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  "fetched_at": "2026-10-24T17:24:07Z",
  "source_url": "https://www.kaggle.com/competitions/compsci367-v2/data",
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    {
      "name": "rules",
      "content": "\n\t<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts. Please create your sign-in using your UPI.</p>\n\n    <h3>Academic Integrity & Privacy </h3>\n    <p>Please DO NOT share the Kaggle link with anyone outside the class. </p>\n    <p>No private sharing of data and solution among classmates. Please remember to submit your report via ADB.</p>\n    <p>Privately sharing code or data  is not permitted.  </p>\n\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 5 entries per day.</p>\n    <p>You may select up to 2 final submissions for judging.</p>\n\n    <h3>Competition Timeline</h3>\n    <p>Entry Deadline: <strong>10 August 2018 11:59 PM  NZT</strong></p>\n    <p>End Date: <strong>10/8/2018 11:59 PM NZT</strong></p>\n\t<!-- End Base Rules -->\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": "Testing",
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      "name": "Description",
      "content": "The goal of the challenge is to predict whether customers will default on their credit card payments. \n\nFor this course, you will enter the competition as an individual. Your goal will be to try out suitable/appropriate algorithms we will see during the course. You are also encouraged to try out other algorithms, combinations of those, and generally explore the challenge data. \n\nThe project will be graded on a 3-page report (including figures and tables) as well as the ranking of your final submissions to the leaderboard. Final submissions to the leaderboard as well as the report are due **August 10, 2018, 23:59 NZT**.\n\n\nFurther instructions on the report is provided on Canvas. Report is submitted via ADB.\n\n**Shortened instructions:**\n\nDownload the datasets. You will see a training dataset, a testing dataset where you will need to provide predictions for, and a sample submission input. \n\nSet up a cross-validation framework for your analysis of your data using WEKA or any other toolkit.\n\nUse the framework for model selection. You are encourage to explore:\n \n - various machine learning algorithms and their parameters,  particularly among those seen in class: linear regression, tree-based approaches, etc.  \n\n - Any transformations of the features, as you may  see fit. \n\n - Explore whether there are any features that are redundant. (There is at least one that is).\n\nSubmit as many of the solutions based on models as you wish (in a limit of 5 per day) to the leaderboard to see their performance on the public validation set. Please note that the limit is not cumulative and is on a per day basis.\n\n\nPerformance Evaluation Metric: Classification Accuracy\n\n\n\n**Please DO NOT share your code on the competition website or other venues as well.** \n\nRemember to have fun and please start on the assignment earlier.",
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