Billy – Buddy Against Cyber Bullying

This research paper examines the design and de- ployment of a victim support chatbot leveraging machine learning (ML) technologies. The study emphasizes the application of natu- ral language processing (NLP) techniques and predictive analytics to enhance user interactions, optimize response efficiency, and minimize operational costs. The chatbot is developed to address victim inquiries, provide tailored solutions, and escalate complex issues to human agents when required. Core features include intent recognition, ethical behavior modeling, and contextual understanding to ensure personalized and accurate responses. The paper delves into the system architecture, covering data preprocessing, model training, and seamless integration with existing support frameworks. Performance metrics such as response time, resolution rates, and user satisfaction are analyzed to evaluate the chatbot’s effectiveness. Through real- world case studies and empirical testing, the research highlights the transformative potential of ML-driven chatbots in victim support services, providing scalable and cost-efficient solutions without compromising service quality. Future recommendations underscore the importance of continuous learning, ethical con- siderations, and enhanced functionality to adapt to evolving user needs.