{
  "fetched_at": "2026-10-19T21:44:46Z",
  "source_url": "https://www.kaggle.com/competitions/clasificacionpuj/data",
  "pages": [
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "<p>Usted encontrará los mismos campos tanto en la base de entrenamiento (score_train)  como en la base de prueba (score_test), excepto que esta última no contiene la variable incumplimiento que usted debe predecir</p>\n<h2>Descripción de campos</h2>\n<ul>\n<li>Empleado\t0=Desempleado, 1=Empleado actualmente</li>\n<li>Tiempo_empleado:\tNúmero de años que lleva en el empleo actual (si aplica)</li>\n<li>Saldo_cuenta:\tSaldo en miles de pesos de la cuenta principal con el banco</li>\n<li>Valor_préstamo:\tValor actual que adeuda en préstamos en el sistema financiero</li>\n<li>Cuentas_otros:\tNúmero de cuentas que tiene en otros bancos</li>\n<li>Autocontrol: Mediante una prueba escrita se mide el nivel de autocontrol de la persona, en puntaje de 0 a 100</li>\n<li>Impulsividad:\tMediante una prueba escrita se mide el nivel de impulsividad de la persona, en puntaje de 0 a 100</li>\n<li>Confianza:\tMediante una prueba escrita se mide el nivel de confianza en sí misma de la persona, en puntaje de 0 a 100</li>\n<li>Incumplimiento: \t0=Cumplimiento; 1=Incumplimiento</li>\n\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p>El reto consiste en saber si un cliente va a pagar efectivamente un crédito o va a caer en default, en un banco colombiano. Todos los clientes son clientes actuales con cuenta en el banco. Es un problema clásico de credit scoring.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\n<p>Submission files should contain two columns: Id and Expected.</p>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}