Movie Revenue Prediction using Machine Learning Models

TL;DR

This paper develops and evaluates multiple machine learning models to predict movie revenues based on various features, aiming to assist the film industry in maximizing profits.

Contribution

It introduces a comprehensive approach using diverse ML models and optimization techniques for accurate movie revenue prediction.

Findings

Random Forest and XGBoost models achieved high accuracy.

Hyperparameter tuning improved model performance.

The models demonstrated good generalization on test data.

Abstract

In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the director, the writer and the leading cast, the country of production of the movie, the budget of the movie, the production company and the runtime of the movie. Through a structured methodology involving data collection, preprocessing, analysis, model selection, evaluation, and improvement, a robust predictive model is constructed. Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained and tested. Model improvement strategies include hyperparameter tuning and cross-validation. The resulting model offers promising accuracy and generalization, facilitating informed decision-making in the film industry to maximize profits.