Classification Model For Credit Data

被引：0

作者：

Al-Zoubi, Ala' M. ^{[1
]}

Rodan, Ali ^{[2
]}

Alazzam, Azmi ^{[2
]}

机构：

[1] Univ Jordan, King Abdullah II Sch Infromat Technol, Amman, Jordan

[2] Higher Coll Technol, CIS, Al Ain Womens Campus, Abu Dhabi, U Arab Emirates

来源：

2018 FIFTH HCT INFORMATION TECHNOLOGY TRENDS (ITT): EMERGING TECHNOLOGIES FOR ARTIFICIAL INTELLIGENCE | 2018年

关键词：

Decision Tree; K-nearest Neighbors; Logistic Regression; Data Mining; Sensitivity; Accuracy;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Data mining algorithms have become over the time more related to business and artificial intelligence. Data mining techniques are used for decision-making and prediction purpose. In this research paper, a model of three data mining algorithms is proposed. The model is tested on a credit dataset for different customers to determine if the credit tested is good (approved) or bad (denied). The three different algorithms used in this model are: Decision Tree (DT), K-Nearest Neighbors Algorithm (KNN), and Logistic Regression (LG). The accuracy, sensitivity and other performance measures of the models are also calculated.

引用

页码：132 / 137

页数：6

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