DADApy

Abstract

1. Introduction

DADApy is a Python package for the characterization of manifolds in high-dimensional spaces.

For more details and tutorials, visit the homepage at: https://dadapy.readthedocs.io/

The Data class is just container of classes. If you need to work with a specific module you can equivalently import it directly.

2. Objective

Distance-based Analysis of DAta-manifolds in python

This project demonstrates how Python, Machine Learning 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.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sissa-data-science/DADApy.git
cd DADApy

pip install dadapy

pip install git+https://github.com/sissa-data-science/DADApy

git clone https://github.com/sissa-data-science/DADApy.git
cd DADApy
python setup.py build_ext --inplace
pip install .

pip install --upgrade "jax[cuda12_pip]==0.4.30" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

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 sissa-data-science and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.