📌 Description:
A machine learning model that predicts heart attack risk based on patient health data. The project compares multiple ML models (Logistic Regression, Random Forest, XGBoost) and uses SMOTE for dataset balancing and PCA for feature selection to improve predictions.
🛠 Skills Used:
✅ Python, Scikit-Learn
✅ Machine Learning (Supervised Learning)
✅ Data Preprocessing & Feature Engineering
✅ Model Training & Evaluation
📂 Topics:
Machine Learning Logistic Regression Random Forest XGBoost Data Preprocessing SMOTE PCA Heart Disease Prediction