The
prediction of movies popularity is challenging problem. Movies prediction is a
hot topic for movie makers, producers, directors, production houses and also
there is a large amount of data related to the movies is available over the
internet, because of that much data available, it is an interesting data mining
topic. Every viewer, producer, directors, production houses all are curious
about the movies that how it will perform in the theatre. In this research, we
have used IMDB for our experimentation. We carried our research by predicting
the rating of movie and by predicting the gross of movie before its theatrical
release. In this research, we have extracted the data from IMDB, this raw data
was then preprocessed, transformed, integrated and cleaned. The relevant
features were selected from data set. We then performed different sets of
experimentation using machine learning techniques to predict rating and gross
of a movie. We tried to build an efficient model that can fairly predict the
rating and gross of movies. The results are presented in bingo and 1-away
accuracy. We are then able to achieve an accuracy of 99% for rating and 94% for
gross. We have also compared our result with other efforts in the same research
area with our results exceeding the previous studies.