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  "fetched_at": "2026-10-19T21:12:10Z",
  "source_url": "https://www.kaggle.com/competitions/classification-prediction-challenge-using-r/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This is a (simplified) dataset. Your focus will be to predict the bribes (\"BribeValue\") paid by the firms in the validation dataset. You'll use the training dataset for developing your prediction models.\n\n## **Files**\n\n- training_data-class.csv - the training dataset with the target value (\"bribeprob\") included for training your prediction models.\n- validation_data.csv - the dataset for which you'll provide predicted probability of bribes.\n- SampleSub-class.csv - a sample submission file in the correct format for your reference. The predicted probabilities of bribe payments are 0 by default -- you'll replace that with your predicted values corresponding to the firms (\"idstd\") in the validation dataset.\n- WorldBankSurvey.PDF - supplemental document with a version of the survey used by the World Bank",
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      "name": "Description",
      "content": "This is a dataset from the World Bank and other resources. The data can be used to examine whether transparency enhancing information technologies make a firm vulnerable to harassment (e.g., tax audits) by corrupt public officials in emerging markets.\n\nIn this competition, students would predict the probability of bribe payments by the firms in the sample.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is Area Under the Curve (AUC) derived from the Receiver Operating Characteristics Curve (ROC).\n\n##**Submission Format**\n\nThe submission file should contain two columns: \"idstd\" which identifies the firm id in the validation dataset and your predicted probability value of bribe payment (\"bribepob\").",
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    {
      "name": "rules",
      "content": "This is an individual assignment, so please follow the usual norms and rules outlined in the Pitt academic integrity code.\n\n- Don't publish or distribute the dataset(s) or your analysis.\n- Don't cheat! This is individual work.\n- Apply yourself!\n- Have fun!",
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