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  "fetched_at": "2026-10-20T06:50:58Z",
  "source_url": "https://www.kaggle.com/competitions/beginners-hypothesis-2023/data",
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      "name": "data-description",
      "content": "The given data contains __2000__ samples. Your performance will be evaluated on the basis of your classifier’s accuracy on the hold-out data of __500__ samples.\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n",
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      "name": "Description",
      "content": "## PROBLEM STATEMENT\nThe data given to you contains the information decoded by astronomers aboard. Each sample(10-tuple) corresponds to one of the black holes viz. __Zeta Draconis, Alpha Boreas, Beta Eioneous, Alpha Hemithea, and Gamma Saggita__. Your task is to build a Classifier from this data, which when given a new sample, would predict the black hole from which this sample is most likely to have been generated. The given data contains 2000 samples. Your performance will be evaluated based on your classifier’s accuracy on the hold-out data of 500 samples. __Any further exploratory analysis and findings from the data would be duly rewarded__.\n",
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      "name": "Evaluation",
      "content": "## EVALUATION \nThe leaderboard uses the fraction of samples predicted correctly as the metric for evaluation.\nYou will first be shortlisted based on your model’s accuracy. We will also evaluate you based on the originality of your code and its interpretability. Any plagiarism would be penalised.",
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    {
      "name": "rules",
      "content": "__HAVE FUN__",
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      "name": "Submission Guidelines",
      "content": "## GUIDELINES\nGive a brief description of your code through Markdown cells in the notebook. You can view the leaderboard and change your code throughout the competition. The winners of this competition will qualify directly for the interview stage. \nAny additional analysis beyond the problem will have additional brownie points. You will first be shortlisted based on your model’s accuracy. We will also evaluate you on the basis of the originality of your code and its interpretability. Any kind of plagiarism would be penalised.\nMake sure you write your name, enrollment number and branch at the end of the notebook.",
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      "name": "Code Submission Link.",
      "content": "# Link for Submission\nThis is the google form in which you are supposed to submit your submission for 'Beginners' Hypothesis'. https://forms.gle/jZjYbSkNWwzSBNwT7\n\nThe form will be editable and accepted only if the institute's G-suite email is used.",
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