In today's digital world, there is a boundless increase in the digital content related to videos, books, movies, food etc. and it has become a tiresome task for users to find the content of their choice from this content. Therefore, there arises a need of recommendation system, that can help user to shortlist the item of their taste and liking by spending minimum time. Recommender System is basically a filtering system that help users to shortlist the content by overcoming information overload. It predicts data for users based upon the previous information/interests about the user and makes recommendation according to the interest model of users. There is also an incipient growth among the digital content providers whose motive is to engross as many operators on their service as possible and that too for maximum time. As this increases their weightage in any recommender system. A movie recommendation system plays a significant role in our social life as this system suggests a set of movies to users based upon their interests. The system is basically a filter that gathers the information from the past history and interests of users, and accordingly give outcome. With the advancement in technology and agile growth in machine learning techniques, the accuracy in the outputs of these recommendation systems is also increased. In this paper, content-based movie recommendation system has been proposed. that uses traits like director, actor, description, genre etc. for making suggestions to users. The perception behind the designed algorithm is that if a user liked a specific type of movie or show, a similar movie or a show will be liked by him/her.