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  "fetched_at": "2026-10-19T23:42:58Z",
  "source_url": "https://www.kaggle.com/competitions/diamonds-datamad0620/data",
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      "name": "data-description",
      "content": "### Data\n\nIn this section you will find data needed to train your model and a detailed description of it.\n\n### Files\n\n- `train.csv`: training dataset\n- `predict.csv`: dataset to predict price for\n- `sample_submission.csv`: sample submission\n\n### Features\n\n- **id**: only for test & sample submission files, id for prediction sample identification\n- **price**: price in USD\n- **carat**: weight of the diamond\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\n- **y**: width in mm\n- **z**: depth in mm\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)",
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    {
      "name": "Description",
      "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3824396%2Fbeaf52135483332c90a86b157e178715%2Fimage%20(12).png?generation=1588785521255575&alt=media)\n\n> 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",
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      "name": "Evaluation",
      "content": "## Evaluation\n\nThe evaluation metric chosen for this competition is the RMSE (Root Mean Squared Error):\n\nhttps://en.wikipedia.org/wiki/Root-mean-square_deviation\n\n## Submit results\n\n- Submissions are limited to 4 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&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 21/07/2020 10:00 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 ![](https://media.giphy.com/media/l2Je00TmqeLx0gavS/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![](https://media.giphy.com/media/82nJoUI8tKg5G/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    PCA\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\n",
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