A collaborative filtering movie recommender using SVD matrix factorization (scikit-surprise) on the MovieLens dataset, with EDA, duplicate-ID data cleaning, GridSearchCV hyperparameter tuning, and joblib model persistence. Full pipeline from raw CSVs to personalized top-N recommendations for any user.
python data-science machine-learning scikit-learn jupyter-notebook eda pandas kaggle collaborative-filtering recommender-system svd data-cleaning hyperparameter-tuning model-evaluation gridsearchcv scikit-surprise joblib movie-recommedation matrix-vectorization
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Updated
Sep 6, 2026 - Jupyter Notebook