INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
TV POPULARITY SHOW ANALYSIS USING MACHINE LEARNING
Authors Name:
HARIHARAN D
, Mrs. V. BAKYALAKSHMI
Unique Id:
IJSDR2303144
Published In:
Volume 8 Issue 3, March-2023
Abstract:
Television is an ever-evolving, multi-billion-dollar business industry. TV shows are more successful Technological society is a huge formula with multiple variables. art Success is not something that happens, it is studied and Duplicate and apply. Hollywood success is unpredictable, with many movies and sitcoms being hyped and the promise of a hit ended in a box office failure Disappointment. In current research, linguistic exploration describes the relationship with the TV series Appeal to your audience community. Have a decision support system you will need to be able to display reliable and predictable results gives you confidence in investing in new TV series. of the model presented in this study uses data to explore and determine. This article uses the description Predictive modeling techniques for assessing persistence Television comedy successes: office, big bang theory, Arrested Development, Scrubs, and South Park. The factor is tested for statistical significance for episode ratings Character presence, director, and writer. View these stats Characters are very important to the show itself, the creation and direction of the show ratings, and therefore the success of the show. Use the machine learning-based predictive models such as linear regression, K nearest Neighbors, Stochastic Gradient Descent, Decision Trees and Forests, Neural Networks, Facebook Prophet accurately predict the success of your show. The model represents Fundamentals for Understanding TV Show Success How Producers Can Boost Current TV Success Use or use this data when creating future shows. Deadline Many factors that go into the series, empirical analysis this study shows that there is no one-size-fits-all model for prediction. Rating or success of a television program. But, even if your variables are statistically significant, you can optimize them positively affect the rating.
Keywords:
Cite Article:
"TV POPULARITY SHOW ANALYSIS USING MACHINE LEARNING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 3, page no.895 - 899, March-2023, Available :http://www.ijsdr.org/papers/IJSDR2303144.pdf
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Publication Details:
Published Paper ID: IJSDR2303144
Registration ID:204579
Published In: Volume 8 Issue 3, March-2023
DOI (Digital Object Identifier):
Page No: 895 - 899
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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