@article{article_281901, title={Estimation of Credit Card Customers Payment Status by Using kNN and MLP}, journal={International Journal of Intelligent Systems and Applications in Engineering}, volume={4}, pages={249–251}, year={2016}, DOI={10.18201/ijisae.281901}, author={Koklu, Murat and Sabancı, Kadir}, keywords={Data mining,Weka,MLP}, abstract={<p class="03Abstract" style="margin-right:0cm;text-align:justify;"> <span lang="en-us" xml:lang="en-us">The Default of Credit Card Clients
dataset in the UCI machine learning repository was used in this study.  The credit card customers were classified if
they would do payment or not (yes=1 no=0) for next month by using 23 information
about them. Totally 30000 data in the dataset’s 66% was used for training and
rest of them as 33% was used for tests. The Weka (Waikato Environment for
Knowledge Analysis) software was used for estimation. In estimation Multilayer
Perceptron (MLP) and k Nearest Neighbors (kNN) machine learning algorithms was
used and success rates and error rates were calculated. With kNN estimation
success rates for various number of neighborhood value was calculated one by
one. The highest success rate was achieved as 80.6569% when the number of
neighbor is 10. With MLP neural network model the estimation success rates was
calculated when there are different number of neurons in the hidden layer of
MLP. The best estimation success rate was achieved as 81.049% when there was
only one neuron in the hidden layer.  MAE
and RMSE values were obtained for this estimation success rate as 0.3237 and
0.388 respectively.  </span> <i> <span lang="en-gb" xml:lang="en-gb"> </span> </i> </p> <p> <i> </i> </p> <i> </i>}, number={Special Issue-1}, publisher={İsmail SARITAŞ}