Beginner score
82/100
Competition workspace
Beginners who want to get started with tabular data and feature engineering. This competition provides a good entry point with community resources and accessible data.
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
82/100
Learning value
74/100
Estimated effort
5-15 hours
Metric
Community Judged (Build Quality)
Beginners who want to get started with tabular data and feature engineering. This competition provides a good entry point with community resources and accessible data.
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
Python
pandas and data manipulation
gradient boosting (XGBoost/LightGBM)
cross-validation
The real friction is usually not library usage. It is validation, time allocation, and task framing.
Requires solid feature engineering and model selection skills.
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.
This competition is already open and has a strong beginner fit right now, so it is a good first move.
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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.
Official metric name was not present on the list/detail payload; evaluation_method points to the Kaggle evaluation page.