Movie Success Prediction using Machine Learning

Movie Success Prediction using Machine Learning


The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed research, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This research proposes a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others on whether a movie will be successful or not. The proposed research provides a quite efficient approach to predict IMDb score on IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. We have used different algorithms in the research work for analysis but among all Random forest gave the best prediction accuracy which is better in comparison to the previous studies. In the exploratory analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.

Existing System:

The study can be used as a proof of concept for applications in other areas, and should highlight some of the challenges one needs to overcome to successfully create a prediction model. This idea could in theory be extended to predict credit ratings, the stock market or housing market. The only requirement being a vast and reliable data source. When combining the questions mentioned above to form a problem statement, formulating good as a measurement of a movies rating and sales, the following problem statement was produced.


There also have been various semantic analysis techniques to analyze user reviews which were applied to analyze the IMDb movie ratings. None of the studies has succeeded in suggesting a model good enough to be used in the industry. In this project, we attempt to use the IMDb dataset to predict the Cinema has a profound impact on our society. Cinema is one of the most powerful media for mass communication in the world. Cinema has the capacity to influence society both locally and globally.

Proposed System:

The first step is to identify a dataset of movie data that’s representative and suitable for analysis. Relevant attributes of such data must include general pre-production information regarding film productions such as genre, language and information about the actors and directors involved. Likewise, the data must also include some measure of success, such as user originated movie ratings. Secondly, the relevant dataset has to be prepared and structured in such a way that the data used is representative of the movie scene at large, as well as viable for analysis by the relevant machine learning techniques and algorithms.