Predicting the Intention of Online Shoppers’ Purchasing

Nowadays the pace of modern life makes people have less time to go shopping. With the development of technology, more and more people choose to purchase things online. For merchants, the competition in electronic business is becoming fierce. Among all of e-commerce platforms, it is clear that who know their customers more can seize the initiative the market. So, this paper tends to have an interest in coming up with a tool that may analyze the factors in several aspects that build individuals a lot of doubtless to get product. During this paper, we recommend a period of time on-line shopper behavior prediction system that predicts the visitor’s looking intent as before long because the web site is visited. To do that, session and visitant data is applied to investigate random forest. What is more, oversampling is used to enhance the performance and therefore the quantifiability of every classifier. The results show that random forest produces considerably high accuracy 86.78% and F1 Score 0.6.