International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 5 Issue 6 November-December 2023 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

Predicting the Success of a Movie using Machine Learning Algorithms: An Analysis

Author(s) Ahaan Anand
Country India
Abstract Machine learning has been an integral part of reshaping the movie industry, and this paper delves into its transformative role. We examine the utilization of machine learning models, including Linear Regression, Decision Trees, and Random Forests, in analyzing data from IMDB's top-rated movies. The results of our analysis demonstrate that Random Forests achieve a remarkable 74% accuracy rate in predicting IMDB ratings. This paper serves as a comprehensive exploration of machine learning's profound impact on the movie industry, emphasizing its transformative role in content creation, production, and audience engagement. It underscores the enormous potential of machine learning in revolutionizing the movie industry, while also paving the way for the integration of Natural Language Processing (NLP) to enhance movie success predictions, propelling innovation within AI-driven cinematic endeavors. In conclusion, this research highlights the pivotal role of machine learning in the film industry, underscoring its ability to elevate the quality of content and engage audiences more effectively, while anticipating a future where technology and creativity harmonize to create unique and captivating cinematic experiences.
Keywords Machine Learning, Movies, Predictions, Data-Driven Decision Making
Field Computer
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-12
Cite This Predicting the Success of a Movie using Machine Learning Algorithms: An Analysis - Ahaan Anand - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8653
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