Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25507
Title: Development of Customs Prediction Model for Online Ordering
Authors: Sharawi, Lina I.
QABBAAH, Hamzah 
SAMMOUR, George 
VANHOOF, Koen 
Issue Date: 2018
Publisher: IEEE
Source: Awajan, Arafat; Shaout, Adnan (Ed.). Proceedings: 2017 International Conference on New Trends in Computing Sciences (ICTCS), IEEE,p. 94-97
Abstract: As a result of the rapid increase of online shopping, and the movement of products in and out among various countries, governments made it mandatory to pay a certain fee referred to as customs to maintain each country’s economy, based on some rules i.e. weight and price. This paper proposes a model to anticipate and inform the customer if his order is going to be subject to customs or not based on some predefined rules. The model is built using a software called SPSS and data mining techniques such as; Decision Tree (DT) and logistic regression. A comparison between the two data mining techniques is made showing that the DT results were better than the logistic regression in terms of accuracy, precision, recall and f-score.
Notes: Sharawi, LI (reprint author), PSUT, Software Engn Dept, Amman, Jordan. lin20148050@std.psut.edu.jo; Hamzah.qabbaah@uhasselt.be; George.sammour@psut.edu.jo; koen.vanhoof@uhasselt.be
Keywords: data mining; customs; decision tree; logistic regression; classification measures
Document URI: http://hdl.handle.net/1942/25507
ISBN: 9781538605271
DOI: 10.1109/ICTCS.2017.33
ISI #: 000425843800017
Rights: © 2017 IEEE
Category: C1
Type: Proceedings Paper
Validations: ecoom 2019
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
sharawi2017.pdf
  Restricted Access
Published version283.28 kBAdobe PDFView/Open    Request a copy
Show full item record

Page view(s)

76
checked on Sep 7, 2022

Download(s)

84
checked on Sep 7, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.