##Contributors
Kashyap, Akash
Motoori, Madhuri
##Abstract Forecasts predict that the entertainment industry will grow to over 679 billion US dollars in value over the next four years, proving its worth in domestic market. Nearly, 600 movies on an average are produced in Hollywood alone and average success rate is approx. 10 percent. Our tool, helps the producers and directors of the movie industry in decision making process by deciding on the optimal combination of actor, director, genre, runtime of movie and country of release.
##Project Architecture:
The project was implemented using MVC pattern. Front end using AngularJs and BootStrap, middleware using NodeJS and express framework for high scalability and performance and mongoDB as Back-end. The training model was developed using IBM SPSS Modeler. This stands forms the core of the predictive model. Neural network boiler plate was used to Mine for the data for hidden relationships and control of the process is maintained by specifying the input variables this in turn is combined with other statistical techniques for greater insight for target variable prediction.
The user movie feed is powered by mongoDB aggregate framework. The user is prompted with past movies for director, actor or genre or any combination of them for movie and how that movie has done at box office using pie chart.
##Project Description:
The objective of the project is to provide producers, investor and production banners with an intelligent tool that predicts success or failure for a movie based combination of director, genre, actor, runtime and country of release. The end user is presented with a simple form for input for director, actor, genre, runtime and things that one has in his mind for prediction based on input values.
The movie feed is populated with movies of the director, actor and genre from the past and the how they did on box can be seen using the pie graph as the user inputs are selected.
#References http://www-03.ibm.com/software/products/en/spss-neural-networks