Abstract
Movie making is a billion-dollar industry. Every month hundreds of movies get released and earn millions of dollars in revenue. However, majority of the movies fail to create an impact on the Box-Office and flop. This not only put a bad impression on the entire cast and crew but also creates a huge setback in financial terms. As a producer or investor, it is crucial for them to have some certainty that the money they are investing in will give a good return otherwise they’ll lose all their capital eventually. The idea of this research is to predict based on certain pre-released variables of the movie, whether an upcoming movie is going to succeed or fail in monetary terms. Many researches have already been doing that in this domain based on different techniques and around different datasets. The novelty of this research is that the proposed approach is not only based on classical movie features, but incorporates all other dependencies as well such as star power, popularity of the cast, track record of director, and actors, to predict whether movie will succeed or fail and whether an investor should invest in the movie proposal or not. This article uses multiple machine learning algorithms and tested them over various evaluation metrics. Among them, CatBoostRegression and Stacking Regression outperformed the remaining by giving the maximum model accuracy of 83.84% and 83.5% respectively. The article have used IMDB Movies Extensive Dataset. This dataset contains information of movies from 1894 to 2020 and has at least 100 votes.
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References
Abbasi MA, Memon ZA, Durrani NM et al (2021) A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IOTs. Cluster Comput 24:2133–2160. https://doi.org/10.1007/s10586-021-03243-1
Bhave A, Kulkarni H, Biramane V, Komsakar P (2015) Role of different factors in predicting movie success, in International Conference on Pervasive Computing
Bistri WR, Zaman Z, Sultana N (2019) Predicting IMDb rating of movies by machine learning techniques, in International Conference on Computing, Communication and Networking Technologies
Bosse T, Memon ZA, Treur J. Emergent storylines based on autonomous characters with mindreading capabilities, 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07), IEEE, pp. 207–214
Bosse T, Memon ZA, Treur J, Umair M (2009) An Adaptive Human-Aware Software Agent Supporting Attention-Demanding Tasks. In: Yang J-J, Yokoo M, Ito T, Jin Z, Scerri P (eds.), Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems, PRIMA'09. Lecture Notes in Artificial Intelligence, vol. 5925. Springer Verlag, pp. 292–307
Laeeq K, Memon ZA (2018) An integrated model to enhance virtual learning environments with current social networking perspective. Int J Emerg Technol Learn (Online) 13(9):252–268. https://www.academia.edu/download/73958405/5163.pdf
Samad F, Abbasi A, Memon ZA, Aziz A, Rahman A (2018) The Future of Internet: IPv6 Fulfilling the Routing Needs in Internet of Things. Int J Futur Gener Commun Netw. https://doi.org/10.14257/ijfgcn.2018.11.1.02
Bosse T, Memon ZA, Treur J (2008) Adaptive Estimation of Emotion Generation for an Ambient Agent Model. In: Aarts E et al. Ambient Intelligence. AmI 2008. Lecture Notes in Computer Science, vol 5355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89617-3_10
Hoogendoorn M, Klein MCA, Memon ZA, Treur J (2013) Formal Specification and Analysis of Intelligent Agents for Model-Based Medicine Usage Management. Comput Biol Med 43(5):444–457
Kashif UA, Memon ZA et al (2018) Architectural design of trusted platform for IaaS cloud computing. Int J Cloud Appl Comput (IJCAC) 8(2):47–65
Khan MA, Memon ZA, Khan S (2012) Highly Available Hadoop NameNode Architecture. In: 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, Malaysia, pp. 167–172. https://doi.org/10.1109/ACSAT.2012.52
Kashif UA, Memon ZA, Balouch AR, Chandio JA (2015) Distributed trust protocol for IaaS Cloud Computing. In: 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, pp. 275–279. https://doi.org/10.1109/IBCAST.2015.7058516
Laghari A, Memon ZA, Ullah S, Hussain I (2018) Cyber Physical System for Stroke Detection. IEEE Access 6:37444–37453
Laghari A, Waheed-ur-Rehman, Memon ZA (2016) Biometric authentication technique using smartphone sensor. In: 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, pp. 381–384. https://doi.org/10.1109/IBCAST.2016.7429906
Bosse T, Hoogendoorn M, Memon ZA, Treur J, Umair M (2010) An Adaptive Model for Dynamics of Desiring and Feeling Based on Hebbian Learning. In: Yao Y, Sun R, Poggio T, Liu J, Zhong N, Huang J (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science, vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_3
Memon ZA, Treur J (2009) Modelling the Reciprocal Interaction between Believing and Feeling from a Neurological Perspective. In: Zhong N, Li K, Lu S, Chen L (eds) Brain Informatics. BI 2009. Lecture Notes in Computer Science, vol 5819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04954-5_12
Memon ZA, Treur J (2008) Cognitive and Biological Agent Models for Emotion Reading. In: Jain L, Gini M, Faltings BB, Terano T, Zhang C, Cercone N, Cao L (eds.), Proceedings of the 8th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'08. IEEE Computer Society Press, pp. 308–313
Memon ZA, Treur J (2010) On the Reciprocal Interaction Between Believing and Feeling: an Adaptive Agent Modelling Perspective. Cogn Neurodyn J 4(4):377–394
Memon ZA, Treur J (2012) An Agent Model for Cognitive and Affective Empathic Understanding of Other Agents. Trans Comput Collective Intell (TCCI) 6:56–83
Bosse T, Duell R, Memon ZA et al (2015) Agent-Based Modeling of Emotion Contagion in Groups. Cogn Comput 7:111–136. https://doi.org/10.1007/s12559-014-9277-9
Siddiqi S, Memon ZA (2016) Internet Addiction Impacts on Time Management That Results in Poor Academic Performance. In: 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, pp. 63–68. https://doi.org/10.1109/FIT.2016.020
Bosse T, Hoogendoorn M, Memon ZA, Treur J, Umair M (2012) A computational model for dynamics of desiring and feeling. Cogn Syst Res 19(20):39–61. https://doi.org/10.1016/j.cogsys.2012.04.002
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Memon, Z.A., Hussain, S.M. Predicting movie success based on pre-released features. Multimed Tools Appl 83, 20975–20996 (2024). https://doi.org/10.1007/s11042-023-16319-4
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DOI: https://doi.org/10.1007/s11042-023-16319-4