Data
Bike_Sharing_Demand

Bike_Sharing_Demand

active ARFF Publicly available Visibility: public Uploaded 17-10-2020 by Joaquin Vanschoren
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Author: Hadi Fanaee-T and Joao Gama Source: [original](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset) - 01-01-2013 Please cite: Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15 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. Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com. Use of this dataset in publications must be cited to the following publication: 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, doi:10.1007/s13748-013-0040-3. Attributes: - season : season (1:spring, 2:summer, 3:fall, 4:winter) - yr : year (0: 2011, 1:2012) - mnth : month ( 1 to 12) - hr : hour (0 to 23) - holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule) - weekday : day of the week - workingday : if day is neither weekend nor holiday is 1, otherwise is 0. - weathersit: - 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 : Normalized temperature in Celsius. The values are divided to 41 (max) - atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max) - hum: Normalized humidity. The values are divided to 100 (max) - windspeed: Normalized wind speed. The values are divided to 67 (max) - casual: count of casual users - registered: count of registered users - cnt: count of total rental bikes including both casual and registered This version was cleaned up up by Joaquin Vanschoren: - Category labels replaced by category names (season, weathersit, year) - Turned back normalization for temperature and windspeed for interpretability - Renamed features for readability - Note: the correct order of the seasons seems to be ['summer', 'winter', 'spring', 'fall']

13 features

count (target)numeric869 unique values
0 missing
seasonnominal4 unique values
0 missing
yearnumeric2 unique values
0 missing
monthnumeric12 unique values
0 missing
hournumeric24 unique values
0 missing
holidaynumeric2 unique values
0 missing
weekdaynumeric7 unique values
0 missing
workingdaynumeric2 unique values
0 missing
weathernominal4 unique values
0 missing
tempnumeric50 unique values
0 missing
feel_tempnumeric65 unique values
0 missing
humiditynumeric89 unique values
0 missing
windspeednumeric30 unique values
0 missing
casual (ignore)numeric322 unique values
0 missing
registered (ignore)numeric776 unique values
0 missing

19 properties

17379
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
11
Number of numeric attributes.
2
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-63.93
Average class difference between consecutive instances.
84.62
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
15.38
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

2 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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