========================================= Dataset characteristics ========================================= day.csv have the following fields: - instant: record index - dteday : date - season : season (1:spring, 2:summer, 3:fall, 4:winter) - yr : year (0: 2018, 1:2019) - mnth : month ( 1 to 12) - holiday : weather day is a 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 : temperature in Celsius - atemp: feeling temperature in Celsius - hum: humidity - windspeed: wind speed - casual: count of casual users - registered: count of registered users - cnt: count of total rental bikes including both casual and registered ========================================= License ========================================= Use of this dataset in publications must be cited to the following publication: [1] 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. @article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} } ========================================= Contact ========================================= For further information about this dataset please contact Hadi Fanaee-T (hadi.fanaee@fe.up.pt)
Dipti028/BikeSharingAssignment
To build a multiple linear regression model for the prediction of demand for shared bikes.
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