A hybrid ensemble machine learning model to predict success of Bollywood movies

Garima Verma, Hemraj Verma and Sushil Kumar Dixit

World Review of Entrepreneurship, Management and Sustainable Development, 2021, vol. 17, issue 2/3, 343-357

Abstract: Bollywood is a multi-billion industry. Hundreds of films are released every year, where each film is an investment of multi-crores. In terms of awards or marketing it has found a place in almost every country and culture. It also contributes and attracts skilled and passionate people to become entrepreneurs. Therefore, it becomes a need as well as a huge concern of the director, producer and all stakeholders involved in a particular film to know the chances of the success of a film on the box office before its release. To address this concern, a hybrid ensemble machine learning model has been proposed. The model uses data sets collected from various sources, such as Boxofficeindia, cinemalytics, YouTube, etc. The model performed pre-processing on data set, which included handling of missing values with mean, cleaning of data, and removal of text values. Feature engineering has been applied in the model to create a new feature called act_direct to make the model more robust. Further, the effectiveness of the model has been tested in terms of accuracy and the AUC-ROC curve. From the experimental results, it is evident that the proposed model ensures relatively better accuracy compared to some recent state-of-art models.

Keywords: Bollywood; entrepreneurs; ensemble; machine learning; feature engineering. (search for similar items in EconPapers)
Date: 2021
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