Data
Amazon---Ratings-(Beauty-Products)

Amazon---Ratings-(Beauty-Products)

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Context Amazon.com is one of the largest electronic commerce and cloud computing companies. Just a few Amazon related facts They lost 4.8 million in August 2013, when their website went down for 40 mins. They hold the patent on 1-Click buying, and licenses it to Apple. Their Phoenix fulfilment centre is a massive 1.2 million square feet. Amazon relies heavily on a Recommendation engine that reviews customer ratings and purchase history to recommend items and improve sales. Content This is a dataset related to over 2 Million customer reviews and ratings of Beauty related products sold on their website. It contains the unique UserId (Customer Identification), the product ASIN (Amazon's unique product identification code for each product), Ratings (ranging from 1-5 based on customer satisfaction) and the Timestamp of the rating (in UNIX time) Acknowledgements A description of the entire Amazon products dataset. This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). For the complete dataset check out Amazon Product Data Inspiration Can a good Recommendation engine be created from this minimal dataset? Give it a try! If you have got any more cool Amazon facts, dataset or queries, just drop a comment.

4 features

UserIdstring1210271 unique values
0 missing
ProductIdstring249274 unique values
0 missing
Ratingnumeric5 unique values
0 missing
Timestampnumeric4231 unique values
0 missing

19 properties

2023070
Number of instances (rows) of the dataset.
4
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
50
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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