A machine learning workflow to address credit default prediction
By Rambod Rahmani, Marco Parola, Mario G.C.A. Cimino
Rating
1288
Battle Count: 80
Relevance
6/10
While focused on credit scoring, the methodology could be adapted for assessing counterparty risk in trading
Implementation Complexity
7/10
Involves multiple steps including data preprocessing, ensemble modeling, and hyperparameter optimization
Reproducibility
4/5
Code and results publicly available on GitHub, datasets are publicly accessible
About this paper
Methodology: Machine Learning Workflow. Problem types: Classification, Credit Default Prediction.
The interactive Everscope explorer (charts, battles, favorites) loads below.