{
  "fetched_at": "2026-10-24T07:47:18Z",
  "source_url": "https://www.kaggle.com/competitions/de-lab-8-2024-2025/data",
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      "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",
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    {
      "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.",
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      "name": "Evaluation",
      "content": "Goal It is your job to predict the price of diamonds. # Evaluation Metric Your score is the standard deviation of the residuals (prediction errors). This is known as Root Mean Square Error (RMSE). # Submission File Format You should submit a csv file with exactly 10788 entries plus a header row. Your submission will show an error if you have extra columns (beyond ID and price) or rows. The file should contain a header and have the following format: ID,price 10788,100",
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    {
      "name": "rules",
      "content": "One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\nCompetition Timeline\nStart Date: 17/12/2023 2:00 PM Jordan Time\n\nEnd Date: 17/12/2023 4:50 PM Jordan Time",
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    {
      "name": "abstract",
      "content": "Overview of How Kaggle’s Competitions Work\nJoin 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",
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