/Recommendation-System

This project is a simple recommendation system developed based on a user-based recommendation model

Primary LanguageHTML

Recommendation-System

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