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  "fetched_at": "2026-10-20T10:12:19Z",
  "source_url": "https://www.kaggle.com/competitions/cloud-faculty-institute-workshop/data",
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      "name": "rules",
      "content": "You may not train your model using any data other than the data that was provided.\n\nYou can submit a maximum of 20 entries per day.\n\nPrizes will be awarded at 9:00am (MDT) on 6/25/2018.\n\n - Prizes will be awarded to the top 5 submissions.\n - Only submissions from public kernels are eligible for prizes.   \n - Click on the \"Make Public\" link within the Kernel Viewer to make your\n   kernel public.\n\n![enter image description here][1]\n\n\n  [1]: https://i.imgur.com/D6eKmnO.png",
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      "content": "## Context\n\nThis dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n\n## Content\n\nThe datasets consists of several medical predictor variables and one target variable, `Outcome`. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.  Specifically, the training dataset has columns Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and Outcome.  The testing dataset has these same columns but does not contain the column 'Outcome'.  This is the column that you predict yourself!\n\n## Acknowledgements\n\nSmith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). [Using the ADAP learning algorithm to forecast the onset of diabetes mellitus][1]. *In Proceedings of the Symposium on Computer Applications and Medical Care* (pp. 261--265). IEEE Computer Society Press.\n\n## Inspiration\n\nCan you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?\n\n  [1]: http://rexa.info/paper/04587c10a7c92baa01948f71f2513d5928fe8e81\n\n\n<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>",
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    {
      "name": "Description",
      "content": "**Cloud Faculty Institute Workshop (6/22/2018)**\n\n - [Predict Diabetes from Medical Records][1]\n   - Kaggle InClass Competition \n    - Kaggle Kernels\n\n\n\n\n  [1]: https://www.kaggle.com/paultimothymooney/exercises-for-cloud-faculty-institute-workshop",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is mean F1 score. The F1 score 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>Submission files should contain two columns: Id and Outcome.\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,Outcome<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">8,0<br />9,0<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">10,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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    {
      "name": "Prizes",
      "content": "Prizes will be awarded at 9:00am (MDT) on 6/25/2018.\n\n - Prizes will be awarded to the top 5 submissions.\n - Only submissions from public kernels are eligible for prizes.   \n - Click on the \"Make Public\" link within the Kernel Viewer to make your\n   kernel public.\n\n![enter image description here][1]\n\n\n  [1]: https://i.imgur.com/D6eKmnO.png",
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