Application Case 4.6 Data Mining Goes to Hollywood: Predicting Financial Success of Movies Application Case 4.6 is about a research study where a number of software tools and data mining techniques are used to build data mining models to predict financial success (box-office receipts) of Hollywood movies while they are nothing more than ideas.

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 4.3 illustrates the definition of the nine classes in terms of the range of box-office receipts.

Table 4.3 Movie Classification Based on Receipts Class No. Range (in millions of dollars) 1 2 3 4 5 6 7 8 9 =1 =1 =10 =20 =40 =65 =100 =150 = 200 {Flop) =610 =20 =640 =665