Data Analyzer Using the Concept of Machine Learning
Minu M.S.1, Prangad Khanna2, Sachit Jain3, Ashutosh Saxena4
1Minu M.S., Department of Computer Science SRM Institute of Science & Technology Chennai (Tamil Nadu), India.
2Prangad Khanna, Department of Computer Science SRM Institute of Science & Technology Chennai (Tamil Nadu), India.
3Sachit Jain, Department of Computer Science SRM Institute of Science & Technology Chennai (Tamil Nadu), India.
4Ashutosh Saxnea, Department of Computer Science SRM Institute of Science & Technology Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 548-552 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6145048419/19©BEIESP
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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: As the workplace is becoming more tech-driven and fast-moving, data analysis has become an imperative part of any industry. Analyzing a data helps in simplification of complex information. This complex information is extremely difficult to interpret and a data analyzer shows only the most necessary outputs from the stored data. It requires detailed procedures and methodology. The biggest advantage of this interpretation of information is that it gives all the technical insights which is extremely important for any organization. Objective behind making a data analyzer is to provide accurate information to the respective organization. Accurate, significant and timely documentation is important for an effective decision-making process. The data presented can be in the form of figures, graphs, mathematical modelling etc. The key algorithm used for basic integration in this project is shunting algorithm.
Keywords: Shunt Yard Algorithm, Fragmentation, Integration, Machine Learning.
Scope of the Article: Machine Learning