ML algorithm for Music Features 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
If the problem you are trying to solve is "I need to guess if the new songs are fit to user's taste with the song feature data I got."

AND if you have some indication what user has liked / listened-to in past; you can use "Recommendation Engine" approach.

Example : https://tampub.uta.fi/bitstream/handle/10024/101198/GRADU-1495623946.pdf?sequence=1

It works on the basis of "likeness"; E.g.: 


Assumption : All Hip-Hop songs have some common pattern among the
features like energy , loudness etc.
If a user likes Hip-Hop (can be judged from user's history based on features of songs), then other Hip-Hop songs can be suggested