Ironhack Data Analytics bootcamp Project 3
Creating an algorithm that compares audio features of an input song to 2 databases with clusters containing different audio feature groups.
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Create dataframe hot 100 songs Scrape website of hottest 100 music (link) Restructure HTML into dataframe with hot 100 songs and artists Get the audio features of the hot 100 songs and add to dataframe
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Create dataframe out of huge playlist(s) using spotipy Gather information in dataframe Get the audio features of the spotify playlist and add to dataframe
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Train a model (algorithm) Using clustering method of audio feats Calculating optimal number of clusters (k) Using model to suggest songs in step 4
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Create function to check if user input = hot 100 If yes, suggest another song of hot 100 with similar audio feat If not, suggest another song with similar audio feat by any artist & popularity