Credit Risk Classification using AWS SageMaker

Table of Contents

• Overview
• Project Structure
• Features
• Installation
• Usage
• Model Training & Evaluation
• Deployment
• Customization
• Contributing
• License
• Acknowledgements
• Contact

---

Overview

Credit Risk Classification using AWS SageMaker is an end-to-end project that demonstrates how to build, train, and deploy a machine learning model to classify credit risk (e.g., predicting if a loan applicant is likely to default), utilizing AWS SageMaker’s managed ML services. The project covers data preprocessing, model development, evaluation, and deployment in a scalable and reproducible way.

---

Project Structure

.
├── data/                   # Raw and processed datasets
├── notebooks/              # Jupyter notebooks for EDA, training, and inference
├── src/                    # Source code for data loading, model, utils, etc.
│   ├── preprocessing.py
│   ├── train.py
│   └── inference.py
├── requirements.txt        # Python dependencies
├── README.md
├── .gitignore
└── config/                 # Configuration files and hyperparameters

---

Features

• Data Preprocessing: Cleaning, feature engineering, and transformation scripts.
• Model Training: Training pipelines using AWS SageMaker SDK.
• Model Evaluation: Metrics and visualization for evaluating model performance.
• Deployment: Scripts and steps for deploying models as SageMaker endpoints.
• Automation: Sample workflow for automating training and deployment.
• Scalability: Easily adaptable to larger datasets and more complex models.

---

Installation

1. Clone the repository

   git clone https://github.com/naman-sriv/Credit_Risk_Classification_AWS_Sagemaker.git
   cd Credit_Risk_Classification_AWS_Sagemaker

2. Create and activate a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. (Optional) Set up AWS credentials

   • Configure your AWS CLI with aws configure, or set environment variables as per AWS docs.

---

Usage

1. Explore the Notebooks

• Check the notebooks/ directory for EDA, training, and deployment walkthroughs.
• Example: notebooks/eda.ipynb, notebooks/train_model.ipynb, notebooks/deploy_model.ipynb

2. Prepare Data

• Place your raw dataset in the data/ folder or update paths in the config files.
• Run data preprocessing scripts:

  python src/preprocessing.py --config config/preprocessing.yaml

3. Train the Model

• Local training:

  python src/train.py --config config/train_config.yaml

• Or use SageMaker:
  • Follow instructions in notebooks/train_model.ipynb to launch a SageMaker training job.

4. Evaluate the Model

• Use evaluation scripts or notebooks to review metrics and visualizations.

5. Deploy the Model

• Deploy using SageMaker endpoint scripts or via notebook.
• Example:

  python src/deploy.py --model-path <model_artifact>

---

Model Training & Evaluation

• Algorithms Used: (e.g., Logistic Regression, XGBoost, Random Forest)
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, etc.
• Validation: k-fold cross-validation, hold-out set, etc.

---

Deployment

• SageMaker Endpoint: Deploy trained model as a REST API endpoint.
• Sample Request:

  import boto3
  
  runtime = boto3.client('sagemaker-runtime')
  response = runtime.invoke_endpoint(
      EndpointName='your-endpoint-name',
      ContentType='text/csv',
      Body='<CSV_DATA>'
  )
  print(response['Body'].read())

---

Customization

• Change hyperparameters in the config/ directory.
• Add new features or models in src/.
• Update data sources as needed.

---

Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements.

1. Fork the repository.
2. Create your feature branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a pull request.

---

License

This project is licensed under the MIT License. See LICENSE for more information.

---

Acknowledgements

• AWS SageMaker Documentation
• Scikit-learn
• Pandas

---

Contact

For questions or feedback, please contact naman-sriv.