Paper Key : IRJ************860
Author: Sooriya Narayanan A J,Mohin P S,Nishanth M,Sadhu Venkata Srinivasulu,K.sasirekha
Date Published: 08 Nov 2024
Abstract
Price comparison websites serve as essential tools for consumers, enabling them to evaluate the prices of various products and services from a multitude of providers. This functionality aids users in making informed purchasing decisions that can lead to significant cost savings in the realm of online shopping. As online shopping continues to evolve as a fundamental aspect of contemporary consumer behaviour, it presents both opportunities and challenges for individuals seeking optimal product selections. This paper examines the implementation of machine learning techniques to enhance the price comparison process.The project emphasizes the importance of gathering diverse and reliable datasets, applying effective preprocessing methods, and establishing mechanisms for real-time updates. The developed machine learning models analyse product characteristics and pricing information to deliver personalized and precise recommendations through an intuitive user interface. This innovative system aims to enrich the online shopping experience while providing valuable insights for future advancements in the e-commerce sector.The proposed solution leverages technologies such as Python, Flask, HTML, CSS, and JavaScript to create a user-friendly web application that empowers consumers to make well-informed purchasing choices. By utilizing the predictive capabilities of linear regression, the system effectively forecasts product prices based on historical data and pertinent attributes, thereby enabling seamless comparisons across various e-commerce platforms.Keywords: Price comparison, machine learning, online shopping, e-commerce, predictive modeling, user interface.