Paper Key : IRJ************124
Author: Kiran Vijay Desai,Tushar Nandkumar Patil
Date Published: 12 Apr 2025
Abstract
A lot of variables, including the genre, cast, budget, marketing campaigns, and audience reaction, make it difficult to forecast a film's earnings. This study examines the use of machine learning to forecast a film's revenue by using historical data. Regression models, decision trees, and neural networks are just a few of the machine learning approaches we use to analyse the relationship between a film's numerous components and its box office performance. We use a dataset for this project that contains information about films, such as their budget, cast, genre, director, production firm, release date, and criticsreviews. Important indicators of success including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R) are used to train and assess the machine learning models. To improve accuracy, we also use feature selection methods to pinpoint the most important factors that influence a movie's revenue.