Fast learner with strong attention to detail and time management skills. Committed to leveraging coursework in Computer Science and collaborative projects to drive success.
Certification
Languages
Technical Skills Programming Languages: Python, Java, JavaScript, C, SQL Web Development: HTML, CSS, Reactjs, Nodejs, Expressjs Machine Learning: Scikit-learn, Basic TensorFlow, CNN, TF-IDF, Naïve Bayes Databases: MySQL, SQLite, Firebase, Supabase Tools: Git, GitHub, VS Code, Postman, Figma Concepts: Data Structures, DBMS, Computer Networks, Cybersecurity Basics,OOP
Problem Solving Communication Team Collaboration Debugging API Integration Responsive Web Design,UI/UX Design
Time management
Strong attention to detail
Attention to detail
Team collaboration
Punctual and reliable
Active listening
Active learning
Computer skills
Energetic and enthusiastic
Customer service
Basic mathematics
Fast learner
Positive attitude
English fluency
Introduction to Machine Learning — NPTEL, IIT Madras | Jan–Apr 2026 | 12-week course, 4 credits Netw
I built a jewelry e-commerce web application called Naari, focused on showcasing and selling fashion jewelry online. The app provides a clean product catalog where users can browse items such as earrings, necklaces, and bracelets, view product details, add products to a cart, and place orders.
The project focuses on a user-friendly shopping experience with responsive design, product images, pricing, category filtering, and an attractive brand-oriented interface. It was designed to support a small jewelry business and can be extended with secure payments, order tracking, inventory management, customer reviews, and an admin dashboard.
Key skills demonstrated: frontend development, UI/UX design, responsive web design, product management, and e-commerce workflow design.
2. Web Link Analyzer — DetechI built a web-based fake-link detection application called Detech. Its goal is to help users identify suspicious or phishing URLs before opening them.
A user enters a link, and the system analyzes features such as URL length, special characters, suspicious keywords, domain-related information, HTTPS usage, and WHOIS details. The machine-learning component uses TF-IDF vectorization and a Naïve Bayes classifier to classify links as safe or suspicious.
The system can also support screenshot analysis using OCR and image-based detection, helping identify phishing pages that imitate trusted websites.
Key skills demonstrated: Python, machine learning, cybersecurity basics, Flask/FastAPI or Node.js backend development, URL feature extraction, API integration, and database handling.
3. Retinal Image Analysis for Alzheimer’s Risk — NeuroVision AINeuroVision AI is an AI-assisted healthcare web application designed to analyze retinal images and estimate Alzheimer’s disease risk. Users upload a retinal fundus image, and the system preprocesses it before sending it to a deep-learning model.
The model uses transfer learning with architectures such as ResNet or VGG16 to extract retinal features and classify the image into categories such as normal or potential Alzheimer’s risk. The application displays the prediction result, confidence score, and a generated report for medical professionals.
The project is intended as a screening and decision-support tool, not a final medical diagnosis. Retinal imaging is being researched as a non-invasive way to study structural and vascular changes associated with Alzheimer’s, but clinical use requires larger, standardized validation studies.
Key skills demonstrated: deep learning, CNNs, transfer learning, image preprocessing, medical-image analysis, React frontend development, backend API development, and report generation.
Technical Skills Programming Languages: Python, Java, JavaScript, C, SQL Web Development: HTML, CSS, Reactjs, Nodejs, Expressjs Machine Learning: Scikit-learn, Basic TensorFlow, CNN, TF-IDF, Naïve Bayes Databases: MySQL, SQLite, Firebase, Supabase Tools: Git, GitHub, VS Code, Postman, Figma Concepts: Data Structures, DBMS, Computer Networks, Cybersecurity Basics,OOP