No offense, but I have seen better ML 101 capstone projects.
You get predictable results like Zeppelin, The Eagles, etc. as the 'most hit-laden' records.
As far as actually measuring the SOUND with machine learning, you could just take a short-cut and study Mutt Lange's mixes, or you could run the computers on it, but this is not actually an interesting question because everyone's been trying to converge on the 'hit song sound' for decades and decades.
You'll get pretty generic and unobjectionable results, and you'll still get blindsided by outliers such as Bohemian Rhapsody (too impossibly long and complicated) and Somebody I Used To Know (very odd arrangement).
Targeting the most mass market possible (which is do-able with AI) is such a shotgun approach that it'll tend to lead to failure because too many talented humans have already beat that approach into the ground.
What you'd want is for your hit-song AI to 'hallucinate' in an interesting way. You want things a little off-model, a 'hook'. In a conceptual sense, not just a 'mathematically optimal catchy phrase' sense.