In addition, we propose a new hybrid method that combines features from both machine learning and econometric methods, based on the recent work of Hirano and Wright (2017)andXie (2015). Problem: revenue data is not good enough. Machine learning, sentient artificial intelligence, humanoid robotics—all of a sudden these terms don’t feel as strictly ‘sci-fi’ as they once did. 2, pp. Applied Economics Letters: Vol. support vector machine, gradient boosting, extreme boosting classifier, and random forest. Once historical box office earnings are available, the contribution of the SNS data to the forecasting performance is not as impressive as it was prior to release, but it certainly drives additional performance improvements. Third, the use of machine learning-based algorithms is effective in forecasting over all of the forecasting periods. DOI: 10.1109/ICCITECHN.2017.8281839 Corpus ID: 20759660. I removed zero revenue rows, resulting in 900 rows lost. Previous studies on predicting the box-office performance of a movie using machine learning techniques have shown practical levels of predictive accuracy. A machine learning approach to predict movie box-office success @article{Quader2017AML, title={A machine learning approach to predict movie box-office success}, author={N. Quader and Md. A voting system is used to make the prediction by averaging the output class-probabilities. Existing methods for movie box-office revenues prediction can be classified according to their choice of movie-related data and prediction techniques. the box office prediction exercises in Lehrer and Xie (2017) by considering eight widely used meth-ods in both machine learning and econometrics literature. Films like Her and Ex Machina offered visions of a digital future that felt almost close enough to touch, in the sense that the very same technology could feasibly be in our own hands soon. Source file: data_prep.py. 26, No. For example, movie trailers were used as input data for a linear support vector machine (SVM) classifier to predict the opening-week box-office … MACHINE LEARNING ON PREDICTING GROSS BOX OFFICE {PENGDA LIU} PENGDA@STANFORD.EDU PROBLEM In this project I explored how several film pa-rameters would help predict the gross box office. We had a … Data points provided include cast, crew, plot keywords, budget, posters, release dates, languages, production companies, and countries. The variables used for model prediction were: User vote (akin to IMDb rating, referred to as ‘rating’ throughout) User-reported box office revenue (referred to as ‘revenue’ throughout) Data preparation. The adaptive behaviour of the presented system is achieved by incorporating conceptually different machine learning classifiers, i.e. 124-130. In our model, the predicting problem is 2017 20th International Conference of Computer and Information Technology (ICCIT), 22-24 December, 2017 A Machine Learning Approach to Predict Movie Box-Office Success Nahid Quader Md. Their works are technically- and methodologically-oriented, focusing mainly on what algorithms are better at predicting the movie performance. Box Office Prediction for Upcoming Films Final Report Rui Xue, Yanlin Chen 1 Introduction In this project, we apply machine learning algorithms including multiclass Naïve Bayes and SVM to predict box office for movies. Machine learning versus econometrics: prediction of box office. Osman Gani and Dipankar Chaki and M. H. Ali}, journal={2017 20th International Conference of Computer and Information Technology (ICCIT)}, … Osman Gani Department of Computer Science and Engineering Department of Computer Science and Engineering School of Engineering and Computer Science School of Engineering and Computer Science BRAC … However, the accuracy of prediction model can also be elevated by taking other … (2019). And Movie industries and persons associated with Movies can use the Machine learning model to predict the revenue of the movie by inputting the above featured. It is divided into two sections, one is using lin-ear models and involving opening weekend box office(it may not serve as a feature, see more de- In this competition, you're presented with metadata on over 7,000 past films from The Movie Database to try and predict their overall worldwide box office revenue.