{
  "fetched_at": "2026-10-19T09:15:02Z",
  "source_url": "https://www.kaggle.com/competitions/age-prediction-spring-25-at-cu-denver/data",
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      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. </p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>wiki_labeled</strong> - the labeled dataset, and should be used to train and test the model. The wiki_labeled/ directory will contains 60327 facial images kept in 100 folders, naming 00-99. Dimension of each image is 100 pixels by 100 pixels. </li>\n<li><strong>wiki_labele.csv</strong> file, containing meta information of each of the 60327 images. It contains the following information about each of the 60327 images:</li>\n<ul>\n    <li> ID: identification number of the subject (starting from 2002)</li>\n    <li> dob: the date of birth of the subject. (It is Matlab’s datenum value calculated based on total number of days since January 0, 0000.)</li>\n    <li>dob_str: the DD-MMM-YYYY format dob value.</li>\n    <li> photo_taken: when the photo was taken (only the year  value)</li>\n    <li> full_path: directory path, including filename of the image</li>\n    <li> gender: Gender of the subject (0: female, 1: male, NaN if unknown)</li>\n    <li> name: name of the subject</li>\n    <li> face_location: location of the face.</li>\n    <li> face_score: detector score (the higher the better). Inf implies that no face was found in the image, and the face_location then just returns the entire image.</li>\n    <li> second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN (not a number) if no second face was detected.</li>\n    <li> age: age of the person (in years), and was calculated based on the “dob” value and the “photo_taken” values.</li>\n</ul>\n<li><strong>wiki_judge_images</strong> containing **1409** facial images (1.png, etc.) of resolution 100x100. This is the judge/test dataset for you based on what you are going to apply your learned model to predict ages of the **1409** subjects.</li>\n<li><strong>wiki_judge.csv</strong> contains some meta information about the **1409** images. For instance,  ID, full_path, gender, face_score, second_face_score. However, the information does not include “age” field.</li>\n\n<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format. Please disregard the values in the age field. They are all made up values.</li>\n</ul>\n\n",
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    {
      "name": "Description",
      "content": "<p>In this assignment you are going to develop a program that would be able to guess age (in years) of a person given his/her photo.</p>\n\n",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [RMSE](\"https://www.statisticshowto.datasciencecentral.com/rmse/), Root Mean Square Error. \n\nRMSE is defined as : \n![RMSE = \\sqrt{MSE} = \\sqrt{\\dfrac{1}{m}\\sum_{i=1}^m\\left(y_i-h_w(x_i)\\right)^2}](https://drive.google.com/thumbnail?id=1a1YhMSdz5TlB6CShdIWT2nHhlxluThF5&sz=w700-h130)\n\nwhere, there are m samples, yi is the true/known target label for the ith sample, and hw(xi) is the predicted target for the input sample xi.\n<hr />\n\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: ID and age </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>ID,age<br />1,38.8<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,25<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have a lot of fun!",
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