{
  "fetched_at": "2026-10-20T07:30:56Z",
  "source_url": "https://www.kaggle.com/competitions/decision-treesrandom-forests-2020/data",
  "pages": [
    {
      "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>sample.csv</strong> - a sample submission file in the correct format</li>\n</ul>",
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      "postTitle": "",
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    {
      "name": "Description",
      "content": "In this competition, you will predict whether or not someone has a cardiovascular disease given info about their health. You can use either a decision tree or a random forest.\n\nThe features you will receive are:\nage (in number of days)\ngender (1 for women, 2 for men)\nheight (in cm)\nweight (in kg)\naphi (systolic blood pressure)\naplo (diastolic blood pressure)\ncholesterol (1 for normal, 2 for above normal, 3 for well above normal)\ngluc (glucose levels, 1 for normal, 2 for above normal, 3 for well above normal)\nsmoke (whether or not the individual smokes)\nalco (whether or not the person consumes alcohol, 0 or 1)\nactive (whether person engages in some form of physical activity)\n\nThe target (what you will be trying to predict):\ncardio (whether or not the individual has a cardiovascular disease)",
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    {
      "name": "Evaluation",
      "content": "Categorization Accuracy is used to score submissions.",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "Don't use any libraries like scikit learn. You should code the decision tree/random forest algorithm yourself. You can use numpy and pandas for simple computation and data IO if you wish. However, it is entirely possible to do this competition without any libraries.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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}