A Shallow Approach to Gradient Boosting (XGBoost_s) for Prediction of the Box Office Revenue of a Movie

Abstract

In the recent past, machine learning paradigms like the ensemble approaches have been used effectively to predict revenue from large volumes of sales data that helped the decision-making process in many businesses. The proposed work in this paper proposes a modified approach of ensemble algorithms to predict box office revenues of upcoming movies. A shallow version of the gradient boosting (XGBoost_s) has been proposed to predict the box office revenue of movies based on several primary and derived features related to the movies in particular. Further studies have found that features such as budget, runtime, budget year ratio can also be considered as some of the more important estimators of the box office revenue. These features along with some other features have been used as an input to the proposed model in this proposed work to make significantly good predictions about the box office collection of a movie. The results are reported by testing and forecasting based on simulation on a standard data set. The precision of the model is tested using popular metrics such as R², MSLE. The results reported gives efficacy of the proposed approach that can be further used in other business models words.