Beginner score
36/100
Competition workspace
Intermediate learners who want to deepen their skills in machine learning. Good for building practical experience with real-world data.
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
36/100
Learning value
73/100
Estimated effort
15-45 hours
Metric
cabt
Intermediate learners who want to deepen their skills in machine learning. Good for building practical experience with real-world data.
Taken from the competition's official page.
Simulator SDK for local training, testing, debugging, and reinforcement learning.
Downloadable submission and other-team episode replay files.
English and Japanese card metadata, card IDs, expansion details, and reference images.
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
Python
scikit-learn or similar ML library
data analysis with pandas
The real friction is usually not library usage. It is validation, time allocation, and task framing.
Requires solid machine learning fundamentals and good experimental methodology.
This guide is the best pre-read if you want a cleaner start instead of trial-and-error.
A simple framework for choosing a competition that teaches instead of overwhelming you.
How to Choose Your First AI CompetitionUse these fields to make a quick decision before you dive deeper.
Registration is open and its signup deadline is close, so it deserves attention first.
Sign in to save competitions and build your own shortlist.
Rules, files, submission details, and the live deadline still come from the official page.
These competitions share a similar domain or difficulty level.
Separate what is confirmed from what still needs review. Official rules and deadlines win — report anything that looks wrong.