Abstract
The movie success factors depend on the critics, storyline, heros, music etc. To predict the movie success various data mining and machine learning techniques such as GuassianNB, MultinomialNB, BernoulliNB, KNeighnorsClassifier, Decision Tree, Logistic regression has been developed but, in this work, we use random forest classifier for the prediction of movie success with reduced cost and schedule. The random forest classifier selects the dataset randomly from the available dataset and the generate the decision tree of the selected dataset and then apply the voting on the prediction results and whose score and accuracy will be maximum that will indicates the success of movie. For the sample of IMDb dataset, we use online resource of kaggle and the experimental results is generated from the widely used machine learning programming language Python which helps in the analysis of the proposed methodology. The performance of proposed methodology is measured using the parameters such as Scor