About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
sis 24(4):

Research Article

Explainable Neural Network analysis on Movie Success Prediction

Download135 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetsis.4435,
        author={S Bhavesh Kumar and Sagar Dhanraj Pande},
        title={Explainable Neural Network analysis on Movie Success Prediction},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={4},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={12},
        keywords={SHAP, ANN, LIME, Glass box model, Black box model, Explanations},
        doi={10.4108/eetsis.4435}
    }
    
  • S Bhavesh Kumar
    Sagar Dhanraj Pande
    Year: 2024
    Explainable Neural Network analysis on Movie Success Prediction
    SIS
    EAI
    DOI: 10.4108/eetsis.4435
S Bhavesh Kumar1, Sagar Dhanraj Pande1,*
  • 1: Vellore Institute of Technology University
*Contact email: sagarpande30@gmail.com

Abstract

These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful.  this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.

Keywords
SHAP, ANN, LIME, Glass box model, Black box model, Explanations
Received
2024-12-04
Accepted
2024-12-04
Published
2024-12-04
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.4435

Copyright © 2023 S. Bhavesh Kumar et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL