Comparing prediction algorithms in disorganized data

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Published: Apr 16, 2017
Updated: 2017-04-16
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2017-04-16 (1)
DOI: https://doi.org/10.18844/gjcs.v6i2.1471

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Erkut Arican
Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey
Adem Karahoca
Department of Software Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey

Abstract

Real estate market is very effective in today’s world but finding best price for house is a big problem. This problem creates a propose of this work. In this study, we try to compare and find best prediction algorithms on disorganized house data. Dataset was collected from real estate websites and three different regions selected for this experiment. KNN, KSTAR, Simple Linear Regression, Linear Regression, RBFNetwork and Decision Stump algorithms were used. This study shows us KStar and KNN algorithms are better than the other prediction algorithms for disorganized data.

Keywords: KNN, simple linear regression, rbfnetwork, disorganized data, bfnetwork.

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How to Cite
Arican, E., & Karahoca, A. (2017). Comparing prediction algorithms in disorganized data. Global Journal of Computer Sciences: Theory and Research, 6(2), 26–35. https://doi.org/10.18844/gjcs.v6i2.1471
Issue
Vol. 6 No. 2 (2016): November
Section
Articles

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