A Technical Guide to Credit Risk Prediction
Abstract: This article presents a systematic analysis of the Kaggle Home Credit Default Risk competition solution, detailing the complete machine learning pipeline from data preprocessing through feature engineering to model ensemble techniques. We examine the architectural decisions, implementation strategies, and performance optimization methods that achieved competitive results in this large-scale credit risk prediction task. The methodology encompasses data quality assessment, sophisticated feature extraction from relational databases, gradient boosting model optimization, and stacking ensemble strategies. Our analysis provides actionable insights for practitioners working on similar structured data prediction problems in financial risk assessment. ...