Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 59 , 05 January 2024
Open
Access | Article
Bicycle Sales Prediction Based on Ensemble Learning
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Abstract
In the field of sales forecasting, there are still various challenges in conducting comprehensive analysis and accurate predictions for bicycle sales, including the diversity of sample data, the range of research scope, and the methods employed. This study aims to fill this research gap by applying a bicycle sales dataset and two ensemble learning methods to investigate the factors influencing bicycle sales and conduct sales predictions and analysis. The research findings indicate that cost, profit, and income are the most significant factors influencing bicycle profit predictions. Compared to the Random Forest model, the Gradient Boosting model performs better in predicting bicycle profits. This paper discusses the relevance and predictive performance of the bicycle sales dataset, providing opportunities for improvement and further optimization in future research to enhance the accuracy and reliability of bicycle sales predictions and offer valuable insights for decision-making and planning. Overall, these results shed light on guiding further exploration of sales prediction.
Keywords
sales forecasting, ensemble learning, the Random Forest model, the Gradient Boosting model
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-209-1
- ISBN (Online)
- 978-1-83558-210-7
- Published Date
- 05 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/59/20231135
- Copyright
- 05 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited