Predicting the Winning Bidding Price of Construction Projects by Ensemble Machine Learning

Louis W. Bray Construction Limited (LWB) is a mid-sized heavy civil contractor with focused efforts in Eastern Ontario. One of its largest challenges is competing with larger competitors and maintaining competitive pricing to all its clients, including all levels of government. Its biggest risk is the procurement process, including tendering and bidding. This project aims to help LWB create a robust winning bidding price prediction model based on their historical data of tenders and cost analysis forms. This project gives LWB further insight into proper bidding approaches and allows it to compete on a larger scale. LWB will be able to use this data to question its pricing model, limit errors, and maximize profitability. Furthermore, if the bid is well understood and there is enough information, there becomes less of a need to price in the risk of errors. This leads to higher competitiveness and better pricing for all clients.

Faculty Supervisor:

Yuan Tian

Student:

Partner:

Louis W. Bray Construction

Discipline:

Computer science

Sector:

Construction and infrastructure

University:

Queen's University

Program: