Analysis of global shipping patterns and data.
Features
- Comprehensive analysis of global shipping data
- Data-driven insights into shipping trends and logistics
- Modular and extensible structure for future data sources and analyses
Tech Stack
- Primary language: Assumed Python or R (common for data analysis)
- Data processing and visualization libraries (e.g., pandas, matplotlib, seaborn, or ggplot2)
- Version control with Git and GitHub
Getting Started
Prerequisites
- Python 3.x or R installed (depending on implementation)
- Required packages/libraries (to be defined in requirements.txt or DESCRIPTION file)
Installation
# Clone the repository
git clone https://github.com/justin-napolitano/Shipping.git
cd Shipping
# Install dependencies (assuming Python)
pip install -r requirements.txt
Running the Analysis
# Run the main analysis script (assumed)
python main.py
Project Structure
Note: Project structure is assumed due to lack of files detected.
Shipping/
├── data/ # Raw and processed shipping data
├── notebooks/ # Jupyter notebooks for exploratory analysis
├── src/ # Source code for data processing and analysis
├── outputs/ # Generated reports and visualizations
├── requirements.txt # Python dependencies
├── README.md # Project documentation
Future Work / Roadmap
- Integrate additional data sources for more comprehensive coverage
- Implement interactive visualizations and dashboards
- Automate data ingestion and update pipelines
- Extend analysis to include predictive modeling and forecasting
- Improve documentation and add unit tests