First Machine Learning Project: Clustering



On Wednesday, students presented their work on Spotify data using either k-Nearest Neighbors, k-Means Clustering or logistic regression.  They needed to also include a visualization of their data.  This was their first "formal" presentation of the cluster. They implemented algorithms to have the machine make suggestions or predictions about data.  Some created their own versions of recommender systems (based on songs, playlists, determining if a song is of a specific genre, etc.). Students determined what features were relevant and commented on any observations.  Below are their presentations. 

Congratulations to Faculty Choice Award winners Faith and Ronit and People's Choice Award winner Angie and Isaac. 

Andrea, Robert and Aamir
Song Recommendation Algorithm: Presentation Slides

Angelina and Stik
Recommendation Spotify Project: Presentation Slides

Minseo and Samuel
Spotify Project: Presentation Slides

Amishi and Rhyan
Spotify Playlist Blend with a Mood: Presentation Slides

Faith and Ronit
Faculty Choice Award Winner
Recommended Playlist: Presentation Slides

Perla and Tomas
Spotify Project: Presentation Slides

Aarushi
Can We Use Machine Learning to Predict the Ranking of a Song?: Presentation Slides

Kathy and Ethan K.
Hip-Hop vs. K-Pop: Presentation Slides

Jyoti and Stanley
Spotify Project: Presentation Slides

Gaby and Naader
Clustering Video Game Music: Presentation Slides

Sagana and Ethan C.
Spotify GENRE-generator: Presentation Slides

Angie and Isaac
People's Choice Award Winner
Spotifriend - Help You Spot a Friend: Presentation Slides





















 

 

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