{
  "fetched_at": "2026-10-20T00:40:39Z",
  "source_url": "https://www.kaggle.com/competitions/binary-classification-with-bank-churn-bny/data",
  "pages": [
    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.\n\n\n## Submission File\nFor each `id` in the test set, you must predict the probability for the target variable `Exited`. The file should contain a header and have the following format:\n\n    id,Exited\n    165034,0.9\n    165035,0.1\n    165036,0.5\n    etc.",
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    {
      "name": "rules",
      "content": " ********Have fun learning !",
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    {
      "name": "data-description",
      "content": "The dataset for this competition (both train and test) was generated from a deep learning model trained on the [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shubhammeshram579/bank-customer-churn-prediction) dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.\n\n## Files\n\n*   **train.csv** - the training dataset; `Exited` is the binary target\n*   **test.csv** - the test dataset; your objective is to predict the probability of `Exited`\n*   **sample_submission.csv** - a sample submission file in the correct format",
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    {
      "name": "abstract",
      "content": "**Welcome to the 2024 Kaggle Playground Series!**  We plan to start a series of playgrounds, providing interesting an approachable datasets for our community to practice their machine learning skills.\n\n**Your Goal:** For this Episode of the Series, your task is to predict whether a customer continues with their account or closes it (e.g., churns). Good luck!",
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