Build institutional-grade end-to-end Machine Learning portfolio projects: Advanced housing regression with XGBoost and SHAP, and high-imbalance financial fraud detection with SMOTE and FastAPI.
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Ames Housing dataset, log target normalization, ColumnTransformer pipelines, XGBoost/Random Forest GridSearchCV tuning, SHAP explainability, and FastAPI/Docker deployment.
Highly imbalanced transaction data (0.17% fraud), SMOTE resampling inside cross-validation, XGBoost classifier, Recall/PR-AUC optimization, and SHAP feature attribution.