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

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	86-92
Sayfa sayısı	7
ISBN (Basılı)	9783031091759
DOI'lar	https://doi.org/10.1007/978-3-031-09176-6_10
Yayın durumu	Yayınlandı - 2022
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Süre: 19 Tem 2022 → 21 Tem 2022

Yayın serisi

Adı	Lecture Notes in Networks and Systems
Hacim	505 LNNS
ISSN (Basılı)	2367-3370
ISSN (Elektronik)	2367-3389

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	19/07/22 → 21/07/22

Bibliyografik not

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

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / 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).

@inproceedings{1f252fa5e09244959ad8993e845ff80d,

title = "Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques",

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.",

keywords = "Box office, Machine learning, Movies, Random Forest model, Revenue prediction, Supervised learning",

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",

year = "2022",

doi = "10.1007/978-3-031-09176-6_10",

language = "English",

isbn = "9783031091759",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "86--92",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Tolga, {A. Cagri} and Selcuk Cebi",

booktitle = "Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2",

address = "Germany",

}

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, hac. 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; Hac. 505 LNNS).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

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

Y1 - 2022

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.

AB - 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.

KW - Box office

KW - Machine learning

KW - Movies

KW - Random Forest model

KW - Revenue prediction

KW - Supervised learning

UR - http://www.scopus.com/inward/record.url?scp=85135064891&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-09176-6_10

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

M3 - Conference contribution

AN - SCOPUS:85135064891

SN - 9783031091759

T3 - Lecture Notes in Networks and Systems

SP - 86

EP - 92

BT - Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Tolga, A. Cagri

A2 - Cebi, Selcuk

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2022

Y2 - 19 July 2022 through 21 July 2022

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, editörler, 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap