Recommendation Systems are models whose algorithm focuses on filtering. It seeks to predict the “rating” or preference of a user, given a set of items. Recommendation systems are of two major types; the collaborative Recommendation system and the content based Recommendation system. The Recommendation system works primarily in the Media and entertainment industry. The collaborative Recommendation system is the type that aggregates ratings or recommendation of objects, identify similarities between the users by using their ratings and then generating new recommendations based on inter-user comparisons. It is based on the assumption that users who agreed in the past will agree in future, and will like similar objects as they did in the past. The content Based Recommendation system is such that the objects are mainly defined by features associated with them. The content based Recommendation operates by learning a profile of the new user’s interests based on the features present in items the user has rated.

The project was about taking songs submission of upcoming artistes on Spotify and analyze the songs through some metrics which involves the genre, valence, tempo and many other features. The aim is to help executives use machine learning algorithm to reduce number of submissions for evaluation. Neural network was used as the model to achieve this.