{
  "fetched_at": "2026-10-20T02:57:44Z",
  "source_url": "https://www.kaggle.com/competitions/epoch-24/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This dataset provides you with various readings and monitoring values. Use the training dataset to make a prediction model and then use it to make predictions on the test.csv dataset, you may refer to the sample_submission.csv to see how your final csv file should look like.\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `ID` - ID of the patient\n*   `age` - Age of the patient\n*   `sex` - Gender of the patient\n*   `myocardial_infarctions_anamnesis` - Quantity of heart attacks in the anamnesis\n*   `Exertional_angina_pectoris_anamnesis` - Exertional angina pectoris in the anamnesis. 0: never 1: during the last year 2: one year ago etc\n*   `FC_of_angina_pectoris` - Functional class of angina pectoris in the last year\n*   `Coronary_heart_disease(CHD)` - CHD in recent weeks\n*   `atr_fib` - Atrial fibrillation\n*   `supr_tach` - Supraventricular tachycardia\n*   `ven_tach` - Ventricular tachycardia\n*   `ven_fib` - Ventricular fibrillation\n*   `th_d_av` - Third-degree AV block\n*   `pul_ede` - Pulmonary edema\n*   `myo_rup` - Myocardial rupture\n*   `dres_syn` - Dressler syndrome\n*   `ch_hf` - Chronic heart failure\n*   `re_myin` - Relapse of the myocardial infarction\n*   `p_inf_ang` - Post-infarction angina\n*   etc.",
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      "postTitle": "",
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    {
      "name": "Description",
      "content": "You have been appointed as the Machine Learning Engineer for a hospital, your task is to utilize the provided dataset to develop predictive models that can accurately determine whether a patient is likely to have specific heart complications. Get ready to apply your machine learning skills!!",
      "isPublished": false,
      "hasIsPublished": false,
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    },
    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on Average Precision\n\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the TARGET variables. The file should contain a header and have the following format:\n\n    ID,atr_fib,supr_tach,ven_tach,ven_fib,th_d_av,pul_ede,myo_rup,dres_syn,ch_hf,re_myin,p_inf_ang\n\n    2,0,0,1,0,1,0,0,0,0,1,0\n    5,0,0,1,0,1,0,0,0,0,1,0\n    6,0,0,1,0,1,0,0,0,0,1,0\n    etc.",
      "isPublished": false,
      "hasIsPublished": false,
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    },
    {
      "name": "rules",
      "content": "Here are the rules -- \n\n - Follow [@acm_manipal](https://www.instagram.com/acm_manipal/) on Instagram for results!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "abstract",
      "content": "Welcome to EPOCH '24, ACM Manipal's first ever Kaggle Contest.",
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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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    }
  ]
}