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US Daily Bike Share Regression Data
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About
Data focuses on understanding the daily demand for shared bikes within the American market, specifically addressing revenue challenges faced by a US provider, BoomBikes, following the effects of the Corona pandemic. The primary business goal is to develop a regression model to predict shared bike usage. This prediction capability will allow management to formulate a mindful business plan for accelerating revenue post-lockdown by anticipating customer needs and understanding how various features influence demand dynamics. The dataset captures key factors like weather, date specifics, and user type (casual versus registered) influence on daily bike rentals.
Columns
- instant: Record identification number.
- dteday: Specific date of the record.
- season: Categorical representation of the season (e.g., Spring, Summer, Fall, Winter).
- yr: Year of observation.
- mnth: Month of the year (1 through 12).
- holiday: Binary indicator for whether the day is an official holiday (0=No, 1=Yes).
- weekday: Numerical representation of the day of the week (0-6).
- workingday: Binary indicator for whether the day is a working day (0=No, 1=Yes).
- weathersit: Categorical description of the weather conditions (1-3 levels).
- temp: Normalized average temperature on that day.
- atemp: Normalized 'feeling' temperature on that day.
- hum: Normalized humidity level.
- windspeed: Normalized wind speed.
- casual: Count of casual users (non-registered).
- registered: Count of registered users.
- cnt: Total count of shared bikes rented (the target demand variable).
Distribution
This data product is supplied in CSV format (
day.csv). It contains 730 valid records across all 16 columns. All features, including date, meteorological factors, and bike counts, have been validated with no missing or mismatched entries observed. The data file size is approximately 57.54 kB.Usage
Ideal applications include building and evaluating Regression models to forecast bike share demand. Users can identify significant variables that predict rental volume and understand the precise relationship between environmental or temporal features and customer demand. This information is key for optimizing resource allocation, managing inventory, and defining strategic marketing efforts. The models generated can also be used by management to understand the demand dynamics of a new market.
Coverage
The data provides daily observations for the American market. It includes details across different seasons and months, covering daily variability in meteorological factors (temperature, humidity, windspeed) and categorical date attributes (holiday, working day). The records span a specific time period, detailed by 730 unique daily entries.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: For conducting quantitative studies and building predictive models like linear regression, focused on time series or demand forecasting.
- Transportation Planners: To understand how external factors like weather and scheduling impact urban mobility choices.
- Business Strategists: To manipulate business strategy effectively to meet customer expectations and demand levels, particularly when assessing new markets.
Dataset Name Suggestions
- BoomBikes Daily Bike Demand Forecast
- Shared Bike Rental Prediction Factors
- US Daily Bike Share Regression Data
- Impact of Weather on Bike Sharing Demand
Attributes
Original Data Source: US Daily Bike Share Regression Data
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