JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Price Comparison using web scrapping and data analysis.
Bi Bi Hajira Khanum, Raghavendra S P
Financial savings can be obtained by purchasing goods online from websites that compare prices of products and services from different suppliers. Online shopping is now a routine part of contemporary consumer behaviour, which has given users great opportunities but poses serious challenges to those looking for the best product options. This paper presents the use of machine learning techniques. In order to ensure real-time updating processes, the project will gather heterogeneous data and apply preprocessing methods. Intuitive user interface is implemented where in Machine Learning models analyze product features and prices in providing personalized recommendations for users. A friendly web application using Python, Flask, HTML, CSS and JavaScript was developed to allow consumers make informed buying choices. Through utilization of web scrapping predictive capabilities, the application uses historical data in determining both relevant attributes of products and how much they are priced at; hence it performs cross-platform comparisons seamlessly among various ecommerce platforms.
Machine learning, Price Data, Web scraping, Quality Purchased, Recommendation.