Automating Residential Construction Cost Estimation Using Machine Learning: Enhancing Accuracy, Efficiency, and Decision-Making in the Prefabricated Building Industry

This research project aims to revolutionize construction cost estimation by developing machine learning models
for automating residential cost estimation in a prefabricated building company. The project focuses on two goals:
creating a revenue forecasting system for business intelligence and automating core processes in the cost
estimation department for efficiency and accuracy. The project involves comprehensive data collection,
preprocessing, and the use of state-of-the-art algorithms to ensure accurate and scalable results. Overall, it
advances cost estimation practices in the industry.

Faculty Supervisor:

Xinming Li

Student:

Partner:

Landmark Group of Companies Inc.

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

University of Alberta

Program:

Accelerate

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