"Music is an undeniable part of our lives - we listen to music during commute, work, or even to get over a heartbreak. The best ones end up in the Billboard 200 chart, but here's a central question:
Is there a method to this madness that is being a chart-topper?
The objective of this project is to build a model that can predict the potential of a song to be a billboard hit based on its features.
This project involves the following:
1. API calling and web scraping - Python3
2. Data cleaning - Python
3. Data reporting - Jupyter Notebook
4. Machine learning - Python
The “Project Billboard” team was formed. The task was to deliver a model predicting if a song would be a Billboard hit.
We had our first meeting at starbucks where we worked out the scope of the project and the resources to use. Spotify’s API was chosen as our primary source of data for song features.
The code for collecting song features from Spotify was completed and we had our first dataset. We proceeded to try simple algorithms for hit/miss prediction, as well as scraping billboard hot 100 charts for unique hits.
Machine learning models proved decent, with accuracies of 60-70%. The next step was to explore how we can refine the model and prepare a user-friendly interface.
Our models have been deployed on a simple website with a user-friendly interface!
The Cool Kid
The Funny Kid
The Crazy Kid
Have an idea for the project? Reach out to any of us!