Built a GenAI application using LLMs to provide intelligent, context-aware responses and automate user interactions.
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MindEase demonstrates that a student-built NLP and ML system can provide meaningful mental wellness support in a responsible, safe, and technically rigorous way. The system successfully combines classical NLP techniques with machine learning classification and intelligent response selection to deliver a conversational experience that adapts to the user's emotional state in real time. The 98.12% classification accuracy and 100% crisis detection rate demonstrate that the system is both technically sound and ethically designed.