Pre-production movie rating prediction using machine learning. (c2017)

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dc.contributor.author Gerges, Firas Abdallah
dc.date.accessioned 2018-07-09T07:44:30Z
dc.date.available 2018-07-09T07:44:30Z
dc.date.copyright 2017 en_US
dc.date.issued 2018-07-09
dc.date.submitted 2017-11-29
dc.identifier.uri http://hdl.handle.net/10725/8176
dc.description.abstract Movie production is one of the most expensive investment fields and can result in enormous financial profit or loss. It is critical for investors and production companies to decide whether to invest in a certain movie given the huge loss that could occur from such investments. Hence, it is very beneficial to construct a model which helps investors in their decision making process. Machine learning has proven its effectiveness in building decision making models and recommender systems in various fields. In this work, we present several machine learning techniques (Support Vectors Machine, K-Nearest Neighbors, C5, Neural Networks and Case-Based Reasoning) along with a genetic algorithm to predict the success of a movie before its production using the IMDB rating as an indicator of the success. Results show that machine learning is useful in this domain and genetic algorithms can be used to build prediction models with relatively good performance. Keywords: en_US
dc.language.iso en en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.subject Motion pictures -- Production and direction -- Accounting en_US
dc.subject Success in motion pictures -- Forecasting en_US
dc.subject Accounting -- Computer programs en_US
dc.subject Motion pictures -- Ratings en_US
dc.title Pre-production movie rating prediction using machine learning. (c2017) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201201287 en_US
dc.author.commembers Harmanani, Haidar
dc.author.commembers Mansour, Nashat
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1 hard copy: xiv, 86 leaves; 30 cm. avaialbe at RNL. en_US
dc.author.advisor Azar, Danielle
dc.keywords Machine Learning en_US
dc.keywords Genetic Algorithms en_US
dc.keywords IMDB en_US
dc.keywords Classification en_US
dc.keywords Data Mining en_US
dc.keywords Forecasting en_US
dc.keywords C5 en_US
dc.keywords Optimization en_US
dc.keywords Predictive Model en_US
dc.keywords Meta-Heuristics en_US
dc.keywords Decision Making en_US
dc.keywords Decision Tree en_US
dc.keywords Instance-Based Learning en_US
dc.keywords Neural Networks en_US
dc.keywords SVM en_US
dc.keywords Movies en_US
dc.keywords Rating en_US
dc.keywords Box-Office en_US
dc.keywords Hollywood en_US
dc.keywords Production en_US
dc.keywords Casting en_US
dc.description.bibliographiccitations Bibliography : leaves 80-82. en_US
dc.identifier.doi https://doi.org/10.26756/th.2018.56 en_US
dc.author.email firas.gerges@lau.edu en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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