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Analysis of success factors of movies based on Internet Movie Database

Pirotte, Nicolas ; Eerebout, Hervé
(2015)

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Pirotte_38691000_2014.pdf
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Details

Supervisors
Deville, Yves ; Verleysen, Michel
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] en sciences informatiques
Abstract
Forecast the success of a movie before its release can be very interesting for the film industry but it is difficult to achieve. One technique is to use instance-based algorithms to predict the movie ratings based on a dataset about existing movies. In this thesis, we apply different machine learning methods based on a dataset from the website Internet Movie Database (IMDb) to analyse the success factors of movies in order to predict the movie ratings. The different methods used are the k-nearest neighbors algorithm, the linear regression, the quadratic regression and the k-means clustering. Thanks to these methods and the huge dataset from IMDb, we developed interesting machine learning techniques to foresee the movie ratings with a good accuracy. The techniques used in this thesis could be applied in the film industry to guide the director of a movie to make choices about some features of its movie.