Enhancement of Accuracy on A Medical Dataset by the Usage of Different Data Preprocessing Techniques
Manda Arpitha1, Ramalakshmi. K2, Venkatesan. R3

1Manda Arpitha, B.Tech, Department of Computer Sciences, Karunya Institute of Technology, Coimbatore (Tamil Nadu), India.
2Dr. K. Ramalakshmi, B. Tech, Madurai Kamaraj University, Madurai (Tamil Nadu), India.
3R. Venkatesan. B. Tech, Madurai Kamaraj University, Madurai (Tamil Nadu), India
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 805-808 | Volume-8 Issue-5, March 2019 | Retrieval Number: E2929038519/19©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 data is increasing exponentially, the real-time data has many inconsistencies. These factors of having inconsistent, incomplete and irrelevant data in the dataset would show its impacts on the knowledge development process. So, the quality of the data determines the success rate of the prediction by the machine learning model. By having a lot of missing and irrelevant values in the dataset would make the training and testing phase more troublesome. It is known that data preparation for analysis would take quite a long time. The primary concentration in this paper would be to clarify the stream of proficient advances that ought to be done amid the procedure of Data Mining.
Keyword: Data Mining, Data Preprocessing Machine Learning, Training, Testing.
Scope of the Article: Machine Learning