ubaar_train: Ubaar Transport Cost Prediction (Training)
In mcnakhaee/dadegan: Some Useful Persian Datasets

In this competition, Ubaar & SADA challenge you to predict transport cost for 15,000 loads. You are provided with data about 50,000 actual transported loads. You can learn more about this competition at http://sanags.ir/ubaar-competition

1	ubaar_train

A tibble with 50000 rows and 14 variables:

ID: an anonymous id unique to a given load
date: the date of the load dispatch. It is a string with ymd format. for example, 960630 is 30th day of 6th month of year 1396.
sourceLatitude: latitude of the source region of transport. Source region is not exactly the location for embarkation of the freight.
sourceLongitude: longitude of the source region of transport. Source region is not exactly the location for embarkation of the freight.
SourceState: state of the source of transport
destinationLatitude: latitude of the destination region of transport. Destination region is not exactly the location for disembarkation of the freight.
destinationLongitude: latitude of the destination region of transport. Destination region is not exactly the location for disembarkation of the freight.
destinationState: state of the destination of transport
distanceKM: driving distance in kilo meters from source region to destination region for usual cars, calculated using google distance services
taxiDurationMin: estimated arrival time in minutes from source region to destination region for usual cars, calculated using google distance services
vehicleType: type of the vehicle that has carried the load. It is a categorical variable
vehicleOption: option of the vehicle that has carried the load. It is a categorical variable. Each vehicle type has its own vehicle options
weight: weight of the load that has been carried in Tons (1000KG)
price: transport cost in Rials. This field is the target and you should predict it for test data set.