Final ProjectCS/ECE/ME 539Professor HuUW-MadisonMWF 1:20pDavid A. GerasimowThe Design and Implementation of a Dynamic Data MLP toPredict Motion Picture RevenueTable of ContentsIntroduction: Preface, Past Research, Improvements Over Past Research 3Initial Data Collection 4Data Collection Improvements, Data Encoding 5Pre-analysis of Data, Development of the Dynamic Data Neural Network, Step 1 of the UpdateWizard: Downloading, Step 2 of the UpdateWizard: Updating 6Step 3 of the UpdateWizard: Creating Training and TestingFiles, Development of the MLP using Dynamic Data 7Using the Dynamic Data MLP, Choice 1 of moviesbp.m, Choice 2 of moviesbp.m 9Figure 1: DataExtractor Screenshot 10Figure 2: DataConcatenator Screenshot 11Figure 3: DataConverter Screenshot: Films Removed From Data File 12Figure 4: DataConverter Screenshot: Films to be Updated 13Figure 5: Results of preanalysis.m 14Figure 6: UpdateWizard Screenshot – Step 1, Figure 7: UpdateWizard Screenshot – Step 2 17Figure 8: UpdateWizard Screenshot – Step 3, Figure 9: NewMovie Screenshot 18Discussion of Results 19Bibliography 20VB Source Code 21Note to Grader: This report is over twenty pages, but this is because I was unsure if the grader has the ability to read and run Visual Basic 6.0 source code.IntroductionPrefaceFor the last century, film has been one of the American public’s favorite entertainment mediums. Large production companies often spend hundreds of millions of dollars to create a single film. However, the amount of money spent on creating a film seems to have little bearing on its success. The Blair Witch Project, for instance, was made for under one million dollars, but it made over twenty-nine million dollars in its first weekend in the box office. On the other hand, Waterworld, starring superstar Kevin Costner, cost roughly one-hundred and seventy-five million dollars to produce, but made back less than half of that amount in domestic box office revenue.Predicting how much a movie will earn in opening-weekend box office revenue is a notoriously difficult thing to do. There are many subjective aspects of a movie. In addition, public taste changes quickly and unpredictably. Developing a mathematical formula to predict how much a film will make will allow production companies to maximize profit and skip film development projects that will hurt their profit margins.Past ResearchIn CS/ECE/ME 539, in the fall semester of 2001, a student attempted to predict the opening weekend box office revenue of a given film using an artificial neural network. He claimed that an accurate prediction of how much a movie will gross in total can be achieved by examining its opening weekend. They are proportional to each other. If a film has a huge opening weekend, it is likely to earn a lot of money in the long run. His logic is correct, and I will use it again in this project.The network’s inputs are the film’s characteristics, such as genre, rating, runtime, etc. Despite his thorough work, there are deficiencies in his project. This project will be a major improvement over his results. Namely, it will produce higher correct classificationresults; while, at the same time, it will allow future users to easily update the data files. The neural network will, over time, accumulate more and more training data. A major component of this report is developing what I call a dynamic data neural network. The training data is automatically updated weekly. Instead of the project ending with the end of the semester, future classes will be able to easily update this project’s results, and the network’s correct classification rates will improve over time.Improvements over Past ResearchAs an avid film ‘buff,’ I came up with the idea to research film box office revenue with respect to neural networks independently, but I was disappointed to find out that it had been done before. As such, I set out to improve upon previous results. By adding more features and more feature vectors, I hoped to receive better results. Moreover, I wrote an UpdateWizard that can automatically and repeatedly update the data files. For example, every week, the top grossing films are listed at www.boxofficeguru.com. The UpdateWizard automatically downloads the list and updates the data file. Finally, it will create new training and testing data files for use with MATLAB and the dynamic data neural network.Initial Data CollectionData pertaining to box office revenue is plentiful on the internet. As such, I sought out the data with the most input features and the most reliability. After a thorough search, I decided to use the information found at www.boxofficeguru.com. Already entered into its.html files are all the films since 1989 that have grossed more then fifteen-million dollars in their first weekend. Also, additional data is posted. The film’s opening date, number of theatres at opening, distributor, number of days in opening weekend, and, most importantly, the exact amount the film grossed in its opening weekend are available.Unfortunately, the data is not in a pleasant, readable format for programming use. Consequently, using Microsoft Visual Basic 6.0 Professional Edition, I wrote a Windows application called DataExtractor (dataextractor.exe) to parse the information out of the *.html files. For this portion of the data collection, which is only performed once, I manually downloaded the data files and renamed them 35plus.htm, 25to35.htm, 20to25.htm, 17to20.htm, and 15to17.htm. After running these five files through the DataExtractor, five readable files are created called 35plus_1.txt, 25to35_1.txt, 20to25_1.txt, 17to20_1.txt, and 15to17_1.txt. A screenshot of the DataExtractor is found on page 10 (Fig. 1).The source code for the DataExtractor is found on pages 21+.After the DataExtractor has parsed the information, the five output files (35plus_1.txt, 25to35_1.txt, 20to25_1.txt, 17to20_1.txt, and 15to17_1.txt) need to be concatenated. Again, using Visual Basic 6.0, I developed another Windows application called DataConcatenator (dataconcatenator.exe). It takes the five aforementioned output files as inputs and creates a single file called concatenated_data.txt. A screenshot of the DataConcatenator is found on page 11 (Fig. 2).The source code for the DataConcatenator is found on pages 21+.Now that a readable, single file exists, more input features needed to be added. In order to avoid unnecessary reentering of
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