Complete fraud detection system implementation

- 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

.gitignore

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