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
78 lines
789 B
Plaintext
78 lines
789 B
Plaintext
# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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# Jupyter Notebook
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.ipynb_checkpoints
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# Virtual environment
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venv/
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env/
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ENV/
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# IDE files
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.idea/
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.vscode/
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*.swp
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*.swo
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# Project specific
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data/processed/
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models/
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*.pkl
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*.h5
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# OS specific
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.DS_Store
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.DS_Store?
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._*
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.Spotlight-V100
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.Trashes
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ehthumbs.db
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Thumbs.db
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src/__pycache__
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src/api/__pycache__
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src/web/__pycache__ |