Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: AI Healthcare Assistant
  • Category: Natural Language Processing
  • Model Type: Sentence Transformers (Semantic Q&A)
  • Github Repo: Link

Technologies Used

The NLP module is built using Python, Flask, and Sentence Transformers. It powers a medical chatbot capable of understanding natural language queries and responding with contextually accurate medical answers. The backend integrates with Firebase for real-time interaction, and the frontend is developed in Next.js.

Details of Project

This component of the AI Healthcare WebApp focuses on natural language understanding through a medical chatbot. It enables users to ask free-form health-related questions and receive meaningful, AI-generated responses. Powered by sentence-transformers, the model semantically matches user input with the most relevant medical information available in its knowledge base.

The chatbot plays a crucial role in democratizing healthcare advice by providing fast, accurate, and easily understandable answers. It is deeply integrated into the system, communicating with a real-time backend and ensuring a smooth user experience across platforms.

Project Features

  • Smart medical chatbot capable of understanding and answering user questions.
  • Built using sentence-transformers for deep semantic matching.
  • Integrated into a real-time AI healthcare platform using Firebase.
  • Deployed with Flask API for NLP and Q&A processing.
  • User-centric design with fast, relevant responses tailored to healthcare queries.