Movie Success Rate Prediction Using Robust Classifier
Balaganesh N1, Bhuvaneswari M S2
1Balaganesh N, Department of Computer Science and Engineering Mepco Schlenk Engineering College, Sivakasi, TamilNadu, India.
2Bhuvaneswari M S, Department of Computer Science and Engineering Mepco Schlenk Engineering College, Sivakasi, TamilNadu, India.
Manuscript received on August 10, 2019. | Revised Manuscript received on August 14 2019. | Manuscript published on August 30, 2019. | PP: 3505-3511 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9342088619/19©BEIESP | DOI: 10.35940/ijeat.F9342.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Film industry is a multi-billion-dollar industry where each movie earns over billions of dollar. Predicting the success of the movie is a difficult task because the success rate is influenced by various factors like running time, actor, actress, genre etc. In this paper a detailed study of machine learning algorithms such as Adaboost, SVM, and K-Nearest Neighbours (KNN) were done and was implemented on IMDB dataset for predicting box office. Based on the results, Adaboost classifier gives better performance compared to SVM and KNN classifier algorithms.
Keywords: Classifier, Movie Prediction, IMDb, Machine Learning.