Author

Eric Lavin

Publication Date

1-1-2016

Document Type

Dissertation/Thesis

First Advisor

Giabbanelli, Philippe J.

Degree Name

B.S. (Bachelor of Science)

Department

Department of Computer Science

Abstract

It has been observed over time that football teams with a huge increase in performance will cause an increase in the amount of applications received in the following year. We would will use classifiers to see if we can predict whether or not a school will see an increase in the number of applications received based on the win-loss record of their football team for the 3 years following that season. In this study we make use of the ADTree, J48, LADTree and Random Forest decision tree methods as well as conjunctive Rule and Decision Table rule sets to perform our analysis. To do this we first gathered data from the IPEDS data set for characteristics about the schools and sports-reference for data on the Division I schools. Python is used to clean the data which is then analyzed through use of WEKA 3.6.13. We have found that with classifiers we could predict with up to 74% accuracy whether or not a school would see an increase in applications received. However, we could not differentiate if the increase was observable from the win-loss record or the included characteristics of the school.

Extent

48 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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