Abstract
Tree Map Disk Visualizer is an open-source Data Science project. TreeMap is a fully downloadable app that can run on mac, windows, and linux. That visualizeses all of your disk-space into charts and square board. It also lets you delete unwanted files and opens files for you aswell. ⬇ Download Features Run it AI / MCP API Safety. It is built using TypeScript. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
⬇ Download Features Run it AI / MCP API Safety
Find itSquarified treemap of every byte Understand itTrends, diffs & duplicate hunting Reclaim itOne-click cleanup → system Trash
TreeMap isn't just a treemap — it's a full disk-hygiene workbench. Seventeen views, one zero-dependency frontend.
2. Objective
TreeMap is a fully downloadable app that can run on mac, windows, and linux. That visualizeses all of your disk-space into charts and square board. It also lets you delete unwanted files and opens files for you aswell.
This project demonstrates how TypeScript can be applied to a real-world Data Science problem.
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/Prithvi-Web/TreeMap-Disk-Visualizer.git
cd TreeMap-Disk-Visualizergit clone https://github.com/Prithvi-Web/TreeMap-Disk-Visualizer.git
cd TreeMap-Disk-Visualizer
npm install
npm run buildpwdFull 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 Prithvi-Web and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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