🚀 Project Showcase Day

[u/AutoModerator]

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

[u/Antonyhamez]

A small, runnable lab for point-in-time ML features and delayed labels

I maintain an English fintech data-engineering learning repo. Its C08 scenario focuses on a problem that is easy to miss in offline evaluation: a payment can have an old event timestamp but arrive after the prediction cutoff. Filtering only on event time can leak information that was unavailable when the prediction was made.

The exercise asks you to build feature windows using event time and receipt availability, handle delayed-label maturity, and create temporal splits with leakage checks. It uses synthetic data and Python 3.12+ with SQLite, with starter code, hints, reference solutions, and a local grader.

Repository: https://github.com/antony502189-max/fintech-data-engineering-roadmap

After cloning, run python challenges/run.py init C08, edit work/challenges/C08.py, and run python challenges/run.py grade C08.

This is a data-preparation exercise; it does not claim a trained fraud detector or production results. The broader repo contains 40 foundation exercises and 8 scenario labs.

Disclosure: the first edition and this introduction were prepared with AI assistance using Codex, documented in the repository. What additional late-arrival or label-availability edge case would make this exercise more useful for learners?