# 第一周作业 利用线性回归技术实现共享单车数量预测 **Repository Path**: igenerator/capitalBikeshare ## Basic Information - **Project Name**: 第一周作业 利用线性回归技术实现共享单车数量预测 - **Description**: 问题描述 一、数据说明： Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的共享单车数据。数据包含每天的日期、天气等信息，需要预测每天的共享单车骑行量。 解题提示 原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset 1) 文件说明 day.csv: 按天计的单车共享次数（作业只需使用该文件） hour.csv: 按小时计的单车共享次数（无需理会） readme：数据说明文件 2) 字段说明 Instant记录号 Dteday：日期 Season：季节（1=春天、2=夏天、3=秋天、4=冬天） yr：年份，(0: 2011, 1:2012) mnth：月份( 1 to 12) hr：小时 (0 to 23) （只在hour.csv有，作业忽略此字段） holiday：是否是节假日（0/1） weekday：星期中的哪天，取值为0～6 workingday：是否工作日（0/1） 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日） weathers - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2019-01-17 - **Last Updated**: 2021-04-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ========================================== Bike Sharing Dataset ========================================== Hadi Fanaee-T Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto INESC Porto, Campus da FEUP Rua Dr. Roberto Frias, 378 4200 - 465 Porto, Portugal ========================================= Background ========================================= 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. ========================================= Data Set ========================================= 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. ========================================= Associated tasks ========================================= - Regression: Predication of bike rental count hourly or daily based on the environmental and seasonal settings. - Event and Anomaly Detection: Count of rented bikes are also correlated to some events in the town which easily are traceable via search engines. For instance, query like "2012-10-30 washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are identified in [1]. Therefore the data can be used for validation of anomaly or event detection algorithms as well. ========================================= 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 ========================================= Dataset characteristics ========================================= Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv - instant: record index - dteday : date - season : season (1:springer, 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 ========================================= 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)