{
  "fetched_at": "2026-10-24T17:53:09Z",
  "source_url": "https://www.kaggle.com/competitions/BYUMovieRatings/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>IMDBTrain.csv</strong> - the training set</li>\n<li><strong>IMDBTest.csv</strong> - the test set</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>color</strong> - Is the movie in color or black & white?</li>\n<li><strong>director_name </strong> - Directors names</li>\n<li><strong>num_critic_for_reviews</strong> - Number of critics who reviewed the movie</li>\n<li><strong>duration</strong> - Length in minutes</li>                  \n<li><strong>director_facebook_likes</strong> - Number of likes on director's facebook page</li>   \n<li><strong>actor_3_facebook_likes</strong> - Number of likes on actor #3 facebook page</li>   \n<li><strong>actor_2_name</strong> - Name of 2nd lead </li>\n<li><strong>actor_1_facebook_likes</strong> - Number of like on lead #1's facebook page</li>    \n<li><strong>gross</strong> - Gross movie revenue</li>                   \n<li><strong>genres</strong> - List of genre's for movie classification </li>                    \n<li><strong>actor_1_name</strong> - Lead actors name</li>              \n<li><strong>movie_title</strong> - Movie title</li>             \n<li><strong>num_voted_users</strong> - Number of IMDB voters who registered a score</li>           \n<li><strong>cast_total_facebook_likes</strong> - Number of facebook likes for whole cast</li> \n<li><strong>actor_3_name</strong> - Name of 3rd lead</li>             \n<li><strong>facenumber_in_poster</strong> - I am not sure :) </li>      \n<li><strong>plot_keywords</strong> - Keywords to the plot </li>            \n<li><strong>movie_imdb_link</strong> - Link to IMDB site </li>          \n<li><strong>num_user_for_reviews</strong> - Number of IMDB users who submitted a review</li>      \n<li><strong>language</strong> - Primary language</li>                 \n<li><strong>country</strong> - Country</li>                  \n<li><strong>content_rating</strong> - MPAA Rating</li>            \n<li><strong>budget</strong> - Budget</li>                    \n<li><strong>title_year</strong> - Year released</li>              \n<li><strong>actor_2_facebook_likes</strong> - Number of likes for lead #2</li>    \n<li><strong>imdb_score</strong> - IMDB score</li>                \n<li><strong>aspect_ratio</strong> - Aspect ratio</li>            \n<li><strong>movie_facebook_likes</strong> - Number of movie likes on facebook page</li>\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "<p>We are using a train-test split of the IMDB 5000 movie database to begin building models for predicting movie ratings. The dataset contains information of 5000 different movies along with the IMDB score.  The IMDB score is from 1 to 10 with 10 suggesting a very well received movie. \n Your goal is to use any available information to predict the IMDB score.  </p>",
      "isPublished": false,
      "hasIsPublished": false,
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    },
    {
      "name": "Evaluation",
      "content": "<p>Prediction scores will be calculated based on the root mean square error criteria:</p>\n<p>\\[ RMSE = \\sqrt{\\frac{1}{n_{test}} \\sum_{i=1}^{n_{test}}(\\hat{y}_i - y_i)^2} \\]</p>\n<p>where \"y\" is the actual movie rating and \"y-hat\" is the predicted movie rating. </p>\n<h2>Submission Format</h2>\n<p>Submission files should contain two columns: movie_title and imdb_score.  The csv file should contain a header and have the following format:</p>\n<pre>movie_title,imdb_score<br>Movie Title, 7.0<br></pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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    },
    {
      "name": "rules",
      "content": "<p> All of the true IMDB ratings you can look up directly but the point of this is to practice building models.",
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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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    }
  ]
}