Velingkar, G.Varadarajan, R.Lanka, S.Anand Kumar, M.2026-02-062022ICPC2T 2022 - 2nd International Conference on Power, Control and Computing Technologies, Proceedings, 2022, Vol., , p. -https://doi.org/10.1109/ICPC2T53885.2022.9776798https://idr.nitk.ac.in/handle/123456789/29963Being a multi-billion dollar business, the film industry contributes largely to helping sustain a country's economy. A movie's box office (the revenue generated by the number of tickets sold of a movie) is an essential indicator of the movie's popularity. It varies depending upon several factors, including a production company, genre, budget, reviews, ratings, etc. Predicting an approximate value for a movie's box office based upon the various parameters helps investors with this business make intelligent and informed decisions. Thus, this paper designs a machine learning model that can predict the revenue a film will generate based on the information available before the movie's release. It also provides a model capable of taking in the planned genre, the required revenue, and using the Random Forest Regression model, provides recommended budget, runtime, star power, and expected popularity. © 2022 IEEE.box-officemachine learningmoviesregressionrevenue prediction