This repository provides a comprehensive guide to modeling credit risk in Python using real-world data. The project covers all the necessary steps, from data preprocessing to model evaluation and maintenance.
The data preprocessing steps include several techniques to prepare the data for modeling, such as dummiezation, weight of evidence, fine classing, coarse classing, and data visualization. These techniques are essential to ensure the data is in a format suitable for modeling.
The data modeling section covers two popular algorithms for credit risk modeling: linear regression and logistic regression. These algorithms are widely used in the industry and provide a solid foundation for modeling credit risk.
Model evaluation is a critical step in credit risk modeling to ensure the model is accurate and reliable. The project covers several metrics for evaluating the models, including area under the curve, receiver operating characteristic curve, Gini coefficient, and Kolmogorov-Smirnov.
Assessing population stability is an essential step to ensure the model's predictions are consistent across different subsets of the data. The project covers techniques to assess population stability and ensure the model is reliable.
Maintaining a model is crucial to ensure it continues to provide accurate predictions over time. The project includes best practices for maintaining a model, such as monitoring performance and updating the model as necessary.
In addition to the practical aspects of credit risk modeling, this repository also includes personal notes on the theoretical concepts underlying credit and data science. These notes provide valuable insights into the theory behind the practical techniques used in the project.
Thank you for visiting this repository, and I hope you find the resources here helpful in your credit risk modeling journey.