This project aims to develop a recommendation system for a music streaming application. It leverages user data and preferences, employing a least-square solution approach. The system analyzes user interactions and applies algorithms to predict and recommend music, creating a personalized user experience. The repository includes datasets, source code, and detailed documentation of the approach and methodologies used, showcasing skills in data analysis, algorithm development in Python, and user preference prediction based on a complex yet explainable recommendation system.
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yonatanko/Recommendation_system
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personalized music recommendation system using a least-square solution based on user preferences and interactions in a music streaming application.
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