Overview

This project delivers a comprehensive analysis of Nassau’s shipping operations using Python and Jupyter Notebook, augmented by AI-assisted workflows to accelerate data processing, pattern discovery, and insight generation. By analyzing historical port activity, cargo volumes, and vessel movement, it identifies key bottlenecks and optimization opportunities to enhance logistics efficiency and operational planning.

Methodology

The Challenge

The primary challenge was dealing with fragmented and inconsistent shipping records spread across multiple data sources. I implemented a robust data cleaning and normalization pipeline to merge these datasets, followed by exploratory analysis to identify traffic trends, peak congestion periods, and route-level performance metrics.