Browsing by Author "MUHAMMAD USMAN MANZOOR"

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    i DEVELOP A BAYESIAN FRAMEWORK FOR PREDICTING LIKELIHOOD OF HIT MOVIES.
    (UMT, Lahore, 2020) MUHAMMAD USMAN MANZOOR
    This study proposes a framework for predicting likelihood of producing a Hit film by implementing probabilistic inference. We propose the Bayesian networks properties are efficient for the problem in hand. We implemented a Bayesian network model to build Stars recommendations system, which is very uncertain in nature. Bayesian Network is Custom-made to the problem in hand. We examine the process through which stars affects the chances of getting an award in film fairs on individual basis and also in group. We performed the Bayesian Network model on the data sets of Lollywood movies and Lux Style Awards from 2002-2019 and the data for the analysis was consisted of all Urdu movies which were released between 2002-2019. The author prepared all the data sets from three different sources which includes IMDB, PAKDB, and PAKMAG and then verified all the data. There were total 239 movies which were part of our initial data set. Our Training Data set was consisted of total 214 movies, 619 stars, and our Test Data set was consisted of 25 movies, which were part of Lux Style award 2019. The Authors validated the model by applying the model on all the movies on the Test Data set on all 25 movies, whether they obtain an award or not. The authors also examine the process through which stars affects the chances of getting an award by Lux style award, that is, whether they influence the movie at least to be selected as a nominee or the best case to get an award. They find that star power influence the success of a film and plays its role. The chemistry between stars also plays key role on screen and is significant factor in the success or failure of the film. The authors also generated a costar network for all the stars and showed the degree of centrality and the closeness centrality as well.