{
  "fetched_at": "2026-10-20T08:03:51Z",
  "source_url": "https://www.kaggle.com/competitions/breastcancer/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The data has been split into two groups:\n\n- training set (train.csv)\n- test set (test.csv)\n\nThe training set should be used to build your machine learning model. The outcomes (also known as “ground truth”) have been provided in the training set.\n\n### Goal\nThe goal is to predict whether the there is a breast cancer patient or not.\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* mean radius\t\n* mean texture\t\n* mean perimeter\t\n* mean area\t\n* mean smoothness\t\n* mean compactness\t\n* mean concavity\t\n* mean concave points\t\n* mean symmetry\t\n* mean fractal \n* dimension\t\n* radius error\t\n* texture error\t\n* perimeter error\t\n* area error\t\n* smoothness error\t\n* compactness error\t\n* concavity error\t\n* concave points error\t\n* symmetry error\t\n* fractal dimension error\t\n* worst radius\t\n* worst texture\t\n* worst perimeter\t\n* worst area\t\n* worst smoothness\t\n* worst compactness\t\n* worst concavity\t\n* worst concave points\t\n* worst symmetry\t\n* worst fractal dimension\t\n* Cancer (target column)\n",
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    {
      "name": "Description",
      "content": "If you are a beginner and familiar with Machine Learning basics and need some hands-on experience, this is the competition to brush-up and revisit your skills.\n\n### Objective\nWith the help of 30 variables (columns), this competition challenges you to predict the possibility of breast cancer ( 1 depicting Yes and 0 depicting No ).",
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    {
      "name": "Evaluation",
      "content": "### Metric\n\nThe evaluation metric for this competition is [Weighted F1-Score](https://en.wikipedia.org/wiki/F-score). It is given by:\n\n`F1 = 2 * (precision * recall) / (precision + recall)`\n\nPrecision is the ratio of true positivesto all predicted positives. Recall is the ratio of true positives  to all actual positives. \n\nThe 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.\n\n## Submission Format\n\nThe file should contain a header and have the following format:\n```\nId,Cancer\n400,1\n401,0\n402,0\nEtc\n```\n\nIt should contain exactly 2 columns:\n- Id\n- Cancer (containing your binary predictions: 1 for Yes, 0 for No)",
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    {
      "name": "rules",
      "content": "### No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n### Team Limits\nThere is no maximum limit on team size.\n\n### Submission Limits\nYou may submit a maximum of 5 entries per day.\n\n### Competition Timeline\n\nDeadline: 02/12/2022 11:59 PM",
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