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@article{191341,
        author = {Pratiksha Badrinath Chindhe and Nikhil Ashruba Chavan and Santosh Vilas Adhe and Jyoti Dinkar Bhosale},
        title = {Design and Implementation of an AI-Based Chatbot Using NLP and Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8909-8915},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191341},
        abstract = {This research presents the design and implementation of an AI-based chatbot system that integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques to deliver automated, accurate, and context-aware conversational support. The study addresses critical limitations in traditional rule-based chatbots, such as poor scalability, limited linguistic understanding, and inability to handle complex or unpredictable user queries. The proposed hybrid system utilizes preprocessing, intent classification, entity extraction, and a structured knowledge base to generate coherent and relevant responses. A modular architecture supported by a Flask backend and a web-based user interface ensures smooth communication between system components. Machine learning algorithms, including Logistic Regression and SVM, improve the accuracy of intent detection, while spaCy-based Named Entity Recognition enhances contextual comprehension. The chatbot supports real-time interaction, achieving faster response generation, reduced human effort, and improved user satisfaction. The results demonstrate that hybrid approaches combining retrieval-based and generative elements significantly enhance response quality, reliability, and adaptability. This work highlights the potential of AI-driven conversational agents to automate routine tasks and provide 24/7 support across various domains.},
        keywords = {AI Chatbot, NLP, Machine Learning, Hybrid Model, Intent Classification, Knowledge Base},
        month = {February},
        }