Exploratory Data Analysis for Credit Scoring with Python

Exploratory data analysis is performed on a credit scoring dataset to understand default risk before building a model. The analysis examines borrower characteristics like age, income, and employment length, as well as loan attributes such as purpose and credit history. By segmenting continuous variables into quartiles and analyzing default rates across categories, key risk drivers are identified. The findings indicate that younger borrowers, individuals with lower incomes, renters, and those with a prior history of default are significantly more likely to be high-risk. This process demonstrates how to derive initial insights and identify predictive features for a credit risk model.