{
  "fetched_at": "2026-10-19T21:30:05Z",
  "source_url": "https://www.kaggle.com/competitions/dataworld/data",
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    {
      "name": "data-description",
      "content": "### PICT CSI DATACUP\nDataCup round 1",
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      "name": "Description",
      "content": "### PICT CSI XENIA'21 event datacup\n\nData Set Information:\n\nThe data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.\n\nThere are four datasets:\n1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]\n2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.\n3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs).\n4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs).\nThe smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).\n\nThe classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).\n\n",
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      "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><strong>For every author in the dataset</strong>, submission files should contain two columns: ID and y.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>ID, y<br>1,no<br></pre>",
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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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