This report explores Natural Language Processing (NLP) with a specific focus on improving word prediction in conversations by detecting changes in context. The main goal of our research is to make existing word prediction models more accurate and user-friendly by addressing the challenge of recognizing when the topic of conversation shifts. We aim to enhance word prediction to reduce user frustration and improve the overall user experience. To achieve this, we introduce a new model that can effectively detect and adapt to changes in the conversation context, making the conversation flow more smoothly and enhancing the user\u27s experience. Our research is based on a carefully curated dataset, which is crucial for developing a system that can better understand and predict words in context-aware conversations
