{
  "fetched_at": "2026-10-22T09:49:04Z",
  "source_url": "https://www.kaggle.com/competitions/careemtrip/data",
  "pages": [
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
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    },
    {
      "name": "data-description",
      "content": "Data provided by careem link to drive.\nPlease keep the data confidential.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "TRAIN DATA: Data with rating, booking and trip data\n\nTEST DATA:  one day of similar data without label \n\nOBJECTIVE: Predict if trip is LOW RATED (0), HIGH RATED (1)\n\nMETRIC to evaluate: AUC\n\nRATED TRIP LABEL: \n\n1. If trip rating is in (1, 2, 3) label is LOW RATED (0)\n\n2. If trip rating is in (4, 5) label is HIGH RATED (1)\n\nThus label to predict is 0 or 1.\n\n",
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    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc\"> AUC</a>. </p>\n\n<p>AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example.</p>\n\n<p>AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.</p>\n\n**AUC is desirable for the following two reasons:**\n\n1. AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values.\n2. AUC is classification-threshold-invariant. It measures the quality of the model's predictions irrespective of what classification threshold is chosen.",
      "isPublished": false,
      "hasIsPublished": false,
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      "hasPostTitle": false,
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      "hasMimeType": false
    }
  ]
}