🩺 Heart Disease Prediction using ML

📌 Overview

This project predicts the likelihood of heart disease using ML models.
Dataset: Kaggle Heart Disease Dataset

📊 Techniques Used

• Data cleaning & EDA (heatmaps, distributions)
• Feature scaling
• Label encoding
• Logistic Regression, Random Forest
• Model evaluation (ROC-AUC, confusion matrix, precision/recall)

🚀 Results

• Logistic Regression Accuracy: ~79%
• Random Forest Accuracy: ~98%

✅ Next Steps

• Hyperparameter tuning
• Flask API deployment