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.