{
  "fetched_at": "2026-10-24T17:17:59Z",
  "source_url": "https://www.kaggle.com/competitions/data-science-nigeria-bank-campaign-subscriptions-2/data",
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      "name": "data-description",
      "content": "**Context**\nThe dataset contains information about marketing campaigns that were conducted via phone calls from a Nigerian banking institution to their clients. The purpose of these campaigns is to prompt their clients to subscribe to a specific financial product of the bank (term deposit). After each call was conducted, the client had to inform the institution about their intention of either subscribing to the product (indicating a successful campaign) or not (unsuccessful campaign).\nThe final output of this survey will be a binary result indicating if the client subscribed ('yes') to the product or not ('no').\n\n**The data has been split into two groups:**\n- training set (train.csv)\n- test set (test.csv)\nThe training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each customer \n\nThe dataset several rows (instances of calls to clients) and 21 columns (variables) which are describing certain aspects of the call. Please note that there are cases where the same client was contacted multiple times - something that practically doesn't affect the analysis as each call will be considered independent from another even if the client is the same.\n\n**Content**\nThe predictor variables (features) contained in the dataset can be divided into the following five sections:\n<ul>\n1. Variables that describing attributes related directly to the client:\n<li><strong> age </strong>\n<li><strong> job </strong>: type of job (e.g. 'admin', 'technician', 'unemployed', etc)\n<li><strong> marital</strong>: marital status ('married', 'single', 'divorced', 'unknown')\n<li><strong> education</strong>: level of education ('basic.4y', 'high.school', 'basic.6y', 'basic.9y','professional.course', 'unknown','university.degree','illiterate')\n<li><strong> default</strong>: if the client has credit in default ('no', 'unknown', 'yes')\n<li><strong> housing</strong>: if the client has housing a loan ('no', 'unknown', 'yes')\n<li><strong> loan</strong>: if the client has a personal loan ? ('no', 'unknown', 'yes')\n</ul>\n\n<ul>\n2. Variables related to the last contact of the current campaign:\n<li><strong> contact</strong>: type of communication ('telephone', 'cellular')\n<li><strong> month</strong>: month of last contact\n<li><strong> dayofweek</strong>: day of last contact\n<li><strong> duration</strong>: call duration (in seconds)\n</ul>\n\n<ul>\n3. Other variables related to the campaign(s):\n<li><strong> campaign</strong>: number of contacts performed during this campaign and for this client\n<li><strong> pdays</strong>: number of days passed by after the client was last contacted from a previous campaign\n<li><strong> previous</strong>: number of contacts performed before this campaign and for this client\n<li><strong> poutcome</strong>: outcome of previous marketing campaign ('nonexistent', 'failure', 'success')\n</ul>\n\n<ul>\n4. Socioeconomic variables:\n<li><strong> emp_var_rate</strong>: employment variation rate - quarterly indicator\n<li><strong> cons_price_idx</strong>: consumer price index - monthly indicator\n<li><strong> cons_conf_idx</strong>: consumer confidence index - monthly indicator\n<li><strong> euribor3m</strong>: euribor 3 month rate - daily indicator\n<li><strong> nr_employed</strong>: number of employees - quarterly indicator\n</ul>\nOf course, the dataset also containt the variable subscribed which is the target variable, indicating if the client subscribed to the product ('yes':1) or not ('no':0).\n\n",
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      "name": "Description",
      "content": "<p><strong>Kowepe bank of Nigeria</strong> conducted marketing campaigns via phone calls with their clients. The purpose of these campaigns is to prompt their clients to subscribe to a specific financial product of the bank (term deposit). This survey had been conducted with some selected individuals that the bank feel is the best representative of their clients so as to minimize the cost of the total client survey.</p>\n<p><strong>You have been contacted as a data scientist</strong> to find patterns and build predictive models on this dataset with the aim of forecasting the percentage of potential customers for the financial products of the bank</p>",
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      "name": "Evaluation",
      "content": "<p>\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\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]\n\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>Check the sample submission</strong>\n</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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