Customer churn prediction in live e-commerce based on stacking ensemble learning

Key Points

• The central aim is to develop a predictive model for customer churn in live e-commerce environments.
• Utilized stacking ensemble learning techniques
• Analyzed customer behavior data
• Implemented predictive modeling approaches
• Achieved improved accuracy in churn predictions
• Demonstrated efficiency of ensemble methods
• Identified key factors influencing customer retention