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Movie success prediction using ensemble classifier

In the film industry, predicting film success has a significant role. For film production, an enormous amount of money is being used. So it will be useful for investors, manufacturers and theaters to analyze the anticipated income from the film within few days of release. There are a number of methods in practice to predict the success of the film. But for prediction, most of the methods either focus on film reviews or on film meta-data. But it is not enough to take any of this. Here we consider both meta-data and film review data for predicting movie success. To generate more precise outcomes, an ensemble classifier is used where the algorithms we used are Logistic Regression, Naive Bayes, Random Forest and Max Voting. The findings showed that the meta-data with an accuracy of 90% yields better results than the review information. We also produce a combined outcome of review andmeta-data.