analysing last.fm and spotify listening history. using playlists as models for classification using SVMs and MLPs
2021-02-20 14:12:51 +00:00
analysis
fixed query numbers, quick cache for playlists
2021-02-03 16:08:06 +00:00
docs
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
.gitignore
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
album.ipynb
added classifier notebook with SVM
2021-02-04 13:34:25 +00:00
analysis.ipynb
fixing spelling error
2021-02-20 14:12:51 +00:00
artist.ipynb
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
playlist-classifier.ipynb
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
playlist.ipynb
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
poetry.lock
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
prep-audio-features.py
restructured, added notebooks, refreshed data, poetry
2021-02-01 01:37:22 +00:00
prep-scrobbles.py
restructured, added notebooks, refreshed data, poetry
2021-02-01 01:37:22 +00:00
pyproject.toml
added playlist and artists books
2021-02-01 21:43:27 +00:00
README.md
added stratifying dataset, added hours per day graph
2021-02-20 00:16:03 +00:00
stats.ipynb
restructured, added notebooks, refreshed data, poetry
2021-02-01 01:37:22 +00:00
Listening Analysis
Notebooks:
analysis for a intro to the dataset and premise
artist , album & playlist investigations
stats for high-level stats about the dataset (Spotify feature miss ratio)
playlist classifier using Scikit to classify tracks using the contents of playlists as models
Combining Spotify & Last.fm data for exploring habits and trends
Uses two data sources,
Last.fm scrobbles
Spotify audio features
The two are joined by searching Last.fm tracks on Spotify to get a Uri, the track name and artist name are provided for the query.
These Uris can be used to retrieve Spotify feature descriptors.