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  "fetched_at": "2026-10-19T23:54:59Z",
  "source_url": "https://www.kaggle.com/competitions/ai-lab-10-spring-2024-section-sunday/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\n# **Content**\n- price: price in US dollars (\\$326--\\$18,823)\n- carat: weight of the diamond (0.2--5.01)\n- cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)\n- color: diamond colour, from J (worst) to D (best)\n- clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\n- x: length in mm (0--10.74)\n- y: width in mm (0--58.9)\n- z: depth in mm (0--31.8)\n- depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\n- table: width of top of diamond relative to widest point (43--95)\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n\n",
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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<img src=\"https://physics.aps.org/assets/58434729-f278-4bc7-ad3c-faf98d3db26b/e40_1.png\" alt=\"Italian Trulli\">\n\n## Practice Skills\n<ul>\n<li>AI </li>\n<li> What is Learning?</li>\n<li>Machine Learning Types</li>\n<li> Supervised Learning: Regression problems. Classification problems.</li>\n<li>Basic regression techniques such as Linear regression </li>\n</ul>\n\n## Overview of How Kaggle’s Competitions Work\n<ol>\n<li>Join the Competition -&gt; Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.</li>\n<li>Get to Work -&gt; 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.</li>\n<li>Make a Submission -&gt; Upload your prediction as a submission on Kaggle and receive an accuracy score.</li>\n<li>Check the Leaderboard -&gt; See how your model ranks against other Kagglers on our leaderboard.</li>\n<li>Improve Your Score</li>\n</ol>\n\n## Acknowledgements\n\n<a href=\"https://www.kaggle.com/datasets/shivam2503/diamonds\">https://www.kaggle.com/datasets/shivam2503/diamonds</a>",
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    {
      "name": "Evaluation",
      "content": "# Goal\nIt is your job to predict the price of diamonds.\n\n# Evaluation Metric\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\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<code>\n    ID,price </code>\n   <code> 10788,100</code>\n",
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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.\n\n# Submission Limits\nYou may submit a maximum of 20 entries per day.\n\n# Competition Timeline\nStart Date: 19/05/2024 2:00 PM Jordan Time\n\nEnd Date: 19/05/2024 5:00 PM Jordan Time",
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    {
      "name": "abstract",
      "content": "This lab aims to familiarize students with machine learning concepts through participating in a Kaggle competition. ",
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