{"library":"scikit-surprise","title":"scikit-surprise","description":"scikit-surprise (Surprise) is a Python scikit for building and analyzing recommender systems. Version 1.1.4 is the current release. It supports prediction-based and neighborhood-based collaborative filtering, matrix factorization, and evaluation metrics. Releases are infrequent (last stable was 1.1.1 in 2020, then 1.1.3/1.1.4 in 2025).","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install scikit-surprise"],"cli":null},"imports":["from surprise import Dataset","from surprise import Reader","from surprise import SVD","from surprise import accuracy","from surprise.model_selection import cross_validate","from surprise.model_selection import GridSearchCV"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from surprise import Dataset, Reader, SVD, accuracy\nfrom surprise.model_selection import train_test_split\n\n# Load the built-in movielens dataset\ndata = Dataset.load_builtin('ml-100k')\ntrainset, testset = train_test_split(data, test_size=0.25)\n\nalgo = SVD()\nalgo.fit(trainset)\npredictions = algo.test(testset)\nrmse = accuracy.rmse(predictions)\nprint(f\"RMSE: {rmse}\")","lang":"python","description":"Loads the Movielens 100k dataset, trains SVD, and evaluates RMSE.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}