Bike Sharing Dataset
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Link: https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset#
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Related: Knowledge Discovery & Data Mining
Files
- Readme.txt
- hour.csv : bike sharing counts aggregated on hourly basis. Records: 17379 hours
- day.csv - bike sharing counts aggregated on daily basis. Records: 731 days
Attributes
In day.csv there is no hr field
- instant: record index
- dteday : date
- 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 : wether 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 :
- Clear, Few clouds, Partly cloudy, Partly cloudy
- Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
- Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
- 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
Learning Tasks
- author suggest regression predicting of bike rentals hourly or daily based on the environmental settings.
- maybe consider % of registered to total and casual to total and how it varies
Project Tasks
- What data to work on
- How the data is managed in your system
- Prepare the data:
- describe and discuss each attribute by visualization
- repair the data if needed, add attributes etc.
- Analyze with an application of choice
- Interpret the results
- Propose a resulting activity