I am trying to build a recommendation system in Python that recommends songs based on a playlist.
What I have is two datasets:
1. One dataset consists of 350 songs from my playlist and 13 acoustic features for each one like timber, energy, key, tempo, etc. that I extracted using the Spotify API
2. The other dataset consists of 340 000 songs and their acoustic features (got it from here: https://components.one/datasets/billboard-200/)
I've gotten both datasets in the same format and ready to be worked with.
Now what I am trying to do in Python is use my first dataset to get 30-40 songs with similar acoustic features from the second dataset but I have no idea how to approach this.
Should I use some machine-learning models or do something entirely else?
I thought about comparing the songs from my first dataset to the songs on my second dataset and pulling the ones with a similarity score of let's say over 70% or something like that but I feel like there is probably a much better way of doing this.