Analysis of Campus Catering Data Based on Machine Learning

At present, sensors and big data create powerful systems capable of real-time monitoring and decision-making. The development of big data in all walks of life is very fast. Big data technology and applications are also gradually accepted by the public, and the data industry is gradually maturing and beginning to enter a rapid development stage. At the same time, the development of the Internet makes data analysis more accurate, and the combination of the two complements each other, contributing to the good development of big data. With the rapid development and wide application of machine learning technology, its application in all walks of life has become increasingly widespread. In this work, we collect the business data of campus restaurants in different periods to ensure the breadth and depth of the data. The regression algorithm and decision tree algorithm in machine learning are used to integrate and analyze the collected data to reflect the demand tendency of the general public and reflect the relationship between cost and benefit. We analyze the catering needs of different consumer groups, develop big data applications with greater potential value, and seek long-term development for catering real economy enterprises.