The movie industry has grown immensely over the past few decades generating approximately $10 billion of revenue for the stakeholders annually. A Movie’s gross revenue prediction is a very important problem in the film industry because it determines all the financial decisions made by producers and investors.Furthermore,prediction system to assess the success of new movies with the help of the predicted revenue can help the movie producers and directors take proper decisions when making the movie in order to increase the chance of profitability and success.While these methods are pretty common, they often only provide a very rough and not a specific estimate of revenue and profitability prediction before a film has been released.
The goal of this project is to analyze the data and compare two models to check the best computational model through evaluating metrics based on public data for movies extracted from a popular online movie database called The Movie Database (TMDb).
We are using the Linear / Logistic Regression as a baseline model and Random Forest as a comparison check against them.Although a wide range of supervised learning algorithms are available, each with its strengths and weaknesses, there is no single learning algorithm that works best on all supervised learning problems. So, we are limiting it to only two of them.
