I'm thrilled to share new task a Song Recommendation System designed and developed using Python and scikit-learn. This task was completed as part of my internship with CodeAlpha, where I received invaluable insights and guidance.
This Song Recommendation System uses natural language processing techniques to provide personalized song recommendations. It is built with a focus on efficiency and scalability, ensuring that it can handle large datasets seamlessly.
Here's what the Song Recommendation System brings to the table:
🔍 Personalized Recommendations: Users can input a song title and receive a list of similar songs based on their TF-IDF cosine similarity.
💾 Efficient Data Handling: The similarity matrix and song data are serialized using pickle, allowing for quick loading and saving, improving performance.
🔠 Text Processing: Utilizes TF-IDF Vectorizer to transform text data into meaningful vectors for similarity comparison.
⚖️ Scalability: Designed to handle large datasets efficiently, making it ideal for larger music libraries.
💻 User Interface: a Flutter Application Will be integrated soon for a seamless recommendation experience.