This project focuses on predicting the likelihood of heart disease using machine learning techniques. The dataset includes medical features like age, blood pressure, cholesterol, and heart rate. The model uses algorithms like CatBoost and Random Forest to predict the presence of heart disease, assisting early diagnosis.
A complete fraud detection pipeline using ML models (CatBoost, XGBoost, LightGBM), class weighting, SMOTE, and custom feature engineering. Achieved strong recall and precision balance for real-world deployment.
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