A comprehensive exploratory data analysis tutorial demonstrates how to assess default risk using Python on a Kaggle credit scoring dataset with 32,581 observations and 12 variables. The analysis covers loan characteristics including amounts from $500 to $35,000 and borrower demographics across medical, personal, educational, and professional loan purposes.

Despite modern AI tools that can automatically generate statistical descriptions, manual EDA remains valuable for understanding data structure and identifying anomalies. The tutorial shows how to analyze categorical variables by default rates and continuous variables through quartile-based discretization. Key findings reveal that over 78% of borrowers have not defaulted, creating an imbalanced dataset requiring careful modeling consideration.

Critical limitations emerge from missing temporal data, preventing analysis of how default rates evolve during economic cycles. The study identifies strongest risk predictors including previous default history with 38% default rates versus 18% for clean borrowers, and income levels showing inverse correlation with default risk. Younger borrowers under 30 represent 70% of the dataset and show highest risk concentrations. Housing status and employment length provide additional predictive signals for credit risk assessment models.