The Billboard magazine is a world renowned music publication since 1984 and it releases a weekly ranking of the top 100 songs in various categories such as rock, pop, hip-hop, etc. Several studies have determined that it is possible to predict the approximate bucket of ranks that a song is likely to chart in using social and subjective indicators. The definitions of these indicators however can change over time, thus rendering the previous classifications erroneous. Here, we report successful results from our experiments in predicting the ranks and the number of weeks the songs are likely to stay on the charts, using objective and well-defined features, obtained from Spotify’s Web API. It extends existing research about classifying songs into rank buckets of Top-10 and Top-40 using these objective features, demonstrating that it is possible to predict exact ranks of the songs within a root-mean-squared-error of 28 ranks and the number of weeks of charting within a root-mean-squared-error of 7 weeks, demonstrating definitive trends between individual features and the ranks of the songs, demonstrating that objective metadata about the songs serve as good indicators about the trends in the Billboard charts, and can be used to predict a song’s performance on the charts within acceptable error rates.