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particular pitch patterns that can be used for prediction.\nThe Spotify API provides features for entire tracks, e.g. its loudness or acousticness scores, as well as the sequence of the individual pitches (notes).\nTotaling 3600 tracks across techno, rock, jazz and classicsal generes were analyzed and used for both classical Machine Learning and Deep Learning modeling methods.\nValidation accuracy of both approaches were similar suggesting that more sophisticated network architectures are needed to increase the model performance.\n\n## Tech stack\n\n- [keras](https://keras.io/) deep learning framework\n- [Tensorflow](https://www.tensorflow.org/) deep learning framework\n- [tidymodels](https://www.tidymodels.org/) machine learning framework\n- [tidyverse](https://www.tidyverse.org/) data wrangling\n- [R targets](https://books.ropensci.org/targets/) pipeline system\n- [spotifyr](https://www.rcharlie.com/spotifyr/) REST API calls\n- [quarto](https://quarto.org/) notebook documentation\n\nKeywords:\n\n- Spatial data analysis\n- deep learning\n- REST APIs\n\n[This project on GitHub Pages](https://danlooo.github.io/spotify-datasci/)\n\n## Development\n\nCreate a file `.env` in the main directory to define the environment variables `SPOTIFY_CLIENT_ID` and `SPOTIFY_CLIENT_SECRET`","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanlooo%2Fspotify-datasci","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanlooo%2Fspotify-datasci","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanlooo%2Fspotify-datasci/lists"}