Global Journal of Computer Sciences: Theory and Research

Global Journal of Computer Sciences: Theory and Research

Comparing prediction algorithms in disorganized data

Yazarlar: Erkut Arican, Adem Karahoca

Cilt 6 , Sayı 2 , 2016 , Sayfalar 26-35

Konular:-

Anahtar Kelimeler:KNN,Simple linear regression,Rbfnetwork,Disorganized data,Bfnetwork.

Özet: 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.

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@article{2016, title={Comparing prediction algorithms in disorganized data}, volume={6}, number={26–35}, publisher={Global Journal of Computer Sciences: Theory and Research}, author={Erkut Arican, Adem Karahoca}, year={2016} }
APA
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Erkut Arican, Adem Karahoca. (2016). Comparing prediction algorithms in disorganized data (Vol. 6). Vol. 6. Global Journal of Computer Sciences: Theory and Research.
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Erkut Arican, Adem Karahoca. Comparing Prediction Algorithms in Disorganized Data. no. 26–35, Global Journal of Computer Sciences: Theory and Research, 2016.
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