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  "fetched_at": "2026-10-19T17:47:43Z",
  "source_url": "https://www.kaggle.com/competitions/32130-summer23-a3-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This third assignment picks up from your second assignment, where you explored the dataset.\nNow you will build different classifiers to predict if a hotel booking is canceled or not.\n\n## Files\n\n*   **Assignment3-SatisfactionDataset.csv** - the training set\n*  **Assignment3-UnknownDatasetcsv** - the test set\n*   **Assignment3-sample_submission.csv** - a sample submission file in the correct format\n\n\n\n\n",
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    {
      "name": "Description",
      "content": "**32130 Fundamentals in Data Analytics Kaggle Competition**\n\nThis third assignment picks up from your second assignment, where you explored the dataset.\nNow you will build different classifiers to predict if a an airline passenger or not.\n\nThe classification goal is to predict if a passenger is satisfied or not.",
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      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how you should format your submission. ## Submission Format For each data point, you need to give the identifier (Id) together with the class value you predicted for \"satisfied\". The \"Id\" is an increasing number from 1 until the end (indicated by `N`) of the `Assignment3-UnknownDataset.csv` (Canvas) or `test.csv` (from this Kaggle competition). The file should contain a header including the `Id` and the prediction for `satisfied` in the following format: ``` Id, satisfied 1, 1 2, 0 3, 0 4, 1 . . . N, 0 ```",
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    {
      "name": "rules",
      "content": "\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n\nThe results and any activities with Kaggle are not subjected or considered for the marking of assignment 3.",
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