An Insight into Machine Learning Techniques for Predictive Analysis and Feature Selection
Deepti Aggarwal1, Vikram Bali2, Sonu Mittal3
1Deepti Aggarwal, Research Scholar, School of Computer and System Sciences, Jaipur National University, Jaipur, India.
2Dr. Vikram Bali, Professor & Head, Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida, India.
3Dr. Sonu Mittal, Associate Professor, School of Computer and System Sciences, Jaipur National University, Jaipur, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 342-349 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10550789S19/19©BEIESP | DOI: 10.35940/ijitee.I1055.0789S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predictive analysis comprises a vast variety of statistical techniques like “machine learning”, “predictive modelling” and “data mining” and uses current and historical statistics to predict future outcomes. It is used in both business and educational domain with equal applicability. This paper aims to give an overview of the top work done so far in this field. We have briefed on classical as well as latest approaches (using “machine learning”) in predictive analysis. Main aspects like feature selection and algorithm selection along with corresponding application is explained. Some of the most quoted papers in this field along with their objectives are listed in a table. This paper can give a good heads up to whoever wants to know and use predictive analysis for his academic or business application.
Keywords: Classification, Clustering, Feature Selection, Machine Learning, Predictive Analysis, Regression.
Scope of the Article: Classification