Author Type

Graduate Student

Date of Award

2003

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Behara, Ravi

Abstract

This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results.

Extent

77 p.

Comments

College of Business|Thesis (M.B.A.)--Florida Atlantic University, 2003.|FAU Electronic Theses and Dissertations Collection|College of Business

Identifier

FADT12992

Format

pdf

Download

Share

COinS