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  "fetched_at": "2026-10-24T12:36:54Z",
  "source_url": "https://www.kaggle.com/competitions/db001/data",
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    {
      "name": "data-description",
      "content": "The dataset (diabetes_risk_prediction_dataset.csv) contains the following columns:\n\nage: Age of the individual\ngender: Gender of the individual (Male/Female)\npolyuria: Presence of polyuria (excessive urination) - Yes/No\npolydipsia: Presence of polydipsia (excessive thirst) - Yes/No\nsudden_weight_loss: Sudden weight loss - Yes/No\nweakness: Weakness - Yes/No\npolyphagia: Polyphagia (excessive hunger) - Yes/No\ngenital_thrush: Presence of genital thrush - Yes/No\nvisual_blurring: Visual blurring - Yes/No\nitching: Itching - Yes/No\nirritability: Irritability - Yes/No\ndelayed_healing: Delayed healing - Yes/No\npartial_paresis: Partial paresis - Yes/No\nmuscle_stiffness: Muscle stiffness - Yes/No\nalopecia: Alopecia (hair loss) - Yes/No\nobesity: Obesity - Yes/No\nclass: Target variable indicating the presence or absence of diabetes (Positive/Negative)\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",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on [Accuracy]\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the class variable. The file should contain a header and have the following format:\n\n```csv\nID,class\n0,Positive\n1,Positive\n2,Negative\n```",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - No AutoML!\n - Show Feature Engineering Skills and Modeling!\n - Get hand dirty!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "Welcome to the Diabetes Risk Prediction competition! \n\nIn this challenge, participants are tasked with developing predictive models to identify individuals at risk of diabetes based on a set of medical and demographic features. \n\nThe dataset provided includes information such as age, gender, and various symptoms that may be indicative of diabetes.",
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