https://github.com/feed0/german-credit

Credit Risk Modeling with Python
https://github.com/feed0/german-credit

credit-modeling credit-risk credit-score machine-learning python statlog uci-machine-learning uci-ml-repository

Last synced: 2 days ago
JSON representation

Credit Risk Modeling with Python

• Host: GitHub
• URL: https://github.com/feed0/german-credit
• Owner: feed0
• License: mit
• Created: 2024-07-23T16:30:24.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-15T19:24:38.000Z (3 months ago)
• Last Synced: 2024-09-16T01:53:24.957Z (3 months ago)
• Topics: credit-modeling, credit-risk, credit-score, machine-learning, python, statlog, uci-machine-learning, uci-ml-repository
• Language: Jupyter Notebook
• Homepage:
• Size: 3.28 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Credit Score Analysis

[Anton Markov et al (Credit scoring methods: Latest trends and points to consider, 2022)](https://www.sciencedirect.com/science/article/pii/S2405918822000095) suggest that University of California Irvine's datasets are among the most popular public sources for credit score modeling. I have chosen the [UCI (Statlog) German Credit Data](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) to begin with.

# German Credit Data 

This dataset contains information about 1000 loan applications, including personal and financial data, credit history, and loan characteristics.

# Objective

Train models in order to predict weather a loan is benefitial or not, in other words predict its creditability for the finantial institution.

# Models

## 1. Logistic Regression

![LogRegConfusionMatrix](https://github.com/user-attachments/assets/f7db9635-87d2-498a-9683-e128d0198dc9)

![LogRegClassificationReport](https://github.com/user-attachments/assets/13f612e6-324c-4d53-964d-e466e2e60f12)

Due to some imbalanced columns, the logreg model presents difficulty in predicting "Bad" loans. To overcome this limitation we might consider oversampling the misrepresented categories in these columns.