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  "fetched_at": "2026-10-19T15:23:57Z",
  "source_url": "https://www.kaggle.com/competitions/2025-2-data-solution-p-01/data",
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      "content": "## Dataset Description\nIn this competition, you will be predicting the probability [0, 1] of a binary target column.\n\nThe data contains binary features (bin_*), nominal features (nom_*), ordinal features (ord_*) as well as (potentially cyclical) day (of the week) and month features. The string ordinal features ord_{3-5} are lexically ordered according to string.ascii_letters.\n\nSince the purpose of this competition is to explore various encoding strategies, the data has been simplified in that (1) there are no missing values, and (2) the test set does not contain any unseen feature values (See this). (Of course, in real-world settings both of these factors are often important to consider!)\n\n## Files\n- train.csv - the training set\n- test.csv - the test set; you must make predictions against this data\n- sample_submission.csv - a sample submission file in the correct format",
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      "name": "Description",
      "content": "## Chapter 7 - [경진대회] 범주형 데이터 이진분류\n2025학년도 2학기 데이터사이언스학과\n데이터문제해결및실습1\n\nA common task in machine learning pipelines is encoding categorical variables for a given algorithm in a format that allows as much useful signal as possible to be captured.\n\nBecause this is such a common task and important skill to master, we've put together a dataset that contains only categorical features, and includes:\n\nbinary features\nlow- and high-cardinality nominal features\nlow- and high-cardinality ordinal features\n(potentially) cyclical features\nThis Playground competition will give you the opportunity to try different encoding schemes for different algorithms to compare how they perform. We encourage you to share what you find with the community.\n\nIf you're not sure how to get started, you can check out the Categorical Variables section of Kaggle's Intermediate Machine Learning course.\n\n\nHave Fun!",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on **area under the ROC curve** between the predicted probability and the observed target.\n\n## Submission File\nFor each id in the test set, you must predict a probability for the target variable. The file should contain a header and have the following format:\n\n```\nid,target\n300000,0.5\n300001,0.5\n300002,0.5\n...\n```",
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    {
      "name": "rules",
      "content": "Rules",
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