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  "source_url": "https://www.kaggle.com/competitions/caltech-cs-155-2019-final/data",
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      "name": "rules",
      "content": "<div class=\"markdown-converter__text--rendered competition-rules__content\">\n<h3>No Additional Data</h3>\n<p>You are not allowed to use any data outside that which has been provided on this Kaggle site. Any infractions will be treated as a violation of the honor code.&nbsp;\n<h3>One account per participant</h3>\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n<h3>No collaboration</h3>\n<p>This is part of the final for this class.</p>\n<h3>Teams</h3>\n<p>Only single-person teams are allowed in this competition.</p>\n<h3>Submission Limits</h3>\n<p>You may submit a maximum of 10 entries per day.</p>\n<p>You may select up to 2 final submissions for judging.</p>\n<h3>Competition Timeline</h3>\n<p>Start Date: <strong>3/13/2019</strong></p>\n<p>Entry Deadline: <strong>None</strong></p>\n<p>End Date: <strong>3/20/2019 8:59 PM UTC&nbsp;(1:59 PM PDT)</strong></p></div>",
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      "content": "The data provided in `X_train.csv`, `X_test.csv`, and `y_train.csv` primarily follows the documentation provided in the file `Codebook.pdf`.  -1 represents a missing value.\n\n### Task\n\nYour task is to predict the `actual_wait div 60000` column corresponding to `X_test.csv`.  `y_train.csv` corresponds to `X_train.csv`. \n\n### Features\n\nThe attached PDF codebook explains what each feature represents.\n\n### File descriptions\n\n- X_train.csv: training data\n- y_train.csv: training labels\n- X_test.csv: testing data\n- sample_submission.csv: a sample for the format of your submissions\n- Codebook.pdf: feature descriptions",
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      "content": "For this Kaggle competition, you will predict time spent waiting in line from information about the line and situation.\n\nWe have received data from [QLess][1], a company that provides solutions to long waiting times spent in lines.\n\n**The top participants will be invited to present their work to QLess!**\n\n<h2>Acknowledgements</h2>\n<p>We thank QLess for providing this dataset.</p>\n\n\n  [1]: https://www.qless.com/",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is MSE. \n\n### Submission Format\n\nFor every row of data in X_train.csv, submission files should contain two columns: `id`, which should contain the position that datapoint holds in the test set, and `actual_wait div 60000`, which should contain the expected wait time.\n\nThe file should contain a header and have the following format:\n\n    id,actual_wait div 60000\n    0,12.2\n    1,1.01\n    2,2.9\n    3,5.1011\n    ...\n    37660,11.393\n",
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