# Prediction Competition - Bike Sharing Demand — Data

来源：https://www.kaggle.com/competitions/csce5300-bikeshare/data

采集时间：2026-10-20T04:31:41Z

You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 16 days of each month, while the test set is the 17-19th day of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period. That is, predict "count" without using "count" or it's components "casual" and "registered".


Data Fields


* **datetime** - hourly date + timestamp  
* **season** -  1 = spring, 2 = summer, 3 = fall, 4 = winter 
* **holiday** - whether the day is considered a holiday
* **workingday** - whether the day is neither a weekend nor holiday
* **weather** -  
    * 1 - Clear, Few clouds, Partly cloudy, Partly cloudy
    * 2 -  Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
    * 3 - Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds  
    * 4 - Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog 
* **temp** - temperature in Celsius
* **atemp** - "feels like" temperature in Celsius
* **humidity** - relative humidity
* **windspeed** - wind speed
* **casual** - number of non-registered user rentals initiated
* **registered** - number of registered user rentals initiated
* **count** - number of total rentals