{
  "fetched_at": "2026-10-20T05:20:20Z",
  "source_url": "https://www.kaggle.com/competitions/bank-marketing-uci/data",
  "pages": [
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      "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>",
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      "name": "Description",
      "content": "<p>This is the home page of the competition. Abstract: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).</p>\n<p>Input variables:\n# bank client data:\n</p>1 - age (numeric)</p>\n</p>2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')</p>\n</p>3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)</p>\n</p>4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')</p>\n</p>5 - default: has credit in default? (categorical: 'no','yes','unknown')</p>\n</p>6 - housing: has housing loan? (categorical: 'no','yes','unknown')</p>\n</p>7 - loan: has personal loan? (categorical: 'no','yes','unknown')\n</p>8 - contact: contact communication type (categorical: 'cellular','telephone')</p>\n</p>9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')</p>\n</p>10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')</p>\n</p>11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.</p>\n</p># other attributes:</p>\n</p>12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)</p>\n</p>13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)</p>\n</p>14 - previous: number of contacts performed before this campaign and for this client (numeric)</p>\n</p>15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')</p>\n</p>Output variable (desired target):</p>\n</p>21 - y - has the client subscribed a term deposit? (binary: 'yes','no')</p>\n<h2>Acknowledgements</h2>\n<p>We thank UCI Machine learning repository  for providing this dataset.</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:</p>\n<hr />\n<p>The evaluation metric for this competition is based on Area Under the Characteristic curve</a>. The AUC score, commonly used in measures accuracy using the statistics,  true positives (tp) & false positives(fp) to all predicted positives (tp + fp). </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 is the order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.\n </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,y<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,0<br />3,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">4,1 <br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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