearch-tuned) (
model_xgboost.pkl)
📁 Files
- Trained models in
.pkl format
README.md (this file)
⚙️ Libraries Used
- Scikit-learn
- XGBoost
- NumPy
- Pandas
- Matplotlib / Seaborn
🧠 Author
Dataset:
This project is part of a broader effort to explore predictive healthcare solutions using real-world datasets.
