Bike sharing dataset from the UCI Machine Learning Repository, containing 17,379 hourly records and 731 daily records, with rental count data combined with weather and seasonal features, suitable for time series analysis and regression modeling.
A multifactor regression dataset integrating time, weather, and seasonal features
Data comes from actual rental records of the Washington, D.C. Capital Bikeshare system from 2011-2012.
Includes weather features such as temperature, feels-like temperature, humidity, wind speed, and weather conditions, enabling analysis of weather's impact on travel.
Includes multidimensional time features such as hour, day, month, year, weekday, and holiday, suitable for time series analysis.
Provides both hourly and daily data, allowing comparison of modeling performance at different aggregation levels.
Distinguishes rental counts between registered and casual users, enabling user behavior analysis.
Sourced from the UCI Machine Learning Repository, a classic dataset in time series regression.
From demand forecasting to urban planning, with a wide range of applications
Predict bike rental volume under different time periods and weather conditions, and practice regression algorithms
Analyze daily cycles and seasonal changes in rental volume, and practice time series decomposition
Analyze the impact weights of factors such as weather, temperature, and holidays on rental demand
Model shared mobility demand and provide data support for urban transportation planning
Below are the first few rows of the hourly bike-sharing 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
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View dataset details on the Ace Data Cloud platform and learn about metadata such as field descriptions, sample size, and licensing terms.
After purchase, download the daily and hourly ZIP delivery packages separately. Each package retains the original CSV (58 KB / 1.1 MB), source attribution, and CC BY 4.0 license text.
Use pandas.read_csv() to load the data and begin time-series analysis and regression modeling.
A classic time-series dataset with previewable licensing information and data samples, available for download after purchase. Complete weather and time features make it an ideal choice for demand forecasting models.
