A Concert-planning Tool for Independent Musicians by Machine Learning Models
This provides a practical tool for independent musicians to optimize concert locations, though it appears incremental as it adapts standard machine learning methods to a specific domain problem.
The researchers tackled the problem of helping independent musicians plan concerts by focusing on location selection, discovering that concert price doesn't vary significantly with factors like music type or time. Their Random Forest classification model improved location prediction performance by 316%, enabling musicians to choose venues with higher network effects.
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.