Machine Learning and Predictive Analytics in E-commerce: A Data-driven Approach


  • 1Lakshmi Nivas Nalla, 1Data Engineer Lead, Florida International University 11200 SW 8th St, Miami, FL 33199, Email:
  • 2Vijay Mallik Reddy Member of Technical Staff, University of North Carolina at Charlotte,


Machine learning, Predictive analytics, E-commerce, Personalization, Dynamic pricing, Demand forecasting


Machine learning and predictive analytics have revolutionized the e-commerce landscape, empowering businesses to leverage data-driven insights for personalized marketing, dynamic pricing, and demand forecasting. This paper explores the applications of machine learning algorithms and predictive analytics in e-commerce, highlighting their role in optimizing customer experiences and driving revenue growth. By analyzing customer behavior, purchase patterns, and market trends, e-commerce platforms can anticipate user preferences and adapt their strategies in real time. Case studies and practical examples demonstrate the effectiveness of machine learning models in enhancing conversion rates, reducing churn, and improving operational efficiency. The paper also discusses challenges and future directions in the adoption of machine learning and predictive analytics in e-commerce, emphasizing the importance of data quality, privacy protection, and algorithmic transparency.