# Bike Rental Prediction — Data

来源：https://www.kaggle.com/competitions/aai-bike-rental-prediction/data

采集时间：2026-10-20T09:39:55Z

## Description of columns in the dataset
* instant: record index
* dteday : date
* season: season (1:winter, 2:spring, 3:summer, 4:fall)
* year:  (0: 2011, 1:2012)
* month: month ( 1 to 12)
* holiday: weather day is holiday or not (extracted from [Web Link])
* weekdaynum: day of the week
* is_workingday: if day is neither weekend nor holiday is 1, otherwise is 0.
*weathersituation:
 - 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
* atemp_normalized: Normalized feeling temperature in Celsius. 
  - The values are derived via (t-tmin)/(tmax-tmin), tmin=-16, t_max=+50 (only in hourly scale)
* humidity_normalized: Normalized humidity. The values are divided to 100 (max)
* windspeed_normalized: Normalized wind speed. The values are divided to 67 (max)
* total_rental: count of total rental bikes including both casual and registered

Train a regression model to predict the total rental count, based on relevant variables per your understanding

*   **train.csv** - the training set
*   **test.csv** - the test set
*   **sample_submission.csv** - a sample submission file in the correct format