Showcasing my work and passion.
Maritime Ops Analytics
Predictive Fuel Modeling & Fleet Optimization
PYTHON • TIME-SERIES FORECASTING • K-MEANS CLUSTERING
Time-Series Fuel Forecasting
Holt-Winters Predictive Modeling
Accurately predicting fuel consumption is critical for maritime cost optimization. I built a time-series forecasting engine using an Additive Holt-Winters model to project future daily fuel usage. The model accounts for seasonal variations and includes 95% confidence intervals, allowing stakeholders to make data-driven, risk-adjusted procurement decisions.
Statistical Anomaly Detection
Z-Score Outlier Identification
To ensure data integrity and flag operational irregularities, I implemented an automated anomaly detection system. Using a rolling Z-score algorithm, the model identifies fuel consumption outliers in real-time. By allowing stakeholders to adjust the sensitivity threshold, the system provides a dynamic early-warning mechanism for vessel inefficiency or reporting errors.
Interactive Scenario Simulator
Predictive Cost-Benefit Analysis
To bridge the gap between statistical modeling and business strategy, I engineered a dynamic scenario simulator. Stakeholders can adjust variables like vessel speed, draft, and sea state to instantly predict total fuel consumption for a voyage. This empowers fleet managers to run real-time cost-benefit analyses and optimize routes before a ship ever leaves the port.