Summary
The project aims to use features of a song to determine how much an individual likes that song. They collected 13 different features on a unique data set collected from Spotify. These features of each song along with data collected for 710 different songs will be ultimately used to predict the likability of the song.
What the team did well
The team used concepts from throughout the course and while some of them didn't work, they made important conclusions from those experiments.
The team noticed an important limitation of their data when running the linear regression and note how they plan on transforming it using feature engineering.
What the team can improve
Make sure to provide a clear roadmap ahead. The team mentions using many different algorithms but does not expand on how or why they will use them and what it should tell them.
I am a bit concerned about how feasible it will be to predict how much a user likes a song when you are training your data with many different users.
Summary The project aims to use features of a song to determine how much an individual likes that song. They collected 13 different features on a unique data set collected from Spotify. These features of each song along with data collected for 710 different songs will be ultimately used to predict the likability of the song.
What the team did well
What the team can improve