Michael Ikehi
f70363e4ca
- Implemented EDA, feature engineering, and model training pipeline - Built ML model with optimized hyperparameters (94% F1-score) - Developed REST API with Flask for real-time fraud prediction - Created responsive web UI for transaction validation - Added Docker containerization for easy deployment - Included comprehensive documentation and usage examples
17 lines
317 B
YAML
17 lines
317 B
YAML
version: '3'
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services:
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fraud-detection:
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build:
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context: ..
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dockerfile: Dockerfile
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ports:
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- "8000:8000" # API port
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- "8501:8501" # Web UI port
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volumes:
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- ../models:/app/models
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- ../data:/app/data
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environment:
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- PYTHONPATH=/app
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restart: unless-stopped
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