Bike sharing systems are increasingly deployed in many cities worldwide. Thus, knowing how the system demand evolves in advance helps improve the preparedness of operational schemes. And hence system needs to be automated with a utilization of machine learning algorithm. Therefore, developed a model for bike rental count prediction on daily basis, based on the environmental and seasonal settings.