This project focuses on building and deploying a Fraud Detection Pipeline using AWS SageMaker, leveraging its robust machine learning capabilities for scalable, efficient, and secure fraud detection. The pipeline is designed to preprocess data, train, evaluate, and deploy a machine learning model to detect fraudulent transactions. The repository includes all relevant code, configurations, and deployment scripts, making it an ideal reference for professionals aiming to implement fraud detection solutions.
Features Data Preprocessing: Handles missing values and outliers in the dataset. Scales numerical features and encodes categorical features. Implements feature engineering to extract meaningful patterns. Includes support for real-time and batch data preprocessing using AWS Lambda or SageMaker Processing.
Model Training: Uses AWS SageMaker Training Jobs for training scalable and efficient machine learning models. Includes Jupyter notebooks for exploratory data analysis (EDA) and model selection. Supports both supervised learning (e.g., Random Forest, XGBoost) and advanced algorithms (e.g., Deep Learning, AutoML).
Model Evaluation: Metrics include accuracy, precision, recall, F1-score, and AUC-ROC for robust evaluation. Visualizations for model performance comparison. Includes bias detection using SageMaker Clarify.
Deployment: Deploys the trained model as an endpoint using SageMaker's hosting services. Supports real-time fraud detection API for transaction analysis. Includes CI/CD integration using AWS CodePipeline and CodeBuild for seamless updates. Monitoring and Logging:
Utilizes AWS CloudWatch for endpoint monitoring. Integrates with SageMaker Model Monitor to detect data drift and anomalies in real-time. Logs all activity for audit purposes, aiding compliance and traceability. Scalability:
Designed to handle large-scale datasets using distributed computing on SageMaker. Auto-scaling capabilities for handling peak loads in fraud detection. Pipeline Architecture
Data Ingestion: Data is ingested from AWS S3, or directly via APIs, into the SageMaker environment. Preprocessing and Feature Engineering: Conducted using SageMaker Processing Jobs.
Model Training: Executed on SageMaker Training Jobs with distributed training if needed. Evaluation and Testing: Model evaluation results are stored in S3 and analyzed via Jupyter Notebooks.
Deployment: The model is deployed as an endpoint for real-time or batch predictions.
Monitoring: CloudWatch and SageMaker Model Monitor oversee model performance post-deployment. Technologies Used
AWS Services: SageMaker, S3, Lambda, CloudWatch, CodePipeline, CodeBuild, SageMaker Model Monitor
Machine Learning Frameworks: Scikit-learn, XGBoost, TensorFlow/PyTorch (optional)
Other Tools: Python, Pandas, NumPy, Matplotlib/Seaborn, Jupyter Notebook Key Benefits Real-time Fraud Detection: High-speed predictions for large-scale transactions. Cost-Efficient: Pay-as-you-go infrastructure with AWS SageMaker.
Scalable: Designed to grow with the demands of the business. Secure: Includes encryption, IAM roles, and VPC support for sensitive data. Getting Started
Clone the repository: bash Copy code git clone https://github.com/rohanrajaggarwal__/fraud-detection-pipeline.git cd fraud-detection-pipeline
Configure AWS CLI: bash Copy code aws configure
Run the pipeline: Use the provided Jupyter Notebooks for exploration and training. Deploy the model using the provided deployment scripts. Contributing Contributions are welcome! Please fork the repository and submit a pull request. For major changes, please open an issue first to discuss your ideas.
License This project is licensed under the MIT License - see the LICENSE file for details
