https://github.com/pjaiswalusf/heart-failure-prediction

A machine learning project predicting heart failure risk using Random Forest and XGBoost. It involves data cleaning, feature engineering, and EDA before training. The best model is saved using Joblib. Key techniques: outlier detection, feature scaling, and optimization.

data-processing data-visualization feature-engineering machine-learning model-training optimization random-forest-classifier saving-model xgboost

Last synced: 07 Mar 2026