BeautyShop Recommendation System

E-commerce will make up 20.4% of global retail sales by 2023.A promising future for online commerce is offered by India’s rising Internet user population. With the exponential growth of e-commerce and the abundance of products available online, consumers often face the challenge of choosing the right products that meet their specific needs and preferences. Online shopping recommendation systems have emerged as powerful tools to address this issue by providing personalized product recommendations based on user data and preferences.For the purpose of recommendation there are various techniques namely Collaborative filtering, Content-based filtering, Matrix Factorization, Association rule, Hybrid Recommender System, Demographic Filtering, Context Aware filtering, Deep-learning based recommender system, Popularity based filtering, Sequential recommendation etc. To recommend a right product to the user the preferred algorithm is Apriori. The Presented Recommendation system generates recommendation using python, flask framework and MySQL as backend. Client’s happiness is positively impacted by the features of the product recommendation system, thus it’s important to meet client demands based on a survey that prioritizes quality.