Yıl 2017, Cilt , Sayı 2, Sayfalar 22 - 25 2017-12-01

Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis

Emine BAHÇE ÇİZER [1] , Ayça AK [2] , Vedat TOPUZ [3]

50 40

Bank and lenders are required to conduct credit analysis to determine the creditworthiness of customers who applying for credit. These organizations apply a number of different methods in order to perform credit analysis with high accuracy, along with various statistical analysis tools. For this purpose, we will use the German Credit data set which is downloaded from UCI Machine Learning Repository open access based site. There are 1000 customer records in the data set and the credit status of these customers is encoded with the appropriate ones 1 and the credit status of these customers is encoded with the inappropriate ones 0. In the first step of this study, SVM analysis will be performed using 21 dependent variables and 1 independent variable in the data set. In the second step of this study, 21 dependent variables will be reduced by performing PCA analysis and SVM analysis will be performed with the dependent variables obtained after the PCA analysis. Will compare the performance of these two different analyzes in the outcome phase of the study.

Support Vector Machine, Principal Component Analysis, Credit Analysis
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Birincil Dil en
Konular Sosyal Bilimler (Genel)
Dergi Bölümü Araştırma Makalesi
Yazarlar

Yazar: Emine BAHÇE ÇİZER (Sorumlu Yazar)
E-posta: emine.bahce@softtech.com.tr

Yazar: Ayça AK
E-posta: emine.bahce@softtech.com.tr

Yazar: Vedat TOPUZ
E-posta: emine.bahce@softtech.com.tr

Bibtex @araştırma makalesi { ijses397775, journal = {Uluslararası Sosyal ve Ekonomik Bilimler Dergisi}, issn = {}, address = {Nobel Bilim ve Araştırma Merkezi Limited}, year = {2017}, volume = {}, pages = {22 - 25}, doi = {}, title = {Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis}, key = {cite}, author = {TOPUZ, Vedat and BAHÇE ÇİZER, Emine and AK, Ayça} }
APA BAHÇE ÇİZER, E , AK, A , TOPUZ, V . (2017). Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis. Uluslararası Sosyal ve Ekonomik Bilimler Dergisi, (2), 22-25. Retrieved from http://dergipark.gov.tr/ijses/issue/34195/397775
MLA BAHÇE ÇİZER, E , AK, A , TOPUZ, V . "Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis". Uluslararası Sosyal ve Ekonomik Bilimler Dergisi (2017): 22-25 <http://dergipark.gov.tr/ijses/issue/34195/397775>
Chicago BAHÇE ÇİZER, E , AK, A , TOPUZ, V . "Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis". Uluslararası Sosyal ve Ekonomik Bilimler Dergisi (2017): 22-25
RIS TY - JOUR T1 - Credit Repayment Analysis Using Support Vector Machine And Principal Component Analysis AU - Emine BAHÇE ÇİZER , Ayça AK , Vedat TOPUZ Y1 - 2017 PY - 2017 N1 - DO - T2 - Uluslararası Sosyal ve Ekonomik Bilimler Dergisi JF - Journal JO - JOR SP - 22 EP - 25 VL - IS - 2 SN - -2146-0078 M3 - UR - Y2 - 2017 ER -
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