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Modelling and Data Analysis
2023. Vol. 13, no. 2, 77–84
doi:10.17759/mda.2023130204
ISSN: 2219-3758 / 2311-9454 (online)

Forecasting the Rating of a New Movie Based on its Metadata

 
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Abstract

The article describes an approach to predicting the rating of a new film based on data known prior to its release, using classic machine learning models.

General Information

Keywords: machine learning, film rating prediction

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex trends: Data Analysis with R, Forecasting Techniques and Applications

Information about the work in OpenAlex

Number of citations: 0

Topics

Data Analysis with R

This cluster of papers focuses on statistical computing and data analysis using the R language. It covers a wide range of topics including statistical analysis, data visualization, parallel computing, time series analysis, spatial data, machine learning, geospatial analysis, statistical modeling, and big data.

Number of works: 202321  |  Total number of citations: 690136

Topic detailsв OpenAlex

Forecasting Techniques and Applications

This cluster of papers focuses on advances in time series forecasting methods, including topics such as exponential smoothing, machine learning techniques, expert judgment, inventory management, demand forecasting, neural networks, and forecast combination.

Number of works: 48174  |  Total number of citations: 633732

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2023130204

Received 17.03.2023

Accepted

Published

For citation: Sologub, G.B., Sazon, N.S. (2023). Forecasting the Rating of a New Movie Based on its Metadata. Modelling and Data Analysis, 13(2), 77–84. (In Russ.). https://doi.org/10.17759/mda.2023130204

© Sologub G.B., Sazon N.S., 2023

License: CC BY-NC 4.0

References

  1. Baev M.A. Predicting movie ratings on IMDB. Materials of the 60th International Student Scientific Conference. Novosibirsk, 2022 - p. 281.
  2. Kirilina N.A., Gorbanyova E.N. Application of machine learning algorithms randomforest, gradientboosting, kneighbors for predicting box office revenues of movies. Software Engineering: Modern Trends in Development and Application (PI-2019) Kursk, March 11-12, 2019 - p. 25-28.
  3. IMDB Datasets [https://datasets.imdbws.com/].
  4. Google Colab [Electronic resource]. - Access mode: https://colab.research.google.com/ (accessed on September 01, 2022).

Information About the Authors

Gleb B. Sologub, Candidate of Science (Physics and Matematics), Associate Professor of the Department of Mathematical Cybernetics of Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5657-4826, e-mail: glebsologub@ya.ru

Nikita S. Sazon, Master's Student at the Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University) (MAI), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-9816-4585, e-mail: nikitaS1598@gmail.com

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