{
  "fetched_at": "2026-10-19T19:53:31Z",
  "source_url": "https://www.kaggle.com/competitions/blg-454e-term-project-competition/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": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.txt</strong> - the training set</li>\n<li><strong>test.txt</strong> - the test set</li>\n<li><strong>sampleSubmission.txt</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li>Explained in dataFields.pdf</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<h2>BLG 454E Learning From Data, Spring 2018 Term Project </h2>\n<p>Suppose that you work as a data analyst in a company. The incurred cost of returns forces your company to take measures to reduce the number of returns without affecting the customer satisfaction. You are asked to solve this problem. </p>\n\n<p>In this project your aim is to predict whether a purchase results with a return or not based on historical purchase data. A model had to be learned generating a prediction of the probability that a certain purchase is converted into a return on the basis of new purchase data of the shop. More explicitly, for each order item a prediction is to be made the value of which is\nwithin the interval [0,1]. The higher the value, the more probable is the return. The error with respect to the real outcome concerning the return of the order item should be as small as possible.</p>\n\nIf a question is not clear, please let the teaching assistants know by email:\n\n<h3> kivrakh@itu.edu.tr or cebeci16@itu.edu.tr</h3>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should \n<p>The evaluation metric for this competition is ..\n\n<h2>Submission Format</h2>\n<p><strong>For every test instances in the dataset</strong>, submission files should contain two columns: orderItemID and returnShipment. \n\n<p>The file should contain a header and have the following format:</p>\n<pre>orderItemID,returnShipment<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,0.9<br />3,0<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">4,0.2<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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