//SVD - is a widely used technique for developing recommendation systems//

Singular Value Decomposition(SVD) - SVD is a classical method from linear algebra is getting popular in the field of data science and machine learning. This popularity is because of its application in developing recommender systems

• A 'recommender system' is an intelligent system that predicts the rating and preferences of users on products

• The primary application of recommender system is finding a relationship between user and products in order to maximise the user-product engagement

• SVD is a 'matrix factorisation technique', which reduces the number of features of a dataset by reducing the space dimension from N-dimension to K-dimension (where K<N).In the context of the recommender system, the SVD is used as a 'collaborative filtering technique'.

• Collaborative filtering technique uses a matrix structure where each row represents a user, and each column represent an item.

• The elements of this matrix are the ratings that are given to items by users.

• Applying SVD: After creating the dataset object, the SVD algorithm is instantiated using the SVD() constructor.

• The cross_validate() method of the surprise library is then used to compute the RootMean Squared Error (RMSE) and Mean Absolute Error (MAE) of the SVD algorithm. The cross_Validate() method performs cross-validation on the data using the specified number of folds (cv=3) and prints the RMSE and MAE values for each fold

  

• The above output shows the evaluation results of the SVD algorithm on the Netflix Price dataset using 3-fold cross-validation The RMSE and MAE values are computed for each fold and the mean and standard deviation are also calculated

• Overall, SVD algorithm has achieved a reasonably "good performance" on the Netflix Prize dataset with a mean RMSE of 0.9977 and a mean of MAE of 0.8029

• Find all the movies that have been rated 5 stars by user with ID 712664 and displays the names of the movies

• The above code predicts the ratings for user 712664 using the trained SVD model and creates a new column 'Estimated_Score' with the predicted ratings for each movie

• The 'Movie_Id' column is then dropped from the dataframe, and the dataframe is sorted based on the predicted ratings in descending order. The top 10 recommendations are printed using the head() method

• Overall, the code recommends the top 10 movies for user 712664 based on their predicted ratings using the SVD algorithm