The Code of Consumer Minds: Decoding E-Commerce User Behavior Through Advanced Models

Abstract

E-commerce platforms must make decisions with significant financial and human resource implications, it is essential to comprehend consumer behavior. Finding patterns in vast amounts of user data is still quite difficult. This project demonstrates a data-driven pipeline that analyzes and forecasts user behavior using data mining techniques. The data comes from two weeks of activity in 2017 on Alibaba's Taobao website. Data preprocessing and exploratory data analysis (EDA) with visualizations are part of the workflow. Customer segmentation is done using K-means clustering and RFM analysis. User behavior is modeled and predicted using Long Short-Term Memory (LSTM) models and Recurrent Neural Networks (RNN). These forecasting methods offer insightful information about user behavior. The findings help e-commerce companies make well-informed, economical judgments. This method improves behavioral data-based strategic planning.