Welcome to my GitHub! I'm a passionate B.Tech. Chemical Engineering student at IIT Guwahati with a keen interest in Web Development, Data Science, and Machine Learning. I love building real-world applications and working on projects that make an impact. 🚀

• CampusEats: A food ordering website using Next.js, React, and MongoDB with secure authentication and an admin panel for managing orders.
• BirthdayFetch: A Streamlit-based tool to extract birthdays from WhatsApp group chats and store them in a Notion template.
• Alcheringa Website: Leading the frontend development with Next.js, React-Three-Fiber, and Three.js for an interactive and engaging experience.

• Deep Learning and AI for advanced projects in image captioning and natural language processing.
• Cloud Computing and DevOps to scale applications and improve deployment pipelines.

• C/C++, Python, JavaScript

• Frontend: HTML, CSS, React, Next.js, Tailwind CSS
• Backend: Node.js, Express.js, Django

• Libraries/Frameworks: TensorFlow, Keras, Pandas, NumPy, Scikit-Learn, Matplotlib
• Techniques: Deep Learning, Computer Vision, NLP, Data Analysis

• MongoDB, MySQL

• Git, GitHub, IBM CPLEX, Notion, Streamlit, PyTorch (Basic proficiency)

Developed a food ordering website with a secure authentication system and user profiles using Next.js and MongoDB.

• Admin panel for managing orders and updating the menu.
• Achieved seamless integration with Google Auth for login.
  Tech Stack: Next.js, React, MongoDB, Tailwind CSS

A Streamlit app that extracts birthdays from WhatsApp group chats and organizes them in a Notion template.

• Automatically associates phone numbers with names using contact details.
• Categorizes users by group type for better organization.
  Tech Stack: Streamlit, Python, Notion API

Led the development of 3D components for the homepage using Next.js, React-Three-Fiber, and Three.js for a dynamic user experience.

• Achieved 66k+ unique visitors with zero downtime.
  Tech Stack: Next.js, React, Three.js

Developed a CNN+LSTM model to generate captions for images using ResNet50 for feature extraction.

• Deployed as a web app using Streamlit.
• Achieved a BLEU score of 0.585.
  Tech Stack: Python, TensorFlow, Streamlit

Fine-tuned the albert-base-v2-squadv2 model to improve contextual question answering.
Tech Stack: PyTorch, Hugging Face

Skills: REACT / JS / Next.js / HTML / CSS / PYTHON / C / C++ / Deep Learnig / SQL / TensorFlow / NODE.JS* / EXPRESS,JS*