This project analyzes streaming platform content data to uncover trends, content gaps, and actionable insights that support data-driven business decisions. Using SQL, Python, and Tableau, the analysis focuses on content distribution, release trends, genre demand, and ratings breakdown to inform content strategy and acquisition planning.

Blending machine learning with music analytics to decode rhythm, mood, and energy. Transforming raw Spotify data into interactive visual insights that reveal how we listen with logic and feel with data.

Building smarter payments through full-stack engineering and seamless user experience — integrating admin dashboards, QR-based transactions, and automated testing to bring effortless campus payments to life.

Applying predictive analytics to understand what truly drives patient satisfaction. Built machine learning models in Python to analyze survey data, identify key experience factors, and visualize actionable insights through interactive dashboards.

Engineering predictive solutions to make supply chains smarter and more sustainable. Designed regression-based models and visualizations to forecast shelf life, optimize logistics, and reduce waste in perishable goods distribution.

Bringing data collection to life through full-stack innovation. Developed a real-time survey application with React, Node.js, and MongoDB — integrating RESTful APIs, asynchronous data flow, and modular design for fast, reliable user feedback.