Performance Study of Classification Algorithms for Consumer Online Shopping Attitudes and Behavior Using Data Mining

The online retail industry is one of the world´s largest and fastest growing industries having huge amount of online sales data. This sales data includes information about customer buying history, goods or services offered for the customers. Hidden relationships in sales data can be discovered from the application of data mining techniques. Data mining is an inter disciplinary promising field that focuses on access of information useful for high level decisions and also include machine learning to help online shopping stores to indentify online customer behavior to recommend for him the appropriate products he/she is interesting to them, because the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. In this paper eleven data mining classification techniques will be comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior according to obtained dataset for big agency of online shopping, the results shows that decision table classifier and filtered classifier gives the highest accuracy and the lowest accuracy is achieved by classification via clustering and simple cart, also this paper will provide a recommender system based on decision table classifier helping the customer to find the products he/she is searching for in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the desired products.