Movie Recommendation Web App
Built a full-stack application using React, Node.js, Express, and MongoDB. Users can add movies to their watched list and enjoy a responsive, easy-to-use interface. Working on this project enhanced my front-end skills, especially around user interface design and state management. I am also working on extending it with an AI model to generate personalized movie suggestions based on viewing history, which will help me gain more experience in machine learning integration.
- Full-stack development with React, Node.js, Express, MongoDB
- Integrated TMDb API for real-time data retrieval and scalable metadata handling.
- Currently building an AI recommendation engine with semantic similarity search, tested on multiple watch-history datasets.
Personal Financial Dashboard
Developed a full-stack web application to track income, expenses, and balances with interactive visualizations. Focused on usability with real-world financial features and backend scalability. This project was inspired by my realization that I often overlooked recreational spending and lacked visibility over my financial responsibilities. The dashboard gave me a clearer picture of my spending habits and helped me learn how to integrate front-end and back-end technologies effectively. Through this project, I strengthened my understanding of full-stack development, database management, and the design of practical tools that address real-life needs.
- Built with React, Node.js, Express, and MongoDB
- Implemented transaction CRUD operations with search and filter functionality
- Designed CI/CD workflows and explored AI-driven financial insights
Spotify VibeMap
Built a collaborative music recommendation app using graph embeddings and Spotify API integration. Focused on authentication, data visualization, and team-based development workflows. The project was divided into frontend and backend components, and working within this structure gave me real insight into team collaboration. I learned the importance of clear communication, managing responsibilities, and contributing to a shared goal. This project showed me the value of teamwork in professional software development.
- Collaborated in a team to build a recommendation system using graph co-occurrence with node2vec embeddings
- Integrated Spotify API for playlist and music data
- Implemented OAuth authentication
- Visualized playlists in an interactive 3D graph
LinkedIn Bot
Developed a bot to track and alert internship postings from LinkedIn job alerts. This project was developed to address the challenge of manually searching for internships. The bot reads LinkedIn notifications from my Gmail account using the Gmail API and posts them directly to a Discord server. This streamlined the internship search process and saved valuable time. Developing this bot gave me experience in automation, parsing, and API interaction while also reinforcing how software can simplify everyday tasks.
- Built with Python for automation and web scraping
- Parsed Gmail job alerts to extract internship and job postings
- Used Gmail API to parse linkedin email.