Movie Prediction System

Predicts the rating of movie using data from IMDB data set

Introduction

The number of movies produced by the American film studios is growing at an exponential rate, the United States is one of the top most prolific producer of films in the world today. The success rate of the box office 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 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. Additionally, being aware of the right time or season to release a particular movie by looking at the overall market will be helpful in in a lot of ways. So, the prediction of the success of a movie is very essential to the film industry. In this project, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score for which we make use of a free, user-maintained and online resource pf production details for over 400,000 TV shows and movies. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth.

Methods used to perform analysis

1. Support Vector Regression
2. Linear Regression
3. Random Forest Classification

Platform used for development

Rstudio

Visualizations have been deployed and can be found at

https://ayesha92ahmad.shinyapps.io/shinymovie/

Licence

The data used in this project is bound by privacy policy