Netflix_Movie_Recommendation

Inspiration:

• 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.

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Data

• Data has been imported from Kaggle
• Code was excuted using Kernel from Kaggle
• Dataset has 24M+ rows and 2 columns

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Data Processing

• 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.

Data Cleaning

• 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)

Machine Learning Algorithm

• 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.

Prediction

• We finally predict the movies that should be recommended to the users and estimate the rating that might be given by the user.