An end-to-end IoT monitoring platform designed to collect, process, and visualize data from distributed sensor nodes in real time. The system focuses on device health, environmental readings, and historical analytics, with a modern dashboard that makes it easy to monitor trends, detect issues, and manage sensors at scale.

A full-stack web application that recommends music based on a user’s emotional state expressed through text input. The app uses a transformer-based sentiment analysis model to infer emotions, maps those emotions to Spotify audio features, and returns personalized song recommendations through a clean, interactive UI.

A machine learning application that predicts NBA player salaries using position-specific Random Forest models trained on cleaned and engineered historical statistics. The project includes a Flask-based API and a React frontend, allowing users to submit player data and explore predicted salary outcomes in a simple, intuitive interface.

A social media application inspired by core Instagram and Twitter functionality, enabling users to create accounts, post content, like, and comment. Built with the MERN stack, the project implements JWT-based authentication, secure password handling, and a RESTful API to manage users, posts, and interactions.

A low-level messaging system simulation that models communication between multiple user processes and a central server. Implemented in C using forks and unidirectional pipes, the system focuses on process coordination, message routing, synchronization, and clean termination of all child processes.