Vol. 6 No. 2 (2025)

Standard Journal Issues

A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets

Published 2025-09-10

  • Sachin Tripathi
    • ,
  • Akhilesh Tiwari
    • ,
  • Kamal Upreti
    • ,
  • G V Radhakrishnan

Sachin Tripathi
Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India

Akhilesh Tiwari
Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India

Kamal Upreti
Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India

G V Radhakrishnan
Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India

Abstract

Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified train–test splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R², RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R² = 0.82) outperformed Random Forest (R² = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variables—purchase intention, brand awareness, and social media engagement—were the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social media–driven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies.

Keywords

Education, Machine Learning, Cognitive learning, Non-cognitive learning, classification, student performance

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