Decision Tree Algorithms for Agile E-Commerce Analytics: Enhancing Customer Experience with Edge-Based Stream Processing

Abstract

Background Information: Advancements in the technology have streamlined e-commerce 
analytics, overcoming many of the present challenges such as data overflow and latency. Further, 
decision tree algorithms, edge processing, and agile analytics could promise improvements on real
time decisions. 
Objectives: This work aims to build a combined approach that increases prediction accuracy and 
scalability while allowing for customer satisfaction through these technologies. 
Method: The combination of decision tree algorithms, edge-based processing, and agile analytics 
ensures e-commerce optimization from real-time data-driven insights. 
Results: The Combined Approach had surpassed individual approaches by giving 93% accuracy, 
95% latency reduction, 95% scalability, and 95% customer satisfaction with 90% cost efficiency. 
Conclusion: This approach clearly reflects the synergy of methodologies for more effective e
commerce operations. This is not an exhaustive work; deeper integration of AI in future 
developments can be explored.