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Credit Default Risk Analysis

In this project, machine learning algorithms were compared for credit default risk assessment using a dataset of 32,581 observations and 12 variables. Key findings include:

  • Logistic Regression:

    • Accuracy: 69.51%
    • AUC: 0.5030

  • Linear Discriminant Analysis (LDA):

    • Accuracy: 86.69%
    • AUC: 0.8669

  • Ridge Regression:

    • Accuracy: 86.61%
    • AUC: 0.8689

  • Classification Tree:

    • Accuracy: 92.18%
    • AUC: 0.8237

Key Insights:

  • The Classification Tree had the highest accuracy, but struggled to distinguish positive and negative classes.
  • Ridge Regression demonstrated superior performance with a higher AUC value.
  • The root node in the Classification Tree was the loan-to-income proportion, emphasizing its importance.
  • Ridge Regression is recommended for risk classification, especially in diverse risk scenarios.

Conclusion

Ridge Regression proves to be a safer choice for credit default risk classification due to its superior AUC value.