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  "fetched_at": "2026-10-19T23:54:54Z",
  "source_url": "https://www.kaggle.com/competitions/data-engineering-lab-9-fall-2022-section-3/data",
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      "content": "### Context\n\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\n### Content\n\nprice: price in US dollars (\\$326--\\$18,823)\ncarat: weight of the diamond (0.2--5.01)\ncut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)\ncolor: diamond colour, from J (worst) to D (best)\nclarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\nx: length in mm (0--10.74)\ny: width in mm (0--58.9)\nz: depth in mm (0--31.8)\ndepth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\ntable: width of top of diamond relative to widest point (43--95)\n\n### Files\n\n**trainingD.csv** - the training set\n**testingD.csv** - the test set\n**sample_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.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3532147%2F4cfa3401b21d61e4c66a12f447515c43%2Fe40_1.png?generation=1671447310335998&alt=media)\n\n\n### Practice Skills\n\n- Data Engineering.\n- Creative feature engineering.\n- Basic regression techniques such as Linear regression\n`\n### Overview of How Kaggle’s Competitions Work\n\n1. Join the Competition -> Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.\n2. Get 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.\n3. Make a Submission -> Upload your prediction as a submission on Kaggle and receive an accuracy score.\n4. Check the Leaderboard -> See how your model ranks against other Kagglers on our leaderboard.\nImprove Your Score\n\n### Acknowledgements\n\nhttps://www.kaggle.com/datasets/shivam2503/diamonds\n",
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    {
      "name": "Evaluation",
      "content": "### Goal\n\nIt is your job to predict the price of diamonds.\n\n### Evaluation Metric\n\nYour score is the standard deviation of the residuals (prediction errors). This is known as Root Mean Square Error (RMSE).\n\n### Submission File Format\n\nYou 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.\n\nThe file should contain a header and have the following format:\n\nID,price\n10788,100",
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    {
      "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!",
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