ML algorithm for Music Features

Question

I am a newbie in machine learning topic and I need to create model from music data.

It contains features of the songs but it is not labeled. How can I create a model from that ?

Do I need to use unsupervised learning algorithms ? Which one is better or is it better if I use deep learning methods.

Data is looking like this:

      danceability  loudness  valence  energy  instrumentalness  acousticness  
136         0.795    -8.334    0.578   0.409          0.000000      0.684000   
442         0.502    -4.556    0.720   0.912          0.000173      0.000025   
92          0.713   -14.590    0.560   0.258          0.006060      0.877000   
67          0.505   -14.951    0.723   0.782          0.930000      0.921000   
127         0.470    -6.740    0.490   0.809          0.006710      0.272000 

Answer 1

While your data is not labeled, you should use unsupervised learning to clusterise them.

You can take each raw as a vector point and apply kmeans or others to have many clusters based on similarity between your data in your training sets. I always try simple algorithms first before passing to deep-learning models

Hope it helps