Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools

A. Jenita Jebamalar¹

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.6 , pp.14-18, Dec-2018

Online published on Dec 31, 2018

Copyright © A. Jenita Jebamalar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Insights are the results of the analytics with various parameters like customer demographics, gender, age, behavior, interests, etc. The objective is to predict which product the customers are least likely and most likely to buy. The result of the analytics is the insights which are provided in the form of tables, charts and graphs. In the technology world, the term agnostic means that the tools are not restricted to a specific systems and it works with various systems rather than being designed for a single system. Agnostic data means that it does not comes from a specific source. In machine learning, feature selection is used to reduce the properties of the class variables by removing the redundancy from the dataset. The goal of this research work is compare and find the efficiency of various data mining algorithms used in analytics insight tools. Dataset is collected from an analytics of a website for the listed algorithms. Data mining utilizes algorithms, statistical analysis and even artificial intelligence to extract data from huge data sets into an apprehensible form. The future work will be the implementation of the selected algorithm in the data analytics insight tool.

Key-Words / Index Term :
Agnostic, Insights, Feature Selection Algorithms, Data Analysis, Data Discovery

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