## 6 Case Study
### Determining Influential Factors Affecting Construction Projects' Profit Using Machine Learning
Larger budgets typically involve more stakeholders, procurement risks, and resource dependencies, which amplify cost overrun risks. This finding aligns with [PERSON] et al. [49], who established that budget size is a proxy for project complexity. Approved change orders (weight: 0.86) indicate deviations from the original plan. Frequent changes often reveal insufficient feasibility studies or fluctuating client demands, diminishing profit margins due to rework and delays [50]. The tight coupling of the current budget (0.82) and actual cost (0.56) weights underscores the impact of dynamic cost control. Projects that proactively revise budgets mitigate losses, while those with rigid plans suffer higher overruns [30]. In contrast, variables like \"city\" (weight: 0.00) failed to segment the data, leading to their exclusion meaningfully. The model's reliance on empirical split efficiency (rather than correlation alone) ensures robust, interpretable feature importance aligned with construction management realities. Moreover, the data for many influential profit factors are available at the early stages of the project, such as the planning stage. Accordingly, these factors can be utilized as inputs for a profit prediction model. The authors highlighted that the factors available at the planning stage with a weight of more than 0.25 are relevant and important to the profit. For instance, factors such as project type, project delivery method (PDM), portfolio, contract type, program, late completion penalty, project category, baseline cost, planned duration in days, and baseline budget. The development of CDH acts as a comprehensive data hub to be utilized to tackle various construction project issues. The outcomes of the case study emphasized the importance of collecting data from multiple dimensions and structures of a construction project. These results are demonstrated by the previously explained studies that a project's profit is impacted by various external, organizational, and project-related factors. To reflect, in this research, many profit influential factors were under the OBS structure; namely, \begin{table}
\begin{tabular}{c c c} \hline
**SI.** & **Attribute** & **Weight** \\ \hline
1 & Currency & 0.02 \\
2 & Joint Venture & 0.04 \\
3 & Joint Venture Partner & 0.06 \\
4 & Company & 0.14 \\
5 & Business Unit & 0.14 \\
6 & Division & 0.15 \\
7 & Complexity Level & 0.17 \\
8 & Province/State & 0.19 \\
9 & Region & 0.22 \\
10 & Project Type & 0.23 \\
11 & Project Delivery Method & 0.23 \\
12 & Portfolio & 0.24 \\
13 & Contract Type & 0.29 \\
14 & Program & 0.33 \\
15 & Late Completion Penalty & 0.36 \\
16 & Actual Duration in Days & 0.43 \\
17 & Project Category & 0.45 \\
18 & Baseline Cost & 0.54 \\
19 & Planned Duration in Days & 0.56 \\
20 & Actual Cost & 0.56 \\
21 & Current Budget & 0.82 \\
22 & Approved Change Orders \% & 0.86 \\
23 & Baseline Budget & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 4: Construction project’s profit influential factors and their weights. company, business unit, division, portfolio, and program. These were categorized as organizational factors. Other factors under the six dimensions mentioned earlier in Table 3 were categorized as project-related factors, such as project type, project delivery method (PDM), contract type, project category, duration, budget, etc. Also, factors such as joint venture, region, and province/state were categorized as external factors. The developed model was unable to identify any correlation between the project stakeholders, such as the project manager, arch/engineer, and owner, with the profit. The absence of work package (WP)-level data significantly limits the model's capacity to identify micro-scale drivers of profit fluctuations. While project-level metrics (e.g., baseline budget, total duration) offer macro-level correlations with profit, they do not capture task-specific inefficiencies that cumulatively affect financial outcomes.