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  "fetched_at": "2026-10-20T02:56:38Z",
  "source_url": "https://www.kaggle.com/competitions/ai-ninja-warrior-july-2023/data",
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    {
      "name": "data-description",
      "content": "The dataset consists of feature vectors belonging to multiple sessions of an e-commerce website\n\nEach session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. \n\nGiven a session the model has to predict if it will end up in a purchase.\n\nThe '**Purchased**' attribute can be used as the target.\n\n**'Internal', 'Internal_Duration', 'Research', 'Research_Duration', 'Goods_Related', 'Goods_Related_time'** represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. \n\nThe values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The \"Bounce_Rates\", \"Exit_Rates\" and \"Page_Values\" features represent the metrics measured by \"Google Analytics\" for each page in the e-commerce site. \n\nThe value of \"**Bounce_Rates**\" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave (\"bounce\") without triggering any other requests to the analytics server during that session. \n\nThe value of \"**Exit_Rates**\" feature for a specific web page is calculated as for all page views to the page, the percentage that were the last in the session. \n\nThe \"**Page_Values**\" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. \n\nThe \"**SpecialOccasion**\" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. \n\nThe dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.",
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    {
      "name": "Description",
      "content": "Hey Ninjas!\nThe primary purpose of the AI Ninja Warrior Hackathon is to provide a platform for participants to expand their AI knowledge, learn new techniques, and put their skills to the test. The event is designed to foster an environment of creativity, innovation, and camaraderie, where individuals compete against each other while also learning from one another's expertise.",
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    {
      "name": "Evaluation",
      "content": "Evaluation metric will be F1 score:\n\n    \"The F1 score can be interpreted as a harmonic mean of the precision and\n    recall, where an F1 score reaches its best value at 1 and worst score at 0.\n    The relative contribution of precision and recall to the F1 score are\n    equal. The formula for the F1 score is::    \n    F1 = 2 * (precision * recall) / (precision + recall)\"",
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    {
      "name": "rules",
      "content": "Remember the goal is to learn. So,\n\nDon't cheat!\nApply yourself!\nIt's ok if you don't win!\nEnsure you take away something after this hackathon!\nHave fun!",
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