- This notebook provides companies(like Netflix, Amazon) to recommend Movies to its users where he/she will be most interested in and is likely to provide better Ratings. This is computed based on the previous reviews provided by the user.
- Data has been imported from Kaggle
- Code was excuted using Kernel from Kaggle
- Dataset has 24M+ rows and 2 columns
- Data with only first 2 columns were imported.
- The number of movies present were 4499.
- Number of customer 470758
- Number of ratings given were 24053764
- The above graph displays the rating percentage of each ratings.
- Data has been updated as Movie_id, by adding the Movie_id as the new column.
- We are keeing the movies that has been reviewed a minimum of 1799 times.
- We have kept only those customers who have reviewed a minimum of 52 times
- Data Size after trmming: (17337458, 3)
- SVD was applied on the dataset.
- We find the prediction for the user_id = 712664 and has given a rating of 5, we merge the movies dataframe to diplay only the movie_id and the name of the movie.
- We finally predict the movies that should be recommended to the users and estimate the rating that might be given by the user.
