{
  "fetched_at": "2026-10-19T14:43:21Z",
  "source_url": "https://www.kaggle.com/competitions/dcds25/data",
  "pages": [
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "postTitle": "",
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      "mimeType": "",
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    },
    {
      "name": "data-description",
      "content": "You only need to download one format of each file. Each has the same contents but use different packaging methods.\n\nFile descriptions\n\ntrain.csv - the training set test.csv - the testing set sample_submission.csv - a sample submission file in the correct format Data dictionary\n\nPostId - ID number of the question PostCreationDate - Date and time the question was asked OwnerUserId - ID number of the question asker OwnerCreationDate - Date and time the question asker's account was created ReputationAtPostCreation - Reputation of the question asker at the time the question was asked OwnerUndeletedAnswerCountAtPostTime - Number of answers (to other questions) that the question asker had written at the time the question was asked Title - Title of the question BodyMarkdown - Body of the question Tag1 through Tag5 - Topical tags applied to the question PostClosedDate - Date and time the question was closed (if it was closed) OpenStatus - 1 indicates open, 0 indicates closed (the response)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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      "mimeType": "",
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    },
    {
      "name": "Description",
      "content": "Kaggle Competition For DS_DC_25\n\nPredict whether a Stack Overflow question will be closed\n\nMillions of programmers use Stack Overflow to get high-quality answers to their programming questions every day. They take quality very seriously and have evolved an effective culture of moderation to safeguard it.\n\nCurrently, about 6% of all new questions end up \"closed\". Questions can be closed as off-topic, not constructive, not a real question, or too localized. Your goal is to build a classifier that predicts whether or not a question will be closed given the question as submitted.\n\nThis is a Kaggle In-Class Competition for the students of General Assembly's course in Washington, DC, and is based on a previous Kaggle competition.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "Your solution should indicate the probability (between 0 and 1) of a given question being open. The evaluation metric is log loss.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}