CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Early Prediction of Movie Success using Machine Learning Models

by D.M.L. Dissanayake, V.G.T.N. Vidanagama
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 44
Year of Publication: 2021
Authors: D.M.L. Dissanayake, V.G.T.N. Vidanagama
10.5120/ijca2021921847

D.M.L. Dissanayake, V.G.T.N. Vidanagama . Early Prediction of Movie Success using Machine Learning Models. International Journal of Computer Applications. 183, 44 ( Dec 2021), 14-21. DOI=10.5120/ijca2021921847

@article{ 10.5120/ijca2021921847,
author = { D.M.L. Dissanayake, V.G.T.N. Vidanagama },
title = { Early Prediction of Movie Success using Machine Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2021 },
volume = { 183 },
number = { 44 },
month = { Dec },
year = { 2021 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number44/32226-2021921847/ },
doi = { 10.5120/ijca2021921847 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:38.296941+05:30
%A D.M.L. Dissanayake
%A V.G.T.N. Vidanagama
%T Early Prediction of Movie Success using Machine Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 44
%P 14-21
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The film industry is a multi-billion-dollar business that is spread all over the world. A very high number of films are being released every year. But only a few are successful and most are failures. If the success of a movie can be predicted and reduce the uncertainty at the early stages of the movie-making process, that will make a significant impact on the film industry because of the immense investments that are made. The success of a movie is based on several factors related to the past, present and future. By identifying the factors that are relevant to the success of a movie, it can be predicted accurately. Creating predictive models with the use of machine learning has become a trend in the recent past due to the availability of large volumes of data and high computational capabilities. Prediction models and currently available machine learning methods can be used to predict the success of a movie. This paper describes a novel approach using machine learning methods to predict the success of a movie in advance. In this paper, multiple regression and classification methods were used for training and testing the dataset and their performances were evaluated to identify the well-fitted model. The Support Vector Machine model showed a movie success prediction rate of 100% on the test data.

References
  1. I. S. Ahmad, A. A. Bakar, M. R. Yaakub and S. H. Muhammad, "A Survey on Machine Learning Techniques in Movie Revenue Prediction," SN Computer Science, p. 235, 2020.
  2. R. Sharda and D. Delen, "Predicting box-office success of motion pictures with neural networks," Expert Systems with Applications, vol. 30 , no. 2, p. 243–254, 2006.
  3. S. Wei, X. Zheng, D. Chen and C. Chen, "A hybrid approach for movie recommendation via tags and ratings," Electronic Commerce Research and Applications, vol. 18, pp. 83 - 94, 2016.
  4. X. (. Wang, J. H. (. Ryoo, N. Bendle and P. K. Kopalle, "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, 2020.
  5. S. M. R. Abidi, Y. Xu, J. Ni, X. Wang and W. Zhang, "Popularity prediction of movies: from statistical," Multimedia Tools and Applications, pp. 47-48, 2020.
  6. I. S. Ahmad, A. A. Bakar and M. R. Yaakub, "Movie Revenue Prediction Based on Purchase Intention Mining," Information Processing and Management, vol. 57, no. 5, 2020.
  7. M. T. Lash and K. Zaho, "Early Prediction of Movie Success: The Who, What and When of Profitability," Journal of Management Information Systems, vol. 33, no. 3, pp. 874 - 952, 2016.
  8. H. Verma and G. Verma, "Prediction Model for Bollywood Movie Success: A Comparative Analysis of Performance of Supervised Machine Learning Algorithms," The Review of Socionetwork Strategies, 2019.
  9. S. Mundra, A. Dhingra, A. Kapur and D. Joshi, "Prediction of a Movie’s Success Using," in Information and Communication Technology for Intelligent Systems, Singapore, 2019.
  10. R. Parimi and D. Caragea, "Pre-release Box-Office Success Prediction for Motion Pictures," Machine Learning and Data Mining in Pattern Recognition, vol. 7988, pp. 571-585, 2013.
  11. K. Lee, J. Park, I. Kim and Y. Choi, "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, vol. 20, pp. 577-588, 2018.
  12. S. Anunaya, "Data Preprocessing in Data Mining -A Hands On Guide," 10 08 2021. [Online]. Available: https://www.analyticsvidhya.com/blog/2021/08/data-preprocessing-in-data-mining-a-hands-on-guide/. [Accessed 12 08 2021].
  13. J. Browniee, "How to Perform Feature Selection With Numerical Input Data," 18 08 2020. [Online]. Available: https://machinelearningmastery.com/feature-selection-with-numerical-input-data/. [Accessed 21 05 2021].
  14. J. Brownlee, "A Gentle Introduction to the Chi-Squared Test for Machine Learning," 31 10 2019. [Online]. Available: https://machinelearningmastery.com/chi-squared-test-for-machine-learning/. [Accessed 21 05 2021].
  15. J. Brownlee, "How to Choose a Feature Selection Method For Machine Learning," 27 11 2019. [Online]. Available: https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/. [Accessed 19 03 2021].
  16. J. Browniee, "How to Calculate Feature Importance With Python," 20 08 2020. [Online]. Available: https://machinelearningmastery.com/calculate-feature-importance-with-python/. [Accessed 21 02 2021].
  17. "Recursive Feature Elimination," [Online]. Available: https://www.scikit-yb.org/en/latest/api/model_selection/rfecv.html. [Accessed 09 07 2021].
  18. M. Kuhn and K. Johnson, "Feature Engineering and Selection: A Practical Approach for Predictive Models," 21 06 2019. [Online]. Available: https://bookdown.org/max/FES/. [Accessed 09 07 2021].
  19. S. Mutuvi, "Introduction to Machine Learning Model Evaluation," 16 04 2019. [Online]. Available: https://heartbeat.fritz.ai/introduction-to-machine-learning-model-evaluation-fa859e1b2d7f. [Accessed 21 05 2021].
  20. V. Jayaswal, "Performance Metrics: Confusion matrix, Precision, Recall, and F1 Score," 14 09 2020. [Online]. Available: https://towardsdatascience.com/performance-metrics-confusion-matrix-precision-recall-and-f1-score-a8fe076a2262. [Accessed 07 08 2021].
  21. V. Jain, "Idiot’s Guide to Precision, Recall, and Confusion Matrix," [Online]. Available: https://www.kdnuggets.com/2020/01/guide-precision-recall-confusion-matrix.html. [Accessed 08 08 2021].
  22. N. Quader, M. O. Gani and D. Chak, "Performance Evaluation of Seven Machine Learning Classification Techniques for Movie Box Office Success Prediction," in 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Machine Learning Movie Prediction Regression

Powered by PhDFocusTM