{
  "fetched_at": "2026-10-19T09:13:49Z",
  "source_url": "https://www.kaggle.com/competitions/etu-ai-club-competition-1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\"train.csv\", \"test.csv\" and \"sample_ submission.csv\" files can be downloaded here. You need to predict the Label which consists of 0s and 1s.\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` - unique identifier for each instance\n*   `feature_0` - feature_0 to be trained ...\n*   `Label` - the class to be predicted",
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    {
      "name": "Description",
      "content": "In this competition, you need to make a binary classification on an imbalanced dataset. We recommend to follow three main steps for beginners:\n\n-Feature Engineering\n-Model Selection\n-Hyperparameter Tuning",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Macro F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision and recall.\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\n**For every instance in the dataset**, submission files should contain two columns: `id` and `Label`. ",
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    {
      "name": "rules",
      "content": " - A team at the max may comprise of 3 members\n - Any machine learning or deep learning model may be used\n - The winners of competition must submit their notebooks to be checked for cheating in the end of the competition",
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      "hasIsPublished": false,
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  ]
}