Buy on fingertips: An E-Commerce Web
- Monica Lakshmi Mandapati - 015219711
- Annapurna Ananya Annadatha - 015218385
- Indhu Priya Reddem - 015930148
- Manasa Bobba - 015945527
Advisor: Dr. Wencen Wu
The COVID-19 pandemic has accelerated the growth of e-commerce websites, making them the fastest growing business in the world. However, to stay competitive, it is essential to provide customers with an exceptional user experience. This project proposes building a full stack web application that integrates a machine learning model to recommend products based on customer preferences. By using content-based and collaborative filtering techniques, the model considers similar products and similar users’ interests to provide a list of top recommendations based on product ratings. Customers can rate and review products, and can also receive notifications when their interested product is back in stock. To better understand their customers’ needs and likes, retailers can use a dashboard to view the velocity of their products and receive insights into their inventory management. A marketing metric is calculated to help retailers align their inventory with market demand, thus helping them gain profit. By using collaborative filtering, content- based filtering, and inventory management, this project aims to create a unique and exceptional e-commerce experience for both customers and retailers. This project aims to leverage machine learning and inventory management techniques to improve the e-commerce experience for both customers and retailers. The proposed web application integrates recommendation systems, product reviews, and inventory management to help retailers make data-driven decisions and improve their profit margins. By enhancing the user experience, this project hopes to contribute to the growth of the e-commerce industry.
https://github.com/mmandapati/EasyBuy/blob/main/Poster_EasyBuy_Final.pdf
https://github.com/mmandapati/EasyBuy/blob/main/EASY_BUY%20Revised.pdf