Predicting User Behavior Models Using M-Commerce Methods Decorated with Weighted Scoring and Mining Techniques

Mobile-based devices and systems are now a common feature of daily life. Mobile trade is a new and more powerful emerging technology. In addition to the fast progress of the increasing popularity of powerful mobile devices and wireless networking technology, smartphone users are now able to access global information from all over the world. In the future, in the mobile business era, some m-commerce platforms catch moving tracks and purchase from customers. An online social network is relevant in many fields including ads, public health programmers, administrative science, and even politics. Leaders and adherents using consumer details will be listed in this online study. Many businesses are strongly involved in SNSs and Social Media Marketing (SMM) is the new “holy marketing grid.” The real importance of SMM remains elusive, as social networking also determines its best practices and actions. Take the latest announcement Shop kick as an example to bring incentives to smartphone consumers when checking in shops and products. The objective of this paper is to develop customer patterns and mining predictions. Patterns are the transactions that a customer buys when he visits a shopping mall. It has information about shops and items. Data mining is used to predict the behavior of customers to gain more knowledge about mining. Data mining is intended to retrieve the F score and use confusion metrics primarily for error percentage determination of data mining algorithms.