IMDB SCORE PREDICTION PROJECT1.INTRODUCTIONFilm industry has been expanded worldwide and now-a- days people, wherever on earth,have an opportunity to watch a movie on the very first day it is released. There is a hugesector behind the preparation phases of each film and lots of directors and movie stars haveburst. Consistently every year hundreds of movies are being produced. These films havedifferent or various types of genres, varying from comedy to romance or war to sciencefiction. In order to monitor and keep a track of every movie produced, an online platformwas very much needed.Internet Movie Database (IMDB) is the most well-known and a popular platform to getinformation about a rich collection of movies. IMDB web site contains downloadable rawdata about the movies, including data like cast, directors, genres, crew, scriptwriters,summaries, gross and even user ratings. This information is used for data mining on the filmsfor making prediction on user ratings of the movies.2. BACKGROUNDA commercial success film not only entertains its audience or viewers, but also facilitatesmovie companies to gain tremendous profit. A ton of factors for instance good directors,experienced actors are considerable for making good movies. However, famous directorsand actors can always bring an expected box-office income but cannot ensure a highly ratedimdb score.In this project, we attempt to use the IMDb dataset to predict what are the important factorsthat make a movie more successful than others using Data mining techniques such asDecision tree & K-NN.we take IMDB scores as response variable and focus on operating predictions by analyzingthe other variables in the IMDB 5000 movie data. The results can help movie companies tounderstand the secret of generating a commercial success movie.