With the rapid increase of the multi-billion dollar movie industry, the volume of data that is generated on the internet related to movies is growing at a lightning speed. Machine learning methods have been used by researchers to build classification models. In this report, a variety of machine learning algorithms are used on a movie dataset for multi-class classification. The aim of this paper is to compare the various Machine learning techniques and conduct an analysis on each of their performances. In this report, the selected machine learning methods are Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron Neural Network and Gaussian Naive Bayes. All these techniques predict an approximate value of the net profit of a movie by examining the historical data collected from varied sources like IMDb, Rotten Tomatoes, Box Office Mojo and Meta Critic. The system predicts box office success based on features that are categorized as pre-released features and post-released features. The performance assessment of these four machine learning methods is done on a movie dataset that contains 755 movies. Among all these techniques, Multilayer Perceptron Neural Network gives a better outcome than the rest.
