{
  "fetched_at": "2026-10-19T16:00:00Z",
  "source_url": "https://www.kaggle.com/competitions/dsua-112-project2/data",
  "pages": [
    {
      "name": "rules",
      "content": "Follow the example in Question 9 for submission of your predictions. You must upload a comma separated value (csv) file containing two columns \n\n- Id : index of the email in the testing set \n- Category : 0 or 1 depending on your prediction of not spam or spam\n\nYou can submit multiple entries before the competition deadline.",
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    {
      "name": "data-description",
      "content": "Here are the training sets and testing sets for Project 2 on spam classification.",
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    {
      "name": "Description",
      "content": "In Project 2, we develop a model that can classify spam emails from non-spam emails. Remember that spam means junk, commercial or bulk emails. We will get practice with\n\n- Encoding text to determine features from documents\n- Using packages to process data and fit models\n- Validating the performance of models to reduce overfitting\n- Generating ROC curves\n\nYou will have the chance to try out your own model in a Kaggle competition. You have until Tuesday May 12 at 11:59PM EST to submit your predictions.\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "We have provided you with a testing set consisting of emails without the label of spam and not-spam. Your job will be to guess these labels. We will evaluate your guesses based on accuracy meaning \n\n$$\\large \\displaystyle \\frac{\\text{True Positive} + \\text{True Negative}}{\\text{True Positive} + \\text{True Negative} + \\text{False Positive} + \\text{False Negative}}$$\n\nPlease see the Rules tab for information about submission.",
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  ]
}