Abstract: The rapid expansion of e-commerce has created a highly competitive marketplace where consumers are often faced with the challenge of finding the best deals across multiple online platforms. Manually comparing product prices is time-consuming, inefficient, and prone to inaccuracies due to inconsistent data across different websites. This project presents the design and development of a Web-Based Ecommerce Price Comparison System using web scraping techniques to provide real-time price comparisons for products from multiple online retailers, including Jumia, Slot, and Jiji. The system integrates a responsive user interface built with React.js and a robust backend powered by Django REST Framework. Web scraping was implemented using Python libraries such as BeautifulSoup, enabling efficient extraction of product details including prices, descriptions, and source platforms. Testing and evaluation showed that the system successfully met its objectives, offering a clean, intuitive, and responsive interface with accurate data retrieval. While challenges such as anti-bot measures, dynamic content handling and inconsistent data formats were encountered, the system provides a functional prototype with significant potential for scalability, future integration of more platforms, and adoption of advanced features such as machine learning-based product matching. This work contributes to improving consumer decision-making, enhancing online shopping efficiency, and promoting transparency in e-commerce pricing.

Keywords: E-commerce, Web Scrapping, Price comparison system, Machine learning, Pricing

Download: | DOI: 10.17148/IMRJR.2026.030402