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  "fetched_at": "2026-10-19T13:16:11Z",
  "source_url": "https://www.kaggle.com/competitions/biketripdurationprediction/data",
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    {
      "name": "data-description",
      "content": "The dataset used for predicting bike trip duration includes various features from a bike-sharing system, combined with weather data to enhance prediction accuracy. The features include trip duration, trip distance, pickup and dropoff locations (latitude and longitude), and several weather conditions such as temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature, and 1-hour average dust concentration.\n\nThese variables are key to understanding the factors influencing trip duration, helping to build a robust predictive model that advances the efficiency of Intelligent Transport Systems (ITS) and traveler information systems.\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format",
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      "name": "Description",
      "content": "In the Bike Trip Duration Prediction challenge, participants are tasked with predicting how long a bike-sharing trip will last based on various features. This is a regression problem, where the goal is to develop a model that can accurately forecast the trip duration, helping optimize bike-sharing systems by improving resource allocation and user satisfaction.",
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      "name": "Evaluation",
      "content": "Submissions are evaluated on the Mean Squared Error (MSE), which measures the average squared difference between the predicted values and the actual target values. Lower MSE indicates a model with better prediction accuracy.\n\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the Duration variable. The file should contain a header and have the following format:\n\n    ID,Duration\n    1,10\n    2,10\n    3,10\n    etc.",
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    {
      "name": "rules",
      "content": " - Learn!\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "## Bike Trip Duration Prediction\nIn this competition, your task is to predict the duration of bike trips using historical data from a bike-sharing service. You'll be working on a regression problem, where the goal is to forecast the continuous variable of trip duration based on factors like start time, station locations, weather, and user information.\n\nBy building an accurate model, you'll help optimize bike-sharing systems, making it easier to manage resources and improve user experience. Dive into feature engineering, time-series analysis, and regression techniques to tackle this real-world problem!",
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