Analysis of E-commerce User Purchase Intention Based on User Behavior Data

This paper analyzes the public data set of “Taobao user shopping behavior data visualization analysis” on Alibaba Cloud Tianchi platform, aiming to analyze users' purchase intention, predict users' future purchase behavior, and determine users' group classification through data mining and machine learning technology. In the process of research and analysis, this paper uses the Numpy and Pandas libraries in Python language to process the original data in the dataset to ensure that the data is more intuitive and effective. In addition, Matplotlib and Seaborn libraries are also used to realize data visualization, intuitively present user behavior characteristics, and Scikit-learn library is introduced to divide user groups. This paper focuses on two types of analysis of user behavior. One is the study of behavior sequence. Through funnel analysis and thermograph analysis, we can preliminarily understand the user behavior conversion, and use Markov chain technology to predict the next behavior. The second is clustering analysis. Through k-means clustering algorithm, different user types and behavior profiles can be obtained. Through these analyses, we can provide strong support for decision-making optimization and precision marketing of e-commerce platforms, and promote the development of e-commerce platforms.