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  "fetched_at": "2026-10-20T09:43:27Z",
  "source_url": "https://www.kaggle.com/competitions/gse-524-f2021/data",
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      "content": "## THE DATA\n\nThe survey that supplied this data was conducted in March 2018 by Survey Sampling International, at the request of Cards Against Humanity. \n\nQuestions from the survey that are included in this dataset are:\n\n*(Note: The bolded question is the target variable.)*\n\n- What gender do you identify with?\n\n- Age\n\n- **In politics today, do you consider yourself a Democrat, a Republican, or Independent?**\n\n- Would you say you are liberal, conservative, or moderate?\n\n- What is your highest level of education?\n\n- What is your race?\n\n- Do you believe that prostitution should be against the law?\n\n- Do you believe that it should be against the law to smoke marijuana for recreation?\n\n- Do you believe the sale of human organs for transplants should be against the law?\n\n- Would you consider yourself a religious person?\n\n- Would you consider yourself pro-life or pro-choice?\n\n- Do you generally disapprove of people who pursue casual sex, hookups, or one-night-stands?\n\n- Based on your general impressions, would you say that people who smoke marijuana tend to have more casual sex than people who abstain from smoking marijuana?\n\n- If abortion became strictly banned, do you think women in general would become more willing to have casual sex, less willing to have casual sex, or behave no differently?\n\n- Do you agree or disagree with the following statement: A woman should have the right to do what she wants with her own body.\n\n- Do you agree or disagree with this next statement: Abortion is morally wrong.\n\n- And do you agree or disagree with this next statement: Sex without love is okay.\n\n- Do you believe that an elected official who has committed sexual misconduct in their personal life can still behave ethically and fulfill their duties while in office?",
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      "name": "Description",
      "content": "## DATA SOURCE\n\nThe board game company Cards Against Humanity (CAH) is known for putting a lot of effort into silly publicity stunts.  In 2017, they launched Cards Against Humanity Saves America, a series of politically-related activities.  One such activity was a monthly poll, asking some silly questions and some serious questions about the world. \n\nTo conduct their polls in a scientifically rigorous manner, they partnered with Survey Sampling International — a professional research firm — to contact a nationally representative sample of the American public.\n\nThe analyses of the data conducted by CAH can be found here: https://thepulseofthenation.com/#poll-12.  You are welcome to reference their analyses to help inform your own.  Note that some questions from the poll have been omitted for purposes of this exam, and that others have been cleaned or edited so that not all possible responses are included.  \n\nPlease also note that, while the data itself is collected by a rigorous neutral party, the discussion on the CAH website is highly opinionated.  I share it for you information, not as an endorsement of their messages.\n\n---\n\n## GOAL\n\nYour mission today is to use the questions asked in the CAH poll to predict the **political affiliation (Democrat, Republican, or Independent)** of the individual.\n\n---\n\n## Only your first two submissions will be graded!",
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      "name": "Evaluation",
      "content": "## METRIC\n\nThe evaluation metric for this competition is the overall **accuracy** of your predictions. \n\n## SUBMISSION FORMAT\n\nSubmission files should contain two columns: `id_num` and `political_affiliation`.  The ID Numbers should be the same as those in the `test.csv` file, and the `political_affiliation_predicted` values should be your predicted affiliation. \n\nYou can generate a properly formatted prediction file as follows, assuming `test_data` is your test dataset and `final_model_fit` is your fitted final chosen model:\n```\nfinal_predictions <- data.frame(\n    id_num  = test_data$id_num,\n    political_affiliation_predicted = predict(final_model_fit, test_data)$.pred_class\n)\n```\n\n```\nfinal_predictions %>% write_csv(\"final_predictions.csv\")\n```\n\n*(Note: This will save the file `final_predictions.csv` to your working directory.  If you aren't sure how to find that, I am happy to help.)*",
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    {
      "name": "rules",
      "content": "For this exam, you may **not**:\n\n- Work with any other student in the class on any aspect of the data or modeling process\n\n- Discuss with any other human, whether or not they are a student and whether or not you know them in real life\n\n- Copy from other analyses of this same dataset that may exist online\n\nYou **are** permitted to use any resource that does not violate the above rules.",
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      "name": "Grading",
      "content": "Your grade on this portion of the Final will be determined by the accuracy score achieved by your submitted predictions.\n\n---\n\n## GRADE TIERS\n\n**35% or below:** F\n**35%-40%:** D\n**40%-45%:** C-\n**45%-50%:** C\n**50%-52%:** C+\n**52%-54%:** B-\n**54%-56%:** B\n**56%-58%:** B+\n**58%-60%:** A-\n**60%-62%:** A\n**62% and above%:** A+\n\n## Challenge Points\n\nThe **top 5 scorers** will receive Challenge Points:\n\n1st place: 25\n2nd place: 20\n3rd place: 15\n4th place: 10\n5th place: 5",
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