Model Drift and Data Quality Dashboard (Credit Card Default, UCI Dataset, 2005)

This dashboard visualizes real model performance and data quality metrics using the UCI Default of Credit Card Clients dataset (30,000 clients, Taiwan, 2005)[1][2][4][6]. It is designed to monitor model health and data integrity in credit risk modeling.

Model Accuracy: Logistic regression achieves 0.819 accuracy (10-fold cross-validation)[2][4].
Feature Drift: Shows mean difference in selected features between clients who defaulted and those who did not.
Pipeline Data Quality: Reports actual missing values and outlier rates in key variables.

Source: UCI ML Repository (Yeh & Lien, 2009)

Model Accuracy (Logistic Regression):

10-fold cross-validation: 0.819 (Yeh & Lien, 2009)[2][4].
Other models: SVM (0.817), Decision Tree (0.818), Random Forest (0.819)[4].

Source: Yeh & Lien, 2009; GitHub analysis[4][6]

Feature Means by Default Status (UCI Dataset):

LIMIT_BAL: Credit limit is much lower among defaulters.
AGE: Defaulters are slightly younger on average.
PAY_0: Defaulters have higher recent payment delays.

Source: UCI dataset summary statistics[2][4][6]

Data Quality: Missing and Outlier Rates

Missing values are extremely rare (0% in all key columns).
Outlier rates (values outside 3 standard deviations) shown for select variables.

Source: UCI dataset EDA[4][6]

All figures are computed from the actual UCI Default of Credit Card Clients dataset. Use this dashboard to benchmark model and data pipeline health in real-world credit risk modeling.

Data: UCI ML Repository (Yeh & Lien, 2009)[2][1]. See also: GitHub Analysis[4].

Model Drift and Data Quality Dashboard (2025)

This dashboard provides a technical, data-driven view of credit risk modeling using the widely recognized UCI Default of Credit Card Clients dataset. It is designed for data scientists, analysts, and risk managers who need to monitor the ongoing health of predictive models and the integrity of the underlying data pipeline.

Model Accuracy

The first chart displays the predictive accuracy of several machine learning models-Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Random Forest-when applied to the credit card default dataset. All accuracy values are derived from real, published analyses using 10-fold cross-validation. This allows users to benchmark model performance and understand how different algorithms stack up on the same real-world problem. Logistic Regression and Random Forest both achieve an accuracy of approximately 82%, indicating strong but not perfect predictive power in identifying potential defaulters.

Feature Means by Default Status

The next three charts provide focused comparisons of key features-credit limit (LIMIT_BAL), age, and most recent payment status (PAY_0)-between customers who defaulted and those who did not.

By separating these features into individual charts, the dashboard makes it easy to spot meaningful differences:

Credit Limit (LIMIT_BAL): Customers who defaulted typically had lower credit limits, suggesting credit exposure is a significant risk factor.
Age: There is a slight trend toward younger ages among defaulters, which may reflect riskier borrowing patterns or less financial stability in younger demographics.
Recent Payment Status (PAY_0): Defaulters show higher average payment delays, highlighting the importance of recent repayment behavior as a warning signal.

This breakdown helps analysts quickly identify which customer attributes are most associated with default risk, supporting more targeted risk management and model refinement.

Data Quality: Missing and Outlier Rates

The final chart addresses the integrity of the data itself, reporting the percentage of missing values and statistical outliers for each key feature. In this dataset, missing values are virtually nonexistent, which is ideal for modeling. Outlier rates are low but detectable, indicating that while the data is generally clean, there are a small number of extreme values that could influence model performance if not properly managed.

By combining real model performance metrics, feature-level comparisons, and data quality diagnostics, you can:

Benchmark model effectiveness using transparent, real-world results.
Detect early signs of model drift by monitoring shifts in feature distributions.
Ensure data pipeline health by tracking missing and anomalous data.
Support regulatory and business requirements for robust, explainable, and auditable credit risk analytics.

This approach empowers organizations to maintain high standards in predictive modeling, reduce operational risk, and make more informed lending decisions.