Using objective data from movies to predict other movies’ approval rating through Machine Learning

  • Iñaki Zabaleta de Larrañaga

Student thesis: Bachelor

Abstract

Machine Learning is improving at being able to analyze data and find patterns in it, but does machine learning have the capabilities to predict something subjective like a movie’s rating using exclusively objective data such as actors, directors, genres, and their runtime? Previous research has shown the profit and performance of actors on certain genres are somewhat predictable. Other studies have had reasonable results using subjective data such as how many likes the actors and directors have on Facebook or what people say about the movie on Twitter and YouTube. This study presents several machine learning algorithms using data provided by IMDb in order to predict the ratings also provided by IMDb and which features of a movie have the biggest impact on its performance. This study found that almost all conducted algorithms are on average 0.7 stars away from the real rating which might seem quite accurate, but at the same time, 85% of movies have ratings between 5 and 8, which means the importance of the data used seems less relevant.

Date of Award2021-Jun-28
Original languageEnglish
SupervisorOla Johansson (Supervisor) & Kamilla Klonowska (Examiner)

Educational program

  • Bachelor programme in Computer Science and Engineering

University credits

  • 15 HE credits

Swedish Standard Keywords

  • Computer Sciences (10201)

Keywords

  • machine learning
  • algorithms
  • movies
  • prediction
  • objective data
  • subjective data
  • imdb

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