{
  "fetched_at": "2026-10-20T07:37:13Z",
  "source_url": "https://www.kaggle.com/competitions/443compreg/data",
  "pages": [
    {
      "name": "data-description",
      "content": "In addition to the variables in the data, you can seek additional variables (i.e.,finding more information on victim such as revenue, etc.) to improve your prediction.\n\n## Files\n\n*   **Train_X.csv** - the training set X's\n*   **Train_Y.csv** - the training set Y\n*   **test_X.csv** - the test set\n*   **test_Y_sample.csv** - a sample submission file in the correct format\n\n\n",
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    {
      "name": "Description",
      "content": "The competition is only for MAT 443: Statistical Learning and Data Mining. Without the permission of the instructor,  the students may not be allowed to share the competition with someone who is not registered for this course.\n\nThe cyber insurance market in the U.S. grew to roughly $4.1 billion in direct written premiums in 2020, an increase of 29.1% from the prior year, reports the National Association of Insurance Commissioners (NAIC). The current cyber insurance industry faces a big challenge on assessing the cyber incident: the top 20 groups in the cyber insurance market reported direct loss ratios in the range of 24.6% to 114.1%. The loss ratio for 2020 for the top 20 groups averaged 66.9%, up from 44.6% in 2019.\n\n\n**We want to help!\n**\nThe purpose of this competition is to predict the loss amount from cyber incidents.\n\n",
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    {
      "name": "Evaluation",
      "content": " \nThe evaluation metric for this competition is mean absolute error (MAE).",
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      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "The evaluation will be based on mean absolute error (MAE). All participators with a satisfactory accuracy will be awarded extra credits for this course.\n\nHave fun! ",
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      "hasIsPublished": false,
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  ]
}