An Intelligent Chatbot Haggling with Ensemble ML Model

In recent years online shopping has gained a huge boom. With this increase, most of the features of online shopping are developed but some features like negotiating with shopkeepers are not available which is sometimes possible in offline purchasing. In this paper we have proposed a Chatbot to conduct product negotiations. Customers can interact with the Chatbot to receive help obtaining a fair price for a product (s). With such a system, which has an impact on key aspects of online buying, there is a chance that the budget of the customer or the product seller could be compromised. To avoid such situations, we have developed an algorithm in Machine learning which works along with prediction of old available data to provide a product price. Sometimes, price prediction is less accurate because some algorithms are inappropriate for a given dataset or because irrelevant features or properties of the data are employed. Due to the potential financial losses associated with even one incorrect price prediction, Ecommerce businesses do not only rely on price prediction systems. Some models also fail when data scales or some feature is unavailable after time on which model prediction was dependent. Our model's accuracy and dependability are maintained by managing these modifications.