Abstract
Timeline Sankey is an open-source Data Science project. A project to visualize time range series data using the Sankey diagram. It is built using TypeScript. Key capabilities include: Visualization layout:; Responsive View, with automatic resizing, and adaptive layout for mobile or desktop devices; Grid layout for multiple node series. The complete source code is publicly available on GitHub under the BSD 3-Clause "New" or "Revised" License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
A project to visualize time range series data using the Sankey diagram.
2. Objective
A project to visualize time range series data using the Sankey diagram.
This project demonstrates how TypeScript can be applied to a real-world Data Science problem.
3. Key Features / Modules
- Visualization layout:
- Responsive View, with automatic resizing, and adaptive layout for mobile or desktop devices.
- Grid layout for multiple node series
- Direct link and cross(series) link
- Node title
- Series(column) title in mobile view
- Interactions:
- Hovering over a node highlights the connected nodes and links
- Zoom on vertical orientation
- Filter by node tags, categories or date range
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Node.js (LTS) and npm / yarn / pnpm
- VS Code or any code editor
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/geekplux/timeline-sankey.git
cd timeline-sankeyFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by geekplux and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.
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