Using Academy Awards to Predict Success of Bollywood Movies using Machine Learning Algorithms

Motion Picture Production has always been a risky and pricey venture. Bollywood alone has released approximately 120 movies in 2017. It is disappointing that only 8% of the movies have made to box office and the remaining 92% failed to return the total cost of production. Studies have explored several determinants that make a motion picture success at box office for Hollywood movies including academy awards. However, same can’t be said for Bollywood movies as there is significantly less research has been conducted to predict their success of a movie. Research also shows no evidence of using academy awards to predict a Bollywood movie’s success. This paper investigates the possibility; does an academy award such as ZeeCine or IIFA, previously won by the actor, playing an important role in movie, impact its success or not? In order to measure, the importance of these academy awards towards a movie’s success, a possible revenue for the movie is predicted using the academy awards information and categorizing the movie in different revenue range classes. We have collected data from multiple sources like Wikipedia, IMDB and BoxOfficeIndia. Various machine-learning algorithms such as Decision Tree, Random Forest, Artificial Neural Networks, Naïve Bayes and Bayesian Networks are used for the said purpose. Experiment and their results show that academy awards slightly increase the accuracy making an academy award a non-dominating ingredient of predicating movie’s success on box office.

@article{Masih2019,
  title     = {Using Academy Awards to Predict Success of Bollywood Movies using Machine Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {2},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Salman Masih and Imran Ihsan},
  doi       = {10.14569/IJACSA.2019.0100257},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100257}
}