Beginner score
60/100
Competition workspace
Learners who want to practice multiclass classification on a large structured dataset with geographic and categorical features.
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
60/100
Learning value
82/100
Estimated effort
5-15 hours
Metric
See official page
Learners who want to practice multiclass classification on a large structured dataset with geographic and categorical features.
Taken from the competition's official page.
Survey dataset on earthquake impacts, household conditions, and demographics
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
Python
pandas and data wrangling
scikit-learn or gradient boosting libraries
understanding of F1 score and multiclass metrics
The real friction is usually not library usage. It is validation, time allocation, and task framing.
The dataset is large with many categorical building-attribute features, the three damage grades have an ordinal relationship that standard classifiers do not exploit, and the micro-averaged F1 metric requires balanced performance across classes.
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.
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Cash prize not listed on the home page (common for practice competitions).