PREDICTIVE MODELING OF BOX OFFICE SUCCESS USING MACHINE LEARNING AND HISTORICAL MOVIE DATA - Shukla, Ankit; Dongre, Ganesh Baliram; Moharana, Gouri; Salve, Prashant Suresh; Sangamwar, Kanchan Makarand; Ahire, Pallavi Pankaj (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Machine learning (ML) methods can now be used to more accurately guess which films will do well at the box office because more past movie data is becoming available. This study shows a complete method for predictive modelling that uses machine learning techniques to guess how well a movie will do at the box office before it comes out. The framework uses a lot of different factors, such as subject, budget, cast popularity, director track record, length, release date, language, and social media talk before the movie comes out. Over 5,000 films produced in the last 20 years were carefully chosen and preprocessed to make sure that the data was consistent, that it was normalized, and that any outliers were removed. Exploratory Data Analysis (EDA) was used to find the most important features and key relationships. It was done using supervised machine learning models like Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machines. They were tested using R² score, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Ensemble methods like Gradient Boosting were the most accurate at predicting the future, with a R² score of more than 0.85 on the test set. A study of feature importance found that the production budget, the popularity of the cast, and the time of the movie's release all have a big effect on its box office earnings. The results show that strong predictive modelling can help directors, companies, and investors make smart choices by estimating how much money a movie will make. This study stresses the importance of using data-driven methods to change the film industry's reliance on gut feelings and past experiences into predicting methods based on science.