Bike sharing company customer count prediction

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

A US bike-sharing provider BoomBikes customer count dataset is provided with attributes related to weather, date, season, year, month, holiday, weekday, working day, and count of rental bikes. The company wants to know:

• Which variables are significant in predicting the demand for shared bikes.
• How well those variables describe the bike demands

• The variables like month, year, weather situation are significant in predicting the demand for shared bikes.
• The coefficients of the variables like month, year, weather situation are very closely related to the affect seen in the box plots for these with basic EDA which confirms the significance of these variables.

• Python - version 3.10.7