Forecasting model of a movie's profitability

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


This study intends to estimate the profit of a movie through the construction of a predictive model that uses several Data Mining techniques, namely neural networks, regression and decision trees. The model will allow obtaining the prediction of box office revenue. Three different dependent variable approaches were used (interval, categorical and binary) aiming to study the difference and predictive influence that each one has on the results. Two metrics were used to determine the most accurate predictions: the misclassification error for the categorical models and the average squared error for the continuous one. In this study, the best predictive results were obtained through the use of multi-layer perceptron. Regarding the representative distinction between the dependent variable, the multiclass model presents a much higher error rate comparing to the remaining ones, which is explained with the increase of the number of classes to predict.

Original languageEnglish
Title of host publicationProceedings of CISTI 2018
Subtitle of host publication13th Iberian Conference on Information Systems and Technologies [Memorias de la CISTI 2018: 13a Conferencia Iberica de Sistemas y Tecnologias de Informacion]
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789899843486
Publication statusPublished - 27 Jun 2018
Event13th Iberian Conference on Information Systems and Technologies, CISTI 2018 - Caceres, Spain
Duration: 13 Jun 201816 Jun 2018


Conference13th Iberian Conference on Information Systems and Technologies, CISTI 2018


  • Box office profit
  • Data Mining
  • Decision Tree
  • Movie Profitability
  • Neural Networks
  • Predictive Analysis
  • Regression


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