{
  "fetched_at": "2026-10-19T23:21:59Z",
  "source_url": "https://www.kaggle.com/competitions/diamonds-datamad0121/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\r\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\r\n<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>train.csv</strong> - the training set</li>\r\n<li><strong>test.csv</strong> - the test set</li>\r\n<li><strong>sampleSubmissi",
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    {
      "name": "Description",
      "content": "![dimantitos](https://media.giphy.com/media/46rXT61bNDxAc/giphy.gif)\n\n&gt; The goal of this competition is the prediction of the price of diamonds based on their characteristics (weight, color, quality of cut, etc.), putting into practice all the machine learning techniques you know.\n\n[![Everything Is AWESOME](//img.youtube.com/vi/lWA2pjMjpBs/0.jpg)](https://www.youtube.com/watch?v=lWA2pjMjpBs \"Everything Is AWESOME\")",
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    {
      "name": "Evaluation",
      "content": "## Evaluation\n\nThe evaluation metric chosen for this competition is the MSE (Mean Squared Error):\n\nhttps://en.wikipedia.org/wiki/Mean_squared_error\n\n## Submit results\n\n- Submissions are limited to 5 times a day.\n- The test set is divided into a public part (with which the public leaderboard is calculated, accessible during the competition) and another private part, with which the final positions are calculated, after the end of the competition.\n- Predictions will be sent in the format indicated in the `sample_submission.csv` file in the data section.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3824396%2Ff6235a3402992f0fc94af9e0ca3465c2%2Finbox_3824396_4554b5824665256889dafdd5c4c59890_Bbf0GWk.jpg?generation=1588773703548655&amp;alt=media)\n",
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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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    {
      "name": "Timeline",
      "content": "###  This competition will finish 28/02/2021 23:59 AM (GMT+2)\n\n![](https://media.giphy.com/media/C0NhnyKT89QdO/giphy.gif)",
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    {
      "name": "Prize",
      "content": "You will receive a great **applause** from your classmates and teachers 👏\nand **knowledge**, a bunch of it. And a little surprise.\n\n![winner](https://media.giphy.com/media/9xt1MUZqkneFiWrAAD/giphy.gif)",
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    {
      "name": "Requirements",
      "content": "- Train a minimum of **4 different models**\n- Perform a minimum of **4 Feature Extraction and Engineering techniques**\n- Documentation needed to reproduce the code\n- The code in ```.py``` and/or ```.ipynb``` files that allows to reproduce the exercise.\n- Functions must be in modules. ;)\n- The ```Readme``` must contain a summary of the machine learning tools and algorithms \nand the results or the score obtained with each of them.\n\n![code](https://media.giphy.com/media/LmNwrBhejkK9EFP504/giphy.gif)",
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    {
      "name": "Models And Feature Extraction",
      "content": "### Possible techniques, in addition to studying the problem and drawing your conclusions:\n\n    One hot encoding\n    Label encoding\n    Get dummies\n    Consumption of external API and / or web scraping\n    Regression\n\n### Possible regression models:\n\n    LinearRegression\n    DecisionTreeRegressor\n    KNeighborsRegressor\n    GradientBoostingRegressor\n    RandomForestRegressor\n\n![data](https://media.giphy.com/media/3osxYc2axjCJNsCXyE/giphy.gif)",
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