Predictive Analytics for Inventory Management in E-commerce Using Machine Learning Algorithms

Abstract

A description of the abstract for the research paper titled “Predictive Analytics for Inventory Management in E-commerce Using Machine Learning Algorithms” is as follows: In this research, a novel strategy for implementing force operations in e-commerce is presented. This strategy involves the utilization of predictive analytics and machine literacy algorithms. When it comes to the ever-changing landscape of online retail, efficient force operation is necessary to satisfy customer demand while simultaneously minimizing the costs that are connected with overstocking and stockouts. To predict future demand for specific products, prophetic analytics methods are utilized, which involve the utilization of literal deal data in addition to other relevant elements. To create accurate prophetic models that are capable of locating complicated patterns and trends in the data, machine literacy methods such as arbitrary timbers, support vector machines, and neural networks are utilized. Through the incorporation of these prophetic models into the process of force operation, ecommerce businesses canoptimize force circumstances, expedite operations, and improve customer happiness. In addition to making a contribution to the expanding body of literature on data-driven approaches to force operation, this investigation demonstrates the potential for predictive analytics and machine literacy to be utilized in the process of tackling the specific issues that are associated with ecommerce.