Comparing prediction algorithms in disorganized data

Main Article Content

Erkut Arican
Adem Karahoca

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.

Downloads

Download data is not yet available.

Article Details

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
Section
Articles

References

[1] Altman, N. S. An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, 1992, pp. 175-185.
[2] Cleary, J. G and L. E. Trigg, K*: An instance-based learner using an entropic distance measure, Proceedings of the 12th International Conference on Machine learning, 1995, pp. 108-114.
[3] Broomhead, D. S. and Lowe, D. Radial basis functions, multi-variable functional interpolation and adaptive networks, 1988.
[4] Iba, W. and Langley, P. Induction of One-Level Decision Trees, Proceedings of the Ninth International Conference on Machine Learning, 1992, pp. 233–240.
[5] Haara, A. and Kangas, A. S. Comparing K nearest neighbours methods and linear regression – is there reason to select one over the other?, MCFNS, 2012, 4 (1), pp. 50-65.
[6] Sahibinden, Sahibinden.com, [Online]. Available from: www.sahibinden.com.
[7] Hurriyet Emlak, Hurriyet Emlak, [Online]. Available from: www.hurriyetemlak.com.
[8] Ripper, V. W. Visual Web Ripper, [Online]. Available from: http://www.visualwebripper.com/.
[9] Witten, H. and Frank, E. Data Mining Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.