Building user-based recommendation model for Amazon.

DESCRIPTION

The dataset provided contains movie reviews given by Amazon 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
- All the users have not watched all the movies and therefore, all 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 users. Netflix 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