Predicting Box Office Revenue Using Machine Learning

The film industry is one of the largest entertainment sectors worldwide, with billions of dollars in income each year. It's critical for investors, producers, and marketers to forecast a film's box office revenues. Modern ways use machine learning techniques to estimate revenue, whereas traditional methods rely on intuition and prior experiences. A machine learning-based method for forecasting box office receipts is presented in this chapter. We collected important revenue statistics, such as average revenue per director, actor, and production company, using information from Wikipedia, IMDB, and BookMyShow. After evaluating several machine learning models, Random Forest was found to be the most effective model. Predictive modeling, feature engineering, and data pretreatment are all part of the methodology. The findings show a significant relationship between box revenue patterns, runtime, and budget. This study explores various machine learning models for predicting box office revenue, evaluates their effectiveness, and Predicts the box office collection using Random Forest Model. Future research directions include deep learning applications and sentiment analysis integration.