My personal inspiration, Elon Musk, once said, “If something is important enough, you should try, even if the likely outcome is failure.” Films have been a passion of mine ever since I saw The Lord of the Rings when I was three. I gasped as an entire world was created before my eyes, complete with large-scale battles and multi-dimensional characters. Even now, I'm fascinated by the sheer amount of substance that can be put on screen and the attention to detail put into doing so. Recently, however, arthouse films have been ignored by the general public and have failed. As a result, studios and producers are discouraged from investing in such projects in favor of big-budget blockbusters that favor spectacle over substance. Many movie studios go to great lengths to change their films to appeal to certain demographics without considering the impact of such changes on 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, movies have become a commercial venture first and an artistic statement second. This harsh reality inspired me as a programmer to take on a project of my own. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Over the summer, I began working on a program that takes into account various factors such as: publicity, popularity of the cast, relevance of content, and other variables for an algorithm that can predict how much the film will gross at the global box office . My goal is to offer this product to movie studios and independent filmmakers as a way to know how to maintain their artistic ideas while working with modern trends in order to keep their film profitable, as well as create riskier hypothetical projects and evaluate the their profitability based on hypothetical creative decisions such as casting choices and the popularity of the genre. When live, this program will change the way filmmakers approach their projects, eliminating their need to cater to mainstream audiences by essentially eliminating that step from the filmmaking process. First I designed the program's user interface. I imported and optimized 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 films over time, reviews from respected critics, and welcome on social media. This laid the foundation for the forecasting algorithm by organizing the variables involved. Over the next few weeks, this algorithm became an obsession to the point that my notebook and whiteboard were cluttered with ideas and equations. Despite my efforts, I had made very little progress with the algorithm. Overwhelmed, I consulted my father, a software engineer, who suggested I research machine learning. I had developed tunnel vision with the desire to perfect my equation, but had never considered other possible solutions. I immediately looked for machine learning courses and found a course from Stanford. Desperate for a solution, I signed up for the course. 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. Although I still have to perfect the program, I have managed to design its architecture. Please note: this is just an example. Get a document now.