E-COMMERCE BASED CHATBOT SYSTEM USING TEXT MINING AND MACHINE LEARNING ALGORITHM

In today's e-commerce environment, internet shopping is rapidly expanding. As a result, product recommendation systems have an opportunity to improve. Because users require a relationship with the system. When a relationship develops, the user receives individualized attention and attraction. The system not only tracks and analyses buyer behavior, but it also entices them to return and spend more. The recommendation system eliminates the tedious job of users searching for what they want in an unending category. Instead, they leverage the dialogue to filter out irrelevant information and deliver the product to the customer. While online purchasing offers numerous advantages, it also has limitations and drawbacks that must be considered. When the product purchased and the request made by the customer do not always match, the customer may be disappointed. As consumers' needs change on a daily basis, improving the present functionality of these systems has become a critical aspect. Based on the history of online buying, recommendation systems will be in high demand in the near future. Research is launching a conversation bot that offers products to clients based on their needs. The chatbot essentially takes orders with minimal user involvement and recommends the best product. This can be done on a wide scale, but in this case, the product database is used. Through the chatbot, the customer provides information regarding the perfume. It will also recommend related products based on the user's description.