Using Predictive Analysis to Improve Small Construction Based Business Success

This research presents a predictive model, which is focused on decreasing the incidence of small construction business failure, resulting in increased long-term viability among these target businesses. Predictive indicators obtained via research will be used to develop and train the model using logistic regression, predicting business success for five years or greater. The completed model will become the backbone of the Small Construction Business Predictor Tool (SCBPT), a user interface employed by small construction businesses in planning for long- term success. Business Owners will input the required prediction indicators and the SCBPT will predict the likelihood of success or failure. In the event of a failure prediction within five years, the tool will provide insight and tips to reduce failures, allowing the user the ability to implement the actions to increase the likelihood of success. Business Owners will then be able to implement the recommended changes identified based on the predicted success of the model.

Author

• Davis, Stanley

Language

• en

Date created

• 2020

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Systems Engineering

Degree

• D.Engr.

Advisor

• Blackburn, Timothry

Committee Member(s)

• Etemadi, Amir
• Adetunji, Oluwatomi

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/qv33rx57p