Application of Gradient Boosting Regression Model in Intelligent Distribution of E-commerce

Abstract

With the fast growth of e-commerce in the nation in the latest days, the quantity of e-commerce purchases has continued to increase, and people's methods of consuming have also transformed. Digital shopping has lately been more practical and effective due to advancements in Internet innovation, particularly wireless connections, and 5G mobile connectivity technologies. Digital shopping has brought about the peak of globalization and has grown to be an essential sector of economic globalization. On the other hand, since e-commerce has grown rapidly, several issues have occurred. One of those is logistics distribution, which has a significant impact on e-commerce growth and is a crucial link in the chain that determines consumer fulfillment. Distribution in e-commerce pertains to the procedure of sending goods or commodities to the final customer after an online transaction. With e-commerce, efficient distribution is essential to ensure that goods are delivered to consumers promptly and effectively. This fosters user retention and encourages repeat purchases.  It is necessary to provide an efficient model for the efficient distribution of e-commerce.  For the effective intelligent distribution of e-commerce platforms, we thus proposed the gradient boosting regression model (GBRM). The efficiency of the suggested system was assessed and contrasted with methods that were previously utilized. The results shows that the suggested GBRM model significantly enhanced the distribution of e-commerce.