AI-powered customer support chatbot leveraging feedforward neural network architecture

In the real-ecosphere applications of today, chatbots are indispensable for resolving complicated issues and improving user engagement. This study explores the development of a chatbot for customer support using cutting-edge artificial intelligence and machine learning methods. In tackling scalability, dynamic integration, and complicated customer-centric needs, preprocessing techniques like tokenization, stemming, and vectorization are implemented. Key criteria such as prediction accuracy, recall score, and common evaluation metrics were used in the performance study. The feed-forward neural network (FFNN) model, which was used as a chatbot, was shown to have a 94% prediction accuracy in the experimental evaluation. Furthermore, earning a recall score and an F1-score of 0.92, the suggested model outperformed current methods. These results demonstrate that the FFNN-based chatbot is more successful than other models at providing increased accuracy and dependability.