Bank marketing ML model (92% ROC-AUC) with XGBoost + Platt scaling. EDA-driven binning, handles 93% class imbalance, addresses data leakage & selection bias. 16 docs covering nuances & business impact.
data-science machine-learning xgboost model-calibration predictive-analytics bivariate-analysis selection-bias univariate-analysis banking-customer-analytics eda-exploratory-data-analysis correlation-causation
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Updated
Dec 29, 2025 - Jupyter Notebook