Ngx Gauge

A highly customizable Gauge component for Angular apps and dashboards

Data ScienceTypeScriptMIT

Abstract

Ngx Gauge is an open-source Data Science project. A highly customizable Gauge component for Angular apps and dashboards. It is built using TypeScript, Angular. 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

A highly customizable Gauge component for Angular apps and dashboards. It provides many configurationable options to customize according to your needs. Checkout the live demo here. (For older versions of Angular, check out the compatibility matrix below).

In version v5.0.0, we introduced markers, ticks and background opacity for gauge as shown below. Checkout the documentation below.

2. Objective

A highly customizable Gauge component for Angular apps and dashboards

This project demonstrates how TypeScript, Angular can be applied to a real-world Data Science problem.

4. Technology Stack

TypeScriptAngular

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/ashish-chopra/ngx-gauge.git
cd ngx-gauge
npm install ngx-gauge
npm install --save ngx-gauge@<version>

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 ashish-chopra 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