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Predicting the ratings of Amazon products using Big Data

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Abstract This paper aims to apply several machine learning (ML) models to the massive dataset present in the area of e‐commerce from Amazon to analyze and predict ratings and to recommend products. For this purpose, we have used both traditional and Big Data algorithms. As the Amazon product review dataset is large, we present Big Data architecture suitable massive dataset for storing and computation, which is not possible with the traditional architecture. Furthermore, the dataset contains 15 attributes and has about 7 million records. With the dataset, we develop several models in Oracle Big Data and Azure Cloud Computing services to predict the review rating and recommendation for the items at Amazon. We present a comparative conclusion in terms of the accuracy as well as the efficiency with Spark ML—the Big Data architecture, and Azure ML—the traditional architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Technologies > Prediction
Spark Hadoop Big Data UDA platform and the scalability
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Technologies > Prediction
Technologies > Machine Learning
Fundamental Concepts of Data and Knowledge > Big Data Mining

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