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Exploratory Data Analysis for Credit Scoring with Python
Understanding default risk through statistical analysis of borrower and loan characteristics.
In a credit scoring project, it is often tempting to jump to modeling. Yet the first step and the most important one is to understand the data.
In our previous post, we presented how the databases used to build credit scoring models are constructed. We also highlight the importance of asking the right questions:
- Who are the customers?
- What types of loans are they granted?
- What characteristics appear to explain default risk?
In this article, we illustrate this foundational step using an open-source dataset available on Kaggle: the Credit Scoring Dataset. This dataset contains 32,581 observations and 12 variables describing loans issued by a bank to individual borrowers.
These loans cover a range of financing needs — medical, personal, educational, and professional — as well as debt consolidation operations. Loan amounts range from $500 to $35,000.
The variables capture two dimensions:
- contract characteristics (loan amount, interest rate, purpose of financing, credit grade, and time elapsed…
