EX5.BIKE: BIKE dataset for Exercise 4 Chapter 5
In profpetrie/regclass: Tools for an Introductory Class in Regression and Modeling
Description
Usage
Format
Details
References
Description
BIKE dataset for Exercise 4 Chapter 5
Usage
1
data("EX5.BIKE")
Format
A data frame with 413 observations on the following 9 variables.
Demanda numeric vector
Daya factor with levels Friday Monday Saturday Sunday Thursday Tuesday Wednesday
Workingdaya factor with levels no yes
Holidaya factor with levels no yes
Weathera factor with levels No rain Rain
AvgTempa numeric vector
EffectiveAvgTempa numeric vector
AvgHumiditya numeric vector
AvgWindspeeda numeric vector
Details
Adapted from the bike sharing dataset on the UCI data repository http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This concerns the demand for rental bikes in the DC area. This is an expanded version of EX4.BIKE with more variables and without the row containing bad data.
Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.
Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.
References
Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.
profpetrie/regclass documentation built on May 26, 2019, 8:33 a.m.
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Description Usage Format Details References
Description
BIKE dataset for Exercise 4 Chapter 5
Usage
1 | data("EX5.BIKE")
|
Format
A data frame with 413 observations on the following 9 variables.
Demanda numeric vector
Daya factor with levels
FridayMondaySaturdaySundayThursdayTuesdayWednesdayWorkingdaya factor with levels
noyesHolidaya factor with levels
noyesWeathera factor with levels
No rainRainAvgTempa numeric vector
EffectiveAvgTempa numeric vector
AvgHumiditya numeric vector
AvgWindspeeda numeric vector
Details
Adapted from the bike sharing dataset on the UCI data repository http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This concerns the demand for rental bikes in the DC area. This is an expanded version of EX4.BIKE with more variables and without the row containing bad data.
Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.
Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.
References
Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.
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