We are provided with hourly bike rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month.We are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.
Dataset: https://www.kaggle.com/c/bike-sharing-demand/data
The train and test data, which can be found at the link given above, contain the following variables:
| Variable | Description |
|---|---|
| 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 |