This project focuses on analyzing transborder freight transportation to identify inefficiencies, trade patterns, and cost implications. Using data-driven insights, the project aims to improve freight operations by examining trends in shipment volumes, transportation modes, delays, and sustainability metrics.
- Understand freight movement trends across borders.
- Identify bottlenecks and inefficiencies in transportation.
- Analyze the cost implications of different freight modes.
- Assess the environmental impact of freight transportation.
- Develop data-driven recommendations for optimization.
- Python: Python is the primary programming language for data analysis, visualization, and machine learning. I used this tool for data cleaning, preprocessing, exploratory data analysis (EDA), and visualization.
- Google Colab: This online tool provides an interactive environment for writing and testing code. I used this tool to write codes, explanations, and visualizations.
- Git and GitHub: Version control and collaboration are essential for tracking changes and sharing work, so I used this tool to store my code, and document progress through commits.