Data mining-based predictive model to determine project financial success using project definition parameters

Abstract

The planning stage is important for project development because the majority of important decisions are made at this stage. Having a well-defined project plan will reduce project uncertainty and increase the likelihood of the project's success. In other words, based on the level of project definition in the planning stage, project success or failure can be predicted. The aim of this study is to generate a predictive model that will forecast project performance in terms of cost, depending on the project definition level during the early stages of the project before a detailed design is started. The predictive model for this study was generated by support vector machine (SVM). A survey of 77 completed construction projects in Korea was conducted in order to collect the project defined level and cost data from each of those projects by questioning selected clients, architects, and construction managers who had participated before beginning the detailed design stage in the project. It is anticipated that prediction results will help clients and project managers revise their project planning when they encounter a poor performance prediction. Furthermore, the research result imply that employing the proposed model can help project participants achieve success by managing projects more effectively.