Comparative Web Product Analysis: A Data-Driven Approach
Jesvi Jonathan, S. K. Muthusundar · 2023
This research endeavor seeks to streamline the intricacies of product discovery and analysis, empowering users to locate their desired products efficiently. Our approach involves indepth analysis encompassing pricing, market insights, sentiment analysis, historical trends, and real-time product comparisons. Leveraging advanced data scraping and data science techniques, we aim to deliver valuable insights from an extensive data pool, facilitating informed decision-making. The implications of this work extend across business, personal, and commercial domains, allowing users to make informed choices based on factors such as product availability, pricing, and specific requirements. Achieving this objective necessitates advanced data abstraction, cleansing, and optimization methods underpinned by a robust architectural framework. Moreover, we introduce a product analysis page for visual data representation, optimizing search and retrieval techniques. Empirical evaluations highlight the efficiency and accuracy of our machine learning classifiers, with a primary focus on handling unstructured and heterogeneous product data sourced from the web and various e-commerce platforms.