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Building user-based recommendation model for NetFilm I Sample Assignment

Updated: Jul 23, 2021


The dataset provided contains movie reviews given by NetFilm customers. Reviews were given between May 1996 and July 2014.

Data Dictionary

UserID – 4848 customers who provided a rating for each movie Movie 1 to Movie 206 – 206 movies for which ratings are provided by 4848 distinct users

Data Considerations

- Viewers who have not watched the movies are not rated. These missing values are represented by NA. - Ratings are on a scale of -1 to 10 where -1 is the least rating and 10 is the best.

Analysis Task

- Exploratory Data Analysis:

  • Which movies have maximum views/ratings?

  • What is the average rating for each movie? Define the top 5 movies with the maximum ratings.

  • Define the top 5 movies with the least audience.

- Recommendation Model: Some of the movies hadn’t been watched and therefore, are not rated by the Viewers. NetFilm would like to take this as an opportunity and build a machine learning recommendation algorithm which provides the ratings for each of the users.

  • Divide the data into training and test data

  • Build a recommendation model on training data

  • Make predictions on the test data

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