Buchtel College of Arts and Sciences

Date of Last Revision

2024-06-04 07:34:44



Honors Course


Number of Credits


Degree Name

Bachelor of Science

Date of Expected Graduation

Spring 2024


Companies, industries, and places of business use artificial intelligence and statistics to predict the characteristics of their employees and staff. Data collected from these individuals is also used to make decisions about them regarding their work life, such as promotions, salaries, or within the hiring process. Two models that are commonly used throughout the field of psychology and specifically in industrial/organizational psychology are the linear regression and the logistic regression. Examining different classification models using Python shows the potential that there may be different models that are more accurate in their predictions of employee success, including a Random Forest model and a LightGBM model, which is short for gradient boosting model. The comparison of these models provides evidence suggesting that the Random Forest model and the LightGBM model predict employee productivity more accurately than a traditional linear regression model or a logistic regression model.

Research Sponsor

James Diefendorff

First Reader

Meghan Thornton-Lugo

Second Reader

Andrea Snell

Honors Faculty Advisor

Charles Waehler

Proprietary and/or Confidential Information



Examining how classification models using Python in industrial/organizational psychology could be more accurate than a linear or logistic regression model in predicting employee productivity and success in the hiring process.

Consultant In This Project: Nedim Ceylan

Honors Project Signatures.jpg (241 kB)



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.