We have described a personalized music recommendation system using K-nearest neighbour that is KNN and machine learning methods in this paper. We present a collaborative filtering and content filtering recommendation algorithm to combine the output of the network with the log files to recommend music to the user in a personalized music recommendation system. The recommended system includes log files that store the past or viewed history of the user's music playlist. The propound music exhortation system pulls the consumer's the beyond records from the log file and provides track tips for each recommendation. Content-based approaches make suggestions based on the audio characteristics. Speedy development of cell phones and internet has made possible for us to access various music resources freely. While the music industry may favour certain types of music more than others, it is salient to understand that there isn’t a single human culture on earth that has existed without music. In this paper, we have sketched, implemented and examined a song recommendation system. We have used Song text provided to find relationship between users and songs and to seek from the preceding listening history of users to deliver recommendations for songs which users may prefer to listen mostly. The dataset bottles up over 10,000 songs and listeners are advocated the first-class available songs based totally at the mood, style, artist and top charts of that yr. With a powerful interactive UI, we show the listener the cover songs that were played the maximum and top charts of the year. Listener also have an option to select his/her favourite artist and albums on which songs are recommended to them by utilizing the dataset. A recommendation system plays a important role in providing a well user experience in an application by providing the most suitable and personalized services for each and every user. Currently, Spotify has one fiftyfive million premium subscribers and three forty five million active users. Spotify’s recommendation system has also played a dominant role in the success of Spotify. In the modern years, music and movie flowing services have grown extremely. Currently, Netflix and Spotify have a bulk number of users, which has made these spurting services victorious. A recommendation system plays a vital role in providing a well user experience in an application by recommending the most acceptable and personalized services for each and every user.

Keywords : K-NN, SVM, Multiple Linear Regression, Random Forest Regression, Popularity Model, ContentBased Model, Collaborative Filtering