{
  "fetched_at": "2026-10-24T17:19:15Z",
  "source_url": "https://www.kaggle.com/competitions/copy-of-data-engineeringlab-9psut-wed/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Context\nThis classic dataset contains the prices and other attributes of almost 54,000 diamonds. It's a great dataset for beginners learning to work with data analysis and visualization.\n\nContent\nprice: price in US dollars (\\$326--\\$18,823)\n\ncarat: weight of the diamond (0.2--5.01)\n\ncut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)\n\ncolor: diamond colour, from J (worst) to D (best)\n\nclarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\n\nx: length in mm (0--10.74)\n\ny: width in mm (0--58.9)\n\nz: depth in mm (0--31.8)\n\ndepth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\n\ntable: width of top of diamond relative to widest point (43--95)\n\nFiles\ntrainingD.csv - the training set\ntestingD.csv - the test set\nsample_submission.csv - a sample submission file in the correct format",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Analyze diamonds by their cut, color, clarity, price, and other attributes to predict the price of a diamond based on the characteristics of cut, color, clarity, and carat.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "Join the Competition -> Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.\n\nGet to Work -> Download the data, build models on it locally or on Kaggle Kernels (our no-setup, customizable Jupyter Notebooks environment with free GPUs) and generate a prediction file.\n\nMake a Submission -> Upload your prediction as a submission on Kaggle and receive an accuracy score.\n\nCheck the Leaderboard -> See how your model ranks against other Kagglers on our leaderboard.\n\nImprove Your Score\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "Rules\nOne account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\nCompetition Timeline\nStart Date: 20/12/2023 2:00 PM Jordan Time\n\nEnd Date: 20/12/2023 4:55 PM Jordan Time\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "abstract",
      "content": "* Any student who:\n (1) records a score better than the benchmark submission; \n(2) improves his/her score at least once; \n(3) Discussing his/her solution with the TA will get 10.\n* The Top-1 on the leaderboard deserves +1\n* Students are not allowed to change the ML part of the solution; they should work on the data preparation part.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}