Computer Science Engineering student with a basic understanding of programming, web development, and AI.
Always curious about technology : how it works, changes, and shapes the world.Â
B.Tech. in Computer Science & Engineering.
Machine Learning Trainee. Completed Python ML projects with an "Excellent" performance rating.Â
As an adaptable software developer, my experience spans across various languages and frameworks, allowing me to quickly learn and apply the right tools for the job. Whether I am building user-facing web features or exploring backend logic, I am driven by a desire to build impactful applications.
Python, C, C++, HTML, CSS, JavaScript, and MySQLÂ
Flask, OpenCV, Scikit-Learn, and Matplotlib.
Machine Learning, Deep Learning, and Computer Vision.Â
Git, GitHub, Open Source, and API integration.
Beyond my academic coursework, I am deeply involved in the tech community and constantly exploring new tools. I believe in continuous learning, participating in hackathons, and sharing knowledge to grow both personally and professionallyÂ
Participated in the Smart India Hackathon (SIH) 2024 and presented an article on 'Gibberlink' in the college magazine AKS – Inked with Inspiration.
Experienced in utilizing modern development environments and version control systems, including Git, GitHub, and VS Code, to build, test, and deploy robust applications.
A closer look at my recent academic and personal projects, showcasing my ability to adapt to new technologies and build practical solutions across different domains.Â
Overview: A web application that allows users to upload an image of a handwritten digit (0–9) and predicts the number with high accuracy.
The Build: Developed and trained a Convolutional Neural Network (CNN) using the MNIST dataset.
Tech Stack: Python, Flask, OpenCV, and Scikit-Learn.
Overview: A full-stack AI application that allows users to upload documents (PDF, DOCX, TXT) and ask questions through a ChatGPT-style interface using a 100% local LLaMA 3 model (zero API dependency).
The Build: Engineered a complete Retrieval-Augmented Generation (RAG) pipeline. Implemented fast semantic search, optimized document chunking, and source tracking to reduce AI hallucinations and provide accurate answers.
Tech Stack: Python, Streamlit (Frontend), FastAPI (Backend), LLaMA 3 via Ollama, FAISS Vector DB, and sentence-transformers.
Overview: Machine learning pipeline for predicting ICU readmission risk using patient data.
The Build: The application is built with Python, Scikit‑Learn, and Streamlit and deployed online for interactive predictions.
Tech Stack: Python, Pandas, NumPy, Scikit-Learn, Streamlit, Joblib, Google Drive (for model hosting)
Description: > Issued: Feb 2026 (ID: 83111997-41d8-4069-a750-99023618301d)
Skills: Prompt Engineering, LLM Integration, Large Language Models.
Details: Learned industry-standard techniques for building applications using LLMs, including summarizing, inferring, transforming, and expanding text.
Issued: July 2025 (ID: 198785)
Skills: NumPy, TensorFlow, Scikit-learn, Pandas.
Description: Completed an intensive 4-week in-house training with an "Excellent" performance rating.
Issued: June 2025 (ID: 5tazsnrtic4)
Description: Focused on professional soft skills, resume building, and interview readiness for the tech industry.
Issued: September 2024 (ID: 549d886)
Skills: C++, GitHub, Core Programming.
Description: Strengthened core programming logic through hands-on development of CLI-based tools and games.
Date: January 2020
Description: Introduced to open-source software development, contributing to real-world projects as a pre-university student.