{
  "fetched_at": "2026-10-20T05:24:58Z",
  "source_url": "https://www.kaggle.com/competitions/digital-democracy/data",
  "pages": [
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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    {
      "name": "data-description",
      "content": "The training and test sets are available in the `/data/face_dataset` directory on JupyterHub.\n\nA sample Kaggle submission file `sample.csv` is provided here.\n",
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    {
      "name": "Description",
      "content": "The Digital Democracy project (www.digitaldemocracy.org) is providing a dataset of screen captures from the videos of legislative hearings that took place in the state of California (and possibly in some other states) since 2015.  The images contain faces of different state legislators and also include some other labelling information. \n\nYour goal is to\n\n- Build a machine learning pipeline that analyzes and properly classifies the images from the Digital Democracy dataset. \n- Use Convolutional Neural Networks and Google’s TensorFlow library as part of your machine learning pipeline.\n- Measure the accuracy of your classification methodology. A Kaggle competition will be set up for this data set.  Attempt to beat the baseline accuracy of about 89%.\n- Write a comprehensive report detailing your work. For this assignment your report shall read as a paper you are intending to submit and publish at a conference or a workshop. \n",
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    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric is categorization accuracy. Your goal is to beat the baseline of 89%.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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}