Headline
AI/ML & Software Developer | CSE (Data Science) Student
About
I am a final-year Computer Science and Data Science student with a strong foundation in software development, artificial intelligence, machine learning, and computer science fundamentals. I have hands-on experience building AI/ML applications, full-stack systems, and Retrieval-Augmented Generation (RAG) solutions through academic and personal projects. My technical skills include Python, Java, JavaScript, React.js, FastAPI, Flask, MongoDB, MySQL, LangChain, FAISS, Hugging Face Transformers, TensorFlow, PyTorch, and Scikit-learn. I have developed projects including an Esports Tournament Management System, a Universal Anti-Hallucination and Self-Correction RAG System, and a Personalized Learning Style Detection System. These projects have strengthened my ability to design APIs, work with databases, develop machine learning models, implement retrieval systems, and translate technical requirements into functional applications. My RAG project achieved 81.7% overall accuracy and 90% hallucination detection, while my learning-style classification system achieved 90.65% accuracy. Alongside technical development, I have participated in hackathons and represented my team at a BIRAC presentation event. I am a motivated problem solver who enjoys learning new technologies and building practical solutions, and I am seeking opportunities to contribute to real-world software, AI, and engineering projects.
Experience
Education
Projects
Esports Tournament Management System
Developed a full-stack tournament management platform using React.js, Flask and MySQL for team registration, bracket generation and match scheduling.
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Personalized Learning Style Detection System
Built a FastAPI, MongoDB and React-based system using Random Forest to classify VARK learning styles from behavioral and demographic features.
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anti halluciantion and self correction through the RAG
my this project is based over the anti halluciantion sytem based project which based which stopping the llm from hallucianting by checking the evidence from the RAP wikepedia api server and if it hallc=uciantion is ther it will self correcting it
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Portfolio
smart-system
I am a final-year Computer Science and Data Science student with a strong foundation in software development, artificial intelligence, machine learning, and computer science fundamentals. I have hands-on experience building AI/ML applications, full-stack systems, and Retrieval-Augmented Generation (RAG) solutions through academic and personal projects. My technical skills include Python, Java, JavaScript, React.js, FastAPI, Flask, MongoDB, MySQL, LangChain, FAISS, Hugging Face Transformers, TensorFlow, PyTorch, and Scikit-learn. I have developed projects including an Esports Tournament Management System, a Universal Anti-Hallucination and Self-Correction RAG System, and a Personalized Learning Style Detection System. These projects have strengthened my ability to design APIs, work with databases, develop machine learning models, implement retrieval systems, and translate technical requirements into functional applications. My RAG project achieved 81.7% overall accuracy and 90% hallucination detection, while my learning-style classification system achieved 90.65% accuracy. Alongside technical development, I have participated in hackathons and represented my team at a BIRAC presentation event. I am a motivated problem solver who enjoys learning new technologies and building practical solutions, and I am seeking opportunities to contribute to real-world software, AI, and engineering projects.
View details