Please use this identifier to cite or link to this item:
http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6692
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DC Field | Value | Language |
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dc.contributor.author | Ameta, Divya | - |
dc.contributor.author | Verma, Rahul | - |
dc.contributor.author | Singh, Hari [Guided by] | - |
dc.date.accessioned | 2022-09-26T05:48:08Z | - |
dc.date.available | 2022-09-26T05:48:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6692 | - |
dc.description.abstract | Big data is the large amount of data that is generated every second. Such large data becomes very difficult to process using manual checking and old methods. Now, we have a number of tools and techniques to handle such data. With continually increasing customer-base on online platforms, it is necessarily required to know customer’s likes and interests. It is really necessary as through this the preferences of the customers can be well known. The best way to handle this problem is by applying Big Data Analytics. This is a project report on “Amazon Rating Analysis and Prediction”. During the exploration of this project, we develop new ideas and functionalities while using the Hive and Machine Learning technologies. This project is based on the Amazon rating details of different products from various categories purchased by a number of customers within a time-span. The project report covers the implementation of the project on Hive and Machine Learning technologies and the reason why there is a shift from Hive to Machine Learning with a concluding result at the end. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Jaypee University of Information Technology, Solan, H.P. | en_US |
dc.subject | Big data | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Amazon | en_US |
dc.title | Amazon Rating Analysis and Prediction | en_US |
dc.type | Project Report | en_US |
Appears in Collections: | B.Tech. Project Reports |
Files in This Item:
File | Description | Size | Format | |
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Amazon Rating Analysis and Prediction.pdf | 1.87 MB | Adobe PDF | View/Open |
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