Bike Sharing Demand — bike_sharing • mlr3data

Regression data to predict the total count of bikes rented. Contains 13 features and 17379 observations. Target column is "count".

Source

https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset

Pre-processing

All columns have been renamed.
instant, "registered" and "casual" column have been removed.
"season" and "weather" have been converted to factor().
"holiday" and "working_day" have been converted to logical().

Examples

data("bike_sharing", package = "mlr3data")
str(bike_sharing)
#> Classes ‘data.table’ and 'data.frame':	17379 obs. of  14 variables:
#>  $ date                : chr  "2011-01-01" "2011-01-01" "2011-01-01" "2011-01-01" ...
#>  $ season              : Factor w/ 4 levels "winter","spring",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ year                : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ month               : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ hour                : int  0 1 2 3 4 5 6 7 8 9 ...
#>  $ holiday             : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
#>  $ weekday             : int  6 6 6 6 6 6 6 6 6 6 ...
#>  $ working_day         : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
#>  $ weather             : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 2 1 1 1 1 ...
#>  $ temperature         : num  0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ...
#>  $ apparent_temperature: num  0.288 0.273 0.273 0.288 0.288 ...
#>  $ humidity            : num  0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ...
#>  $ windspeed           : num  0 0 0 0 0 0.0896 0 0 0 0 ...
#>  $ count               : int  16 40 32 13 1 1 2 3 8 14 ...
#>  - attr(*, ".internal.selfref")=<externalptr>