This project and exercises / homeworks were made for the Models in credit and operational risk course at the AGH UST in 2022. All provided methods are a result of my work after hours, when I was solving given tasks (topics).
This project aims to introduce the reader to classification scoring methods in credit risk by presenting the substantive content supported by examples. For this reason, the first part of the work focuses on discussing the theoretical aspects of scoring. The next stage is the preparation and analysis of the selected data set, which will be used to demonstrate the operation of selected classifiers:
- KNN
- Logistic Regression
- Random Forest
- Decission Tree
- SVM
Ultimately, the project provides for an evaluation of the results completed with a summary. The project is implemented using the Python language in Jupyter Notebook.
Lab 1 Homework
- LDA
- Altman Z-score
Lab 2 Homework
- Basel I
- Basel II
Lab 3 Homework
- Naive Bayes
- Logistic Regression
- LDA
Lab 4 Homework
- LDA
- OpVaR and OpES using the Monte Carlo method
- Python
- R programming language
- Jupyter Notebook
- Kaggle: Your Machine Learning and Data Science Community https://www.kaggle.com/datasets/dansbecker/aer-credit-card-data