Machine Learning Nanodegree Capstone Project : 

Recommender Systems are a subclass of information filtering system t ​ hat seek to predict the "rating" or "preference" that a user would give to an item .Recommender systems have become extremely common in recent years, and are utilized in a variety of areas, some popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general .
In this project, we will build a recommender systems to make predictions related to reviews of Books on Amazon. The dataset is 1,000,000 Amazon Book Reviews, which provided us purchase information. Our target was to predict whether the user purchased the item based on these reviews.


In a terminal or command window, navigate to the top-level project directory project 5/ and run one of the following commands:

   ""ipython notebook RS_book.ipynb""
   
Assignment Data is available on:
http://jmcauley.ucsd.edu/data/assignment1.tar.gz
- itemID: The ID of the item. This is a hashed product identifier from Amazon.
- reviewerID: The ID of the reviewer. This is a hashed user identifier from Amazon.
- helpful: Helpfulness votes for the review. 
- reviewText: The text of the review.
- summary: Summary of the review.
- unixReviewTime: Time of the review in seconds since 1970.
- reviewTime: Plain-text representation of the review time.
- category: Category labels of the product being reviewed.

Task
Purchase prediction: predict given a (user,item) pair from ‘pairs_Purchase.txt’ whether the user purchased the item (really, whether it was one of the items they reviewed). Predictions should be labeled 1 (purchased) or 0 (not purchased). Accuracy will be measured in terms of the classification accuracy.
