This project implements an advanced transaction success rate optimization system using machine learning, reinforcement learning, and real-time inference to dynamically route and improve financial transaction outcomes. The solution analyzes transaction patterns, predicts success probabilities, and implements optimal routing strategies to maximize success rates.
- Intelligent Transaction Routing: Predicts transaction success probability and routes accordingly
- Real-time Inference API: FastAPI-based endpoint for integrating with payment systems
- Advanced ML Models: XGBoost, Neural Networks, and Reinforcement Learning approaches
- Feature Engineering Pipeline: Comprehensive preprocessing for transaction data
- Model Retraining: Continuous learning from new transaction outcomes
- Performance Monitoring: Dashboards for tracking model performance and business metrics
- AWS Deployment: Containerized deployment to AWS ECS
- A/B Testing: Evaluation framework for comparing routing strategies
transaction_success_optimization/
├── app/ # API and deployment files
│ ├── app.py # FastAPI application
│ ├── deploy_to_aws.py # AWS ECS deployment script
│ ├── monitoring.py # API and model monitoring
│ └── static/ # Static assets for the API
│ ├── favicon.ico # Favicon for the API
│ └── site.webmanifest # Web app manifest
├── configs/ # Configuration files
│ └── config.yaml # Main configuration parameters
├── dashboards/ # Interactive analysis dashboards
│ └── home.py # Main Streamlit dashboard
├── data/ # Transaction datasets
│ ├── batches/ # Batch prediction datasets
│ │ ├── holiday_week.parquet # Holiday period transactions
│ │ └── normal_week.csv # Normal period transactions
│ ├── processed/ # Processed feature data
│ │ ├── full_feature_engineered_data.csv # Data with all features
│ │ └── processed_transaction_data.csv # Cleaned transaction data
│ └── raw/ # Original transaction data
│ ├── batches/ # Raw batch data
│ └── transactions_march_2023.csv # Raw transaction data
├── models/ # Trained models and pipelines
│ ├── feature_engineering_pipeline.pkl # Feature preprocessing pipeline
│ ├── model_metadata.json # Model performance metrics
│ ├── Optimized XGBoost_model_20250320.joblib # Optimized model
│ ├── Optimized XGBoost.pkl # Latest optimized model
│ ├── rl_agent.pth # Reinforcement learning agent
│ └── XGBoost_model_20250320.joblib # Base XGBoost model
├── notebooks/ # Development and analysis notebooks
│ ├── 01_data_exploration.ipynb # Initial data analysis
│ ├── 02_feature_engineering.ipynb # Feature development notebook
│ ├── 03_model_training_and_evaluation.ipynb # Model training notebook
│ ├── 05_deployment_and_integration.ipynb # Deployment notebook
│ └── imgs/ # Notebook images and diagrams
├── reports/ # Generated reports and evaluations
│ └── overview.pdf # Project overview document
├── src/ # Source code
│ ├── data_generator.py # Synthetic data generation
│ ├── dependencies.py # Project dependencies
│ ├── feature_engineering.py # Feature creation and transformation
│ ├── model_retraining.py # Continuous model improvement
│ └── utils.py # Utility functions
├── tests/ # Test suite
│ ├── ab_testing.py # A/B testing framework
│ └── test_app.py # API tests
├── requirements.txt # Project dependencies
├── setup.py # Package installation configuration
└── README.md # Project documentation
# Clone the repository
git clone https://github.com/omar-kabeer/transaction_success_optimization.git
cd transaction_success_optimization
# Install with base dependencies
pip install -r requirements.txtInstall specific dependency groups:
# For machine learning components only
pip install -e ".[ml]"
# For development (includes testing tools)
pip install -e ".[dev]"
# For all dependencies
pip install -e ".[all]"# Generate synthetic data (if needed)
python -m src.data_generator
# Process transaction data
python -m src.data_preprocessing# Train the XGBoost model
python -m src.model_training --model xgboost
# Train the reinforcement learning model
python -m src.model_training --model rl# Run the FastAPI application locally
cd app
uvicorn app:app --reload --port 8000# Launch the model performance dashboard
streamlit run dashboards/model_performance_dashboard.pyThe project includes several monitoring components:
- API Health Monitoring: Real-time checks of endpoint availability and response times
- Model Drift Detection: Identifies when model performance degrades
- Transaction Analysis: Visual dashboards for business metrics
- A/B Testing: Framework for comparing routing strategies
The system employs multiple models:
- XGBoost Classifier: Primary model for success prediction
- Reinforcement Learning Agent: Dynamic optimization of routing decisions
- Feature Engineering Pipeline: Automated transformation of raw transaction data
The system is evaluated on:
- Transaction success rate improvement
- Precision and recall for fraud detection
- Average response time for real-time predictions
- Business impact metrics (revenue, customer satisfaction)
The system implements:
- Feedback loop for model retraining
- Monitoring of prediction vs. actual outcomes
- Concept drift detection
- Automated model retraining when performance degrades
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.
For any questions or feedback, please reach out to [email protected].