Movie Success Prediction and Performance Comparison using Various Statistical Approaches

Abstract

Movies are among the most prominent contributors to the global entertainment industry today, and they are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial Neural Network. The models stated above were compared on a variety of factors, including their accuracy on the training and validation datasets as well as the testing dataset, the availability of new movie characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered that certain characteristics have a greater impact on the likelihood of a film's success than others. For example, the existence of the genre action may have a significant impact on the forecasts, although another genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best performing model of all the models discussed.