Song Recommendation based on user’s Activity using Ensemble Learning and Clustering

Abstract

The Song Recommendation System Based on User Schedule project is designed to provide users with personalized music recommendations that match their daily activities and mood swings. With a busy and hectic schedule, it can be challenging to find time to select music that matches a user’s current activity and mood. This project aims to provide a solution to this problem by analyzing the user’s daily schedule, including their planned activities and time of day, and using machine learning algorithms to recommend songs that fit their mood and energy level during each activity. The project utilizes a variety of technologies, such as React.js for the front-end and various machine learning algorithms using python for the back-end, to provide a user-friendly interface that allows users to input their schedules and receive song recommendations.