Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 7th International Conference on Economic Management and Green Development

Series Vol. 43 , 10 November 2023


Open Access | Article

Research on the Distributions of Products for Big Mart

Zixuan Zhao * 1
1 South China University of Technology

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 43, 32-39
Published 10 November 2023. © 2023 The Author(s). Published by EWA Publishing
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.
Citation Zixuan Zhao. Research on the Distributions of Products for Big Mart. AEMPS (2023) Vol. 43: 32-39. DOI: 10.54254/2754-1169/43/20232121.

Abstract

In order to have a brief insight into the process of business data analysis for the big mart’s product and through which to find out the inner logic about data analysis. This research did a brief research based on the big mart sales dataset from Kaggle. The data are collected in 2013 for 1559 products across 10 stores in different cities. This research aims to build a predictive model and forecast the sales of each product at the specific stores and then try to understand the properties of products and outlets which play a key role in increasing sales. After using some basic analysis methods based on python, the author gets the distribution outcome of a big mart’s product and creates five simple models to predict the final outlet-sales and find out the most performed model using MAE criteria. The outcome shows that finally the XGB Regressor model performed best and for the real business, it is the most suitable selection.

Keywords

big mart, XGB Regressor, business

References

1. Ramírez-Hassan, A., Montoya-Blandón, S.: Forecasting from others’ experience: Bayesian estimation of the generalized Bass model. International Journal of Forecasting 36(2), 442-465 (2020).

2. Verstraete, G., Aghezzaf, E. H., Desmet, B.: A data-driven framework for predicting weather impact on high-volume low-margin retail products. Journal of Retailing and Consumer Services 48, 169-177 (2019).

3. Gong, L., Wang, C.: Model of Automobile Parts Sale Prediction Based on Nonlinear Periodic Gray GM (1, 1) and Empirical Research. Mathematical Problems in Engineering (2019).

4. Ozhegov, E. M., Teterina, D.: Methods of machine learning for censored demand prediction. In Machine Learning, Optimization, and Data Science: 4th International Conference, 441-446 (2019).

5. Massaro, A., Maritati, V., Galiano, A.: Data Mining model performance of sales predictive algorithms based on RapidMiner workflows. International Journal of Computer Science & Information Technology 10(3), 39-56 (2018).

6. Correlation matrix. Corporate Finance Institute. (2023, March 24). i. https://corporatefinanceinstitute.com/resources/excel/correlation-matrix/,last accessed 2023/7/5

7. Rankfeatures. MathWorks. (n.d.). https://www.mathworks.com/help/stats/feature-selection.html, last accessed 2023/7/5

8. The difference between training data vs. test data in machine learning. Data Science without Code(2022,February 11), https://www.obviously.ai/post/, last accessed 2023/7/5.

9. Machine learning models: What they are and how to build them. Coursera.(n.d.). i. https://www.coursera.org/articles/machine-learning-models last accessed 2023/7/5.

10. Bevans, R. (2022, November 15). Simple linear regression: An easy introduction & examples. Scribbr. https://www.scribbr.com/statistics/simple-linear-regression/last accessed 2023/7/5.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International 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 Instruction).

Volume Title
Proceedings of the 7th International Conference on Economic Management and Green Development
ISBN (Print)
978-1-83558-107-0
ISBN (Online)
978-1-83558-108-7
Published Date
10 November 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/43/20232121
Copyright
© 2023 The Author(s)
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

Copyright © 2023 EWA Publishing. Unless Otherwise Stated