{
  "fetched_at": "2026-10-20T08:39:57Z",
  "source_url": "https://www.kaggle.com/competitions/competition1/data",
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      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "content": "Start here if...\nYou're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions.\n\nCompetition Description\nThe sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n\nOne of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n\nIn this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n\nPractice Skills\nBinary classification\nPython and R basics",
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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 PassengerId in the test set, you must predict a 0 or 1 value for the Survived variable.\n\nMetric\nYour score is the percentage of passengers you correctly predict. This is known simply as \"accuracy”.\n\nSubmission File Format\nYou should submit a csv file with exactly 418 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\nPassengerId (sorted in any order)\nSurvived (contains your binary predictions: 1 for survived, 0 for deceased)\nPassengerId,Survived\n 892,0\n 893,1\n 894,0\n Etc.\nYou can download an example submission file (gender_submission.csv) on the Data page.",
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