Automated Product Price Comparison using Python

Abstract

The advent of e-commerce has revolutionized shopping by enabling users to compare prices across multiple platforms. Automated Product Price Comparison (APPC) systems use data science techniques to streamline the process of price comparison, offering consumers the best deals while saving time. This research paper explores the development of an APPC system using Python, leveraging data extraction, cleaning, and analysis techniques. The paper discusses methodologies for web scraping, data preprocessing, and machine learning integration for price trend analysis. The study also examines the challenges in implementing APPC systems, including data heterogeneity and dynamic pricing, and provides solutions to address them.