International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Dealaxe: A Smart E-commerce Aggregation Platform for Optimal Product Selection Prof. Sheetal Mhatre1, Mr. Vishram Bapat2, Shreya Pandey3, Anchal Pandey4 , Sneha Waghmare5 1,2,3,4,5 Department of Data Science Usha Mittal Institute of Technology SNDT University Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The swift growth of e-commerce has resulted in
Dealaxe works on API-based integration. Such an integration ensures that its users are provided with the latest updates regarding prices, availability of products, delivery options, and much more.
an over-whelming volume of online online stores providing identical products, making it difficult for users to compare and choose the best ones. Current product comparison tools lack real-time updates, are not personalized, and can support only a limited set of e-commerce websites. Dealaxe is a new ecommerce product aggregation site that combines info-mation from different online stores like Amazon, Myntra, and Flipkart. It offers a comfortable shopping experience to customers through a user friendly interface comparing products on the basis of price, colour, cash on delivery availability (COD), time of delivery, and other filters that can be made as per the customer's choice. Using API-based product extraction, optimized ranking algorithm, and interactive React-based UI, Dealaxe achieves quick and precise product retrieval. The system architecture, data processing algorithms, filtering strategies, performance measurement, and future optimizations are discussed in this paper. Experimental outcomes show that Dealaxe dramatically minimizes search time and maximizes user experience over available tools.
One of the highlights of Dealaxe is the advanced filter system that provides users with what they want by empowering refined searches according to multiple criteria. Users can filter products based on factors that include but are not limited to price range, colour, cash-on-delivery (COD) availability, estimated delivery time, and personalized userdefined features. This feature greatly helps shoppers narrow down their selections rapidly, thus facilitating quick and sound purchasing decisions. This paper delivers a detailed analysis and design of the system architecture for Dealaxe, explaining how the platform integrates smoothly with third-party APIs and controls data movement throughout the service. Further-more, it discusses the data processing pipeline that ex-tracts, cleans, and stores the incoming data to ensure its correctness and consistency. It further surveys the filtering methods incorporated in Dealaxe, including the algorithms and techniques applied towards making personalized and contextually relevant suggestions for the users.
Key Words: E-commerce, product aggregation, API integration, recommendation system, price comparison
1. INTRODUCTION In the past couple of years, online shopping increased exponentially with millions of product seekers shifting their buying needs onto the internet. Moreover, several ecommerce platforms come up with similar or identical products, yet charge differently. And although having such numbers may be useful, it, nevertheless, creates an overload for the buyer: manually comparing prices, de-livery time, and product specifications for numerous websites may be exhausting, slow, and inefficient. Traditional price comparison tools are very helpful. However, they do not overcome the great limits of off-late data updates, backing of limited platforms, and lack of advanced filtering options, thus affecting users conversely in making due and right decisions. Dealaxe is the solution these problems solve, being aware of a much-whole solution offered in one go to streamline the product comparison process.
In addition, experimental evaluation of Dealaxe is shown to provide timely and accurate comparisons and discussions about user satisfaction and performance metrics. To conclude, possible areas for improvement have also been explored: expanding platform support, enhancing user interface, machine learning to embed suggested buys, and price trend analysis. Dealaxe aims to transform online shopping into something that is real-time, follows contemporary standards, yet rich in features to be helpful for individual users.
2. RELATED WORK The emergence of e-commerce gave way to various price comparison tools to help consumers find the best prices for products on different online platforms. Some of the most recognized price comparison sites-such as Google Shopping, PriceGrabber, and Shopzilla provide users with lists of products aggregated from various retailers. At the same time,
In contrast with traditional comparison tools, for the purpose of instant data retrieval from many of the premier ecommerce sites such as Myntra, Amazon, and Flipkart,
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