Credit Scorecard 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. The development of credit score model are done manually without the help of optbinning (like the previous one).

Project Scope

The main objective is not only to create a reliable credit score model and develop a comprehensive credit scorecard, but also emphasizes on model deployment through web application. Some of the concepts involve python package development, continuous integration, and continuous deployment.

Tools and Technologies

The project is built using Python 3.10, with the following libraries and tools:

1. pandas and numpy for data manipulation.
2. matplotlib and seaborn for data visualization.
3. scikit-learn for training and evaluation credit score model.
4. gradio for the development of the web application.

Installation and Setup

To run this project locally, you can use Anaconda. Ensure your Python version is 3.10. Recommended using linux environment for setting up environment. Then, install the required libraries from the requirements.txt file:

  make create_environment  # create conda environment
  conda activate credit-scorecard-modelling  # access the environment
  make requirements  # install all libraries from the requirements.txt file
  make create_ipykernel  # create ipykernel

With this you can use run the Python notebook using the exact same dependencies that I used for this project. For the web application you can access through this link https://huggingface.co/spaces/marcellinus-witarsah/credit-score-app.