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JoeKell / repository
The goal of this project is to analyze the data available from Spotify to answer questions about Spotify Audio Features by song year, correlation between Audio Features and country metrics, and the Spotify Audio Features by song year. Technologies to be used are Python, Jupyter Notebooks, Pandas, Requests, and Matplotlib. Optionally, the Spotify API can be used but will match the Kaggle Data
The goal of this project is to analyze the data available from Spotify to answer questions about Spotify Audio Features by song year, correlation between Audio Features and country metrics, and the Spotify Audio Features by song year. Technologies to be used are Python, Jupyter Notebooks, Pandas, Requests, and Matplotlib. Optionally, the Spotify API can be used but will match the Kaggle Data. For this we used the following data sources:
Our presentation slides can be found here.
The analysis for this question is in the Features Over Time notebook.
Valence – The average valence was the lowest in 1946, meaning this was the saddest year of music in our dataset. This is likely due to WWII.
Acousticness – We noticed a general drop is acousticness over the decades, which makes sense considering the rise of digital music making
Popularity – Considering “The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are”, this analysis confirmed our assumption that the more recent a song was made, the more popular it would be.