The aim of this this project was to predict songs rating using audio features as predictors. The algorithms selected for this purpose will be Linear Regression, Tree Model (default, maximal, and tuned), Bootstrap Aggregation (BAG) Model, and Random Forest Model (tuned and default with ranger and randomForest library). The evaluation metric discussed is RMSE (root mean square error). What makes some songs have higher ratings than the others? The dataset describes most rated songs based on auditory features, such as key and loudness.

The analysis comprised of the most trending categories, top 10 channels on entertainment category, and average views per publish hour. These insights based on Youtube data in U.S. on 2017.