{
  "fetched_at": "2026-10-19T22:20:31Z",
  "source_url": "https://www.kaggle.com/competitions/bank-marketing-prediction/data",
  "pages": [
    {
      "name": "rules",
      "content": " - Don't cheat! The test data is available online if you google for it. It is not allowed to use this data in any way!  \n    For my students: I may ask you to share your code with me, to make sure you are not cheating.\n - Have fun!",
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      "content": "Bank Marketing Data Set.  \nThe 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).  \n\n- **train.csv** - training samples with target variable  \n- **test.csv** - testing samples. Predict target variable for them  \n- **sample_submission.csv** - example of how to save your predictions\n\nColumns:\n\n- age (numeric)\n- job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\n                                       \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n- marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n- education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n- default: has credit in default? (binary: \"yes\",\"no\")\n- balance: average yearly balance, in euros (numeric) \n- housing: has housing loan? (binary: \"yes\",\"no\")\n- loan: has personal loan? (binary: \"yes\",\"no\")\n\n    *related with the last contact of the current campaign:*\n- contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n- day: last contact day of the month (numeric)\n- month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n- duration: last contact duration, in seconds (numeric)\n\n    *other attributes:*\n- campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n- pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n- previous: number of contacts performed before this campaign and for this client (numeric)\n- poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n\n    *target variable:*\n- y - has the client subscribed a term deposit? (binary: true, false)",
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    {
      "name": "Description",
      "content": "This competition is a homework for students of the *reboot_ds* program.  \nEveryone else is welcome to participate as well!\n\nApply your skills to a classic machine learning problem.  \nCompete with other students and teachers.",
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    {
      "name": "Evaluation",
      "content": "This competition uses a [**F1-score**](https://en.wikipedia.org/wiki/F1_score):\n\n![](https://latex.codecogs.com/gif.download?%5Clarge%20F1%20%3D%202%20%5Cfrac%7Bprecision%20%5Ccdot%20recall%7D%7Bprecision%20+%20recall%7D)\n\n## Submission File\nEach row contains `SampleId`, which is the ID of a test sample, and `y`, which is your predicted value: either `True` or `False`. The first 6 lines of the `sample_submission.csv` file:\n\n```\nSampleId,y\n22605,False\n22606,False\n22607,False\n22608,False\n22609,False\n```\n\n## Public and private test\nYour submission will be evaluated on 50% of test data, and you will see the evaluation results in the public leaderboard. When the competition ends, all submissions will be evaluated on other, private 50% of data. Winners will be defined based on the private leaderboard.",
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    },
    {
      "name": "Prizes",
      "content": "Students will get additional points for beating teacher's results.",
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