Machine Learning Approaches Used For Prediction in Diverse Fields
Ierin Babu1, R V Siva Balan2, Paul P Mathai3
1Ierin Babu, Research Scholar, Department of Computer Science and Engineeering, Noorul Islam Centre for Higher Education, Kumaracoil, Kanya Kumari (Tamil Nadu), India.
2Dr. R V Siva Balan, Associate Professor, Department of Computer Science and Application, Noorul Islam Centre for Higher Education, Kumaracoil, Kanya Kumari (Tamil Nadu), India.
3Dr. Paul P Mathai, Associate Professor, Department of Computer Science and Engineeering, Federal Institute of Science and Technology, Angamaly, (Kerala), India.
Manuscript received on 07 July 2019 | Revised Manuscript received on 17 August 2019 | Manuscript Published on 27 August 2019 | PP: 762-767 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B11540782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1154.0782S419
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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: Although machine learning has long provided a powerful approach to prediction, its applicability has been somewhat emerging right now because of the large requirements in the various field. In recent years a number of new predictions with greatly reduced algorithm requirements have been developed. The purpose of this paper is to survey the various techniques that using now a days. The approaches of machine learning and the algorithms are included in this review. Several applications of the new techniques are discussed.
Keywords: ML, Random Forest, Decision Tree Algorithm, Supervised Learning.
Scope of the Article: Machine Learning