Bike-Sharing-Dataset

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
weekday：星期中的哪天，取值为 0～6
workingday：是否工作日
1=工作日 （非周末和节假日）
0=周末
weathersit：天气
1：晴天，多云
2：雾天，阴天
3：小雪，小雨
4：大雨，大雪，大雾
temp：气温摄氏度
atemp：体感温度
hum：湿度
windspeed：风速

y值

casual：非注册用户个数
registered：注册用户个数
cnt：给定日期（天）时间（每小时）总租车人数，响应变量 y

注意：后三个特征均为要预测的 y，作业里只需对 cnt 进行预测
黑色标记的特征为输入特征 x

Field Descriptions
Instant: Record number
Dteday: Date
Season: Season
1 = Spring, 2 = Summer, 3 = Autumn, 4 = Winter
yr: Year, (0: 2011, 1: 2012)
mnth: Month (1 to 12)
hr: Hour (0 to 23) [only included in hour.csv, this field should be ignored for assignments]
holiday: Whether it is a holiday
weekday: Day of the week, values range from 0 to 6
workingday: Whether it is a working day
1 = Working day (non-weekend and non-holiday), 0 = Weekend
weathersit: Weather situation
1: Clear, few clouds
2: Misty, overcast
3: Light snow, light rain
4: Heavy rain, heavy snow, dense fog
temp: Air temperature (degrees Celsius)
atemp: Feels-like (apparent) temperature
hum: Humidity
windspeed: Wind speed

Target Variables (y)
casual: Number of non-registered users
registered: Number of registered users
cnt: Total number of hourly bike rentals on a given date, the response variable y

Note: The last three features are all target variables y, and only cnt prediction is required for this assignment. Features marked in black are input features x.

搜集汇总

背景与挑战

背景概述

Bike-Sharing-Dataset是一个关于自行车租赁的数据集，包含日期、季节、天气、温度等多种特征，目标变量为总租车人数。数据集适用于预测分析任务，特别是对自行车租赁需求的预测。