The Hollywood movie production business has a very instinct and contact driven low-tech decision-making process that generates a portfolio of movies that a production house decides to fund in any given year. The same type of decision-making process is employed by movie stars and their agents to decide which projects to pursue and which ones to pass. This leads to a high degree of variation in the success rate of projects (as measured by gross box office receipts and return in investment). Most production houses employ a portfolio driven approach and diversify their risk across a number of low, medium and high budget movies.
I have attempted several data centric ML approaches to solve this interesting predictive problem.