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My personal inspiration, Elon Musk once said, “If something is important enough, you should try, even if the probable outcome is failure.” Films have been a passion of mine ever since I saw The Lord of the Rings when I was three years old. My jaw dropped as an entire world was created before my very eyes, complete with large scale battles and multi-dimensional characters. Even now, I am captivated by the sheer amount of substance that can be put on screen and the attention to detail taken to do so. Recently, however, artistic films have been ignored by the general public and flopped. As a result, studios and producers are discouraged from investing in such projects in favor of high budget blockbusters that prioritize spectacle over substance. Many studios go out of their way to change their films to target certain demographics without considering the impact of those changes to the quality of the film. Cast members and directors are selected for roles based on their fame and popularity rather than talent. As much as I hate to admit it, films have become a business venture first and an artistic statement second. This harsh reality inspired me as a programmer to take upon a passion project of my own.
Over the summer, I began working on a program that takes in various factors such as: hype, popularity of the cast, pertinancy of content, and other variables for an algorithm to predict how much the film will earn at the global box office. My goal is to offer this product to film studios and independent filmmakers as a way for them to know how to keep their artistic ideas while working with modern trends in order to keep their film profitable as well as create riskier hypothetical projects and evaluate their profitability based on hypothetical creative decisions such as casting choices and genre popularity. When active, this program will change the way filmmakers approach their projects, eliminating their need to cater to the general public by essentially cutting out that step from the filmmaking process.
I first designed the program’s user interface. I imported and tweaked with information from IMDB and integrated the data with other online sources to create my own database that tracks how films are received, complete with graphs showing interest in the films over time, reviews by respected critics, and social media reception. This laid the foundation for the prediction algorithm by organizing the variables involved.
Over the next few weeks, this algorithm became an obsession to the point where my notebook and whiteboard were cluttered with ideas and equations. Despite my efforts, I had made very little progress on the algorithm. Overwhelmed, I consulted my father, a software engineer, who suggested that I research machine learning. I had developed tunnel vision with my desire to perfect my equation, but had never considered any other possible solutions. I immediately looked for machine learning courses and found a course from Stanford. Desperate for a solution, I enrolled in the class. There, I learned how to create algorithms that can analyze data to create a recommendation system for users. This knowledge gave me a much better idea of how to design my algorithm. While I have yet to perfect the program, I have managed to design its architecture.
With my passion for film, I would hate to see the art form diluted and manipulated in the name of profits that are incorrectly predicted and measured. I hope that my program can push movie studios to value artistry in films as well as marketability. As a child, I fell in love with the art of filmmaking. However, through my quest to contribute to the industry, I have realized how complex and involved the filmmaking process is. This journey has also honed my skills as a programmer and made me realize how much more there is to learn.
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