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

Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the project files
+COPY . .
+
+# Create necessary directories
+RUN mkdir -p data/processed models
+
+# Expose ports for API and Web UI
+EXPOSE 8000 8501
+
+# Set environment variables
+ENV PYTHONPATH=/app
+
+# Command to run the API and Web UI
+CMD ["sh", "-c", "python -m src.api.app & python -m src.web.app"]