Classification of Movie Success: A Comparison of Two Movie Datasets

Abstract

This work presents a classification problem to classify a movie's success based on features of a given movie. Two movies' datasets along with features generated from web scraping are utilized to generate the training and testing datasets. Four Machine Learning classifiers are applied to these datasets: Stochastic Gradient Descent, Random Forests, LinearSVC and Extra Trees. This study compares the performance metrics for these Machine Learning models on these two movies datasets and draws conclusions based on the results.