OpenML

JavaScript is required to properly view the contents of this page!

Explore
- Data
- Task
- Flow
- Run
- Study
- Task type
- Measure
- People
Help
Blog
Contact
Please cite us

Uber-and-Lyft-Dataset-Boston-MA

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

Issue	#Downvotes for this reason	By

Loading wiki

Help us complete this description Edit

Uber vs Lyft This is a very beginner-friendly dataset. It does contain a lot of NA values. It is a good dataset if you want to use a Linear Regression Model to see the pattern between different predectors such as hour and price. A really amazing part of this dataset is that I have included the corresponding weather data for that hour with a short summary of the weather. Other important factors are temperature, wind, and sunset.

56 features

id (ignore)	string	693071 unique values 0 missing
timestamp	numeric	36179 unique values 0 missing
hour	numeric	24 unique values 0 missing
day	numeric	17 unique values 0 missing
month	numeric	2 unique values 0 missing
datetime	string	31350 unique values 0 missing
timezone	string	1 unique values 0 missing
source	string	12 unique values 0 missing
destination	string	12 unique values 0 missing
cab_type	string	2 unique values 0 missing
product_id	string	13 unique values 0 missing
name	string	13 unique values 0 missing
price	numeric	147 unique values 55095 missing
distance	numeric	549 unique values 0 missing
surge_multiplier	numeric	7 unique values 0 missing
latitude	numeric	11 unique values 0 missing
longitude	numeric	12 unique values 0 missing
temperature	numeric	308 unique values 0 missing
apparentTemperature	numeric	319 unique values 0 missing
short_summary	string	9 unique values 0 missing
long_summary	string	11 unique values 0 missing
precipIntensity	numeric	63 unique values 0 missing
precipProbability	numeric	29 unique values 0 missing
humidity	numeric	51 unique values 0 missing
windSpeed	numeric	291 unique values 0 missing
windGust	numeric	286 unique values 0 missing
windGustTime	numeric	25 unique values 0 missing
visibility	numeric	227 unique values 0 missing
temperatureHigh	numeric	129 unique values 0 missing
temperatureHighTime	numeric	23 unique values 0 missing
temperatureLow	numeric	133 unique values 0 missing
temperatureLowTime	numeric	31 unique values 0 missing
apparentTemperatureHigh	numeric	124 unique values 0 missing
apparentTemperatureHighTime	numeric	27 unique values 0 missing
apparentTemperatureLow	numeric	136 unique values 0 missing
apparentTemperatureLowTime	numeric	32 unique values 0 missing
icon	string	7 unique values 0 missing
dewPoint	numeric	313 unique values 0 missing
pressure	numeric	316 unique values 0 missing
windBearing	numeric	195 unique values 0 missing
cloudCover	numeric	83 unique values 0 missing
uvIndex	numeric	3 unique values 0 missing
visibility.1	numeric	227 unique values 0 missing
ozone	numeric	274 unique values 0 missing
sunriseTime	numeric	110 unique values 0 missing
sunsetTime	numeric	114 unique values 0 missing
moonPhase	numeric	18 unique values 0 missing
precipIntensityMax	numeric	65 unique values 0 missing
uvIndexTime	numeric	20 unique values 0 missing
temperatureMin	numeric	131 unique values 0 missing
temperatureMinTime	numeric	25 unique values 0 missing
temperatureMax	numeric	128 unique values 0 missing
temperatureMaxTime	numeric	23 unique values 0 missing
apparentTemperatureMin	numeric	137 unique values 0 missing
apparentTemperatureMinTime	numeric	29 unique values 0 missing
apparentTemperatureMax	numeric	125 unique values 0 missing
apparentTemperatureMaxTime	numeric	27 unique values 0 missing