{
  "fetched_at": "2026-10-19T23:58:16Z",
  "source_url": "https://www.kaggle.com/competitions/diamonds-part-datamad0122/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## 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*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "![diamantes](https://media.giphy.com/media/46rXT61bNDxAc/giphy.gif)\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.```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "### Evaluation\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- 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![partes](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3824396%2Ff6235a3402992f0fc94af9e0ca3465c2%2Finbox_3824396_4554b5824665256889dafdd5c4c59890_Bbf0GWk.jpg?generation=1588773703548655&alt=media)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Timeline",
      "content": "This competition will finish 27/06/2022 23:59 AM (GMT+2)\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Models And Features Extraction",
      "content": "### Possible techniques, in addition to studying the problem and drawing your conclusions:\n```\nOne hot encoding\nLabel encoding\nGet dummies\nPCA\nConsumption of external API and / or web scraping\nRegression\n```\n### Possible regression models:\n```\nLinearRegression\nDecisionTreeRegressor\nKNeighborsRegressor\nGradientBoostingRegressor\nRandomForestRegressor\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}