User Behavior Prediction Based on Machin Learning

In the age of big data, online shopping is now overgrowing and becoming an emerging trend among customers. Understanding user behaviors can let e-commerce platforms identify target customers more effectively and provide guests with more interest. For the fact that as the time customer stayed on the interface increases, the possibility that he or she would buy the product increases. Besides, buyers tend to browse details and comments more carefully. This paper proposed a method to predict user decisions based on user behavior. After collection and rebuilding, data were gotten from one product page. This paper uses them for training the Logistic Regression Model. After several iterations, optimal solutions can be obtained using the steepest descent method, and user behavior can be predicted. This paper uses the F1 to evaluate the model by combining the confusion matrix. Our method opens a new route to analyze and predict whether users will achieve specific behavior. It can be extended to more areas to perform more functions, like indicating whether the user is on schedule repayment.