Browsing by Author "1620252042"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Open Access
    Product Recommendation System by Analyzing Customer Reviews and implementation using Django Based E-commerce website
    (North South University, 2021) KAYSER AHMED; SUMIAYA KHAN; MD. AMZAD HOSSEN; Mohammad Monirujjaman Khan; 1620252042; 1711879642; 1620396642
    This report presents a recommendation system and its implementation on a real time Ecommerce website. This will help the customer to discover new products that would please a customer, recommend products by monitoring customers activity like searching of products, along with the previous set of things a user enjoyed and also find a selection of products that would be enjoyed by a community of people. The biggest secret of achieving success in business is to provide better quality customer services that ensures customer satisfaction. A statistic shows that about 70% of purchase decisions are taken relying on how consumers believe, they are being served. Online shopping is on trend now a days where customer buys things or services without any middle way. However, to give customer satisfaction and earn more revenue, number of advertising ways are introduced. One of which is intelligently recommendation services or products. A product recommender system is a software that detects the consumer’s behaviour on e-commerce sites and on the basis of that, suggests products that meets interest of consumers. This paper presents the design and implementation of a recommendation system on Ecommerce which will suggest the consumer with their relevant and desired products and make their online shopping more comfortable. For implementing this system using machine learning, three algorithms (Model Based Collaborative Filtering, Popularity Based Filtering, K means) are used. The system is designed in such a way that focused on a fresh customer's first visit on the company's website to when they perform repeat purchases. The system will be proved as user friendly and will effectively predict which products customers would like the most. The accuracy in matrix factorization using SVD is 97.7 percent and with KNN using item similarity is 84.56 percent.