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  "fetched_at": "2026-10-20T13:13:21Z",
  "source_url": "https://www.kaggle.com/competitions/2401-iris-data-set/data",
  "pages": [
    {
      "name": "rules",
      "content": " - Improve yourself!\n - Have fun!\n - Respect others\n - Help others if you can",
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      "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 submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n1. sepal length in cm\n2. sepal width in cm\n3. petal length in cm\n4. petal width in cm\n5. class:\n - Iris Setosa<br>\n - Iris Versicolour<br>\n - Iris Virginica<br>",
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    {
      "name": "Description",
      "content": "# Introduction\nFollowing **24 Ultimate Data Science Projects To Boost Your Knowledge and Skills** [[Link]](https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/). We will go through a sequence of classic data science projects to increase our skill\n\n# Source\n[archive.ics.uci.edu](https://archive.ics.uci.edu/ml/datasets/Iris)\n\n# About this data set\nIts a simple and classic dataset. A simple dataset with 150 records with 4 attributes. According to wiki it was introduced by the British statistician and biologist Ronald Fisher in his 1936 paper.\n\n# Target\nIt's a classification project, that you need to predict the class of the flowers",
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      "name": "Evaluation",
      "content": "<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<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\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>For every author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">8,8<br />9,9 10<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">10,10 9<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre",
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