Data Analytics Apps Suite for E-Commerce

Abstract

In today's era, with the increasing popularity of Online shoppers demand of the efficient recommendation system is also increasing to help the customer select the interesting product as well as supporting the marketing campaign. Researchers are putting their continuous efforts to make it more efficient and effective. This thesis focuses on recom- mendation system and churn prediction. Collaborative Filtering is a well-known approach to suggest products based on user's review. Content-based Filtering helps to Find the similar product based on its feature. The proposed recommendation system use a hybrid approach of collaborative and content-based Filtering to consider both user's review and products features. Churn prediction attracts users losing or reducing shopping activity. The Hereby proposed churn prediction covers long history, unlike other existing systems.