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Level: Intermediate
Recommended Use: Regression Models
Domain: Social

Bike Sharing Data Set

Predict bike rental count (hourly/daily) based on the environmental & seasonal settings


This intermediate level dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. 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. This contains 2 files: Bike sharing counts aggregated on hourly basis (hour.csv - 17379 rows, 17 columns) & bike sharing counts aggregated on daily basis (day.csv - 731 rows, 16 columns)

This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, and regression modelling techniques. This data set could also be used to discover important trends and relationships. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set. All columns (except hr) are similar in both the data sets:


Data Dictionary

Column Position Atrribute Name Definition Data Type Example % Null Ratios
1 instant Record Index Quantitative 190, 7, 17180 0
2 dteday Date (Format: YYYY-MM-DD) Quantitative 2012-12-23, 2012-01-01, 2012-06-24 0
3 season Season (1: springer, 2: summer, 3: fall, 4: winter) Quantitative 1, 2, 4 0
4 yr Year (0: 2011, 1:2012) Quantitative 0, 1 0
5 mnth Month (1 to 12) Quantitative 1, 6, 12 0
6 hr Hour (0 to 23) - Not in day.csv dataset Quantitative 4, 6, 14 0
7 holiday Weather day is holiday or not Quantitative 0, 1 0
8 weekday Day of the week Quantitative 0, 6, 3 0
9 workingday Working Day: If day is neither weekend nor holiday is 1, otherwise is 0 Quantitative 0, 1 0
10 weathersit Weather Situation (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) Quantitative 1, 2, 3 0
11 temp Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale) Quantitative 0.08, 0.22, 0.34 0
12 atemp Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale) Quantitative 0.0909, 0.2727, 0.303 0
13 hum Normalized humidity. The values are divided to 100 (max) Quantitative 0.53, 0.8, 0.31 0
14 windspeed Normalized wind speed. The values are divided to 67 (max) Quantitative 0.194, 0, 0.2985 0
15 casual Count of casual users Quantitative 0, 2, 57 0
16 registered Count of registered users Quantitative 1, 0, 118 0
17 cnt Count of total rental bikes including both casual and registered Quantitative 1, 2, 175 0

Acknowledgement

This data set has been sourced from the Machine Learning Repository of University of California, Irvine Bike Sharing Data Set (UC Irvine). The UCI page mentions the following publication as the original source of the data set:

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