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http://dx.doi.org/10.7838/jsebs.2020.25.2.049

A Study on the Performance Evaluation of Machine Learning for Predicting the Number of Movie Audiences  

Jeong, Chan-Mi (Graduate School(Big Data Analytics), Ewha Womans University)
Min, Daiki (School of Business, Ewha Womans University)
Publication Information
The Journal of Society for e-Business Studies / v.25, no.2, 2020 , pp. 49-63 More about this Journal
Abstract
The accurate prediction of box office in the early stage is crucial for film industry to make better managerial decision. With aims to improve the prediction performance, the purpose of this paper is to evaluate the use of machine learning methods. We tested both classification and regression based methods including k-NN, SVM and Random Forest. We first evaluate input variables, which show that reputation-related information generated during the first two-week period after release is significant. Prediction test results show that regression based methods provides lower prediction error, and Random Forest particularly outperforms other machine learning methods. Regression based method has better prediction power when films have small box office earnings. On the other hand, classification based method works better for predicting large box office earnings.
Keywords
Box Office Forecasting; Machine Learning; Classification Model; Regression Model; Random Forest; K-NN; SVM;
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Times Cited By KSCI : 5  (Citation Analysis)
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