DOI:10.20894/IJBI.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.065
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
American film studios collectively produce several hundred movies every year, making the United States the third most prolific producer of films in the world. The budget of these movies is of the order of hundreds of millions of dollars, making their box office success absolutely essential for the survival of the industry. Knowing which movies are likely to succeed and which are likely to fail before the release could benefit the production houses greatly as it will enable them to focus their advertising campaigns which itself cost millions of dollars, accordingly. And it could also help them to know when it is most appropriate to release a movie by looking at the overall market. So the prediction of movie success is of great importance to the industry. Machine learning algorithms are widely used to make predictions such as growth in the stock market, demand for products, nature of tumors etc. This paper presents a detailed study of Logistic Regression, SVM Regression and Linear Regression on IMDB data to predict movie box office.
