We can all agree that music has become a big part of human life. We all have favorite artists and genres, as well as listen to music based on our moods or the activity we are doing. People are eager to listen to music from their favorite artists or genre, but that confines their diversity in music. To solve this, we can utilize recommendation systems to encourage listeners to listen to new music that come from them artist, genre, or maybe songs other people with similar tastes in music listen too. There are multiple methods creating recommendations, but we will focus on two methods, Content based filtering and Collaborative Filtering. In this project we will focus on the implementation of these two recommendation system methods and analyze which is more effective in recommending new music to listeners.