The bike sharing dataset from the UCI Machine Learning Repository contains 17,389 hourly records and 731 daily records, combined with weather and seasonal features of rental count data, suitable for time series analysis and regression modeling.
A multi-factor regression dataset that integrates time, weather, and seasonal features
The data comes from the actual rental records of the Capital Bikeshare system in Washington, D.C. from 2011 to 2012.
Includes weather features such as temperature, feels-like temperature, humidity, wind speed, and weather conditions, allowing analysis of the impact of weather on travel.
Includes multi-dimensional time features such as hour, day, month, year, weekdays, and holidays, suitable for time series analysis.
Provides data at both hourly and daily granularity, allowing comparison of modeling effects at different aggregation levels.
Distinguishes between registered users and temporary users in terms of rental quantity, enabling user behavior analysis.
Originates from the UCI Machine Learning Repository and is a classic dataset in the field of time series regression.
From demand forecasting to urban planning, the application scenarios are extensive
Forecast the bike rental volume under different time periods and weather conditions, practicing regression algorithms
Analyze the daily cycle and seasonal variations of rental volume, practicing time series decomposition
Analyze the weight of factors such as weather, temperature, and holidays on rental demand
Modeling shared travel demand to provide data support for urban transportation planning
The following are examples of the first few rows of the shared bicycle hourly dataset
instant,dteday,season,yr,mnth,hr,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt 1,2011-01-01,1,0,1,0,0,6,0,1,0.24,0.2879,0.81,0,3,13,16 2,2011-01-01,1,0,1,1,0,6,0,1,0.22,0.2727,0.8,0,8,32,40 3,2011-01-01,1,0,1,2,0,6,0,1,0.22,0.2727,0.8,0,5,27,32 4,2011-01-01,1,0,1,3,0,6,0,1,0.24,0.2879,0.75,0,3,10,13 5,2011-01-01,1,0,1,4,0,6,0,1,0.24,0.2879,0.75,0,0,1,1
From browsing to analysis, you can start your data science project in minutes
View dataset details on the Ace Data Cloud platform, understand field descriptions, sample size, and licensing agreements.
Download daily (58 KB) and hourly (1.1 MB) CSV files, data is ready to use.
Use pandas.read_csv() to load the data and start time series analysis and regression modeling.
A classic time series dataset, open license, available for immediate download. Complete weather and time features make it an ideal choice for demand forecasting modeling.
