Paper Title
Statistical Modelling of Movie Ratings: Lasso and Ridge Regression Perspectives
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Registration ID: IJNRD_300985
Published ID: IJNRD2409413
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Keywords
Keywords: IMDb Ratings Prediction, Lasso Regression, Ridge Regression, Movie Metadata, Regression Analysis.
Abstract
It is crucial to accurately predict how people would receive movies in the entertainment industry. In this study, Lasso and Ridge Regression models are used to forecast IMDb ratings, which are an important indicator of public opinion based on a comprehensive set of movie metadata. The dataset contains variables like budget, revenue, runtime, vote count, director and lead actors. We used Lasso and Ridge Regression as regression analysis techniques to create a model that predicts IMDb ratings from movie attributes. When using Lasso Regression penalization terms for the size of coefficients for individuals features rather than groups to encourage sparsity and provide greater interpretability to the model; but when using Ridge Regression all these coeficients can be shrinked so as to reduce the effect of colinearity among them. Continuous preprocessing,feature engineering, and model training enable accurate IMDb ratings predictions.Performance measures such as mean absolute error (MAE), mean squared error (MSE),and R-squared test statistic are utilized in evaluating models’ performances. The results showed that both Lasso and Ridge Regression models could effectively predict IMDb ratings with their mean absolute errors (MAE) being 0.453 and 0.449 respectively, mean squared errors (MSE) were 0.573 and 0.591 while R-squared scores were 0.942 and 0.940 respectively. These figures underscore the value of regression analysis in enhancing predictive accuracy within the entertainment industry, offering valuable insights for filmmakers, production studios, and distributors
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How To Cite (APA)
JATAN PATEL & KAUSTUBH SHINDE (September-2024). Statistical Modelling of Movie Ratings: Lasso and Ridge Regression Perspectives. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(9), e73-e78. https://ijnrd.org/papers/IJNRD2409413.pdf
Issue
Volume 9 Issue 9, September-2024
Pages : e73-e78
Other Publication Details
Paper Reg. ID: IJNRD_300985
Published Paper Id: IJNRD2409413
Research Area: Science and Technology
Author Type: Indian Author
Country: MUMBAI, MAHARASHTRA, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2409413.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409413
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