Product Recommendation System Using Machine  Learning

Abstract : With numerous choices to find online, making the correct selection is perplexing and time-wasting. It is made simple with a properly designed recommendation system, which proposes items depending on users' liking and past search history. The current project tries to do it in a new and different approach using online reviews posted by customers, which offer authentic feedback and users' views. Our system operates in three primary steps: Authentication, Product recommendation, selling. The initial step protects the system so that only authenticated individuals have access to it. It improves security and assists with the provision of personalized recommendations. For the second step, we concentrate on product recommendations through machine learning to read customer opinions. A ranking algorithm has already ranked products based on sentiment from customer reviews to give users the best possible suggestions based on actual experience. The third step is selling, where we employ a K-Means and DBSCAN model to enable sellers to comprehend customer behavior and enhance the marketing strategy. It ensures enhanced presentation of the products and aids in sales increase. The motivation behind creating this system is the increasing need for more intelligent recommendation systems that are capable of managing various user requirements. Conventional recommendation techniques usually do not take into account personal experiences and opinions. By incorporating sentiment analysis from customer reviews, our system provides more relevant and helpful suggestions.

Keyword Recommendation System, Sentiment Analysis, Product Reviews, K-means, DBSCAN.