# Fraud Detection System

## Docs

- [API Overview](https://mintlify.wiki/sujith52/fraud/api/endpoints.md): Overview of the Fraud Detection ML System API endpoints
- [GET / - Home Page](https://mintlify.wiki/sujith52/fraud/api/home.md): Web interface for insurance fraud prediction
- [KMeansClustering](https://mintlify.wiki/sujith52/fraud/api/modules/clustering.md): K-Means clustering module for data segmentation
- [Data Loader](https://mintlify.wiki/sujith52/fraud/api/modules/data-loader.md): Data ingestion modules for training and prediction
- [File_Operation](https://mintlify.wiki/sujith52/fraud/api/modules/file-operations.md): Model persistence and file management module
- [Model_Finder](https://mintlify.wiki/sujith52/fraud/api/modules/model-finder.md): Hyperparameter tuning and model selection module
- [Preprocessor](https://mintlify.wiki/sujith52/fraud/api/modules/preprocessing.md): Data preprocessing and transformation module for fraud detection
- [POST /predict](https://mintlify.wiki/sujith52/fraud/api/predict.md): Make fraud predictions on new transaction data
- [POST /train](https://mintlify.wiki/sujith52/fraud/api/train.md): Train a fraud detection model with new data
- [Technical Architecture](https://mintlify.wiki/sujith52/fraud/concepts/architecture.md): Understand the system components, data flow, and module organization
- [Data Pipeline](https://mintlify.wiki/sujith52/fraud/concepts/data-pipeline.md): Deep dive into data ingestion, validation, and transformation processes
- [Fraud Detection Methodology](https://mintlify.wiki/sujith52/fraud/concepts/fraud-detection.md): Understanding insurance fraud indicators, features, and the ML approach
- [System Overview](https://mintlify.wiki/sujith52/fraud/concepts/overview.md): Learn about the fraud detection ML system and its capabilities
- [Heroku Deployment](https://mintlify.wiki/sujith52/fraud/deployment/heroku.md): Deploy your fraud detection ML system to Heroku
- [Monitoring Dashboard](https://mintlify.wiki/sujith52/fraud/deployment/monitoring.md): Monitor your fraud detection system with Flask-MonitoringDashboard
- [Production Setup](https://mintlify.wiki/sujith52/fraud/deployment/setup.md): Configure your fraud detection system for production deployment
- [Installation Guide](https://mintlify.wiki/sujith52/fraud/installation.md): Complete installation instructions for the Fraud Detection ML System
- [Introduction](https://mintlify.wiki/sujith52/fraud/introduction.md): Welcome to the Fraud Detection System documentation
- [Batch Prediction](https://mintlify.wiki/sujith52/fraud/prediction/batch-prediction.md): Processing batch files to generate fraud predictions
- [Prediction Data Validation](https://mintlify.wiki/sujith52/fraud/prediction/data-validation.md): Schema validation and data quality checks for prediction files
- [Prediction Output](https://mintlify.wiki/sujith52/fraud/prediction/output.md): Understanding and interpreting fraud detection prediction results
- [Prediction Overview](https://mintlify.wiki/sujith52/fraud/prediction/overview.md): Understanding how fraud detection predictions work in the system
- [Quickstart](https://mintlify.wiki/sujith52/fraud/quickstart.md): Get started with the fraud detection system in minutes
- [K-Means Clustering](https://mintlify.wiki/sujith52/fraud/training/clustering.md): Divide training data into clusters to train specialized models for different fraud patterns
- [Data Preparation](https://mintlify.wiki/sujith52/fraud/training/data-preparation.md): Learn how to prepare your fraud detection training data with the correct format, schema, and naming conventions
- [Data Validation](https://mintlify.wiki/sujith52/fraud/training/data-validation.md): Understand the automated validation process that ensures data quality before training
- [Model Selection](https://mintlify.wiki/sujith52/fraud/training/model-selection.md): Hyperparameter tuning and model comparison using GridSearchCV and AUC scores
- [Model Training](https://mintlify.wiki/sujith52/fraud/training/model-training.md): Complete pipeline for training cluster-specific fraud detection models
- [Preprocessing](https://mintlify.wiki/sujith52/fraud/training/preprocessing.md): Transform and clean validated data through imputation, encoding, and scaling