• Sushira Somavarapu
• Vikhyat Gupta

The rapid expansion of e-commerce platforms has generated an immense volume of user interaction data, offering opportunities to enhance user experience, optimize business strategies, and increase profitability. This paper presents a comprehensive study on analyzing and visualizing user behavior in e-commerce environments using machine learning techniques. We explore various dimensions of user behavior, including browsing patterns, purchasing tendencies, and product preferences, leveraging advanced clustering, classification, and predictive modeling algorithms. The approach involves data preprocessing, feature engineering, and model training to uncover valuable insights into customer segmentation, product recommendations, and churn prediction. Additionally, interactive visualization tools are employed to facilitate real-time monitoring of key behavioral trends and decision-making. Our results demonstrate the effectiveness of machine learning models in capturing user behavior nuances and highlight the potential of data-driven strategies in personalizing e-commerce experiences. This study provides a scalable framework that can be adapted by online retailers to better understand their customers and foster long-term engagement.

E-commerce, user behavior analysis, machine learning, customer segmentation, predictive modeling, product recommendation, data visualization, churn prediction, browsing patterns, purchasing behavior

"Analyzing and Visualizing User Behavior in E-commerce: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.h347-h368, February-2025, Available :http://www.jetir.org/papers/JETIR2501736.pdf

"Analyzing and Visualizing User Behavior in E-commerce: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. pph347-h368, February-2025, Available at : http://www.jetir.org/papers/JETIR2501736.pdf