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  "fetched_at": "2026-10-19T21:38:26Z",
  "source_url": "https://www.kaggle.com/competitions/DATACUP-ROUND1/data",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>XENIA_DATACUP_R1_TRAIN.csv</strong> - the training set</li>\n<li><strong>XENIA_DATACUP_R1_X_TEST.csv</strong> - the test set</li>\n<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - an anonymous id unique to a given customer</li>\n<li><strong>schooling</strong> - schooling details</li>\n<li><strong>age</strong> - age of candidate</li>\n<li><strong>marriage status</strong> - marriage status of candidate</li>\n<li><strong>position</strong> - work or office position of candidate</li>\n<li><strong>housing</strong> - does candidate owns house</li>\n<li><strong>default</strong> - default column</li>\n<li><strong>balance</strong> - bank balance of candidate</li>\n<li><strong>day</strong> - day on which candidate was contacted</li>\n<li><strong>month</strong> - month on which candidate was contacted</li>\n<li><strong>ROI</strong> - return on investment candidate earns</li>\n<li><strong>duration</strong> - duration for which candidate was contacted</li>\n<li><strong>campaign</strong> - campaign</li>\n<li><strong>pdays</strong> - previous campaign day</li>\n<li><strong>previous</strong> - previous campaign detail about candidate</li>\n<li><strong>card_owned</strong> - type of card owned by candidate</li>\n<li><strong>poutcome</strong> - previous outcome</li>\n<li><strong>y</strong> - y is target variable, it specifies that candidate buys or rejects our offer</li>\n\n\n</ul>",
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    {
      "name": "Description",
      "content": "<h2>Xenia 2021 Datacup round 1.</h2>\n<p>Pass the benchmark accuracy to qualify for second round</p>\n",
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    {
      "name": "Evaluation",
      "content": "<h2>Xenia 2021 Datacup Round 1</h2>\n<hr>\n<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. y should be prediction on given dataset.</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": "RULES\n\n - Don't cheat!\n - Apply yourself!\n - If needed, participants will also be asked to submit their notebooks for validation of results.\n - Any malpractices (if found), will result in the disqualification of the team/ individual.",
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