Cardiovascular diseases are among the leading causes of mortality worldwide. Early prediction of heart attack risks can significantly improve outcomes by enabling timely intervention and lifestyle changes. To address this pressing issue, I developed a Heart Attack Prediction Shiny App.

To improve the model’s performance, I used a Random Forest algorithm and handled class imbalance with SMOTE (Synthetic Minority Over-sampling Technique)