Customer Service Chatbot With ML

Dr . N Thrimoorthy¹ , Assistant Professor SCSE&IS¹

MadarNaik Sami Ali Khan¹, Bar Shaik Muhammed Gouse², Khasab Adil Ahamed³, Shaik Faiz⁴ UG Student SCSE¹, UG Student SCSE², UG Student SCSE³, UG Student SCSE⁴

Presidency University, Bangalore-560 064

Abstract
In the modern business landscape, customer support plays a critical role in shaping customer experiences and building brand loyalty. With the growing demand for instant responses and 24/7 availability, organizations are increasingly exploring artificial intelligence (AI) solutions to enhance the efficiency and effectiveness of their customer service operations. This work aims to design and develop an intelligent customer support chatbot that leverages machine learning (ML) and natural language processing (NLP) techniques to deliver automated, yet personalized, customer assistance. The core objective is to build a machine learning-based chatbot capable of understanding and responding to a wide array of customer queries in real-time. Traditional customer service models often require significant human resources, leading to high operational costs and long response times. By integrating an AI powered chatbot into the customer support process, businesses can streamline operations, reduce response time, and provide customers with timely, accurate information.

Keywords:
· Natural Language Understanding (NLU):

The chatbot uses NLTK and Speech recognition to process and understand the user's input in natural language. It can identify the intent and extract key details from user queries to provide relevant responses[6].

· Integration in Flask Backend

Flask supports the light-weight fast backend with requests that routes those to the correct machine learning model. It manages all the interactions with the users by processing inputs and passing on[4].

· Model Training of Machine Learning Model:

The OpenAI models are trained on labelled data for the improvement in recognizing intents of the chatbot. user and giving suitable responses of the model keeps learning and evolving as it processes new data of the users[3].

· Real-Time Error Handling:

The system handles errors such as unrecognized queries or technical issues efficiently. It logs errors and provides feedback to the user, ensuring a smooth experience[2].

· Sentiment Analysis:

Based on NLTK, it analysis the sentiment of all user inputs to establish possible emotions such as frustration and satisfaction. This will provide the chatbot with means to respond in addition to adjusting its tone[4].

Customer Service Chatbot With ML

Customer Service Chatbot With ML