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Data Mining Engine using Predictive Analytics

by Sakshi Rungta, Vanita Jain, Akanksha Utreja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 5
Year of Publication: 2015
Authors: Sakshi Rungta, Vanita Jain, Akanksha Utreja
10.5120/21537-4545

Sakshi Rungta, Vanita Jain, Akanksha Utreja . Data Mining Engine using Predictive Analytics. International Journal of Computer Applications. 121, 5 ( July 2015), 22-26. DOI=10.5120/21537-4545

@article{ 10.5120/21537-4545,
author = { Sakshi Rungta, Vanita Jain, Akanksha Utreja },
title = { Data Mining Engine using Predictive Analytics },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 5 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number5/21537-4545/ },
doi = { 10.5120/21537-4545 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:07:39.880738+05:30
%A Sakshi Rungta
%A Vanita Jain
%A Akanksha Utreja
%T Data Mining Engine using Predictive Analytics
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 5
%P 22-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Predictive analytics is a field of data mining which extracts information from the past and use it to predict the future trends. This paper establishes the importance of predictive analysis. In this paper, we present a system to analyse user stories incorporating the data of energy and health demands of four countries – namely India, China, United States of America and Brazil; for the past 30 years, depict them graphically using Business Intelligence and finally predict the future trend of the parameters. The correlation between various entities is found out using Pearson's coefficient. Finally we can see the predicted values of 30-40 years ahead and predict the emerging trends in the form of Power View charts. We present lessons learned and future directions for improving the user in the loop workflow for predictive analytics.

References
  1. World Bank. 2015, January 10. Indicators[Online]. Available: http://data. worldbank. org/indicator
  2. Frank Buytendijk and Lucie Trepanie, 2010. "Predictive Analytics: Bringing The Tools To The Data", Oracle White.
  3. T. Muhlbacher and H. Piringer, 2013. "A partition-based framework for building and validating regression models", IEEE Transactions on Visualization and Computer Graphics, vol 19 ,no. 12, pp. 1962–1971.
  4. R. Amar and J. Stasko, 2004. "A knowledge task-based framework for design and evaluation of information visualizations", IEEE Symposium on Information Visualization, pp 143–150
  5. G. G. Vining, D. C. Montgomery and E. A. Peck. 2012. Introduction to Linear Regression Analysis. Wiley.
  6. Mengwei Lu, Haga Helia , 2013. "Discovering Microsoft Self-service BI solution: Power BI,20148", University of Applied Science.
  7. R Core Team. 2014. "R: A Language and Environment for Statistical Computing",R Foundation for Statistical Computing, Vienna, Austria.
  8. SAS institute Inc. SAS/STAT software, Version 9. 3. Cary, 2011.
  9. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. . 2009 . The WEKA Data Mining Software: An Update. SIGKDD Explo- rations Newsletter, vol. 11, no. 01, pp. 10–18, Nov.
  10. S. Publishing et al. JMP 10 modeling and multivariate methods. SAS Institute, 2012.
  11. WCF Data Service 5, Available: http://msdn. microsoft. com/en-us/library/dn259731%28v=vs. 113%29. aspx.
  12. Mike Wasson, 2014. Supporting OData Query Options in ASP. NET Web API 2.
  13. Fern Halper, 2014. "Predictive Analytics for Business Advantage",TDWI Research,Boston.
  14. Wadud Z Dey,HS, Kabir,MA and Khan,SI ,2015. " Modelling and forecasting natural gas demand in Bangladesh Energy Policy",vol 39, no. 11,ISSN 0301-4215.
  15. Aaron Skonnard and Pluralsight(2015, April 1), A Guide to Designing and Building RESTful Web Services with WCF[Online],Available: https://msdn. microsoft. com/en-in/library/dd203052. aspx.
  16. International Energy Agency, World Energy Outlook, 2014
  17. J. Seo and B. Shneiderman, "A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections", IEEE symposium on information visualization, pp. 65–72, 2004
  18. Pearson. (2014, March 15). R-correlation Coefficient [Online],Available: http://www. strath. ac. uk/aer/materials/4dataanalysisineducationalresearch/unit4/pearsonr-correlationcoefficient. com
  19. Predictive inference. Chapman & Hall, New York, 1993.
  20. David Grey(2014, January). Machine Learning: powerful cloud based predictive analytics [Online], Available: http://azure. microsoft. com/en-in/services/machine-learning. com
  21. Microsoft(2014),Microsoft Power BI [Online],Available: http://research. gigaom. com/2013/07/microsoft-power-bi. com
  22. James Taylor, 2014, "Putting Predictive Analytics to Work in Operations" Decision Management Solutions.
  23. Fern Halper, 2014. "Predictive Analytics for Business Advantage", TDWI Research.
  24. Dickson Wai Hei lam, 2014. " Survey of Predictive Analytics in Data Mining with Big Data", Athabasca University.
Index Terms

Computer Science
Information Sciences

Keywords

Predictive Analytics Regression Analysis Health and Energy Demands

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