This is a machine learning project to predict the popularity of a track on Spotify.
The dataset was sourced from Kaggle. The dataset contains 170,653 tracks from 1921 to 2020. It also contains 16 features. Visualizing feature importances reveals that 'year' is the most important predictor of the popularity of a track (Well, the Spotify documentation does state that popularity is calculated with respect to year).
The approach to predict popularity is simple. It is broken into four steps:
- Create a simple decision tree model
- Optimize the decision tree model with GridSearchCV
- Tweak only the max_leaf_nodes parameter of the decision tree model
- Create a random forest model
The random forest model has the best results, with a mean absolute error of 6.75.
| Model | MAE |
|---|---|
| Simple decision tree | 9.196 |
| Decision tree (GridSearchCV) | 6.915 |
| Decision tree (max_leaf_nodes) | 6.829 |
| Random Forest | 6.750 |