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@article{173662,
        author = {Ritesh Kumar and Rajvee Sakariya},
        title = {Prediction for bike sharing demand machine learning technique},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1694-1696},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173662},
        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.},
        keywords = {Bike-sharing, Machine Learning, Demand Forecasting, Random Forest, Gradient Boosting, Predictive Models},
        month = {March},
        }