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  "fetched_at": "2026-10-19T13:39:46Z",
  "source_url": "https://www.kaggle.com/competitions/coms4771-spring-2022-regression-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "There are three data files: train_examples.csv, train_labels.csv, test_exampes.csv.\n\n- train_examples.csv contains 400,000 training examples, where each example has 11 features\n- train_labels.csv contains 400,000 training labels, in the same order as train_examples (and with ids).\n- test_examples.csv contains 100,000 training examples.",
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      "name": "Description",
      "content": "Welcome to COMS 4771 Spring 2022 large-scale regression task. Your goal for this assignment is to develop a good quality regressor for the given dataset.\n\nThe given dataset comes from a Uber-esque transportation service. It consists of about 400000 rides taken by users. The task is to predict the duration (in seconds) of the ride.\n\nYou can use any resource/code/package/toolbox that is publicly available to develop your regressor. No external data sources can be used. Your primary goal for this assignment is to develop a good quality trip-duration predictor. (This of course will be only possible if you employ good Machine Learning practices).",
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    {
      "name": "Evaluation",
      "content": "##Evaluation Criteria\nYou must get a Mean Absolute Error (MAE) score of at most 500 on the private holdout test-dataset to get full credit. If your MAE score is over the simple baseline (see leaderboard), then you will get zero credit. If your MAE score is between the simple baseline and the full credit threshold, then you will be awarded points linearly proportional to your deviation from the full credit threshold. For example, if your MAE is halfway between the simple baseline and full credit threshold, then you will get half credit. Extra points will be awarded to the highest performing students, using the following scoring mechanism:\n\n$$\\text{Extra Credit Points}= 5(1−\\frac{\\text{Your Rank}−1}\n{\\text{Number of Participants with MAE < 300 }}\n)⋅1[\\text{Your MAE}< 300]$$",
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    {
      "name": "rules",
      "content": "- You must design your regressor individually. Teams of more than one student are not permitted.\n- You must use your UNI as your \"team\" name in order to get points.\n- No additional data sources may be used.",
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