R-codebase for a scientific research article, titled "Defining and comparing SICR-events for classifying impaired loans under IFRS 9"
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Updated
Nov 27, 2024 - R
R-codebase for a scientific research article, titled "Defining and comparing SICR-events for classifying impaired loans under IFRS 9"
Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.
🎯 Machine Learning Credit Risk Model Advanced credit risk assessment model using logistic regression with WoE transformation. Achieves 0.85 AUROC and 0.71 Gini coefficient for accurate loan default prediction. 📊 Key Metrics: 85% AUROC 98% PR-AUC 0.56 KS Statistic 🛠️ Built with Python, scikit-learn, pandas & imblearn Tags: #MachineLearning
Built a Logistic Regression model for loan risk prediction, focusing on credit risk and improving high-risk loan detection.
The objective of this project is to build a model to predict probability of a client defaulting a loan.
R-codebase for a scientific research article, titled "The TruEnd-procedure: Treating trailing zero-valued balances in credit data"
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