analysing last.fm and spotify listening history. using playlists as models for classification using SVMs and MLPs
| analysis | ||
| docs | ||
| .gitignore | ||
| album.ipynb | ||
| analysis.ipynb | ||
| artist.ipynb | ||
| playlist-nn.ipynb | ||
| playlist-svm.ipynb | ||
| playlist.ipynb | ||
| poetry.lock | ||
| prep-audio-features.py | ||
| prep-scrobbles.py | ||
| pyproject.toml | ||
| README.md | ||
| stats.ipynb | ||
| track.ipynb | ||
Listening Analysis
Notebooks:
- analysis for a intro to the dataset and premise
- artist, album, track & playlist investigations
- stats for high-level stats about the dataset (Spotify feature miss ratio)
- playlist SVM using Scikit to classify tracks using the contents of playlists as models
- playlist NN using a multi-layer perceptron 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.
