Bike Sharing Dataset is a dataset that contains the hourly and daily count of rental bikes between the years 2011 and 2012 in the Capital Bikeshare system with the corresponding weather and seasonal information.
Dataset Information
- 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 [Web Link])
- weekday : day of the week
- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
- weathersit :
- Clear, Few clouds, Partly cloudy, Partly cloudy
- Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
- Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
- Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)
- atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)
- 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
- Clone this repository
git clone https://github.com/Anashaneef/capital-bike-share-analysis.git
- Install all library
pip install numpy pandas matplotlib seaborn jupyter streamlit babel
or
pip install -r requirements.txt
- Go to dashboard folder
cd dashboard
- Run with Streamlit
streamlit run dashboard.py
Or you can or you can directly visit this website