Movies are a subjective entertainment platform; every person has a different choice for movies they like to watch. Some people prefer drama, some prefer action, or some even prefer comedy combined with action and so on. With the introduction of online platforms such as Netflix, Amazon Prime we are seeing mass amounts of movies hitting the market than we saw 10 years ago. We cannot expect viewers to keep up with all these new released movies, so we need to get help from data collected by various movie rating websites and create a model to recommend movies related to viewers’ choice.

When we watch one movie in Netflix or Amazon Prime etc., we get a recommendation for movies that are similar to previously watched movies. This recommendation is very interesting and useful from a business model perspective as well as user experience. Movie streaming services that introduced these kinds of recommendation could retain customer base and enhanced customer experience. Identifying a pattern of movies rating has with viewers can have drastic impact on viewers watching habits and overall user experience.

We are building a similar application that will recommend movies based on user’s preference. User will select best movies they like and the application will recommend similar types of movies that meets users taste and preference. This Application will find rating and genre selected by the user as input and recommend same kind of movies that meets similar genre. Success of the Application depends on finding right kind of movies that meets users’ taste.