Analysis of buying intension online based on decision tree and support vector machine

Abstract

With the development of the Internet, online shopping has gradually emerged and changed people's living habits. On this basis, the purchase intention of users has become a hot topic. In this study, the data set on Kaggle is used to train and analyse the model with decision tree as well as support vector machine (SVM). To be specific, 30% of the data is used for testing. According to the feature correlation analysis, one can see that some features, such as ExitRates and BounceRates, are highly correlated, and PageValue has a great impact on income. In addition, precision, recall, f1-score and other data were processed to evaluate the model, and its limitations and future development were explained. It is of great significance to forecast the income of online merchants and how merchants purchase goods and reduce inventory, and to understand and predict the intention of users to purchase online from the income of merchants.