Performance evaluation of seven machine learning classification techniques for movie box office success prediction

Movie industry is a multi-billion-dollar industry and now there is a huge amount of data available on the internet related to movie industry. Researchers have developed different machine learning methods which can make good classification models. In this paper, various machine learning classification methods are implemented on our own movie dataset for multi class classification. The main goal of this paper is to conduct performance comparison among various machine learning methods. We choose seven machine learning techniques for this comparison such as Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron Neural Network, Gaussian Naive Bayes, Random Forest, AdaBoost and Stochastic Gradient Descent (SGD). All of these methods predict an approximate net profit value of a movie by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Meta Critic. For all these seven methods, the system predicts a movie box office profit based on some pre-released features and post-released features. This paper analyzes the performance assessment of all these seven machine learning techniques based on our own dataset which contains 755 movies. Among these seven algorithms, Multilayer perceptron Neural Network gives better result.