Skore

Abstract

1. Introduction

Elevate ML Development with Built-in Recommended Practices \ Documentation — Community — YouTube — Skore Hub

When it comes to data science, you have excellent tools at your disposal: pandas and polars for data exploration, skrub for stateful transformations, and scikit-learn for model training and evaluation. These libraries are designed to be generic and accommodate a wide range of use cases.

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2. Objective

Track your Data Science. Skore's open-source Python library accelerates ML model development with automated evaluation reports, smart methodological guidance, and comprehensive cross-validation analysis.

This project demonstrates how Python, Machine Learning, scikit-learn 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/probabl-ai/skore.git
cd skore

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 probabl-ai 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.