Credit Score Modelling

Figure 1: Credit Score Illustration (Source).

Project Summary

In this project, we developed a credit score model leveraging Logistic Regression and Weight of Evidence techniques. The scoring methodology is based on the "point to double the odds" approach, utilizing Logistic Regression parameters, Weight of Evidence, and specific user-defined constraints to assign credit points for based on each predictor variable.

Project Scope

The main objective is to create a reliable credit score model and develop a comprehensive credit scorecard.

Tools and Technologies

The project is built using Python 3.10.13, with the following libraries and tools:

1. pandas and numpy for data manipulation.
2. matplotlib and seaborn for data visualization.
3. optbinning for calculating Weight of Evidence and Information Value.
4. scikit-learn, and optbinning for training and evaluation credit score model.

Installation and Setup

To run this project locally, you can use Anaconda or venv. Ensure your Python version is 3.10.13. Then, install the required libraries from the requirements.txt file:

  cd credit-scorecard-modelling
  pip install -r requirements.txt

For a detailed explanation of the project, please visit my Medium blog post.