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

requirements.txt

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+# Data processing and analysis
+numpy>=1.20.0
+pandas>=1.3.0
+scipy>=1.7.0
+
+# Data visualization
+matplotlib>=3.4.0
+seaborn>=0.11.0
+plotly>=5.3.0
+
+# Machine learning
+scikit-learn>=1.0.0
+xgboost>=1.5.0
+imbalanced-learn>=0.8.0
+
+# API and web development
+flask>=2.0.0
+fastapi>=0.68.0
+uvicorn>=0.15.0
+pydantic>=1.8.0
+
+# Jupyter notebooks
+jupyter>=1.0.0
+ipykernel>=6.0.0
+
+# Utilities
+python-dotenv>=0.19.0
+joblib>=1.0.0