2026-05-22T06:49:28Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1545892024-12-20T18:25:12Zoai_pmh:alloai_pmh:repo_items154589
https://hdl.handle.net/2286/R.I.38651
http://rightsstatements.org/vocab/InC/1.0/
All Rights Reserved
2016
x, 85 pages : illustrations (some color)
Masters Thesis
Academic theses
Text
eng
Ejaz, Samira
Davulcu, Hasan
Balasooriya, Janaka
Candan, Kasim
Arizona State University
Partial requirement for: M.S., Arizona State University, 2016
Includes bibliographical references (pages 56-57)
Field of study: Computer science
Bank institutions employ several marketing strategies to maximize new customer acquisition as well as current customer retention. Telemarketing is one such approach taken where individual customers are contacted by bank representatives with offers. These telemarketing strategies can be improved in combination with data mining techniques that allow predictability of customer information and interests. In this thesis, bank telemarketing data from a Portuguese banking institution were analyzed to determine predictability of several client demographic and financial attributes and find most contributing factors in each. Data were preprocessed to ensure quality, and then data mining models were generated for the attributes with logistic regression, support vector machine (SVM) and random forest using Orange as the data mining tool. Results were analyzed using precision, recall and F1 score.
Computer Science
Mathematics
Industrial Engineering
Classification
Data Mining
Logistic Regression
Random Forest
Sensitivity Analysis
Support Vector Machines
Data Mining
Logistic regression analysis
Support Vector Machines
Telemarketing
Predicting demographic and financial attributes in a bank marketing dataset