The main goal of our project was to develop a music recommendation system that utilized the power of ML to provide users with accurate and personalized recommendations.
Some key features of our system include:
- Seamless integration with popular music streaming services
- Real-time updates based on user behaviour
- Advanced machine learning algorithms for accurate recommendations
In order to develop our Music Recommendation System, we utilized a large dataset of user listening histories and preferences. This data was collected from popular music streaming services and included information such as:
- User demographics
- Music preferences and ratings
- Music Characteristics We also used various web scraping tools to gather additional data about music genres, artists, and albums.
Our Music Recommendation System comprises several interconnected components that work together to provide accurate and personalized recommendations. These components include:
- Data collection and preprocessing
- Machine learning model training and deployment
- Real-time recommendation
Our Music Recommendation System was implemented using Python as the primary programming language. We used several popular libraries and frameworks such as Pandas, NumPy, seaborn, scikit to build our machine learning models. We utilized various integrated development environments (IDEs) in Google Colaboratory for development. These tools allowed us to streamline our development process and collaborate more effectively.
Our Music Recommendation System's AI/ML aspects were critical to its success. We utilized advanced machine learning algorithms to analyze user listening and preferences and provide personalized recommendations in real-time. For some specific AIML techniques, we used the concept of content-based filtering by including feature vectorization and cosine similarity, this can also be done using collaborative filtering. These techniques allowed us to build highly accurate models that provided relevant and engaging recommendations to our users.