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  "fetched_at": "2026-10-20T02:54:16Z",
  "source_url": "https://www.kaggle.com/competitions/aalto-wine-quality-prediction-t-61-3050-challenge/data",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>training_classification_regression_2015.csv</strong> - the training set. The last two columns of the training set are 'quality' and 'type' respectively. The same dataset should be used for both the classification and regression challenges</li>\r\n<li><strong>challenge_public_test_classification_regression.csv</strong> - the test set. The same set should be used for both the classification and regression challenges. However, each challenge has it's own submission format (refer challenge_regression_sample_submission.csv)</li>\r\n<li><strong>challenge_regression_sample_submission.csv-</strong> a sample submission file in the correct format</li>\r\n</ul>",
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    {
      "name": "Description",
      "content": "<p>Jorma, Alex and the T.A's need your help selecting the perfect bottle of wine for that special occasion. Since we don't know much about wine itself, we're hoping that training data with expert ratings will help us decide what makes a 'good' wine good. Use the training data to predict the quality of unrated wines in the test data.</p>",
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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>\r\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\r\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>\r\n<h2>Submission Format</h2>\r\n<p>Submission Format</p>\r\n<p>For every wine entry in the dataset, submission files should contain two columns: 'id' and 'quality'. The quality should be an integer between 1 and 7 (1 and 7 included)</p>\r\n<p>The file should contain a header and have the following format:</p>\r\n<pre>id,quality<br />1,3<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,4<br />3,7<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">14,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>The maximum size of a team is 2 participants.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 1 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>9/11/2015 8:33 PM UTC</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>11/23/2015 12:15 PM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>Refer MyCourses page for a detailed description of the contest rules.</p>",
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