VisuX - Data Visualisation and Dimensionality Reduction Engine

Overview

VisuX is a web-based tool designed to provide users with interactive visualization and advanced techniques for their datasets.

Features

• File Upload & Download: Supports CSV, Excel, and JSON formats.
• Data Visualisation: Provides multiple chart types, including scatter plots, line charts, bar charts, pie charts, heatmaps, and 3D visualizations using Plotly.js.
• Dimensionality Reduction: Supports PCA, t-SNE, and UMAP for feature reduction.
• Interpolation & Extrapolation: Applies linear, polynomial, and exponential methods to extend datasets.
• Correlation Analysis: Computes Pearson, Spearman, and Kendall correlations.
• Graph Editing: Allows customization of axes, colors, and styles.
• Logging System: Tracks all user actions and operations for better data management.

Installation

Prerequisites

Ensure you have the following installed:

• Node.js & npm (for frontend development)
• Python 3.8+ & pip (for backend development)

Setup

1. Clone the Repository

 git clone https://github.com/WangJing3383/VisuX.git
 cd Visux\my-visualization-app\

2. Backend Setup

 python -m venv venv  # Create a virtual environment
 source venv/bin/activate  # Activate virtual environment (Mac/Linux)
 venv\Scripts\activate  # Activate virtual environment (Windows)
 pip install -r requirements.txt  # Install dependencies
 python manage.py migrate  # Apply database migrations
 python manage.py runserver  # Start the backend server

3. Frontend Setup

 cd frontend
 npm install  # Install dependencies
 npm start  # Start the frontend server

Usage

1. Upload a dataset through the interface.
2. Select visualization options or apply data processing tools.
3. Customize graphs using the Graph Editor.
4. Download processed datasets or export visualisations.

Technologies Used

Frontend

• React.js (Component-based UI)
• Ant Design (UI components)
• Plotly.js (Data visualisation)
• Axios (API communication)

Backend

• Django (Web framework)
• Django REST Framework (API development)
• SQLite (Database management)
• Pandas, NumPy (Data manipulation)
• Scikit-learn, SciPy (Machine learning & statistical analysis)

Contributors

• Shengjie Yin
• Jing Wang
• Ezgi Yircali
• Yufei Lin
• Mengjia Cao

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to our supervisors at the Karlsruhe Institute of Technology (KIT) for their guidance and support!

• Gürol Saglam
• Shanmukha Mangadahalli Siddaramu

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