Product-Recommendation

Discription

A product recommender system is proposed to predict and compile lists of items customers are likely to purchase. Collaborative filtering methods, including user-based and item-based approaches, are utilized. The data is processed, removing product returns and handling missing values. Customer-item matrices are created to represent customer purchase behavior. User-based collaborative filtering identifies similar customers, while item-based filtering suggests similar products. The system aims to enhance targeted product recommendations and boost conversions among both existing and new customers.

Dataset:- https://github.com/trinoysaha159/Product-Recommendation/blob/main/Online%20Retail.xlsx