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The easy guide for building python collaborative filtering recommendation system in 2017

The easy guide for building python collaborative filtering recommendation system in 2017: 
surprise_tutorial.py
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import zipfile from surprise import Reader, Dataset, SVD, evaluate # Unzip ml-100k.zip zipfile = zipfile.ZipFile('ml-100k.zip', 'r') zipfile.extractall() zipfile.close() # Read data into an array of strings with open('./ml-100k/u.data') as f: all_lines = f.readlines() # Prepare the data to be used in Surprise reader = Reader(line_format='user item rating timestamp', sep='\t') data = Dataset.load_from_file('./ml-100k/u.data', reader=reader) # Split the dataset into 5 folds and choose the algorithm data.split(n_folds=5) algo = SVD() # Train and test reporting the RMSE and MAE scores evaluate(algo, data, measures=['RMSE', 'MAE']) # Retrieve the trainset. trainset = data.build_full_trainset() algo.train(trainset) # Predict a certain item userid = str(196) itemid = str(302) actual_rating = 4 print algo.predict(userid, itemid, actual_rating)
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