{
  "fetched_at": "2026-10-20T05:57:59Z",
  "source_url": "https://www.kaggle.com/competitions/execmba-2023-daii/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>Data used here came from the Darden Case Study: \"Sarah Gets a Diamond\" (UVA-QA-0702). <\\p>\n\n<p>Diamonds are primarily based on four criteria known as the “four Cs”: cut, color, clarity, and carat weight. In addition to these factors, other less-known qualifiers (e.g., fluorescence, polish, and symmetry) marginally influence the value of the stone. </p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set of 6000 rows and 9 columns</li>\n<li><strong>test.csv</strong> - the test set of 3142 rows and 9 columns. The final column of 'price' has all NA</li>\n<li><strong>Submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>ID</strong> - a unique id given to each diamond</li>\n<li><strong>Carat Weight</strong> - size of the diamond. </li>\n<li><strong>Cut</strong> - diamond cut ratings, in ascending order, are: poor, fair, good, very good, ideal, and signature ideal</li>\n<li><strong>Color</strong> - diamonds are graded on a letter scale from Z (noticeable color) to D (absolutely colorless)</li>\n<li><strong>Clarity</strong> Gems free of blemishes are known to be FL (flawless), followed by IF (internally flawless), VVS1 and VVS2 (very, very slight inclusions), VS1 and VS2 (very slight inclusions), SI1 and SI2 (slight inclusions), and I1, I2, and I3</li>\n<li><strong>Polish</strong> - graded using G (good), VG (very good), and EX (excellent) categories.</li>\n<li><strong>Symmetry</strong> - graded using G (good), VG (very good), and EX (excellent) categories. </li>\t\n<li><strong>Report</strong> - graded using G (good), VG (very good),  EX (excellent) and ID (ideal) categories. </li>\t\n<li><strong>Price</strong> the price of the diamonds in USD. </li>\n</ul>",
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    {
      "name": "Description",
      "content": "In this competition you will be using the data from the case Sarah Gets a Diamond. You will be predicting diamond prices based on attributes of the diamond.\n\nThe training set contains information on 6000 diamonds. You will be predicting the prices of 3142 other diamonds. We will use mean absolute percentage error (MAPE), to evaluate your submissions.",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is MAPE</a>. The MAPE is a measure of prediction accuracy, where the forecast error is measured relative to the magnitude of the quantity of interest.  MAPE can be expressed as:  </p>\n<p>\\[ MAPE = \\frac{1}{n} \\sum_{i=1}^n (\\frac{\\left|A_{t}-F_{t}\\right|}{A_{t}})  \\]</p>\n\n<p>where At is the actual value and Ft is the forecast value.</p>",
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    {
      "name": "rules",
      "content": "**Rules**\n\n - One account per participant  \n - You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple    \n   accounts. \n - Privately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all      \n   participants on the forums.\n - You may submit a maximum of 5 entries per day.\nYou may select up to 2 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>May 16, 2023</strong></p>\n    <p>Entry Deadline: <strong>June 6, 2023</strong></p>\n    <p>End Date: <strong>June 6, 2023</strong></p>\n\t<!-- End Base Rules -->",
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}