You can use content-based filtering or collaborative filtering to build a recommendation system in Python. Libraries such as scikit-learn and Surprise facilitate the creation of item-based or user-based models for collaborative filtering. User preferences and item features are used in content-based filtering; pandas and scikit-learn are frequently used for feature extraction and similarity calculations. SVD and other matrix factorisation techniques are widely used for creating more intricate models. Consider enrolling in a Python certification course for in-depth instruction and practical experience.
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