SeafaringIT - Anomaly Detection for Water & Energy Consumption | Hi Interns

Goals:
Build an intelligent anomaly detection system to identify water leaks and energy overconsumption
Provide explainable insights into anomaly causes
Prioritize interventions by impact and savings
Student roles: Machine learning engineers, data scientists, backend engineers, MLOps specialists
Expected outcomes: ML-powered anomaly detection with explainability, API exposure, model tracking, and impact analysis
Key features:
Dataset construction (open/synthetic) with feature engineering
Multiple anomaly detection models (statistical + ML) with benchmarking
Explainability layer (SHAP) and error analysis
Alert API with scoring and prioritization
Model tracking/versioning with MLflow
Concept drift monitoring and performance tracking
Business impact report (savings, environmental impact)
Technologies: Python, scikit-learn, XGBoost, Isolation Forest, SHAP, MLflow, FastAPI, PostgreSQL, Elasticsearch, visualization tools

Anomaly Detection for Water & Energy Consumption