Predicting Online Shopper Behavior: Machine Learning Approaches for Enhanced E-Commerce Insights

1Dannala Appaji Sesha Sai Kumar

The ability to predict online shopper behavior is essential for e-commerce platforms striving to enhance customer satisfaction and optimize operational efficiency. This paper presents a machine learning-based approach to forecasting user preferences, engagement patterns, and purchasing likelihood. Leveraging the UCI Online Shoppers Purchasing Intention Dataset, we evaluate various algorithms, including Random Forest, Logistic Regression, and Naive Bayes, to determine the most effective predictor of shopper behavior. Exploratory Data Analysis (EDA) highlights key features influencing customer decisions, such as session duration and bounce rates. Our findings underscore the potential of predictive analytics in driving personalized marketing, improving inventory management, and increasing conversion rates. The Random Forest algorithm outperforms other models in accuracy and generalization, demonstrating the transformative role of machine learning in the digital retail landscape.

Published Paper   E-Certificate

"Predicting Online Shopper Behavior: Machine Learning Approaches for Enhanced E-Commerce Insights", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 12, page no.a166-a173, December-2024, Available :https://ijsdr.org/papers/IJSDR2412021.pdf

Volume 9 Issue 12, December-2024

Pages : a166-a173

Paper Reg. ID: IJSDR_300019

Published Paper Id: IJSDR2412021

Downloads: 000347174

Research Area: Science and Technology

Country: vishakapatnam, andhra pradesh, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2412021

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2412021

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition