Cristalyse

Abstract

1. Introduction

Visit our complete documentation for step-by-step guides, interactive examples,and everything you need to master data visualization in Flutter.

Interactive scatter plots with smooth animations and multi-dimensional data mapping

Progressive line drawing with customizable themes and multi-series support

2. Objective

High-performance data visualization library for Flutter with native rendering and cross-platform support

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

3. Key Features / Modules

• Size Encoding - Third dimension mapped to bubble radius
• Color Grouping - Categorical data with distinct colors
• Custom Labels - Show formatted values on bubbles
• Interactive Tooltips - Rich hover information
• Dual Y-Axis Support - Use with secondary scales
• Animation Support - Smooth entrance effects
• Grammar of Graphics API - Familiar ggplot2-style syntax
• Smooth 60fps animations with customizable timing and curves
• Dual Y-axis support with independent scales and data routing
• Multiple built-in themes (Light, Dark, Solarized Light/Dark)

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Flutter SDK
• Android Studio or VS Code with Flutter plugin
• Android / iOS device or emulator
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/rudi-q/cristalyse.git
cd cristalyse

flutter pub add cristalyse

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 rudi-q 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.