Research On Scoring Of Meituan Takeaway Stores Based On Data Mining

The popularity of the Internet has brought many conveniences to our lives.With the rapid development of mobile applications,the booking app has successfully occupied the entire food delivery industry market.More and more people choose to order meals on the takeaway platform,which makes the takeaway industry continue to grow rapidly,which not only promotes the development of online and offline integration,but also expands consumer application scenarios,and injects new momentum into the development of the restaurant industry.Online consumption often has information asymmetry.For merchants,various factors such as the type of food,pricing,and delivery time will affect consumers' purchase intentions,thereby affecting store sales.For consumers,facing a large number of takeaway merchants,it is difficult to make a satisfactory purchase decision without fully understanding the merchant information.Based on this,this paper selects the Meituan takeaway platform with the largest market transaction value,and uses a web crawler to obtain information on the Meituan takeaway merchants from Changqing University in Jinan City.It conducts research through takeaway merchant data to provide decisionmaking references for merchants and consumers.The specific research contents of this thesis are as follows:(1)Analysis of the basic situation of Meituan takeaway merchants.After clarifying the research purpose and research object,first obtain the merchant data of Meituan Takeaway Online University City,Changqing District,Jinan,through octopus data collector,and conduct data processing and index selection.Visual analysis of the basic situation,category distribution and operating status of take-out merchants;(2)Construction of a store classification model based on K-means clustering.Choose the main indicators that affect the store classification,use SPSS to perform correlation analysis on the above indicators,and use the principal component analysis to reduce the dimensions of the highly relevant indicators,and then use the K-means clustering algorithm to obtain the takeaway merchant classification.All businesses are divided into three categories: "word-of-mouth" merchants,"popular" merchants,and "net red" merchants,which analyze the characteristics of different types of merchants and give corresponding shop operating decisions;(3)Construction of Consumer Choice Model Based on C4.5 Algorithm.The Kmeans clustering results are divided into two categories: recommended merchants and non-recommended merchants as classification attributes.Based on the C4.5 algorithm,a decision tree model is established.The distribution fees,store ratings,and positive rate affect whether to recommend consumers to choose a business,then extract classification rules from the decision tree,evaluate the accuracy of the model,and finally give consumers recommendations for purchasing decisions.