{
  "fetched_at": "2026-10-19T07:26:06Z",
  "source_url": "https://www.kaggle.com/competitions/etu-ai-club-competition-2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\"train.csv\", \"test.csv\" and \"sample_ submission.csv\" files can be downloaded here. You need to predict the Target which consists of float numbers.\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*   `Target` - the float number to be predicted",
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    {
      "name": "Description",
      "content": "In this competition, you need to make a regression on a noisy dataset. We recommend to follow three main steps for beginners:\n\n-Feature Engineering\n-Model Selection\n-Hyperparameter Tuning",
      "isPublished": false,
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Root mean squared error (RMSE)](https://www.kaggle.com/general/215997). Root mean squared error (RMSE) is the most popular metrics. It is simply the square root of MSE. MSE is the mean of all squared errors.\n\nSquared Error = ( True Value – Predicted Value )**2\nMean Squared Error = 1/N*(SE)\n\nRoot mean squared error = SQRT(MSE)\n\n## Submission Format\n\n**For every instance in the dataset**, submission files should contain two columns: `id` and `Target`. ",
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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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}