Paper Key : IRJ************298
Author: Ruchita Vilas Saindane,Ashwini Sunil Patil,Rajashri Sunil Bhoi,Sanskruti Yogesh Bhoite
Date Published: 01 May 2024
Abstract
: Price comparison sites are designed to compare the price of various products and services from a wide range of providers, which will help consumers in making decision to choose products that will save their money through online. Online shopping has become an integral part of modern consumer behavior, presenting both opportunities and challenges for users seeking optimal product choices. This explores the implementation of machine learning techniques. The project focuses on collecting diverse and reliable data, employing preprocessing techniques, and developing real-time updating mechanisms. The machine learning models analyze product features and prices to provide users with personalized and accurate recommendations through an intuitive user interface. This system will improve online shopping experiences and providing valuable recommendations for future developments in the e-commerce domain. The proposed system utilizes Python, Flask, HTML, CSS, and JavaScript to develop a user-friendly web application that empowers consumers to make informed purchasing decisions. By harnessing the predictive capabilities of linear regression, the system accurately predicts product prices based on historical data and relevant attributes, thereby facilitating seamless comparison across multiple e-commerce platforms.