Web
Developer
Hello it's me
Naman
Passionate 🌐 Full Stack Web Developer pursuing B.Tech in 🤖 AI & Machine Learning at 🎓 Vivekanand Institute of Professional Studies.I am a fervent 🐧 Linux enthusiast, and open-source advocate.
I am a Full Stack Web Developer and Linux Enthusiast. I have experience with a wide range of technologies and tools, including HTML, CSS, JavaScript, React, Next js,Node.js, Express, MongoDB, Redis,MySQL and PostgresSQL. I am also experienced in design tools such as Figma, GIMP and Inkscape. I am a quick learner and have a strong attention to detail. I am a motivated self-starter who is always looking for new challenges.
typescript
react
git
GraphQL
tailwind
sass
React Query
zustand
GNU/linux
NodeJS
Nest JS
PostgreSQL
prisma
JWT
mongoDB
redis
docker
Numpy
Pandas
Matplotlib
Scikit-Learn
TensorFlow
< Projects />
CsTimer - Rubik's Cube Timer
This web app is a clone of a rubik's cube timer app with full auth features such as reset password and confirmation email.It is a highly interactive web app with lots of features such as scrambles for different puzzle types 3x3,2x2,4x4 up to 7x7.It also has charts to show your progess of your solve times.
Graphing Calculator for Complex Functions
I've been working on a tool designed to make graphing and analyzing complex functions easier and more intuitive. This advanced graphing calculator features vector fields for detailed function analysis and domain coloring to visualize complex functions in a unique way.
Ecommerce Website
This is a fully functional ecommerce webiste made with next js , nest js and sanity.Users can login and register.There is functionality for forgot password and confirmation of user email.Users can add products to cart, remove them from cart and change the quantity of items.Users can purchase items via stripe checkout feature.
Heart Disease Classification
This project aims to predict the likelihood of heart disease in individuals based on their medical and demographic data. By leveraging Logistic Regression, a popular statistical model for binary classification, the project classifies individuals into two categories: those likely to have heart disease and those who are not.
