A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

TL;DR

This paper compares various statistical and machine learning models to predict movie success, finding neural networks perform best with about 86% accuracy and providing insights into success factors.

Contribution

It offers a comprehensive comparison of multiple models for predicting movie success and identifies the neural network as the most effective model.

Findings

Neural network achieved approximately 86% accuracy.

Regression and machine learning models were also evaluated.

Insights into factors influencing movie success were obtained.

Abstract

The film industry is one of the most popular entertainment industries and one of the biggest markets for business. Among the contributing factors to this would be the success of a movie in terms of its popularity as well as its box office performance. Hence, we create a comprehensive comparison between the various machine learning models to predict the rate of success of a movie. The effectiveness of these models along with their statistical significance is studied to conclude which of these models is the best predictor. Some insights regarding factors that affect the success of the movies are also found. The models studied include some Regression models, Machine Learning models, a Time Series model and a Neural Network with the Neural Network being the best performing model with an accuracy of about 86%. Additionally, as part of the testing data for the movies released in 2020 are analysed.