Employability of Predictive Analysis in Mitigating the Probable Risks in Data Mining
Rishit Garkhel
Download PDF
Abstract
In this time of ML and AI, Business Intelligence is likewise acquiring importance as associations overall need to incorporate knowledge into their business measures so they can more readily comprehend the client conduct or designs and give experiences to business pioneers to settle on the ideal choices in the commercial centre to keep them serious and effective by decreasing the danger enhancing tasks or Fighting misrepresentation to the degree conceivable. In its simple structure, Business Intelligence consistently existed before IT regarding experience and business aptitude, with the workers taking care of specific business measures over many years. In any case, this cycle doesn't ensure every one of the elements has been represented and no real way to show their examination out before any choices to be made by the association. Business Intelligence is informationdriven and has a logical interaction behind it to dissect the data and give models to test the What-If situations to settle on less danger inclined choices. We can't make it 100% solid. However, it is way obviously better than speculating out of one individual's point of view. This paper intends to investigate Data Mining and Predictive Analysis regarding business applications and the methods in question, which at last form the insight required in Business Intelligence.
- https://www.datasciencecentral.com/profiles/blogs/the-7-most-important-data-mining-techniques
- https://searchsqlserver.techtarget.com/definition/data-mining
- https://www.predictiveanalyticstoday.com/what-is-predictive-analytics/
- Desheng Dash Wu, Shu-heng Chen, David L. Olson, Introduction to the Special Issue on “Business I Intelligence (BI) in Risk Management”. Article in Information Sciences · January 2014. DOI: 10.101 6 /j.ins.2013.10.008
- https://channels.theinnovationenterprise.com/articles/pros-and-cons-of-datamining-social-interactions
- http://bus237datamining.blogspot.com/2012/11/advantages-disadvantages.html
- https://www.wideskills.com/data-mining-tutorial/data-mining-techniques
- https://www.educba.com/predictive-analytics-vs-data-mining/
- https://www.expertsystem.com/data-mining-predictive-analytics-difference/
- https://en.wikipedia.org/wiki/Predictive_analytics
- https://www.allerin.com/blog/transforming-risk-management-with-predictive-analytics
- https://www.neuraldesigner.com/blog/6_Applications_of_predictive_analytics_in_business_intelligence
- http://www.rmmagazine.com/2014/05/01/the-new-math-bringing-predictive-analytics-into-the-mainstream/
- Wazurkar, P., Bhadoria, R. S., & Bajpai, D. (2017). Predictive analytics in data science for Business Intelligence (BI) solutions. 2017 7th International Conference on Communication Systems and Network Technologies (CSNT). Page no: 367-370.doi:10.1109/csnt.2017.8418568
- McCue, C. (2006). Data Mining (DM) and Predictive Analytics in Public Safety and Security. IT Professional, 8(4), page no:12–18. doi:10.1109/mitp.2006.84
- A. Martin, T. Miranda Lakshmi, V. Prasanna Venkatesan. An Analysis on Business Intelligence (BI)Models to Improve Business Performance.IEEE- International Conference on Advances in Engineering, Science and Management (ICAESM -2012) March 30, 31, 2012.Page no:503-508, ISBN: 978-81-909042-2-3 2012 IEEE
- http://www.blog-geographica.com/2016/11/15/how- data-mining-is-used-to-generate-business-intelligence/