Tree Map Disk Visualizer

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.

Data ScienceTypeScriptMIT

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

TypeScript

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-Visualizer
git clone https://github.com/Prithvi-Web/TreeMap-Disk-Visualizer.git
cd TreeMap-Disk-Visualizer
npm install
npm run build
pwd

Full 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.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. 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.

Want to build this as your internship project?

Work on a Data Science project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for Data Science Internship