Online sales prediction approach using methodology of CRISP-DM

¹ Siberian Federal University, Vuzovsky Lanc,3, Krasnoyarsk, 660025, Russia
² Reshetnev Siberian State University of Science and Technology, System Analysis and Operation Research, 31, Krasnoyarsky Rabochy Av., Krasnoyarsk. Krasnoyarsk, 660037, Russia

^* Corresponding author: h677hm@gmail.com

Abstract

This article studies the sales forecasting problem in the field of e-commerce. Based on the CRISP-DM methodology, innovative data mining technology is used to construct a variety of forecasting models, and is compared and optimized. This article improves the quality and quantity of sales forecasts and provides enterprises with more accurate and effective decision support. In terms of modeling optimization in this article, data mining models such as random forest, support vector machine, and neural network are used for comprehensive prediction, and comparative analysis is conducted with the classic multiple linear regression model. Through model evaluation and optimization, this paper achieved better prediction performance and accuracy. This research has certain theoretical significance and practical value, and provides new ideas and methods for the marketing decisions and business development of e-commerce enterprises.