International Research Journal of Education and Technology

This project details the development of an automated web scraping application designed to efficiently extract product data from Amazon.com. Utilizing the Python programming language in conjunction with the Selenium library, the application provides a user-friendly experience through a simple dialog box interface. Users can input specific product search terms, such as 'laptops,' 'bags,' or 'smartphones,' and initiate the scraping process. Upon activation, the system autonomously launches a Chrome browser instance, navigates to the Amazon website, and systematically gathers relevant product information. This information includes key attributes such as product titles, prices, and customer ratings. The extracted data is then meticulously organized and saved into a Comma-Separated Values (CSV) file, enabling seamless integration with data analysis tools. Furthermore, the project incorporates data visualization capabilities, generating a PNG image that visually represents key trends and insights derived from the scraped data. This visualization facilitates a deeper understanding of product market dynamics. Ultimately, this project demonstrates the effectiveness of automated web scraping for acquiring valuable e-commerce data, with potential applications in market research, competitive analysis, and informed decision-making.