Download pdf
Walmart sales prediction based on random forest model and application of feature importance
- 1 Whittier Christian High School
* Author to whom correspondence should be addressed.
Abstract
Sales forecasting is crucial for efficient resource allocation and inventory management in retail. This study employs Random Forest to predict weekly sales for 45 Walmart stores, leveraging a diverse dataset with store-specific sales and external factors. Through meticulous preprocessing and model application, one achieves outstanding accuracy, with a Weighted Mean Absolute Error (WMAE) as low as 1.2030 and an impressive accuracy rate of 98.8%. Additionally, integrating feature importance ranking sheds light on influential variables in sales forecasting. This study provides a blueprint for developing precise and adaptable sales forecasting models, offering profound significance for the retail industry. It underscores the effectiveness of machine learning techniques, e.g., Random Forest and insightful feature engineering in achieving highly accurate predictions. By enhancing the industry's understanding of intricate sales dynamics, this research contributes to optimizing resource allocation, inventory management, and strategic planning. Ultimately, it drives operational efficiency and success in the dynamic landscape of the retail sector.
Keywords
Random forest, feature importance, WMAE
[1]. Gustriansyah R, Ermatita E and Rini D P 2022 Expert Systems with Applications vol 207 p 118043.
[2]. Kahn K B and Adams M E 2001 The Journal of Business Forecasting Methods and Systems vol 19 p 19.
[3]. Liu N, Ren S, Choi T M, Hui C L and Ng S F 2013 Mathematical Problems in Engineering vol 2013 pp 1–9.
[4]. Xia Z, Xue S, Wu L, Sun J, Chen Y and Zhang R 2020 Distributed and Parallel Databases vol 38(3) p 713–738.
[5]. Edling E 2016 Intelligent Decision-Making Models for Production and Retail Operations Createspace Independent Publishing Platform (Berlin Heidelberg: Springer)
[6]. Aras S Deveci K İ and Polay C 2017 Journal of Business Economics and Management vol 18(5) pp 803–832.
[7]. Walmart Sales Prediction (nd) Wwwkagglecom Retrieved from: https://wwwkagglecom/datasets/divyajeetthakur/walmart-sales-prediction/data
[8]. Segal M R 2004 Machine learning benchmarks and random forest regression (New York: Escholar).
[9]. Zhang J, Zulkernine M and Haque A 2008 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) vol 38(5) pp 649-659.
[10]. Cassidy A P and Deviney F A (2014 October) Calculating feature importance in data streams with concept drift using Online Random Forest In 2014 IEEE International Conference on Big Data (Big Data) pp 23-28.
[11]. Cleger-Tamayo S, Fernández-Luna J M and Huete J F 2012 RUE@ RecSys pp 24-26.
[12]. Wojtas M and Chen K 2020 Advances in Neural Information Processing Systems vol 33 pp 5105-5114.
Cite this article
Chen,D. (2024). Walmart sales prediction based on random forest model and application of feature importance. Applied and Computational Engineering,53,264-273.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).