{
  "fetched_at": "2026-10-19T22:46:42Z",
  "source_url": "https://www.kaggle.com/competitions/data-science-lab-kaggle-sp2020/data",
  "pages": [
    {
      "name": "rules",
      "content": "Every student is competing on their own for this competition. If you want to make a comment/remark or suggestion you can post something on Piazza for everyone to see. No private communication allowed. ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Welcome to the in-class Kaggle competion. \nDo your best to make the best predictions by the closing time of the competition. \nWrite a report that indicates what you tried, what worked and what did not. \nYou will be scored based on your private and public leaderboard score but also on your creativity on trying various ideas. \n\nThis is a binary classification competition. The meanings of the features were not revealed by the data provider for privacy reasons. \nThe metric you are evaluated as is called Area Under the Curve (AUC). It is a binary accuracy metric that works well even for unbalanced problems. We will explain how the metric works in class. \n\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sample-submission.csv</strong> - a sample submission file in the correct format</li>\n\nEvery row has an Id, an anonymous unique Identification given to each customer. Include the Id column in your predictions. \n\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The metric you are evaluated as is called Area Under the Curve (AUC). It is a binary accuracy metric that works well even for unbalanced problems. We will explain how the metric works in class. \n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}