  Our project aims at helping independent musicians to plan their concerts
based on the economies of agglomeration in the music industry. Initially, we
planned to design an advisory tool for both concert pricing and location
selection. Nonetheless, after implementing SGD linear regression and support
vector regression models, we realized that concert price does not vary
significantly according to different music types, concert time, concert
location and ticket venues. Therefore, to offer more useful suggestions, we
focus on the location choice problem by turning it to a classification task.
The overall performance of our classification model is pretty good. After
tuning hyperparameters, we discovered the Random Forest gives the best
performance, improving the classification result by 316%. This result reveals
that we could help independent musicians better locate their concerts to where
similar musicians would go, namely a place with higher network effects.
