Use of Regression Algorithm for Bike Ride Sharing Demand Projection

Bike-sharing systems are becoming an increasingly popular means of transportation in areas of urban populations around the world. Accurate prediction of bike sharing demand is critical for the efficient management of these systems, as it allows for the provision of resources and the optimization of the user experience. Machine learning models have emerged as a powerful tool for bike-sharing demand prediction in recent years. These models can consider a range of factors, including weather conditions, time of day, and the location of the bike-sharing station, in order to generate accurate projections of demand. In this research chapter, with the help of machine learning techniques, the data sources and pre-processing steps through evaluated metrics were used to assess the performance in terms of hyperparameter tuning techniques. Several regression algorithms such as linear, lasso, random forest, gradient boosting, XGBoost, and lightweight regression evaluated performance on the training data and selected the one that performed best. Out of all, XGBoost regression achieved greatly with 88.81% accuracy. Further, it had evaluated that through this experimentation, root means square log error (RMSLE) was a suitable evaluation metric for bike sharing demand prediction since it was well-suited for continuous, positive-valued targets and was sensitive to the relative size of the errors. Thus, this estimation provided a valuable resource for researchers and practitioners interested in the use of machine learning for bike-sharing demand forecasts.