Developing an Efficient Ensemble Machine Learning Model for Evaluating Construction Project Bidding Quality and Optimal Winning Strategies

PledgX is interested in building a solution that aims to optimize the bidding process to maximize key performance indicators for contactors and vendors. For bidding optimization, several strategies and methods have been proposed; however, with the massive amount of available bidding datasets, the quality and performance of such methods are questionable. Machine learning introduces intelligent solutions to optimize the bidding decision, however these solutions are applicable to a range of prediction or classification tasks. Thus, ensemble modelling is introduced for efficient performance and to overcome drawbacks for individual modeling. In this project, we propose a novel data-driven bidding model based on ensemble predictive learning, which extracts sophisticated features and learns to bid automatically using the collected data. The model is composed of sub-models aggregated to form a more robust global model. The proposed ensemble learning model enables PledgX to learn complex rules of bidding with optimized overall bidding performance.

Faculty Supervisor:

Rasha Kashef


Alireza Ghasemieh




Engineering - biomedical


Information and cultural industries


Ryerson University



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