/Amazon-Recommendation-system

Using amazon dataset

Primary LanguageJupyter Notebook

Amazon-Recommendation-system

Problem Statement:

Everyday a million products are being recommended to users based on popularity and other metrics on e-commerce websites. The most popular e-commerce website boosts average order value by 50%, increases revenues by 300%, and improves conversion. In addition to being a powerful tool for increasing revenues, product recommendations are so essential that customers now expect to see similar features on all other e-commerce sites. Amazon is referred to as "one of the most influential economic and cultural forces in the world", as well as the world's most valuable brand. Our objective is to make a recommendation system that recommends new products based on user’s habits.

Data Definition:

ID : Product ID.

Brand : Name of the Brand to which the product belongs..

Categories : Category of the product..

dateAdded : Date on which the product was added..

dateUpdated : Date on which the product was updated..

Manufacturer : Name of the manufacturer of the product..

Manufacturer Number : Number given to the product by the manufacturer..

Name : Name of the product..

reviews.numHelpful : Number of helpful reviews for the product..

reviews.rating : Rating for the product on each review..

reviews.text : Informative Review on the product..

reviews.title : Title for each review on the product..

reviews.username : Username of person posting the review.