Design of E-commerce Customer Churn Prediction System Based on Data Mining Techniques

An e-commerce customer churn prediction system was designed based on data mining techniques. Following relevant theories, data mining, machine learning, and customer relationship management were applied for model construction. A decision tree model and the random forest model were used to build the system. The decision tree model is a decision-making model based on a tree structure, using the construction of a tree for classification or regression tasks. Random forest is an ensemble learning method that improves prediction accuracy and stability by building multiple decision trees and averaging their results. In the implementation of the system, data were preprocessed for data cleaning, handling missing values, and addressing outliers to ensure data quality and integrity. Correlation analysis and principal component analysis were conducted to identify features that significantly impacted customer churn prediction. Finally, continuous variables were determined for model construction and prediction. Using data mining techniques, an effective system was constructed. The system helps e-commerce businesses understand and manage customer relationships and enhance customer satisfaction and loyalty.