Analysis and Prediction of Commercial Big Data Based on WIFI Probe

Abstract

With the development of the Internet economy, offline physical transactions are facing a huge test. The number of merchants in many shopping malls has decreased, and the volume of physical transactions has fallen sharply. As an important data of the operation status of the shopping mall, passenger traffic can grasp the best operating time, arrange employees reasonably, and judge the marketing volume of the shopping mall. Therefore, excellent mall operators need to know the status of the passenger flow in the market in a timely manner, and analyze the overall operation situation and make reasonable operational decisions. Based on the above problems, this paper designs and implements a commercial big data analysis and prediction system based on WIFI probe, obtains basic data through WIFI probe hardware sensor, and provides relatively accurate passenger flow information for commercial field, combined with big data analysis and machine learning prediction. The algorithm analyzes a series of information about passenger flow, store quantity, bounce rate, dwell time and other forecasting trend of passenger flow in the future, providing reliable data support for the commercial field and becoming the reference for various decisions.