Beginner score
16/100
Competition workspace
Advanced practitioners with experience in general machine learning. Requires strong technical foundations and comfort with complex ML pipelines.
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
16/100
Learning value
66/100
Estimated effort
25-70 hours
Metric
See official page
Advanced practitioners with experience in general machine learning. Requires strong technical foundations and comfort with complex ML pipelines.
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. The competitive landscape is strong, with many experienced teams.
This guide is the best pre-read if you want a cleaner start instead of trial-and-error.
How to learn feature engineering, validation, and competition workflow without heavy hardware.
No GPU? Pick Competitions That Still Teach You Good HabitsUse these fields to make a quick decision before you dive deeper.
This competition is better treated as a comparison option inside your shortlist before you invest more time.
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.
Official metric name was not present on the list/detail payload; evaluation_method points to the Kaggle evaluation page.