Box Office Revenue Prediction Using Linear Regression in ML

There are many uncertainties in the film industry, especially when it comes to predicting the future overall performance of movie theaters. Predictive strategies are mostly based on historical data and decisions, and it is difficult to capture the important factors that affect the video in the process of developing the ground panorama for poisoning. This research proposes a machine learning-driven approach to estimate Agency revenue by reading various variables including production costs, cast, genre, release date and advertising usage. After analysis and testing, the application provides more accurate results than traditional methods, providing studios, distributors and business personnel with effective tools for resource allocation and risk management. However, other factors such as statistics, complex models, target market and international conditions among the required products need to be carefully considered. The ultimate goal is to bridge the gap between traditional forecasting methods and data-driven inference methods, enabling business professionals to understand the unimaginable variety of business options.