svd movielens data train and test

from surprise import Dataset, Reader, SVD, accuracy
from surprise.model_selection import train_test_split
 
# instantiate a reader and read in our rating data
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(ratings_f[['userId','movieId','rating']], reader)
 
# train SVD on 75% of known rates
trainset, testset = train_test_split(data, test_size=.25)
algorithm = SVD()
algorithm.fit(trainset)
predictions = algorithm.test(testset)
 
# check the accuracy using Root Mean Square Error
accuracy.rmse(predictions)
RMSE: 0.7724
 
# check the preferences of a particular user
user_id = 7010
predicted_ratings = pred_user_rating(user_id)
pdf = pd.DataFrame(predicted_ratings, columns = ['movies','ratings'])
pdf.sort_values('ratings', ascending=False, inplace=True)  
pdf.set_index('movies', inplace=True)
pdf.head(10)