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Predicting attributes based movie success through ensemble machine learning

The film industry has grown into a multi-billionaire industry in terms of entertainment. The success of the film industry depends on the criteria that how much profit a movie would make which gives the tag of a ‘hit’ or a ‘flop’. Predicting the success is guided by various factors like genre, date o...

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Main Authors:	Vedika, Vedika, Jain, Nikita, Garg, Harshit, Jhunthra, Srishti, Mohan, Senthilkumar, Omar, Abdullah Hisam, Ahmadian, Ali
Format:	Article
Published:	Springer Nature 2023
Subjects:	Q Science (General) T58.5-58.64 Information technology
Online Access:	http://eprints.utm.my/105915/ http://dx.doi.org/10.1007/s11042-021-11553-0
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id	my.utm.105915
record_format	eprints
spelling	my.utm.1059152024-05-26T09:06:56Z http://eprints.utm.my/105915/ Predicting attributes based movie success through ensemble machine learning Vedika, Vedika Jain, Nikita Garg, Harshit Jhunthra, Srishti Mohan, Senthilkumar Omar, Abdullah Hisam Ahmadian, Ali Q Science (General) T58.5-58.64 Information technology The film industry has grown into a multi-billionaire industry in terms of entertainment. The success of the film industry depends on the criteria that how much profit a movie would make which gives the tag of a ‘hit’ or a ‘flop’. Predicting the success is guided by various factors like genre, date of release, actors, net gross and many more. Understanding the stakes involved with a movie release that can affect its success or a failure, before-hand can be a great step towards the expansion of the film industry business. Therefore, this study proposes an ensemble learning strategy as a solution to analyze such understanding where predictions from previously guided attribute calculations can be used to enhance future success/failure accuracy. This study shows various strategies used in the literature to analyze and compare the results obtained. The various machines learning algorithms SVM, KNN, Naive Bayes, Boosting Ensemble Technique, Stacking Ensemble Technique, Voting Ensemble Technique, and MLP Neural Network are applied on the dataset to predict the box office success of a movie. The paper uses various algorithms and their trends in predicting the outcome of a movie and shows that the proposed methodology outperforms the existing studies. The most effective algorithm in the study is Gradient Boosting with a success rate of 84.1297%. Springer Nature 2023-03 Article PeerReviewed Vedika, Vedika and Jain, Nikita and Garg, Harshit and Jhunthra, Srishti and Mohan, Senthilkumar and Omar, Abdullah Hisam and Ahmadian, Ali (2023) Predicting attributes based movie success through ensemble machine learning. Multimedia Tools and Applications, 82 (7). pp. 9597-9626. ISSN 1380-7501 http://dx.doi.org/10.1007/s11042-021-11553-0 DOI:10.1007/s11042-021-11553-0
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	Q Science (General) T58.5-58.64 Information technology
spellingShingle	Q Science (General) T58.5-58.64 Information technology Vedika, Vedika Jain, Nikita Garg, Harshit Jhunthra, Srishti Mohan, Senthilkumar Omar, Abdullah Hisam Ahmadian, Ali Predicting attributes based movie success through ensemble machine learning
description	The film industry has grown into a multi-billionaire industry in terms of entertainment. The success of the film industry depends on the criteria that how much profit a movie would make which gives the tag of a ‘hit’ or a ‘flop’. Predicting the success is guided by various factors like genre, date of release, actors, net gross and many more. Understanding the stakes involved with a movie release that can affect its success or a failure, before-hand can be a great step towards the expansion of the film industry business. Therefore, this study proposes an ensemble learning strategy as a solution to analyze such understanding where predictions from previously guided attribute calculations can be used to enhance future success/failure accuracy. This study shows various strategies used in the literature to analyze and compare the results obtained. The various machines learning algorithms SVM, KNN, Naive Bayes, Boosting Ensemble Technique, Stacking Ensemble Technique, Voting Ensemble Technique, and MLP Neural Network are applied on the dataset to predict the box office success of a movie. The paper uses various algorithms and their trends in predicting the outcome of a movie and shows that the proposed methodology outperforms the existing studies. The most effective algorithm in the study is Gradient Boosting with a success rate of 84.1297%.
format	Article
author	Vedika, Vedika Jain, Nikita Garg, Harshit Jhunthra, Srishti Mohan, Senthilkumar Omar, Abdullah Hisam Ahmadian, Ali
author_facet	Vedika, Vedika Jain, Nikita Garg, Harshit Jhunthra, Srishti Mohan, Senthilkumar Omar, Abdullah Hisam Ahmadian, Ali
author_sort	Vedika, Vedika
title	Predicting attributes based movie success through ensemble machine learning
title_short	Predicting attributes based movie success through ensemble machine learning
title_full	Predicting attributes based movie success through ensemble machine learning
title_fullStr	Predicting attributes based movie success through ensemble machine learning
title_full_unstemmed	Predicting attributes based movie success through ensemble machine learning
title_sort	predicting attributes based movie success through ensemble machine learning
publisher	Springer Nature
publishDate	2023
url	http://eprints.utm.my/105915/ http://dx.doi.org/10.1007/s11042-021-11553-0
_version_	1800714775726915584
score	13.211869

Predicting attributes based movie success through ensemble machine learning

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