Abstract
This paper presents an innovative approach to e-commerce data analysis using web scraping and machine learning techniques. By aggregating data from platforms like Amazon, eBay, Snapdeal, and Ajio, the system facilitates product comparison and price prediction. Leveraging state-of-the-art scraping tools such as Selenium and BeautifulSoup, and machine learning algorithms, including K-Means clustering and Linear Regression, the system ranks products and forecasts price trends. A detailed graph showcasing predicted price trends over time provides actionable insights for consumer decision-making. Results emphasize the integration of analytics and data visualization to enhance the e-commerce experience.
Published Version
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