학술논문
Big Mart Sales Prediction Using Machine Learning
Document Type
Conference
Author
Source
2024 10th International Conference on Communication and Signal Processing (ICCSP) Communication and Signal Processing (ICCSP), 2024 10th International Conference on. :742-747 Apr, 2024
Subject
Language
ISSN
2836-1873
Abstract
In today’s competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment.