risk-modeling 1.1.0

ML modeling tools in python for risk management domain including loan, credit card, etc.

About The Project

While GitHub hosts many excellent risk modeling libraries, most emphasize traditional logistic regression approaches. This project addresses modern needs by:

1. Focusing on tree-based methodologies (XGBoost, LightGBM)
2. Providing essential modeling tools (PSI, IV, Bivar, etc.)
3. Supporting documentation of all modeling steps

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Library Functionality

📈 Modeling Procedure

• Hyperparameter Tuning:
  random_search_xgboost(), random_search_lightgbm()
• Model Training:
  train_single_model_xgboost(), train_single_model_lightgbm()
• Feature Analysis:
  feature_importance() (gain-based),
  variable_reduction() (feature-importance-driven selection)
• Performance Metrics:
  model_performance() (AUC, KS, Top Capture Rate, Top Bad Rate)

🛠️ Modeling Tools

• Stability Analysis:
  calculate_numeric_psi(), calculate_categorical_psi()
• Predictive Strength:
  calculate_numeric_iv(), calculate_categorical_iv()
• Feature Evaluation:
  compute_numeric_bivar(), compute_categorical_bivar(),
  proc_means() (quality checks)
• UAT Support:
  proc_compare() (feature-by-feature value validation)

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Installation

Install the latest stable release from PyPI using pip:

pip install risk-modeling

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Example

Detailed examples will be added in upcoming documentation updates.