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  "fetched_at": "2026-10-20T05:10:56Z",
  "source_url": "https://www.kaggle.com/competitions/data-marketing/data",
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      "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": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<h4>Input variables:</h4>\n<p><br /># bank client data:</p>\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')</p>\n<p># related with the last contact of the current campaign:</p>\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><br /># 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><br /># social and economic context attributes</p>\n<p>16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)</p>\n<p>17 - cons.price.idx: consumer price index - monthly indicator (numeric)</p>\n<p>18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)</p>\n<p>19 - euribor3m: euribor 3 month rate - daily indicator (numeric)</p>\n<p>20 - nr.employed: number of employees - quarterly indicator (numeric)</p>\n<h4>Output variable (desired target):</h4>\n<h6><br />21 - y - has the client subscribed a term deposit? (binary: 'yes','no')</h6>",
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      "name": "Description",
      "content": "<p>The 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.</p>\n<h2>Relevant Papers:</h2>\n<p>S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014</p>\n<p>S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]</p>\n<h2>Acknowledgements</h2>\n<p>We thank Professors S. Moro, P. Cortez and P. Rita for providing this dataset.</p>",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is MCC (Matthews correlation coefficient).</p>\n<h2>Submission Format</h2>\n<p><strong>For every transaction in the test dataset</strong>, submission files should contain two columns: Id and prediction. Prediction should be 0 (no) or 1 (yes).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,prediction<br />1,0<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,1<br />3,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">4,0<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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