Authors - C. R. Patil, Arundhati Sarvadnya, Diksha Shejwal, Sakshi Nehe, Sobiya Shaikh Abstract - The rapid expansion of the Internet, together with the pervasive diffusion of mobile technologies, has fundamentally reshaped contemporary socio-economic activities, positioning e-commerce as a core pillar of the digital economy. In response to increasing competitive pressures and the growing demand for personalized consumer experiences, enterprises have progressively adopted advanced analytical technologies, among which machine learning has emerged as a key strategic instrument. This study develops and empirically evaluates a machine learning–based product recommendation framework that integrates historical transaction data with sentiment information extracted from user-generated reviews. Data were collected from multiple e-commerce platforms and assessed using widely adopted evaluation metrics, including Accuracy, Recall, and F1-score. The experimental findings demonstrate that the XGBoost algorithm consistently outperforms alternative models, exhibiting superior capability in identifying latent consumer preferences and behavioral patterns. Overall, the results provide robust empirical evidence supporting the effectiveness of the proposed approach and underscore its practical potential for enhancing personalization quality and improving recommendation performance in large-scale e-commerce environments.
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