{
  "fetched_at": "2026-10-19T23:12:05Z",
  "source_url": "https://www.kaggle.com/competitions/3er-hands-on-immune-institute/data",
  "pages": [
    {
      "name": "data-description",
      "content": "El dataset consiste en un conjunto de 30 parámetros calculados a partir del Electrocardiograma (ECG). Estos parámetros, recogen distintas características del ECG, desde su comportamiento temporal como a amplitud, pendiente, etc. Hasta carácterísticas un poco más complejas como variables espectrales. A continuación, se muestra la lista completa de parámetros:\n\n**Parámetros termporales** \n\n* TCI = threshold crossing interval \n\n* TCSC = threshold crossing sample count \n\n* Exp = exponencial estandard\n\n* Expmod = exponencial modificada \n\n* MAV = valor medio absoluto\n\n* count1 \n\n* count2 \n\n* count3\n\n* x1\n\n* x2\n\n* bCP\n\n**Parámetros espectrales** \n\n* vFleak = VF filter\n\n* M\n\n* A1\n\n* A2\n\n* A3\n\n* x3\n\n* x4\n\n* x5\n\n* bWT\n\n**Parámetros de tipo tiempo-frecuencia**\n\n* Li = basada en el análisis de ondas del ECG.\n\n**Parámetros de complejidad del ECG** \n\n* CM = medida de la complejidad\n\n* CVbin = covarianza\n\n* abin = area\n\n* Frqbin = frecuencia\n\n* Kurt = Curtosis de una señal binaria extraída del ECG\n\n* PSR = La reconstrucción del espacio de fase  \n\n* HILB = La transformación de Hilbert\n\n* SamEn = Muestra de entropía",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Final challenge of the Supervised ML models module",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "You should upload to **kaggle** the prediction file generated using the **eval best model** function in your Notebook. Remember that the input parameters of this function are: \n- 1. Best trained model\n- 2. Student name \"Quotes\".\n\nThis function will generate the following file: **submission_\"name\".csv** which will have the following format: \n\n| id |output  |\n| --- | --- |\n|  11111| 0 |\n|  12111| 1 |\n|  11510| 0 |\n\nAdditionally, you will have to upload your notebook in Kaggle in the \"notebooks\" section.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}