We employ the support vector machine (SVM) classifier, over different types of kernels, to investigate whether observable variables of individuals and their household information are able to describe their consumption decision of film at theaters in Brazil. Using a very big dataset of 340,000 individuals living in metropolitan areas of a whole large developing economy, we performed a Knowledge Discovery in Databases to classify the film consumers, which results in 80% instances correctly classified. To reduce the degrees of freedom for SVM and to learn the more important determinants of film consumption, we apply the Linear Discriminant Analysis that allows us to identify the key determinants of this consumption. The main individual characteristics are age, education (that merges to be a student), income, and preferences for cultural goods. Regarding the main geographic characteristics, these are the timing of sample, population concentration, and supply of movie theaters. The results point to an ineffective policy for the sector at the time investigated.
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Nishijima, M., Nieuwenhoff, N., Pires, R. et al. Movie films consumption in Brazil: an analysis of support vector machine classification. AI & Soc 35, 451–457 (2020). https://doi.org/10.1007/s00146-019-00899-7
- Film at theaters
- Individual data