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.
machine-learning optimization data-visualization xgboost feature-engineering data-processing model-training random-forest-classifier saving-model
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Updated
Feb 21, 2025 - HTML