Our Berkeley Data Science Capstone Project: Rap Analysis

The goal of our system was to predict whether or not a rap song would appear on the Billboard Top 100 Charts. We treated this as a supervised machine learning problem, and we narrowed down our dataset of songs 24,175 entries. This dataset contained 1,491 rap songs that had successfully made it onto the top 100 charts, and the remaining songs from those artists that did not make it onto the charts. With such a large offset between the number of successful and unsuccessful songs, we used randomized sampling to better balance the data. We used combinations of the features we described above and various machine learning algorithms to better hone our predictive score.