For application case 4.6 – Data Mining Goes to Hollywood, describe the research study, the methodology, the results and the conclusion.
Data Mining Goes to Hollywood: Predicting Financial Success of Movies
Predicting box-office receipts (i.e., financial success) of a particular motion picture is an interesting and challenging problem. According to some domain experts, the movie industry is the "land of hunches and wild guesses" due to the difficulty associated with forecasting product demand, making the movie business in Hollywood a risky endeavor. In support of such observations, Jack Valenti (the longtime president and CEO of the Motion Picture Association of America) once mentioned that "…no one can tell you how a movie is going to do in the marketplace…not until the film opens in darkened theatre and sparks fly up between the screen and the audience." Entertainment industry trade journals and magazines have been full of examples, statements, and experiences that support such a claim. Like many other researchers who have attempted to shed light on this challenging real-world problem, Ramesh Sharda and Dursun Delen have been exploring the use of data mining to predict the financial performance of a motion picture at the box office before it even enters production (while the movie is nothing more than a conceptual idea). In their highly publicized prediction models, they convert the forecasting (or regression) problem into a classification problem; that is, rather than forecasting the point estimate of box-office receipts, they classify a movie based on its box-office receipts in one of nine categories, ranging from "flop" to "blockbuster," making the problem a multinomial classification problem. Table 5.4 illustrates the definition of the nine classes in terms of the range of box-office receipts.
Data
Data was collected from variety of movie-related databases (e.g., ShowBiz, IMDb, IMSDb, AllMovie, etc.) and consolidated into a single data set. The data set for the most recently developed models contained 2,632 movies released between 1998 and 2006. A summary of the independent variables along with their specifications is provided in Table 5.5. For more descriptive details and justification for inclusion of these independent variables, the reader is referred to Sharda and Delen (2007). Business Intelligence Spring 2017
Methodology
Using a variety of data mining methods, including neural networks, decision trees, support vector machines, and three types of ensembles, Sharda and Delen developed the prediction models. The data from 1998 to 2005 were used as training data to build the prediction models, and the data from 2006 was used as the test data to assess and compare the models’ prediction accuracy. Figure 5.15 shows a screenshot of IBM SPSS Modeler (formerly Clementine data mining tool) depicting the process map employed for the prediction problem. The upper-left side of the process map shows the model development process, and the lower-right corner of the process map shows the model assessment (i.e., testing or scoring) process (more details on IBM SPSS Modeler tool and its usage can be found on the book’s Web site).