scikit-learn compatible tools for building credit risk acceptance models
Monotone Weight Of Evidence Transformer and LogisticRegression model with scikit-learn API
High-performance binning library specifically designed for Credit Risk Modeling and Scorecard Development.
A PyQt6 GUI application for interactive credit risk analysis. Load data, select variables, perform binning, and build scorecards efficiently.
risk3r
Enterprise-style Credit Risk Analytics & Scorecard Modeling System using WOE, IV, Logistic Regression, XGBoost, KS, AUC, Credit Scoring, PSI & Drift Monitoring.
Package for creating custom score cross tables.
Logistic Regression vs AdaBoost in ScoreCard model
Tools for creating analytic reports and testing scorecards
Project_Case study: preprocessing, modeling, model validation and maintenance in Python
The "Comprehensive Machine Learning Framework in R" is an all-inclusive toolkit for data preprocessing, WOE calculation, and model evaluation, designed for robust machine learning applications and equipped with cross-validation and extensibility features.
Creating a credit scoring model to manage, understand, and model credit risk that will be handled optimally.
This repo contains a GBQ script that pulls operational performance metrics of an E-commerce platform's suppliers and ranks them against one another for benchmarking purposes
Add a description, image, and links to the scorecard-model topic page so that developers can more easily learn about it.
To associate your repository with the scorecard-model topic, visit your repo's landing page and select "manage topics."