{
  "fetched_at": "2026-10-24T16:08:11Z",
  "source_url": "https://www.kaggle.com/competitions/capstonefall2021/data",
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      "name": "Description",
      "content": "###File descriptions\n\n- amazon_reviews.csv - 560,804 unique reviews from Amazon Fine Food Reviews, with their associated star ratings and metadata. It is not necessary to use all reviews, or metadata for training. Some reviews will be missing a value in the 'Score' column. That is because, these are the scores you want to predict.\n\n- sampleSubmission.csv - a sample submission file. Kaggle will only evaluate submission files in this exact same format.\n\n###Data fields\n\n- ProductId - unique identifier for the product\n- UserId - unique identifier for the user\n- HelpfulnessNumerator - number of users who found the review helpful\n- HelpfulnessDenominator - number of users who indicated whether they found the review helpful\n- Score - rating between 1 and 5\n- Time - timestamp for the review\n- Summary - brief summary of the review\n- Text - text of the review\n- Id - a unique identifier associated with a review\n\nNote: Some of the rows of the table may have some of these values missing.\n\n###Dataset Citation\nJ. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\"> RMSE</a>. RMSE is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed.</p>",
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    {
      "name": "rules",
      "content": "You can work with up to two people in this project. You can send up to 10 submissions per day.",
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