{
  "fetched_at": "2026-10-20T04:10:10Z",
  "source_url": "https://www.kaggle.com/competitions/adquisicion-producto-financiero/data",
  "pages": [
    {
      "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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    {
      "name": "data-description",
      "content": "<h1>File descriptions</h1>\n<ul>\n<li><strong>data_train.csv</strong> - the training set</li>\n<li><strong>data_test.csv</strong> - the testing set</li>\n</ul>",
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      "name": "Description",
      "content": "<h1>Variables description:</h1>\n\n<h2> Input variables: </h2>\n\n<h3>   # bank client data: </h3>\n<p>   1 - age (numeric) <br/>\n   2 - job : type of job (categorical: \"admin.\",\"blue- collar\",\"entrepreneur\",\"housemaid\",\"management\",\"retired\",\"self-employed\",\"services\",\"student\",\"technician\",\"unemployed\",\"unknown\") <br/>\n   3 - marital : marital status (categorical: \"divorced\",\"married\",\"single\",\"unknown\"; note: \"divorced\" means divorced or widowed) <br/>\n   4 - education (categorical: \"basic.4y\",\"basic.6y\",\"basic.9y\",\"high.school\",\"illiterate\",\"professional.course\",\"university.degree\",\"unknown\") <br/>\n   5 - default: has credit in default? (categorical: \"no\",\"yes\",\"unknown\") <br/>\n   6 - housing: has housing loan? (categorical: \"no\",\"yes\",\"unknown\") <br/>\n   7 - loan: has personal loan? (categorical: \"no\",\"yes\",\"unknown\") <br/>\n</p>\n\n<h3>   # related with the last contact of the current campaign: </h3>\n<p>   8 - contact: contact communication type (categorical: \"cellular\",\"telephone\")  <br/>\n   9 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\") <br/>\n  10 - day_of_week: last contact day of the week (categorical: \"mon\",\"tue\",\"wed\",\"thu\",\"fri\") <br/>\n  11 - duration: last contact duration, in seconds (numeric). \nImportant 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. </br>\n</p>\n\n<h3>   # other attributes: </h3>\n<p>  12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) </br>\n  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) </br>\n  14 - previous: number of contacts performed before this campaign and for this client (numeric) </br>\n  15 - poutcome: outcome of the previous marketing campaign (categorical:  \"failure\",\"nonexistent\",\"success\") </br>\n</p>\n\n<h3> # social and economic context attributes </h3>\n <p>  16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) </br>\n  17 - cons.price.idx: consumer price index - monthly indicator (numeric)     </br>\n  18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)     </br>\n  19 - euribor3m: euribor 3 month rate - daily indicator (numeric) </br>\n  20 - nr.employed: number of employees - quarterly indicator (numeric) </br>\n</p>\n\n<h3>  Output variable (desired target): </h3>\n<p>  21 - y - has the client subscribed a term deposit? (binary: 1=\"yes\", 0 = \"no\") </br>\n</p>\n  \n<h3>*Missing Attribute Values: </h3> There are several missing values in some categorical attributes, all coded with the \"unknown\" label. These missing values can be treated as a possible class label or using deletion or imputation techniques. ",
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    {
      "name": "Evaluation",
      "content": "<h1>\n<p>The evaluation metric for this competition is <a href=\"https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5\"> AUC</a> \n</h1>",
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