Prediction for bike sharing demand machine learning technique

Q: How many days will it take for my paper to be published?

The review time for papers is not fixed. However, if the paper is accepted and the author completes the processing charges formalities, the paper will be published within a few working days.

Q: I would like to receive a hard copy of the journal materials. Are there any additional charges?

You can log in to the author portal and pay 500 INR to receive the hard copy materials.

Ritesh Kumar; Rajvee Sakariya

Prediction for bike sharing demand machine learning technique

Authors: Ritesh Kumar, Rajvee Sakariya

Unique Paper ID: 173662
Volume: 11
Issue: 10
PageNo: 1694-1696

Keywords: Bike-sharing Machine Learning Demand Forecasting Random Forest Gradient Boosting Predictive Models

Abstract:
Bicycle sharing systems have evolved into efficient and environmentally friendly transportation. Forecasting the demand for such services is important for optimizing your business. This study uses machine learning techniques to analyze historical data and identify key factors affecting bicycle demand. Several models, including some linear regression (MLR), decision trees (DT), Randallswald (RF), and Gradient Boosting Machines (GBM), have been evaluated to determine the most effective prediction framework. The results show that ensemble models, particularly random forests and gradient boosts, provide excellent accuracy in demand forecasting.

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 1694-1696

Prediction for bike sharing demand machine learning technique

Available:https://ijirt.org/article?manuscript=173662

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now