Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions,
precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to
the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is
publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then
extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com.
CERTIFICATE ANALISI DATA DENGAN PYTHON
conda create --name main-ds python=3.9.6
conda activate main-ds
pip install numpy pandas matplotlib seaborn jupyter streamlit babel
streamlit run app.py