https://github.com/alihassanml/credit-risk-modeling-using-automl

This project demonstrates the use of AutoML and Explainable AI (XAI) to predict credit default risk for a large-scale dataset. The goal is to build an interpretable machine learning model using AutoGluon, XGBoost, and LightGBM with SHAP for model interpretability.
https://github.com/alihassanml/credit-risk-modeling-using-automl

autogluon lightgbm pyspark sklearn xgboost

Last synced: 3 months ago
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This project demonstrates the use of AutoML and Explainable AI (XAI) to predict credit default risk for a large-scale dataset. The goal is to build an interpretable machine learning model using AutoGluon, XGBoost, and LightGBM with SHAP for model interpretability.

• Host: GitHub
• URL: https://github.com/alihassanml/credit-risk-modeling-using-automl
• Owner: alihassanml
• License: mit
• Created: 2025-05-05T15:05:32.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-05-05T18:11:17.000Z (5 months ago)
• Last Synced: 2025-06-20T09:08:55.042Z (4 months ago)
• Topics: autogluon, lightgbm, pyspark, sklearn, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 3.83 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Credit Risk Modeling Using AutoML

This project demonstrates the use of **AutoML** and **Explainable AI (XAI)** to predict **credit default risk** for a large-scale dataset. The goal is to build an interpretable machine learning model using **AutoGluon**, **XGBoost**, and **LightGBM** with **SHAP** for model interpretability.

### 📊 Dataset

The dataset used in this project is the **UCI Credit Card Default Dataset** available at [UCI Repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).

* **Number of records**: 30,000

* **Number of features**: 23 features related to credit, demographics, and payment history.

* **Target**: Whether the client will default on their payment next month (`1` = Default, `0` = No Default).

### 🔧 Technologies

* **LightGBM**: Gradient Boosting decision trees used for model training.

* **XGBoost**: Another powerful gradient boosting model.

* **AutoGluon**: AutoML library for easy model training and hyperparameter tuning.

* **SHAP**: SHapley Additive exPlanations for model interpretability.

* **Apache Spark**: Distributed processing for handling large-scale datasets.

### 🛠️ Installation

1. **Clone the Repository**:

   ```bash

   git clone https://github.com/alihassanml/Credit-Risk-Modeling-Using-AutoML.git

   cd Credit-Risk-Modeling-Using-AutoML

   ```

2. **Create a virtual environment** (recommended):

   ```bash

   python3 -m venv venv

   source venv/bin/activate  # On Windows, use venv\Scripts\activate

   ```

3. **Install dependencies**:

   ```bash

   pip install -r requirements.txt

   ```

### 🚀 Running the Project

1. **Prepare Data**:

   The dataset is already available. If you want to download it manually, visit [UCI Credit Card Default Dataset](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).

2. **Preprocessing and Feature Engineering**:

   Run the following script to clean and process the data:

   ```bash

   python preprocess.py

   ```

3. **Train Models**:

   The model training pipeline is automated using **AutoGluon** and **LightGBM**. To train the models, use:

   ```bash

   python train_model.py

   ```

4. **Model Explainability**:

   After training, SHAP will be used to generate interpretability plots:

   ```bash

   python explain_model.py

   ```

5. **Evaluate the Model**:

   You can evaluate the trained model by running:

   ```bash

   python evaluate_model.py

   ```

### 📁 Directory Structure

```

.

├── data/                      # Store raw and processed data here

├── src/                       # Source code for preprocessing, training, etc.

│   ├── preprocess.py          # Data preprocessing and feature engineering

│   ├── train_model.py         # AutoML model training (AutoGluon, LightGBM, XGBoost)

│   ├── evaluate_model.py      # Evaluate the trained model

│   ├── explain_model.py       # Model explainability using SHAP

│   └── feature_engineering.py # Custom feature engineering (AutoGluon setup)

├── requirements.txt           # Python dependencies

├── README.md                  # Project documentation

└── outputs/                   # Model outputs and visualizations

```

### 🔑 Key Features

* **AutoML Model Selection**: Uses **AutoGluon** to automatically select the best model and hyperparameters.

* **Interpretable Results**: Leverages **SHAP** for explainability, visualizing high-risk patterns for default prediction.

* **Scalable**: Utilizes **Apache Spark** for large dataset processing and distributed computing.

### 🧑‍🏫 Example Workflow

1. **Preprocess Data**

   Clean data and generate derived features (e.g., `AVG_PAY_DELAY`, `MAX_PAY_DELAY`, `PAYMENT_RATIO`).

2. **Train Model**

   Use **AutoGluon** to automatically select the best models (e.g., **LightGBM**, **XGBoost**), fine-tune hyperparameters, and perform cross-validation.

3. **Evaluate Model**

   Evaluate the models using metrics such as **Accuracy**, **AUC**, **Precision**, and **Recall**.

4. **Model Explainability**

   Use **SHAP** to interpret model predictions, highlighting which features are most indicative of credit default risk.

### 🧑‍💻 Example Code Snippets

* **Feature Engineering (`feature_engineering.py`)**:

  * Includes custom feature generation like `AVG_PAY_DELAY`, `BILL_TOTAL`, and `PAYMENT_RATIO`.

* **Model Training (`train_model.py`)**:

  ```python

  from autogluon.tabular import TabularPredictor

  # Load preprocessed data

  df = pd.read_csv("processed_data.csv")

  # Train AutoML model

  predictor = TabularPredictor(label="default").fit(df)

  ```

* **Model Explainability with SHAP (`explain_model.py`)**:

  ```python

  import shap

  model = predictor.load("model.pkl")

  # Create SHAP explainer

  explainer = shap.TreeExplainer(model)

  shap_values = explainer.shap_values(df)

  # Plot SHAP summary

  shap.summary_plot(shap_values, df)

  ```

### 📊 Evaluation Metrics

* **Accuracy**: Measures overall prediction accuracy.

* **AUC (Area Under the ROC Curve)**: Evaluates classification performance at various thresholds.

* **Precision/Recall/F1 Score**: Measures model performance, especially for imbalanced datasets.

### ⚠️ Limitations

* The dataset may not fully represent all types of credit risk situations.

* The models are highly sensitive to hyperparameter tuning and require careful optimization for optimal results.

### 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.