ML Problems - mlops
#01 Classical Ml
Build a Customer Lifetime Value Regression Model Free
Predict 24-month net revenue per customer on a zero-inflated, heavy-tailed target using a LightGBM Tweedie objective, lifting top-decile revenue capture from 31% to 52% and Spearman correlation from 0.41 to 0.68.
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#02 Classical Ml
Build a Loan Default Predictor with XGBoost and SHAP Explainability Free
Predict 12-month default on unsecured personal loans with XGBoost and per-decision SHAP attributions, lifting Gini from 0.41 to 0.58 while producing adverse action reasons a lender can legally send to every declined applicant.
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