SCRS Conference Proceedings on Intelligent Systems
Performance Comparison of ML Regression Algorithms in Predicting Supermarket Sales
Authors: Balaji Jayakrishnan, Gunja Pandey, Nitika Verma, Ritika Sarkar, Muskan Dhingra and Palak Tande
Publishing Date: 11-10-2021
ISBN: 978-93-91842-08-6
Abstract
The ability of regression algorithms to reliably identify the influencing factors of any data on the desired result is irrefutable. With the available techniques, we can investigate the main reason behind the influence of distinguishing factors on a supermarket's sales. We’ll be building a machine learning model that can accurately predict the sales in millions of units for a given product. Our work will investigate the ability of some of the most popular ML regression algorithms to provide this information. Seven regression algorithms will be trained using data collected through supermarket sales. To gain key insights, the algorithms are compared along two axes, prediction quality and usefulness of output. This class of algorithms produces models that can be used to predict performance in sales and indicate the sources of potential market problems and quantify the potential gain.
Keywords
Machine Learning; Regression Algorithm; Supermarkets; Sales Analysis; Prediction.
Cite as
Balaji Jayakrishnan, Gunja Pandey, Nitika Verma, Ritika Sarkar, Muskan Dhingra and Palak Tande, "Performance Comparison of ML Regression Algorithms in Predicting Supermarket Sales", In: Raju Pal and Praveen Kumar Shukla (eds), SCRS Conference Proceedings on Intelligent Systems, SCRS, India, 2021, pp. 181-187. https://doi.org/10.52458/978-93-91842-08-6-16