Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/utkarsh-284/credit-default-risk

This Repository deals with Credit Risk evaluation and prediction of default
https://github.com/utkarsh-284/credit-default-risk

credit-card credit-risk default econometrics essemblelearning exploratory-data-analysis jupyter-notebook logistic-regression machine-learning neural-network python support-vector-machines

Last synced: about 2 months ago
JSON representation

This Repository deals with Credit Risk evaluation and prediction of default

Awesome Lists containing this project

README 

          
# Credit Default Risk Analysis



## Overview

This project analyzes credit card default risk using machine learning techniques on a dataset of 30,000 Taiwanese credit card clients. The analysis includes comprehensive exploratory data analysis, statistical modeling, and machine learning approaches to predict credit card defaults and identify key risk factors.



## Key Findings



### 🎯 **Best Model Performance**

- **Support Vector Machine (SVM)**: 80.5% accuracy, 0.70 AUC-ROC score

- **Ensemble Methods**: Voting Classifier achieved 80.5% accuracy

- **Neural Network**: Deep learning model achieved 80.0% accuracy



### 📊 **Critical Risk Factors**

1. **Recent Payment Status (PAY_0)**: Most predictive feature (coefficient = 0.515)

2. **Payment History (PAY_2)**: Secondary predictor (coefficient = 0.111)

3. **Bill Amount (BILL_AMT1)**: Financial capacity indicator

4. **Credit Limit (LIMIT_BAL)**: Risk tolerance measure

5. **Age Demographics**: U-shaped risk curve (highest for young adults and seniors)



### 🔍 **Demographic Insights**

- **Gender**: Females take more loans but have lower default rates

- **Education**: University graduates borrow most, but all education levels show similar default proportions

- **Age**: Peak borrowing at 25-30 years, with highest default risk for young adults and seniors

- **Marital Status**: Singles borrow more but have higher default rates than married individuals



## Dataset Details



**Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients)



### Variables:

- **Demographic Features**: Credit limit (`LIMIT_BAL`), gender (`SEX`), education (`EDUCATION`), marital status (`MARRIAGE`), age (`AGE`)

- **Payment History**: Repayment status for past 6 months (`PAY_0` to `PAY_6`)

- **Billing Statements**: Bill amounts for past 6 months (`BILL_AMT1` to `BILL_AMT6`)

- **Payment Amounts**: Amounts paid in past 6 months (`PAY_AMT1` to `PAY_AMT6`)

- **Target Variable**: DEFAULT (binary indicator: 1 = default, 0 = non-default)



## Methodology



### Data Preprocessing

- **Missing Values**: No missing values detected

- **Categorical Variables**:

  - EDUCATION: Categories 0, 5, 6 merged into "Others"

  - MARRIAGE: Category 0 merged into "Others"

- **Feature Engineering**:

  - Created `TOTAL_BILL_AMT` (sum of all bill amounts)

  - Created `TOTAL_PAY_AMT` (sum of all payment amounts)

- **Outlier Removal**: Applied IQR method, reducing dataset from 30,000 to 25,174 observations



### Exploratory Data Analysis (EDA)

- Comprehensive demographic analysis revealing risk patterns

- Financial behavior correlation analysis

- Age-based risk segmentation

- Payment history impact assessment



### Statistical Analysis

- **Logistic Regression**: Pseudo R-squared = 0.121 (12.1% variance explained)

- **Key Statistical Findings**:

  - PAY_0 highly significant (p < 0.001)

  - PAY_2 significant (p < 0.001)

  - BILL_AMT1 significant (p < 0.001)



### Machine Learning Models Tested



| Model | Accuracy | AUC-ROC | Performance Rank |

|-------|----------|---------|------------------|

| **Support Vector Machine** | **80.5%** | **0.70** | **🥇 Best** |

| Voting Classifier | 80.5% | - | 🥈 Second |

| Random Forest | 80.0% | - | 🥉 Third |

| Deep Neural Network | 80.0% | - | 🥉 Third |

| Logistic Regression | 79.3% | - | 4th |

| Lasso Regression | 76.2% | - | 5th |

| Elastic Net | 76.3% | - | 6th |



## Business Impact



### Risk Management Applications

1. **Early Warning System**: Monitor recent payment status for immediate intervention

2. **Credit Limit Optimization**: Adjust limits based on risk scores

3. **Customer Segmentation**: Target high-risk demographics proactively

4. **Collection Strategy**: Prioritize collection efforts based on risk assessment



### Operational Benefits

- **Proactive Risk Mitigation**: Identify high-risk clients before default

- **Reduced Financial Losses**: Targeted interventions based on risk scores

- **Improved Customer Retention**: Personalized risk-based strategies

- **Optimized Credit Policies**: Data-driven lending decisions



## Model Performance Analysis



### Support Vector Machine (Best Model)

- **Accuracy**: 80.5%

- **AUC-ROC**: 0.70

- **Advantages**: 

  - Excellent for binary classification

  - Handles non-linear relationships

  - Robust to outliers

- **Business Value**: Highest predictive power for default risk identification



### Feature Importance Ranking

1. **PAY_0** (Most recent payment status) - Primary predictor

2. **PAY_2** (2nd most recent payment status) - Secondary predictor  

3. **BILL_AMT1** (Most recent bill amount) - Financial capacity indicator

4. **LIMIT_BAL** (Credit limit) - Risk tolerance indicator

5. **Age-related features** - Life stage risk factors



## Technical Implementation



### Dependencies

```

Python 3.7+

pandas, numpy, matplotlib, seaborn

scikit-learn, statsmodels

tensorflow (for neural networks)

```



### Key Libraries Used

- **Data Processing**: Pandas, NumPy

- **Visualization**: Matplotlib, Seaborn

- **Statistical Analysis**: Statsmodels

- **Machine Learning**: Scikit-learn

- **Deep Learning**: TensorFlow



## Future Work



### Model Enhancements

- **Advanced Algorithms**: XGBoost, LightGBM implementation

- **Class Imbalance**: SMOTE or cost-sensitive learning

- **Feature Engineering**: Payment-to-bill ratios, temporal trends

- **Cross-validation**: Robust model validation



### Data Improvements

- **Additional Features**: Macroeconomic indicators, employment data

- **Real-time Data**: Live transactional data integration

- **Geographic Expansion**: Multi-market validation

- **Temporal Analysis**: Time series modeling



### Deployment Considerations

- **API Development**: Real-time prediction endpoints

- **Dashboard Creation**: Risk visualization interface

- **Model Monitoring**: Performance tracking and drift detection

- **Scalability**: Cloud deployment for production use



## Files Structure

```

Credit Risk Analysis/

├── README.md                           # Project overview and documentation

├── MODEL_REPORT.md                     # Comprehensive model analysis report

├── Credit_Default_Prediction.ipynb     # Complete Jupyter notebook analysis

└── content/

    ├── default of credit card clients.xls    # Original dataset

    └── default+of+credit+card+clients.zip    # Compressed dataset

```



## Quick Start



1. **Clone the repository**

2. **Install dependencies**: `pip install pandas numpy matplotlib seaborn scikit-learn statsmodels tensorflow`

3. **Open the Jupyter notebook**: `Credit_Default_Prediction.ipynb`

4. **Run the analysis**: Execute cells sequentially for complete analysis



## Results Summary



### Model Performance

- **Best Model**: Support Vector Machine (80.5% accuracy)

- **Key Predictor**: Recent payment status (PAY_0)

- **Business Impact**: Proactive risk identification and mitigation



### Key Insights

- Recent payment behavior is the strongest default predictor

- Age shows U-shaped risk pattern (highest for young adults and seniors)

- Higher credit limits correlate with lower default rates

- Gender and education show nuanced risk relationships



## Contributor

**Utkarsh Bhardwaj**  

**Publish Date**: February 28, 2025  

**Contact**: ubhardwaj284@gmail.com



[LinkedIn](https://www.linkedin.com/in/utkarsh284/) | [GitHub](https://github.com/utkarsh-284)



---



**Note**: This analysis provides a solid foundation for credit risk assessment. The SVM model with 80.5% accuracy can be effectively deployed for real-time default prediction and risk management applications.