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  "fetched_at": "2026-10-19T11:37:24Z",
  "source_url": "https://www.kaggle.com/competitions/caiis-dogfood-day-2020/data",
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      "name": "rules",
      "content": "# Rules\n\n1. Have fun! \n2. Take the time to learn and ask questions!\n3. You can make up to 50 submissions.\n4. Use CAIIS products where possible and document where you cannot.\n5. Instructions on how to make submissions are on the [Evaluation](www.kaggle.com/c/caiis-dogfood-day-2020/overview/evaluation) page\n6. Please be aware that this competition is viewable by the general public, but only those with the invite URL can enter the competition.\n\n### Team Limits\n\nThe maximum team size is 5.\n\n### Submission Limits\n\nYou may submit a maximum of 50 entries.\nYou may select up to 2 final submissions for judging.\n\n**WINNER LICENSE TYPE**: n/a\n<br>**DATA ACCESS AND USE**: Competition-Use only\n\nCompetitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, Cuba, Iran, Syria, North Korea, Sudan, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Sponsor reserves the right to award alternative Prizes where needed to comply with local laws.\n\n**ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.**\n\nThe Competition named above is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. Your competition submissions (\"Submissions\") must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to participants with the best scores, based on the merits of the data science models submitted. ",
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      "name": "Description",
      "content": "Nothing ruins the thrill of buying a brand new car more quickly than seeing your new insurance bill. The sting’s even more painful when you know you’re a good driver. It doesn’t seem fair that you have to pay so much if you’ve been cautious on the road for years.\n\n<p><img src=\"https://images.unsplash.com/photo-1489824904134-891ab64532f1?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9\" alt=\"beep beep\" width=\"250\" style=\"float: right\"></p>\n\nInaccuracies in car insurance company’s claim predictions raise the cost of insurance for good drivers and reduce the price for bad ones. But Kaymo &trade;  wants to stop that!\n\nIn this competition, you’re challenged to build a model that predicts the probability that a driver will initiate an auto insurance claim. While Kaymo &trade; has used machine learning for the past 10 years, they’re looking to the CAIIS machine learning community to explore new, more powerful methods. A more accurate prediction will allow them to further tailor their prices, and hopefully make auto insurance coverage more accessible to more drivers.",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.\n\n\n## Submission File\nFor each `id` in the test set, you must predict a probability for the `target` variable. The file should contain a header and have the following format:\n\n    id,target\n    3,0.1\n    9,0.7\n    11,0.5\n    etc.\n",
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      "name": "data-description",
      "content": "Each row in this dataset represents an insurance policy. You must predict the value for the `target` column, whether a policy resulted in a claim `1` or not `0` over a fixed time period. Variables prefaced with `cat` are categorical (nominal or ordinal, but not specified), while those prefaced with `cont` are continuous.\n\n*   **train.csv** - the training data\n*   **test.csv** - the test data; you must predict a `target` for each row (`id`)\n*   **sample_submission.csv** - a sample submission file in the correct format\n",
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