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Project Overview

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.

Objectives

  • 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.

Selected tools

  • 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.