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Siddhant Dwivedi Portfolio

Welcome to my personal portfolio website! This repository hosts my full-stack portfolio showcasing my projects, skills, and professional experience. It demonstrates my proficiency in web development, full-stack engineering, and machine learning deployment.


About Me

I am a B.Tech student in Chemical Engineering (MNNIT Allahabad, 2022–2026) with a strong passion for software development, AI/ML, and full-stack web applications. I have hands-on experience building scalable and responsive applications and integrating machine learning solutions into interactive web apps.


Features

  • Fully responsive portfolio website showcasing projects, skills, and achievements.
  • Projects section with detailed descriptions, tech stack, GitHub links, and live demos.
  • Skills and interests with visual tags for front-end, back-end, and ML tools.
  • Contact section for easy outreach and professional networking.
  • Smooth animations and transitions powered by Framer Motion.
  • Light/Dark mode (if implemented) with modern UI/UX design principles.

Projects Featured

1. QuickStay — Hotel Booking Platform

  • Description: Hotel booking web app featuring live availability, filters, location-aware search, secure booking flow with JWT, role-based access, and booking history tracking. Admin dashboard integrated. Deployed on Vercel simulating CI/CD.
  • Tech Stack: React.js, Node.js, Express.js, MongoDB, Tailwind CSS, JWT, REST API, Vercel
  • GitHub: QuickStay

2. MediConnect — HACK-36

  • Description: Full-stack doctor–patient appointment booking platform with calendar scheduling for 50+ simulated appointments. JWT authentication, role-based access for 5 user roles, REST APIs with optimized queries. React.js & Tailwind CSS UI components.
  • Tech Stack: React.js, Node.js, Express.js, MongoDB, Tailwind CSS, JWT, REST API
  • GitHub: MediConnect

3. Email Spam Classifier — Streamlit + NLTK

  • Description: ML web app classifying emails as spam or ham using NLTK and Tf-idf Vectorizer. Trained on 5,000 labeled emails achieving 98% accuracy & 99% precision. Deployed with Streamlit & Render.
  • Tech Stack: Python, NLTK, Scikit-learn, Tf-idf, Streamlit, Render
  • GitHub: Email Spam Classifier
  • Live Demo: View App

Tech Stack

  • Front-end: React.js, Next.js, Tailwind CSS, HTML5, CSS3, JavaScript/TypeScript, Framer Motion
  • Back-end: Node.js, Express.js, MongoDB, PostgreSQL, JWT, REST APIs
  • Machine Learning: Python, NLTK, Scikit-learn, Tf-idf
  • Deployment: Vercel, Render, CI/CD pipelines, Cloud hosting
  • Tools: Git/GitHub, VS Code, Postman

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