Exploratory Data Analysis for Credit Scoring with Python

What’s Happening

Here’s the thing: Understanding default risk through statistical analysis of borrower and loan characteristics.

The post Exploratory Data Analysis for Credit Scoring with Python appeared first on Towards Data Science. In a credit scoring project, it is often tempting to jump to modeling. (plot twist fr)

Yet the first step and the most important one is to understand the data.

The Details

In our previous post , we presented how the databases used to build credit scoring models are constructed. We also highlight the importance of asking right questions: Who are the users?

What types of loans are they granted? What characteristics appear to explain default risk?

Why This Matters

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.

As AI capabilities expand, we’re seeing more announcements like this reshape the industry.

Key Takeaways

• Loan amounts range from $500 to $35,000.
• The models target variable is default, which takes the value 1 if the customer is in default and 0 otherwise.
• Today, many tools and an increasing number of AI agents are capable of automatically generating statistical descriptions of datasets.

The Bottom Line

In this article, we take a simple instructional approach to statistically describing each variable in the dataset. For categorical variables, we analyze the number of observations and the default rate for each category.

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