Mood-Based Music Playlist Recommendation App

Added Jun 2025 3 design docs

Choosing music is emotional, but most players force users to translate feelings into search terms. Letting someone simply say how they feel, whether stressed before an exam or energized for a run, and receiving a fitting playlist is a small experience with real delight, and it happens to be a classic gateway into recommendation systems. The intern builds a React application with a Node.js backend where users describe their current mood and machine learning maps that input to playlist suggestions. The pipeline applies sentiment analysis to free-text mood entries, classifies them into mood categories, and matches those categories to songs tagged by energy, positivity, and genre using models built with standard data science tooling. Users can rate the suggestions they receive, and that feedback loop tunes future recommendations, giving the intern a first taste of learning from implicit user signals. The project introduces sentiment analysis, recommendation logic, and full-stack integration in one approachable build, and leaves the intern able to explain precisely how a mood becomes a feature vector and how a feature vector becomes a playlist.

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