A music recomendation system built off machine learning.
Many of today's current music systems (Pandora, Spotify) fail to effectively play music for a variety of audiences on a variety of platforms. Many are inadequete for mobile or tablet devices, and feature clunky layouts.
The Summer Project (name pending) aims to combat these issues and employ a wholistic approach to music recomendation. This includes taking things like the user's mood into account when recommending songs.
The process of building a recomendation system is broken down into three parts.
- Build a scraper to collect data to build a music library
- Implement machine learning as to analyze patterns and give recomendations
- Build a clean and sleek user interface via both web based and mobile applications.
The project is currently on step one, as we are currently building a music library.