Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques

Anil Gürbüz; Ezgi Biçer; Tolga Kaya

doi:10.1007/978-3-031-09176-6_10

Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques

Anil Gürbüz^*, Ezgi Biçer, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The gross revenue of a movie in the box office has been a concern of the movie industry. In the last few years, there have been studies on predicting various movie attributes. The field lacks a gross movie revenue prediction model that specifically concerns the gross movie revenues in the Turkish box office. The aim of this study is to build a model to predict the gross movie revenue in the Turkish box office using machine learning techniques. This study is conducted on 150 movies that were in the Turkish box office in 2018. The techniques involved multiple regression analysis including the ridge regression and the lasso, tree-based methods including random forest and boosting, SVM and KNN regression. All models were built using the R programming language. Methods were compared using their MSE values. The lowest MSE was obtained with the Random Forest model.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	86-92
Number of pages	7
ISBN (Print)	9783031091759
DOIs	https://doi.org/10.1007/978-3-031-09176-6_10
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Duration: 19 Jul 2022 → 21 Jul 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	505 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Country/Territory	Turkey
City	Izmir
Period	19/07/22 → 21/07/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Box office
Machine learning
Movies
Random Forest model
Revenue prediction
Supervised learning

Access to Document

10.1007/978-3-031-09176-6_10

Cite this

Gürbüz, A., Biçer, E., & Kaya, T. (2022). Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, A. C. Tolga, & S. Cebi (Eds.), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2 (pp. 86-92). (Lecture Notes in Networks and Systems; Vol. 505 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09176-6_10

Gürbüz, Anil ; Biçer, Ezgi ; Kaya, Tolga. / Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques. Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; A. Cagri Tolga ; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 86-92 (Lecture Notes in Networks and Systems).

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abstract = "The gross revenue of a movie in the box office has been a concern of the movie industry. In the last few years, there have been studies on predicting various movie attributes. The field lacks a gross movie revenue prediction model that specifically concerns the gross movie revenues in the Turkish box office. The aim of this study is to build a model to predict the gross movie revenue in the Turkish box office using machine learning techniques. This study is conducted on 150 movies that were in the Turkish box office in 2018. The techniques involved multiple regression analysis including the ridge regression and the lasso, tree-based methods including random forest and boosting, SVM and KNN regression. All models were built using the R programming language. Methods were compared using their MSE values. The lowest MSE was obtained with the Random Forest model.",

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author = "Anil G{\"u}rb{\"u}z and Ezgi Bi{\c c}er and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2022 ; Conference date: 19-07-2022 Through 21-07-2022",

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Gürbüz, A, Biçer, E & Kaya, T 2022, Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, AC Tolga & S Cebi (eds), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. Lecture Notes in Networks and Systems, vol. 505 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 86-92, International Conference on Intelligent and Fuzzy Systems, INFUS 2022, Izmir, Turkey, 19/07/22. https://doi.org/10.1007/978-3-031-09176-6_10

Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques. / Gürbüz, Anil; Biçer, Ezgi; Kaya, Tolga.
Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; A. Cagri Tolga; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. p. 86-92 (Lecture Notes in Networks and Systems; Vol. 505 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques

AU - Gürbüz, Anil

AU - Biçer, Ezgi

AU - Kaya, Tolga

PY - 2022

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N2 - The gross revenue of a movie in the box office has been a concern of the movie industry. In the last few years, there have been studies on predicting various movie attributes. The field lacks a gross movie revenue prediction model that specifically concerns the gross movie revenues in the Turkish box office. The aim of this study is to build a model to predict the gross movie revenue in the Turkish box office using machine learning techniques. This study is conducted on 150 movies that were in the Turkish box office in 2018. The techniques involved multiple regression analysis including the ridge regression and the lasso, tree-based methods including random forest and boosting, SVM and KNN regression. All models were built using the R programming language. Methods were compared using their MSE values. The lowest MSE was obtained with the Random Forest model.

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ER -

Gürbüz A, Biçer E, Kaya T. Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Tolga AC, Cebi S, editors, Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. Springer Science and Business Media Deutschland GmbH. 2022. p. 86-92. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-09176-6_10

Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques

Abstract

Publication series

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Bibliographical note

Keywords

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