In the era of digital commerce, consumers are increasingly reliant on online platforms to instruct consumer decisions. However, the vast number of online shopping websites and dynamic pricing strategies often make it challenging to identify the best deals. This project presents a product price comparison system that leverages web scraping techniques to extract real-time price information from multiple online retailers such as Amazon, Flipkart, and Croma. The system aggregates and analyzes this data to provide users with a unified view of product prices, enabling efficient comparison and cost-effective decision-making. The solution is to be built using technologies such as SQL, PHP, HTML, CSS, JS with scraping logic designed to handle both static and dynamic web content. The backend stores historical price data, allowing for trend analysis and alert generation when prices drop below user-defined thresholds. Visualization tools and a user-friendly interface further enhance the usability of the system. This project not only simplifies customer experience but also offers insights into market behavior, pricing strategies, and competitive dynamics. It demonstrates the potential of data-driven approaches in optimizing e-commerce interactions and highlights the role of automation in modern retail analytics.
Neha et al. (Tue,) studied this question.