Big Mart Sales Prediction Using Machine Learning

DOI

10.2991/978-94-6463-738-0_77How to use a DOI?

Keywords

Machine learning; sales prediction; Big Mart; regression; XG-Boost regression; decision tree; random forest; prediction model; linear regression; K-Nearest Neighbors

Abstract

Current day companies can neither understand nor predict the marketing events. Any management will find it really difficult to survive in that particular field if detailed and regular data analysis is not done. Using a few machine learning techniques. Can be used to demonstrate visualization of current and future sale By analyzing the sales trends of Big Mart, the company can make informed predictions or forecasts, enabling better planning and decision-making for the retailer and design its production, marketing and promotion activities accordingly. More importantly, it is necessary to pre-process missing data and utilize performance feature engineering to build the model before applying it. It is evident from the experimental observations that a random forest predictor has performed the best of the four compared to ridge regression, XG-Boost Regression, and decision tree models used in this study out of the four machine learning techniques.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ishu Sahu
AU  - Dhaneshwari Sahu
AU  - Poonam Gupta
PY  - 2025
DA  - 2025/06/22
TI  - Big Mart Sales Prediction Using Machine Learning
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 988
EP  - 1001
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_77
DO  - 10.2991/978-94-6463-738-0_77
ID  - Sahu2025
ER  -