Comparative Analysis of Different Imputation Techniques For Handling Missing Dataset
Gopal Krishna M1, Durgaprasad N2, Deepa Kanmani S3, Sravan Reddy G4, Revanth Reddy D5
1Durgaprasad N, Computer Science and Engineering, Karunya institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
2Gopal Krishna M, Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
3Deepa Kanmani S, Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
4Sravan Reddy G, Computer science and Engineering, Karunya institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
5Revanth Reddy D, Computer science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 347-351 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5450058719/19©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: In last two decades data became the wealth because of its importance in different fields. But it’s very difficult to collect all the information and store it as data in real time which results in some missing data. Missing data cannot be omitted because even small piece of data plays a major role in the output. Imputation plays a major role in handling missing data before we predict the hidden patterns in it. In this paper our aim is to, discuss about different techniques to handle missing data, together with some relatively simple approaches that can often yield reasonable results. However our aim is to replace the missing values by the predicted values with the help of eight different imputation algorithms and we will conclude with the best algorithm.
Keyword: Handling incomplete data, Imputation, Imputation Techniques and Missing Data.
Scope of the Article: Predictive Analysis.