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  "source_url": "https://www.kaggle.com/competitions/caltech-cs-155-2019-part-1/data",
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      "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 the two Kaggle sites. Any infractions will be treated as a violation of the honor code.&nbsp;After submitting your predictions to this competition, don't forget to submit your predictions for the 2012 election to Part 2 of this miniproject <a href=\"https://inclass.kaggle.com/c/caltech-cs-155-2017-part-2\" rel=\"nofollow\">here</a>!</p>\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 private sharing outside teams</h3>\n<p>Privately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.</p>\n<h3>Team Mergers</h3>\n<p>Team mergers are not allowed in this competition.</p>\n<h3>Team Limits</h3>\n<p>The maximum size of a team is 3 participants.</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>2/6/2019</strong></p>\n<p>Merger Deadline: <strong>None</strong></p>\n<p>Entry Deadline: <strong>None</strong></p>\n<p>End Date: <strong>2/12/2019 9:59 PM UTC&nbsp;(1:59 PM PST)</strong></p></div>",
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      "content": "The data provided in `train_2008.csv` and `test_2008.csv` primarily follows the documentation provided in the file `2008-Codebook.pdf`. The following fields in the codebook have been removed and thus not in the `.csv` files:\n\n`['HRHHID', 'PROLDRRP', 'PUPELIG', 'HXFAMINC', 'PUSCK4', 'PES1', 'PES2', 'PES3', 'PES4', 'PES5', 'PES6', 'PES7', 'PES8']`\n\nThe field `HRHHID` has been renamed as `id` in the `.csv` files, which starts from 0 and uniquely identifies each row in the dataset.\n\n### Task\n\nYour task is to predict the `target` column (in `train_2008.csv`, but not in `test_2008.csv`). \n\nIn `train_2008.csv`, if `target` is 0, the person did not vote in the election; if `target` is 1, the person voted in the election. (In fact, this `target` column directly corresponds to the field `PES1` in the codebook. Except that `PES1` has values 1 and 2, and we shifted it to 0 and 1.)\n\nPlease note that your submission should be **_the probabilities_** of each testing sample being `1` (i.e., floating point numbers), instead of binary 0/1.\n\n### Features\n\nThe attached PDF codebook explains what each feature represents.\n\n### File descriptions\n\n- train_2008.csv: the training set, including the labels\n- test_2008.csv: the test set, with no labels\n- sample_submission.csv: an example submission showing the format required\n- 2008-Codebook.pdf: explanation of the various features\n\n",
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      "name": "Description",
      "content": "<p>For this Kaggle competition, you will use 2008 demographic data to predict voter turnout in the 2008 presidential election.</p>\n\n<h2>Acknowledgements</h2>\n<p>We thank Prof. Mike Alvarez for providing this dataset.</p>",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is AUC. \n\n### Submission Format\n\nFor every row of data in test_2008.csv, submission files should contain two columns: `id` and `target`. The `id` columns should just contain integers in order in the interval [0, 15999]. The `target` column should contain the predicted probability of each row of data being `1` (i.e., shows up to vote).\n\nThe file should contain a header and have the following format:\n\n    id,target\n    0,0.257221\n    1,0.985442\n    2,0.332613\n    3,0.000013\n    ...\n    15999,0.123456\n\n",
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