{
  "fetched_at": "2026-10-20T09:13:01Z",
  "source_url": "https://www.kaggle.com/competitions/data-boot-camp-challenge/data",
  "pages": [
    {
      "name": "data-description",
      "content": "To get descriptions of the columns, see [this page](https://www.kaggle.com/c/titanic/data)",
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    {
      "name": "Description",
      "content": "Hello class! See [this page](https://www.kaggle.com/c/titanic) for a description of the project. \n\nI encourage all of you to try doing things on your own for a while before looking at the answers, but know that in the \"code\" and \"discussions\" section you can sort by \"most voted\" to see some phenomenal tutorials and tips for working with this data set. For a fantastic data analysis and machine learning guide in Jupyter Notebook form, see [this page](https://www.kaggle.com/startupsci/titanic-data-science-solutions) and/or [this page](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide).",
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    {
      "name": "Evaluation",
      "content": "## Goal\nIt is your job to predict if a passenger survived the sinking of the Titanic or not.\nFor each passenger in the test set, you must predict a 0 or 1 value for the variable.\n\n## Metric\nYour score is the R2 score.\n\n## Submission File Format\nYou should submit a csv file with exactly 179 entries plus a header row. Your submission will show an error if you have extra columns (beyond PassengerId and Survived) or rows.\n\nThe file should have exactly 2 columns:\n* PassengerId (sorted in any order)\n* Survived (contains your binary predictions: 1 for survived, 0 for deceased)\n```\nPassengerId,Survived\n892,0\n893,1\n894,0\n```\nEtc.\nYou can download an example submission file (gender_submission.csv) on the Data page.",
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    {
      "name": "rules",
      "content": "This is just for fun, no prizes awarded. It is fun though....\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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